Unlike emergent brokers, formal brokers are appointed to mediate information exchanges in a highly structured setting. While the use of formal brokers has increased, particularly in globally distributed work, the extant literature has so far paid little attention to the information brokering challenges they face. Our case analysis revealed that, despite the structured setting, information users often

This paper employs a three-pronged approach to examine price patterns in a substantial chunk of trades in nonfungible token (NFT) transactions to identify suspicious trading activities. Tests based on Benford's Law, clustering via Student's t-test, and Pareto-Levy analyses identify nonconformity. This potentially signals manipulation. Reapplying Benford's Law to a subset of 50 highly popular NFTs’

Intermittent discontinuance acts as a precursor to user loss and has become a crucial and challenging issue in the operation and management of social media. From the perspective of alternative competition, we integrate the push–pull–mooring framework and expectancy violation theory to explore how contradictory psychology occurs and impacts users’ intermittent discontinuance of strong-ties social functions

Focusing on the passive use of Instagram, we apply the affordance perspective to deeply explore its use and use-related outcomes. In the qualitative study, we uncover the affordances of focal social media features. Two distinct groups of affordances (self- and others-oriented) emerge. Following the grounded theory methodology, we develop the affordances-actualizations-outcomes model, explaining how

As the organizational demand for ICT (Information Communication Technologies) undergoes rapid changes, organizations must integrate other enterprise systems into ICT to continually address their current challenges. However, few studies have considered the dynamic fabric of ICT, especially since its usage is evolving with the development of digital technology and the organization's needs. Therefore

We investigate the strategies of an e-commerce platform (EP), selecting a supplier for its store brand and determining whether to provide information that can reduce consumers’ uncertainty about their quality preferences. The EP can choose either a non-competing outside supplier or a competing inside supplier that sells a high-quality brand product through the EP. Consumers have complete knowledge

This study investigates the temporal dynamics of herding behavior in equity crowdfunding, and especially when herding momentum is likely to occur during a funding campaign under the influence of different information disclosures. Our results are consistent with the multidimensional uncertainty theory in which herding does not occur in the first stage of funding campaigns but arises in the later stages

Visual inspection is an important procedure in manufacturing industries to guarantee product quality and reduce costs of poor quality. Several challenges exist for manufacturing inspection automation, including the false positive and false negative trade-off, autonomous decision-making, and inspection algorithm selection. This paper addresses these challenges by proposing a two-level visual analytics

This article investigates the role played by Information and Communications Technology (ICT) and specifically social media in reshaping the mediation (‘Kulturvermittlung’) processes enacted by museums along with their digital transformation. We apply a multi-methods research design, including qualitative research conducted within a selection of museums in France, an intercept study at Le Louvre Museum

The following are those that were assigned reviews between November 2022 and October 2023.

The increasing production of solid waste rate in urban areas plays a critical role in sustainable development. To mitigate the adverse effects of waste and enhance waste management efficiency, this paper introduces a holistic approach that notably reduces the overall cost while mitigating social and environmental impacts. Central to the system's efficacy is the critical process of waste sorting, which

Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capability. To address these issues, we propose a

There is limited evidence on why decision makers extend beyond routine use toward more advanced use of Business Intelligence (BI) systems. This study developed an extended DeLone and McLean information system success model hypothesizing the effects of system, data, information, and service quality, along with self-efficacy and task complexity, on routine and advanced use of BI. Task complexity was

A high brand reputation is usually associated with a brand premium and high profit. Does it still hold in the online market with rich information? How do the sales volume information and ratings information change the influence of the existing brand reputation? This study investigates a two-period pricing model of duopoly firms with different levels of brand reputation (high vs. low) in the presence

As frequent service failures raise user concerns, guarantee compensation has become a competitive instrument for cloud service providers (CSPs) in addition to price. This study proposes a game-theoretical model where two CSPs compete on both price and compensation. We consider two roles of compensation: (1) remedying user losses and (2) helping users to form quality perception. The results indicate

This study develops artifact and design principles for a campus-wide crisis management system amid the COVID-19 pandemic and beyond. Drawing upon crisis management literature and the uncertainty reduction theory, we develop a preliminary crisis management system with three fundamental components: dashboard, portal, and forum, geared toward aggregating environmental information, expert knowledge, and

The steep development of artificial intelligence (AI) is accompanied by a completely new set of challenges for information systems (IS) research and practice, especially in the area of individual-level technology adoption. In this article, we elaborate on the important role that biases play regarding the adoption of AI-based IS by individuals in a work environment and for which, in addition, an alarmingly

