Reviewerly/exHarmony huggingface dataset:
Implementation of Quantum Machine Learning Algorithms: A Literature Review

Recently, machine learning is benefiting from advan-tages of quantum computing which has resulted in a new stream of algorithms known as quantum machine learning algorithms. This paper presents the literature describing implementations of quantum machine learning algorithms in the various quantum machine learning frameworks. In addition, for each of the ob-served algorithms and frameworks, the literature in which they are described is stated. To the best of our knowledge, this is so far the most comprehensive overview of the existing QML algorithms with their corresponding implementation frameworks.
Securing Internet of Things Environment using Lightweight Blockchain Approach

Internet of things (IoT) is an environment where heterogenous devices are interconnected to perform a specific function. IoT applications has gradually become an essential part in different fields due to the ascent of information-based society and the diversity of IoT domains and applications. However, like any other emerging technology, IoT has not attained the level of maturity necessary to attain relevant required security and vulnerability suppression levels that may affect the potential benefits of IoT. The limited computational resources and memory of IoT devices are the main challenges one faces when dealing with IoT. Therefore, we need to guarantee a certain level of security for this system. On the other hand, blockchain technology has shown a promising capability of providing secure transactions and therefore, may help in securing IoT environment. In this paper, we explore the utilization of blockchain technology in the authentication of Iot environment. Hence, we propose a new solution that takes the advantage of decentralized blockchain and secure nature and combines it in IoT system that works with MQTT protocol. Furthermore, the system is built on fog computing technology that acts as the backbone for this system to enhance the overall performance and add an extra layer of defense to the system. Finally, we conducted a simulation of the proposed model to measure the environment performance key metrics and the results showed that the proposed model has a strong potential with good performance.
The Device Compact Model Based on Multi-gradient Neural Network and Its Application on MoS<sub>2</sub> Field Effect Transistors

Transistor compact model (TCM) is the key bridge between process technology and chip design. A TCM based on multi-gradient neural network (MNN) is developed to capture the nonlinear device electronic characteristics and their high order derivatives in high precision. The typical MNN model creation time of a single transistor is < 3 hours with accuracy > 95% (error < 5%). Further, this MNN model methodology is applied to the MoS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> FET, and the created model is characterized and implemented for the simulations of logic circuits such as ring oscillator (RO) and various standard cells. Simulation results agree well with experimental data.
Exploratory data analysis for data center energy management

The continuous improvement in energy efficiency of existing data centers would help reduce their environmental footprints. Greening of Data Centers could be attained using renewable energy sources or more energy efficient compute systems and effective cooling systems. A reliable cooling system is necessary to generate a persistent flow of cold air to cool servers that are subjected to increasing computational load demand. As a matter of fact, servers' dissipated heat effects a strain on the cooling systems and consequently, on electricity consumption. Generated heat in the data center is categorized into different granularity levels namely: server level, rack level, room level, and data center level. Several datasets are collected at ENEA Portici Data Center from CRESCO 6 cluster - a High-Performance Computing Cluster. The cooling and environmental aspects of the data center is also considered for data analysis. This research aims to conduct a rigorous exploratory data analysis on each dataset separately and collectively followed in various stages. This work presents descriptive and inferential analyses for feature selection and extraction process. Furthermore, a supervised Machine learning modelling and correlation estimation is performed on all the datasets to abstract relevant features. that would have an impact on energy efficiency in data centers.
Analysing academic paper ranking algorithms using test data and benchmarks: an investigation

Abstract Research on academic paper ranking has received great attention in recent years, and many algorithms have been proposed to automatically assess a large number of papers for this purpose. How to evaluate or analyse the performance of these ranking algorithms becomes an open research question. Theoretically, evaluation of an algorithm requires to compare its ranking result against a ground truth paper list. However, such ground truth does not exist in the field of scholarly ranking due to the fact that there does not and will not exist an absolutely unbiased, objective, and unified standard to formulate the impact of papers. Therefore, in practice researchers evaluate or analyse their proposed ranking algorithms by different methods, such as using domain expert decisions (test data) and comparing against predefined ranking benchmarks. The question is whether using different methods leads to different analysis results, and if so, how should we analyse the performance of the ranking algorithms? To answer these questions, this study compares among test data and different citation-based benchmarks by examining their relationships and assessing the effect of the method choices on their analysis results. The results of our experiments show that there does exist difference in analysis results when employing test data and different benchmarks, and relying exclusively on one benchmark or test data may bring inadequate analysis results. In addition, a guideline on how to conduct a comprehensive analysis using multiple benchmarks from different perspectives is summarised, which can help provide a systematic understanding and profile of the analysed algorithms.
Didactic fairy tale designing as a key to proactive training of Physics and Mathematics at primary schools

