Reviewerly/exHarmony huggingface dataset:
An Enhanced Method for Neural Machine Translation via Data Augmentation Based on the Self-Constructed English-Chinese Corpus, WCC-EC

In an era of increasing globalization, the imperative for understanding multilingual texts elevated the role of translation to an everyday necessity. The efficacy of contemporary Neural Machine Translation (NMT) systems was heavily dependent on the availability of substantial training data. As such, the creation of an expansive parallel corpus became a strategic focal point, providing a bedrock for the evolution of high-caliber NMT systems. However, building a parallel corpus often posed challenges due to its complexity and cost, especially for language pairs that lacked sufficient parallel data. To tackle this challenge, this study proposed a novel data augmentation method for the corpus. Bilingual news texts sourced from the KEKE English website were utilized, and SentenceBERT was employed to ensure accurate sentence alignment. Subsequently, the parallel partial sentences within the corpus were filtered and used to augment the dataset. Finally, the effectiveness of the method was assessed by calculating BLEU, chrF and METEOR scores based on both the original corpus and the data-augmented corpus using the base translation model. The experimental results indicated that compared to the baseline method, the optimal method showed an improvement of 0.4-2.1 points in BLEU scores, an improvement of 0.5-2.7 points in chrF scores, and an increase of 0.5-1.6 points in METEOR scores.
Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign

Traffic sign recognition plays a crucial role in the intelligent vehicle’s environment perception system. However, due to varying weather conditions, illumination, and complicated backgrounds, recognizing traffic signs becomes very challenging. A novel lightweight detection model based on YOLOv5s, namely Sign-YOLO, is proposed to overcome these challenges. Firstly, the CA (Coordinate Attention) module is incorporated into the backbone network to improve the extraction of key features. Secondly, the improved High-BiFPN is used to enhance YOLOv5s’ neck structure’s capability in fusing multi-scale semantic information. Finally, the improved Better-Ghost Module is employed to reduce the model’s parameters and accelerate the detection speed. We used the CCTSDB2021 dataset to evaluate our model. Compared to YOLOv5s, the proposed Sign-YOLO algorithm in this paper reduces the model parameters by 0.13 M. The precision, recall, F-1 score, and mAP value have improved by 1.02%, 7.01%, 1.84%, and 4.61%, respectively. The FPS value remains around 86 fps. The results show that Sign-YOLO has achieved the optimal balance between accuracy and real-time performance.
A Spin-off Version of Jason for IoT and Embedded Multi-Agent Systems

Embedded artificial intelligence in IoT devices is presented as an option to reduce connectivity dependence, allowing decision-making directly at the edge computing layer. The Multi-agent Systems (MAS) embedded into IoT devices enables, in addition to the ability to perceive and act in the environment, new characteristics like pro-activity, deliberation, and collaboration capabilities to these devices. A few new frameworks and extensions enable the construction of agent-based IoT devices. However, no framework allows constructing them with hardware control, adaptability, and fault tolerance, besides agents' communicability and mobility. This work presents an extension of the Jason framework for developing Embedded MAS with BDI agents capable of controlling hardware, communicating, and moving between IoT devices capable of dealing with fault tolerance. A case study of an IoT solution with a smart home, a monitoring center, and an autonomous vehicle is presented to demonstrate the framework's applicability.
Fitness Landscape Analysis of TPOT Using Local Optima Network

AutoML addresses the challenge of automatically configuring machine learning pipelines for specific data analysis tasks. These pipelines encompass techniques for preprocessing and classifying data. Numerous approaches exist for discovering the optimal pipeline configuration, with most focusing on optimization methods such as Bayesian optimization and evolutionary algorithms. Nevertheless, limited knowledge exists regarding the structure of the search space that these methods operate within. What is certain is that these spaces incorporate categorical, continuous, and conditional hyperparameters, and effectively handling them is not straightforward. To shed light on this matter, the present study conducts an examination of AutoML search spaces generated by the Tree-based Pipeline Optimization Tool (TPOT) algorithm utilizing local optimal networks (LON). The goal is to gain deeper insights into the overall characteristics of the search space, enhancing comprehension of the search strategy employed and the algorithm's limitations. This investigation aids in understanding the search strategy and constraints of the algorithm, ultimately contributing to the advancement of novel optimization algorithms or the refinement of existing ones within the scientific literature. The findings have implications for enhancing optimization algorithms by illuminating how the search space is explored and the consequent impact on the discovered solutions.
Machine Teaching: An Explainable Machine Learning Model for Individualized Education

