Reviewerly/exHarmony huggingface dataset:
Implicit user relationships across sessions enhanced graph for session-based recommendation

Session-based recommendation aims to predict users' next preference based on the sequence of their own history preferences in a short period. Most state-of-the-art methods model the session as a graph using graph neural networks (GNN) to capture the dynamic transitions between items within sessions. However, the complex and hidden correlations between different sessions are not adequately addressed, especially during the testing stage. We argue that session-based recommendation tasks can be improved by exploiting the correlations between different sessions in both training and testing. To this end, we propose a novel three-GNN-based recommendation framework to exploit the intra- and inter-session item correlations and the session-session correlations. The first one is a graph-based multi-layer perceptron to learn the inter-session item representations in a contrastive learning scheme guided by a contrastive loss function. The second one is a multi-relation graph attention network for intra-session item representations. The two item embeddings are combined through a position attention scheme to form the session representation, which is modulated and enhanced by the third extra session GNN by capturing the session-session correlations. The three levels of correlations are used in the training and testing stages in a joint prediction manner. To alleviate the data sparsity issue faced by the session GNN, we expand the session items by incorporating the neighboring items in the global item graph built from the entire training sessions. We have evaluated our method on multiple benchmark datasets. The results have shown that our joint recommendation method based on session correlation has significantly improved the recommendation accuracy over the state-of-the-art by more than 10%.
A blockchain-based dynamic and traceable data integrity verification scheme for smart homes

A smart home system composed of numerous smart devices has benefited people’s life due to the feature of convenience. With the development of smart home technology, huge amounts of data stored on external servers flow between different smart devices, and the integrity of these data is of great importance to the security of smart home systems. In this paper, we propose a blockchain-based data integrity verification scheme that supports efficient dynamic operations for a smart home system. In the verification scheme, we utilize the home gateway (HG) to aggregate all the data information and design a data structure that can be efficiently and dynamically operated to store the tag information of the data, which can reduce the local storage pressure in smart home systems. Moreover, homomorphic verifiable tags are used to formulate verification strategies and record the interactions between HG, cloud service providers (CSP), and third-party auditor (TPA) on the blockchain to make the verification service traceable and useful. Then, the theoretical analysis and experiments prove that the scheme is effective and cost-acceptable in smart home systems.
Beyond robustness: Resilience verification of tree-based classifiers

In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings. To mitigate the limitations of robustness, we introduce a new measure called resilience and we focus on its verification. In particular, we discuss how resilience can be verified by combining a traditional robustness verification technique with a data-independent stability analysis, which identifies a subset of the feature space where the model does not change its predictions despite adversarial manipulations. We then introduce a formally sound data-independent stability analysis for decision trees and decision tree ensembles, which we experimentally assess on public datasets and we leverage for resilience verification. Our results show that resilience verification is useful and feasible in practice, yielding a more reliable security assessment of both standard and robust decision tree models.
StarNet: Joint Action-Space Prediction with Star Graphs and Implicit Global-Frame Self-Attention

In this work, we present a novel multi-modal multi-agent trajectory prediction architecture, focusing on map and interaction modeling using graph representation. For the purposes of map modeling, we capture rich topological structure into vector-based star graphs, which enable an agent to directly attend to relevant regions along polylines that are used to represent the map. We denote this architecture StarNet, and integrate it into a single-agent prediction setting. As the main result, we extend this architecture to joint scene-level prediction, which produces multiple agents’ predictions simultaneously. The key idea in joint-StarNet is integrating the awareness of one agent in its own reference frame with how it is perceived from the points of view of other agents. We achieve this via masked self-attention. Both proposed architectures are built on top of the action-space prediction framework introduced in our previous work, which ensures kinematically feasible trajectory predictions. We evaluate the methods on the interaction-rich inD and INTERACTION datasets, with both StarNet and joint-StarNet achieving improvements over state of the art.
Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline

