Although omni-channel retailers have made substantial investments in online-offline channel integration, they face the challenge of how to best present these valuable systems to retain customers. Based on cue congruence theory, this study examined the congruence and incongruence between perceived information integration and fulfillment integration on customer retention, and further considered the moderating

Presents the front cover for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

A typical Internet-of-Things (IoT) system consists of three major layers: 1) sensing; 2) communication; and 3) application (i.e., actuation and control) layers. The co-design of these layers has been studied for over two decades, dating back to the concept of communication, computing, and control, i.e., 3C, convergence in the 1990s. Nowadays, with the emergence of wireless-networked machine-type applications

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Existing deep reinforcement learning (DRL)-based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with a homogeneous vehicle fleet, in which the fleet is assumed as repetitions of a single vehicle. Hence, their key to construct a solution solely lies in the selection of the next node (customer) to visit excluding the selection of vehicle. However, vehicles in real-world

This article investigates the cooperative output regulation problem for discrete-time heterogeneous multiagent Markov jump systems. Two cases are studied: 1) output regulation quadratic control in the case where the exosystem is accessible to all agents and 2) cooperative output regulation quadratic control in the case where only a part of agents can directly communicate with the exosystem. The hidden

A product can receive Electronic Word-of-Mouth (eWOM), e.g., user reviews, at both retail and review platforms, with considerable variance in volume and valence. We define such eWOM variation across platforms as the platform concentration of eWOM. A lower platform concentration is measured by a higher level of volume equality and valence equality, i.e., a more comparable number of user reviews and

Technology has facilitated platform-mediated work, and individuals are often encouraged to engage with technology using various digital solutions. However, for platform workers, the use of technology can lead to digital overload, which may negatively impact their burnout and performance. In this paper, we examine the interplay between individuals’ cognitive absorption in technology and technology overload

The newly issued International Financial Reporting Standards (IFRS) have significantly transformed the accounting process and the accounting information system (AIS). Confronting the changes in information technologies (IT), using the AIS appropriately is crucial to derive accurate information from which to process business transactions and report information in compliance with the new IFRS. Built

This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE)-Softmax, for the fault identification of complex industrial processes (CIPs). Specifically, sparse denoising autoencoder (SDAE) is established by integrating a sparse AE (SAE) with a

In many financial applications, such as fraud detection, reject inference, and credit evaluation, detecting clusters automatically is critical because it helps to understand the subpatterns of the data that can be used to infer user's behaviors and identify potential risks. Due to the complexity of human behaviors and changing social environments, the distributions of financial data are usually complex

Information systems literature suggests that reputation is the main judgment form that captures user-related information online. Drawing on social judgment literature, we contend that a user's online characteristics and past actions are not all reputation-based, but also legitimacy-based, and that transaction stake determines whether users will use reputation or legitimacy judgment to evaluate other

Presently, people use social media at a greater rate to share their personal investment experiences. This plentiful user-generated data source has been promisingly used by investors for portfolio creation. A new type of investing platform that allows investors to copy the portfolios of experienced investors has grown dramatically. In this research, we propose a collective intelligence mechanism that

We investigate how employees react when confronted with an enterprise system (ES) that does not fit with work processes dictated by local realities. We draw on interview data (n=31) from a multinational company to reveal how employees devised and coordinated workarounds that were not compliant with corporate IT policy, but enabled the completion of essential activities, thereby creating value for the

This study aims to develop a new methodology in IT Portfolio Management (ITPM) to address the impact of portfolio priorities on IT strategies in a multi-business unit firm. The methodology includes a new ITPM model and uses simulated scenarios to overcome limitations due to insufficient real-world organizational IT portfolio data. The weight scores generated by the proposed ITPM model can enable the

Amid the skyrocketing increase in social media content, understanding the information-sharing mechanism is crucial. We analyze Twitter messages on recent public issues and IT trends to examine the network's bi-directional message diffusion patterns through incoming and outgoing ties, based on the users’ retweeting and mentioning behavior toward one another. We find context-based patterns. This study

Many reward-based crowdfunding platforms encourage entrepreneurs to introduce their projects and make a personal appeal with a video clip. In this study, we investigate the impact of such a pitch video on financing outcomes. Grounded in social perception literature, we propose that effective use of nonverbal cues in a pitch video increases funding success. We coded and analyzed videos of crowdfunding

Current information security behavior research assumes that lone individuals make a rational, informed decision about security technologies based on careful consideration of personally available information. We challenge this assumption by examining how the herd behavior influences users’ security decisions when coping with security threats. The results show that uncertainty about a security technology

