Reviewerly/exHarmony huggingface dataset:
Assessment of barriers to the implementation of blockchain technology in construction supply chain management in Nigeria

Purpose The purpose of this study is to assess the barriers to the implementation of blockchain technology in construction supply chain management in Nigeria. Design/methodology/approach This study employed a quantitative research approach through a questionnaire survey that was conducted among professionals in the Nigerian construction industry using the snowball sampling method, which resulted in a selection of 155 respondents. The collected data were analysed using descriptive and exploratory factor analysis (EFA), while Cronbach's alpha was used to assess the reliability. Findings The analysis revealed that all barriers ranked above the average mean item score. It also revealed that all professionals have a convergent opinion on the barriers. EFA was used in clustering the identified barriers into two categories: technological and socio-political barrier. Research limitations/implications This research was carried out in the Southwestern region which is one of the six geo-political zones in Nigeria using a cross-sectional survey method. Practical implications The findings provide valuable insights into the barriers to the implementation of blockchain in supply chain management for professionals and practitioners in the Nigerian construction industry. Originality/value The research categorised the barriers into technological and social-political barrier and identified that lack of digitalisation is the major barrier to the implementation of blockchain technology in construction supply chain.
A systematic review of online personalized systems for the autonomous learning of people with cognitive disabilities

ABSTRACTA lack of access to learning opportunities is among the main reasons for social exclusion of people with cognitive disabilities in the professional field. It has been accentuated in the last few years by the rapid development of online learning strategies that limit the physical presence of students and instructors. Personalized systems can help overcome access barriers in learning for people with disabilities by helping them learn autonomously without the direct intervention of tutors. However, despite the wide range of research initiatives in this area to deal with individual differences, learners with cognitive disabilities still suffer from a lack of consideration of their conditions. Indeed, most e-learning platforms have been designed without taking into account users with multiple profiles and impairments. This paper systematically reviewed papers to provide insights about personalization within e-learning systems. We highlight personalization goals and approaches and review whether they improve learning accessibility and outcomes. The objective is to discuss the strengths and weaknesses of current research and suggest some opportunities to improve research in personalized e-learning systems for people with cognitive disabilities.KEYWORDS: Personalizatione-learning systemstechnology enhanced learningcognitive disabilities Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsNadia HocineNadia Hocine received her PhD in computer science from the University of Montpellier, France in 2014 and is now an associate professor at the University of Mostaganem, Algeria. Her research interests include adaptive and personalized systems, technology enhanced learning, and serious games for health and education.Karim SehabaKarim Sehaba is a professor of computer science at the University of Mostaganem, Algeria. His main research Interest lies in the field of adaptive systems, extractions of knowledge, and the analysis of behaviors.
Improving Sequence-to-sequence Tibetan Speech Synthesis with Prosodic Information

There are about 6,000 languages worldwide, most of which are low-resource languages. Although the current speech synthesis (or text-to-speech, TTS) for major languages (e.g., Mandarin, English, French) has achieved good results, the voice quality of TTS for low-resource languages (e.g., Tibetan) still needs to be further improved. Because prosody plays a significant role in natural speech, the article proposes two sequence-to-sequence (seq2seq) Tibetan TTS models with prosodic information fusion to improve the voice quality of synthesized Tibetan speech. We first constructed a large-scale Tibetan corpus for seq2seq TTS. Then we designed a prosody generator to extract prosodic information from the Tibetan sentences. Finally, we trained two seq2seq Tibetan TTS models by fusing prosodic information, including feature-level and model-level prosodic information fusion. The experimental results showed that the proposed two seq2seq Tibetan TTS models, which fuse prosodic information, could effectively improve the voice quality of synthesized speech. Furthermore, the model-level prosodic information fusion only needs 60% ~ 70% of the training data to synthesize a voice similar to the baseline seq2seq Tibetan TTS. Therefore, the proposed prosodic information fusion methods can improve the voice quality of synthesized speech for low-resource languages.
Collocated Clothing Synthesis with GANs Aided by Textual Information: A Multi-Modal Framework

