Reviewerly/exHarmony huggingface dataset:
Learn and Tell: Learning Priors for Image Caption Generation

In this work, we propose a novel priors-based attention neural network (PANN) for image captioning, which aims at incorporating two kinds of priors, i.e., the probabilities being mentioned for local region proposals (PBM priors) and part-of-speech clues for caption words (POS priors), into a visual information extraction process at each word prediction. This work was inspired by the intuitions that region proposals have different inherent probabilities for image captioning, and that the POS clues bridge the word class (part-of-speech tag) with the categories of visual features. We propose new methods to extract these two priors, in which the PBM priors are obtained by computing the similarities between the caption feature vector and local feature vectors, while the POS priors are predicated at each step of word generation by taking the hidden state of the decoder as input. After that, these two kinds of priors are further incorporated into the PANN module of the decoder to help the decoder extract more accurate visual information for the current word generation. In our experiments, we qualitatively analyzed the proposed approach and quantitatively evaluated several captioning schemes with our PANN on the MS-COCO dataset. Experimental results demonstrate that our proposed method could achieve better performance as well as the effectiveness of the proposed network for image captioning.
SCA: A Secure CNN Accelerator for Both Training and Inference

Convolutional neural networks (CNNs), while being widely deployed to edge devices, face increasingly requirements for IP protection, i.e., the protection of the models and their weights. This becomes particularly challenging for those that demand post-deployment training to enhance inference performance. Existing schemes focus mainly on IP protection at the inference phase, and lack the ability to extend to the training phase. In this paper, we propose SCA, a secure CNN accelerator that exploits stochastic computing to achieve IP protection at both training and inference phases. We propose hybrid stochastic addition and weight remapping to further optimize space utilization and design robustness. Our experimental results show that SCA effectively prevents pirating the CNN IP from the authorized devices. In addition, it achieves 4.8× and 34.2× speedups and 84.3% and 98.5% energy reductions over a non-secure baseline and an inference-only secure baseline, respectively.
Research on Image Super Resolution Reconstruction Model Based on Deep Learning

Artificial neural network has made the rapid development of artificial intelligence with its super-learning ability, making artificial neural network become a research hotspot again. At present, deep learning has been widely used in various fields such as computer vision, speech processing, and natural language processing, and has even played a leading role in some fields. The single image super-resolution reconstruction technique aims to reconstruct a low-resolution image through a series of algorithms to reconstruct a corresponding high-resolution image. This paper first briefly introduces the relevant theories of artificial neural networks, then studies the fast super-resolution reconstruction model, and improves the model layer and filter size to establish a new improved model.
Attribute Memory Transfer Network for Unsupervised Cross-Domain Person Re-Identification

Person re-identification (re-ID) presents various applications in surveillance system, but most existing models are proposed under supervised framework. These methods require large amounts of annotated pedestrian data, which limits their scalability and flexibility in a new application scenario. Aiming to relax this limitation, this article exploits the attribute-invariant characteristics and domain correlations into cross-domain person re-ID, while most unsupervised methods only consider the identity features and ignore the different importance of each source image to the target domain. Specifically, this article proposes an Attribute Memory Transfer Network (AMTNet) with two major contributions of domain-balanced memory and attribute-invariant memory modules. The first domain balanced-memory integrates a domain correlation learning method to evaluate the importance of each source image to the target domain, which is involved into the transfer learning; The second attribute-invariant memory can transfer the source attribute knowledge into the target domain with preserving the identity information to conduct the re-ID process. Extensive evaluated experiments elaborate the superiority of AMTNet on two large datasets of Market-1501 and DukeMTMC-reID, compared with hand-crafted and deep learning feature-based methods.
Future English Teachers’ Professional Image Forming

