Reviewerly/exHarmony huggingface dataset:
Blockchain adoption challenges in supply chain

Blockchain is proving to be a game changer in supply chain management; with expectations, it may change the competitive landscape within the global business arena. In a complex supply chain network, blockchain has the potential to address numerous disruptions such as inefficient transactions, fraud, pilferage, lack of trust, poor information sharing and verifiability, all of which lead to inferior supply chain performance. However, there are many impediments hindering the widespread adoption of blockchain in global supply chains. While studies on blockchain technology have gained popularity, the focus on blockchain adoption challenges in the global supply chain is lacking. Considering this gap in the literature, this study aims to identify and prioritise blockchain adoption challenges in the global supply chain through an extensive literature review. We use our findings here to develop a conceptual model. After which, the fuzzy analytic hierarchy process (AHP) is used to prioritise challenges using expert opinion. Theoretical and managerial significance are then discussed.
Experimental Study of Hybrid Genetic Algorithms for the Maximum Scatter Travelling Salesman Problem

We consider the maximum scatter travelling salesman problem (MSTSP), a travelling salesman problem (TSP) variant.The problem aims to maximize the shortest edge in the tour that travels each city only once in the given network.It is a very complicated NP-hard problem, and hence, exact solutions are obtainable for small sizes only.For large sizes, heuristic algorithms must be applied, and genetic algorithms (GAs) are observed to be very successful in dealing with such problems.In our study, a simple GA (SGA) and four hybrid GAs (HGAs) are proposed for the MSTSP.The SGA starts with initial population produced by sequential sampling approach that is improved by 2-opt search, and then it is tried to improve gradually the population through a proportionate selection procedure, sequential constructive crossover, and adaptive mutation.A stopping condition of maximum generation is adopted.The hybrid genetic algorithms (HGAs) include a selected local search and perturbation procedure to the proposed SGA.Each HGA uses one of three local search procedures based on insertion, inversion and swap operators directly or randomly.Experimental study has been carried out among the proposed SGA and HGAs by solving some TSPLIB asymmetric and symmetric instances of various sizes.Our computational experience reveals that the suggested HGAs are very good.Finally, our best HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes.Our computational experience reveals that our best HGA is better.
Cryptanalysis of ITRU

ITRU cryptosystem is a public key cryptosystem and one of the known variants of NTRU cryptosystem.Instead of working in a truncated polynomial ring, ITRU cryptosystem is based on the ring of integers.The authors claimed that ITRU has better features comparing to the classical NTRU, such as having a simple parameter selection algorithm, invertibility, and successful message decryption, and better security.In this paper, we present an attack technique against the ITRU cryptosystem, and it is mainly based on a simple frequency analysis on the letters of ciphertexts.
Fault-Tolerant Consensus in Wireless Blockchain System

In recent years, the blockchain system on multi-agents has drawn much attention from both of the broad academic research and industries due to its popular applications, such as Bitcoin []. Meanwhile, it is also an important part in city/industrial Internet-of-Things and artificial intelligence areas. In this paper, we investigate the fault-tolerant consensus problem in wireless blockchain system. Considering that the multi-agents in reality are inevitable to break down or send wrong messages because of some uncertain errors, in this paper, we propose a fault-tolerant consensus protocol, which can achieve consensus over multi-agents in wireless blockchain network within \(O((f+1)\log n)\) time steps with high probability. f is the upper bound of invalid agents, and n is the number of agents in the blockchain system. Rigorous theoretical analysis and extensive simulations are given to show the efficiency of our algorithm.
Integration of Accountability and Fraud Diamond Theories as a Framework to Predict Fraud in the General Education Sector: A Case Study of Zambia

Purpose:The purpose of this paper is to present a critical review of the contributions of more than one theory to fraud research (which is often referred to as "theoretical triangulation or integration" or "theoretical pluralism) in a public organization,", with a particular focus on the manner in which chosen variables from a given theory could be employed to develop research assumptions.Design/methodology/approach: The authors conduct an analysis of how to employ theoretical integration and methodological monism in fraud research.To this end, they use three theories for illustrative purposes. Findings:The authors argue that using theories with varying epistemological notions that are captured by appropriate research methods enables one to explore and account for different layers of theoretical concepts pertaining to fraud in organizations.Originality/value: The paper contributes to the thinking about the interaction between theory applications by demonstrating that theoretical integration is more meaningful than single theories in order to provide deeper understandings of fraud phenomena.
Development of a Job Applicants E-government System Based on Web Mining Classification Methods

Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicated that the algorithm j48 had the highest precision (94.80%) compared to other algorithms for the aforementioned dataset.
Knowledge Extraction in Digit Recognition Using MNIST Dataset

In handwriting recognition, traditional systems have relied heavily on handcrafted features and a massive amount of prior data and knowledge. Deep learning techniques have been the focus of research in the field of handwriting digit recognition and have achieved breakthrough performance in the last few years for knowledge extraction and management. KM and knowledge pyramid helps the project with its relationship with big data and IoT. The layers were selected randomly by which the performance of all the cases was found different. Data layers of the knowledge pyramid are formed by the sensors and input devices, whereas knowledge layers are the result of knowledge extraction applied on data layers. The knowledge pyramid and KM helps in making the use of IoT and big data easily. In this manuscript, the knowledge management principles capture the handwritten gestures numerically and get it recognized correctly by the software. The application of AI and DNN has increased the acceptability significantly. The accuracy is better than other available software on the market.
Harnessing Multi-label Classification Approaches for Economic Phenomena Categorization

One fashion to report a country’s economic state is by compiling economic phenomena from several sources. The collected data may be explored based on their sentiments and economic categories. This research attempted to perform and analyze multiple approaches to multi-label text classification in addition to providing sentiment analysis on the economic phenomena. The sentiment and single-label category classification was performed utilizing the logistic regression model. Meanwhile, the multi-label category classification was fulfilled using a combination of logistic regression, support vector machines, k-nearest neighbor, naïve Bayes, and decision trees as base classifiers, with binary relevance, classifier chain, and label power set as the implementation approaches. The results showed that logistic regression works well in sentiment and single-label classification, with a classification accuracy of 80.08% and 92.71%, respectively. However, it was also discovered that it works poorly as a base classifier in multi-label classification, indicated by the classification accuracy dropping to 13.35%, 15.40%, and 30.65% for binary relevance, classifier chain, and label power set, respectively. Alternatively, naïve Bayes works best as a base classifier in the label power set approach for multi-label classification, with a classification accuracy of 63.22%, followed by decision trees and support vector machines.
Informational Quality Labeling on Social Media: In Defense of a Social Epistemology Strategy

Social media platforms have been rapidly increasing the number of informational labels they are appending to user-generated content in order to indicate the disputed nature of messages or to provide context. The rise of this practice constitutes an important new chapter in social media governance, as companies are often choosing this new “middle way” between a laissez-faire approach and more drastic remedies such as removing or downranking content. Yet information labeling as a practice has, thus far, been mostly tactical, reactive, and without strategic underpinnings. In this paper, we argue against defining success as merely the curbing of misinformation spread. The key to thinking about labeling strategically is to consider it from an epistemic perspective and to take as a starting point the “social” dimension of online social networks. The strategy we articulate emphasizes how the moderation system needs to improve the epistemic position and relationships of platform users — i.e., their ability to make good judgements about the sources and quality of the information with which they interact on the platform — while also appropriately respecting sources, seekers, and subjects of information. A systematic and normatively grounded approach can improve content moderation efforts by providing clearer accounts of what the goals are, how success should be defined and measured, and where ethical considerations should be taken into consideration. We consider implications for the policies of social media companies, propose new potential metrics for success, and review research and innovation agendas in this regard.
Product Ownership of a Legacy Institutional Repository

