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The notion of visibility of knowledge is important to gain insights into an evolving knowledge-sharing practice through social media use. This article offers a conceptual definition of and an instrument to measure the visibility of knowledge in online communities facilitated by social media. Following a review of the relevant literature and interviews with experts and knowledge workers, we produced

Interest in Business Intelligence and Analytics (BI&A) has led to a growing body of impactful scholarly articles. We investigate the state of BI&A research by answering what is the state of BI&A research in terms of constructs studied, article's macrostructure, and theoretical contributions, and how do the constructs studied, macrostructure, and theoretical contributions, influence an article's impact

Industrial automation is a multi-disciplinary study field that is constantly evolving. This research proposes IoT-based real-time liquid level monitoring and control in a single tank system. A PID controller with parameters set using a novel modified Grey Wolf Optimization (mGWO) algorithm is used to manage the liquid level. The PID controller was then built on the ESP32 microcontroller module, and

Understanding how to enhance online leadership in online Q&A communities is important because an online leader plays a role model or knowledge coordinator who can strengthen member commitment in the community. Considering the essential role of communication in establishing leadership, this study aims to understand how the linguistic complexity of two types of knowledge contribution, i.e., knowledge

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The papers in this special section focus on security and privacy issues associated with Industry 4.0. In 2011, a group of delegates from business and academia, and politics in German initially proposed the conception of the Fourth Industrial Revolution (or Industry 4.0), which aims to improve the competitive ability in the manufacturing industry of their country. Along with the emergence of the Industry

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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With the rapid progress of deep neural models and the explosion of available data resources, dialogue systems that supports extensive topics and chit-chat conversations are emerging as a research hot-spot for many communities, e.g., information retrieval (IR), natural language processing (NLP), and machine learning (ML). Building a chit-chat system with retrieval techniques is an essential task and

In many classification scenarios, the data to be analyzed can be naturally represented as points living on the curved Riemannian manifold of symmetric positive-definite (SPD) matrices. Due to its non-Euclidean geometry, usual Euclidean learning algorithms may deliver poor performance on such data. We propose a principled reformulation of the successful Euclidean generalized learning vector quantization

Deep reinforcement learning (DRL) has been researched for computer room air conditioning unit control problems in data centers (DCs). However, two main issues limit the deployment of DRL in actual systems. First, a large amount of data is needed. Next, as a mission-critical system, safe control needs to be guaranteed, and temperatures in DCs should be kept within a certain operating range. To mitigate

Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction

As a classical and effective method for solving various time-varying problems, the zeroing neural network (ZNN) is widely applied in the scientific and industrial realms. In plentiful studies on the ZNN model, its robustness and convergence have been two essential criteria to evaluate the quality of the model. Improvements in the ZNN model have been focused on its convergence speed; however, the adjustability

The adaptive tracking control problem of leader-following nonlinear multiagent systems (MASs) subject to unknown perturbations and limited network bandwidth is investigated by the robust adaptive event-triggered sliding-mode control method. A distributed integral sliding mode is established to realize the finite-time reachability of the states of the leader-following nonlinear MAS. An adaptive triggering

Constrained multiobjective optimization problems (CMOPs) pose great difficulties to the existing multiobjective evolutionary algorithms (MOEAs), in terms of constraint handling and the tradeoffs between diversity and convergence. The constraints divide the search space into feasible and infeasible regions. A key to solving CMOPs is how to effectively utilize the information of both feasible and infeasible

Distributed clustering based on the Gaussian mixture model (GMM) has exhibited excellent clustering capabilities in peer-to-peer (P2P) networks. However, more iterative numbers and communication overhead are required to achieve the consensus in existing distributed GMM clustering algorithms. In addition, the truth that it cannot find a closed form for the update of parameters in GMM causes the imprecise

The data generated by modern industrial processes often exhibit high-dimensional, nonlinear, timing, and multiscale characteristics. Presently, most of the fault diagnosis methods based on deep learning only consider the part of the characteristics of industrial data, which will cause the loss of part of the feature information during training, thereby affecting the final diagnosis effect. In order

Semisupervised classification with a few labeled training samples is a challenging task in the area of data mining. Moore-Penrose inverse (MPI)-based manifold regularization (MR) is a widely used technique in tackling semisupervised classification. However, most of the existing MPI-based MR algorithms can only generate loosely connected feature encoding, which is generally less effective in data representation

In this article, the event-triggered robust control of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated by using adaptive dynamic programming. To relax the requirement of system dynamics, a neural network-based identifier is constructed by using the system input-output data. Subsequently, by designing a nonquadratic value function, which contains the bounded

The rapid development of information and communication technologies has facilitated machining condition monitoring toward a data-driven paradigm, of which the Industrial Internet of Things (IIoT) serves as the fundamental basis to acquire data from physical equipment with sensing technologies as well as to learn the relationship between the system condition and the collected condition monitoring data

In real industrial processes, factors, such as the change in manufacturing strategy and production technology lead to the creation of multimode industrial processes and the continuous emergence of new modes. Although the industrial SCADA system has accumulated a large amount of historical data, which can be used for modeling and monitoring multimode processes to a certain extent, it is difficult for

The radial basis function (RBF) model and the Kriging model have been widely used in the surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global and local surrogate-assisted differential evolution algorithm (GL-SADE) for high-dimensional expensive problems is proposed in this article, in which a global RBF model is trained with all samples to estimate a global trend

Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs (TWSVMs) assign membership weights to reduce the effect of outliers, however, it ignores the positioning of the input data samples and hence fails to distinguish between support vectors and noise. To overcome this issue, intuitionistic

Published in Journal of Management Information Systems (Vol. 39, No. 2, 2022)

