Reviewerly/exHarmony huggingface dataset:
Unlocking Efficiency

Blockchain technology has gained considerable attention in recent years due to its potential to revolutionize various industries. Human resources (HR) management in large enterprises is an area that can greatly benefit from the transparency, security, and efficiency offered by blockchain technology. This study explores the concept of blockchain as a service (BaaS) in enterprise HR management and its potential to unlock efficiency, streamline processes, and enhance data security elaborating a comprehensive exploration of the theoretical underpinnings, practical application, and potential ramifications of BaaS within HR operations, highlighting the profound transformative prospects inherent in this emerging technology. The integration of blockchain as a service (BaaS) in enterprise human resources management is the focus of this study. The findings suggest that BaaS has the potential to revolutionize HR management by offering a secure, transparent, and efficient platform for various HR processes, from recruitment and onboarding to payroll and talent management.
Blockchain Technology for Sustainable Investment

The purpose of this review is to provide a comprehensive analysis of how blockchain technology can enhance sustainable investment practices. The authors aim to assess the current applications, benefits, and challenges of blockchain in sustainable investment and identify opportunities for future development. They conducted a thorough review of existing literature and case studies to analyze the current landscape of blockchain technology in sustainable investment. This involved examining various blockchain platforms and applications, as well as assessing their impact on transparency, security, and efficiency in sustainable investment. This analysis reveals that blockchain technology offers significant advantages for sustainable investment, including improved transparency, reduced fraud, and increased accessibility for smaller investors. However, challenges such as scalability and regulatory hurdles persist. The review also highlights successful cases and real-world examples of blockchain implementation in sustainable investment.
A two-step passenger flow anomaly detection scheme based on machine learning methods

Abstract Subway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.
Statistical Fault Analysis of TinyJambu

Abstract The resource-constrained IoT devices have limited resources such as processing power, memory, and battery capacity. Therefore it is challenging to adopt traditional cryptographic algorithms on them. In order to find a solution, the National Institute of Standards and Technology (NIST) initiated the Lightweight Cryptography (LWC) competition to standardize cryptographic algorithms for resource-constrained devices. The primary aim of our work is to implement and analyse selected finalist algorithms from the NIST competition using modern cryptanalysis techniques, with a focus on statistical fault attacks. Traditional analysis methods, such as linear and differential analysis, were not prioritized as most finalist algorithms have established defences against these methods. We implemented six of the selected finalist algorithms from the competition: Ascon, Elephant, GIFT-COFB, ISAP, TinyJambu, and Xoodyak. We chose TinyJumbu for statistical fault analysis because of its attractiveness, compact block size, and provision of a more lightweight keyed permutation.
Global Classroom

In this paper Brownlie discusses a module entitled 'Global Classroom'. There are three participating universities: Le Mans Université, France where the module is part of the Masters in International Cultural Studies; the University of Pretoria, South Africa; and Fundação Armando Alvares Penteado, São Paulo, Brazil; participants from the latter two universities are fourth year students in political science. In order to facilitate communication, the module operates via the video-conferencing platform Zoom. Each week focuses on a particular topic, for example, green action, media and misinformation, democracy in action, national elections and youth attitudes, with guest speakers, student presentations and discussion. The paper defines and is structured by four concepts/practices: "small cultures", "postdigitality", "virtual exchange", and "postdigital critical cosmopolitanism." The Global Classroom module is analyzed through the prism of these concepts/practices. With respect to virtual exchange and postdigital critical cosmopolitanism, it is found that the module partially equates with these concepts/practices. It is suggested that in future iterations of the module, particular choices with respect to topic and task design could better allow students to develop intercultural skills and self-reflection.
Virtual Exchange as a Mechanism for Digital Education

Virtual exchange (VE), a pedagogical approach towards intercultural dialogue among young people supported by technology, has been prominent over the past two to three decades. In this chapter, I offer a general introduction to the different types of Virtual Exchange and focus on a particular approach provided by the Sharing Perspectives Foundation (SPF). The logic and theoretical foundation on which the SPF programmes and courses are built are presented and the efficacy of using Virtual Exchange as a tool to prepare the ground for the seeds of social inclusion and civic engagement are debated, while various virtual exchange programmes offered by the SPF and its partners are also discussed. This chapter combines scientific research with years of practice to offer a holistic view of the application of Virtual Exchange in peace education. I dwell on my own ten years of experience in the field of VE, wearing the various hats of participant, facilitator, coach and mentor, trainer, programme officer and partnership coordinator at the Sharing Perspectives Foundation (SPF), as well as a lecturer at Hebron University in Palestine.
Exploring the Interplay of Lifewide Learning, Migration, and Social Network Sites in the Postdigital Field of Action

