Reviewerly/exHarmony huggingface dataset:
A Black-Box Attack on Neural Networks Based on Swarm Evolutionary Algorithm

Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from adversarial examples generated to attack them. However, most of the generation approaches either assume that the attacker has full knowledge of the neural network model or are limited by the type of attacked model. In this paper, we propose a new approach that generates a black-box attack to neural networks based on the swarm evolutionary algorithm. Benefiting from the improvements in the technology and theoretical characteristics of evolutionary algorithms, our approach has the advantages of effectiveness, black-box attack, generality, and randomness. Our experimental results show that both the MNIST images and the CIFAR-10 images can be perturbed to successful generate a black-box attack with 100% probability on average. In addition, the proposed attack, which is successful on distilled neural networks with almost 100% probability, is resistant to defensive distillation. The experimental results also indicate that the robustness of the artificial intelligence algorithm is related to the complexity of the model and the data set. In addition, we find that the adversarial examples to some extent reproduce the characteristics of the sample data learned by the neural network model.
YOUNG LEARNERS’ PROCESSING OF MULTIMODAL INPUT AND ITS IMPACT ON READING COMPREHENSION: AN EYE-TRACKING STUDY – CORRIGENDUM

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Research on Software Defect Prediction Framework Based on ISFLA in IoT Communication Software

Software defect feature selection has problems of feature space dimensionality reduction and large search space. This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm (ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages, the feature values are sorted, and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow. The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks. At the same time, this framework further reduces the dimension of the feature space. After the contrast simulation experiment with other common defect prediction methods, we used the actual test data set to verify the framework for multiple iterations on Internet of Things (IoT) system platform. The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software. This framework can save the testing time of IoT communication software, effectively improve the performance of software defect prediction, and ensure the software quality.
Probabilistic storage and retrieval of qubit phase gates

Probabilistic storage and retrieval (PSR) of unitary quantum dynamics is possible with exponentially small failure probability with respect to the number of systems used as a quantum memory [PRL 122, 170502 (2019)]. Here we study improvements due to a priori knowledge about the unitary transformation to be stored. In particular, we study $N \rightarrow 1$ PSR of qubit phase gates, i.e. qubit rotations a round $Z$ axis with an unknown angle, and show that if we access the gate only $N$-times, the optimal probability of perfect retrieving of its single use is $N/(N+1)$. We propose a quantum circuit realization for the optimal protocol and show that programmable phase gate [PRL 88, 047905 (2002)] can be turned into $(2^k-1)\rightarrow 1$ optimal PSR of phase gates and requires only $k$ CNOT gates, while having exponentially small failure probability in $k$.
Treewidth-aware Reductions of Normal ASP to SAT - Is Normal ASP Harder than SAT after All?

Answer Set Programming (ASP) is a paradigm and problem modeling/solving toolkit for KR that is often invoked. There are plenty of results dedicated to studying the hardness of (fragments of) ASP. So far, these studies resulted in characterizations in terms of computational complexity as well as in fine-grained insights presented in form of dichotomy-style results, lower bounds when translating to other formalisms like propositional satisfiability (SAT), and even detailed parameterized complexity landscapes. A quite generic and prominent parameter in parameterized complexity originating from graph theory is the so-called treewidth, which in a sense captures structural density of a program. Recently, there was an increase in the number of treewidth-based solvers related to SAT. While there exist several translations from (normal) ASP to SAT, yet there is no reduction preserving treewidth or at least being aware of the treewidth increase. This paper deals with a novel reduction from normal ASP to SAT that is aware of the treewidth, and guarantees that a slight increase of treewidth is indeed sufficient. Then, we also present a new result establishing that when considering treewidth, already the fragment of normal ASP is slightly harder than SAT (under reasonable assumptions in computational complexity). This also confirms that our reduction probably cannot be significantly improved and that the slight increase of treewidth is unavoidable.
Reversible Logic Synthesis Using Binary Decision Diagrams With Exploiting Efficient Reordering Operators

