Reviewerly/exHarmony huggingface dataset:
Information Theory of Blockchain Systems

In this paper, we apply the information theory to provide an approximate expression of the steady-state probability distribution for blockchain systems. We achieve this goal by maximizing an entropy function subject to specific constraints. These constraints are based on some prior information, including the average numbers of transactions in the block and the transaction pool, respectively. Furthermore, we use some numerical experiments to analyze how the key factors in this approximate expression depend on the crucial parameters of the blockchain system. As a result, this approximate expression has important theoretical significance in promoting practical applications of blockchain technology. At the same time, not only do the method and results given in this paper provide a new line in the study of blockchain queueing systems, but they also provide the theoretical basis and technical support for how to apply the information theory to the investigation of blockchain queueing networks and stochastic models more broadly.
On Problems Related to Absent Subsequences

The paper introduces the absent subsequence automaton as a compact representation of shortest absent subsequences and minimal absent subsequences and describes its application to various related problems. It also reveals interesting combinatorial properties of minimal absent subsequences and derives an algorithm for computing the number of minimal absent subsequences.
Machine Learning with Low-Resource Data from Psychiatric Clinics

Amidst the rapid growth of big data, the success of machine learning is critically tethered to the availability and quality of training data. A pertinent challenge faced by this symbiotic relationship is the issue of “low-resource data,” characterized by insufficient data volume, diversity, and representativeness, and exacerbated by class imbalances within datasets. This study delves into the intersection of machine learning and big data, exploring innovative methodologies to counteract the challenges of data scarcity. Focusing on psychiatric clinic data, marked by subjectivity and inconsistency, we outline the unique challenges posed by the nature of data in this domain. To address these challenges, we explore the potential of data augmentation-using transformations or operations on available data-and transfer learning, where knowledge from a pre-trained model on a large dataset is transferred to a smaller one. Through a comprehensive exploration of these methodologies, this research aims to bolster the effectiveness of machine learning in low-resource environments, with a vision of advancing the digital landscape while navigating inherent data constraints.
Single Image Dehazing Based on Dynamic Convolution and Transformer

In this paper, an end-to-end multi-stage dehazing network based on convolution and Transformer is proposed. The network design is divided into three parts: encoding network, feature fusion network and decoding network. The encoding network extracts primary features of the haze image, the feature fusion network uses the serial Transformer module and the dynamic convolution module to make the information extracted from the features richer, and the decoding network is used to recover the image resolution. In the resolution restoration stage of the decoding module, the MixUp module is used to restore the high resolution image by combining the extracted primary features to reduce the loss of information. Extensive experiments were conducted on synthetic and real datasets to validate the role of Transformer module and Dynamic Convolution module in dehazing respectively. The results show that the proposed method achieves a good objective evaluation score and reconstructs a subjectively better dehazing image with a PSNR of 35.37 and a SSIM of 0.9849 on the SOTS test dataset.
An Efficient Local Search Algorithm for Correlation Clustering on Large Graphs

Correlation clustering (CC) is a widely-used clustering paradigm, with many applications to problems such as classification, database deduplication, and community detection. CC instances represent objects as graph nodes, and clustering is performed based on relationships between objects (positive or negative edges between pairs of nodes). The CC objective is to obtain a graph clustering that minimizes the number of incorrectly assigned edges (negative edges within clusters, and positive edges between clusters). For large CC instances, lightweight algorithms like the Pivot method have been preferred due to their scalability. Because these algorithms do not have state-of-the-art approximation guarantees, LocalSearch (LS) methods have often then been applied to refine their clustering results. Unfortunately, LS does not enjoy the same ability to scale since it is inherently sequential and has the potential to converge slowly. We propose a lightweight, parallelizable LS method called InnerLocalSearch (ILS) to use in conjunction with the Pivot algorithm. We show that ILS still provides a significant improvement to clustering quality while dramatically reducing the additional running time costs incurred by LS. We demonstrate our algorithm’s effectiveness against several LS benchmarks and other popular CC methods on real and synthetic data sets.
Practical Attribute-Based Multi-keyword Search Scheme with Sensitive Information Hiding for Cloud Storage Systems

