Reviewerly/exHarmony huggingface dataset:
Enhanced Detection Model and Joint Scoring Strategy for Multi-Vehicle Tracking

Multi-vehicle tracking is one of the most crucial components of an intelligent transportation system (ITS). However, when it comes to busy traffic flow, tracking targets robustly becomes more problematic due to occlusion, motion blur, high appearance similarity, etc. To achieve accurate and efficient tracking performance, we present a novel multi-vehicle tracking method based on the enhanced detection model and joint scoring strategy. Specifically, the former aims to (1) adopt lightweight yet efficient YOLOv5s to improve detection accuracy and running speed, and (2) incorporate the CBAM and transformer encoder modules into the detection model to generate the refined features for the target localization. The Latter preferentially provides high-confidence detections and tracklets for subsequent data association, significantly reducing the number of identity switches and redundant vehicle trajectories caused by mutual occlusion, similar object interference, etc. We evaluated the proposed multivehicle tracking approach on the UA-DETRAC vehicle tracking dataset and demonstrated its superior capabilities through intensive comparison and analysis. Moreover, our proposed method runs at 24.4 FPS on a single GPU and meets the real-time requirement.
FastSecNet: An Efficient Cryptographic Framework for Private Neural Network Inference

Private neural network inference has demonstrated great importance in various privacy-critical scenarios. However, the primary challenge remaining in prior works is that the evaluation on encrypted data levies prohibitively high run-time and communication overhead. In this work, we present FastSecNet, an efficient two-party cryptographic framework for private inference in the dealer-based pre-processing setting. Specifically, (1) FastSecNet provides an efficient ReLU protocol for the evalution of non-linear layers, which is built up on a recent advanced cryptographic primitive, function secret sharing (FSS). The core of this construction are an optimized ReLU representation and a customized FSS-based ReLU protocol. (2) For linear layer evaluation, we first propose an efficient PRG-based preprocessing protocol based on the fact that one of the inputs is uniformly random in the offline phase. Then, the online phase only communicates one element and consists of lightweight secret-sharing operations in a ring. Extensive evaluations conducted on 4 real-world datasets and 9 neural network models demonstrate that during the online phase, FastSecNet achieves 14× less runtime and 18× less communication cost compared to the state-of-the-art.
CD-DAA-MD: A Cross-domain DAA Scheme with Mimic Defense for Internet of Vehicles

The direct anonymous attestation (DAA) scheme is a promising authentication scheme in a single trust domain. However, the existing schemes fail to effectively solve the problem of mutual authentication of vehicle nodes between different trust domains. This paper designs a cross-domain remote DAA scheme combined with a mimic defense mechanism (CD-DAA-MD) for the Internet of Vehicles (IoV). Specifically, the trust chain is constructed, and the mimic defense mechanism is combined with a trusted auditor (TA) to meet the urgent needs of the security of the IoV. Furthermore, a dynamic heterogeneous redundancy structure (DHR) composed of multiple heterogeneous executors is established, where the heterogeneous redundancy between each heterogeneous executor is applied to effectively enhance the security of the TA and simultaneously strengthen the trustworthiness of the TA’s identity when participating in the cross-domain link We analogize the proof method that combines the random oracle model and the protocol judgment to evaluate the security issues at the protocol level, where a model based on the probability of heterogeneous executors being compromised is utilized. Simulation results show that our scheme effectively reduces the communication overhead without compromising security. The communication overheads of the TA-Join protocol in our scheme are reduced by about 15% compared with existing schemes. The efficiency of completing the entire cross-domain interactive authentication process is nearly 25% higher than the existing schemes.
Federated Learning and Adaptive Privacy Preserving in Healthcare

Real-world health data for machine learning tasks addresses many issues related to storage, access and privacy concerns on the centralized databases. Distributed data silos need to be secured and privacy assured for the access to multiple sites and reduce the risk of breach. A federated learning framework that learns global model from the distributed health data at different sites and provide secure access to emanate. The adaptive privacy mechanism on the sites is further a protective model to prevent potential attacks. In this paper a federated model with adaptive privacy is discussed. A simulation of the federated learning mechanism with federated sites with data silos and the differential privacy as adaptive privacy has been experimented. The results are satisfactory and found the variability of the differential privacy before and after the application of the privacy preservation.
A Comparative Review on Approaches of Aspect Level Sentiment Analysis

