Reviewerly/exHarmony huggingface dataset:
Light Gradient Boosting Machine in Software Defect Prediction: Concurrent Feature Selection and Hyper Parameter Tuning

Predicting software defects is critical for ensuring software quality. Many supervised learning approaches have been used to detect defect-prone instances in recent years. However, the efficacy of these supervised learning approaches is still inadequate, and more sophisticated techniques will be required to boost the effectiveness of defect prediction models. In this paper, we present a light gradient boosting methodology based on ensemble learning that uses simultaneous feature selection (Recursive Feature Elimination (RFE)) and hyperparameter tuning (Random search). Our proposed technique LGBM + Randomsearch + RFE method is evaluated using the AEEEM dataset, including Apache Lucene, Eclipse JDT Core, Equinox, Mylyn, and Eclipse PDE UI. The experimental findings demonstrate that the proposed approach outperforms LGBM + Randomsearch, LGBM, and the top classical machine learning algorithms on all performance criteria considered.
Intellectual Property Rights and Legal Research: Issues and Challenges

Intellectual property rights are very important rights that help in the economic development of a country by promoting healthy competition and encouraging industrial development. How IPR is used as a tool in legal research is significant to note. Research is a systemized effort to gain new knowledge. Legal communication via writings shall manifest higher moral values; shall maintain high degree of competence discipline. Legal research inculcates necessary theoretical and practical skills to deal with the diverse and expanding world of law, change and satisfaction. So, the issues that will arise during legal research and the challenges faced by the researchers during the period of research is discussed in this article. This article basically elaborates copyright related issues and challenges during legal research writing. It also specifies provisions for infringement of copyright and rules relating to plagiarism. Hence, present study furnishes a brief overview of intellectual property rights with special reference on research in law.
THE ROLE OF PARENTS AND TEACHERS IN ADOLESCENT DEVELOPMENT: A CASE STUDY OF CONSTRAINTS TO EFFECTIVE DISCIPLINE AMONG HIGH SCHOOL STUDENTS IN AWKA, NIGERIA

The study was designed to investigate the constraints to the effective discipline of secondary school students in Awka South Local Government Area of Anambra state. The survey design was adopted for the study. Five research questions guided the study. The population of the study comprised 1320 teachers. A simple random sampling technique was used to select 150 respondents for the study. The instrument for data collection was a structured questionnaire titled “Constraints to Effective Discipline of Secondary School Student Questionnaire (CEDSSSQ).” The instrument was face validated by three specialists, two from Educational Management and Policy and one from Educational Measurement and Evaluation, all in the Faculty of Education, Nnamdi Azikiwe University, Awka. The questionnaire was trail-tested using Pearson Product Moment Correlation and was used to determine the reliability coefficient of the instrument and this yielded a reliability coefficient (0.74). Data were analyzed using frequency means to answer the research questions. Based on the findings, it was revealed that discipline problems encountered in secondary schools include: Truancy, stealing, assault on school prefects, absenteeism, fighting, and examination malpractice, among others. It was recommended, among others, that in order to stem indiscipline, specific disciplinary actions should be meted out against undisciplined students to discourage others from being unruly.
A Lightweight Image Colorization Model Based on U-Net Architecture

For the problem of grayscale image colorization, many authors propose their methods to produce the most plausible, vivid images from a gray input. Almost all of them introduce a quite large neural network model with hundreds of megabytes of parameters. This paper proposes a relatively lightweight model to solve the problem which has equivalent performance to recent methods. Our model is based on famous U-net architecture which is frequently used for semantic segmentation problems. The model is trained to predict the chromatic ab channels given the lightness L channel in Lab color space to finally produce a colorful image. Our method applies self-supervised representation learning where input and labeled output are different channels of the same image. Experiments on commonly used PASCAL VOC 2012 and Places205 datasets show that our method has equivalent performance compared to other state-of-the-art algorithms while the model size is relatively smaller and consumes less computing resources.
Multilingual Identification Using Deep Learning

Language identification (LID) problem is one of the main applications of natural language processing (NLP). It is used to identify the languages from the given corpus. This paper proposes a deep neural network-based model that can perform language identification of English, Sanskrit, Malayalam, Tamil, Kannada, and Hindi parallelly, with an overall percentage accuracy of 97.3. The model can recognize the input character as Sanskrit, Malayalam, Tamil, Kannada, Punjabi, and Hindi. Recurrent neural network (RNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) are used to formulate the problem.
Car-Like Robot Tracking Using Particle Filter

