Reviewerly/exHarmony huggingface dataset:
Constructing a Digital Competence Evaluation Framework for In-Service Teachers’ Online Teaching

The focus on online teaching and teachers’ digital competence (DC) has reached a new level following the emergence of COVID-19 and its dramatic influence on the educational industry, requiring teachers to be equipped with DC. However, there is no consensus on the measuring framework of teachers’ DC. Therefore, this study aimed to construct a reliable self-evaluation framework for in-service teachers’ DC during online teaching. The data of 1342 teachers with online teaching experience were obtained. The methods of data analysis included exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and item analysis. Results demonstrated that the constructed evaluation framework performed consistently with the collected data. CFA also confirmed a good model fitting of the relevant 10 factors of the teachers’ DC framework. Therefore, in the teachers’ DC evaluation framework, the constructs interacting with each other consist of technical knowledge (TK, four items), learner knowledge (LK, three items), pedagogical knowledge (PK, three items), ethical knowledge (EK, three items), learner technical knowledge (LTK, four items), learner pedagogical knowledge (LPK, four items), learner ethical knowledge (LEK, four items), technical pedagogical knowledge (TPK, three items), technical ethical knowledge (TEK, four items), and pedagogical ethical knowledge (PEK, three items), but in total the scale comprises 35 items. It can be an effective instrument to support in-service teachers’ DC measurement for their online teaching.
On Solar Radiation Prediction for the East–Central European Region

The aim of this paper is to present the results of the Weather Research and Forecasting (WRF) model of solar radiation for moderate climatic zones. This analysis covered the area of northeastern Germany. Due to very unfavorable solar energy conditions in this region for at least 1/3 of the year, we decided to select the dates with the most representative conditions: passing warm fronts, cold fronts, and occluded fronts (two cases each). As the reference, two cloudless conditions during high-pressure situations were chosen. Two different shortwave radiation schemes—Rapid Radiative Transfer Model for general circulation model (RRTMG) and Dudhia—were tested. The obtained results were compared with in situ data measured at Deutscher Wetterdienst (DWD) stations and then with European Medium-Range Weather Forecast reanalysis (ERA5) data. The results showed that for high-pressure situations, the mean correlations with measured data were above 90%. The Dudhia scheme, in addition to the expected good results for the high-pressure situation, showed better results than RRTMG for the warm and cold fronts as well. The forecast using the RRTMG scheme gave the best results for the occluded front, which were also better than those of the ERA5 model.
Ukemochi: A Video See-through Food Overlay System for Eating Experience in the Metaverse

The widespread use of Head-Mounted Displays (HMDs) allows ordinary users to interact with their friends daily in social Virtual Environments (VEs) or metaverse. However, it is not easy to eat in a metaverse while wearing an HMD because the Real Environment (RE) is not visible. Currently, users watch the RE's food through the gap between the user's face and the HMD (None) or superimposing a video see-through (VST) image on the VE, but these methods reduce the sense of presence. To allow natural eating in a VE, we propose Ukemochi that improves the presence and ease of eating. Ukemochi seamlessly overlays a food segmentation image inferred by deep neural networks on a VE. Ukemochi can be used simultaneously as a VE created with the OpenVR API and can be easily deployed for the metaverse. In this study, we evaluated the effectiveness of Ukemochi by comparing three visual presentation methods (None, VST, and Ukemochi) and two meal conditions (Hand condition and Plate condition). The experimental results demonstrated that Ukemochi enables users to maintain a high presence in VE and improve the ease of eating. We believe that our study will provide users with the experience of eating in the metaverse and encourage further research on eating in the metaverse.
High-performance Reconfigurable DNN Accelerator on a Bandwidth-limited Embedded System

