Reviewerly/exHarmony huggingface dataset:
ANALYSIS AND POTENTIAL IMPROVEMENT OF SOFTWARE FAULT PREDICTION APPROACHES

Inadequate testing may cause serious consequences, and hence is considered such a vital part of the development process.The fault prediction is a technique of predicting that software parts are most likely to contain faults.Several recent studies have explored the usage of various fault prediction techniques based on different algorithms and datasets.The research objectives are deep analysis of existing fault prediction approaches, findings, and discussions on potential research gaps, with respect to the proposed novel and promising ensemble techniques in software fault prediction.A complete literature review of 29 approaches is performed with deep analyzes of a set of 5 selected approaches in the field.The results of provide a deeper understanding of the generalizability, robustness, and other key aspects of each approach, giving a clearer indication of their potentials.Among the key results is the diversity of the selected approaches in their scope, experiment data, classifiers, and evaluation measures.The results of the ensemble technique usage showed higher accuracy rates compared to their respective base classifiers.Novel potential ensemble techniques are proposed based on theoretic analysis results of selected approaches aiming to be more effective than their basic models and overpass them.This review provides insights into the effectiveness of the studied approach solutions in fault prediction.It discovers the necessity of new novel techniques, where collaborative ones are promising in fault prediction accuracy and diverse applications.Further analyzes are needed to investigate the feasibility of the proposed ensemble approaches and results, ideally for real industrial projects.
Research on Construction and Application of Digital Ecosystem for Innovation and Entrepreneurship Education—The Case of National Vocational Education Innovation and Entrepreneurship Repository

Digital transformation is reshaping the society, the labor market and the future of work.Accelerating the digital transformation of education has become a consensus of the international education community.This paper takes the national vocational education innovation and entrepreneurship education repository as an entry point, to carry out research on the construction and application of education digital ecosystem.Targeting the pain points of the digital ecosystem construction of innovation and entrepreneurship education now, the author proposes solutions in five dimensions, including the construction concept, top-level design, institutional support, platform application, and teachers development.The aspiration is that the construction of digital education ecology can help serve people's lifelong growth and sustainable development.
Review of Optical and Inertial Technologies for Lower Body Motion Capture

Motion capture systems play a vital role in diverse fields of knowledge, impacting multimedia content creation, sports, and assistive technologies in health sciences.This article offers an overview of human motion capture technologies with a focus on lower body analysis.Recent advancements are presented by compiling papers selected through the PRISMA method.The data were searched and extracted from various databases such as MDPI, IEEE Xplore, and Scopus, narrowing the observation window from 2018 to 2022, where 24 articles form the core of the review.The results indicate that 41% of the papers deal with technologies based on optical sensors, 24% with technologies based on MEMS sensors, 14% with technologies based on radar sensors, and 21% report the use of MEMS and optical sensor technologies.The technologies used were analyzed for their characteristics, motion information processing techniques, the type of results derived from each system, and their final applications.Based on this review, it is evident that there is a need to develop robust wearable systems that allow the fusion of different types of sensors to compensate for the weaknesses of each motion capture technology.In conclusion, the current state of motion capture technology emphasizes the development of health and rehabilitation applications.
A Hybrid TF-IDF and RNN Model for Multi-label Classification of the Deep and Dark Web

The classification of content on the deep and dark web has been a topic of interest for researchers.Researchers focus on adopting more efficient and effective classification methods as the data available on deep and dark web platforms continues to grow.Multi-label classification is the approach for simultaneously categorizing content into multiple classes.To address this, a hybrid approach combining Term Frequency-Inverse Document Frequency (TF-IDF) and Recurrent Neural Network (RNN) has been proposed.The approach involves preprocessing a dataset of Hypertext Markup Language (HTML) documents, selecting specific HTML tags to generate embeddings using TF-IDF, and using an RNN model for multi-label classification.The proposed model was evaluated against commonly used methods (Binary Relevance, Classifier Chains, and Label Powerset) using precision, recall, and F1-score as evaluation metrics, demonstrating promising results in accurately classifying data from the deep and dark web.This contribution represents a noteworthy advancement for researchers and analysts working in this field.
The Effect of Using Picture Stories Media and Learning Activities Towards Students’ Ability in Writing

