Reviewerly/exHarmony huggingface dataset:
Estimation Fusion Based on Simplified Model for Cross-Covariance of Local Estimation Errors

Cross-correlation generally exists between local estimation errors in distributed estimation fusion but is hard to know exactly. In some cases, the cross-correlation is partially known (e.g., only correlation level is known approximately or known within an interval). Utilizing correlation information benefits estimation. To use it, one way is to model the correlation by a generalized Pearson's correlation coefficient times the matrix product of local MSE matrices. The correlation coefficient is used to measure linear relation between two random vectors, and is assumed to be random (e.g., uniformly distributed) over the provided interval. Based on the way the cross-covariance matrix is modeled, the assumption about correlation coefficient and by applying best linear unbiased estimation (BLUE) fusion, an estimation fusion algorithm, called expected BLUE fuser (EBF), is presented. Compared with similar algorithms, its comparable or better performance demonstrates its effectiveness. Considering the fusion results under various given correlation intervals, we observe that strong correlation benefits fusion performance, and point out that EBF gets good fusion results when the given correlation interval cover the true correlation coefficient tightly.
The Context of Designing a Partnership Ecosystem of a Pedagogical University in the New Reality of Education Transformation

The research aims to substantiate the trends of ensuring the transformation of pedagogical education in the context of actively addressing the discourse of expanding the multidimensional partnership of segments of the educational system at the level of the municipality and the region. The paper considers the aspects of construction, functioning and development of educational ecosystems studied as one of the priority areas of modern education; identifies the relevant formation tracks of a regional ecosystem of the innovative territorial educational cluster, describing the experience of the Rostov Region pedagogical community in the context of understanding the “plots” of interaction between the Taganrog Institute named after A. P. Chekhov (branch) of RSEU (RINH) and the subjects of the general education system among other things. The research is novel in that it substantiates the factors that form the “growth points” of the higher pedagogical education system in the aspect of the content-related and sense-bearing and organisational-methodological components of the functioning of innovative educational ecosystems. As a result, the authors have presented their own vision regarding the possibilities of developing ecosystem cluster models for building an integral educational environment, which act as productive drivers of strategic initiatives of the modern educational policy corresponding to innovative pedagogical trends.
Global Aggregation Node Selection Scheme in Federated Learning for Vehicular Ad Hoc Networks (VANETs)

Federated learning allows multiple users and parties to collaborate and train machine learning models in a distributed and privacy-preserving manner in Vehicular Adhoc Networks VANETs. This computing paradigm addresses privacy concerns; however, it comes at a considerable cost of network resources. After training the machine learning models in conventional federated learning frameworks, devices share that model with a central server, mostly cloud, where the global aggregation is performed. Multiple devices communicating with a central server raise network bandwidth and congestion concerns. To solve this problem, we proposed a federated learning framework for VANETs where instead of using a fixed global aggregator, we used variable global aggregation nodes. The global aggregation node is selected based on communication delay and workload in the proposed framework. We also believe that, in a vehicular Adhoc network, all network nodes cannot participate in the learning process due to network, computation, and energy resource limitations. We Also proposed a client selection algorithm that adapts itself and selects some clients based on specific criteria. Finally, the proposed technique is compared with the hierarchical federated learning framework (HFL) and FedAvg where proposed method outperformed in terms of accuracy.
The characteristics of mineralogy and its implications for ore-forming mechanism of the Qian'echong molybdenum deposit in Xinyang, Henan Province

To explore the genesis of Qian'echong molybdenum deposit in Xinyang, the mineralogical analysis of metal sulfide was carried out by using electron microprobe data, and the regional geological background and deposit characteristics were connected to provide indicative significance for the genesis of Qian'echong molybdenum deposit. The results show that the ore-forming stage of the deposit can be divided into six stages. The electron microprobe data of pyrite show that the S content of pyrite in quartz-potash feldspar stage is 53.51%, the Fe content is 46.44%, and the Co/Ni ratio ranges from 0.18 to 0.82, showing the characteristics of sedimentary pyrite. The pyrite in molybdenite-quartz stage contains 53.21% S, 46.58% Fe, and the Co/Ni ratio ranges from 1.01 to 5.68, showing the characteristics of magmatic-hydrothermal pyrite. Combined with the basic geological characteristics, it is considered that Qian'echong molybdenum deposit is a typical porphyry deposit.
Optimization Algorithms for Efficient Workflow Scheduling in IaaS Cloud