This paper proposes the method that tracks a remote-controlled car-like robot in outdoor environment by using particle filter algorithm. The proposed model uses a sampling-based recursive Bayesian algorithm which is implemented in the state estimator particle filter object. A geometrical model tracker distinguishes particles by resampling from invalid particles to make more precise prediction. Commands are sent to the robot and the robot pose measurement is provided by an on-board global positioning system (GPS). The proposed algorithm predicts the estimated path, and the error has been calculated to evaluate the performance of the tracking model.
Hyper Chaos Random Bit-Flipping Diffusion-Based Colour Image Cryptosystem

In today’s digital environment, sensitive and personal images are transmitted over different social media applications over an insecure network. By considering the user’s privacy and security, their sensitive images have to be transferred in a secure manner. This work proposes a new colour image crypt architecture by utilizing the hyper Lorenz chaotic map. A new methodology random bit-flipping process is employed in the diffusion process to increase the security level of the cryptosystem. The developed architecture is the combination of R, G, and B channels confusion and random bit-level diffusion of each pixel. Experimental results and security analysis indicate that the developed model has the ability to resist statistical, differential, and exhaustive attacks and also achieves sufficient robustness.
Characterization, Detection, and Segmentation of Work-Zone Scenes From Naturalistic Driving Data

This paper elucidates the automatic detection and analysis of work zones (construction zones) in naturalistic roadway images. An underlying motivation is to identify locations that may pose a challenge to advanced driver assistance systems (ADAS) or autonomous vehicle navigation systems. We first present an in-depth characterization of work-zone scenes from a custom data set collected from more than a million miles of naturalistic driving data. We then describe two machine learning algorithms based on the ResNet and U-Net architectures. The first approach works in an image classification framework that classifies an image as a work-zone scene or non-work-zone scene. The second algorithm was developed to identify individual components representing evidence of a work zone (signs, barriers, machines, etc.). These systems achieved an [Formula: see text] score of 0.951 for the classification task and an [Formula: see text] score of 0.611 for the segmentation task. We further demonstrate the viability of our proposed models through saliency map analysis and ablation studies. To our knowledge, this is the first study to consider the detection of work zones in large-scale naturalistic data. The systems demonstrate potential for real-time detection of construction zones using forward-looking cameras mounted on automobiles. Such a system can be incorporated with ADAS to assist drivers in navigating through challenging environments such as construction zones, making those areas safer for commuters. The code is available on our GitHub page: https://github.com/VTTI/Segmentation-and-detection-of-work-zone-scenes .
Quantifying Complementarity via Robustness of Asymmetry

Complementarity plays a central role in the conceptual development of quantum mechanics, and also provides practical applications in quantum information technologies. How to properly quantify it is an important problem in quantum foundations, and there exists different types of complementarity relations. In this paper, a complementarity relation is established with the robustness of asymmetry. Specifically, the two complementary aspects are quantified by applying the robustness of asymmetry corresponding to two cyclic groups whose generators are linked by the Fourier matrix. This complementarity relation is compared with known results and considered in other perspectives, especially its operational meaning regarding quantum state discrimination. We conclude that the internal asymmetry of quantum states is closely related to other fundamental concepts, such as complementarity and coherence, and it is possible to quantitatively investigate complementarity and quantum state discrimination using the robustness of asymmetry.
Drivers of Blockchain Adoption in Pharmaceutical Sector

Blockchain is fast emerging as the next foundational technology after internet, given its increasing applications in business. With the phenomenal popularity and media attention given to Bitcoin-a blockchain-based cryptocurrency, businesses and governments worldwide are gearing up to embrace this technology, lest it becomes an opportunity lost. While major central banks worldwide are exploring the possibilities of incorporating blockchain, organizations around the world have started studying the implications in different areas like supply chain, record management (health and education) as well as pharma. Further, it’s not just the private sector but also the governments around the world, that have started engaging with this technology. Dubai’s 2020 Blockchain Strategy (Government of Dubai, 2016) and Georgia’s land registration indicate a beginning. Blockchain technology with its defining features of transparency, consensus, data immutability, traceability among others is emerging to be a very useful tool. One application area is product tracking. Pharmaceutical products are vulnerable to counterfeiting and heavy losses due to leakage in supply chains. Global pharmaceutical companies are exploring the potential of blockchain technology as single source of truth for tracking products from research to consumption. The present study employs ISM modelling to prioritize the drivers of blockchain adoption in pharmaceutical industry. The analysis indicates that the key drivers of blockchain adoption for the Pharmaceutical Industry are data immutability and data governance which will lead to other drivers. Cost Reduction, Better preservation of IPR, Security and trust are all outcomes of data immutability and decentralization. The analysis can help the Pharmaceutical industry to focus on the priorities while adopting the Blockchain technology and thus leading to better implementation of this technology.
Determining metamorphic grade from greenschist to amphibolite using Hyperion data: the case of an inverted metamorphic gradient of Almora nappe (lesser Himalaya)

