Reviewerly/exHarmony huggingface dataset:
Recognition of Hand Gestures using Visible Light and a Probabilistic-based Neural Network

Gestures by hand are an effective and efficient means to interact with machines. We present a method for recognizing gestures via the analysis of interrupted light patterns using visible light. Utilizing low-cost and easily accessible components, the system can exploit existing light communications infrastructures. A probabilistic type neural network is applied to learn the sequence of obfuscated light patterns during movement of the hands. A novel preprocessing of the received light is introduced enabling the use of an accurate but low-complexity class of neural network.
Interactive Web-Based Visual Analysis on Network Traffic Data

Network traffic data analysis is important for securing our computing environment and data. However, analyzing network traffic data requires tremendous effort because of the complexity of continuously changing network traffic patterns. To assist the user in better understanding and analyzing the network traffic data, an interactive web-based visualization system is designed using multiple coordinated views, supporting a rich set of user interactions. For advancing the capability of analyzing network traffic data, feature extraction is considered along with uncertainty quantification to help the user make precise analyses. The system allows the user to perform a continuous visual analysis by requesting incrementally new subsets of data with updated visual representation. Case studies have been performed to determine the effectiveness of the system. The results from the case studies support that the system is well designed to understand network traffic data by identifying abnormal network traffic patterns.
A Novel Density Peaks Clustering Algorithm with Isolation Kernel and K-Induction

Density peaks clustering (DPC) algorithm can process data of any shape and is simple and intuitive. However, the distance between any two high-dimensional points tends to be consistent, which makes it difficult to distinguish the density peaks and easily produces “bad label” delivery. To surmount the above-mentioned defects, this paper put forward a novel density peaks clustering algorithm with isolation kernel and K-induction (IKDC). The IKDC uses an optimized isolation kernel instead of the traditional distance. The optimized isolation kernel solves the problem of converging the distance between the high-dimensional samples by increasing the similarity of two samples in a sparse domain and decreasing the similarity of two samples in a dense domain. In addition, the IKDC introduces three-way clustering, uses core domains to represent dense regions of clusters, and uses boundary domains to represent sparse regions of clusters, where points in the boundary domains may belong to one or more clusters. At the same time as determining the core domains, the improved KNN and average similarity are proposed to assign as many as possible to the core domains. The K-induction is proposed to assign the leftover points to the boundary domain of the optimal cluster. To confirm the practicability and validity of IKDC, we test on 10 synthetic and 8 real datasets. The comparison with other algorithms showed that the IKDC was superior to other algorithms in multiple clustering indicators.
SIFT-CNN Pipeline in Livestock Management: A Drone Image Stitching Algorithm

Images taken by drones often must be preprocessed and stitched together due to the inherent noise, narrow imaging breadth, flying height, and angle of view. Conventional UAV feature-based image stitching techniques significantly rely on the quality of feature identification, made possible by image pixels, which frequently fail to stitch together images with few features or low resolution. Furthermore, later approaches were developed to eliminate the issues with conventional methods by using the deep learning-based stitching technique to collect the general attributes of remote sensing images before they were stitched. However, since the images have empty backgrounds classified as stitched points, it is challenging to distinguish livestock in a grazing area. Consequently, less information can be inferred from the surveillance data. This study provides a four-stage object-based image stitching technique that, before stitching, removes the background’s space and classifies images in the grazing field. In the first stage, the drone-based image sequence of the livestock on the grazing field is preprocessed. In the second stage, the images of the cattle on the grazing field are classified to eliminate the empty spaces or backgrounds. The third stage uses the improved SIFT to detect the feature points of the classified images to o8btain the feature point descriptor. Lastly, the stitching area is computed using the image projection transformation.
Health Chain

The research shows that the blockchain has a wide range of healthcare applications including electronic medical records, drug and pharmaceutical supply chain management, biomedical research and education, remote patient monitoring, health data analysis, among others. A number of blockchain-based healthcare applications have been developed as examples based on emerging blockchain paradigms, such as smart contracts, etc. In this research, a health chain system is introduced. HealthChain is an online portal that maintains a digital copy of a patient's medical history, along with all his/her diagnoses, prescriptions, and medical receipts.
<scp>ELSTM</scp>: An improved long <scp>short‐term</scp> memory network language model for sequence learning

