Reviewerly/exHarmony huggingface dataset:
Development of 'Ruang Alumni' Information System Based on Web

Alumni are graduates of a school, college, or university. As at Private University in Jakarta, every year there are new alumni. After graduating from college, Alumni are usually busy working or continuing to higher education, they will be separated by distance. Based on these problems, a media is needed to interact with other alumni and this media can also be used by Alumni in helping the campus in collecting data on other alumni. The media is a social network that connects people to one another. Our research is aimed at the alumni of Private University in Jakarta. Even though nowadays there are many social media, not all alumni use usernames according to their real names, not even all alumni use these social media, this of course makes it difficult for alumni to communicate again with other alumni. With the website-based application, namely the Ruang Alumni, it will make it easier for Alumni to connect with each other, and can help the campus to complete alumni data even though not all of them are complete because it is only based on other Alumni who have registered in the application. This application was developed using the Waterfall method. With this Ruang Alumni website-based application, Alumni can connect with each other by sharing data and information in this application, such as posting personal photos, posting job vacancies and posting lots of information in this application, alumni can also update personal data like on other social media. Alumni can also find information on campus such as events or competitions held by faculties, study programs, or student organizations at University.
Optimization of Artificial Potential Field Parameters based on Enhanced Butterfly Algorithm

The artificial potential field algorithm in the global path planning of the robot is to calculate the combined force vector of the gravitational force of the destination and the repulsive force of the obstacle to determine the rotation angle and forward distance of the vehicle. It can effectively plan the path for the current robot position. Still, if the parameters in the algorithm are not appropriately set, it is easy to generate problems such as not being able to reach the specified location, a jagged planning path, or stagnant movement. In this paper, we propose an enhanced butterfly algorithm based on stochastic excellent community vectors for parameter optimization of the artificial potential field algorithm with the shortest path as the objective function under the condition of the finite number of steps. The butterfly algorithm converts the fitness values into impact factors for the update terms. It uses random number squares as coefficients, which has the disadvantages of requiring more iterations and slow convergence. Therefore, the concept of fitness value normalization and the stochastic excellent community vector mechanism is introduced to enhance the performance of the original algorithm. Finally, several robot path planning simulations show that the artificial potential field parameters based on the enhanced butterfly algorithm can effectively improve pathway planning.
Query-Document Topic Mismatch Detection

Query-document topic match is one of the central signals for ranked information retrieval. Returning documents which are too broad or insufficient or off-topic compared to the query leads to a poor user experience. Thus, given a query and a document, it is critical to estimate degree of topical mismatch between the two. Previous work has either focused on very broad topics (like health, education, science, etc.) or predicted extremely-fine-level topic mismatch using query reformulations which suffer from sparsity. Predicting query-document topic mismatch is difficult because it needs semantic understanding of both the query and the document. In this paper, we model the problem as a five-class classification problem using a novel Transformer-based architecture. Our technique takes the query as input along with a detailed document representation including title, URL, snippet, key-phrases and topic distribution, and outputs one of these five grades of topic match: Very Unsatisfactory, Unsatisfactory, Neutral, Satisfactory, and Very Satisfactory. On a large dataset of $$\sim $$ 2.43M query-document pairs, we show that our proposed method can provide an AUC of 0.75.
An Enhanced Opposition-Based Evolutionary Feature Selection Approach

This paper proposes an enhanced feature selection (FS) approach to improve the classification tasks, taking into account data dimensionality as a significant criterion of the dataset. High dimensionality may cause serious problems in classification that degrade the performance of the classifier. Among these problems: generating complex models (overfitting), increasing the learning time, and including redundant and irrelevant features in the learning model. FS is a data mining technique to minimize the number of dimensions (features) by getting rid of redundant and irrelevant features. Meanwhile, FS tries to maximize the classification performance. As FS is an optimization problem, meta-heuristic optimization algorithms can take place to achieve superior results in solving such problems. This paper proposes the Moth Flame Optimization (MFO) algorithm to tackle the FS problem. A new initialization method called opposition-based is proposed. Furthermore, a new update strategy is proposed to alleviate the local minima. The comparative results find that the proposed approach improves the MFO performance and outperforms other similar approaches.
Chatbot for Hospitality Service

