Reviewerly/exHarmony huggingface dataset:
The Use and Abuse of Machine Translation in Vocabulary Acquisition among L2 Arabic-Speaking Learners

Using Machine Translation (MT) for vocabulary acquisition is an inevitable phenomenon among Arabic-speaking L2 learners. But has MT fully succeeded in replacing traditional dictionaries and providing an ideal tool for vocabulary acquisition among L2 learners? This study aimed to research the value and implications of using machine translation in vocabulary acquisition. The study is significant as it investigates a neglected area in teaching a second language focusing on the role of MT in vocabulary acquisition. The empirical study adopted a combined quantitative and qualitative methodology that tested students’ skills in answering vocabulary questions in context by using online translators. Forty-seven participants took part in the assessment voluntarily, and they were all fourth-year students about to graduate from an Omani private university. The findings confirmed the results of earlier research about the challenges ESL learners face in vocabulary acquisition, including difficulties in processing sequential lexical patterns and using vocabulary communicatively. The results further revealed that online translators do not provide an optimal solution to overcome obstacles in using vocabulary unless accompanied by higher metacognitive skills such as critical thinking and using words in context. The study concluded with some implications and recommendations for further relevant research.
Fostering creativity and critical thinking with the use of ICT: Theoretical foundations and empirical examples

The purpose of this article is to highlight some theoretical foundations for a connection between critical thinking and creativity as concepts to develop at school, to find the feasibility of use of ICT in such lessons, and to give examples of lessons conducted in such a logic. The competence approach, despite the ambiguous attitude towards it in secondary vocational education, has been re-examined by researchers in connection with a new wave of discussion on how to develop key competencies in secondary education. Critical thinking and creativity are seen as part of key competencies. The article describes in detail these constructs and correlates with well-known works of Russian and foreign practitioners and psychologists, such as L. Vygotsky, J. Piaget, and J. Dewey. The concepts overlap in many ways, which should be taken into account when evaluating innovation and designing educational experience of students. Despite the some differences, the existing modern didactic Russian and foreign approaches (such as problem-based learning, activity-based approach, student-centered learning, and others) meet the objectives and organization of a lesson, which foster critical thinking and creativity. Portable digital devices contribute to the development of greater student autonomy, which is one of the most important components of the development of key competencies. The final part of the article is structured through three theses on implementation of ICT in such lessons with examples of use of such practices by teachers during the project “Fostering creativity and critical thinking in education with the use of ICT” in 2018–2019. Examples allow us to determine how theoretical concepts, having many diachronic intersections, can be implemented in practice.
Adversarial Attack with KD-Tree Searching on Training Set

With the emergence of adversarial examples, target detection models based on deep learning have been found to be susceptible to well-designed input samples. Most adversarial examples are imperceptible to humans, but they will purposefully mislead the target detection system to produce various errors. This article provides a new idea of adversarial example generation: a method of adversarial attack based on training set data. In this paper, by establishing the training set KD-tree of the target model, it is used to retrieve the reference image that is closest to the original image and does not belong to the same category. By moving the image closer to the reference image, the purpose of constructing an adversarial example with a stronger attack ability is achieved. We show through experiments that moving the image closer to the training set image that is most similar to it can quickly achieve the attack effect with less disturbance.
U-Net Medical Image Segmentation Based on Attention Mechanism Combination

The accuracy of medical image segmentation is of great significance to the diagnosis of patients. With the development of deep learning, the segmentation of medical images using convolutional neural networks has become a research hotspot. After the U-Net model was proposed, it has gradually become a commonly used convolutional neural network model in the field of medical image segmentation. However, medical images have the characteristics of different shapes of target organs and the image boundaries are not easy to determine. These problems lead to poor segmentation performance of the U-Net model. In view of the above problems, the attention mechanism is introduced from the two dimensions of space and channel to improve the U-Net model. Use Dice coefficient and IOU (Intersection Over Union) as evaluation metrics to compare model performance on multiple medical image datasets. The experimental results show that the U-Net model after introducing the attention mechanism has a better segmentation effect.
Factors that Affect Merge Conflicts: A Software Developers’ Perspective