Amodal perception terms the ability of humans to imagine the entire shapes of occluded objects. This gives humans an advantage to keep track of everything that is going on, especially in crowded situations. Typical perception functions, however, lack amodal perception abilities and are therefore at a disadvantage in situations with occlusions. Complex urban driving scenarios often experience many different types of occlusions and, therefore, amodal perception for automated vehicles is an important task to investigate. In this paper, we consider the task of amodal semantic segmentation and propose a generic way to generate datasets to train amodal semantic segmentation methods. We use this approach to generate an amodal Cityscapes dataset. Moreover, we propose and evaluate a method as baseline on Amodal Cityscapes, showing its applicability for amodal semantic segmentation in automotive environment perception. We provide the means to re-generate this dataset on github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/ifnspaml/AmodalCityscapes
PackChain: Toward a Blockchain-based Management Platform for Last-mile Delivery

In this paper, a blockchain-based management platform, PackChain, for last-mile delivery is proposed. The growing popularity of online shopping has put immense pressure on the supply-chain industry, especially on last-mile delivery. The available solutions suffer from high cost, and lack of transparency. Therefore, assuring traceability of users' actions has become a critical requirement to establish trust between parties. The proposed PackChain framework uses the Ethereum blockchain to offer a crowdsourcing platform for last-mile delivery with autonomous and transparent processes. PackChain provides all the core functions needed for the delivery framework to operate through smart contracts such as managing user information, accepting offers, verifying transactions, and handling payments. In addition, the framework relies on proofs of delivery as an arbitration mechanism between users to release or hold funds. The proposed framework is implemented using Solidity and Web3.js to interact between clients and carriers. The emulation results demonstrate the feasibility and cost-efficiency of the proposed solution <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The full code of the smart contract and the related logic is also made publicly available on Github..
Real-Time Application for Recognition and Visualization of Arabic Words with Vowels based DL and AR

Text is difficult to read in some cases due to text orientation, writing style, very light colors, etc. Visually impaired or visually impaired people have difficulty reading text in all of these situations. The architecture proposed in this work is intended to detect and identify Arabic characters with vowels in natural environments. This architecture can help visually impaired or blind people to read the text correctly. It allows users to read the text in a better and more immersive way by combining augmented reality with digital material. The approach uses both deep learning and more specifically the VGG 19 model and augmented reality to improve the efficiency, clarity, and accuracy of text reading. For text detection and identification we use the VGG 19 model, and for text visualization, we use augmented reality. The implementation technique presented in this research for an augmented reality interactive virtual assistant system is for users to use their smartphone's camera to receive enhanced text information via a text image and a three-dimensional image to understand the displayed text. It offers an interesting way to understand their environment. The use of augmented reality to better display recognized text in 3D is a fantastic feature. User research studies are conducted to assess usability and user satisfaction.
Deep Learning Model based Novel Semantic Analysis

Nowadays each and every person connected through the digital platform. The digital market take place the physical market. People purchase everything not going physically on market and buy everything. In the e-commerce platform initially, people purchase any product firstly read the review regarding the product as well as organization provided by the previous customer. The paper illustrates a sentiment analysis on the huge amazon customer review data from the e-commerce website. Initially the dataset in the json format and preprocessing on the data set and convert the dataset into CSV file. Secondly, load the dataset and break the collective information into the training and testing information. Using the method inverse document frequency and term frequency and get features of the information dataset. At last, the deep learning model is use and evaluate the reviews of the customer. The result final outcomes result shows the model precision, recall, f-measure, accuracy and time of the developed model on the real text data.
Development of Mobile Application For E-Voting System Using 3-step Security for preventing phishing attack

Smart Technologies are used to improve the quality of human life, especially the invention of the Internet. Thanks to the rapid growth of the Internet, more and more things can be solved and done in simple and faster ways compared to the old days. A relatively new field of e-voting has been discovered. There are many types and ways of voting, such as going to the polling station, online webpage and mobile application. Because of the rapid growth of the internet, the security of applications cannot be ignored. In this research we have developed an android application and provided a 3-step security before voting to prevent phishing attacks. This enables students to vote online using their own mobile phone from anywhere and anytime. The application is developed and deployed with Android Studio. This research follows the software development life cycle while developing the voting application. The outcome of this research is to develop a mobile app that is intuitive and easy for students to use, as well as serving as a functional application to let students vote with 3-step of security before voting.
Evaluation of Cluster Approach for Detecting Black Hole Attacks in Wireless Ad Hoc Networks using Deep Learning

