Reviewerly/exHarmony huggingface dataset:
Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions

The emergence of the Internet of Things (IoT) has enabled billions of devices that collect large amounts of data to be connected.Therefore, IoT security has fundamental requirements.One critical aspect of IoT security is data integrity.Cryptographic hash functions are cryptographic primitives that provide data integrity services.However, due to the limitations of IoT devices, existing cryptographic hash functions are not suitable for all IoT environments.As a result, researchers have proposed various lightweight cryptographic hash function algorithms.In this paper, we discuss advanced lightweight cryptographic hash functions for highly constrained devices, categorize design trends, analyze cryptographic aspects and cryptanalytic attacks, and present a comparative analysis of different hardware and software implementations.In the final section of this paper, we highlight present research challenges and suggest future research topics related to the design of lightweight cryptographic hash functions.
Low-Light Image Enhancement: A Comparative Review and Prospects

Low-light image enhancement is a key prerequisite for diverse applications in the field of image processing and computer vision.Various approaches for this task have been introduced over last few decades, and the current state of the art methods have shown remarkable advances based on deep neural networks.However, there are still technical issues to be resolved, e.g., dependency on subjective re-touching results and inconsistency with subjective evaluations.The goal of this work is to provide a comprehensive overview and a practical guide for experts as well as beginners.This paper covers a systematic taxonomy of existing algorithms, representative methodologies, and the performance analysis on benchmark datasets.To pave the way of the development direction for low-light image enhancement, constructive discussions and prospects are also provided.
Augmented Reality-Based English Language Learning: Importance and State of the Art

Augmented reality is increasingly used in the educational domain. However, little is known concerning the actual importance of AR for learning English skills. The weakness of the English language among English as a foreign Language (EFL) students is widespread in different educational institutions. Accordingly, this paper aims at exploring the importance of AR for learning English skills from the perspectives of English language teachers and educators. Mixed qualitative methods were used. To achieve the objective of this study, 12 interviews were conducted with English teachers concerning the topic under investigation. Second, a systematic literature review (SLR) that demonstrates the advantages, the limitation, and the approach of AR for learning English was performed. This study is different from other studies in using two methods and conducting comprehensive research on the importance of AR in improving English language skills in general. Thus, the study concluded that AR improves language skills and academic achievements. It also reduces students’ anxiety levels, improves students’ creativity, and increases students’ collaboration and engagement. Moreover, the students have positive attitudes towards using AR for learning the English language. The findings present important implications for the integration and development of AR for learning.
Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator

In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities.The improved algorithm has three parts.First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity.Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability.Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development.A numerical simulation experiment based on 36 different types of benchmark functions showed that the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA while exhibiting superior performance compared to those of other swarm intelligence algorithms.
The use of digital escape room in educational electronic environment of maritime higher education institutions

The paper is tended to investigate the gamification activities use in educational electronic environment of maritime higher education institutions. Gamification methods with examples are described (gamification testing, QR Code quest, storytelling and escape room). Comparative characteristic of traditional learning and learning using gamification in educational electronic environment is given in the article according to different criteria: the place and role of teacher or students in the learning process; type of information communication; methods of training; equipment; level of freedom of the actions; presence of the problems in educational process; level of its control and learning outcomes. The paper also presents examples of gamification activities based on escape room quest to form communicative competency of future maritime professionals. Escape room activity presented in the article contains storytelling element, crossword and electronic testing questions of different types. Question types listed in the paper are Drag and drop to the text, Short answer and Multiple choice. Escape room activity was done by second year cadets of Kherson State Maritime Academy. According to the received results, knowledge quality increased by 10% and success by 20%. Further investigation of gamification activities can also be done for learning system of maritime higher education institutions using simulation technologies of virtual, augmented and mixed realities.
Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

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Synthetic data in medical research

### Key messages > Synthetic data have the potential to improve medical research while minimising the need to access personal data; Theodora Kokosi and Katie Harron explain what they are and how they are used. Demand to access high quality data at the individual level for medical and healthcare
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x' that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x, from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: it allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this paper we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g.the choice of pre-trained models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts, but also release other resources, e.g., a website http://pretrain.nlpedia.ai/ including constantly-updated survey, and paperlist.
Design of 3D Virtual Reality in the Metaverse for Environmental Conservation Education Based on Cognitive Theory

