• arXiv.cs.CY Pub Date : 2020-01-15
Andreas Kamilaris; Nicolo Botteghi

As the Internet of Things (IoT) penetrates different domains and application areas, it has recently entered also the world of robotics. Robotics constitutes a modern and fast-evolving technology, increasingly being used in industrial, commercial and domestic settings. IoT, together with the Web of Things (WoT) could provide many benefits to robotic systems. Some of the benefits of IoT in robotics have been discussed in related work. This paper moves one step further, studying the actual current use of IoT in robotics, through various real-world examples encountered through a bibliographic research. The paper also examines the potential ofWoT, together with robotic systems, investigating which concepts, characteristics, architectures, hardware, software and communication methods of IoT are used in existing robotic systems, which sensors and actions are incorporated in IoT-based robots, as well as in which application areas. Finally, the current application of WoT in robotics is examined and discussed.

更新日期：2020-01-17
• arXiv.cs.CY Pub Date : 2020-01-16
Jan A. Bergstra; Mark Burgess

By reasoning about the claims and speculations promised as part of the public discourse, we analyze the hypothesis that flaws in software engineering played a critical role in the Boeing 737 MCAS incidents. We use promise-based reasoning to discuss how, from an outsider's perspective, one may assemble clues about what went wrong. Rather than looking for a Rational Alternative Design (RAD), as suggested by Wendel, we look for candidate flaws in the software process. We describe four such potential flaws. Recently, Boeing has circulated information on its envisaged MCAS algorithm upgrade. We cast this as a promise to resolve the flaws, i.e. to provide a RAD for the B737 Max. We offer an assessment of B-Max-New based on the public discourse.

更新日期：2020-01-17
• arXiv.cs.CY Pub Date : 2020-01-14
Vivian Lai; Han Liu; Chenhao Tan

To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real-time assistance, we explore model-driven tutorials to help humans understand these patterns in a training phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large-scale, randomized human-subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real-time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human-centered tutorials and explanations towards a synergy between humans and AI.

更新日期：2020-01-17
• arXiv.cs.CY Pub Date : 2020-01-16
Matúš Medo; Manuel S. Mariani; Linyuan Lü

Online news can quickly reach and affect millions of people, yet little is known about potential dynamical regularities that govern their impact on the public. By analyzing data collected from two nation-wide news outlets, we demonstrate that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems. In particular, we find that the news comment count follows a universal exponential distribution which is explained by the lack of the otherwise omnipresent rich-get-richer mechanism. Exponential aging induces a universal dynamics of article impact. We finally find that the readers' collective attention does "stretch" in the presence of high-impact articles, thus effectively canceling possible competition among the articles. Our findings challenge the generality of widespread popularity dynamics patterns as well as common assumptions of attention economy, suggesting the need to critically reconsider the assumption that collective attention is inherently limited.

更新日期：2020-01-17
• arXiv.cs.CY Pub Date : 2020-01-16
Sagar Joglekar; Daniele Quercia; Miriam Redi; Luca Maria Aiello; Tobias Kauer; Nishanth Sastry

In the area of computer vision, deep learning techniques have recently been used to predict whether urban scenes are likely to be considered beautiful: it turns out that these techniques are able to make accurate predictions. Yet they fall short when it comes to generating actionable insights for urban design. To support urban interventions, one needs to go beyond predicting beauty, and tackle the challenge of recreating beauty. Unfortunately, deep learning techniques have not been designed with that challenge in mind. Given their "black-box nature", these models cannot be directly used to explain why a particular urban scene is deemed to be beautiful. To partly fix that, we propose a deep learning framework called Facelift, that is able to both beautify existing urban scenes (Google Street views) and explain which urban elements make those transformed scenes beautiful. To quantitatively evaluate our framework, we cannot resort to any existing metric (as the research problem at hand has never been tackled before) and need to formulate new ones. These new metrics should ideally capture the presence/absence of elements that make urban spaces great. Upon a review of the urban planning literature, we identify five main metrics: walkability, green spaces, openness, landmarks and visual complexity. We find that, across all the five metrics, the beautified scenes meet the expectations set by the literature on what great spaces tend to be made of. This result is further confirmed by a 20-participant expert survey in which FaceLift have been found to be effective in promoting citizen participation. All this suggests that, in the future, as our framework's components are further researched and become better and more sophisticated, it is not hard to imagine technologies that will be able to accurately and efficiently support architects and planners in the design of spaces we intuitively love.

更新日期：2020-01-17
• arXiv.cs.CY Pub Date : 2018-09-04
Juste Raimbault

Co-evolutionary processes are according to the evolutionary urban theory at the center of urban systems dynamics. Their empirical observation or within models of simulation remains however relatively rare. This chapter is focused on the co-evolution of transportation networks and cities and applies high performance computing numerical experiments to the SimpopNet co-evolution model in order to understand its behavior. We introduce specific indicators to quantify trajectories of such models for systems of cities, and apply these to exhibit co-evolutionary regimes of the model. This illustrates how the systematic exploration of a simulation model can qualitatively transform the knowledge it provides.

更新日期：2020-01-17
• arXiv.cs.CY Pub Date : 2018-10-30
Mark C. Ballandies; Marcus M. Dapp; Evangelos Pournaras

