Reviewerly/exHarmony huggingface dataset:
A Review of Ethical Leadership Practice and Corporate Social Responsibility Approach during COVID-19

The current COVID situation has brought some serious ethical dilemmas and challenges for employers who are acting in leadership roles.Different companies responded in different ways to contribute within their allowed capacity to stand strong against the economic and humanitarian crisis.This paper begins with exploring the changing role of CSR during COVID times and inferred that its focus is shifting towards the idea of conscious capitalism and stakeholder relationship management.With that, it reinforces the articulation of authentic CSR which is required for a sustainable business model after the crisis is over.Next, it emphasized the importance of CSR in the COVID times as it enables companies to secure high consumer and employee loyalty and trust levels which contributes somehow towards the economic growth of the company for a long-term basis.Then, it analyzed the role of ethical leadership in light of the COVID situation to see if it can add to the CSR effectiveness.It was argued that customer orientation encourages ethical leaders to not compromise their ethical standards while regarding the whole community-wide perspective.Finally, the value of having a strong responsible culture was discussed which can generate more impactful CSR outcomes.
The utilization of paving blocks as environmental heat reduction materials

Global warming increases the overall temperature of the earth's surface and leads to climate change. Therefore, it plays an important impact on human life, especially in the architecture field. This study aims to identify the rise in environmental temperature due to parked cars with running engines and the use of paving blocks as heat-reducing material. This is a quantitative research with data collected and measured directly in the field, before being processed and explored of natural and waste materials in the laboratory. The results showed: (1) an increase in environmental temperature of ± 34.80C - 39.40C, or 50C to 100C due to parked cars with running engines, (2) the use of rectangular and hexagon paving blocks as heat-reducing material. Therefore, the increase in environmental temperature is reduced by the use of paving blocks, which comprises of basic (cement, sand, and water), natural (grass and fibers), and recycled materials (sawdust and styrofoam).
Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors

Statistical significance testing is widely accepted as a means to assess how well a difference in effectiveness reflects an actual difference between systems, as opposed to random noise because of the selection of topics. According to recent surveys on SIGIR, CIKM, ECIR and TOIS papers, the t-test is the most popular choice among IR researchers. However, previous work has suggested computer intensive tests like the bootstrap or the permutation test, based mainly on theoretical arguments. On empirical grounds, others have suggested non-parametric alternatives such as the Wilcoxon test. Indeed, the question of which tests we should use has accompanied IR and related fields for decades now. Previous theoretical studies on this matter were limited in that we know that test assumptions are not met in IR experiments, and empirical studies were limited in that we do not have the necessary control over the null hypotheses to compute actual Type I and Type II error rates under realistic conditions. Therefore, not only is it unclear which test to use, but also how much trust we should put in them. In contrast to past studies, in this paper we employ a recent simulation methodology from TREC data to go around these limitations. Our study comprises over 500 million p-values computed for a range of tests, systems, effectiveness measures, topic set sizes and effect sizes, and for both the 2-tail and 1-tail cases. Having such a large supply of IR evaluation data with full knowledge of the null hypotheses, we are finally in a position to evaluate how well statistical significance tests really behave with IR data, and make sound recommendations for practitioners.
Convergence of Mini-Batch Learning for Fault Aware RBF Networks

In between online and batch modes, there is a mini-batch concept that takes a subset of the training samples for updating the weights at each iteration. Traditional analysis of mini-batch is based on the stochastic gradient descent in which we assume that the process of taking mini-batches is performed in a random manner. In fact, practically, the mini-batch process is not in a random manner. In the last decade, many online and batch learning algorithms for fault aware radial basis function (RBF) networks. However, not much works on mini-batch for fault aware RBF networks are reported. This paper proposes a mini-batch learning algorithm for fault aware RBF networks. In our approach, rather than using the assumptions of the stochastic gradient descent, we consider that the partitions of mini-batches are fixed, and that those mini-batches are presented in a fixed order. Even with the above fixed arrangement, we are still able to prove that the training weight vector converges to the fault aware batch mode solution. In addition, we present the sufficient condition for the convergence.
idris-ct: A Library to do Category Theory in Idris

