Reviewerly/exHarmony huggingface dataset:
Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems

The purpose of this study is to propose a framework for accurate and efficient vehicle distance estimation from a monocular camera. The proposed framework consists of a transformer-based object detector, a transformer-based depth estimator, and a distance predictor. The object detector detects various objects that are mostly symmetrical from an image captured by the monocular camera and provides the type of each object and the coordinate information of a bounding box around each object. The depth estimator generates a depth map for the image. Then, the bounding boxes are overlapped with the depth map to extract the depth features of each object, such as the mean depth, minimum depth, and maximum depth of each object. The present study then trained three models—eXtreme Gradient Boosting, Random Forest, and Long Short-Term Memory—to predict the actual distance between the object and the camera based on the type of the object, the bounding box of the object (including its coordinates and size), and the extracted depth features. The present study proposes including the trimmed mean depth of an object to predict the actual distance by excluding the background pixels around an object but within the bounding box of the object. The evaluation results show that the proposed framework outperformed existing studies.
Movie Recommendation System using Machine Learning

The recommendation engine filters information using specific algorithms and recommends high quality content to customers. It starts capturing more consumer behavior and based on that, recommends products that consumers can purchase. Three key strategies are used in our recommendation structures. One Demographic Filtering i.e. They offer general suggestions for each individual, based entirely on the film's image and genre. The system recommends similar films to all the users. if you consider that each person is of the same type, this method is considered very simple. The simple idea behind is that the movies which are more popular can be liked by the more people. The second method is content based filtering, which considers all the features like director, actors and movie related content and based on that the movies will be recommended. The third one is collaborative filtering, which implement the item based collaborative filtering and single value decomposition. The obtained results have showcased the proposed strategies with good accuracy.
Virtual Alphabet Recognition using Deep Convolution Neural Networks

Air-based visual perception plays an important role in human interactions with computers. With the advent of many new technological advances such as biometric authentication, which is now regularly used in smartphones, similarly touch recognition is a modern way of interacting between a computer and humans i.e. the system can be controlled by showing or moving the hands in front of a webcam. A hand touch detection can be helpful for all types of people. Spiritual writing recognition plays a major role in intelligent programming and application. To date, some of the most common isolation challenges have not been successfully resolved. In this case, the user drawn character in the air is captured by a computer camera, following the prediction of drawn character. Therefore, while capturing aerial footage, the video camera should be turned ON. Now, an object is defined based on its color to determine the movement of the user. The color is captured by the lower and upper limit of HSV (hue saturation value), which leads to the final acquisition of the object at all times. It uses open CV and machine learning algorithms to capture and predict the written text.
Infrared image denoising via adversarial learning with multi-level feature attention network

We propose an infrared image denoising method based on the adversarial learning with a multi-level feature attention network (MLFAN). A multi-level feature attention block (MLFAB) is designed to extract features from different levels and establish connections through feature fusion to enhance the details of reconstructed images. Specifically, the MLFAB is constructed by the residual block, non-local attention and bicubic interpolation. A noise map estimation module is used to extract noise features from input noisy infrared images. Moreover, a discriminator is developed to provide the adversarial learning loss. A series of experiments are conducted on a public dataset and the real scene datasets obtained from an experimental system. The experimental results demonstrate that the proposed method achieves competitive performance and excellent denoising effect on different kinds of noisy infrared images compared with previously reported state-of-the-art methods.
Comparing Machine Learning Algorithms for Improving the Maintenance of LTE Networks Based on Alarms Analysis

Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided.To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network.Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure.This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network.Three machine learning techniques are presented in this study and compared with regard to accuracy.This study demonstrates that the appropriate machine learning technique improves the accuracy of the model.By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%.In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain; resulting in reduced service disruption time and improved the network availability which is a key network performance index.
Improved quantum (t, n) threshold group signature

