Reviewerly/exHarmony huggingface dataset:
A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports

Software practitioners often strive to achieve a “bug-free” software, though, it is a myth. Software Bug Categorization (SBC) models, which assigns levels (viz. “low”, “moderate” or “high”) to a software bug aid effective bug management. They assist in allocation of proper maintenance resources for bug elimination to improve software quality. This study proposes the development of SBC models that allocate levels on the basis of three software bug aspects i.e., maintenance effort required to correct a bug, its change impact and the combined effect of both of these. In order to develop SBC models, we use text mining approach, which extracts relevant features from bug descriptions and relates these features with different software bug levels. The results of the study indicate that the categorization of software bugs in accordance with maintenance effort and change impact is possible. Furthermore, the combined approach SBC models were also found to be effective.
ORCCA: Optimal Randomized Canonical Correlation Analysis

Random features approach has been widely used for kernel approximation in large-scale machine learning. A number of recent studies have explored data-dependent sampling of features, modifying the stochastic oracle from which random features are sampled. While proposed techniques in this realm improve the approximation, their suitability is often verified on a single learning task. In this article, we propose a task-specific scoring rule for selecting random features, which can be employed for different applications with some adjustments. We restrict our attention to canonical correlation analysis (CCA) and provide a novel, principled guide for finding the score function maximizing the canonical correlations. We prove that this method, called optimal randomized CCA (ORCCA), can outperform (in expectation) the corresponding kernel CCA with a default kernel. Numerical experiments verify that ORCCA is significantly superior to other approximation techniques in the CCA task.
Chromosome Image Enhancement for Efficient Karyotyping

Chromosome images are susceptible to sensor and staining noises, inhomogeneity, and blurring which prevent efficient karyotyping. In this research work, image processing methods are systematically extended for the preprocessing of chromosome images, and a novel approach for denoising and enhancing the chromosome images is proposed. The proposed approach is mathematically modeled and evaluated with subjective and objective measures. Promising results are obtained which are further substantiated with the post-classification of the segmented chromosomes from the preprocessed input image. Performance of the proposed method is quantified in terms of MSE (Mean Squared Error), PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index Measure), FSIM(Features Similarity Index Measure), SAM(Spectral Angle Mapper), and SRE(Signal to Reconstruction Error ratio). An MSE of 8.164, PSNR of 39.037, SSIM of 0.9654, SAM of 81.729, SRE of 63.842, and FSIM of 0.6128 are obtained, on average for a set of 10 test images which were previously degraded with Gaussian noise and Gaussian blur. Post-classification accuracy improved from 88% to 95% as and when the proposed preprocessing is followed by the classification task.
The Influence of Industry Leaders’ Behavior on the Decisions of Common Enterprise Leaders in Enterprise Clustering: An Event-Related Potential Study

This study aims to explore the influence of industry leaders' behavior on common enterprise leaders' decisions in enterprise clustering by recognizing top executives' cognitive processes of brains.Twenty-one real top executives from twelve textile enterprises were recruited in the lab experiment, and decisions about whether entering an industrial zone under two conditions of following an industry leader or a common enterprise were designed as the experiment task. Throughout the formal experimental task, participants' electroencephalograms were recorded.The behavioral results preliminarily proved the effect of industry leaders' behaviors on the real top executives' decisions in common enterprises: participants had a higher acceptance rate with a shorter reaction time in the condition of following an industry leader rather than that in the condition of following a common enterprise. Event-related potential results indicated that choices of following an industry leader led to a more positive perception of emotional valence (reflected by a smaller P2 amplitude) and better evaluation categorization and greater decision confidence (reflected by a larger late positive potential amplitude) than choices of following a common enterprise.Top executives from common enterprises tend to evaluate industry leaders' behaviors better than other common enterprises' behaviors, and they tend to make a similar business decision to keep their enterprises consistent with these industry leaders.
The Impact of Quantum Computing on Security in Emerging Technologies

Quantum computing is rapidly shifting from high-tech lab to mainstream commercial use in the next few years, representing the next breakthrough in modern ICT. It is motivated by quantum computer's ability to perform computational tasks with efficiencies that are not possible with classical computers. Quantum computing technology has a lasting potential impact on society and broad implications on many aspects of our lives. Although quantum computing has several advantages, it threatens the security of current cryptographic techniques. It is expected that quantum computers will be able to solve the key distribution problem and break every single cryptography and authentication algorithm based on asymmetric cryptography. New emerging technologies such as the Internet of Things, blockchain, autonomous vehicles, 5G, artificial intelligence and robotics all rely on cryptography for their secure operation. Therefore, the realisation of quantum computing will pose significant challenges to the current progress and future development of these technologies. In this paper, the quantum technology preliminaries are discussed. The possible impact of quantum technologies on emerging technologies is investigated. Finally, some of the currently available quantum-safe cryptosystems devoted to protecting computer systems in the quantum era are discussed.
A Study on Security Threats to Blockchain &amp; Cryptocurrencies

