Reviewerly/exHarmony huggingface dataset:
A real time intelligent detection and counting method of cells based on YOLOv5

Blood cell count plays an important role in the diagnosis and treatment of many diseases. This paper proposes and implements a blood cell detection and count method based on YOLOv5. On the basis of YOLOv5, 346 blood cell images screened by BCCD dataset were used for 300 epochs of training. The mAP of 46 samples in the verification set was as high as 0.96, and the actual counting accuracy of red blood cells, white blood cells and platelets was close to 100%. The detection accuracy of untrained new data is also close to 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and the recall rate is slightly weaker than the verification set data. The inferencing time of single sample in batch detection is only 16 ms, which has the ability of real-time detection and counting. However, in the SSD blood cell detection and counting control model with MobileNet as the backbone, the verification set mAP is only 0.62, and the platelets can hardly be detected and counted. Experiments show that the blood cell detection and counting method based on YOLOv5 has the ability to replace and assist artificial cell counting in clinic.
Advanced Object Detection Algorithms and Its Application Based on Deep Learning

Object detection is one of the most basic and challenging problems in such a field, which has become a paramount topic for scholars in recent years. In the past two decades, object detection has developed rapidly from the beginning to the application in all aspects of life, with the improvement both in detection accuracy and detection speed. In this paper, firstly, we focus on the research progress of object detection algorithm based on deep learning in the light of its technical evolution and application. Secondly, we compare and analyze the two-stage and single-stage detection framework from series algorithms based on R-CNN to series algorithms based on Yolo, and introduce the common data sets and index evaluation as well as the application process of the algorithm in the detection fields of pedestrian, face, text, medical image, sign language, etc. Finally, we predict the prospects of deep learning-based object detection algorithms according to the existing challenges.
Data Filtering and Deep Learning for Enhanced Human

Human Activity Recognition (HAR) is one of the most popular research topics. Indeed, recognizing human activities will help provide appropriate assistance to older adults, fighting the consequences of the population aging at the same time. For example, specific organizations, such as United Nations, reported an important risk of qualified human resources shortage to care for older adults in-home care. Hence, HAR solutions based on Ambient Intelligence (AmI) have been proposed to face these consequences. Therefore, we denoted an increasing interest in Ultra-WideBand (UWB) radars for HAR these last years. In this work, we exploited three UWB radars to recognize 15 human activities performed by 9 participants in a prototype apartment. The main contribution in this paper is data cleaning. Indeed, data provided by UWB radars have been cleaned with a band-pass Chebyshev type I filter of order 2. The application of that kind of filter is uncommon in the literature with the data of UWB radars. Also, this filter has been selected according to tests made with different filter types, and classification performances attest that data filtering is efficient. In addition, two different deep learning architectures to classify the cleaned data have been exploited. The first is a relatively simple Convolutional Neural Network (CNN), and the second is Efficient-CapsNet. We obtained similar performances between these two architectures for the top-1 with an accuracy of approximately 64%.
A Study About Cyber Security Design Basis Threat for Control System in Nuclear Power Plant

As the nerve center of a nuclear power plant, the industrial control system is used to ensure the safety and economy of power plant operation. With the integration of industrialization and informatization, as well as the development of industrial Internet and big data in the field of nuclear power, more and more intelligent control systems and equipment are applied in nuclear power plants, introducing the cyber security threats of traditional IT systems. Safety hazards will directly affect the operation of the power plant. The identification of cyber security threats is an important basis for vulnerability analysis and risk assessment, and it is also an important input for effective cyber security design. This paper analyzes the cyber security characteristics of nuclear power plant control systems and equipment, identifies and categorizes design basis threats, and proposes countermeasures for design basis threats (DBT) to provide a reference for the design of nuclear power plant industrial control system cyber security.
Automated Multiple-Choice Question Creation Using Synonymization and Factual Confirmation

