Reviewerly/exHarmony huggingface dataset:
Bring your own device or organisation provided device classroom: problematising the issue of the digital divide in teaching and learning contexts in Poland

This comparative study contributes to new knowledge on how digital inclusion can be supported in two differently designed school settings through the meaningful use of technology to enhance the students’ learning and their overall skills development. This study is a comparative, empirically grounded case research conducted over a one year period in two primary schools in Poland. Our research involved a number of methods, including video-ethnography and interviews, which were used to gather qualitative data from headteachers, teachers, students and parents. The results problematise the issues of the digital divide in teaching and learning practices in both formal and informal contexts. We have checked in what way both OPD and BYOD influence the existing divides in the area of learning processes.
A Case Study: Comparison of Software Cost Estimation of Smart Shopping List Application

The aim of this study is to evaluate development cost of smart shopping software which is going to be planned to developed using cost estimation methods of software development in software engineering. The smart shopping software aims that faster and easier preparing of shopping lists which is prepared by consumer with the aims of buying product from supermarket with lower cost. The most of consumers demand that a products price which they are going to buy or total amount of shopping price are lowest while shopping. Therefore, product prices of supermarkets need to be known. They try to find out which supermarket lowest price of products is in by researching of product price before shopping and they go shopping with the aim of lowest total cost of shopping. The smart shopping software planned to develop in this study aims to make consumers to shop with lower total cost within the help of artificial intelligence and applied mathematics. It is going to provided that to be found out supermarkets where optimal product prices are. The cost of best shopping route for shopping from these supermarkets is going to be determined. In this study, it has been made an estimate of development cost of smart shopping software. Therefore, the attributes of the software were firstly defined. These attributes are classified to determine complexity of them in following categories; interface design, database, controller classes, web API, artificial intelligence. So, the difficulties of performing these tasks have been obtained. After that, the tasks required to develop these attributes were determined within the scope of the functional points. In conclusion, the required workforce and time costs and project size were estimated by the methods using different software development cost estimation approaches and the results of the methods were evaluated.
An Illumination Insensitive and Structure-Aware Image Color Layer Decomposition Method

To decompose of an image into a set of color layers can facilitate many image editing manipulations, but the high-quality layering remains challenging. We propose a novel illumination insensitive and structure-aware layer decomposition approach. To reduce the influence of non-uniform illumination on color appearance, we design a scheme of letting the decomposition work on the reflectance image output by intrinsic decomposition, rather than the original image commonly used in previous work. To obtain fine layers, we leverage image specific structure information and enforce it by encoding a structure-aware prior into a novel energy minimization formulation. The proposed optimization considers the fidelity, our structure-aware prior and permissible ranges simultaneously. We provide a solver to this optimization to get final layers. Experiments demonstrate that our method can obtain finer layers compared to several state-of-the-art methods.
Data-driven Face Hallucination by Inverse Degradation Neural Network

Face hallucination refers to the technology that inferring its potential corresponding high-resolution (HR) image from the input low-resolution (LR) facial image. At present, most face hallucination algorithms improve reconstruction performance by optimizing models. However, the common approach will out of operation when meeting more complex problem, etc, the input image contains degraded pixels (noise), their reconstruction performance will drop sharply. In order to solve the problem, we propose an inverse degradation neural network (IDNN), which can mine the essential features of the images under data-driven. In this network, we design different network structures in different task stages. Firstly, the more accurated face structure is generated by the denoising network in the LR space. But the details from the face image is lacked in this stage. In order to further enhance the face image details, we utilize the reconstruction network to restore the missing details. The experimental results on FEI face database show that IDNN outperforms some state-of-the-art approaches in subjective and objective measures.
Usability Evaluation of e-Kocaeli M-Goverment Application

Nowadays, mobile applications are preferred by local governments as much as in other fields in terms of providing the municipal services to their end users and enabling them to perform their transactions. In this study, the usability of e-Kocaeli, which is an m-Government application, was evaluated. A usability test was conducted with 25 participants. Participants were given 6 tasks to complete through the application, and the time to complete the tasks, the difficulties and errors they experienced, and their think-aloud data were recorded by the researchers. Participants were asked to fill the System Usability Scale. The findings revealed that there were some usability problems that hindered the performances of users. In order to improve the usability of the application, suggestions such as rearranging the menu categories on the main page, providing categories in alphabetical order and adding in-app search function were presented.
Making Truth Safe for Intuitionists