The cluster approach consists of a huge level of significance as it can easily detect the black hole attacks on various networks such as “wireless ad hoc networks' ‘(WANET), MANET and many others. This research paper comprises a detailed introduction part where important definitions and clear ideas have been given on some key topics such as Cluster approach, black hole attack on various networks and a detailed view about WANET. The types of this network, some advantages and the usage of this network have been discussed thoroughly in this context. The literature review part also reflects the challenges as well as disadvantages of the proposed networking systems and various proposed mechanisms to detect black hole attacks in a short time. This literature review part has constructed as a critical manner and several mechanisms of identification of black hole precisely mentioned with providing clear as well as relevant pictures. The research methodology part clearly has expressed the overall process of concluding over the effectiveness of the clustering approach. After that, the discussion part clearly has delivered a transparent mechanism by following which black holes can be detected easily. It also supports the survey responses also. In the result portion, a detailed survey has occurred in the presence of 35 individuals and two direct questions have been asked to evaluate the effectiveness of the cluster approach for detecting WANET.
Comparative Analysis of Blockchain Technology and Artificial Intelligence and its impact on Open Issues of Automation in Workplace

In the modern decade, utilizing advances in emerging technologies is essential in operating automation in the workplace. Blockchain constructs a distributed point-to-point system that is capable of balancing the financial economy. In the automation process, the rapid adoption of blockchain technology has made both public and social services flexible. In order to achieve the objectives and goals of an individual organization, all the advancements of Artificial Intelligence (AI) bring both opportunities and challenges. This paper provides a competitive literature review regarding the factors related to blockchain technology and artificial intelligence to operate the parking system in automation service by mitigating its potential issues. In this study, the researcher has discussed blockchain security enhancement solutions by adopting AI to develop several blockchain transportation systems. The purpose of this research article is to investigate the impact of blockchain technology and artificial intelligence on the automation service. The researcher has adopted a secondary data collection method to gather relevant data and information related to the research topic to make the study more valuable and authentic. Similarly, the collected resources used quantitative methods to malaise the data to be understandable. Moreover, this study will help the readers to understand the appropriate effectiveness of AI to control automation activity. In this research article, the description of the Industrial Revolution and its benefits are also described. This research article focuses on the open issues of automation in the workplace that can be controlled by artificial intelligence and blockchain technology.
Virtual Reality, Augmented Reality, and Mixed Reality Applications: Present Scenario

With tremendous research, innovation in human computer interaction, infographics and greater accessibility of visualization technologies i.e., augmented reality; virtual reality and mixed reality is to find new possibilities for its applications. The ability to transform the user's perception and interaction with his environment has increased his popularity of use in all spheres of life. This study analyses the literature on augmented reality, virtual reality and mixed reality applications in order to find the research gaps in these fields. This review study has taken 103 papers of 2010-2021 into account from SCOPUS, SCI and IEEE databases. The results of the analysis can provide some useful insights regarding potential and use of augmented reality, virtual reality and mixed reality in the different areas of application.
The Block Chain Technology to Protect Data Access Using Intelligent Contracts Mechanism Security Framework for 5g Networks

The introductory part of the research mainly focuses on the importance of using block chain facilities by using the 5G Network that can be useful for data privacy and security. It can be said that the research mainly focuses on all the benefits of using block chain technology in order to protect all the access of relevant data by implementing intelligent contracts for enhancing the security framework related to the use of 5G networks on the data protection activities. The Literature review of the research mainly concentrates on the benefits and merits of applying the block chain facilities for enhancing both the growth as well as the development of data protection and data privacy. All the merits, as well as demerits of using the block chain facility, have been also discussed throughout the overall research paper. On the other hand, various methods, as well as strategies for applying the block chain facilities, also have been analyzed throughout the literature review section of this research paper. A survey was conducted in this particular scenario to get a clear comprehension of the situation. A survey was conducted with fifty one random people that enable the researches to get a clear picture of the trend while fetching some real life data in this particular scenario.
The Use of Supervised Text Classification Techniques: A Comprehensive Study

