Reviewerly/exHarmony huggingface dataset:
A Computer Vision Based on Vehicle Detection and Counting System Using Sensor Security

Unique—Vehicle identification and counting framework, which assumes a significant part in keen transportation framework, and the administration of the progression of traffic. In this real, we refuse a video metric technique for the place and whole of vehicles self-reliant on PC vision innovations. The proposed lack employs the foundation deduction strategy is to see closer view objects into the video. For a more precise recognition of moving vehicles, and afterward there are some PC vision strategies, including tear an opening to fill, and keeping in mind that the morphology of the exercises. At long last, the vehicle, the statistics will be done with the assistance of a virtual discovery zone. The trial results show ordinarily the exactness and precision of the deliberate vehicle counting framework, it is around 96%.
Influence of Digital Assistive Technologies Used in Higher Education on the Development of Individual Educational Strategies among Students with Disabilities

The digitalization of education significantly facilitates access to the educational process for people with special needs. The study aims at determining the main possibilities of using digital technologies as a means of supporting the education of students with disabilities. The research strategy requires both quantitative and qualitative methods to understand what university staff considers effective when using digital technologies for students with disabilities with due regard to their experience, knowledge and abilities to meet the needs of such students. To achieve this objective, the authors used a set of theoretical and empirical methods: theoretical, empirical and numerical methods. The main research method was an online survey. Based on the online survey of teachers, IT specialists and network administrators (from six Russian universities), the authors of the article have revealed the advantages, directions and opportunities of digital technologies to support the learning of students with disabilities, considered the use of digital assistive technologies in teaching such students and the issues of introducing digital technologies as a means of supporting students with disabilities and creating conditions for overcoming them. The study results have demonstrated that the pedagogically balanced and expedient use of digital technologies will help students with disabilities fully engage in the educational process and develop individual educational strategies acceptable to them.
TECHNOLOGY FOR THE DEVELOPMENT OF SOFTWARE ALGORITHMS FOR OPTOELECTRONIC MONITORING SYSTEMS FOR REMOTE OBJECTS

The issues of constructing algorithms for obtaining coordinate and non-coordinate information used to solve the problem of multiplexing images obtained from several optoelectronic systems are considered. A unique mathematical method for finding the corresponding points in images, based on algorithms for continuous wavelet transform of the brightness structure of an image, is proposed. The technology of development of algorithms intended for multi-position optoelectronic systems for monitoring remote objects based on software from National Instruments is considered. A technique for constructing software for obtaining information is proposed, which ensures high accuracy in determining the coordinates of the corresponding fragments in images. It is shown that the parallel use of several methods makes it possible to assess the reliability of the information obtained under conditions of changing observation parameters. The use of several methods makes it possible to assess the reliability of the information obtained under conditions of changing observation parameters. Computational experiments have confirmed that a more accurate search for image alignment regions is provided by the double wavelet transform method by increasing the number of extrema of the curves of the continuous wavelet transform coefficients, expanding the area from localization and additional filtering. An example of the practical implementation of the developed algorithms in a two-channel optoelectronic system is presented.
Accelerating sailfish optimization applied to unconstrained optimization problems on graphical processing unit

The Sailfish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing high convergence speed. However, taking a lot of time to solve optimization problems has become a challenge for metaheuristic algorithms. Due to independence of the metaheuristics components, parallel processing is a good option to reduce the computational time and to find high quality solutions that are close to the optimum with an acceptable cost. Nowadays, combination of parallel processing and metaheuristic algorithms can provide high performance solutions to quickly solve combinatorial optimization problems. In this paper, we elaborate a novel GPU based and accelerated method of sailfish optimizer (ASFO), which improves the execution time and speedup while maintaining the results of optimization in high quality. In depth of study, we present the implementation details and performance observations of ASFO algorithm. Also, a comparative study of accelerated and sequential SFO is performed on a set of standard benchmark optimization functions and it compared with other parallel algorithms to show the speed of proposed algorithm for solving unconstrained optimization problems. The results indicate the ability of proposed approach in continuous, non-separable, non-convex and scalable optimization problems.
Pembelajaran Ekonomi Pada Masa Pandemi Covid-19