Cryptocurrency is a digital asset that works through distributed ledger technology such as a blockchain. Blockchain was dubbed as un-hackable by many, but recent incidents indicate that it is not the case. This paper aims to examine different types of cyberattacks. Attacking cryptocurrencies is much more intriguing to criminals, mainly because of the reason that reverting those funds is a laborious task in itself. This paper surveys 5 different kinds of cryptocurrency attacks and also examines different consensus algorithms used by cryptocurrencies. Despite blockchain being a leading edge technology, the vulnerabilities in the technology has caused difficulties in the past and cannot be ignored.
Blockchain-Enabled Trust Management for Digital Marketing in the Industry 4.0 Era

Blockchain technology is the fastest-developing technology in recent years, and it has had a significant impact on a wide range of industries and companies in the industry 4.0 era. Privacy, security, and trust are three of the most pressing issues facing digital marketing today. The purpose of this study is to investigate security, privacy, and trust challenges in digital marketing and how blockchain technology can be used to influence digital marketing to increase consumer trust and security. This was done via a systematic review of literature published between 2017 and 2021. The review identified that the use of blockchain technology in digital marketing and marketing management will continue to grow, and it has proven to be effective in providing solutions to both existing and upcoming company challenges and situations in industry 4.0. Blockchain can influence digital marketing by removing intermediaries and delivering trusted cybersecurity services with a high level of transparency and accountability. Also discussed are the possible problems and limitations of blockchain-enabled digital marketing.
A Smart Cradle System to Monitor Infants for Healthcare Baby Wards Based on IoT and Blockchain

In countries like India, stealing of newborn babies is common now. Despite the CCTV Cameras installed on the healthcare baby centers premises left police with lack of solid evidence to zero on the person who stole the infant. Moreover, there are so many babies who are under supervision in the baby ward. It is difficult for the healthcare center staff to track each and every infant for changing their wet nappies, which leads to severe infection and rashes. However, with the help of emerging technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Blockchain Technology (BT) can provide a solution, as these are the interrelated computing technologies which provides the ability to share the data on a network without any human interference. In this research article the authors have proposed a solution based on IoT and BT for monitoring the infants or toddlers. This proposed solution will help to reduce the burden of parents and healthcare staff by enabling the features of security and alarm systems.
A New Framework for Taxing Cryptocurrencies

This Article explores the tax law challenges associated with the taxation of cryptocurrencies and offers proposals to address such challenges. Specifically, the Article addresses the proper tax treatment of different cryptocurrency transactions and activities. It examines various aspects associated with the taxation of cryptocurrency through its life cycle, starting from earning cryptocurrency, through its disposal or exchange. The Article also examines the tax treatment of two special crypto events, hard forks and airdrops.
Review on Insight into Elliptic Curve Cryptography

In this paper we focus on the preliminary mathematical concepts that are needed to take off with elliptic curve cryptography such as discrete logarithm problem which is underlying problem behind elliptic curve cryptography, interpretation of quadratic graphs, slope of a line, slope intercept form of a line, equation of a line and arithmetic principles behind elliptic curves. Then we discuss with identification of points on elliptic curve using method of Diaphantus. Finally we come to a conclusion that elliptic curve cryptography takes a lead with its peer cryptographic techniques due to its high efficiency and shorter key sizes.
Byzantine Fault Tolerant Distributed Stochastic Gradient Descent Based on Over-the-Air Computation

Wireless distributed machine learning is envisaged to facilitate advanced learning services and applications in wireless networks consisting of devices with limited computing capability. Distributed machine learning algorithms are more vulnerable in wireless systems since information exchange in learning is limited by wireless resources and channel conditions. Moreover, their performance can be significantly degraded by attacks of the Byzantine devices, and information distorted by channel fading can be treated as Byzantine attacks. Consequently, protection of wireless distributed machine learning from Byzantine devices is paramount. Leveraging over-the-air computation, we put forth a novel wireless distributed stochastic gradient descent system which is resilient to Byzantine attacks. The proposed learning system is underpinned by two novel and distinct features which enable more accurate and faster distributed machine learning resilient to Byzantine attacks: <xref ref-type="disp-formula" rid="deqn1-deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</xref> collecting training data in the PS to obtain its own training results and <xref ref-type="disp-formula" rid="deqn1-deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</xref> grouping the distributed devices. We derive upper bounds of the mean square error of the global parameter when the proposed algorithms are used in the cases with and without Byzantine devices, and prove the convergence of the proposed algorithms with the derived bounds. The effectiveness of the algorithms is validated by showing the accuracy and convergence speed.
SusTriage: Sustainable Bug Triage with Multi-modal Ensemble Learning

