Early and accurate detection of network intrusions is crucial to ensure network security and stability. Existing network intrusion detection methods mainly use conventional machine learning or deep learning technology to classify intrusions based on the statistical features of network flows. The feature extraction relies on expert experience and cannot be performed until the end of network flows, which

In recent years, there has been an irresistible need for the agri-food industry to prioritize the development of efficient supply chain systems considering circular economy concepts. Agri-food products are critical to sustaining human life, as they provide essential nutrients required for maintaining bodily functions. Producing the appropriate quantity of agri-food products to match the potential demand

The American Multimodal Energy System (AMES) is a system-of-systems comprised of four separate but interdependent infrastructure enterprises: the electric grid, the natural gas enterprise, the oil enterprise, and the coal enterprise. Their interdependence creates the need to better understand the underlying architecture in order to move towards a more sustainable, resilient and accessible energy system

Overload derived from the wide use of multiple information and communication technologies (ICTs) has become increasingly pervasive. Nevertheless, our understanding of the nature of ICT-mediated overload and how such overload affects individual productivity in multi-ICT context is limited. This study classifies ICT-mediated overload into congruent and incongruent types based on content relevance. Drawing

One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local

This study explores whether and how a holiday effect exists in consumers’ review rating behavior, which is well explained by expectancy-disconfirmation theory (EDT). After utilizing the secondary data of online restaurant reviews and conducting an online experiment, we find that consumers have the tendency to form high expectations before holidays, thus leading to more negative disconfirmation and

The transitioning into motherhood can be a stressful and resource-intensive experience, filled with numerous concerns and uncertainties during pregnancy, such as biological changes and unforeseen outcomes. Expectant mothers, those lacking prior motherhood experience, frequently seek solace within online communities, utilizing them as sources of social support to navigate the intricate landscape of

We investigate the way service providers who operate on an online peer-to-peer (P2P) platform readapted their marketing choices to face the Covid-19 pandemic. Through an empirical investigation on a large dataset of Airbnb properties in Rome, observed from January 2018 to December 2020, we provide a threefold contribution by investigating how Airbnb hosts reacted to the Covid-19 pandemic shock, in

The increasing use of information technology artifacts in daily life makes security a shared responsibility of both users and companies. In recent years, increasing a user's objective (i.e., actual) security knowledge and providing applications with more secure default settings appear among the most ubiquitous tools companies use to broaden their efforts to help users make more secure decisions. Examining

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies

Due to the immense benefits of guaranteeing user privacy, popular messaging platforms have shown enthusiasm for deploying End-to-End Encryption (E2EE). However, E2EE could be misused for bypassing media moderation, opening a shortcut for the viral spreading of harmful media. Private hash-matching techniques are proposed to identify harmful content in E2EE. Unfortunately, the pioneering solution incurs

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine learning that effectively deals with the privacy leakage problem by utilizing cryptographic primitives. However, how to prevent poisoning attacks in distributed situations has recently become a major FL concern. Indeed, an adversary can manipulate multiple edge nodes and submit malicious gradients to disturb the

A Blockchain network consists of a distributed ledger and a set of nodes participating in the network. The consistency in the ledger’s state is maintained by a Blockchain consensus mechanism. This property is essential since it is assumed that the participants in the network do not trust each other. Most Blockchain consensus algorithms are unsuitable for resource-constrained nodes due to their power

Hedonic information consumption (HIC) refers to the consumption of information for non-task-oriented purposes such as entertainment or pleasure. With the explosive growth of information availability through online channels such as social media, HIC has become a common and growing phenomenon; however, most information systems research to date has focused on factors surrounding information consumption

With the explosive growth of Internet traffic, large sensitive and valuable information is at risk of cyber attacks, which are mostly preceded by network reconnaissance. A moving target defense technique called host address mutation (HAM) helps facing network reconnaissance. However, there still exist several fundamental problems in HAM: 1) current approaches cannot be self-adaptive to adversarial

The Scanning the Literature column provides concise summaries of selected papers that have recently been published in the field of networking. Each summary describes the paper's main idea, methodology, and technical contributions. The purpose of the column is to bring the state of the art of networking research to readers of IEEE Network. Authors are also welcome to recommend their recently published