Cloud computing is a novel way of delivering resources over the Internet. Cloud computing, albeit a newer computer paradigm, has many obsolete predecessors. In addition, it is so popular that it doesn't need taxonomies to define. Our early study looked at the origins of cloud computing and their significance to cloud services. Many applications in research and industry have made workflow scheduling in cloud computing popular. Several articles have proposed using the heuristics method to account for energy savings, cost, and makespan. However, most heuristics or hybrids methodology does not provide near-optimal solutions. Therefore, we examine the limitations and potential advancements of cloud computing workflow scheduling technologies. The objective is to develop time and cost-efficient heuristic and hybrid workflow scheduling methods. Finally, we discuss and propose strategies to overcome impediments to the growth, application, and development of cloud computing.
Sci-K 2022 - International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment

In this paper we present the 2nd edition of the Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2022) workshop. Sci-K aims to explore innovative solutions and ideas for the generation of approaches, data models, and infrastructures (e.g., knowledge graphs) for supporting, directing, monitoring and assessing the scientific knowledge and progress. This edition is also a reflection point as the community is seeking alternative solutions to the now-defunct Microsoft Academic Graph (MAG).
Experimental analysis and investigation of dementia detection framework using EHR-based variant LSTM model

This chapter introduces a deep learning model for forecasting the progression of dementia disease. Because the condition is fundamentally progressive, the model takes into account the temporal data gathered from the cases. In contrast to existing methodologies, our model can predict the disease&apos;s future condition rather than just categorise the state of a present diagnosis. Experimental results have displayed that our prototype outclasses the vast majority of existing techniques. Furthermore, this method contains a table for various data sizes. Simultaneously, the findings demonstrate that the TA characteristic is a substantial predictor of DD progression.
Machine learning-based blockchain technologies for data storage: challenges, and opportunities

Technical problems have dominated machine learning (ML) research. ML is being increasingly commonly employed in healthcare, and it is assisting patients and physicians in a variety of ways. ML is a pattern recognition technology that may be used on medical pictures. After that, the ML algorithm system determines the optimum combination of these image attributes for categorizing the picture or generating a metric for the specified image area. The goals were to promote fundamental and applied research in the application of ML methods to medical problem solving and research, to provide a forum for reporting significant results, to determine whether ML methods are capable of underpinning research and development on intelligent systems for medical applications, and to identify areas that needed more research. Several research agenda suggestions were presented, covering both technical and human-centered issues.
7 When do discourse markers affect computational sentence understanding?

The capabilities and use cases of automatic natural language processing (NLP) have grown significantly over the last few years. While much work has been devoted to understanding how humans deal with discourse connectives, this phenomenon is understudied in computational systems. Therefore, it is important to put NLP models under the microscope and examine whether they can adequately comprehend, process, and reason within the complexity of natural language. In this chapter, we introduce the main mechanisms behind automatic sentence processing systems step by step and then focus on evaluating discourse connective processing. We assess nine popular systems in their ability to understand English discourse connectives and analyze how context and language understanding tasks affect their connective comprehension. The results show that NLP systems do not process all discourse connectives equally well and that the computational processing complexity of different connective kinds is not always consistently in line with the presumed complexity order found in human processing. In addition, while humans are more inclined to be influenced during the reading procedure but not necessarily in the final comprehension performance, discourse connectives have a significant impact on the final accuracy of NLP systems. The richer knowledge of connectives a system learns, the more negative effect inappropriate connectives have on it. This suggests that the correct explicitation of discourse connectives is important for computational natural language processing.
Introduction and Overview

An introduction to formal Logic and to the contrast and connections between syntax and semantics. The unique pedagogical approach of the book of capturing logic in a computer program and of proving by programming.
Fifth International Workshop on Experiences and Empirical Studies on Software Reuse (WEESR 2022)

In the Workshop on Experiences and Empirical Studies on Software Reuse (WEESR) researchers and practitioners discusses in-progress research regarding experiences and empirical studies applying reuse techniques in non-academic environments. The fifth edition of this workshop, the WEESR 2022, was co-located with the 26th International Systems and Software Product Line Conference (SPLC'22). There, attendees discussed two original papers presenting empirical studies related to reuse in the software industry, one using an interview-based methodology and the other using a survey-based methodology.
A Study on Cyber Security in Various Critical IT Infrastructure Organizational Sectors: Challenges and Solutions