Abstract Relevance of the selected research topic is due to its development results aiming to resolve contradictions between society’s needs for personality-oriented training of Physics and Mathematics basics at comprehensive secondary school and the inadequate basic (necessary and sufficient) conditions to provide this in primary grades. Teachers also lack for a systematic and scientifically substantiated practice of immersing their students into the didactic fairytale environment. The research primarily aims to reveal the author’s conceptual vision of the essence and principles of didactic fairy tale designing in order to convert it into a basic tool of Physics and Mathematics training to 1st – 4th grade pupils.
A Novel Deep Feature Extraction Engineering for Subtypes of Breast Cancer Diagnosis: A Transfer Learning Approach

Feature extraction from histological images is a challenging part of computer-aided detection of breast cancer. For this research, we present a novel technique for deep feature extraction for breast cancer diagnosis subtypes based on a transfer learning approach using the BreaKhis dataset. This approach consists of five phases: feature extraction, concatenation, transformation, selection, and classification. In the first phase, nineteen pre-trained convolutional neural networks were used as feature extractors to extract features from the input images. A Support Vector Machine was used at the feature extraction phase to calculate the misclassification rate of each feature generated by the pre-trained networks used. The feature extraction results showed that the two networks achieved the highest accuracy on the dataset and outperformed the other networks. The two networks considered were selected and connected to create the DRNet model, combining the pre-trained networks ResNet50 and DenseNet201. The extracted features were decomposed into five sub-hand low-level features using a multilevel discrete wavelet transform in the transformation phase. An iterative neighborhood component analyzer was used to select the minimum number of features needed in the classification phase. A cubic support vector machine was used as a classifier in the final phase. Average classification accuracy of 98.61%, 98.04%, 97.68%, and 97.71% for the 40×, 100×, 200×, and 400× magnification levels, respectively, was achieved.
Beobench

Reinforcement learning (RL) is often considered a promising approach for controlling complex building operations. In this context, RL algorithms are typically evaluated using a testing framework that simulates building operations. To make general claims and avoid overfitting, an RL method should be evaluated on a large and diverse set of buildings. Unfortunately, due to the complexity of creating building simulations, none of the existing frameworks provide more than a handful of simulated buildings. Moreover, each framework has its own particularities, which makes it difficult to evaluate the same algorithm on multiple frameworks. To address this, we present Beobench: a Python toolkit1 that provides unified access to building simulations from multiple frameworks using a container-based approach. We demonstrate the power of our approach with an example showing how Beobench can launch RL experiments in any supported framework with a single command.
Segmentation of Mammogram Images Using Deep Learning for Breast Cancer Detection

Breast Cancer is one of the most common forms of cancer occurring in women. According to GLOBCON, 24.2% of new cancer types are related to breast cancer. Early detection followed by a proper diagnosis can help to combat this kind of serious disease. Segmentation of mammography lesions has been proven to be a valuable source of information for the detection and classification of breast cancer. Segmentation can assist both in extracting shape-related features and providing accurate localization of the lesions. In this paper, detailed research on the segmentation of the mammography images using two different networks, namely U-Net and BCDU-Net is done. A comparison of the performance concerning Dice Coefficient and Jaccard Similarity is done on two subsets of the same dataset, one on the full mammogram image and another on the ROI extracted from the mammogram. The evaluation results of the proposed research achieve the highest Jaccard and Dice coefficient of 0.7872 and 0.8376 respectively on the full mammogram. Whereas the figures for ROI segmentation are 0.8098 and 0.8723 respectively. These results demonstrate that, with equal data size, both the network provides better segmentation performance on ROI than on full mammogram.
A Fast Noniterative Visually Lossless Compression of Dental Images Using BPG Coder