Synthesizing realistic images of fashion items which are compatible with given clothing images, as well as conditioning on multiple modalities, brings novel and exciting applications together with enormous economic potential. In this work, we propose a multi-modal collocation framework based on generative adversarial network (GAN) for synthesizing compatible clothing images. Given an input clothing item that consists of an image and a text description, our model works on synthesizing a clothing image which is compatible with the input clothing, as well as being guided by a given text description from the target domain. Specifically, a generator aims to synthesize realistic and collocated clothing images relying on image- and text-based latent representations learned from the source domain. An auxiliary text representation from the target domain is added for supervising the generation results. In addition, a multi-discriminator framework is carried out to determine compatibility between the generated clothing images and the input clothing images, as well as visual-semantic matching between the generated clothing images and the targeted textual information. Extensive quantitative and qualitative results demonstrate that our model substantially outperforms state-of-the-art methods in terms of authenticity, diversity, and visual-semantic similarity between image and text.
Grouping and categorizing library services at the official websites of central libraries of RF constituent territories: The need for standardized diversification

In the context of evolving digital environment, the authors point out the urgency of developing the library services classification, friendly to the users whose needs are satisfied primarily via the library websites. The findings of the study conducted in 2021–2022 on positioning of the services of the central libraries of 77 subjects of the Russian Federation on their official websites are discussed. The evidence is provided that information about services is often scattered across different website headings and subheadings, and there are no hyperlinks between them. The biased nature of structuring classifications of services, that are not comparable one with another and often confusing, is also emphasized. The barriers that prevent the prompt search for information about library services on official websites are specified: i. e. the absence of “Services” heading linking users to the entire range of library services, scattered information about services under different headings with no hyperlinks, lacking rubricated lists of services and price lists, irrelevant prices, etc. The authors suggest that one of the most promising areas of theoretical research is the development of linguistic tools that enable structuring and ordering of information about services, in particular, building rubricators of charged and free services, designing “aspect grids” to allow formalization and uniformness of characteristics of the most important headings on the official websites; and building unified lists of normalized lexical units for wording of headings on the library websites. The need for unified approaches to presenting the results of the activities of the central libraries of the subjects of the Russian Federation in providing services to users (in particular, remote ones) on official websites is substantiated. The authors argue that it is the standardized diversification that will allow the services to be adequately positioned on the official websites of libraries. This will enable to provide full representation of multiaspect library activities, to increase library competitiveness and to ensure the growth in the number of physical visits and online users.
Real World Evidence: methodological issues and opportunities from the European Health Data Space

The current Evidence-Based-Medicine (EBM) approach is generally based on data coming from Randomized Clinical Trials or and, epidemiological observational studies. However, the past few years, with the explosion of available data derived from e-technology, a novel aspect regarding EBM arose, the Real-World-Data (RWD). RWD refers to data collected outside traditional studies, such as e-health records, claims data, patient-generated information, registries, etc. This type of information provides invaluable insights into the effectiveness, safety, and value of medical treatments and interventions when applied in real world settings. European Health Data Space (EHDS) is an initiative launched by the European Commission to create a secure and protected platform for exchanging health data across borders within European Union. The powerful combination of RWD within the EHDS serves as a valuable resource, supporting research initiatives. By analyzing diverse RWD sources, researchers generate Real-World Evidence (RWE) broadening medical knowledge. In this comment paper, methodological issues and opportunities of the application of EHDS in member states are discussed. Undoubtedly, EHDS creates a health-specific ecosystem empowering individuals through increased digital access and control their health data, providing a consistent, truthful and proficient set-up for the use of health data for research, innovation, policy-making and regulatory activities.
Topologically protected Grover's oracle for the partition problem

The number partitioning problem (NPP) is one of the NP-complete (nondeterministic polynomial-time complete) computational problems. Its definite exact solution generally requires a check of all $N$ solution candidates, which is exponentially large. Here we describe a path to the fast solution of this problem in $\sqrt{N}$ quasi-adiabatic quantum annealing steps. We argue that the errors due to the finite duration of the quantum annealing can be suppressed if the annealing time scales with $N$ only logarithmically. Moreover, our adiabatic oracle is topologically protected, in the sense that it is robust against small uncertainty and slow time dependence of the physical parameters or the choice of the annealing protocol. We also argue that our approach can solve many other famous NP-complete computational problems in $\sqrt{N}$ steps.
MHlinker: Research on a Joint Extraction Method of Fault Entity Relationship for Mine Hoist