To begin a decent life, medical care is vital. Be that as it may, it is exceptionally hard to the counsel the specialist if any medical problems. The proposed thought is to make a medical care Chatbot utilizing Natural Language Processing procedure it is the piece of Artificial Intelligence that can analyze the sickness and give essential. Chatbots can open up a doorway into the reality where Artificial Intelligence is getting progressed, particularly in the space of medical care, where patients will pass on their issues with a courier and get a discussion without burning through any time. The innovation at the center of the ascent of the Chatbot is natural language processing (“NLP”). The utilization of common language handling (NLP) strategies and their application to creating conversational frameworks for wellbeing analysis expands patients’ admittance to clinical information. The clinical visit bots working relies upon natural language preparing that encourages clients to present their concern about the wellbeing. The user can ask any close to home inquiry identified with medical care through the visit Bot without genuinely accessible to the emergency clinic. By Using Google API for voice-text and text-voice transformation. Inquiry is shipped off ChatBot and finds related solution and show answer on android application. The system’s significant worry behind building up this online stage is breaking down client’s feelings.
ASIS vs. LibAdalang

This paper compares the origins, features, and status of two different tools intended to facilitate static analysis of Ada programs: ASIS and LibAdalang. It stresses the differences in principles, features, and intended usages, and shows use cases where each is more appropriate.
Fault Prediction Method for Complex Distribution Network Based on Data Association Characteristics

In order to exploit the data value of cyber-physical system and improve the risk prevention and control ability of distribution network, a fault prediction method for complex distribution network based on data association characteristics is proposed. Through data preprocessing, the fault level is divided, and the relevant meteorological characteristics of distribution network fault are summarized. The feature weight is calculated and sorted by random forest algorithm, and the optimal fault feature set is obtained. Then the fault level is predicted based on multi classification support vector machine algorithm. Through the analysis of practical examples, the proposed method can achieve high prediction effect, and the prediction results can provide an effective basis for distribution network risk pre control, which has practical application value.
Conv-Vit-Hgr: A Transformer Based Hybrid Model for Hand Gesture Recognition in Complex Background

Nowadays, hand gesture recognition has become an alternative for human-machine interaction. Recognizing gestures is a challenging task specially from RGB images in complex background. Our model works on two stage (segmentation and classification) to recognize hand gestures from RGB images. Images are first segmented using a fully convolutional residual network and atrous spatial pyramid pooling. For the next stage we propose a hybrid model based on CNN and Vision Transformer (ViT) for recognising gestures from the RGB images and estimated segmentation maps. In this paper, we show a comparison of different classification models for classifying RGB images of hand gestures in complex background. Experiments are carried on publicly available dataset named OUHANDS dataset and it is observed that the proposed hybrid model; Convolution- Visual Transformer- Hand Gesture Recognition (Conv-ViT-HGR); produces good results in terms of accuracy and time taken to recognize each gesture.
Review on Phishing Attack Detection using Recurrent Neural Network

Phishing is a crime that involves the theft of personal information from users. Individuals, corporations, cloud storage, and government websites are all targets for the phishing websites. Anti-phishing technologies based on hardware are commonly utilised, while software-based options are preferred due to cost and operational considerations. Current phishing detection systems have no solution for problems like zero-day phishing assaults. To address these issues, a three-phase attack detection system called the Phishing Attack Detector based on Web Crawler was suggested, which uses a recurrent neural network to precisely detect phishing incidents. Based on the classification of phishing and non-phishing pages, it covers the input features Web traffic, web content, and Uniform Resource Locator (URL).
Strategic Diagnostics of Stress and Impulse Control for Second Order Change: Inclusion of Forgotten Effects in Diffuse Cognitive Maps

The determination of the strategic cognitive factors that intervene in the Second Order Change (SOC) are fundamental to achieve the transformation of organizational systems [6]. This work proposes a methodology based on the Fuzzy Cognitive Maps (FCM) model and the Forgotten Effects (FE) model, for the formalization and identification of these factors that intervene in the inference matrix. The inclusion of FE allows reducing inference errors in the matrix. The objective being to give certainty to the inference matrix in its state of reliability when applying FE, with the trade-off that this model only handles positive elements in the analysis matrix. This condition requires, first, to separate the elements into two matrices, one containing positive elements and the other, negative ones. The negative-element matrix is then temporarily transformed into a state of positive elements so that both matrices can operate the fuzzification-inferential-defuzzification process in the FE model [34, 16]. Once the matrices have been defuzzified, the matrix of temporarily positive elements returns to its original (negative) state and, finally, both matrices are added to obtain an adjusted inference matrix so it can be applied to the FCM model. A case study is presented where the main objective is to find the cognitive factors that intervene in the behavior of four companies’ staff, starting the procedure with two factors highly correlated: Stress Tolerance and Impulse Control. The main results represent the metrics of the absolute Hamming distances, laying the foundations for the formalization of the cognitive factors that hinder a SOC.
A Meta-review of Blockchain Adoption Literature in Supply Chain