Abstract The gated structure of the long short‐term memory (LSTM) alleviates the defects of gradient disappearance and explosion in the recurrent neural network (RNN). It has received widespread attention in sequence learning such as text analysis. Although LSTM has good performance in handling remote dependencies, information loss often occurs in long‐distance transmission. We propose a new model called ELSTM based on the computational complexity and gradient dispersion in the traditional LSTM model. This model simplifies the input gate of LSTM, reduces some time complexity by reducing some components, and improves the output gate. By introducing the exponential linear unit activation layer, the problem of gradient dispersion is alleviated. Comparing the new model with multiple existing models, when predicting language sequences, the time used by the model has been greatly reduced, and the language confusion has been reduced, showing good performance.
Blockchain and IoT's role in Agri Food Supply Chain Traceability

In this work, we show how blockchain can be used to manage the supply chain for non-perishable agricultural food (NPAF). Keeping product quality high across the supply chain is a major challenge for the food business. Traditional foods are severely affected. The system's major purpose is to make product history accessible. At each stage of the supply chain, the product can be checked for quality and cleanliness. We created a trust less environment by utilising Blockchain, RFID, and IoT. Encryption and the network are more trusted than public or private institutions. This research suggests an execution model for the supply chain management of non-perishable agricultural foods that addresses these challenges by combining Ethereum smart contracts with IPFS, RFID, and IoT technologies. These technologies were combined in an effort to find a solution to these problems. The technology will give consumers a clear view of the complete process from raw material to the finished product and make it simple for a controlling authority to conduct operational examinations on the excellence of the goods and decent working practises.
Enhancing Two Dimensional Magnetic Resonance Image Using Generative Adversarial Network

Magnetic Resonance Imaging is often used in medical imaging techniques. The particular magnetic resonance imaging needs to be clear and sharp for precise and effective medical diagnosis. The image quality can be severely harmed by a slight movement in the muscle or the intended area. It is difficult to obtain high-quality scans due to hardware limitations and health risks associated with magnetic resonance imaging radiation. The existing research has shown that the generative adversarial network approach with deep neural networks gives impressive results compared to traditional approaches such as bicubic interpolation. In the proposed methodology, generative adversarial networks is used to improve the resolution and quality of the magnetic resonance imaging. The proposed architecture converts the low-resolution image input to high-resolution image output. Two different neural networks are used in the generative adversarial network i. e., the discriminator and the generator. These two architecture compete against one another to enhance the final output. The high-resolution results are generated by a generator, and the generator's performance is improved by a discriminator.
Detecting SQL Injection Attack using Natural Language Processing

In today's digital era, online attacks are increasing in number and are becoming severe day by day, especially those related to web applications. The data accessible over the web persuades the attackers to dispatch new kinds of attacks. Serious exploration on web security has shown that the most hazardous attack that affects web security is the Structured Query Language Injection(SQLI). This attack addresses a genuine threat to web application security and a few examination works have been directed to defend against this attack by detecting it when it happens. Traditional methods like input validation and filtering, use of parameterized queries, etc. are not sufficient to counter these attacks as they rely solely on the implementation of the code hence factoring in the developer's skill-set which in turn gave rise to Machine Learning based solutions. In this study, we have proposed a novel approach that takes the help of Natural Language Processing(NLP) and uses BERT for feature extraction that is capable to adapt to SQLI variants and provides an accuracy of 97% with a false positive rate of 0.8% and a false negative rate of 5.8%.
Batch Image Encryption and Compression using Chaotic Map Infused Autoencoder Network

Image encryption has been an active research area in computer science. Secure transmission of images over any transmission medium is very important to ensure security and privacy. In this pursuit, many algorithms have been proposed that use chaotic map-based encryption followed by the use of Autoencoders to compress the image in a lossless manner. We have proposed an Encoder-Decoder-based method that makes use of chaotic map infusion during the training process along with OTP to generate an encrypted compressed representation of the image. The encrypted representation is then flattened to generate a one-dimensional sequence that is transmitted over the transmission medium. The trained encoder is used to generate this encrypted representation and the trained decoder is used to regenerate the original image. The advantage of this method is twofold: the spatial information is totally removed in encrypted representation and the representation is compressed in a lossless manner. The comparative analysis is done to examine the similarity between the decrypted and plain images to ascertain the efficacy of the proposed model. On evaluation, it is found that the PSNR, MSE, and SSIM values are surpassing the other related work on most Standard Test Images. The proposed model algorithm provides High PSNR (>80dB), Low MSE (>0.00007), High SSIM (>99%) in comparison with other existing neural network-based algorithms on Standard Test Images. The proposed model achieves high-performance scores on entire datasets as well. The experimental results suggest that using deep learning-based architecture can provide fruitful results in cryptography.
Blockchain technology adoption in SMEs: the extended model of UTAUT

