Reviewerly/exHarmony huggingface dataset:
A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm

Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different needs and different scenarios. Many approaches have been followed, both before and after the introduction of machine and deep learning algorithms as intelligence computation. The network traffic analysis is the process of incarcerating traffic of a network and observing it deeply to predict what the manifestation in traffic of the network is. To enhance the quality of service (QoS) of a network, it is important to estimate the network traffic and analyze its accuracy and precision, as well as the false positive and negative rates, with suitable algorithms. This proposed work is coining a new method using an enhanced deep reinforcement learning (EDRL) algorithm to improve network traffic analysis and prediction. The importance of this proposed work is to contribute towards intelligence-based network traffic prediction and solve network management issues. An experiment was carried out to check the accuracy and precision, as well as the false positive and negative parameters with EDRL. Also, convolutional neural network (CNN) machines and deep learning algorithms have been used to predict the different types of network traffic, which are labeled text-based, video-based, and unencrypted and encrypted data traffic. The EDRL algorithm has outperformed with mean Accuracy (97.20%), mean Precision (97.343%), mean false positive (2.657%) and mean false negative (2.527%) than the CNN algorithm.
Privacy-preserving deep learning techniques for wearable sensor-based big data applications

Augmented reality (AR) technologies enable people to observe the world in new ways. Generally, AR uses computer-generated graphics to overlay improvements on the user's perspective of reality using big data and machine learning techniques. In this paper, we present a comprehensive survey of recent privacy-preserving big data analytics applications for the emerging applications of machine learning in the era of 5G. We highlight the fundamental features of security and privacy for 5G-based wearable device applications. We then present a study on privacy-preserving machine learning techniques. Finally, we explain the implementation details of a study case of a secure prediction service using the Convolutional Neural Network (CNN) model with the CKKS homomorphic encryption technique. We aim to anticipate how security technology will be useful for the safe processing of the collected data from wearable devices, particularly in increasing the utility and performance of privacy-preserving machine learning analytics in the era of 5G and beyond.
A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attribute-Based Cryptographic Mechanisms

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect patients’ anonymity and safeguard their EHRs. All the EHR activities and access control events are recorded permanently as blockchain transactions. We develop the ABSA module on Hyperledger Ursa cryptography library, MA-ABE module on OpenABE toolset, and blockchain network on Hyperledger Fabric. We measure the execution time of ABSA’s signing and verification functions, MA-ABE with different access policies and homomorphic encryption schemes, and compare the results with other existing blockchain-based EHR systems. We validate the access activities and authentication events recorded in blockchain transactions and evaluate the transaction throughput and latency using Hyperledger Caliper. The results show that the performance meets real-world scenarios’ requirements while safeguarding EHR and is robust against unauthorized retrievals.
QSFM: Model Pruning Based on Quantified Similarity Between Feature Maps for AI on Edge

Convolutional neural networks (CNNs) have been applied in numerous Internet of Things (IoT) devices for multifarious downstream tasks. However, with the increasing amount of data on edge devices, CNNs can hardly complete some tasks in time with limited computing and storage resources. Recently, filter pruning has been regarded as an effective technique to compress and accelerate CNNs, but existing methods rarely prune CNNs from the perspective of compressing high-dimensional tensors. In this article, we propose a novel theory to find redundant information in 3-D tensors, namely, quantified similarity between feature maps (QSFM), and utilize this theory to guide the filter pruning procedure. We perform QSFM on data sets (CIFAR-10, CIFAR-100, and ILSVRC-12) and edge devices and demonstrate that the proposed method can find the redundant information in the neural networks effectively with comparable compression and tolerable drop of accuracy. Without any fine-tuning operation, QSFM can compress ResNet-56 on CIFAR-10 significantly (48.7% FLOPs and 57.9% parameters are reduced) with only a loss of 0.54% in the top-1 accuracy. For the practical application of edge devices, QSFM can accelerate MobileNet-V2 inference speed by 1.53 times with only a loss of 1.23% in the ILSVRC-12 top-1 accuracy.
Evaluating and Prioritizing Barriers for Sustainable E-Learning Using Analytic Hierarchy Process-Group Decision Making

