Reviewerly/exHarmony huggingface dataset:
A geometric approach to rank metric codes and a classification of constant weight codes

In this work we develop a geometric approach to the study of rank metric codes. Using this method, we introduce a simpler definition for generalized rank weight of linear codes. We give a complete classification of constant rank weight code and we give their generalized rank weights.
JPEG quantization step estimation with coefficient histogram and spectrum analyses

This paper proposes a new method for estimating quantization steps (QSs) from an image that has been previously JPEG-compressed and stored in a lossless format. In this method, DCT coefficients of each frequency band of JPEG-compressed image are aggregated in the QS and its multiples. The entire estimation process can be grouped into two categories: alternating and direct current bands. Considering that DCT coefficients under different QSs show different periodicity, QS estimation for each band is then further divided into three steps, which involve identifying whether the QS is one, two, or another value. For each step, the periodicity of DCT coefficients can be well exploited with the analyses of the DCT-coefficient histogram and its corresponding frequency magnitude spectrum. Experimental results demonstrate the efficacy of the proposed method and the superiority in QS estimation for previously JPEG-compressed images, especially in the case that the actual QSs are higher than two.
On the Radon Transform and Linear Transformations of Images

We present a novel original method for estimating and recovering a general geometric transformation which is applied to an image. Our main tool is the Radon Transform; we develop analysis to address the behavior of this transform under a Linear Transformation in terms of the singular value decomposition of the Transformation's matrix. We derive a mathematical exact solution to this problem. We then implement our analysis and validate the work on synthetic images as well as real images. In so doing, we developed efficient numerical tools for carrying out such analysis.
The market for bitcoin transactions

Transaction fees in the bitcoin system work differently from those in conventional payment systems due to the design of the bitcoin mining algorithm. In particular, transaction fees and transaction volume in the bitcoin system increase whenever the network is congested, and our VAR results confirm that is indeed the case. To account for the empirical findings, we build a model where users and miners together determine transaction fees and transaction volume. Even though the mechanism of fluctuating transaction fees in bitcoin introduces an extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fixed transaction fee rate. However, this calculation may underestimate the cost due to the crowding-out effect on small transactions during the congested period.
File system anti-forensics – types, techniques and tools

Forensics paved the way for the growth of anti-forensics, and the time has come for anti-forensics to return the favour. For that purpose, it is imperative that forensic investigators and practitioners are armed with the knowledge of contemporary anti-forensics types, techniques and tools. This article aims to provide technical information and a comprehensive understanding of file system anti-forensics types, techniques and tools so as to facilitate investigators' ability to collect technically credible and legally admissible digital evidence from crime scenes. Forensics paved the way for the growth of anti-forensics, which tries to prevent, hinder or corrupt the forensic process of evidence acquisition and analysis. The time has come for anti-forensics to return the favour. Mohamad Ahtisham Wani, Ali AlZahrani and Wasim Ahmad Bhat provide technical information and a comprehensive understanding of file system anti-forensics types, techniques and tools so as to facilitate investigators' ability to collect technically credible and legally admissible digital evidence from crime scenes.
Thesaurus

In this paper, we identify a previously untapped source of compressibility in cache working sets: clusters of cachelines that are similar, but not identical, to one another. To compress the cache, we can then store the "clusteroid" of each cluster together with the (much smaller) "diffs" needed to reconstruct the rest of the cluster. To exploit this opportunity, we propose a hardware-level on-line cacheline clustering mechanism based on locality-sensitive hashing. Our method dynamically forms clusters as they appear in the data access stream and retires them as they disappear from the cache. Our evaluations show that we achieve 2.25× compression on average (and up to 9.9×) on SPEC~CPU~2017 suite and is significantly higher than prior proposals scaled to an iso-silicon budget.
A Novel Approach to Solve Security and Privacy Issues for IoT Applications Using Blockchain

Internet of Things(IoT) has emerge as a revolution in last decade. The IoT deals with lots of smart things interconnected with each others. The smart things are used to monitor, read, access the different smart applications environments. An IoT application has different challenges such as bandwidth/traffic issue, scalability challenges, device mobility, along with security and privacy issues. To make use of IoT in real-life application confidentiality, integrity, authentication, authorization, trust, verification, information storage, and management, availability challenges need to be addressed. In this work, initially, authors have explained the security and privacy challenges that currently exist in IoT applications. Following which, authors have identified that Blockchain Technology addressed the security and privacy issue in some papers. Blockchain technology being distributed architecture is secure and tamper-proof explained. Finally, paper concluded that the distributed Blockchain technique has the potential to provide a solution to the existing security and privacy challenges of IoT application.
Rear‐side spectral irradiance at 1 sun and application to bifacial module power rating

