Reviewerly/exHarmony huggingface dataset:
A new human-based metaheuristic algorithm for solving optimization problems based on preschool education

In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA's performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration-exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications.
Engineering Responsible And Explainable Models In Human-Agent Collectives

In human-agent collectives, humans and agents need to work collaboratively and agree on collective decisions. However, ensuring that agents responsibly make decisions is a complex task, especially when encountering dilemmas where the choices available to agents are not unambiguously preferred over another. Therefore, methodologies that allow the certification of such systems are urgently needed. In this paper, we propose a novel engineering methodology based on formal model checking as a step toward providing evidence for the certification of responsible and explainable decision making within human-agent collectives. Our approach, which is based on the MCMAS model checker, verifies the decision-making behavior against the logical formulae specified to guarantee safety and controllability, and address ethical concerns. We propose the use of counterexample traces and simulation results to provide a judgment and an explanation to the AI engineer as to the reasons actions may be refused or allowed. To demonstrate the practical feasibility of our approach, we evaluate it using the real-world problem of human-UAV (unmanned aerial vehicle) teaming in dynamic and uncertain environments.
Sensing Playgrounding: Playful Design Workshops to Reimagine the City as Playground

Cities are complex spaces of contesting infrastructures, media, humans and more-than-human interactions. They are places that house multiple visible and invisible cartographies that draw on multisensorial ways of being in the world. In the city, our senses are amplified and intensified in ways that reorient our processes of worlding. Cities have, more recently, been defined and approached as spaces for play. In this article, we explore how understanding the city as a playground—or through the activity of playgrounding—can invite new ways of designing urban games and playful interventions that acknowledge the uneven and multisensorial ways we inhabit the world. We reflect upon dominant, innovative, and interventionist ideas of playgrounds and notions such as the playful or playable city. We then reflect on findings from a placemaking game workshop designed around the Spanish Superillas (superblocks) in Barcelona. Superillas are playgrounds designed into street intersections that have transformed specific sections of the city in a playground. What does it mean to think about the city as a playground? Can this speculation/ provocation help us rethink the city in more inclusive and sensorial ways?
Probability distribution of dependency distance and dependency type in translational language

Abstract As a “third code”, translational language attracts considerable attention in linguistics research due to its distinctive features. Adopting the quantitative linguistic approach, the current study examines its features by investigating the mean dependency distance (MDD), as well as the probability distribution of the individual dependency distances (DDs) and distribution of a high-frequency dependency type in translational language. The MDD and the distributions were tested in a self-built corpus which contains parallel and comparable language materials in both Chinese-English and English-Chinese translations. The results show that: (1) compared with source texts and native texts, translated texts in both translation directions yield an MDD in between; (2) both the distribution of DDs and that of the dependency type nsubj follow the Zipf-Alekseev distribution in translated texts, as in source texts and native texts; (3) the in-between feature is further confirmed by parameters a and b in Chinese-English translation materials when fitting the distribution of DDs to Zipf-Alekseev distribution; (4) translational texts in both directions show higher a and lower b than their source and native texts when fitting the DD Distribution of dependency type nsubj to Zipf-Alekseev distribution. These findings suggest that, on the one hand, dependency distance minimization (DDM) occurs in translational language, which is consistent with native language and reflects a general tendency of natural languages to reduce cognitive load; on the other hand, translational language presents distinctive feature in nsubj type, but in most cases, it is subject to the gravitational pull of both source and target language systems, exhibiting a “compromise” feature in between. The current study highlights the contribution of syntactic quantitative methods to deeper understanding of the complexity of translational language and its cognitive underpinnings.
Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization

The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine.It determines the class membership of unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample.The choice of K has been the topic of various studies and proposed KNN-variants.Yet no variant has been proven to outperform all other variants.In this paper a new KNN-variant is proposed which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way.The proposed algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints.It achieves a higher classification accuracy than the KNN in the tests, while requiring having the same computational demand.
High-Throughput Key Switching Accelerator for Homomorphic Encryption

We propose an ASIC architecture for key switching in RNS-CKKS Homomorphic Encryption scheme. With the pipeline design, the frequent RAM access is avoided and high throughput is achieved. This key switching design is a general architecture for several operations in RNS-CKKS, such as reliearization, rotation and conjugation. Furthermore, we show the flexibility of the design in changing the degree of parallelism. Increasing parallelism leads to an almost quadratic growth in throughput, which further accelerates the calculation. Compared to previous design, the throughput is increased to 1.7 times for key switching operation.
Legal Tech: Unravelling the nature and purpose of modern law in the digital era