The purpose of this study was to analyze: (1) the difference in the average of students’ ability in writing paragraphs using pictorial story media and those without using pictorial media. (2) the influence of students who have high learning activities using picture stories media on writing skills is better than students who have high learning activities without using picture stories media. (3) the average score of students’ ability in writing paragraphs with high learning activities who use the picture stories is higher than those who do not use picture stories media. (4) the average score of students’ ability in writing paragraphs with low learning activities who use the picture stories is higher than those who do not use picture stories media. The method used in this research is the experimental method. The results of this study indicate that learning activities and picture stories media have a positive effect towards the students’ ability in writig paragraphs. The interaction of students who have high learning activities and are taught using picture stories media has the highest average value in writing paragraph ability, that is 16.74. Furthermore, the average scores of students with low learning activities and being taught with picture stories media is 15.03, then students with high learning activities and being taught without media is 10.67, and finally the average score of students who have low learning activities and are taught without picture stories media is 10,13.
Long-range zero-shot generative deep network quantization

Quantization approximates a deep network model with floating-point numbers by the model with low bit width numbers, thereby accelerating inference and reducing computation. Zero-shot quantization, which aims to quantize a model without access to the original data, can be achieved by fitting the real data distribution through data synthesis. However, it has been observed that zero-shot quantization leads to inferior performance compared to post-training quantization with real data for two primary reasons: 1) a normal generator has difficulty obtaining a high diversity of synthetic data since it lacks long-range information to allocate attention to global features, and 2) synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness. To overcome these problems, we propose a novel deep network quantizer called long-range zero-shot generative deep network quantization (LRQ). Technically, we propose a long-range generator (LRG) to learn long-range information instead of simple local features. To incorporate more global features into the synthetic data, we use long-range attention with large-kernel convolution in the generator. In addition, we also present an adversarial margin add (AMA) module to force intra-class angular enlargement between the feature vector and class center. The AMA module forms an adversarial process that increases the convergence difficulty of the loss function, which is opposite to the training objective of the original loss function. Furthermore, to transfer knowledge from the full-precision network, we also utilize decoupled knowledge distillation. Extensive experiments demonstrate that LRQ obtains better performance than other competitors.
Blockchain-Based Framework for Supply Chain Traceability

Alcohol counterfeiting, which negatively affects people’s lives in terms of economic, health, and social aspects, is one of the crucial problems that arise in many countries. For this reason, information should be made visible and traceable in all current flows of manufactured products from the raw material supplier to the retailer. In this study, a blockchain-based system architecture is proposed to ensure the traceability of raki (an anise-flavored Turkish drink), which is heavily counterfeited in Turkey. The information that adds value to the product is recorded in blocks in the blockchain, which is the building block of the system architecture, and this recorded information can be viewed by the relevant stakeholders. Product traceability is carried out with two smart contracts. With the Asset Registration Smart Contract (ARSC), assets are identified in the digital layer and the lot ID is assigned to the assets. The security of these smart contracts is ensured by storing recorded information in the blockchain, where it cannot be deleted or changed. Hyperledger Fabric, a blockchain platform, is used in the proposed architecture. In addition, QR codes integrated into the system act as a bridge between the physical and digital layers, directing consumers to the blockchain layer for traceability. Thus, the consumer will be able to see all the stages of the product that have transparently reached him and will be able to notice any counterfeiting event that may occur. In this context, it is foreseen that the proposed architecture will prevent raki counterfeiting as much as possible and provide transparency and traceability in all processes from raw material to bottle. This paper is arranged as follows. In Sec. 1 , the problem is defined, the solution for the problem is explained, information is given about the technologies proposed for the solution, and current studies on the subject are discussed. In Sec. 2 , the system architecture created for traceability is explained in detail. Section 3 describes the results.
PENGARUH MEDIA GAMBAR BERSERI DENGAN TEMA PENDIDIKAN TERHADAP KEMAMPUAN MENULIS CERPEN PADA SISWA KELAS X SMK TKJ I WIRA KESUMA JAYA NAMORAMBE TAHUN AJARAN 2021/2022

This study aims to determine the effect of serial image media with the theme of education on the ability to write short stories in class X TKJ 1 SMK TKJ I Wira Kesuma Jaya Namorambe in the 2021/2022 academic year.This type of research is quantitative research, with a quasi-experimental research approach.The population in this study were students of class X TKJ 1 SMK Wira Kesuma Jaya Namorambe.The sample in this study was class X TKJ 1. Sampling used the purposive sampling method as many as 30 people from class X TKJ 1.This research data was collected using LKPD to write short story pictures with the theme of education.Then the data were analyzed using the t-test to see if there was a significant effect of giving a series of educational themes on students' writing skills.This can be seen from the results of the t-test which shows that the t-level test obtained the calculation results with the initial test score (pre-test) obtained an average score of 58 with a standard deviation of 14.7.While the score (post-test) obtained an average of 85.6 with a standard deviation of 6.94.That is, the description of the level of ability to write short stories in class X TKJ 1 SMK Wira Kesuma Jaya Namorambe.It can be seen from the average score of students before using the educational theme series, which was lower than after using the educational theme series, and the educational theme series had an effect on writing ability.
Teaching English to Young Learners Through Pocket Bee Instructional Media