Deep convolutional neural networks (DNNs) have been widely used in many applications, particularly in machine vision. It is challenging to accelerate DNNs on embedded systems because real-world machine vision applications should reserve a lot of external memory bandwidth for other tasks, such as video capture and display, while leaving little bandwidth for accelerating DNNs. In order to solve this issue, in this study, we propose a high-throughput accelerator, called reconfigurable tiny neural network accelerator (ReTiNNA), for the bandwidth-limited system and present a real-time object detection system for the high-resolution video image. We first present a dedicated computation engine that takes different data mapping methods for various filter types to improve data reuse and reduce hardware resources. We then propose an adaptive layer-wise tiling strategy that tiles the feature maps into strips to reduce the control complexity of data transmission dramatically and to improve the efficiency of data transmission. Finally, a design space exploration (DSE) approach is presented to explore design space more accurately in the case of insufficient bandwidth to improve the performance of the low-bandwidth accelerator. With a low bandwidth of 2.23 GB/s and a low hardware consumption of 90.261K LUTs and 448 DSPs, ReTiNNA can still achieve a high performance of 155.86 GOPS on VGG16 and 68.20 GOPS on ResNet50, which is better than other state-of-the-art designs implemented on FPGA devices. Furthermore, the real-time object detection system can achieve a high object detection speed of 19 fps for high-resolution video.
Robust Motion Planning in the Presence of Estimation Uncertainty

Motion planning is a fundamental problem and focuses on finding control inputs that enable a robot to reach a goal region while safely avoiding obstacles. However, in many situations, the state of the system may not be known but only estimated using, for instance, a Kalman filter. This results in a novel motion planning problem where safety must be ensured in the presence of state estimation uncertainty. Previous approaches to this problem are either conservative or integrate state estimates optimistically which leads to non-robust solutions. Optimistic solutions require frequent replanning to not endanger the safety of the system. We propose a new formulation to this problem with the aim to be robust to state estimation errors while not being overly conservative. In particular, we formulate a stochastic optimal control problem that contains robustified risk-aware safety constraints by incorporating robustness margins to account for state estimation errors. We propose a novel sampling-based approach that builds trees exploring the reachable space of Gaussian distributions that capture uncertainty both in state estimation and in future measurements. We provide robustness guarantees and show, both in theory and simulations, that the induced robustness margins constitute a trade-off between conservatism and robustness for planning under estimation uncertainty that allows to control the frequency of replanning.
An Effective Encrypted Traffic Classification Method Based on Pruning Convolutional Neural Networks for Cloud Platform

With the rapid development of cloud computing technology, the traffic of cloud platform also increases rapidly. Most of these traffic adopt encryption technology, the original traffic classification method is no longer effective. In this paper, we propose an efficient encrypted traffic classification method based on pruning convolution neural network: PCNN. This method does not need to extract features manually, but automatically extracts high-level features through CNN, which makes the model have generalization ability and reduces the cost. Then the pruning technology is adopted to prune the model and retain the parameters that are highly important to the model, which greatly reduces the size and computation of the model and will not affect the performance of the model. We used the public network dataset ISCXVPN2016 to verify our proposed method, the experiments show that the F1 score of our proposed approach is 93% which is improved by 3%, and the precision is 94%, which is improved by 5%.
A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations

We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. Making effective recommendations to these time-sensitive cold-start users is critical to maintain the user base of a recommender system. Due to the sparse recent interactions, it is challenging to capture these users' current preferences precisely. Solely relying on their historical interactions may also lead to outdated recommendations misaligned with their recent interests. The proposed model leverages historical and current user-item interactions and dynamically factorizes a user's (latent) preference into time-specific and time-evolving representations that jointly affect user behaviors. These latent factors further interact with an optimized item embedding to achieve accurate and timely recommendations. Experiments over real-world data help demonstrate the effectiveness of the proposed time-sensitive cold-start recommendation model.
Malaria Detection Using Image Processing And Machine Learning

Malaria which has now become a common human disease is diagnosed in the present scenario starting with a clinical screening and then by medical treatment. Automated classification of malaria parasites using images is a bit challenging task due to the large amount of variability found in the display of skin abrasion. Many deep convolution neural networks show possibility for general highly variable tasks across many fine-grained object categories. Here we are working by using CNN network and datasets. CNN which is known as convolutional neural network is used to differentiate images on the basis of image pixel patterns. Our model mostly focusses on image processing using keras image generator for creating real time images. We basically train our model to easily differentiate between positive and negative images from the given datasets. Detecting malaria by image processing is very fast and reliable method as it does not require any experience. The main aim of using image processing is that our model can detect cells from multiple images taken from microscope via thin blood smear and detect them as positive and negative human blood cell and also it performs classification on human blood cell by using deep learning.
MCW-Net: Single image deraining with multi-level connections and wide regional non-local blocks