Education is the single most important investment that people can make in their futures, and since the Universal Declaration of Human Rights in 1948, the goal of achieving universal education has been on the international agenda. In this regard, there is no doubt that the Web has had a profound impact on making education both universally available and more relevant. Nevertheless, online courses are based on "static" learning material (one-size-fits-all). For that reason, it is not straightforward to assess learning with a great number of learners who differ considerably in their educational background, engagement styles, and cognitive skills. In this work, we aim to address these aforementioned challenges by proposing an explainable Machine Learning algorithm for personalizing web-based education systems. The method has a "deep" architecture mimicking the information representation structure in human brains, and it is continuously adapted based on the signals of the students, understanding their performance through micro-steps and maximizing the learning outcome. While our methodology is general and can be applied in numerous scenarios, we demonstrate its performance by a real case study which comprises a non-mandatory, standardized exam, that evaluates high school students in Brazil.
BLUEX: A Benchmark Based on Brazilian Leading Universities Entrance eXams

One common trend in recent studies of language models (LMs) is the use of standardized tests for evaluation. However, despite being the fifth most spoken language worldwide, few such evaluations have been conducted in Portuguese. This is mainly due to the lack of high-quality datasets available to the community for carrying out evaluations in Portuguese. To address this gap, we introduce the Brazilian Leading Universities Entrance eXams (BLUEX), a dataset of entrance exams from the two leading universities in Brazil: UNICAMP and USP. The dataset includes annotated metadata for evaluating the performance of NLP models on a variety of subjects. Furthermore, BLUEX includes a collection of recently administered exams that are unlikely to be included in the training data of many popular LMs as of 2023. The dataset is also annotated to indicate the position of images in each question, providing a valuable resource for advancing the state-of-the-art in multimodal language understanding and reasoning. We describe the creation and characteristics of BLUEX and establish a benchmark through experiments with state-of-the-art LMs, demonstrating its potential for advancing the state-of-the-art in natural language understanding and reasoning in Portuguese. The data and relevant code can be found at https://github.com/Portuguese-Benchmark-Datasets/BLUEX.
Towards Cloud Storage Tier Optimization with Rule-Based Classification

Cloud storage adoption has increased over the years as more and more data has been produced with particularly high demand for fast processing and low latency. To meet the users' demands and to provide a cost-effective solution, cloud service providers (CSPs) have offered tiered storage; however, keeping the data in one tier is not a cost-effective approach. Hence, several two-tiered approaches have been developed to classify storage objects into the most suitable tier. In this respect, this paper explores a rule-based classification approach to optimize cloud storage cost by migrating data between different storage tiers. Instead of two, four distinct storage tiers are considered, including premium, hot, cold, and archive. The viability and potential of the approach are demonstrated by comparing cost savings achieved when data was moved between tiers versus when it remained static. The results indicate that the proposed approach has the potential to significantly reduce cloud storage cost, thereby providing valuable insights for organizations seeking to optimize their cloud storage strategies. Finally, the limitations of the proposed approach are discussed along with the potential directions for future work, particularly the use of game theory to incorporate a feedback loop to extend and improve the proposed approach accordingly.
Towards a Decentralised Federated Learning Based Compute Continuum Framework