This study seeks to find out how economics teachers carry out online learning during the COVID-19 pandemic. This research method uses a qualitative descriptive type, while data collected using interviews with several economics teachers in the Surakarta senior high schools using WhatsApp. The data were collected using purposive and snowball sampling. The results showed that the economics teachers prepared learning instruments at the beginning of the semester. However, the instruments were only the edited instruments from the previous semester. The economics teachers only adjusted them. The instruments made were only as administrative requirements for reports to the local education office. The instruments were not implemented during the learning process. Economics teachers used several media to connect with their students such as WhatsApp Group, Google Classroom, Google Meet, and YouTube. For learning assessment, they used Google Form, Quizzzz, Quipper School, Mentimeter, Kahoot, and the school's e-learning software.
Frequent Itemset Generation Using Association Rule Mining Based on Hybrid Neural Network Based Billiard Inspired Optimization

The concept based on data mining has drawn considerable attention from various database professionals and research scholars. The progression of computer-based advancements, namely database management and data storage has facilitated the storage of large data and the data mining approaches are employed to gain valuable information from huge databases. Recently, several techniques to association rule mining (ARM) and frequent itemset mining (FIM) have been established; yet the efficiency based on execution time and scalability continues to be seen as a significant limitation that results in poor solution quality. Therefore, it is necessary to enhance the consistency that signifies the total number of frequently discovered frequent itemsets. This paper proposes three different phases namely the pre-processing phase, FIM phase and ARM phase. In the first pre-processing phase, the Twitter databases are pre-processed and converted into a suitable format for FIM. Here, the tweets are converted into related feature sets and items. In the second FIM phase, an improved Apriori algorithm is 1utilized in mining and extracting the frequent Then in the final phase, an adaptive billiard inspired optimization (ABIO) algorithm which is the integration of neural network (NN) optimization algorithm and billiard inspired optimization (BIO) algorithm is proposed for the optimal generation of association rules with minimum support and confidence from the huge itemsets. Finally, the recent tweets based on covidvaccine, BTSlivestreaming, KFC, McDonald’s as well as lockdown achieved using the hashtag is evaluated for various performance measures, like precision, recall, [Formula: see text]-measure, execution time and memory utilization. Also, comparative analyses are performed to evaluate the efficiency of the proposed technique.
Data as Symbolic Form: Datafication and the Imaginary Media of W. E. B. Du Bois

This article explores datafication as a speculative discourse that fundamentally and instrumentally misunderstands data, not as a representational system, but as an ontology. This analysis of datafication takes a semiotic and media-archaeological approach to datafication, understanding it as an imaginary media system, and the article looks to supplementary discourses in data visualization and big data to clarify and expand an understanding of datafication as a prescriptive and speculative idea. This critique is sharpened through the exploration of a detailed study of the early visualizations and unpublished fictions of W. E. B. Du Bois, whose imagined technology of the “megascope” gives a remarkable blueprint for contemporary discourses in data visualization and data science. Du Bois’s liberatory perspective is contrasted to a more paranoid example coming from the self-documented delusional system of Daniel Paul Schreber, who provides a much different vision of a totalizing media system in the form of an Aufschreibesysteme, which seems to presage another side of datafication as a surveillance system. Taken together, datafication, the megascope, and the Aufschreibesysteme work as three speculative media that imagine themselves as different instances or visions of a totalizing media system, a notion that has become especially significant in the technological, cultural, and economic systems of the twenty-first century.
Beyond NVD: Cybersecurity meets the Semantic Web.

Cybersecurity experts rely on the knowledge stored in databases like the NVD to do their work, but these are not the only sources of information about threats and vulnerabilities. Much of that information flows through social media channels. In this paper we argue that security experts and general users alike can benefit from the technologies of the Semantic Web, merging heterogeneous sources of knowledge in an ontological representation. We present a system that has an ontology of vulnerabilities at its core, but that is enhanced with NLP tools to identify cybersecurity-related information in social media and to launch queries over heterogeneous data sources. The transformative power of Semantic Web technologies for cybersecurity, which has been proven in the biomedical field, is evaluated and discussed.
Autonomous underwater vehicles (AUVs) path planning based on Deep Reinforcement Learning

For autonomous underwater vehicles (AUVs), autonomous navigation in an unknown underwater environment is still a difficult problem. In recent years, people have proposed some machine learning-based methods to solve this problem, but the existing methods still cannot meet the complex and changeable underwater environment. This paper conducts technical research on the path planning of autonomous underwater vehicles, combines deep learning and reinforcement learning, uses WL interpolation surface to model the seabed, and proposes a path planning model for autonomous underwater vehicles based on deep reinforcement learning. And train the path planning model in the simulation environment, and finally achieve the goal of path planning for the underwater robot in the complex and changeable underwater environment.
Group Time-based One-time Passwords and its Application to Efficient Privacy-Preserving Proof of Location

