Reviewerly/exHarmony huggingface dataset:
Electronic and Remote Voting Systems: Solutions to Existing Problems – An Experimental Approach to Secure Voting Platforms on Endpoints

The need for secure delivery solutions on endpoints has become essential as true and neutral outcomes can be provided by the systems themselves. This has arisen due to several discrepancies found in the manual voting systems and various vulnerability assessments done on the existing electronic and remote voting systems. Though these systems provide the convenience of voting using any endpoint desired, there are still loopholes that are not looked into that can harm the endpoint as well as the end user as well. The paper will be looking into the reasons why a secure endpoint delivery platform for electronic and remote voting systems is needed we experiment using a random online voting system and then test in to two phases which are the effectiveness of the antivirus software to invoke a protected browser and the second phase of the test the same online voting system on the same localhost environment by the use of the alternative of the On Vue Software such as the safe exam browser.
Privacy-Protective Distributed Machine Learning Between Rich Devices and Edge Servers Using Confidence Level

Many recent studies have focused on using the personal information collected by edge devices for machine learning on servers while protecting privacy. In most cases, a high-performance server aggregates and manages all data. However, users may refuse to pass on their personal information to an external server owing to the risk of information leakage. To address this concern, we propose a distributed machine learning model that does not extract any sensitive data from the edge device. In the proposed model, edge devices also run machine learning and integrates learning results obtained at edge servers with those obtained at edge devices by comparing their confidence levels. To validate the proposed model, we performed experiments on facial image recognition using Jetson Nano. Experimental results confirm that the proposed model enables the use of personal data for machine learning without transferring any sensitive information to the edge server. With this learning model, each user could use personal information securely and could receive the best match results.
A Secure Sharing Framework Based on Cloud-Chain Fusion for SWIM

The air traffic management (ATM) data sharing has attracted extensive attention as the key content of the collaborative operation in the next generation of aviation sector, and the corresponding secure issues and controls have become research hotspots. The SWIM concept aimed at enabling the secure sharing of data among ATM stakeholders was proposed. According to the SWIM regulation on efficient, reliable and secure data sharing, this paper intends to design a data sharing framework with security protections. By sorting out the characteristics and sharing requirements of ATM big data, it is found that the sharing framework based on distributed architecture has become the optimal choice for large-scale ATM systems. On the basis of cloud storage and blockchain, the concept of ATM data security sharing based on cloud-chain fusion is proposed, the feasibility of combining the two technologies is demonstrated, and the ATM data sharing cloud-chain fusion framework named CB-ATM is constructed. CB-ATM is built on the data publishing and discovery mechanism, operation record and traceability mechanism, and data security protection mechanism. Then, the security of this framework is demonstrated in accordance with ATM data secure sharing requirements. Finally, three future research directions for ATM data sharing are proposed. This paper provides a new idea for establishing an efficient, secure and reliable ATM data sharing architecture.
Optimizing Data Stream Throughput for Real-Time Applications

Many problems, like recommendation services, website log activities, commit logs, and event sourcing services, are often very high volume and high velocity as many activity messages are generated. Various cloud vendors offer services for building message transport and processing pipelines. We present the Apache Kafka optimization process for using Kafka as a messaging system in high throughput and low latency systems. Experiments are conducted with various configurations for observing their effects on performance metrics. The performance, Durability, and reliability trade-offs of Apache Kafka are presented for various data rates, resource utilization, and configurations on how to utilize resources more economically and choose the best Kafka configuration for use cases. This research presents by tuning configuration parameters leveraged throughput by 19%.
Improving Performance of Docker Instance via Image Reconstruction

Docker has become a key paradigm for application deployment in cloud computing. A running Docker instance need to access its corresponding docker image for searching the required file from time to time. The more layers a docker image has, the longer the search time is. The number of image layers has significant impact on the efficiency of the application. However, reducing the number of image layers via layer merging will reduce the shareability of image layers and increase the storage occupation. To address the above issue, we exploit image reconfiguration to achieve a trade-off between the operation efficiency and storage occupation. We formulate the image reconstruction problem as a knapsack problem, and design a dynamic programming-based offline image reconstruction algorithm named DPOIRA to solve it. We conduct experiments on the busybox and crate, and the experiment results show the effectiveness of our algorithm.
LTCS: Lightweight Tree-Chain Scheme for ADS-B Data Authentication

