Reviewerly/exHarmony huggingface dataset:
Fully Convolutional Network for Object Direction Estimation in the Wild

In recent years, object detection has been widely applied in various fields, such as aircraft navigation, and power line inspection. However, the object pose estimation in complex background still remains challenging. In this paper, an end-to-end object direction regression system based on the fully convolutional network is proposed. The system can learn deep features from data and use convolutional layers to regress the direction angle of the object. The experiments show that this method can accurately estimate the direction angle of the object from the image.
Digital Educational Technology in a Higher Education Institution

Digitization is becoming a global trend in the development of society.It`s a process that is based on the conversion of information into digital form and leads to an increase in the efficiency of the economy and an improvement in the quality of life.Due to digitization, new conditions for the development of the industry are created, the formats of production and consumption are changed, ideas about professionalism, a culture of behavior and communications, and ways of perception and thinking are changing.There are new requirements for the basic competencies of specialists in the labor market.An urgent task is the digitization of education.The main objective of the Russian policy in the field of education is quality assurance based on maintaining its fundamental nature and meeting current and future needs of the person, society and the state.The organization of the educational process in higher education institution depends, first of all, on the administration of the institution.The presence or absence of a specific educational strategy largely determines the principles of the educational process organization, its subsequent support and expected results at the "output".The basic principles of the formation of the educational strategy are accessibility, quality and mobility, and the current federal state educational standards act as the basis.Each university has its system of practical implementation of internal mechanisms for guaranteeing the quality of education, and each system is built according to its principles.The article is devoted to the use of digital technologies in higher education.The determining factors for the successful process of education digitization in Russia are highlighted in the article.
Exponential Adaptive Strategy in Spider Monkey Optimization Algorithm

Spider monkey optimization algorithm (SMOA) is one of the powerful techniques in the arena of swarm intelligence (SI)-based strategies. This article proposes a modified variant of SMOA that is based on an exponential adaptive strategy for step size. During the search of the optimal solution, this exponential strategy is used to adjust the step size so that it can speed up the convergence ability of the swarm. The proposed algorithm is termed as exponential adaptive spider monkey optimization (EASMO) algorithm. This evinced algorithm is tested over 14 standard optimization problems to examine its authenticity. Further, the obtained results are compared with the artificial bee colony (ABC), differential evolution (DE), Gbest-guided artificial bee colony (GABC), particle swarm optimization (PSO), SMOA, and three of its momentous variants, namely levy flight SMOA (LFSMOA), modified limacon SMOA (MLSMOA), and power law-based local search in SMOA (PLSMOA). The analysis of the results proved the competence of EASMO in the field of SI-based strategies.
Development of cognitive approach to the organization of business processes in virtual machine-building enterprises within Industry 4.0

The paper sets out an approach to improving the efficiency of business processes at virtual machine-building enterprises (VMBE), which consists in improving the existing methodological and software tools to support the life cycle of products of such enterprises within Industry 4.0. In the approach, the concept of creating, deploying and implementing business processes at VMBE and principles of this concept, as well as methods for implementing the basic principles, are considered. The essence of the approach is to organize a full production cycle at VMBE, with almost complete absence of fixed assets at such an enterprise. This involves the fixed assets of subcontractors distributed within the country and, in some cases, abroad. The peculiarity of the approach lies in taking into account the specifics of the Industry 4.0 concept, the main feature of which is the individualization of production and consumption. Practical application of the discussed approach will ensure: the formation of VMBE marketing policy by creating a multi-agent web-basedenvironment; decision support for the adaptation of VMBE production to the current market conditions; alternative choice of subcontractors in the organization of virtual production using multi-agent technologies; information support for decisions on the feasibility of manufacturing VMBE products based on OLAP technology; adjustment of the life cycle of VMBE products by applying special information support methods and models for organizational management of VMBE. The effectiveness of the described approach is shown by analyzing the activities of a typical VMBE. The results of the study can be used to organize, deploy and support the effective functioning of virtual production enterprises in various industries
Logistic Profile Generation via Clustering Analy

