Reviewerly/exHarmony huggingface dataset:
Visualization Resources: A Survey

Visualization, a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data visualization as a distinct field. As such, the number of visualization resources and the demand for those resources is increasing at a rapid pace. After a decades-equivalent long search process, we present a survey of open visualization resources for all those with an interest in interactive data visualization and visual analytics. Because the number of resources is so large, we focus on collections of resources, of which there are already many ranging from literature collections to collections of practitioner resources. Based on this, we develop a classification of visualization resource collections with a focus on the resource type, e.g. literature-based, web-based, developer focused and special topics. The result is an overview and details-on-demand of many useful resources. The collection offers a valuable jump-start for those seeking out data visualization resources from all backgrounds spanning from beginners such as students to teachers, practitioners, developers, and researchers wishing to create their own advanced or novel visual designs. This paper is a response to students and others who frequently ask for visualization resources available to them.
A Novel Densely Search based Fire-Fly (DSFF) Optimization Algorithm for Image Classification Application

Data processing and analytics are wide spread study with profound applications. Data analytics deals with deriving or applying an algorithm to an application that work with dataset. The proposed work analyses the image data with optimization algorithm by using novel method of Fire-Fly (FF) algorithm, which is named as Densely Search Fire-Fly (DSFF) optimization algorithm. The Neural Network (NN) is applied to classify the optimized data. In this process, the optimized data refers to selective attributes from the raw data of image features. To test the performance of proposed optimization, the Gabor feature extraction method is used to fetch the features from raw image data. The Gabor method retrieves the pattern in various angle of projections. This produces 5 × 8 number of patterns to represent the image feature. From this feature attributes of whole image dataset, the optimization search for the best attributes by the reference of weight value is calculated from the particles of Fire-Fly. According to the best selection of attributes from the objective function, the neurons in a network that can segregate the different classes in the training dataset. The performance of the proposed FF algorithm are compared with the traditional optimization methods in the image classification application.
Attacking DoH and ECH: Does Server Name Encryption Protect Users’ Privacy?

Privacy on the Internet has become a priority, and several efforts have been devoted to limit the leakage of personal information. Domain names, both in the TLS Client Hello and DNS traffic, are among the last pieces of information still visible to an observer in the network. The Encrypted Client Hello extension for TLS, DNS over HTTPS or over QUIC protocols aim to further increase network confidentiality by encrypting the domain names of the visited servers. In this article, we check whether an attacker able to passively observe the traffic of users could still recover the domain name of websites they visit even if names are encrypted. By relying on large-scale network traces, we show that simplistic features and off-the-shelf machine learning models are sufficient to achieve surprisingly high precision and recall when recovering encrypted domain names. We consider three attack scenarios, i.e., recovering the per-flow name, rebuilding the set of visited websites by a user, and checking which users visit a given target website. We next evaluate the efficacy of padding-based mitigation, finding that all three attacks are still effective, despite resources wasted with padding. We conclude that current proposals for domain encryption may produce a false sense of privacy, and more robust techniques should be envisioned to offer protection to end users.
Blockchain based Internet of Vehicles (IOV) InformationTransmission Mechanisms

The automotive industry has certainly reshaped society and becoming sophisticated, intelligent, and highly developed industries with discoveries ranges from hybrid, battery-powered, and self-driving modern vehicles to the advent of the Internet of Vehicles (IoV). Because of its complication, it necessitates the use of numerous Industry 4.0 technology such as modern manufacturing operations, robotics, cyber security, augmented reality and so on. The blockchain is a new trend in the automotive industry that can improve data security, anonymity, traceability, integrity, accountability, privacy, robustness, authentication transparency and trustworthiness. Internet of Vehicles (IoV) relates to the transmission of actual data between infrastructures and vehicles via intelligent terminal devices, information platforms, automotive navigation processes, and mobile telecommunication technologies. The article gives a systematic review of Internet of Vehicles (IoV). The analysis is based on evaluation metrics, limitations, technological advancements and techniques over a span of time. This essence has created an opportunity to collect and distribute information regarding vehicles and infrastructures more easily. It moreover enables data collection, processing, and sharing in social media. Most vehicular messages need to be shared in close proximity vehicles whereas IoV helps to improve traffic effectiveness and security. Some messages may need to be distributed more widely in a larger region. Currently, the technique is generating a lot of research interest in other areas, such as the Internet of Vehicles (IoVs). Some centralized issues can besolved in the blockchain technology using a decentralized and reliable vehicular environment.
Research and Design of 3D Path Obstacle Avoidance Method Between UAVs

