Reviewerly/exHarmony huggingface dataset:
Hate Speech Detection in Thai Social Media with Ordinal-Imbalanced Text Classification

Cyberbullying has become a serious problem in Thai social media. For example, some Thai people posted hate speeches on Myanmar workers in Thailand during the COVID-19 pandemic, which might elevate hate crime. It is imperative and urgent to detect cyberbullying on Thai social media. The task is a text classification problem. Moreover, hate speeches contain the order of severity levels, but many pieces of work did not consider this point in the model. Therefore, we developed a Thai hate-speech classification method with various loss functions to detect such hate speeches accurately. We evaluated them on a corpus of ordinal-imbalanced Thai text. The evaluated outcomes indicated that the best-in terms of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score-model was the model with a loss function of a hybrid between an Ordinal regression loss function and Pearson correlation coefficients (common in similarity function). It yielded an average F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score of 78.38 %-0.88 % significantly higher than the score achieved by a conventional loss function-and an average mean squared error of 0.2478-5.49 % relative improvement. Thus, the proposed hybrid loss function improved the efficiency of the model.
TAPE: Tether-Aware Path Planning for Autonomous Exploration of Unknown 3D Cavities Using a Tangle-Compatible Tethered Aerial Robot

This letter presents the first method for autonomous exploration of unknown cavities in three dimensions (3D) that focuses on minimizing the distance traveled and the length of tether unwound. Considering that the tether entanglements are little influenced by the global path, our approach employs a 2-level hierarchical architecture. The global frontier-based planning solves a Traveling Salesman Problem (TSP) to minimize the distance. The local planning attempts to minimize the path cost and the tether length using an adjustable decision function whose parameters play on the trade-off between these two values. The proposed method, TAPE, is evaluated through detailed simulation studies as well as field tests. On average, our method generates a 4.1% increase in distance traveled compared to the TSP solution without our local planner, with which the length of the tether remains below the maximum allowed value in 53% of the simulated cases against 100% with our method.
Higher-Order MDS Codes

An improved Singleton-type upper bound is presented for the list decoding radius of linear codes, in terms of the code parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k,d]$ </tex-math></inline-formula> and the list size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> . <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> -MDS codes are then defined as codes that attain this bound (under a slightly stronger notion of list decodability), with 1-MDS codes corresponding to ordinary linear MDS codes. Several properties of such codes are presented; in particular, it is shown that the 2-MDS property is preserved under duality. Finally, explicit constructions for 2-MDS codes are presented through generalized Reed–Solomon (GRS) codes.
Semi-Supervised Federated Learning Over Heterogeneous Wireless IoT Edge Networks: Framework and Algorithms

Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems to underpin network edge intelligence for smart cities applications. However, most of the data collected by the Internet of Things devices in such applications is unlabeled, necessitating the use of semi-supervised learning. Existing studies have introduced solutions to run semi-supervised FL; however, they overlooked the inherent critical impacts of the wireless characteristics at the network edge. We fill this gap by proposing novel solutions to run semi-supervised FL over wireless network edge, considering the limited computation and communication resources and deadline constraints and realizing that unlabeled data can be automatically labeled during the training rounds to improve the performance of the global model. The problem is first formulated as an optimization problem followed by a two-phase solution. In the first phase, we propose a bisection-based algorithm to find the transmit power and local processing speed that optimally fit the new injected labeled data. In the second phase, we propose three algorithms to control the local updates and injected samples that meet the deadline constraint. We analyze the performance of each algorithm concerning the tradeoffs between learning performance, training time, and total energy consumption. Targeting two applications in smart cities, human activity recognition and object detection, we conduct extensive simulations using realistic federated data sets under nonindependent and identically distributed settings. Numerical results show that the proposed algorithms effectively utilize unlabeled samples while accounting for the characteristics of wireless edge networks in smart cities.
An Interactive Augmented Reality Graph Visualization for Chinese Painters