Attribute-based multi-keyword search (ABMKS) facilitates searching with fine-grained access control over outsourced ciphertexts. However, two critical issues impede wide application of ABMKS. Firstly, the majority of ABMKS schemes have suffered huge computation and communication costs in the process of ciphertexts matching and transmission. Secondly, the contents of data file containing sensitive information are encrypted as a whole, and data users with varying roles should have different access rights to the ciphertext returned by cloud, thereby preventing sensitive information in data files from being leaked to semi-trusted data users. In this paper, we tackle the issue of content access rights by introducing sensitive information hiding, a novel concept in the field of attribute-based keyword search. Specifically, we propose a practical multi-keyword search scheme with sensitive information hiding by integrating a modified blindness filtering technique into ciphertext policy attribute-based encryption under the multi-keyword search model. To minimize communication costs in the ciphertext transmission process, we utilize a super-increasing sequence to aggregate multiple blinding data blocks into a single ciphertext. The ciphertext can be recovered by using a recursive algorithm. Security analysis proves that our scheme is provably secure within the random oracle model, it guarantees keyword secrecy and selective security against chosen-keyword attacks. Performance evaluations demonstrate that our scheme surpasses state-of-the-art ABMKS schemes, making it highly suitable for cloud storage systems.
Zero-Visibility Cops and Robber Game on Cage Graph

We consider zero-visibility cops and robber game that the cops lack of information on the location of the robber at all times, which is a variant of the classical cops and robbers game. First of all, we use the idea of splitting to study properties of cage graphs. Then we apply properties of cage graphs to investigate the lower bounds of cop number and the monotonic zero-visibility cop number of cage graphs. We also propose a searching algorithm to calculate the monotonic zero-visibility cop number of cage graphs.
Comparison of physics-based deformable registration methods for image-guided neurosurgery

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration.
Analysis of Foreign Language Teachers´ Attitudes Towards Digital Teaching in the European Union Countries

Abstract In the present era, both learning and teaching, including foreign language learning (FLL) and teaching, are being radically influenced by a massive implementation of digital technologies. The purpose of this study is to analyze foreign language teachers’ attitudes towards the use of technologies in foreign language teaching across Europe and to identify clear implications for their efficient implementation. The methodology of this study includes a mixed-method research design (quantitative and qualitative) with a survey conducted with altogether 234 foreign language (FL) teachers from different universities across Europe. The results clearly show that FL teachers generally have a positive attitude towards the use of technologies in their classes and that they frequently use them in their teaching since they can make students more engaged in learning a foreign language. In fact, the FL teachers have always been at the forefront of innovative approaches to teaching and learning. In spite of this, they admit that they would welcome more professional support from the management of their home institutions and demand training in the use of new technologies. The results of the research also indicate that special attention should be paid to the lack of personal contact, students' reactions, and the expression of emotions, which is difficult and almost impossible to achieve through the use of digital media. These findings are unique since they aim specifically at current FL teachers´ attitudes and needs across Europe and can be utilized by stakeholders and FLL course designers. Moreover, they can also be extended to a larger scale, i.e., the global level.
Do cybercrime laws address emergent IoT security threats? The case of UAE federal cybercrime law regarding RFID technology

Abstract With the widespread use of radio-frequency identification (RFID) technology as a means for commerce and identification, new types of cybercrime have emerged. Accordingly, revisions to extant cybercrime law are required. This research attempts to determine the relevance of current UAE laws in addressing new cybercrimes on emergent technologies. We reviewed the literature to identify threats to RFID systems, classifying works into three categories: blocking, interception, and physical attacks. We mapped these categories to 50 articles of the UAE cybercrime law to assess their relevance. According to the distribution of law articles pertaining to RFID attacks, it was found that RFID communication interception attacks are not sufficiently addressed in the law and that further amendments are required in the UAE. Example of international statutes are highlighted as models.
Host Security Technology

Digging into a more concrete topic, this chapter covers both attacks and defenses on host computer systems. The first half of the chapter focuses on building a robust system using isolation and access control through operating system design, hardware enforcement and software enforcement. The second half of the chapter digs into attacks on these systems through exploitation of design flaws and/or malicious software.
Core Cryptography Technology

