Reviewerly/exHarmony huggingface dataset:
Implementation K-Means Algorithm to Group Provinces By Factors Influenced Criminal Act in Indonesia in 2019

A criminal act is an act that is prohibited by a criminal law accompanied by a sanction in the form of a particular crime for whoever violates the prohibition. Criminal action as a social phenomenon is more influenced by various aspects of life in society, including poverty and unemployment factors. Grouping the factors that influence a crime is necessary to find the most recent information that was not previously known. This research uses the K-Means method, a non-hierarchical cluster analysis that seeks to partition data with the same characteristics into one cluster. The results showed that 3 clusters formed, with cluster 1 covering 17 provinces are areas with the characteristics of the lowest percentage of poverty and the highest average unemployment, the cluster group 2 includes 12 provinces which are areas with the characteristics of the percentage of moderate poverty and the lowest average unemployment, the cluster group 3 includes five provinces which are areas with the characteristics of the highest percentage of poverty and moderate unemployment.
Acute lymphoblastic leukemia prediction using covering-oriented rough k-means segmentation algorithm

Acute Lymphoblastic Leukemia (ALL) is a type of dreadful cancer that has an effect on the bone marrow and blood. In recent decades, leukemia mainly affects youngsters and grown-ups. Timely diagnosis of leukemia is crucial to give right treatment to patients, predominantly in the case of children. Computational tecniques for medical image analysis have incredible use and become the focus of research in medical image processing. The major objective of this research work is to predict the acute lymphoblastic leukemia cells using the proposed segmentation algorithms with the assistance of machine learning classifiers. In this study, hybrid clustering based segmentation methods Covering-orientedRough K-Means (CRKM) algorithm is proposed to segment the image of the leukemia nucleus. Then, the performance of the segmentation algorithms were analyzed using different machine learning classifiers. From the experimental analysis, it is observed that the machine learning classification algorithms achieved better prediction accuracy when compared to existing segmentation algorithms.
Sparse Visual Counterfactual Explanations in Image Space

Visual counterfactual explanations (VCEs) in image space are an important tool to understand decisions of image classifiers as they show under which changes of the image the decision of the classifier would change. Their generation in image space is challenging and requires robust models due to the problem of adversarial examples. Existing techniques to generate VCEs in image space suffer from spurious changes in the background. Our novel perturbation model for VCEs together with its efficient optimization via our novel Auto-Frank-Wolfe scheme yields sparse VCEs which lead to subtle changes specific for the target class. Moreover, we show that VCEs can be used to detect undesired behavior of ImageNet classifiers due to spurious features in the ImageNet dataset.
StoreSim: Optimizing Information Leakage in Multi-Cloud Storage Services

Abstract: Many schemes have been recently advanced for storing data on multiple clouds. Distributing data over different cloud storage providers (CSPs) automatically provides users with a certain degree of information leakage control, as no single point of attack can leak all a user’s information. However, unplanned distribution of data chunks can lead to high information disclosure even while using multiple clouds. In this paper, to address this problem, we present Store-Sim, an information leakage-aware storage system in multi cloud. Store-Sim aims to store syntactically similar data on the same cloud, thus minimizing the user’s information leakage across multiple clouds. We design an approximate algorithm to efficiently generate similarity- preserving signatures for data chunks based on Min-Hash and Bloom filters, and design a function to compute the information leakage based on these signatures. Next, we present an effective storage plan generation algorithm based on clustering for distributing data chunks with minimal information leakage across multiple clouds. Finally, we evaluated our scheme using two real datasets from Wikipedia and GitHub. We show that our scheme can reduce information leakage by up to 60-70 Percent. Keywords: Min-Hash, Bloom-Filter, Multi-Cloud, StoreSim, Data Leakage.
Secure Cryptography by Using Hash Application

This project is basically design for ensuring security to all kinds of data format file such as pdf, text, images, audio, videos etc. This is carried out using various keys and hashing algorithm. This project basically helps the end user to protect his/her personal files from the unauthorized user and can also restrict its access to the limited people. This complete process is carried out in two methods that are encryption and decryption. Encryption process required varchar key while encryption the data file which convert the original file in to incomprehensible format. Further in decryption process, again by providing the same key file can be converted in to its original format. This helps the user to protect personal file on a public systems like schools, colleges, cyber cafes, hospitals, banks etc. The cryptography is acquired through various algorithms and keys. We are using a XOR key function and Hash function for encrypting and decrypting the data. In this certain key given by user will be XOR with the actual byte code of file for encryption and vice versa for decryption. This ensures to restrict the personal files on a public system.
Social Distance detection using Deep Learning