More than 1000 distributed ledger technology (DLT) systems raising 600 billion in investment in 2016 feature the unprecedented and disruptive potential of blockchain technology. A systematic and data-driven analysis, comparison and rigorous evaluation of the different design choices of distributed ledgers and their implications is a challenge. The rapidly evolving nature of the blockchain landscape hinders reaching a common understanding of the techno-socio-economic design space of distributed ledgers and the cryptoeconomies they support. To fill this gap, this paper makes the following contributions: (i) A conceptual architecture of DLT systems with which (ii) a taxonomy is designed and (iii) a rigorous classification of DLT systems is made using real-world data and wisdom of the crowd. (iv) A DLT design guideline is the end result of applying machine learning methodologies on the classification data. Compared to related work and as defined in earlier taxonomy theory, the proposed taxonomy is highly comprehensive, robust, explanatory and extensible. The findings of this paper can provide new insights and better understanding of the key design choices evolving the modeling complexity of DLT systems, while identifying opportunities for new research contributions and business innovation. 更新日期：2020-01-17 • arXiv.cs.CY Pub Date : 2019-07-26 The Anh Han; Luis Moniz Pereira; Francisco C. Santos; Tom Lenaerts Rapid technological advancements in AI as well as the growing deployment of intelligent technologies in new application domains are currently driving the competition between businesses, nations and regions. This race for technological supremacy creates a complex ecology of choices that may lead to negative consequences, in particular, when ethical and safety procedures are underestimated or even ignored. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements. As there is no easy access to data describing this AI race, theoretical models are necessary to understand its dynamics, allowing for the identification of when, how and which procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the time-scale in which AI supremacy can be achieved plays a crucial role. When this supremacy can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that blindly imposing regulations may not have anticipated effect as only for specific conditions a dilemma arises between what individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short term situation, certain conditions require the promotion of risk-taking as opposed to compliance to safety regulations in order to improve social welfare. These results remain robust when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for AI, a clear understanding about the time-scale of the race is required. 更新日期：2020-01-17 • arXiv.cs.CY Pub Date : 2020-01-14 Abeba Birhane; Jelle van Dijk The 'robot rights' debate, and its related question of 'robot responsibility', invokes some of the most polarized positions in AI ethics. While some advocate for granting robots rights on a par with human beings, others, in a stark opposition argue that robots are not deserving of rights but are objects that should be our slaves. Grounded in post-Cartesian philosophical foundations, we argue not just to deny robots 'rights', but to deny that robots, as artifacts emerging out of and mediating human being, are the kinds of things that could be granted rights in the first place. Once we see robots as mediators of human being, we can understand how the robots rights' debate is focused on first world problems, at the expense of urgent ethical concerns, such as machine bias, machine elicited human labour exploitation, and erosion of privacy all impacting society's least privileged individuals. We conclude that, if human being is our starting point and human welfare is the primary concern, the negative impacts emerging from machinic systems, as well as the lack of taking responsibility by people designing, selling and deploying such machines, remains the most pressing ethical discussion in AI. 更新日期：2020-01-16 • arXiv.cs.CY Pub Date : 2020-01-14 Aaron D. Tucker; Markus Anderljung; Allan Dafoe Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data efficiency. Specifically, we examine the intuition that data efficiency will erode the barriers to entry protecting incumbent data-rich AI firms, exposing them to more competition from data-poor firms. We find that this intuition is only partially correct: data efficiency makes it easier to create ML applications, but large AI firms may have more to gain from higher performing AI systems. Further, we find that the effect on privacy, data markets, robustness, and misuse are complex. For example, while it seems intuitive that misuse risk would increase along with data efficiency -- as more actors gain access to any level of capability -- the net effect crucially depends on how much defensive measures are improved. More investigation into data efficiency, as well as research into the "AI production function", will be key to understanding the development of the AI industry and its societal impacts. 更新日期：2020-01-16 • arXiv.cs.CY Pub Date : 2020-01-15 Camille Roth; Antoine Mazières; Telmo Menezes The role of recommendation algorithms in online user confinement is at the heart of a fast-growing literature. Recent empirical studies generally suggest that filter bubbles may principally be observed in the case of explicit recommendation (based on user-declared preferences) rather than implicit recommendation (based on user activity). We focus on YouTube which has become a major online content provider but where confinement has until now been little-studied in a systematic manner. Starting from a diverse number of seed videos, we first describe the properties of the sets of suggested videos in order to design a sound exploration protocol able to capture latent recommendation graphs recursively induced by these suggestions. These graphs form the background of potential user navigations along non-personalized recommendations. From there, be it in topological, topical or temporal terms, we show that the landscape of what we call mean-field YouTube recommendations is often prone to confinement dynamics. Moreover, the most confined recommendation graphs i.e., potential bubbles, seem to be organized around sets of videos that garner the highest audience and thus plausibly viewing time. 更新日期：2020-01-16 • arXiv.cs.CY Pub Date : 2020-01-15 Florian Buettner; John Piorkowski; Ian McCulloh; Ulli Waltinger To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems. Not only in safety-critical applications such as autonomous driving or medicine, but also in dynamic open world systems in industry and government it is crucial for predictive models to be uncertainty-aware and yield trustworthy predictions. Another key requirement for deployment of AI at enterprise scale is to realize the importance of integrating human-centered design into AI systems such that humans are able to use systems effectively, understand results and output, and explain findings to oversight committees. While the focus of this symposium was on AI systems to improve data quality and technical robustness and safety, we welcomed submissions from broadly defined areas also discussing approaches addressing requirements such as explainable models, human trust and ethical aspects of AI. 更新日期：2020-01-16 • arXiv.cs.CY Pub Date : 2019-12-22 Stanisław Saganowski; Anna Dutkowiak; Adam Dziadek; Maciej Dzieżyc; Joanna Komoszyńska; Weronika Michalska; Adam Polak; Michał Ujma; Przemysław Kazienko Wearables like smartwatches or wrist bands equipped with pervasive sensors enable us to monitor our physiological signals. In this study, we address the question whether they can help us to recognize our emotions in our everyday life for ubiquitous computing. Using the systematic literature review, we identified crucial research steps and discussed the main limitations and problems in the domain. 更新日期：2020-01-16 • arXiv.cs.CY Pub Date : 2020-01-13 Na Du; Feng Zhou; Elizabeth Pulver; Dawn M. Tilbury; Lionel P. Robert; Anuj K. Pradhan; X. Jessie Yang In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not. 