Time-based One-Time Password (TOTP) provides a strong second factor for user authentication. In TOTP, a prover authenticates to a verifier by using the current time and a secret key to generate an authentication token (or password) which is valid for a short time period. Our goal is to extend TOTP to the group setting, and to provide both authentication and privacy. To this end, we introduce a new authentication scheme, called Group TOTP (GTOTP), that allows the prover to prove that it is a member of an authenticated group without revealing its identity. We propose a novel construction that transforms any asymmetric TOTP scheme into a GTOTP scheme. Our approach combines Merkle tree and Bloom filter to reduce the verifier’s states to constant sizes.
Detection and localization for watermarking technique using LSB encryption for DICOM Image

Watermarking is an effective way of transferring hidden data from one place to another, or proving ownership of digital content. The hidden data can be text, audio, images GIF etc., the data is embedded in a cover object usually an image or a video sequence. Usually the watermarking system(s) rely on their hidden aspect, as their primary security measure, once this is established that the cover object is counting some hidden data, then it is generally possible to recover the hidden information. The author proposed an in-genuine technique for DICOM color image water marking by combining Multi Quadrant LSB with truly random mixed key cryptography. This system provides a high level of security by just the water marking technique, as it breaks the cover image into up to four quadrants, & does LSB replacement of two bytes each quadrant. The bit sequence as the quadrant sequence can be randomized to increase the randomness, use of truly random mixed key cryptography, by using a pre shared, variable length, truly random, private key, turns hidden data into noise, which can only be recovered by having the private key. Thus, the proposed technique truly diminishes the probability of recovering hidden data, even if it is detected that something is hidden in cover object.
Binary Duck Travel Optimization Algorithm for Feature Selection in Breast Cancer Dataset Problem

To predict if a tumor is malignant or benign is the challenge to all the researchers achieving efficient prediction of women in breast tumor. Unrestrained cell expansion in hankies of the breast is called breast cancer disease. Successful detection of breast cancer in earlier stage is required to secure women against high mortality. To select the best features among breast cancer classification of benign/malignant from the input mammogram images originated from Wisconsin dataset is satisfying with the new algorithm binary duck travel optimization algorithm (bDTO). Sigmoid activation function (logistic regression) is used for binary classification of proposed method. Food forage activation of ducks position is updated by sigmoid with maximum likelihood function (SMLE) of entire duck flock. Classifying the given mammogram is cancer or non-cancerous is based on the optimal feature selection by bDTO through (SMLE). Feature extraction of mammogram Corpus is done with the aid of sigmoid activation function (SAF) classifier. (bDTO-SMLE-SAF) is an intrinsic procedure to eliminate irrelevant scope and select the optimal highlights by using Wisconsin families normal nucleoli, single epithelial cell size, bare nuclei, uniformity of cell size, uniformity of cell shape, bland chromatin, mitoses, marginal adhesion, clump thickness features that are evaluated by the quality measures exactness, compassion, specificity, accuracy, evoke, and F-value clearly shows that bDTO classifier has the maximum accuracy 92% while compared with the DTO and SAF classifiers. The efficiency of an algorithm is proved by the promising results for selecting the best feature of malignancy classification through bDTO algorithm.
Artificial Intelligence-Based Rubber Tapping Robot

Rubber tapping is the process of extracting latex from rubber trees. Rubber tree (Hevea brasiliensis) is the most prevalent plantation crop in Kerala, India, and it was discovered to be the main source of income for the over 11.5 lakh people who live there. Because of its outstanding qualities, natural rubber is often used in everyday life. Manual tapping is currently the most popular method of accessing natural rubber. Rubber tree tapping is considered a skilled labor-intensive task, and the availability of such labor is dwindling. This is considered to be one of the factors affecting natural rubber production in Indian plantations. As a solution to the problems faced by the rubber industry, an AI-based rubber tapping robot is proposed that can automatically move from one tree to another in a rubber plantation and tap each tree.
SousChef System for Personalized Meal Recommendations: A Validation Study

