Reviewerly/exHarmony huggingface dataset:
20‐1: <i>Invited Paper:</i> Rethinking Display Requirements for Esports and High Interactivity Applications

Media technology is continuing its transition from passive streaming to participatory interactive experiences, including well‐known applications such as web browsing, video conferencing and gaming, as well as emerging and more demanding uses like AR/MR/VR and esports. How should display traits such as latency, refresh rate and size change to meet this trend? We review recent studies from NVIDIA Research and others on requirements for esports as the cutting edge of this trend toward interactivity, and discuss the studies’ implications for other interactive applications.
6‐2: Potential Failure Detection using Unsupervised Clustering and Anomaly Detection

Automated Visual Inspection(AVI) is an effective way for mobile display product to detect defects which may cause quality problem. The rules of determining defects are based on knowledge for an accumulated engineer's experience. However, it is difficult to detect potential failure that may cause in the production process, because we determine failure of panel based on boundary samples. In this study, we propose an analysis method to find abnormalities by quantifying the potential failure with AVI data using unsupervised learning.
50‐1: Full area local dimming switchable privacy solution with color compensation

Multi display cockpit designs are increasingly popular and aim to provide the content not only to the driver, but also the co‐driver (front seat passenger). This poses a potential safety risk of driver distraction by some content intended for co‐driver entertainment. One potential solution to address it, is switchable (active) privacy allowing to control the display viewing modes.
Block Policy Mirror Descent

In this paper, we present a new policy gradient (PG) method, namely, the block policy mirror descent (BPMD) method, for solving a class of regularized reinforcement learning (RL) problems with (strongly) convex regularizers. Compared to the traditional PG methods with a batch update rule, which visits and updates the policy for every state, the BPMD method has cheap per-iteration computation via a partial update rule that performs the policy update on a sampled state. Despite the nonconvex nature of the problem and a partial update rule, we provide a unified analysis for several sampling schemes and show that BPMD achieves fast linear convergence to the global optimality. In particular, uniform sampling leads to worst-case total computational complexity comparable to batch PG methods. A necessary and sufficient condition for convergence with on-policy sampling is also identified. With a hybrid sampling scheme, we further show that BPMD enjoys potential instance-dependent acceleration, leading to improved dependence on the state space and consequently outperforming batch PG methods. We then extend BPMD methods to the stochastic setting by utilizing stochastic first-order information constructed from samples. With a generative model, (resp., ) sample complexities are established for the strongly convex (resp., non-strongly convex) regularizers, where denotes the target accuracy. To the best of our knowledge, this is the first time that block coordinate descent methods have been developed and analyzed for policy optimization in reinforcement learning, which provides a new perspective on solving large-scale RL problems.
THREE-DEGREE-OF-FREEDOM ELECTRIC MACHINE WITH INTERNAL AND EXTERNAL ROTOR: COMPARATIVE ANALYSIS OF CONTROL PERFORMANCE

Two structures of electric machines with three degrees of freedom of the rotational movement of the rotor are considered - with an external and an internal rotor held on an internal cardan suspension. A three-plane symmetric magnetic system was chosen for the comparative analysis and such a choice was justified. Limitations are formulated that allow considering the rotor as a conservative mechanical system, as well as considering the magnetic field in the electric machine as magnetostatic. Mathematical models of the rotational motion of a body with a fixed center of mass, as well as the force interaction of the magnetic field of a permanent magnet located on the rotor and the control winding located on the stator, are given. The peculiarities of the distribution of electromagnetic forces in both structures are analyzed and the greater efficiency of the formation of these forces in the structure with an internal rotor is shown. Expressions for the components of the electromagnetic moment for use in the "COMSOL Multiphysics" interface are obtained. Forced precession movement for both structures was analyzed in the case of equal radii of the control windings and in the case of the same dimensions of the magnetic systems. It was determined that the structure with an external magnetic circuit is able to provide an order of magnitude smaller angular velocity of precessional movement and, at the same time, an equally smaller range of nutation. References 14, figures 10.
Hyperspectral images classification for white blood cells with attention-aided convolutional neural networks and fusion network