E-assessment is the method of end-to-end automated evaluation in which cyber-physical technology is used to present evaluation events and document responses. This perspective requires a full-length assessment mechanism for teachers, tutors, educational agencies, granting institutions and regulators, and the public at large. Various methodologies are implemented for increasing the quality of the assessment through automation of question generation through machine learning and deep learning concepts. Though there are various amounts of contributions toward this challenge, an efficient and a stable method has not been yet formulated. This paper introduces a process that can help to make a contribution decreasing the intensity of the challenge by using abstractive LSTM series to model sequence summarization with attention mechanism for summarization along with key generation using synonymizing with factual validation and knowledge-centric distractor generation using Wiki data for the MCQ generation. The performance of the proposed model is measured and compared to baseline models, and the proposed solution was found to be superior in terms of performance with the findings of 94.87%, 96.74%, 95.84%, and 0.03%, respectively, of precision, recall, accuracy, F-measure, and false negative rate.
Unsupervised Domain-Adaptive Image Classification Algorithm Incorporating Generative Adversarial Networks

With the rapid development of computing power of computers, deep neural networks have achieved great success in recent years, but these successes rely on huge amounts of labeled data, which have the disadvantages of low timeliness and high cost, and thus cannot rapidly deploy deep learning models when new tasks come. Unsupervised domain adaptation techniques can take a classifier learned from a source domain rich in labeled data and use it for a target domain with only a small amount of data with labels or no labels at all, and use the knowledge learned from the source domain for a target domain with no labels at all, effectively solving the data labeling problem. However, the unsupervised domain adaptive technique suffers from the problem of target domain data without explicit labeled data and domain discrepancy, which affects the image classification performance; Therefore, this paper proposes an unsupervised domain adaptive image classification algorithm incorporating generative adversarial networks, which combines the advantages of deep generative adversarial networks and applies the trained labeled source domain depth model accurately to the unlabeled target domain. Finally, the algorithm of this paper is compared with other classical methods, and the experiments show that the efficiency and accuracy of this algorithm are higher.
Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model

Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine.The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery.Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells.In medical practices, histopathological investigation of tissue specimens generally takes place in a conventional way, whereas automated tools that use Artificial Intelligence (AI) techniques can produce effective results in disease detection performance.In this background, the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification (AAI-CCDC) technique.The proposed AAI-CCDC technique focuses on the examination of histopathological images to diagnose colorectal cancer.Initially, AAI-CCDC technique performs preprocessing in three levels such as gray scale transformation, Median Filtering (MF)-based noise removal, and contrast improvement.In addition, Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature vectors.Furthermore, Glowworm Swarm Optimization (GSO) with Stacked Gated Recurrent Unit (SGRU) model is used for the detection and classification of colorectal cancer.The proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches.
Messengers integration methods with corporate information systems

Today we cannot imagine our life without the introduction of digital technologies into the life of society. Messengers have become popular not only among ordinary users, but also among IT companies. Such companies tend to use and support one or multiple corporate systems. In this case, events that require prompt response from users of those systems are possible to occur. To solve this problem, the corporate information system is integrated with other communication channels, including messengers, social networks, etc. The article discusses integration of corporate information systems with the Telegram messenger using bots. Having studied publications in one way or another related to this problem, it turned out that almost all of them do not reflect the topic of the introduction and application of instant messengers to the corporate information system. Most of them describe the importance of notifications, calculate the speed of notifications, or create separate information systems, but do not talk about the possibilities of using messengers as a method of interaction between user and system. Therefore, the task arose of studying approaches to integrating messengers.
Recommender Systems in E-commerce and their Challenges

With the advancement in technology and the usage of internet, everything is just a click away from purchasing. It can be online shopping, banking, communicating or even watching movies. Many large e-commerce websites like Amazon and Netflix have already incorporated recommender systems to increase their sales. The competition for providing the best products and services is evident between e-commerce websites. In this paper we have discussed about the common recommendation techniques that are being used in the electronic commerce industry to help customize recommendations. We also discuss ways in which the recommender system enhances the e-commerce business. The paper has also thrown light on the prominent issues and challenges the system faces which includes sparsity and latency. It also proposes areas in which the recommender system needs to be improved so as to give optimal suggestions and tackle with the problems of future.
SHELBRS: Location-Based Recommendation Services Using Switchable Homomorphic Encryption