Climate change causes devastating impacts with extreme weather conditions, such as flooding, polar ice caps melting, sea level rise, and droughts. Environmental conservation education is an important and ongoing project nowadays for all governments in the world. In this paper, a novel 3D virtual reality architecture in the metaverse (VRAM) is proposed to foster water resources education using modern information technology.A quasi-experimental study was performed to observe a comparison between learning involving VRAM and learning without VRAM. The 3D VRAM multimedia content comes from a picture book for learning environmental conservation concepts, based on the cognitive theory of multimedia learning to enhance human cognition. Learners wear VRAM helmets to run VRAM Android apps by entering the immersive environment for playing and/or interacting with 3D VRAM multimedia content in the metaverse. They shake their head to move the interaction sign to initiate interactive actions, such as replaying, going to consecutive video clips, displaying text annotations, and replying to questions when learning soil-and-water conservation course materials. Interactive portfolios of triggering actions are transferred to the cloud computing database immediately by the app.Experimental results showed that participants who received instruction involving VRAM had significant improvement in their flow experience, learning motivation, learning interaction, self-efficacy, and presence in learning environmental conservation concepts.The novel VRAM is highly suitable for multimedia educational systems. Moreover, learners' interactive VRAM portfolios can be analyzed by big-data analytics to understand behaviors for using VRAM in the future to improve the quality of environmental conservation education.
Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4

Insulator devices are important for transmission lines, and defects such as insulator bursting and string loss affect the safety of transmission lines. In this study, we aim to investigate the problems of slow detection speed and low efficiency of traditional insulator defect detection algorithms, and to improve the accuracy of insulator fault identification and the convenience of daily work; therefore, we propose an insulator defect detection algorithm based on an improved MobilenetV1-YOLOv4. First, the backbone feature extraction network of YOLOv4 'Backbone' is replaced with the lightweight module Mobilenet-V1. Second, the scSE attention mechanism is introduced in stages of preliminary feature extraction and enhanced feature extraction, sequentially. Finally, the depthwise separable convolution substitutes the 3 × 3 convolution of the enhanced feature extraction network to reduce the overall number of network parameters. The experimental results show that the weight of the improved algorithm is 57.9 MB, which is 62.6% less than that obtained by the MobilenetV1-YOLOv4 model; the average accuracy of insulator defect detection is improved by 0.26% and reaches 98.81%; and the detection speed reaches 190 frames per second with an increase of 37 frames per second.
Brain connectivity meets reservoir computing

The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? At a meso/macro-scale level of the connectivity, ANNs' architectures are carefully engineered and such those design decisions have crucial importance in many recent performance improvements. On the other hand, BNNs exhibit complex emergent connectivity patterns at all scales. At the individual level, BNNs connectivity results from brain development and plasticity processes, while at the species level, adaptive reconfigurations during evolution also play a major role shaping connectivity. Ubiquitous features of brain connectivity have been identified in recent years, but their role in the brain's ability to perform concrete computations remains poorly understood. Computational neuroscience studies reveal the influence of specific brain connectivity features only on abstract dynamical properties, although the implications of real brain networks topologies on machine learning or cognitive tasks have been barely explored. Here we present a cross-species study with a hybrid approach integrating real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks, allowing us to probe the potential computational implications of real brain connectivity patterns on task solving. We find results consistent across species and tasks, showing that biologically inspired networks perform as well as classical echo state networks, provided a minimum level of randomness and diversity of connections is allowed. We also present a framework, bio2art, to map and scale up real connectomes that can be integrated into recurrent ANNs. This approach also allows us to show the crucial importance of the diversity of interareal connectivity patterns, stressing the importance of stochastic processes determining neural networks connectivity in general.
Sentiment Analysis of Twitter Data

Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA.
Blockchain and 6G-Enabled IoT

Ubiquitous computing turns into a reality with the emergence of the Internet of Things (IoT) adopted to connect massive numbers of smart and autonomous devices for various applications. 6G-enabled IoT technology provides a platform for information collection and processing at high speed and with low latency. However, there are still issues that need to be addressed in an extended connectivity environment, particularly the security and privacy domain challenges. In addition, the traditional centralized architecture is often unable to address problems associated with access control management, interoperability of different devices, the possible existence of a single point of failure, and extensive computational overhead. Considering the evolution of decentralized access control mechanisms, it is necessary to provide robust security and privacy in various IoT-enabled industrial applications. The emergence of blockchain technology has changed the way information is shared. Blockchain can establish trust in a secure and distributed platform while eliminating the need for third-party authorities. We believe the coalition of 6G-enabled IoT and blockchain can potentially address many problems. This paper is dedicated to discussing the advantages, challenges, and future research directions of integrating 6G-enabled IoT and blockchain technology for various applications such as smart homes, smart cities, healthcare, supply chain, vehicle automation, etc.
Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data

Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification. The existing research has concentrated more on sentiment analysis and offensive language identification in a monolingual data set than code-mixed data. Code-mixed data is framed by combining words and phrases from two or more distinct languages in a single text. It is quite challenging to identify emotion or offensive terms in the comments since noise exists in code-mixed data. The majority of advancements in hostile language detection and sentiment analysis are made on monolingual data for languages with high resource requirements. The proposed system attempts to perform both sentiment analysis and offensive language identification for low resource code-mixed data in Tamil and English using machine learning, deep learning and pre-trained models like BERT, RoBERTa and adapter-BERT. The dataset utilized for this research work is taken from a shared task on Multi task learning DravidianLangTech@ACL2022. Another challenge addressed by this work is the extraction of semantically meaningful information from code-mixed data using word embedding. The result represents an adapter-BERT model gives a better accuracy of 65% for sentiment analysis and 79% for offensive language identification when compared with other trained models.
Development of an augmented reality mobile physics application to study electric circuits

The use of virtual teaching aids with AR technology in various areas of education, including physics, has been studied. The similarity between physical and electrical models has been established, and the development of a mobile app for studying simple electric circuits has been validated. The feasibility of developing the technique of augmented reality mobile apps has been established. The following milestones have been identified in the development of the augmented reality app: production of electronic models, installation of the game engine Unity3D, development of all program scenes, operation testing, and demonstration. The application of scenarios for electronic models rotation and movement has received special attention. In-house created augmented reality mobile app for mobile devices "Augmented reality program for studying the simplest electric circuit" has been offered. The developed mobile app reads, recognizes, and displays the product electronic model on the screen. It has been demonstrated that the augmented reality application produced by the author team as mobile teaching software can be used to complete assignments for students' individual work as well as classroom studies at colleges.
Deformable Feature Aggregation for Dynamic Multi-modal 3D Object Detection

Point clouds and RGB images are two general perceptional sources in autonomous driving. The former can provide accurate localization of objects, and the latter is denser and richer in semantic information. Recently, AutoAlign [6] presents a learnable paradigm in combining these two modalities for 3D object detection. However, it suffers from high computational cost introduced by the global-wise attention. To solve the problem, we propose Cross-Domain DeformCAFA module in this work. It attends to sparse learnable sampling points for cross-modal relational modeling, which enhances the tolerance to calibration error and greatly speeds up the feature aggregation across different modalities. To overcome the complex GT-AUG under multi-modal settings, we design a simple yet effective cross-modal augmentation strategy on convex combination of image patches given their depth information. Moreover, by carrying out a novel image-level dropout training scheme, our model is able to infer in a dynamic manner. To this end, we propose AutoAlignV2, a faster and stronger multi-modal 3D detection framework, built on top of AutoAlign. Extensive experiments on nuScenes benchmark demonstrate the effectiveness and efficiency of AutoAlignV2. Notably, our best model reaches 72.4 NDS on nuScenes test leaderboard, achieving new state-of-the-art results among all published multi-modal 3D object detectors. Code will be available at https://github.com/zehuichen123/AutoAlignV2 .
Chessbot: A voice-controlled chess board with self-moving pieces

Automated chess is an emerging challenge from the field of robotics to human interaction. Customarily, chess playing robots use video camera for detecting the state of the board and robotic arm to manipulate the pieces, which makes it either expensive or too fragile to move. In this study, an automated chess board called “Chessbot” was built by combining a multitude of technologies and techniques such as voice command recognition, x and y plotting and recognition of chess pieces in a Cartesian plane, artificial intelligence algorithm, and Android mobile application. To properly execute chess moves either from a human player or computer via voice commands or a mobile game interface, the microcontroller provides motors the current and landing squares of chess pieces on the board. For the evaluation, several matches were simulated to perform various testing procedures such as voice command recognition, move log accuracy, display and user input acceptance accuracy, self-arrangement, and chess move reliability. Upon testing, Chessbot performs all the necessary tasks efficiently and accurately, which indicates the possibility of using this automated device for chess games at a hobby-level or professional matches.
Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation

A C TLiver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-ofuse, portability, and non-invasive nature.Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures.In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy.Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance.We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024while delivering a real-time performance of 37 frames per second (FPS).