Reviewerly/exHarmony huggingface dataset:
An Implementation of Multimedia Resources Learning Topic for Interactive Applications in Android Programming Learning Assistance System

Recently, Android programming has become an essential subject in IT departments in universities due to the intense market demands. We have proposed and developed the Android Programming Learning Assistance System (APLAS) as a self-learning platform in the Android programming course to support students. APLAS offers rich learning materials with four stages, where each step has several learning topics. This paper implements the Multimedia Resources topic as the fourth topic in the Interactive Application stage. This topic aims to understand the utilization of video, image, YouTube video, and animations in Android applications. The implemented case was applied to 50 students in an IT department in Indonesia Al to confirm the effectiveness. The students correctly solved the assignments in the topic with positive comments.
Next Generation Learning and Assessment: What, Why and How

Computational psychometrics is a blend of stochastic processes theory, computer science-based methods, and theory-based psychometric approaches that may aid the analyses of complex data from performance assessments. This chapter discusses the grounds for using complex performance assessments, the design of such assessments so that useful evidence about targeted abilities will be present in the data to be analysed, and roles that computational psychometric ideas and methods can play. It first provides background on a situative, sociocognitive, perspective on human capabilities and how we develop them and use them—a perspective we believe is necessary to synthesize the methodologies. Next it reviews the form of evidentiary argument that underlies the evidence-centered approach to design, interpretation, and use of educational assessments. It then points out junctures in extensions of the argument form where computational psychometric methods can carry out vital roles in assessment of more advanced constructs, from more complex data, in new forms and contexts of assessment. It concludes by reflecting on how one reconceives and extends the notions of validity, reliability, comparability, fairness, and generalizability to more complex assessments and analytic methods.
Accounting for the Effect of Inter-Task Similarity in Continual Learning Models

Catastrophic forgetting has long been a major obstacle to continual learning. In this paper, we explore the effect of certain task characteristics, in particular inter-task similarity, on the extent of forgetting. Moreover, we experimentally study the effect of these characteristics on the effectiveness of recent state-of-the-art continual learning approaches. We show that the performance of some methods, for example, the recently proposed Learning without Forgetting (LwF) and elastic weight consolidation (EWC) models, is significantly dependent on these characteristics. We propose a rehearsal-based extension to continual learning models to address this vulnerability. We develop this extension first in the context of LwF and later demonstrate its effectiveness with other models such as EWC. We show that a memory budget of 1% of training data is sufficient to significantly improve on performance in cases of low inter-task similarity.
tHyENA: Making HyENA Even Smaller

This paper proposes a lightweight short-tweak tweakable blockcipher (tBC) based authenticated encryption (AE) scheme tHyENA, a tweakable variant of the high profile NIST LWC competition submission HyENA. tHyENA is structurally similar to HyENA, however, proper usage of short-tweaks for the purpose of domain separation, makes the design much simpler compact. We know that HyENA already achieves a very small hardware footprint, and tHyENA further optimizes it. To realize our claim, we provide NIST API compliant hardware implementation details and benchmark for tHyENA against HyENA and several other well-known sequential feedback-based designs. The implementation results depict that when instantiated with the tBC TweGIFT, tHyENA achieves an extremely low hardware footprint - consuming only around 680 LUTs and 260 slices while maintaining the full rate and the almost birthday bound security. To the best of our knowledge, this figure is significantly better than all the known implementation results of other lightweight ciphers with sequential structures.
End-to-End Medical Image Denoising via Cycle-consistent Generative Adversarial Network

Since the radiation of X-ray is detrimental to patients, low-dose computed tomography (CT) has been developed in medical field. Nevertheless, it may degrade the quality of CT images and affect the clinical diagnosis. To address this issue, various advanced low-dose CT denoising methods have been developed to improve the quality of CT images. The majority of these methods require well-matched low-dose CT and normal-dose CT images for the training. These methods have made much progress in low-dose CT denoising field. However, it is not practicable in medical field to acquire well-paired images in medical field which restricts the application of these methods. To deal with this matter, we propose an improved end-to-end CT image denoising without aligned low-dose and normal-dose CT images inspired by Cycle-consistent Generative Adversarial Network. This method can learn the data distributions of low-dose CT and normal-dose CT in order to build a mapping from low-dose CT images to normal-dose CT images without paired CT images. Additionally, the perceptual loss is integrated into the proposed method to enhance the visual quality of denoised images. The experiments on the real clinic CT dataset show our methods can not only suppress noise but also preserve structural information.CCS-Applied computing~Computers in other domains~Computing in government~E-government
Functional Modification of Advanced Encryption Standard Algorithm by Perturbing the Diffusion and Confusion Properties

