Reviewerly/exHarmony huggingface dataset:
SMART: A Stratified Machine Reading Test

We present a Stratified MAchine Reading Test (SMART) data set for Chinese in which each question is assigned a "level" that reflects the type of reasoning that is needed to answer the question. This data set consists of close to 40 K question-answer pairs and its stratified design allows machine reading researchers to quickly focus in on areas that present the most challenge for a machine comprehension system. We further establish a baseline for future research with BERT, and present results that show the levels we have designed correspond well with the level of difficulty that BERT experiences in answering these questions, as reflected by the lower accuracy for higher levels. We have also collected human answers to the questions in the test portion of this data set, and show that humans and the machine have different challenges when answering these questions. This means that even though the machine is approaching human-level performance on this task, humans and the machine perform this task with very different mechanisms.
GloSOPHIA: An Enhanced Textual Based Clustering Approach by Word Embeddings

Textual case based reasoning (TCBR) is a challenging problem because a single case may consist of different topics and complex linguistic terms. Many efforts have been made to enhance retrieval process in TCBR using clustering methods. This paper proposes an enhanced clustering approach called GloSOPHIA (GloVe SOPHIA). It is based on extending SOPHIA by integrating word embeddings technique to enhance knowledge discovery in TCBR. To evaluate the quality of the proposed method, we will apply the GloSOPHIA to an Arabic newspaper corpus called watan-2004 and will compare the results with SOPHIA (SOPHisticated Information Analysis), K-means, and Self-Organizing Map (SOM) with different types of evaluation criteria. The results show that GloSOPHIA outperforms the 3 other clustering methods in most of the evaluation criteria.
Development of a Computer Package on Organic Chemistry for Colleges of Education Students in Nigeria

The low achievement of chemistry students in Nigerian Colleges of Education has been attributed to a number of factors including a lack of indigenous computer software for enhancing course teaching. This study therefore sought to develop and validate an instructional computer package on organic chemistry for Colleges of Education in Nigeria. The development of the package involved the use of Macromedia Flash and Macromedia Time Line and Frames on HTML platform. CorelDraw and Microsoft Word programs were used for texts and graphics, Macromedia Fireworks was used for some texts, buttons, and graphics, while Macromedia Flash was utilised for animations. In the end, an organic chemistry computer instructional package (OCIP) has been produced and validated. It is recommended among others that lecturers teaching organic chemistry to first year College of Education students should make use of the developed package to improve the understanding of the course by the students.
Latent Space Embedding for Unsupervised Feature Selection via Joint Dictionary Learning

With the prevalence of unlabeled data, unsupervised feature selection is vital for comprehensive analysis of unlabeled high-dimensional data. Most existing unsupervised feature selection methods first generate cluster labels by specific techniques and then select features that can preserve cluster structure well. However, the selected features only reflect the distribution information of pseudo label space but ignore that of feature space. Instead, we propose a novel method, latent space embedding for unsupervised feature selection, which considers the common distribution of feature space and pseudo label space, spectral analysis and feature selection simultaneously. Inspired by the success of joint dictionary learning in cross-modality cases, we introduce a latent space shared by feature space and pseudo label space. By utilizing the mapping between feature space and latent space, features that can well maintain the common distribution of features and pseudo labels are selected. The ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm minimization constraint is added to the objective function to handle outliers and noises. Experimental results on benchmark datasets demonstrate that our algorithm outperforms the compared methods in terms of clustering tasks.
Neuromorphic information processing using silicon photonics

We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. First, we dicuss how passive reservoir computing can be used to perform non-linear signal equalisation in telecom links. Then, we introduce a training method that can deal with limited weight resolution for a hardware implementation of a photonic readout.
Secure Human-Computer Interaction

In this chapter, CAPTCHA was presented as a measure for secure human-computer interaction. A multi-factor CAPTCHA scheme that integrates facial recognition and real-time functionality as a secure verification mechanism to check the activities of bots that try to assume human status was designed, developed, and tested. The real-time functionality is premised on the human user's ability to complete trivial tasks which though simple for human is difficult to break by bots. This was motivated by the need to combat attackers' tendencies to beat existing CAPTCHA schemes through optical character recognition, image annotation, tag classifier, etc. Literature on a number of existing schemes was reviewed with a view to identifying gaps and establishing the research agenda. The system design and analysis were done using scalable design techniques. Implementation was done using Javascript and a set of APIs. The scheme was tested on an intel core i7 3GHz computer and further evaluated. Preliminary results and findings show a promising effectiveness and efficiency of the developed system.
STRUCTURE OF THE INFORMATION EDUCATION ENVIRONMENT AND ITS USE IN PREPARATION OF FUTURE EDUCATION PROFESSIONALS