Aiming at finding the detailed scheme of teaching English to young learners through Pocket Bee instructional media, this scientific article scrutinizes Pocket Bee’s basis concept, Pocket Bee’s way of implementing, and young learners’ responses toward Pocket Bee. This scientific article was used qualitative method with descriptive qualitative as the research design. The subjects of this scientific article were well-filtered through convenience sampling with its inclusive sampling on young learners’ range of age. Twenty young learners of Lembaga Pendidikan As Salim (Ponpes/TPQ/RA/PAUD) were contributed to the entire process of teaching English to young learners through Pocket Bee instructional media. The researcher of this scientific article concludes that Pocket Bee is a TEYL instructional media in a form of interactive and child familiar design (i.e. bee form) that is created by the researcher in a purpose of providing young learners an instructional media that is able to maximize the possibility of enhancing young learners’ English proficiency. The implementation can be formed into five main steps on its main activity, namely (1) giving prior knowledge, (2) letting all students roll the dice, (3) picking picture card from the pocket bee, (4) testing students’ understanding, and (5) instructing all students to draw and to write down the picture card. The students' response agrees that Pocket Bee instructional media is capable to provide young learners a fun and happiness during the learning process because the scale of positive respondents reach out 100%. Those responses were validated by legal representative of Lembaga Pendidikan As Salim (Ponpes/TPQ/RA/PAUD).
University library in the modern information space: new opportunities, problems, and risks

The article addresses the problems of a new direction in the development of university libraries in the information and educational environment caused by the pandemic. Libraries have developed new skills in distance communication, and the introduction of new services and the creation of new electronic and media resources have enhanced the role of the library in providing information to various categories of users.
Visual Analysis of Research Trends on Language Teachers in the Era of Artificial Intelligence

This study reviews and analyzes language teacher research in the era of artificial intelligence between 2013 and 2023 using CiteSpace.The results indicate that research hotspots encompass language teachers' technological integration ability, affective attitudes, teaching content, and instructional effectiveness.The developmental trajectory of the research is evident in the shift of language teachers' instructional content from PCK to TPACK, the exploration of language teachers' affective research from internal beliefs to external environments, and the evolution of language teachers' educational technology from CALL to AI.
DAResNet Based on double-layer residual block for restoring industrial blurred images

Image acquisition and restoration have always been an indispensable part of industrial production. Due to the harsh environment in which industrial cameras are used and problems such as network transmission loss, the images captured are not high definition enough, making image restoration particularly important. Existing image restoration methods have a large number of parameters and time-consuming training, resulting in inefficiency. In this paper, we propose DAResNet for the problem of balancing model size and image restoration performance to improve image restoration while reducing the number of parameters. By analyzing the characteristics of industrial blurred images and combining the features of the proposed model, we propose the Half-Channel Attention Mechanism (HCAM) while constructing the Half-Channel Attention Instance Normalization Block (HCAIN Block) and the Double-Layer Residual Block (DARes Block). Meanwhile, we establish the Industrial Blurred Image Dataset (IBID). Finally, four state-of-the-arts (SOTAs) are selected for comparison on IBID, and DAResNet achieves excellent results.
Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers

Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in scenarios where data is scarce. Visual transformers (ViT) have recently emerged as a powerful tool for image classification, due to their ability to learn highly expressive representations of visual data using self-attention mechanisms. In this work, we explore Semi-ViT, a ViT model fine tuned using semi-supervised learning techniques, suitable for situations where we have lack of annotated data. This is particularly common in e-commerce, where images are readily available but labels are noisy, nonexistent, or expensive to obtain. Our results demonstrate that Semi-ViT outperforms traditional convolutional neural networks (CNN) and ViTs, even when fine-tuned with limited annotated data. These findings indicate that Semi-ViTs hold significant promise for applications that require precise and fine-grained classification of visual data.
Probot 2: upgrading a Mastodon bot