The purpose of this study is to measure the behavioral intention to adopt blockchain technology (BT) among SME managers based on the theory of "Unified theory of acceptance and use of technology" (UTAUT).The study was conducted among SME managers in Iraq who adopted blockchain technology. The study deployed the quantitative method and the purposive sample technique was used. The data collected by questionnaires and the valid questionnaires was 192 analyzed by Smart-PLS after ensuring the validity and reliability of the proposed model.The results indicated that performance expectancy, facilitating conditions, technology readiness, and blockchain transparency positively and significantly impact SME managers' intention to adopt blockchain technology. On the other hand, social influence has an insignificant impact on BI’s decision to adopt the blockchain among SME managers.
Real-time Depth Completion using Radar and Camera

One of the active challenges in intelligent driving systems is depth completion. The KITTI benchmark depth completion challenge is concerned with lidar-camera fusion. Recently, researchers attempted to address the problem using radar and camera fusion due to the common use of radar sensors in the automotive industry. In this study, we take a closer look at one of the developed models for radar-camera fusion. We propose introducing preprocessing algorithms for radar data such as CPU-based methods to improve the radar measurements and subsequently the depth estimation. The methods include dilation, closure, and hole filling. With the usage of late fusion between radar and camera, the quantitative results show that the proposed methods improve the estimation with almost no additional computational cost. Additionally, the qualitative analysis demonstrates how the proposed model performs better.
Performance Evaluation of a Variational Quantum Classifier

Quantum Machine Learning (QML) is a newly emerging research area at the intersection of classical machine learning (CML) and quantum computing (QC). Data is becoming voluminous rapidly, so it is challenging for classical computers to train Machine Learning (ML) models over massive datasets. The hope is that quantum physics' intrinsic features, such as entanglement, superposition, and interference, could be exploited as resources for training ML models on big datasets that would otherwise be relatively impossible for classical computers. It is theoretically proven that quantum computers have an exponential-time advantage over their classical counterparts in solving several problems, e.g., complex large dimensional matrix multiplication, factorization problem, unstructured database search, etc. QML models attempt to find a quantum advantage over their classical counterparts. Variational Quantum Classifiers (VQC) are hybrid quantum neural networks to perform the task of classification using QML models. VQC models in the present NISQ (Noisy Intermediate Scale Quantum — 50 to 100 qubits) era can produce comparable and even better results than Classical models. In this article, we examined the performance of a VQC while performing a simple binary classification task. In this article, we use a VQC to evaluate this method's performance empirically. We constructed a VQC to predict the label of fresh input for the typical Iris dataset comprising pairings of target outputs and training inputs. Our quantum classifier can reasonably predict species labels using only four qubits. These levels are fairly good compared to the accuracy levels attained by classical classifiers.
Big Data De-duplication Using Classification Scheme based on Histogram of File Stream

Modern technologies generate millions of data every second. Managing and storing this data is very challenging. Data deduplication reduces storage requirements and data redundancy. It is one of the most effective solutions for Big data storage. This paper proposes a new method to accelerate and improve the deduplication technique. It separates the data into many classes before entering the deduplication system based on the Pearson correlation between the histograms of different data extensions. A new method has been developed to divide the file appropriately by generating a list of divisors based on data repeating patterns. In addition, a mathematical algorithm is proposed to construct the hash functions for each chunk that are twice faster as traditional hash functions (MD5 and SHA-1). As a result, the Deduplication Ratio (DR) of the proposed method is about ten times more potent than the Basic Sliding Window (BSW) method and approximately five times more powerful than the Two Thresholds Two Divisors (TTTD) method.
A reduction-based cut-free Gentzen calculus for dynamic epistemic logic