The article analyzes the definition of information educational environment by national and foreign scientists, on the basis of which the author defines the specified concept; structural blocks, functions, principles, components of the information educational environment are analyzed; the structure of information educational environment of Vinnytsia State Pedagogical University named after Mikhail Kotsyubynsky and the possibility of its use in the process of formation of information competence of future teacher of professional training are considered; the structure of the main page of the University site is described, the its sections and their purpose are characterized, the view of the site of the electronic library of the educational establishment is characterized, its structure and purpose are characterized, the structure of the information and educational portal of the Department of Innovative and Information Technologies in Education, Educational and Scientific Institute of Psychology, training of highly qualified specialists were reviewed. Based on the analysis of scientific works by foreign and national scientists, the main characteristics of the informational educational environment were determined: openness, possibility of expansion, scalability, integration, adaptability, and their definitions were provided; the functions of the informational educational environment are defined: information, interactive, communication, coordinating, developmental, occupational. The scientific and pedagogical directions of formation of information educational environment are characterized: organizational, methodical, technical, resource, explanation of these principles is given. The structure and possibilities of using the educational environment are described on the example of the portal of the Chair of Innovative and Information Technologies in Education. The article also presents the results of a pedagogical study regarding the use of an informational educational environment to improve the quality of training of future vocational education teachers.
Automatic Decision Support for Public Opinion Governance of Urban Public Events

Though the process of urbanization in China is accelerating and the size of the cities continues to expand, the urban danger sources are increasing. When public emergencies such as natural disasters, major accidents, environmental hazards, and vandalism occur in the city, people receive information from major media platforms; it is easy to cause rapid spread of public opinion by smart terminals. Therefore, the research on the public opinion governance model of urban public events has great practical significance for ensuring the economic development and social stability. This paper mainly focuses on the prediction, analysis, and governance of the current public sentiment caused by urban public emergencies. Facing different sensational outbreak nodes, the system analyzes the current node’s emotional inclination, assists the government to improve their supervisory capability, and proposes rationalized constructive opinions and solutions.
The Impact of Image Resolution on Facial Expression Analysis with CNNs

While deep learning has achieved state-of-the-art results on many computer vision tasks it is still challenged when interpreting human facial expressions, namely: poor generalisation ability of models across datasets, failure to account for individual differences in similar emotional states, and inability to recognise compound facial expressions and low-intensity or subtle emotional states. This study analyses how the resolution of face images that are input to various Convolutional Neural Network (CNN) models impacts on their ability to recognise compound and low-intensity emotions. Several high-resolution facial expression databases were combined to compile a simple dataset containing high-intensity emotions and a complex data set consisting of compound and low-intensity emotions. In the experiments, standard pre-trained CNN models that were further fine-tuned achieved higher validation accuracies than CNN models that were trained from scratch on the simple data set. However, when tested on the complex data the models trained from scratch generalised better than the refined pre-trained models. Using a technique of output visualisation we could show how our high-resolution CNN models were able to generalise to the complex data where they utilised small facial features that previously were not detectable.
Smooth Deep Network Embedding

Network embedding is an efficient method to learn low-dimensional representations of vertexes in networks since the network structure can be captured and preserved through this process. Unlike shallow models, deep neural network framework is able to capture the highly non-linear network structure. Therefore, it can achieve much better performance in comparison of traditional network embedding methods. However, few attention has been paid to the smoothness of such models, in contrast to numerous research works for image and text fields. Methods without smoothness are not robust enough, which means that slight changes on network may lead dramatic changes on the embedding results. Hence, how to find a smooth deep framework is still an open yet important problem. To this end, in this paper, we propose a Smooth Deep Network Embedding method, namely SmNE, which generates stable and reliable embedding results. Empirically, we conduct experiments on real-world networks. The results show that compared to the state-of-the-art methods, our proposed method can achieve significant gains in several applications.
Software Development and Customer Satisfaction: A Systematic Literature Review