Earlier this year I set up a bot on Mastodon. The bot, AlbumsX3, posts an album suggestion twice-a-day. Performance has been good. It has only missed a few posts due – I think – to server glitches. However, I have made a couple of tweaks to upgrade the bot since my last post, so I thought I would detail them here. Preventing duplicate posts In the last post I wrote: This means that duplicate posts will happen, but I didn’t try too hard to find a way around this. I might revisit it in the future. Well, it wasn’t long before I needed to revisit this issue.
Dezvent - Digitalizing Attendance System with 2FA and Face Recognition Implementation

The traditional way of taking attendance has been said to be inefficient and had to take a longer time to mark every attendee's presence. With that in mind, the research aims to eliminate the issue brought by the manual attendance system by developing a web-based attendance system that can record attendance in a faster and more effective way with the implementation of a face recognition system. The attendance will be taken with just one scan of the face of the attendees, ensuring their presence at the event. Not only that but also with the implementation of two-factor authentication (2FA) to develop a secure web-based system, as well as to protect users against cyber-attack. This system not only solved the problems brought by the current attendance system but also protects the environment by eliminating the need of using paper to record attendance.
A parametrized axiomatization for a large number of restricted second-order logics

Abstract By limiting the range of the predicate variables in a second-order language, one may obtain restricted versions of second-order logic such as weak second-order logic or definable subset logic. In this note, we provide an infinitary strongly complete axiomatization for several systems of this kind having the range of the predicate variables as a parameter. The completeness argument uses simple techniques from the theory of Boolean algebras. This article is dedicated to our friend John N. Crossley on the occasion of his 86th birthday.
VTK-m: Visualization for the Exascale Era and Beyond

A recent trend in modern high-performance computing is the increasing use of hybrid architectures, where the vast majority of performance comes from accelerators. Modern accelerators are based on Graphics Processing Units (GPU) that contain many low power cores that in their aggregate provides an extremely high computation rate. Current and future CPU processors are requiring more explicit parallelism as each successive version of the hardware packs in more cores, and technologies like hyperthreading and vector operations require even more parallel processing to leverage each core’s full potential. As an example, the Frontier supercomputer installed at Oak Ridge National Laboratories recently hit a record breaking 1.1 exaflops1 on the LINPACK HPC benchmark [Shoemaker 2022]. The system contains 37632 AMD MI250x GPUs which requires more than half a billion threads to keep the system fully utilized [Khizeran 2022].
Multi-modal Fine-grained Retrieval with Local and Global Cross-Attention

The goal of cross-modal retrieval is that the user gives any sample as a query sample, and the system retrieves and feeds back various modal samples related to the query sample. At present, the cross-modal retrieval method mainly focuses on coarse-grained, which is far from being satisfied in practical application. However, there are many difficulties in fine-grained retrieval, such as the heterogeneous gap and semantic gap between multi-modal data, the difficulty of similarity measurement, and the small difference in fine-grained sample features. To overcome these limitations, we propose a novel multi-modal fine-grained retrieval method with the LAGC-Attention module, which can fully extract and fuse feature information from different modalities and represent them in a common space. Specifically, we use local and global cross self-attention to extract the neighboring and global context information for each single modal data, which greatly enhances the feature representation capability of each modality (image, text, audio, video), and especially reduce the gap between different feature distributions. Finally, Extensive experiments and ablation studies demonstrate that our method achieves state-of-the-art on the public dataset PKU FG-XMedia.
Home IoT Authority Control Method Based on DID Auth

The Home IoT device market is expanding rapidly, which has led to the increasing adoption of these devices in the home. However, IoT devices have long been challenged by various vulnerabilities and pose unique security challenges compared to other devices. This paper aims to redefine the process of entering the Home IoT environment by leveraging Decentralized Identifier(DID) authentication, and proposes a comprehensive approach to using Verifiable Credential(VC) and Verifiable Presentation(VP) to control access from outsiders like visitors. Through security analysis, this paper also highlights how these authentication and permission controls address traditional vulnerabilities and provide improved security over traditional IoT authentication methods. The results of this study shed light on the significant impact this methodology can have on the security of the Home IoT environment.
Reinforcement Learning-Based Bonobo Optimizer for Efficient Load Balancing in Cloud Computing

The field of optimization has undergone a paradigm shift with the advent of reinforcement learning techniques, which are widely used for solving complex problems in various domains. In this paper, we propose a novel optimization algorithm, called the Bonobo Optimization. The algorithm is inspired by the social behaviour of Bonobo apes, which are known for their collaborative and communicative behaviour. This algorithm is designed to learn from its interactions with the environment and adapt to new situations, making it a robust and adaptive optimization tool. We demonstrate the effectiveness on several benchmark optimization problems, where it outperforms them.