Reviewerly/exHarmony huggingface dataset:
Mobile Application as Appropriate Support for the Retention of New English Words and Phrases in English-Language Learning

When learning a foreign language, it is essential to learn new words and phrases. However, students often have problems remembering and retaining new vocabulary. Therefore, it is important to revise new vocabulary outside the classroom as well. In this process, modern technologies can help. Currently, mobile learning is one of the approaches to learning new vocabulary because it enables learners to perform their learning anytime and anywhere, mobile devices are easy to carry and usually adapted to learners’ needs. The purpose of this article is to illustrate how vocabulary supported with a mobile app was learned and retained in a practical English class at the Faculty of Informatics and Management of the University of Hradec Kralove, Czech Republic. In addition, the effectiveness of such mobile learning was assessed. The methodology of this study was based on an experiment. The findings of this study, as well as of other research studies on this topic, confirm effectiveness of mobile apps on the learning of foreign-language vocabulary. However, they also indicate that especially teacher’s encouragement enhanced through different methods, such as feedback or notifications, is essential for students’ motivation to use the mobile application and study the new words and phrases.
A System to Match Behaviors and Performance of Learners From User-Object Interactions

Intelligent environments in educational settings are aimed at supporting the learning process with an unobtrusive monitoring of the student while doing his/her activities. A desk is a common object in these settings, so if it is enhanced with sensing capabilities, it would enable gathering information of user-object interaction in a natural and unobtrusive way. An intelligent system is needed to analyze that information to reach conclusions faster than with traditional, manual observational process, and to provide timely valuable information. In this article, a design of a system to semi-automatically identify relationships between behaviors and the task performance of learners from user-object interaction logs is presented. The aim of detecting such relationships is to help teachers and students in the learning process, supporting their activities to identify special needs. Its components are designed to address four main functions: data acquisition, behavior identification, student performance identification, and computing relationships between student task performance and behaviors.
A Linear Reduction Model for Parallel Prolog System

As one of the most influential artificial intelligence programming languages, Prolog can naturally express human’s thinking and reasoning process. Thus, it has been widely applied in mechanical theorem proving, robot, intelligent planning, etc. However, the reasoning process of traditional Prolog is inefficient since it requires a lot of backtracking. This paper presents a novel linear reduction model different from the previous prolog derivation--generalized and/or graph search model, which can clearly represent variable (binding) constraints, so no backtracking is needed. In this model, a successful derivation of Prolog corresponds to a solution sub-graph of the corresponding generalized and/or graph. Therefore, the prolog reasoning is transformed into a searching process without backtracking, which can be parallel executed and greatly improves the efficiency of the prolog deduction. Furthermore, we discuss the transformation of artificial intelligence heuristic search technology, which is mature in both theory and practice, to Prolog reasoning, and obtain a more effective algorithm.
Learning to Predict 2D Object Instances by Applying Model-Based 6D Pose Estimation

Object detection and pose estimation still are very challenging tasks for robots. One common problem for many processing pipelines is the big amount of object data, e.g. often it is not known beforehand how many objects and which object classes can occur in the surrounding environment of a robot. Especially available model-based object detection pipelines often focus on a few different object classes. However, new deep learning algorithms have been developed in the last years. They are able to handle a big amount of data and can easily distinguish between different object classes. The drawback is the high amount of training data needed. In general, both approaches have different advantages and disadvantages. Thus, this paper presents a new way to combine model-based 6D pose estimation with deep learning to reduce time for training and to improve the 6D pose estimation pipeline.
Mobile Phone Sales Forecast Based on Support Vector Machine

Abstract In this paper, we select price, wear resistance, resistance to fall, charging interval, battery life, communication stability, photo effect, appearance design, memory size and whether to buy again as input variables, take different mobile phone sales foreground grade as output variables based on the survey data of all kinds of mobile phone users in the current Chinese market, using support vector machine regression algorithm (SVMR), BP Neural Network Algorithms and K-Nearest Neighbor Algorithms to establish models and predict the sales prospects of various kinds of mobile phones in China. The prediction results show that the predicted value of the mobile phone sales prediction model constructed by SVMR is basically consistent with the actual sales of all kinds of mobile phones in the market, which can provide some guidance for the manufacture and sale of various of mobile phones.
Product Quality Assessment Based on Online Reviews