Background: Customer satisfaction is one of the vital components of a successful software company. It is not possible to develop successful products with functional and/or non-functional properties that are not able to satisfy the customer’s needs. To this end, it is important to identify factors that affect customer satisfaction and approaches to measure them also in relation with the adopted development methodology. Goals: The purpose of this work is to provide an extensive investigation of the existing studies related to evaluation of customer satisfaction and analyze them. Method: The Systematic Literature Review approach was applied. We have identified an initial set of 310 studies obtained from the three largest digital libraries that was reduced to 34 after the application of a number of filters. These studies were analyzed in depth in this paper. Results: The analysis performed points out that in the majority of the identified studies, one of the main factor that affects customer satisfaction is related to the application of Agile Software Development approaches due to their deep involvement of the customer in the development process.
Exploring Transferability in Deep Neural Networks with Functional Data Analysis and Spatial Statistics

Recent advances in machine learning have brought with them considerable attention in applying such methods to complex prediction problems. However, in extremely large dataspaces, a single neural network covering that space may not be effective, and generating large numbers of deep neural networks is not feasible. In this paper, we analyze deep networks trained from stacked autoencoders in a spatio-temporal application area to determine the extent to which knowledge can be transferred to similar regions. Our analysis applies methods from functional data analysis and spatial statistics to identify such correlation. We apply this work in the context of numerical weather prediction in analyzing large-scale data from Hurricane Sandy. Results of our analysis indicate high likelihood that spatial correlation can be exploited if it can be identified prior to training.
Attention-based Adversarial Training for Seamless Nudity Censorship

The amount of digital pornographic content over the Internet grows daily and accessing such a content has become increasingly easier. Hence, there is a real need for mechanisms that can protect particularly-vulnerable audiences (e.g., children) from browsing the web. Recently, object detection methods based on deep neural networks such as CNNs have improved the effectiveness and efficiency of identifying and blocking pornographic content. Even though improvements in detecting intimate parts have been significant, the occlusion of the content is still primarily done by either blurring or removing regions of the image in an intrusive fashion. A recent study has addressed the problem of censoring the pornographic content in a non-intrusive way by generating the so-called seamless censorship via cycle-consistent generative adversarial networks. Such an approach has managed to automatically add bikinis to naked women without explicit supervision or paired training data. In this paper, we extend that method by designing a novel cycle-consistency framework that leverages sensitive information from an attention-based multi-label convolutional neural network. We evaluate the quality of our novel generative model by conducting a web survey with over 1000 opinions regarding the resulting images from our method and from baseline approaches. Results of the survey show that our method considerably improves the state-of-the-art on the seamless censorship task.
PROFESSIONAL COMPETENCIES FORMATION OF OFFICERS-PENINENTIARIES THROUGH THE PRISM OF VIRTUAL AND AUGMENTED REALITY

The article addresses to the idea of using virtual and augmented technologies for the educational environment expanding with the aim of forming modern professional competencies of future officers-penitentiaries of Ukraine. At present, a vector of higher education reform is clearly defined, aimed at developing conceptually new approaches to the organization of educational space for the training highly skilled, competitive specialists in various fields of professional activity, including the penitentiary profile. Penitentiary sphere is characterized by quasi-professionalism, which causes the penitentiaries to have a multifaceted spectrum of knowledge, skills and abilities. Particular attention is paid to the practice of providing various types of security to the penitentiary institutions (physical, administrative, dynamic), since this directly affects the level of society's security. Therefore, the author of the article paid much attention to the teaching methods of tactical training disciplines in the Academy of the State Penitentiary Service, which is based on the use of gaming (business learning game, role playing, training) and non-gaming (analysis of specific situations, group discussions, discussion platforms, brainstorming, methods of cooperative studying) teaching methods that are conducted in the form of special trainings. It is the form of conducting practical classes that promotes the formation of certain professional competencies of future officers in the field of penitentiary institutions security. The article proposes to improve the forms of conducting practical classes by using technologies of augmented reality. This will provide opportunities to create the development conditions of a system for obtaining modern knowledge in the field of future professional activity of cadets; to solve the multifaceted task of future penitentiary officers training based on active and competent approaches; to create a practice-oriented interesting learning content, aimed at motivating students for self-education, improving certain skills and abilities, working out modern professional competencies that are important for the performance of service-professional activities at a decent level.
Strong-Background Restrained Cross Entropy Loss for Scene Text Detection