The aim of the research is to substantiate, design pedagogical system of future English teachers’ professional image forming at pedagogical institutions of higher education and verify experimentally its effectiveness. The research was conducted within the mixed methodology in two stages: theoretical and empirical. In the context of theoretical stage the essence of the professional image of English teacher was defined, based on personality’s role theory, its structure was clarified and pedagogical system of its targeted forming at pedagogical institutions of higher education was developed. Research findings made it possible to identify pedagogical factors influencing the future English teachers’ professional image forming as well as pedagogical conditions, which allow achieving positive results in the process of its formation at pedagogical higher educational institutions. Obtained theoretical results have been introduced into the educational process of pedagogical higher educational institutions of Ukraine within the pedagogical experiment (empirical stage) in order to be experimentally testified. Statistical processing of obtained experimental data ensured the validity and objectivity of the research results and verified the effectiveness of the proposed pedagogical system. The research results point to the fact that the proposed pedagogical system provides the high quality of image training of future English teachers’ at higher pedagogical educational institutions.
An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Keyboard input has played an essential role in human-computer interaction with a vast user base, and the keyboard design has always been one of the fundamental objects of studies on smart devices. With the development of screen technology, more precise data and indicators could be collected by smartphones to in-depth evaluate the keyboard design. The enlargement of the phone screen has led to unsatisfactory input experience and finger pain, especially for one-handed input. The input efficiency and comfort have attracted the attention of researchers and designers, and the curved keyboard with size-adjustable buttons, which roughly accorded with the physiological structure of thumbs, was proposed to optimize the one-handed usage on large-screen smartphones. However, its real effects remained ambiguous. Therefore, this protocol demonstrated a general and summarized method to evaluate the effect of curved QWERTY keyboard design on a 5-inch smartphone through a self-developed software with detailed variables, including objective behavioral data, subjective feedback, and the coordinate data of each touchpoint. There is sufficient existing literature on evaluating virtual keyboards; however, only a few of them systematically summarized and took reflection on the evaluation methods and processes. Therefore, this protocol fills in the gap and presents a process and method of the systematic evaluation of keyboard design with available codes for analysis and visualization. It needs no additional or expensive equipment and is easy to conduct and operate. In addition, the protocol also helps to get potential reasons for the disadvantages of the design and enlightens the optimization of designs. In conclusion, this protocol with the open-source resources could not only be an in-class demonstrative experiment to inspire the novice to start their studies but also contributes to improving the user experience and the revenue of input method editor companies.
Tackling 21st Century Geoscience Problems with Machine Learning

A new cross-journal special collection invites contributions on how machine learning can be used for solid Earth observation, modeling and understanding.
Realistic Fault Models and Fault Simulation for Quantum Dot Quantum Circuits

Testing for quantum circuits (QC) is a challenging task because QC is intrinsically probabilistic. Existing fault models for QC, such as missing gate faults, are not suitable for quantum dot QC. This paper proposes realistic fault models and fault simulation for quantum dot QC. Our fault models are based on real physical phenomenon of quantum dot devices so that they represent real defect behavior or control errors. Our fault simulation does not need to fully expand gate matrices to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> x 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , where n is the number of qubits. Using sparse matrix multiplication, our fault simulation saves a lot of memory and CPU time. We also calculate the test repetition of each test pattern so that we can estimate our test time. Based on fault simulation of a full adder QC, we can select a small test set of six test patterns, totally 526 repetitions, to detect all faults with 99% confidence level.
The Evolution Is Now: Service Robots, Behavioral Bias and Emotions

Purpose – The purpose of this chapter is to conduct a critical literature review that examines the origins and development of research on service robots in organizations, as well as the key emotional and cognitive issues between service employees, customers, and robots. This review provides a foundation for future research that leverages the emotional connection between service robots and humans. Design/Methodology/Approach – A critical literature review that examines robotics, artificial intelligence, emotions, approach/avoid behavior, and cognitive biases is conducted. Findings – This research provides six key themes that emerge from the current state of research in the field of service robotics with 14 accompanying research questions forming the basis of a research agenda. The themes presented are as follows: Theme 1: Employees have a forgotten “dual role”; Theme 2: The influence of groups is neglected; Theme 3: Opposing emotions lead to uncertain outcomes; Theme 4: We know how robots influence engagement, but not experience; Theme 5: Trust is necessary but poorly understood; and Theme 6: Bias is contagious: if the human mind is irrational…so too are robot minds. Practical Implications – Practically, this research provides guidance for researchers and practitioners alike regarding the current state of research, gaps, and future directions. Importantly for practitioners, it sheds light on themes in the use of AI and robotics in services, highlighting opportunities to consider the dual role of the employee, examines how incorporating a service robot influences all levels of the organization, addresses motivational conflicts for employees and customers, explores how service robots influence the whole customer experience and how trust is formed, and how we are (often inadvertently) creating biased robots.
Determination of the Inaccuracies of Calculated EEG Indices