更新日期：2020-01-15 • arXiv.cs.CY Pub Date : 2020-01-13 W. J. A. van Heeswijk The 1996 Donald Duck Holiday Game is a role-playing variant of the historical Game of the Goose, involving characters with unique attributes, event squares, and random event cards. The objective of the game is to reach the camping before any other player does. We develop a Monte Carlo simulation model that automatically plays the game and enables analyzing its key characteristics. We assess the game on various metrics relevant to each playability. Numerical analysis shows that, on average, the game takes between 69 and 123 rounds to complete, depending on the number of players. However, durations over one hour (translated to human play time) occur over 25% of the games, which might reduce the quality of the gaming experience. Furthermore, we show that two characters are about 30% likely to win than the other three, primarily due to being exposed to fewer random events. We argue that the richer narrative of role-playing games may extend the duration for which the game remains enjoyable, such that the metrics cannot directly be compared to those of the traditional Game-of-the-Goose. Based on our analysis, we provide several suggestions to improve the game balance with only slight modifications. In a broader sense, we demonstrate that a basic Monte Carlo simulation suffices to analyze Game-of-the-Goose role-playing variants, verify how they score on criteria that contribute to an enjoyable game, and detect possible anomalies. 更新日期：2020-01-15 • arXiv.cs.CY Pub Date : 2020-01-14 Min Jin Park; Joshua I. James Many artificial intelligence (AI) speakers have recently come to market. Beginning with Amazon Echo, many companies producing their own speaker technologies. Due to the limitations of technology, most speakers have similar functions, but the way of handling the data of each speaker is different. In the case of Amazon echo, the API of the cloud is open for any developers to develop their API. The Amazon Echo has been around for a while, and much research has been done on it. However, not much research has been done on Google Home Mini analysis for digital investigations. In this paper, we will conduct some initial research on the data storing and security methods of Google Home Mini. 更新日期：2020-01-15 • arXiv.cs.CY Pub Date : 2020-01-14 Manuel S. Mariani; Yanina Gimenez; Jorge Brea; Martin Minnoni; René Algesheimer; Claudio J. Tessone Can we predict the future success of a product, service, or business by monitoring the behavior of a small set of individuals? A positive answer would have important implications for the science of success and managerial practices, yet recent works have supported diametrically opposite answers. To resolve this tension, we address this question in a unique, large-scale dataset that combines individuals' purchasing history with their social and mobility traits across an entire nation. Surprisingly, we find that the purchasing history alone enables the detection of small sets of "discoverers" whose early purchases consistently predict success. In contrast with the assumptions by most existing studies on word-of-mouth processes, the social hubs selected by network centrality are not consistently predictive of success. Our approach to detect key individuals has promise for applications in other research areas including science of science, technological forecasting, and behavioral finance. 更新日期：2020-01-15 • arXiv.cs.CY Pub Date : 2020-01-07 Shahpar YakhchiMacquarie University- Sydney-Australia; Amin BeheshtiMacquarie University- Sydney-Australia; Seyed Mohssen GhafariMacquarie University- Sydney-Australia; Mehmet OrgunMacquarie University- Sydney-Australia Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users' personal interests and level of knowledge, as a key factor to increase recommendations' acceptance. Differently, we identifying users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations. 更新日期：2020-01-15 • arXiv.cs.CY Pub Date : 2020-01-14 Jason M. Pittman; Nikki Robinson The purpose of this study was to measure whether participant education, profession, and technical skill level exhibited a relationship with identification of password strength. Participants reviewed 50 passwords and labeled each as weak or strong. A Chi-square test of independence was used to measure relationships between education, profession, technical skill level relative to the frequency of weak and strong password identification. The results demonstrate significant relationships across all variable combinations except for technical skill and strong passwords which demonstrated no relationship. This research has three limitations. Data collection was dependent upon participant self-reporting and has limited externalized power. Further, the instrument was constructed under the assumption that all participants could read English and understood the concept of password strength. Finally, we did not control for external tool use (i.e., password strength meter). The results build upon existing literature insofar as the outcomes add to the collective understanding of user perception of passwords in specific and authentication in general. Whereas prior research has explored similar areas, such work has done so by having participants create passwords. This work measures perception of pre-generated passwords. The results demonstrate a need for further investigation into why users continue to rely on weak passwords. The originality of this work rests in soliciting a broad spectrum of participants and measuring potential correlations between participant education, profession, and technical skill level. 更新日期：2020-01-15 • arXiv.cs.CY Pub Date : 2019-08-21 Daisuke Moriwaki; Komei Fujita; Shota Yasui; Takahiro Hoshino In online display advertising, selecting the most effective ad creative (ad image) for each impression is a crucial task for DSPs (Demand-Side Platforms) to fulfill their goals (click-through rate, number of conversions, revenue, and brand improvement). As widely recognized in the marketing literature, the effect of ad creative changes with the number of repetitive ad exposures. In this study, we propose an efficient and easy-to-implement ad creative selection algorithm that explicitly considers user's psychological status when selecting ad creatives. The proposed system was deployed in a real-world production environment and tested against the baseline algorithms. The results show superiority of the proposed algorithm. 更新日期：2020-01-15 • arXiv.cs.CY Pub Date : 2020-01-12 Oka Kurniawan; Norman Tiong Seng Lee; Christopher M. Poskitt Traditional pen and paper exams are inadequate for modern university programming courses as they are misaligned with pedagogies and learning objectives that target practical coding ability. Unfortunately, many institutions lack the resources or space to be able to run assessments in dedicated computer labs. This has motivated the development of bring-your-own-device (BYOD) exam formats, allowing students to program in a similar environment to how they learnt, but presenting instructors with significant additional challenges in preventing plagiarism and cheating. In this paper, we describe a BYOD exam solution based on lockdown browsers, software which temporarily turns students' laptops into secure workstations with limited system or internet access. We combine the use of this technology with a learning management system and cloud-based programming tool to facilitate conceptual and practical programming questions that can be tackled in an interactive but controlled environment. We reflect on our experience of implementing this solution for a major undergraduate programming course, highlighting our principal lesson that policies and support mechanisms are as important to consider as the technology itself. 更新日期：2020-01-14 • arXiv.cs.CY Pub Date : 2020-01-13 Yekai Xu; Zuofang Wan; Qingqian He; Shiguang Ni This project describes an approach to analyze public sentiments with social media data and provides an example of the Twitter discourse during the 2019 Chinese National Day. The objective is to study the online discourse towards China with NLP algorithms, as well as observe the temporal, spatial and lingual characteristics of the expressed sentiments. Firstly, the Twitter data sets were collected between Sept 30 and Oct 3 through API and part of them were manually labeled to train the SVM. Then, a hybrid method of SVM and dictionary was applied to evaluate the sentiments of the collected tweets. After that, the tweets sentiments' time fluctuation, spatial distribution and frequently used words were given. Finally, we conclude by highlighting the possible consequences of the overall negative image of China in the English-speaking discourses and indicating future directions. 更新日期：2020-01-14 • arXiv.cs.CY Pub Date : 2020-01-13 Carina Prunkl; Jess Whittlestone One way of carving up the broad "AI ethics and society" research space that has emerged in recent years is to distinguish between "near-term" and "long-term" research. While such ways of breaking down the research space can be useful, we put forward several concerns about the near/long-term distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used, and argue that while there are differing priorities within this broad research community, these differences are not well-captured by the near/long-term distinction. We unpack the near/long-term distinction into four different dimensions, and propose some ways that researchers can communicate more clearly about their work and priorities using these dimensions. We suggest that moving towards a more nuanced conversation about research priorities can help establish new opportunities for collaboration, aid the development of more consistent and coherent research agendas, and enable identification of previously neglected research areas. 