Reviewerly/exHarmony huggingface dataset:
Computing the Approximation Error for Neural Networks with Weights Varying on Fixed Directions

We obtain a sharp lower bound estimate for the approximation error of a continuous function by single hidden layer neural networks with a continuous activation function and weights varying on two fixed directions. We show that for a certain class of activation functions this lower bound estimate turns into equality. The obtained result provides us with a method for direct computation of the approximation error. As an application, we give a formula, which can be used to compute instantly the approximation error for a class of functions having second order partial derivatives.
TRRS

Recent years has witnessed great emerge of online video websites, including the exploded number of videos and users. As a result, there appears a lot of personlized recommender systems. However there remain some challenging problems to tackle such as cold start problem, which scientists have made use of all kinds of sideinformation, e.g. gender, age or comments, to release. Currently a new type of video comments, called TSCs (TSC), plays a more and more important role in video watching activity. In this paper we utilize TSC to recommend videos for users. We developed a deep nueral network model called Temporal Recurrent Recommder System (TRRS) which combine multi-layers neural network to extract feature for users and videos. The first layer convert TSC to embeddings, then RNN layer analyze each comment from user or video, and fianlly the merge layer combine all output from prior layer and produce the feature. We use the feature from the network for users and videos to make personlized recommendation.
Correction to: Implementation and Assessment Methodologies of Teachers’ Training Courses for STEM Activities

The article listed above was initially published with incorrect copyright information.
PRIVACY-PRESERVING K-NEAREST NEIGHBOUR INTERPOLATION METHOD IN AN OUTSOURCED ENVIRONMENT

One&#x0D; of the most emerging computer technologies of this decade is cloud computing&#x0D; that allows data owners to outsource their storage and computing requirements.&#x0D; It enables data owners to avoid the costs of building and maintaining a private&#x0D; storage infrastructure. While outsourcing data to cloud promises significant&#x0D; benefits, it possesses substantial security and privacy concerns, especially&#x0D; when data stored in the cloud is sensitive and confidential, like a business&#x0D; plan. Encrypting the data before outsourcing can ensure privacy. However, it&#x0D; will be very difficult to process the cipher text created by the traditional&#x0D; encryption method. Considering this fact, we propose an efficient protocol that&#x0D; allows a query owner to retrieve the interpolation of the top k records from two&#x0D; different databases that are closest to a query point. Note that the databases&#x0D; are stored in two different cloud service providers in encrypted form. We also&#x0D; show that the proposed protocol ensures the privacy and the security of the&#x0D; data and the query point.
Seven ways scientists handle technology challenges in resource-poor settings

Researchers share their tips for working in environments with unreliable infrastructure. Researchers share their tips for working in environments with unreliable infrastructure.
ICT-Based Didactic Strategies to Build Knowledge Models in Electronics in Higher Education

This paper presents a didactic strategy based on information and communication technologies (ICTs) to help students build knowledge mental models in the context of Higher Education. It presents a methodology that combines the flipped classroom with other active methodologies and traditional lessons to improve the teaching/learning process of Electronics in university studies in Physics. Using the flipped classroom as the main strategy, the proposed methodology allows devoting more classroom time to active learning so that the instructor can follow the student learning process and evaluate model construction, while at the same time it increases student implication and fosters autonomy and cooperation with peers, contributing to a better construction of knowledge mental models in Electronics.
Low complexity versatile video coding for traffic surveillance system

The versatile video coding (VVC) standard can efficiently compress the video data of traffic surveillance system, however, the encoding complexity of VVC is quite high. In this paper, we propose an efficient motion estimation (ME) algorithm for improving the encoding complexity of VVC. First, we propose a content oriented adaptive search range setting algorithm, where the search range size of the children coding units (CUs) can be adaptively set by using the best motion vector information of their parent CU. In addition, based on the high spatial correlation and similar characteristics, we propose a fast reference frame direction decision algorithm to further reduce the ME encoding complexity. The simulation results show that the encoding complexity saving performance of the proposed algorithm is quite well, i.e., the total encoding time is saved by an average of 34.27%, and the ME encoding time is saved by an average of 40.79%.
DESCRIÇÃO ACÚSTICA DAS VOGAIS TÔNICAS DO ESPANHOL RIOPLATENSE E DE UMA VARIEDADE DO PORTUGUÊS DO SUL DO BRASIL DE MONOLÍNGUES E BILÍNGUES: UMA DISCUSSÃO DINÂMICA SOBRE DESENVOLVIMENTO LINGUÍSTICO