With the development of high-speed and wide application of UAV technology, at the same time, UAV is faced with more and more complex applications, so its path obstacle avoidance has become a very key problem. One UAV can no longer meet the requirements of the application occasions under the current UAV application background, so the advantages of multiple UAVs are highlighted. UAVs are developing towards the direction of cluster cooperative operation, which is the main development direction of UAVs in the future. UAVs cluster operation means that multiple UAVs communicate with each other to exchange information to achieve coordinated flight, coordinated reconnaissance and coordinated operations. UAV cluster cooperative flight operation must consider the problem of obstacle avoidance between UAVs. Based on the Boid model, this paper proposes a UAV cooperative flight algorithm, and tests the feasibility of the algorithm by using 10 and 20 UAVs in a gradually increasing number based on the 3D test environment. Finally, the simulation results verify that the algorithm has good feasibility in the obstacle avoidance of large-scale UAV 3D cooperative flight between UAVs.
Recommendation and Rating System using Machine Learning

This paper deals with a recommendation and rating system using machine learning algorithms like linear regression, random forest regression and support vector model. Recommendation and rating systems are subdivision of structures that filter information. These structures normally are dedicated software program components, which contribute to a bigger software program machine, but also can be standalone equipment. Our recommendation and rating system’s main aim is to give suggestions for items that the user demands which can be favorable in a collaborative approach using machine learning models. The recommendations are associated with specific choice-making mechanisms, distinctive techniques, which includes, what commodities to shop for, what shows to watch, or what holiday places to look for. This collaborative technique should be able to compute the relationship among distinct clients and depending upon their ratings and prescribe items to others who’ve comparable tastes and also finally allowing users to discover more.
Comparative Evaluation of Noise Immunity Characteristics of Special Subsets of M-sequences with the Joint Processing Possibility

The object of consideration is asynchronous multicast radio systems that use code features to divide objects. The subject of the analysis is the characteristics of noise immunity of variants of constructing signal ensembles using special subsets of M-sequences, assuming their joint simplified processing. The purpose of the analysis is to compare the noise immunity characteristics of systems using variations for constructing special subsets of M-sequences with joint processing properties. The solution is based on the formation and use of hybrid models of device blocks that provide detection and recognition of objects by code attributes. A number of examples show that the variants for constructing special subsets of code sequences of the type under consideration differ (sometimes significantly) both in terms of the general parameters of the produced ensembles and in terms of noise immunity characteristics. This result is generated by the singularities of algebraic representations of code sequences and is not accidental in this sense. It indicates the potential variety of application scenarios for the formed ensembles, which may be useful to the design engineers of the corresponding radio systems, especially when choosing complex signals with a large base.
Transparency in Carbon Credit by Automating Data-Management Using Blockchain

Climate change has raised an alarm for world to ponder about and if not stopped can cause serious repercussions. A feasible solution to alleviate greenhouse gas emissions is via a mechanism that is laid out in Kyoto protocol in 1997. It was signed by close to 192 nations thus creating a system to monetize carbon emissions with an aim to build an authority over it. There have been efforts to build a system in previous decade too but there have been certain shortcomings. The major challenges which exist are data manipulation, lack of transparency and substantial costs. So we have come up with the idea of Blockchain which will build a carbon credit ecosystem thus guaranteeing us security, transparency, accessibility to the standardized carbon markets. We will also be using sensors to automate the data to out consortium.
Improving the performance of code vulnerability prediction using abstract syntax tree information

The recent emergence of the Log4jshell vulnerability demonstrates the importance of detecting code vulnerabilities in software systems. Software Vulnerability Prediction Models (VPMs) are a promising tool for vulnerability detection. Recent studies have focused on improving the performance of models to predict whether a piece of code is vulnerable or not (binary classification). However, such approaches are limited because they do not provide developers with information on the type of vulnerability that needs to be patched. We present our multiclass classification approach to improve the performance of vulnerability prediction models. Our approach uses abstract syntax tree n-grams to identify code clusters related to specific vulnerabilities. We evaluated our approach using real-world Java software vulnerability data. We report increased predictive performance compared to a variety of other models, for example, F-measure increases from 55% to 75% and MCC increases from 48% to 74%. Our results suggest that clustering software vulnerabilities using AST n-gram information is a promising approach to improve vulnerability prediction and enable specific information about the vulnerability type to be provided.
API + code = better code summary? insights from an exploratory study