更新日期：2020-01-14 • arXiv.cs.CY Pub Date : 2019-06-16 Xuewei Wang; Weiyan Shi; Richard Kim; Yoojung Oh; Sijia Yang; Jingwen Zhang; Zhou Yu Developing intelligent persuasive conversational agents to change people's opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a subset. Based on the annotation, we built a baseline classifier with context information and sentence-level features to predict the 10 persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed the relationships between individuals' demographic and psychological backgrounds including personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depending on the individuals' personal backgrounds. This work lays the ground for developing a personalized persuasive dialogue system. 更新日期：2020-01-14 • arXiv.cs.CY Pub Date : 2019-10-02 Jess Whittlestone; Aviv Ovadya This paper explores the tension between openness and prudence in AI research, evident in two core principles of the Montr\'eal Declaration for Responsible AI. While the AI community has strong norms around open sharing of research, concerns about the potential harms arising from misuse of research are growing, prompting some to consider whether the field of AI needs to reconsider publication norms. We discuss how different beliefs and values can lead to differing perspectives on how the AI community should manage this tension, and explore implications for what responsible publication norms in AI research might look like in practice. 更新日期：2020-01-14 • arXiv.cs.CY Pub Date : 2020-01-09 Jason Radford; Kenneth Joseph Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, scholars have identified myriad ways in which machine learning, when applied without care, can also lead to incorrect and harmful claims about people (e.g. about the biological nature of sexuality), and/or to discriminatory outcomes. Here, we argue that such issues arise primarily because of the lack of, or misuse of, social theory. Walking through every step of the machine learning pipeline, we identify ways in which social theory must be involved in order to address problems that technology alone cannot solve, and provide a pathway towards the use of theory to this end. 更新日期：2020-01-13 • arXiv.cs.CY Pub Date : 2020-01-09 Jeffrey Ding; Allan Dafoe What resources and technologies are strategic? This question is often the focus of policy and theoretical debates, where the label "strategic" designates those assets that warrant the attention of the highest levels of the state. But these conversations are plagued by analytical confusion, flawed heuristics, and the rhetorical use of "strategic" to advance particular agendas. We aim to improve these conversations through conceptual clarification, introducing a theory based on important rivalrous externalities for which socially optimal behavior will not be produced alone by markets or individual national security entities. We distill and theorize the most important three forms of these externalities, which involve cumulative-, infrastructure-, and dependency-strategic logics. We then employ these logics to clarify three important cases: the Avon 2 engine in the 1950s, the U.S.-Japan technology rivalry in the late 1980s, and contemporary conversations about artificial intelligence. 更新日期：2020-01-13 • arXiv.cs.CY Pub Date : 2020-01-10 Gian Maria Campedelli; Iain Cruickshank; Kathleen M. Carley Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow finding latent clusters of similar terror groups using information on their operational characteristics. Specifically, using open access data of terrorist attacks occurred worldwide from 1997 to 2016, we build a multi-partite network that includes terrorist groups and related information on tactics, weapons, targets, active regions. We propose a novel algorithm for cluster formation that expands our earlier work that solely used Gower's coefficient of similarity via the application of Von Neumann entropy for mode-weighting. This novel approach is compared with our previous Gower-based method and a heuristic clustering technique that only focuses on groups' ideologies. The comparative analysis demonstrates that the entropy-based approach tends to reliably reflect the structure of the data that naturally emerges from the baseline Gower-based method. Additionally, it provides interesting results in terms of behavioral and ideological characteristics of terrorist groups. We furthermore show that the ideology-based procedure tends to distort or hide existing patterns. Among the main statistical results, our work reveals that groups belonging to opposite ideologies can share very common behaviors and that Islamist/jihadist groups hold peculiar behavioral characteristics with respect to the others. Limitations and potential work directions are also discussed, introducing the idea of a dynamic entropy-based framework. 更新日期：2020-01-13 • arXiv.cs.CY Pub Date : 2020-01-10 Gian Maria Campedelli; Francesco Calderoni; Mario Paolucci; Tommaso Comunale; Daniele Vilone; Federico Cecconi; Giulia Andrighetto Criminal organizations exploit their presence on territories and local communities to recruit new workforce in order to carry out their criminal activities and business. The ability to attract individuals is crucial for maintaining power and control over the territories in which these groups are settled. This study proposes the formalization, development and analysis of an agent-based model (ABM) that simulates a neighborhood of Palermo (Sicily) with the aim to understand the pathways that lead individuals to recruitment into organized crime groups (OCGs). Using empirical data on social, economic and criminal conditions of the area under analysis, we use a multi-layer network approach to simulate this scenario. As the final goal, we test different policies to counter recruitment into OCGs. These scenarios are based on two different dimensions of prevention and intervention: (i) primary and secondary socialization and (ii) law enforcement targeting strategies. 更新日期：2020-01-13 • arXiv.cs.CY Pub Date : 2020-01-10 Johannes Himmelreich The ongoing debate on the ethics of self-driving cars typically focuses on two approaches to answering ethical questions: moral philosophy and social science. I argue that these two approaches are both lacking. We should neither deduce answers from individual moral theories nor should we expect social science to give us complete answers. To supplement these approaches, we should turn to political philosophy. The issues we face are collective decisions that we make together rather than individual decisions we make in light of what we each have reason to value. Political philosophy adds three basic concerns to our conceptual toolkit: reasonable pluralism, human agency, and legitimacy. These three concerns have so far been largely overlooked in the debate on the ethics of self-driving cars. 更新日期：2020-01-13 • arXiv.cs.CY Pub Date : 2020-01-10 Luoying Yang; Zhou Xu; Jiebo Luo Women have always been underrepresented in movies and not until recently do women representation in movies improve. To investigate the improvement of women representation and its relationship with a movie's success, we propose a new measure, the female cast ratio, and compare it to the commonly used Bechdel test result. We employ generalized linear regression withL_1\$ penalty and a Random Forest model to identify the predictors that are influential on women representation, and evaluate the relationship between women representation and a movie's success in three aspects: revenue/budget ratio, rating and popularity. Three important findings in our study have highlighted the difficulties women in the film industry face in both upstream and downstream. First, female filmmakers especially female screenplay writers are instrumental for movies to have better women representation, but the percentage of female filmmakers has been very low. Second, lower budgets are often made to support movies that could tell good stories about women, and this usually cause the films to in turn receive more criticisms. Finally, the demand for better women presentation from moviegoers has also not been strong enough to compel the film industry for a change, as movies that have poor women representation can still be very popular and successful in the box office.