Recent research in the area of immersive analytics demonstrated the utility of augmented reality for data analysis. However, there is a lack of research on how to facilitate engaging, embodied, and interactive AR graph visualization. In this paper, we explored the design space for combining the capabilities of AR with node-link diagrams to create immersive data visualization. We first systematically described the design rationale and the design process of the mobile based AR graph including the layout, interactions, and aesthetics. Then, we validated the AR concept by conducting a user study with 36 participants to examine users’ behaviors with an AR graph and a 2D graph. The results of our study showed the feasibility of using an AR graph to present data relations and also introduced interaction challenges in terms of the effectiveness and usability with mobile devices. Third, we iterated the AR graph by implementing embodied interactions with hand gestures and addressing the connection between the physical objects and the digital graph. This study is the first step in our research, aiming to guide the design of the application of immersive AR data visualization in the future.
High-Temporal-Resolution Object Detection and Tracking Using Images and Events

Event-based vision is an emerging field of computer vision that offers unique properties, such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes, to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution object detection and tracking approach that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple, low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze tracking performance at different temporal rates. Our approach shows promising results, with minimal performance deterioration at higher temporal resolutions (48-384 Hz) when compared with the baseline frame-based performance at 24 Hz.
Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review

Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns.This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques.This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding.Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively.The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.
Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

Virtual reality is an emerging field in the whole world.The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities.Hence, the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games.The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room.To track the human movement, sensors Micro Processor Unit (MPU6050) are used that are connected with Bluetooth modules and Arduino responsible for sending the sensor data to the game.Further, the sensor data is sent to a machine learning model, which detects the game played by the user.The detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used, which utilizes triaxial accelerometers attached to the subject's wrist, knee, and below neck regions to capture important aspects of human motion.The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity.The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction, education, offices and the educational sector.Extensive experimentation proved the validity of the proposed system.
Metaverse: Implications for Business, Politics, and Society

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Evaluation of Research Data Management (RDM) services in academic libraries of India: A triangulation approach

Research is a planned and scientific method of increasing knowledge that is typically funded by a country's government or funding agencies. Research activity produces valuable data. Research Data Services (RDS) or Research Data Management (RDM) services are considered vital services provided by an academic library and are focused on the management, archiving, processing, and reuse of critical research data. This study evaluates the current status of the adaptation of RDS or RDM services in Indian academic libraries (which includes a total of 186 institutions, including all of India's Central Universities (54) and Institutes of National Importance (132)). A method triangulation approach was used for the data collection, including a literature survey, library website study, online survey, and telephonic interview with LIS professionals from Indian academic libraries. Academic libraries in India are yet to keep up with those in developed countries in adopting RDM services owing to a lack of RDM policy, institutional support, and technological challenges, according to the data. The study also presents suggestions to decision-makers, higher authorities of academic institutions, and the government to develop a strong RDM policy at both the institutional and national levels defining the role and duties of the libraries in RDM.
Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic features

When cybercriminals communicate with their customers in underground markets, they tend to use secure and customizable instant messaging (IM) software, i.e. Telegram. It is a popular IM software with over 700 million monthly active users (MAU) up to June 2022. In recent years, more and more dark jargons (i.e. an innocent-looking replacement of sensitive terms) appear frequently on Telegram. Therefore, jargons identification is one of the most significant research perspectives to track online underground markets and cybercrimes. This paper proposes a novel Chinese Jargons Identification Framework (CJI-Framework) to identify dark jargons. Firstly, we collect chat history from Telegram groups that are related to the underground market and construct the corpus TUMCC (Telegram Underground Market Chinese Corpus), which is the first Chinese corpus in jargons identification research field. Secondly, we extract seven brand-new features which can be classified into three categories: Vectors-based Features (VF), Lexical analysis-based Features (LF), and Dictionary analysis-based Features (DF), to identify Chinese dark jargons from commonly-used words. Based on these features, we then run a statistical outlier detection to decide whether a word is a jargon. Furthermore, we employ a word vector projection method and a transfer learning method to improve the effect of the framework. Experimental results show that CJI-Framework achieves a remarkable performance with an F1-score of 89.66%. After adaptation for English, it performs better than state-of-the-art English jargons identification method as well. Our built corpus and code have been publicly released to facilitate the reproduction and extension of our work.
Bitcoin's hedging attributes against equity market volatility: empirical evidence during the COVID-19 pandemic