portuguesResumo: A abordagem convencional de “transferencia linguistica unidirecional”, isto e, da L1 para a L2, deve ser interrogada a luz de uma visao de lingua como sistema dinâmico. Assim, este estudo busca descrever, em termos de altura/anterioridade, os sistemas vocalicos do espanhol rio-platense e do portugues do Sul do Brasil em monolingues, assim como os sistemas vocalicos de bilingues hispânicos falantes das variedades referidas, residentes no Brasil, de modo a apontar uma dinamicidade na interacao entre os sistemas dos participantes. Os resultados mostram que fenomenos de influencia linguistica ocorrem bidireccionalmente, resultando em formas imprevistas, hibridas, em ambas as linguas dos bilingues. EnglishAbstract: The conventional approach of unidirectional linguistic transfer, from the L1 to the L2, must be questioned in a view of language as a dynamic system. In this study we aim to describe, in terms of height/frontness, the vowel systems of Riverplate Spanish and Southern Brazilian Portuguese produced by monolingual speakers, as well as the production of these two languages by bilingual Hispanic speakers living in Southern Brazil, aiming to highlight the dynamic interaction between the two language systems. Our results show that language influence is bidirectional, resulting in hybrid and unpredictable forms in the two languages produced by the bilinguals.
Special Issue:Deep Learning in Computer Graphics

The five papers in this special section focus on the use of deep learning for computer graphics. The great success of deep learning techniques in computer vision, speech recognition, and natural language processing has recently attracted much attention. It is expected that the use of deep learning techniques can significantly advance the performance of many state-of-theart graphics and visualization algorithms.
Query-Aware Bayesian Committee Machine for Scalable Gaussian Process Regression

The Gaussian process (GP) model is a powerful tool for regression problems. However, the high computational costs of the GP model has constrained its applications over large-scale data sets. To overcome this limitation, aggregation models employ distributed GP submodels (experts) for parallel training and predicting, and then merge the predictions of all submodels to produce an approximated result. The state-of-the-art aggregation models are based on Bayesian committee machines, where a prior is assumed at the start and then updated by each submodel. In this paper, we investigate the impact of the prior on the accuracy of aggregations. We propose a query-aware Bayesian committee machine (QBCM). The QBCM model partitions the testing data (i.e., queries) into subsets, and incorporates a query-aware prior when merging the predictions of submodels. This model improves the prediction accuracy, while retaining the advantages of aggregation models, i.e., closed-form inference and parallelizability. We conduct both theoretical analysis and empirical experiments on real data. The results confirm the effectiveness and efficiency of the proposed model QBCM.MSC codesGaussian processAggregation methodsBayesian committee machine
Interoperability Optimization using a modified PSO algorithm

Optimizing efforts in interoperability implementation is considered a key requirement.This way one can effectively sets up, develops, and evolves intra and inter organizational collaboration.Therefore, the objective of the present paper is to initiate a novel method for linear modeling of the interoperability optimization between involved information systems.Interoperability degree is assessed using a novel Particle Swarm Optimization (PSO) model with dynamic neighborhood topology associated to parallel computation.The idea behind using dynamic neighborhood topology is to overcome premature convergence of PSO algorithm, by well exploring and exploiting the search space for a better solution quality.Parallel computation is used to accelerate calculations especially for complex optimization problems.The obtained results demonstrate good performance of the proposed algorithm in solving interoperability optimization.
The impact of organizational justice on job involvement level on Saudi postal corporation

The purpose of this study is to analyze the impact of organizational justice on level of job involvement in the Saudi Postal Corporation of the Northern Border Region.The study uses analytical and descriptive methodology.The data were collected from different sources through the tool of questionnaire.The sample of 174 participants from the Saudi Postal Corporation was surveyed.The study uses the Statistical Package for Social Sciences (SPSS) program for testing the hypotheses and analyzing the data.The level of organizational justice and job involvement is above the average, also there is no significant difference between employees' perception, organizational justice dimensions, and job involvement level.Moreover, there is a positive correlation between organizational justice and job involvement.The dimensions of organizational justice inducing; distributive, procedural, and interactive are explained by (79.6%) of the changes that happened in the job involvement.The study addresses the necessity of reinforcing organizational culture on employees through rewarding the workers materially and morally.Moreover, it is necessary to connect moral and material incentives with a set of objective measurements and standards that are associated with the dimensions and requirements of organizational justice in order to increase the workers engagement opportunities in administrative decision making process and to create good relations with workers to guarantee organizational loyalty.
Fuzzy logic-based moving obstacle avoidance method

