Reviewerly/exHarmony huggingface dataset:
GUARDIAN: A Hardware-Assisted Distributed Framework to Enhance Deep Learning Security

The ubiquity of artificial intelligence (AI) has led to its extensive research and application in various fields, such as computer vision, natural language processing, and medical image analysis. However, responsible AI faces severe security challenges, including the leakage of pretrained models and valuable training data. The existing solutions adopt new algorithm designs (such as federated learning) or cryptography (such as homomorphic encryption) to prevent possible security vulnerabilities. We observe that hardware-assisted trusted execution environments (TEEs) can further improve machine learning responsibility. Intel Software Guard Extension (SGX) is a popular, trusted execution hardware that enables users’ programs to run in an untrusted execution environment, such as a malicious operating system, but ensures the confidentiality and integrity of data. Therefore, we have designed GUARDIAN, a hardware-assisted secure machine learning training framework that protects data security during the training process. We have analyzed the typical characteristics of machine learning applications and characterized GUARDIAN through extensive experiments. Our findings demonstrate that introducing security guarantees causes performance degradation, which provides a feasible optimization direction in the near future.
Multi-scale local explanation approach for image analysis using model-agnostic Explainable Artificial Intelligence (XAI)

The recent success of deep neural networks has generated remarkable growth in Artificial Intelligence (AI) research and has received much interest over the past few years. One of the main challenges for the broad adoption of deep learning-based models such as Convolutional Neural Networks (CNN) is the lack of understanding of their decisions. To address this issue, Explainable Artificial Intelligence (XAI) has been proposed to shift toward more transparent AI, resulting in the development of techniques to explain decisions by AI models. This paper aims to explore and develop a multi-scale scheme of LIME (Local Interpretable Model-Agnostic Explanations) applied to image classification to explain decisions made by CNN models through heatmaps of coarse to finer scales. More precisely, when LIME highlights large superpixels from a coarse scale, there may be smaller regions in the corresponding superpixel that influenced the model’s prediction at some finer scale. In the proposed multi-scale scheme, two weighting approaches, one based on Gaussian distribution and another parameter-free framework will be introduced to produce visual explanations observed from different scales. Promising results for multi-scale classification heatmaps of histopathology images are presented. More specifically, we investigated the proposed multi-scale approach on Camelyon16 dataset. The results show that the explanations are faithful to the underlying model, and the visualizations are reasonably interpretable.
Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

The goal of this article is to analyze the problem of low computational efficiency and propagation error rate in entity recognition and relation extraction. This paper proposes a personalized education resource recommendation algorithm framework XMAMBLSTM based on deep learning in an intelligent education robot environment. XMAMBLSTM uses XLNet to assign word vectors to text sequences, employs a Multi-Bi-LSTM layer to represent complex information of word vectors, and combines a multi-headed attention layer to realize weight distribution of each word vector. The experimental results show that compared with the traditional collaborative filtering algorithm, the comprehensive evaluation indexes of the proposed method, based on the intelligent education robot environment on the two platforms, are higher than 5.05% and 17.3%, respectively.
Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis

The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion words. The application of syntactic dependency trees is one such resolution. However, the widely-used dependency parsers still have challenges in obtaining a solid sentiment classification result. In this work, an information propagation graph convolutional network based on syntactic structure optimization is proposed on the task of ABSA. To further complement the syntactic information, the semantic information is incorporated to learn the representations using graph information propagation mechanism. In addition, the effects of syntactic and semantic information are adapted via feature separation. Experimental results on three benchmark datasets show that the proposed model achieves satisfying performance against the state-of-the-art methods, indicating that the model can precisely build the relation between aspect and its context words.
Towards the Distributed Wound Treatment Optimization Method for Training CNN Models: Analysis on the MNIST Dataset

