Reviewerly/exHarmony huggingface dataset:
Anonymous Signcryption for Securing Knowledge Management System

The need to safeguard knowledge management systems (KMS) against security breaches has increased as attackers increasingly steal company secrets for nefarious motives such as blackmail, extortion, etc. Various cryptographic techniques have been developed in order to secure the KMS's shared data. However, none consider the case in which an employee wishes to exchange confidential information with the business manager without exposing his or her identity in order to prevent profiling or retaliation by other employees. This study proposes an anonymous signcryption system for secure information sharing without revealing the identities of the message's encryptor and signer. The proposed method was tested in a computer simulation, and the outcomes demonstrate that the system functions effectively and might be applied in real world.
Relation Heterogeneous Graph Neural Network

In heterogeneous graph, we can mine high-order neighbor information or semantic information using meta-path, or only use the original connection, and then obtain high-order neighbor information indirectly through residual connections. Both two methods can get good results, but the latter can improve the efficiency without prior knowledge and meta-path mining. We take the second approach, proposing a novel relation heterogeneous graph neural network (RHGN) which adds edge features to the message aggregation of nodes and updates edge information by comparing different edge types through auxiliary tasks. Extensive experiments on two real-world heterogeneous graphs of node classification tasks show that our proposed model works better.
Query Optimization Scheme for Edge Device Data Management Based on Consortium Chain

As a distributed ledger jointly maintained by multiple parties that do not trust each other [1], the data services provided by the blockchain system must ensure that the results cannot be tampered with. However, the query on blockchain is affected by the chain structure. This paper adds relational semantics to block data. So, it can support three basic query methods and accelerate a unique query method for blockchain. In addition, we design four types of indexes to ensure the efficiency of data query. We optimized the query processing and tested it in the edge device scenario and taking the Internet of Vehicles as an example. The result shows that the response time for the query is significantly reduced.
An Unsupervised Domain Adaptation Method for Compressed Video Quality Enhancement

In recent years, the field of deep learning based compressed video quality enhancement (CVQE) has developed rapidly. However, due to the domain shift, the model struggles to perform effectively on those videos which has inconsistent distribution with training sets. Recently, a domain adaptation method called mean teacher has been proposed in the field of object detection. The mean teacher technique has already had a lot of success with cross-domain recognition. In this paper, we introduce the mean teacher method to CVQE, interfere with the output of teacher and student model by adding noise, and use consistency loss to guide the training of student model. This is the first work of source-free domain adaptation on CVQE. Our experiment demonstrates the practicality of the unsupervised approach on CVQE.
Robust Graph Embedding via Self-Supervised Graph Denoising

Graph embedding has attracted increasing attention in the past few years. Graph neural network is the most popular type of algorithm to provide high-quality graph embedding. It brings stunning success but causes an important and easy-to-be-ignored problem, i.e., vulnerability, which makes the embedding quality dramatically degrade and affects the performance of downstream tasks like node classification. In this paper, we propose an algorithm for robust graph embedding via self-supervised graph denoising (SSGD). The key idea is to learn normal patterns about how a graph is organized and apply the patterns to reorganize the structure and remove noisy edges in the graph. Since the vulnerability is mainly caused by noisy edges, graph neural networks are supposed to work well on denoised graphs. In experiments, we introduce 6 state-of-the-art algorithms and 3 real-world datasets to demonstrate the superiority of our algorithm.
A New Blind Road Classification Method Based on Wavelet Feature Fusion and Transfer Learning for Mobile Environment

It's very useful to sense blind road environment, a new real-time detection for blind road detection by wavelet feature fusion and transfer learning based on MobileViT is proposed. Firstly, the wavelet-based features are extracted through wavelet transformation with different scales and channels, some bottleneck features are recovered from pretrained model in other big-data domain, and then these features are incorporated together. Secondly, the new model is trained again based on pretrained model parameters with fine tuning. Finally, we used the trained model for real time blind road scene detection. In our experiments, the blind road image datasets are collected at different districts in Chengdu for tests with different model parameters. At the same time, our inference models are tested with the embedded device in real time environment. Our test results show that the new method has advantages on classification precision with directly using MobileViT by transfer learning. The proposed scheme is more effective than the original scheme, and the total accuracy is increased from 88.09% to 91.58% on the standard flower photos dataset. We got the total precision of 99% and 95% respectively on two blind road datasets collected by ourself.
Collaborative And Popularity Based Book Recommender System

