Reviewerly/exHarmony huggingface dataset:
Class Difficulty based Mixed Precision Quantization for Low Complexity CNN Training

Low-bit quantization of CNN training is highly needed for reducing the computational complexity of convolutional neural network (CNN) training. In CNN training, some of the classes can finish training early (reaches high accuracy in early training epochs) while other classes need more time (epochs) to finish training. This measure of training difficulty can be efficiently exploited for the mixed precision quantization to reduce the computational complexity of CNN training. In this paper, we present a training difficulty based mixed precision training approach, where easy-to-train classes are trained using low-bit quantization and the hard-to-train classes are trained using high bit quantization. The simulation results show that the proposed mixed precision training can achieve 1.33X improved compression ratio with the same accuracy compared to 8-bit (activations and weights) and 16-bit (gradients of activation and weight) uniform quantization training for ResNet-20 using the CIFAR-10 dataset.
Improving Resistance of Matrix Factorization Recommenders To Data Poisoning Attacks

In this work, we conduct a systematic study on data poisoning attacks to Matrix Factorisation (MF) based Recommender Systems (RS) where a determined attacker injects fake users with false user-item feedback, with an objective to promote a target item by increasing its rating. We explore the capability of a MF based approach to reduce the impact of attack on targeted item in the system. We develop and evaluate multiple techniques to update the user and item feature matrices when incorporating new ratings. We also study the effectiveness of attack under increasing filler items and choice of target item.Our experimental results based on two real-world datasets show that the observations from the study could be used to design a more robust MF based RS.
Filter Pruning Method for Inference Time Acceleration Based on YOLOX in Edge Device

Convolutional neural network (CNN) has a lot of parameters and floating point operations (FLOPs), so it is difficult to use it in edge devices with limited resources. To solve this problem, the filter pruning method of our previous study was extended and applied to the state-of-the-art object detection network, YOLOX. In addition, the inference time of the pruned network was measured on NVIDIA Jetson Xavier NX using the PASCAL VOC dataset to confirm performance improvement in the actual edge device. When the target pruning rates of parameters and FLOPs were 40% and 30%, mean average precision <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{mAP})(0.5)$</tex> improved by 0.07%, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}(0.5:0.95)$</tex> decreased by 0.8%, and inference time improved by 19.48%. Also, when the target pruning rates of parameters and FLOPs were 40% and 50%, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}(0.5)$</tex> decreased by 0.57%, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}(0.5:0.95)$</tex> decreased by 2.84%, but the inference time was improved by 36.21%.
Non-Sequential Indexing of Videos using Linguistic Computation

Since the outbreak of Corona Virus Disease(COVID-19), the education sector has seen a drift from traditional in-person teaching methods to virtually-assisted learning. This new trend has paved its path for students to easily gain access to a variety of educational instructors across the globe. But online education comes with its own potential and challenges. Factors like high availability, flexibility, and affordability of the online learning platforms add to the effective deliverance of the content in this progressive present-day online learning. Some key disadvantages are lack of powerful conveyance of content to listeners and sequential navigation of videos. Linearly searching for specific topics through long videos is a common problem that students face, while learning from the internet. This research study proposes a novel approach to promote the application of non-sequential navigation of videos by identifying key-topics and automatically generating timestamps. The model utilizes Natural Language Processing (NLP) and Optical Character Recognition (OCR) techniques for determining the key topics from the video. Timestamps are identified for the keywords before they are uttered, using a novel algorithm for audio indexing. Finally, timestamps are successfully generated for every keyword. Through this study, the objective of non-sequential navigation of videos using a new audio-indexing algorithm is achieved.
Research on Data Sharing and Privacy Protection System of Power “One Network Office”

Due to the fact that the business scenario of “One-stop service” for electric power has not been truly opened at this stage, there are low real-time data exchange, difficult data life cycle management, low data correctness and security, and difficulty in information interconnection and “data island” problem. A credible and reliable data sharing mechanism has not yet been formed between government affairs platforms, which has seriously hindered the development of “One-stop service” for electricity. In response to the above problems, this paper first analyzes the applicability of blockchain technology in the energy and power industry, and then proposes a decentralized and trustless data sharing framework based on blockchain technology. Secondly, from the perspectives of data privacy protection and data privacy sharing, different blockchain-based data privacy security protection strategies are proposed. Since federated learning and blockchain have strong commonalities in application fields, architectural features, and privacy protection mechanisms, we combine federated learning to study distributed, secure and trusted data sharing. Finally, the application scheme of blockchain in the power sharing economy is proposed, which provides new ideas and feasible directions for the research and application development of blockchain technology in the power industry.
Design and Security Analysis of Isoidentity based Isobeta Cryptosystem