Convolutional neural network (CNN) is a prominent algorithm in Deep Learning methods. CNN architectures have been used successfully to solve various problems in image processing, for example, segmentation, classification, and enhancement task. However, automatic search for suitable architectures and training parameters remain an open area of research, where metaheuristic algorithms have been used to fine-tuning the hyperparameters and learning parameters. This work presents a bio-inspired distributed strategy based on Wound Treatment optimization (WTO) for training the learning parameters of a LenNet CNN model fast and accurate. The proposed method was evaluated over the popular benchmark dataset MNIST for handwritten digit recognition. Experimental results showed an improvement of 36.87% in training time using the distributed WTO method compared to the baseline with a single learning agent, and the accuracy increases 4.69% more using the proposed method in contrast with the baseline. As this is a preliminary study towards the distributed WTO method for training CNN models, we anticipate this approach can be used in robotics, multi-agent systems, federated learning, complex optimization problems, and many others, where an optimization task is required to be solved fast and accurate.
The Impacts of Maintenance Weather and Aging on Solar Power Generation Forecasting and Prediction

Solar energy forecasting has seen tremendous growth by using weather and photovoltaic (PV) parameters. This study presents new approach that predicts solar energy production by using the scheduled, unscheduled maintenance activities and weather data. The dataset is obtained from the 1MW solar power plant of PDEU (our university), which has 12 structured columns and 1 unstructured column with manual text entries about different scheduled and unscheduled maintenance activities, and weather conditions on the daily basis. The unstructured column is used to create new features by using Hash-Map, flag words and stop words. The solar power generation forecasting is formulated as a vector auto regression (VAR) optimization problem and total power generation forecasting is presented with the results of four different cases. The results have shown that the root mean square percentage error (RMSPE) in total power generation forecasting is less than 10% for different lag (p) values. The vector auto regression can forecast the unscheduled maintenance activities like Grid failure, Inverter Failure, scheduled maintenance activity like module cleaning, weather activity like cloudy along with total power generation forecasting for effective and efficient management of solar power plants. The power generation decay is different for all the PV sets which show the variations in the impacts of weather, aging and maintenance on the solar power plant. This research work has proven that the peaks of total power generation forecasting and prediction can be tracked in a better way by using daily unscheduled, scheduled maintenance activities and weather conditions.
Evolutionary algorithms based on oversampling techniques for enhancing the imbalanced credit card fraud detection

Online services have advanced to the point where they have made our lives much easier, but many problems should be solved to make these services safer for consumers. Numerous transactions are conducted daily, and much personal information is published and shared on e-commerce and social media platforms. This makes security, privacy, and problematic reliability barriers to overcome. One of these problems is detecting credit card fraud because thieves aim to make all transactions legitimate by stealing credit card information. Imbalanced data is a potential problem in machine learning that impairs the performance of the classifiers used in real-world systems. For example, anomaly detection and fraudulent transactions. The term “data imbalance” refers to the problem in which the sample distribution is skewed or skewed towards a particular class. Due to its inherent nature, the software failure prediction dataset falls into the same category as non-defective software modules. The main objective of this paper is to solve the problem of the imbalanced fraud credit card dataset for enhancing the detection accuracy of using machine learning algorithms. This paper provides a unique fraud detection model using the Particle Swarm Optimization (PSO) based on oversampling technique of the minority class to solve the imbalanced dataset problem compared with the Genetic Algorithm (GA) technique. Random Forest (RF) algorithm shows up with sensitivity, specificity, and accuracy. The experimental results achieved 99.3% and 99.4% for GA and PSO within seconds, respectively. Experiments show that the proposed methods outperform other methods, evidenced by the higher classification accuracy obtained.
Real-time respiratory motion prediction using photonic reservoir computing

