Reviewerly/exHarmony huggingface dataset:
Special Issue “Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov”: Foreword by Guest Editors

Nonsmooth optimization refers to the general problem of minimizing (or maximizing) functions that have discontinuous gradients. This Special Issue contains six research articles that collect together the most recent techniques and applications in the area of nonsmooth optimization. These include novel techniques utilizing some decomposable structures in nonsmooth problems—for instance, the difference-of-convex (DC) structure—and interesting important practical problems, like multiple instance learning, hydrothermal unit-commitment problem, and scheduling the disposal of nuclear waste.
Automated video game world map synthesis by model-based techniques

World maps contribute a significant part of the interactivity and entertainment to modern video games. While large-scale industrial world map generation tools exist, their use usually implies a substantial learning curve, and the cost of licences restricts the accessibility of these tools to individual game developers.
Safety Instructions in a Virtual Machining Process: The Use of Motion Capture to Develop a VR App for Industrial Safety Purposes

Safety in industry has always been an important issue since 1950, following safety standards can reduce incidents and accidents throughout the manufacturing process, for instance, in the operation of a lathe. On the other hand, the prices for modern HMDs (ex. Oculus, HTC VIVE) have decreased, and, the amount of developers have increased around the world, so, the virtual reality on the last decade has been used across a wide range of sectors. This works shows a process flow to build a virtual environment for training operators on safety instructions for the lathe operation, where motion capture is used. The virtual environment is according to our real facilities where the building was modeled by using a laser scanner to maintain physical measures and distribution, the trainer body shape was also modeled, and, their motions were captured in order to make the virtual environment more immersive for training operators (i.e., they will see the virtual environment the same as the real facilities). Based on the developed methods, and, the capture motion to build the avatar which will teach operators to follow safety instructions, we found that participants felt immersed and comfortable while walking around the virtual environment, they also listened very carefully to the safety instructions given by the trainer’s avatar. This was due to the emotional link built between the trainer and the participants by meeting each other before the test.
Co-Designing Personas for User Experience and Engagement in Automation

The aim of this workshop is to engage in co-design of personas to explore the interplay of autonomous technologies with user experience and engagement. Automating a process that is embedded into people's everyday lives and activities will surely impact their experience. In a time where there is strong push towards more and more automation in our daily life, the workshop will explore the value of co-design in bringing to the fore the opportunities and issues of such trend on users' experiences and engagements in multiple contexts such as work, health, entertainment and learning. Through the co-design of personas in future scenarios of automation the workshop will concretely identify valuable automation design goals for user experience and engagement drawing on participants' knowledge from industry projects and academic research.
A Multiple-Replica Provable Data Possession Algorithm Based on Branch Authentication Tree

Aiming at multiple-replica data integrity check in cloud storage environment, a Provable Data Possession (PDP) algorithm named MRPDP-BAT is proposed in the paper, which is composed of seven phases: key generation, replica data generation, data block signature, challenge information generation, evidence generation, verification, and data dynamic operation. In the proposed algorithm, the tag of data block is generated by vector dot product with less computation, replacing the original complex power exponent tag. Besides, it is combined with multi branch authentication tree to support verification for the dynamic update of data. Experimental results validate the effectiveness of the proposed algorithm.
Utilizing Digitized Surveys for Data Collection: The Case of Developing Countries

A major concern for public and private organizations worldwide is to ensure that its constituents are healthy. However, the degree to which this is ensured differ from country to country and even from community/individual to community/individual based on the level of wealth/income, education, etc. In developing countries the concern for the health of its population is particularly important the populations of since these countries often suffer from serious health issues. These health issues have attracted the attention of international organizations who have been focusing on raising awareness of the health issues within the local communities through education. Assessment of the status of the health of a population is dependent on data. This data is used to determine the allocation of the resources that are used to improve the health of the population. Traditionally, organizations have used paper surveys for collecting such data. Since this is a manual process it is prone to errors that also consumes a significant amount of time and effort. It is possible to overcome the limitations of paper surveys by using advanced technology for the collection process. This leads to digitized surveys which may be conducted using hand-held devices. Hand held devices can help getting more reliable and secure data in less time, with lower cost and with less effort. This paper contributes to these efforts by proposing a hand held device framework that has a user-friendly visual interface suitable for data collection in the field. The proposed framework has been implemented and used for the first time in Uganda. It has been well received by domain experts who showed that it was very successful and it was capable of collecting reliable data in less time than when they were using the paper-based process. We describe various components of the proposed framework along with the data storing feature for in-the-field accumulation of data. This was combined with after hours bulk data transmittal of the accumulated data from central points to a data repository.
Data Exchange Between JADE and Simulink Model for Multi-agent Control Using NoSQL Database Redis