Jailbreaking, which refers to security-cracking behavior targeted at systems, threatens closed two-sided platforms. By allowing customers to access unauthorised content freely, jailbreaking may hurt developers’ enthusiasm and have complicated influences on the platform's profit. We develop an analytical model to investigate jailbreaking's influence on closed platforms. The findings reveal that platforms

Frequent and habitual engagement with social media can reinforce certain activities such as sharing, clicking hyperlinks, and liking, which may be performed with insufficient cognition. In this study, we aimed to examine the associations between personality traits, habits, and information processing to identify social media users who are susceptible to phishing attacks. Our experimental data consisted

As requirements for 6G systems start being shaped, we propose the provoking vision that the future generations of mobile networks shall reach beyond their traditional role of enablers for communication services. In fact, mobile networks have an impressive untapped potential for dual use as a pervasive remote sensing platform, which, if duly exploited, can support compelling original applications across

The IoT world is evolving with the latest technology trends, like edge computing, augmented & virtual reality, machine learning, robotics, and 5G. With the digital transformation happening in Industry 4.0, many industries are moving toward private 5G networks. There are massive number (hundreds to thousands) of IoT devices in a single factory depending on the scale of the industry and these factories

This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection

In this paper, we investigate the secure transmission of multi-resource heterogeneous radio access networks (RANs) in space-air-ground integrated network (SAGIN) from the perspective of physical layer security. Considering the network heterogeneity, resource constrain, and channel similarity, it is challenging to implement the physical layer security in SAGIN. Particularly, digital twin (DT) is considered

Facial age estimation aims to rank the face aging data by taking in the correlation among age categories. Conventional age estimation models are trained based on assumed high-quality training annotations in a totally-supervised manner. However, noisy data in a sparse distribution collected from unconstrained environment usually account for the corruption of produced gradients and ordinal relationships

Multi-label pedestrian attribute recognition (PAR) involves assigning multiple attributes to pedestrian images captured by video surveillance cameras. Despite its importance, learning robust attribute-related features for PAR remains a challenge due to the large intra-attribute variations in the image space. These variations, which stem from changes in pedestrian poses, illumination conditions, and

Ontologies play a pivotal role in knowledge representation, particularly beneficial for the Architecture, Engineering, and Construction (AEC) sector due to its inherent data diversity and intricacy. Despite the growing interest in ontology and data integration research, especially with the advent of knowledge graphs and digital twins, a noticeable lack of consolidated academic synthesis still needs

Solutions based on Blockchain (BC) technologies are gaining more and more attention in the context of Industry 4.0 (I4.0). While the research community has provided significant effort in either BC or I4.0 research domains, the complex implications of the adoption of BC technologies in this specific context are not analyzed in depth. This is a serious drawback because of the multiple and heterogeneous

The challenge of information control of complex systems has been studied especially précised during the last years due to the increased level of digital transformation. The integrated nature of modern economic systems requires advanced solutions based on combination of state-of-the-art digital techniques with revised classical managerial, economical and mathematical approaches. The research gap is

Ateniese et al. introduced the primitive of matchmaking encryption (ME) at CRYPTO 2019 and left open several important questions, which include extending ME to fuzzy cases or giving an efficient ME in the identity-based setting without relying on random oracles. The main challenge is to achieve fuzzy bilateral access control while providing identity privacy of the sender and receiver, message confidentiality

Several tools and methodologies were developed for manufacturing organizations to evaluate their sustainability performance. However, the growing number of methods and tools casts some doubt upon the applicability, efficiency, and capability of those methods by real manufacturing companies. Therefore, tools that offer a standard assessment of the sustainability performance of the organization with

Conversational agents are systems with great potential to enhance human-computer interaction in industrial settings. Although the number of applications of conversational agents in many fields is growing, there is no shared view of the elements to design and implement for chatbots in the industrial field. The paper presents the combination of many research contributions into an integrated conceptual