Abstract Dynamic epistemic logic (DEL) is a multi-modal logic for reasoning about the change of knowledge in multi-agent systems. It extends epistemic logic by a modal operator for actions which announce logical formulas to other agents. In Hilbert-style proof calculi for DEL, modal action formulas are reduced to epistemic logic, whereas current sequent calculi for DEL are labelled systems which internalize the semantic accessibility relation of the modal operators, as well as the accessibility relation underlying the semantics of the actions. We present a novel cut-free ordinary sequent calculus, called $ \textbf{G4}_{P,A}[] $, for propositional DEL. In contrast to the known sequent calculi, our calculus does not internalize the accessibility relations, but—similar to Hilbert style proof calculi—action formulas are reduced to epistemic formulas. Since no ordinary sequent calculus for full S5 modal logic is known, the proof rules for the knowledge operator and the Boolean operators are those of an underlying S4 modal calculus. We show the soundness and completeness of $ \textbf{G4}_{P,A}[] $ and prove also the admissibility of the cut-rule and of several other rules for introducing the action modality.
Contact-based object inspection with mobile manipulators at near-optimal base locations

This paper presents a control and motion planning algorithm for a mobile vehicle-manipulator system such that the mobile vehicle and the manipulator mounted on it work in harmony to inspect unknown objects. Forward Dynamic Control method is used for the manipulator to accomplish a stable interaction with the environment and constrained particle swarm optimization is applied so that the vehicle can be localized at the estimated points maximizing the dexterity of the manipulator. Quartic splines are implemented to generate a smooth path for the vehicle in between the optimal locations. The proposed architecture is validated via an experimental setup consisting of a robotic arm with a force sensor at its end-effector mounted on a parallel manipulator. These experiments emulate an underwater vehicle-manipulator system, where the mobile base is subject to disturbances due to the physical interaction of the end-effector with the environment, typically a pipe. The advantage of the proposed approach is that it allows continuous and smooth movement of the base in harmony with the robotic manipulator while executing a task on a large surface (larger than the manipulator workspace can cover from a fixed position) and maintains a high level of dexterity index for the manipulator.
What are the characteristics of highly-selected packages? A case study on the npm ecosystem

With the popularity of software ecosystems, the number of open source components (known as packages) has grown rapidly. Identifying high-quality and well-maintained packages from a large pool of packages to depend on is a basic and important problem, as it is beneficial for various applications, such as package recommendation and package search. However, no systematic and comprehensive work focuses on addressing this problem except in online discussions or informal literature and interviews. To fill this gap, in this paper, we conducted a mixed qualitative and quantitative analysis to understand how developers identify and select relevant open source packages. In particular, we started by surveying 118 JavaScript developers from the npm ecosystem to qualitatively understand the factors that make a package to be highly-selected within the npm ecosystem. The survey results showed that JavaScript developers believe that highly-selected packages are well-documented, receive a high number of stars on GitHub, have a large number of downloads, and do not suffer from vulnerabilities. Then, we conducted an experiment to quantitatively validate the developers' perception of the factors that make a highly-selected package. In this analysis, we collected and mined historical data from 2,527 packages divided into highly-selected and not highly-selected packages. For each package in the dataset, we collected quantitative data to present the factors studied in the developers' survey. Next, we used regression analysis to quantitatively investigate which of the studied factors are the most important. Our regression analysis complements our survey results about highly-selected packages. In particular, the results showed that highly-selected packages tend to be correlated by the number of downloads, stars, and how large the package's readme file is.
An improved IoT forensic model to identify interconnectivity between things

The Internet of Things (IoT) is permeating our daily lives, blurring the boundaries between the cyber world and the physical world. With an increased number of identified sources, obtaining potential digital evidence has become a significant aspect of investigations and incident responses. However, multi-level interconnections are bringing new challenges to identifying connected things in IoT ecosystems. This study focuses on IoT services that can connect or be connected to various things to reveal their connected relationship by examining forensic artifacts under six interconnected scenarios. We found that not all services retain traces of interconnection. Based on our findings from scenario-based experiments and existing IoT forensic frameworks, we propose an improved IoT forensic model to identify interconnectivity between things. This framework suggests additional phases for identifying the interconnectivity of things. The proposed framework is designed to investigate IoT-related cases by identifying all the connected things, ensuring relevant data collection, narrowing down areas of investigation, and integrating the data. Our proposed model discovers pieces of connected things, which helps reveal the whole interconnected IoT ecosystems where users are involved in. This approach can also be applied for a thorough understanding of events relating to things and services around a suspect in cybercrime response.