Reviewerly/exHarmony huggingface dataset:
Pancreatic Cancer Diagnosis using Swarm Optimization Combined with Machine Learning Techniques

When compared to different types of malignant tumors, pancreatic malignancy is the 12 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> widespread tumor disease among humans globally. Pancreatic cancer can be identified at the earlier stages using microarray gene analysis. The objective of this work is to classify the gene samples as normal and tumoral through the usage of Particle Swarm Optimization technique along with the supervised machine learning classifiers. A variance-based feature selection is employed to select the most appropriate features and the selected features are transformed using particle swarm optimization to improve the classification performance. Four different machine learning supervised algorithms are realized as classification techniques. When these four vanilla classifiers are considered, the random forest algorithm performs relatively well with balanced accuracy score of 87.5%. This score is improved to 94.4% through the usage of the proposed technique. In addition, the proposed technique enhances the prediction of the other two classifiers as well.
An Empirical Study on the Customer Acceptance of Unified Payment Interface

There have been significant efforts to promote digital banking and payment in India. As a part of digital banking, the unified payments interface became the fastest payment system in India with its distinctive features. This platform plays a strong role to amplify the Digital India initiative launched by the Indian government. In this paper, the present understanding is enhanced through the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). The novelty of the proposed model considers three new parameters namely, security, privacy, and technology proficiency of user as a measurement of customer acceptance of UPI. The model was empirically validated with data collected from 198 Indian adults. User habits, hedonic motivation, facilitating condition and data privacy impacted behavioural intention to use UPI apps among users. UPI usage habits and data privacy significantly impacted the respondents' use behaviour. The study offered implications to theory and practice for enhancing the usage and percolation of UPI among the Indian populace.
Application of Deep Learning in Signal Modulation Recognition System for Wireless Communication

The development of Industrial Internet has promoted broad connections between people, machines, objects and systems, among others,and build a new manufacturing and service system covering the entire industrial chain and the entire value chain, providing a way to realize the digitization, networking and intelligent development of industry and even industry.Communication system in the high reliability and ultra-high capacity requirements at the same time, but also more and more complex, modulation methods are diverse, how to quickly and efficiently identify the signal type in order to make a deeper degree of processing becomes very meaningful, this paper introduces the relevant content of deep learning, mainly using convolutional neural network algorithm for signal modulation recognition, through Matlab simulation, the signal recognition accuracy is close to 87% and the stability is good under high SNR.
Assessing the Threat Level of Software Supply Chains with the Log Model

The use of free and open source software (FOSS) components in all software systems is estimated to be above 90%. With such high usage and because of the heterogeneity of FOSS tools, repositories, developers and ecosystem, the level of complexity of managing software development has also increased. This has amplified both the attack surface for malicious actors and the difficulty of making sure that the software products are free from threats. The rise of security incidents involving high profile attacks is evidence that there is still much to be done to safeguard software products and the FOSS supply chain.Software Composition Analysis (SCA) tools and the study of attack trees help with improving security. However, they still lack the ability to comprehensively address how interactions within the software supply chain may impact security.This work presents a novel approach of assessing threat levels in FOSS supply chains with the log model. This model provides information capture and threat propagation analysis that not only account for security risks that may be caused by attacks and the usage of vulnerable software, but also how they interact with the other elements to affect the threat level for any element in the model.
Construction of an Improved Intuitionistic Fuzzy Cloud Theory Performance Evaluation Model

Traditional performance evaluation models are difficult to effectively handle a large amount of fuzzy and uncertain information, while intuitionistic fuzzy cloud theory has the advantage of being suitable for solving uncertainty and fuzziness problems. This article analyzed the construction of an improved intuitionistic fuzzy cloud theory performance evaluation model. Based on the principles of intuitionistic fuzzy analytic hierarchy process, indicator weights were constructed, and fuzzy logic and cloud modeling were combined to construct an intuitionistic fuzzy cloud theory performance evaluation model. It was found that the prediction accuracy, accuracy of determining evaluation factor weights, accuracy of overall performance evaluation, and accuracy of decision-making assistance of the improved model were higher than traditional model. The improved intuitionistic fuzzy cloud theory performance evaluation model was more satisfactory to users, with an increase of approximately 12.6% in user satisfaction. This article provided a new perspective for the field of performance evaluation, and was expected to provide new methods.
Improved SENet for Pedestrian Detection