Merge conflicts are very common in collaborative software development. Such collaborative work is usually supported by the use of branches, which at some point can be merged. In the technical literature, several studies propose mechanisms to avoid conflicts, even though this is not always possible. Some recent studies have tried to identify factors that lead to conflicts. In our study, we aim to investigate factors that can lead to conflicts, or that can somehow reduce the chances of conflicts, from the developers’ perspective. To do so, we conducted a survey research with 109 software developers to understand the way they use branches, the occurrence of conflicts and resolution process, and factors that can lead or avoid conflicts. Results show that the use of branches is very common and mostly has the purpose of creating a new feature or fixing a bug. According to the participants, in most projects, developers have autonomy to create new branches and sometimes conflicts happen. The main factors that can lead to conflicts are “the time a branch is isolated” and “lack of communication”. On the other hand, the factors cited as good practices to avoid conflicts were “improve team communication” and “less branching-duration”.
Improved Proxy Re-encryption Scheme with Equality Test

A proxy re-encryption with keyword search (PRES) provides simultaneously the function of keyword search and flexible sharing of data. The concept is more efficient than the traditional data-sharing method by performing "search-download-decrypt-encrypt." However, a PRES is limited to searching for encrypted keywords using the same public key. To address this problem, Li et al. introduced a new primitive, called proxy re-encryption with equality test (PREET), which can search for ciphertext under different public keys. PREET effectively solves these problems and provides an equality test of keywords under different public keys. Unfortunately, we found that their scheme was unable to effectively verify the integrity of the ciphertext during the verification process of the decryption step. Therefore, we propose an improved scheme to make the verification effectively verify the integrity of the ciphertext. In addition, a security proof is provided to show that our proposed scheme is secure against chosen-ciphertext attacks based on the Diffie–Hellman assumptions under random oracle models.
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning

Vision-based reinforcement learning (RL) is a promising approach to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in terms of sample efficiency, especially when using image observations. This has led to increased attention on integrating state representation learning (SRL) techniques into the RL pipeline. Work in this field demonstrates a substantial improvement in sample efficiency among other benefits. However, to take full advantage of this paradigm, the quality of samples used for training plays a crucial role. More importantly, the diversity of these samples could affect the sample efficiency of vision-based RL, but also its generalization capability. In this work, we present an approach to improve sample diversity for state representation learning. Our method enhances the exploration capability of RL algorithms, by taking advantage of the SRL setup. Our experiments show that our proposed approach boosts the visitation of problematic states, improves the learned state representation, and outperforms the baselines for all tested environments. These results are most apparent for environments where the baseline methods struggle. In simple environments, our method contributes to stabilizing the training, reducing the reward variance, and improving sample efficiency.
MP-BADNet: A Backdoor-Attack Detection and Identification Protocol among Multi-Participants in Private Deep Neural Networks

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks where the adversary can inject malicious data during the DNN training. Such kind of attacks is always activated when the input is stamped with a pre-specified trigger which results in a pre-setting prediction of the DNN model. Due to increasing applications of DNNs, it is necessary to detect the backdoors whether the DNN model has been trojaned before implementation. Since the data come from the various data holders during the model training, it is also important to protect the privacy both of input data and models. In this paper, we propose a framework MP-BADNet, the first work on the backdoor attack detection and identification protocol among multi-participants in private deep neural networks. MP-BADNet can not only detect and identify backdoors in the privacy-preserving DNN model, but also achieve privacy preserving of input data and the model in secure multi-party computation (MPC) ways. The implemental results show that the scheme can effectively detect and identify backdoor attacks in the privacy-preserving DNN model.
Designing and Evaluating Ubicomp Characteristics of Intelligent In-Car Systems

In this paper, we present a preliminary taxonomy for designing and evaluating intelligent systems from the viewpoint of ubiquitous computing (ubicomp). As an example of intelligent systems, we examine a few novel in-car systems, which are already commercially available. We also discuss some ergonomics issues related to the design of in-car systems and argue that rationale for solving these issues can be found from the ideal qualities of ubicomp systems. The five characteristics we define to be ideal for genuine ubicomp systems are context-awareness, natural interaction methods, invisibility, support for everyday tasks, and interconnectivity. Based on these characteristics, we create a framework to analyse the differences between the ubicomp characteristics of the examined novel in-car systems. To assess the utility of the framework in the development of entirely new systems, we use it to evaluate two intelligent prototype in-car systems. The results suggest that the framework suits well for guiding the design and evaluation of in-car computing systems from the ubicomp point of view. Future work needs to be conducted to improve the applicability of the framework in evaluating also other intelligent systems than only in-car systems.
Predicting Cybersecurity Incidents Through Mandatory Disclosure Regulation

