Reviewerly/exHarmony huggingface dataset:
Structural regularization based discriminative multi-view unsupervised feature selection

Multi-view unsupervised feature selection (MUFS) has recently aroused considerable attention, which can select the compact representative feature subset from original multi-view data. Despite the promising preliminary performance, most previous MUFS methods fail to explore the discriminative ability of multi-view data. In addition, they usually utilize spectral analysis to maintain the geometrical structure, which will inevitably increase the difficulty of parameter selection. To address these issues, we present a novel MUFS method, named structural regularization based discriminative multi-view unsupervised feature selection (SDFS). Specifically, we calculate the similarity matrix of sample space from different views and automatically weight each view-specific graph to learn a consensus similarity graph, in which these two types of graphs can promote each other. Further, we treat the learned latent representation as the cluster indicator, and employ a graph regularization without introducing additional parameters to maintain the geometrical structure of data. Besides, a simple yet efficient iterative updating algorithm with theoretical convergence property is developed. Extensive experiments on several benchmark datasets verify that the designed model is superior to several state-of-the-art MUFS models.
Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding (CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure, thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives, thereby promoting further research into this emerging and important field.
Iterative Configuration of Programmable Unitary Converter Based on Few-Layer Redundant Multiplane Light Conversion

Programmable unitary photonic devices are emerging as promising tools to implement unitary transformation for quantum information processing, machine learning, and optical communication. These devices typically use a rectangular mesh of Mach-Zehnder interferometers, which has a clear mathematical structure and can be configured deterministically. However, this mesh architecture is sensitive to fabrication errors, and the correction techniques are still under investigation. In contrast, the multiplane light-conversion (MPLC) architecture is more robust against fabrication errors, but a deterministic method for configuring the converter has not yet been developed due to its complex mathematical structure. In this work, we propose a fast iterative configuration method for MPLC, following the mathematical review of the matrix distance and proposal of an alternative norm. We show through numerical simulations that adding a few redundant layers significantly improves the convergence of the MPLC architecture, making it a practical and attractive option. We also consider the effects of finite resolution and cross-talk in phase shifters in our simulations. In addition, we propose a phase-insensitive distance suited for applications using only intensity detections. Our method demonstrates orders of magnitude better accuracy and a 20-fold speedup compared to previous approaches.
Determining the optimal number of folds to use in a K-fold cross-validation: A neural network classification experiment

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Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review

The research objective of this paper is to advance knowledge about the role of artificial intelligence (AI) in complex problem-solving. A problem is complex due to the large number of highly inter-connected variables affecting the problem state. Complex problem-solving situations often change decremental or worsen, forcing a problem solver to act immediately, under considerable time pressure. While research findings support the assumption that (1) affective, (2) (meta-)cognitive, and (3) social processes support complex problem-solving, opportunities of AI for supporting complex problem-solving need to be further investigated. This article presents a scoping review of relevant literature from the last five years. The study included, N = 38 studies for coding and analysis. Our findings show that in addition to the increased number of publications, the current trend suggests increased quality of published work. Human-AI collaboration in complex problem-solving has been explored across a broad variety of AI application domains. However, the four dimensions of complex problem – namely, cognitive, metacognitive, social and affective - have been augmented by AI to a different extent. Although most of the work has been done in the cognitive domain, it is encouraging to see progress across social and affective dimensions as well. Implications for future research and practice are being discussed.
Gender differences in cybersickness: Clarifying confusion and identifying paths forward

Cybersickness is a barrier to widespread adoption of virtual reality (VR). We summarize the literature and conclude that women experience more cybersickness than do men, but that the size of the gender effect is modest. We present a mediation and moderation framework for organizing existing research and proposing new questions about gender and cybersickness. A mediator causally connects gender and cybersickness, and a moderator changes the magnitude of the gender difference in cybersickness.
Terrain Synthesis for Treadmill Exergaming in Virtual Reality