Threshold signature is an important branch of the digital signature scheme, which can distribute signature rights and avoid the abuse of signature rights. With the continuous development of quantum computation and quantum information, quantum threshold signatures are gradually becoming more popular. Recently, a quantum ( t , n ) threshold group signature scheme was analyzed that uses techniques such as quantum-controlled-not operation and quantum teleportation. However, this scheme cannot resist forgery attack and does not conform to the design of a threshold signature in the signing phase. Based on the original scheme, we propose an improved quantum ( t , n ) threshold signature scheme using quantum ( t , n ) threshold secret sharing technology. The analysis proves that the improved scheme can resist forgery attack and collusion attack, and it is undeniable. At the same time, this scheme reduces the level of trust in the arbitrator during the signature phase.
Knowledge-Guided Blind Image Quality Assessment With Few Training Samples

Blind image quality assessment (BIQA) for in-the-wild images has achieved great progress by training advanced deep neural networks. However, the current BIQA models are suffering the generalization challenge, meaning that a well-trained BIQA model is still very limited in evaluating images with different distributions. Deep BIQA models are data-intensive, but the annotation of image quality labels is extremely expensive. To design a generalizable BIQA model with few training samples is highly desired. Motivated by the above fact, this paper presents a knowledge-guided BIQA (KG-IQA) framework by integrating domain knowledge from the human visual system (HVS) and natural scene statistics (NSS). Specifically, the quality-aware HVS and NSS features are first extracted as prior knowledge. Then, we embed the two types of knowledge into the conventional deep neural network by learning to predict the HVS and NSS features, producing the knowledge-enhanced quality features, based on which the final image quality score is obtained. We conduct extensive experiments and comparisons on five authentically distorted IQA datasets. The experimental results demonstrate that the introduction of knowledge greatly reduces the requirement on the amount of training images, and the proposed KG-IQA model achieves superior performance in terms of both prediction accuracy and generalization ability.
Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models

Abstract Clothing image in the e-commerce industry plays an important role in providing customers with information. This paper divides clothing images into two groups: pure clothing images and dressed clothing images. Targeting small and medium-sized clothing companies or merchants, it compares traditional machine learning and deep learning models to determine suitable models for each group. For pure clothing images, the HOG+SVM algorithm with the Gaussian kernel function obtains the highest classification accuracy of 91.32% as compared to the Small VGG network. For dressed clothing images, the CNN model obtains a higher accuracy than the HOG+SVM algorithm, with the highest accuracy rate of 69.78% for the Small VGG network. Therefore, for end-users with only ordinary computing processors, it is recommended to apply the traditional machine learning algorithm HOG+SVM to classify pure clothing images. The classification of dressed clothing images is performed using a more efficient and less computationally intensive lightweight model, such as the Small VGG network.
Elementary Number Theory Problems. Part V

Summary This paper reports on the formalization of ten selected problems from W. Sierpinski’s book “250 Problems in Elementary Number Theory” [5] using the Mizar system [4], [1], [2]. Problems 12, 13, 31, 32, 33, 35 and 40 belong to the chapter devoted to the divisibility of numbers, problem 47 concerns relatively prime numbers, whereas problems 76 and 79 are taken from the chapter on prime and composite numbers.
Enabling Remote School Education using Knowledge Graphs and Deep Learning Techniques

An automated question-answering system allows students to learn as an integral part of digitized learning. This system responds to queries using text. We also include a knowledge graph, which significantly enhances the model's intrigue and improves learners’ understanding. The features of knowledge entity extraction, information point evaluation and analysis, knowledge graph construction from unstructured text, and knowledge entity integration are all explored. The question-answering paradigm we suggest in this study uses knowledge graphs and BERT (Bidirectional Encoder Representations from Transformers) to provide diverse learners with quick feedback on the subject. In order to facilitate non-native learners’ understanding, we also include an English to Hindi translation. As a result, access to and continued learning can be very beneficial for educators.
American Sign Language Recognition for Alphabets Using MediaPipe and LSTM

With the advancement of today's technologies in artificial intelligence, humans tend to use hand gestures in their communication to convey their ideas. Gesture recognition is an active area of research in the human-computer interface (HCI). Gesture recognition is important for communication between deaf-mute people, HCI, robot control, home automation, and medical applications. In this article, a simple and efficient vision-based approach for American Sign Language (ASL) alphabets recognition has been discussed to recognize both static and dynamic gestures. Mediapipe introduced by Google had been used to get hand landmarks and a custom dataset has been created and used for the experimental study. Hand gesture recognition has been done by using Long short-term memory (LSTM). The proposed system has been investigated with 26 alphabets and an accuracy of 99% has been achieved. This work can be used to convert hand gestures into text.
Text Summarization Using Document and Sentence Clustering