Local sporting clubs make a major contribution by encouraging sport participation. Whilst digital technologies assist clubs to achieve their objectives, many face challenges with their implementation due to limited resources. This paper extends the Valuable, Rare, Imitability and Organisation (VRIO) framework in resource-based theory to examine the value that clubs gain from websites. Interviews were

In this article, the distributed set-membership fusion filtering problem is investigated for a class of nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. The sensors are communicated with their neighbors according to a given topology through wireless networks of limited bandwidth. With the purpose of relieving the communication burden as well as enhancing

Pattern recognition is significantly challenging in real-world scenarios by the variability of visual statistics. Therefore, most existing algorithms relying on the independent identically distributed assumption of training and test data suffer from the poor generalization capability of inference on unseen testing datasets. Although numerous studies, including domain discriminator or domain-invariant

This article investigates the stabilization of discrete-time stochastic neural networks with time-varying delay via aperiodically intermittent control (AIC). A comprehensive analysis of the stabilization of discrete-time delayed systems via AIC is provided, where the Lyapunov function method and the Lyapunov-Krasovskii functional method are investigated, respectively. Then, three stabilization criteria

Abstract-domain adaptation action recognition is a hot research topic in machine learning and some effective approaches have been proposed. However, samples in the target domain with label information are often required by these approaches. Moreover, domain-invariant discriminative feature learning, feature fusion, and classifier module learning have not been explored in an end-to-end framework. Thus

Multiview learning (MVL), which enhances the learners' performance by coordinating complementarity and consistency among different views, has attracted much attention. The multiview generalized eigenvalue proximal support vector machine (MvGSVM) is a recently proposed effective binary classification method, which introduces the concept of MVL into the classical generalized eigenvalue proximal support

Skin lesion diagnosis is a key step for skin cancer screening, which requires high accuracy and interpretability. Though many computer-aided methods, especially deep learning methods, have made remarkable achievements in skin lesion diagnosis, their generalization and interpretability are still a challenge. To solve this issue, we propose an interpretability-based multimodal convolutional neural network

The finite-time output consensus (FTOC) issue of heterogeneous fractional-order multiagent systems (HFO-MASs) is investigated in this article. First, a new principle of finite-time convergence for absolutely continuous functions is developed if a fractional derivative inequality is satisfied. Next, in order to remove the assumption that the leader's system matrix is known to all agents in previous

Prescribed-time consensus tracking for second-order nonlinear multiagent systems (MASs) with the unknown nonlinear dynamics satisfying a time-varying Lipschitz growth rate is investigated in this article. A time-varying function is introduced as a part of the controller gains, and it plays an important role in overcoming the rapid growth of nonlinear terms and in ensuring that the consensus can be

Classic portfolio selection problems mainly focus on high-risk financial markets with tradeoffs between returns and risk. However, more risk-averse investors pursue long-term portfolio planning with the objectives of maximizing final returns and maximizing flexibility. This article addresses a new type of the portfolio problem, called periodic investment portfolio selection problems (PIPSPs), in which

This article is centered on the cybersecurity research of dynamic state estimation for power systems with measurement delays. Relying on mixed measurements from phasor measurement units (PMUs) and remote terminal units (RTUs), a delayed measurement model is constructed. A modified state estimator based on the Kalman filter (KF) is designed, which can obtain the optimal estimated states under measurement

The general tensor-based methods can recover missing values of multidimensional images by exploiting the low-rankness on the pixel level. However, especially when considerable pixels of an image are missing, the low-rankness is not reliable on the pixel level, resulting in some details losing in their results, which hinders the performance of subsequent image applications (e.g., image recognition and

This article is concerned with the local stabilization of memristive neural networks subject to actuator saturation via aperiodic sampled-data control. A dynamic partitioning point is elegantly introduced, which is placed between the latest sampling instant and the present time to utilize more information of the inner sampling. To analyze the stability of the closed-loop system, a time-dependent two-side

During the past two decades, many evolutionary algorithms have been proposed to solve nonlinear equation systems (NESs). However, the benchmark test sets have not received enough attention. Some features of NESs (e.g., high dimension, large search range, the connectivity of the feasible region) are rarely considered in the original benchmark test sets, which results in that they cannot represent the

Strong nonlinearities, uncertainties, and disturbances present great challenges to the control of the dissolved oxygen (DO) in a wastewater treatment process (WWTP). To deal with those undesired issues, in this article, a scalable-bandwidth extended state observer (SESO) is proposed, and the SESO-based adaptive sliding-mode control (ASMC) is designed. By the SESO, the time-varying total disturbance