Supply chains are increasingly adopting industry 4.0 technologies to meet exceeding stakeholder expectations. Blockchain technology offers an opportunity to facilitate the digital transformation of supply chains. Supply chains can benefit from the characteristics of blockchain including through transparency, traceability, and immutable data, to enable for example quality, sustainability, provenance, and safety. Adoption considerations for blockchain are important to ensure needs are met in the early adoption stages and further stages of deployment. This study aims to explore the adoption considerations for blockchain across supply chain domains reported in the literature, focusing on adoption factors and readiness. Research methodology used is a meta-analysis of literature review studies on blockchain adoption in supply chains to identify themes. The review identified 102 papers from four databases, and 33 are selected for analysis, identifying 64 blockchain adoption factors. Security, system integration, trust, scalability, costs, and traceability are found to be important blockchain adoption factors for supply chain. The adoption factors show a spread over a people-process-technology framework. Limitations of the research and areas for future research are highlighted.
Invest in Splitting: User Story Splitting Within the Software Industry

Context and Motivation: Requirements as captured in user stories often must be split to facilitate further work. There are many different theoretical descriptions on how user story splitting should be conducted in agile software development. However, current research does not give insight into how teams and team members conduct user story splitting in practice. Question/problem: Our research aims to decrease the gap between the theory and the practice by exploring why, how, and what the impact is of user story splitting for the participants within the context. Principal ideas/results: Through interviews and observations, we see indications of purposes which the majority of the participants have in common. Their practices are similar to those found in the literature but not specifically prescribed from any specific source. As a result of their practices, many participants describe that they receive an understanding that they previously did not have. The participants are also able to deliver results incrementally and estimate their work with higher precision. Contribution: We use our results to provide guidelines on user story splitting and to guide further research on the topic.
Research on Semantic Retrieval System of Scientific Literature Based on Deep Learning

Based on the background of the application of scientific literature retrieval, this paper puts forward that the current retrieval systems can not meet the semantic retrieval needs of the majority of scientific researchers. In order to solve this problem, the semantic retrieval effect is improved by deep learning. First, the deep learning technology is used to annotate the deep information of semantic knowledge and rich knowledge relationship in scientific literature. Then, the rich semantic information is displayed to users through the friendly retrieval interface. In the process of practical application, the system brings users more fine-grained knowledge level of association and navigation, and improves the semantic retrieval experience of the retrieval system.
Automated segmentation and classification of nuclei in histopathological images

Various kinds of cancer are detected and diagnosed using histopathological analysis. Recent advances in whole slide scanner technology and the shift towards digitisation of whole slides have inspired the application of computational methods on histological data. Digital analysis of histopathological images has the potential to tackle issues accompanying conventional histological techniques, like the lack of objectivity and high variability. In this paper, we present a framework for the automated segmentation of nuclei from human histopathological whole slide images, and their classification using morphological and colour characteristics of the nuclei. The segmentation stage consists of two methods, thresholding and the watershed transform. The features of the segmented regions are recorded for the classification stage. Experimental results show that the knowledge from the selected features is capable of classifying a segmented object as a candidate nucleus and filtering out the incorrectly identified segments.
When Does a Nonfungible Token (NFT) Become a Security?

Non-fungible tokens (NFTs) gained prominence in the news cycle during March 2021when $69 million was paid in a cryptocurrency known as ether for a single piece of unique digital art titled “Everydays – The First 5000 Days.” Regulation of NFTs is complicated by the fact that the technology encompasses so many varied applications. Therefore, it is the particular use of a given NFT that will determine its appropriate regulatory regime, since it may take the form of a collectible, data associated with a physical item, financial instrument, or a permanent record associated with a person, such as marriage license, or property deed. Just as in the case of digital art in the form of NFTs, our laws and regulations are in a constant struggle to keep pace with rapid introduction and diffusion of technological changes. Unlike digital or cryptocurrencies which are fungible, NFTs are not. The effective regulation of U.S. securities markets has a significant impact on capital formation, job creation, economic security, and growth of both the American and global economies. In recent years, the advent of the Internet has created novel regulatory challenges for the SEC.The focus of our article is how and when an NFT becomes a security for purposes of U.S. securities law. We proceed in six parts. First, we briefly explain the evolution of the digital world and emergence of virtual economies within. Second, we describe blockchain technology and the growth in virtual currencies. Third, is an explanation of NFTs along with some examples of their various uses. Fourth, we discuss when a nonfungible token is a security. Fifth, we explore SEC interpretations of when a crypto-asset is a regulatable security. And last, we conclude. Given the importance of U.S, securities markets in fostering job creation and global economic growth, we believe this work contributes to the understanding of this new technology and is of considerable interest to securities issuers, investors and the regulatory community.
Explainable Concept Graph Completion by Bridging Open-Domain Relations and Concepts