Aspect-level sentiment analysis carries out a detail-oriented task of retrieving the aspects in a particular document and categorizing the opinion about every aspect. The applications of Aspect Level Sentiment Analysis (ALSA) are extended because more data is readily available on social networking sites. The applications include reviewing the opinions of customers, examining the mental health status of patients based on social media posts, market research and competitor analysis, product analysis, and stock price prediction. The extensive applications encouraged researchers to devise new techniques and approaches for ALSA. In this survey, the techniques and approaches of ALSA are reviewed. The review is carried out based on the categorization which classifies the studies into three approaches: Knowledge-based, Statistical, and Hybrid. Various techniques for implementing these approaches and performance on different datasets are reviewed. Furthermore, analyzing the benefits and drawbacks of each literature and also finds their research gaps to keep up with current trending research areas. This study is finally concluded by highlighting few research challenges and further research directions in this domain.
Find It With A Pencil: An Efficient Approach for Vulnerability Detection in Authentication Protocols

Smart devices improve the quality of life by collecting, analyzing, and transmitting data across different channels. Unfortunately, public media are prone to adversaries, and such devices must protect the privacy and confidentiality of users’ data. Although authentication and key agreement protocols achieve the goal, they may suffer from hidden vulnerabilities; detecting them requires solid mathematical knowledge. Informal methods can discover protocol vulnerabilities, but most of them are limited to analyzing a specific protocol. Therefore, it is essential to provide a way for analyzing arbitrary ones. This paper proposes several algorithms to perform informal analyzes in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ×log( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> )). Also, the article offers some ideas for optimizing these algorithms to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> is the number of involved parameters in the protocol. Additionally, this paper introduces three graphical representations for vulnerability detection and examines their strengths and weaknesses. The compact version of such an expression has a better performance compared to the others. This method enables students with a weak mathematical background to analyze complex protocols on a piece of paper. The article explores <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">four</i> protocols published in recent years and describes how adversaries can obtain session keys in different scenarios.
Comparative Study of Blockchain Technology in Supply Chain Management

Nowadays internet is the basic need for everyone’s life. Internet is widely used in communication, business, music, video, online money transfer, to check weather conditions, online shopping and lot more applications. In this way, internet has a great impact on human lives. In the same way, Blockchain will influence the daily activities of humans in the near future. Each and every business has online and offline market. For every commercial organization the basic need is proper supply chain management. Every business organization wish to build trust among the customers. Blockchain technology can be use in supply chain management to build trust among the customer. This paper gives overview of the importance of blockchain technology in supply chain management. This article provides an introduction to supply chain management followed by blockchain technology. The study also focuses on how researchers have applied blockchain technology along with other technology to drive effective supply chain in various areas.
Heterogenous Center Alignment of Dual-Path Features for Text-Image Person Re-identification

Text-image person Re-identification aims at searching a target person's image through its corresponding textual descriptions. Although many current methods have made great progress, most of these methods needed auxiliary networks, have a long training time, and are unsuitable for practical industrial applications. In this paper, We propose a novel heterogenous center alignment loss (Hetero-center alignment loss) for dual-path features, one path for global features, and the other path for local features. The hetero-center alignment loss focuses on relaxing the strict constraint of sample-based alignment loss by replacing the alignment of one sample to all the other modality samples by the sample center to all the other modality sample centers, saving the training time. Specially, we first extract initial visual and textual features from the image and language backbones, respectively. Then, we design dual-path embedding learning modules to obtain global and part-level features for both text and image input. Finally, we train our network with hetero-center alignment loss, cross-modality projection match(CMPM) loss, and a cross-modality projection category (CMPC) loss to increase the similarity of text descriptions and images with the same identity. Numerous experiments conducted on the CUHK-PEDES dataset demonstrate the superiority of our proposed method.
Context-Adaptive Online Reinforcement Learning for Multi-view Video Summarization on Mobile Devices