This chapter provides an overview of modern symmetric encryption and asymmetric encryption.
Cryptographic Systems Technologies

Using the building blocks from the previous chapter, this chapter puts them together in two example systems. One of these, Transport Layer Security (TLS), is used to secure Internet communications. One key consideration for the design of a cryptographic system is protection against a man-in-the-middle, a model wherein the attacker can intercept, and potentially modify or generate, messages.
Analysis of the Practical Path of Vocational Education Serving Rural Revitalization by Digital Means and Philosophy of Technology

Abstract In this paper, based on digital means and technical philosophy, the histogram algorithm in the Light GBM model is used to calculate the floating point values of the raw data for the analysis of vocational education service for rural revitalization so that each of its features is converted into a histogram. In order to prevent the feature training overfitting problem of the Light GBM model, the LightGBM model is optimized by the iterative tree MPA algorithm, and the prediction model of returning to poverty risk based on IMPA-LightGBM is constructed. Starting from the current situation of vocational education service for rural revitalization, we put forward research hypotheses to realize the research design of vocational education accurate poverty alleviation service for rural revitalization and carry out an example analysis of vocational education service for rural revitalization combined with digital technology. The results show that in terms of model performance, the WAPE values of the return-to-poor risk values obtained from the prediction of the IMPA-LightGBM model are all lower than 5.5%, so the prediction effect is relatively satisfactory, and the return-to-poor risk values of poverty-eradicating households can be effectively predicted. On the practice road analysis, the standard deviation (SD) of the rural revitalization development index in China as a whole decreased from 0.63 to 0.52, which means that the differences in rural revitalization among provinces are decreasing. This study explores the synergistic development of the community of interest between vocational education and rural revitalization through the cultivation of new vocational farmers.
Authentication Technology

In this chapter, you will learn about how parties (typically human parties) can be identified to a computer system. This process is called authentication. For identifying a human party, there are three common approaches: something you know (like a password), something you have (like your phone), or something you are (like a fingerprint). Although it is popular to talk about passwords as “weak” and other mechanisms like biometrics as “strong,” every approach has pros and cons, strengths and weaknesses.
Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises

Abstract This paper constructs a model of the particle swarm algorithm, compares and analyzes the performance of the particle swarm algorithm under the two parameters of w and k in detail, and solves the constrained optimization problem by the particle swarm algorithm. On the basis of the local optimal value to find the global optimal value, the particle swarm algorithm is improved with reference to the particle’s motion state and behavior. Based on the particle swarm algorithm, a digital enterprise management system is constructed to plan enterprise management operations and optimize efficiency. Finally, we compare the performance of different algorithms in enterprise management risk prediction, analyze the correlation between the management system and enterprise management efficiency, and compare the management efficiency of different enterprises to explore the effect of the particle swarm algorithm in digital enterprise management. The results show that the predictive classification effect of the particle swarm algorithm model reaches more than 95% correct rate, and the management system of the particle swarm algorithm presents significance at 1% and 5% significance level for enterprise management efficiency, respectively.
The semantic map of when and its typological parallels

In this paper, we explore the semantic map of the English temporal connective when and its parallels in more than 1,000 languages drawn from a parallel corpus of New Testament translations. We show that there is robust evidence for a cross-linguistic distinction between universal and existential WHEN . We also see tentative evidence that innovation in this area involves recruiting new items for universal WHEN which gradually can take over the existential usage. Another possible distinction that we see is between serialized events, which tend to be expressed with non-lexified constructions and framing/backgrounding constructions, which favor an explicit subordinator.
THE IMPACT OF GOVERNANCE STRUCTURES ON ECONOMIC GROWTH IN AFRICA: A PANEL DATA ANALYSIS OF 47 AFRICAN COUNTRIES

