Reviewerly/exHarmony huggingface dataset:
The Technology of Developing Media Culture in Secondary School Students

This article is written about the result of research on the development of technology for the development of media culture of students of secondary schools. The aim of the research is development of media culture of secondary school students, and the object of the research development of media culture of secondary school students. The tasks of the research are following from analysis of the condition of the technology to develop media culture in students of secondary educational institutions; defining of the educational process of developing media culture in students of secondary education institutions based on media culture development technologies employed abroad; improving the mechanism and content of media culture development in secondary school students; suggesting recommendations on expansion of pedagogical opportunities of media culture development in secondary school students. The article consists of from introduction, literature survey, methodology, recommendation, discussions, and conclusion.
Construction of distance teaching platform based on mobile communication technology

With the development and application of mobile communication technology, people have higher requirements for the transmission and reception of information, and this brings opportunities for distance learning. In view of this, this article has carried on the research to the long-distance teaching platform construction based on the mobile communication technology. First of all, this paper briefly introduces the application type of mobile communication technology in distance education; and then, this paper explores the framework and function of distance learning platform based on mobile communication technology, and gives the specific platform system framework and function modules of figure; finally, the main functions of the distance learning platform based on mobile communication technology are implemented and tested in this paper. The results of this paper show that the system can meet the design requirements, and can achieve distance learning and mobile learning. At the same time, the distance education institutions can transfer more learning resources to learners at a higher speed. The distance teaching platform based on mobile communication technology has a good application prospect, and the platform can exchange information with the traditional campus network to realise the data sharing.
Electroencephalogram signal quality enhancement by total variation denoising using non-convex regulariser

Medical practitioners have great interest in getting the denoised signal before analysing it. EEG is widely used in detecting several neurological diseases such as epilepsy, narcolepsy, dementia, sleep apnea syndrome, Alzheimer's, insomnia, parasomnia, Creutzfeldt-Jakob diseases (CJD) and schizophrenia, etc. In the process of EEG recordings, a lot of background noise and other kind of physiological artefacts are present, hence, data is contaminated. Therefore, to analyse EEG properly, it is necessary to denoise it first. Total variation denoising is expressed as an optimisation problem. Solution of this problem is obtained by using a non-convex penalty (regulariser) in the total variation denoising. In this article, non-convex penalty is used for denoising the EEG signal. The result has been compared with wavelet methods. Signal to noise ratio (SNR) and root mean square error have been computed to measure the performance of the method. It has been observed that the approach used here works well in denoising the EEG signal and hence enhancing its quality.
Factors influencing consumer's adoption of internet of things: an empirical study from Indian context

This study aims to find out the impact of safety, status, cost, and convenience on the consumer's awareness regarding internet of things (IoTs) as well as the consumer's adoption of IoTs. The population of the study is the total number of students of Aligarh Muslim University in which the sample size of 310 students has been taken for the study. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) is applied for testing reliability and validity. Also, structural equation modelling (SEM) is used for testing the hypotheses of the study. The results reveal that the students have a positive attitude of awareness in the case of the cost of IoTs devices while they have a negative attitude towards awareness regarding safety, convenience and status. Besides, the results show students' positive attitude of adoption in case of convenience, and status.
Mobile Phone Maintainability Prediction using MCDM Methodology

For software engineering it is essential to build a software that is qualitative from every perspective. As the interest of the clients in mobile applications getting higher and higher day by day, the quality turns out to be a major concern. Ample number of researches shows issues with the maintenance of the applications once they are public. Different studies showed various evaluation methods for estimating maintainability but only few were defined for mobile applications. Also, none of the approaches propounded gives a specified way for evaluating maintainability of these portable applications. The present research work, examines the key idea of maintenance of apps considering 9 vital factors for the estimation of maintainability utilizing an MCDM approach to evaluate the identified factors.
An Examination Of Big Data And Blockchain Technology

