Reviewerly/exHarmony huggingface dataset:
An Empirical Study of the Feasibility of Teaching EFL Reading in Chinese Universities Based on WeChat Platform

With the extensive application and popularization of Internet technology, E-learning has gradually played a more and more important role in education. In China, WeChat provides teachers and students with a larger platform for English learning. Based on the plentiful resources on the platform, teachers now have various ways of imparting knowledge and interacting with students. Students are active to take part in such reading activities, during which their ability of reading and the awareness of cooperation can be improved greatly. Taking EFL reading teaching in a Chinese university as an example, this paper expounds the research of WeChat platform in EFL teaching and learning by looking at the beneficial aspects and existing problems of applying WeChat platform in college English teaching and aiming at improving students' reading ability and trying to find a teaching mode combining English reading and the E-learning teaching platform. Based on an empirical study, this paper makes an analysis of the existing problems and discusses the feasibility, so that WeChat platform can be used more reasonably in the teaching of EFL reading.
FPGA-Based Hardware Accelerators for Selected Computational Problems

This chapter is dedicated to several computational problems that can efficiently be solved in FPGA-based hardware accelerators. The base of them, which is described in detail, is Hamming weight counting and comparison, which has many practical applications. Three core methods are presented that are counting networks, low-level designs from elementary logic cells, and designs using arithmetical units. Mixed solutions that involve composition of different methods are also discussed. Automatic generation of constants for look-up tables is suggested. Many practical examples with synthesizable VHDL code are shown. Finally, applicability of the proposed methods is demonstrated on some problems from the scope of combinatorial search (namely, matrix/set covering), frequent items computations, filtering, and information processing.
HISTORY OF PANOPTICISM: FROM ORACLES TO PALANTIRS

Technological progress is interpreted as the history of panopticism - the development of all-seeing and omniscience mechanisms intended to study human life. Panopticism practices evolutionized from pagan priests-oracles to gadgets-palantirs, which nowadays penetrate social space. New algorithms, providing numerous benefits, have their negative effects. Artificial intelligence systems can be a panacea or the humanity’s last invention. Panopticism in its radical form can create human-unfriendly environment. Transparent palantirization world leads to dehumanization of the humanity.
How Implicit Negative Evidence Improve Weighted Context-Free Grammar Induction

Probabilistic context-free grammars (PCFGs) or in general a weighted context-free grammars (WCFGs) are widely used in many areas of syntactic pattern matching, especially in statistical natural language parsing or biological modeling. For a given a fixed set of context–free grammar rules, probabilities of its rules can be estimated by using the Inside-Outside algorithm (IO), which is a special case of an expectation-maximization method. The IO algorithm implies only positive examples in the data given. In this paper a modified IO algorithm to estimate probabilistic parameters over implicit positive and also negative evidence is proposed. We demonstrate that a Contrastive Estimation based method outperforms a standard IO algorithm in terms of Precision, without any loss of Recall.
Modeling Approach of Software Components Based on Use Cases

The software industry is moving towards development, modeling and building software components and research is still needed to provide reliable and effective software components. All the components of a system meet the defined and common goals of the system, which requires a modeling method, to achieve these system goals. The focus of this approach is on systems development by identifying the set of system components and their interfaces from use cases. This approach consists of three phases based on the cutting process in the system into a modeling component representing a logical grouping of use cases that achieves a given work, the component modeling cutting in parts, and cutting the use case in activities to develop the component interfaces. This is where the authors proposed a new shcema, to get final system software components.
Extracting and Optimizing Formally Verified Code for Systems Programming

MCQC is a compiler for extracting verified systems programs to low-level assembly, with no runtime or garbage collection requirements and an emphasis on performance. MCQC targets the Gallina functional language used in the Coq proof assistant. MCQC translates pure and recursive functions into C++17, while compiling monadic effectful functions to imperative C++ system calls. With a few memory and performance optimizations, MCQC combines verifiability with memory and runtime performance. By handling effectful and pure functions separately MCQC can generate executable verified code directly from Gallina, reducing the effort of implementing and executing verified systems.
A New Ranking based Semantic Hashing Method for Deep Image Retrieval

