Reviewerly/exHarmony huggingface dataset:
Dataset: Global Trends in Digital Forensics and Fingerprint Research: A Comprehensive Analysis

The purpose of retrieving this data is to determine the number of journal articles related to digital forensics and fingerprinting. Through lens.org analysis with appropriate keywords and filters, 133 relevant documents were found. The study revealed that the University of Lausanne is the most active institution in this field, with the most research in 2019. The most explored fields of study include forensic science and computer security. The most active countries in this research were the UK, the US, and Switzerland. These results suggest that this data can serve as a quick guide to further research and development methods, such as forensic 3D modelling and photogrammetry. In addition, this data is useful for authors, readers, and researchers interested in the topic of digital forensics and fingerprinting.Highlights:&#x0D; &#x0D; Dominant institutions and countries: University of Lausanne leads the research, with the United Kingdom, United States, and Switzerland as the most active countries.&#x0D; Peak research activity: The highest number of journal articles published in 2019, focusing on various aspects of digital forensics and fingerprinting.&#x0D; Interdisciplinary exploration: Research spans across forensic sciences, computer security, and law, indicating diverse applications and implications of digital forensics and fingerprinting techniques.&#x0D; &#x0D;
Dataset: Cyberlaw and Copyright in the 21st Century: A Comprehensive Analysis of Scholarly Works

This data article presents a collection of scholarly works related to Cyberlaw and Copyright in the field of law, obtained through the Lens.org website. The data were obtained by searching for scholarly works using keywords Cyberlaw or Cyber Law and Copyright and filtering them by document type (journal article), date range (2012-2022), and subject matter (law). The data were exported in CSV and BibTex formats and are available in the Zenodo repository. The analysis of the data using VOSViewer and Lens.org tools revealed the top institutions, fields of study, active authors, countries/regions, publishers, and journals. The results of this study provide a valuable resource for researchers and practitioners interested in the legal aspects of Cyberlaw and Copyright.&#x0D; Highlights:&#x0D; &#x0D; Valuable resource for Cyberlaw and Copyright research&#x0D; Comprehensive analysis of scholarly works from 2012-2022&#x0D; Reveals top institutions, authors, and publishers in the field.&#x0D; &#x0D;
Treatment of Waste Water from Complex Organic Substances

This paper covers the issue of extraction of polycyclic hydrocarbons from waste water by using physical and chemical technology including the use of flotation, sedimentation, filtration, adsorption, ozonation. The use of this technology makes it possible to significantly reduce the concentration of hazardous substances such as benzapyrene in the waste water. Under certain conditions of wastewater treatment using physical and chemical technology the concentration of benzapyrene does not exceed the regulatory values. The results of the studies showed that the microflora of wastewater contaminated with Polycyclic aromatic hydrocarbons (PAHs) is capable of destroying PAHs, including BP. Prospects for the use of chemoflotocombines for the treatment of waste water containing complex organic substances are considered.
ASSESSMENT-FOR-LEARNING TO ENHANCE SPEAKING FLUENCY AT THE FOURTH SEMESTER STUDENTS OF MUHAMMADIYAH UNIVERSITY OF MAKASSAR

This research aimed to find out the improvement of students’ speaking fluency skill by using Assessment-for-learning approach. The sample of the research was the fourth semester students from UNISMUH MAKASSAR which consisted of 20 students. The sample was taken by using purposive sampling. The research findings showed that the fourth semester students of UNISMUH MAKASSAR had quite low score in pre-test. After treatment, their speaking skill was significantly improved. The result of the research was the mean score of speaking fluency obtained by the students pre-test was 44.75 and post-test was 70.25 with the significant score of the t-test is (0.00) or &lt;(0.05). It means that there was significance difference between before and after giving the treatment. It indicated that the alternative hypothesis (Ha) was accepted and the null hypothesis (H0) was rejected. It was concluded that the use Assessment-for-learning approach is effective in improving the students’ speaking fluency. Assessment-for-learning approach was one of the approach that can be considered in teaching English in order to help improved the students’ speaking fluency skill.
The Effect of Using Android-Based Learning Media on Student Interest and Learning Outcomes in Lessons Islamic Education

This study aims to determine the interest in learning, and student learning outcomes before and after the treatment is given learning media based on android applications on fardhu kifayah material, in terms of increasing student interest and learning outcomes. This research was conducted at SMK Negeri 7 Bandar Lampung by dividing into two groups, namely the experimental class and the control class. The experimental research design was carried out by collecting interest in learning questionnaire data and learning achievement tests. The collected description data will be processed using SPSS 23. The results showed that the experimental and control class students' learning interest after treatment was 87.91%. The results of the t-count calculation of 6.416 are greater than the t-table of 1.668. The experimental learning outcomes obtained an average of 79.15, while the control class was 63.27. Increased learning outcomes indicate this from before treatment (52.30 to 79.15). There is a significant difference between the control, and experimental classes, the t-count of 5.321 is greater than the t-table of 1.668. Thus the hypothesis that there is a significant influence of the media on learning interest and student learning outcomes in the use of android-based applications for fardhu kifayah is acceptable.
INTERACTIVE METADISCOURSE FEATURES USED BY TEACHERS IN BIPA ONLINE CLASSES