The process of characterizing a city to generate logistic profiles involves the analysis of many different aspects.These profiles are based on secondary sources of data, mainly road network infrastructure, socio-economic data and population density.Following previous research, the final profiles are given by a K-Means algorithm, which uses principal component analysis (PCA) for correlation analysis.A caveat in this method is that prior research has shown that PCA is sensitive to outliers and high dimensionality, which may mislead the following analysis and research.As such, this paper proposes a methodology to evaluate the performance of different clustering techniques to generate logistic profiles, applying it to a case study in the city of Lima, Perú.
Fake colorized and morphed image detection using convolutional neural network

A normal smartphone on a normal client produces approximately 1000 photos.As the quantity of pictures continues expanding, there is additionally an inundation of a practically equivalent number of phony pictures.In the present generation, images can be modified using advanced editing software technologies.The morphed images are planned with evil purposes to ruin the first proprietor by people with malevolent expectations.[1] Because of countless produced pictures, it is important to have the option to produce an original picture from the manufactured ones.This has driven numerous information researchers to examine in this field and that has empowered a far more noteworthy comprehension of the frauds that are submitted.The commonly using image fabrication method is an Image splicing and CopyMove technique.The copy move strategy is increasingly regular in contrast to the Image splicing method.With the new image editing applications like Photoshop, Photo Editor, the procedure of faking an image has been improved.
Analisis kualitas struktur dan keberadaan literasi kuantitatif pada lembar kerja peserta didik biologi SMA

The structure quality of student worksheet will determine the actualization of learning objectives required by curriculum. Besides that, as an effort to support the science development to be more quantitative, biology student worksheets should have the quantitative literacy indicators, such as interpretation, representation, calculation, application/ analysis, assumption, and communication. This research is aimed to explain the structure quality and the existence of quantitative literacy in biology student worksheets for senior high school. The research method is quantitative descriptive. The subject of this research is 40 student worksheets in biology book for class 11 published by A, B, and C publisher. The result shows that the student worksheets from A has the best structure quality according to Vee Diagram. The percentage of structure quality of student worksheet in A book is 55,5%, followed by B (53,2%) and C (29,9%). The existence of quantitative literacy that can be identified from the books are interpretation, calculation, application/analysis, and communication. All of the interpretation and calculation indicator found in record/transformation component, while all of the application/analysis indicator found in knowledge claim component. Communication indicator found in two components, record/ transformation (50%) and knowledge claim (50%).
HBase or Cassandra? A Comparative study of NoSQL Database Performance

A significant growth in data has been observed with the growth in technology and population.This data is non-relational and unstructured and often referred to as NoSQL data.It is growing in complexity for the traditional database management systems to manage such vast databases.Present day cloud services are offering numerous NoSQL databases to manage such non-relational databases ad-dressing different user specific requirements such as performance, availability, security etc. Hence there is a need to evaluate and find the behavior of different NoSQL databases in virtual environments.This study aims to evaluate two popular NoSQL databases and in support to the study, a benchmarking tool is used to compare the performance difference between HBase and Cassandra on a virtual instance deployed on OpenStack.
Load Management with Predictions of Solar Energy Production for Cloud Data Centers

Power supply of big infrastructures is today a tremendous operational cost for providers and the expected growth of Internet traffic and services will lead to a further expansion of the computing and networking infrastructures and this, in its turn, raises also concerns in terms of sustainability. In this context, renewable energy generators can help to both reduce costs and alleviate the concerns of sustainability of big infrastructures. In this paper, we consider the case of Data Centers (DCs) composed of a few sites located in different geographical positions and powered with solar energy. Due to the intermittent nature of solar energy, different time zones and price of electricity in different locations, load management strategies are fundamental. We consider predictions of the solar energy production performed through Artificial Neural Networks and we assess the impact of predictions on load management decisions and, ultimately, on the DC performance.
Aproximación a la ciudadanía digital y el perfil del profesor