更新日期：2020-01-13
• arXiv.cs.CY Pub Date : 2020-01-10
Peter Cihon; Matthijs M. Maas; Luke Kemp

Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.

更新日期：2020-01-13
• arXiv.cs.CY Pub Date : 2019-08-30
Amanda Coston; Alan Mishler; Edward H. Kennedy; Alexandra Chouldechova

Algorithmic risk assessments are increasingly used to help humans make decisions in high-stakes settings, such as medicine, criminal justice and education. In each of these cases, the purpose of the risk assessment tool is to inform actions, such as medical treatments or release conditions, often with the aim of reducing the likelihood of an adverse event such as hospital readmission or recidivism. Problematically, most tools are trained and evaluated on historical data in which the outcomes observed depend on the historical decision-making policy. These tools thus reflect risk under the historical policy, rather than under the different decision options that the tool is intended to inform. Even when tools are constructed to predict risk under a specific decision, they are often improperly evaluated as predictors of the target outcome. Focusing on the evaluation task, in this paper we define counterfactual analogues of common predictive performance and algorithmic fairness metrics that we argue are better suited for the decision-making context. We introduce a new method for estimating the proposed metrics using doubly robust estimation. We provide theoretical results that show that only under strong conditions can fairness according to the standard metric and the counterfactual metric simultaneously hold. Consequently, fairness-promoting methods that target parity in a standard fairness metric may --- and as we show empirically, do --- induce greater imbalance in the counterfactual analogue. We provide empirical comparisons on both synthetic data and a real world child welfare dataset to demonstrate how the proposed method improves upon standard practice.

更新日期：2020-01-13
• arXiv.cs.CY Pub Date : 2019-11-20
Javier Sánchez-Monedero; Lina Dencik

This paper critically examines a recently developed proposal for a border control system called iBorderCtrl, designed to detect deception based on facial recognition technology and the measurement of micro-expressions, termed 'biomarkers of deceit'. Funded under the European Commission's Horizon 2020 programme, we situate our analysis in the wider political economy of 'emotional AI' and the history of deception detection technologies. We then move on to interrogate the design of iBorderCtrl using publicly available documents and assess the assumptions and scientific validation underpinning the project design. Finally, drawing on a Bayesian analysis we outline statistical fallacies in the foundational premise of mass screening and argue that it is very unlikely that the model that iBorderCtrl provides for deception detection would work in practice. By interrogating actual systems in this way, we argue that we can begin to question the very premise of the development of data-driven systems, and emotional AI and deception detection in particular, pushing back on the assumption that these systems are fulfilling the tasks they claim to be attending to and instead ask what function such projects carry out in the creation of subjects and management of populations. This function is not merely technical but, rather, we argue, distinctly political and forms part of a mode of governance increasingly shaping life opportunities and fundamental rights.

更新日期：2020-01-13
• arXiv.cs.CY Pub Date : 2020-01-09
Md. Aminur Rab Ratul

To ensure the security of the general mass, crime prevention is one of the most higher priorities for any government. An accurate crime prediction model can help the government, law enforcement to prevent violence, detect the criminals in advance, allocate the government resources, and recognize problems causing crimes. To construct any future-oriented tools, examine and understand the crime patterns in the earliest possible time is essential. In this paper, I analyzed a real-world crime and accident dataset of Denver county, USA, from January 2014 to May 2019, which containing 478,578 incidents. This project aims to predict and highlights the trends of occurrence that will, in return, support the law enforcement agencies and government to discover the preventive measures from the prediction rates. At first, I apply several statistical analysis supported by several data visualization approaches. Then, I implement various classification algorithms such as Random Forest, Decision Tree, AdaBoost Classifier, Extra Tree Classifier, Linear Discriminant Analysis, K-Neighbors Classifiers, and 4 Ensemble Models to classify 15 different classes of crimes. The outcomes are captured using two popular test methods: train-test split, and k-fold cross-validation. Moreover, to evaluate the performance flawlessly, I also utilize precision, recall, F1-score, Mean Squared Error (MSE), ROC curve, and paired-T-test. Except for the AdaBoost classifier, most of the algorithms exhibit satisfactory accuracy. Random Forest, Decision Tree, Ensemble Model 1, 3, and 4 even produce me more than 90% accuracy. Among all the approaches, Ensemble Model 4 presented superior results for every evaluation basis. This study could be useful to raise the awareness of peoples regarding the occurrence locations and to assist security agencies to predict future outbreaks of violence in a specific area within a particular time.

更新日期：2020-01-10
• arXiv.cs.CY Pub Date : 2020-01-09
Chris Dulhanty; Alexander Wong

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.