E-Learning is a popular computer-based teaching–learning system that has been rapidly gaining global attention during and post COVID-19. The leaping changes in digital technology have enabled E-Learning to become more effective in recent years. It offers freedom from restrictions caused by geographical boundaries and provides time flexibility in the teaching–learning process. Apart from its numerous advantages, the success of E-Learning depends upon many critical success factors (CSFs) and barriers. If the barriers that lie in the way of successful E-Learning implementation are not addressed diligently, it will limit E-Learning success. It has been revealed through past research that these barriers are serious threats that need immediate attention in their redressal. This paper attempts to reveal sixteen barriers under four different dimensions by going through a comprehensive review of the literature and engaging decision makers. Furthermore, it uses the Analytic Hierarchy Process-Group Decision Making (AHP-GDM) methodology to evaluate and prioritize them. The results obtained show that barriers related to the Institutional Management Dimension (BIMD), Infrastructure and Technological Dimension (BITD), Student Dimension (BSD), and Instructor Dimension (BID) pose the greatest challenges in the successful implementation of E-Learning. The AHP-GDM methodologies reveal the comparative relationship among these barriers as BIMD &gt; BITD &gt; BSD &gt; BID and further quantify their negative effects as 46.35%, 29.88%, 12.30%, and 11.4%, respectively, on successful E-Learning systems (‘&gt;’ indicates comparative challenges).
An Empirical Investigation into the Reproduction of Bug Reports for Android Apps

One of the key tasks related to ensuring mobile app quality is the reporting, management, and resolution of bug reports. As such, researchers have committed considerable resources toward automating various tasks of the bug management process for mobile apps, such as reproduction and triaging. However, the success of these automated approaches is largely dictated by the characteristics and properties of the bug reports they operate upon. As such, understanding mobile app bug reports is imperative to drive the continued advancement of report management techniques. While prior studies have examined high-level statistics of large sets of reports, we currently lack an in-depth investigation of how the information typically reported in mobile app issue trackers relates to the specific details generally required to reproduce the underlying failures. In this paper, we perform an in-depth analysis of 180 reproducible bug reports systematically mined from Android apps on GitHub and investigate how the information contained in the reports relates to the task of reproducing the described bugs. In our analysis, we focus on three pieces of information: the environment needed to reproduce the bug report, the steps to reproduce (S2Rs), and the observed behavior. Focusing on this information, we characterize failure types, identify the modality used to report the information, and characterize the quality of the information within the reports. We find that bugs are reported in a multi-modal fashion, the environment is not always provided, and S2Rs often contain missing or non-specific enough information. These findings carry with them important implications on automated bug reproduction techniques as well as automated bug report management approaches more generally.
Automatic True/False Question Generation for Educational Purpose

In field of teaching, true/false questioning is an important educational method for assessing students' general understanding of learning materials.Manually creating such questions requires extensive human effort and expert knowledge.Question Generation (QG) technique offers the possibility to automatically generate a large number of questions.However, there is limited work on automatic true/false question generation due to the lack of training data and difficulty finding question-worthy content.In this paper, we propose an unsupervised True/False Question Generation approach (TF-QG) that automatically generates true/false questions from a given passage for reading comprehension test.TF-QG consists of a template-based framework that aims to test the specific knowledge in the passage by leveraging various NLP techniques, and a generative framework to generate more flexible and complicated questions by using a novel masking-and-infilling strategy.Human evaluation shows that our approach can generate high-quality and valuable true/false questions.In addition, simulated testing on the generated questions challenges the state-of-the-art inference models from NLI, QA, and fact verification tasks.
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency

Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that such algorithms can cause we developed an open source toolbox that can analyse selected fairness, accountability and transparency aspects of the machine learning process: data (and their features), models and predictions, allowing to automatically and objectively report them to relevant stakeholders. In this paper we describe the design, scope, usage and impact of this Python package, which is published under the 3-Clause BSD open source licence.
An Intelligent Automated System for Detecting Malicious Vehicles in Intelligent Transportation Systems