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Explainable Concept Graph Completion by Bridging Open-Domain Relations and ConceptsYue Zhang, Mingming Sun, Jingyuan Zhang, and Ping LiYue Zhang, Mingming Sun, Jingyuan Zhang, and Ping Lipp.675 - 683Chapter DOI:https://doi.org/10.1137/1.9781611977172.76PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Entity relations and concepts are the most critical information in knowledge-based systems. In traditional closed-domain knowledge bases (KBs), the entity relations and concepts are tightly bound together in the human-designed schema. The relations of entities (relations in the KB) are correlated with its concepts (entity types in the KB). However, the relations and concepts described by the closed-domain KBs are limited. When we extend the investigation to the open domain, we find that the relations of entities (from Open Information Extraction data sets) and the concepts of entities (from large-scale concept graphs) reside in the different data sources, and there is no connection between them. In this paper, we proposed a matching network-based concept graph completion model, which leverages open-domain relations to represent the query entity to predict the entity's concept, where relations are extracted from the open-domain corpus (e.g., Wikipedia). By comparing with other neural baselines, which leverage the whole sentences, we show that our model gets superior performance. Furthermore, we use the open-domain relations as the explanation basis and build an explanation model to answer the question “why an entity belongs to a concept”. The model gives clear and informative explanations with high relevance to human understanding. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:Concept Graph Completion, Open IE, Interpretability
A Robust Reversible Data Hiding Framework for Video Steganography Applications

Reversible Data Hiding (RDH) is a special form of data hiding approach for data integrity and confidentiality protection where the secret image bits (SI) are embedded into Cover Media (CM) by altering its intrinsic pixel attributes.However, in RDH the CM along with the secret message is recovered at the end of computing phase.However, despite of its potential use-cases for enhancing the embedding performance, when it comes to security for various network standards, the traditional RDH mechanisms cannot fully comply with the standards for different set of attacks during the bit-stream transmission scenarios.Therefore, the proposed study contributes towards a computational framework of a robust RDH framework for Video Steganography (VS) which is modeled and simulated under various attack effects and the observation outcome are produced in before and after attack situations to justify the improvement over Embedding Capacity (EC) and Peak Signal-to-Noise Ratio (PSNR) performance for both CM and secret message unlike traditional difference expansion-based methods (DE).The outcome of the study shows that the formulated RDH method not only achieves better reversibility at lower cost of computing but also ensures effective PSNR and imperceptibility outcome for both CM and secret image.
Image Quality Enhancement : A Linear Programming Approach

Picture improvement has been discovered to be perhaps the main vision applications since it can upgrade the computerized pictures with the goal that the outcomes are more appropriate for show or further picture examination. To work on the nature of computerized pictures there are remarkable strategies that have been proposed. The goal is to manage picture handling and its major strides after that we had zeroed in on the diverse picture upgrade procedures. Since picture clearness is effectively influenced by lighting, climate, or gear that has been utilized to catch the picture. These conditions lead to loss of data. The principle motivation behind picture improvement is to bring out detail that is covered up in a picture or to expand contrast during a low difference picture. It gives countless decisions for working on the visual nature of pictures. Its article is to dissect the specific picture attributes for examination, end and further use.
Construction of College Students’ Innovation and Entrepreneurship Training System Under the Background of Educational Informatization

College Students’ innovation and entrepreneurship education is the main direction of modern higher education and a key &#x0D; step to promote local education. Combined with the main characteristics of university education, this paper reviews the problems &#x0D; related to innovation and entrepreneurship, and puts forward the rapid development based on educational informatization, so as to &#x0D; develop an innovation and entrepreneurship training system, increase students’ innovative spirit in the University, and lay a good &#x0D; foundation for university innovation and entrepreneurship education.