In this paper, we investigate the issue of class imbalance in scene text detection. Class Balanced Cross Entropy (CBCE) loss is often adopted for addressing this imbalance problem. We find that CBCE excessively restrains the backward gradients of background. Negative samples own extremely small weights which are offered by CBCE during training of text detectors. These tiny weight values lead to insufficient learning of background. As a result, the CBCE-based text detection methods only can achieve sub-optimal performance. We propose a novel loss function, Strong-Background Restrained Cross Entropy (SBRCE), to deal with the disadvantage in CBCE. Specifically, SBRCE effectively down-weights the loss assigned to the strong background which means well-classified negative samples. Our SBRCE can make training focused on all positive samples and weak background(i.e., hard-classified negative samples). Moreover, it can prevent the enormous amount of strong background from overwhelming text detectors during training. Experimental results show that the proposed SBRCE can improve the performance of the efficient and accurate scene text detector (EAST) by F-score of 3.3% on ICDAR2015 dataset and 1.12% on MSRA-TD500 dataset, without sacrificing the training and testing speed of EAST.
A Discretization-based Ensemble Learning Method for Classification in High-Speed Data Streams

Data stream mining has attracted much attention of the machine learning community in the last decade. Motivated by the upcoming issues associated with data stream applications, such as concept drift and the velocity into which data needs to be processed, several methods have been proposed in the literature, most of them resulting from adaptations of traditional algorithms. Such methods are forced to satisfy hard requirements of restricted memory and processing time, while keeping track of the performance at the same time. In the classification context, ensembles are an effective and elegant way to handle this task. And mostly, the bottleneck of processing time and memory of an ensemble relies on the employed base learner and on the ensemble updating policy. This paper addresses both issues by proposing: 1) a fast base learning algorithm, which relies on discretizing every attribute range into disjoint intervals and associating, to each of them, a posterior probability relating it to a class; and 2) a static ensemble that comprises such base learners and handles concept drift without replacing base learners. Results comparing the proposed ensemble method to six ensemble approaches, on artificial and real data streams, showed it yields comparable results but with lower computational time; which makes the proposed ensemble an efficient alternative to high-speed data streams.
Coupled Dictionary Learning for Multi-label Embedding

With the booming of social networks, such as Facebook and Flickr, the candidate labels of an instance can be numerous. Hence, traditional multi-label learning algorithms are out of capability to handle a large quantity of labels for the unaffordable time complexity. To alleviate this problem, label space dimension reduction (LSDR) is proposed by transforming the original label space into a lower dimensional one. Inspired by the effectiveness of coupled dictionary learning (CDL) in dealing with cross-modal data, in this paper, we proposed a novel algorithm named Coupled Dictionary Learning for Multi-label Embedding (ML-CDL) to track the problem of LSDR. We novelly treat feature and label as coupled domains. Then CDL is utilized to generate the low-dimensional latent space that leverages the information between feature and label spaces. In particular, the sparse representation coefficients embody the properties of interpretability, discriminability and sparsity. Experimental results on benchmark datasets demonstrate the effectiveness of our algorithm.
Gideon—An Artificial Intelligent Companion

Technology cannot run essentially without the input of a human. The rate of technological advancement is increasing with time, society is looking to create and develop easier ways to live and lengthen their lives. The internet is a massive source of information that millions of people use and depend on every day. Artificial Intelligence (AI) is intended to do the thinking for us often thinking through things very quickly that we do not have enough information or time to process ourselves. So Gideon will help to do daily task and it acts like artificial companion. As compared to other application Gideon has additional feature face recognition which can able to detect faces in real world and try to recognize known faces, which we may have forgotten. It can able to listen to you and provide appropriate response precisely and quickly.
Proposed User Identification Algorithm Across Social Network Using Hybrid Techniques

Social networking sites such as Facebook, Twitter, Instagram, and Chat are stronger communication tools. Users use multiple social network applications for different purposes such as business or communications and multiple accounts in different social network applications for each user. In order to identify the same user in an accurate method is important in many services. In this paper, we proposed an algorithm to identify users based on check-in and profile matching using fuzzy matching method in profile and Bayesian algorithm in check-in. This method called “Profile Matching and Check-in” PMC. The proposed method simulated using Python programming language version (2.7) for the Gowalla dataset, the accuracy of the proposed method is 84%. This method achieves high security and privacy to the system.