Advanced Encryption Standard is the most acceptable and widely used symmetric algorithm. However, cyber attacks and security breaches are becoming very common day by day. Cyber attacks hit businesses all over the world, not only financially but also in the terms of reputation. That is why more robust security measures are needed to protect privacy and prevent data breaches. In this paper, we analyzed the traditional AES algorithm and proposed some enhancements. We modified both the diffusion and confusion section by employing modified key scheduling and cipher rounds. While making the encryption stronger, we did not compromise with the complexity and user-friendliness. Through simulation studies we showed that our modification increased the avalanche effect of the algorithm by 6%, and our proposed algorithm takes 35.23% less time than the original.
Speech Recognition System for Writing Dentist Medical Records

This paper describes the development of a speech recognition system for dental medical records. The dental domain text corpus is taken from medical record scenarios developed by dentists. The text corpus that has been collected is then recorded by 100 speakers. Text corpus and speech corpus are used as training data and test data in developing speech recognition systems. The speech recognition system was developed using three different techniques, the GMM-HMM technique as the baseline and the CNN and LSTM technique as the proposed technique. The experimental results show that the CNN technique produces better performance than the GMM-HMM and LSTM techniques.
Мethodological Support for University Students’ Independent Work

The article reveals the relevance of the organization of independent work of students and the need for methodological support thereof, the quality of which determines the effectiveness of the educational process and professional training at university. The author of the article analyzes various views of domestic scholars on the essence and content of independent work at university. She considers various types and forms of classroom and extracurricular work of students, and defines the status of methodological support as a process and result. She also proposes specific types of educational and methodological support for various types of independent work of students.
School Makerspace Manifesto

Abstract This contribution describes a sustainable model for makerspaces in primary and lower secondary schools. Based on Indire research on innovative school spaces, it discusses the theoretical background that schools should adopt and create before starting a makerspace lab. It also looks at which aspects of the maker culture can successfully be combined with active pedagogy. In this way, educational institutions and makers can come together to build makerspaces within schools that will be useful to both.
An Effective Neural Architecture Optimization Algorithm for CNN based on Search Economics

Developing a good convolutional neural network (CNN) architecture manually by trial and error is typically extremely time-consuming and requires a lot of effort. That is why several recent studies have attempted to develop ways to automatically construct a suitable CNN architecture. In this study, an effective neural architecture search (NAS) algorithm based on a novel metaheuristic algorithm, search economics (SE), is presented for CNN to improve the accuracy of image classification. The basic idea of the proposed algorithm is to use the "expected value" instead of the "objective value" to evaluate a set of searched solutions, i.e., neural architectures in this case. As such, the searches of the proposed algorithm will trend to high potential regions in the solution space. Simulation results show that the proposed algorithm outperforms genetic algorithm-based NAS algorithm in terms of the accuracy, especially for complex image classification problems.
Detection of Small Targets in Aerial Images Based on Feature Fusion

Most existing target detection models are designed for image targets in natural scenarios and the detection results of small targets in aerial images are less than satisfactory. This paper tries to solve the locating and classification problem of small targets in aerial images from the perspectives of fusion between shallow features and deep features, receptive field enhancement, and a lightweight feature extraction network. To realize accurate detection of small targets in aerial images, on the basis of SSD (Single Shot MultiBox Detector), an aerial image target detection model MF-SSD based on multi-scale feature fusion and receptive field enhancement is proposed. In this model, a feature fusion sub-network is established, in which, the two-level fusion of shallow features and skip-connection fusion of intermediate features are provided. In addition, a multi-branch Inception structure D-Inception based on dilated convolution is utilized to achieve receptive field enhancement, which enables the system to better extract target features and adapt to the scale change of target. To address the increased computational complexity of the model after feature fusion and receptive field enhancement processing, a lightweight convolutional neural network based on Mobilenet is used to replace the basic network, which can not only reduce the complexity of the network model but also maintain high detection precision. The experimental results show that the target detection model MF-SSD can be used to effectively detect small targets in aerial images.
Continuous Authentication Using Behavioural Biometrics: A Step Towards Enhancing Authentication for Distance Learning in Jamaica

