Reviewerly/exHarmony huggingface dataset:
Research on Blockchain Based Electric Carbon Intelligent Collaborative Carbon Verification Platform

This paper uses blockchain distributed storage, immutable, peer-to-peer network and other technical characteristics. Based on the greenhouse gas accounting methods of all industries released by the state, the paper uses electricity data such as online electricity quantity and purchased electricity quantity to build a electric carbon intelligent collaborative carbon verification platform, and carries out carbon emission, carbon verification and monitoring from the technical verification. The collaborative management and application mode of power grid enterprises, government departments, emission control enterprises and third-party verification institutions shall be formed, so as to establish a multi-party trust mechanism, realize online business handling, peer-to-peer information transparency, reliable data tamper-proof, and effectively improve carbon verification efficiency, mutual trust and user satisfaction.
Research on Cross-Device Bypass Attacks Based on Deep Learning

SPECK algorithm is a typical lightweight algorithm with ARX structure, which is widely used in resource-constrained portable devices. In this paper, we address cross-device scenarios and explore the feasibility of accuracy as an index for evaluating the efficiency of attacks based on the bypass analysis of SPECK encryption algorithm. Three different scenarios are considered, i.e., different measurement locations for the same device, different IC sockets for the same chip, and different keys for different chips, to explore the impact of changes in the environment on the attack efficiency. The experiments show that using different devices and keys or changing chip IC sockets during the analysis and attack phases will seriously affect the accuracy metrics compared to changing the measurement locations of the probes. It is concluded that while accuracy is the most commonly used metric for monitoring and evaluating neural networks, it leads to a significant underestimation of the efficiency of attacks in cross-device conditions, and in fact models with low accuracy can still successfully attack the attacking device.
Program Kegiatan Taman Baca Masyarakat (TBM) Ridha Di Kelurahan Teladan Kota Kisaran Kabupaten Asahan untuk Menarik Perhatian Masyarakat

This study aims to find out the program of activities at the Ridha Community Reading Center (TBM) and ways to educate the Asahan community by making people like reading and improving the quality of reading interest in Teladan Village, Kisaran Regency. The method used in this research is qualitative research with a descriptive approach. Data collection techniques were carried out by means of observation, interviews and documentation. In the data analysis section using data reduction, data presentation and drawing conclusions. The validity of the data in this study used source triangulation, time triangulation, and method triangulation. The results of this study indicate that the Ridha Community Reading Center (TBM) activity program in the exemplary urban village of the Asahan district can attract the public's attention by creating various programs, while the activity programs provided by Ridha's TBM to attract the public's attention are: 1). reading corner; 2). library cart; 3). reading taxis and smart stalls; 4). Declaration of literacy village; 5). Workshops; 6). Storytelling; 7). Dancing the Gubang dance; 8). white swallow martial arts; 9). Poetry reading and poetry reading competitions; 10). Traditional game practice; 11). Social inclusion (learning to make savory banana chips and roll cakes); 12). Aerobic Gymnastics and 13). TBM Ridha Studio. An activity program that makes the Asahan community gain knowledge and very useful learning.
Synthetic Speech Detection Using Extended Constant-Q Symmetric-Subband Cepstrum Coefficients and CBAM-ResNet

Synthetic speech detection has always played a considerable role in the security of automated speaker verification systems. This work proposes an extended constant-Q symmetric-subbandd cepstrum coefficients (eCQSCC) feature with phase-symbol magnitude phase spectrum information (PMPS). The eCQSCC symmetrically divides the octave spectrum and extends the dynamic information of the octave spectrum and the linear PMPS. This division and extended information make eCQSCC more capable of deceptive artifact extraction of synthetic speech. Then we improved the residual network as the back-end classifier using the convolutional block attention module (CBAM). The network proposed consists of thirty-three basic residual blocks and two CBAMs. Finally, we fed eCQSCC into this network for synthetic speech detection. Experiments on the ASVspoof2019 synthetic speech dataset show that the EER and t-DCF of this method are 0.04% and 0.001, respectively, which are significantly better than many recently known methods.
ACTIVITY MANAGEMENT INFORMATION SYSTEM FOR DIGITALIZATION REPORTING TOWARD SOCIETY 5.0 ERA

This research was motivated by the problem at a one of Mosque which currently does not have an information system in managing mosque activities. The number of activities in a Mosque that have not been well organized and manual data processing makes it difficult for mosque administrators to process data and takes a long time. To overcome this problem, a management system is needed that can be easily accessed by mosque administrators and provide precise and accurate information about the activities of Mosque to the community. The purpose of the system to be created is to make it easier for mosque administrators to manage data on mosque activities and make it easier for the public to find information. And for users who apply for donations or activities do not need to come directly to the mosque because they can apply through the system
DESIGNING A WEB-BASED ACTIVITY MANAGEMENT SYSTEM FOR ENHANCED DATA ANALYTICS PERFORMANCE

