Reviewerly/exHarmony huggingface dataset:
AI based Event Management Web Application

The AI-Based Event Management web application is an online event management web application that allows users to search for and register for a variety of events in one place. The app allows registered users to see the events that are recommended to them based on their interests. All users have access to a list of their most recently viewed events. The web application allows new users to register. This online application will assist users in locating events of interest and registering in the event of a connected issue. This web application provides the most basic tasks required for an event, such as a Techfest, a cooperative event, a webinar, a workshop, a conference, a seminar, a school & college festival, and so on. The gateway allows customers to choose an event category and search for it by name and description. Users need to select the interesting category of events that will register on the portal then whenever the users log in, they can see recommended events based on their selected interest.
CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.
Greedy Squirrel Search Algorithm for Large-Scale Traveling Salesman Problems

A greedy strategy based on squirrel search algorithm is proposed and used to solve the TSP problem. First, a new path initialization operator is designed for partial path initialization. Secondly, two search operators and a mutation strategy are designed to improve the convergence speed and performance of the algorithm: (1) Greedy crossover operator, which is used to improve the information interaction ability between each individual. (2) RC2opt+R3opt operator, which is used to enhance the local search ability of individuals. (3) Local mutation strategy, which uses seasonal constants to measure population differences and the number of iterations, and performs mutation operations on individuals in the middle and late stages of the algorithm to prevent the algorithm from falling into the local optimum. Finally, a large number of TSP examples in the open dataset TSPLIB are selected to verify the effectiveness of the algorithms and operators. The comparison results with 3 classical algorithms and 7 latest algorithms show that the algorithm has higher precision and better stability.
Optimization Improvement and Clustering Application Based on Moth-Flame Algorithm

Optimization problem is widely used in production management, military command and scientific experiments and other fields, moth-flame optimization algorithm as a new swarm intelligence optimization algorithm, has the advantages of fast convergence, simple structure, strong robustness, memory mechanism, it is also one of the focus of scholars. K-means clustering is the most famous partition clustering algorithm. Given a set of data points and the required number of clustering k, k is specified by the user, and the K-means algorithm repeatedly divides the data into K clusters according to a certain distance function. This article first on moth-flame optimization algorithm (MFO) the existence of complex or slightly larger scale function to solve the problems of slow convergence speed, put forward by the flame number greater than the number of moths reference grey wolf optimizer (GWO) comes first algorithm do rectilinear flight, later periods the scaling factor are introduced to improve moth-flame optimization algorithm, in order to realize broaden the moths search area, improve the ability of global optimization and convergence rate of the target. Through experiments to verify the feasibility of the improved Moth-flame optimization algorithm (IMFO), the convergence speed is significantly higher than MFO algorithm, and the solution accuracy is also greatly improved. Then the algorithm is used to guide the clustering center of k-means clustering algorithm to improve the clustering accuracy. The three algorithms of K-means, MFO Fusion K-means (MFO-KM) and IMFO Fusion K-means (IMFO-KM) algorithms were compared in the international standard data set Iris, Seeds and Wine Quality. The results showed that: IMFO-KM algorithm has the best performance improvement in Wine Quality data set, with the accuracy improved by 3.82%–6.37% compared with other algorithms, the class average distance G reduced by 7.18%–13.58%, and the standardized mutual information improved by 14.17%.
Adaptive Clustering by Fast Search and Find of Density Peaks

Clustering by fast search and find of density peaks is a new density-based clustering algorithm, which is widely used in various fields owing to its simplicity and efficiency, unique parameters, and recognition of arbitrary shape clusters. However, when selecting the cluster center requires human participation, which makes the clustering result to be subjectively affected by the operator, thus reducing the availability of clustering and interrupting the fluency of the algorithm. In this study, to eliminate artificial participation in the selection of cluster centers, a weighted decision measurement slope change method is proposed to select cluster centers, and the F-Measure, ARI, and AMI of the algorithm are tested in the UCI and synthetic datasets. Experimental results show that the proposed algorithm addresses the limitation of human participation in the selection of cluster centers and improves the clustering performance of the algorithm.
On the Fault‐Tolerance Threshold for Surface Codes with General Noise