A recent line of convolutional neural network-based works has succeeded in capturing rain streaks. However, difficulties in detailed recovery still remain. In this paper, we present a multi-level connection and wide regional non-local block network (MCW-Net) to properly restore the original background textures in rainy images. Unlike existing encoder-decoder-based image deraining models that improve performance with additional branches, MCW-Net improves performance by maximizing information utilization without additional branches through the following two proposed methods. The first method is a multi-level connection that repeatedly connects multi-level features of the encoder network to the decoder network. Multi-level connection encourages the decoding process to use the feature information of all levels. In multi-level connection, channel-wise attention is considered to learn which level of features is important in the decoding process of the current level. The second method is a wide regional non-local block. As rain streaks primarily exhibit a vertical distribution, we divide the grid of the image into horizontally-wide patches and apply a non-local operation to each region to explore the rich rain-free background information. Experimental results on both synthetic and real-world rainy datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art models. Furthermore, the results of the joint deraining and segmentation experiment prove that our model contributes effectively to other vision tasks.
String Kernels for Document Classification: A Comparative Study

In machine learning and data mining, String Kernels combined with classifiers like Support Vector Machines (SVM) show state-of-the-art results for tasks such as text classification. Traditional pairwise comparisons of strings on large datasets are computationally expensive and result in quadratic runtimes. This work compares the performance of various String Kernels and similarity measures on the document classification task. We compare different String Kernels such as Spectrum Kernel, String Subsequence Kernel, Weighted Degree Kernel, and Distance Substitution Kernel in this paper for classifying text documents. A detailed comparative study of these Kernel techniques on real-life document corpus such as Reuters-21578 shows different insights when used with and without other feature extraction techniques. The results indicate that string similarity measures give the best performance when run over the entire corpus but for small and medium-sized datasets. The complexity increases with an increase in the size of the dataset.
How Effective Is Automated Trace Link Recovery in Model-Driven Development?

[Context and Motivation] Requirements Traceability (RT) aims to follow and describe the lifecycle of a requirement. RT is employed either because it is mandated, or because the product team perceives benefits. [Problem] RT practices such as the establishment and maintenance of trace links are generally carried out manually, thereby being prone to mistakes, vulnerable to changes, time-consuming, and difficult to maintain. Automated tracing tools have been proposed; yet, their adoption is low, often because of the limited evidence of their effectiveness. We focus on vertical traceability that links artifacts having different levels of abstraction. [Results] We design an automated tool for recovering traces between JIRA issues (user stories and bugs) and revisions in a model-driven development (MDD) context. Based on existing literature that uses process and text-based data, we created 123 features to train a machine learning classifier. This classifier was validated via three MDD industry datasets. For a trace recommendation scenario, we obtained an average F $$_2$$ -score of 69% with the best tested configuration. For an automated trace maintenance scenario, we obtained an F $$_{0.5}$$ -score of 76%. [Contribution] Our findings provide insights on the effectiveness of state-of-the-art trace link recovery techniques in an MDD context by using real-world data from a large company in the field of low-code development.
Ouroboros: An Efficient and Provably Secure KEM Family

In this paper we introduce Ouroboros, a new family of Key Exchange protocols based on coding theory. The protocols propose a middle ground between the cryptosystems based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {QC}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {MDPC}$ </tex-math></inline-formula> codes, which feature small parameter sizes, but have a security reduction to two problems: the syndrome decoding problem and the indistinguishability of the code, and the HQC protocol, which features bigger parameters but has a security reduction to the syndrome decoding problem only. Ouroboros features a reduction to the syndrome decoding problem with only a small overhead compared to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {QC}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {MDPC}$ </tex-math></inline-formula> based cryptosystems. The approach is based on an ideal structure and also works for the rank metric. This yields a simple, secure and efficient approach for key exchange, the Ouroboros family of protocols. For the Hamming metric we obtain the same type of parameters (and almost the same simple decoding) as for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {MDPC}$ </tex-math></inline-formula> based cryptosystems, but with a security reduction to decoding random quasi-cyclic codes in the Random Oracle Model. This represents a reduction of up to 38% on the public key size compared to HQC, for the most secure parameters. For the rank metric, we obtain better parameters than for RQC, saving up to 31% on the public key for the most secure set of parameters, using non homogeneous errors in Ouroboros. In this full version, the protocol and decoding algorithm have been slightly improved, additional details are given in the security proof, and the protocol is fully described for the rank metric.
MARL-Based Dual Reward Model on Segmented Actions for Multiple Mobile Robots in Automated Warehouse Environment