We consider a handful of solutions to the liar paradox which admit a naive truth predicate and employ a non-classical logic, and which include a proposal for classical recapture. Classical recapture is essentially the property that the paradox solvent (in this case, the non-classical interpretation of the connectives) only affects the portion of the language including the truth predicate—so that the connectives can be interpreted classically in sentences in which the truth predicate does not occur. We consider a variation on this theme where the logic to be recaptured is not classical but rather intuitionist logic, and consider the extent to which these handful of solutions to the liar admit of intuitionist recapture by sketching potential ways of altering their various methods for classical recapture to suit an intuitionist framework.
Multi-scale Comparison Network for Few-Shot Learning

Few-shot learning, which learns from a small number of samples, is an emerging field in multimedia. Through systematically exploring influences of scale information, including multi-scale feature extraction, multi-scale comparison and increased parameters brought by multiple scales, in this paper, we present a novel end-to-end model called Multi-scale Comparison Network (MSCN) for few-shot learning. The proposed MSCN uses different scale convolutions for comparison to solve the problem of excessive gaps between target sizes in the images during few-shot learning. It first uses a 4-layer encoder to encode support and testing samples to obtain their feature maps. After deep splicing these feature maps, the proposed MSCN further uses a comparator comprising two layers of multi-scale comparative modules and two fully connected layers to derive the similarity between support and testing samples. Experimental results on two benchmark datasets including Omniglot and $$\textit{mini}$$Imagenet shows the effectiveness of the proposed MSCN, which has averagely 2% improvement on $$\textit{mini}$$Imagenet in all experimental results compared with the recent Relation Network.
A Compact Deep Neural Network for Single Image Super-Resolution

Convolutional neural network (CNN) has recently been applied into single image super-resolution (SISR) task. But the applied CNN models are increasingly cumbersome which will cause heavy memory and computational burden when deploying in realistic applications. Besides, existing CNNs for SISR have trouble in handling different scales information with same kernel size. In this paper, we propose a compact deep neural network (CDNN) to (1) reduce the amount of model parameters (2) decrease computational operations and (3) process different scales information. We devise two kinds of channel-wise scoring units (CSU), including adaptive channel-wise scoring unit (ACSU) and constant channel-wise scoring unit (CCSU), which act as judges to score for different channels. With further sparsity regularization imposed on CSUs and ensuing pruning of low-score channels, we can achieve considerable storage saving and computation simplification. In addition, the CDNN contains a dense inception structure, the convolutional kernels of which are in different sizes. This enables the CDNN to cope with different scales information in one natural image. We demonstrate the effectiveness of CSUs, dense inception on benchmarks and the proposed CDNN has superior performance over other methods.
Effective Utilization of Hybrid Residual Modules in Deep Neural Networks for Super Resolution

Recently, Single-Image Super-Resolution (SISR) has attracted a lot of researchers due to its numerous real-life applications in multiple domains. This paper focuses on efficient solutions of SISR with Hybrid Residual Modules (HRM). The proposed HRM allows the deep neural network to reconstruct very high quality super-resolved images with much lower computation compared to the conventional SISR methods. In this paper, we first describe the technical details of our HRM in SISR and introduce interesting applications of the proposed SISR method, such as surveillance camera system, medical imaging, astronomical imaging.
Cloud-based Artificial Bee Colony Programming Web Application

To solve a problem without any knowledge of its solution, a variety evolutionary computation approach based automatic programming methods have been developed. Artficial Bee Colony Programming (ABCP) is one of the recently proposed automatic programming methods. ABCP has been preferred and applied to various problems by researchers from different disciplines like other evolutionary computation approach based algorithms. In this study, a cloud-based web application (ABCPWeb) that allows to use the ABCP with no need to have any programming knowledge is proposed. ABCPWeb is tested on the benchmark problems that are previously solved by ABCP. The success of ABCPWeb is revealed by comparing the experimental results with the results of basic ABCP.
Similarity Graph Convolutional Construction Network for Interactive Action Recognition