Language features used by BIPA teachers become one of essential parts during online teaching process. This study incorporated interactive metadiscourse (IMD) proposed by Hyland (2005). It was aimed at describing the functions of the categories and understanding the ways teachers employing IMD markers in BIPA digital classes. The data sources were 4 online BIPA learning videos. Aside from employing a qualitative-descriptive method, this study also used documentation technique and content analysis for collecting and analyzing the data through the process of transcribing the video, coding the data, inputting the data in AntCont 3.4.3, selecting the target words, calculating the occurrences and average, interpreting the data, and drawing conclusion. The results of this study revealed that BIPA teachers incorporated all five categories of IMD markers. The most common IMD markers used are: ok, nah, jadi, lihat, coba, dan karena, kalau,and misalnya. The discourse markers mostly assisted the BIPA teachers to direct students, to point on the slides, to shift from one topic to another, and to express agreements. The findings of this research are expected to give benefits theoretically for scholars and practically for BIPA teachers in conducting online learning.
Research on Texture Classification Based on Multi-Scale Information Fusion

Texture feature is an important visual cue for an image, which is the unified description of human visual attributes and sensory attributes. The inherent problem of texture image is that the difference of intra-class images is large and the disparity of inter-class images is small. This problem increases the difficulty of texture image recognition. Therefore, improving the relevance embedding of intra-class images can reduce the classification errors caused by this problem. To solve this problem, this paper proposes a multi-scale information fusion network algorithm, which adopts a cascade structure. It combines multi-scale feature information with the corresponding background information. The shallow background information guides the next stage of feature formation and enhances the similarity of intra-class images. The intra-class feature information obtained is more general. The algorithm has been tested on data sets describable texture database (DTD) and Flickr material dataset (FMD), which has achieved good results.
Statistical Analysis of Effectiveness of Cross-Disciplinary Team’s Practice in Engineering Project Based Learning

Although cross-disciplinary team learning has been proposed, there have been few studies that have evaluated its effectiveness quantitatively. In this paper, we have conducted PBL training for teams with the same curriculum and specialties (single-disciplinary teams) and teams with different curriculum and specialties (cross-disciplinary teams) over the past four years. Based on the results of the PROG (Progress Report on Generic skills) test before and after the PBL exercise, we examined and analyzed the difference in competency between the single-disciplinary teams and cross-disciplinary teams, and showed that the students who participated in the cross-disciplinary teams tended to improve their personal skills in the PROG test.
Cybersecurity interventions in low- and middle-income country healthcare organizations: A scoping review (Preprint)

<sec> <title>BACKGROUND</title> Healthcare organizations globally have seen a significant increase in the frequency of cyber-attacks in recent years. Cyber-attacks cause massive disruption to health service delivery and directly impact patient safety through disruption and treatment delays. Given increasing cyber-attacks in low- and middle-income countries (LMICs), there is a need to explore the interventions put in place to plan for cyber-attacks and develop cyber-resilience. </sec> <sec> <title>OBJECTIVE</title> To describe cybersecurity interventions implemented in LMICs to date and to evaluate their impact on the likelihood and impact of attacks. Secondary objective was to describe the main barriers and facilitators for the implementation of such interventions, where reported. </sec> <sec> <title>METHODS</title> A systematic search of the literature published between January 2017 and September 2022 was performed on Ovid Medline, Embase, Global Health and Scopus, using a combination of controlled terms and free text. A search of grey literature within the same time parameters was undertaken on the websites of relevant stakeholder organizations to identify possible additional studies that meet the inclusion criteria. Findings from included papers were mapped against dimension areas of the Essentials of Cybersecurity for Healthcare Organizations (ECHO framework) and presented as a narrative synthesis. </sec> <sec> <title>RESULTS</title> Seventeen studies were included in this review. The sample size of the majority of studies (58.8%) was 1-5 facilities and the studies were conducted in 13 countries. Studies were categorized into the thematic dimensions of the ECHO framework, including Context; Governance; Organizational strategy; Risk management; Awareness, education, and training; and Technical capabilities. Few studies (29.4%) discussed cybersecurity intervention(s) as the primary focus of the paper and so information on intervention(s) implemented had to be deduced. There was no attempt to report on the impact and outcomes in all but one paper. Facilitators and barriers identified were grouped and presented across national/regional, organizational, and individual staff levels. </sec> <sec> <title>CONCLUSIONS</title> The scoping review findings highlight the limited body of research published on cybersecurity interventions implemented in healthcare organizations in LMICs and large heterogeneity across existing studies in interventions, research objectives, methods, and outcome measures used. The impact of cybersecurity interventions on likelihood and impact of cyber-attacks remains unclear. Future research should specifically focus on the evaluation of cybersecurity interventions and objectively evaluate their impact to build a robust evidence base to inform evidence-based policy and practice. </sec>
Adaptive Sv-Borderline Smote-Svm Algorithm for Imbalanced Data Classification