The intricate nature of metamorphic terrain remains a complex problem for many remote sensing scientists. The metamorphic rock assemblages are composed by intrinsic mixtures of index minerals which are defined at different pressure and temperature zones. Metamorphic assemblage’s nature and geothermal gradient can be identified using reflectance spectroscopy. The study aims to determine the nature and gradient of metamorphism using spectral identification of the index mineral phases. A model for metamorphic assemblage was developed to predict the nature and grade of metamorphism in order to address the problem with the inadequate quantification of mineral spectral mixing. The study addresses the problem by incorporating semi-empirical method of classification of metamorphic rock assemblage based on reflectance characteristics. We use the simulated spectral library of Hyperion satellite data which is favourable for classifying geological outcrops in metamorphic terrain of the Almora nappe of lower Himalayas. The Hyperion data analysis reveals the nature and trend of change of the metamorphic grades within different zones based on mass proportion of index mineral assemblages. An inverse thermal grade of the metamorphic Almora group is observed along North Almora Thrust and South Almora Thrust, where reverse thrusting might have resulted in lowering of pressure and temperature condition. The developed model can be utilized for geological survey’s involving metamorphic terrain in inaccessible areas.
Phase 2—Co-design Workshops

This chapter describes the second phase of the process followed for the Rival(u)ta Rivalta project, which consisted of an intensive programme of co-design workshops involving a variety of stakeholders, with the aim of generating many ideas for the future of the Ducal Palace and its park. The text begins with a reflection on the methodologies for co-design and scenario building, with the aim to design an effective process and its boundary objects for the specific case. Then, the chapter details the co-design methodology, the methods and tools employed, the structure of each workshop and its outcomes organised into a set of 6 co-created preliminary scenarios that were used to inform the next phase of the work, which is the connection with the international landscape design competition.
An Asymptotic Expansion of Continuous Wavelet Transform for Small Dilation Parameter

In this paper, we derive asymptotic expansion of the wavelet transform for small values of the dilation parameter a by using Lopez and Pagola technique. Asymptotic expansion of Morlet wavelet, Mexican Hat wavelet and Haar wavelet transform are obtained as a special cases.
Using DNS Tunneling to Transfer Malicious Software

Purpose of the article: to develop a way to increase the level of protection of an information system from an attack using DNS tunneling. Method: using entropy to identify domains and subdomains used when transferring data through a DNS tunnel. The result: a method of data transmission through the DNS protocol bypassing the information security tools is considered. A malicious file was transferred using DNS tunneling, and an analysis was made of the operation of information protection tools during transmission. Information security tools do not detect the transfer of a malicious file via the DNS protocol, but they do if it is transferred in clear text. The concept of information entropy, its role in data processing is given. By calculating the entropy for domain names, the domain used in the transmission of a malicious file through the DNS tunnel was identified. It is concluded that entropy can be used not only to detect data transfer through the DNS tunnel, but also to detect the activity of malicious software that uses random domain and subdomain names in its work. The scientific novelty lies in the fact that malicious activity is detected without using the knowledge base. There is no need to signature check each DNS request, it is enough to calculate the entropy to detect an attack.
The Modular Journey: Uncovering analogue aesthetics in digital landscapes

This article draws on a practice theory perspective to investigate instances of sound practice in a particular community of technology use by focusing on the community and product offerings in and around contemporary modular synthesisers and their growing popularity in the ‘Eurorack’ format in order to investigate the attraction and allure of analogue things in a digital age. This article identifies issues of authenticity, legitimacy and creativity as key drivers of the way we project our identities onto objects and the intimate technologies we own, and the search for meaningful technological encounters. In the realm of sound practice, the follow-up questions are similar: why when there is software and affordable digital alternatives, do some musicians swear by modular synthesis given the commitment this practice requires in terms of money, time, self-education and exploration (and for a lack of a better term) tinkering? With Eurorack as a case study, this article investigates the attraction and allure of analogue things in a digital age by investigating meaningful sound practice as emerging out of the discourses of online communities around the modular synth phenomenon.
Few‐shot classification using Gaussianisation prototypical classifier