The huge amount of video data produced by ubiqui tous cameras imposes significant challenges for users to efficiently obtain useful video information. Multi-view video summarization (MVS) aggregates multi-view videos into information-rich video summaries by considering content correlations within each view and between multiple views. Existing MVS methods fail to concentrate on performance across scenarios and usually achieve satisfactory performance on specific training datasets. However, when faced with unseen video scenarios, the quality of the summaries generated by existing methods may degrade. Moreover, they usually only use cameras for data acquisition, which require a large amount of network bandwidth to transfer the data to the server for processing. To bridge this gap, we propose a context-adaptive online reinforcement learning multi-view video summarization framework (COORS) that meets the low response latency performance requirements of context adaptation while ensuring camera hardware compatibility. Specifically, COORS enables retraining in new contexts by extracting contextindependent rewards, while improving model convergence speed based on representation learning and replica playback. Extensive experiments show that COORS has better performance compared to the state-of-the-art baselines.
Decentralized Federated Learning with Data Feature Transmission and Neighbor Selection

In edge computing (EC), federated learning (FL) has been widely used to cooperatively train machine learning models and protect privacy over the traditional approaches. In order to avoid the possible communication bottleneck and single point failure in parameter server (PS) framework, we mainly focus on decentralized federated learning (DFL) in which each worker disseminates information through peer-to-peer (P2P) communication network. However, the high communication cost and the non-IID local data hinder the efficient training of DFL. In this paper, we propose a mechanism (termed DFL-DF) to achieve efficient communication and improve the performance on non-IID data. Specifically, each worker will exchange data features with neighbors rather than models (or gradients). Different from the traditional fixed topology methods, each worker will communicate with its neighbors selected at each round. Extensive experiment results show the high effectiveness of DFL-DF. Specifically, DFL-DF can reduce communication cost by 29.4%-82.06% and improve test accuracy 2.5%-9.1% under communication cost constraints, compared with the traditional model exchanging methods.
Concealing the Data using Cryptography

Data security has emerged as a major challenge. Many techniques and procedures have been developed in order to reach a level of security like data encryption, data backup, data masking, etc., which may require high initial costs and maintenance. Also, once sensitive data has been masked, it cannot be changed back to its original form. Cryptography can be used for data security by data encryption and decryption. One can quickly discover the existence of concealed information in carrier files by employing cryptography. The strategy used in this project utilizes cryptographic methods. In cryptography, the proposed method uses AES algorithm. This helps to maintain the data in a secure way. Cryptography is used to conceal the data in any text file document by utilizing the AES technique. This ensures that no other network user can access the information that is protected. The original message can only be viewed by the sender and can be exchanged with the recipient with the help of a key which is used to encrypt and decrypt the data. The method uses three types of AES algorithms namely AES-128, AES -192 and AES-256. AES-256 is taken as the most efficient one when compared to other two algorithms. This study offers an ASCII-based cryptography technique as a solution to data security.
Health Information Broadcast Distributed Pattern Association based on Estimated Volume

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. Data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery.
Advanced Data Mining Enabled Robust Sentiment Analysis on E-Commerce Product Reviews and Recommendation Model

Currently, digital review plays a crucial role in influencing consumer buying patterns and improving global communications amongst consumers. E-commerce giants such as Flipkart, Amazon, and so on, provide real insights about the performance of the product to future buyers and provide a platform for the consumer for sharing their experiences. In order to extract valuable insight from a huge set of reviews, classification of reviews into negative and positive sentiments is needed. Sentiment Analysis (SA) is a computational study for extracting subjective data from text. This article focuses on the design of Advanced Data Mining Enabled Robust Sentiment Analysis on E-Commerce Product Reviews (ADMRSA-EPR) model. The presented ADMRSA-EPR technique mainly relies on the differentiation of the sentiments exist in the online product reviews. In the presented ADMRSA-EPR technique, the first step is to analyze the raw product reviews into useful format and word embedding process takes place. To analyze sentiments exist in product reviews, stacked auto encoder (SAE) model is applied. At the final stage, the parameters related to the SAE model get optimally adjusted using the manta ray foraging optimization (MRFO) algorithm. The experimental result analysis of the ADMRSA-EPR technique on distinct datasets reports a promising performance over the other existing models.
Blockchain-based Health Data Sharing for Continuous Disease Surveillance in Smart Environments