This paper describes the way for data exchange between Simulink model and JADE multi-agent control. Simulink model is the predictive thermal model for datacenter. Multi-agent control is aimed to optimize energy consumption of the datacenter ventilation system. The data exchange is carried out via NoSQL database Redis. The paper offers reasons for choosing Redis as middleware in the interaction of multi-agent control with Simulink model as well as the paper describes the Simulink blocks and agent in JADE that are developed for interaction (reading/writing) with Redis.
Fisheye Rectified Image Super-resolution Using Deep Symmetrical Neural Network

Abstract Influenced by the fast developments of imaging technologies, fisheye cameras have been widely applied. But the resolution of images captured by fisheye cameras is limited due to lens distortions. In our work, we use a deep symmetrical network model to deal with single fisheye rectified image super-resolution task. It is shown by the experiments that the proposed method can achieve satisfactory results for fisheye rectified image super-resolution.
MODERN SCHOOL GEOGRAPHY TEXTBOOKS: PROS, CONS AND WAYS OF MODERNIZATION

Приводится критический анализ школьных учебников по географии на соответствие предъявляемым Федеральным государственным образовательным стандартом основного общего образования методическим, психологическим, эстетическим, гигиеническим требованиям. Выявлено, что наряду с положительными моментами имеются и отрицательные. Рассмотрен национальный проект «Цифровая школа», в соответствии с его требованиями описаны способы использования в образовательном процессе учебника географии совместно с другими средствами обучения, в том числе и цифровыми. Доказывается, что учебник географии играет важную роль в формировании географического мировоззрения школьников, способствует достижению личностных, метапредметных и предметных результатов обучения. На основе анализа состояния школьного российского географического образования выявлены пути модернизации учебников географии. A critical analysis of modern school textbooks on geography is given in the article. The analysis is related to the compliance of the Federal state educational standard of basic General education with the following requirements: methodological, psychological, aesthetic and hygienic. The presented positive aspects of geography textbooks were identified: wellbuilt methodological apparatus; colorful design, the presence of maps, schemes, diagrams, drawings; a system of questions and tasks aimed at organizing independent cognitive activity of pupils through the use of research methods; integration of physical-geographical and economic-geographical branches of geography. The authors of the article revealed the given below negative points in geography textbooks: simplification of scientific geographical information; insufficient description of physical-geographical processes in solving practical problems; lack of connection between the simplified theory of initial courses of geography and practical tasks that are given in Russian exams (the Basic State Exam and the Unified State Exam) in geography; incomplete reflection of the local history approach. Specific examples from existing geography textbooks were provided to confirm the established disadvantages. The national project «Digital school» was reviewed. This project suggests using geography textbooks in the educational process together with other teaching tools, including digital ones. Thus, the geography textbook plays an essential role in shaping the geographical outlook of pupils. In addition, textbooks contribute to the achievement of personal, metasubject and subject learning outcomes. Ways to modernize geography textbooks were defined based on the analysis of the state of Russian school geographical education, namely, the converting textbooks to digital format using interactive applications, videos, demonstrations of experiments and observations, electronic maps and electronic test tasks.
A Multi-class Classification Algorithm Based on Geometric Support Vector Machine

A multi-class classification algorithm based on geometric support vector machine (SVM) is proposed. For each class of training samples, a convex hull is constructed in the sample space using the Schlesinger-Kozinec (SK) algorithm. For a sample to be classified, the class label is determined according to the convex hull in which it is located. If this sample is in more than one convex hull, or is not in any convex hull, the nearest neighbor rule is further employed. Subsequently, its class label is identified by the class of centroid closest to the sample. The experimental results show that compared with the existing multi-class SVM methods, the proposed algorithm can improve the classification accuracy.
경영시스템의 효과성 확인을 위한 중소기업의 품질기록에 대한 작성, 저장, 활용, 모니터링관련 표준화 사례