In today's digital era, information is becoming important assset for the various organizations. The production of data and information is moving on electronic platforms and replacing traditional paper based methods. The integration of computer and communication capabilities in traditional business activities, the probability of threat of cyber insecurity is increasing. The threat of data leakage due to different known and unknown vulnerabilities, insiders, lack of knowledge etc. are becoming challenges for every organization, specially for critical IT infrastructure. This paper analyses a cyber-crime perspective and the range of various cyber-attacks experienced by various critical IT infrastructure such as Banking, Finance & Insurance Government, Defense, Power & Energy, Telecom, Health, Transport and Strategic & Public Enterprises. Moreover, this literature review also identifies role of nodal agencies of cyber security and various cyber security framework to protect the information stored in computer resources.
How to Bake X Cake

What kind of reduction does a recipe perform? This short piece considers the recipe as a purpose-oriented list. Based on a recipe for baking cakes of different flavours by following a similar procedure, it reflects on experience, time, chance, generalization, and secrecy.
Reduction in Computer Music

In the age of pervasive computing the way our body interacts with reality needs to be reconceptualized. The reduction of embodiment is a problem for computer music since this music relies heavily on different layers of (digital) technology and mediation in order to be produced and performed. The article shows that such a mediation should not be conceived of as an obstacle but rather as a constitutive element of a permanent, complex negotiation between the artist, the machinery, and the audience, aimed at shaping a different temporality for musical language (as the Italian artist Caterina Barbieri develops).
Blockchain-Based Solutions for Cybersecurity: Architecture, Applications, and Review

In recent times, blockchain technology has gradually come out to be the most frequent leveraged mechanism for securing the data storage and transfer via the trustless, decentralized, and peer-to-peer ecosystems. Blockchain is essentially a distributed ledger that operates in a peer-to-peer network and contains a record of all the transactions that have been executed or confirmed among the participants or nodes. In this book chapter, the authors have explained the detailed architecture of blockchain technology and its integration with cybersecurity applications. This integrated architecture delivers a clear understanding to the readers about the working mechanism of blockchain technology in empowering the security and privacy of the user data in various cybersecurity domains. This book chapter talks about the roles and responsibility of blockchain technology in transforming the cybersecurity applications such as privacy, security, accountability, and integrity of data in various security domains such as mitigating DDoS attacks, biometric private keys, Securing DND, and data veracity. In addition, in this book chapter, the authors have discussed various applications of blockchain to cater cybersecurity, such as data storage and sharing, IoT, Network Security, Utility of World Wide Web (WWW), and Private user data.
The consequences of upholding deceptive marketing practices and its effect on consumers’ related attitudes including consumer loyalty and consumer trust: Evidence from the fashion industry in the UAE

Aim: This research aims to bring to light the negative effects of deceptive marketing on their consumers, especially on their loyalty and trust in the fashion industry of the UAE. The research objectives of this study are to analyze the effects of deceptive marketing and understand how consumer behavior is affected, especially consumer loyalty and trust. This project aims to analyze the evidence found in previous studies and connect their findings to the people of the UAE.Methodology: This research was conducted through a qualitative approach, focusing on opinionated responses and personal experiences. The interview was the selected method of collecting data from the respondents. Thematic analysis was applied to this research to better understand the responses and analyze any similarities between their answers. The respondents of this research are currently UAE residents and actively viewing advertisements from the fashion industry.Findings: Based on the information provided by 3 respondents, they clearly know how deceptive marketing can affect their consumer’s behavior. However, 2 of the remaining respondents implied that they are completely unaware of deceptive marketing. They understand that consumers should be given truthful information about the products they purchase, especially when they use that information to make decisions. The UAE is generally safe due to its strict laws, such as its consumer protection law, that prioritizes consumer safety. However, companies are still capable of tampering with their advertisements and creating small changes that are unnoticeable but will have a big impact on consumers.Implications/Novel Contribution: These findings imply the need to spread consumers’ awareness of the negative effects of deceptive marketing on their decisions. Consumers should never take any information at face value. Instead, they should research to verify the information they will utilize for decision-making. Further study should be developed on the role of deceptive marketing in the global fashion industry, including the company’s service quality and brand image; this will provide the most in-depth study based on deceptive marketing.
The Protection of Data Privacy of College Students

Privacy data belongs to big data, and it is personal information from our lives. The common problem is privacy leaking and cyber attacks. The current solution uses a complex algorithm to encrypt data, but it is expensive and inefficient. In the paper, we combine students' information at university to make four layers based on a reliable framework named FMM. The proposed framework FMM, according to different privacy levels, will choose other encryption methods to protect these data and increase efficiency to keep trade-offs.
Sharing data from residential solar installations

Publicly available dataset contains power measurements and a wealth of information on residential solar installations, along with a quality control algorithm for error correction.
Command