Blockchain-based data storage has become an emerging paradigm, providing a fair and transparent data platform for decentralized applications. However, how to achieve secure on-chain verification for arbitrary SQL queries in such a decentralized storage remains under-explored. Due to the limitations of authenticated data structure (ADS), existing works either do not consider arbitrary query verification

Drawing on the resource-based view, this study investigates the interactive effects of green information technologies/systems (IT/IS) adoption, environmental motivation, and IT–business strategic alignment on organizations’ perceived relative environmental performance. We use data from a field study of 587 firms in China to test our hypotheses. The results confirm that green IT/IS positively affects

Although emotions are often evoked when medical professionals are required to employ new systems at work, their roles in information system post-adoption literature are relatively less examined. We draw on appraisal theories of emotions to unveil the emotional mechanisms underlying medical professionals' behaviors at different post-adoption stages. Results from data collected from a two-round survey

We propose a Universal Defence against backdoor attacks based on Clustering and Centroids Analysis (CCA-UD). The goal of the defence is to reveal whether a Deep Neural Network model is subject to a backdoor attack by inspecting the training dataset. CCA-UD first clusters the samples of the training set by means of density-based clustering. Then, it applies a novel strategy to detect the presence of

In this article, a practical finite-time command-filtered adaptive backstepping (PFTCFAB) control method is presented for a class of uncertain nonlinear systems with nonparametric unknown nonlinearities and external disturbances. Unlike PFTCFAB control techniques that use neural networks (NNs) or fuzzy-logic systems (FLSs) to deal with system uncertainties, the proposed method is capable of handling

Reconstructing a high-resolution hyperspectral image (HSI) from a low-resolution HSI is significant for many applications, such as remote sensing and aerospace. Most deep learning-based HSI super-resolution methods pay more attention to developing novel network structures but rarely study the HSI super-resolution problem from the perspective of image dynamic evolution. In this article, we propose that

This article proposes a data-efficient model-free reinforcement learning (RL) algorithm using Koopman operators for complex nonlinear systems. A high-dimensional data-driven optimal control of the nonlinear system is developed by lifting it into the linear system model. We use a data-driven model-based RL framework to derive an off-policy Bellman equation. Building upon this equation, we deduce the

With the increase of open government data (OGD) supply in many countries, promoting its reuse becomes an important issue to a variety of stakeholders. Considering that most citizens have never used OGD, this study adopted a mixed methods design that includes an experiment and a survey to explore the factors that affect OGD reuse intention. The findings include: (1) user information need, significantly

The antecedents of employees’ cyberloafing (internet use for nonwork purposes during office hours) have become a burgeoning research topic. This research therefore explores how and when participative leadership serves as an important antecedent to employees’ cyberloafing. Drawing upon self-concept-based theory, we hypothesize that participative leadership can reduce the cyberloafing of employees with

Machine unlearning as a fundamental requirement in Machine-Learning-as-a-Service (MLaaS) has been extensively studied with increasing concerns about data privacy. It requires MLaaS providers should delete training data upon user requests. Unfortunately, none of the existing studies can efficiently achieve machine unlearning validation while preserving the retraining efficiency and the service quality

Dimensionality reduction (DR) targets to learn low-dimensional representations for improving discriminability of data, which is essential for many downstream machine learning tasks, such as image classification, information clustering, etc. Non-Gaussian issue as a long-standing challenge brings many obstacles to the applications of DR methods that established on Gaussian assumption. The mainstream

Vertical federated learning (VFL) is being used more and more widely in industry. One of its most common application scenarios is a two-party setting: a participant (i.e., the host), who exclusively owns the labels but possesses insufficient number of features, wants to improve its model performance by combining features from another participant (i.e., the client) of a different business group. The

As Unmanned Aerial Vehicle (UAV) is more and more frequently used in farming and logistics, civil and military alike, researches involving UAVs also starts to boom. In the civil field, UAV is generally flown in urban areas, so buildings are the main factors hindering the normal flight of UAV. Therefore, it is necessary to calculate an optimal flight path of UAV under constraints. The intelligent logistics

Numerous fault models with distinct characteristics and effects have been developed. The costs, repeatability, and practicability of these models should be assessed. Moreover, there must be effective ways to use the injected fault to retrieve the secret key, particularly if the implementation includes any countermeasures. In this paper, we introduce a new fault analysis called “linked fault analysis”

