Reviewerly/exHarmony huggingface dataset:
Generic Error SDP and Generic Error CVE

This paper introduces a new family of CVE schemes built from generic errors (GE-CVE) and identifies a vulnerability therein. To introduce the problem, we generalize the concept of error sets beyond those defined by a metric, and use the set-theoretic difference operator to characterize when these error sets are detectable or correctable by codes. We prove the existence of a general, metric-less form of the Gilbert-Varshamov bound, and show that - like in the Hamming setting - a random code corrects a generic error set with overwhelming probability. We define the generic error SDP (GE-SDP), which is contained in the complexity class of NP-hard problems, and use its hardness to demonstrate the security of GE-CVE. We prove that these schemes are complete, sound, and zero-knowledge. Finally, we identify a vulnerability of the GE-SDP for codes defined over large extension fields and without a very high rate. We show that certain GE-CVE parameters suffer from this vulnerability, notably the restricted CVE scheme.
Algebraic Algorithm for the Alternating Trilinear Form Equivalence Problem

The Alternating Trilinear Form Equivalence ( $$\textsf{ATFE}$$ ) problem was recently used as a hardness assumption in the design of a digital signature scheme by Tang et al. using the Fiat-Shamir paradigm. It is a hard equivalence problem known to be in the class of equivalence problems that includes, for instance, the Tensor Isomorphism ( $$\textsf{TI}$$ ), Quadratic Maps Linear Equivalence ( $$\textsf{QMLE}$$ ) and the Matrix Code Equivalence ( $$\textsf{MCE}$$ ) problems. Due to the increased cryptographic interest, the understanding of its practical hardness has also increased in the last couple of years. Currently, there are several combinatorial and algebraic algorithms for solving it, the best of which is a graph-theoretic algorithm that also includes an algebraic subroutine. In this paper, we take a purely algebraic approach to the $$\textsf{ATFE}$$ problem, but we use a coding theory perspective to model the problem. This modelling was introduced earlier for the $$\textsf{MCE}$$ problem. Using it, we improve the cost of an algebraic attack against $$\textsf{ATFE}$$ compared to previously known ones. Taking into account the algebraic structure of alternating trilinear forms, we show that the obtained system has less variables but also less equations than for $$\textsf{MCE}$$ and gives rise to structural degree-3 syzygies. Under the assumption that outside of these syzygies the system behaves semi-regularly, we provide a concrete, non-asymptotic complexity estimate of the performance of our algebraic attack. Our results show that the complexity is below the estimated security levels of the signature scheme of Tang et al. and comparable to the currently best graph-theoretic attack by Beullens.
Factors affecting the development of university lecturers’ competencies

Developing university lecturers’ competencies to meet the requirements of fundamental and comprehensive innovation in higher education has been raised as an urgent requirement in recent years. Through the analysis, synthesis and systematization of researches by international and domestic authors on the factors affecting the development of university lecturers’ competencies in general, including lecturers in the field of Information - Libraries, the article presents the requirements for the development of university lecturers’ competencies, the theoretical basis of the research issue, and the factors affecting the development of university lecturers’ competencies. The research results have identified the factors related to the development of lecturers’ competencies, including macro-environmental characteristics such as the social context and views on education, as well as the State and Government’s policies on higher education; the factors of the organizational environment such as the support of resources and the working environment; and the factors of professional features and activities, in addition to lecturers’ personal characteristics. This article serves as a suitable reference for researchers in research design process and for managers at different levels at higher education institutions in perfecting and strengthening policies and measures to develop lecturer's competencies.
IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0

In recent years, most of the research exhibits in the field of Education 4.0 Training Systems (ETS) and Industry 4.0 Training Systems (ITS) that has the ability to learn the behavior of the learners, interns, or trainees. Understanding the feelings and emotions of learners toward learning is essential for creating a practical and exciting learning experience. Patience-emotions detection and sentiments analysis have emerged as an integral part of the understanding of the behaviors of learners, thus there is a need to expand the overall educational or training process in academics and industries. This model enables teachers, trainers and instruction designers to obtain valuable information, which can be used to optimize teaching strategies to improve learning outcomes. To achieve this goal with IoT-enabled objects, an Academician can create a more personalized and effective learning environment for students, trainees and interns. A novel emulated framework is designed and implemented with IoT and machine learning techniques to analyze the performance of learners. The model receives feedback from 1000 learners using IoT devices and analysis the missing information in the learning systems, that missing information lacked effective learning. This emulated framework analyzes the performance of the model. A novel and innovative early warning system is also created to send the warning on WhatsApp or email to several users in a single shot, when achieving certain goals such as file’s size limits and so on. In this research SVM, MCC, NLP and CNN machine learning algorithms are applied to detect students’ feelings and emotions to track the feedback via IoT enabled system.
A recent survey of permutation trinomials over finite fields