Reviews can reflect the degree of consumers' satisfaction and views on product quality, and consumers tend to read product reviews and then get helpful information about product quality before placing an order in e-commerce platforms. However, the existing research mainly focus on the assessment of review quality, fake review detection, opinion mining, and there is little research to assess product quality from the perspectives of product features based on reviews objectively and quantifialy. Therefore, the authors propose a method to assess product quality based on reviews in a granularity of product feature. The authors define the related quality dimensions and develop the corresponding assessment models, assess the review quality crawled from an e-commerce platform, then extract product features and opinion words from the quality reviews, and finally assess product quality on the extracted and consumer-concerned features. Experiment results demonstrate the methodology can achieve the assessment of product quality on any feature objectively and quantificationally.
Construction of Network-based Teaching Platform in University Courses in the Background of MOOC

MOOC has attracted the attention of the global universities, the network-based teaching platform combines the advantages of traditional classroom teaching and MOOC, integrates the traditional classroom learning and online learning, and carries out teaching through flipped classroom, mixed learning, etc. This paper first describes the MOOC and the reform of higher education, it is pointed out that the MOOC of traditional classroom teaching reform and influence; then On the basis of the construction principle, the structure of college teaching model based on MOOC is given.; and then, taking Huangshan University as an example, discusses the related problems of university network teaching platform construction.
Technological Model for the Exchange of Goods in the Peruvian Agricultural Business Sector Using the Smart Contracts and Blockchain

To make the purchase or sale of a product through the internet, an intermediary is needed to validate certain operations, which make the products more expensive; according to INEI (National Institute of Statistics and Informatics), 68% of Peruvian agricultural sector distrusts the use of the internet for the sale of their products, so it is unlikely that they will carry out certain operations over the internet. As a result, two emerging technologies were created that offer new opportunities for the design of decentralized markets, allowing to increase the security of transactions, saving money by not depending on an intermediary, increasing the confidence of farmers and ensuring transparency and immutability of each transaction; these technologies are the Smart Contracts and Blockchain. They are technologies store information in a shared and transparent system for all the members; likewise, that the records of transactions cannot be altered, thus ensuring the transparency and immutability of the information. This document proposes a model that uses the Smart Contract and Blockchain technologies applied in the Peruvian agricultural business sector, which allows both the buyer and seller to commercialize agricultural products.
Multilevel Collaborative Attention Network for Person Search

Person search aims to apply pedestrian detection and person re-identification simultaneously to search persons in images, which inevitably introduces pedestrian box misalignment during the procedure. And the detected boxes usually have a large variety of scales on a single image. Together with cluttered background and occlusion, all these distracting factors make it difficult to extract discriminative pedestrian representations. However, these problems are usually ignored by current person search systems. In this work, we propose a novel Multilevel Collaborative Attention Network (MCAN) to fulfill person search task efficiently. A multilevel selective learning is introduced to extract scale-aware features in different levels, and a collaborative attention module consisting of hard regional attention and soft pixel-wise attention is designed to deal with misalignment, background noise and occlusion. MCAN achieves 60.1% top-1 accuracy and 29.1% mAP on PRW benchmark, demonstrating its superiority over current state-of-the-art methods.
FriendGuard

With the prevalence of online social networking, a large amount of studies have focused on online users' privacy. Existing work has heavily focused on preventing unauthorized access of one's personal information (e.g. locations, posts and photos). Very little research has been devoted into protecting the friend search engine, a service that allows people to explore others' friend lists. Although most friend search engines only disclose a partial view of one's friend list (e.g., k friends) or offer the ability to show all or no friends, attackers may leverage the combined knowledge from views obtained from different queries to gain a much larger social network of a targeted victim, potentially revealing sensitive information of a victim. In this paper, we propose a new friend search engine, namely FriendGuard, which guarantees the degree of friend exposure as set by users. If a user only allows k of his/her friends to be disclosed, our search engine will ensure that any attempts of discovering more friends of this user through querying the user's other friends will be a failure. The key idea underlying our search engine is the construction of a unique sub social network that is capable of satisfying query needs as well as controlling the degree of friend exposure. We have carried out an extensive experimental study and the results demonstrate both efficiency and effectiveness in our approach.
Gated Time Delay Neural Network for Speech Recognition

Abstract In deep neural networks, the gate mechanism is a very effective tool for controlling the information flow. For example, the gates of Long Short-Term Memory (LSTM) help alleviate the gradient vanishing problem. In addition, these gates preserve useful information. We believe that it will benefit if the system learns to explicitly focus on the relevant dimensions of the input. In this paper, we propose Gated Time Delay Neural Networks (Gated TDNN) for speech recognition. Time-delay layers are utilized to model the long temporal context correlation of speech signal while the gate mechanism enables the model to discover the relevant dimensions of the input. Our experimental results on the Switchboard and the Librispeech data sets demonstrate the effectiveness of the proposed method.
Unified multivariate hypergeometric interpoint distances