更新日期：2020-01-10
• arXiv.cs.CY Pub Date : 2019-01-30
Alexandre Louis Lamy; Ziyuan Zhong; Aditya Krishna Menon; Nakul Verma

Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates under the assumption that the sensitive feature available in one's training sample is perfectly reliable. This assumption may be violated in many real-world cases: for example, respondents to a survey may choose to conceal or obfuscate their group identity out of fear of potential discrimination. This poses the question of whether one can still learn fair classifiers given noisy sensitive features. In this paper, we answer the question in the affirmative: we show that if one measures fairness using the mean-difference score, and sensitive features are subject to noise from the mutually contaminated learning model, then owing to a simple identity we only need to change the desired fairness-tolerance. The requisite tolerance can be estimated by leveraging existing noise-rate estimators from the label noise literature. We finally show that our procedure is empirically effective on two case-studies involving sensitive feature censoring.

更新日期：2020-01-10
• arXiv.cs.CY Pub Date : 2019-10-16
Mayank Agrawal; Joshua C. Peterson; Thomas L. Griffiths

Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models---the biggest errors they make in predicting the data---to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting instead that the predictions of these data-driven models should be used to guide model-building. We call this approach "Scientific Regret Minimization" (SRM) as it focuses on minimizing errors for cases that we know should have been predictable. We demonstrate this methodology on a subset of the Moral Machine dataset, a public collection of roughly forty million moral decisions. Using SRM, we found that incorporating a set of deontological principles that capture dimensions along which groups of agents can vary (e.g. sex and age) improves a computational model of human moral judgment. Furthermore, we were able to identify and independently validate three interesting moral phenomena: criminal dehumanization, age of responsibility, and asymmetric notions of responsibility.

更新日期：2020-01-10
• arXiv.cs.CY Pub Date : 2019-12-15
Xinyu Xiao; Qiuming Kuang; Shiming Xiang; Junnan Hu; Chunhong Pan

Numerical Weather Prediction (NWP), is widely used in precipitation forecasting, based on complex equations of atmospheric motion requires supercomputers to infer the state of the atmosphere. Due to the complexity of the task and the huge computation, this methodology has the problems of inefficiency and non-economic. With the rapid development of meteorological technology, the collection of plentiful numerical meteorological data offers opportunities to develop data-driven models for NMP task. In this paper, we consider to combine NWP with deep learning. Firstly, to improve the spatiotemporal modeling of meteorological elements, a deconstruction mechanism and the multi-scale filters are composed to propose a multi-scale deconstructed ConvLSTM (MSD-ConvLSTM). The MSD-ConvLSTM captures and fuses the contextual information by multi-scale filters with low parameter consumption. Furthermore, an encoder-decoder is constructed to encode the features of multiple meteorological elements by deep CNN and decode the spatiotemporal information from different elements by the MSD-ConvLSTM. Our method demonstrates the data-driven way is significance for the weather prediction, which can be confirmed from the experimental results of precipitation forecasting on the European Centre Weather Forecasts (EC) and China Meteorological Forecasts (CM) datasets.

更新日期：2020-01-10
• arXiv.cs.CY Pub Date : 2019-12-30
Laura Kinkead; Ahmed Allam; Michael Krauthammer

Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional. Low quality health information, which is common on the internet, presents risks to the patient in the form of misinformation and a possibly poorer relationship with their physician. To address this, the DISCERN criteria (developed at University of Oxford) are used to evaluate the quality of online health information. However, patients are unlikely to take the time to apply these criteria to the health websites they visit. We built an automated implementation of the DISCERN instrument (Brief version) using machine learning models. We compared the performance of a traditional model (Random Forest) with that of a hierarchical encoder attention-based neural network (HEA) model using two language embeddings, BERT and BioBERT. The HEA BERT and BioBERT models achieved average F1-macro scores across all criteria of 0.75 and 0.74, respectively, outperforming the Random Forest model (average F1-macro = 0.69). Similarly, as measured by F-micro, HEA BERT and BioBERT scored on average 0.80 and 0.81 vs. 0.76 for the Random Forest model. Overall, the neural network based models achieved 81% and 86% average accuracy at 100% and 80% coverage, respectively, compared to 94% manual rating accuracy. The attention mechanism implemented in the HEA architectures provided 'model explainability' by identifying reasonable supporting sentences for the documents fulfilling the Brief DISCERN criteria. Our research suggests that it is feasible to automate online health information quality assessment, which is an important step towards empowering patients to become informed partners in the healthcare process.

更新日期：2020-01-10
• arXiv.cs.CY Pub Date : 2020-01-08
Marco Mamprin; Jo M. Zelis; Pim A. L. Tonino; Svitlana Zinger; Peter H. N. de With

Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. Statistical predictors are not robust enough to correctly identify patients who would benefit from Transcatheter Aortic Valve Implantation (TAVI). This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI. We adopt a modern gradient boosting on decision trees algorithm, specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling to identify the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 TAVI cases, reaching an AUC of 0.83. Our approach outperforms several widespread prognostic risk scores, such as logistic EuroSCORE II, the STS risk score and the TAVI2-score, which are broadly adopted by cardiologists worldwide.

更新日期：2020-01-09
• arXiv.cs.CY Pub Date : 2020-01-08
Midas Nouwens; Ilaria Liccardi; Michael Veale; David Karger; Lalana Kagal

New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet the minimal requirements that we set based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22--23 percentage points; and providing more granular controls on the first page decreases consent by 8--20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance.

更新日期：2020-01-09
• arXiv.cs.CY Pub Date : 2020-01-08
Benjamin Nguyen; Claude Castelluccia

In this document, we present a state of the art of anonymization techniques for classical tabular datasets. This article is geared towards a general public having some knowledge of mathematics and computer science, but with no need for specific knowledge in anonymization. The objective of this document it to explain anonymization concepts in order to be able to sanitize a dataset and compute reindentification risk. The document contains a large number of examples to help understand the calculations. ----- Dans ce document, nous pr\'esentons l'\'etat de l'art des techniques d'anonymisation pour des bases de donn\'ees classiques (i.e. des tables), \a destination d'un public technique ayant une formation universitaire de base en math\'ematiques et informatique, mais non sp\'ecialiste. L'objectif de ce document est d'expliquer les concepts permettant de r\'ealiser une anonymisation de donn\'ees tabulaires, et de calculer les risques de r\'eidentification. Le document est largement compos\'e d'exemples permettant au lecteur de comprendre comment mettre en oeuvre les calculs.