Text documents have important information and it will be very large in size. Getting the relevant information from the text document is very much challenging criteria in the field of information retrieval. This can be done using the text summarization method. A text document is compressed using a summarizing system to produce a new form that conveys the core idea of the content it contains. The issue of information overload demands access to reliable and properly crafted summaries. Users can quickly find the information they need using data minimization. Saving the time and effort from browsing through the entire collection of documents is main advantage of text summarization. The proposed system is focused on an extractive technique of text summarization using a text clustering and word-graph approach. The proposed System uses the term Frequency, Inverse Document Frequency (TFIDF), Jaccard similarity and Euclidian distance which are important techniques for clustering the text. This hybrid approach deals with the novel method for comprising of document and sentence clustering.
Real-time Influencer Detection In Twitter Using A Hybrid Approach.

Social media is attaining popularity day by day across domains. Approximately about 40% of the world's population uses social media. Automatically these people look the influencers in social media to guide them with their decision-making in a day-to-day life. An influencer in social media is someone who can influence others. Finding the best influencer as a seed node among social media users is relevant to research. This paper proposes a methodology that can get the most suitable influencer list based on context analysis. A contextual analysis analyzes a text in different media that helps us assess that text within the context. The hybrid way of using the graph networks and the machine learning algorithms like LSTM and Naive Bayes together as suggested in this article helps us assess that text and find the influencer significantly better than the existing approaches. We prove that the hybrid model can produce more accurate results in such a way that we only get the users who can positively influencer other users. Based on our result, we are sure that this hybrid model can produce more accurate results for funding the best set of influencers. Along with that, the combined use of LSTM and Naive Bayes can produce better results.
The Adoption of Metaverse Systems: A hybrid SEM - ML Method

seeing high-tech medical devices from other nations and witnessing surgery to learn has become nearly unattainable. The pandemic of coronavirus disease 2019 (COVID-19) has created cross-border medical education challenging. Nevertheless, to cater to the increase in non-face-to-face education, instructional techniques entailing the “metaverse” are being initiated in the medical field, since medical staff from all over the globe who frequented the UAE to acquire skills in medical technology and medical students who require to exercise have already had minimal prospects to collaborate closely with patients attributable to COVID-19. Employing video-conferencing technology like Zoom to provide effective medical education is similarly difficult. The research's goal is to learn the perception of students in the UAE towards the metaverse system (MV) used for medical training. The conceptual model includes The Technology Acceptance Model (TAM) elements and adoption aspects of perceived value. The research's conceptual model, which connects both personal-based traits and technological features, is what makes it novel. Additionally, the novel hybrid analysis approach will be applied in the present research to conduct machine learning (ML) driven structural equation modeling (SEM) evaluation.
Thalassemia Diagnosis Through Medical Imaging: A New Artificial Intelligence-Based Framework

Thalassemia is the most common single-gene disorder throughout the world and represents a major public health problem resulting in abnormal ratios of hemoglobin subunits. It is widely spread throughout the Mediterranean region, Middle East, Southeast Asia, and some parts of Africa. There are different types of Thalassemia characterized by abnormal hemoglobin production. Most of the current hemoglobinopathy screening methods include High Performance Liquid Chromatography (HPLC), hemoglobin electrophoresis, screening of Polymerase Chain Reaction (PCR) mutations, and Deoxyribonucleic Acid (DNA) tests. However, all these methods are costly and require specialized instrumentation and trained technicians. Despite some studies used Artificial Intelligence (AI) they focused merely on Machine Learning (ML) and results were not optimal. This study aims to design a new AI-based framework for Thalassemia diagnosis using Deep Learning (DL) and a new combination of metrics for evaluation. This was achieved through the development and evaluation of a supervised semantic image segmentation model, and the implementation of different data engineering methods such as data annotation, augmentation, pre-processing, and preparation. Transfer learning was utilized and the Prediction Time Augmentation (PTA) was employed to get smoother and more accurate predictions. Quantitative results showed that, the mean Intersection Over Union (IoU) score of prediction of Thalassemia was 88% with PTA and 82% without PTA. Results also showed that as the combined metric of loss scores decreases the prediction of Thalassemia increases. Qualitative results showed that the final prediction of Thalassemia focuses on the Codocytes and labels the other unidentified cells as the background. Also, the resulting image was smoother and less bulky than the original annotated ground truth, and thus could be feasibly diagnosed. As a future prospect we aim to implement more algorithms and extend the diagnosis to include other diseases.
Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network