We establish (a) the probability mass function (p.m.f.) of the interpoint distance (IPD) between random vectors drawn from the unified multivariate hypergeometric (UMHG) family of distributions; (b) obtain the distribution of the IPD within one sample and across two samples from this family; (c) determine the distribution of the UMHG Euclidean norm and distance from fixed point in Zd; and (d) provide the distribution of the IPDs of vectors drawn from a mixture of the UMHG distribution. For application, we present a test the homogeneity of multivariate hypergeometric samples against mixture alternatives.
Reanimating the Answerable Body: Rhetorical Looking and the Digital Interface

Extending the work of Fleckenstein, Gage, and Bridgman (2017), this article presents rhetorical looking as a framework for addressing the covert violence that accrues across digital interfaces through the reinforcing dynamic of speed, transparency, and forgetful seeing. This triad invites users to approximate the normate body, and in doing so, elides users’ unique embodiments and answerability. Rhetorical looking disrupts this process through four nonlinear tactics: looking-through, which illuminates vision as active and embodied; looking-at, which invites an audit of the values at play through interface-user interactions; looking-with, during which users remap their relation to digital interfaces; and looking-into, which encourages action answerable to bodies, past, present, and future. Enacting rhetorical looking in response to digital interfaces, students in an advanced composition course contested speed, transparency, and forgetful seeing, and through this work, reclaimed both their embodiments and their answerability.
Facial Keypoint Detection Using Deep Learning and Computer Vision

With the advent of Computer Vision, research scientists across the world are working constantly working to expedite the advancement of Facial Landmarking system. It is a paramount step for various Facial processing operations. The applications range from facial recognition to Emotion recognition. These days, we have systems that identify people in images and tag them accordingly. There are mobile applications which identify the emotion of a person in an image and return the appropriate emoticon. The systems are put to use for applications ranging from personal security to national security. In this work, we have agglomerated computer vision techniques and Deep Learning algorithms to develop an end-to-end facial keypoint recognition system. Facial keypoints are discrete points around eyes, nose, mouth on any face. The implementation begins from Investigating OpenCV, pre-processing of images and Detection of faces. Further, a convolutional Neural network is trained for detecting eyes, nose and mouth. Finally, the CV pipeline is completed by the two parts mentioned above.
Motion tracking of iris features for eye tracking

Current video-based eye trackers fail to acquire a high signal-to-noise (SNR) ratio which is crucial for specific applications like interactive systems, event detection, the study of various eye movements, and most importantly estimating the gaze position with high certainty. Specifically, current video-based eye trackers over-rely on precise localization of the pupil boundary and/or corneal reflection (CR) for gaze tracking, which often results in inaccuracies and large sample-to-sample root mean square (RMS-S2S). Therefore, it is crucial to address the shortcomings of these trackers, and we plan to study a new video-based eye tracking methodology focused on simultaneously tracking the motion of many iris features and investigate its implications for obtaining high accuracy and precision. In our preliminary work, the method has shown great potential for robust detection of microsaccades over 0.2 degrees with high confidence. Hence, we plan to explore and optimize this technique.
A novel automated magnetic resonance image segmentation approach based on elliptical gamma mixture model for breast lumps detection

Abstract This article introduces a novel semisupervised automated segmentation approach for breast magnetic resonance (MR) image on multicore CPU‐GPU systems. The basic idea of the proposed method is clustering‐based semisupervised classifier devised by elliptical gamma mixture model (EGMM). Parameters of EGMM are identified by the iterative log‐expectation maximization (EM) algorithm. The suggested classifier labels the groups of voxels in an input image first and then classifies the image slices using the EGMM. Two different implementations of the proposed algorithm have been developed based on two different types of high‐performance computing architectures such as graphics processing units (GPUs) and multicore processors. To realize the real‐time segmentation performance of our algorithm with two distinctive architecture, we have tested a set of breast MR images collected from MedPix. Comparison between two architectures in terms of segmentation performance and computational cost is assessed by the analysis of simulation and experimental results.
<sub>1</sub>GBDT, LR &amp; Deep Learning for Turn-based Strategy Game AI

