Reviewerly/exHarmony huggingface dataset:
Crossing edge minimization in radial outerplanar layered graphs using segment paths

Crossing minimization is a natural problem in graph drawing, which consists of drawing it in the plane with a minimum number of crossings on the edges. We present an algorithm to minimize the crossing edges in radial layered graphs. The algorithm determines the optimal location of nodes in their corresponding circle layer and consists of two phases. In the first phase, a counterclockwise rotation of concentric circles is performed; in the next phase, edges are converted into segment paths. Unlike other algorithms with circular drawings, our algorithm does not require the insertion or removal of nodes and edges; only rotation and path construction operations are utilized. Two versions of the algorithm were created; the exhaustive version tests all the possible paths, while the random version tests a few number of paths. The goal is to minimize the crossing edges to obtain a clear visualization using three criteria: rotation, crossing edge detection and segment path construction. The algorithm has been successfully implemented in 30 instances of graphs with 20, 35 and 50 layers, where crossings are minimized in 86–93%, 93–96% and 95–97%, respectively.
Regulation of the Use of Information Technologies in Higher Education in the Russian Federation

The article outlines the legal basis for the use of information technology in higher education. Digital technologies are considered, which are actively used both in the practice of Russian universities and foreign ones. At the same time, it is noted that in foreign countries the process of digitalization of educational services is quite broader than in Russia. Within the framework of the domestic legal order, only the basic development of both legal regulation in this area and the practical implementation of the researched direction takes place. Particular emphasis is placed on modern problems of using information technologies in the field of higher education, which are typical for domestic practice and legal regulation. The author identified problems of a legal and organizational nature. As conclusions, directions for improving legal regulation and practical implementation of information technologies in the field of higher education in Russia are formulated. The author proposes to resolve possible problems of an organizational, technical and legal nature, while taking into account the existing experience of distance learning in the state.
A coarse-to-fine vehicle detection in large SAR scenes based on GL-CFAR and PRID R-CNN

With the development of deep learning and traditional algorithms in synthetic aperture radar (SAR) image target detection, vehicle target detection under large scenes in SAR images remains a challenging research topic. This is due to various factors: the difficulty of accuracy and speed of target detection; the complexity of background interference in the large scene; partition of small block object detection; and the slow window traversal process of vehicle detection method in large scene under deep learning. To address these limitations, the key is to establish a background-target adaptation model to determine the target position and improve the performance of algorithm detection. A vehicle detection algorithm based on GL-CFAR and PRID RCNN is proposed in this study by combining traditional algorithms with deep learning algorithms. GL-CFAR is used to provide fast position coarse detection of vehicle targets in large SAR scenes, and the proposed PRID RCNN network can effectively adapt to vehicle detection in SAR large scenes, which greatly improves the detection of vehicle under different backgrounds. By adding the Pyramid Real Image Denoising Network (PRID) module to the Faster RCNN network and using the K-means method to reduce the number of detection anchor boxes, reduce background interference, and achieve feature enhancement. Experiment results show that the algorithm proposed in this study has high accuracy and processing speed.
Vocal Composition in Creating the Commedia dell’Arte Characters

"The paper presents a possible approach to the construction of a comic character from both a vocal and a verbal point of view, by using various language disorders. For the scenic procedure of language composition, voice and pronunciation as well as rhythm and influence disorders can be important tools for students in their creating a comic character, since these techniques have a significant potential also outside the Commedia dell’Arte. Hence, conscious training and practice of speech/language disorders, such as dyslalia, as well as of rhythm and fluency disorders, develops and improves both the students’ voice usage abilities and their vocal creativity, so that they can manifest themselves artistically in a more expressive way. Keywords: acting, character, training, speech/language disorders, rhythm and fluency disorders, Commedia dell’Arte. "
Role of Blockchain Technology in Creating Blue Ocean Strategy for Banking Products and Services