Interaction action recognition is a challenging problem in the research of computer vision. Skeleton-based action recognition shows great performance in recent years, but the non-Euclidean distance structure of the skeleton brings a huge challenge to the design of deep learning neural network. When meeting interaction action recognition, research in the previous study is based on a fixed skeleton graph, capturing only information about local body movements in a single action and do not deal with the relationship between two or more people. In this article, we present a similarity graph convolutional network that contains two-person interaction information. This model can represent the relationship between two people. Simultaneously, for different body parts (such as head and hand), the relationship can be handled. The model has two construction modes, a skeleton graph and a similarity graph, and the features from the two composition modes is better fused by the hypergraph. Similarity graph is obtained from a two-step construction. First, an encoder is designed, which is aimed to map different characteristics of one joint to a same vector space. Second, we calculate the similarity between different joints to construct the similarity graph. Follow the steps above, similarity graph can indicate the relationship between two people in details. We perform experiments on the NTU RGB+D dataset and verify the effectiveness of our model. The result shows that our approach outperforms the state-of-the-art methods and similarity graph can solve the relationship modeling problem in interactive action recognition.
Research on Quantifier Phrases Based on the Corpus of International Chinese Textbooks

Chinese is rich in quantifiers, and studying various types and structures of quantifier phrases is a necessary way to master quantifiers. Using Chinese information processing technology to study quantifier phrases in international Chinese teaching is conducive to promoting deep integration in the two fields. This paper first constructs a knowledge base of quantifier phrase structural modes by tagging the quantifier phrases in the corpus of a certain scale of international Chinese textbooks. Then the characteristics of quantifier phrases in the field of international Chinese teaching are analyzed through the constructed structural mode knowledge base. Finally, on the basis of the structural mode knowledge base, automatic recognition of quantifier phrases in the corpus of international Chinese textbooks is studied.
Development of Augmented Reality Based Mobile Robot Maintenance Software

Augmented Reality (AR) helps to increase the perception of reality by adding digital data to the real world. Nowadays, AR is used effectively in many areas from entertainment sectors to health and education applications. In this study, a mobile application has been developed by using Augmented Reality technology for maintenance and repair of the mobile robot (evarobot). In this study, battery, motor, sensor and electronic card replacement scenarios of Evarobot and maintenance and repair steps were determined. These steps are supported with visuals in accordance with the instructions given and are intended to enable the user to carry out the maintenance process easily without the need for an expert or maintenance document. At the end of the study, the digitization of the experience and the transfer of the information without getting lost has been realized in an easy way.
High Accuracy Disparity Estimation via Weighted Phase Correlation Disparity Tracker

When using Phase Correlation (PC) to estimate disparity map of remote sensing images with narrow baseline, disparity precision is usually influenced by matching window size, low texture areas and motion noises. To solve these problems, we propose a weighted PC based disparity tracker: First, a hierarchical meth-od is applied to put images divided into multistage structure; Then, at first level, weighted PC employs initial window to esti-mate a rough disparity matrix. Meanwhile, Kronecker delta peak of initial window is applied to get a reliable threshold. Next, disparity tracker employs three sub-steps to refine the disparity matrix: first, window size is reduced according to adaptive win-dow strategy; then, in target image, the reduced window is moved to a new position under the guide of prior disparity ma-trix; third, weighted PC is employed to estimate the disparity matrix of next level. Finally, the disparity tracker is iteratively executed until it converges. Moreover, according to the reliable threshold, a reliability evaluation approach is imbedded into disparity tracker for the reliability of each disparity. Comparing with the state-of-the-art PC methods, the experimental results demonstrate our method obtains better results in visual and quantitative evaluations, especially in minimizing the influence of tremendous depth differences and motion noises caused by high mountains and river areas.
Integrated Approach to the Self Adaptive Software Development Based on Reflection and Observation of the Information Environment

The approach proposed by the authors for building self-adaptive software systems is based on the joint use of two concepts: reflexive self-adaptation and observation of the information environment. The first concept involves the analysis of information circulating within the system and the identification of patterns in it. The restructuring of the program will be carried out in the process of implementation on the basis of the found patterns. The central idea of the second concept is the analysis of information entering the system from outside (for example, in the form of user reviews), and the subsequent restructuring of the system. The implementation of both concepts is based on the application of two technologies: technology of dynamic lines of software products used to build a universal behavioral structure of an adaptive software application, and data mining technology, which is used to implement the process of self-adaptation. The study was carried out with the financial support of the Russian Foundation for Basic Research in the framework of the research project No. 18-07-00408.
Consensus Approaches of High-Value Crypto Currencies and Application in SHA-3