This paper proposes an AI fighting strategies generation approach implemented in the turn-based fighting game StoneAge 2 (SA2). Our research aim is to develop such AI for choosing the logical skills and targets to the player. The approach trained the logistical regression (LR) model and deep neural networks (DNN) model, individually. And combined both output at inference process. Meanwhile, to transform the features into a higher dimension binary vector without any manual intervention or any prior knowledge, we put all category features into Gradient Boosted Decision Tree (GBDT) before LR component. The main advantage of this procedure is the approach combines the benefits of LR models (memorization of feature interactions) and DL (generation the unseen feature combination through low-dimensional dense feature) for the AI fighting system. In our experiment, we evaluated our model with some other AI strategies (Reinforcement Learning (RL), GBDT, LR, DNN) to against a robot script. The results shown that the players, participating in the experiment, are capable of using reasonable strategic skills on the different targets. As a consequence, the win rate (versus with the robot script) of our system is higher than the others. Finally, we productionized and evaluated the system on SA 2, a commercial mobile turn-based game.
Development of the Multi-platform Human-Computer Interaction for Mobile and Wearable Devices

The paper concerns the problem of dependability of multi-platform mobile applications developed with FMX framework. Intended for simultaneous development of applications dedicated to multiple mobile platforms, FMX enables faster testing at desktop environment, convenient porting the project to another target platform, as well as internationalization and localization of the software. In this paper we discuss the details of several case-studies illustrating the functionalities listed above and provide comprehensive experimental examples with indication of encountered issues, alongside with the general solutions for the proper user experience design. Our observations show possible weaknesses and advantages of multi-platform applications developed with FMX and therefore can be a valuable reference for designers of multi-platform human-computer interaction and software engineers dealing with the mobile and wearable development and testing.
Adapting Self-Organizing Map Algorithm to Sparse Data

Machine learning techniques applied to data-mining face the challenge of time and memory requirements, and for this purpose should make full profit of the increase in power that recent multi-core processors bring. When applied to sparse data, it is also sometimes necessary to find an appropriate reformulation of the algorithms, keeping in mind that memory load was and still is an issue. In [1], we presented a mathematical reformulation of the standard and the batch versions of the Self-Organizing Map algorithm for sparse data, proposed a parallel implementation of the batch version, and carried out initial performance evaluationMelka, Josué tests. We here reproduce and extend our experiments on a more powerful hardware architecture and compare the results to ourMariage, Jean-Jacques previous ones. A thorough quantitative and qualitative analysis confirms our preceding results.
An Orientation-correction Detection Method for Scene Text Based on SPP-CNN

Scene text detection plays an important role in computer vision and pattern recognition field in recent years due to extract the accurate and rich text information. At present, component-based methods have become the trend, and there are still some challenges because of illumination, blur and difficult background. In this paper, an orientation-correction detection method for scene text based on Spatial Pyramid Pooling Convolutional Neural Networks, SPP-CNN , is proposed. Firstly, an enhanced multi-channel MSER model, which is constructed from R, G, B, H, S, V and grey channels of manually blurred and sharpened images, is built. Then, the manually-designed features are embedded in SPP-CNN as the effective text feature detector and classifier. Finally, a two-layer text grouping algorithm is achieved which can handle slightly-slanted text. Experiments on ICDAR 2011 show the f-measure has improved to 84%, the precision and f-measure of ICDAR 2013 have reached 87% and 85% respectively.
Açık kaynak yazılımlarda bakım yapılabilirliği ve güvenilirliği ölçmek için iki boyutlu değerlendirme metodu

 Evaluation method for maintainability and reliability in Open Source Software  Code-based and community-based evaluation  Implementation of the proposed method for three Open Source Software products and a comparison of the results with other methodsIncreased popularity of open source software (OSS) has led to a considerable proliferation of alternative software.However, this being the case, an evident lack of studies that would contribute to evaluation of OSS by organizations has turned the process of selecting the most suitable product into an appealing research problem.In this article, a method to evaluate reliability and maintainability of OSS products by using both code-based and community-based aspects is proposed.Figure A. Process followed to evaluate OSS productsPurpose: In this article, a method to evaluate reliability and maintainability of OSS by using both code-based and community-based aspects is proposed through the synthesis of existing studies in literature.Theory and Methods: To perform code-based evaluation, some internal attributes of the most recent quality model, ISO/IEC 25010, are selected and object-oriented C&K metrics are employed in an attempt to measure these attributes.To perform community-based evaluation, metrics derived from historical data such as emailing lists, problem reports, frequently asked questions, and etc. are utilized to identify and satisfy information needs as conformant to ISO/IEC 15939 standard for software measurement process. Results:The two-dimensional method was used in selection of the Java build tools written in Java, and the results obtained by applying the proposed method and the results obtained by using OSMM and OpenBRR which are common methods in the literature were compared.According to the evaluation and comparison results, the results obtained with the existing models confirm each other and the results obtained with the proposed method Conclusion:The proposed method can be subject to further empirical studies on OSS selection with respect to maintainability and reliability attributes.