Abstract Visual search in millions of samples in high-dimensional feature space is computationally expensive and challenging. A natural solution is to reduce the dimension of image representation by mapping each sample into compact binary code. In this paper, we propose a Ranking Based Semantic Hashing (RBSH) method to tackle with this problem. Observing that semantic structures carry complementary information, this paper takes advantage of semantic supervision for training high quality hashing, the semantic mapping between the high-dimensional feature space of samples and the reduced representation space of binary code, with the help of pre-trained word2vec. Specifically, the proposed method learns the mapping based on two criteria: the contrastive ranking loss and the orthogonality constraint. The former preserves the ordering of relative similarity in image pairs, while the latter makes different bit in the hash stream as orthogonal as possible. Extensive experimental study has been conducted on VOC2012 and ILSVRC2014 image sets, demonstrating that the proposed approach generally outperforms the state-of-the-art hashing techniques based methods in image search.
Engineering Solutions on Multimodal Profiling Tool for Digital Jobs Analysis and Matching of Requirements Competences Frameworks

Skill sets, knowledge, skills and a proactive attitude are all required to play an active part in society, and to access and progress in the labour market. This is a massive challenge, without adding gender disadvantage. Therefore, individuals, such as young women, who have neither completed compulsory education, nor entered the labour market, urgently need special initiatives and guidance. The paper presents engineering solutions of the multimodal profiling tool. The aim of an online automated employability profiling tool is to identify the user's existing skills, competences, attitudes and experience against identified digital jobs profiles these then be combined with the age, personal goals and formal education to place the user in an employment matrix. A digital employability pathway defines what individual training needs, experiences and work needs to be undertaken to achieve the objectives.
Earthquake early warning systems

More accurate sensors that track the speed difference between P-waves and S-waves created by fault ruptures have facilitated the development of effective earthquake early warning systems. Japan has been using this technology since the 1960s to stop bullet trains and prevent earthquake-induced derailments. The seismically active Palm Springs, California, area adopted this technology in the 1980s to increase the speed of fire department response. The U.S. Geological Survey, California Institute of Technology at Pasadena, University of California at Berkeley, California Office of Emergency Services, and other agencies and organizations are partnering to create a public warning network for the heavily populated earthquake-prone areas of Los Angeles and San Francisco. Earthquake early warning (EEW) enhances resilience by encouraging investment in retrofitting for seismic strengthening, by developing policies for responding to the warnings, and through public education about the meaning of warnings and appropriate responses to them. Challenges remain, however, in the development of effective public warning within the blind zone near the epicenter of an earthquake where the time difference between the P- and S-waves is so short that no warning can be issued, as was demonstrated in the 2014 Napa Earthquake. Other challenges include the high cost of developing and maintaining public EEW systems. This chapter discusses how these challenges factor into the design of such a system.
Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core

Toward on-device learning on IoT devices, this paper implements an online sequential learning and unsupervised anomaly detection core and explores its design options, such as pipeline structure. They are evaluated in terms of performance and cost.
Isogeny-based cryptography in Julia/Nemo

The Couveignes-Rostovtsev-Stolbunov key-exchange protocol based on isogenies of elliptic curves is of interest because it may resist quantum attacks, but its efficient implementation remains a challenge. We briefly present the computations involved, and efficient algorithms to achieve the critical steps, with timing results for our implementations in Sage and Julia/Nemo.
Unsupervised Transformation Network Based on GANs for Target-Domain Oriented Multi-domain Image Translation

Multi-domain image translation with unpaired data is a challenging problem. This paper proposes a generalized GAN-based unsupervised multi-domain transformation network (UMT-GAN) for image translation. The generation network of UMT-GAN consists of a universal encoder, a reconstructor and a series of translators corresponding to different target domains. The encoder is used to learn the universal information among different domains. The reconstructor is designed to extract the hierarchical representations of the images by minimizing the reconstruction loss. The translators are used to perform the multi-domain translation. Each translator and reconstructor are connected to a discriminator for adversarial training. Importantly, the high-level representations are shared between the source and multiple target domains, and all network structures are trained together by using a joint loss function. In particular, instead of using a random vector z as inputs to generate high-resolution images, UMT-GAN rather employs the source domain images as the inputs of the generator, hence help the model escape from collapsing to a certain extent. The experimental studies demonstrate the effectiveness and superiority of the proposed algorithm compared with several state-of-the-art algorithms.
Fingerspelling Alphabet 3D Modeling and Recognition Base on CNN Technology for Cross Platform Applications

The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units (fingerspelling alphabet) of sign language, animation between states of gesture units with a combination of gestures (words).Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input.With the cross platform means technology achieves the ability to run on multiple platforms without re-implementing for each platform.
PKULAE: A Learning Attitude Evaluation Method Based on Learning Behavior