Utilization of online websites to shop for a range of products has been frequent in our day to day lives. As a result, consumer demand is becoming more diverse, making it difficult for a general store to deliver the proper products based on the tastes of its customers. To deliver a favorable buying experience for the consumer, these E-commerce websites use various recommendation system algorithms. Recommendation systems are a tool for dealing with this problem; they allow you to meet consumer’s demands and expectations while also attracting new ones. A product recommendation system is essentially a filtering system that suggests particular things to customers depending on their interests. Recommendation systems have exploded in popularity in recent years with applications in music, news, movies search queries and others. The bulk of today’s E- commerce sites such as Amazon, Flipkart ,Myntra, make use of their own recommendation algorithms to better offer their customers with products they are likely to like .Recommendation engines are data filtering technologies that use algorithms and data to suggest the most relevant items to the user.
Search-based Feature Selection for Cross-Project Fault Prediction

Cross-project fault prediction (CPFP) is a current field of research in the realm of software engineering. CPFP comes into play when there is a scarcity of within-project training data. In particular, it involves constructing a fault prediction model for software project ‘X’ using the defect/fault data of software project ‘Y’. However, the distribution dissimilarity between the two project's data creates a bottleneck in its success. Many existing approaches addressed this issue by selecting relevant instances from the training data without giving any attention to feature selection (FS). Thus, to assess the power of FS for effective CPFP, we investigated two search-based FS algorithms namely Binary Genetic Algorithm (BGA) and Binary Particle Swarm Optimization (BPSO) algorithm. We performed 26 CPFP experiments based on 8 software projects and compared their performance with a CPFP model (ALL_CPFP), built with all features. Although both BPSO _CPFP and BGA _CPFP showed their potential over ALL_CPFP, BPSO_CPFP performed better than BGA_CPFP in capturing the important features for effective CPFP.
Artificial Intelligence and Deep Learning based Face Mask Detection System using Generic Camera

In the past two years 2020 and 2021, the COVID-19 outburst has had a serious effect on human life. The effects and side effects of COVID-19 are being stroked in almost every discipline relevant to survival and development. The healthcare system was in a problematic situation during this tough time in pandemic situation all over the world. One of the many precautions and protections used to break the chain of spreading of this virus is wearing a mask and keeping safe distance. In a network of smart cities where entirely public spaces are monitored by Closed-Circuit Television (CCTV) cameras, we are offering a strategy in this study that restricts the spread of COVID-19 by identifying people not wearing mask. The network alerts the proper authority whenever a person without a mask is discovered. It is believed that our research will help many countries throughout the world stop the spread of this infectious disease. We've examined this on 200 real people in the present, with a 100% success rate. It is also observed that when more than one person in front of CCTV success rate reduced exponentially
A Modified Approach of Hyper-parameter Optimization to Assess The Classifier Performance

Modern algorithms are remarkably adept at identifying data that is too large or complex for humans to comprehend. It has become difficult to identify the list of hyperparameters that deliver an improvement in performance for a given geometry of the data set. This has shifted the emphasis from processing data (model improvement) to the hyper parameters (tuning) of the classifier. Since hyper parameters are set to default values for a generic case, they need not be specially tuned to the given classification task. The purpose of this paper is to demonstrate a strategy that avoids unnecessary tuning attempts and shows the best performance for various classifiers on various shapes of geometry. The findings of this experiment will assist the user in determining whether hyper parameter tuning activities is worth the time and computational resources.
Effective utilization of Machine Learning Techniques to Classify Breast Cancer Tumors

Breast Cancer occurs when alterations called mutations to take place in the genes that cause anomalous cell advancement in the breast. One of the ways to achieve success in this field of cancer is by digging deep into machine learning techniques to diagnose the disease better as well as attempt to cure it. This paper aims at identifying breast cancer tumors fast and efficiently. The system suggested in the research uses the Wisconsin Breast Cancer Dataset, which was downloaded from the UCI repository, and allows binary classification, classifying tumors as malignant or benign. Techniques used to implement classification are Support Vector Machines and Random Forest. To comprehend the trends and patterns in the Wisconsin Breast Cancer Dataset, a thorough data visualization of the dataset has been conducted. The system employs data processing techniques to retrieve useful data, followed by Principal Component Analysis to carry out feature extraction. For SVM, to reiterate through the predefined hyperparameters, Grid Search CV has been implemented. For the Random Forest algorithm, k-fold cross-validation has been applied to achieve a unique set of results. The highest accuracy achieved using the random forest algorithm is 99.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and the same for SVM is 98.2%. The following algorithms have been highlighted since their implementation has helped to retrieve significant accuracy levels. The models have been evaluated by computing the precision, recall score, f1 score, and confusion matrix. Models have also been compared using truepositive rate, true negative rate, false positive rate, and false negative rate.
Augmented Reptile Feeder