The proliferation of sensing device technologies, and the growing demand for data intensive IoT applications calls for a seamless interconnection of IoT, edge and cloud resources in one computing system, to form a Compute Continuum, also referred to as edge-to-cloud. This paper targets self-adaptive Machine Learning applications that rely on data coming from IoT sensors. These applications are often "context-aware", with high context sensitivity, different physical settings and complex usage patterns. Their intelligence, deployed on the edge, is updated on the fly. We present two Compute Continuum strategies for the deployment of such applications: (1) a centralised approach, which involves training a model on a centralised server, and (2) a decentralised approach using Federated Learning. The former approach involves centralising data from multiple sources onto a single server, while the latter locally decentralises both the training process and the aggregation and communication tasks across edge devices. In both cases the inference model is deployed on edge devices close to the collected data. The decentralised architecture relies on a coordination platform favouring self-adaptation and decentralised Federated Learning. Results show that the decentralised Federated Learning approach offers networking performances and privacy-preserving advantages compared to non-private centralised models, with a slight trade-off in prediction accuracy. According to our simulations, the deployment cost of the decentralised architecture is much lower than that of deployment on the centralised architecture.
Study of Modifications of Gender Genetic Algorithm

This study compares several modifications of a gender genetic algorithm (GGA). Aside the difference between the genders in the probability of mutation, we introduce two additional modifications: different implementations of selection and different laws of dependence of the probability of mutation on gene number within a chromosome. We use four test optimization problems in spaces of various dimensions to compare conventional GA, conventional GGA, and GGA with the additional modifications implemented separately or together. It is demonstrated that the proposed additional modifications outperform conventional GA and conventional GGA in the achieved value of the fitness function, especially in high-dimensional spaces. With increase in the problem dimension, they degrade more slowly. Also, the new modifications prevent premature convergence of the algorithm.
Athlete target detection method in dynamic scenario based on nonlinear filtering and YOLOv5

In this paper firefly optimization algorithm is improved, including the method of relative firefly fluorescence brightness, the method of attraction and the method of firefly position update. The dynamic step size factor and dynamic difference factor are introduced, and the improved firefly algorithm is used to optimize the particle filter, so that the particle swarm can be concentrated to the high likelihood region as much as possible, so as to ensure the overall quality of the particle swarm. In this paper, the most commonly used non-maximum suppression algorithm in the post-processing stage of target detection model is discussed. The original YOLOv5 model and the fusion model of nonlinear filtering and YOLOv5 were respectively used to simulate the two data sets after data enhancement. Some mainstream object detection models and the improved model in this paper are analyzed experimentally in two datasets. In dataset 1, the small increase in mAP value is the addition of CBAM's attention module, which increases by 2.8%. For data set 2, when the original Focal loss was replaced by VFLoss, the mAP increased to 89.8%, an increase of 0.95%. For the case where all the improvements were added, the mAP value increased by 7%.
Cultural psychology of english translation through computer vision-based robotic interpretation

Computer Vision-based English translation approaches pledge robots to master complicated functions. Conversely, the debate remains unanswered as to how to extend persuasion skills to real-world relationships. The stable operation of robots in English translation is an upcoming development in the educational field. The development of science and technology in the form of robots helps in computer vision technology for object detection and learning. In education, the usage of advanced technology is still facing challenges in English translation based on the computer vision of robots. In the article, researchers investigate robotic simulation, movement identification, and target tracking in computer vision, learning from one illustration of the third person's perspective. Researchers consider using a previous information basis like a text repository to deduce the feature to be dealt with as part of a robot to promote its generalization through object detection and learning. A Robot translation based on computer vision of English translation (RT-CV) framework is proposed in the research. Robots' word recognition, facial expression, speech, and movement are captured and based on computer vision; the translation is permitted with the basic functions. RT-CV is implemented in real-world applications with manipulative functions with generalized outcomes. The results are obtained as emotional interaction with robots' ratio is 87.6%, improving computer vision ratio is 88.7%, the Estimation of translating speed ratio is 84.5%, the efficiency of English translation ratio is 93.8%, and anxiety reduction through communication ratio is 82.2%.
Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction

The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction.
Deep learning in news recommender systems: A comprehensive survey, challenges and future trends