In this study, a fuzzy logic-based collision avoidance method is studied. Mobile robots operating in a dynamic environment may encounter moving obstacles. The distance from the obstacles to the robot is provided as a control input. The change of distance is provided as the secondary input. The output speed value is expressed as â€˜stopâ€™, â€˜very slowâ€™, â€˜slowâ€™, â€˜mediumâ€™ and â€˜fastâ€™, respectively. Each of these expressions has an exact numerical value, but it depends on the inputs and the rule base, and therefore the numerical values belong to the variable ratios. The input parameters do not have an exact value and they are assigned to different numerical ranges at different ratios. The input membership functions defined in this study are defined as triangular functions. The defuzzification process of the output parameter is performed by the weighted average method. Keywords: fuzzy; logic-based; mobile; avoidance method
A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic Controller

Autonomous mobile robots have been used to carry out different tasks without continuous human guidance.To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them.Navigation means that the robot can move through the environment to reach a destination.Obstacles avoidance considers a challenge which robot must overcome.In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment.All robots cooperate with each other to avoid obstacles.The robots detect the obstacles position around them and store their positions in shared memory.By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them.To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).
Analisis Pengembangan Fitur Obrolan Baru Berbasis Scan QR Code Pada Aplikasi Paziim

Quick Response (QR) Code is an image in the form of a two-dimensional matrix that has the ability to save the data in it. The QR Code feature is added to several social media, especially the Paziim chat application which aims to speed up starting a new chat with people who are not yet on the phone's contact list.The research method in the form of an interpretive qualitative approach is carried out with in depth interviews in several informants, observation and data collection at PT. Paziim AIO Platformindo. The results of the research in the form of the Paziim QR code feature can be developed to minimize the time to add new chat, features can help maintain privacy between users, scan QR Code accounts enter the chat room with contact add or block options, features can be used based on the version settings in the application Paziim and the new chat feature on Paziim which is the work of the nation's children can also be a differentiator with similar foreign-owned social media applications.
Coin classification in a complex environment

Coin classification automatically plays important roles in many applications, e.g., vending systems. Glossy reflection is one of the key factor that affect the performance of vision-based coin classification, especially in a complex environment. In this paper, we propose a novel method for robust coin classification. Contrary to the previous method, we get the glossy area first. Edge features and texture features are used in glossy area detection. Then the deep learning features are extracted based on non-glossy area instead of the whole coin image. Finally, the coin classification results are got from the VGG nets scheme. Comprehensive experiments show that our method is robust under various complex environments. The comparison experiments demonstrate that our method can outperform the state-of-the-art method. Our method achieves 95.80% accuracy.
FUNGSI BAHASA PADA KAOS DI KALANGAN REMAJA

Language as a communication tool plays a very important role in human life because human language can interact and talk about anything. Language as a tool to convey thoughts, ideas, concepts, or feelings. An intellectual must think and the process of thinking definitely requires language. Language also functions as a means for social interaction and the media conveys ideas, the purpose of this study is to describe the function of language in counsels among adolescents. The method used in this study is a qualitative descriptive method. The data in this study are various languages ​​including the form of language, language functions, and the meaning of expressions. The form of language in the form of words, phrases, clauses, and sentences. Language functions in the form of expression functions, information functions, exploration functions, persuasion functions, entertainment functions, Data sources in this study are the function of language on shirts among teenagers. The technique used in this data collection is documentation, see, and note. The results of research on the function of shirts on teenagers that the function of language is a tool of social interaction, as a medium for conveying ideas, concepts, thoughts, and expressions of feelings including expression functions, information functions, exploration functions, persuasion functions, and entertainment functions on shirts. among teenagers.
Attacking Intel UEFI by Using Cache Poisoning

The Unified Extensible Firmware Interface (UEFI) is a software interface between an operating system and platform firmware designed to replace a traditional BIOS. In this paper, we evaluated the security mechanisms used to protected SPI Flash, and then analyzed the attack surface presented by those security mechanisms. Intel provides several registers in its chipset relevant to locking down the SPI Flash chip that contains the UEFI in order to prevent arbitrary writes. Since these registers implement their functions through the system management mode, the main attack surface is concentrated in the system management mode. In this paper, we propose an attack vector for the system management mode, which uses the method of cache poisoning to attack the system management mode and destroy the protection mechanism of SPI Flash. This method can overcome the limitations for the traditional attacks. Experimental results proved that this kind of attack can arbitrarily write to the UEFI.
DIGITAL WATERMARKING OF 3D MEDICAL VISUAL OBJECTS