The purpose of the study is to investigate how computer technology is used in legal practice. The primary techniques employed in this article are systematization, which made it possible to place the results in a logical order for understanding and reproduction of the researched, as well as analysis and synthesis for a literary review of thematic literature and the determination of the main scientific trends reflected in it. The study’s findings demonstrate how computer technology has improved the convenience, speed, visualization, and predictability of legal practice. The research’s practical significance lies in the fact that computer technology, specialized software, and mathematical techniques must be incorporated into law enforcement and law-making processes in order to collect, store, and process legal information in a way that best enables the provision of various legal services. In conclusion, digital computer technologies are developing in the legal field in a number of areas, including the automation of standard legal services, the use of online legal services, the digitalization of public services and their online provision, the shift to an e-justice system, modeling of legal solutions based on artificial intelligence, and more. The automation of many social processes is gaining momentum, including in the legal profession.
Cryptocurrency Adoption and Its Implications: A Literature Review

Cryptocurrencies have emerged as a disruptive technology with the potential to revolutionize the financial landscape. This research paper aims to explore the topic of cryptocurrency adoption and its implications from multiple perspectives. This research paper is based on secondary data collected by researchers from various sources and papers reviewed by the researcher. It was clear from the secondary data that the cryptocurrency landscape continues to mature and overcome the challenges it faces, and the potential for Bitcoin and other digital assets to transform the global financial system and revolutionize the way we conduct transactions, store value, and access financial services seems increasingly likely. A cryptocurrency-based ecosystem can provide opportunities for new market entrants and support startups by facilitating the process of fundraising. For instance, initial coin offerings enable entrepreneurs and investors to finance new projects without the recourse to intermediaries and endorsements of traditional investors and financial bodies. Cryptocurrency is a constant evolution driven by a diverse and passionate community of developers, entrepreneurs, investors, and users. The future of money is being reimagined, and cryptocurrencies such as Bitcoin are at the forefront of this exciting transformation.
Lightweight and Secure Multi-Message Multi-Receiver Certificateless Signcryption Scheme for the Internet of Vehicles

The Internet of Vehicles (IoV) improves traffic efficiency and enhances driving safety through the real-time collection and analysis of traffic-related data. Numerous secure and privacy-preserving communication protocols have been proposed for the IoV. However, various security threats, privacy leakage, and inefficient communications remain unaddressed. Therefore, a lightweight and secure multi-message multi-receiver certificateless signcryption (LS-MRCLSC) scheme based on elliptic curve cryptography (ECC) is proposed. The proposed scheme guarantees secure communication and promotes messaging efficiency with multi-cast mode. Multiple key generation centers (KGCs) collaborate to generate and update the system master key (SMK) using Feldman’s verifiable secret-sharing (FVSS) algorithm, avoiding the single point of failure (SPoF) problem. Formal security proofs under the random oracle model (ROM) demonstrate that the proposed scheme meets requirements such as data confidentiality, message unforgeability, anonymity, and unlinkability. Performance evaluations confirm that the LS-MRCLSC scheme is better than similar schemes in terms of efficiency, feasibility, and scalability.
ByteRCNN: Enhancing File Fragment Type Identification with Recurrent and Convolutional Neural Networks

File fragment type identification is an important step in file carving and data recovery. Machine learning techniques, especially neural networks, have been utilized for this problem, some with very promising results. This paper presents a novel neural network architecture for identifying file fragment types using a combination of byte embeddings as well as recurrent and convolutional elements. The corresponding classification model, ByteRCNN, has been trained on the publicly available file fragment FiFTy dataset and evaluated in closed-set and open-set recognition settings using FiFTy and other available file fragment datasets. Evaluation results have demonstrated that ByteRCNN can compete with state-of-the-art models described in literature in terms of classification accuracy, with 71.1% average accuracy on 512-byte fragments and 83.9% average accuracy on 4,096-byte fragments from the FiFTy dataset. When evaluated on other publicly available datasets in closed-set and open-set recognition settings, ByteRCNN similarly or slightly better than the FiFTy classification model. Obtained results overall suggest that ByteRCNN is a competitive file fragment classification model, but they also reveal that there is still plenty of space for further improving file type identification methods using more complex datasets or in open-set recognition settings. ByteRCNN is publicly available at [GitHub, after publication].
Improving the Process of Training Staff in Software Companies Through Specialized Software

Modern human resource management practice advocates personnel training processes. Traditional ways of training can be replaced by specialized software, through which each employee can follow an individual line of training. The development and implementation of this type of software is essential in the process of innovation of training programs and increasing competitiveness in the software business. Theoretical aspects of the need to develop a human resources training system are presented, and the advantages and necessity for its development are analyzed. The ways of functioning and development of the training system are described, based on the results of a study, and a test application in a real working environment was also made.
Multivalued Network Logic with One Real and Two Imaginary Logic Structures

This paper explores and proposes a multivalued network logic in which one real and two imaginary propositional logics are created. Each of the six states of this logic has a different degree of truth. Their arrangement in a chain in descending order is carried out by the relation. In this study it is shown that developed as such the multivalued network logic satisfies the axioms of Boolean algebra and is a propositional logic. It is also proven that network configuration is a general structure in which sequential and hierarchical configuration can be implemented in it. Two Kelly tables for truthfulness are proposed for disjunction, conjunction, and negation. Possibilities for further research on network multivalued logic and its applications are presented.
Towards Blockchain Wallets Classification and Implementation