Purpose: The banking sector took the initiative to improve it by releasing a new blockchain application. This innovative approach connects customers from various geographic locations and also gives them a sense of banks’ global presence. Competition is one of the most important market factors because consumer tastes, interests and demands constantly change, making it difficult to meet these problems.Methodology: Blockchain develops a Blue Ocean Approach in this competitive climate by enticing numerous market segments and giving the financial industry a fresh perspective that benefits the potential consumer. This chapter illustrates how the Blue Ocean Approach can be unlocked by a disruptive technology called blockchain, which generates value innovation and renders the competition obsolete.Findings: This paradigm shifts the emphasis away from the present competition and generates value and demand for the product. The researcher advises that the Blue Ocean Strategy in retail banking, which uses blockchain technology, works very well since it eliminates cut-throat competition and favours costs, operations, and meeting financial targets on time.Practical Implications: The study focuses on the bank’s real-world application of the Blue Ocean Strategy and the discovery of sustainable marketing strategies that will aid in their pursuit of innovation. It also highlights the elements introduced in the banking industry to support innovation and the development of long-lasting markets.
Checking the Effectiveness of Blockchain Application in Fraud Detection with A Systematic Literature Review Approach

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.
Learning asymmetric encryption using adversarial neural networks

We propose a multi-agent adversarial neural networks model where a sender (Alice) and a receiver (Bob) are able to learn to use a pair of public/private keys in order to protect their communication from one or more attackers/eavesdroppers. Existing work in the field required shared symmetric information between Alice and Bob before initiating the training process. To the best of our knowledge, this is the first work in which Alice and Bob with asymmetric information can train themselves to protect their communication. Our initial model setup contains five agents: sender Alice, receiver Bob, eavesdropper Eve and two neural networks (we call them public keys generator and private keys generator) that, based on a (secret) random noise from Bob, will generate a pair of public/private keys that allows Alice to encrypt a message with the public key and Bob to decrypt the message with the private key while preventing Eve from decrypting the secret message using the public key. We show that the neural networks are able to establish a communication and secure it from Eve. Finally, we consider adversaries stronger than Eve to model leakage attacks, chosen plaintext attacks (CPA) and test the distinguishability between ciphertexts. The last three experiments show that neural networks (with asymmetric information) can secure the communication providing stronger security guarantees and resilience to leakage attacks which may include leakage from the private key.
Sequence Graph-Based Query Auto-Suggestion (SGQAS)

Query autosuggestion or auto-completion is a query prediction service that returns suggested queries for text-based queries when users type in the search box. It is a search-assistant feature of almost all search engines that helps users complete the queries without typing the entire search query. The process of query auto-suggestion typically involves analyzing the user's partial query and generating a list of suggestions based on factors such as popular search terms, the user's search history, and the search context. The suggestions are then displayed to the user in real-time, often in a drop-down menu or other interface types, allowing them to select and refine their search query easily. This chapter proposes a content-based query auto-suggestion using a graph-based word sequence representation of documents using a knowledge graph. It uses the whole sequence of all entered query terms to retrieve the names of all nodes connected to the end node of the entered path sequence of query terms to provide user suggestion queries.
Smart ATM With Tracking of Criminals Using Novel Di-Pattern and C-LDP (Combined Local Directional Pattern)

Automated teller machine (ATM) surveillance system is a smart system based on image processing that incorporates various sensors and machine learning algorithms to continuously monitor its surroundings for suspicious activities like physical attack. To prevent these attacks, there is a need to find the criminal immediately and save the person's life. In this chapter, two ways are followed to detect the criminals. The first one is weapon detection; the second one is criminal facial identification. A novel magnitude-based feature extraction technique creates the magnitude pattern for the image using Di-Pattern (DiP). Di-Pattern utilizes both horizontal and vertical derivatives to create a unique feature vector of the objects. Based on thresholding, weapons are detected. Once weapon detection as well as facial identification is done, it gives the alert. This system makes its effective usage in the remote locations where threatening is more, thus providing security. The proposed method achieves better accuracy than the other existing methods.
Track A Missing Child Using Local Binary Pattern Histogram Based Face Recognition Method