The classification of White blood cells (WBCs) plays an important role. However, the traditional method of blood smear analysis is laborious. This paper presented a classification method of WBCs based on hyperspectral images and Deep learning (DL). The U-net network was proposed to extract spectral features of WBCs region of interest (ROI) under the pseudo-color images with specific hyperspectral images (420.8, 557.2 and 667.4 nm). For spectral features and the pseudo-colour images of hyperspectral data, attention-aided one and two-dimensional convolutional neural networks were applied to establish WBCs classification models, respectively. The overall average accuracy can reach 94.20% and 92.60%, respectively. A fusion network was constructed to make full use of the spectral and image spatial features, and its classification accuracy reached 96.20%. In terms of overall average accuracy, the fusion network model was the optimal, but for individual types of WBCs, each network had its own unique advantages.
Conditional mixture modelling for heavy‐tailed and skewed data

Overparameterization is a serious concern for multivariate mixture models as it can lead to model overfitting and, as a result, mixture order underestimation. Parsimonious modelling is one of the most effective remedies in this context. In Gaussian mixture models, the majority of parameters is associated with covariance matrices and parsimonious models based on factor analysers and spectral decomposition of dispersion parameters are the most popular in literature. Some drawbacks of these models include the lack of flexibility in imposing different covariance structures for individual components and limitations in modelling compact clusters. Recently introduced conditional mixture models provide substantial flexibility in addressing these concerns. The components of such mixtures are formulated as a product of conditional distributions with univariate Gaussian densities being the primary choice. However, the presence of heavy tails or skewness in any dimension can lead to fitting problems. We propose a flexible model that is free of the above‐mentioned limitations and name it a contaminated transformation conditional mixture model and demonstrate on a series of simulation studies that it can effectively account for skewness and heavy tails. Applications to real‐life data sets show good results and highlight the promise of the proposed model.
Predicting Employee Voice Behavior: Exploring the Roles of Empowering Leadership, LMX and the Mediation Effect of Psychological Empowerment

When employees consciously suppress important information, suggestions or concerns from their managers, negative implications for organizational performance can emerge. Some studies suggested that employees often choose to remain silent when faced with the choice of whether or not to raise an issue. Therefore, the main objective of this research is to examine the factors that impact employee voice behavior (VB). The research theorizes that empowering leadership and Leader-Member Exchange (LMX) significantly and positively impacts employee voice behavior in UAE public sector (N=146). Moreover, this study broadens the previous research on the empowering leadership, LMX and employee voice relationship by introducing employee psychological empowerment as a mediator. The data was gathered using the online survey. The results of the statistical analysis using structural equation modeling with Smart-Partial Least Squares (PLS).3 showed that empowering leadership directly and indirectly (through psychological empowerment) impact on employee voice behavior. Surprisingly, the results presented no significant relationships between LMX and voice behavior. However, the relationships only exist through the psychological empowerment (fully mediate). Implications of the study model for management or human resource management as well as for future research are discussed. Keywords: Empowering leadership, Leader-member exchange, psychological empowerment, employee voice behavior
A novel approach for explicit song lyrics detection using machine and deep ensemble learning models

The content of music is not always suitable for all ages. Industries that manage music content are looking for ways to help adults determine what is appropriate for children. Lyrics of songs have become increasingly inappropriate for kids and can negatively impact their mental development. However, it is difficult to filter explicit musical content because it is mostly done manually, which is time-consuming and prone to errors. Existing approaches lack the desired accuracy and are complex. This study suggests using a combination of machine learning and deep learning models to automatically screen song lyrics in this regard. The proposed model, called ELSTM-VC, combines extra tree classifier and long short-term memory and its performance is compared to other models. The ELSTM-VC can detect explicit content in English lyrics and can be useful for the music industry. The study used a dataset of 100 songs from Spotify for training, and the results show that the proposed approach effectively detects explicit lyrics. It can censor offensive content for children with a 96% accuracy. The performance of the proposed approach is better than existing approaches including machine learning models and encoding-decoding models.
Exploring the public sentiment of local community on major infrastructure development: Evidence from media news and Twitter data