The blockchain technology continues to evolve, and new use cases and applications are emerging. A key component of the blockchain ecosystem is the blockchain wallet. This paper presents the key components in the concept behind blockchain wallets, analyzes and systematizes their classification, and proposes an extended working definition. The proposed classification can be viewed as a classical multi-criteria problem for framework design selection of various custom wallet solutions. As a practical solution, the implementation of a desktop version of a non-custodial, software hot wallet, which supports tokens/NFT (ERC-20, ERC-721) with biometric authentication. The wallet is designed for the Antelope/IPFS based platform for data exchange in smart crop production. The technology framework requirements are presented, along with two groups of functionalities - account management and working with tokens, and three main applications for token transfer and user file exchange authentication. The wallet is an extension of the infrastructure and functionalities of the platform prototype. The obtained results provide opportunities for future development in direction of deployment a web-based application and wallet, enabling the client-side of the platform to be implemented as a dApp.
Improving nonalcoholic fatty liver disease classification performance with latent diffusion models

Integrating deep learning with clinical expertise holds great potential for addressing healthcare challenges and empowering medical professionals with improved diagnostic tools. However, the need for annotated medical images is often an obstacle to leveraging the full power of machine learning models. Our research demonstrates that by combining synthetic images, generated using diffusion models, with real images, we can enhance nonalcoholic fatty liver disease (NAFLD) classification performance even in low-data regime settings. We evaluate the quality of the synthetic images by comparing two metrics: Inception Score (IS) and Fréchet Inception Distance (FID), computed on diffusion- and generative adversarial network (GAN)-generated images. Our results show superior performance for the diffusion-generated images, with a maximum IS score of 1.90 compared to 1.67 for GANs, and a minimum FID score of 69.45 compared to 100.05 for GANs. Utilizing a partially frozen CNN backbone (EfficientNet v1), our synthetic augmentation method achieves a maximum image-level ROC AUC of 0.904 on a NAFLD prediction task.
Low area and high throughput hardware implementations for the LILLIPUT cipher

Summary The widespread use of Internet of Things devices has increased the demand for lower cost and more efficient lightweight ciphers. However, there is a difficult trade‐off between cost and efficiency for lightweight block ciphers. The optimizations of area and throughput are important for some constrained environments. This paper proposes two novel hardware architectures for the LILLIPUT cipher. In the novel low area structure, a new permutation layer is provided for LILLIPUT. The relationship between encryption algorithm and key scheduling algorithm is utilized to achieve optimal sharing among components, which significantly reduces hardware area. The experimental results show that the number of XOR gates and S‐boxes required for low area optimization is reduced by 52 and 8, respectively. The total area is reduced by about 18%. For high throughput structure, this paper provides 2‐round, 5‐round, and 15‐round loop unrolling designs for LILLIPUT to improve throughput. The experimental results show that the throughput of the 5‐round loop unrolling structure reaches a good level, which is relatively the most cost‐effective. In practical application, ciphers can be unrolled implementations according to the needs of devices to improve the execution speed, which can greatly reduce the execution time and complexity of the algorithm.
Improvement of the key point detection algorithm based on yolov8

Human key point detection has essential application prospects in human and computer interaction and disease prediction. However, in the current scenario, there is still some room for improvement in the accuracy of key point detection when facing complex scenes and some key points are missing. To address the above situation this paper proposes an improved YOLOv8-SW model based on the YOLOV8 model, adding the SA attention mechanism to improve the model's ability to extract channel and spatial features, replacing IOU with WIOU, and increasing the proportion of the model's weight on the general quality box. The validation results on the COCO2017 dataset show that the improved model can achieve better detection performance for complex scenes. Compared with the baseline method, the accuracy is improved by 1.0%, which is significant in promoting the practical application of human key point detection.
A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation

The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.
Bitcoin and Renewable Energy Mining: A Survey

Bitcoin, the most valuable and energy-consuming cryptocurrency, has recently been at the center of a heated debate over its environmental impact. This controversy has caught the public’s attention, prompting us to investigate the energy consumption of Bitcoin. In this paper, we have conducted a review of the literature on various aspects of Bitcoin mining, including its mechanisms, energy consumption, mining sites, and the potential for renewable energy use. Our findings reveal that the power consumption of Bitcoin is bound to increase with the continued adoption of the proof-of-work (PoW) consensus algorithm. Nonetheless, the growing availability of affordable renewable energy sources worldwide brings hope that Bitcoin mining will shift towards cleaner energy in the near future.
Tunable, Nonmechanical, Fractional Talbot Illuminators

Inside an optical Fourier processor, we inserted a varifocal system to continuously magnify the frequency of a master grating. The proposed system does not involve any mechanical compensation for scaling the Fourier spectrum. As the magnification, M, varies, the Fourier spectrum remains at the same initial location. We identified a previously unknown quadratic phase factor for generating, in the fixed output plane, Talbot images of any fractional order. We applied this result to setting a structured illumination beam, which does not have occluding regions. This illuminating beam can be useful for Talbot interferometry.