In our society, a countless number of children are missing every-day. Even though missing cases are getting filed against them in nearby police stations but it is complicated to trace them through investigation as it requires lot of efforts and it is time consuming also it becomes quite challenging for police to find them and it’s really getting impossible to find them in most cases. So to make it convenient we planned to design a website for “Tracking the missing child using Face Recognition ”, which makes the task quite simpler. The main intention of this project is to reunite the missing children with their parents, so we are going to design a web page where one can be able to upload the missing child details and store it in database. If the child who is missing is identified on road by any random person, then he/she will upload the photo of that child on web page then our system can be able to detect that lost child using face recognition algorithms. Whenever the system confirms the matching then it will set up custom alerts along with location and send them directly to family members and concerned investigation officers . And if the match is not found , then the person will be provided with the option of registering child photo as a new entry to our database with the location they found. So, in this way we can find the lost children in more easy and convenient way. In this model, an automated facial recognition system for Missing Child database was proposed using known Haar feature-based cascade classifier. This system will be able to detect face and recognize face automatically in real time. In our model we are going to use Face Recognition module of python.
Excavated Earth Area Maximization as a Basement and Outdoor Living Space on a Sloped Terrain Housing

Abstract The cut-and-fill technique frequently creates a space for housing on sloped terrain. Some developers use the contours of the land on sloped terrain for garden areas instead of developing it into space to reduce production costs when building houses. By developing structures for building reinforcement, this research seeks to use the excavated earth area in the sloped terrain as storage space and outdoor living space. A single case study in a Malang City home situated on a sloping terrain served as the research approach. Primary data were collected through field surveys and customer interviews to determine the design of the room based on space requirements. Secondary data for this study also comes from a literature review. The first step for the architect is to plan the room’s layout following the client’s requirements. The next stage is to choose the foundation for the project by taking the soil’s structure and condition. The final stage is also decided upon the outdoor living space’s finishing material and the furniture for the outdoor room. The final result indicates that the excavated earth area is beneficial as a storage place at the bottom and an outdoor living area at the top. Outdoor living spaces are beneficial because they provide extra space and address home design issues by reducing the spread of airborne viruses like Covid-19. Maximizing excavated earth as a warehouse space and functioning as an outdoor living space is one of the applications of sustainable design in architecture.
Twitter Sentiment Analysis using Machine Learning

Social media makes it easier for people to communicate with one another online. Social media encompasses a wide range of applications and platforms, including Facebook for entertainment, Instagram for youth, Twitter for social and political, and YouTube, that let users share information, communicate online, and create communities. More than 4.7 billion individuals, or nearly 60% of the world's population, utilise social media. Twitter is a popular social media platform where users may express their feelings and opinions. In order to determine user sentiments, this Twitter sentiment analysis study uses sentiment analysis to data from tweets on the social media site. A whole new set of problems, such as the usage of slang and acronyms, are brought about by the relatively small size of the tweet format. Our objective is to carry out research on Twitter sentiment analysis while outlining the methodology, models, and generalised Python-based approach that was employed.
Polynomial combined first-order rewritings for linear and guarded existential rules

We consider the problem of ontological query answering, that is, the problem of answering a database query (typically a conjunctive query) in the presence of an ontology. This means that during the query answering process we also need to take into account the knowledge that can be inferred from the given database and ontology. Building, however, ontology-aware database systems from scratch, with sophisticated optimization techniques, is a highly non-trivial task that requires a great engineering effort. Therefore, exploiting conventional database systems is an important route towards efficient ontological query answering. Nevertheless, standard database systems are unaware of ontologies. An approach to ontological query answering that enables the use of standard database systems is the so-called polynomial combined query rewriting, originally introduced in the context of description logics: the conjunctive query q and the ontology Σ are rewritten in polynomial time into a first-order query qΣ (in a database-independent way), while the database D and the ontology Σ are rewritten in polynomial time into a new database DΣ (in a query-independent way), such that the answer to q in the presence of Σ over D coincides with the answer to qΣ over DΣ. The latter can then be computed by exploiting a conventional database system. In this work, we focus on linear and guarded existential rules, which form robust rule-based languages for modeling ontologies, and investigate the limits of polynomial combined query rewriting. In particular, we show that this type of rewriting can be successfully applied to (i) linear existential rules when the rewritten query can use the full power of first-order queries, (ii) linear existential rules when the arity of the underlying schema is fixed and the rewritten query is positive existential, namely it uses only existential quantification, conjunction, and disjunction, and (iii) guarded existential rules when the underlying schema is fixed and the rewritten query is positive existential. We can show that the above results reach the limits (under standard complexity-theoretic assumptions such as ) of polynomial combined query rewriting in the case of linear and guarded existential rules.
Search Engine on Vedas