Blockchain technology is the process of development of bitcoin, the blockchain technology as a distributed ledger of cryptocurrency transactions for digitized, decentralized, trusted and secured manner. The mainstream of blockchain technology is bitcoin, bitcoin concept made with ledger of every single transaction, transactions allows for hashing mechanism to verify the large amounts of data. Big data task requires that large amount of computational space, to generate the terabytes of data for ensuring the successful data processing techniques. The major impact on big data analytics requires more number of data and generated data can be depending upon different sectors from different organizations. This paper presents a state of definition, characteristics, transaction process, and applications, along with discussion of big data analytics are introduced. In blockchain technology covers the flaws of big data in fruitful relationship, with the factors of security, transparency, decentralization and flexibility, so that data to be analyze in different and efficient way for organizations all sizes in data analytics form.
Improving the cooperation of fuzzy simplified memory A* search and particle swarm optimisation for path planning

Problem solving is a very important subject in the world of AI. In fact, a problem can be considered one or more goals along with a set of available interactions for reaching those goals. One of the best ways of solving AI problems is to use search methods. The simplified memory bounded A* (SMA*) is one of the best methods of informed search. In this research, a hybrid method was proposed to increase the performance of SMA* search. The combining fuzzy logic with this search method and improving it with PSO algorithm brought satisfactory results. The use of fuzzy logic leads to increase the search flexibility especially when a robot dealing with lots of barriers and path changes. Furthermore, combining PSO saves the search from being trapped into local optimums and provides for search some correct and accurate suggestions. In the proposed algorithm, the results indicate that the cost of search and branching factor are decreased in comparison with other methods.
Visualization System for Water Levels and Environmental Conditions in the Flood Monitoring Information System

Flood disaster is a national disaster that takes a lot of victims and material for now. The government nationally implements short-term programs to anticipate disasters by supporting the various government and private institutions in developing disaster mitigation systems to reduce sustainable casualties. Based on this support, this research aims to develop a flood monitoring information system by implementing a real-time flood visualization system. System development is done by making two systems, namely hardware-based flood detection systems that are used as client systems and software-based flood monitoring information systems as servers. Flood detection systems are built using ultrasonic sensors, temperature sensors, rain sensors, Arduino microcontrollers, and Sim900A GSM modules. On the flood monitoring information system server using the Xampp component as a server application and Gammu as an SMS application. The communication system between the two systems uses the SMS Gateway communication system. The parameter values sent through the flood detection system will be visualized by the server in the form of animation and text. From the research, it was found that the flood detection system as a client has been able to send flood data, temperature, and rain conditions in real-time. And flood monitoring information systems have been able to capture data from client systems and store them in MySQL DBMS.
Learning SpatioTemporal and Motion Features in a Unified 2D Network for Action Recognition

Recent methods for action recognition always apply 3D Convolutional Neural Networks (CNNs) to extract spatiotemporal features and introduce optical flows to present motion features. Although achieving state-of-the-art performance, they are expensive in both time and space. In this paper, we propose to represent both the two kinds of features in a unified 2D CNN without any 3D convolution or optical flows calculation. In particular, we first design a channel-wise spatiotemporal module to present the spatiotemporal features and a channel-wise motion module to encode feature-level motion features efficiently. Secondly, we combine these two modules and an identity mapping path into one united block that can easily replaces the original residual block in the ResNet architecture, forming a simple yet effective network termed STM network by introducing very limited extra computation cost and parameters. Thirdly, we propose a novel Twins Training framework for action recognition by incorporating a correlation loss to optimize the inter-class and intra-class correlation and a siamese structure to fully stretch the training data. We extensively validate the proposed STM on both temporal-related datasets (i.e., Something-Something v1 \& v2) and scene-related datasets (i.e., Kinetics-400, UCF-101, and HMDB-51). It achieves favorable results against state-of-the-art methods in all the datasets.
Training Time Minimization in Quantized Federated Edge Learning under Bandwidth Constraint

In this paper, the training time minimization problem is investigated in a quantized FEEL system, where the heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing number of communication rounds. The intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Constrained by total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization via successive convex approximation and the subproblem of bandwidth allocation via bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed algorithm are demonstrated by the experimental results.
Optical metrology embraces deep learning: keeping an open mind

Optical metrology practitioners ought to embrace deep learning with an open mind, while devote continuing efforts to look for its theoretical groundwork and maintain an awareness of its limits.
Toward Smart Communication Components: Recent Advances in Human and AI Speaker Interaction

