Reviewerly/exHarmony huggingface dataset:
The deaf’s e-learning challenges during COVID-19: Moroccan case study

Aim: The lockdown that followed the outbreak of the COVID-19 pandemic necessitated implementing several emergency measures, including some in the realms of education and social services.Classes at all schools have been canceled and replaced by online instruction.Health and protection decisions have also resulted from the pandemic, significantly affecting many people's lives, especially those with particular requirements.By adapting and improving their innovative methods, Association Marocaine pour Enfants Sourds (AMES) provided effective remote instructions.In this paper, we look into how deaf students in Morocco coped with online education during the country's 2011 lockdown.Methodology: The challenges and communication barriers that deaf students in Morocco face are first examined and discussed.After that, we look into why, unlike most deaf schools around the world, AMES shut its doors on its deaf students and left them out.We used a qualitative and quantitative approach by conducting a survey and a thematic analysis of the results.Findings: Issues with education, communication, digital illiteracy, and e-learning absenteeism were all highlighted in this research.After that, some suggestions are made.Implications/Novel Contribution: The challenges faced by deaf students in Morocco as they transitioned to online education during the pandemic are investigated in this study.In Morocco, where COVID-19 has had a significant impact, e-learning is still in its infancy compared to other countries.
Paragraph Comprehension from the Perspective of Conjunctions and Structure under Structuralism

People frequently struggle with comprehending the text they read and require more intensive support to develop proficiency in reading comprehension. Some find it perplexing observing all the elements and constituents of the paragraph theory. Good readers, however, can understand what a paragraph is by studying the conjunction relationships between sentences and group of sentences in the paragraph and can also understand the organization of the text by analyzing the conjunction relationships in a paragraph or a text when they deal with a written text. Such analysis of the conjunction relationships in a paragraph or a text can be used as a basis for understanding how the author organizes and presents materials. The inspiration of structuralist view of education is that students need to learn and discover the structure of knowledge. The research methods of the paper are ones of knowledge, experience, observation, logical reasoning and keen analysis of English examples or materials. This paper provides guidance for the skills of logical relationship comprehension, text structure determination and main idea dentification, and introduces some practices of using the skills. These skills have brought us some findings: We can use the conjunction relationships between the sentences in a paragraph to determine the structure of the paragraph, whether they are signaled or unsignaled, including finding the main idea; The contextual clues can be adverbs, pronouns, nouns, verbs, and preposition groups besides conjunctions; Items in one type of relationships are of equal weight and one sentence in another type of relationships is given more importance than the others; There is a relationship between the type of conjunction relationship involved and the location of the topic sentence; The role of the main idea or topic sentence is not in whether it always provides important information, but in its organizational function. These findings guide readers to apply the knowledge of paragraph organization system to help them deal with English texts.
Small Text Extraction from Documents and Chart Images

&lt;p&gt;Text recognition is an important area in computer vision which deals with detecting and recognizing text from an image. Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied to text with small font size like the text data of chart images, the recognition rate is less than 30%. In this work, Our aim is to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We observed that the text recognition rate increases by 24% by applying the proposed method, which involves super resolution and character segmentation using projection profile followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.&lt;/p&gt;
Adaptive Split-Merge Sampling of Dirichlet Mixture Models

&lt;p&gt;Draft of manuscript.&lt;/p&gt;
A development of visual-feedback automatic control for robotic manipulator

In this work, an automatic robotic manipulator control based on visual information for object tracking is presented. The tracking process employ Recurrent Neural Network and Kalman filter techniques to predict the location of target object which is used to calculate movement of the robot. A manipulator control process was developed to provide consistent motion and reduce vibration. We also propose an object analysis based on image processing technique for object size determination. We implemented the proposed system with an actual robotic manipulator with a gripper for experiments. The experiments shown that object size estimation method results the maximum accuracy at 1.13%. The proposed object tracking and manipulator control process achieved vibration reduction 3.05 times compared to simple control method and reduced the time of end-effector reaching the target object by 1.29sec.
Voice Assistant ZIA using HDG algorithm