Triplet extraction is the key technology to automatically construct knowledge graphs. Extracting the triplet of mechanical equipment fault relationships is of great significance in constructing the fault diagnosis of a mine hoist. The pipeline triple extraction method will bring problems such as error accumulation and information redundancy. The existing joint learning methods cannot be applied to fault texts with more overlapping relationships, ignoring the particularity of professional knowledge in the field of complex mechanical equipment faults. Therefore, based on the Chinese pre-trained language model BERT Whole Word Masking (BERT-wwm), this paper proposes a joint entity and relation extraction model MHlinker (Mine Hoist linker, MHlinker) for the mine hoist fault field. This method uses BERT-wwm as the underlying encoder. In the entity recognition stage, the classification matrix is constructed using the multi-head extraction paradigm, which effectively solves the problem of entity nesting. The results show that this method enhances the model’s ability to extract fault relationships as a whole. When the small-scale manually labeled mine hoist fault text data set is tested, the extraction effect of entities and relationships is significantly improved compared with several baseline models.
Dynamic 3D Measurement without Motion Artifacts Based on Feature Compensation

Phase-shift profilometry (PSP) holds great promise for high-precision 3D shape measurements. However, in the case of measuring moving objects, as PSP requires multiple images to calculate the phase, the movement of the object causes artifacts in the measurement, which in turn has a significant impact on the accuracy of the 3D surface measurement. Therefore, we propose a method to reduce motion artifacts using feature information in the image and simulate it using the six-step term shift method as a case study. The simulation results show that the phase of the object is greatly affected when the object is in motion and that the phase shift due to motion can be effectively reduced using this method. Finally, artifact optimization was carried out by way of specific copper tube vibration experiments at a measurement frequency of 320 Hz. The experimental results prove that the method is well implemented.
Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing (NLP) that aims to extract triplets from comments. Each triplet comprises an aspect term, an opinion term, and the sentiment polarity of the aspect term. The neural network model developed for this task can enable robots to effectively identify and extract the most meaningful and relevant information from comment sentences, ultimately leading to better products and services for consumers. Most existing end-to-end models focus solely on learning the interactions between the three elements in a triplet and contextual words, ignoring the rich affective knowledge information contained in each word and paying insufficient attention to the relationships between multiple triplets in the same sentence. To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and Position Information (DGCNAP). This model jointly considers both the contextual features and the affective knowledge information by introducing the affective knowledge from SenticNet into the dependency graph construction of two parallel channels. In addition, a novel multi-target position-aware function is added to the graph convolutional network (GCN) to reduce the impact of noise information and capture the relationships between potential triplets in the same sentence by assigning greater positional weights to words that are in proximity to aspect or opinion terms. The experiment results on the ASTE-Data-V2 datasets demonstrate that our model outperforms other state-of-the-art models significantly, where the F1 scores on 14res, 14lap, 15res, and 16res are 70.72, 57.57, 61.19, and 69.58.
A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition

The effective and accurate recognition of communication jamming is crucial for enhancing the anti-jamming capability of wireless communication systems. At present, a significant portion of jamming data is decentralized, stored in local nodes, and cannot be uploaded directly for network training due to its sensitive nature. To address this challenge, we introduce a novel distributed jamming recognition method. This method leverages a distributed recognition framework to achieve global optimization through federated learning. Each node independently trains its local model and contributes to the comprehensive global model. We have devised an adaptive adjustment mechanism for the mixed weight parameters of both local and global models, ensuring an automatic balance between the global model and the aggregated insights from local data across devices. Simulations indicate that our personalization strategy yields a 30% boost in accuracy, and the adaptive weight parameters further enhance the recognition accuracy by 1.1%.
Privacy-Preserving Blockchain Technologies

The main characteristics of blockchains, such as security and traceability, have enabled their use in many distinct scenarios, such as the rise of new cryptocurrencies and decentralized applications (dApps). However, part of the information exchanged in the typical blockchain is public, which can lead to privacy issues. To avoid or mitigate these issues, some blockchains are applying mechanisms to deal with data privacy. Trusted execution environments, the basis of confidential computing, and secure multi-party computation are two technologies that can be applied in that sense. In this paper, we analyze seven blockchain technologies that apply mechanisms to improve data privacy. We define seven technical questions related to common requirements for decentralized applications and, to answer each question, we review the available documentation and gather information from chat channels. We briefly present each blockchain technology and the answers to each technical question. Finally, we present a table summarizing the information and showing which technologies are more prominent.
Enhancing Cyber Security Governance and Policy for SMEs in Industry 5.0: A Comparative Study between Saudi Arabia and the United Kingdom