Location-Based Recommendation Services (LBRS) has seen an unprecedented rise in its usage in recent years. LBRS facilitates a user by recommending services based on his location and past preferences. However, leveraging such services comes at a cost of compromising one’s sensitive information like their shopping preferences, lodging places, food habits, recently visited places, etc. to the third-party servers. Losing such information could be crucial and threatens one’s privacy. Nowadays, the privacy-aware society seeks solutions that can provide such services, with minimized risks. Recently, a few privacy-preserving recommendation services have been proposed that exploit the fully homomorphic encryption (FHE) properties to address the issue. Though, it reduced privacy risks but suffered from heavy computational overheads that ruled out their commercial applications. Here, we propose SHELBRS, a lightweight LBRS that is based on switchable homomorphic encryption (SHE), which will benefit the users as well as the service providers. A SHE exploits both the additive as well as the multiplicative homomorphic properties but with comparatively much lesser processing time as it’s FHE counterpart. We evaluate the performance of our proposed scheme with the other state-of-the-art approaches without compromising security.
Electronic information and educational environment as a condition for the implementation of English language programs in a maritime university

The relevance of the research topic is determined by the need to reconsider the prospects for the language training of seafarers in the context of a pandemic and related socio-economic changes in the state and in the world, including the employment of seafarers, the competitiveness of graduates of Russian maritime universities, wages, etc. Effective communication in English has always been of great importance for the safe operation of ships and protection of the marine environment from shipping. The research problem is how does the electronic information and educational environment contribute to the successful implementation of English language programs and improve the quality of language training at a maritime university? The purpose of the research is to substantiate the effectiveness of the implementation of English language programs at the EIEE of the Institute. The research methods used are analysis, synthesis, generalization. The Institute ofWater Transport named after G.Ya. Sedov was the experimental basis of the research. The results of the research are the following: the English language programs implemented by the Institute have been described (goals, objectives, benefits, competencies being formed, form of organization and training mode); the forms of interaction between participants in the educational process at the EIEE of the Institute in the process of implementing English language programs have been disclosed; further prospects for the implementation of English language programs have been presented. Key conclusions: the electronic information and educational environment created at the Institute has proved the effectiveness of the implementation of English language programs, based on cooperation, consultation, and dialogue between participants in the educational process, including employers, which contributes to the development of teamwork skills and constructive resolution of problem situations.
Deployment of Sentiment Analysis of Tweets Using Various Classifiers

Twitter is a social media forum that permits individuals to share and express their perspectives regarding the matter and post messages. A great deal of examination has been done on the sentiment analysis of Twitter data. Our proposed methodology aims to incorporate the techniques of sentimental analysis, assessing the polarity of the tweets, and improving the accuracy of the model. This paper includes classifying of tweets into sentiments: positive and negative. For this paper, we have used different data preprocessing techniques for cleaning up the data by eliminating stop word, slangs, emoticons, and hashtags. To build the productivity and the precision of the model, we have used term frequency inverse data frequency, count vectorizer, stemming, and N-gram features. Several techniques have been used currently for sentiment analysis which is discussed in brief in this paper. Out of which, we have used supervised learning approach and compared different algorithms such as Naive Bayes, support vector machines, linear regression, decision tree, XGBoost, and random forest.
Quantum Key Agreement simulation using pattern-based encoding

This paper proposes a novel dynamic multi-party key agreement method. The algorithm proposed is used for an individual or a group of receivers. The algorithm imbibes a unique method of storing the bits resulting in reduced retransmission. The algorithm uses pattern-based private key generation to encode the message. The private key is mutually decided and agreed upon by the sender and the intended recipient. The message is represented in a block cipher while the transmission occurs row-wise. Providing an opportunity to jumble the message according to the pattern before the transmission. Due to row-based transmission, the bandwidth requirement and channel utilization are efficient. The algorithm reduces the probability of interception such that all the channels should be inferred correctly to identify the key hence decoding the message. The algorithm is implemented, the results are simulated and verified.
Devanagari Character Recognition using Deep Convolution Neural Networks

In the presented paper, a Deep Learning architecture for Devanagari handwritten character recognition is introduced. The dataset comprises of 94742 images of 58 distinct classes containing numerals, vowels and consonants from Devanagari script. Deep Convolution Neural Network (DCNN) have indicated better outcomes than traditional Machine Learning algorithms and Shallow networks in recognition tasks. The proposed algorithm achieved an accuracy of 99.20% on testing data with 98.31% accuracy on training data.
IoT-Blockchain in Remote Patient Monitoring