Abstract The air mass 1.5 global (AM1.5G) reference spectral irradiance distribution is defined in the international standard IEC 60904‐3, accounts for the direct, diffuse, and albedo components directing 1000 W/m 2 (1 sun) of global irradiance at a photovoltaic (PV) module's front side. Bifacial PV modules, however, receive an additional irradiance contribution from the rear side, which is not accounted for in the reference irradiance. This work strives to shed light on the amount of expected light that arrives on the rear side of a PV module, when the front‐side irradiance and ambient conditions match the conditions used for the calculation of AM1.5G. For this purpose, an optical model was developed to simulate the rear‐side spectral irradiance of PV modules, whose validity was verified by outdoor measurements. The results show that the average rear‐side irradiance at a set of conditions as defined in IEC 60904‐3 lies in the range of 130 to 150 W/m 2 . The results of this work lay the foundation to define the bifacial Standard Test Conditions (BSTC), which can be used to support the technical specification IEC TS 60904‐1‐2 and international standard IEC 60904‐3 and establish supplementary requirements for the power rating and label verification of bifacial PV modules.
Comparative Study of Machine Learning Algorithms for SMS Spam Detection

The short message service (SMS) became popular after it was initially provided as a service in the second-generation (2G) terrestrial mobile network architecture (Global System for Mobile Communication - GSM). Its popularity has been exploited by some advertising companies and others to spread unwanted advertising, communicate advertising offers, and send unwanted material to the end users. These undesirable messages, known as spam, make it difficult for the users to receive the desirable messages and make them frustration and irritation. Consequently, there are measures that various experts have implemented in filtering out these spam messages and blocking them from reaching the end users. Most of the solutions have followed the success of email spam filtering and utilized machine learning techniques to filter spam messages. The popular machine learning techniques that have successfully been used include logistical regression, Naïve Bayes algorithms, Support Vector Machine (SVM), and neural networks. The present study adopts these techniques in filtering spam messages and measures their accuracy to determine the most effective method of filtering spam messages. Based on the findings, the neural network performs best as the trained classifier model used to classify incoming messages as ham or spam.
Using Convolutional Neural Network with BERT for Intent Determination

We propose an Intent Determination (ID) method by combining the single-layer Convolutional Neural Network (CNN) with the Bidirectional Encoder Representations from Transformers (BERT). The ID task is usually treated as a classification issue and the user’s query statement is usually of short text type. It has been proven that CNN is suitable for conducting short text classification tasks. We utilize BERT as a sentence encoder, which can accurately get the context representation of a sentence. Our method improves the performance of ID with the powerful ability to capture semantic and long-distance dependencies in sentences. Our experimental results demonstrate that our model outperforms the state-of-the-art approach and improves the accuracy of 0.67% on the ATIS dataset. On the ground truth of the Chinese dataset, as the intent granularity increases, our method improves the accuracy by 15.99%, 4.75%, 4.69%, 6.29%, and 4.12% compared to the baseline.
Pose‐invariant face recognition based on matching the occlusion free regions aligned by 3D generic model

Face recognition systems perform accurately in a controlled environment, but an unconstrained environment dramatically degrades their performance. In this study, a novel pose‐invariant face recognition system is proposed based on the occlusion free regions. This method utilises a gallery set of frontal face images and can handle large pose variations. For a 2D probe face image with an arbitrary pose, the head pose is first obtained using a robust head pose estimation method. Then, this 2D face image is normalised by a novel 3D modelling method from a single input image. In consequence, pose invariant face recognition is converted to a frontal face recognition problem. The 3D structure is reconstructed using a new method based on the estimated head pose and only one facial feature point, which is significantly reduced in comparison with the number of landmarks used in previous methods. According to the estimated poses, occlusion free regions are extracted from normalised images as feature extraction. Finally, face matching and recognition is performed using these regions from normalised test images and the corresponding regions of gallery images. Experimental results on FERET and CAS‐PEAL‐R1 databases demonstrate that the proposed method outperforms other methods, and it is robust and efficient.
Quantisation and Pruning for Neural Network Compression and Regularisation

Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices. In this paper, we investigate reducing the computational and memory requirements of neural networks through network pruning and quantisation. We examine their efficacy on large networks like AlexNet compared to recent compact architectures: ShuffleNet and MobileNet. Our results show that pruning and quantisation compresses these networks to less than half their original size and improves their efficiency, particularly on MobileNet with a 7x speedup. We also demonstrate that pruning, in addition to reducing the number of parameters in a network. can aid in the correction of overfitting.
Mechanisms of the breathing contribution to bodily self‐consciousness in healthy humans: Lessons from machine‐assisted breathing?