Abstract Few-shot classification (FSC) aims at classifying query samples into correct classes given only a few labelled samples. Prototypical Classifier (PC) can be chosen to be an ideal classifier for settling this problem, as it has good properties of low-capacity and parameter-free. However, the mean-based prototypes suffer from the issue of deviating from its ground-truth centre. In order to solve such problem of prototype bias, Gaussianisation Prototypical Classifier (GPC) is proposed, which is a kind of one-step prototype rectification method. Specifically, the authors first perform Gaussianisation operation over the feature extracted from the backbone network so that the features fit the particular Gaussian distribution. Second, the authors use prototype feature of the base class as prior information and employs Maximum a Posteriori estimation method to obtain the reliable prototype for each novel class. Finally, the query sample of novel class is classified to be its nearest prototype with non-parametric classifiers. Extensive experiments have been conducted on multiple FSC benchmarks. Comparative results also demonstrate that the authors' method is superior to existing state-of-the-art FSC methods.
Determinants of Technological and non-technological Innovations: Evidence from Ghana’ Manufacturing and Service Sectors

This paper aims at investigating the various factors driving technological and non-technological innovations in the manufacturing and service sectors in Ghana. We argue that numerous previous studies have shown that digitalization, firms’ collaborations, access to finance, engaging in research and development and certain firm characteristic such as age, size and ownership influence firms’ aptitude and incentives to be innovative. However, in the context of developing countries like Ghana, we do not know whether these same determinants also have positive effects on stimulating innovations at the firm-level. Using a sample of 549 firms sourced from the World Bank Enterprises Survey conducted between 2007 and 2013, this study finds that the adoption of digitalization promotes non-technological (organizational) innovations than technological innovations. We also find that firms’ innovation collaboration with consultants and universities rather exert no impact on technological and non-technological innovations. Our finding show that internal R&D enhance technological innovations and not non-technological innovations. The main practical implications of the study are that attempts to boosting firm-level and developing countries innovation potentials should mainly focus on improving internal R&D and innovation support activities, expanding access to finance, and upgrading Information and Communication Technology (ICT) infrastructure to enhance digitalization.
THE INFLUENCE OF GRAPHIC DESIGN TRAINING ON THE FORMATION OF SPATIAL REPRESENTATIONS OF YOUNGER SCHOOLCHILDREN

В статья посвящена проблеме формирования пространственных представлений у младших школьников посредством занятий графическим дизайном; представлена характеристика средств и методов формирования пространственных представлений на занятиях графическим дизайном в условиях дополнительного образования. The article is devoted to the problem of the formation of spatial representations of younger schoolchildren through graphic design classes; the characteristics of the means and methods of formation of spatial representations in graphic design classes in the conditions of additional education are presented.
Bayes‐optimal tracking of two statistically correlated targets in general clutter

The Bernoulli filter is a very general, computationally feasible Bayes-optimal approach for tracking a single disappearing and reappearing target, using a single sensor whose observations are corrupted by missed detections and a known, general point-clutter process. This paper shows how to generalise it to the dyadic labelled random finite set (DLRFS) filter—that is, a very general, computationally feasible Bayes-optimal approach for tracking two disappearing and reappearing and possibly correlated targets, using a single sensor whose observations are corrupted by missed detections and a known, general clutter process. It is further shown that, like the Bernoulli filter, the DLRFS filter is an exact special case of the labelled multitarget recursive Bayes filter (LMRBF)—and thus that, given the target and sensor models, there cannot be a theoretically better tracking filter. The paper also describes the relationship between the DLRFS filter and the (unlabelled) Gauss–Poisson filter of Singh, Vo, Baddeley, and Zuyev.
Cognitive excursion analysis of uncertainty concepts based on cloud model

Abstract Based on the characteristics of human cognition and the cloud model, the excursion of uncertain concepts by the similarity between uncertain concepts from the perspective of conceptual cognition is studied. Firstly, considering the different meanings of the numerical characters in cloud concept, the properties of cloud concepts similarity are given. Furthermore, in order to reflect the various similarities that may exist in uncertain concept, five similarities between cloud concepts are constructed, specifically include the similarity between concept expectations, the similarity between concept entropy, the similarity between concept hyper entropy, the shape similarity and the overall similarity. Secondly, the rationality of these proposed similarity measurements is illustrated by comparing with the existing methods through the specific data analysis. Finally, two cognitive experiments, including concept cognitive processes without prior knowledge and concept cognitive processes with prior knowledge (including positive prior knowledge and negative prior knowledge), are designed. These experiments are all used to study the excursion in the process of concept cognition by the similarity of cloud concepts. The experiment results show that the proposed similarities are reasonable by comparing the proposed method with the existing ones, and the effectiveness of the proposed method is also verified by the excursion of concept cognition.