Deep Neural Networks (DNNs) are vulnerable to adversarial patch attacks, which raises security concerns for face recognition systems using DNNs. Previous attack methods focus on the perturbation texture and generate adversarial patches with fixed shapes at random or pre-designed locations, which causes poor adversarial transferability. This paper proposes a Spatial Mutable Adversarial Patch (SMAP)

In face antispoofing, it is desirable to have multimodal images to demonstrate liveness cues from various perspectives. However, in most face recognition scenarios, only a single modality, namely visible lighting (VIS) facial images is available. This paper first investigates the possibility of generating polarized (Polar) images from VIS cameras without changing the existing recognition devices to

Trace alignment can improve the subsequent side-channel analysis against the trace. Most current trace alignment schemes are, however, typically operated under a high signal-to-noise ratio (SNR), which demands them to be noise reduced before alignment when practical applications in the complex environment. In this paper, we propose a novel strategy for applying adaptive filtering in trace alignment

Quality management (QM) of additive manufacturing (AM) processes is currently immature in terms of transparency, traceability and security. In particular, quality-relevant documents are not documented and communicated in a traceable and transparent manner, which often leads to quality deficiencies. However, combining AM with blockchain technology can enable a solution that maps the AM value chain digitally

Published in Journal of Management Information Systems (Vol. 40, No. 3, 2023)

Published in Journal of Management Information Systems (Vol. 40, No. 3, 2023)

ABSTRACT Conversational Agents (CAs) are becoming part of our everyday lives. About 10 percent of users display aggressive behavior toward CAs, such as swearing at them when they produce errors. We conducted two online experiments to understand user aggression toward CAs better. In the first experiment, 175 participants used either a humanlike CA or a non-humanlike CA. Both CAs worked without errors

Organizations undergo significant change in the pursuit of digital transformation. Among those changes is replacing legacy information systems (LIS) with updated systems that integrate data, platfo...

Aircraft assembly demands efficient and accurate drilling and fastening of a large number of complex thin-walled aircraft parts. In this paper, a robotic assembly system is developed. The machine design and component functions of the robotic system are first discussed. Subsequently, the formation of the relative positioning error between the end-effector and an aircraft part is analyzed and modeled

Otoacoustic emission (OAE) biometrics are inherently robust to replay and falsification attacks. The widely studied transient-evoked OAE (TEOAE) is non-stationary and offers biometric value only in normal-hearing individuals since it is more susceptible to hearing loss. To address these issues, this paper presents a novel yet promising OAE biometric modality-stimulus-frequency OAE (SFOAE). Unlike TEOAE

Fuzzy message detection is a novel cryptographic primitive in which the remote storage cloud can help the client carry out fuzzy detections with some false-positive rate. This primitive protects the privacy of clients and does not reveal exactly the matching messages to the untrusted cloud. However, the existing public-key-based schemes require many public keys to generate a single flag ciphertext

Cyber-physical systems are becoming more intelligent with the adoption of heterogeneous sensor networks and machine learning capabilities that deal with an increasing amount of input data. While this complexity aims to solve problems in various domains, it adds new challenges for the system assurance. One issue is the rise in the number of abnormal behaviors that affect system performance due to possible

Unconstrained Palmprint Recognition (UPR) shows engaging potential owing to its high hygiene and privacy. The unconstrained acquisition usually produces wide variations, against which deep methods resort to large samples that are unavailable in practice, however. We focus on Few-Shot UPR (FS-UPR), a more general problem, recognizing query samples given a few support samples per class. Because scarce

This paper investigates the impact of social media utilization on problem-solving routines in organizations undergoing Lean Production (LP) implementation. A multi-case study was conducted in three firms from different sectors with distinct maturity levels of LP implementation. Empirical evidence was collected through complementary ways, such as semi-structured interviews, secondary data, and in loco

ABSTRACT A contemporary business challenge for bundling theory is the distribution of digital content such as media, entertainment, software, and other information goods. Consumers can use a number of devices to interact with software, online services, music, video, news, and other forms of digital content. For instance, Netflix videos and Kindle ebooks, initially accessed only on television sets and