We introduce idris-ct, a Idris library providing verified type definitions of categorical concepts.idris-ct strives to be a bridge between academy and industry, catering both to category theorists who want to implement and try their ideas in a practical environment and to businesses and engineers who care about formalization with category theory: It is inspired by similar libraries developed for theorem proving but remains very practical, being aimed at software production in business. Nevertheless, the use of dependent types allows for a formally correct implementation of categorical concepts, so that guarantees can be made on software properties.
Quantum Advantage by Relational Queries About Equivalence Classes

Relational quantum queries are sometimes capable to effectively decide between collections of mutually exclusive elementary cases without completely resolving and determining those individual instances. Thereby the set of mutually exclusive elementary cases is effectively partitioned into equivalence classes pertinent to the respective query. In the second part of the paper, we review recent progress in theoretical certifications (relative to the assumptions made) of quantum value indeterminacy as a means to build quantum oracles for randomness.
MUSICAL TALENT IN THE WORKS OF FOREIGN RESEARCHERS

Object. The article analyzes the theoretical approaches of foreign researchers to the definition of musical talent. Empirical material devoted to the study of the determinants of the manifestation of musical talent in foreign music psychology and pedagogy is substantiated and concretized. Research methods (analytical, historical and comparative, retrospective, comparative analysis of scientific and pedagogical literature, educational materials) allowed to distinguish different approaches to understanding the significance of the development of musical abilities (M. Shawen, R. Mueller-Freinfels, M. Hassler, U. Mursell, B. Probst and others). Results. It is determined that at the beginning of the twentieth century in Western musical psychological and pedagogical works outlined two approaches to the essence of musicality: musicality as a holistic property of the individual; musicality as a category of personality ability. It is established that in the foreign scientific literature there are several approaches to the problem of identifying musical abilities. The first approach is that musical talent consists of a number of components that are assessed by tests separately. The second approach is to test the ability to perceive classical musical forms. It is established that the perfection of a musician is largely determined by the presence of innate inclinations to musical activity. It is proved that the development of musical talent has a direct relationship between the level of development of musical abilities and external socio-pedagogical factors. At the same time, natural data and specially organized classes complement each other, and professional music training helps a person to reach his full potential. Conclusions. In foreign music psychology and pedagogy, a large amount of empirical material has been accumulated, devoted to the study of the determinants of the manifestation of musical talent. However, the question of the structure of musical talent and its components remains open. At the theoretical level, the issues of the relationship between the concepts of «musical abilities», «musical talent», «musicality» remain unsolved.
Ethical hacking for boosting IoT vulnerability management: a first look into bug bounty programs and responsible disclosure

The of the Internet of Things (IoT) has attracted much attention due to the growing number of IoT-oriented incidents. IoT hardware and software vulnerabilities are exploited affecting many companies and persons. Since the causes of vulnerabilities go beyond pure technical measures, there is a pressing demand nowadays to demystify IoT security complex and develop practical guidelines for both companies, consumers, and regulators. In this paper, we present an initial study targeting an unexplored sphere in IoT by illuminating the potential of crowdsource ethical hacking approaches for enhancing IoT vulnerability management. We focus on Bug Bounty Programs (BBP) and Responsible Disclosure (RD), which stimulate hackers to report vulnerability in exchange for monetary rewards. We carried out a qualitative investigation supported by literature survey and expert interviews to explore how BBP and RD can facilitate the practice of identifying, classifying, prioritizing, remediating, and mitigating IoT vulnerabilities in an effective and cost-efficient manner. Besides deriving tangible guidelines for IoT stakeholders, our study also sheds light on a systematic integration path to combine BBP and RD with existing practices (e.g., penetration test) to further boost overall IoT security.
A flexible class of dependence-aware multi-label loss functions

Abstract The idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology that allows to analyze the dependence-awareness of MLC algorithms. With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical framework of non-additive measures and integrals. Roughly speaking, a non-additive measure allows for modeling the importance of correct predictions of label subsets (instead of single labels), and thereby their impact on the overall evaluation, in a flexible way. The well-known Hamming and subset 0/1 losses are rather extreme special cases of this function class, which give full importance to single label sets or the entire label set, respectively. We present concrete instantiations of this class, which appear to be especially appealing from a modeling perspective. The assessment of multi-label classifiers in terms of these losses is illustrated in an empirical study, clearly showing their aptness at capturing label dependencies. Finally, while not being the main goal of this study, we also show some preliminary results on the minimization of this parametrized family of losses.
METHODS OF DIAGNOSING STUDENTS’ KNOWLEDGE OF CONDUCTING AND CHORAL DISCIPLINES