Abstract Fault‐tolerant quantum computing based on surface codes has emerged as a popular route to large‐scale quantum computers capable of accurate computation even in the presence of noise. Its popularity is, in part, because the fault‐tolerance or accuracy threshold for surface codes is believed to be less stringent than competing schemes. This threshold is the noise level below which computational accuracy can be increased by increasing physical resources for noise removal, and is an important engineering target for realizing quantum devices. The current conclusions about surface code thresholds are, however, drawn largely from studies of probabilistic noise. While probabilistic noise is a natural assumption, current devices experience noise beyond such a model, raising the question of whether conventional statements about the thresholds apply. This work attempts to extend past proof techniques to derive the fault‐tolerance threshold for surface codes subjected to general noise with no particular structure. Surprisingly, no nontrivial threshold is found, i.e., there is no guarantee the surface code prescription works for general noise. While this is not a proof that the scheme fails, it appears that current proof techniques are likely unable to provide an answer. A genuinely new idea is needed to reaffirm the feasibility of surface code quantum computing.
RANCANG BANGUN SISTEM INFORMASI PENGOLAHAN DATA PRAKTIK KERJA INDUSTRI DAN LAPANGAN (STUDI KASUS DI SMK PASUNDAN RANCAEKEK)

Data processing of industrial and field work practices in SMK Pasundan Rancaekek is still semi manual using a spreadsheet application. This can lead to various problems, namely checking students who have not made payment and registration of PRAKERIN, making evidence of transactions and reports effectively, placing students in accordance with the capacity of the place of PRAKERIN and scheduling supervisors in conducting visits. The method used is the System Development Life Cycle (SDLC), which includes system analysis, system design, system implementation, and system operation. Whereas to analyze user system requirements using Flowmap analysis, Context Diagrams, Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), Data Dictionaries, and Structure Charts. The design of the PRAKERIN data processing information system uses Microsoft Visual FoxPro 9.0 as a system implementation, report generation, and for database processing. Thus the system designed by the author is expected to facilitate the PRAKERIN data processing process so that it is more effective and efficient.
A Sub-captions Semantic-Guided Network for Image Captioning

The fine-grained representation of image is important for cross-modal transformation tasks. Existing image captioning methods of illustrating images are often limited to enumerating or describing the visual content but ignore capturing deep semantic information. So there is a big semantic gap between the image representation learned by the encoder and the representation required by the decoder to generate the description, which result in the generation of repetitive and simple sentences. To address this issue, we propose a Sub-Captions Semantic-Guided (SCSG) network. Our network selectively extracts detailed sub-captions information from the decomposed scene graph of the image. Thereby, the rich image information obtained by aligning and integrating image visual features and semantic features of the sub-captions can generate accurate captions. Moreover, our network achieves controllable captioning by intentionally selecting sub-graphs. Extensive experiments demonstrate the effectiveness of SCSG on three benchmark datasets: MSCOCO, Flickr30K and Flickr30K Entities, and show that our method achieves comparable results than in terms of accuracy, diversity and controllability.
Detection of Personal Protective Equipment in Factories: A Survey and Benchmark Dataset

There are many accidents in chemical plants and laboratories caused by workers not wearing the required personal protective equipment (PPE). Yet, manually ensuring that workers wear their PPE all the time is often infeasible. Object detection based on deep learning could provide a good solution to reduce the safety risks. Developing and testing such approaches requires a large amount of high-quality training data. However, there is a lack of datasets on chemical factory scenes and related protective equipment. With the Personal Protective Equipment Dataset (PPED), we provide 3300 pictures of personnel wearing protective glasses, face shields, gas masks, masks, protective clothing, and protective gloves. On the basis of this dataset, several widely-used state-of-the-art object detection models including YOLOv3-SPP, YOLOv5, SSD, and Faster R-CNN are used to conduct comparative experiments to create a benchmark for the field. In our study, YOLOv5 achieves the best detection result with a mAP of 93.6% and a recall of 87.5%.
Sistem Informasi Pengelompokan Pembayaran Denda Tilang Menggunakan Algoritma K-Means Clustering