Here we consider lossy compression of grayscale dental images. To preserve valuable diagnostic information, we propose to carry out visually lossless compression. For this purpose, it is proposed to apply better portable graphics (BPG) coder for which quality and compression ratio is controlled by the quality parameter Q. To provide invisibility of distortions and artifacts, visual quality metric called PSNR-HVS-M is analyzed for which the threshold of distortion invisibility is known. Experiments with image fragments are carried out for dental images produced by Morita imager. It is shown that quality of compressed images slightly depends on image acquisition mode. We give recommendations concerning Q setting to provide invisibility of distortions. These recommendations are also based on the results of analysis of dental image visual quality carried out by a group of dentistry experts. It is shown that BPG coder offers several benefits compared to several counterparts.
Enhancing anomaly detection methods for energy time series using latent space data representations

The increasing number of recorded energy time series calls for an automated operation of smart grid applications such as load forecasting and load management. While these applications require anomaly-free data to perform well, the recorded data often contains anomalies. The numerous methods to detect these anomalies are typically applied directly to the recorded data. However, for other tasks such as forecasting, promising performance has been achieved when directly applying methods to a meaningful feature space of the data, i.e., the latent space data representation. We, therefore, propose a novel approach to generally enhance anomaly detection methods for energy time series by taking advantage of their latent space representation. We create latent space data representations using a conditional Invertible Neural Network (cINN) and a conditional Variational Autoencoder (cVAE) and directly apply existing supervised and unsupervised detection methods to this representation. We evaluate the latent space data representation qualitatively by visualizing the separation of anomalies and non-anomalous data. We also quantitatively evaluate our approach by applying supervised and unsupervised detection methods to real-world load data containing two groups of artificially inserted anomalies: technical faults and unusual consumption. We show that our approach generally improves the anomaly detection performance of the considered methods while only moderately increasing computational cost.
BSPASS: A Beam Search-Based Phase Association and Source Scanning Earthquake Location Method

Abstract We propose a novel phase association and source scanning method by incorporating the norm-grid search and a beam search technique together to associate seismic phases, and to determine the accurate location of a particular event simultaneously. The method is called the beam search-based phase association and source scanning (BSPASS). Using synthetic travel times for two seismic networks with different station spacings, we examine the practicability and accuracy of the BSPASS method. The results show that BSPASS could constrain event location with high precisions, if the standard deviation of travel-time errors is less than 0.4 s. The method works well even for those earthquakes located at the network boundary. We applied the BSPASS method to the beginning day of the 2018 Purcell mountains earthquake swarm. Two times more events are detected automatically, and similar spatiotemporal distribution of these earthquakes is recognized by our method compared to the U.S. Geological Survey catalog. We also investigate the flexibility and robustness of the BSPASS method by adding Gaussian noises to phase arrivals. With the increasing of noise levels, the location errors grow accordingly. Based on our tests, we suggest that there might be an upper limit of phase picking errors for a particular source array configuration, for example, 0.4 s in this study, above which the global optimization procedure fails to resolve a better source location under the constraint of travel-time residuals than that of the norm-grid searching.
Application of efficient recognition algorithm based on deep neural network in English teaching scene

The recognition of English texts in teaching scenes is a practical research direction. English text recognition can be widely used in English teaching scenes, such as assisting teachers to recognise students’ English homework, text positioning before text translation, developing outdoor classrooms, assisting junior students in scene understanding and so on. To identify English information in different scenes as accurately as possible, identifying the corresponding text content is the key. Based on a deep neural network, this paper proposes GCN-Attention English recognition algorithm. The experiment adopts the deep learning framework Tensorflow, which combines 104 × 104 size GCN with an attention mechanism for training. The output of GCN is used to train the cyclic neural network to continuously predict the next most likely letter in the sequence. The goal of training is to match the output words with the expected words as much as possible. The test results show that the model can have a good recognition accuracy for the scene image data set used in teaching.
A multi-level feature-fusion-based approach to breast histopathological image classification

Previously, convolutional neural networks mostly used deep semantic feature information obtained from several convolutions for image classification. Such deep semantic features have a larger receptive field, and the features extracted are more effective as the number of convolutions increases, which helps in the classification of targets. However, this method tends to lose the shallow local features, such as the spatial connectivity and correlation of tumor region texture and edge contours in breast histopathology images, which leads to its recognition accuracy not being high enough. To address this problem, we propose a multi-level feature fusion method for breast histopathology image classification. First, we fuse shallow features and deep semantic features by attention mechanism and convolutions. Then, a new weighted cross entropy loss function is used to deal with the misjudgment of false negative and false positive. And finally, the correlation of spatial information is used to correct the misjudgment of some patches. We have conducted experiments on our own datasets and compared with the base network Inception-ResNet-v2, which has a high accuracy. The proposed method achieves an accuracy of 99.0% and an AUC of 99.9%.
A highly efficient, confidential, and continuous federated learning backdoor attack strategy