ABSTRACT Although vendor firms invest heavily in technologies to digitalize business-to-business (B2B) service orchestration, little is known about the mechanisms through which high quality information, a key resource generated from digitalization, creates customer loyalty. Positioning our work in the B2B logistics services context, we take a customer needs fulfillment perspective to understand value

ABSTRACT We propose a theoretical model based on the judge-advisor system (JAS) and empirically examine how algorithmic advice, compared to identical advice from humans, influences human judgment. This effect is contingent on the level of transparency, which varies with whether and how the prediction performance of the advice source is presented. In a series of five controlled behavioral experiments

ABSTRACT Although mobile platform ecosystems are highly competitive environments, they provide important entrepreneurial opportunities for app developer organizations. To facilitate app innovations, platform owners regularly release platform boundary resources (PBRs) by adding novel technologies at the platform level. App developer organizations, however, face high levels of technological uncertainty

ABSTRACT Markets in which similar goods of different qualities are sold suffer from information asymmetries and their negative consequences. Dealers have established themselves, and mediate these markets through their use of quality signals. While these signals help to mitigate information asymmetries, these markets still function well below their optimum: a large share of goods sold are overpriced

ABSTRACT This study examines how the use of algorithmic control within gig economy platforms relates to the well-being and behavior of workers. Specifically, we explore how two different forms of algorithmic control—gatekeeping and guiding—correspond with (positive) challenge technostressors and (negative) threat technostressors experienced by Uber drivers. We also examine the moderating impact of

ABSTRACT Techno-invasion refers to constant connectivity of being “always exposed” that blurs desired boundaries between work and personal life and represents an emerging phenomenon of scholarly investigation. As work-related use of information technology (IT) during non-work time leads to techno-invasion, a minimal amount is known about whether techno-invasion may engender employees’ deviant behaviors

ABSTRACT We aim to understand why employees take information technology (IT)-mediated shortcuts, that is, skipping one or several steps for completing tasks quicker by bending the rules. This is a specific and often detrimental form of noncompliant behavior. Adopting an ego-depletion perspective, we posit that IT complexity drives IT-mediated shortcuts by increasing employees’ ego-depletion. Extending

ABSTRACT Approximately 55 percent of the world’s 7.3 billion people access the Internet, creating a significant demand in information technology (IT) services for both organizations and consumers. The number of data centers continue to increase to meet this demand, consuming 2 percent of the energy produced worldwide. Many organizations moving towards cloud computing due to its ability to meet users’

ABSTRACT As predictive analytics increasingly applies supervised machine learning (SML) models to inform mission-critical decision-making, adversaries become incentivized to exploit the vulnerabilities of these SML models and mislead predictive analytics into erroneous decisions. Due to the limited understanding and awareness of such adversarial attacks, the predictive analytics knowledge and deployment

ABSTRACT Driven by the need to enhance user traffic on social media (SM) platforms for increasing their advertising revenues, SM platforms are experimenting with new content creation features. However, it is unclear if such initiatives are also beneficial for SM profile owners such as influencers, who are the prime content creators on the SM platforms who use SM posts to build their influence within

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Internet of Things (IoT) refers to an ecosystem where applications and services are driven by data collected from devices interacting with each other and the physical world. Although IoT has already brought spectacular benefits to human society, the progress is actually not as fast as expected. From network structures to control flow graphs, IoT naturally generates an unprecedented volume of graph

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In this article, a parametric predictor is constructed to predict the future state of a discrete-time linear system with a known constant input delay and external disturbances by introducing a tuning parameter. Then, a predictor-based feedback controller is given to attenuate the unknown disturbance signal. The ultimate bound of the state of the resulted closed-loop system is obtained when the state

In this article, a novel error-driven variable gain active disturbance rejection control (ADRC) approach is developed for pneumatic servo system, and the desired performance of trajectory tracking and the disturbance rejection can be guaranteed. The proposed variable gain ADRC includes three parts: 1) variable gain tracking differentiator (TD); 2) variable gain extended state observer (ESO); and 3)

Support vector machine (SVM), as a supervised learning method, has different kinds of varieties with significant performance. In recent years, more research focused on nonparallel SVM, where twin SVM (TWSVM) is the typical one. In order to reduce the influence of outliers, more robust distance measurements are considered in these methods, but the discriminability of the models is neglected. In this

This article deals with the leader-following consensus problem of multiple uncertain Euler-Lagrange systems with unknown nonlinear dynamics. By introducing a dynamic compensator for each agent, a fully distributed control strategy is developed based on the fuzzy approximation approach, which is independent of any priori global information associated with the communication topology. Meanwhile, a distributed

Due to the frequent changes in operating conditions, time-varying behaviors, including slow-varying dynamics and switching modes, commonly exist in industrial processes, resulting in different degrees of shifting in the process data distribution. When the data distribution shifts in a relatively wide range, conventional adaptive methods become ineffective since they are unable to distinguish normal

This article explores an adaptive robust control scheme for satellite formation-containment flying in a way of constraint following. For safe flight and uncertainty suppression, both collision avoidance and uncertainty suppression are addressed. First, for uncertainty suppression, (possibly fast) time-varying but bounded uncertainty is considered, and then an adaptive law is proposed to estimate the

Android security has received a lot of attention over the last decade, especially malware investigation. Researchers attempt to highlight applications’ security-relevant characteristics to better understand malware and effectively distinguish malware from benign applications. The accuracy and the completeness of their proposals are evaluated experimentally on malware and goodware datasets. Thus, the

Dynamic time warping (DTW) is a popular technique for sequence alignment, and is the de facto standard for dynamic signature verification. In this paper, we go a significant step further to enhance DTW with the capability of deep representation learning, and propose an end-to-end trainable Deep soft-DTW (DsDTW) model for dynamic signature verification. Specifically, we design a convolutional recurrent

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