This study aims to investigate how humans and artificial intelligence (AI) speakers interact and to examine the interactions based on three types of communication failures: system, semantic, and effectiveness. We divided service failures using AI speaker user data provided by the top telecommunication service providers in South Korea and investigated the means to increase the continuity of product use for each type. We proved the occurrence of failure due to system error (H1) and negative results on sustainable use of the AI speaker due to not understanding the meaning (H2). It was observed that the number of users increases as the effectiveness failure rate increases. For single-person households constituted by persons in their 30s and 70s or older, the continued use of AI speakers was significant. We found that it alleviated loneliness and that human-machine interaction using AI speaker could reach a high level through a high degree of meaning transfer. We also expect AI speakers to play a positive role in single-person households, especially in cases of the elderly, which has become a tough challenge in the recent times.
Moving Target Interception Considering Dynamic Environment

The interception of moving targets is a widely studied issue. In this paper, we propose an algorithm of intercepting the moving target with a wheeled mobile robot in a dynamic environment. We first predict the future position of the target through polynomial fitting. The algorithm then generates an interception trajectory with path and speed decoupling. We use Hybrid A* search to plan a path and optimize it via gradient decent method. To avoid the dynamic obstacles in the environment, we introduce ST graph for speed planning. The speed curve is represented by piecewise Bézier curves for further optimization. Compared with other interception algorithms, we consider a dynamic environment and plan a safety trajectory which satisfies the kinematic characteristics of the wheeled robot while ensuring the accuracy of interception. Simulation illustrates that the algorithm successfully achieves the interception tasks and has high computational efficiency.
XST: A Crossbar Column-wise Sparse Training for Efficient Continual Learning

Leveraging the ReRAM crossbar-based In-Memory-Computing (IMC) to accelerate single task DNN inference has been widely studied. However, using the ReRAM crossbar for continual learning has not been explored yet. In this work, we propose XST, a novel crossbar column-wise sparse training framework for continual learning. XST significantly reduces the training cost and saves inference energy. More importantly, it is friendly to existing crossbar-based convolution engine with almost no hardware overhead. Compared with the state-of-the-art CPG method, the experiments show that XST's accuracy achieves 4.95 % higher accuracy. Furthermore, XST demonstrates ~5.59 × training speedup and 1.5 × inference energy-saving.
Cinematographic Shot Classification with Deep Ensemble Learning

Cinematographic shot classification assigns a category to each shot either on the basis of the field size or on the movement performed by the camera. In this work, we focus on the camera field of view, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. The automation of this task can help freelancers and studios belonging to the visual creative field in their daily activities. In our study, we took into account eight classes of film shots: long shot, medium shot, full figure, american shot, half figure, half torso, close up and extreme close up. The cinematographic shot classification is a complex task, so we combined state-of-the-art techniques to deal with it. Specifically, we finetuned three separated VGG-16 models and combined their predictions in order to obtain better performances by exploiting the stacking learning technique. Experimental results demonstrate the effectiveness of the proposed approach in performing the classification task with good accuracy. Our method was able to achieve 77% accuracy without relying on data augmentation techniques. We also evaluated our approach in terms of f1 score, precision, and recall and we showed confusion matrices to show that most of our misclassified samples belonged to a neighboring class.
Fractional Order Darwinian Pigeon-Inspired Optimization for Multi-UAV Swarm Controller

This paper presents a novel multiple unmanned aerial vehicle (UAV) swarm controller based on the fractional calculus theory. This controller is designed based on fractional order Darwinian pigeon-inspired optimization (FODPIO) and PID algorithm. Several comparative simulations are conducted in the paper. The simulation results reveal that FODPIObased multi-UAV formation controller is superior to the basic PIO and differential evolution (DE) method. The fractional coeffcient in FODPIO algorithm makes it effective optimization with fast convergence rate, small overshoot, and better stability. Therefore, the controller proposed in this paper is feasible and robust.
Using textual bug reports to predict the fault category of software bugs