espanolEl objetivo de este documento es analizar la idea que tienen los profesores mexicanos de alumnos de nivel elemental a nivel intermedio sobre la ciudadania digital, tomando las siguientes variables independientes: genero, edad, region y nivel educativo impartido. La hipotesis es que tales variables afectan a lo que los profesores creen que es la ciudadania digital. Esta investigacion se ha llevado a cabo con un grupo de 220 maestros de educacion primaria e intermedia en un Estado ubicado en el noreste de Mexico. Los hallazgos muestran que los maestros varones estan mas inclinados a usar indicadores relacionados con la mejora del rendimiento academico, y menos interesados en los indicadores relacionados con la armonia en el entorno de grupo / escuela. Ademas, los maestros jovenes tienden a trabajar con ideas que estan mas centradas en el estudiante y menos dirigidas por el maestro, en contraste con sus companeros mayores, que prefieren ideas mas centradas en el maestro sobre las centradas en el estudiante. Finalmente, los maestros de educacion primaria buscan enfocar sus ideas en los padres y la comunidad en lugar de en los estudiantes, mientras que aquellos que ensenan a nivel intermedio enfocan sus ideas mas en los estudiantes y menos hacia sus padres y la comunidad. Si las escuelas planean centrarse en el desarrollo de competencias para la ciudadania digital, es importante considerar como parte de la estrategia educativa el perfil del profesor en terminos de genero, edad y nivel educativo en el que ensenan, puesto qeu influye en sus ideas. EnglishThe aim of this paper is to analyse ideas about digital citizenship in Mexican teachers of pupils from elementary through to intermediate level, taking the following as independent variables: gender, age, region and the educational level taught. The hypothesis is that such variables affect what the teachers believe digital citizenship to be. This research is carried out on a group of 220 elementary and intermediate education teachers in a state located in northeast Mexico. Findings show that male teachers are more inclined to use indicators related to the improvement of academic performance, and less interested in indicators related to harmony in the group/school environment. In addition, young teachers tend to work with ideas that are more student-focused and less teacher-led, in contrast to their older peers, who favour more teacher-focused ideas over student-centred ones. Finally, elementary education teachers seek to focus their ideas on parents and the community instead of on students, while those teaching at intermediate level target their ideas more on students and less towards their parents and the community. If schools are planning to focus on the development of competences for digital citizenship, it is important to consider as part of the educational strategy how the profile of the teacher in terms of gender, age, and the educational level at which they teach influences their ideas.
STUDENTS’ PERCEPTIONS ON THE USE OF INTERNET AS LEARNING MEDIA IN READING CLASSROOM

This study investigated students’ perception of using the internet as learning media in reading comprehension skills. This study uses descriptive statistics. The objective of the research was to find out the students’ perception of using the internet as learning media in the reading comprehension skill of the English Education Study Program at Baturaja University. The population of this research was 79 students of English Study Program. The researcher only took 54 students as the sample by applying purposive sampling. The data was collected through the questionnaire that consisted of 20 statements. Then the researcher used five points of Likert scale for analyzing the data. The result of this research there agreed (4.03) students had a positive perception of using the internet as learning media in reading comprehension skills. It means that the internet is a facility to improve his proficiency in language skills. So the use internet for English learning makes language learning an easier process by being fun, interactive, and interesting.
Guaranteeing Performance SLAs of Cloud Applications Under Resource Storms

In modern data centers, enterprise cloud instances run not only foreground applications like web and databases, but also different background services (e.g., backup, virus/compliance scan, batch) to manage the cloud instances securely and improve the overall resource utilization. These background services often incur resource storms that suddenly consume a lot of shared resources on cloud instances. The resource storms significantly degrade the performance of foreground applications by interfering in the preemption of the shared resources, resulting in frequent SLA violations. However, stock OS schedulers are not designed to handle these situations, and prior works are insufficient to address such resource storms under highly dynamic cloud workloads. This article presents Orchestra, a cloud-specific framework for controlling multiple applications in the user space, aiming at meeting corresponding SLAs. Orchestra takes an online approach with lightweight monitoring and performance models for both applications on the fly. It optimizes the resource allocations to meet corresponding SLAs. We evaluate the performance of Orchestra on a production cloud with a diverse range of SLAs. Orchestra guarantees the foreground application's performance SLAs at all times. At the same time, Orchestra maintains the background's performance by minimizing its performance penalty with proper allocation of the shared resources.
“Defense of the Ancients”, Gamification in Learning: Improvement of Student’s Social Skills