Digital data protection frameworks inclusive of cryptographic mechanisms and signature crypto mechanisms are essential for stable data communication in open network systems. An extensive open-key folder is not necessary for $\mathcal{J}\mathcal{D}$ -based crypto mechanisms like $\mathcal{J}\mathcal{D}$ -based crypto mechanisms, the $\mathcal{J}\mathcal{D}$ basedsignature mechanisms, else $\mathcal{J}\mathcal{D}$ -basedkey circulation mechanisms are between the enhanced an opposing for create stable and protected digital data communication for enormous-scale open network systems recommended by Shamir. This article suggests an isoidentity based isobeta crypto mechanism build on the generalized isodiscrete isologarithm isoproblem and isointeger isofactorization isoproblem that is one of the initial implementations of Shamir's for security norms. Also, we examine the security adverse a conjuration of certain clients in the suggested mechanism, and demonstrate the probability of establishing a higher secure mechanism.
Immersive VR IS Based on Virtual Reality Technology

The rapid development of interactive technology makes immersive VR a trend, which is widely used in various fields. VR technology (VRT) realizes the process of human-computer interaction by simulating the complex and changeable natural environment and human activity scenes in the real world through the digital operation of the computer. For this reason, the immersive VR interactive system (IS) for VRT is conducive to improving people’s understanding of ISs and VRT. This article mainly uses experimental methods and case analysis methods to combine the immersive VR IS with art, analyze the role of the IS, and provide data support for visual feedback and multi-channel feedback. The experimental results show that the visual feedback and multi-channel feedback time are 4~14s shorter than the other two types of feedback time. This is enough to illustrate the visual effect of the immersive VR Interactive System.
EHLS: Beginner to Advanced Ethical Hacking Learning Platform

The Ethical Hacking Learning System (EHLS) is a comprehensive learning platform that includes all necessary resources in both theoretical and practical aspects. It will enable students to learn and practice ethical hacking in a secure and legal setting. It will be user-friendly to attract users and present them with a unique and engaging learning approach in ethical hacking programmers. EHLS also assists students and lecturers in managing and monitoring their learning processes, such as quizzes and performance tasks.
Channel-Wise Activation Map Pruning using Max-Pool for Reducing Memory Accesses

While neural network pruning can reduce the amount of data transfers, pruning techniques such as fine-grained pruning cannot be efficiently implemented as indexing overhead is high. In order to design a hardware friendly pruning technique, structured pruning, which removes groups of data together to minimize the indexing overhead, is highly required. In this paper, to reduce the overall number of main memory accesses when implementing convolutional neural network (CNN) accelerators, we propose a hardware friendly L2-norm based structured channel-wise activation pruning using max-pooling. Max-pooling is typically deployed in CNN to decrease the height and width of CNN activation maps. The simulation results of the proposed technique shows that the activation maps of ResNet20 and ResNet56 can be reduced to 48% and 51%, respectively, with less than 1% accuracy degradation on CIFAR-10.
Automotive Features Controlling using APDS 9960

A gesture-based system is proposed in this paper that can take away some of the driver’s responsibilities. While driving, controlling multiple devices steals drivers’ attention from the road and in most of the cases is becoming the cause of accidents. Several research projects are underway to develop, test, and create a Human-Machine Interface that will allow drivers to operate automotive equipment and functions without being distracted. In this work a basic system to operate some of the car functions using gesture recognition is presented. This system recognizes a few hand gestures and each gesture has been assigned to a unique operation with a locking-unlocking function. The sensor used here is the APDS 9960 gesture sensor. The detection of gestures to enhance the driver’s experience is mentioned in this paper.
Soccer Curveball Trajectory Optimized by Adaptive Fuzzy Algorithm

Football, as the world’s largest sport, plays an important role in the improvement of modern society. Fuzzy algorithms simulate human thinking, avoid the difficulty of creating mathematical models, and can use simplified designs to complete more complex tasks. This paper studies a multi-modal and motion modeling-based model and an overall algorithm for soccer trajectory prediction based on adaptive online adjustment of parameters to achieve The automatic optimization of the motion trajectory model is realized, the generation of the model parameter library and the training of the model parameters are realized. The purpose is to predict the trajectory of the football arc based on the adaptive fuzzy algorithm, through a large amount of experimental data and the decision tree algorithm in the machine learning algorithm, and at the same time use the target space positioning, starting from the actual needs of the football positioning vision system, to achieve The fast and accurate football identification and positioning has laid a good foundation for the subsequent motion trajectory analysis.
Classification of WBC based on Deep Learning using Microscopic Images