&lt;abstract&gt;&lt;p&gt;Constructing permutation polynomials is a hot topic in the area of finite fields, and permutation polynomials have many applications in different areas. Recently, several classes of permutation trinomials were constructed. In 2015, Hou surveyed the achievements of permutation polynomials and novel methods. But, very few were known at that time. Recently, many permutation binomials and trinomials have been constructed. Here we survey the significant contribution made to the construction of permutation trinomials over finite fields in recent years. Emphasis is placed on significant results and novel methods. The covered material is split into three aspects: the existence of permutation trinomials of the respective forms $ x^{r}h(x^{s}) $, $ \lambda_{1}x^{a}+\lambda_{2}x^{b}+\lambda_{3}x^{c} $ and $ x+x^{s(q^{m}-1)+1} +x^{t(q^{m}-1)+1} $, with Niho-type exponents $ s, t $.&lt;/p&gt;&lt;/abstract&gt;
Learning about simulated adversaries from human defenders using interactive cyber-defense games

Abstract Given the increase in cybercrime, cybersecurity analysts (i.e. defenders) are in high demand. Defenders must monitor an organization’s network to evaluate threats and potential breaches into the network. Adversary simulation is commonly used to test defenders’ performance against known threats to organizations. However, it is unclear how effective this training process is in preparing defenders for this highly demanding job. In this paper, we demonstrate how to use adversarial algorithms to investigate defenders’ learning using interactive cyber-defense games. We created an Interactive Defense Game (IDG) that represents a cyber-defense scenario, which requires monitoring of incoming network alerts and allows a defender to analyze, remove, and restore services based on the events observed in a network. The participants in our study faced one of two types of simulated adversaries. A Beeline adversary is a fast, targeted, and informed attacker; and a Meander adversary is a slow attacker that wanders the network until it finds the right target to exploit. Our results suggest that although human defenders have more difficulty to stop the Beeline adversary initially, they were able to learn to stop this adversary by taking advantage of their attack strategy. Participants who played against the Beeline adversary learned to anticipate the adversary’s actions and took more proactive actions, while decreasing their reactive actions. These findings have implications for understanding how to help cybersecurity analysts speed up their training.
The mediating effects of employee trust and job satisfaction in the relationship between empowerment leadership and employee loyalty

This research investigates the mediating role of employee trust and job satisfaction in the association between empowerment leadership and employee loyalty among medical staff in the private healthcare system located in southern Vietnam. The study involved 374 doctors, nurses, and other medical personnel employed within this private healthcare system. Data analysis was conducted using partial least squares structural equation modelling (PLS-SEM) with the SmartPLS 4.0 software. The findings from the structural model analysis indicate that empowerment leadership has a direct positive influence on medical staff's loyalty to the organisation, as well as a positive impact on intermediate factors such as employee trust and job satisfaction. Furthermore, employee trust and job satisfaction were found to have a positive effect on employee loyalty towards the organisation.
Modelling and Sizing a Grid-connected PV-Battery System Using DIgSILENT for Powering UTeM Main Campus

Universities encounter challenges stemming from the escalating electrical bills attributed to the substantial energy consumption of their expansive buildings. This serves as a clear impetus for universities to transition to renewable energy technologies, which offer the advantages of cost-effective operation and minimal environmental footprint. This paper presents the design of a grid-connected photovoltaic (PV) system with battery storage to fulfil the electricity consumption needs of Universiti Teknikal Malaysia Melaka (UTeM) main campus. The objective is to reduce grid dependency, lower electricity costs, and minimize carbon dioxide (CO 2 ) emissions. The system was modelled and simulated using DIgSILENT software. Load demand and energy consumption data were extracted from the Tenaga Nasional Berhad (TNB) electricity bill, while the PV profile and irradiance data were obtained from the UTeM solar laboratory. Based on the findings, it has been determined that a 12 MWp PV system, coupled with a 25.8 MWh battery, represents the optimal solution for satisfying the total electricity demand of UTeM's main campus. The installation of this system is projected to result in estimated monthly electricity bill savings of MYR 422,611 for UTeM. Furthermore, the proposed system offers a significant environmental benefit by potentially reducing CO 2 emissions by up to 1,507,520 kg per month. The findings of this study can inform decision-makers in implementing a cost-effective and environmentally friendly energy solution for UTeM's main campus.
Performance of Meta-heuristic Algorithm for a Photovoltaic System under Partial Shade