This study Violation can be defined as a situation where there is a mismatch between the rules and implementation. The regulation is contained in Law Number 22 Year 2009, concerning "Road Traffic and Transportation" which has been stipulated by the state and is valid legally. The current problem shows, there is no information service system for ticket data management, unable to manage the process of grouping the results of the payment of fines properly, because the process is still manual by checking the data of offenders who have made payments, so it takes longer to process the grouping and data management payment results. The objectives of this study are: Knowing the success of the k-means clustering algorithm in the process of grouping the ticket data to the process of grouping the results of the payment of fines. This research uses the clustering method with the k-means algorithm which is one of the algorithms for the formation of data clusters. This algorithm works by dividing the data into k-clusters by grouping data based on certain classes which are then formulated the results by analyzing the articles that are violated and average. the total amount of the penalty payment. Research shows that information services for ticket data management using the k means clustering algorithm can simplify the process of grouping the payment of fines by more than 70%. This can be proven through the results of the hypothesis test which states that t count is smaller than t table with a value of -2.430 &lt;2.178 so that Ha can be accepted and H0 is rejected.
MPCNN with Knowledge Augmentation: A Model for Chinese Text Classification

Since TextCNN was proposed, Convolutional Neural Networks have shined in the field of NLP, especially for text classification tasks. In recent years, many models based on convolutional neural networks have been proposed, and DPCNN is one of them. It expands the receptive field by continuously deepening the number of network layers, thereby obtaining the long-distance dependence of text. However, the traditional DPCNN has problems such as single feature extraction, ignoring the importance of different semantic spaces and different n-gram features. This paper improves and proposes the MPCNN model to extract grammatical features from multiple scales. Integrate different semantic spaces to build multiple pyramids. At the same time, considering the lack of contextual information and insufficient semantics in short Chinese texts, the knowledge base is used to obtain the concept set of the text. Then the existing text is enhanced by prior knowledge. Experiments on the THUCNews dataset have achieved better classification results than other mainstream classification models.
Perancangan Sistem Electronic Voting untuk Pemilihan Ketua OSIS di SMAN 25 Garut

The development of increasingly modern technology has been widely used by humans in all aspects, be it education, defense, health, psychology, communication and so on. This is inseparable from the role of technology which makes human work easier, faster and more accurate. The election of the student council president is one of the activities that must be carried out by SMAN 25 Garut every year. Currently the method of selecting the student council president is carried out using conventional techniques, namely voting by punching ballots, then calculating the results of the recapitulation manually. This technique is in fact ineffective and inefficient because it requires a lot of resources. To answer these problems, an electronic voting (e-voting) system is proposed. E-voting is seen as being able to answer these problems because it can facilitate and speed up the voting process and vote counting so that the spent resources can be minimized. The e-voting system was built using the system development life cycle (SDLC). Which consists of identification, analysis, design, construction, testing, implementation. The testing phase was carried out by observing, interviewing, and studying literature. The results of the analysis were then analyzed, then designed using UML modeling. Meanwhile, the application program is designed using the CodeIgniter framework, the PHP programming language and the MySQL database. Based on the results of research and testing, it is concluded that the e-voting system is more effective in terms of implementation, and more efficient in terms of costs, personnel and implementing personnel.
Finite Sample Analysis of Minmax Variant of Offline Reinforcement Learning for General MDPs

In this work, we analyze the finite sample complexity bounds for offline reinforcement learning with general state, general function space and state-dependent action sets. The algorithm analyzed does not require the knowledge of the data-collection policy as compared to earlier works. We show that one can compute an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -optimal Q function (state-action value function) using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(1/\epsilon ^{4})$</tex-math></inline-formula> i.i.d. samples of state-action-reward-next state tuples.
The Potential for Enhancing the Quality of Electronic Banking Service in Light of the Adoption of Digital Marketing"A field study in the keycard outlets in Anbar Governorate"