ABSTRACTThis study analyzes public sentiment on news sites and Twitter concerning the development of the Trans-Sumatra Toll Road (JTTS). Applying a lexicon-based approach to calculate sentiment scores, this study found a predominance of positive sentiments in news articles and tweets regarding the JTTS development. Employing multinomial logit estimation to examine the determining factors, this study however found no specific relationship between JTTS segment completion and public sentiment on news sites or Twitter. Nonetheless, the association changed and became noticeable on Twitter after controlling the effects of socioeconomic characteristics on sentiment scores. The development status of the JTTS segment influenced public sentiment considerably. This study highlights the potential of social media to facilitate public engagement and inform government decision making. Furthermore, it offers a comprehensive analysis of public sentiment regarding toll road infrastructure development by incorporating multiple stakeholders, including government agencies, business actors, community organizations (via news articles), and local communities (via Twitter data), and highlights various interests, concerns, and conflicts surrounding toll road projects. The investigation of socioeconomic and demographic factors in relation to public sentiment further contributes to our understanding of the underlying influences and dynamics that shape opinions on toll-road infrastructure through sentiment analysis and quantitative methods.KEYWORDS: Infrastructurenewspublic participationsocial mediatransportationTwitter Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author, KK, upon reasonable request.Additional informationFundingThis work was supported by the Universitas Indonesia under the PUTI Grant [NKB-305/UN2.RST/HKP.005.00/2023].
Lateral interaction by Laplacian‐based graph smoothing for deep neural networks

Abstract Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions. Linear self‐organising map (SOM) introduces lateral interaction in a general form in which signals of any modality can be used. Some approaches directly incorporate SOM learning rules into neural networks, but incur complex operations and poor extendibility. The efficient way to implement lateral interaction in deep neural networks is not well established. The use of Laplacian Matrix‐based Smoothing (LS) regularisation is proposed for implementing lateral interaction in a concise form. The authors’ derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means, and they both show the topology‐preserving capability. The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders. Additionally, the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated. Furthermore, the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks. Overall, LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.
A Hybrid Classification Approach for Fasteners Based on Transfer Learning with Fine-Tuning and Deep Features

Deep learning, which has seen frequent use in recent studies, has helped solve the problem of classifying objects of many different types and properties. Most studies both create and train a convolutional neural network (CNN) from scratch. The time spent training the network is thus wasted. Transfer learning (TL) is used both to prevent the loss of time due to training the dataset and to more effectively classify small datasets. This study performs classification using a dataset containing eighteen types of fastener. Our study contains three different TL scenarios. Two of them use TL with fine-tuning (FT), while the third does so with feature extraction (FE). The study compares the classification performance of eighteen different pre-trained network models (i.e., one or more versions of EfficientNet, DenseNet, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, Xception, and VGGNet) in detail. When compared to other research in the literature, our first and second scenarios provide excellent implementations of TL-FT, while our third scenario, TL-FE, is hybrid and produces better results than the other two. Furthermore, our findings are superior to those of most previous studies. The models with the best results are DenseNet169 with an accuracy of 0.97 in the TL-FT1 scenario, EfficientNetB0 with 0.96 in TL-FT2, and DenseNet169 with 0.995 in TL-FE.
Trending Technologies: A Corpus-based Genre Analysis of Abstracts on ChatGPT

Genre studies have dominated academic contexts, with a particular focus on research article abstracts. Such studies have focused on single and cross-discipline discourse practices, rather than how different disciplines address a common subject based on its communicative purpose. Against this backdrop, this study conducted a corpus-based genre analysis of RA abstracts on ChatGPT. Synergising ESP textual analysis, and corpus-based analysis, the study analysed 72 abstracts. The analyses revealed the ‘Product’ and ‘Purpose’ moves were the most frequent in the abstracts. The ‘Product’ move was afforded much textual space. Concerning the lexicogrammatical features, chatgpt was typified in all the abstracts, occurring frequently at the ‘Introduction’ and ‘Purpose’ moves. Furthermore, the potential and use of ChatGPT, as well as its educational implications, were themes developed from the lexicogrammatical characteristics. Based on these findings, the study provides recommendations for practice and further research.
Application of Authority Control Process in Koha Cataloguing System