The Search Engine on Vedas Project is an effort to create a comprehensive and user-friendly search engine for the vast corpus of Vedic literature. The project involves digitizing and indexing a diverse range of Vedic texts, including hymns, rituals, commentaries, and translations, and developing a user-friendly interface that allows efficient searching and browsing. The search engine employs advanced search algorithms, such as natural language processing and machine learning, to enable users to search for specific keywords, phrases, or concepts within the Vedic texts. The interface is designed to be intuitive and customizable, with options for filtering and sorting search results based on various criteria, such as author, type of text, language, and publication date. The project also includes interactive features, such as multimedia resources, glossaries, and forums, to facilitate a more engaging and immersive user experience. Additionally, the search engine aims to provide a platform for collaborative research and knowledge-sharing among scholars and enthusiasts of Vedic literature.
Multi-modal simultaneous machine translation fusion of image information

Simultaneous translation is to translate a sentence before people finish it, to understand the speaker's intention in real-time. At present, simultaneous machine translation still relies on text-to-text data resources. However, the output information from the encoder side is used for the decoder as the input data recourse in the pure text translation system. This information is only derived from the text content, and the input information is single, causing a shortage of decoding information at the decoder and the vocabulary is missed in translation. The translator will also visually capture the information of the surrounding scenes to assist himself in the translation work, based on this feature, we propose a multi-modal simultaneous machine translation of fusion image information. We extract information from the image, add the information to the decoder side of the translation system, increase the input data resource of the decoder, and help the system improve the translation quality. We use the Multi30K dataset for experimental verification. Compared with the translation system of plain text, the method we propose can translate more complete sentences, richer content, and better translation results.
Learning to decode to future success for multi-modal neural machine translation

Existing only-text NMT (neural machine translation) systems can benefit from explicitly modelling target future contexts as recurrent states. However, the modelled target future context is implicit in the conventional only-text NMT as the target is invisible in inference. For the Multi-modal Neural Machine Translation (MNMT), the visual annotation presents the content described in the bilingual parallel sentence pair, so-called multi-modal consistency. This consistency provides an advantage that future target context can be simulated in visual features. This paper proposes a novel translation model that allows MNMT to encode the future target context from the visual annotation in auto-regressive decoding. Our model uses visual-target consistency to enhance the target generation. Moreover, we use the multi-modal consistency that fully uses the visual annotation to encourage the semantic agreement between bilingual parallel sentences and the pivoted visual annotation. Empirical results on several recent multi-model translation datasets demonstrated the MNMT model which we proposed significantly improved translation performance on a strong baseline, especially achieving new state-of-the-art results on all three language pairs with visual annotations. Our code will be available after acceptance.
Cave Meal

Cave Meal is an end user support and android application consultation service. Here we propose a system that provides users with good quality home - made food and spacious rooms for their accommodation. To provide better client relationship management for these service providers. This system aims to build an environment where various users who need these basic service-help can consult service providers, tell them their requirements and discuss expenses. A facility where large quantity of food, different types of accommodations acquiring a huge area which is in minimal rent intended for individual services and consumption are routinely provided. “Cave Meal” provides an entire ecosystem aimed to help users have a quality lifestyle and solve day-to-day survival issues with help of our service-providers. The user can consult various service-providers to fulfil the basic requirements under one platform. This project examines the aspects of the hospitality industry. In the 21st century the use of the internet, computers and other electronic devices have made handling different jobs and aspects of management very easy.