This paper presents an enhanced version of the conventional SENet algorithm in computer vision to tackle the problem of missed or challenging detection of pedestrians when they are obscured. The proposed approach introduces a new method for enhancing the feature extraction performance of pedestrian targets. The paper employs an attention module to learn the correlation between the spatial information of feature channels and feature maps. Next, a distance cross-merge ratio loss function is introduced to improve the regression of detection frames by focusing on the cross-merge ratio between candidate frames and real frames. Last, a non-maximum suppression algorithm is employed for post-processing to eliminate redundant prediction frames and retain more accurate prediction frames, ultimately leading to the best object detection location. Experimental results indicate that the proposed network has a simpler structure and achieves higher detection accuracy compared to traditional methods when tested on the WiderPerson dataset.
A Pose and Shape-Aware Cross-Skeleton Motion Retargeting Framework

Motion retargeting technology plays a vital role in fields such as computer animation, virtual reality, and gaming industries. Users can save a lot of costs in art design and animation production. However, the current motion retargeting methods still has many strict requirements. For example, some methods require the source and target skeletons need to have the same number of joints or share the same topology. In addition, we find many methods currently use the Mixamo dataset as the training set and test set, and use joint position errors optimize retargeting results, but ignore the source-target differences at the shape geometry level. This may result in interpenetration or loss of contact. Therefore, we introduce a novel framework and use evaluation metrics such as position error related to skeleton and interpenetration ratio related to shape, which makes the retargeting result more realistic.
Decentralized Metaverse: Towards a Secure, Autonomous, and Inclusive Virtual World

The metaverse is a universal and immersive virtual world, which are components of cyber-physical-social systems (CPSS). The traditional centralized approach to building a metaverse poses risks to user privacy, security, and autonomy. A growing movement towards a decentralized metaverse, built on blockchain and distributed networks, has emerged as a response. This paper presents a conceptual framework for building a de-centralized metaverse emphasising security, user autonomy, and inclusivity within situation-aware CPSS. Drawing on literature from blockchain, virtual worlds, and decentralized systems, we explore the technical and regulatory challenges that must be addressed to create a more secure, autonomous, and inclusive virtual world. Our framework highlights the importance of community-driven governance and transparent data management for building a decentralized metaverse that empowers users and promotes collaboration.
Factors Affecting the Popularity of VR-Related Educational Videos on TikTok

Virtual reality (VR) has emerged as a useful educational tool with widespread applications, leading to benefits such as increased student engagement and improved memory retention. These applications and benefits are often discussed on social media platforms such as TikTok, one of the fastest-growing social networks. This research explores the factors affecting the popularity of VR-related educational videos on TikTok. Relevant videos were downloaded from TikTok, and their speech and video content were then analysed through a data pipeline. It was found that videos that included descriptive words, such as specific VR hardware, were more popular. Furthermore, videos that were presented in a split-screen format, featuring concise scenes and on-screen subtitles received the most viewer engagement. Additionally, videos that covered gameplay were the most popular, highlighting the dominance of the game industry in the VR sector. These findings will be of value to companies looking to create and promote VR learning resources.
Computer Network Security Evaluation Method Based on GABP Model

Network security is the key to protect the computer network from malicious attacks and illegal access. Routine evaluation methods have some limitations, such as the failure to accurately reflect the dynamic changes and complex associations in the network. Therefore, this study proposed a GABP(Genetic Algorithm Back Propagation)model. First, network traffic data and security event data are collected for preprocessing and feature extraction. Then, the network traffic data is modeled and analyzed using the GABP model to identify potential security threats and vulnerabilities. Finally, the cybersecurity risks are evaluated and quantified based on the evaluation indicators. Through the experimental validation of the real network environment, the method of this study showed high accuracy and efficiency in detecting network security threats and vulnerabilities, and the test results of the four basic performances are close to 1. At the same time, this method can detect and respond to network security incidents in time, improve the protection ability of network security, has high practicability and feasibility, can effectively evaluate network security risks, and provide targeted security protection measures. Future studies could further refine and optimize the approach to the changing means of cybersecurity threats and attack.
GPU Acceleration for Data Processing and Analytics