This paper describes a method of gaming based on AR[Augmented Reality] and Computer Vision in the field of AI[Artificial Intelligence]. AI and AR include a wider range of applications in upcoming technologies. These technologies are becoming an integrated part of our way of life. These technologies are gaining more popularity in the gaming sector. AR games achieve popularity with the masses, causing growing addiction. We have developed a game called Augmented Reptile Feeder (ARF). We describe an algorithm based on the hand tracking module. The Augmented Reptile Feeder system is intended for 2D gaming innovations with real world interaction. This will be achieved by tracking the movements of the index finger using hand detection in the CV zone. Operating this game is pretty simple and straightforward for users. ARF ultimately provides an enhanced platform unlike the rest available in the gaming market.
Simulative Results of Time-Variant Nonlinear Equation Systems Solved by Integral-Aided Zhang Neuronet with Various Noises Suppressed

To solve time-variant nonlinear equation systems (TVNES), a neuronet model called integral-aided Zhang neuronet (IAZN) model is presented and investigated. For comparison, a traditional Zhang neuronet (TZN) model is presented. In addition, simulative results show that the presented IAZN model possesses superior performance to the TZN model for solving TVNES with various noises suppressed. In detail, the presented IAZN model converges to the theoretical solution of TVNES with arbitrary constant noises, and has excellent ability to suppress bounded random noises and linear-form time-variant noises. Illustrative numerical experiments substantiate the efficacy and superiority of the presented IAZN model for solving TVNES with various noises suppressed.
Pivot-based Unsupervised Domain Adaptation for Pre-trained Language Model

In the task of text classification, natural language processing technology provides an effective solution for automatically identifying text content classification, but labeled data is difficult to obtain in specific domains. To reduce manual labeling, some researchers have proposed unsupervised domain adaptation technology, which is a special transfer learning technology, transferring source domain models suitable for general knowledge to the target domain with less labeled data, to improve the generalization effect of the model in the target domain. However, the current unsupervised domain adaptation methods are mostly to fine-tune the pre-trained model directly using unsupervised data from the target domain. This method needs a large amount of unsupervised data as usual to improve the training effect. Therefore, this paper presents a pivot-based unsupervised domain adaptation method, which extracts and masks pivots from unsupervised data, fine-tunes the pre-trained language model, and finally validates the method using supervised training, compared with the method of directly using unsupervised data to fine-tune the original model. The pivot-based domain adaptation method effectively improves the efficiency of domain knowledge transfer for the specific domain.
Learning Emotion Representations on a Manifold

Previous works on emotion embedding construct models for emotion categories to analyze the emotion relations, but they neglect the point that the emotion embedding result is actually in the form of a high dimension array. The result being as a high dimension array means that it could also be a manifold of lower dimension existing in such high dimension. Ignoring the assumption of the manifold will distract the actual relation information among emotion categories, and undermine the application effect of emotion embedding on downstream tasks. To solve this problem, we utilize LLE (Locally Linear Embedding), an unsupervised learning algorithm, to capture the manifold characteristics of the emotion embedding. We visualize and analyze the result in 2 dimensions, making it more effective for downstream applications.
Design and Implementation of Digital Information Platform for Home-based Elderly Care Services

China has entered a comprehensive aging society, and home-based elderly care is an important form of elderly care services. With the development of society and the progress of technology, how to use digital technology to provide services for more people is one of the important topics at present. According to the current demand for home-based elderly care services in China, this paper puts forward the framework of building a digital information platform for home-based elderly care services, and provides technical support for elderly care services by establishing databases, constructing XML virtual data sources, and building search and display systems.
MAT-GT: An Automated Ground Truth Collection Tool for Mobile Application Traffic

Due to the rapid update of mobile applications, the characteristics of network traffic may change greatly, which leads to the degradation of network traffic classification model. The network traffic classification model requires accurate labelled data sets. Therefore, it is necessary to build high quality network traffic ground truth for mobile applications. This paper suggests an automated ground truth collection system for mobile application traffic, called MAT-GT, which can collect the network traffic of mobile applications and quickly construct accurate ground truth. It contains four components: controller engine, capture engine, traffic process engine and noise filter engine. MAT-GT mainly uses the user identifier of application and netfilter log to collect and label traffic of the mobile applications. For the labelling of fine-grained behavior traffic, we propose to use the injection timing behavior labelling method to slice the traffic of the application. After the traffic traces processed, we combine the noise recognition model to filter out the noisy traffic in the dataset. Then, we accurately form labelled traffic data sets for 150 mobile applications and 33 behaviors traffic. Finally, we apply non-noisy application traffic datasets for mobile application traffic classification, which further verifies that the accurately labelled data set provided by our system can improve the classification effect of application traffic.
Research on Construction of Armament Knowledge Graph by Protégé