Learning attitude is an important factor related to students' academic achievement. The existing E-learning systems has paid little attention to the students' learning attitude, which lacks the ability of supervision for students' learning behavior. This paper designs a learning attitude evaluation method PKULAE based on 17 kinds of learning behaviors. This method analyses the students' learning behavior from both global and local aspects. It judges the student's learning attitude from global and local aspects by regression analysis and GBDT regression tree constructed by the students' behavior characteristic matrix. Finally, it determines the students' learning attitude in a certain period by voting. The paper classifies learning attitudes into positive attitudes and negative attitudes. We did experiments based on the online behavior data of 125 students in Peking University. The result shows that PKULAE method has been significantly improved compared with GBDT and RF. When the recall and the TNR are no less than GBDT and RF, the accuracy is 0.778, which is at least 0.07 higher than GBDT and RF.
Fast Object Detector Based on Convolutional Neural Networks

We propose a fast object detector, based on Convolutional Neural Network (CNN). The object detector, which operates on RGB images, is designed for a mobile robot equipped with a robotic manipulator. The proposed detector is designed to quickly and accurately detect objects which are common in small manufactories and workshops. We propose a fully convolutional architecture of neural network which allows the full GPU implementation. We provide results obtained on our custom dataset based on ImageNet and other common datasets, like COCO or PascalVOC. We also compare the proposed method with other state of the art object detectors.
Fast Implementation of Image Structural Similarity Algorithm

Abstract The traditional Structural SIMilarity (SSIM) index has higher computation complexity compared with Peak-Signal-to-Noise-Ratio (PSNR). In order to solve this problem, we propose a two-stage optimization algorithm to reduce the computation complexity of SSIM. First, floating-point operations in the calculation process of SSIM are converted to fixed-point ones. Then, the repeated calculation rule of the iteration operation in SSIM is analyzed and the repeated operations are subsequently removed via modifying SSIM calculation method. Experiment results show that, in comparison with the original SSIM algorithm, the proposed algorithm obtains 24.12% of computation time reduction with negligible accuracy decrease.
LBP Pattern-Processed Log-Gabor Filter for Expression and Illumination Robust Facial Recognition

In this paper, an illumination and expression robust feature face is generated to improve the accuracy of facial recognition system. In the pre-processing stage, the median filter is applied to heal the noise impact and to extract the structural features. The weighted LBP filter is applied on smoothen face to generate the pattern region. This frequency and angle-based Log-Gabor filter is applied on LBP-weighted face to generate the expression and illumination robust pattern-feature set. The PCA-based rules are generated on pattern-feature-based training set to recognize the input face image. The proposed model is applied on three samplesets of expression and illumination-affected images of ORL dataset. The comparative results against PCA, Gabor PCA, Log-Gabor PCA, and LBP-PCA methods have observed a significant gain in accuracy ratio of face recognition.
On improved estimation in multivariate Dirichlet regressions

In this paper we consider the multivariate Dirichlet regression model proposed by Melo et al. (2009 Melo, T. F. N., K. L. P. Vasconcellos, and A. J. Lemonte. 2009. Some restriction tests in a new class of regression models for proportions. Computational Statistics and Data Analysis 53(12):3972–9. doi:10.1016/j.csda.2009.06.005.[Crossref], [Web of Science ®] , [Google Scholar]), which is tailored to situations where the multivariate response consists of multivariate positive observations summing to one and the regression structure involves regressors and unknown parameters. We discuss maximum likelihood estimation for the model parameters and derive modified maximum likelihood estimators that are bias-free to second order. Monte Carlo simulation experiments are conducted in order to investigate the performance of the corrected estimators. The numerical results reveal that the bias correction scheme yields nearly unbiased estimates without increasing the mean squared errors. An empirical application is considered for illustrative purposes.
Reflection of Students’ Language Needs Analysis in the e-Course—Comparative Study

Research in this comparative study highlights the utilization of social applications, web language educational portals and current possibilities of virtual learning space in teaching/learning languages in the university setting. The main research tool is a survey based on the questionnaire “Students’ Language Needs Analysis”, where students’ perspective is of key importance. Findings from the needs analysis map students experience with online sources and their perception and potential benefits in mastering the language. The other key explored area refers to students’ language competencies measured according to the CEFR scale. Reached levels of students’ language competences are put into the context with students’ requirements on the desirable improvement of their language skills as well as applied methods of learning language. The paper brings not only a comparison of findings from two latest surveys but also as an added value it also brings an example of adapted study material on the virtual platform reflecting findings from students’ needs analysis.
Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation

The large variety of computerized solutions (software and information systems) calls for a systematic approach to their comparison and evaluation. Different methods have been proposed over the years for analyzing the similarity and variability of systems. These methods get artifacts, such as requirements, design models, or code, of different systems (commonly in the same domain), identify and calculate their similarities, and represent the variability in models, such as feature diagrams. Most methods rely on implementation considerations of the input systems and generate outcomes based on predefined, fixed strategies of comparison (referred to as variability views). In this paper, we introduce an approach for mining relevant views for comparison and evaluation, based on the input artifacts. Particularly, we equip SOVA – a Semantic and Ontological Variability Analysis method – with data mining techniques in order to identify relevant views that highlight variability or similarity of the input artifacts (natural language requirement documents). The comparison is done using entropy and Rand index measures. The method and its outcomes are evaluated on a case of three photo sharing applications.
From a Desktop Application to a Web API A Code Conversion Approach

Nowadays, hybrid solutions that manage the interaction between desktop platforms and Web applications and mobile devices are steadily gaining more and more importance. In this context, the use of Web services stands as the development technology to use. In this paper an automatable approach for the conversion of code from a desktop application to a Web service is presented. In order to demonstrate this approach, a real-life based scenario is presented, in which the services of an ERP (Enterprise Resource Planning) system were made available, automatically, via Web services, using the Web API (Application Programming Interface) framework from Microsoft.
A Computational Camera with Programmable Optics for Snapshot High-Resolution Multispectral Imaging

Spectral imaging has many uses in the field of conservation of cultural heritage, medical imaging, etc. It collects spectral information at each location of an image plane as an image cube. Among various approaches, snapshot multispectral imaging techniques measure the cube within one integration period. Previous work has addressed the issue of optical design, while recent developments have shifted the focus towards computation. In this paper, we present a snapshot multispectral imaging technique with a computational camera and a corresponding image restoration algorithm. The main characteristics are: (1) transferring spectral information to the spatial domain by engineering user-defined PSFs; (2) measuring spectral images by computationally inverting the image formation. The design of our computational camera is based on a phase-coded aperture technique to generate spatial and spectral variant PSFs. The corresponding algorithm is designed by adapting single-channel and cross-channel priors. We show experimentally the viability of our technique: it reconstructs high resolution multispectral images from a snapshot. We further validate that the role of PSF design is critical.
Research on Intelligent Detection System for Information Abnormal Defect Based on Personalized Recommendation of E-Book

With the accelerated pace of life, e-book reading has become a way for people to learn and play in their leisure time. When faced with a large number of e-books, the recommendation system and e-book rankings have become the entrance for readers to quickly find books of their own interest. Through the current situation of cash development of e-books, this paper analyzes the abnormality of information recommended by personalities, and discusses the problems of intelligent detection systems.
Presentation of Several Secure Access Systems and Implementations

In the last years, security has been placed in the center of every technology and domain, which enabled its fast and efficient development. Starting from the simple PIN code based system and heading to biometric technology, the access systems became more secure and safe, especially against unauthorized access in institutions. In today’s times, biometric technology is very appreciated, although it is not as developed as "old" technologies. That is why these techniques are used in combination with Near-Field Communication (NFC) cards or PIN codes. Another aspect is the fact that biometric systems are sensitive to the changing body; a disease or even daily routine like makeup can corrupt the results. In order to improve recognition results, several architectures and algorithms were presented in this paper, starting with simple Arduino-based implementations. The systems of secure access were designed, implemented and tested, and in the end, we propose a more secure system based on a two-step recognition. The focus in this system relies on the use of a biometric identification method.
Comprehensive Study of Keyphrase Extraction Metrics for Uncertain User-Generated Data

Social media analysis has received much attention from academic researchers and practitioners. Twitter is one of the most widely used social media platforms and contains ill-formed, uncertain and user-generated information. The word co-occurrence networks represent the statistical and computational evaluation of contextual data which are also called textual networks. Identifying influential nodes as keywords in textual networks of given data is of theoretical and practical significance. Different network metrics can be used to understand the salient features of textual networks. To overcome this research gap, the comprehensive study of different keyword extraction and keyphrase extraction techniques has been explored which gives useful insight into patterns in word co-occurrence networks. The significant insights obtained from textual networks can be used for different applications for social media analytics. The ‘First Story Detection’ data set has been used for experimental evaluation to identify significant traditional measures.