Abstract: In this computerized world, machine human interaction system was more common. For that kind of interaction, we achieve this voice assistant using machine learning and artificial intelligence. Using this we deploy executable code which works according to the user’s voice command. Voice assistants are the kind of software that use voice recognition technology, natural language processing and AI to response humans. using this technology, the device synthesizes the user’s message, breaks it down, evaluates it, and offers a meaningful response in return. So far google assistance, apple’s siri, amazon’s alexa, microsoft’s cortana were being well known assistance but they were all developed for specific platforms. We developed a software that can be bridge with all the platforms, as we did for the windows machine because we mainly concentrated to achieve some more features to give better experience. The purpose of this paper is to build the voice assistant with the speech recognition algorithm, interactional graphical interface and has a voice cloning technology to perform a task. Keywords: Artificial intelligence, Machine learning, Natural language processing, Speech Recognition, Graphical user interface, Voice cloning
A review of Knowledge graph link prediction using graph neural networks

Abstract Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). In order to use KGs in downstream tasks it is desirable to predict missing links in KGs. In this review we will study the current approaches and the challenges exists..
PERHITUNGAN VOLUME BATUGAMPING MENGGUNAKAN METODE PENAMPANG

In the mining industry, it is very important to estimate the amount of mineral resources. This estimation aims to find out the volume of resources at the certain mining survey area. The estimation method used is the cross-section method, which is based on the rule of gradual changes and the rule of nearest point. The estimation using the rule of gradual changes shows that the total volume of limestone resources is 31,420 m³, and the volume of overburden is 27,572.6 m³. Meanwhile, the estimation using the rule of nearest point shows that the total limestone resource is 125,680 m³, and the overburden volume is 30,290 m³.
Adaptively coping with concept drifts in energy time series forecasting using profiles

Accurate electrical load forecasts are necessary to stabilize the electricity grid, e.g., by optimally operating energy storage systems or using demand-side management. However, an implicit assumption of most load forecasting methods is that future data looks similar to past data. Unfortunately, this assumption often does not hold; for example, recruiting new staff or a pandemic can lead to demand changes resulting in so-called concept drifts in the underlying data. Most methods for coping with such concept drifts rely on computationally expensive retraining. We propose a new method for coping with concept drifts based on profiles and a linear regression model that avoids expensive retraining. Compared to a simple baseline and five state-of-the-art benchmark models on two different data sets, our method has lower computational costs and higher forecast accuracy, making it especially interesting for smart grid applications.
Creative hobby S.F. Platonov-school student

Sergei Fedorovich Platonov (1860-1933) Russian and Soviet historian, teacher. Corresponding member of the St. Petersburg Academy of Sciences in the Historical and Philological Department, full member of the Russian Academy of Sciences. At first, he did not think about devoting himself to history, he wrote poetry and dreamed of a career as a professional writer.Much in the life of S.F. Platonov of the last years of the gymnasium period remains unknown. It is to this period that researchers have turned today. And it turned out that at that time young Platonov had diverse interests, primarily in the field of poetry, theater hobbies. It was during these years that there were meetings with people, communication with whom had a great impact on the formation of his worldview and hobbies. Such acquaintances should include meetings with E.N. Kalaidovich, V.N. Berkut, V.F. Kenevich, a high school literature teacher widely known in scientific circles for his works in the field of wing studies. It is about close communication with these people, already being a mature man, a world-renowned scientist, S.F. Platonov will write many warm lines in his biographical works. Not only long conversations were conducted with V.F. Kenevich, he was initiated by a high school student of the Bychkov gymnasium into his poetic experiments. And acquaintance with individual poems caused him enthusiastic responses.
DESIGN FOR EMOTIONAL DURABILITY: THE CASE OF HOUSEHOLD LIGHT DESIGN

Designers’ hands-on experience in research through design process contributes them to produce&#x0D; and share knowledge from their own design practices. In this study, the primary purpose was exploring&#x0D; sustainable design concepts and developing solutions for improving the product lifespan of household light&#x0D; with research through design process. With a particular focus on “emotionally durable design”, “product&#x0D; personalisation”, and “localisation” were considered fruitful concepts. The do-it-yourself (DIY) approach,&#x0D; which means that users participate in production, and the halfway product rationale, which leaves space for&#x0D; users to complete the production, were the concepts used in personalisation. The outcome of the research&#x0D; through design process was a wall lamp manufactured with different scales of production, involving&#x0D; personalisation features facilitated by DIY and halfway product rationale. For “localisation”, different scales of&#x0D; production opportunities were explored, and a design kit as a halfway product was considered as a sustainable&#x0D; design solution. DIY approach was benefited for producing complementary lace lampshade. In the design&#x0D; for sustainability framework, it is seen that experimenting and engaging with locally available materials and&#x0D; manufacturing capabilities in the research through design process nurtured developing sustainable design&#x0D; solutions.
Generative time series model based on encoder-decoder architecture

