Reviewerly/exHarmony huggingface dataset:
Spatial and Temporal Hand-Raising Recognition from Classroom Videos Using Locality, Relative Position-Aware Non-Local Networks and Hand Tracking

Hand-raising gesture is one of the most popular signs of communication, whose frequency is related to the classroom’s atmosphere, the attractiveness of the subject, and the level of interactions between students and teachers. However, automatic hand-raising gesture detection and recognition remains still a challenging problem mainly due to low hand resolution, hand occlusion, various backgrounds, and viewpoint changes. While majority of the existing methods focus on static hand-raising posture detection, in this paper, we propose a framework for dynamic hand gesture recognition from classroom videos consisting of two main stages: hand posture detection and dynamic hand gesture recognition. In hand posture detection stage, we extend the previous work by adding relative position-aware in non-local network. After detecting the hand-raising posture on static pixels, which in turn accelerates the performance images, at the dynamic hand gesture recognition stage, we incorporate object detection and tracking to associate the hand-raising detection results at consecutive frames to supplement the missing detection due to the occlusion issue and obtain hand-raising gesture recognition at the event level. The experimental results show that the proposed method outperforms three benchmark models for static hand posture detection that are Faster-RCNN, Libra-RCNN, and Libra-RCNN+RDA on our dataset with the gains obtained for mAP being 7.68%, 5.76%, and 0.35% higher, respectively. In terms of event-level recognition results, the proposed method achieves the value of frame-wise accuracy, temporal_IoU, F1-score@0.3, and Levenshtein-score are 90.0%, 84.4%, 83.2%, and 84.3%, respectively. The code and dataset used in the paper will be made publicity available for research community.
Cost of Security of Layer 2 Network — Evidence From Polygon Network

Propelled by the decentralized finance (Defi) hype, the use of decentralized exchange (DEX) platforms has been growing in the last few years. The recent innovation of Layer 2 (L2) blockchain has provided better scalability and lower fees than the Ethereum blockchain (L1). The security of L2 relative to L1 is unclear and is difficult to identify. Using a structural model and a novel and comprehensive data set, we find that traders anticipate a 1.93% chance of losing the transaction value when trading on the L2 compared to L1, a very considerable amount compared to the 0.3% liquidity pool fee charged on each trade.
Reordering Decision Diagrams for Quantum Computing Is Harder Than You Might Think

Decision diagrams have proven to be a useful data structure in both, conventional and quantum computing, to compactly represent exponentially large data in many cases. Several approaches exist to further reduce the size of decision diagrams, i.e., their number of nodes. Reordering is one such approach to shrink decision diagrams by changing the order of variables in the representation. In the conventional world, this approach is established and its availability taken for granted. For quantum computing however, first approaches exist, but could not fully exploit a similar potential yet. In this paper, we investigate the differences between reordering decision diagrams in the conventional and the quantum world and, afterwards, unveil challenges that explain why reordering is much harder in the latter. A case study shows that, also for quantum computing, reordering may lead to improvements of several orders of magnitude in the size of the decision diagrams, but also requires substantially more runtime.
Spatial-Temporal Information Aggregation and Cross-Modality Interactive Learning for RGB-D-Based Human Action Recognition

The RGB-D-based human action recognition is gaining increasing attention because the different modalities can provide complementary information. However, the recognition performance is still not satisfactory due to the limited ability to learn spatial-temporal feature and insufficient inter-model interaction. In this paper, we propose a novel approach for RGB-D human action recognition by aggregating spatial-temporal information and implementing cross-modality interactive learning. Firstly, a spatial-temporal information aggregation module (STIAM) is proposed to utilizes sample convolutional neural networks (CNNs) to aggregate the spatial-temporal information in entire RGB-D sequence into lightweight representations efficiently. This allows the model to extract richer spatial-temporal features with limited extra memory and computational cost. Secondly, a cross-modality interactive module (CMIM) is proposed to fully fuse the multi-modal complementary information. Moreover, a multi-modal interactive network (MMINet) is constructed for RGB-D-based action recognition by embeding the above two modules into the two-stream CNNs. In order to verify the universality of our approach, two backbones are deployed in the two-stream architecture, successively. Ablation experiments demonstrate that the proposed STIAM can bring significant improvement in recognizing actions. CMIM can further play the advantages of complementary features of multiple modalities. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120 and PKU-MMD datasets proved the effectiveness of the proposed approach. The proposed approach achieves an accuracy of 94.3% and 96.5% for cross-subject and cross-view on NTU RGB+D 60, 91.7% and 92.6% for cross-subject and cross-setup on NTU RGB+D 120, 93.6% and 94.2% for cross-subject and cross-view on PKU-MMD datasets, which are the state-of-the-art performance. Further analysis denotes that our approach has advantages in recognizing subtle actions.
Roles of Middle Managers in Agile Project Governance