Abstract: Our research paper presented below is about social distance among people in public places by utilizing Deep learning technique on captured video by at public place Camera for detecting and controlling distance between them so that virus spread can be contained. This application is created to give alarms to individuals for keeping social separation in jam-packed areas. The real time video used to analysis objects by using object detection method and YOLOv3 calculation. We can figure out whether individuals are following social separating or not and in view of that we are alarming by making alert. It is likewise chipping away at web cameras, CCTV, and so forth, and can identify individuals continuously. Thismight assist specialists with overhauling the design of public spots or to make preparatory moves to relieve high-hazard zones. The technology is useful in different fields likewise like independent vehicles, human activity acknowledgment, swarm investigation. Keywords: Social separating. Object identification. Swarm examination, Coronavirus
Attention Vision Transformers for Human Fall Detection

Abstract In recent years, attention transformers have proven to be instrumental in natural language processing (NLP)-based tasks such as sentence classification and language translation. However, their application has been recently extended to large-scale object recognition tasks. In this work, Vision Transformer with attention has been investigated for the detection of human falls and ADLs (Activities of Daily Living) from time series-based signals. The Vision Transformer model has been trained and validated using the acceleration signals of waist-worn Inertial Measurement Unit (IMU) sensors obtained from the IMU Falls dataset[1]. The model is also trained and validated on the popular SiSFall dataset[2]. The model is also investigated by independently training three different cases of patch size and attention heads. It was observed that a larger patch size resulted in significant performance deterioration. Additionally, a smaller patch size took longer to train and was computationally expensive. The model performed (best case) with an Accuracy (%) of 99.9 ± 0.1 and a True Positive Rate (%) of 99.9 ± 0.1 on the SFU-IMU dataset and with an Accuracy (%) of 99.8 ± 0.25 and a true positive rate (%) of 99.87 ± 0.3 on the SISFALL dataset. Overall, the results show that Transformers are highly robust in the detection of human falls and nonfalls/ADLs, subject to the appropriate patch size.
Twenty-two years since revealing cross-site scripting attacks: a systematic mapping and a comprehensive survey

Cross-site scripting (XSS) is one of the major threats menacing the privacy of data and the navigation of trusted web applications. Since its reveal in late 1999 by Microsoft security engineers, several techniques have been developed in the aim to secure web navigation and protect web applications against XSS attacks. The problem became worse with the emergence of advanced web technologies such as Web services and APIs and new programming styles such as AJAX, CSS3 and HTML5. While new technologies enable complex interactions and data exchanges between clients and servers in the network, new programming styles introduce new and complicate injection flaws to web applications. XSS has been and still in the TOP 10 list of web vulnerabilities reported by the Open Web Applications Security Project (OWASP). Consequently, handling XSS attacks became one of the major concerns of several web security communities. In this paper, we contribute by conducting a systematic mapping and a comprehensive survey. We summarize and categorize existent endeavors that aim to protect against XSS attacks and develop XSS-free web applications. The present review covers 147 high quality published studies since 1999 including early publications of 2022. A comprehensive taxonomy is drawn out describing the different techniques used to prevent, detect, protect and defend against XSS attacks. Although the diversity of XSS attack types and the scripting languages that can be used to state them, the systematic mapping revealed a remarkable bias toward basic and JavaScript XSS attacks and a dearth of vulnerability repair mechanisms. The survey highlighted the limitations, discussed the potentials of existing XSS attack defense mechanisms and identified potential gaps.
Negative Sentiment Analysis: Hate Speech Detection and Cyber Bullying

Abstract: Sentiment Analysis or opinion mining in general, is the use of natural language processing, computer linguistics and related technology used to determine whether the text data is positive, neutral or negative. Hate Speech, on the other hand is abusive or threatening speech or writing that expresses prejudice, hate and / or encourages violence towards a person or a group of persons based on their race, religion, sex, or sexual orientation. With the exponential rise in the number of people making use of social media, where tons of content is posted daily, which is visibly harmless in nature, there has been a sharp rise in hate speech as well. The need and interest for identifying and detecting hate speech on such social media platforms, especially Twitter has risen significantly, as now major firms move towards development of systems which help tackle hate speech online. This project aims to cater to the need of identifying negative tweets which promote hate speech.
Current Status and Development Plan in the Area of Character Education at Universities