Lifewide learning encompasses all forms of learning and personal development in formal, non-formal and informal modalities. This article discusses the relationship between Lifewide Learning, Social Network Sites (SNSs) and Migration by reflecting on the role of SNSs as a resource for informal learning in the context of migration and its impact on the postdigital field of action. This article argues that SNSs are fields of action that are ubiquitously used and are deeply interwoven in everyday life, especially for people who wish to/or have already relocated. In these fields of action, interaction, emotional support and constant learning take place, impacting the lives and experiences of people undergoing migration. Therefore, this article considers that SNSs are highly relevant resources for learning 'informally', where sharing personal experiences not only provides concrete information regarding a situation but perhaps, more importantly, people who share or seek information find companionship in the realization that they are not alone with their doubts or situations in their new location.
Digital Competences in the Educational Sphere

Digital skills are one of the most contested key competences in the educational sphere. On the one hand, they have become essential to guarantee standards of educational quality and progress, on the other hand they tend to further hamper educational inequalities among the contemporary highly-diversified student population. This paper ties into these premises with a case study located in Turin, Italy based on 19 teachers and educators from all school levels (primary, lower-secondary and upper-secondary education) to highlight the transformation of needs and challenges related to the different phases of the life cycle of students. Three main issues were investigated from a comparative perspective among the study participants: (1) the coherence of their knowledge regarding opportunities and the challenges of digitalization; (2) their skills to implement digital instruments in their working context; and (3) their attitude towards the transfer of potential benefits that enhance learning outcomes through digital instruments. Findings suggest that high-quality and effective staff formation represents one of the most critical issues when talking about digitality in the educational sphere. A chronic lack of time for training, the ongoing work in emergency conditions, the heterogenous institutional endowment with technical devices (PCs, digital infrastructure, etc.), and the suspicion towards the benefits of digitality in the classroom are some of the major barriers to the forwarding of digital competences as a set of skills, knowledge and attitudes within the educational context.
Embrace sustainable AI: Dynamic data subset selection for image classification

Data selection is commonly used to reduce costs and energy usage by training on a subset of available data. However, determining the appropriate subset size requires extensive dataset knowledge and experimentation, limiting transferability. Varying the validation set also produces unstable results and wastes computational resources. In this paper, we propose a data selection method for dynamically determining subset ratios based on model performance using only a training set. The data search space is narrowed through weighted sampling, leveraging statistical selection patterns. Parallel analysis of class distributions identifies the most representative samples with high selection potential. Extensive experiments validate our approach and demonstrate improved training efficiency. Our method speeds up various subset ratios by up to 2.2x on CIFAR-10, 1.9x on CIFAR-100, 2.0x on TinyImageNet, and 2.1x on ImageNet with negligible accuracy drops.
Discriminating quartz host rock based on its trace element chemistry using machine learning-a new tool for sedimentary provenance studies

Quartz is one of the most abundant mineral in the continental crust, occurring in a wide variety of igneous, metamorphic, and sedimentary rocks. It incorporates several trace elements such as Ti, Al, Li, Ge, B, and P in its structure during crystallization/precipitation from magma/fluid, which can potentially be used as petrogenetic indicators. Being physically and chemically resilient during sedimentary recycling, detrital quartz can retain the signatures of the physicochemical environment of their formation. Earlier studies have mostly attempted to discriminate quartz from magmatic and magmatic-hydrothermal systems using bivariate or ternary discrimination diagrams. In this study, we developed three robust machine learning (ML) methods (XGBoost, LightGBM, and CatBoost models) to discriminate quartz from a wide variety of magmatic/magmatic-hydrothermal host rocks based on their trace element chemistry. For this purpose, we compiled published 11,267 trace element analyses of quartz grains considering 50 trace elements from 14 different host rocks, namely A-, S-, I- types granitoids and pegmatites, A-, I- types volcanic rocks, A-, S- types granite greisen, hydrothermal veins associated with A-, S-, I- type intrusions, and metamorphic rocks. The three ML classifiers can successfully discriminate quartz from the aforementioned groups with an average accuracy of ~95%. Quartz hosted in each system can be correctly predicted with >75% classification accuracy, except for I-type pegmatite. The Shapley Additive exPlanations (SHAP) algorithm, which measures the contribution of each element to the output predicted by the ML models, indicates that Ti, Li, Ge, Al, B, and P exert the highest control on the ML classifiers. We propose our ML models as a new tool for constraining sedimentary provenance and demonstrate the usefulness of our approach using the trace element data of detrital quartz from sediments in the catchment area of the Bega River in the Lachlan Fold Belt, Australia.
Evaluation of 3D optical techniques for modal analysis during climatic test inside environmental chamber