Graphics processing units, or GPUs, are widely employed as hardware accelerators in various applications, such as algorithmic trading, computer vision, and large language model training. In particular, NVIDIA’s GPUs, together with its Compute-Unified Device Architecture (CUDA) interface, provide a massively parallel platform for general-purpose computing. However, it is often challenging to accelerate data processing and analytical tasks on the GPU when they are irregular and do not match well the GPU architecture or programming paradigm. In this talk, I will discuss general methodologies as well as specific design and implementation techniques on using the GPU to accelerate such tasks, and compare them with CPU-based solutions. With the prevalence of GPU-equipped computing resources and big data applications of increasing complexity and scale, more opportunities and challenges will arise in this space.
Uncertainty-Aware Optimisation for Sustainable Multimedia Event Processing in Big Data Streams

Multimedia Event Processing (MEP) systems play a critical role in various Internet of Things (IoT) applications, including Smart Cities and Health and Safety, by processing large amounts of multimedia data streams. These systems often leverage state-of-the-art Deep Neural Network (DNN) models to enhance their capabilities. However, the growth of Cloud and Edge Big Data applications has imposed a significant environ-mental burden, further intensified by the substantial energy consumption associated with certain DNN model operations.This study addresses the environmental impact of growing Big Data stream applications and focuses on optimising MEP systems to mitigate this issue. We tackle uncertainties arising from user-defined Quality of Service (QoS) interpretations and service worker measurement imprecisions by using uncertainty-aware solutions for the service selection problem in order to improve the QoS within MEP systems.Our results reveal substantial advantages in employing uncertainty-aware strategies. These approaches consistently enhance QoS metrics, outperforming their uncertainty-oblivious counterparts. Specifically, we report improvements in more than 67%, 69%, and 20% of the scenarios, on average, for energy consumption, latency, and accuracy, respectively. These enhancements become evident within just three hours of processing, resulting in energy savings of up to 1.2 kilowatt-hours and latency reductions of 213 seconds, with a 0.29% average loss in query accuracy. These strategies improve system efficiency and ecological sustainability while incurring a small accuracy trade-off. When extrapolated over a year, the environmental benefits become even more noticeable, surpassing the energy requirements for a 1000 Km electric vehicle round-trip from Amsterdam to Paris and back.
Product Completion with Fuzzy Association Analysis: Recommendation System Application for an Insurance Company

Recommendation systems have been widely accepted and have attracted significant attention both from practitioners and academicians. Utilising past behavioural patterns or indicators of customers, recommendation systems determine items or contents that would be related to a user. From this perspective, association rule mining provides extracting rules for related items according to the sequence of past behaviour of the customers. Thus, recommendation systems can be enhanced by using these ‘valuable’ rules to find out what kind of items customers might prefer and what could be shown that is not provided before. This study includes the following: (i) The way of association rule mining can be used to improve state-of-the-art association rule mining algorithms (ii) an ensemble of the extracted rules can be used as an alternative way for current state-of-the-art recommendation approaches providing fuzzy set theory and (iii) a case study to prove the way for implementation of fuzzy ensemble association rule mining. The case study is conducted using an insurance dataset, and results are reported for building the infrastructure of ensemble-based recommendation systems.
Eyetracking Correlated in the Matching Pairs Game

The video games industry has made available frame-works and methodologies that can support many applications in education, medical assessments, and rehabilitation programs. This study proposes a gamified approach based on Matching Pairs (MP) game to evaluate memory skills and performance in a cohort of 18 healthy subjects. An eye-tracking (ET) device was adopted to acquire the participants' gaze patterns while playing three consecutive instances of the card memory game. Eye gaze data has been analyzed to extract eye movements and velocity. Moreover, a statistical analysis was carried out to investigate possible differences in eye dynamics between trials, and a correlation analysis was performed between game statistics (i.e., total score, time, number of flipped cards) and ET-related parameters. Results showed that the maximum eye velocity differed between the first and second trials and eye movements (i.e., saccade and fixation count) and velocity significantly correlated with game duration and the number of flipped cards, respectively. These preliminary outcomes highlight possible relationships between eye dynamics and mental mechanisms for memory recall strategy. Furthermore, this study explored the feasibility of the proposed strategy to be applied to elderly or fragile people to contrast cognitive decline.
Will Blockchain Technology Change How Well National Archives Preserve the Trustworthiness of Digital Records?: Preliminary Results of a Survey