Purpose : The purpose of this paper is to focus on the importance of records management standards to verify management system’s effectiveness and to share the best practices for standardizing and establishing records management.Methods : Pields Engineering Co., small and medium-sized businesses, has successfully established records management standards in its corporate quality management culture. We will introduce the characteristics and distinguishing advantage of Pields Engineering's records management standards.Results : Pields Engineering provides differentiated quality records retrieval tools to employees, to help create appropriate quality records for each stage of their work and helps to improve the actual quality performance rate.Conclusion : The establishment of specific records management standards can decrease the rate of rework for engineering outputs and confirm whether the management performance is improved We hope that by sharing the documentary and systemic features of Pields Engineering's records management standards, we can help SMEs establish a successful management system.
Securing Classifiers Against Both White-Box and Black-Box Attacks using Encrypted-Input Obfuscation

Machine Learning as a Service (aka MLaaS) and Smart Grid as a Service (aka SGaaS) are expected to grow at a significant rate. Just like most cloud services, MLaaS and SGaaS can be subject to a number of attacks. In this paper, we focus on white-box attacks (informally defined as attacks that try to access some or all internal data or computation used by the service program), and black-box attacks (informally defined as attacks only use input-output access to the attacked service program).
EDUCO: An Augmented Reality Application to Enrich Learning Experience of Students for History and Civics Subjects

Project Educo focuses on the upliftment of the subject History & Civics of class 10th. The subject is considered as one of the most boring subjects due to its theoretical approach and lack of interactivity. Through immersive technologies like AR and VR, the approach to the subject, as well as the number of students preferring to take the subject for further education, is supposed to increase at an exponential rate. In this project, the research was conducted in the preliminary phase followed by literature reviews, market analysis and user surveys (N=26). The data was analysed using card sorting technique. In the ideation phase, User Interface had two directions to continue the project of which we choose the most affordable and versatile one. With the direction confirmed, explorations were done for the UI after Information Architecture and Wireframing. The prototype was made in Adobe XD followed by Unity for the AR and VR content. Heuristic analysis was conducted to evaluate and improve the experience as well as the visual part.
6DOF Inertial IMU Head Gesture Detection: Performance Analysis Using Fourier Transform and Jerk-Based Feature Extraction

In this paper, we further develop the system used in head gesture recognition, based on an inertial sensor, previously developed and presented in another research paper. In this research, we evaluated the performance of the head gesture recognition system using Fourier Transform and Jerk-based features. The main focus of this paper was to develop and check an alternative method for increasing the head gesture classification rate. The proposed method converts the acquired time series to the frequency domain and, from here, selects only the necessary head gesture frequency components. After selection, a conversion to the time domain with the help of the Inverse Fourier Transform was performed. During the experiments, we discovered the fact that the head gesture pattern is located in the frequency band started at 0.3 Hz and up to 23Hz. The evaluation of the classification accuracy, in this paper, was made with the help of 9 predictive algorithms and 5 deep learning algorithms. The results show that the classification accuracy of the proposed method can reach a value equal to 99.48% when the predictive models are used and 87.23% when the deep neural networks were employed.
3D shape measurement by a novel structured light encoding and decoding method

In this article, we propose a 3D shape measurement technique based on a novel structured light encoding and decoding scheme . In this technique, as few as three images can be used to obtain the absolute 3D shape of the measured object. By adding perturbations to the Hamiltonian coding patterns, new coding patterns which have higher performance can be obtained. These patterns are projected onto the measured object, then normalized cross-correlation (ZNCC) decoding is performed using the obtained image sequence. The experimental results verified that our method is feasible.
Research On Genetic Algorithm Of Mechanical Structure Optimization Of Weight Reduction System

In this paper, genetic algorithm is proposed to optimize the mechanical structure. In order to assist patients with lower limb movement disorder in rehabilitation training, assist patients to stand, and achieve the purpose of rehabilitation training, a lower limb rehabilitation mobile device was designed. Because the patient is in the early stage of recovery, lower limbs cannot stand on their own. The weight reduction mechanism is an important part in the whole mobile equipment. The weight reduction mechanism can reduce the requirement of leg strength and satisfy the precondition of early rehabilitation training. At present, the orthogonal method has been used to design the weight reduction mechanism. Using genetic algorithm to optimize mechanical structure can achieved the goal of reducing the volume and weight of the structure without decreasing the output force. From the optimization simulation results, it can be seen that the genetic algorithm can be used to optimize the mechanical structure design, and the volume of the weight reduction mechanism decreases by 48%. Compared with orthogonal design, genetic algorithm is an efficient structural optimization method.
Guided adaptive interpolation filter