The diagnosis of White Blood cells (WBC) also named as Leukocytes is to classify and detect the total number of WBCs in the human blood cells in order to find the infections, allergies, and diseases. The WBCs are analyzed beneath the microscope, wherein variations in structure and shape reveal the presence of certain diseases. While the examination of WBC images by physicians clinically, a variety of issues may occur due to individual misinterpretations. The Deep Learning method is the best method for accurately and rapidly obtaining white blood cell types. The samples are preprocessed with pixel enhancement, augmentation, normalization, and resizing steps. The Convolutional Neural Network (CNN) method-based results are obtained for WBC classifications. At that point, a Transfer Learning model is used for fine-tuning. As a result, the new model produces classification using the Softmax classifier and finds a higher accuracy equated to other classification approaches.
Premises of Conservativity Violations in Ontology Alignments

we improve the quality of ontology alignments by detecting and repairing conservativity violations that arise from interactions of ontologies through alignments. An alignment of ontologies which only states equivalent relations between entities of ontologies is an ontological signature isomorphism if and only if it preserves the logical closures of ontologies. Otherwise, it violates the isomorphism constraint. In this paper, we show that isomorphism violations constitute the premises of violations. Hence, conservativity violations repair can be reduced to repairing isomorphism violations. Isomorphism violations have the advantage of being detectable by local reasoning over each ontology, which is particularly relevant since most approaches require reasoning over the merge of both ontologies, an operation with high computational cost. We compare the result of our approach and the result of the approaches applied on the reference alignments used in the conference organization track provided by the Ontology Alignment Evaluation Initiative (OAEI). The experimental results show that the number of violations detected by our method is strongly reduced which might heavily change the results of this track.
Real-Time Face Mask Detection from CCTV Video Frames using Deep Neural Networks

The coronavirus, commonly known as SARS COVID-19, is causing a pandemic that is affecting individuals all over the world. The spread of the virus compelled the authorities to impose a rigorous lockdown on its citizens. Every person in society may experience a variety of issues as a result of this. According to WHO (World Health Organization) regulations, the sole method to halt the virus's spread is to wear a face mask. Therefore, the suggested approach makes sure that everyone appropriately wears a face mask in public locations. The objective of this approach is to detect people without face masks and people who wear facemasks incorrectly in social environments. This system consists of multiple face detection modules to find the area of interest within the video frames. In the next level, using the trained Deep Learning model, the presence of a mask is detected and faces without mask and faces wearing masks incorrectly are highlighted. The dataset for face mask identification comprises of 8190 photos with unique facial annotations from the Kaggle and RMFD datasets that come into two categories: "with mask" and "without mask".
An Efficient and Robust Breast Cancer Detection in Mammogram Image using Improved Threshold Extraction Method

Although deaths from lung cancer are more prevalent overall, breast cancer is the most common malignancy among women. A number of distinct genetic codes work together to induce cancer growth in the breast. Less is known about the succeeding stages in gene changes and how they contribute to tumor formation. Breast cancer detection methods have advanced significantly in recent years. This work describes an image processing method using MATLAB for detecting breast cancer in mammography pictures. Data about the different MATLAB functions used throughout the breast cancer detection process is provided. This technique aids the doctor in detecting breast cancer and keeping tabs on the progress of therapy.
A Machine Learning based Approach for Breast Cancer Prediction

Breast cancer is currently a rather frequent kind of cancer. Recently, it has been observed in many women, and it has been responsible for several fatalities. In order to prevent this terrible disease, it is important to forecast the likelihood of cancer in its earliest stages. Machine learning is a novel AI approach whose potential for cancer prediction has not yet been fully understood. In this work, the ability of machine learning classifiers is employed to forecast breast cancer. Machine learning algorithms applied in this work to predict the breast cancer are Support Vector Classifier, Random Forest Classifier, KNN Classifier, and Logistic Regression Classifier. The experiment's findings show that Logistic Regression outperforms the other three prediction methods.
Informatization of Constructive English Learning Platform Based on Improved GLR Algorithm