Abstract. At present, medical equipment provides often 3D models of scanning organs instead of ordinary 2D images. This concept is supported by Digital Imaging and COmmunications in Medicine (DICOM) standard available for telemedicine. This means that the confidential information under transmission ought to be protected by special techniques, particularly digital watermarking scheme instead of textual informative files represented, for example, on CD disks. We propose a multilevel protection, for which a fragile watermark is the first level of protection. The Region Of Interest (ROI) watermark and textual watermarks with information about patient and study (the last ones can be combines as a single textual watermark) form the second level of protection. Encryption of the ROI and textual watermarks using Arnold’s transform is the third level of protection. In the case of 3D models, we find the ROI in each of 2D sliced images, apply the digital wavelet transform or digital shearlet transform (depending on the volume of watermarks) for the ROI and textual watermarks embedding, and embed a fragile watermark using digital Hadamard transform. The main task is to find the relevant regions for embedding. To this and, we develop the original algorithm for selecting relevant regions. The obtained results confirm the robustness of our approach for rotation, scaling, translation, and JPEG attacks.
The interrelationship between concepts about agency and students’ use of teachable-agent learning technology

To successfully interact with software agents, people must call upon basic concepts about goals and intentionality and strategically deploy these concepts in a range of circumstances where specific entailments may or may not apply. We hypothesize that people who can effectively deploy agency concepts in new situations will be more effective in interactions with software agents. Further, we posit that interacting with a software agent can itself refine a person's deployment of agency concepts. We investigated this reciprocal relationship in one particularly important context: the classroom. In three experiments we examined connections between middle school students' concepts about agency and their success learning from a teachable-agent-based computer system called "Betty's Brain". We found that the students who made more intentional behavioral predictions about humans learned more effectively from the system. We also found that students who used the Betty's Brain system distinguished human behavior from machine behavior more strongly than students who did not. We conclude that the ability to effectively deploy agency concepts both supports, and is refined by, interactions with software agents.
Free-Form Feature Classification for Finite Element Meshing based on Shape Descriptors and Machine Learning

Lithophane lampshade is an artwork that has been commonly used in home decorations.Traditional lithophane lampshade creation requires a lot of user interactions, which is time-consuming.In this paper, we present an automatic method for personalized lithophane lampshade modeling.Given a 2D target image and a personalized 3D lampshade surface, our method first applies Laplacian-based mesh deformation to flatten the surface, and establishes mapping relation with the target image.Then, Poisson-based surface reconstruction is used to generate the lithophane.In the end, an optimization step is applied to adjust the coordinates of the lithophane.In our modeling framework, users are allowed to freely adjust the input source and the thickness of the lampshade.Then, our method automatically output the modeling result in a short period of time.Experiments with a range of models show that our method is effective in modeling lithophane lampshade with target image shown under lighting.
Peran Budaya Literasi Pada Peningkatan Karakter Siswa Sekolah Dasar

The purpose of this study is to see the effect of the implementation of literacy culture on improving the character of elementary school students. The research method used in this study is quantitative research. The research design used in this study is quasi-experimental design with the design of one group Pretest-Posttest. The sampling technique used in this study was simple random sampling. Data analysis in this study used pair sample t-test with the help of SPSS 25.0. Based on the results of different tests using the Wilcoxon formula shows that the significance value is smaller than 0.05, it can be concluded that there is an influence of the Implementation of Literacy Culture on Improving Character of Elementary School Student.
Training an Agent for Third-person Shooter Game Using Unity ML-Agents

The development of deep reinforcement learning has been widely used in the field of games. FPS and TPS are very suitable. At the same time, there are many training platforms and simulation environments. In this paper, a third-person shooting environment is built under the environment of Unity ML-Agents and some agents are trained by PPO and actor-critic algorithm. Through this visual in-depth reinforcement learning, agents learn skills such as searching for enemies in unfamiliar areas, searching for cartridges after injury, supplying ammunition and multi-batch of dangerous targets. Experiments show that deep reinforcement learning can fully improve the intelligence level of agent in the field of game, which is obviously superior to the ordinary intelligent behavior based on state machine.
Efficient Task Scheduling in Cloud Computing using an Improved Particle Swarm Optimization Algorithm

An improved multi-objective discrete particle swarm optimization (IMODPSO) algorithm is proposed to solve the task scheduling and resource allocation problem for scientific workflows in cloud computing. First, we use a strategy to limit the velocity of particles and adopt a discrete position updating equation to solve the multi-objective time and cost optimization model. Second, we adopt a Gaussian mutation operation to update the personal best position and the external archive, which can retain the diversity and convergence accuracy of Pareto optimal solutions. Finally, the computational complexity of IMODPSO is compared with three other state-of-the-art algorithms. We validate the computational speed, the number of solutions found and the generational distance of IMODPSO and find that the new algorithm outperforms the three other algorithms with respect to all three metrics.