Bug report triage refers to the process of selecting appropriate developers to resolve newly submitted bug reports. Most existing methods primarily focus on improving the recommendation accuracy. Such methods suffer serious popularity bias favoring more experienced developers, while ignoring the implications of recommendation results to long-term, noteworthy sustainability of the open source communities. To alleviate the problem, this paper proposes SusTriage to assign bug reports targeting at balancing both accuracy and sustainability of developer recommendation, based on multi-modal deep learning and ensemble learning. When a new bug report is submitted, we firstly match it with all the developers who have experience in the bug resolution of corresponding product. These developers are divided into three groups and we build multi-modal deep learning scoring model for each group separately, i.e., core, active and peripheral developers. Secondly, the textual and meta information of the bug report with a developer are fed into a specified scoring model according to the developer type. Thirdly, we adopt ensemble learning weight to adjust the output score of each developer, and recommend top Q developers for the bug resolution. We conduct experiments on Eclipse and Mozilla projects and the results demonstrate that SusTriage outperforms state-of-the-art automatic bug report triage methods on both recommendation performance and sustainability of bug resolution.
Exploring Dynamic Geometry Through Immersive Virtual Reality and Distance Teaching

Virtual realityVirtual Reality (VR) provides an interesting environment to teach and learn 3D geometry. In this article, we discuss the use of Neotrie VRNeotrie VR as a 3D whiteboard for distance teachingDistance teaching that we have carried out during the 2020–21 academic year, with studentsStudent of the Mathematics degree at the University of Almería. We describe a concrete case on parametric equations ofParametric equations of surfaces surfaces, for which a 3D graphing calculator3D graphing calculator has been implemented, as well as a stereoscopic view camera to show 3D videos, which the studentsStudent can view with cheap stereoscopic glasses for mobile phones. From the side of the teacher, it is certainly much easier to explain 3D concepts on a 3D whiteboard like Neotrie than to use paper and pencil, blackboard, or any 2D digital tablet. StudentStudent feedbackFeedback is also analyzed after using various supports for manipulating and observing learning, includingGeoGebra GeoGebra, which can also serve to know how to use virtual realityVirtual Reality (VR) for distance learning.
Literature Review Profiles of Specialization in Education and Profession as the basis for the development of Artificial Intelligence Website

Profiles of specialization in education and profession are currently heavily influenced by the development of industry 4.0. The era of disruption is marked by massive changes due to innovations that change business systems and arrangements to newer levels. In various studies, there will be many professions that are extinct but there will also be many new professions that will be born. This study aims to conduct a literature review of profiles of specialization in education and profession that are relevant to industry 4.0 trends which will then be developed in a measurement through an artificial intelligence-based website. The research method used is a bibliometric approach through scientific studies of various library sources using VOSViewer with cartographic overlay techniques and density visualization maps, to represent sequences and their relationships. While the method used to develop website profiles of specialization in education and profession based on artificial intelligence is to use the waterfall method. The results of the literature review found 10 words with a high level of bibliometric correlation as the basis for developing artificial intelligence-based websites.
GANSAT: A GAN and SATellite Constellation Fingerprint-Based Framework for GPS Spoof-Detection and Location Estimation in GPS Deprived Environment

This paper presents a robust system for mitigating adversarial and natural GPS disruptions by presenting: (1) a software-based defense mechanism against spoofing attacks using generative adversarial networks (GANs), The system detects unauthorized or spoofed GPS signals from a hardware based spoofer, and (2) deep neural network models to infer positioning information in GPS-degraded /denied environments using the novel idea of GPS satellite constellation fingerprint. As the GAN and Satellite constellation fingerprinting are used together in a unified framework, we call it the “GANSAT positioning system.” Intuitively, the GANSAT neural networks implicitly learn a representation of the aggregation of the hardware fingerprints of the satellite’s in the GPS constellation at a given location and time. To demonstrate the approach, raw GPS signals were collected from the satellite transmitters using a software defined radio (SDR) at five different locations in the Florida panhandle area of the United States. Additionally, a GPS spoofer is implemented using a SDR and an open source software and used in an uncontrolled laboratory environment for spoofing the GPS signals at the aforementioned locations. In our experiments, the GANSAT framework yields ~99.5% accuracy for the task of identifying and filtering the spoofed GPS signals from real ones. It also achieves ~100% accuracy for the task of location estimation.
Research on real-time detection of road vehicle targets based on YOLOV4 improved algorithm

