We examine how firms configure alliance portfolios—that is, networks of partnering firms—in order to exchange, share, or codevelop the capabilities they require to engage in digital innovation. We analyze data from 550 U.S. firms and the strategic alliances they formed within and across industrial sectors to study how the configuration of alliance portfolios in terms of size, degree of exploration

AI-powered learning technologies are increasingly being used to automate and scaffold learning activities (e.g., personalised reminders for completing tasks, automated real-time feedback for improving writing, or recommendations for when and what to study). While the prevailing view is that these technologies generally have a positive effect on student learning, their impact on students’ agency and

Over the past decade, there has been an enormous upsurge in the use of educational apps in primary schools. However, few studies have examined how children interact with these apps and how their interaction patterns relate to learning outcomes. An interaction pattern that is potentially detrimental to learning is repeated mistakes, defined as making the same mistake more than once when answering a

Topic modeling includes a variety of machine learning techniques for identifying latent themes in a corpus of documents. Generating an exact solution (i.e., finding global optimum) is often computationally intractable. Various optimization techniques (e.g., Variational Bayes or Gibbs Sampling) are employed to generate topic solutions approximately by finding local optima. Such an approximation often

Extended reality (XR) applications—encompassing virtual reality, augmented reality, and mixed reality—are finding their way into multiple domains. Each area has different motivations for employing and different criteria for evaluating XR. Multiple surveys describe XR and its evaluation in particular fields. However, these surveys do not always agree on the definition of XR. This lack of consensus makes

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further

Predictive modeling of various systems around the world is extremely essential from the physics and engineering perspectives. The recognition of different systems and the capacity to predict their future behavior can lead to numerous significant applications. For the most part, physics is frequently used to model different systems. Using physical modeling can also very well help the resolution of complexity

The astounding success made by artificial intelligence in healthcare and other fields proves that it can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data dependent and require large datasets for training. Many junior researchers face a lack of data for a variety of reasons. Medical image acquisition, annotation, and analysis are costly

Neural networks (NN s) have become one of the most important tools for artificial intelligence. Well-designed and trained NN s can perform inference (e.g., make decisions or predictions) on unseen inputs with high accuracy. Using NN s often involves sensitive data: Depending on the specific use case, the input to the NN and/or the internals of the NN (e.g., the weights and biases) may be sensitive

In the evolution of agriculture to its next stage, Agriculture 5.0, artificial intelligence will play a central role. Controlled-environment agriculture, or CEA, is a special form of urban and suburban agricultural practice that offers numerous economic, environmental, and social benefits, including shorter transportation routes to population centers, reduced environmental impact, and increased productivity

Outsourcing data to the cloud has become prevalent, so Searchable Symmetric Encryption (SSE), one of the methods for protecting outsourced data, has arisen widespread interest. Moreover, many novel technologies and theories have emerged, especially for the attacks on SSE and privacy-preserving. But most surveys related to SSE concentrate on one aspect (e.g., single keyword search, fuzzy keyword search)

As the adoption of IoT-based smart homes continues to grow, the importance of addressing potential conflicts becomes increasingly vital for ensuring seamless functionality and user satisfaction. In this survey, we introduce a novel conflict taxonomy, complete with formal definitions of each conflict type that may arise within the smart home environment. We design an advanced conflict model to effectively

Though the sheer volume of data that is collected is immense, it is the rare categories that are often the most important in many high-impact domains, ranging from financial fraud detection in online transaction networks to emerging trend detection in social networks, from spam image detection on social media platforms to rare disease diagnosis in medical decision support systems. The unique challenges

Quadrotor Unmanned Aerial Vehicle (UAV) is an unstable system, so it needs to be controlled efficiently and intelligently. Moreover, due to its non-linear, coupled, and under-actuated nature, the quadrotor has become an important research platform to study and validate various control theories. Different control approaches have been used to control the quadrotor UAV. In this context, a comprehensive

Purpose Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process

One discussion in the context of education for sustainable development centres on the importance of cognitive as well as affective processes for promoting pro-environmental behaviour. In our study, we investigate how affordances of immersive Virtual Reality (iVR) such as the virtual embodiment of a tree might provide new opportunities to achieve this goal. The aim of our study was twofold: Firstly

Automated co-located human–human interaction analysis has been addressed by the use of nonverbal communication as measurable evidence of social and psychological phenomena. We survey the computing studies (since 2010) detecting phenomena related to social traits (e.g., leadership, dominance, and personality traits), social roles/relations, and interaction dynamics (e.g., group cohesion, engagement

The Internet of Things (IoT) encompasses a network of physical objects embedded with sensors, software, and data processing technologies that can establish connections and exchange data with other devices and systems via the Internet. IoT devices are incorporated into various products, ranging from ordinary household items to complex industrial appliances. Despite the increasing demand for IoT, security

Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce due to the severe overfitting problem. Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to

Recent advances in extended reality (XR) technologies have enabled new and increasingly realistic empathy tools and experiences. In XR, all interactions take place in different spatial contexts, all with different features, affordances, and constraints. We present a systematic literature survey of recent work on empathy in XR. As a result, we contribute a research roadmap with three future opportunities

For the past several decades, VLSI design has been focused on lowering the size, power, and delay. As of now, this miniaturization does not seems to be a possible way to address the demands of consumers. Quantum Dot Cellular Automata (QCA) technology is a promising technique that is able to provide low-power high-speed circuits at nano-scale. Much work has been done in this area where the researchers

Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit

Abstract not available

This study explored the effects of out-of-class interactions with intelligent personal assistants (IPAs) versus human interlocutors on EFL learners' willingness to communicate (WTC) in English. A total of 92 first-year college students were recruited to participate in interactive out-of-class activities, which were held in 10-min sessions twice a week for 12 weeks. The participants were divided into

Purpose Integrating transaction costs economics and task-technology fit theory, this study distinguishes two categories of social media–enabled interactions, namely task-related interactions and tie-related interactions, and explores the match between these two and firms' use of contracts in achieving safeguarding and coordinating purposes in interfirm governance. Design/methodology/approach Two studies

Scholars and practitioners have called for personalized and widely accessible professional development (PD) for teachers. Yet, a long-standing tension between customizing support and increasing access to such support has hindered the scale-up of high-quality PD for individual teachers. This study addresses this challenge by developing a computerized program for middle school mathematics teachers that

Digital social innovation (DSI) offers incumbents a strategic field of action to leverage the opportunities of digital technologies to address pressing societal challenges. By proposing a taxonomy and 12 clusters of incumbents’ DSI initiatives based on a sample of 296 real-world objects, we develop a unified understanding of DSI and its characteristics. This lays the foundation for further theorising

Computational thinking is widely recognized as an essential skill for adapting to the current era, with programming learning being the most effective means to develop it. It is recommended that computational thinking and learning programming be introduced as early as elementary school. However, elementary school students often possess limited prior knowledge of programming, posing challenges in their

The yearly global production of data is growing exponentially, outpacing the capacity of existing storage media, such as tape and disk, and surpassing our ability to store it. DNA storage—the representation of arbitrary information as sequences of nucleotides—offers a promising storage medium. DNA is nature’s information-storage molecule of choice and has a number of key properties: It is extremely

In this paper, we review and meta-analyze the findings of experimental studies published between 2006 and 2022 that examined the effects of coding and programming interventions on children's core and higher order executive functions (response inhibition, working memory, cognitive flexibility, planning and problem solving). The systematic review and meta-analysis aimed to address three research questions:

Purpose This study is based on the heuristic-systematic model (HSM) to dynamically examine the effect of review variance on sales and the boundary conditions that mitigate this effect. Design/methodology/approach Based on the theoretical domain of HSM, a conceptual model is proposed that analyzes the nonlinear relationship between review variance and sales and the interaction and motivation factors

Abstract not available

In this systematic review, Kitchenham’s framework is used to explore what tasks, techniques, and benchmarks for Sentiment Analysis have been developed for addressing topics about the natural environment. We comprehensively analyze seven dimensions including contribution, topical focus, data source and query, annotation, language, detail of the task, and technology/algorithm used. By showing how this

Bark beetle outbreaks can have serious consequences on forest ecosystem processes, biodiversity, forest structure and function, and economies. Thus, accurate and timely detection of bark beetle infestations in the early stage (known as green-attack detection) is crucial to mitigate the further impact, develop proactive forest management activities, and minimize economic losses. Incorporating remote

This survey examines approaches to promote Collaborative Learning in distributed systems for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of Intelligent Autonomous Systems based on Collaborative Learning, analyzing aspects in four dimensions: computing environment, performance concerns, system management, and privacy concerns, mapping the significant requirements

Distributed Scrum adapts the Scrum project management framework for geographically distributed software teams. Experimentally evaluating the effectiveness of Distributed Scrum is impractical, but many case studies and experience reports describe teams and projects that used Distributed Scrum. This article synthesizes the results of these cases using case meta-analysis, a technique for quantitatively

Context: Compilers are the fundamental tools for software development. Thus, compiler defects can disrupt development productivity and propagate errors into developer-written software source code. Categorizing defects in compilers can inform practitioners and researchers about the existing defects in compilers and techniques that can be used to identify defects systematically. Objective: The goal of

Recent years have seen remarkable progress in deep learning powered visual content creation. This includes deep generative 3D-aware image synthesis, which produces high-fidelity images in a 3D-consistent manner while simultaneously capturing compact surfaces of objects from pure image collections without the need for any 3D supervision, thus bridging the gap between 2D imagery and 3D reality. The field