Respiration induced motion is a well-recognized challenge in many clinical practices including upper body imaging, lung tumor motion tracking and radiation therapy. In this work, we present a recurrent neural network algorithm that was implemented in a photonic delay-line reservoir computer (RC) for real-time respiratory motion prediction. The respiratory motion signals are quasi-periodic waveforms subject to a variety of non-linear distortions. In this work, we demonstrated for the first time that RC can be effective in predicting short to medium range of respiratory motions within practical timescales. A double-sliding window technology is explored to enable the real-time establishment of an individually trained model for each patient and the real-time processing of live-streamed respiratory motion data. A breathing dataset from a total of 76 patients with breathing speeds ranging from 3 to 20 breaths per minute (BPM) is studied. Motion prediction of look-ahead times of 66.6, 166.6, and 333 ms are investigated. With a 333 ms look-ahead time, the real-time RC model achieves an average normalized mean square error (NMSE) of 0.025, an average mean absolute error (MAE) of 0.34 mm, an average root mean square error (RMSE) of 0.45 mm, an average therapeutic beam efficiency (TBE) of 94.14% for an absolute error (AE) < 1 mm, and 99.89% for AE < 3 mm. This study demonstrates that real-time RC is an efficient computing framework for high precision respiratory motion prediction.
Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news.
Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach

In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences.
Efficient Conformer for Agglutinative Language ASR Model Using Low-Rank Approximation and Balanced Softmax

Recently, the performance of end-to-end speech recognition has been further improved based on the proposed Conformer framework, which has also been widely used in the field of speech recognition. However, the Conformer model is mostly applied to very widespread languages, such as Chinese and English, and rarely applied to speech recognition of Central and West Asian agglutinative languages. There are more network parameters in the Conformer end-to-end speech recognition model, so the structure of the model is complex, and it consumes more resources. At the same time, we found that there is a long-tail problem in Kazakh, i.e., the distribution of high-frequency words and low-frequency words is not uniform, which makes the recognition accuracy of the model low. For these reasons, we made the following improvements to the Conformer baseline model. First, we constructed a low-rank multi-head self-attention encoder and decoder using low-rank approximation decomposition to reduce the number of parameters of the multi-head self-attention module and model’s storage space. Second, to alleviate the long-tail problem in Kazakh, the original softmax function was replaced by a balanced softmax function in the Conformer model; Third, we use connectionist temporal classification (CTC) as an auxiliary task to speed up the model training and build a multi-task lightweight but efficient Conformer speech recognition model with hybrid CTC/Attention. To evaluate the effectiveness of the proposed model, we conduct experiments on the open-source Kazakh language dataset, during which no external language model is used, and the number of parameters is relatively compressed by 7.4% and the storage space is relatively reduced by 13.5 MB, while the training speed and word error rate remain basically unchanged.
On the Summarization of Meteorological Data for Solar Thermal Power Generation Forecast

The establishment of the typical weather conditions of a given locality is of fundamental importance to determine the optimal configurations for solar thermal power plants and to calculate feasibility indicators in the power plant design phase. Therefore, this work proposes a summarization method to statistically represent historical weather data using typical meteorological days (TMDs) based on the cumulative distribution function (CDF) and hourly normalized root mean square difference (nRMSD). The proposed approach is compared with regular Sandia selection in forecasting the electricity produced by a solar thermal power plant in ten different Brazilian cities. Considering the determination of the annual generation of electricity, the results obtained show that when considering an overall average of weather characteristics, commonly used for analyzing solar thermal power plant designs, the normalized mean average error (nMAE) is 20.8 ± 4.8% relative to the use of historical data of 20 years established at hourly intervals. On the other hand, a typical meteorological year (TMY) is the most accurate approach (nMAE = 1.0 ± 1.1%), but the costliest in computational time (CT = 381.6 ± 56.3 s). Some TMD cases, in turn, present a reasonable trade-off between computational time and accuracy. The case using 4 TMD, for example, increased the error by about 11 percentual points while the computational time was reduced by about 81 times, which is quite significant for the simulation and optimization of complex heliothermic systems.
Transformer tracking with multi-scale dual-attention