更新日期：2020-01-09
• arXiv.cs.CY Pub Date : 2018-11-10
Juste Raimbault

Several approaches and corresponding definitions of complexity have been developed in different fields. Urban systems are the archetype of complex socio-technical systems concerned with these different viewpoints. We suggest in this chapter some links between three types of complexity, namely emergence, computational complexity and informational complexity. We discuss the implication of these links on the necessity of reflexivity to produce a knowledge of the complex, and how this connects to the interdisciplinary of approaches in particular for socio-technical systems. We finally synthesize this positioning as a proposal of an epistemological framework called applied perspectivism, and discuss the implications for the study of urban systems.

更新日期：2020-01-09
• arXiv.cs.CY Pub Date : 2019-06-14
David Lopez; Bilal Farooq

Blockchain has the potential to render the transaction of information more secure and transparent. Nowadays, transportation data are shared across multiple entities using heterogeneous mediums, from paper collected data to smartphone. Most of this data are stored in central servers that are susceptible to hacks. In some cases shady actors who may have access to such sources, share the mobility data with unwanted third parties. A multi-layered Blockchain framework for Smart Mobility Data-market (BSMD) is presented for addressing the associated privacy, security, management, and scalability challenges. Each participant shares their encrypted data to the blockchain network and can transact information with other participants as long as both parties agree to the transaction rules issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility data-market. In a case study of real-time mobility data sharing, we demonstrate the performance of BSMD on a 370 nodes blockchain running on heterogeneous and geographically-separated devices communicating on a physical network. We also demonstrate how BSMD ensures the cybersecurity and privacy of individual by safeguarding against spoofing and message interception attacks and providing information access management control.

更新日期：2020-01-09
• arXiv.cs.CY Pub Date : 2019-11-07
Denise Pumain; Juste Raimbault

At the end of the five years of work in our GeoDiverCity program, we brought together a diversity of authors from different disciplines. Each person was invited to present an important question about the theories and models of urbanization. They are representative of a variety of currents in urban research. Rather than repeat here the contents of all chapters, we propose two ways to synthesize the scientific contributions of this book. In a first part we replace them in relation to a few principles that were experimented in our program, and in a second part we situate them with respect to a broader view of international literature on these topics.

更新日期：2020-01-09
• arXiv.cs.CY Pub Date : 2019-12-12
Inioluwa Deborah Raji; Jingying Yang

We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating disparate efforts across a variety of stakeholders, as well as bringing in the perspectives of currently missing voices that will be valuable in shaping future conversations. We describe the details of the initiative and the gaps we hope this project will help address.

更新日期：2020-01-09
• arXiv.cs.CY Pub Date : 2020-01-07
Zheyuan Ryan Shi; Claire Wang; Fei Fang

Artificial intelligence for social good (AI4SG) is a research theme that aims to use and advance artificial intelligence to address societal issues and improve the well-being of the world. AI4SG has received lots of attention from the research community in the past decade with several successful applications. Building on the most comprehensive collection of the AI4SG literature to date with over 1000 contributed papers, we provide a detailed account and analysis of the work under the theme in the following ways. (1) We quantitatively analyze the distribution and trend of the AI4SG literature in terms of application domains and AI techniques used. (2) We propose three conceptual methods to systematically group the existing literature and analyze the eight AI4SG application domains in a unified framework. (3) We distill five research topics that represent the common challenges in AI4SG across various application domains. (4) We discuss five issues that, we hope, can shed light on the future development of the AI4SG research.

更新日期：2020-01-08
• arXiv.cs.CY Pub Date : 2020-01-07
Austin P. WrightPolo; Omar ShaikhPolo; Haekyu ParkPolo; Will EppersonPolo; Muhammed AhmedPolo; Stephane PinelPolo; Diyi YangPolo; Duen HorngPolo; Chau

As toxic language becomes nearly pervasive online, there has been increasing interest in leveraging the advancements in natural language processing (NLP), from very large transformer models to automatically detecting and removing toxic comments. Despite the fairness concerns, lack of adversarial robustness, and limited prediction explainability for deep learning systems, there is currently little work for auditing these systems and understanding how they work for both developers and users. We present our ongoing work, RECAST, an interactive tool for examining toxicity detection models by visualizing explanations for predictions and providing alternative wordings for detected toxic speech.

更新日期：2020-01-08
• arXiv.cs.CY Pub Date : 2019-06-28
Michael Fröwis; Thilo Gottschalk; Bernhard Haslhofer; Christian Rückert; Paulina Pesch

Analyzing cryptocurrency payment flows has become a key forensic method in law enforcement and is nowadays used to investigate a wide spectrum of criminal activities. However, despite its widespread adoption, the evidential value of obtained findings in court is still largely unclear. In this paper, we focus on the key ingredients of modern cryptocurrency analytics techniques, which are clustering heuristics and attribution tags. We identify internationally accepted standards and rules for substantiating suspicions and providing evidence in court and project them onto current cryptocurrency forensics practices. By providing an empirical analysis of CoinJoin transactions, we illustrate possible sources of misinterpretation in algorithmic clustering heuristics. Eventually, we derive a set of legal key requirements and translate them into a technical data sharing framework that fosters compliance with existing legal and technical standards in the realm of cryptocurrency forensics. Integrating the proposed framework in modern cryptocurrency analytics tools could allow more efficient and effective investigations, while safeguarding the evidential value of the analysis and the fundamental rights of affected persons.