Object. The article covers the issues of diagnostics of academic success of students of the Faculty of Arts in conducting and choral disciplines, in particular in the discipline of «Choral Arrangement». Methods. The study used a set of methods: analysis, synthesis, generalization, classification, comparative, prognostic, which highlighted the problems of diagnosing the success of students of arts faculties in conducting and choral disciplines (including choral arrangement), means and forms of its implementation. Results. Diagnostics is an integral part of the educational process. Thanks to it, the quality of achievement of the set purpose is defined. Without diagnostic, it is impossible to effectively manage the didactic process. The need to consider the problems of organization, structure and content of diagnostics is due to the radical changes taking place in the national higher school: introduction into practice of state standards and credit-modular system of organization of educational process. The author defines the concept of «diagnostics» as a system of accurate determination of the results of the didactic process, highlights the main types of control in higher education. The study analyzes the main forms, methods and pedagogical conditions for the quality of the diagnosis of academic success of students of the Art Faculty in conducting and choral disciplines, especially in «Choral Arrangement». Conclusions. The author describes the methods of control as ways by which the effectiveness of educational and cognitive students` activities and teachers` pedagogical work is determined. The study identifies the place of educational creative discipline «Choral Arrangement» in the system of training a professional music teacher of the school, who has practical skills in translating choral, solo, folk works for different choirs, is familiar with the basic theoretical principles, has principles and means of translating musical works for choirs. a certain composition and level of performance.
WITHDRAWN: A HMAC algorithm based secure online transaction system using block chain technology

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Classical evolution in quantum systems

Abstract We investigate quantum effects in the evolution of general systems. For studying such temporal quantum phenomena, it is paramount to have a rigorous concept and profound understanding of the classical dynamics in such a system in the first place. For this reason, we derive from first principles equations of motions that describe the classical propagation in quantum systems. A comparison of this classical model with the actual temporal quantum behavior enables us to identify quantum phenomena in the system’s dynamics and distinguish them from static quantum features at individual points in time. For instance, we show how Newton’s second law emerges as a special case of our general treatment, connecting it to a Schrödinger-type equation. As applications of our universal technique, we analyze nonlinear optical processes, semiclassical models, and the multipartite entanglement dynamics of macroscopic ensembles.
Multi-dimensional Arrays with Levels

We explore a data structure that generalises rectangular multi-dimensional arrays. The shape of an n-dimensional array is typically given by a tuple of n natural numbers. Each element in that tuple defines the length of the corresponding axis. If we treat this tuple as an array, the shape of that array is described by the single natural number n. A natural number itself can be also treated as an array with the shape described by the natural number 1 (or the element of any singleton set). This observation gives rise to the hierarchy of array types where the shape of an array of level l+1 is a level-l array of natural numbers. Such a hierarchy occurs naturally when treating arrays as containers, which makes it possible to define both rank- and level-polymorphic operations. The former can be found in most array languages, whereas the latter gives rise to partial selections on a large set of hyperplanes, which is often useful in practice. In this paper we present an Agda formalisation of arrays with levels. We show that the proposed formalism supports standard rank-polymorphic array operations, while type system gives static guarantees that indexing is within bounds. We generalise the notion of ranked operator so that it becomes applicable on arrays of arbitrary levels and we show why this may be useful in practice.
Semantic and Influence aware k-Representative Queries over Social Streams.