In this study, character education, which has a deep origin in the educational history of both the East and West, began to be newly illuminated in the 4th Industrial Revolution and AI era. In line with the trend of the times, this study was conducted as a basic study to review the role and meaning of university character education and to redefine its status. The demand for character education is on the rise, with several universities in Korea already opening and operating subjects related to character education as essential subjects. The Center for Liberal Arts Education has established a literacy education area and has presented it as a standard model for basic liberal arts education. But there is a sense that both the concept and meaning in the education area have not yet been sufficiently disseminated. In light of this problem, this study explored the direction that the newly established status in the character education field is taking. To this end, the research trend of university character education was analyzed, and a survey was conducted on those in charge of liberal arts education in the university’s education field. In addition, a focus group interview (FGI) was conducted for nine liberal arts education policy experts. As a result of examining the research trend of literacy education, universities perceived that character education is mainly related to cultivating basic or practical skills in the chosen fields or majors of the students. Thus, it proved to be different from the area of character education that aims for personality education. However, as a result of a survey conducted by a person in charge of liberal arts education at domestic universities, 31.4% were already applying to the area of character education, and this number increased by 7% from the previous survey. In addition, 34.3% of the respondents said that the basic framework of the liberal arts curriculum of the “Standards Plan” was already accepted and had entered the settlement stage at many universities. As a result of the FGI, a liberal arts education policy expert sympathized with the importance of character education, but there were divided opinions for and against classifying it into character education areas. Based on the results of this study, it is suggested that both the details of the current status and a subject analysis in the area of character education being conducted at domestic universities need to be conducted as a follow-up study.
Generalizing to New Tasks via One-Shot Compositional Subgoals

The ability to generalize to previously unseen tasks with little to no supervision is a key challenge in modern machine learning research. It is also a cornerstone of a future "General AI". Any artificially intelligent agent deployed in a real world application, must adapt on the fly to unknown environments. Researchers often rely on reinforcement and imitation learning to provide online adaptation to new tasks, through trial and error learning. However, this can be challenging for complex tasks which require many timesteps or large numbers of subtasks to complete. These "long horizon" tasks suffer from sample inefficiency and can require extremely long training times before the agent can learn to perform the necessary longterm planning. In this work, we introduce CASE which attempts to address these issues by training an Imitation Learning agent using adaptive "near future" subgoals. These subgoals are recalculated at each step using compositional arithmetic in a learned latent representation space. In addition to improving learning efficiency for standard long-term tasks, this approach also makes it possible to perform one-shot generalization to previously unseen tasks, given only a single reference trajectory for the task in a different environment. Our experiments show that the proposed approach consistently outperforms the previous state-of-the-art compositional Imitation Learning approach by 30%.
Intelligent Management of Land Resources Based on Internet of Things and GIS Technology

In order to improve the effect of land resource management, this paper combines the Internet of Things technology and GIS technology to build an intelligent management system for gradient resources to improve the efficiency of land resource management. Aiming at the hybrid intelligent model of wetland resource remote sensing monitoring technology, this paper analyzes and studies the remote sensing image processing theory. Moreover, this paper studies in detail remote sensing image restoration, TM image reflectivity simulation imaging, image enhancement technology, optimal band selection based on the characteristics of wetland resources, expert decision analysis, deep mining of image data, knowledge reasoning, and decision tree analysis to form a theoretical support system for a hybrid intelligent classification model for wetland resources. The research shows that the intelligent management system of land resources based on the Internet of Things and GIS technology has a good effect in the collection and processing of land resource information and can effectively improve the management efficiency of land resources.
Meningkatkan Pemahaman Siswa tentang Teks Iklan melalui Metode Presentasi Berbantuan Media Power Point di Kelas VIII C SMP Negeri 2 Kumai Tahun Pelajaran 2019/2020

In cycle I students still look confused with their assignments in class, this can be due to learning with computer media being a new thing and has not been applied before so the achievement of learning outcomes achieved by students is not optimal. With diligent effort and guidance, it is expected to increase students' understanding in the hope of increasing the achievement of better student learning outcomes. One of the factors that determine the success or failure of a person in learning is a factor that comes from within the person who is learning (internal). In the implementation of the second cycle of learning, the learning carried out by the teacher using the media-assisted presentation method, the power point-assisted presentation method, was better than the previous cycle and the explanation of the concept of the learning material could be understood by most students. The increase was due to the teacher's voice being able to be heard to the back, this could be influenced by the condition of the class that the teacher had mastered when the teacher delivered an explanation of the material. From the results of students' responses, students stated that they could understand what the media displayed. The results of the study: There was an increase in the percentage of the number of students who understood from the observations of 71.87% in the first cycle, 81.2% in the second cycle. The increased understanding of students affects the activeness of students to participate in the teaching and learning process.
REDESAIN BODI SEPEDA MOTOR LISTRIK BABY GANESHA 1.0 GENERASI II DAN PENGARUHNYA TERHADAP PENURUNAN COEFFICIENT OF DRAG