Every second in social networking, a massive amount of feedback, comments, and posts are made, resulting in a massive increase in the social database. It's now necessary to go through enormous amounts of data to figure out how people feel about a certain company or product. Most online evaluations are published in English, but as technology and people's understanding improve, Hindi-language material is becoming more prevalent on the internet. In addition, knowing people's views about a particular item is an important component of Indian language sentiment analysis; we consider all of their thoughts to be equally valid. To boost the accuracy of our categorization, we used the Hindi language repository for general news stories from a number of news sources. Text classification accuracy was investigated using Naive Bayes and other machine learning classification techniques, as well as Random Forest, Support Vector Machines, and Logistic Regression approaches.
Comparative Approaches by using Machine Learning Algorithms in Breast Cancer Prediction

Cancer is most dangerous disease in the world now. It affects the unusual segments of the human body. A sum of Breast cancer patients in the world is second-most among all other cancers. Nowadays it is challenging to detect breast cancer in its early stage. If the researcher gets success to detect cancer in its early stage then it is quite easier to reduce the death rate caused due to this disease. Artificial Neural Network(ANN) can be the solution for automated disease detection in the early stage. Machine Learning (ML) approaches can be the best suit for prediction as these algorithms have intelligent learning ability for improved performance. This work is meant for the prediction of breast cancer in women by using different ML ability-based algorithms and choosing the best performance model. For the total number of 569 data points number of features are included radius, texture, perimeter, area, smoothness, compactness, concavity, and concave points. Here three classification algorithms as Support Vector Classifier (SVC), Random Forest (RF), and XGBoost (XGB) are taken for the performance measure. the classifiers are trained, tested, and validated by taking different parameter settings. In conclusion, XGBoost (XGB) is the best classification in predicting breast cancer using the Kaggle dataset with an accuracy of 98.7%.
A Comprehensive Analysis of Internet of Things (IOT) in Enhancing Data Security for Better System Integrity - A Critical Analysis on the Security Attacks and Relevant Countermeasures

The dynamic business environment has fuelled the overall growth and development of critical technologies like of the Internet of Things (IoT), which supports in creating various opportunities in addressing different business needs, support in enhancing the business operations, create and engage with the customers for efficient delivery of goods and services. With the increase in implementation of IoT and related tools there is an increased concern on security attacks and the critical measures which the management need to take in order to protect the data and enhance efficiency in business operations. The paper is more focused in analysing the critical determinants of IoT technologies which can be used by the organisation in order to safeguard from the security attacks and relevant counter measures can be taking for attaining sustainable development. It has been regarded that each category of security attack is connected to one or more various layers of the architecture and hence appropriate authentication, confidentiality and validity need to be implemented for better protection. The study involves in using qualitative research design and secondary sources are used for preparing the study..
Heart Attack Anxiety Disorder using Machine Learning and Artificial Neural Networks (ANN) Approaches

Heart attack and anxiety disorder are proving to be two major medical complexities in the present scenario. This study illustrates the role of machine learning and artificial neural networks in making successful predictions of these two medical abnormalities. This research paper presents the significance of incorporating artificial intelligence-based mechanisms in minimising the errors of the diagnosis process. In this context, a brief description of the evolution of the usage of AI technology in medical diagnosis has been portrayed here. The different branches of AI like machine learning, artificial neural networks and their contribution to the analysis and interpretation of heart diseases are elaborated in this research paper. Besides primary research has been conducted for the purpose of gaining knowledge about what is the human perception of this technology and its relevance in the modern healthcare system. Analysis and interpretation have also been provided in this research paper to present a clear description of experimental result. Findings suggested that “Random Forest” and Vector Machine are the most used algorithms in heart attack risk prediction and ANN is the least used algorithm. However, developing ANN by integrating autoencoder and feature classifier can perform better in anxiety disorder heart attack prediction.