This study suggests a pragmatic approach to the authority control process in library cataloguing systems in order to develop a better database that facilitates speedy information retrieval from library OPACs. The authority control method also creates a unified bibliographic database with separate access points for both library users and library professionals. To review the authority control procedure for the sake of this study, we used free and open-source library management software, namely Koha. Different methods are followed by the Koha administration and its cataloguing module. The bibliographic and authority frameworks are very helpful and conducive to all the libraries and information centers. The originality of this study has been successfully demonstrated by library cataloguers’ observations on frequent strategies for carrying out the authority control processes.
Item Selection Algorithm Based on Collaborative Filtering for Item Exposure Control

Abstract In computerized adaptive testing, overexposure of items in the bank is a serious problem and might result in item compromise. We develop an item selection algorithm that utilizes the entire bank well and reduces the overexposure of items. The algorithm is based on collaborative filtering and selects an item in two stages. In the first stage, a set of candidate items whose expected performance matches the examinee's current performance is selected. In the second stage, an item that is approximately matched to the examinee's observed performance is selected from the candidate set. The expected performance of an examinee on an item is predicted by autoencoders. Experiment results show that the proposed algorithm outperforms existing item selection algorithms in terms of item exposure while incurring only a small loss in measurement precision.
Improved versions of crow search algorithm for solving global numerical optimization problems

Abstract Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named Crow Search Algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length ( fl ) and awareness probability ( AP ) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values of fl and AP . A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others.
RoboREIT: An interactive robotic tutor with instructive feedback component for requirements elicitation interview training

Abstract Interviewing stakeholders is the most popular technique for eliciting requirements. The success of an interview depends on the interviewer's theoretical knowledge, preparedness, and communication skills. Practice interviews allow students to apply their knowledge and improve their skills through experience. This practical training is resource‐intensive, requiring the time and effort of a stakeholder for each student, which may not be feasible for a large number of students. This paper introduces RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. RoboREIT addresses the scalability problem of practice sessions with a robotic tutor acting as a stakeholder during the interview and providing feedback after the interview. We performed an exploratory user study to evaluate RoboREIT and demonstrate its applicability in requirements elicitation interview training. The quantitative and qualitative analyses of the users' responses reveal the appreciation of RoboREIT. Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's design incorporates replaying faulty interview stages and allows the student to learn from mistakes by a second time practicing. All participants praised the feedback component, which is not present in the state of the art, for being helpful in identifying the mistakes. A favorable response rate of 81% for the system's usefulness indicates the positive perception of the participants.
A rule-based obfuscating focused crawler in the audio retrieval domain

Abstract The detection of violations of intellectual properties on multimedia files is a critical problem for the current infrastructure of the Internet, especially within very large document collections. To contrast such a problem, either proactive or reactive methods are used. The first category prevents the upload of infringing files themselves by comparing illegal files with a reference collection, while the second one responds to reports made by third parties or artificial intelligence systems in order to delete files deemed illegal. In this article we propose an approach that is both reactive and proactive at the same time, with the aim of preventing the deletion of legal uploads of files (or modifications of such files, such as remixes, parodies and other edits) due to the presence of illegal uploads on a platform. We developed a rule-based obfuscating focused crawler able to work with audio files in the Audio Information Retrieval (AIR) domain, but its use can be easily extended to other multimedia file types, such as videos or textual documents. Our proposed model automatically scans multimedia files uploaded to the public collection only when a user query is submitted to it. We will also show experimental results obtained during tests on a known musical collection. Several combinations of specific Neural Network-Similarity Scorer solutions are shown, and we will discuss the strength and efficiency of each combination.