As distance learning platforms become increasingly attractive among the Jamaican cohort of university students, there are renewed concerns around the exploitation of authentication protocols. The literature suggest that static authentication alone may not be sufficient in validating user identity. In the context of the distance learning environment, it is often challenging to demonstrate that the integrity of user sessions has not been compromised. In this paper, the researchers experimented and demonstrated the use of Behavioral Biometrics to; continuously authenticate students in a Distance Learning Environment. The key features/dataset used in this experiment originates from two (2) primary physical input devices (Keyboard and Mouse). This dataset aided in the development of a user biometric profile that would, in turn, authenticate a user based on statistical matching, utilizing a combination of algorithms. The results garnered from the study revealed evidence that suggested that the application of behavioural biometrics seems promising in enhancing user authentication as a mean of identifying genuine and imposter users on the distance learning platform thus meeting the regulatory requirement as established by the University Council of Jamaica (UCJ).
Implementation of Quadrotor Path Planning Using Fluid Flow Equations

In this paper, an offline path planning algorithm based on fluid flow line equations is applied and demonstrated for quadrotor UAV s. A flowing inviscid fluid is known to have an inherent tendency to avoid obstacles by simply moving around them. Moreover, such fluid is always capable of finding its route to the point that has the minimum potential, provided that this route exists. The proposed algorithm uses the panel method to analyse automatically the environment and generate potential flow lines of a flowing inviscid fluid. These flow lines are then used to draw a safe path for a quadrotor from its starting point to its target. The resulting path is seen to be smooth and promising for the navigation of air vehicles. Experimental indoor tests on real simple maps using AR Drone 2 quadrotor emphasize the effectiveness of the algorithm that is able to generate smooth and safe paths.
Multi-class Feature Selection Based on Softmax with $$L_{2,0}$$-Norm Regularization

In recent years, feature selection (FS) methods based on sparsity have been extensively investigated due to their high performance. These methods solve the FS problem mainly by introducing some kinds of sparsity regularization terms. However, recent existing feature selection algorithms combine sparsity regularization with simple linear loss function, which may lead to deficient in performance. To this end, we propose a fresh and robust feature selection method that combines the structured sparsity regularization, i.e., $$\ell _{2,0}$$ -norm regularization, with the Softmax model to find a stable row-sparse solution, where we can select the features in group according to the solution of the projected matrix, and the classification performance can be improved by Softmax. Extensive experiments on six different datasets indicate that our method can obtain better or comparable classification performance by using fewer features compared with other advanced sparsity-based FS methods.
A Simple Generative Network

Generative neural networks are able to mimic intricate probability distributions such as those representing handwritten text, natural images, etc. Since their inception several models were proposed. The most successful of these were based on adversarial (GAN), auto-encoding (VAE) and maximum mean discrepancy (MMD) relatively complex architectures and schemes. Surprisingly, a very simple architecture (a single feed-forward neural network) in conjunction with an obvious optimization goal (Kullback–Leibler divergence) was apparently overlooked. This paper demonstrates that such a model (denoted SGN for its simplicity) is able to generate samples visually and quantitatively competitive as compared with the fore-mentioned state of the art methods.
An Efficient Learning Algorithm Based on Log-Sum Regularization and Modified Momentum for Rbm

Restricted Boltzmann Machine (RBM) is a strong representation and generative model for unsupervised feature extraction in deep learning. Deep architectures can effectively enhance the capability of image feature expression in image recognition. Inspired by the log-sum norm which can provide more effective sparsity inducing properties in compressed sensing, we incorporate it into RBM to get sparse representation. In order to enhance the RBM's model representation capability and computational efficiency, we propose an improved gradient approximation algorithm with log-sum regularization and modified momentum for training RBM. This algorithm is a better approximation of the log-likelihood gradient than the traditional CD. By adding a log-sum term to the likelihood function of an RBM, the sparsity of each hidden unit can be adaptively learnt. Different combinations of the momentum term are added in the pre-training and fine-tuning phases of the RBM to accelerate the network convergence. Experiments on MNIST, Extended Yale B and CMU-PIE databases illustrate the effectiveness and advantages of our proposed method in terms of recognition performance and computational efficiency.
Information Technology for Adaptive Semantic Testing of Knowledge Level of Educational Materials

The use of adaptive testing of knowledge level allows to avoid an excessive test tasks number in the test and to fully cover the educational material semantic structure. Semantic structure is formed by the system of rubrication of educational material and semantic units of rubrics in form of key terms or other elements; and each semantic unit has its own numerical mark of semantic importance in rubric content. The deeper the mastering of education material semantic content, the less important semantic units student learns. This paper offers information technology that allows calculating the knowledge level by adaptive selection of tasks in testing, and each test task allows checking the knowledge level of specific semantic units of educational materials. Features of this information technology are automation of whole testing process, maximum variety of test tasks in the test, possibility of using different algorithms for starting testing, testing dynamics and assessing the knowledge level of educational material.
Quality metrics of generative models