Nowadays, people prefer to read news articles from online sources worldwide due to their easiness and availability. For the last few years, online searching for required information or content has been replaced by item recommendation, and news recommendation is also not an exception. For news recommendations, News Recommender System (NRS) helps the users to find the appropriate and pertinent content, alleviate the problem of information overload, and propose news that would be of interest to news readers. NRS also assists different users all around the world in this regard by recommending the most recent news articles based on their interests and past preferences. Many techniques such as traditional, Deep Learning (DL), and hybrid have been proposed to solve the NRS challenges and issues. DL techniques are considered one of the best techniques and have been successfully applied in various fields such as Natural Language Processing (NLP) and Computer Vision (CV). This survey article provides a detailed analysis of DL models-based techniques to build NRS. In this regard, firstly, a comprehensive comparison is provided between published survey articles on NRS and this research work. Secondly, it discusses the background of recommendation systems and their techniques. Furthermore, NRS is explored along with its current research challenges. Then background knowledge of DL and its methods have been discussed along with the analysis of year-wise published relevant articles having DL as the applied technique. The survey also presents widely used datasets and performance evaluation metrics used in the relevant literature. Finally, a detailed discussion provides several future directions and open research challenges for the researchers to consider DL applications in NRS.
Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM)

A large number of medical images are generated for diagnostic purposes, disease monitoring, research and education, quality control in health services, and so on. The secure transmission and storage of them demand a significant effort. Most of the available encryption schemes are designed for non-medical images, whereas medical images need a higher level of security and robust authentication. Additionally, in certain cases, only a specific part of the image, which may be separated into the region of interest and the region of background, medical images can be divided into these two regions. A region-based medical image encryption using a 2D logistic sine map (2DLSM) and an advanced zig zag transform is used to secure medical images. First, the Region of Interest (ROI) is extracted from the original medical image using basic morphological techniques, including edge detection, dilation, and erosion. Secondly, the ROI is encrypted using a complex zigzag transform and a 2D logistic sine map (2DLSM). Advanced zigzag transform that crosses in both directions while beginning at random points to jumble the image. This new zigzag transform method is more complex than existing zigzag transform techniques because the number of sequence types is equal to the number of pixels in the plaintext image. The confused image is diffused using a random sequence created using the 2D logistic sine map approach after numerous iterations of an advanced zigzag transformation. In order to save time and computational resources, the background region pixels are eliminated during encryption. Experiments and security analyses show that the suggested approach is strong in defending against diverse assaults and can effectively secure ROI of different types and sizes of medical photos.
Model Checking of Hyperledger Fabric Smart Contracts

Conducting interactions between shared-purpose organizations that are not entirely trustworthy of each other without centralized oversight is an idea that emerged with the advent of private blockchains such as Hyperledger Fabric and its smart contracts. It is critical to check contracts to ensure their proper functionality, as organizations may collaborate with competitors. Due to the new architecture of Hyperledger Fabric, tools in this area are limited. To formally verify the source code of contracts, we mapped Fabric contract concepts into the Rebeca modeling language. Rebeca is an actor-based language that enables the modeling of concurrent and distributed systems and is supported by a model checking tool, Afra. We have identified vulnerabilities such as deadlock and starvation by examining the desired properties. Using the model checking approach, we could debug the code and hence benefit from speeding up the transactions, creating fewer extra blocks, requiring less storage space to store the ledger, and avoiding wasting computing resources.
Basic study on linear optical quantum computers with liquid crystal devices

Abstract A basic experiment on 2-bit quantum calculations related to the Deutsch–Jozsa algorithm on a linear optical quantum computer using a liquid crystal (LC) device as a linear optical element was investigated. By controlling the phase of each of the two split laser beams and checking the number of photons at the output, it is possible to determine if the input bits are “not equal” or “not uniform.” The phase of the laser light was controlled using an LC device and a half-wave plate, and changes in the interference state of the output light and the number of photons were observed. The voltage application to the LC device is set at the input bit. As a result, the interference state changed according to the input bit, and changes in the number of photons were observed.