Reviewerly/exHarmony huggingface dataset:
Study of Drainage Capacity in Ujung Kalak Village, West Aceh District

Flood inundation occurs on main roads in Meulaboh City, especially when it rains. Johan Pahlawan District is one of the sub-districts in West Aceh Regency. This sub-district includes a densely populated area of 4243 people, which is in the urban area of Meulaboh City One is the road in Ujung Kalak Village, where floods often occur even though the village already has drainage. A study of the drainage profile is needed to determine the ability of the drainage to accommodate rainwater discharge. The purpose of this study is to find out whether the drainage capacity is able or not to accommodate rainwater discharge. Then proceed with redesigning of the drainage dimensions if the study results show insufficient drainage dimensions to accommodate flood discharge. The research method uses descriptive qualitative. Data collection begins with conducting field surveys to measure the dimensions of the existing drainage channel, including measuring the channel's width, height, and slope. The data needed in this study are rainfall data for at least ten years used to calculate the planned flood discharge and land cover maps to calculate the flow coefficient. The resulting study showed that 24 out of 29 channel segments needed to be enlarged in dimension. Of the 24 channel segments, 70 percent of the channels require cleaning of sediment and channel maintenance so that rainwater can flow, and inundation does not occur again.
Unsupervised Person Re-identification via Multi-label Classification

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. The label prediction comprises similarity computation and cycle consistency to ensure the quality of predicted labels. To boost the ReID model training efficiency in multi-label classification, we further propose the memory-based multi-label classification loss (MMCL). MMCL works with memory-based non-parametric classifier and integrates multi-label classification and single-label classification in a unified framework. Our label prediction and MMCL work iteratively and substantially boost the ReID performance. Experiments on several large-scale person ReID datasets demonstrate the superiority of our method in unsupervised person ReID. Our method also allows to use labeled person images in other domains. Under this transfer learning setting, our method also achieves state-of-the-art performance.
Women’s Role in the Foundation and Development of Private Libraries During the Second Half of the 19th and the Beginning of the 20th Centuries (Based on the Materials from the Upper Volga Area)

The article deals with little-studied issues related to the role of women, who represented various social classes, in the foundation and development of private libraries in three provinces of the Upper Volga area during the second half of the 19th and the beginning of the 20th centuries. As exemplified by the Vladimir, Kostroma and Yaroslavl Provinces, womens contribution to the foundation of libraries in both urban and rural areas has been studied. Based on numerous archival materials from todays Russian regions, the author concludes that private libraries founded upon womens initiative at the beginning of the 20th century were one of the significant components of the library realm in the Upper Volga area.
Design and Implementation of a Real-Time Face Recognition System Based on Artificial Intelligence Techniques

Abstract This paper mainly discusses the asymmetric face recognition problem where the number of names in a name list and the number of faces in the photo might not be equal, but each face should be automatically labeled with a name. The motivation for this issue is that there had been many meetings in the past. After each meeting, the participant took group photos. The meeting provided only a corresponding name list of participants without one-to-one labels. In the worst case, the group photo might mix with the faces that were not participating in the meeting. Another reason for asymmetric face recognition is that some meeting personnel did not appear in photos because they assisted in taking pictures. This paper proposes an Asymmetric Face Recognition Mechanism, called AFRM in short. Initially, the proposed AFRM adopts the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) to detect and extract all faces from photos. Next, AFRM extracts the features from each face using the convolution feature map (Conv_FF) and adopts the features to partition the faces into different classes. Then the AFRM applies the statistic-based mechanism to map each name in the name list to each face class. According to this mapping, each face will be associated with one name. To quickly identify a face during the meeting, the AFRM applies the K-Nearest Neighbors (KNN) to represent the features of each face. During the new meeting, the proposed AFRM can extract the feature of one face and then adopts KNN to derive the features. Experimental results show that the proposed mechanism achieves more than 97% accuracy without one-to-one name and face labeling.
Partition-based SIMD Processing and its Application to Columnar Database Systems