Cybersecurity risk is an increasingly common concern for organizationsthat collect and maintain vast troves of data. In 2011, the United StatesSecurities and Exchange Commission (SEC) provided guidelines for howpublicly traded companies should convey these risks to potential investors. Butdoes this mandatory disclosure regime effectively serve this purpose in thecybersecurity context? This Article uses machine learning and natural languageprocessing techniques to analyze firmsâ€™ mandatory risk disclosure statements,predict which firms are at the greatest risk of suffering cybersecurity incidents,and evaluate how well disclosure meets the goals of the broad regulatoryregime. More broadly, this study highlights the potential for using legallymandated disclosures to bolster regulatory efforts, particularly in the context ofprediction policy problems.
Data Security and Privacy Preserving with Augmented Homomorphic Re-Encryption Decryption (AHRED) Algorithm in Big Data Analytics

The process of Big data storage has become challenging due to the expansion of extensive data; data providers will offer encrypted data and upload to Big data. However, the data exchange mechanism is unable to accommodate encrypted data. Particularly when a large number of users share the scalable data, the scalability becomes extremely limited. Using a contemporary privacy protection system to solve this issue and ensure the security of encrypted data, as well as partially homomorphic re-encryption and decryption (PHRED). This scheme has the flexibility to share data by ensuring user's privacy with partially trusted Big Data. It can access to strong unforgeable scheme it make the transmuted cipher text have public and private key verification combined identity based Augmented Homomorphic Re Encryption Decryption(AHRED) on paillier crypto System with Laplacian noise filter the performance of the data provider for privacy preserving big data.
Population Management Approaches in the OPn Algorithm

This paper deals with the problem of selecting the population size for the population-based algorithm with dynamic selection of operators (OPn). This research was undertaken to check how population size changes affect the optimization of problems in which both the parameters of the solution and its structure should be selected. Moreover, variants in which the size of the population changes dynamically were considered. The simulations were performed for a small selection/variety of examples of control problems in which the structures and parameters of controllers based on PID systems had to be selected.
A Stackelberg Game Approach toward Migration of Enterprise Applications to the Cloud

With the development of cloud computing, more and more cloud resources are rented or purchased by users. Using an economics approach to achieve cloud resource management has been thought of as a good choice for an enterprise user to complete an application’s migration and deployment into the public cloud. During an application’s migration process, it is important but very challenging to achieve the satisfaction of both the enterprise user and the public cloud provider at the same time. In this paper, we apply an economics approach to investigate the migration optimization problem during the migration process of applications from the enterprise user’s data center to the remote public cloud. We consider the application migration time of the enterprise user and the energy consumption of physical machines, and establish a single static round optimization problem for both the enterprise user and the cloud provider on the premise of satisfying the quality of experience (QoE) based on the Stackelberg game, where the public cloud provider is leader and the enterprise user is follower. Then we propose a novel algorithm to find the optimal physical machine placement for application migration. After that, we further consider that an enterprise user needs to migrate several applications, and extend the single-round static game to the multi-round dynamic game, where the energy consumption costs of the physical machines are reduced by adjusting the states of the physical machines in each round. We finally illustrate the performance of our scheme through some simulation results.
Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference

Image inpainting is a well established problem in computer vision which aims to fill in missing regions in images. In medical applications, it can be combined with other tools to remove artifacts or fill in occluded regions, allowing better understanding of the images from doctors or downstream algorithms. However, current methods that solve the problem usually pay no attention to the underlying pixel-intensity distributions in the missing input regions, and most approaches deterministically provide only one result per input region instead of several plausible results. We estimate the intensity distributions within each masked region using a novel Variational Autoencoder (VAE) based hierarchical probabilistic network. Our approach then generates a diverse set of inpainted images, all of which appear visually appropriate.
Scene-Perception Graph Convolutional Networks for Human Action Prediction

Human action recognition and prediction with skeleton-based data has been widely applied in intelligent robots and machine vision. Current advanced methods mainly focus on using recurrent neural network (RNN) to predict human motion based only on skeleton data. However, objects that interact with the human body will have a great impact on human behaviors. Extracting body information without considering environmental objects constraints reduces the accuracy of behavior prediction. In this paper, we propose a scene-perception architecture, which implements an end-to-end human behavior prediction task based on both objects and human skeleton. We propose a scene-perception Graph Convolutional Network (SPGCN) to formula the natural constraint between skeleton nodes and objects in the scene to predict human postures. SPGCN is composed of graph convolutional network with scene-aware and RNN, GCN is used to learn the dependence relationships between human and objects. Then, the learned features are fed into RNN to predict future pose and action labels. We evaluate SPGCN on CAD-120 dataset. Experiments show that our proposed method achieves promising results compared with the state-of-the-art methods.
Enhanced Face Recognition Using Adaptive Local Tri Weber Pattern with Improved Deep Learning Architecture