The data obtained as a result of an EEG measurement are burdened with inaccuracies related to the measurement process itself and the need to remove recorded disturbances. The article presents an example of how to calculate the Approach-Withdraw Index (EEG-AW) and Memorization Index (MI) indices in such a way that their inaccuracy resulting from the removal of artifacts can be periodically calculated. This inaccuracy is expressed in terms of standard deviation. This allows you to determine the reliability of the obtained conclusions in the context of examining elements in a 2D computer game created in the Unity engine.
TindART: A Personal Visual Arts Recommender

We present TindART - a comprehensive visual arts recommender system. TindART leverages real time user input to build a user-centric preference model based on content and demographic features. Our system is coupled with visual analytics controls that allow users to gain a deeper understanding of their art taste and further refine their personal recommendation model. The content based features in TindART are extracted using a multi-task learning deep neural network which accounts for a link between multiple descriptive attributes and the content they represent. Our demographic engine is powered by social media integrations such as Google, Facebook and Twitter profiles the users can login with. Both the content and demographics power a recommender system which decision making processed is visualized through our web t-SNE implementation. TindART is live and available at: https://tindart.net/.
New Projects in Developmental Learning

The article outlines the existing practices of developing learning which are being implemented in educational institutions of the Russian Federation since 1998 to the present day, taking into account modern trends and challenges to the world education: individualization and personalization; digitalization, networking, collaboration (cooperation, team work); interdisciplinary content of education as a norm; metacompetency as a result of education; smart learning in the era of the information society with the availability of communication technologies and team work; gamification as an introduction to learning educational games, etc. To all these challenges and trends of the modern reality, the educational system of D.B. Elkonin—V.V. Davydov has its own ‘answers’, through certain practices that are realized in the network of classes and schools of developmental learning in Russia. In addition, we describe new projects that can be viewed as a resource for the development of the Russian educational system.
Spectral solar irradiance on inclined surfaces: A fast Monte Carlo approach

Estimating spectral plane-of-array (POA) solar irradiance on inclined surfaces is an important step in the design and performance evaluation of both photovoltaic and concentrated solar plants. This work introduces a fast, line-by-line spectral, Monte Carlo (MC) radiative transfer model approach to simulate anisotropic distributions of shortwave radiation through the atmosphere as photon bundles impinge on inclined surfaces. This fast Monte Carlo approach reproduces the angular distribution of solar irradiance without the undesirable effects of spatial discretization and thus computes detailed POA irradiance values on surfaces at any orientation and also when surfaces are subjected to the anisotropic ground and atmospheric scattering. Polarization effects are also easily incorporated into this approach that can be considered as direct numerical simulation of the physics involved. Here, we compare our Monte Carlo radiative transfer model with the most widely used empirical transposition model, Perez4, under various conditions. The results show that the Perez4 model reproduces the more detailed Monte Carlo simulations with less than 10% deviation under clear skies for all relevant surface tilt and azimuth angles. When optically thin clouds are present, observed deviations are larger, especially when the receiving surface is strongly tilted. Deviations are also observed for large azimuth angle differences between the receiving surface and the solar position. When optically thick clouds are present, the two models agree within 15% deviation for nearly all surface orientation and tilt angles. The overall deviations are smaller when compared with cases for optically thin clouds. The Perez4 model performs very well (∼6.0% deviation) in comparison with the detailed MC simulations for all cases, thus validating its widespread use for practical solar applications. When detailed atmospheric profiles and cloud optical properties are available, the proposed fast Monte Carlo radiative model reproduces accurate spectral and angular POA irradiance levels for various atmospheric and cloud cover conditions, surface orientations, and different surface and ground properties.
Sentiment Analysis of Visitors in Borobudur Temple With Support Vector Machine Based on TripAdvisor Review

The number of online sites in Indonesia has recently experienced significant growth, both in tourism and other sectors.Borobudur Temple is one of the most well-preserved tourist attractions that is frequently visited by domestic as well as foreign tourist.TripAdvisor is a platform of an online forum that provides anyone to share information related to travel where enormous reviews in daily are posted.Tourists can read the accumulated opinions as a consideration for their travel plans.In this paper, we aim to analyze the English reviews with sentiment analysis by identifying and categorizing opinions as generally positive, negative, or neutral.Data used in this paper is a set of online reviews from TripAdvisor from December 29, 2017, to May 12, 2019.Data were analyzed using Support Vector Machine (SVM) classification methods.The result of each category shows that there are 305 positive reviews, 60 neutral reviews and the rest 36 are negative reviews.The accuracy performed by the SVM model is 100% that has been tested with 401 testing data and trained by 247 data.
Evaluation of the impact the hyper-converged infrastructure storage subsystem synchronization on the overall performance