Major international institutions like the World Bank, African Union, and International Monetary Fund have made the development of robust governance systems a “sacred utterance” since the 1990s. Our study aimed to refute this common thinking by examining how governance structures have affected the expansion of the financial market in 47 African countries from 2008 to 2019. Using the availability of venture capital as a proxy for financial market development, our article departed from existing literature which used stock exchange characteristics as benchmarks for growth in the financial market. The governance variables comprise the six global governance indices: control of corruption, government effectiveness, political stability, regulatory quality, the rule of law, and voice and accountability. The model’s control variables include GDP per capita, inflation, and trade openness. Our finding suggests that a great quality governance climate is significant in explaining the growth of the financial market in Africa using the generalized method of moments (GMM) methodological approach with corrected standard errors. Considering these findings, our research makes the case that solid institutional frameworks might encourage the degree of financial systems growth in Africa. Therefore, the financial development rate in the African region will be significantly influenced by improving the quality of governance through strengthening legal and institutional frameworks to facilitate financial inclusion.
Latest Advancements in Perception Algorithms for ADAS and AV Systems Using Infrared Images and Deep Learning

Perception system plays an important role in Advanced driver assistance systems (ADAS) &amp; Autonomous vehicles (AV) to understand the surrounding environment and further navigation. It is highly challenging to achieve the accurate perception of ego vehicle mimicking human vision. The available ADAS and AV solutions could able to perceive the environment to some extent using multiple sensors like Lidars, Radars and Cameras. National Highway Traffic Safety Administration Crash reports of ADAS and AV systems shows that the complete autonomy is challenging to achieve using the existing sensor suite. Particularly, in extreme weather, low light and night scenarios, there is a need for additional perception sensors. Infrared camera seems to be one of the potential sensors to address such extreme and corner cases. This chapter aimed to discuss the advantage of adding infrared sensors to perceive the environment accurately. The advancements in deep learning approaches further leverages to enhance ADAS features. Also, the limitations of current sensors, the need for infrared sensors and technology, artificial intelligence and current research focus using IR images are discussed in detail. Literature shows that by adding IR sensor to existing sensor suite may lead a way to achieve level 3 and above autonomous driving precisely.
Chronicling Information Communication Technology into Teaching and Learning in the Eastern Cape Province, South Africa

This study aimed to investigate the integration of Information Communication Technology (ICT) during the teaching and learning encounter in the Eastern Cape province of South Africa. The study employed a qualitative approach guided by a case study design. The population of the study comprised teachers of one secondary school and its feeder schools in the OR Tambo inland district. The sample size for this study was three principals and nine computer teachers of the senior secondary school and its two feeder schools. An open-ended interview schedule was used for the face-to-face, in-depth interviews on the extent of the implementation of computer integration into teaching and learning in the selected schools in the OR Tambo inland district. Schools must enable a culture of computer-integrated teaching and learning to meet diverse needs. ICT integration training, competence and appropriate software and materials need to be attended to in a considerable way.
The Role of Architects in Search and Rescue Technologies: A Comparative Analysis of Global Examples and Türkiye

Search and rescue (SAR) operations require the integration of various technologies and expertise, to effectively respond to emergencies. In this study it was investigated how architects play an essential part in SAR technologies both globally and in Türkiye contexts. Architects play an invaluable role in designing SAR facilities, by optimizing spatial layouts, assuring structural integrity, integrating advanced technologies and encouraging sustainability. By conducting an in-depth comparison between international and Turkish SAR examples, this study highlights key similarities, distinctions and potential areas for improvement. These results emphasize the value of architectural expertise when conducting SAR operations; further providing insight into how Türkiye could enhance its approach to increase SAR capabilities.
Dongpo dream machine: notes towards an exhibition

This article presents a concept for an imagined exhibition relating to the work of Su Shi (1037–1101), also known as Su Dongpo. A renowned poet and calligrapher from China's Song Dynasty, Su Shi stands out as one of the pivotal figures in traditional Chinese literature; credited with penning many of China's iconic poems that remain part of the educational curriculum in China today. The proposed project is situated within the historical sites of Hainan, an island where Su Shi was exiled near the end of his life. Taking a contemporary approach to heritage, the exhibition concept draws upon recent developments in artificial intelligence large language models, with the aim of enabling visitors to generate and explore ‘lost’ poems of Su Shi.