As a widely used air traffic monitoring and information transmission technology, the Automatic Dependent Surveillance - Broadcast (ADS-B) is gradually deployed to aircraft around the world. However, due to the openness of its communication protocol, it is vulnerable to malicious attacks. Aiming at the security threats of ADS-B system, this paper proposes an ADS-B data security scheme on the basis of tree-chain structure to realize the lightweight guarantee of ADS-B message reliability. Firstly, we used the idea of symmetric key delay disclosure in classical data authentication scheme to construct a tree-chain authentication model, which reduces the computation amount in authentication process and the length of key chain. Next, we used SM2 digital signature, which is more secure and computationally efficient, to authenticate the initial information in the signature phase. Compared with the original authentication scheme, our scheme improves the computation speed by about 4 times and reduces the number of keys in the authentication process, and is more applicable for ADS-B use cases with high real-time demand and tight computing resources.
MagicBatch: An Energy-Aware Scheduling Framework for DNN Inference on Heterogeneous Edge Servers in Space-Air-Ground Computation

With the fast development of space-air-ground computing scenarios, large UAVs, airships or HAPS (high altitude platform station), and satellites, are in the trend to have more powerful computation resources (e.g., heterogeneous types of GPUs), and can act as edge servers in the air. They are increasingly used for a large number of deep neural networks (DNN) inference applications, such as disaster monitoring, remote sensing, and agriculture inspection. However, these edge servers in the air always have a very limited energy supply. Thus, how to reduce their energy consumption to extend their working hours, while meeting the delay requirements of DNN inference tasks becomes a very important demand. In this paper, we propose MagicBatch, an energy-aware scheduling framework for DNN inference workloads on edge servers (with heterogeneous GPUs) in the air. MagicBatch is based on our key finding, that various GPUs can have different energy and latency performance under different DNN inference batch sizes. Thus, MagicBatch is designed in two phases: In the offline analysis phase, it analyzes the execution latency and energy consumption performance of different DNN inference tasks on heterogeneous GPUs; In the online scheduling phase, we propose a heuristic energy-aware scheduling algorithm (PSO-GA) to better allocate heterogeneous GPU computing resources to various inference tasks. Evaluation on our emulation testbed shows that MagicBatch can achieve more than 31.3% energy savings and 41.1% throughput improvement compared with the state-of-the-art methods.
PHR and Personalized Health Recommendations Using Rule-Based Approach

Personal health record (PHR) systems have been increasingly popular among patients and consumers in recent years. Nowadays, the interest in following a healthy life style has increased. The needs for health information and prevention of disease have also been gaining people’s interest. A personal health record (PHR) is now a patient-centric method for exchanging medical data. In this study, we provide a user-centered approach to rule-based reasoning for designing a tailored healthcare assistance system. The suggested system performs five main tasks, including modifying PHR log data and analyzing user profiles, visualization of user profile, self-tracking and recommendation of the proper diet suggestions for each user. The proposed system was implemented as a web application. Ultimately, Various alternative menus are used to give nutritional diet recommendations, ensuring that each menu is a part of a healthy, balanced diet. This web application is useful in real time to keep track of one’s health related information and feedback the results to enable individual self-health care. A rule-based technique is used to construct a personalized recommendation system that aids the end user in structuring diet recommendations in accordance with numerous personal criteria, such as food ideas, nutritious diet, and deficits.
A Framework for Identifying Image Dissimilarity

Images are captured to keep memories of events in and around a person's life, documents and many such use cases. After capturing images, comparing the images captured over time and finding the differences between similar captured images requires significant effort. The proposed framework aims to distinguish between two comparable photographs and computes the dissimilarity features. The model will also assist the user in recognizing the local terrain from an image and help the user take a second image identical to the first reference image. The similarity between the two images is based on various factors, including the facial structure, camera orientation, terrane, focus object on the image, background, face points, and boundaries. The proposed framework is designed to identify the similarity between two images from possible known categories like groups, portrait, and landscape images. The framework can identify the differences between the first shot and the second reference shot of the image using the extracted features from both images and will be able to display the affecting factors for dissimilarity.
Using Genetic Algorithm for the Optimization of RadViz Dimension Arrangement Problem

Visualization of high dimensional data aims to eliminate the difficulties and efforts of working with tabular and abstract data forms. One of the critical challenges for visualization methods is the dimension arrangement problem; where the final result of visualization is completely affected by the set and the order of the dimensions along the visualization anchors. According to the nature of this problem it is treated as an NP-complete problem; where optimization tools are required for solving such a problem. In this study, Researchers implemented the genetic algorithm (GA) to be used for the dimension arrangement optimization of radial coordinate visualization tools. During the testing of GA we work with a dataset of proteomic data to preserve the pairwise structural relations of the dataset instances as much as possible. We compared the result obtained using our GA optimization with some solutions obtained without optimization, and we found that our result was close to the optimal solution 4 times more than non-optimized solution.
Semantic Aware Video Clipper Using Speech Recognition Toolkit