Edge-aware smoothing has proved to be a fundamental technique for various image processing and computer vision tasks. In this study, the authors introduce a local, non-iterative, and effective edge-preserving filter namely guided adaptive interpolation filter (GAIF). GAIF can be used as a post-processing step after any smoothing filter to improve its edge preservation performance without reformulation. GAIF has an computation complexity, where N is the total number of pixels in the image. To further increase the efficiency of GAIF at edge-preservation, two techniques are introduced and demonstrated. GAIF efficiency is demonstrated and compared to state-of-the-art techniques on a number of tasks including image smoothing, flash/no-flash image denoising/fusion, single image dehazing, and image details enhancement.
Reconstruction and Re-ranking: A Simple and Effective Approach for Question Answering

With the rapid growth of knowledge bases (KBs), question answering over knowledge base, a.k.a. KBQA has drawn huge attention in recent years. Most of the existing methods follow the simply matching method and search the answer from the whole knowledge bases. Despite the effectiveness of above approaches, there are two key issues should be settled, i.e., how to reconstruct knowledge bases effectively and how to search answer efficiently. In this paper, we introduce a simple model to construct large-scale knowledge base as graph and generate a set of candidate answer from efficient answer search. To verify our model, we conduct extensive experiments on Simple Question benchmarks. The experimental results greatly confirm the effectiveness of our model.
An evaluation of machine learning and latent semantic analysis in text sentiment classification

In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public.
Adversarial Defense via Attention-Based Randomized Smoothing

Recent works have shown the effectiveness of randomized smoothing in adversarial defense. This paper presents a new understanding of randomized smoothing. Features that are vulnerable to noise are not conducive to the prediction of model under adversarial perturbations. An enhanced defense called Attention-based Randomized Smoothing (ARS) is proposed. Based on smoothed classifier, ARS designs a mixed attention module, which helps model merge smoothed feature with original feature and pay more attention to robust feature. The advantages of ARS are manifested in four ways: 1) Superior performance on both clean and adversarial samples. 2) Without pre-processing in inference. 3) Explicable attention map. 4) Compatible with other defense methods. Experiment results demonstrate that ARS achieves the state-of-the-art defense against adversarial attacks on MNIST and CIFAR-10 datasets, outperforming Salman’s defense when the attacks are limited to a maximum norm.
Continuity and Smoothness Analysis and Possible Improvement of Traditional Reinforcement Learning Methods

At present, the deep reinforcement learning method has become one of the important branches in the field of artificial intelligence. Its model-free feature makes it considered as one of the ways to achieve the goal of strong artificial intelligence. In addition to discrete decision-making tasks, deep reinforcement learning has been gradually applied to continuous control tasks. Nevertheless, compared with classical control strategies and methods, the instability of deep reinforcement learning limits its extensive application in real scenarios. The instability of reinforcement learning mainly comes from two aspects: the first is the inherent discontinuity of reinforcement learning action strategies; the second is the randomness of reinforcement learning action strategies. This paper will discuss theoretical reasons of this instability and evaluate the instability of the current mainstream reinforcement learning algorithms by time-frequency analysis. Finally, we give an improved framework based on stochastic differential equation, and theoretically solve the inherent discontinuity of reinforcement learning action strategy.
Attention Mechanism for Uyghur Personal Pronouns Resolution

Deep neural network models for Uyghur personal pronoun resolution learn semantic information for personal pronoun and antecedents, but tend to be short-sighted—they ignore the importance of each feature. In this article, we propose a Uyghur personal pronoun resolution model based on Attention mechanism, Convolutional neural networks and Gated recurrent unit (ATCG). Our model studies the grammatical structure and semantic features of Uyghur, and extracts 11 key features for Uyghur resolution task. Attention mechanism can focus on the importance of words in sentences. Gated Recurrent Unit (GRU) is applied in this model to achieve the interdependent features with long distance. The ATCG model effectively makes up for the shortcomings of relying only on the features of the content level and achieves better classification performance. Experimental results on Uyghur resolution dataset show that our model surpasses the state-of-the-art models.