Reviewerly/exHarmony huggingface dataset:
Procurement 4.0: Drivers, Challenges, Remedies, and Benefits

Procurement is the process that manages business relationships of suppliers, including negotiations or services, and plays a key role for the success of many companies.In this work we discuss some of the important aspects around procurement (and towards Procurement 4.0), including the main drivers, challenges, remedies, and benefits.The main drivers are related to efficiency improvements, risk minimization and reducing human efforts in procurement process.The challenges include the adaption to Procurement 4.0, data security and lack of employee trainings.The mainly remedies concern technology and data management.The benefits of improved procurement are process optimization with digitization, skilled supplier selections, on time demand fulfillment, and on time invoicing management.Recommendations for industrial procurement are provided aiming for achieving improved operations.
Determinants of Organizational Citizenship Behavior and Project Performance: The Role of Communication Frequency as Moderating Variable

The purpose of this study was to determine the effect of the determinants of organizational citizenship behavior and project performance: the role of communication frequency as moderating variable.The method used in this research is a quantitative method.The subjects in this study were employees of the property industry with a sample of 100 taken using random sampling.The analysis used in this study was a structural approach to the Equation Model (SEM) with the help of smart PLS. with the results that Leader Member Exchange has a significant negative effect on Project Performance, Quality Work Life has a significant negative effect on Project Performance, Innovative Work Behavior has a significant positive effect on Project Performance, Leader Member Exchange has a significant positive effect on Organizational Citizenship Behavior.Quality Work Life has a significant positive effect on Organizational Citizenship Behavior, Innovative Work Behavior has a significant positive effect on Organizational Citizenship Behavior, Organizational Citizenship Behavior has a significant positive effect on Project Performance, Communication Frequency has a significant negative effect on Project Performance, Leader Member Exchange mediated Organizational Citizenship Behavior has a positive effect significant on Project Performance, Quality Work Life mediated by Organizational Citizenship Behavior has a significant positive effect on Project Performance, mediation of Organizational Citizenship Behavior moderated by Communication Frequency has a significant positive effect on Project Performance.
Learnable: A Web and Mobile Based Learning Management System

Technology is something that is very influential in various fields of human life today, one of which is in the field of education.As is well known, the use of technology in the field of education today certainly has a very significant impact on the progress of education in Indonesia.However, in this pandemic era, face-to-face learning activities are becoming very limited so various online learning platforms must be involved as a solution.The higher the number of learning platforms used tends to be less effective and makes students or even teachers more confused.Therefore, an online learning application is designed that is equipped with various features to facilitate the online learning process and minimize the use of many different platforms so that the learning process becomes more effective.
The concept of a software and technological platform for digital twins based on energy dynamics methods

The present paper proposes the concept of a software and technological platform of digital twins based on the methods of energodynamics. Energodynamic theory, which incorporates modern nonequilibrium thermodynamics, mechanics, theory of electric and magnetic fields, electrodynamics, is a unified approach to describe processes of different physical and chemical nature within a macroscopic approach. This provides an opportunity to use the proposed software and technological platform of digital twins in various subject areas (in particular, in engineering and technology, in medicine, in meteorology, in life safety). The proposed in this paper software and technological platform of digital twins, based on the methods of energy dynamics, has a microservice architecture. Such architecture ensures high reliability of the platform, as well as its horizontal scalability. This makes it possible to build digital twins of sufficiently complex systems in the presence of a sufficient number of computational nodes.
Second Language Learning Through an Emergent Narrative in a Narrative-Rich Customizable Metaverse Platform

EFL students often struggle with second language (L2) learning, particularly because they learn L2 out of context, which they find unmeaningful and demotivating. To provide a meaningful context for L2 learning and make learning more interesting, the present study utilized a customizable metaverse platform. First, we developed a scenario of a rose theft, and then based on that, we created a town in the metaverse, which offered a variety of multimodal language input and missions to solve the problem and identify the thief. The entire activity was developed grounded in constructivist language learning principles (e.g., contextualized, motivating, interactive, and task-based learning), thus, learners could explore, play, and learn in the playful constructivist space. Seventy-two Korean EFL students participated in the activity. The study employed a mixed method for more robust research. Quantitative data (the pre- and posttests and the post-survey) indicated the students had a great interest in the activity and the learning outcome increased. Qualitative data (the students' journals, the interviews with the teachers and the screen recordings) further evidenced the students' enjoyment of the activity. Particularly, the screen recordings showed how the students participated in the activity in the metaverse, collaboratively created an emergent narrative through interaction, and produced their own narrative to reach the answer. The study suggested pedagogical implications for using the metaverse for L2 learning based on the results of the study.
Neural Pde Solvers for Irregular Domains

Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention. However, the large majority of neural PDE solvers only apply to rectilinear domains and do not systematically address the imposition of boundary conditions over irregular domain boundaries. In this paper, we present a neural framework to solve partial differential equations over domains with irregularly shaped (non-rectilinear) geometric boundaries. Our network takes in the shape of the domain as an input (represented as a binary mask) and can generalize to novel (unseen) irregular domains; the key technical ingredient to realizing this model is a novel approach for identifying the interior and exterior of the computational grid in a differentiable manner. We also perform a careful error analysis which reveals theoretical insights into several sources of error incurred in the model-building process. Finally, we showcase a wide variety of applications in 2D and 3D, along with favorable comparisons with ground truth solutions.
Reveal: Robustness-Aware Vnf Placement and Request Scheduling in Edge Clouds

In the edge cloud network, service providers place virtual network functions (VNFs) in edge clouds to serve users’ requests. Thus, it is essential to consider VNF placement and request scheduling in edge clouds. Existing works often focus on minimizing request completion time or maximizing network throughput to utilize network resources and ensure users’ QoS efficiently. However, they ignore two practical factors: malicious users and failed VNFs, leading to poor network robustness. To this end, this paper studies robustness-aware VNF placement and request scheduling, named Reveal. Specifically, we limit the number of VNFs each user can access and the number of users each VNF can serve to control the influence scope of malicious users and VNF failures. Since placing VNFs is time-consuming and requests arrive dynamically, we solve this problem through two phases: robust VNF placement and assignment, and online request scheduling. For the first phase, we design an efficient knapsack-based rounding algorithm with bounded approximation factors. For online request scheduling, we propose a primal-dual based algorithm with a competitive ratio of [1 − ε, O(log 1/ε)] where ε ∈ (0, 1). Experiment and simulation results show that Reveal can achievebetter performance and robustness than other alternatives.
Crypto-Trading Platforms as Exchanges

An impressive series of scandals and market crashes, including FTX, Celsius and Voyager, have shown the weaknesses of crypto-economy’s market infrastructures. Crypto-trading platforms have become dominant financial institutions, acting as trading venues, broker-dealers, and lenders at the same time. This Article conducts a conceptual, and empirical analysis of crypto-trading platforms and their activities, identifies and documents the causes of this concentration of functions, and illustrates how such concentration poses risks to both investors and the financial system. Therefore, this Article proposes regulating crypto-trading platforms as exchanges, limiting the perimeter of their activities.This Article examines the transformation of trading venues, from public stock exchanges to fragmented private marketplaces. It contextualizes the rise of crypto-trading platforms as part of this phenomenon and compares them with both public stock-exchanges as well as broker-dealers and alternative trading systems (ATS) managing private trading venues. Through a theoretical and empirical analysis focusing on crypto-trading platforms’ activities, this Article demonstrates that these market infrastructures have shifted towards a full-service business model for the crypto-economy, changing the traditional market structure. They act as intermediaries, issuing their own stablecoins, offering custodial services, accepting deposits, issuing loans, and distributing risky financial instruments. This creates unprecedented conflicts of interest and systemic risks that have not been previously associated with market infrastructures in traditional finance.The current business model of crypto-trading platforms and the resulting market structure emerging in the crypto-economy have profound implications for financial law and policy. This Article suggests strengthening international cooperation to prevent regulatory fragmentation of crypto-trading platforms, and regulating them as exchanges rather than broker-dealers. This approach would limit the number of services they can provide, and would lead to a shift in their business model. It would also contribute to enhancing investor protection while reducing systemic risks and effectively addressing the challenges presented by technology-driven financial capitalism.
Learning Multi-expert Distribution Calibration for Long-tailed Video Classification

Most existing state-of-the-art video classification methods assume that the training data obey a uniform distribution. However, video data in the real world typically exhibit an imbalanced long-tailed class distribution, resulting in a model bias towards head class and relatively low performance on tail class. While the current long-tailed classification methods usually focus on image classification, adapting them to video data is not a trivial extension. We propose an end-to-end multi-expert distribution calibration method to address these challenges based on two-level distribution information. The method jointly considers the distribution of samples in each class (intra-class distribution) and the overall distribution of diverse data (inter-class distribution) to solve the issue of imbalanced data under long-tailed distribution. By modeling the two-level distribution information, the model can jointly consider the head classes and the tail classes and significantly transfer the knowledge from the head classes to improve the performance of the tail classes. Extensive experiments verify that our method achieves state-of-the-art performance on the long-tailed video classification task.
V-Crypto Images/videos/texts by Two Key Authentication Using Aco Algorithm Technique