Imbalance data classification has become a challenging task in recent years, and the borderline synthetic minority oversampling technique (Borderline-SMOTE) combined with the support vector machine (SVM) is a general used algorithm for dealing with class-imbalance problems. However, Borderline-SMOTE utilize a linear operation to find out and over-sample the borderline minority samples for classification by SVM in a kernel space, which may then generate noise samples and reduce the classification accuracy. This paper proposes an adaptive SV-Borderline SMOTE-SVM method. The essential idea is to provide a reasonable method to combine borderline SMOTE with SVM. Firstly, support vectors (SVs) are selected as borderline samples to generate new samples adaptively in the kernel space of SVM. The neighbors of SVs are calculated by kernel distance, and according to the class distribution of neighbors, the SVs are labeled as “concave” or “convex”. New samples are then adaptively generated by two different calculations of different label SVs. The Gram matrix is then designed, and the SVM decision function can be constructed without the explicit expression of new samples in the kernel space. Experiments on multiple datasets show that the proposed algorithm can effectively improve the classification results on imbalanced datasets.
General Public’s Awareness of the Industrial Accident and Serious Accident Punishment Act

Purpose: This study is to identify the seriousness of industrial accidents recognized by the general public and the effect of the Serious Accident Punishment Act. Methods: The survey period was from January 18 to January 19, 2022, and data collection was conducted by automated phone call, and the number of respondents was 1,000. Results: The response that industrial accidents are serious was very high at 93.4%, and the response that the Serious Accident Punishment Act is helpful in preventing industrial accidents was high at 80.1%. As for measures to reduce industrial accidents, 31.1% of respondents said active investment and efforts by business owners were needed, and 28.7% of respondents said thorough government supervision and management were needed. Conclusion: Since the public’s awareness of the seriousness of industrial accidents was found to be very high, active efforts should be made to prevent industrial accidents. Since the public responded that the Severe Accident Punishment Act would be effective in preventing industrial accidents, efforts should be made to ensure that the Severe Accident Punishment Act is properly implemented. It is necessary for business owners and the government to make more efforts to prevent industrial accidents.
HÁBITOS DE SUEÑO, ALIMENTACIÓN Y USO DE DISPOSITIVOS TECNOLÓGICOS DURANTE LA PANDEMIA POR COVID-19 EN POBLACIÓN INFANTIL ARGENTINA

Introduction: The lifestyle of children was affected by the Covid-19 pandemic. Objectives: Identify sleep-wake behaviours, use of technology, participation in the purchase, preparation and sharing of meals and understand their nutritional value. Materials and Methods: A descriptive, observational and cross-sectional study was performed in children from 6 to 12 years old from La Pampa, Argentina. Results: 348 were recruited, 80% ate the main meals at home and 17% participated in the preparation of meals, cooking pizzas, mash potatoes, pastas, stews, empanadas, which include food with complex carbohydrates and fats. Post dinner, 43 % chose to eat fruit and 24.7 % biscuits and sweets. 73.3% watched television while eating. 38.8 % spent between 3-4 hours with technological devices. 33.5 % ate cookies, snacks, cereals, chips, sweets, sandwiches, and drank juices while using technology. 82.5 % slept 8-10 hours a day and 37.4% reported sleeping more hours than pre-pandemic. 52% used screens before falling asleep, of the 31% who found it difficult to fall asleep, 52% continued to use devices and 11% ate food. Conclusions: snacking, food intake with low nutritional value, changes in sleeping and waking-up times, and excessive screen exposure were observed. Children could benefit from more education with regards to technological hygiene routines and the subsequent benefits of efficient sleep, meal times, food selection, playtime, and physical activity. Keywords: COVID-19; Child Nutrition Sciences; pandemics; Feeding Behavior; Meals.
Intrinsically Morphological Effect of Perovskite Batio3 Boosting Piezocatalytic Uranium Extraction Efficiency and Mechanism Investigation