The Internet learning framework was presented as not a single one of us could meet up like previously, during the pandemic. Most understudies go to their web-based classes on portals like Google Meet, Microsoft Groups, Zoom. Students having some doubt regarding a topic, or they somehow missed the lesson, to cover the classes they can go through the recordings, but in these recordings, students are only interested in watching the content where the teacher was teaching a particular concept/topic, but the video also has a lot of the content which is not so useful, i.e., unrelated to the content being taught and also want to skip the part where nothing is happening. So, to eliminate these problems, we propose Semantic Aware Video Clipper (SAVC), a software to clip the irrelevant stuff and the part where no activity is there (silence) and make these videos/recordings relevant to the topic and hence save the priceless time of users, with respect to learning. SAVC will automatically cut some video fragments and then join these fragments together and this entire process will be completely automatic, without human intervention.
Detecting Spam Comments on YouTube by Combining Multiple Machine Learning Models

In recent times, online media has seen an influx with billions of concurrent users. YouTube is one such platform for streaming media, allowing its users to post their ideas in the form of comments. Although it is one of the biggest platforms, it suffers from spam comments from adversaries and bots. To tackle such comments, this paper examines different machine learning models (Multinomial Naive Bayes, Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest) on the comments of five popular videos on the platform. It was found that the Ensemble model with soft voting outperformed the other classifiers when these techniques were coupled with hard and soft voting.
Hand Anatomy and Neural Network Based Recognition of Isolated and Real-Life Words of Indian Sign Language

The primary source of communication used by deaf-mute people is sign language. The task of automatically recognizing sign language motions is difficult. For the recognition of Indian sign language, a model based on hand anatomy and neural networks is proposed in this research. Features are extracted through Features from the Accelerated Segment Test (FAST) and Scale Invariant Feature Transform (SIFT) techniques, and hand anatomy is utilized for the selection of hand landmarks out of these detected points. Finally, training and testing of the model have been done by the neural network. Experimentation was performed on challenging and publicly available datasets for isolated single-letter gestures (alphabets and numbers) and word gestures (various real-life gestures) with different backgrounds and lighting conditions. Experimental results prove that fast and highly accurate results are achieved by the proposed model when compared with other available models. An accuracy of 98.90% has been achieved for isolated gestures and 97.58% for word gestures.
Evaluating Image Data Augmentation Technique Utilizing Hadamard Walsh Space for Image Classification

Deep neural networks are complex networks that need millions of parameters to be trained and perform well for huge datasets, however result in poor generalization for small datasets. Data augmentation is an effective method of addressing generalization where an existing dataset is enlarged by generating synthetic images. Commonly used geometric transformation-based augmentation techniques such as rotation, flipping, random cropping etc. cannot be applied generally to all datasets. This paper proposes an effective and yet easy to implement data augmentation technique utilizing frequency domain coefficients obtained using the Hadamard and Walsh transforms which we believe can be applied generally to any dataset. The experiments performed using the VGG16 model on the Cifar-10 dataset show that the Hadamard based technique outperforms the single geometric transformation technique. It also achieves comparable results even with a smaller dataset when compared with multiple geometric transformation techniques. Further, the complexity of proposed techniques is less than that of traditional geometric transformation techniques.
Predictive Analytics for Fake Currency Detection

Fake currency is the imitation of the real currency without the legal sanction of the government. Producing or using fake currency is a form of forgery and it is illegal. The circulation of fake currency increases inflation and raises demands for goods which also affects the producers and consumers thereby leads to currency devaluation. Classifying fake currency on the fly is a major challenge. The aim of this work is to detect fake currencies using predictive analytic models by considering the statistical characteristics of the fake currency note as features. The US dollar images are considered for classification of fake currency notes. The statistical characteristics variance, skewness, kurtosis and entropy of the fake currency images are extracted from images and are treated as the independent variables for model building. The dataset is splitted into 80:20 as training and test datasets. The classifiers Naïve Bayes, Random Forest, AdaBoost, Logistic Regression, Support Vector Machine, K- Nearest Neighbor and Multi-layer Perceptron are built. The performance metrices accuracy, precision, recall and F1 score of the built models are compared. The predictive analytics models SVM, KNN and MLP yield the best accuracy 100% and these models are not suffering from drift between the train and test dataset.
Video Captioning Using Deep Learning Approach-A Comprehensive Survey