The increasing demand for ensuring the reliability of industrial components under diverse temperature and dynamic solicitations has led to develop standards and testing methods that incorporate both thermal and vibration loads to analyse the dependency of mechanical properties with temperature. This research presents an assessment of a novel experimental approach that combines 3D full-field optical techniques and an environmental chamber to perform modal identification under different temperatures. The study performs a thorough comparative of scanning Laser Doppler Vibrometry (SLDV) and Digital Image Correlation (DIC) techniques. By integrating the environmental chamber with these optical methodologies, valuable insights into the behaviour of components were obtained. Both SLDV and DIC successfully identify variations in modal parameters due to temperature changes. Nevertheless, the results obtained employing SLDV were proved less effective for a reasonable test duration.
A new McEliece-type cryptosystem using Gabidulin-Kronecker product codes

This paper presents a new McEliece-type cryptosystem using Gabidulin-Kronecker product codes in the rank metric. The contributions of this paper are as follows. Firstly, we propose a new Gabidulin-Kronecker product code which is a kind of block circulant code, and give an efficient decoding algorithm. Secondly, we design a one-way secure public key encryption scheme based on the Gabidulin-Kronecker product codes. Thirdly, we obtain an IND-CCA2 secure public key encryption scheme by converting our one-way secure public key encryption scheme under the hardness assumption of the RSD Dual Problem. In terms of efficiency, our scheme has a smaller public key size by taking advantage of the block circulant structure. For 128-bit security, the public key size of our proposal is 13% of Lau-Tan's cryptosystem (in the rank metric), and 19% of BIKE (in the Hamming metric). In terms of security, our scheme can resist Overbeck attack, Coggia-Couvreur attack and Sendrier attack.
Sparsifying Count Sketch

The seminal work of Charikar et al. [1] called Count-Sketch suggests a sketching algorithm for real-valued vectors that has been used in frequency estimation for data streams and pairwise inner product estimation for real-valued vectors etc. One of the major advantages of Count-Sketch over other similar sketching algorithms, such as random projection, is that its running time, as well as the sparsity of sketch, depends on the sparsity of the input. Therefore, sparse datasets enjoy space-efficient (sparse sketches) and faster running time. However, on dense datasets, these advantages of Count-Sketch might be negligible over other baselines. In this work, we address this challenge by suggesting a simple and effective approach that outputs (asymptotically) a sparser sketch than that obtained via Count-Sketch, and as a by-product, we also achieve a faster running time. Simultaneously, the quality of our estimate is closely approximate to that of Count-Sketch. For frequency estimation and pairwise inner product estimation problems, our proposal Sparse-Count-Sketch provides unbiased estimates. These estimations, however, have slightly higher variances than their respective estimates obtained via Count-Sketch. To address this issue, we present improved estimators for these problems based on maximum likelihood estimation (MLE) that offer smaller variances even w.r.t. Count-Sketch. We suggest a rigorous theoretical analysis of our proposal for frequency estimation for data streams and pairwise inner product estimation for real-valued vectors.
Distributed representations of entities in open-world knowledge graphs

Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge graphs where new entities emerge frequently. To address this limitation, we introduce Decentralized Attention Network (DAN). DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings. To effectively train a DAN, we introduce self-distillation, a technique that guides the network in generating desired representations. Theoretical analysis validates the effectiveness of our approach. We implement an end-to-end framework and conduct extensive experiments to evaluate our method, showcasing competitive performance on conventional entity alignment and entity prediction tasks. Furthermore, our method significantly outperforms existing methods in open-world settings.
Dataset for Bibliometric Data-Driven Research Team Formation: Case of Politehnica University of Timisoara scholars for the interval 2010-2022