Think about a trivial variable assignment, such as meaningOfLife = 42. The variable got assigned, but there's no record anywhere that the assignment took place. Nobody can give us the previous value. We cannot take the fact of assignment and serialize it somewhere. This is problematic, because without a record of the change, we are unable to roll back to previous values, perform audits, or do history-based debugging.
Verbalization Vs Visualization: The Problems Of The Issues Of Interaction And Communication

A feature of our power and management practice is the adoption of only unambiguous decisions - alternatives are not considered at any stage, neither before nor after the adoption of any decisions. The distance in the education system forced by the covid-19 pandemic turned into an unequivocal decision: only on line - and no other alternatives, but they exist and they are probably more effective both in terms of their effectiveness and costs. Only one of these alternatives is considered here.
Assess The Role of Social Media in Transforming Police Services. The Case of Zrp Harare Province [2010-2020]

There has been a very significant widespread use of social media by the large sections of the population including in the uniformed forces. This study was assessing the role of social media in transforming police force in Harare Province of Zimbabwe Republic Police between 2010 to 2020. To identify the current services being offered by the police in Zimbabwe. Some of the objectives of the study included investigating the importance of social media use within ZRP in Zimbabwe and examining the challenges faced by police in using social media in the operations of the police. Some of the research questions included what is the importance of using social media in the operations of ZRP and what are the benefits and challenges of the police using social media in Zimbabwe? The research used a descriptive research design as this design suited the topic which is not quantitative in nature. The study is critical to ZRP as it will assist to improve efficiency and effectiveness in the operations of the police force. The research adopted the use of convenient sampling in selecting respondents for this study Questionnaires and interviews were used to collect data from the respondents. Findings of the research do indicate that there is widespread use of social media within the Zimbabwe Republic Police. It further indicated that there is official position with regards to use of social media but officers in various ranks have resorted to use of the social media in their operations and this has enhanced the success of the police work. The study concludes by arguing in favour of using social media for police work. Finally, the study recommends adoption of policy that regulates the use of social media within the police force and the police training should integrate the issues of cyber-crime and social media.
Characteristic Features of Metropolitan and Provincial Silver Education and AgingPedagogy

Adult education is becoming more and more popular in Russia. It acquires different features and trajectories in Moscow and in the provinces. Aging pedagogy accumulates experience and begins to acquire its own, very peculiar methodology and methodology. The problems of metropolitan and provincial education for pensioners have both general and specific features.
A Robust Speech Features Extractor &amp; Reconstructor For Artificial Intelligence Frontends

Human speech consists mainly of three components: a glottal signal, a vocal tract response, and a harmonic shift. The three respectively correlate with the intonation (pitch), the formants (timbre), and the speech resolution (depth). Adding the intonation of the Fundamental Frequency (FF) to Automatic Speech Recognition (ASR) systems is necessary. First, the intonation conveys a primitive paralanguage. Second, its speaker-tuning reduces background noises to clarify acoustic observations. Third, extracting the speech features is more efficient when they are computed together at the same time. This work introduces a frequency-modulation model, a novel quefrency-based speech feature extraction that is named Speech Quefrency Transform (SQT), and its proper quefrency scaling and transformation function. The cepstrums, which are spectrums of spectrums, are suggested in time unit accelerations, whereby the discrete variable, the quefrency, is measured in Hertz-per-microsecond. The extracted features are comparable to Mel-Frequency Cepstral Coefficients (MFCC) integrated within a quefrency-based pitch tracker. The SQT transform directly expands time samples of stationary signals (i.e., speech) to a higher dimensional space, which can help generative Artificial Neural Networks (ANNs) in unsupervised Machine Learning and Natural Language Processing (NLP) tasks. The proposed methodologies, which are a scalable solution that is compatible with dynamic and parallel programming for refined speech and cepstral analysis, can robustly estimate the features after applying a matrix multiplication in less than a hundred sub-bands, preserving precious computational resources.
Alouette: Yet another encapsulated TAUOLA, but revertible

We present an algorithm for simulating reverse Monte Carlo decays given an existing forward Monte Carlo decay engine. This algorithm is implemented in the Alouette library, a TAUOLA thin wrapper for simulating decays of τ-leptons. We provide a detailed description of Alouette, as well as validation results. Program title: Alouette CPC Library link to program files: https://doi.org/10.17632/w2z5w546c5.1 Developer's repository link: https://github.com/niess/alouette Code Ocean capsule: https://codeocean.com/capsule/9167021 Licensing provisions: LGPL-3.0 Programming language: C, Fortran and Python Nature of problem: Perform reverse Monte Carlo decays. Solution method: Invert an existing forward Monte Carlo engine using the Jacobian backward method. Apply the algorithm to τ decays generated by TAUOLA.