Abstract Project governance is an important activity in agile software development (ASD) projects for project success. Middle managers are part of the governance structure in ASD projects. Despite the efficacy of project governance and existence of middle managers in agile teams, project governance and middle management in ASD projects are under-researched. This multiple-case study investigates the roles of middle managers in agile project governance activities within two Nigerian ASD projects through the lens of activity theory. We collected data in semi-structured interviews, observations, questionnaires, and company documents. Our findings show that middle managers performed 25 roles related to planning and coordination for project alignment and execution, continuous improvement and organisational change, agile and technical leadership, monitoring, and capability building. We conclude that middle managers are pivotal to project governance practice and the effectual functioning of agile teams in ASD projects. The study will help agile practitioners to better understand the roles of middle managers in agile project governance. Results from this work contribute to the ‘middle management in agile’ debate and offer an alternative view that may change beliefs about middle managers in agile project settings.
Informatics at Primary Education: Teachers’ Motivation and Barriers in Lithuania and Turkey

Scientific research shows that the role of teachers is crucial in the integration of informatics at all educational stages. Besides some other barriers, teachers’ lack of knowledge of informatics is believed to be a reason why educators are reluctant to get involved in informatics teaching. Hence, this study focused on the factors influencing primary school teachers’ motivation and possible barriers for integrating informatics in lessons. The data were collected from an accessible sample by conducting semi-structured interviews with primary school teachers in Lithuania and Turkey in 2022. This paper also presents an overview of informatics primary education curricula in both countries. Results showed that despite different curriculum conditions and sociodemographic issues in two countries, primary school teachers’ factors of motivation and barriers are the same. Motivation depends on teachers’ willingness to acquire new digital competences, to go beyond being a regular teacher and to innovate in their school. Teachers understand the importance of keeping their students up-to-date with the newest educational technologies and CT competences, however they face serious challenges to overcome. We sum up with a discussion comparing curricula and teachers’ attitudes in both countries and conclude with some insights and recommendations.
Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Speech recognition systems have become a unique human-computer interaction (HCI) family.Speech is one of the most naturally developed human abilities; speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR (Automatic Speech Recognition) has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented, along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication, emotion recognition, healthcare, etc., this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.
Breast DCE-MRI Segmentation for Lesion Detection by Multilevel Thresholding Using Arithmetic Optimization Algorithm

Patra, Dipak Kumar Si, Tapas Mondal, Sukumar Mukherjee, PrakashIt is becoming more common to employ Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to diagnose breast disorders. DCE-MRI, which is assisted by Computer-Aided Design (CAD) methods, shows its effectiveness in detecting breast cancer. In image segmentation, multilevel thresholding is an important and easy to implement technique. This paper proposes breast DCE-MRI segmentation by multilevel thresholding using the Arithmetic Optimization Algorithm (AOA). First, the anisotropic diffusion filter is used to denoise MR images, and then, the correction of Intensity Inhomogeneities (IIHs) is performed. The lesions are then retrieved from the segmented images and located in the initial MR images. 50 Sagittal T2-Weighted DCE-MRI images are used to test the suggested approach. Particle Swarm Optimizer (PSO) and Hidden Markov Random Field (HMRF) are compared to the suggested AOA technique. The devised technique achieves a high level of accuracy of 99.80%, sensitivity of 98.06%, and Dice Similarity Coefficient (DSC) of 85.52%. The devised method outperforms the two compared methods.
Fuzzy-Based Methods for the Selection and Prioritization of Software Requirements: A Systematic Literature Review

Software requirements (SRs) selection is one of the major research issues in the field of software engineering which is used to select the requirements from the list of the elicited SRs on the basis of their ranks. There are different fuzzy-based techniques for computing the ranking values of SRs. Based on our review, we found different studies which focus on the literature review of the SRs selection and prioritization. There is no study which synthesizes the SRs selection methods under a fuzzy environment. Therefore, to address this issue, this paper presents a systematic literature review (SLR) in the area of fuzzy-based methods for the selection of SRs. Four research questions (RQs) have been formulated to identify the research gaps in the literature: RQ1: What are the different fuzzy-based methods for the selection of the SRs? RQ2: Which fuzzy number is mostly used during the computation process? RQ3: What kinds of datasets have been used in the SRs selection methods? RQ4: Which system/application has been used to select the SRs? Search items were extracted from the Journals, Conferences, and Workshops based on the RQs. Our SLR has identified 54 different studies. Selected studies were accessed based on the RQs thus identifying the research issues in the literature. After the analysis of the 54 studies, it was identified that the area of SRs selection has lots of research issues which can be addressed by applying the other soft computing techniques to find out the ranking order of SRs.
An Effective Approach for Detecting Acute Lymphoblastic Leukemia Using Deep Convolutional Neural Networks