Massive volumes of data continuously generated on social platforms have become an important information source for users. A primary method to obtain fresh and valuable information from social streams is \emph{social search}. Although there have been extensive studies on social search, existing methods only focus on the \emph{relevance} of query results but ignore the \emph{representativeness}. In this paper, we propose a novel Semantic and Influence aware $k$-Representative ($k$-SIR) query for social streams based on topic modeling. Specifically, we consider that both user queries and elements are represented as vectors in the topic space. A $k$-SIR query retrieves a set of $k$ elements with the maximum \emph{representativeness} over the sliding window at query time w.r.t. the query vector. The representativeness of an element set comprises both semantic and influence scores computed by the topic model. Subsequently, we design two approximation algorithms, namely \textsc{Multi-Topic ThresholdStream} (MTTS) and \textsc{Multi-Topic ThresholdDescend} (MTTD), to process $k$-SIR queries in real-time. Both algorithms leverage the ranked lists maintained on each topic for $k$-SIR processing with theoretical guarantees. Extensive experiments on real-world datasets demonstrate the effectiveness of $k$-SIR query compared with existing methods as well as the efficiency and scalability of our proposed algorithms for $k$-SIR processing.
Trajectory Tracking based on Adaptive Weights Receding Horizon Control by Differential Drive Robot

In this paper, an adaptive weights receding horizon control (AWRHC) for trajectory tracking of a robot is presented. Because of the physical constraints of the robot, the conventional receding horizon control (RHC) may result in some perturbations from the reference trajectory when it is near the desired trajectory. The adaptive weights adjust the preferences for the way-points selected from the reference trajectory. The simulation results of RHC were compared with AWRHC for straight and circular trajectory tracking. The evaluation of both RHC strategies were carried out by Grey Wolf Optimizer (GWO). The AWRHC approach significantly reduces perturbations which results in quicker path following with lower path transverse at the cost of negligible increase in computation time.
Adversarial Turing Patterns from Cellular Automata

State-of-the-art deep classifiers are intriguingly vulnerable to universal adversarial perturbations: single disturbances of small magnitude that lead to misclassification of most inputs. This phenomena may potentially result in a serious security problem. Despite the extensive research in this area, there is a lack of theoretical understanding of the structure of these perturbations. In image domain, there is a certain visual similarity between patterns, that represent these perturbations, and classical Turing patterns, which appear as a solution of non-linear partial differential equations and are underlying concept of many processes in nature. In this paper, we provide a theoretical bridge between these two different theories, by mapping a simplified algorithm for crafting universal perturbations to (inhomogeneous) cellular automata, the latter is known to generate Turing patterns. Furthermore, we propose to use Turing patterns, generated by cellular automata, as universal perturbations, and experimentally show that they significantly degrade the performance of deep learning models. We found this method to be a fast and efficient way to create a data-agnostic quasi-imperceptible perturbation in the black-box scenario. The source code is available at https://github.com/NurislamT/advTuring.
Trainable Thresholds for Neural Network Quantization

Embedded computer vision applications for robotics, security cameras, and mobile phone apps require the usage of mobile neural network architectures like MobileNet-v2 or MNAS-Net in order to reduce RAM consumption and accelerate processing. An additional option for further resource consumption reduction is 8-bit neural network quantization. Unfortunately, the known methods for neural network quantization lead to significant accuracy reduction (more than 1.2%) for mobile architectures and require long training with quantization procedure. To overcome this limitation, we propose a method that allows to quantize mobile neural network without significant accuracy loss. Our approach is based on trainable quantization thresholds for each neural network filter, that allows to accelerate training with quantization procedure up to 10 times in comparison with the standard techniques. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the accuracy loss not exceeding 0.1%. Ready-for-use models and code are available at: https://github.com/agoncharenko1992/FAT-fast-adjustable-threshold .
Proof of Authenticity of Logistics Information with Passive RFID Tags and Blockchain

In tracing the (robotically automated) logistics of large quantities of goods, inexpensive passive RFID tags are preferred for cost reasons. Accordingly, security between such tags and readers have primarily been studied among many issues of RFID. However, the authenticity of data cannot be guaranteed if logistics services can give false information. Although the use of blockchain is often discussed, it is simply a recording system, so there is a risk that false records may be written to it. As a solution, we propose a design in which a digitally signing, location-constrained and tamper-evident reader atomically writes an evidence to blockchain along with its reading and writing a tag. By semi-formal modeling, we confirmed that the confidentiality and integrity of the information can be maintained throughout the system, and digitally signed data can be verified later despite possible compromise of private keys or signature algorithms, or expiration of public key certificates. We also introduce a prototype design to show that our proposal is viable. This makes it possible to trace authentic logistics information using inexpensive passive RFID tags. Furthermore, by abstracting the reader/writer as a sensor/actuator, this model can be extended to IoT in general.