The Named Data Networking (NDN) architecture is a futuristic internet infrastructure that aims to deliver content efficiently. However, NDN is faced with the challenge of ensuring the privacy of both content and names. Traditional solutions have focused on encrypting and signing content before injecting the resultant ciphertext into the NDN platform to provide confidentiality and integrity. However

Heterogeneous face analysis is an important and challenge problem in face recognition community, because of the large modality discrepancy between heterogeneous face images. Existing methods either focus on transforming heterogeneous faces into the same style via face synthesis process, or intend to directly recognize heterogeneous face via modality invariant descriptors. However, the tasks of cross

Industries, such as manufacturing, are accelerating their embrace of the metaverse to achieve higher productivity, especially in complex industrial scheduling. In view of the growing parking challenges in large cities, high-density vehicle spatial scheduling is one of the potential solutions. Stack-based parking lots utilize parking robots to densely park vehicles in the vertical stacks like container

Federated learning (FL) allows multiple clients to train deep learning models collaboratively while protecting sensitive local datasets. However, FL has been highly susceptible to security for federated backdoor attacks (FBA) through injecting triggers and privacy for potential data leakage from uploaded models in practical application scenarios. FBA defense strategies consider specific and limited

In federated learning, standard machine learning (ML) techniques are modified so they can be applied to data held by separate participants without the need for exchanging said data and while preserving privacy. Other data modelling techniques, such as singular value decomposition, have been similarly federated, enabling federated principal component analysis (PCA), which is a popular preprocessing

With the property of helping users further enhance the anonymity of transactions, mixing services in blockchain have gained wide popularity in recent years. However, the strong untraceability offered by mixing services has led to the abuse of them by criminals for money laundering and committing fraud. These illegal actions pose significant threats to the blockchain ecosystem and financial order. In

As innovative technologies such as autonomous driving, over-the-air technology, and vehicle-to-everything are widely applied to intelligent connected vehicles, people can gain a more convenient and safer driving experience. Although the application of these technologies facilitates our lives, they also bring a series of vulnerable interfaces (such as 5G, Bluetooth, and WiFi), which pose a significant

In side-channel analysis (SCA), Points-of-Interest (PoIs), i.e., the informative sample points remain sparsely scattered across the whole side-channel trace. Several works in the SCA literature have demonstrated that the attack efficacy could be significantly improved by combining information from the sparsely occurring PoIs. In Deep Learning (DL), a common approach for combining the information from

This paper deals with reversible contrast enhancement (RCE). Image enhancement is achieved by histogram specification, the most popular contrast enhancement technique. A low bitrate procedure for inverting histogram specification is developed. The data for original image recovery is reversibly embedded into the contrast enhanced version. Very low distortion RDH schemes that exploit the sparse histogram

The increasing connectivity in the 5G Internet of Things networks has enlarged the attack surface and made the traditional security defense inadequate for sophisticated attackers, who can move laterally from node to node with stored credentials once build a foothold in the network. There is a need to shift from the perimeter-based defense to a zero-trust security framework that focuses on agent-centric

Post-processing of the raw bits produced by a true random number generator (TRNG) is always necessary when the entropy per bit is insufficient for security applications. In this paper, we derive a tight bound on the output min-entropy of the algorithmic post-processing module based on linear codes, known as linear correctors. Our bound is based on the codes’ weight distributions, and we prove that

Authentication and cryptography are critical security functions and, thus, are very often included as part of code. These functions require using credentials, such as passwords, security tokens, and cryptographic keys. However, developers often incorrectly implement/use credentials in their code because of a lack of secure coding skills. This paper analyzes open-source projects concerning the correct

Sustainable, smart connected cars (SSCCs) are one of the representative sustainable products that leverage smart technologies (e.g., the internet of things, artificial intelligence, big data). Although many studies have investigated consumers’ purchase decisions regarding sustainable products, little research has addressed SSCCs and the relationship between privacy, disclosure intentions, and purchase

Insiders have the potential to do a great deal of damage, given their legitimate access to organisational assets and the trust they enjoy. Organisations can only mitigate insider threats if they understand what the different kinds of insider threats are, and what tailored measures can be used to mitigate the threat posed by each of them. Here, we derive VISTA (inclusiVe InSider Threat tAxonomy) based