This article introduces a learning innovation called the Dota learning model. Dota (Defense of the Ancients) actually is a game or fortress defense game that is in great interest among various groups. This game inspires the researcher to adapt and adopt into a learning model. The Dota learning model provides an opportunity for students to train critical thinking, train cooperation, improve competitive spirit between teams, and be able to play the emotions of students. The research method used in this study is a research and development with an ADDIE (Analyze-Design-Development-Implementation-Evaluation) model. The feasibility of the Dota learning model is validated by learning design expert, educator, and students. The learning design validation score is 85%, the educator validation score is 95%, and the student’s validation score is 80%. Based on the result, it can be concluded that the Dota learning model that developed has been valid and feasible to use
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making potentially important decisions in high-regret applications. However, despite GNNs' impressive performance, it has been observed that carefully crafted perturbations on graph structures (or nodes attributes) lead them to make wrong predictions. Presence of these adversarial examples raises serious security concerns. Most of the existing robust GNN design/training methods are only applicable to white-box settings where model parameters are known and gradient based methods can be used by performing convex relaxation of the discrete graph domain. More importantly, these methods are not efficient and scalable which make them infeasible in time sensitive tasks and massive graph datasets. To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner. On several applications, we show that the proposed techniques are significantly less computationally expensive and, in some cases, more robust than the state-of-the-art methods making them suitable to large-scale problems which were out of the reach of traditional robust training methods.
Anefficient Alternative to Network Pruning Through Ensemble Learning

Convolutional Neural Networks (CNNs) currently represent the best tool for classification of image content. CNNs are trained in order to develop generalized expressions in form of unique features to distinguish different classes. During this process, one or more filter weights might develop the same or similar values. In this case, the redundant filters can be pruned without damaging accuracy.Unlike normal pruning methods, we investigate the possibility of replacing a full-sized convolutional neural network with an ensemble of its narrow versions. Empirically, we show that the combination two narrow networks, which has only half of the original parameter count in total, is able to contest and even outperform the full-sized original network. In other words, a pruning rate of 50% can be achieved without any compromise on accuracy. Furthermore, we introduce a novel approach on ensemble learning called ComboNet, which increases the accuracy of the ensemble even further.
Manipulating Users’ Trust on Amazon Echo: Compromising Smart Home from Outside

Nowadays, voice control becomes a popular application that allows people to communicate with their devices more conveniently.Amazon Echo, designed around Alexa, is capable of controlling devices, e.g., smart lights, etc.Moreover, with the help of IFTTT (if-this-then-that) service, Amazon Echo's skill set gets improved significantly.However, people who are enjoying these conveniences may not take security into account.Hence, it becomes important to carefully scrutinize the Echo's voice control attack surface and the corresponding impacts.In this paper, we proposed MUTAE (Manipulating Users' Trust on Amazon Echo) attack to remotely compromise Echo's voice control interface.We also conducted security analysis and performed taxonomy based on different consequences considering the level of trust that users have placed on Echo.Finally, we also proposed mitigation techniques that protect Echo from MUTAE attack.
Dynamic Prediction of the Thermal Nonlinear Process Based on Deep Hybrid Neural Network

Nonlinear system prediction plays an important role in the practical thermal process, and deep learning algorithm is now popular in nonlinear dynamic system modeling because of its powerful learning ability. In this paper, the dynamic artificial neural networks (DANNs), which can be divided into two different types with external dynamic characteristics and internal dynamic characteristics, are analyzed. The mathematical formulations of feedforward deep neural network (DNN), traditional recurrent neural network (RNN) and Long-Short Term Memory network (LSTM) models are given. Furthermore, the structure of deep Hybrid Neural Network (DHNN) is described. Finally, the applicability of the above models in the thermal nonlinear process with different structural features is discussed. Simulation experiments reveal that DANNs with internal dynamic characteristics more suitable for solving thermal nonlinear system modeling problems with unknown order, and DHNN based on LSTM model has performed much better in approximating the dynamics of the thermal process with state parameters.
Construction of a Criterion for Assessing the Level of Objectivity of Experts Based on a Modified Interval Expert Appraisal Method

The problem of assessing the level of objectivity and qualification of experts in the project evaluations submitted by them according to the given criteria was investigated in the article. An effective approach to organizing of the estimations of experts group is to collectively discuss individual opinions or select groups of the most qualified reputable experts to obtain ratings. Determined feasibility of constructing quality estimation criteria for each expert based on modified interval method of expert evaluation. The formulated criterion is based on the consistency of the expert's interval estimation with two important indicators: the initial given interval estimation of the customer and the interval estimation of the end user. The article represents screen forms of the developed program system of expert evaluation, which allow the project manager to clearly trace the stages of the process of estimation: from entering the project name and criteria of its evaluation to the result of consistency of expert assessments.