This study sought to shed light on the variables of digital marketing tools and the quality of electronic banking service because of their great importance to banks today as there are one of the basic issues that enable banks to confront environmental changes. This reflects the objectives of the study, the most important of which was diagnosing what the study can produce in the light of the relationship of impact and the correlation between its variables.To achieve the desired objectives of the study, the descriptive analytical method was used. A sample of individuals working in the keycard outlets in Anbar Governorate with a size of (66) individuals was chosen to conduct the study using a variety of statistical tools and methods to analyze data and information. The SPSS.V23 software was used in the process of extracting the results. The study reached a set of conclusions, the most important of which is (there is a positive significant correlation between the two variables of digital marketing tools and the quality of the electronic banking service at the overall level, and the strongest correlation was between online marketing and the quality of the electronic banking service).Depending on the conclusions reached by the study, proposals were made that are consistent with these conclusions, the most important of which (it requires the outlets in question to consider the variable digital marketing tools in order to keep pace with growth and continuous development, as a major growth strategy in the business environment.
The impact of information technology components on process design decisions / a field study in the Oil Products Distribution Company / Kirkuk branch

The study aimed to shed light on the role played by the information technology to achieve right decision in the field of processes design, To achieve this goal, the researchers relied on the descriptive analytical method, which focuses on studying the phenomenon as it exists in reality, as it is an accurate description and expresses it qualitatively and quantitatively. The Oil Products Distribution Company / Kirkuk branch was chosen as the field for the study, and in order to achieve the goal and answer the questions of the intellectual problem of the study, a hypothetical outline was built showing the correlation and influence relations between the study variables. A random sample of (266) respondents was selected from the employees of the Oil Products Distribution Company / Kirkuk branch, The questionnaire was relied on as a main tool for collecting data on the field part. As for the theoretical part, it was covered by relying on the contributions of researchers collected from Arab and foreign sources. The study reached a set of conclusions, the most important of which was the existence of a significant correlation and impact of information technology on process design decisions.
Dyslexia: Impact of mobile phones educational programs on academic achievement of elementary school dyslectic students of India and Sudan

Although mobile phones have emerged in large numbers in the past few decades, little is known about their impact. Especially the impact on the attitude and academic performance of DYSLECTIC students. Although people are becoming more and more interested in mobile devices and their relevance and consequences on children, the impact of related studies varies, and the overall effect remains unclear. The purpose of this study is to further investigate any differences that may exist: based on gender and school type; on the impact of mobile phone educational programmes on academic achievement of elementary school dyslectic students of India and Sudan.
Rancang Bangun Sistem Informasi Berobat Jalan di Klinik Cikijing Rancaekek

Information technology provides benefits for human life. Utilization of technology in the service sector is needed because it can provide optimal service to clients. Meanwhile, currently the outpatient data processing at the Cikijing Rancaekek Clinic is still manual, causing several obstacles, namely difficulty in finding patient data because they are still looking through medical record cards, difficulty in making reports quickly and accurately and difficulty in calculating treatment costs.The purpose of this study is to make it easier to find patient data, make reports quickly, and make the calculation of patient treatment costs more effective. The method used is SDLC with a waterfall model and data collection techniques. System analysis and design tools used for this final project are Flow Map, Data Flow Diagram (DFD), Entity Relationship Diagram (ERD), Data Dictionary (Data Dictionary), and Structure Chart. For program implementation using Microsoft Visual Foxpro9.0 programming language. In this system, it is concluded to overcome the problems that have occurred in the old system, so that it can assist in the process of searching for patient data, making reports and calculating treatment costs.
Sistem Informasi Absensi Pegawai Berbasis Web di Kantor Desa Nagreg

Employee attendance very influential on work productivity in achieving company goals, especially in government agencies related to community services as an indicator in employee performance appraisal. The attendance process at the Nagreg Village office as one of the government agencies is still done manually so that data collection on attendance hours, outgoing hours, recapitulation of employee attendance requires a relatively long time in the calculation and search process. An attendance information system is needed to overcome these problems. Attendance Information System in this study was built using the waterfall method which consists of the stages of analysis, design, coding, and testing. UML (Unified Model Language) is also used as a modeling tool in the analysis and system design stages