Aiming at the problem of low accuracy of single-step detection algorithm and long time-consuming detection of two-step detection algorithm, this paper proposes a convolutional network model that has the characteristics of fast single-step detection algorithm and high accuracy of two-step detection algorithm. It can meet the requirements of real-time detection of road vehicle targets. The algorithm proposed in this paper is based on the end-to-end network yolov4, by deepening the number of layers of the convolutional network to obtain more semantic features. At the same time, in combination with the anchor mechanism, the K-means++ clustering algorithm is used to cluster out suitable vehicle widths and heights. The benchmark frame uses four feature acquisition frames, and then uses FPN+PAN to perform feature fusion, and finally uses CIOU_Loss as the loss function of coordinate prediction to obtain a more excellent algorithm. Through comparison and analysis of the results, the algorithm in this paper is significantly improved compared to other similar algorithms.
Counterfactual-Guided and Curiosity-Driven Multi-hop Reasoning over Knowledge Graph

Recently, multi-hop reasoning over incomplete Knowledge Graphs (KGs) to predict missing facts has attracted widespread attention due to its desirable effectiveness and interpretability. It typically adopts the Reinforcement Learning (RL) framework and traverses over the KG to reach the target answer and find evidential paths. However, existing methods often give all reached paths equal hit rewards. Intuitively, not all paths have the same contribution to the proof of the reasoning process. Moreover, the severely sparse rewards obtained after a multi-step traversal are usually insufficient to encourage a sophisticated RL-based model to work well. In order to tackle the above two problems, we propose a novel Counterfactual-guided and Curiosity-driven Knowledge Graph multi-hop Reasoning model (CoCuKGR). CoCuKGR constructs counterfactual relation reasoning tasks to estimate the semantic contribution to the query relation of each path and give each arrival path a different soft reward that can distinguish its validity. In addition, our method leverages the curiosity mechanism to generate curiosity-driven intrinsic rewards, which can not only alleviate the reward sparsity issue but also drive the agent to explore the environment more thoroughly to find more abundant paths. Experimental results show that our proposed model outperforms existing multi-hop reasoning methods significantly.
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis

In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody encoder to obtain the decomposed prosody representations in an unsupervised manner. Rely on its advantages, the speaking styles, such as pitch, speaking velocity, local pitch variance, etc., are decomposed automatically into the latent quantize vectors. We also investigate the internal mechanism of VQ disentangle process by means of a latent variables counter and find that higher value dimensions usually represent prosody information. Experiments show that our model can control the speaking styles of synthesis results by directly manipulating the latent variables. The objective and subjective evaluations illustrated that our model outperforms the popular models.
Recognition of Feline Epidermal Disease using Raspberry-Pi based Gray Level Co-occurrence Matrix and Support Vector Machine

As of 2018, with over 373 million population of cats worldwide, cases of feline skin diseases have drastically increased as well, with 6-15% of feline patients who have experienced at least one form of dermatopathy in their lifetimes. Research on detecting feline skin diseases has been focused on using different diagnostic methods in the past. Examples of these methods include the Fur Pluck Method, which uses microscopic hair evaluation to detect parasites. Another study uses Wood’s Lamp method that uses a UV light source to detect dermatophytosis. In this study, the image processing technique in diagnosing two types of skin diseases, Dermatophytosis and Ectoparasitic Skin Disease, was identified using GLCM and SVM to extract the features and classify the images, respectively. The system trained 270 images with 90 photos each on Dermatophytosis, Ectoparasitic, and unknown skin diseases and tested 45 skin disease images. These feline skin disease images used are primarily from Philippine short-haired cats or Puspins. These feline skin disease images used are primarily from domestic short-haired cats. Confusion Matrix was used in determining the accuracy of the system. The accuracy of the system reached 86.776% with 80% on Dermatophytosis Skin Diseases, 93.33% for Ectoparasitic Skin Diseases, and 87% for the Unknown Parameter with 13.224% Error of Commission.