The article investigates the components of hyper-convergent infrastructure that have a mutual influence on each other and assesses the level of this influence. It is important for the evaluation of maximum performance as well as the determination of approaches to hyper-convergent infrastructure scaling. Due to the complexity of the infrastructure and a large number of components, the assessment is now usually done on an experimental basis, with the addition of new components if necessary. Due to the research, it is possible to improve the accuracy of initial planning as well as to make the system scaling more predictable.
Are Strong Policies Also Good Playout Policies? Playout Policy Optimization for RTS Games

Monte Carlo Tree Search has been successfully applied to complex domains such as computer Go. However, despite its success in building game-playing agents, there is little understanding of general principles to design or learn its playout policy. Many systems, such as AlphaGo, use a policy optimized to mimic human expert as the playout policy. But are strong policies good playout policies? In this paper, we take a case study in real-time strategy games. We use bandit algorithms to optimize stochastic policies as both gameplay policies and playout policies for MCTS in the context of RTS games. Our results show that strong policies do not make the best playout policies, and that policies that maximize MCTS performance as playout policies are actually weak in terms of gameplay strength
Quantitative Analysis on the Interaction Fatigue of Natural Gestures

With the popularity of natural user interface (NUI), natural gesture interaction has become the mainstream. Using improper natural gestures for a long time will cause muscle fatigue, which leads to an increase in pathological problems such as tenosynovitis. To avoid the harm caused by improper gestures, this paper selects three daily interactive gestures as the research objects including browsing information, playing games and typing, and divides them into nine independent gestures. After denoising, filtering, segmenting, and extracting the parameters of the acquired surface electromyography (sEMG) signals, the time-domain, frequency-domain and time-frequency-domain are analyzed. The characteristics of the envelope waveform and power spectrum threshold, as well as the fatigue characteristics of nine independent gestures are obtained. The long short-term memory (LSTM), one of the recurrent neural network (RNN) methods is used to train nine independent gesture models. The fatigue characteristics of the integrated gestures are predicted by the trained LSTM series model. The energy consumption characteristics of integrated gestures in smartphones and PCs are obtained. It is found that the simple behavior of browsing in the integrated behavior is suitable for natural interactive gestures, and complex behaviors such as games or typing on PC have lower energy consumption than that of smartphones. Among the independent natural gestures, the energy consumption of clicking is higher than that of dragging. Compared with a behavior for the same purpose, the energy consumption of mouse gestures for PCs is much lower than that of smartphones. This study of gesture fatigue provides a theoretical basis for the design of gestures and the development of internet user products.
Design and Simulation of Computer Aided Chinese Vocabulary Evaluation System

This article first explains the research background of computer-aided Chinese vocabulary assessment. Vocabulary assessment occupies an important position in Chinese learning. Computer-aided assessment has advantages in statistical analysis and organization and management. Subsequently, the development of Chinese vocabulary assessment and computer-assisted Chinese assessment was reviewed, and the design scheme of computer-assisted Chinese vocabulary assessment system was constructed. The functional framework, assessment methods, and feedback of the assessment system were explained. Finally, this paper verifies the function of the system through experimental investigation.
Applied Orientation and Interdisciplinary Integration as a Factor in Increasing Student Learning Motivation

The research is devoted to the study of the process of increasing students' motivation to study physics, one of the most complex sciences of the school course.The process of increasing motivation is considered through the introduction of an optional course in high school.The research represents the organizational and methodological features of the formation of sustainable motivation of students to physics in the process of teaching the optional course "Physical Basis of Information Security", which is a program for giving theoretical knowledge about the physical foundations of information security, providing active practical work in the process of studying different ways of information security and performing laboratory work.Teaching is based on the application of the problem-based learning and active learning, as the optimal methods of searching and obtaining new knowledge.The increased level of motivation and students' knowledge is achieved through the introduction of the optional course "Physical Basis of Information Security", which includes pedagogical technologies, problem situations and practical exercises.Monitoring analysis of the results of a learning experiment proves that the experimental group, unlike the control group, has a higher level of motivation and knowledge.