The advancement of mobile computing technology and various information terminals has greatly promoted the development of mobile learning platforms based on Internet+. Digital and mobile online learning has become a new way for people to receive education. The learning method of mobile learning platform allows people to use fragmented time to learn without being limited by time and space. In this paper, the CE (constructive English) learning platform based on GLR algorithm aims to improve students' interest in autonomous learning and to organically integrate traditional offline teaching and online autonomous learning. This paper uses the unified integration theory to add the variable indicators of mobile situation and flow experience respectively in terms of platform and user perception, and builds an informatization model of English mobile learning platform according to the characteristics of English online learning platform with a wide range of current user groups. Constructivist learning is a new development of cognitive learning theory. It emphasizes the dominant position of students in the learning process and the guiding role of teachers. It believes that students' knowledge acquisition is based on their existing knowledge and experience, through real situations, meaning construction completed actively through collaboration and interaction with teachers and classmates. The final results of the research show that the satisfaction of content quality, teacher strength, learning resources and curriculum arrangement are 71.45%, 62.45%, 60.13% and 64.45% respectively, and generally more than 60% of users are satisfied with the service content of the English learning platform. expressed satisfaction.
The Optimization of Binary Feature Selection Problem using GSK Algorithm

In the data organizations, lots of tenders have produced datasets and decision-makers are required to apply several logical methods to extract valued data from databases, such as data removal, neural system, and statistical analysis. There has been a growth in real-world applications’ structures for machine learning and design acknowledgment.Among these features, an amount could be redundant or unrelated but it is hard to guess which one is valuable in the absence of previous skill. The algorithms for feature selection are commonly separated into three categories such as embedded, wrapper and filter. This research proposed the data imbalance and high dimensions for feature selection by using the GSK algorithm. The imbalanced data sets are categorized in two various types of approaches, namely, the wrapper and filter approach stage. The filter algorithm chooses topographies from the dataset and then customs the designated feature selection to teach the beginner. This research is focused to resolve the optimization of the binary feature selection problem. This comprehensive research supports the researchers in accomplishing a better solution for the feature selection optimization problem.
Aging Building Renovation based on Programming Aided Design

With the increasing proportion of social aging in China, the requirements of the elderly for the quality of living environment have also been improving. Based on China’s national conditions and traditional cultural habits, home-based elderly care is still the main mode of elderly care for a long time now and in the future. For the old residential areas where the elderly live more, how to adapt to the aging transformation in combination with the actual situation, so as to better meet the needs of the elderly at home, is an important topic concerned by the architectural community at present. Taking the combination of programming aided design(PAD) and architectural design as the research object, this paper analyzes and studies the programming aided architectural design, briefly explains the process of PAD, discusses the aging building renovation(ABR) design based on PAD, applies PAD to aging renovation, and puts forward the theory and method of aging renovation of old houses; Through the investigation and analysis of the aging population in rural areas of a county, it is confirmed that the research on the aging transformation of old houses based on PAD has important theoretical significance and practical value for coping with the trend of aging society in China, improving the quality of life of the elderly, and creating a harmonious social environment.
Knowledge Graph Completion based on Hyperbolic Graph Contrastive Attention Network

Knowledge graph completion technology is important for the integrity of knowledge graph. However, the feature mining of knowledge graph is not sufficient due to the lack of hierarchical and neighborhood information. To solve such issues, this paper proposes a knowledge graph completion method based on the Hyperbolic Graph Contrastive ATtention network(HyGCAT). HyGCAT embedded the knowledge graph into the hyperbolic space with constant negative curvature to capture the complex hierarchical relations between entities with less memory. Meanwhile, HyGCAT uses the attention mechanism to learn the latent representations of neighborhood entities. Furthermore, HyGCAT strengthens the correlation between representations of entities and neighbor subgraphs through contrastive learning. The proposed method can improve the performance of link prediction for knowledge graphs completion significantly.
Currency and Fake Currency Detection using Machine Learning and Image Processing – An Application for Blind People using Android Studio

Any development in technology is made with the intention of solving the difficulties in that field. One such identified problem is blind people were unable to make out the type of currency. The proposed model efficiently identifies the type of currency. The converse of this leads to the printing and scanning of currencies and releasing them into the market, which not only affects the economy but also reduces the value for money. This further causes some conflicts and a great threat to the country. Thus, a need for efficient currency and fake currency detection methods is required. Finding fake currencies is done by any ways. The proposed work identifies the currency’s originality by examining its images. The code simulated using MATLAB extracts the features of currencies. Machine learning models are used for mapping the extracted feature to its standard value, it is able to identify the fake currencies efficiently.