Developing a convenient, efficient and eco-friendly approach for the recovery of U(VI) ion is a key measure to solve the environmental problems arising from the utilization of nuclear energy. Herein, the high efficiency of uranium extraction is realized by the piezo property of perovskite BaTiO3, revealing the intrinsically morphological engineering effect on the piezocatalytic performance. Especially, BaTiO3 nanowires (BTO NWs) exhibit not only an excellent piezocatalytic activity with U(VI) extraction rate of 96.8% in a UO2(NO3)2 aqueous solution compared to 71.3% of BaTiO3 nanoparticles (BTO NPs), but also a promising piezocatalyst for U extraction in a real U-mining wastewater with various pH ranges. Piezo response force microscopy and finite elemental simulation show that the piezo response of BTO NWs is much higher than BTO NPs. Additionally, some factors (pH, various ions, different powers) are explored on piezocatalytic efficiency for U(VI) extraction. The results from electron spin resonance and the charge/radical capture experiments confirm that the active species (e-, •O2-, •OH) stemmed from the piezo induction of BTO NWs and BTO NPs in the piezocatalytic U(VI) reduction process. The present work reveals the structure-performance correlation during piezocatalysis and highlights the crucial role of piezocatalysis in dealing with environmental problems.
MIGC: Multi-intent Graph Contrastive Learning in Recommendation

Contrastive learning has been highly successful with computer vision and natural language processing. It can effectively address the under-sample situation. Contrastive learning has also been successfully implemented in recommender systems. It can not only address the problem of the small number of samples but also improve the learning impact of long-tailed data. Recommender systems contain large amounts of graph data. Graph neural networks are good at learning graph node representations. Through the neighbor information in the graph, it is possible to understand the potential intention of the user. Contrastive learning mainly includes sequence-based and graph-based contrastive learning in recommender systems. Currently, the modeling of both sequence contrastive learning and graph comparison learning in recommender systems is based on the user's single intent. However, the user's behavior consists of multiple intents. This paper proposes a new method which is named MIGC for modeling of user's numerous intents. Graph contrastive learning is introduced into the recommendation system recall algorithm and User's multi-interest modeling. This approach not only learns multiple users' intents but also improves the representation of long-tail data. Firstly, we construct a bipartite graph from user-to-item behavior data. Secondly, the multi-intents of users are a model of the graph. Finally, vector representations of users and items are obtained through contrastive learning of graph neural networks for vector recall in recommender systems. The experiments in this paper used the public dataset MovieLens and the private dataset e-commerce. And both offline and online have achieved a certain improvement. This study aims to start a new approach to users' multi-intent recall.
CCTV-FullyAware: toward end-to-end feasible privacy-enhancing and CCTV forensics applications

It is estimated that over 1 billion Closed-Circuit Television (CCTV) cameras are operational worldwide. The advertised main benefits of CCTV cameras have always been the same; physical security, safety, and crime deterrence. The current scale and rate of deployment of CCTV cameras bring additional research and technical challenges for CCTV forensics as well, as for privacy enhancements.This paper presents the first end-to-end system for CCTV forensics and feasible privacy-enhancing applications such as exposure measurement, CCTV route recovery, CCTV-aware routing/navigation, and crowd-sourcing. For this, we developed and evaluated four complex and distinct modules (CCTVCV [1], OSRM-CCTV [2], BRIMA [3], CCTV-Exposure [4]), all of which are novel, unique, peer-reviewed, and can be used either separately or within an integrated end-to-end system such as CCTV-FullyAware. We release all our artefacts as open-source/open data. We hope our work will bootstrap policy-driving discussions and large-scale applications such as CCTV forensics and privacy-enhancing technologies.
Automated Synthesis of Quantum Circuits using Neural Network

While the ability to build quantum computers is improving dramatically, developing quantum algorithms is very limited and relies on human insight and ingenuity. Although several quantum programming languages have been developed, it is challenging for software developers unfamiliar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing through a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.
Shared Incentive System for Clinical Pathway Experience

The phenomenon of unbalanced regional medical resources has led to large differences in the implementation experience of clinical pathways in hospitals with different medical levels. However, due to the fear of privacy leakage and the lack of sharing motivation, there is a lack of effective collaborative communication channels for clinical pathways among medical institutions. In response to these problems, we propose a blockchain-based sharing incentive scheme for clinical pathway experience, which uses the clinical pathway implementation effect evaluation system to evaluate the quality of clinical pathway experience data. Specifically, we designe a two-phase cross-domain shared transaction model for the transaction of clinical pathway experience data. Moreover, we introduce the shared transaction alliance committee to verify and review the transaction, and solve the problems in the transaction in the arbitration phase. Finally, the functional test results show that the system meets the experience sharing incentive requirements, and the performance test results show that the TPS of sharing clinical pathway experience read and writed can reach around 600 and 1000, and the transaction latency of each phase is within 3 s.