Within the framework of this article, modern metrics for evaluating generative models are considered. Particular attention is paid to metrics that are used in the field of natural language processing - BLUE (evaluates quality based on a comparison of the result obtained by a model and a person), NIST (based on the BLUE metric), METEOR (based on the harmonic mean of unigrams of accuracy and completeness), ROUGE (. The article presents a new metric, which is based on subjective assessments. The subjective estimates used in the considered metric are collected using pairwise comparison in the form of evaluation scales. The article also proposes an algorithm for generating music based on automatic models of working with ABC notation, models of distributive semantics and generative models of deep neural networks - Transformers. The new quality metric (SS-metric) presented in the article is used to assess the quality of the proposed algorithm for generating music in comparison with the solutions offered by humans and baseline models. Music generation based on the baseline model builds a continuation of a musical fragment by randomly selecting bars from the first half of the musical fragment. During the experiments, it was found out that the SS-metric allows you to formalize and generalize subjective assessments, this can be used to assess the quality of various objects.
LOWESS Smoothing and Random Forest Based GRU Model: A Short-Term Photovoltaic Power Generation Forecasting Method

Accurate forecasting of photovoltaic power generation is vital importance to guarantee the smooth operation of photovoltaic power stations and ensure the power consumption of end-users. As a good forecasting tool, Gated Recurrent Unit method has been widely used in many fields. However, existing studies ignore the impact of data fluctuations on prediction accuracy since photovoltaic power generation is intermittent and uncertain, then the prediction results of Gated Recurrent Unit are no longer enough accurate. To fill the gaps in the previous literatures and enhance prediction accuracy, this paper proposes an improved Gated Recurrent Unit photovoltaic generation prediction method. Several different data smoothing techniques are introduced to reduce fluctuations, the Random Forest method is used for feature selection, and the RepeatVector layer with extended attribute dimensions and TimeDistributed layer with full connectivity are utilized to optimize the Gated Recurrent Unit model. A real-world case from the photovoltaic power plant in Xuhui District, Shanghai, China, is employed to evaluate the performance of proposed model. After comparing with the prediction results of Recurrent Neural Networks and Long Short-Term Memory, and the actual data as well, it is found that the proposed prediction method can effectively improve the accuracy of photovoltaic power generation prediction.
METHODS FOR CONSTRUCTING A DISTRIBUTED SCHEMATIC CAD-SYSTEM BASED ON SERVICE-ORIENTED ARCHITECTURE AND DATA PACKAGING

The article discusses the evolution of service-oriented architecture. The prerequisites for the emergence of an architectural style for the development of distributed service-oriented software systems based on the ESB bus are determined. A standardized ESB bus structure is provided with the assignment of basic and optional components. The main advantages and disadvantages of SOA architecture based on the use of ESB bus, as well as its impact on the development of service-oriented architecture are considered. The principles of construction and organization of distributed web-oriented systems based on microservice architecture are determined. The main advantages and disadvantages of microservices, both the architectural approach and the organization of the development process, are given. Serverless technologies for building information systems are considered as a stage in the evolution of a service-oriented architecture based on a paradigm shift towards an event-driven approach of the participants of the software complex. The structure of distributed web-oriented systems and the purpose of their components built on the basis of a serverless architectural approach are determined. Middleware is proposed to propagate a transaction between the components of a serverless event-driven system and to cancel it, if it is incomplete. The paper describes the methodology for constructing mathematical and software support for automation systems for circuit design based on data packaging. The main tasks of automation of circuit design are given. The efficiency of known methods of matrix packing, their advantages and disadvantages of use are considered. The modernization of the description separation method by means of a two-stage array formation technology is proposed. The scheme and algorithm for the practical implementation of the two-stage technology of array formation are described. An algorithm for the transformation of the vector X is given in the final calculation of the variables of the modeled circuit, taking into account the permutations in the case of LU-factorization.
Classification of Recyclable Waste Generated in Indian Households

MohanKumar, Harshith Jamadar, Mohammed Zayd Natarajan, S. Nithin, H.Detecting and classifying recyclable material is one of the hardest computer vision problems. In this study, we present an optimized residual network with the inclusion of an SVM classification layer to categorize single object images containing recyclable waste. The model is trained on three thousand images of which $$\frac{1}{3}$$ rd were of recyclable products commonly found in India. The results of the trained model have shown to contain competitive accuracy rates and reliably fast inference times.