Data-driven expert team formation is a complicated and multifaceted process that requires access to accurate information to identify researchers’ areas and level of expertise and their collaborative prospects. In this respect, bibliometric data represents a valuable and reliable source of information that can be effectively employed in revealing key insights regarding candidates. Due to its complex and complete structure of publication metadata records, IEEE Xplore database may offer the possibility to compute an extensive set of indicators about researchers’ publication production and how they have interacted during time. Considering the case of Politehnica University of Timisoara scholars for the interval 2010-2022, current dataset encapsulates relevant and rich information for assembling multidisciplinary research teams, being also a testing ground for experimenting and calibrating the expert team formation methods and mechanisms.
Improved likelihood ratios for face recognition in surveillance video by multimodal feature pairing

In forensic and security scenarios, accurate facial recognition in surveillance videos, often challenged by variations in pose, illumination, and expression, is essential. Traditional manual comparison methods lack standardization, revealing a critical gap in evidence reliability. We propose an enhanced images-to-video recognition approach, pairing facial images with attributes like pose and quality. Utilizing datasets such as ENFSI 2015, SCFace, XQLFW, ChokePoint, and ForenFace, we assess evidence strength using calibration methods for likelihood ratio estimation. Three models-ArcFace, FaceNet, and QMagFace-undergo validation, with the log-likelihood ratio cost (Cllr) as a key metric. Results indicate that prioritizing high-quality frames and aligning attributes with reference images optimizes recognition, yielding similar Cllr values to the top 25% best frames approach. A combined embedding weighted by frame quality emerges as the second-best method. Upon preprocessing facial images with the super resolution CodeFormer, it unexpectedly increased Cllr, undermining evidence reliability, advising against its use in such forensic applications.
A semi-automatic approach for joint orientation recognition using 3D trace network analysis

Identifying rock mass discontinuities and their plane orientation are crucial factors when determining rock mass characteristics. Rock mass discontinuity mapping is fundamentally dependent on joint trace surveying since traces of them are most often the only visible features in rock outcrops. Traditional methods for joint trace surveying using tape and a geological compass are challenging, time-consuming, and hazardous. Fortunately, non-contact measuring techniques offer the advantage of generating accurate objective records of rock masses and enabling the measurement of discontinuities from digital surface models and 3D point clouds of outcrops without requiring direct access to the rock mass and associated constraints. Herein, an innovative approach for identifying discontinuity planes in rock formations using 3D trace data is presented. We introduce the concept of curved and straight traces, with a curvature index indicating the trace's accuracy by representing its discontinuity plane. In addition, we identified co-planar traces by analyzing intersecting straight traces, thereby further contributing to discontinuity plane determination. The methodology's effectiveness was established through validation using a predefined 3D trace network of discontinuity planes with known orientations on a 3D digital rock outcrop model. The methodology was then applied to trace data collected from an actual rock outcrop. The algorithm successfully matched >66% of traces with their corresponding discontinuity planes. After clustering, 80% of the identified planes aligned with principal joint sets within the rock mass. These identified planes accurately aligned with their expected jointing pattern, thereby validating the robustness of the method. Despite challenges presented by discontinuous and complex trace segments obtained from semi-automatic trace detection techniques, the algorithm effectively processed such traces, thereby enabling a comprehensive understanding of the rock mass's jointing system. This enables swift identification of the main joint orientations. We leveraged stereonet analysis to identify principal joint sets using kernel-based and fuzzy C-means clustering techniques. Our approach represents an important advancement in the characterization of rock mass structural properties.
Security issues in visual cryptography scheme with known random number sequence

The visual cryptography scheme (VCS) is usually considered unconditionally secure, meaning that participants in the forbidden subset cannot access any secret information. However, this assumes that the random number sequence (RNS) used in the secret sharing process is completely inaccessible to attackers. In reality, the random number generator could have vulnerabilities or backdoors, allowing attackers to obtain RNS. Thus, we study an extreme scenario where attackers successfully acquire RNS. Our work explores the relationship between VCS security and RNS, revealing intriguing findings when there is complete or partial leakage of secret information. To enhance VCS security, we propose three innovative algorithms designed to prevent attackers from accessing all original secret image information, even when they possess RNS and shares within the forbidden subset. Theoretical and experimental results demonstrate the effectiveness of these proposed schemes in enhancing the security of existing VCSs.