In order to connect haptic feedback with visual content in treadmill VR exergames for getting more immersive user experiences, we present a novel optimization-based approach to automatically generate terrains for treadmill exergames that can match well with the user-specified treadmill workout profile. In order to validate the effectiveness of our approach, a series of numerical experiments are conducted to investigate the visual effects of the generated terrain, the virtual walking experience of the treadmill exergame, and the comparison between the manual terrain creation approach and the terrain synthesis approach.
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks

Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the input, concatenating multi-modality features inside models, or applying attention to each data modality. These fusion strategies are straightforward yet insufficient. In this letter, we propose a novel fusion method named Explicit Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of data. Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features. EAEF uses one branch to enhance feature extraction for i) and iii) and the other branch to remedy insufficient representations for ii). The outputs of two branches are fused to form complementary features. As a result, the proposed fusion method outperforms state-of-the-art by 1.6% in mIoU on semantic segmentation, 3.1% in MAE on salient object detection, 2.3% in mAP on object detection, and 8.1% in MAE on crowd counting.
Enhanced sentence representation for extractive text summarization: Investigating the syntactic and semantic features and their contribution to sentence scoring

The primary challenge faced in extractive text summarization is related to the scoring of sentences, with the critical factor for scoring being the manner in which the sentence representation is conducted. This study aims to investigate this hypothesis and to perform a detailed analysis of the impact of sentence representation techniques that have been used both semantically and syntactically. The study initially evaluated the empirical impact of individual syntactic and semantic features on the accuracy of summarization. To examine syntactic usage, a comprehensive list of 40 syntactic features was developed, while semantic representation was accomplished using sentence embeddings. Subsequently, an improved feature set was proposed that jointly utilizes syntactic and semantic features. To assess the impact of this feature set on the resulting summaries, the proposed sentence representation was tested on three distinct summarization corpora consisting of lengthy scientific documents across diverse domains. The assessment of summary evaluation and classification performance evaluation metrics was conducted to evaluate the quality of the resulting summaries. The findings of the experiments indicated that the summaries generated by the proposed feature set performed better than not only those obtained using individual features but even summaries produced by state-of-the-art methods.
Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature Review

Based on the principles of the biological evolution of nature, bio-inspired algorithms are gaining popularity in developing robust techniques for optimization.Unlike gradient descent optimization methods, these metaheuristic algorithms are computationally less expensive, and can also considerably perform well with nonlinear and high-dimensional data.Objectives: To understand the algorithms, application domains, effectiveness, and challenges of bio-inspired feature selection techniques.Method: A systematic literature review is conducted on five major digital databases of science and engineering.Results: The primary search included 695 articles.After removing 263 duplicated articles, 432 studies remained to be screened.Among those, 317 irrelevant papers were removed.We then excluded 77 studies according to the exclusion criteria.Finally, 38 articles were selected for this study.Conclusion: Out of 38 studies, 28 papers discussed Swarmbased algorithms, 2 papers studied Genetic Algorithms, and 8 papers covered algorithms in both categories.Considering the application domains, 21 of the articles focused on problems in the healthcare sector, while the rest mainly investigated issues in cybersecurity, text classification, and image processing.Hybridization with other BIAs was employed by approximately 18.5% of papers, and 13 out of 38 studies used S-shaped transfer functions.The majority of studies used supervised classification methods such as k-NN and SVM for building fitness functions.Accordingly, we conclude that future research should focus on applying bio-inspired feature selection to a diverse area of applications such as finance and social networks.And further exploration into enhancement techniques such as quantum representation, rough set theory, chaotic maps, and Lévy flight is necessary.Additionally, we suggest investigating other transfer functions besides S-shaped, such as V-shaped and X-shaped.Moreover, clustering and deep learning models for constructing fitness functions in bio-inspired feature selection algorithms need to be investigated further.
A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill

This paper presents a new method for the construction and training of interval type-3 fuzzy logic systems whose inputs are uncertain type-2 non-singleton numbers (IT3 NSFLS-2).The proposed methodology is divided in two processes: 1) The novel construction of the structure of the IT3 NSFLS-2 systems based on: a) The level-alpha-0 of the interval type-2 fuzzy logic system (IT2-alpha-0 FLS), and on b) The secondary membership function using Gaussian modeling to construct each rule of the alpha-k fuzzy rule base (FRB), the firing intervals of the antecedent and the centroids of the consequent, and 2) The training methodology based on gradient descent algorithm to train the antecedent and consequent parameters of the alpha-0 FRB.The primary membership functions (MF) of the antecedents of the IT3 NSFLS-2 system are modeled as Gaussians with uncertain means and with common standard deviation.The proposal was applied and tested with the prediction of a transfer bar's surface temperature in an industrial hot strip mill facility located in Monterrey México.The modeling results show that the proposal supports the stability required by this critical process and shows the best performance when compared with similar methods.INDEX TERMS General type-2 fuzzy systems, gradient
Online higher education: current landscape and future trends

This critical review and perspective of the literature features the current landscape of online learning in higher education institutions (HEIs). The review starts by exploring the changing nature of student needs and staff and institutional roles in the face of online learning, and how digital transformation is shaping the culture and ways of learning. The aim is to better understand the challenges faced by higher education leaders to adopt online offerings and the factors affecting their decision-making. Emergence and fast evolution of third-party online programme management providers (OPMs) and forces which encourage or oppose their partnership with HEIs are critically discussed. The review also highlights the impact of COVID-19 emergency online learning on institutional practices and future adoption of online offerings.
Does workplace digitalization matter in linking transformational leadership and innovative culture to employee engagement?

Purpose Engaged employees are an organization’s competitive advantage because the employees are willing to strive for organizational excellence. This paper examines employee engagement (EE), taking workplace digitalization as the mediator. The authors developed an integrated framework introducing transformational leadership and innovative culture as EE antecedents in workplace digitalization settings. Specifically, the authors argue that transformational leadership and innovative culture influence EE directly and indirectly through workplace digitalization. Design/methodology/approach A total of 256 valid online-survey samples were used in the structural equation modeling (SEM) tests. The respondents were the management-level executives of Selangor/Kuala Lumpur-based Malaysian companies. Findings The authors' findings support that workplace digitalization positively influences EE. Unlike transformational leadership, innovative culture positively influences workplace digitalization. Further, innovative culture directly affects EE and indirectly through workplace digitalization, albeit partially. Transformational leadership directly influences EE but is insignificant through workplace digitalization. Practical implications The findings suggest that organizations that wish to fast-track EE can cultivate an innovative culture to facilitate employees' acceptance of workplace digitalization and enhance EE. Originality/value The authors' research expands the interdisciplinary theoretical foundation on how employees' perception and acceptance of workplace digitalization add to EE by highlighting the roles of transformational leaders and innovative culture. The authors' research is among the first few investigating how transformational leadership and innovative culture affect EE in the presence of workplace digitalization. The authors also discussed workplace digitalization as a mediator to innovative culture–EE relationships.
Finite mixture modeling in time series: A survey of Bayesian filters and fusion approaches

From the celebrated Gaussian mixture, model averaging estimators to the cutting-edge multi-Bernoulli mixture of various forms, finite mixture models offer a fundamental and flexible means to deal with uncertainties arisen in the estimation and learning realm. In the context of recursive estimation, both the uncertainties due to model maneuvering and multi-target data association derive a need for representing the single/multiple-target probability distribution by a finite mixture described by a number of parameters that are iteratively updated over time in the framework of Bayesian inference, which we call generally the Bayesian mixture (BM) filtering. In addition, density fusion between netted agents/sensors may be addressed by linearly combining their local estimations which are often correlated with each other in a complicated means, leading to a fused mixture too. In either case, the final estimate is given by the arithmetic average (AA) of all the weighted components in the mixture, regarding either a single target or multiple targets. Along with this are versatile mixture adaption and optimization strategies which aim to use a small number of components for the best fit to the target distribution. This survey provides a comprehensive overview of representative single/multiple-target BM filters and fusion approaches with the use of either a single sensor or multiple sensors in a unified and coherent fashion. State-of-the-art, relevant adaption and optimization technologies and remaining challenges are discussed. All strive to gain more insights of the approach.
A global blockchain-based agro-food value chain to facilitate trade and sustainable blocks of healthy lives and food for all