The purpose of this study is to examine the viewpoint of national archives on blockchain and distributed ledger technologies, discover their activities in relation to the application of these technologies, and analyse their thoughts on how these technologies can play a role in the preservation of records’ trustworthiness. A survey method was adopted in the study. The survey consisted of 18 questions about national archives’ attitude and actions in relation to application of blockchain and distributed ledger technologies. The survey was sent to the 194 national archives listed in the Directory of National Archives. Eighteen responses have been acquired which, while low, provides initial insights into how national archives are responding to these technologies. This study has three hypotheses. The first one is “blockchain technology will change archiving practices “, the second one is “the trustworthiness of digital records can be preserved better with blockchain technology”, and the last one is “national archives are reluctant to implement blockchain networks that use tradable crypto-assets”. According to the results obtained from the survey, the first hypothesis has not been verified. The second hypothesis is likely, as national archives that are keen to adopt blockchain and distributed ledger technologies, but a majority of the archives are hesitant to adopt these technologies for archiving, suggesting that the third and final hypothesis might also true, though the reasons for national archives’ reluctance to adopt these technologies could be more varied than originally hypothesized. This study is one of the first systemic analyses of the viewpoint and activities of national archives on blockchain and distributed technologies.
Improved Breast Cancer Detection from Ultrasound Images Using YOLOv8 Model

The number of cases of breast cancer has steadily risen during the past few decades. It is one of the major causes of death in women. There is a good chance of recovery if it is identified early. In order to discover and classify breast cancers, a modified version of the YOLOv8 network is employed, which overcomes the drawbacks of earlier models. Publicly accessible data sets of ultrasound breast cancer images (BUSI) are used for the detection. Preprocessing is also done in the initial phase, which comprises image enhancement methods as well as the removal of labels and pectoral muscles. The data set is annotated, augmented and divided into three parts for training (70%), validation (15%), and testing (15%). The simulation is done with the parameters of batch size of 10, learning rate 0.01 and epoch value of 300. This proposed model is compared to YOLOv7 and YOLOv6 and other models for comparative performance analysis. The outcomes demonstrate that YOLOv8 model achieved mAP (mean average precision) 99.5%, recall 98.40%, and accuracy 96.50%. The experimental results show that the proposed approach outperforms state-of-the-art methods on breast ultrasound cancer detection.
Padang Panjang Community Reading Park toward UNESCO version of literacy city

Padang Panjang City Community Reading Park has role developing community literacy skills. Activity in community reading park and efforts to strengthen community literacy to realize the UNESCO version of the Literacy City. The purpose this research to know the role Padang Panjang Community Reading Park to developing community literacy skills and and efforts to strengthen community literacy to realize the UNESCO version of the Literacy City. The research method used the descriptive qualitative. Eight informants from Rimba Bulan, Anggrek Bulan Garbage Bank, Red Orchid, and Makrame Community Reading Park. The study results showed that TBMs were actively developing community literacy skills by training children to read and write and for mothers to do handicrafts such as knitting, and processing plastic waste. The study concludes that TBMs in Padang Panjang City play a significant role in developing community literacy skills and contributing to the realization of the UNESCO version of the Literacy City. TBMs are active, involved in training children in reading and writing and empowering women through craft workshops and plastic waste processing. The TBMs focus on promoting social inclusion and increasing social inclusion literacy. Conclusion this research is community reading park through the diverse programs and collaboration to enhance creativity and service for the community.
“STOP THIS NONSENSE, HARRY!”: An analysis of impoliteness strategies in cyberbullying commentary targeting Harry Maguire

Cyberbullying encompasses a range of negative online behaviours, including harassment, threats, and insults, which can significantly impact individuals’ mental and emotional wellbeing. This study aims to examine the nature and prevalence of impoliteness strategies within cyberbullying commentary targeting Harry Maguire, a prominent figure in the football world. The data consists of 100 purposively selected comments showcasing impoliteness, extracted from the comments section of a post by Maguire, on his official X platform (formerly Twitter) account dated 7th November 2021. Culpeper’s (2005) Model of Impoliteness serves as the theoretical framework while a mixed method approach is adopted in this study. Out of the five strategies proposed by Culpeper (2005), this study discovers four strategies present in the selected cyberbullying comments. The findings show that Positive Impoliteness (39.6%) is the most frequently employed strategy, and this is followed by Bald on Record Impoliteness, which represents 27.7% of the total comments. The study concludes that within the digital sports community on the X platform, fans frequently employ strategies of positive impoliteness and bald-on-record impoliteness as a means to challenge the face wants of players, such as Harry Maguire, particularly when their performances are below expectation.