ABSTRACT We study an online environment where a firm provides strategic product recommendations to consumers. We develop an analytical framework to integrate recommendations into the consumer search process. The firm sells two imperfectly substitutable products with different profit margins and makes a personalized product recommendation to each consumer based on its uncertainty (lack of knowledge)

ABSTRACT Incentivized blockchain-based online social media (BOSM), where creators and curators of popular content are paid in cryptocurrency, have recently emerged. Traditional social media ecosystems have experienced significant bot involvement in their platforms, which has often had a negative impact on both users and platforms. BOSM can provide additional direct financial incentives as motivation

ABSTRACT Multi-sided platforms must make decisions on both pricing and engineering investment and must continually adjust them as the platform scales over its lifecycle. Engineering investments can be allocated to features that improve a platform’s standalone value, social features to take advantage of same-side network effects, or integration tools and boundary resources to facilitate third-party

ABSTRACT We examine how hospital cost efficiency can improve because of knowledge spillover effects, arising from the experiences of members of a healthcare system. We identify two types of functional technology networks—that we term repository-type and workflow-type networks—which are defined by the adoption patterns of different types of electronic health record (EHR) systems application functions

This paper analyzes the herding behavior that characterizes lenders’ lending decisions on a microloan platform and explains how rational herding behavior can resolve the information-asymmetry probl...

ABSTRACT Media capabilities influence consumers’ trust in online exchanges. However, in the sharing economy, where consumers interact with service providers through a platform, conventional models of trust must be revisited. Our research identifies how media synchronicity and anonymity influence the relative importance of institution-based trust in sharing economy exchanges. We collected data from

ABSTRACT Participants in open collaboration communities coproduce knowledge despite minimal explicit communication to coordinate the efforts. Studying how participants coordinate around the knowledge artifact and its impacts are critical for understanding the open knowledge production model. This study builds on the theory of stigmergy, wherein actions performed by a participant leave traces on a knowledge

Collaborative manufacturing systems have become a key component of Industry 4.0, supported by Industrial Information Integration Engineering (IIIE) applications. Efficient scheduling and coordination of manufacturing tasks are vital for these systems, directly impacting factory operational efficiency. One of the most prominent optimization problems in collaborative manufacturing is the hybrid flow

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data aggregation, incentive design, and privacy protection, simultaneously. However, existing solutions usually focus on one or, at most, two of these issues. To this end, this paper presents CROWD FA, a novel paradigm for privacy-preserving MCS through federated analytics (FA), which aims to achieve a well-rounded solution

Most person re-identification (re-ID) approaches are based on representation learning of pedestrian images, which assume that the person’s appearance captured by cameras in the target is fully available. However, the exposure of appearance could cause serious privacy leakages. To address this issue, we focus on a new privacy-protected person re-ID task where the person’s appearance is unavailable for

We use the lens of the intrinsic/extrinsic motivation theory to holistically model the effects of intangible awards on member contribution in virtual communities. Using multiple studies on Yelp, we validate that the influence of award stimuli on contribution outcomes become significantly moderated by the aspect of contribution (quantity versus quality) and the novelty of stimuli (time and repetition

Originally a part of the study of physics, quantum mechanics is fast becoming an interdisciplinary field. Quantum computing offers the potential to reach the higher levels of accuracy and security associated with a much faster speedup. Many different approaches have been proposed to building a quantum computer. Seeking the best solution for quantum computers is the mission of both practitioners and

The primary objective of anti-jamming techniques is to ensure that the transmitted data arrives at the intended receiver without being disturbed or jammed with by any jamming signal or other hostile activities to ensuring the security of the communication system. Deep reinforcement learning (DRL) has been extensively utilized in solving the dynamic spectrum anti-jamming problem. However, most of existing

Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce perceptible perturbations to the images have attracted the interest of researchers. Existing patch attacks rely on the architecture of the model or the probabilities of predictions and perform poorly in the decision-based

Wireless energy harvesting (WEH) enabled Device-to-Device (D2D) communication emerges as an effective technique to improve spectral and energy efficiencies. However, D2D users are usually power-constrained devices that may be monitored or attacked by adversaries easily. To confuse randomly distributed adversaries in WEH-enabled D2D underlaying cellular networks, power beacons (PBs) can be used to send