Many academic libraries have developed and/or purchased digital systems over the years, including digital collection platforms, institutional repositories, and other online tools on which users depend. At Hesburgh Libraries, as with other institutions, some of these systems have aged without strong guidance and resulted in stale services and technology. This case study will explore the lengthy process of stewarding an aging service that satisfies critical external needs. Starting with a brief literature review and institutional context, the authors will examine how the current product owners have embraced the role of maintainers, charting a future direction by defining a clear vision for the service, articulating firm boundaries, and prioritizing small changes. The authors will conclude by reflecting on lessons learned and discussing potential future work, both at the institutional and professional level.
MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunately, multimodal research has seen limited resources to study (1) generalization across domains and modalities, (2) complexity during training and inference, and (3) robustness to noisy and missing modalities. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. MultiBench provides an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. To enable holistic evaluation, MultiBench offers a comprehensive methodology to assess (1) generalization, (2) time and space complexity, and (3) modality robustness. MultiBench introduces impactful challenges for future research, including scalability to large-scale multimodal datasets and robustness to realistic imperfections. To accompany this benchmark, we also provide a standardized implementation of 20 core approaches in multimodal learning. Simply applying methods proposed in different research areas can improve the state-of-the-art performance on 9/15 datasets. Therefore, MultiBench presents a milestone in unifying disjoint efforts in multimodal research and paves the way towards a better understanding of the capabilities and limitations of multimodal models, all the while ensuring ease of use, accessibility, and reproducibility. MultiBench, our standardized code, and leaderboards are publicly available, will be regularly updated, and welcomes inputs from the community.
Text Categorization Techniques and Current Trends

With the development of online data, text categorization has become one of the key procedures for taking care of and sorting out content information. Text categorization strategies are utilized to order reports, to discover fascinating data on the world wide web. Text Categorization is a task for categorizing information based on text and it has been important for effective analysis of textual data frameworks. There are systems which are designed to analyse and make distinctions between meaningful classes of information and text, such system is known as text classification systems. The above-mentioned system is widely accepted and has been used for the purpose of retrieval of information and natural language processing. The archives can be ordered in three different ways unsupervised, supervised and semi supervised techniques. Text categorization alludes to the procedure of dole out a classification or a few classes among predefined ones to each archive, naturally. For the given text data, these words that can be expressed in the correct meaning of a word in different documents are usually considered as good features. In the paper, we have used certain measures to ensure meaningful text categorization. One such method is through feature selection which is the solution proposed in this paper which does not change the physicality of the original features. We have taken into account all meaningful features to distinguish between different text categorization approaches and highlighted the evaluation metrics, advantages and limitations of each approach. We conclusively studied the working of several approaches and drew conclusion of best suited algorithm by performing practical evaluation. We are going to review different papers on the basis of different text categorization sections and a comparative and conclusive analysis is presented in this paper. This paper will present classification on various kinds of ways to deal and compare with text categorization.
Examination of Barriers of Criminal Information Sharing Between Law Enforcement Agencies and Their Effect in Crimes Management in Nairobi County, Kenya

The paper examined barriers of criminal information sharing between law enforcement agencies and their effect in crimes management in Nairobi County, Kenya, using data from an MA Thesis. This study was guided by four objectives; to examine the types of criminal information that is shared among law enforcement agencies; to assess the individual, organizational and technological barriers that hinders sharing of criminal information among law enforcement agencies in Nairobi County, Kenya. This study was guided by Social Exchange Theory as propounded by Homans in (1958). This study adopted a qualitative study design and non-probabilistic sample design paradigms. The main method of data collection was the interview. Interview schedule and key informants guide were used to collect data. A sample comprised of 47 law enforcement officers purposively selected from Directorate of criminal Investigation (DCI) Operation wing and the Kenya Police Service Headquarters operation wing. In addition, 10 Key informants were also purposively selected and interviewed to inform the study. Both individual barriers, organizational and technological barriers affected criminal information sharing among law enforcement agencies. Major individual barrier was competition for credit between the agencies in achieving positive outcomes which was perceived to have a backlash on security operations. Other individual barriers included the deeply entrenched culture of secrecy and the need-to-know rather than the need to share policy. Major organizational barriers include Mistrust of other organizations (89.1%), Organizational culture such as culture of secrecy (83.2%). Major technological barriers included both intra and inter-information technological gaps exacerbated by wanting levels of information technology skills. The barriers were perceived to cause 'linkage blindness,' where the information available in one agency is not available with other law enforcement agencies affecting remarkable achievement of outcomes. As a policy measure, the study recommended speedy removal of criminal information barriers at all levels and fast mitigation on both intra and inter-organizational information technological skill gaps. The deeply entrenched culture of secrecy and the need-to-know rather than the need to share information policy should further be re-visited and dealt with. Finally, there is an urgent need for National-wide Criminal Information Management System with certain rights for all law enforcement agencies. The use of a database will reduce the technological barrier since criminal information will be made accessible to all the agencies
Two-stage hybrid sem-neural network approach and Van city residents’ perception of brand