Robot manipulation, for example, pick-and-place manipulation, is broadly used for intelligent manufacturing with industrial robots, ocean engineering with underwater robots, service robots, or even healthcare with medical robots. Most traditional robot manipulations adopt 2-D vision systems with plane hypotheses and can only generate 3-DOF (degrees of freedom) pose accordingly. To mimic human intelligence

Along with the success of deep neural network (DNN) models, rise the threats to the integrity of these models. A recent threat is the Trojan attack where an attacker interferes with the training pipeline by inserting triggers into some of the training samples and trains the model to act maliciously only for samples that contain the trigger. Since the knowledge of triggers is privy to the attacker,

Learning local representations is important for face recognition (FR). Recent attention-based networks emphasize few facial parts, while ignoring other potentially discriminative ones. This is more serious when there are large pose variations, occlusions (e.g. face masks), or other image quality changes. To address this, we propose Diverse and Sparse Attentions, called DSA-Face. First, a divergence

Batch steganography aims at properly allocating a large payload to multiple covers, so as to keep the whole covert communication at a satisfactory level of security. JPEG is currently one of the most widely used formats for image storage and transmission. This paper presents an efficient JPEG batch steganographic scheme, which allocates the payload in a linear manner w.r.t. a new heuristic measure

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse

Federated learning (FL), as a distributed machine learning setting, has received considerable attention in recent years. To alleviate privacy concerns, FL essentially promises that multiple parties jointly train the model by exchanging gradients rather than raw data. However, intrinsic privacy issue still exists in FL, e.g., user’s training samples could be revealed by solely inferring gradients. Moreover

One existing challenge associated with large scale skyline queries on cloud services, particularly when dealing with private information such as biomedical data, is supporting multi-party queries with curious-but-honest parties on encrypted data. In addition, existing solutions designed for performing secure skyline queries incur significant communication and computation costs due to ciphertext calculation

The problem of covert communication over Multiple-Input Multiple-Output (MIMO) Additive White Gaussian Noise (AWGN) channels is investigated, in which a transmitter attempts to reliably communicate with a legitimate receiver while avoiding detection by a passive adversary. The covert capacity of the MIMO AWGN channel is characterized under a variational distance covertness constraint when the MIMO

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Advertisement.

Advertisement.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

With the rapid development in face recognition, most of the existing systems can perform very well in unconstrained scenarios. However, it is still a very challenging task to detect face spoofing attacks, thus face anti-spoofing has become one of the most important research topics in the community. Though various anti-spoofing models have been proposed, the generalisation capability of these models

Low Entropy Masking Schemes (LEMS) had been proposed to mitigate the high-performance overhead results from the Full Entropy Masking Schemes (FEMS) while offering good protection against side-channel attacks. The masking schemes usually rely on Boolean masking, however, splitting sensitive variables in a multiplicative way is more amenable to non-linear functions and it had been applied to both software

In this paper, we present a new technique for recovering the secret inner components of block cipher structures. This technique does not simply distinguish a block cipher structure from a random permutation but recovers the secret inner components. In addition, our technique is more general than ad hoc structural cryptanalysis for specific structures. A new tool, the Inequality Constraints Table (

Leakage detection tests have become an indispensable tool for testing implementations featuring side channel countermeasures such as masking. Whilst moment-based techniques such as the Welch’s $t$ -test are universally powerful if there is leakage in a central moment, they naturally fail if this is not the case. Distribution-based techniques such as the $\chi ^{2}$ -test then come to the rescue, but

Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut-off value to distinguish whether the connections exist

Generating various goal-oriented movements via the flexible muscle model of the musculoskeletal system as fast and accurately as possible is a pressing problem, which is also the basis of most human adaptive behaviors, such as reaching, catching, interception, and pointing. This article focuses on the adaptive motion generation of fast goal-oriented motion on the musculoskeletal system by implementing

A mixed-integer programming (MIP) problem contains both constraints and integer restrictions. Integer restrictions divide the feasible region defined by constraints into multiple discontinuous feasible parts. In particular, the number of discontinuous feasible parts will drastically increase with the increase of the number of integer decision variables and/or the size of the candidate set of each integer

A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this

Vision inspection as an important automation technology in manufacturing industry undertakes many appearance quality related tasks, such as product positioning, recognition, and measurement. Intelligent manufacturing increases the demand for vision methods in production scenes. However, the diversity and increment of the targets, and the feature robustness and scheme migration have restricted further