Renewable energies are very attractive for the production of electrical energy and consequently the installation of power plants is growing Rsharply in order to meet the constant demand for energy in emerging countries. Indeed, these countries then invest too many means to develop their natural resources, in particular hydraulic, solar, wind, biomass, etc.., in order to reduce their dependence on fossil fuels. The production of electricity from solar energy as an alternative to fossil fuels continues to be the subject of several research works. However, the problem is that the production of these plants is intermittent because it depends mainly on the level of irradiation and the temperature. However, several methods have been developed to ensure operation at the maximum power point of the photovoltaic generator whatever the climatic conditions. This work concerns the study of the effect of the non-uniform variation of the level of irradiation known under the name of partial shading on photovoltaic installations and to evaluate the performance and the interest of the use. Meta-heuristic algorithms for tracking the maximum power point compared to those of classical optimization. The study validated by numerical simulations taking into account different partial shading scenarios. The analysis of the results proves the best performances of the meta-heuristic algorithms, whatever the position of the point of maximum power.
Research on Joint Representation Learning Methods for Entity Neighborhood Information and Description Information

To address the issue of poor embedding performance in the knowledge graph of a programming design course, a joint representation learning model that combines entity neighborhood information and description information is proposed. Firstly, a graph attention network is employed to obtain the features of entity neighboring nodes, incorporating relationship features to enrich the structural information. Next, the BERT-WWM model is utilized in conjunction with attention mechanisms to obtain the representation of entity description information. Finally, the final entity vector representation is obtained by combining the vector representations of entity neighborhood information and description information. Experimental results demonstrate that the proposed model achieves favorable performance on the knowledge graph dataset of the programming design course, outperforming other baseline models.
Multi-Perspective Frame Element Representation for Machine Reading Comprehension

Semantics understanding is a critical aspect of Machine Reading Comprehension (MRC). In recent years, researchers have started leveraging the semantic knowledge provided by FrameNet to enhance the performance of MRC systems. While significant efforts have been dedicated to Frame representation, there is a noticeable lack of research on Frame Element (FE) representation, which is equally crucial for MRC. We propose a groundbreaking approach called the Multi-Perspective Frame Elements Representation (MPFER) method. It aims to comprehensively model FEs from three distinct perspectives: FE definition, Frame (semantic scenario), and FE relation. By considering these multiple perspectives, our proposed model significantly improves the representation and understanding of FEs in MRC tasks. To validate the effectiveness of the MPFER method, we conducted extensive experiments. The results clearly demonstrate that our proposed model outperforms existing state-of-the-art methods. The superiority of our approach highlights its potential for advancing the field of MRC and showcasing the importance of properly modeling FEs for better semantic understanding.
Event-Centric Opinion Mining via In-Context Learning with ChatGPT

Events represent fundamental constituents of the world, and investigating expressions of opinion centered around events can enhance our comprehension of events themselves, encompassing their underlying causes, effects, and consequences. This helps us to understand and explain social phenomena in a more comprehensive way. In this regard, we introduce ChatGPT-opinion mining as a framework that transforms event-centric opinion mining tasks into question-answering (QA) utilizing large language model. We employ this approach in the context of the event-centric opinion mining task that utilizes an event-argument structure. In our study, we primarily leverage in-context learning methods to construct demonstrations. Through comprehensive comparative experiments, we illustrate that the model achieves superior results within the ChatGPT-opinion mining framework when the demonstrations exhibit diversity and possess higher semantic similarity, comparable to those of supervised models. Moreover, ChatGPT-opinion mining surpasses the supervised model, particularly when there is limited availability of the same data.
Prior Knowledge-Guided Transformer for Remote Sensing Image Captioning

Remote sensing image captioning aims to generate meaningful and grammatically accurate sentences for remote sensing images. However, in comparison to natural image captioning, remote sensing image captioning encounters additional challenges due to the unique characteristics of remote sensing images. The first challenge arises from the abundance of objects present in these images. As the number of objects increases, it becomes increasingly difficult to determine the main focus of the description. Moreover, the objects in remote sensing images often share similar appearances, which further complicates the generation of accurate descriptions. To overcome these challenges, we propose a Prior Knowledge-guided Transformer for remote sensing image captioning. Firstly, scene-level and object-level features are extracted in a Multi-level Feature Extraction module. To further refine and enhance the extracted multi-level features, we introduce a Feature Enhancement module. This module utilizes a combination of graph neural networks and attention mechanisms to capture the correlation and difference between different objects or scene regions. Moreover, we propose a Prior Knowledge augmented Attention mechanism to select the objects that are more relevant to the scene regions by establishing the relationships between them. This attention mechanism is seamlessly integrated into the Transformer structure, providing valuable prior knowledge that promotes the caption generation process. Extensive experiments on three remote sensing image captioning datasets verify the superiority of the proposed method. Compared with the baseline methods, the proposed method achieves more impressive performance. The code will be publicly available at https://github.com/One-paper-luck/PKG-Transformer.
Online Sparse Representation Clustering for Evolving Data Streams