Internet of Things (IoT) and Blockchain technologies have been utilized heavily in diverse domains, including remote patient monitoring (RPM). Expeditious development of wearable IoT medical devices with a variety of functionality, real-time sensory data from patients can be provided and further processed. IoT data are centrally collected, processed, and stored. But this centralization may face different problems including single-point failure, data tampering, privacy issues, etc. Blockchain can address these problems using its decentralized architecture. Hence, the combination of IoT and blockchain is a plausible solution to design a smart RPM system. In this paper, we provide a systematic review of the existing literature that has integrated blockchain with IoT-based systems from the perspective of remote patient monitoring. We have also proposed comprehensive IoT-blockchain based RPM framework guideline that addresses all aspects of designing an RPM system. Our proposed framework addresses the challenges of IoT-blockchain based systems, fulfills the requirements of real-time remote patient monitoring systems, and integrates the best practices of the literature in the relevant domains.
Dynamic Path Planning of UAV Based on Pheromone Diffusion Ant Colony Algorithm

Due to the dynamic uncertainty factors in a complex environment, such as flight conditions, movable obstacles and other sudden threats. It is a challenge to realize the real-time path planning of Unmanned Aerial Vehicles (UAV). In this paper, the method is proposed with a model of the dynamic environment and a method of pheromone diffusion ant colony optimization (PDACO) to solve the real-time path planning of UAV in a dynamic environment. The translational obstacle method and the random obstacle method can efficiently simulate the dynamic environment. PDACO takes advantage of pheromone diffusion characteristics in an ant colony, and diffuses the pheromones to adjacent paths after each iteration, thus expanding the guidance range of pheromones. When the environment changes, the pheromone diffusion method can quickly plan new paths and accelerate the convergence of the algorithm. Simulation results show that the dynamic environment model accords with the actual situation. Compared with four algorithms, PDACO ensures that the UAV can optimize a new path with shorter path length and computing time when environment changes. The proposed method is feasible and effective.
A Blockchain-Based Location Privacy-Preserving Scheme in Location-Based Service

With the rapid popularization and development of location-based services (LBS), location privacy preserving has become a mainstream concern. Existing anonymity-based methods rely mainly on anonymous location selection based on geographic information, which neglects the semantic information on the location, and behavior of self-interested users may raise a privacy vulnerability. A blockchain-based approach for location privacy protection schemes has been proposed to address these issues. In this scheme, the privacy level of the location is improved using private chain-based collaborative anonymous communication with anonymous set construction methods that consider the semantic diversity associated with the related user. Compared with other existing methods, evaluation based on real-world datasets shows that the proposed method prevents location privacy leakage from collaborative users and constructs a semantical anonymity set, thus effectively protecting the user’s location privacy.
Online Product Advertisement Prediction and Explanation in Large-scale Social Networks

Online advertisement has become a major commercial campaign in social networks. Many big companies have invested massive resources for collecting data about the users and their web surfing habits. Utilising these data, the advertisement companies can get valuable insights about the users and their interests. The gathered information can improve the effectiveness of advertisement campaigns by identifying potential customers of a product/service or by identifying purchase patterns. A successful advertisement campaign depends on the company's ability to fully leverage these data assets. As the artificial intelligence flourish with the machine learning models which were offered as a solution for such a problem depending on dataset availability and computation power but the resulting systems suffer from a loss of transparency and interpretability, especially for end-users.In order to overcome the aforementioned problem of explainability of the models, we propose an explainable and interpretable approach to solve this problem. In the first stage, machine learning model will be used to develop a predictive model that is capable of predicting potential customers who are likely to click the advertisement of a particular product/services. This approach is tested on the public advertising dataset. In the second stage, the predictive model is further utilised by local surrogate model initially using Local Interpretable Model-agnostic Explanations (LIME) to locally approximating the model around a given prediction and then with global interpretable explanations by considering whole machine learning model at once. Finally, Contextual Importance and Utility (CIU) is used for global explanations to generate the explanations and interpretation of the prediction based on the contributing features of the dataset.