Recording activities in an organization or institution is very important so that activities can run well, as well as recording activities related to worship activities. This research is motivated by problems in team, the study of mosques in Central Java, which currently does not have an information system in managing mosque activities and implementation data analitycs for enhanced performance activity. The large number of activities in the place of worship and all activities have not been properly recorded and manual data processing makes it difficult for mosque administrators to process data and takes a long time. To overcome this problem, a management system is needed that can be easily accessed by mosque administrators and provides precise and accurate information about activities to the community. The research conducted in this final project is to create and design a web-based information system for mosques. Which information system is made with Hypertext Preprocessor (PHP), CodeIgniter Framework and MySQL database. The research method used is waterfall. Starting with system analysis, design, coding and the final stage is system testing. The purpose of the system design that will be made is to make it easier for mosque administrators to manage data on mosque activities and make it easier for the public to find information. And for users in submitting donations or activities, they don't need to come directly to the mosque because they can apply through the system
DESIGNING A WEB-BASED ACHIEVEMENT MANAGEMENT INFORMATION SYSTEM

The development of technology is increasingly advanced, and increasingly entering the digital era, everything we need is made easier with technology. One is in the achievement management of each student by using the achievement management information system. This information system uses a development research method consisting of analysis, design, flow and factor design, implementation (coding) and trials. The purpose of creating this information system is to design and build an achievement management information system at SMA Negeri 1 Wonosari Klaten. This system begins with analyzing system needs, creating business processes, designing ERD, making system designs, and designing user interfaces. In conducting the test, this system uses the BlackBox method. The result of this system is that it runs well and corresponds to its functionality
Conceptualizing an Inductive Learning Situation in Online Learning Enabled by Software Engineering

Our work highlights the importance of adopting a systematic and methodical software engineering approach to the development of information technology projects for e-learning. We place particular emphasis on conceptualizing pedagogical scenarios and an inductive online learning situation. To ensure effective management of the information systems development process, we applied instructional design principles and adopted the 2TUP process, a refined version of the Rational Unified Process (RUP) suitable for projects of all sizes. To provide a visual representation of the system architecture and inform instructional design decisions, we used the Unified Modeling Language (UML) to create class, use case, activity, and sequence diagrams. We aim to demonstrate the potential of a structured software engineering approach to creating effective and efficient e-learning systems by conceptualizing an inductive online learning situation and five concrete examples illustrating the system's functionality. Our work underlines the importance of using standardized modeling languages such as UML to facilitate communication between stakeholders and collaboration between instructional designers and software developers.
Encryption Traffic Classification Method Based on ConvNeXt and Bilinear Attention Mechanism

The rapid growth in internet traffic resulted to the emergence of network traffic categorization as a crucial area of research in network performance and management. This technological advancement has demonstrated its efficacy in aiding network administrators to identify anomalies within network behavior. However, the widespread adoption of encryption technology and the continual evolution of encryption protocols present a novel challenge in the classification of encrypted traffic. Addressing this challenge, this paper introduces an innovative methodology for classifying encrypted traffic by harnessing ConvNeXt and a fusion attention mechanism. Through the representation of traffic data as images and the integration of a bilinear attention mechanism into the model, our proposed approach attains heightened precision in the classification of encrypted network traffic. To substantiate the effectiveness of our methodology, experiments were conducted employing the publicly available ISCX VPN-nonVPN dataset. The experimental findings showcase superior recognition performance, underscoring the efficacy of the proposed approach.
Status and Development Trend of Blockchain Application in the Private Equity Industry

As an essential part of the digital economy revolution, blockchain is rapidly developing and penetrating various fields of China’s economy as a new generation of information infrastructure.
Rainfall Prediction Using Fuzzy Logic Method For Early Warning System In Flood Disaster Mitigation In Nganjuk

In general, flooding often causes problems in the form of flood disasters. This is because the flooding of shipments is unpredictable and causes a large area of impact. This problem will certainly have an impact on various life activities, such as threatened public safety, the emergence of congestio
Uji Kadar Zat Besi (Fe) Sediaan Obat Tablet Tambah Darah (Ferro Fumarat) dengan Metode Kompleksometri