Debugging is a time-consuming and expensive process. Developers have to select appropriate tools, methods and approaches in order to efficiently reproduce, localize and fix bugs. These choices are based on the developers’ assessment of the type of fault for a given bug report. This paper proposes a machine learning (ML) based approach that predicts the fault type for a given textual bug report. We built a dataset from 70+ projects for training and evaluation of our approach. Further, we performed a user study to establish a baseline for non-expert human performance on this task. Our models, incorporating our custom preprocessing approaches, reach up to 0.69% macro average F1 score on this bug classification problem. We demonstrate inter-project transferability of our approach. Further, we identify and discuss issues and limitations of ML classification approaches applied on textual bug reports. Our models can support researchers in data collection efforts, as for example bug benchmark creation. In future, such models could aid inexperienced developers in debugging tool selection, helping save time and resources.
Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction

Accurate forecasting of solar radiation (Rs) is significant to photovoltaic power generation and agricultural management. The National Centers for Environmental Prediction (NECP) has released its latest Global Ensemble Forecast System version 12 (GEFSv12) prediction product; however, the capability of this numerical weather product for Rs forecasting has not been evaluated. This study intends to establish a coupling algorithm based on a bat algorithm (BA) and Kernel-based nonlinear extension of Arps decline (KNEA) for post-processing 1–3 d ahead Rs forecasting based on the GEFSv12 in Xinjiang of China. The new model also compares two empirical statistical methods, which were quantile mapping (QM) and Equiratio cumulative distribution function matching (EDCDFm), and compares six machine-learning methods, e.g., long-short term memory (LSTM), support vector machine (SVM), XGBoost, KNEA, BA-SVM, BA-XGBoost. The results show that the accuracy of forecasting Rs from all of the models decreases with the extension of the forecast period. Compared with the GEFS raw Rs data over the four stations, the RMSE and MAE of QM and EDCDFm models decreased by 20% and 15%, respectively. In addition, the BA-KNEA model was superior to the GEFSv12 raw Rs data and other post-processing methods, with R2 = 0.782–0.829, RMSE = 3.240–3.685 MJ m−2 d−1, MAE = 2.465–2.799 MJ m−2 d−1, and NRMSE = 0.152–0.173.
P-Lite: A study of parallel coordinate plot literacy

Visualization literacy, the ability to interpret and comprehend visual designs, is recognized as an essential skill by the visualization community. We identify and investigate barriers to comprehending parallel coordinates plots (PCPs), one of the advanced graphical representations for the display of multivariate and high-dimensional data. We develop a parallel coordinates literacy test with diverse images generated using popular PCP software tools. The test improves PCP literacy and evaluates the user’s literacy skills. We introduce an interactive educational tool that assists the teaching and learning of parallel coordinates by offering a more active learning experience. Using this pedagogical tool, we aim to advance novice users’ parallel coordinates literacy skills. Based on the hypothesis that an interactive tool that links traditional Cartesian Coordinates with PCPs interactively will enhance PCP literacy further than static slides, we compare the learning experience using traditional slides with our novel software tool and investigate the efficiency of the educational software with an online, crowdsourced user-study. User-study results show that our pedagogical tool positively impacts a user’s PCP comprehension.
Stability selection enables robust learning of differential equations from limited noisy data

We present a statistical learning framework for robust identification of differential equations from noisy spatio-temporal data. We address two issues that have so far limited the application of such methods, namely their robustness against noise and the need for manual parameter tuning, by proposing stability-based model selection to determine the level of regularization required for reproducible inference. This avoids manual parameter tuning and improves robustness against noise in the data. Our stability selection approach, termed PDE-STRIDE, can be combined with any sparsity-promoting regression method and provides an interpretable criterion for model component importance. We show that the particular combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast and robust framework for equation inference that outperforms previous approaches with respect to accuracy, amount of data required, and robustness. We illustrate the performance of PDE-STRIDE on a range of simulated benchmark problems, and we demonstrate the applicability of PDE-STRIDE on real-world data by considering purely data-driven inference of the protein interaction network for embryonic polarization in Caenorhabditis elegans. Using fluorescence microscopy images of C. elegans zygotes as input data, PDE-STRIDE is able to learn the molecular interactions of the proteins.