Federated learning is a kind of distributed machine learning. Researchers have conducted extensive research on federated learning's security defences and backdoor attacks. However, most studies are based on the assumption federated learning participant's data obey iid (independently identically distribution). This paper will evaluate the security issues of non-iid federated learning and propose a new attack strategy. Compared with the existing attack strategy, our approach has three innovations. The first one, we conquer foolsgold [1] defences through the attacker's negotiation. In the second one, we propose a modified gradient upload strategy for fedsgd backdoor attack, which significantly improves the backdoor attack's confidentiality on the original basis. Finally, we offer a bit Trojan method to realize continuous on non-iid federated learning. We conduct extensive experiments on different datasets to illustrate our backdoor attack strategy is highly efficient, confidential, and continuous on non-iid federated learning.
An optimization method for the inverted pendulum problem based on deep reinforcement learning

Abstract The inverted pendulum problem is a classical problem. The inverted pendulum starting at a random position keeps moving upwards and aims to reach an upright position. The problem has been solved through some methods based on deep reinforcement learning (DRL) such as Deep Deterministic Policy Gradient (DDPG). However, DDPG also has disadvantages. Deterministic policy is not conducive to action exploration. Moreover, the Q value needs to be estimated reasonably accurately for the policy to be accurate. Nevertheless, at the beginning of the learning, there is a certain difference in the Q value estimation, and the parameters learned at this time are easy to deviate. Therefore, this paper combining AdaBound with DDPG algorithm proposes an optimization method for the inverted pendulum problem, and compares the performance with that of four published baselines. The experimental results show that for the inverted pendulum problem, the proposed method outperforms the above four baselines to a certain extent.
Digitalization in Education and Learning Activities

To analyze the essence of digital transformation and the ways in which it becomes possible. This article focuses on students’ reactions, their assessments and views on ongoing online learning. The main research questions posed in the article are: is ongoing digital learning effective? Do students evaluate what happened as a digital transformation in education? What are the main challenges facing ongoing digital learning?
Arabic Medical Community Question Answering Using ON-LSTM and CNN

In this paper, we address the problem of Arabic community question answering. We propose a model that leverages both the archived question and answer representations in the similarity computation with the user’s question. The proposed model considers the interaction of the user’s question with both archived questions and answers separately to address the noisy information problem in Arabic community question answering. The proposed model is a combination of two parts that covers question-question similarity and question-answer relevance. We used twin ON-LSTM with an attention mechanism and Arabic ELMo embeddings as input for the question-question similarity. For the question-answer relevance, we used a combination of twin ON-LSTM and CNN networks which can capture the relevance score even with long answers and questions. We evaluated the proposed model on the biomedical Arabic community question answering dataset cQA-MD. The proposed model outperformed the previous studies evaluated on the same dataset.
FPGA implementation of histogram-based thresholding

Edge detection or segmentation results in a binary image. Thresholding is most often used for this purpose. The final binary image depends significantly on used threshold value. Several methods have been published to solve the problem of determining the optimal threshold. In this paper we propose FPGA implementation of Otsu's method for automatic unsupervised and nonparametric determination of picture threshold value based on picture histogram. This method has not been used in FPGA circuits so far because of relatively complicated implementation of the calculation algorithm.
A New Chaos-Based Lightweight Encryption Mechanism for Microcomputers

Many casual objects are now connected to the internet. The objects are managed with micro-computers for cost-effectiveness. Because this computer has limited processing and storage, they usually act as a collection of data. The data are processed by powerful computers. Therefore, data needs to be transferred to the computers. However, since most of the objects are connected to the internet in a wireless environment, they cause significant security weakness. In this study, a new chaos-based lightweight encryption mechanism is proposed to provide a solution to the problem. The aim of the study is to reduce the overhead of microcomputers and to have strong encryption of data. The 3-dimensional Lorenz chaotic system is used to test the performance of the study. According to the experimental results obtained, it has been observed that the proposed working image data is strongly encrypted and with less processing and storage than traditional encryption algorithms. This study will be used for the security of microcomputers to be used in future studies and more detailed performance tests of this method will be carried out.