The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.
Why Do People Resist Drone Food Delivery Services? An Innovation Resistance Theory Perspective

Recently, the food service delivery industry started using drones for delivering food. The drone food delivery services are not only beneficial for marketers but also provide several benefits to the consumers. Despite several advantages for different stakeholders, the adoption of drone-based delivery is not encouraging. Thus, it is crucial to know why consumers show resistance behavior toward drone-based food delivery services. Toward that end, this study examines the effects of different functional and psychological barriers on consumers’ resistance to drone-based food delivery services through the lens of innovation resistance theory. A moderated-mediation framework has been proposed using anticipated negative emotions as a mediator and consumers’ innovativeness as a moderator. The hypotheses are examined by analyzing the 355 samples collected from online food delivery service users. The results suggest that usage, risk, image, and experience barriers are significantly associated with resistance. The study's findings enrich emerging drone-based delivery services and online food delivery literature. The study's findings furthermore offer practical knowledge to help online food delivery service providers implement strategies to reduce consumers' resistance to the drone food delivery system.
A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential Election

With the increasing popularity of Twitter as both a social media platform and a data source for companies, decision makers, advertisers, and even researchers alike, data have been so massive that manual labeling is no longer feasible. This research uses a semi-supervised approach to sentiment analysis of both English and Tagalog tweets using a base classifier. In this study involving the Philippines, where social media played a central role in the campaign of both candidates, the tweets during the widely contested race between the son of the Philippines’ former President and Dictator, and the outgoing Vice President of the Philippines were used. Using Natural Language Processing techniques, these tweets were annotated, processed, and trained to classify both English and Tagalog tweets into three polarities: positive, neutral, and negative. Through the Self-Training with Multinomial Naïve Bayes as base classifier with 30% unlabeled data, the results yielded an accuracy of 84.83%, which outweighs other studies using Twitter data from the Philippines.
Evolution and Evaluation: Sarcasm Analysis for Twitter Data Using Sentiment Analysis

This paper addresses the evolution and evaluation of sarcasm in textual form. The growing popularity of social networking sites is well known, and every individual generates a whole new set of opinions in form of blogs, microposts, etc. Sentiment analysis is one of the fastest evolving aspects of artificial intelligence categorizing opinions under positive, negative, or neutral sentiments. One such part of sentiment analysis is sarcasm. Sarcasm is becoming a common phenomenon in networking sites where expressing murky feelings wrapped by positive words for conveying contempt is highly used, making it difficult to understand the actual meaning of a statement. When reading customer reviews or complaints, it might be helpful to understand the consumers’ genuine intentions in order to enhance the efficiency of customer support or after-sales services. In this paper, different classifiers—decision tree, Naïve Bayes, k-nearest, and support vector machine are used to predict a statement under the category sarcastic or nonsarcastic using tweeter data; the following proposed methodology is used for the experimental evaluation concluding that the given classifiers SVM gains the highest accuracy of 93%, whereas Naïve Bayes and decision tree are performing well with an accuracy of 83% and 86%, respectively, along with the lowest of 51% attained by KNN.
Analog instrument pointer monitoring and parameter estimation via line scan vision

Analog instruments are widely used in energy engineering. However, an analog instrument is only human-readable because it does not have a built-in digital communication interface. In power system, it is necessary not only to record the final output value but also to monitor the associated dynamic power process. The present paper develops an analog instrument pointer monitoring and parameter estimation system via line scan vision. A line scan camera is used to collect the dynamic process data of the analog instrument. The captured images can be regarded as a discrete temporal sequence. The light-spot centroid method is implemented to extract the initial pointer position. Data normalization is used to process the initial data. By analysing the system step response function, we construct a cost function, and Least-squares identification algorithm is used to estimate the damp and natural frequency. The proposed method monitors the dynamic process of the analog instrument with various inputs, such as sine signals and random signals, thereby enabling condition monitoring. Experimental prediction results show that the proposed estimation method is effective and robust.
Sampling Agnostic Feature Representation for Long-Term Person Re-Identification