In this paper, we present a fully automatic acute lymphoblastic leukemia detection method based on deep neural networks. ALL is a condition affecting the leukocytes. Children tend to be prone to have this melanoma. The fundamental problem with this type of cancer is that, unlike other cancers, it does not create tumors, making it extremely difficult to identify. Prior to automation, manual microscopic testing procedures were used, but they were time-consuming and error-prone. To overcome this issue, many automated systems were introduced, which used machine learning techniques. But, because we are dealing with medical information, we may require better efficiency and accuracy, so as an improvement, our proposed system employed several convolutional neural networks architectures. In the proposed system, five such CNN algorithms were implemented to classify and separate the cancerous and non-cancerous cells. The system accepts the blood cell image from the user and predicts whether the cell contains one/more blasts depending upon the prediction value obtained from the CNN algorithm on the stained cell image. As a result, it significantly reduces the research costs, increases the speed of testing, and can be a lifesaver for millions of cancer patients.
Fault Classification of Pump Using Support Vector Machine (SVM) Method

A machine is mechanical or electrical equipment that converts energy to aid human activities or manufacture specific items. A machine&#x0027;s condition should be maintained and checked in proper working order. As a result, the machine&#x0027;s state must be determined before major harm occurs. The goal of this research is to use machine learning to detect the type of pump damage. The focus of this investigation was a Panasonic GP-129 water pump. The purpose of this research is to classify three types of pump faults: misalignment, imbalance, and bearing fault. Based on the results obtained, the classification of pump fault using Support Vector Machine (SVM) methods had average accuracy of 98.35&#x0025; on the Linear SVM and Cubic SVM models, and average accuracy of 100&#x0025; on the Quadratic SVM model.
MMKP: A mind mapping knowledgebase prototyping tool for precision medicine

With significant advancements in the area of precision medicine, the breadth and complexity of the relevant knowledge in the field has increased significantly. However, the difficulty associated with dynamic modelling and the disorganization of such knowledge hinders its rapid development potential.To overcome the difficulty in using the relational database model for dynamic modelling, and to aid in the organization of precision medicine knowledge, we developed the Mind Mapping Knowledgebase Prototyping (MMKP) tool. The MMKP implements a novel design that we call a "polymorphic foreign key", which allows the establishment of a logical linkage between a single table field and a record from any table. This design has advantages in supporting dynamic changes to the structural relationships in precision medicine knowledge. Knowledge stored in MMKP is presented as a mind map to facilitate human interaction. When using this tool, medical experts may curate the structure and content of the precision knowledge in a flow that is similar to the human thinking process.The design of polymorphic foreign keys natively supports knowledge modelling in the form of mind mapping, which avoids the hard-coding of medical logic into a rigid database schema and significantly reduces the workload that is required for adapting a relational data model to future changes to the medical logic. The MMKP tool provides a graphical user interface for both data management and knowledgebase prototyping. It supports the flexible customization of the data field constraints and annotations. MMKP is available as open-source code on GitHub: https://github.com/ZjuLiangsl/mmkp.
Security of medical images over insecure communication channels using zero-steganography

Secure and private communication has gained much focus on the international arena recently due to the ability of certain organizations to eavesdrop on private communication. This ability is further increased in the Internet of Medical Things scenario where medical images and private data are shared over insecure channels. This work is focused on a new and exciting question related to medical image steganography: is it possible to send hidden information through a cover medium, that is a medical image, without making any alteration to the cover medium in an effective manner? The proposed work has shown that it is possible to send hidden information through a medical image without making any alteration to the original medical image. This solves two problems at the same time. The first problem is that privacy and security approaches that depend on watermarking and steganography usually need some sort of modification to the medical images. The second is the knowledge that there is steganography taking place is completely hidden as no change is made to the medical image. Experiments have shown that the proposed approach under investigation demonstrates effective robustness against attacks like filtering, compression, and addition of noise.
An Improved Whale Optimization Algorithm for Robot Time-jerk Optimal Trajectory Planning