In view of the widespread use of information technologies, the security of data against third parties should be maintained. Blockchain technology is used for this data and especially for transactions in finance. However, although bitcoin is well known as the crypto currency, the use of the blockchain technology is gaining importance in different areas as well. Thus, consensus algorithms have been developed in order to increase the security and especially the integrity of the crypto currencies. These consensus algorithms indirectly influence the market value of crypto currencies. In this study, consensus algorithms of some crypto currencies have been investigated. In addition to the SHA256 algorithm, the advantages and disadvantages of using Shake and Keccak algorithms as a part of the consensus algorithms, have been analyzed. SHA256, Keccak and Shake algorithms are compared regarding to their performance. As a result, we recommend that the cryptographic hash function can be replaced with the Shake algorithm can be an extendable output function for new crypto currencies.
Specifying a New Requirement Model for Secure Adaptive Systems

Abstract Security is a growing concern in developing software systems. It is important to face unknown threats in order to make the system continue operating properly. Threats are vague and attack methods change frequently. Coping with such changes is a major feature of an adaptive software. Therefore, designing an adaptive secure software is an appropriate solution to address software security challenges. Through estimation of maximum amount of system assets security, one can determine whether the system is protecting the assets or not; if not, reconfiguration can be employed. This paper proposes a new requirement model for secure adaptive systems using fuzzy, goal modeling and Description Logic concepts. The model contains three phases of modeling security aspects of the system, identifying formalizations and relations between the requirements and monitoring and adapting, when needed. To illustrate the relations between the requirements, goal modeling is used in the first phase and fuzzy Description Logic in the second phase. For the third phase, four algorithms are proposed to monitor and determine whether reconfiguration is needed or not. Theorems are given to prove concept satisfaction of the requirements. Furthermore, examples and case studies are discussed to evaluate and show applicability of the proposed model.
Block-Based Neural Network Optimization with Manageable Problem Space

In this paper, a simple method based on Genetic Algorithm (GA) is proposed to evolve Block-Based Neural Network (BbNN) model. A BbNN consists of a 2-D array of memory-based modular component NNs with flexible structures and internal configuration that can be implemented in reconfigurable hardware such as a field programmable gate array (FPGA). The network structure and the weights are encoded in bit strings and globally optimized using the genetic operators. Asynchronous BbNN (ABbNN), which is a new model of BbNN, suggests high-performance BbNN by utilizing parallel computation and pipeline architecture. ABbNN's operating frequency is stable for all scales of the network, while conventional BbNN's is decreasing according to the network size. However, optimization by the genetic algorithm requires more iterations to find a solution with increasing problem space and the memory access in GA operation is one of the causes degrading the performance. ABbNN optimized with the proposed evolutionary algorithm is applied on general classifiers to verify the effectiveness with increasing problem space. The proposed method is confirmed by experimental investigations and compared with the conventional genetic algorithm.
Aspectual coding asymmetries: Predicting aspectual verb lengths by the effects frequency and information content

Abstract The topic of this paper is the interaction of aspectual verb coding, information content and lengths of verbs, as generally stated in Shannon’s source coding theorem on the interaction between the coding and length of a message. We hypothesize that, based on this interaction, lengths of aspectual verb forms can be predicted from both their aspectual coding and their information. The point of departure is the assumption that each verb has a default aspectual value and that this value can be estimated based on frequency – which has, according to Zipf’s law, a negative correlation with length. Employing a linear mixed-effects model fitted with a random effect for LEMMA, effects of the predictors’ DEFAULT – i.e. the default aspect value of verbs, the Zipfian predictor FREQUENCY and the entropy-based predictor AVERAGE INFORMATION CONTENT – are compared with average aspectual verb form lengths. Data resources are 18 UD treebanks. Significantly differing impacts of the predictors on verb lengths across our test set of languages have come to light and, in addition, the hypothesis of coding asymmetry does not turn out to be true for all languages in focus.
Pulmonary Nodules Image Retrieval via Supervised Deep Hashing

Content-based image retrieval can quickly find images associated with query instance from large-scale medical image databases, which can provide effective aided diagnosis information for doctors, and is beneficial to improve the accuracy of diagnosis. Deep supervised hashing not only has the ability to extract rich features from deep neural networks but also can make full use of supervised information to improve retrieval accuracy. It is also more efficient and memory-saving due to reducing the dimension of the feature vectors by the hash mapping. Therefore, it has attracted much attention. In the paper, a novel end-to-end supervised deep hashing method is proposed, where feature extraction and binary code learning are carried out by joint optimization. The semantic similarity is maintained by label information and the neighborhood structures are preserved by graph regularization. Moreover, the discrete constrained objective function is optimized directly without relaxation. Experiments have been conducted on the pulmonary nodule image dataset and the results demonstrate the proposed method can yield better retrieval performance by comparing with the state-of-the-art hashing methods.