With the in-depth development of military informationization, the construction of weapon and equipment knowledge graph is the basis for the intelligent development of battle commanding, and is one of the important technologies to improve the decision-making level of combat data. This paper proposes a framework of knowledge graph construction of weapons and equipment based on Protégé, and describes the corresponding key technologies of each link of the knowledge graph building of weapons and equipment, to provide theoretical and methodological support for improving design and construction of knowledge graph of weapons and equipment.
Construction of Ocean Information Network with All Time, Space and Elements

Marine information network involves many links such as marine information perception, transmission and fusion application. This paper proposes a new generation of marine information network from the perspective of information perception, transmission and fusion application. On the basis of traditional marine information network, new information nodes such as large floating islands, surface and underwater combined floating tower stations, self-service navigation vehicles, tethered UAVs, etc. are supplemented to form a new architecture of marine three-dimensional large network, forming a new architecture of shore, sea The new system of integrated ocean information network of air, space and submarine realizes the "four all-around" information coverage of the ocean all day, all weather, all sea areas and all elements. This paper introduces the architecture, scientific issues, key technologies, and application prospects of this new generation of marine information network, which is an important exploratory work in the research of marine information system and helps to promote the formation of a new architecture and standard of marine information network. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup>
Detection and Classification of Breast Cancer from Microscopic Biopsy Images using Modified Neural Network

Cancer is widely recognized as being among the most lethal illnesses currently affecting humans. Cancer, which has life-threatening effects, has the opportunity of being eradicated if it is caught at a preliminary phase and treated appropriately. In contrast to that, the precision with which one makes predictions is an important factor. As a result, the development of a dependable model that makes significant contributions to the healthcare community in the prompt identification of biopsy pictures with 100% accuracy becomes the primary focus of attention. In the context of clinical cancer research, the purpose of this article is to work toward the development of improved prediction models by utilizing multivariate information and high-resolution diagnostic techniques. In order to categorize microscopic biopsy pictures of cancer, the socially spider optimization (SSO) encryption method neural network is presented as a possible solution in this research. The relevance of the approach that was developed is dependent on the SSO method's ability to effectively tune the weights assigned to the neural network classification. The performance of the proposed technique is evaluated using measurement systems like accuracy, sensitivity, specificity, as well as MCC assessments, and the conclusions drawn are 95.91 percent, 94.25 percent, 97.12 percent, and 97.68 percent, correspondingly. These results demonstrate that the proposed technique is efficient for the diagnosis of cancer disease.
A Generic Algorithm for Controlling an Eyeball-based Cursor System

This paper introduces a specific human-computer interaction system. To get input from the user, a system solely depends on various input devices. But the people who are afflicted by specific ailments or disorders, are unable to use computers. Allowing persons with disabilities and vision impairments to operate computers with their eyes will be very beneficial to them. Additionally, this form of control will reduce the need for other parties to assist in operating the computer. The individual who is handless and can just utilize their eye motions to work will find this measure to be most useful. The center of the pupil is intimately related to how the cursor moves. The electronic device controls the movement of the computer cursor. In this proposed system, OpenCV libraries and the Haar cascade algorithm are used for detecting eye movements.
Modeling of Chicken Swarm Optimization with Hybrid Networks for Visual Places Recognition

Visual Place Recognition (VPR) is procedure of appropriately recognizing previously visited places under variable viewpoint conditions. VPR becomes a stimulating process because of the difference in lighting conditions (daytime or night), shadow, weather conditions, view points, or even diverse seasons. So, it is needed to design effectual VPR models to manage the variations in the visual appearances. With this motivation, this article develops a chicken swarm optimization with hybrid network for VPR (CSOHN-VPR) model. The presented CSOHN-VPR model aims to recognize the visual places effectively and accurately. To accomplish this, the presented CSOHN-VPR model allows for automatically extracting the features using the HybridNet technique. For optimal hyperparameter tuning process, the CSO algorithm is exploited to it which helps in improving the recognition performance. At last, city block similarity measure is used to recognize the visual places. To demonstrate the enhanced place recognition outcomes of the presented CSOHN-VPR method, a detailed experimentation process is made. The obtained values pointed out the improvements of the CSOHN-VPR model over other models. Therefore, the CSOHN-VRP model is able to recognize places regardless of varying illuminations, seasons, and viewpoints.