Data stream clustering can be performed to discover the patterns underlying continuously arriving sequences of data. A number of data stream clustering algorithms for finding clusters in arbitrary shapes and handling outliers, such as density-based clustering algorithms, have been proposed. However, these algorithms are often limited in their ability to construct and merge microclusters by measuring the Euclidean distances between high-dimensional data objects, e.g., transferring valuable knowledge from historical landmark windows to the current landmark window, and exploiting evolving subspace structures adaptively. We propose an online sparse representation clustering (OSRC) method to learn an affinity matrix for evaluating the relationships among high-dimensional data objects in evolving data streams. We first introduce a low-dimensional projection (LDP) into sparse representation to adaptively reduce the potential negative influence associated with the noise and redundancy contained in high-dimensional data. Then, we take advantage of the l2,1 -norm optimization technique to choose the appropriate number of representative data objects and form a specific dictionary for sparse representation. The specific dictionary is integrated into sparse representation to adaptively exploit the evolving subspace structures of the high-dimensional data objects. Moreover, the data object representatives from the current landmark window can transfer valuable knowledge to the next landmark window. The experimental results based on a synthetic dataset and six benchmark datasets validate the effectiveness of the proposed method compared to that of state-of-the-art methods for data stream clustering.
CausE: Towards Causal Knowledge Graph Embedding

Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC). However, KGE models often only briefly learn structural correlations of triple data and embeddings would be misled by the trivial patterns and noisy links in real-world KGs. To address this issue, we build the new paradigm of KGE in the context of causality and embedding disentanglement. We further propose a Causality-enhanced knowledge graph Embedding (CausE) framework. CausE employs causal intervention to estimate the causal effect of the confounder embeddings and design new training objectives to make stable predictions. Experimental results demonstrate that CausE could outperform the baseline models and achieve state-of-the-art KGC performance. We release our code in https://github.com/zjukg/CausE .
ODKG: An Official Document Knowledge Graph for the Effective Management

Effective management of massive electronic documents is one of the hot topics for social services. There exist several knowledge bases of documents published for researchers to explore downstream applications such as PubLayNet and DocBank. Nevertheless, these datasets are mainly designed for document layout analysis and do not consider the linkages among documents. To improve this issue, in this paper, we present an official document knowledge graph, namely ODKG, which aims to collect the offical documents for effective management. We design a lightweight ontology of official documents. It can bring a well-defined schema of collected documents so that they could share more linkages with each other. We present the algorithms of element extraction, document archiving, and knowledge alignment during the process of ODKG construction, and further evaluate the corresponding algorithms based on our constructed datasets. Experimental results show that several algorithms can be competent to above tasks to some extent. Finally, we list three use cases of ODKG that are helpful for managers to improve the efficiency of their document management.
Rapid Detection of Iron Ore Grades Based on Fractional-Order Derivative Spectroscopy and Machine Learning

The time-consuming nature of chemical testing techniques makes them lag behind mineral processing. Therefore, this paper combines Visible-infrared reflectance spectroscopy with machine learning (ML) algorithms to achieve rapid detection of iron ore grades and meet the requirements of mining production. Firstly, the standard normal variate and de-trending are used to eliminate noise and baseline drift in the original spectral data. Then, extraneous signals are removed using direct orthogonal signal correction (DOSC). In addition, fractional-order derivative (FOD) is performed on the DOSC spectrum to further amplify the spectral details. To extract spectral features and reduce the spectral dimension, a multilayer incremental extreme learning machine auto-encoder (MIELM-AE) is proposed in this paper. MIELM-AE can automatically match the optimal number of network nodes and network layers to minimize the reconstruction error. The experimental results show that the Pearson correlation coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of the extreme learning machine (ELM) built using MIELM-AE improves from 0.715 to 0.821, compared with the ELM built without the dimensionality reduction method. To increase the measurement accuracy, this paper uses Tikhonov regularization and truncated singular value decomposition to alleviate the ill-conditioned matrix of the hidden layer of the ELM and uses the incremental method to match the optimal network nodes. Finally, double regularization incremental ELM (DRIELM) is proposed in this paper. Experiments show that DRIELM obtained the highest detection accuracy with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.932 at an FOD of 0.4.