Abstract Transformer-based trackers greatly improve tracking success rate and precision rate. Attention mechanism in Transformer can fully explore the context information across successive frames. Nevertheless, it ignores the equally important local information and structured spatial information. And irrelevant regions may also affect the template features and search region features. In this work, a multi-scale feature fusion network is designed with box attention and instance attention in Encoder–Decoder architecture based on Transformer. After extracting features, the local information and structured spatial information is learnt by multi-scale box attention, and the global context information is explored by instance attention. Box attention samples grid features from the region of interest. Therefore, it effectively focuses on the region of interest (ROI) and avoids the influence of irrelevant regions in feature extraction. At the same time, instance attention can also pay attention to the context information across successive frames, and avoid falling into local optimum. The long-range feature dependencies are learned in this stage. Extensive experiments are conducted on six challenging tracking datasets to demonstrate the superiority of the proposed tracker MDTT, including UAV123, GOT-10k, LaSOT, VOT2018, TrackingNet, and NfS. In particular, the proposed tracker achieves AUC score of $$64.7 \% $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>64.7</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on LaSOT, $$78.1 \%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>78.1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on TrackingNet and precision score of $$89.2 \%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>89.2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on UAV123, which outperforms the baseline and most recent advanced trackers.
Technical debt in the engineering of complex systems

Abstract The metaphor of technical debt (TD) is widely adopted in the software engineering field, referring to short‐term compromises in software artifacts in exchange for speed or to meet release schedules or other constraints. The implication is that TDs accumulate over time, and may eventually make rework or maintenance very expensive or even impossible. The analogy is generally applicable in the systems engineering field, particularly concerning numerous program cancellation and obsolescence challenges due to premature decisions made in early acquisition phases. This paper adapts this metaphor of TD to the systems engineering field, and proposes a TD taxonomy to support the early identification and assessment of TD items in engineering complex systems, especially in the early life cycle phases of engineering complex, distributed systems. The taxonomy identifies seven TD types: functionality, performance, interoperability, version conflicts, documentation and support, system evolution, and organic, based on systematic indicators and signs discoverable during early acquisition activities. We expect that the notion and the taxonomy of TD will offer an additional perspective for design decisions that will help mitigate challenging integration and obsolescence issues in the engineering of complex systems.
INFOGRAPHICS AS A MODERN METHOD OF TEACHING ENGLISH

The purpose of this paper is to acquaint the reader with infographics as the modern method of education and teaching English based on a review of scientific articles on this topic. Infographics is a graphical way of presenting information that allows to convey data in a concise form while carrying significant visual material in the form of illustrations, graphs, charts, and diagrams. In addition, the use of infographics in teaching increases the creative potential of the teacher, diversifying the possibilities for conducting lessons. The infographic method significantly improves the quality and efficiency of the lesson, in particular the English lessons, which was demonstrated by the authors of the reviewed studies. A scoping review of 10 articles written for the period from 2014 to 2022 was carried out. The documents were analyzed, and the elements of the research were distributed according to such classifications as the level of education, the level of students' knowledge, the number of students, the form of studying, skills for the development of which infographics and research results were used. Case studies were conducted by the authors in both online and blended classrooms at various educational institutions. The document also includes feedback from students, as well as suggestions for developing the use of infographics in the future. The paper will be useful to anyone interested in modern methods of teaching English and the introduction of infographics in education.
Dataset of human-written and synthesized samples of keystroke dynamics features for free-text inputs

The data presented in this article comprises human-written samples of keystroke dynamic features for free-text inputs, in the form of sentences written in natural language, together with synthesized samples that share the same text sequences. The human-written samples originate in three publicly available datasets that have been previously used in several keystroke dynamics studies; the corresponding synthesized samples, which have been forged as detailed in the companion article, share the same keystroke sequences as the human-written ones to facilitate comparison. The human-written samples were collected, and the synthesized samples created, with the objective of training and evaluating a liveness detection model. For each human-written sample of each source dataset and each method, 25 synthetic samples were included in the dataset here presented; these were forged using five different methods, a between-subject profile (only samples from users other than the target were available to the attacker) or with varying partial knowledge of the legitimate users' keystroke dynamics that ranged from only 100 keystrokes to all the available information. This dataset can be used by researchers to evaluate the performance of liveness detection methods for keystroke dynamics against a variety of state-of-the-art methods of sample synthesis.