Nutrition is an essential part of our life. A healthy diet can help to prevent several chronic diseases like diabetes, obesity, cancer, and cardiovascular diseases, being influenced by social, cultural, and economic factors. Meal recommender systems are a trend to assist people in finding new recipes to cook and adopt healthier eating habits. However, food choice is complex and driven by multiple factors which need to be reflected in the personalization process of these systems to ensure their adoption. We present SousChef, a meal recommender system that can help to plan multiple meals considering an individual’s food preferences, restrictions, and nutritional needs. Our approach uses recipes rather than individual food items, limiting recommendations to tasteful and culturally acceptable food combinations. Several experiments were performed to evaluate the system from different perspectives: nutritional, food preferences, and restrictions, and the recommendations’ variability. Our results highlight the importance of using extensive and diverse content in recommendations to meet food preferences, restrictions, and nutritional needs of people with different characteristics.
BACKGROUND OF U AND Th IN SEDIMENTS OF BEDROCK RIVERS

The Miño River is a good example of bedrock rivers, where sediment geochemistry is scarcely studied. Its urban reach when passing through the city of Ourense gathers some characteristics that provide interest to its sediments, like scarcity of fine sediments accumulation and the impact of several human activities. Sediments trapped by potholes and other rock cavities were considered. In order to evaluate society-nature interactions through sediment composition it is critical to determine the compositional background (in absence of human alterations), particularly when working with trace elements. This work presents an exploratory assay to determine background in sediments from bedrock rivers by using two uncommon elements, uranium (U) and thorium (Th). To determine their background different statistical techniques were applied in order to set the background composition value and calculate possible enrichments. Background was calculated by simple least squares lineal regression by using Al as independent variable (reference element) resulting in 8.7 mgU kg-1 and 5.6 mgTh kg-1. Enrichments were found in some particular samples and can be attributed to intrinsic microenvironment complexities inside rock cavities.
Assessing the Impact of Financial Support on Ghana’s Agricultural Development

Agriculture is a type of economic activity, livelihood, and provider of environmental services, contributing to the development process.It can participate, in concert with other sectors, in realizing faster growth, poverty reduction, and environmental sustainability.Agriculture has a special ability to reduce poverty.The objectives of the study are to identify agricultural development in Ghana and identify challenges of modern agricultural development in Ghana.Data from the field were collected from the primary and secondary sources.Depending on the resources available to the researcher, all the beneficiary farmers were interviewed to understand the situation on the ground to improve credit delivery and agriculture development in Ghana.It was identified that the several Rural Banks disburses the entire loan earmark for particular year to the farmers.However, the banks satisfy only 83.3 percent and 63.4 percent of the applicants in 2015 and 2016 financial years respectively.According to the project officers of the various rural banks, the reduction is due to low rate of recovery (i.e.3.45) for the 2015 financial year.However, the farmers' complaint of late receipts of the loan and lack of storage facilities compelled them to sell the produce at uneconomic prices which led to low income, low investment and low savings.In 2019, it was recommended that the rural banks should take into account the advantages of providing credit in kind for purchased inputs.This would relieve the smallholder farmers of further transactions with which he may be unfamiliar and provide the bank with some assurances that the credit is used for the purposes intended.This credit must be timely as it will prevent the farmers from diversion and loss.Subjecting the problem to critical analysis, it is recommended that the economy and lifestyle of Ghana are dependent on agriculture.It is the major employer of her population and contributes massively to her foreign exchange, supplier of food and raw materials for the population and industries, provider of import substitution to solve the balance of payment situations in the country.However, the performance of this sector is inhibited by finance even though there is institutional source of credit from the com-
Differentially Private Learning with Grouped Gradient Clipping

While deep learning has proved success in many critical tasks by training models from large-scale data, some private information within can be recovered from the released models, leading to the leakage of privacy. To address this problem, this paper presents a differentially private deep learning paradigm to train private models. In the approach, we propose and incorporate a simple operation termed grouped gradient clipping to modulate the gradient weights. We also incorporated the smooth sensitivity mechanism into differentially private deep learning paradigm, which bounds the adding Gaussian noise. In this way, the resulting model can simultaneously provide with strong privacy protection and avoid accuracy degradation, providing a good trade-off between privacy and performance. The theoretic advantages of grouped gradient clipping are well analyzed. Extensive evaluations on popular benchmarks and comparisons with 11 state-of-the-arts clearly demonstrate the effectiveness and genearalizability of our approach.
An optical neural network operating with less than 1 photon per multiplication

We report an experimental demonstration of an optical neural network performing image classification with high accuracy using less than a single photon per scalar multiplication, validating theoretical predictions about quantum-limited performance of ONNs.