Video captioning is a technique used to represent videos using phrases of natural language. This technique automatically generates phrases to explain the contents of input videos. Deep learning-based video captioning methodologies are used for generating descriptions in natural language for a given video or an image. The significance of video captioning in the present era has advanced research in this field during the last few decades. A methodical review of recent works, various benchmarks used for performance evaluation, and various data sets for training are explained in this review paper for the benefit of new researchers. The most widely used neural network variants for visual and spatiotemporal feature extraction, as well as an approach for language generation, are included in this survey paper. The results show that ResNet and VGG are the most widely used visual feature extractors, and 3D convolutional neural networks are the most widely used Spatio-temporal feature extractors. Aside from that, the language model has primarily been used as Long Short-Term Memory (LSTM). The most widely used metrics for video description evaluation are BLEU, ROUGE, METEOR, CIDEr, SPICE, and WMD. This article is intended to be an extensive overview of deep learning-based captioning approaches for a given image and video.
Identification of Social Accounts’ Responses Using Machine Learning Techniques

Twitter bots, or “zombies,” are automated Twitter accounts run by bot software. Bots carry out tasks like sending tweets, following other users in bulk, and tweeting and retweeting material on a vast scale to achieve predetermined objectives. The proliferation of social bots—automated agents typically exploited for nefarious purposes—on social media sites like Twitter is a significant issue. These include using the impact of these accounts to influence a community on a particular issue, disseminate false information, enlist individuals in criminal organisations, control people’s behaviour in the stock market, and extort people into disclosing their data. This work has investigated how bots react to unexpected political events compared to human accounts, explained the general prevalence of political bots on Twitter, and created and implemented a model to recognise them only based on user profiles.
Comparing Neural Architectures to Find the Best Model Suited for Edge Devices

Training large-scale neural network models is computationally expensive and demands a great deal of resources. It is an important area of study with a lot of potential for the future of the AI industry. In recent years, the power of computer hardware has significantly improved and we have new breakthroughs in deep learning. With these innovations, the computational cost of training large neural network models has declined by at least 10 folds in high- and average-performance machines. In this research, we explore NAL, AutoML, and other frameworks to determine the best suitable model for edge devices. The biggest improvements compared to reference models can be acquired if the NAS algorithm is co-designed with the corresponding inference engine.
A Novel Remote Sensing Image Captioning Architecture for Resource Constrained Systems

Machine learning, specifically deep learning, has proven to be successful in many aspects along with groundbreaking results in various fields. One such area where the applications of Deep Learning are yet to show differences is in Resource Constrained Environment. In such conditions, not only the accuracy but also the feasibility with which one can process computations on the machines play an important role. The biggest challenge demanded by such systems is the simple yet accurate application that can operate under such challenging conditions. In this paper, we present a novel architecture for Image Captioning on remote sensing images, which delivers information based on satellite images with decent accuracy and can work efficiently in a resource-constrained atmosphere. The model is trained and tested on standard Remote Sensing Datasets such as UCM-Captions and Sydney Captions. Our proposed model has achieved the BLUE-1 score of 71.2 with only 8.28M parameters which is significantly less when compared to other benchmark models in Remote Sensing Image Captioning.
Dynamic Priority Based Resource Scheduling in Cloud Infrastructure Using Fuzzy Logic

Cloud computing is a viable solution for infrastructure services in distributed applications with the primary goal of optimizing resource utilization. Cloud infrastructure services may offer thousands of resources to complete a single operation. Appropriate resource scheduling results in optimal usage and an improvement in the service provider’s QoS. In this work, a fuzzy logic-based technique is used, and an efficient algorithm for scheduling resources is introduced. Implementation of the proposed approach and its efficacy is tested through the Cloudsim and MATLAB tools. The proposed technique’s waiting and turnaround times were compared with prominent algorithms SJF and FCFS. The simulation findings are remarkable when compared with the turnaround time and waiting time of existing algorithms. The proposed approach has an improvement of 80% in waiting time and 17% in turnaround time.
Geometry Enhancements from Visual Content: Going Beyond Ground Truth

This work presents a new self-supervised cyclic architecture that extracts high-frequency patterns from images and re-insert them as geometric features. This procedure allows us to enhance the resolution and bittage of low-cost depth sensors capturing fine details on the one hand and being loyal to the scanned scene on the other. We present state-of-the-art results for depth super-resolution tasks as well as visually attractive, enhanced generated 3D models. Our main focus is on situations in which there is no large-scale accurate ground-truth depth map to train on, since the depth maps are captured by a low-cost scanner, making it either noisy or quantized.