SEM is multivariate statistical analysis. In addition to its advantageous aspects, it needs to cope with many assumptions. The ANN methods developed in recent years have advantages both in themselves and in the black box section, but there are cases when they cannot explain or fall short. In this study, we aimed to show that the two methods can be used together as a hybrid model and can support each other. In the study, we aimed to show the perceived brand personality and satisfaction about the city in Van province as a data set and to examine its effects on behavioral intentions. SEM-ANN, the Hybrid method, has been shown to be a supportive analysis method.In the research, the survey technique was used as a data collection tool. In this study, SPSS 22.0 was used for confirmatory factor analysis and ANN analysis, and R program was used for SEM analysis within the scope of the reliability and validity studies for the scales.
Applying EEND Diarization to Telephone Recordings from a Call Center

In this paper, we focus on the issue of speaker diarization of data from a real call center. We have previously proposed a specialized solution to the problem, which employed additional knowledge about the identities of the phone operators (in our case, the language counselors from the Language Consulting Center), thus improving performance over the baseline. But a recent end-to-end diarization method, EEND, has since proven very successful on other data and was shown to surpass the previous state of the art in the field. Thus, we chose to compare this new method with our own previous approach. Using an existing implementation of the EEND method (adapted using a small amount of the target data from the Language Consulting Center), we successfully surpass the performance of our previous approach (17.42% vs. 19.39% DER), without the need for any additional information about speaker identities. The majority of the remaining diarization error of the EEND system is due to incorrect decisions between speech and silence, rather than speaker confusion. For comparison, we also show the results of a more standard diarization approach, represented by the method used in the Kaldi toolkit.
A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period

Gene expression programming (GEP) uses simple linear coding to solve complex modeling problems. However, the performance is limited by the effectiveness of the selected method of evaluating population individuals, the breadth and depth of the search domain for the solution, and the ability of accuracy of correcting the solution based on historical data. Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. It takes measures to specialize origin data to reserve the elite individuals, reevaluate the target individuals, and process data and solutions via the similar period mode, which avoids the tendency to get stuck in local optimum and the complexity of the precisions of correcting complex modeling problems due to insufficiency scope of the search domain, and subsequently, better convergence results are obtained. If we take the leek price and the sunspot observation data as the sample to compare the new algorithm with the GEP simulation test, the results indicate that the new algorithm possesses more powerful exploration ability and higher precision. Under the same accuracy requirements, the new algorithm can find the individual faster. Additionally, the conclusion can be drawn that the performance of new algorithm is better on the condition that we take another set of sunspot observations as samples, combining the ARIMA algorithm and BP neural network prediction algorithm for simulation and comparison with the new algorithm.
Adapting your teaching during the pandemic? How social science research education adapted to the COVID-19 pandemic

University lecturers worldwide had to adopt to online teaching at very short notice due to restrictions related to COVID-19. This is a particular challenge for social sciences research methods education, which often requires face-to-face interactions. Data from an online survey of lecturers (n = 105; March 2020) who teach social science methods and methodology at Austrian public universities was used to discuss their adaption behavior and the corresponding determinates. Consequently, a measure for the rate of adaption of teaching materials and methods per lecturer was constructed and a linear regression model employed to discuss the determinants of this adaption. The results show the following: Understanding online teaching as a permanent solution for the future, the extent of an individual’s teaching load and a tenured employment were identified as significant influences fostering the adaption of teaching methods and materials to the online context. The predictors discovered differ from previous studies, and it has to be assumed that the adaption behavior in the wake of the pandemic had a profound impact on the ongoing digitalization of university education.