Abstract This article presents a holistic and integrated framework for blockchain deployment in global Agro-food ‘supply’ chains and how to transition them to accountable and sustainable global ‘value’ chains. Though many a scholarly contributions have assessed blockchain implementation at various levels in the chain, this research holistically looks at impediments to blockchain implementation at each level in the value chain. The study first establishes interlinkages between the three United Nations Sustainable Development Goals (SDGs), namely food for all (SDG 2), health for all (SDG 3), and sustainable consumption and production (SDG 12). It assesses the legal framework (namely trade law) and regulatory requirements therein. It then employs a case-study-based approach to assess blockchain deployment from the lens of operations management. These inter-disciplinary insights offer an enabling framework to successfully implement a vertically-integrated blockchain across the entire global Agro-food value chain. An end-to-end blockchain promises accountability, and thereby, enhanced trust in trade by offering a time-stamped ledger of transactions from the farm to the fork, to the end consumer. This systematic study, and its findings therein, are expected to serve as a ready reference guide to managers and policymakers for a truly integrated farm-to-fork blockchain deployment.
Acquisition and retention of spatial knowledge through virtual reality experiences: Effects of VR setup and locomotion technique

The fidelity level of virtual reality (VR) setups can affect different aspects of virtual experiences, but its effects on knowledge acquisition and retention need clarification. The two studies in this paper focus on spatial knowledge. The first study compared three VR setups, one using a VR headset and two using a tablet, differing in display and interaction fidelity. Since the type of virtual environment (VE), and the locomotion technique employed to explore it, might affect spatial knowledge acquisition, we studied each setup in an indoor and an outdoor VE with two widely used locomotion techniques (teleport and steering). Results showed that setups offering higher display and interaction fidelity can improve acquisition of spatial knowledge in terms of distance estimations (with teleport in the indoor VE, and with steering in the outdoor VE) and object-to-object spatial relations (with steering in the outdoor VE), but not in terms of object locations. This can provide guidance about how to choose the appropriate combination of setup and locomotion technique based on the type of VE and the types of spatial knowledge to acquire. Moreover, sickness and usability results showed that VR headset was more usable than tablet, and suggest using teleport on VR headset and steering on tablet. Therefore, the second study focused on VR headset with teleport and tablet with steering, and extended the assessment of their effects to spatial knowledge retention after two weeks. Results showed that spatial knowledge decreased in both conditions, but the VR headset with teleport led to better acquisition and 2-weeks retention of distance estimations.
Single Image Dehazing via Fusion of Multilevel Attention Network for Vision-Based Measurement Applications

Nowadays, researchers use vision-based measurement tools to record, detect, and monitor an atmospheric phenomenon called haze. It impedes the proper functioning of many outdoor industrial systems, such as autonomous driving, surveillance, satellite imagery, etc. Conventional visibility restoration methods cannot accurately recover image quality due to inaccurate estimations of haze thickness and the presence of color-cast effects. Deep neural networks are evolving due to their ability to directly dehaze images from hazy scenes. Therefore, a unique attention-based end-to-end dehazing network named Oval-Net has been proposed in this study to restore clear images from its counterpart without employing the atmospheric scattering model. The Oval-Net is an encoder-decoder architecture that uses spatial and channel attention at each stage to focus on dominant and significant information while avoiding the transmission of irrelevant information from the encoder to the decoder, allowing quicker convergence. The proposed approach outperforms seven state-of-the-art algorithms in quantitative and qualitative assessments of a variety of synthetic and real-world hazy images, proving its effectiveness for vision-based industrial systems.