Generative Adversarial Networks (GANs) are a remarkable creation with regard to deep generative models. Thanks to their ability to learn from complex data distributions, GANs have been credited with the capacity to generate plausible data examples, which have been widely applied to various data generation tasks over image, text, and audio. However, as with any powerful technology, GANs have a flip

Machine learning (ML) has gained widespread adoption in a variety of fields, including computer vision and natural language processing. However, ML models are vulnerable to membership inference attacks (MIAs), which can infer whether access data was used in training a target model, thus compromising the privacy of training data. This has led researchers to focus on protecting the privacy of ML. To

Although highly valuable for a variety of applications, urban mobility data are rarely made openly available, as it contains sensitive personal information. Synthetic data aims to solve this issue by generating artificial data that resembles an original dataset in structural and statistical characteristics, but omits sensitive information. For mobility data, a large number of corresponding models have

Wikipedia is the world’s largest online encyclopedia, but maintaining article quality through collaboration is challenging. Wikipedia designed a quality scale, but with such a manual assessment process, many articles remain unassessed. We review existing methods for automatically measuring the quality of Wikipedia articles, identifying and comparing machine learning algorithms, article features, quality

The sophistication and complexity of cyber attacks and the variety of targeted platforms have grown in recent years. Adversaries are targeting a wide range of platforms, e.g., enterprise networks, mobile phones, PCs, and industrial control systems. The past few years have also seen various cyber attacks on transportation systems, including attacks on ports, trains, airports, and aircraft. Due to the

As machine learning becomes more widely used, the need to study its implications in security and privacy becomes more urgent. Although the body of work in privacy has been steadily growing over the past few years, research on the privacy aspects of machine learning has received less focus than the security aspects. Our contribution in this research is an analysis of more than 45 papers related to privacy

Data scientists are tasked with obtaining insights from data. However, suitable data is often not immediately at hand, and there may be many potentially relevant datasets in a data lake or in open data repositories. As a result, data discovery and exploration are necessary, but often time consuming, steps in a data analysis workflow. Data discovery is the process of identifying datasets that may meet

The rain removal task is to restore a clean image from the contaminated image by separating the background. Since the rise of deep learning in 2016, the task of image deraining has also stepped into the era of deep learning. Numerous researchers have devoted themselves to the field of computer vision and pattern recognition. However, there is still a lack of comprehensive review papers focused on using

Although cloud computing is associated with organizational agility, anecdotal evidence points to resistance to cloud computing by employees in information technology (IT) units. We explored the links between software as a service (SaaS) and organizational agility by conducting two stages of interviews with key informants in large organizations, and by employing affordance and inertia-theoretical lenses

Purpose Drawing upon social cognitive theory, this study aims to investigate the potential predictors and consequences of social media health-misinformation seeking behavior during the coronavirus (COVID-19) pandemic. Design/methodology/approach Using a sample of 230 international students studying at Wuhan University and Beijing Language and Cultural University, China, this study employs structural

Game learning analytics (GLA) is an emerging technology that facilitates teachers’ evidence-based pedagogical design and assessments. Despite its affordances and potential in K–12 classrooms, teachers’ integration of GLA in teaching practices remains largely unexplored. This study implemented an educational game on collaborative problem solving (CPS) and a GLA system for assisting K–12 teachers in

Recent meta-analytic evidence suggests that students’ minds are likely to wander off-task frequently, regardless of the learning modality; yet virtual reality (VR) has been notably unexplored in this space. VR may present an opportunity to mitigate task-unrelated thought (TUT; the most common operationalization of mind wandering) because it minimizes audio-visual distractions and increases feelings

The relevance of code comprehension in a developer’s daily work was recognized more than 40 years ago. Consequently, many experiments were conducted to find out how developers could be supported during code comprehension and which code characteristics contribute to better comprehension. Today, such studies are more common than ever. While this is great for advancing the field, the number of publications

Economic systems play pivotal roles in the metaverse. However, we have not yet found an overview that systematically introduces economic systems for the metaverse. Therefore, we review the state-of-the-art solutions, architectures, and systems related to economic systems. When investigating those state-of-the-art studies, we keep two questions in mind: (1) What is the framework of economic systems

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and

Dynamic graphs serve as a generic abstraction and description of the evolutionary behaviors of various complex systems (e.g., social networks and communication networks). Temporal link prediction (TLP) is a classic yet challenging inference task on dynamic graphs, which predicts possible future linkage based on historical topology. The predicted future topology can be used to support some advanced

Purpose This study investigates the communication behavior of public health organizations on Twitter during the COVID-19 vaccination campaign in Brazil. It contributes to the understanding of the organizational framing of health communication by showcasing several instances of framing devices that borrow from (Brazilian) internet culture. The investigation of this case extends the knowledge by providing