The emergence of Industry 5.0 has revolutionized technology by integrating physical systems with digital networks. These advancements have also led to an increase in cyber threats, posing significant risks, particularly for small and medium-sized enterprises (SMEs). This research investigates the resistance of SMEs in Saudi Arabia and the United Kingdom (UK) to cyber security measures within the context of Industry 5.0, with a specific focus on governance and policy. It explores the cultural and economic factors contributing to this resistance, such as limited awareness of cyber security risks, financial constraints, and competing business priorities. Additionally, the study examines the role of government policies and regulations in promoting cyber security practices among SMEs and compares the approaches adopted by Saudi Arabia and the UK. By employing a mixed methods analysis, including interviews with SME owners and experts, the research highlights challenges and opportunities for improving cyber security governance and policy in both countries. The findings emphasize the need for tailored solutions due to the differing cultural and economic contexts between Saudi Arabia and the UK. Specifically, the study delves into the awareness and implementation of cyber security measures, focusing on SMEs in Saudi Arabia and their adherence to the Essential Cyber Security Controls (ECC-1:2018) guidelines. Furthermore, it examines the existing cyber security awareness practices and compliance in the UK, while also comparing official guidance documents aimed at supporting SMEs in achieving better cyber security practices. Based on the analysis, greater engagement with these documents is recommended in both countries to foster awareness, confidence, and compliance among SMEs, ultimately enhancing their cyber security posture. This paper offers a comparative research study on governance and policy between Saudi Arabia and the UK, presenting a set of recommendations to strengthen cyber security awareness and education, fortify regulatory frameworks, and foster public–private partnerships to combat cyber security threats in the Industry 5.0 landscape.
A Comparative Analysis of Measured and Simulated Data of PV Rooftop Installations Located in Poland

In recent years, photovoltaics (PVs) is the main driver of the renewable energy market growth in Poland. The number of photovoltaic installations, most of which are rooftop prosumer systems, is consistently growing. Therefore, the determination of the applicability and feasibility of photovoltaic systems under different climate conditions is of great significance. This study presents the performance analysis of four prosumer photovoltaic installations situated in the Eastern part of Poland, Lublin Voivodeship. The influence of various tilt angles, ranging from 19° to 40°, and azimuths (south, east, south–east, and east–west) on the final yield have been determined under one year of operation (2022). The average yearly final yield was found to be 1022 kWh·kW−1, with the highest value obtained for the installation oriented towards the south, equal 1079 kWh·kW−1. Then, the PV systems were simulated by the use of four specialized photovoltaic software: DDS-Cad 16, PVGIS 5.2, PVSOL premium 2022, and the PVWatts Calculator 8.2.1. A comparative analysis of the measured and simulated data in terms of the final yield was carried out. The data obtained from PVGIS and PVSOL demonstrated the highest degree of overall alignment of 92% and 91%, respectively. The most significant underestimation was noticed for the DDS-Cad software, which was equal to 77%. The most accurate predictions stand out for the system oriented to the south, while the weakest was found for the E–W installation.
Renewable energy forecasting: A self-supervised learning-based transformer variant

Reliable and accurate renewable energy forecasting (REF) has substantial impact on society by helping with daily planning and mitigating the instability of power system. Providing a state-of-the-art (SOTA) solution for REF can power it to take on more sophisticated tasks and solve frontier problems to play a greater role in modern society. Aiming this goal, this work proposes a novel Transformer-based model, named Graph Patch Informer (GPI), for REF. Compared with existing REF models and mainstream Transformer, our model has three main characteristics: (1) Segment-wise self-attention is designed, which benefits Transformer by preserving the temporal information hidden in the continuous signals, (2) Graph Attention Networks with adaptive adjacent matrix is proposed to capture the inter-temporal dependencies automatically, (3) A new training strategy, that is, self-supervised pre-training followed by fine-tuning, is introduced to enhance the representation learning. To validate the performance of GPI, five experiments are conducted on four datasets covering solar radiation (SR), photovoltaic power (PVP), wind speed (WS) and wind power (WP). Experiments show that GPI goes beyond SOTA Autoformer by 23.67%–40.75% on MSE. It demonstrates that GPI can provide an effective solution for REF and have SOTA performance on SR, PVP, WS, WP forecasting tasks. Experiments also show that GPI can mitigate the adverse influences caused by missing values to a certain degree.