The aerodynamic aspect is an aspect that really considers the force generated by the fluid flow which has an impact on performance and energy consumption while driving. The purpose of this research is to modify the standard design of the baby ganesha 1.0 generation II electric motorcycle so that it gets a design with a smaller coefficient of drag. The need for fluid flow simulation analysis To determine the coefficient of drag, one way of simulation analysis is to use solidwork, solidworks is known to have the speed and accuracy required for a successful analysis. The conceptual research paradigm R2D2 (Reflective, Recursive, Design and Development) is used to conduct research and development. Development (R&amp;D). After the fluid flow analysis process was carried out, it was found that the modified body design of 2 baby Ganesha 1.0 generation II electric motorcycles obtained a Coeficient Of Drag of 0.0372 lower than the standard body design of Baby Ganesha 1.0 generation II electric motorcycles which only had a coefficient value. of drag is 0.0433. Through expert judgment, the modified design of 2 Baby Ganesha 1.0 generation II electric motorcycles received a percentage with a value of 82% with good qualifications.
Research on the Evaluation of College Students' Algorithmic Literacy based on Factor Analysis and Cluster Analysis

This paper takes 16 prefectures and cities under Anhui Province as the research object, and constructs an index group that affects the level of algorithm literacy. First, based on the qualitative research on the quality level of algorithm literacy in Anhui Province, a combination model of the influencing factors of algorithm literacy level was established by factor analysis, and the comprehensive score of algorithm literacy was used as its representative index; secondly, the algorithm literacy level was established by cluster analysis method. To explore the development model of algorithm literacy in different regions. The final research results show that: in the combined model, the three factors of regional education comprehensive strength, per capita education level and material resource input have a greater impact on the algorithm literacy level of regional college students; in the classification model, Hefei City is the core of Anhui Province. In the city, the algorithm literacy level of its college students ranks first. Finally, research on the commonality of the two models is conducted to reasonably evaluate the algorithm literacy of college students in various cities in Anhui Province, and provide feasible suggestions for improving the algorithm literacy of college students in Anhui Province.
Analisis dan Desain Sistem Informasi Berbasis Website Gereja Katolik Santo Yusup Jember

An organization or community institution consists of a group of people who work together for the purposes or interests of their organization or institution. . Paroki Santo Yusup Jember is a community institution located on Jalan R.A. Kartini 28, Jember. As a community institution that requires an integrated information needed for all their activities, both about displaying information, registering, processing the required data in a practical and efficient manner. With the creation of this information system, all information and all registrations will be greatly assisted and facilitate the work of the Church secretariat effectively and precisely in the future. Therefore, a journal was created for the Analysis and Design of Website-Based Information Systems for Gereja Katolik Santo Yusup Jember. It is hoped that the existence of this information system can help in the effectiveness and efficiency of activities around the church.
Pre-Distribution of Entanglements in Quantum Networks

Quantum network communication is challenging, as the No-Cloning theorem in quantum regime makes many classical techniques inapplicable. For long-distance communication, the only viable approach is teleportation of quantum states, which requires a prior distribution of entangled pairs (EPs) of qubits. Establishment of EPs across remote nodes can incur significant latency due to the low probability of success of the underlying physical processes. To reduce EP generation latency, prior works have looked at selection of efficient entanglement-routing paths and simultaneous use of multiple such paths for EP generation. In this paper, we propose and investigate a complementary technique to reduce EP generation latency--to pre-distribute EPs over certain (pre-determined) pairs of network nodes; these pre-distributed EPs can then be used to generate EPs for the requested pairs, when needed, with lower generation latency. For such an pre-distribution approach to be most effective, we need to address an optimization problem of selection of node-pairs where the EPs should be pre-distributed to minimize the generation latency of expected EP requests, under a given cost constraint. In this paper, we appropriately formulate the above optimization problem and design two efficient algorithms, one of which is a greedy approach based on an approximation algorithm for a special case. Via extensive evaluations over the NetSquid simulator, we demonstrate the effectiveness of our approach and developed techniques; we show that our developed algorithms outperform a naive approach by up to an order of magnitude.