Encoder-decoder neural network models have found widespread use in recent years for solving various machine learning problems. In this paper, we investigate the variety of such models, including the sparse, denoising and variational autoencoders. To predict non-stationary time series, a generative model is presented and tested, which is based on a variational autoencoder, GRU recurrent networks, and uses elements of neural ordinary differential equations. Based on the constructed model, the system is implemented in the Python3 environment, the TensorFlow2 framework and the Keras library. The developed system can be used for modeling continuous time-dependent processes. The system minimizes a human factor in the process of time series analysis, and presents a high-level modern interface for fast and convenient construction and training of deep models.
A Semantic Study on Emerging Risk and Security Management in Cloud Computing

Abstract: Cloud computing is a flexible, cost-effective, and proven delivery platform for providing business or consumer IT services over the Internet. However, cloud Computing presents an added level of risk because essential services are often outsourced to a third party, which makes it harder to maintain data security and privacy, support data and service availability, and demonstrate compliance. One of the most significant problems that has hampered the expansion of cloud computing is security. It complicates data privacy, and data protection continues to have an impact on the market. Users must be aware of the dangers of data breaches in the cloud. This study focuses on cloud computing security concerns. Keywords: Cloud Security, Cloud Threats, Cloud Computing, Cloud Risk and Management
PULP: Extreme Energy Efficiency for Extreme Edge AI Acceleration

The next wave of pervasive AI pushes machine learning (ML) acceleration toward the extreme edge, with mW power budgets, while at the same time it raises the bar in terms of accuracy and capabilities, with new ML models being propose on a daily basis.To succeed in this balancing act, we need principled ways to walk the line between flexible and highly specialized ML acceleration architectures.In this talk I will detail on how to walk the line, drawing from the experience of the open PULP (Parallel Ultra-Low Power) platform, based on ML-enhanced RISC-V processors coupled with domain-specific acceleration engines.
Motion Attitude Prediction Algorithm Based on Data Mining and Intelligent Motion Training Model Construction

Data mining refers to the process of obtaining information from a huge amount of data through algorithms, which provides reception support for people to apply data from simple problems to extracting and discovering knowledge in data. This study examines traditional data mining methods and their applications in order to increase the timeliness and usability of data extraction algorithms. Through data mining and recognition of motion gestures, the most accurate algorithm data are given. This study begins by looking at data mining classification methods and associated algorithms. Then, using a neural network, a motion attitude prediction is created and the neural network is used to test the algorithm. The experimental results show that the single-stage neural network and the bipolar neural network can achieve an average accuracy of 87.9%, while for the WTO model, it can achieve an average accuracy of 95.7%.
Fashion Recommendation System Using Machine Learning

Abstract: Lately, the business and dress have shown quick improvement in design. An online business website with a wide scope of choices requires a decent method for sending, sort, and successfully convey items and data to clients. Online display is a program that is utilized to feature and sell clothing at closeout, paying little heed to identity, orientation, or different qualities. Individuals can join online to become individuals, and everybody can send an advanced duplicate of their work by classification. They can organize their drawings at sell off or at assigned costs. Every client can set up their own store to get their number one plans and access them with next to no issues. Picture based directing frameworks (FRSs) draw in an ever-increasing number of individuals to quick design retailers since it permits shoppers to shop. With the progression of innovation, the best-in-class innovation offers numerous amazing open doors for picture handling, investigation, sequencing, and detachment. In spite of the numerous open doors, the quantity of investigations on this theme is little. Potential investigations won't think about the plan of the innovation and the separating strategies. As the creators know, this is the primary point in the exploration of the new plan of the drawing framework and the strategy for separating. Moreover, this audit inspects the different potential ways that can be executed in the future to give a model. This article will help specialists, researchers, and professionals intrigued by AI, PC vision, and dress deals to figure out the idea of various advising frameworks.
CLASSIFICATION OF BLOOD DONOR DATA USING C4.5 AND K-NEAREST NEIGHBOR METHODS (CASE STUDY: UTD PMI BALI PROVINCE)