Iron is a very important element to form hemoglobin (Hb). Blood supplement tablets which are often consumed in the process of g iron are ferro fumarate. The aims is determine the levels of iron (Fe) contained in blood supplement drugs (ferro fumarate). This type of research was a laboratory experiment using the complexometric method and a complex forming substance, namely disodium ethylene diamine tetra acetate (Na2EDTA). A sample of blood supplement tablets (ferro fumarate) which was prepared in 10 tablets was made into powder and then 150 mg was taken and titrated with Na2EDTA as the titrant and using the Erichrome Black T (EBT) indicator resulting in a purple titration end point turning blue. The results showed that the level of iron (Fe) in the blood supplement sample (ferro fumarate) was 200%.
Handling E-evidence in Egyptian and Comparative Legislation

This article aims to introduce e-evidence, describe its characteristics, examine its legal authenticity in Egyptian law, shed light on the requirements for the collection of e-evidence and its admissibility before the criminal judiciary, and highlight how e-evidence is criminally protected in Egyptian law along with the procedures for gathering and documenting it. The pervasive usage of technology in all spheres of life may be traced back to the significance of the research topic. We have come to the logical conclusion that it is improbable that a traditional or new crime would occur without leaving behind E-evidence that may be used to identify the offender thanks to the proliferation of electrical and technological equipment and the Internet. The research concluded that the effect of the changing nature of intangible forensic evidence on its reliability before the criminal courts, in a way that requires a precise legal regulation of this issue. The legislator defines a set of conditions for the procedures for collecting and documenting E-evidence to achieve the idea of its reliability and then produces its impact on the formation of the criminal judge's doctrine. It is recommended strengthening cooperation with international organizations to exchange information related to E-evidence.
Misleading Design Implications of Adopting Embodied Interface in Everyday Objects

Abstract What happens when an object is consciously designed not to suggest how to interact with it explicitly? This research theme is controversial and peculiar. It is rooted in the concepts of agency and affordance of objects and their interfaces, proposing a change of perspective. Rather than conceiving functions clearly expressing themselves, embedded technology allows an extension of the possible levels of manipulation on seemingly silent objects. This implies a semantic reconfiguration that begins with aesthetics and impacts interaction. Operating at the level of attribution of meaning, these objects challenge the ecological approach, resulting in a misleading design. The topic is tackled from the point of view of the communication designer and design researcher who look at the design of interaction and interface. The study relies on the lessons learnt and knowledge from a five-year-long research-through-design experimentation, triangulated with evidence emerging from the analysis on five relevant cases.
Texture and major element geochemistry of channel sediments in the Orsang and Hiren River Basins, Gujarat, India: Implications for provenance and weathering

Size, shape, degree of sorting, and composition of sediments in the river channels are controlled by climate, lithology, weathering, sorting, and medium of transportation. The present investigation is focused on the grain-size and geochemical analysis of the channel sediments of the Orsang and Hiren river basins. Major outcrops in the study area are Archaean granites, granitic gneisses, Upper Cretaceous to lower Eocene Deccan Volcanic Basalts (DVB), Quaternary sediments and minor proportion of Proterozoic low grade metamorphic rocks. The sediments are poorly to moderately sorted, very finely skewed, suggesting its derivation from heterogenous sources, while the kurtosis value indicates a high-energy depositional environment. The sediments are with gravelly sand texture and the mean grain size is varying from 581.9µm to 1284.2µm. The DVB provenance of the Hiren river basin and granitic provenance of the Orsang river basin is clearly reflected in the texture and geochemical composition of sediments. The TiO2 and Fe2O3 contents of sediments from the Hiren river basin are distinctly higher and are comparable to the basalts of the Saurashtra region of the Deccan Province. Sediments collected after Orsang and Hiren rivers confluence and from Narmada river show higher concentration of felsic sources, indicating that Orsang river's sediment supply significantly outweighs Hiren rivers. The arkosic-litharenite nature points towards less transportation and moderate chemical weathering for the Orsang river sediments. The low Chemical Index of Alteration (CIA) values (Avg. 48.45 and 56.99 for Orsang and Hiren rivers, respectively) and A-CN-K plot also suggest the supply of sediments from minimally weathered detritus under a semi-arid condition.
The Next Normal Terminologies in Malaysia: Morphological Landscape in the Future of Work

This paper analysed the Morphological Landscape in the perceptibility and prominence of word formation on commercial and public signs in the digital platforms during the next normal in Malaysia. The surge of new terminologies accompanying the next normal created new lexical and sociolinguistic changes that have become part of our lives. It can’t be denied that many Malaysians are having problems understanding the new terms used in digital platforms during the pandemic. If someone occasionally struggles to understand the terms and their importance, they are not alone, a quote from a daily newspaper. This study aimed to 1) record new terminologies of the future of work in Malaysia and 2) analyse the headedness of compounds and blends to determine their meaning. It can be hard to keep up with the next normal terminology used in media reports and official statements about employment and employability, such as acronym (WFH), blend (workcation work + vacation), compound (remote work) and an alphabet-and-numbers blend of virus (COVID-19). The researchers used a descriptive qualitative approach to gather samples from various digital platforms (online advertisements) to identify new terminologies and adapted Yule’s and Lieber’s theoretical frameworks to analyse the concepts morphologically. It also identified compounding contributed to the rise of these new terminologies. The analysis revealed that new terms are coined from word-formation processes of acronyms, blending, compounding, and multiple processes.This study will educate the public about the new terminologies of the next normal in the future of work in Malaysia. regarding its meanings and function in daily life.
Breast Cancer Detection using Image Processing and Machine Learning: A Comprehensive Review and Improved Segmentation Approach