Everyone has a query, whether our data is safe when shared across internet services like emails, websites, apps and so on for a variety of purposes. Companies grub these data to extract the user needs and grow their business because this is one of the main strategies to taste the victory. This research discusses the unique method of protecting our own data ourselves before we send it. Cryptography is the method to secure the data and there are several methods and algorithms evolved in due course of time. After the research, I have come up with the unique cryptographic method that can encrypt and decrypt text files, images and videos. My method merges multi secret keys and 2 different techniques such as ACO algorithm and logical operation technique. A customer must provide two different secret keys as an input to encrypt and decrypt the data. Second key tells the distance of the path for the encryption to take place. This proposed method makes the encryption stronger as it is difficult for anyone to hack the data while decryption. The unique design algorithm helps the user to secure the data safely at source itself. This method has been experimented on multiple text files, images and videos.
THEMATIC MODELING AND SUMMARIZATION OF TEXTS IN THE FIELD OF CYBER SECURITY (BASED ON JOURNAL PUBLICATIONS)

Purpose: improving the quality of text document analysis through the use of machine learning and intellectual analysis models in summarizing and topic modeling tasks, which will reduce the load on an expert who analyzes and generalizes significant volumes of semi-structured text data on information security topics from various sources. Methods: machine learning methods were used for the operational processing and analysis of large volumes of heterogeneous ill-structured information in natural language (NL). Methods of thematic modeling and summarization of texts based on deep neural networks, including neural network language models based on the architecture of transformers, are applied. Practical relevance: the main stages of the machine procedure of thematic modeling and summarization of professional texts in the field of information security are highlighted. The results of a comparative evaluation of the effectiveness of using clustering models, latent semantic analysis, Fast Text, Text Rank language models and BERT transformers for these purposes are presented. Recommendations are given regarding the prospects for the practical application of these models as a means of intellectual support for the professional activities of cybersecurity specialists. Scientific novelty: a complex of machine learning models for thematic modeling and summarization of professional texts is proposed, based on neural network models of attachments and transformer models, characterized by an algorithm for preparing a corpus of texts for training models and the use of a learning transfer algorithm, which will increase the efficiency of analysis and generalization of domain-specific corpora of texts.
Human Action Recognition Using Deep Learning Technique

Recognizing human activities from videos is a difficult computer vision task that involves human action recognition. In this work, we offer a Mediapipe and LSTM-based deep learning-based technique for human action recognition. Using Mediapipe, we decode human stance and movement information from videos, and then we feed the information into an LSTM network to categorize the actions. We test our method using a custom dataset that we collected under a number of difficult conditions, such as shifting lighting, shifting camera angles, and occlusion. On our custom dataset, our suggested method performs at the cutting edge, with a maximum accuracy of 92.40%. We run comprehensive tests to assess how well our method stands up to various scenarios, such as changing lighting conditions, shifting the camera's angle, and occlusion. In order to assess how each element of our strategy contributes to the overall result, we also perform ablation experiments. Potential applications for our suggested methodology include surveillance, sports analysis, and human-robot interaction, among other areas.
Multi-Valued Model Checking Iot and Intelligent Systems with Commitment Protocols in Multi-Source Data Environments

In today's world of connectivity, various domains use different multi-sensor Internet of Things (IoT) and Intelligent Systems (IS) applications. These applications involve extensive interactions between thousands or millions of components in open environments, which challenges verifying their reliability and efficiency. This paper introduces the first investigation in verifying IoT applications and intelligent systems in multi-source data settings with multi-agent commitment protocols in uncertain or inconsistent environments. Specifically, we present original and efficient frameworks for modelling and verifying these systems with conditional and unconditional commitment protocols under uncertain or inconsistent settings. We introduce (4v-CTLc) and (4v-CTLcc), multi-valued logics of commitment for reasoning about inconsistency over these systems that expands (3v-CTLc) and (3v-CTLcc) for reasoning about uncertainty. Moreover, we introduce new reduction algorithms for reducing our multi-valued model checking problems to the classical ones. To implement these algorithms, we develop two new automatic verification tools. The first tool translates the multi-valued logics to (CTL) and automatically interacts with the NuSMV model checker. The second tool translates these logics to the classical versions (CTLc and CTLcc) and automatically interacts with the MCMAS+ model checker. We apply our verification approaches to a Smart Home, a Smart Hospital and a Smart Mortgage system with multi-source data as case studies using sets of properties, including safety, liveness and reachability. The experimental results obtained by the proposed multi-valued model checking techniques proved these techniques' high efficiency and applicability to modelling and verifying intelligent and autonomous multi-source data systems.
Binlets: Data-Fusion Aware Denoising Enables Accurate and Unbiased Quantification of Multichannel Signal