Abstract The Single Instruction Multiple Data (SIMD) paradigm became a core principle for optimizing query processing in columnar database systems. Until now, only the instructions are considered to be efficient enough to achieve the expected speedups, while avoiding is considered almost imperative. However, the instruction offers a very flexible way to populate SIMD registers with data elements coming from non-consecutive memory locations. As we will discuss within this article, the instruction can achieve the same performance as the instruction, if applied properly. To enable the proper usage, we outline a novel access pattern allowing fine-grained, partition-based SIMD implementations. Then, we apply this partition-based SIMD processing to two representative examples from columnar database systems to experimentally demonstrate the applicability and efficiency of our new access pattern.
ARTIFICAL NEURAL NETWORKS

This is a survey of neural network applications within the real-world situation. It provides a taxonomy of artificial neural networks (ANNs) and furnishes the reader with data of current and rising trends in ANN applications analysis and space of focus for researchers. The study assesses ANN contributions, compare performances and critiques strategies. The study found that neural-network models like feedforward and feedback propagation artificial neural networks is performing arts higher in its application to human issues. Therefore, we tend to plan feedforward and feedback propagation ANN models for analysis focus supported information analysis factors like accuracy, process speed, latency, fault tolerance, volume, measurability, convergence, and performance. Moreover, we tend to suggest that instead of applying one technique, future analysis will concentrate on combining ANN models into one network-wide application.
Analysis of Pattern Searching Algorithms and Their Application

Nowadays data is growing tremendously. Therefore, there is a great need to store and process data. The problem of Pattern Searching has different applications. When searching for text or words in computer application systems, Pattern searching is used to display the search results. The purpose of Pattern searching is to find text within another text. For example, searching for text in books will take a long time and is hard work. Using Pattern searching will save you time and effort. If similar words are found within the requested text, it will underline the word similar to what was requested, otherwise it does not display any matches if there are no similar words within a text. This paper presents comparisons of the speed of different Pattern searching algorithms, precisely the Naive, KMP, Rabin-Karp, Finite Automata, Boyer-Moore, Aho-Corasick, Z Algorithm algorithms. We will test the time complexity of these algorithms in the three programming languages C#, Java and Python using three different CPUs. According to the results that appear in this comparison, we are able to perform the comparison between the programming languages and the comparison between the CPUs used in this research.
Preserving the Privacy of Sensitive Data Using Bit-Coded-Sensitive Algorithm (BCSA)

Organizations now deal with massive amounts of data. Data is collected from various points such as hospitals, credit card companies, and search engines. After collecting this voluminous data, it is published and shared for research. Data that is collected may have sensitive information that might be used to identify an individual and consequently lead to privacy violations when published. To address this challenge, privacy-preserving data publishing (PPDP) seeks to remove threats to privacy while ensuring that the necessary information is released for data mining. Various techniques have been proposed to solve the problems associated with sensitive information. One such technique is k- anonymity. This technique is the best and very efficient. However, it also leads to loss of information, reduces data utility, and works well only with static tables. In this paper, we proposed a technique that addresses the challenges of K-anonymity known as the Bit-Coded-Sensitive Algorithm (BCSA). This algorithm is more efficient and effective and ensures that the privacy of the individual is preserved by avoiding disclosure, and linkages and at the same time ensuring high quality and utility of data. BCSA first identifies the source of data and based on that, uses bits to code sensitive data with a key.
States’ Regulation of Virtual Currencies

The federal government, acting slowly in promulgating regulations concerning cryptocurrencies to date, in great part has ceded to the states the enactment of legislation or guidance concerning their use within their jurisdictions. The problem is that there is no model uniform legislation which a sizeable number of states find acceptable to promulgate but rather have enacted a variety of statutes and regulations.
The VoiceMOS Challenge 2022

We present the first edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthetic speech. This challenge drew 22 participating teams from academia and industry who tried a variety of approaches to tackle the problem of predicting human ratings of synthesized speech. The listening test data for the main track of the challenge consisted of samples from 187 different text-to-speech and voice conversion systems spanning over a decade of research, and the out-of-domain track consisted of data from more recent systems rated in a separate listening test. Results of the challenge show the effectiveness of fine-tuning self-supervised speech models for the MOS prediction task, as well as the difficulty of predicting MOS ratings for unseen speakers and listeners, and for unseen systems in the out-of-domain setting.
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios

Visual detection is a key task in autonomous driving, and it serves as a crucial foundation for self-driving planning and control. Deep neural networks have achieved promising results in various visual tasks, but they are known to be vulnerable to adversarial attacks. A comprehensive understanding of deep visual detectors' vulnerability is required before people can improve their robustness. However, only a few adversarial attack/defense works have focused on object detection, and most of them employed only classification and/or localization losses, ignoring the objectness aspect. In this paper, we identify a serious objectness-related adversarial vulnerability in YOLO detectors and present an effective attack strategy targeting the objectness aspect of visual detection in autonomous vehicles. Furthermore, to address such vulnerability, we propose a new objectness-aware adversarial training approach for visual detection. Experiments show that the proposed attack targeting the objectness aspect is 45.17% and 43.50% more effective than those generated from classification and/or localization losses on the KITTI and COCO traffic datasets, respectively. Also, the proposed adversarial defense approach can improve the detectors' robustness against objectness-oriented attacks by up to 21% and 12% mAP on KITTI and COCO traffic, respectively.
Prospects of Development of the Metaverse: Empirical Evidence

The purpose of the research is to analyze the key features of the metaverse, the possibilities and advantages of its tools, security issues and the format of user participation in the metaverse virtual space. The relevance of the study is caused by the changes in the global economic order and the metaverse technologies awareness from both the expert community and ordinary users. The authors use methods of comparative analysis of Russian and foreign scientific sources, as well as statistical analysis and forecasting from leading consulting, rating and analytical agencies (Gartner, Statista, Emergenresearch, Moscow Innovation Agency, World Economic Forum, Tadviser). The scope of the achieved results is determined by the possibility of using the practical part of the study in building scenarios for the metaverse technologies application by various stakeholders. The authors draw conclusion that there is a wide range of use of the metaverse tools in various felds of human activity (professional, educational, social, entertainment, etc.), as well as about stimulating the development of the digital market using metaverse technologies. The prospects of non-interchangeable tokens, one of the innovative tools of the metaverse, are discussed and the audience growth rate of the metaverse innovative technologies potential consumers in the future is predicted.
RegMiner: Towards Constructing a Large Regression Dataset from Code Evolution History

Bug datasets consisting of real-world bugs are important artifacts for researchers and programmers, which lay empirical and experimental foundation for various SE/PL research such as fault localization, software testing, and program repair. All known state-of-the-art datasets are constructed manually, which inevitably limits their scalability, representativeness, and the support for the emerging data-driven research. In this work, we propose an approach to automate the process of harvesting replicable regression bugs from the code evolutionary history. We focus on regression bug dataset, as they (1) manifest how a bug is introduced and fixed (as normal bugs), (2) support regression bug analysis, and (3) incorporate a much stronger specification (i.e., the original passing version) for general bug analysis. Technically, we address an information retrieval problem on code evolution history. Given a code repository, we search for regressions where a test can pass a regression-fixing commit, fail a regressioninducing commit, and pass a working commit. In this work, we address the challenges of (1) identifying potential regression-fixing commits from the code evolution history, (2) migrating the test and its code dependencies over the history, and (3) minimizing the compilation overhead during the regression search. We build our tool, RegMiner, which harvested 537 regressions over 66 projects for 3 weeks, created the largest replicable regression dataset within shortest period, to the best of our knowledge. Moreover, our empirical study on our regression dataset shows a gap between the popular regression fault localization techniques (e.g, delta-debugging) and the real fix, revealing new data-driven research opportunities.
Estimation of New Student Admissions Using the Moving Average Method

Acceptance of new students at several tertiary institutions, both public and private, will affect the learning and teaching process at these tertiary institutions. Because knowing the number of new students accepted is one way that can be used in determining the teaching and learning process.&#x0D; This study aims to predict the number of new students for the coming year with the Moving Average method and calculate the absolute mean error rate for this type of data analysis. This method is applied to one of the private universities in Indonesia. To know the prediction of the number of new students every year, or to find out the estimated number of new students for the coming year.&#x0D; The data used is the previous 7 years at one of the Indonesian private universities.&#x0D; The results of this study are expected to be able to assist these tertiary institutions in planning a good teaching and learning process, because the number of new students will affect the number of existing lecturer ratios.
A Variant of the RSA Cryptosystem with Smaller Keys