Automatic code summarization techniques aid in program comprehension by generating a natural language summary from source code. Recent research in this area has seen significant developments from basic Seq2Seq models to different flavors of transformer models, which try to encode the structural components of the source code using various input representations. Apart from the source code itself, components used in source code, such as API knowledge, have previously been helpful in code summarization using recurrent neural networks (RNN). So, in this article, along with source code and its structure, we explore the importance of APIs in improving the performance of code summarization models. Our model uses a transformer-based architecture containing two encoders for two input modules, source code and API sequences, and a joint decoder to generate summaries combining the outputs of two encoders. We experimented with our proposed model on a dataset of java projects collected from GitHub containing around 87K <Java Method, API Sequence, Comment> triplets. The experiments show our model outperforms most of the existing RNN-based approaches, but the overall performance does not improve compared with the state-of-the-art approach using transformers. Thus, the results show that although API information is helpful for code summarization, we see immense scope for further research focusing on improving models and leveraging additional API knowledge for code summarization.
Profiling developers to predict vulnerable code changes

Software vulnerability prediction and management have caught the interest of researchers and practitioners, recently. Various techniques that are usually based on characteristics of the code artefacts are also offered to predict software vulnerabilities. While other studies achieve promising results, the role of developers in inducing vulnerabilities has not been studied yet. We aim to profile the vulnerability inducing and vulnerability fixing behaviors of developers in software projects using Heterogeneous Information Network (HIN) analysis. We also investigate the impact of developer profiles in predicting vulnerability inducing commits, and compare the findings against the approach based on the code metrics. We adopt Random Walk with Restart (RWR) algorithm on HIN and the aggregation of code metrics for extracting all the input features. We utilize traditional machine learning algorithms namely, Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) to build the prediction models.We report our empirical analysis to predict vulnerability inducing commits of four Apache projects. The technique based on code metrics achieves 90% success for the recall measure, whereas the technique based on profiling developer behavior achieves 71% success. When we use the feature sets obtained with the two techniques together, we achieve 89% success.
Secured E-Market Auction for Advertisement Slots Using Blockchain

The Television broadcasting firms has not upgraded its strategies for selling their advertisement space/slots or willingly downgrading themselves for higher profits through unpleasant means. Currently every broadcasting company in India works independently with rules and regulations defined by the TRAI [Telecom Regulatory Authority of India], commonly the broadcasting firms earn by selling their advertisement slots. At times less appealing slots go unsold and force the broadcasters to sell these slots at cheaper rates and also few slots create huge demand such that it paves a path for unpleasant transactions or other unfair business deals rather than being equal to the society. So the entire plan is to develop an e-commerce website where these ad slots can be sold in an efficient and secure manner to increase the profits and to be fair to all the stakeholder. The method of selling most of the ad slots would be auction because it is the most natural and trusted methodology to sell product at proper rates. To secure the entire environment we propose the use of Blockchain technology as a security layer over the platform and allowing access only to the users in the blockchain. Since maintaining huge number of auctions at the same moment is difficult, we propose auto bidding as a solution for this problem and Flash sale as a tool to sell less popular products. The next major problem is with finding the right advertisement slot for the users to spot on the right product in a huge lot will be done by automatic suggesting bot and also manual filters.
NER in Hindi Language Using Transformer Model:XLM-Roberta

Natural language processing (NLP) is a computer program that trains computers to read and understand the text and spoken words in the same way that people do. In Natural Language Processing, Named Entity Recognition (NER) is a crucial field. It extracts information from given texts and is used to translate machines, text to speech synthesis, to understand natural language, etc. Its main goal is to categorize words in a text that represent names into specified tags like location, organization, person-name, date, time, and measures. In this paper, the proposed method extracts entities on Hindi Fraud Call (publicly not available) annotated Corpus using XLM-Roberta (base-sized model). By pre-training model to build the accurate NER system for datasets, the Authors are using XLM-Roberta as a multi-layer bidirectional transformer encoder for learning deep bidirectional Hindi word representations. The fine-tuning concept is used in this proposed method. XLM-Roberta Model has been fine-tuned to extract nine entities from sentences based on context of sentences to achieve better performance. An Annotated corpus for Hindi with a tag set of Nine different Named Entity (NE) classes, defined as part of the NER Shared Task for South and Southeast Asian Languages (SSEAL) at IJCNLP. Nine entities have been recognized from sentences. The Obtained F1-score(micro) and F1-score(macro) are 0.96 and 0.80, respectively.
Blockchain based Secure and Effective Authentication Mechanism for 5G Networks