Purpose The purpose of this paper is to analyze the hedging capacity of Bitcoin in relation to the S&amp;P 500 index during the COVID-19 pandemic. Design/methodology/approach In order to investigate the hedging features of Bitcoin in relation to the S&amp;P 500 index during the COVID-19 pandemic, the authors use the Granger causality applied on a daily sample of observations ranging from January 1st, 2019 to December 31st, 2020. As robustness checks, the authors use autoregressive models to test the validity of the findings. Findings Using time series of daily data from 1st January 2019 to 31st December 2020, the results show that Bitcoin is not considered as a safe haven because it moves at the same pace as the S&amp;P 500. As a robustness check, the authors use the exponential GARCH model and confirm our previous findings. Overall, the study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises. Originality/value The study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises.
A Graph Skeleton Transformer Network for Action Recognition

Skeleton-based action recognition is a research hotspot in the field of computer vision. Currently, the mainstream method is based on Graph Convolutional Networks (GCNs). Although there are many advantages of GCNs, GCNs mainly rely on graph topologies to draw dependencies between the joints, which are limited in capturing long-distance dependencies. Meanwhile, Transformer-based methods have been applied to skeleton-based action recognition because they effectively capture long-distance dependencies. However, existing Transformer-based methods lose the inherent connection information of human skeleton joints because they do not yet focus on initial graph structure information. This paper aims to improve the accuracy of skeleton-based action recognition. Therefore, a Graph Skeleton Transformer network (GSTN) for action recognition is proposed, which is based on Transformer architecture to extract global features, while using undirected graph information represented by the symmetric matrix to extract local features. Two encodings are utilized in feature processing to improve joints’ semantic and centrality features. In the process of multi-stream fusion strategies, a grid-search-based method is used to assign weights to each input stream to optimize the fusion results. We tested our method using three action recognition datasets: NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA. The experimental results show that our model’s accuracy is comparable to state-of-the-art approaches.
PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

Pedestrian and vehicle detection plays a key role in the safe driving of autonomous vehicles. Although transformer-based object detection algorithms have made great progress, the accuracy of detection in rainy scenarios is still challenging. Based on the Swin Transformer, this paper proposes an end-to-end pedestrian and vehicle detection algorithm (PVformer) with deraining module, which improves the image quality and detection accuracy in rainy scenes. Based on Transformer blocks, a four-branch feature mapping model was introduced to achieve deraining from a single image, thereby mitigating the influence of rain streak occlusion on the detector performance. According to the trouble of small object detection only by visual transformer, we designed a local enhancement perception block based on CNN and Transformer. In addition, the deraining module and the detection module were combined to train the PVformer model through transfer learning. The experimental results show that the algorithm performed well on rainy days and significantly improved the accuracy of pedestrian and vehicle detection.
An Algorithm for Task Allocation and Planning for a Heterogeneous Multi-Robot System to Minimize the Last Task Completion Time

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min-max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal-dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min-max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time.
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.
Technical debts and faults in open-source quantum software systems: An empirical study

Quantum computing is a rapidly growing field attracting the interest of both researchers and software developers. Supported by its numerous open-source tools, developers can now build, test, or run their quantum algorithms. Although the maintenance practices for traditional software systems have been extensively studied, the maintenance of quantum software is still a new field of study but a critical part to ensure the quality of a whole quantum computing system. In this work, we set out to investigate the distribution and evolution of technical debts in quantum software and their relationship with fault occurrences. Understanding these problems could guide future quantum development and provide maintenance recommendations for the key areas where quantum software developers and researchers should pay more attention. In this paper, we empirically studied 118 open-source quantum projects, which were selected from GitHub. The projects are categorized into 10 categories. We found that the studied quantum software suffers from the issues of code convention violation, error-handling, and code design. We also observed a statistically significant correlation between code design, redundant code or code convention, and the occurrences of faults in quantum software.