In this paper we introduce an efficient variant of the RSA cryptosystem which will need lesser memory for key storage which is lessen the computational cost. Introduction of the RSA cryptosystem by Rivest, Shamir, and Adleman in 1978 was a revolutionary step in cryptography. The regular RSA system needs huge cost for decryption due to large size of the private decryption key. The proposed variant will be a solution to this conundrum. Dhaka Univ. J. Sci. 70(2): 15-17, 2022 (July)
PENGEMBANGAN MEDIA VISUAL SCRAPBOOK PADA TEMA PERISTIWA DALAM KEHIDUPAN DI KELAS V SDN SUKASABA 1 PANDEGLANG

The development of this learning media is a process of activities carried out to produce a learning media based on existing theories and product validation that researchers have made so that they are suitable for use in learning activities. This study aims to determine: the results of the design of visual scrapbook media development on the theme of 7 events in life. The purpose of this thesis is for researchers to determine learning outcomes using learning media using Scrapbook Media. Methods This research uses the Risech and delepoment (R&amp;D) research method. The subjects in this study were the fifth grade students of SDN Sukasaba 1 Pandeglang Regency for the academic year 2021/2022, totaling 16 students, with 10 female students and 10 male students. The data collection instrument in this study used observation sheets and interviews. From the results of the Scrapbook visual media research on the material of events in life, there is an increase in learning in the classroom. And the results of the validation of media experts obtained 85% categorized as very feasible, the results of material expert validation obtained 85% categorized as very feasible, the results of linguistic expert validation 84% were categorized as very feasible and the results of the student responses obtained 81.5% categorized as very good. From the results of these studies, it is shown that learning on the material of events in life using visual media Scrapbook is able to increase the readiness and focus of students in understanding learning faster. Therefore, in learning events in life, it is recommended for teachers to use the Scrapbook media because it can improve learning outcomes and make students understand learning faster.
Construction of quaternary Hermitian LCD codes

We introduce a general construction of many Hermitian LCD $[n, k]$ codes from a given Hermitian LCD $[n, k]$ code. Furthermore, we present some results on punctured codes and shortened codes of quaternary Hermitian LCD codes. As an application, we improve some of the previously known lower bounds on the largest minimum weights of quaternary Hermitian LCD codes of length $12 \le n \le 30$.
An Improved Probability Propagation Algorithm for Density Peak Clustering Based on Natural Nearest Neighborhood

Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance ($d_c$), the cluster number ($k$) and the selection of the centers of clusters. Moreover, the final allocation strategy is sensitive and has poor fault tolerance. The shortcomings above make the algorithm sensitive to parameters and only applicable for some specific datasets. To overcome the limitations of DPC, this paper presents an improved probability propagation algorithm for density peak clustering based on the natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural nearest neighborhood and probability propagation, DPC-PPNNN realizes the nonparametric clustering process and makes the algorithm applicable for more complex datasets. In experiments on several datasets, DPC-PPNNN is shown to outperform DPC, K-means and DBSCAN.
The use of cake application to improve speaking ability

In an era where learning cannot be separated from technology, there are various Android-based applications that are very helpful, interesting and make learning adapt to the learner's time. Learning media is the learning source component that is able to stimulate students to study (Arsyad: 2005). In this paper the learning media is application. One such application is the Cake application. This application is a learning application that deserves to be tested because it has several advantages. One of the advantages of using learning applications is that there is no limited space and time for learners to learn. Learning is no longer limited to lecture rooms or lecture time. learning can be anytime and anywhere. The purpose of this research is to find out whether the Cake application has an effect on students who use it, especially in the speaking field, more specifically the pronunciation. This research is a type of quantitative research with a quasi-experimental approach. The design used is "Pretest – Posttest Non-Equivalent Control Group Design". The groups used in this study amounted to 2 groups, namely the experimental group using online learning and the control group using online learning as well but added with the application of Cake. In this study, according to determine the experimental class and control class, random assignment was done, namely, the selection was made randomly to choose the class. This research will provide benefits for teachers who want to try to apply the method so that it will be a reference later in the future.