The increasing number of devices and frequent interactions of agents from networked multiagent systems (MASs) exacerbate the risks of potential cyber attacks, especially the different point attacks and multiple pattern attacks. This article considers the output formation-containment problem for MASs under multipoint multipattern false data injection (FDI) attacks. The multipoint describes the attacks

Modeling correlations between multimodal physiological signals e.g., canonical correlation analysis (CCA) for emotion recognition has attracted much attention. However, existing studies rarely consider the neural nature of emotional responses within physiological signals. Furthermore, during fusion space construction, the CCA method maximizes only the correlations between different modalities and neglects

Over the last decades, finger vein biometric recognition has generated increasing attention because of its high security, accuracy, and natural anti-counterfeiting. However, most of the existing finger vein recognition approaches rely on image enhancement or require much prior knowledge, which limits their generalization ability to different databases and different scenarios. Additionally, these methods

Blockchain, as an emerging technology, is constantly evolving due to its remarkable advantages but is also subject to its unalterability, which leads to the misuse of blockchain storage and causes adverse effects. Hence, the redactable blockchain was proposed, which can alleviate the above issues in a controlled manner. However, a situation exists in which the modifiers specified by customized identities

This study reveals the paradox of technostress on the elderly due to the large-scale COVID testing during the post-COVID-19 period. Drawing upon the stressor-strain-outcomes framework, we examined how technology-driven stressors affect the elderly's avoidance behaviors. Results indicate that two types of technology-driven stressors exert opposite influences on the elderly's avoidance behaviors through

This study examines how an individual cognitively generates creative ideas using mobile collaboration platforms (MCPs). Applying the cognitive creativity perspective and the knowledge of the idiosyncratic capabilities of MCPs, the study proposes a research model that explains MCP-driven creative idea generation mechanisms. The model is validated using a field study where individuals were involved in

In recent years, high-order fully actuated (HOFA) systems, founded by Prof. GR Duan, have recorded rapid progress for deterministic systems. However, the control issue of stochastic fully actuated systems is still an open problem. This study develops a novel stochastic HOFA system model that complements the existing HOFA methodology. Notably, stochastic signals can be considered in the proposed model

This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIM $\epsilon $ ) delivers minimum privacy budget ( $\epsilon $ ) value to protect personal or sensitive data during data mining and publication. In this work, the proposed Pattern identification in the Locale of Users with

In this paper, we propose an Aggregation and Separation Domain Generalization (ASDG) method for Audio DeepFake Detection (ADD). Fake speech generated from different methods exhibits varied amplitude and frequency distributions rather than genuine speech. In addition, the spoofing attacks in training sets may not keep pace with the evolving diversity of real-world deepfake distributions. In light of

The serious concerns over the negative impacts of Deepfakes have attracted wide attentions in the community of multimedia forensics. The existing detection works achieve deepfake detection by improving the traditional backbone networks to capture subtle manipulation traces. However, there is no attempt to construct new backbone networks with different structures for Deepfake detection by improving

Set-valued data are widely used to represent information in the real word, such as individual daily behaviors, items in shopping carts and web browsing history. By collecting set-valued data and identifying heavy hitters, service providers (i.e., the collector) can learn usage preferences of costumers (i.e., users), and improve the quality of their services by the learned information. However, the

Machine Learning (ML) techniques have been applied to many real-world applications to perform a wide range of tasks. In practice, ML models are typically deployed as the black-box APIs to protect the model owner’s benefits and/or defend against various privacy attacks. In this paper, we present Gradient-Leaks as the first evidence showcasing the possibility of performing membership inference attacks

In this paper, the issue of tracking control for connected servosystems with coupling input and false data injection (FDI) attacks is studied. A fuzzy linguistic knowledge reasoning-based secure control scheme is proposed. Firstly, the dynamic model of connected nonlinear servosystems suffer from coupling input and FDI attacks is modeled. Then, a fuzzy linguistic estimator based on experimental observation

Since Deep Learning (DL) backdoor attacks have been revealed as one of the most insidious adversarial attacks, a number of countermeasures have been developed with certain assumptions defined in their respective threat models. However, their robustness is currently inadvertently ignored, which can introduce severe consequences, e.g., a countermeasure can be misused and result in a false implication

We propose a protection mechanism for password-based credential databases maintained by service providers against leakage, dubbed PCDL. In PCDL, each authentication credential is derived from a user’s password and a salt, where a service provider employs a set of key servers to share the salt in a threshold way. With PCDL, an external adversary cannot derive any information about the underlying passwords