Classification of blood donor data at UTD PMI Bali Province by applying the C4.5 and K-Nearest Neighbor algorithms. The number of blood donor data donors is 34,948, of which 90% of the data, namely 31,454 is used as training data. Meanwhile, 10% of the data, which is 3,494 data, is used as the implementation of data testing using the Orange application. C4.5 obtained an accuracy score of 92.9%, F1 of 92.2%, Precision of 93.1%, Recall of 92.9%, specificity of 68.2%. While K-nearest neighbor obtained an accuracy score of 91%, F1 of 90.1%, Precision of 90.8%, Recall of 91%, specificity of 63%. With the AUC (Area Under Curve) value for the C4.5 algorithm is 0.875 and the K-nearest neighbor is 0.813 Good Classification. The results of the evaluation using the confusion matrix C4.5 obtained an accuracy score of 92.6%, F1 of 95.7%, Precision of 99.4%, Recall of 92.4%, specificity of 96%. While k-nearest neighbor obtained an accuracy score of 90.9%, F1 of 94.6%, Precision of 98.4%, Recall of 91.2%, specificity of 88.4%. Based on the evaluation of the confusion matrix and the ROC Analysis Graph, the C4.5 algorithm obtained higher results than the K-Nearest Neighbor algorithm. Based on the data on the characteristics of blood donors at UTD PMI Bali Province, it shows that the gender is male, Badung area, Age 20 to &lt; 30, the occupation of private employees dominates in blood donation.
Recent Research Topic Detection and Analysis using IFCM

Abstract Computational geometry has become one of the most important technologies in computer science domain. To assist decision makers establish action plans based on computational geometry, it is an emergency task to analyze recent topic in computational geometry technology. Moreover, existing methods not concentrate to identify the recent topic in computational geometry topic. Hence, in this paper, improved fuzzy c means clustering is developed to identify the recent topic from the computational geometry. The fuzzy c means clustering is utilized to identify the topics from the collated resources. The fuzzy c means clustering process can be improved with the help of mayfly algorithm to find optimal membership index value. The proposed approach is a combination of word to vector and clustering approach. This proposed approach is to perform an annual trend analysis in computational geometry. The proposed method is implemented in MATLAB and performances are analyzed with statistical measurements such as accuracy, precision, recall, specificity and F_Measure respectively. The proposed method is compared with the existing methods such as K nearest neighbor (KNN), Fuzzy c means clustering with Particle Swarm Optimization (FCM-PSO) and Fuzzy c means clustering with Firefly Algorithm (FA) respectively.
Artificial Intelligence for Insurance Industry: A Survey

Abstract The cost of insurance is rising all over the world, with frequent proposed policies and fraud playing a role. In recent years, AI techniques have been increasingly used for numerous insurance tasks. However, AI researchers frequently struggle technically to understand the vast and sophisticated domain knowledge as well as rapidly changing literature. Although several studies have already been published on the use of artificial intelligence in the context of specific insurance tasks, our work provides a unified general survey of the existing literature, incorporating various AI techniques into all major insurance tasks by providing a systematic literature survey of the rapidly growing literature on artificial intelligence in the insurance industry. From 2017 to the present, we focus our research on machine learning, big data, blockchain, data mining, chatbot theory, and its application in fraud detection, insurance policy, claim prediction, rick prediction, and other areas to systematically introduce existing work in the insurance sector using AI techniques.
E-library management system

Online Library Management System is a system which maintains the information about the books present in the library, their authors, the members of library to whom books are issued, library staff and all.. Owing to the advancement of technology, organization of an Online Library becomes much simple This computerization of library helps in many instances of its maintenances
On Asymptotic Convergence Rate of Evolutionary Algorithms. Discrete and Continuous Optimization

&lt;p&gt;Asymptotic Convergence Rate (AsCR) measures how fast an iterative optimization method converges to the global solution as the number of iterations increases to infinity. If less than one, it determines fast exponential convergence mode. We provide general theory for the AsCR in case of both discrete and continuous state space models. We start from showing that the AsCR may be defined by the geometric mean of the subsequent one step ratios of the expected fitness error and thus this paper extends the previous studies on Average Convergence Rate, a recently introduced measure. We show that in discrete optimization those convergence measures do not depend on the values of the fitness function but only on the algorithm itself and that the convergence rate in the solutions’ space is the same as the convergence rate in the fitness space ( any particular conditions on the optimization algorithm like the Markov property are not necessary). Next we focus on continuous optimization and we analyse how the change of fitness function may influence the value of AsCR. Surprisingly, even in continuous case there are some limitations on how much the change of the fitness function may influence the convergence rate of the process and we provide lower and upper bounds based on the asymptotic relation between the optimization process and the fitness functions. We also discuss examples and applications, including some specific instances of Evolutionary Algorithms and Simulated Annealing. In particular, we show that some algorithms cannot converge exponentially fast for any nontrivial fitness function.&lt;/p&gt;