Facial expression recognition technology was extensively used.This paper develops a multi-branch attention convolutional neural network based on a multiple-branch structure to recognize facial expressions.First, features are extracted from facial images in a multi-branch architecture, and features from three branches are fused.Second, to address the issue of insufficient feature extraction and poor recognition performance, the Convolutional Block Attention Module is added as attention module.Third, our model reduces parameters and computation loads by using depth-wise separable convolutions.The experiments on the FER2013, FERPLUS, and CK+ datasets show that the recognition rates of the proposed model are 69.49%,84.633%, and 99.39%, respectively.The proposed method has a higher efficiency in extracting image features than traditional deep learning counterparts and achieves high accuracy without complicated artificial feature technology.
Cycle Consistency Based Pseudo Label and Fine Alignment for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain with a correlative distribution. Numerous existing approaches process this hard nut by directly matching the marginal distribution between two domains, which confront the obstacle of rough alignment and blurred decision boundary. Recent advances in UDA introduce target pseudo-label and subdomain adaptation to reduce misalignment and distribution discrepancy. Whereas, they frequently ignore that the production of target pseudo-label is so dependent on the source-trained classifier, which without reasonable restriction to discriminate generated pseudo-label is whether confident. Meanwhile, many methods in the subdomain alignment metric ignore exploring the potential distribution discrepancy between same-class samples of the intra-domain. To address these two issues simultaneously, this paper proposes a Cycle Consistency based Pseudo Label and Fine Alignment (CCPLFA) approach for UDA. In particular, firstly, a novel cycle-consistency based pseudo label module is designed, which is a simple yet effective way to alleviate the noise of pseudo labels and improve their semantic correctness. Secondly, we develop a Fine-Alignment distribution matching metric. Which can maximize the feature distribution density of intra-class cross-domains and not overlook the distribution structure of the global aspect. Comprehensive experiment results on four benchmarks demonstrate the capability of plug and play and the well generalization performance of our proposed method.
An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles, and obtains an optimal solution through three stages, namely selection stage, search stage and swooping stage.However, BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap, we propose an improved bald eagle algorithm (CABES) that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly, CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage, to select a better search range.Secondly, in the search stage, CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES, the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm (PSO), Whale Optimization Algorithm (WOA) and Archimedes Algorithm (AOA).The experimental results show that CABES can provide good exploration and development capabilities, and it has strong competitiveness in testing algorithms.Finally, CABES is applied to four constrained engineering problems and a groundwater engineering model, which further verifies the effectiveness and efficiency of CABES in practical engineering problems.
Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability

Selfish mining is a typical malicious attack targeting the blockchain-based bitcoin system, an emerging crypto asset. Because of the non-incentive compatibility of the bitcoin mining protocol, the attackers are able to collect unfair mining rewards by intentionally withholding blocks. The existing works on selfish mining mostly focused on cryptography design, and malicious behavior detection based on different approaches, such as machine learning or timestamp. Most defense strategies show their effectiveness in the perspective of reward reduced. No work has been performed to design a defense strategy that aims to improve bitcoin dependability and provide a framework for quantitively evaluating the improvement. In this paper, we contribute by proposing two network-wide defensive strategies: the dynamic difficulty adjustment algorithm (DDAA) and the acceptance limitation policy (ALP). The DDAA increases the mining difficulty dynamically once a selfish mining behavior is detected, while the ALP incorporates a limitation to the acceptance rate when multiple blocks are broadcast at the same time. Both strategies are designed to disincentivize dishonest selfish miners and increase the system’s resilience to the selfish mining attack. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability made by the proposed defense strategies. Statistical analysis is applied to evaluate the feasibility of the proposed strategies. The proposed DDAA and ALP methods are also compared to an existing timestamp-based defense strategy, revealing that the DDAA is the most effective in improving bitcoin’s dependability.