The simple and labor-intensive tasks of workers on the job site are rapidly becoming digital. In the work environment of logistics warehouses and manufacturing plants, moving goods to a designated place is a typical labor-intensive task for workers. These tasks are rapidly undergoing digital transformation by leveraging mobile robots in automated warehouses. In this paper, we studied and tested realistically necessary conditions to operate mobile robots in an automated warehouse. In particular, considering conditions for operating multiple mobile robots in an automated warehouse, we added more complex actions and various routes and proposed a method for improving sparse reward problems when learning paths in a warehouse with reinforcement learning. Multi-Agent Reinforcement Learning (MARL) experiments were conducted with multiple mobile robots in an automated warehouse simulation environment, and it was confirmed that the proposed reward model method makes learning start earlier even there is a sparse reward problem and learning progress was maintained stably. We expect this study to help us understand the actual operation of mobile robots in an automated warehouse further.
More data breaches compromise millions of records

Another rash of data breaches has put the personal information of millions of people at risk, although not all of the leaks were the result of malicious hackers.
Performance Analysis of Machine Learning Algorithms in Credit Card Fraud Detection

Credit cards are very useful to make payments nowadays. Security is a major issue in making transactions using credit cards. Lots of frauds happen in different ways using credit cards. Machine learning algorithms are very useful in making predictions. In this paper, machine learning algorithms are used to analyze and predict the fraud in a credit card transaction. Different machine learning algorithms such as decision tree, random forest, linear regression, support vector machine, XG-Boost are applied on a standard data set. The prediction accuracy of these algorithms is measured and compared to each other. It is concluded that machine learning algorithms are very useful in predicting frauds using credit cards. The prediction accuracy of XG-Boost and the random forest is best among all.
Anomaly Detection for Physics Analysis and Less Than Supervised Learning

Artificial Intelligence for High Energy Physics, pp. 85-112 (2022) No AccessChapter 4: Anomaly Detection for Physics Analysis and Less Than Supervised LearningBenjamin NachmanBenjamin NachmanLawrence Berkeley National Laboratory, Berkeley, CA 94720, USAhttps://doi.org/10.1142/9789811234033_0004Cited by:1 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Modern machine learning tools offer exciting possibilities to qualitatively change the paradigm for new particle searches. In particular, new methods can broaden the search program by gaining sensitivity to unforeseen scenarios by learning directly from data. There has been a significant growth in new ideas and they are starting to be applied to experimental data. This chapter introduces these new anomaly detection methods, which range from fully supervised algorithms to unsupervised, and include weakly supervised methods. FiguresReferencesRelatedDetailsCited By 1'Flux+Mutability': a conditional generative approach to one-class classification and anomaly detectionC Fanelli, J Giroux and Z Papandreou4 November 2022 | Machine Learning: Science and Technology, Vol. 3, No. 4 Recommended Artificial Intelligence for High Energy PhysicsMetrics History PDF download
Chaotic inertia weight and constriction factor-based PSO algorithm for BLDC motor drive control

The population algorithms have a number of advantages over classical methods for solving complex optimisation problems such as tuning of controller parameters of motor drives These algorithms for solving various problems of global optimisation is often called as methods inspired by nature, methods in this class are based on the modelling of intelligent behaviour of organised members of the population. Particle swarm optimisation (PSO) algorithm is population-based algorithm which has ability to fine tune the controller parameters. In this paper, chaotic inertia weight and constriction factor-based PSO algorithms are proposed for tuning of proportional-integral-derivative (PID) controller parameters to control brushless direct current (BLDC) motor drive. The BLDC is modelled in MATLAB/Simulink and trapezoidal back emf waveforms are modelled as a function of rotor position using MATLAB code. The simulation results of PSO algorithms are compared and results shown the effectiveness of C-inertia weight and C-factor in tuning PID controller parameters.
Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged. The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm. Moreover, the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position. Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-ofthe-art methods for images with different texture complexity.