Effective face recognition is accomplished using the extraction of features and classification. Though there are multiple techniques for face image recognition, full face recognition in real-time is quite difficult. One of the emerging and promising methods to address this challenge in face recognition is deep learning networks. The inevitable network tool associated with the face recognition method with deep learning systems is convolutional neural networks (CNNs). This research intends to develop a new method for face recognition using adaptive intelligent methods. The main phases of the proposed method are (a) data collection, (b) image pre-processing, (c) normalization, (d) pattern extraction, and (e) recognition. Initially, the images for face recognition are gathered from CPFW, Yale datasets, and the MIT-CBCL dataset. The image pre-processing is performed by the Gaussian filtering method. Further, the normalization of the image will be done, which is a process that alters the range of pixel intensities and can handle the poor contrast due to glare. Then a new descriptor called adaptive local tri Weber pattern (ALTrWP) acts as a pattern extractor. In the recognition phase, the VGG16 architecture with new chick updated-chicken swarm optimization (NSU-CSO) is used. As the modification, VGG16 architecture will be enhanced by this optimization technique. The performance of the developed method is analyzed on two standards face database. Experimental results are compared with different machine learning approaches concerned with noteworthy measures, which demonstrate the efficiency of the considered classifier.
IMPLEMENTATION OF PROJECT-BASED TRAINING AT THE DEPARTMENT OF PHYSICS OF SSUGT

The experience of the Department of Physics of SSUGT, as a general education department, in the field of implementing the elements of project-based learning in the educational process is analyzed. The possibilities of transition from micro- and mini-projects to full-scale projects by opening its own bachelor degree program are considered.
Evolving Delta-Oriented Product Lines: A Case Study on Feature Interaction, Safe and Partially Safe Evolution

Software product line engineering is a well-known approach for building a set of configurable systems for a specific domain, and different techniques have been used to manage product line variability, including source-code preprocessing, aspect-oriented programming (AOP), and delta-oriented programming (DOP). Although existing studies have explored the design and evolution of product lines using techniques such as source-code preprocessing and AOP, little is known about the practical implications of using DOP to bootstrap and evolve software product lines. In this paper we address this issue, reporting our experience of using DeltaJ to implement two product lines (Reminder-PL and Iris-PL). This experience covers different scenarios of evolution (such as the inclusion of mandatory, optional, and alternative features) that indeed led to several feature interactions. Altogether, this work brings several contributions, including evidence that existing templates for safe and partially safe evolution of product lines can also help developers to evolve delta-oriented SPLs—although we revealed the need for two additional templates for safe evolution. Also, we present a description of the feature interactions that appeared during the evolution of both product lines and how we modularized these interactions using DOP constructs.
Two-Stage Pre-Training for Sequence to Sequence Speech Recognition

The attention-based encoder-decoder structure is popular in automatic speech recognition (ASR). However, it relies heavily on transcribed data. In this paper, we propose a novel pre-training strategy for the encoder-decoder sequence-to-sequence (seq2seq) model by utilizing unpaired speech and transcripts. The pre-training process consists of two stages, acoustic pre-training and linguistic pre-training. In the acoustic pre-training stage, we use a large amount of speech to pre-train the encoder by predicting masked speech feature chunks with their contexts. In the linguistic pre-training stage, we first generate synthesized speech from a large number of transcripts using a text-to-speech (TTS) system and then use the synthesized paired data to pre-train the decoder. The two-stage pre-training is conducted on the AISHELL-2 dataset, and we apply this pre-trained model to multiple subsets of AISHELL-1 and HKUST for post-training. As the size of the subset increases, we obtain relative character error rate reduction (CERR) from 38.24% to 7.88% on AISHELL-1 and from 12.00% to 1.20% on HKUST.
Information Technology-Based Picture Book Design of Children's Intellectual and Educational Anecdotes

With concise and vivid language and delicate and beautiful pictures, picture books have become a fashion for children's reading training in the 21st century. In addition, with the improvement of people's living standards, people focus on the spiritual level, cultural improvement, more and more attention to children's reading education. The development of picture books based on information technology has a broad development prospect. The following through the picture book introduction, picture book creation, development prospects and other contents are discussed.