更新日期：2020-01-08
• arXiv.cs.CY Pub Date : 2019-10-08
Shriphani Palakodety; Ashiqur R. KhudaBukhsh; Jaime G. Carbonell

The Rohingya refugee crisis is one of the biggest humanitarian crises of modern times with more than 600,000 Rohingyas rendered homeless according to the United Nations High Commissioner for Refugees. While it has received sustained press attention globally, no comprehensive research has been performed on social media pertaining to this large evolving crisis. In this work, we construct a substantial corpus of YouTube video comments (263,482 comments from 113,250 users in 5,153 relevant videos) with an aim to analyze the possible role of AI in helping a marginalized community. Using a novel combination of multiple Active Learning strategies and a novel active sampling strategy based on nearest-neighbors in the comment-embedding space, we construct a classifier that can detect comments defending the Rohingyas among larger numbers of disparaging and neutral ones. We advocate that beyond the burgeoning field of hate-speech detection, automatic detection of \emph{help-speech} can lend voice to the voiceless people and make the internet safer for marginalized communities.

更新日期：2020-01-08
• arXiv.cs.CY Pub Date : 2020-01-03
Inioluwa Deborah Raji; Timnit Gebru; Margaret Mitchell; Joy Buolamwini; Joonseok Lee; Emily Denton

Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect. This concern is even more salient while auditing biometric systems such as facial recognition, where the data is sensitive and the technology is often used in ethically questionable manners. We demonstrate a set of five ethical concerns in the particular case of auditing commercial facial processing technology, highlighting additional design considerations and ethical tensions the auditor needs to be aware of so as not exacerbate or complement the harms propagated by the audited system. We go further to provide tangible illustrations of these concerns, and conclude by reflecting on what these concerns mean for the role of the algorithmic audit and the fundamental product limitations they reveal.

更新日期：2020-01-07
• arXiv.cs.CY Pub Date : 2020-01-03
Inioluwa Deborah Raji; Andrew Smart; Rebecca N. White; Margaret Mitchell; Timnit Gebru; Ben Hutchinson; Jamila Smith-Loud; Daniel Theron; Parker Barnes

Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, emergent issues can become difficult or impossible to trace back to their source. In this paper, we introduce a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle. Each stage of the audit yields a set of documents that together form an overall audit report, drawing on an organization's values or principles to assess the fit of decisions made throughout the process. The proposed auditing framework is intended to contribute to closing the accountability gap in the development and deployment of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity.

更新日期：2020-01-07
• arXiv.cs.CY Pub Date : 2020-01-04
Cole Freeman; Hamed Alhoori; Murtuza Shahzad

Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared---a conclusion that contradicts previous studies of other social media platforms.

更新日期：2020-01-07
• arXiv.cs.CY Pub Date : 2020-01-05
Pedro Ramaciotti Morales; Robin Lamarche-Perrin; Raphael Fournier-S'niehotta; Remy Poulain; Lionel Tabourier; Fabien Tarissan

Diversity is a concept relevant to numerous domains of research as diverse as ecology, information theory, and economics, to cite a few. It is a notion that is continuously gaining attention in the information retrieval, network analysis, and artificial neural networks communities. While the use of diversity measures in network-structured data finds a growing number of applications, no clear and comprehensive description is available for the different ways in which diversities can be measured in data described by these structures. In this article, we develop a formal framework for the application of a large family of diversity measures to heterogeneous information networks (HINs), a flexible, widely used, network data formalism. This allows for an extension of the application of diversity measures, from systems of classifications and apportionments, to more complex relations that can be better modeled by networks. In doing so, we do not only provide an effective organization of multiple practices from different domains, but we also unearth new observables in systems modeled by heterogeneous information networks. The pertinence of the approach is illustrated by the development of different applications related to various domains concerned by both diversity and networks. In particular, we illustrate the usefulness of these new proposed observables in the domains of recommender systems and social media studies among other fields.

更新日期：2020-01-07
• arXiv.cs.CY Pub Date : 2020-01-05
Mang Tik Chiu; Xingqian Xu; Yunchao Wei; Zilong Huang; Alexander Schwing; Robert Brunner; Hrant Khachatrian; Hovnatan Karapetyan; Ivan Dozier; Greg Rose; David Wilson; Adrian Tudor; Naira Hovakimyan; Thomas S. Huang; Honghui Shi

The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity. These challenges are not present in most of the common object datasets, and we show that they are more challenging than many other aerial image datasets. To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. We collected 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition. Our experiments demonstrate several challenges Agriculture-Vision poses to both the computer vision and agriculture communities. Future versions of this dataset will include even more aerial images, anomaly patterns and image channels. More information at https://www.agriculture-vision.com.

更新日期：2020-01-07
• arXiv.cs.CY Pub Date : 2020-01-06
Murat Ozer; Nelly Elsayed; Said Varlioglu; Chengcheng Li

In this paper, we proposed a novel automated model, called Vulnerability Index for Population at Risk (VIPAR) scores, to identify rare populations for their future shooting victimizations. Likewise, the focused deterrence approach identifies vulnerable individuals and offers certain types of treatments (e.g., outreach services) to prevent violence in communities. The proposed rule-based engine model is the first AI-based model for victim prediction. This paper aims to compare the list of focused deterrence strategy with the VIPAR score list regarding their predictive power for the future shooting victimizations. Drawing on the criminological studies, the model uses age, past criminal history, and peer influence as the main predictors of future violence. Social network analysis is employed to measure the influence of peers on the outcome variable. The model also uses logistic regression analysis to verify the variable selections. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, we discuss the concerns about the presumption of innocence right.

更新日期：2020-01-07
• arXiv.cs.CY Pub Date : 2020-01-06
Evangelos Pournaras

Digital societies come with a design paradox: On the one hand, technologies, such as Internet of Things, pervasive and ubiquitous systems, allow a distributed local intelligence in interconnected devices of our everyday life such as smart phones, smart thermostats, self-driving cars, etc. On the other hand, Big Data collection and storage is managed in a highly centralized fashion, resulting in privacy-intrusion, surveillance actions, discriminatory and segregation social phenomena. What is the difference between a distributed and a decentralized system design? How "decentralized" is the processing of our data nowadays? Does centralized design undermine autonomy? Can the level of decentralization in the implemented technologies influence ethical and social dimensions, such as social justice? Can decentralization convey sustainability? Are there parallelisms between the decentralization of digital technology and the decentralization of urban development?

更新日期：2020-01-07
• arXiv.cs.CY Pub Date : 2020-01-06
Rupak Sarkar; Hirak Sarkar; Sayantan Mahinder; Ashiqur R. KhudaBukhsh