Breast cancer stands as one of the most prevalent health concerns for women. Early detection of breast cancer can significantly improve the chances of survival. In developed countries, more than 19.9% of women will die per year due to breast cancer. Regular breast cancer screening is an important way to detect cancer early. Image processing techniques are highly used for different types of cancer detection applications with medical screening. Segmentation of the breast tumor region is a critical step in image processing related to this manner. Lots of research work can be found on developing ways for detecting breast cancers. However still, there is a need for a standard and robust cancer region segmentation method. Right cancer region segmentation is significant for better feature extraction and better classification. This study presents a comprehensive literature review about technologies used with image enhancement and tumor segmentation. Further, the study proposes a robust approach to image enhancement and cancer region segmentation for breast cancer detection using image processing techniques and machine learning. In this work, mammograms are enhanced using Contrast Limited Adaptive Histogram Equalization and denoised using the Median blurring filter. This study enables an effective image segmentation approach with two stages: removing the background using thresholding, tumor region segmentation using a combination of thresholding, and Canny edge detection. Segmented tumor region’s features are extracted using the Gabor filter. Here the accuracy of the approach is compared with three main machine learning classifiers: Decision Tree, Random Forest, Multinomial Logistic Regression and mammograms are classified into three classes (malignant, benign and normal). Finally, an ensemble approach is proposed using the hard voting mechanism to improve the accuracy. With the dataset of mini-MIAS this proposed approach achieved 78.89% accuracy. Results demonstrated that the proposed breast cancer detection approach improves the performance of segmentation breast tumor regions.
Households Intention to Formally Dispose E-waste Using Theory Planned Behavior

Electronic waste or e-waste is a global issue that continues to escalate. The e-waste contains hazardous materials, which could lead to health issues and pollution if improperly disposed. The most common e-waste sources are household appliances namely refrigerators, air conditioners, washing machines, radios, televisions, and telecommunications devices containing electric or electronic parts. Each e-waste item also encompasses valuable materials that will profit the business, which in turn can benefit the growth of the economy besides the preservation of resources. This study refers to the Theory of Planned Behaviour as a heuristic guideline to investigate the intention to formally dispose of e-waste among residents at Taman Bahau, Negeri Sembilan. As this study applied a quantitative approach, a questionnaire was utilized as the primary data collection tool, and three hundred seventy-five (375) respondents were selected using a convenience sampling technique to partake in the survey. The demographic characteristics and profiling of the respondents were analyzed using descriptive analysis. In contrast, regression analysis was employed to examine the contribution of variables towards the intention to dispose of e-waste formally. The finding shows the attitudes, subjective norms, and perceived behavioral controls constructed towards the intention to dispose of e-waste formally. The result also implies that perceived behavioural control explains the most variance in the respondents' intention to dispose of their e-waste items formally, compared to other variables. This research holds significance as it can enhance community awareness and encourage relevant stakeholders to find solutions to protect the environment from the detrimental consequences of improperly discarded electronic waste&#x0D;
RL-NMT: Reinforcement Learning Fine-tuning for Improved Neural Machine Translation of Burmese Dialects

In this study, we investigate the use of Reinforcement Learning (RL) for fine-tuning neural machine translation models for Burmese dialects. We perform experiments using two extremely low-resource Burmese dialect datasets: Burmese-Beik and Burmese-Rakhine, employing two different deep learning modeling techniques: bi-LSTM (Seq2Seq) and the Transformer. Our training procedure involved initially training models over varying numbers of epochs: 30, 40, 50, 60, and 70 epochs, and then fine-tuning them with RL for additional epochs, such that the total number of epochs for each model equaled 100. For instance, a model initially trained for 30 epochs was fine-tuned for an additional 70 epochs using RL. The results show that better quality machine translation was attained with RL over all initial models. Moreover, RL yields a significant improvement of Bilingual Evaluation Understudy (BLEU) scores (+4.73 BLEU for Burmese-to-Beik translation with Seq2Seq RL, +2.58 for Rakhine-to-Burmese translation with Transformer RL) over some of the baselines not utilizing RL training.