As monitoring multiple signals becomes more cost-effective, combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process. Here, we present a method based on the Haar wavelet transform, that trades off resolution against accuracy based on statistical significance. It denoises multichannel signals taking advantage of correlations between channels, and offers a superior performance when a non-linear transformation of the signal is involved. We demonstrate its efficacy by benchmarking our method against standard wavelet thresholding for synthetic single and multichannel time series, and a multichannel two dimensional image. The method has a simple interpretation as an adaptive binning of the signal, and neither requires training data nor specialized hardware to run fast. A reference Python implementation is available on GitHub and PyPI, making it simple to integrate into any analysis pipeline.
Fs: Score Fused with Density as the Basis for Nms

The existing pedestrian detection algorithms, such as YOLO and Faster RCNN, generate multiple proposal boxes with associated scores. These proposal boxes are then filtered using NMS (Non-Maximum Suppression) to derive the final detection boxes. However, the traditional NMS is based on the score-first strategy, which is inadequate because the score only indicates the likelihood of the corresponding anchor box containing the pedestrian to be detected, rather than its actual probability of being a ground truth. We have observed that a proposal box has a greater probability of being a ground truth if it possesses both higher local density and higher distance from a proposal box of higher density. Therefore, this study have incorporated local density and distance from the proposal box of higher density into the score of the proposal box, and named it FS(Fused Score). FS has excellent composability and can be easily incorporated into different NMS, such as Greedy NMS, Soft NMS, WBF NMS, because it is calculated based only on the coordinates of the proposal boxes. The experimental results indicate that after Greedy NMS, Soft NMS, and WBF NMS are applied FS, the corresponding mAP(mean Average Precision, IOU 0.5:0.95) is improved by 2.49%, 2.73%, and 1.06% on the Citypersons dataset, and by 1.96%, 1.81%, 0.51% on the CrowdHuman dataset. Furthermore, FS can also be extended to multi-object detection. Experimental results with the COCO dataset demonstrate that AP@0.75 also significantly be improved.
Consolidating Cybersecurity in Europe: A Case Study on Job Profiles Assessment (Dataset)

Data consists of expert assessments about the cybersecurity skills needed to perform in six cybersecurity-oriented job profiles relevant for Europe. Data was collected via surveys, targeted at European experts in cybersecurity from academia and industry. Respondents characterised the skills needed to perform in six job profiles using the CSEC+ framework: a spreadsheet where cybersecurity skills must be ranked in a Likert scale from 0 (irrelevant) to 4 (advance knowledge needed). Metadata requested included the type of organisation of the respondent (Large company, SME, Academic/Research, Public administration, Other) and the country of origin.There were three data-collection phases: (1) an initial phase, used also to refine later larger-scale processes, carried out in Oct 2021–Jan 2022 and resulting in 13 expert assessments from four EU countries; (2) a second phase implemented as an online service broadcast to a larger audience, carried out in Mar–Apr 2022 and resulting in 15 assessments from eight European countries; (3) and a third phase, allowing direct online input and distributed in PC and mobile form, carried out in Sep–Oct 2022 and resulting in 32 assessments from ten European countries.The raw data gathered was stored and processed via spreadsheets, computing statistical information (mean, stdev) on how much each cybersecurity skill and area was deemed necessary to perform in each job profile. This is visualised as a heatmap where colour intensity symbolises value, and circle diffusion symbolises spread. Processed data further includes visualisations on how the area of origin of the respondent (academia, as in “producer of education”, vs. industry, as in “consumer of education”) influences the responses. This is shown as bar plots, where whiskers represent confidence intervals used for statistical-significance tests.This data can serve as basis to understand the educational needs for the cybersecurity sector in Europe. It can be reused for comparison against frameworks, other than CSEC+, to assess the need of education in specific cybersecurity sectors such as human security. Furthermore, the Qualtrics survey template (included) is a ready-made solution for replication studies.