Abstract Previous studies investigated bodily self‐consciousness (BSC) by experimentally exposing subjects to multisensory conflicts (i.e., visuo‐tactile, audio‐tactile, visuo‐cardiac) in virtual reality (VR) that involve the participant's torso in a paradigm known as the full‐body illusion (FBI). Using a modified FBI paradigm, we found that synchrony of visuo‐respiratory stimulation (i.e., a flashing outline surrounding an avatar in VR; the flash intensity depending on breathing), is also able to modulate BSC by increasing self‐location and breathing agency toward the virtual body. Our aim was to investigate such visuo‐respiratory effects and determine whether respiratory motor commands contributes to BSC, using non‐invasive mechanical ventilation (i.e., machine‐delivered breathing). Seventeen healthy participants took part in a visuo‐respiratory FBI paradigm and performed the FBI during two breathing conditions: (a) “active breathing” (i.e., participants actively initiate machine‐delivered breaths) and (b) “passive breathing” (i.e., breaths’ timing was determined by the machine). Respiration rate, tidal volume, and their variability were recorded. In line with previous results, participants experienced subjective changes in self‐location, breathing agency, and self‐identification toward the avatar's body, when presented with synchronous visuo‐respiratory stimulation. Moreover, drift in self‐location was reduced and tidal volume variability were increased by asynchronous visuo‐respiratory stimulations. Such effects were not modulated by breathing control manipulations. Our results extend previous FBI findings showing that visuo‐respiratory stimulation affects BSC, independently from breathing motor command initiation. Also, variability of respiratory parameters was influenced by visuo‐respiratory feedback and might reduce breathing discomfort. Further exploration of such findings might inform the development of respiratory therapeutic tools using VR in patients.
Usability Testing: A Practitioner's Guide to Evaluating the User Experience

It is all too common for products, such as consumer appliances, information systems, mobile apps, and websites, to cause trouble and frustration. For example, products are often difficult or dull to use, make tasks less flexible or more tedious, shift attention away from important or gratifying activities, and simply fail to deliver expected benefits or experiences. By identifying such trouble and frustration in the lab prior to widespread use, usability tests have proven a valuable method for informing redesign efforts. A usability test consists of having test users exercise a product and think aloud about their experience using it, while an evaluator observes the users and listens in on their thoughts. On this basis, the evaluator identifies usability problems and assesses the user experience. This book describes how to conduct usability tests. After providing context about concepts and testing, the main chapters of the book cover the steps involved in preparing for a usability test, executing the test sessions, and analyzing the test data. Throughout the chapters, concrete guidance is balanced against more complex issues with an impact on the robustness, validity, completeness, impact, and cost of a usability test. The book concludes with an outlook to variations of usability testing and alternatives to it.
Server Scalability Using Kubernetes

An enterprise that has implemented virtualization can consolidate multiple servers into fewer host servers and get the benefits of reduced space, power, and administrative requirements. Sharing their hosts' operating system resources, containerization significantly reduces workloads, and is known as a lightweight virtualization. Kubernetes is commonly used to automatically deploy and scale application containers. The scalability of these application containers can be applied to Kubernetes with several supporting parameters. It is expected that the exploitation of scalability will improve performance and server response time to users without reducing server utility capabilities. This research focuses on applying the scalability in Kubernetes and evaluating its performance on overcoming the increasing number of concurrent users accessing academic data. This research employed 3 computers: one computer as the master node and two others as worker nodes. Simulations are performed by an application that generates multiple user behaviors accessing various microservice URLs. Two scenarios were designed to evaluate the CPU load on single and multiple servers.On multiple servers, the server scalability was enabled to serve the user requests. Implementation of scalability to the containers (on multiple servers) reduces the CPU usage pod due to the distribution of loads to containers that are scattered in many workers. Besides CPU load, this research also measured the server's response time in responding user requests. Response time on multiple servers takes longer time than that on single server due to the overhead delay of scaling containers.
Cell Nuclei Classification in Histopathological Images using Hybrid O <sub>L</sub> ConvNet