Abstract Optimal path planning in the welding process enhances the processing and manufacturing efficiency. An effective algorithm for optimum path planning of a six-axis welding industrial robot is established in this paper. The robot’s trajectory is constructed using 5-th B spline interpolation in the joint space, and a cost function is constructed in terms of both time and mean jerk. An improved whale optimization algorithm (IWOA) is presented to address the time-jerk optimal trajectory planning issue. The robot’s joint positions are considered the IWOA input, while the MATLAB 2018b software is utilized to perform the optimization algorithm. Numerical simulations demonstrate that IWOA can effectively reduce the jerk of the robot while improving its work efficiency.
Analysing the Institutional building for Solar Radiation and Photovoltaic Energy using AutoDesk-Revit

Abstract Unconventional sources of energy like solar are not only green but sustainable too. Adopting sustainable sources of energy is a futuristic approach. Working in similar directions in current research, paper authors analyzed and modeled various aspects of an institutional building for solar energy suitability and efficiency by using Autodesk Revit software solar Plugin 2021. The various aspects of solar analysis such as sun path, PV potential and solar radiation value are observed and recorded. After ranging the different criteria like PV surface coverage and PV efficiency the mean energy cost tends to be reduced. Authors observed that the energy cost got reduced to 18.8$ from 24.3$.
Target recognition of heterogeneous fusion images based on improved YOLO model deep learning algorithm

Abstract Aiming at the problem of target recognition of heterogeneous fusion images, the algorithm flow and network structure of the YOLO convolutional neural network model are analyzed and explained based on the deep learning of the convolutional neural network. Combining the characteristics of the fusion image, the interface, structure and parameters of the YOLOv3 convolutional neural network are improved and adjusted while the recognition effect is guaranteed, the YOLO model is streamlined, and the training is successfully implemented and the training weights are output. The algorithm is effective and stable, and the recognition speed is faster.
Enforcement of the Use of Digital Contact-Tracing Apps in a Common Law Jurisdiction

Digital contact-tracing systems have been widely implemented worldwide with different system designs and implementation policies for the purpose of tracking potentially exposed individuals. The use of a digital contact-tracing app in Hong Kong has been mandated for visiting certain premises by legislations. This paper reviewed the regulations promulgated specifically for the prevention and control of COVID-19 and identified those associated with the digital contact-tracing system. A comprehensive search in newspaper databases was performed to explore the enforcement of the mandated use of the digital contact-tracing app. The three facets of regulations in relation to digital contact tracing were examined: duty to disclose information, requirements and directions to businesses, and compulsory testing. The use of digital contact-tracing data for non-public health purposes was also reported. Our analyses showed that prosecution of non-use or the use of fraudulent digital contact-tracing apps was not limited to COVID-19-specific legislations. The flexible approach ensured the enforcement of the use of the digital contact-tracing app, but the judiciary's test must be passed in future cases.
Eliminating Data Duplication in CQA Platforms Using Deep Neural Model

Primary research to detect duplicate question pairs within community-based question answering systems is based on datasets made of English questions only. This research put forward a solution to the problem of duplicate question detection by matching semantically identical questions in transliterated bilingual data. Deep learning has been implemented to analyze informal languages like Hinglish which is a bilingual mix of Hindi and English on Community Question Answering (CQA) platforms to identify duplicacy in questions. The proposed model works in two sequential modules. First module is a language transliteration module which converts input questions into a mono-language text. The next module takes the transliterated text where a hybrid deep learning model which is implemented using multiple layers is used to detect duplicate questions in the mono-lingual data. The similarity between the question pairs is done utilizing this hybrid model combining a Siamese neural network with identical capsule network as the subnetworks and a decision tree classifier. Manhattan distance function is used with the Siamese network for computing the similarity between questions. The proposed model has been validated on 150 pairs of questions which were scrapped from various social media platforms, such as Tripadvisor and Quora which achieves accuracy of 87.0885% and AUC-ROC value of 0.86.
Innovation and Practical Methods of Music Education Path in Colleges and Universities under the Popularization of the 5G Network

In the era of the 4G network, the scale and pattern of online music education have been formed, and its necessity and educational achievements have emerged. The 5G network era is coming, and online music education is once again facing new challenges, and it is imperative to make necessary changes. This paper, by expounding on the changes brought about by the 5G network era, combined with the characteristics of online music education in colleges and universities, will summarize and analyze the current work status and problems faced. Improve the level of work and make online music education reach a new height.