Person re-identification is a problem of identifying individuals across non-overlapping cameras. Although remarkable progress has been made in the re-identification problem, it is still a challenging problem due to appearance variations of the same person as well as other people of similar appearance. Some prior works solved the issues by separating features of positive samples from features of negative ones. However, the performances of existing models considerably depend on the characteristics and statistics of the samples used for training. Thus, we propose a novel framework named sampling independent robust feature representation network (SirNet) that learns disentangled feature embedding from randomly chosen samples. A carefully designed sampling independent maximum discrepancy loss is introduced to model samples of the same person as a cluster. As a result, the proposed framework can generate additional hard negatives/positives using the learned features, which results in better discriminability from other identities. Extensive experimental results on large-scale benchmark datasets verify that the proposed model is more effective than prior state-of-the-art models.
BreastMultiNet: A multi-scale feature fusion method using deep neural network to detect breast cancer

Breast cancer is predominantly seen in women and is the leading cause of death in females worldwide. Diagnosis of breast cancer using biopsy tissue images is expensive, time-intensive, and fraught with conflicts among doctors. Pathologists can now diagnose breast cancer more consistently and promptly because of advances in the Computer-Aided Diagnosis (CAD) system. As a result, there has been a surge in demand for CAD-based machine learning techniques. This study describes a “BreastMultiNet” framework that focuses on the transfer learning concept for identifying distinct types of breast cancer by utilizing two publicly available datasets. The suggested “BreastMultiNet” architecture allows rapid and comprehensive breast cancer diagnosis. The suggested scheme extracts features from microscope images with the help of well-known conventional and deep learning models such as HOG, LBP, SURP, DenseNet201, and VGG19. Comparatively, transfer learning models provide good accuracy than conventional models. The collected properties of transfer learning models are subsequently dispatched into the summing layer, resulting in a fused vector. The proposed framework achieves 99% and 95% classification accuracy on both BreakHis and ICIAR dataset respectively, outperforming all the other state of the art techniques. In terms of accuracy, the “BreastMultiNet” framework may be employed as a modeling approach in hospitals and medical care contexts.
Squeezing of nonlinear spin observables by one axis twisting in the presence of decoherence: An analytical study

In an ensemble of two-level atoms that can be described in terms of a collective spin, entangled states can be used to enhance the sensitivity of interferometric precision measurements. While non-Gaussian spin states can produce larger quantum enhancements than spin-squeezed Gaussian states, their use requires the measurement of observables that are nonlinear functions of the three components of the collective spin. In this paper we develop strategies that achieve the optimal quantum enhancements using non-Gaussian states produced by a nonlinear one-axis-twisting Hamiltonian, and show that measurement-after-interaction techniques, known to amplify the output signals in quantum parameter estimation protocols, are effective in measuring nonlinear spin observables. Including the presence of the relevant decoherence processes from atomic experiments, we determine analytically the quantum enhancement of non-Gaussian over-squeezed states as a function of the noise parameters for arbitrary atom numbers.
Electron-photon vertex and dynamical chiral symmetry breaking in reduced QED: An advanced study of gauge invariance

We study the effect of a refined electron-photon vertex on the dynamical breaking of chiral symmetry in reduced quantum electrodynamics. We construct an educated {\em ansatz} for this vertex which satisfies the required discrete symmetries under parity, time reversal and charge conjugation operations. Furthermore, it reproduces its asymptotic perturbative limit in the weak coupling regime and ensures the massless electron propagator is multiplicatively renormalizable in its leading logarithmic expansion. Employing this vertex {\em ansatz}, we solve the gap equation to compute dynamically generated electron mass whose dependence on the electromagnetic coupling is found to satisfy Miransky scaling law. We also investigate the gauge dependence of this dynamical mass as well as that of the critical coupling above which chiral symmetry is dynamically broken. As a litmus test of our vertex construction, both these quantities are rendered virtually gauge independent within a certain interval of values considered for the covariant gauge parameter.