Computer-aided histopathological image analysis for cancer detection is a major research challenge in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose a lot of challenges in developing state of the art algorithms due to the heterogeneity of cell nuclei and data set variability. Recently, a multitude of classification algorithms has used complex deep learning models for their dataset. However, most of these methods are rigid and their architectural arrangement suffers from inflexibility and non-interpretability. In this research article, we have proposed a hybrid and flexible deep learning architecture OLConvNet that integrates the interpretability of traditional object-level features and generalization of deep learning features by using a shallower Convolutional Neural Network (CNN) named as $CNN_{3L}$. $CNN_{3L}$ reduces the training time by training fewer parameters and hence eliminating space constraints imposed by deeper algorithms. We used F1-score and multiclass Area Under the Curve (AUC) performance parameters to compare the results. To further strengthen the viability of our architectural approach, we tested our proposed methodology with state of the art deep learning architectures AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 as backbone networks. After a comprehensive analysis of classification results from all four architectures, we observed that our proposed model works well and perform better than contemporary complex algorithms.
Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System

In urban areas, the accuracy and reliability of global navigation satellite systems (GNSS) vehicle positioning decline due to substantial non-line-of-sight (NLOS) signals and multipath effects. Recently, positioning enhancement approaches with supervised GNSS signal type classification based on 3D building model-aided labelling have received widespread attention. Despite the reduced computing costs and improved real-time performance, the strict requirements of 3D building models on accuracy and timeliness limit the popularization of the technology to some extent. Meanwhile, the diversity of anomalous observation sources is beyond the reach of NLOS/multipath detection methods. This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. The experimental results based on the measured vehicle GPS/BeiDou data show that after excluding anomalous observations the single point positioning accuracy of the offline dataset is improved by 87.0%, 45.9%, and 69.6% in the east, north, and up directions, respectively, and the positioning accuracy of two online datasets is improved by 48.4%/45.7%, 39.6%/63.3%, and 49.6%/49.1% in the three directions. Through a large number of comparative experiments and discussion on key issues, it is certified that the proposed method is highly feasible and has great potential in the practical application of urban GNSS vehicle positioning.
Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning

We develop a distributed stochastic gradient descent algorithm for solving non-convex optimization problems under the assumption that the local objective functions are twice continuously differentiable with Lipschitz continuous gradients and Hessians. We provide sufficient conditions on step-sizes that guarantee the asymptotic mean-square convergence of the proposed algorithm. We apply the developed algorithm to a distributed supervised-learning problem, in which a set of networked agents collaboratively train their individual neural nets to recognize handwritten digits in images. Results indicate that all agents report similar performance that is also comparable to the performance of a centrally trained neural net. Numerical results also show that the proposed distributed algorithm allows the individual agents to recognize the digits even though the training data corresponding to all the digits is not locally available to each agent.
An Intelligent Hybrid Technique of Decision Tree and Genetic Algorithm for E-Mail Spam Detection

One of the most serious and irritating problem for decades is E-mail spam. These unwanted and unrequested commercial emails are also known as junk emails. Several machine learning approaches are used for detecting emails as spam or ham. These techniques detect spam emails from inbox and pass it to junk email folder. Even though among these approaches, it has been found that simple text classification techniques are not sufficient to detect spam e-mails. It is more preferable to use hybrid techniques to make detecting spam e-mails more efficiently. In this paper, a novel of hybrid machine learning technique of decision tree and genetic algorithm known as GADT is proposed for e-mail spam detection. It is believable that using genetic algorithm to improve the decision tree performance for text classification is effective and precise. Genetic algorithm is used to optimize and find the optimal value of a parameter named confidence factor that control the pruning of the decision tree. A major problem of any application of text classification, as spam detection, is its huge number of features that decrease in the accuracy of the classifiers. In our application, most of the extracted features that are extracted from the content of the email messages are irrelevant noise that can misled the classifier. So, it is found that dimension reduction stage is essential to reduce this huge number of features. In this paper, principle component analysis (PCA) technique is found to be a good choice to eliminate unsuitable features with less processing. The experimental results show the enhancement of accuracy of the hybrid approach GADT for detecting spam e-mail compared to the traditional decision tree. Also, these results show the high performance of GADT after using PCA compared to other traditional text classifiers.