5G technology's rapid development revolutionized the field of cellular networks by providing higher data rate up to 10 Gbit/s using millimetre waves. Though 5G network can carry huge data with a higher speed than 4G, it can cover only a shorter province, around 1000 feet which is not even 2% of 4G's coverage. Numerous 5G cell towers and antennas are required to ensure reliable 5G signal and connectivity. This resulted in frequent and increased authentication when User Equipment (UE) moves between cells. Therefore, there is a critical need for a fast and frequent authentication mechanism. Though existing 4G authentication protocols can be adopted for 5G, they can only satisfy the basic requirements on the security front but cannot handle frequent authentications effectively. Blockchain technology with the features such as security, anonymity, immutability and transparency has been considered a promising technology to evade redundant authentications. Therefore, an effective and secure authentication scheme by completely utilizing features of Blockchain technology has been proposed in this paper. The proposed methodology works in two steps, the first by the introduction of a novel optimized authentication specific consensus algorithm for improving the Authentication Rate. Second, the reduction of the authentication by utilizing the authentication results stored in UE-AP Blockchain aiming to handle the redundant authentications.
Blockchain-Based User-Centric Electronic Health Record Management System

The adoption of Blockchain technology in the medical sector has started gaining popularity because it mitigates crucial security, privacy, and interoperability challenges. Moreover, the massive volume of data produced and exchanged via electronic health records has raised various issues concerning data ownership. It is challenging to trace ownership information and medical history because these records are scattered among numerous medical institutions, and individuals have no control over their data, thus jeopardising the privacy and record archiving. Therefore, we created and put into practice a solution based on blockchain, IoT, and AI. The developed system can improve healthcare management by building a patient-centric model and increasing the privacy and interoperability of medical data. Along with solving the preceding issues, we have also incorporated machine learning features to monitor and predict the risk of arrhythmia and heart diseases respectively.
Lightweight Auditable Secure Cloud Storage With Privacy Enabled Data Storage Optimization

Cloud computing platforms are the widely used state of the art platforms by various organizations. Data storage and data sharing services are the most widely used services in the cloud, while the maintenance of data integrity is a big challenge. A public cloud platform which is not reliable, users must generate digital signature of their data and then share generated signature for integrity auditing. Any attack on cloud can compromise the users valuable data which is most likely carried out by external entity. By generating signature we can write the rules of who can access update or delete the data. If data is updated by unauthorized user, then auditing can identify which data is compromised. Here we are using asymmetric keys that is when user upload his/her data over cloud then digital signature will get created with users private key, and when TPA (Third Party Auditor) wants to check the integrity of that users data then he can generate digital signature with users public key. Not only carried out low cost for data storage by compressing data but also established data access protocol to maintain data privacy.
A Knowledge- Distillation - Integrated Pruning Method for Vision Transformer

Vision transformers (ViTs) have made remarkable achievements in various computer vision applications such as image classification, object detection, and image segmentation. Since the self-attention mechanism introduced by itself can model the relationship between all pixels of the input image, the performance of the ViTs model is significantly improved compared to the traditional CNN network. However, their storage, runtime memory and computing requirements hinder their deployment on edge devices. This paper proposes a ViT pruning method with knowledge distillation, which can prune the ViT model and avoid the performance loss of the model after pruning. Based on the idea that knowledge distillation can make the student model improve the performance of the model by learning the unique knowledge of the teacher model, the convolution neural network (CNN) which has the unique ability of parameter sharing and local receptive field is used as a teacher model to guide the training of the ViT model and enable the ViT model to obtain the same ability. In addition, some important parts may be cut during pruning, resulting in irreversible loss of model performance. To solve this problem, this paper designs the importance score learning module to guide the pruning work, and determines that the pruning work removes the unimportant parts of the model. Finally, this paper compares the pruned model with other methods in terms of accuracy, Floating Point Operations(FLOPs) and model parameters on ImageNet-1k.