The Covid-19 pandemic ushered in multiple paradigms of personal health data sharing with particular emphasis on Person-to-Institution sharing and Institution-toInstitution sharing. While the data aggregated by technology companies and health authorities was instrumental in the development of vaccines and ultimately flattening the curve of infection rates, egregious abuses of privacy occurred. In many instances acceptable guarantees of appropriate utility for the data were not made available. Personal health data sharing for the containment of infections with privacy limitations present a classic case of collaboration among mutually distrustful entities. In this regard the blockchain network and attendant protocols for data integrity, transaction transmission and provenance can prove useful. Thus, in this paper we present a blockchain-based method for disease surveillance in a smart environment where smart contracts are deployed to monitor public locations instead of individuals. The data aggregated is analysed and tagged with a lifetime commensurate with the time for infection. Once the data utility period has elapsed the monitored data are removed from the active surveillance pool and the entities involved can be notified. Such a method of continual surveillance protects privacy by shifting the emphasis from individuals to locations. Experimental data suggests this method is efficient and can be implemented on top of existing disease surveillance strategies for later pandemics.
Machine Learning Framework for Breast Cancer Detection

India has vouchered half-hour cases of carcinoma numerous times throughout the former, and it is doubtless to extend. Carcinoma is the most typical reason for death because of cancer. It’s necessary to discover at its early stages. Carcinoma develops cells within the bone that formerly expanded and disunited uncontrollably, leading to a lump kerchief as a tumor. This lump of the scarf is a nominated excrescence. When melanoma, bone cancer is the most common cancer among ladies, varied Machine literacy ways are offered for diagnosing melanoma knowledge. There are different Machine Learning ways handed for the future of diagnosing melanoma knowledge. Convolution Neural Network (CNN) is employed as a classifier model, and algorithmic point Elimination (RFE) is used to select the points. Additionally, five algorithms—SVM, Random Forest, KNN, Logistic Regression, Naive Bayes theory, and classifier— are compared in this work. The trial showed that the random forest classifier, with a delicacy of 99%, is superior to others.
Job Recommendation System using Machine Learning

Millions of students graduate from college every year and start looking for jobs, but hunting for a suitable job that pays according to the applicant’s skills is not an easy process. On the other hand, recruiters and companies look for graduates to join them according to their requirements. Unfortunately, there is often a gap between the applicants (job seekers) and recruiters (job providers). To bridge this gap and solve this problem, this study has come up with a job recommendation system that will recommend jobs in the IT sector that match the applicants’ skills and expectations. This study has built an Android application to recommend jobs to the users based on their interests, databases, frameworks, platforms, and languages comfortable with. The recommendation is done by using a Machine Learning (ML) model (written in Python), which makes use of the content-based filtering algorithm. This study has built the application using Kotlin, Jetpack Compose, Ktor, and the UI has been designed by using Material 3 Design Principles.
Smart Attendance System with and Without Mask using Face Recognition

Education institutions today are concerned with students' reliability of performance. One aspect of the drop in student performance is the insufficient attendance. The most common methods for keeping track of attendance are having students sign or be called. It was difficult and needed more time. It is now necessary to have a computerized system for keeping track of students' attendance that also allows the teacher to do so. This project uses a face-recognition-based intelligent attendance system. This study has proposed the establishment of a "Smart Attendance System for Face Recognition" that has several applications. Face permission allows for facial recognition in the current system, saving time and removing the potential of proxy attendance. Additionally, it establishes the time a person entered a certain location and tracks their attendance. Each student needs to sign up with the system initially so that it can immediately identify them. This project is implemented using Python, and development is done using Visual Studio. In our study, the Local Binary Pattern Histogram algorithm is employed. This system gives pupils attendance marks based on two factors. Specifically, students may choose to wear a mask or not. The first step is to photograph and register the students. They must train their images as the second assignment. Finding their faces and recording their attendance is the third duty. The implementation of our project is done using the Tkinter interface. This strategy eliminates the potential of fake attendance because it uses the face as a biometric for authentication.