With the continuous shrinkage of transistor sizes in very large scale integrated circuits, power consumption forms a serious concern to be tackled.With their ability to allow for zero energy dissipation, reversible circuits have been considered as promising solution to meetup with low power design requirements.Moreover, advances in their synthesis methodologies can be easily applied to quantum circuits due to the inherited reversibility of the latter.Although numerous algorithms have been proposed in the literature to synthesize reversible circuits and map them into their corresponding quantum circuits, the scalability and computational effort of such algorithms form a serious concern when synthesizing large size input functions.Binary Decision Diagram-based synthesis for reversible circuits has shown great evidence in realizing reversible circuits with low quantum cost through exploiting proper reduction rules for smaller graph size.However, the order of the variables in the decision diagram impacts its overall size, and thus, the cost of its corresponding reversible circuit.While several reordering algorithms have been proposed in this manner, their direct impact on the quantum cost has not been considered.In this article, a Binary Decision Diagram-based algorithm for reversible circuit synthesis is proposed to synthesize reversible circuits for a given Boolean function with low quantum cost through exploiting a linearized relationship between the decision diagram size and the corresponding quantum cost.Thereafter, different decision diagram reordering algorithms have been integrated with the proposed algorithm and compared in terms of their impact on the quantum cost.Experimental results show that Genetic Algorithm-based reordering for decision diagram, supported with, cycle crossover, inverse mutation, and tournament selection, results in the least quantum cost of the output circuit if compared with other algorithms due to its property in preserving the nodes of the decision diagram in their near-optimal locations during the optimiation recipe.
Hardness-Aware Dictionary Learning: Boosting Dictionary for Recognition

Sparse representation is a powerful tool in many visual applications since images can be represented effectively and efficiently with a dictionary. Conventional dictionary learning methods usually treat each training sample equally, which would lead to the degradation of recognition performance when the samples from same category distribute dispersedly. This is because the dictionary focuses more on easy samples (known as highly clustered samples), and those hard samples (known as widely distributed samples) are easily ignored. As a result, the test samples which exhibit high dissimilarities to most of intra-category samples tend to be misclassified. To circumvent this issue, this paper proposes a simple and effective hardness-aware dictionary learning (HADL) method, which considers training samples discriminatively based on the AdaBoost mechanism. Different from learning one optimal dictionary, HADL learns a set of dictionaries and corresponding sub-classifiers jointly in an iterative fashion. In each iteration, HADL learns a dictionary and a sub-classifier, and updates the weights based on the classification errors given by current sub-classifier. Those correctly classified samples are assigned with small weights while those incorrectly classified samples are assigned with large weights. Through the iterated learning procedure, the hard samples are associated with different dictionaries. Finally, HADL combines the learned sub-classifiers linearly to form a strong classifier, which improves the overall recognition accuracy effectively. Experiments on well-known benchmarks show that HADL achieves promising classification results.
Research data sharing in the Australian national science agency: Understanding the relative importance of organisational, disciplinary and domain-specific influences

This study delineates the relative importance of organisational, research discipline and application domain factors in influencing researchers' data sharing practices in Australia's national scientific and industrial research agency. We surveyed 354 researchers and found that the number of data deposits made by researchers were related to the openness of the data culture and the contractual inhibitors experienced by researchers. Multi-level modelling revealed that organisational unit membership explained 10%, disciplinary membership explained 6%, and domain membership explained 4% of the variance in researchers' intentions to share research data. However, only the organisational measure of openness to data sharing explained significant unique variance in data sharing. Thus, whereas previous research has tended to focus on disciplinary influences on data sharing, this study suggests that factors operating within the organisation have the most powerful influence on researchers' data sharing practices. The research received approval from the organisation's Human Research Ethics Committee (no. 014/18).
An Assistive Technology Solution for User Activity Monitoring Exploiting Passive RFID

Population ageing is having a direct influence on serious health issues, including hampered mobility and physical decline. Good habits in performing physical activities, in addition to eating and drinking, are essential to improve the life quality of the elderly population. Technological solutions, aiming at increasing awareness or providing reminders to eat/drink regularly, can have a significant impact in this scenario. These solutions enable the possibility to constantly monitor deviations from users’ normal behavior, thus allowing reminders to be provided to users/caregivers. In this context, this paper presents a radio-frequency identification (RFID) system to monitor user’s habits, such as the use of food, beverages, and/or drugs. The device was optimized to fulfill specifications imposed by the addressed application. The approach could be extended for the monitoring of home appliances, environment exploitation, and activity rate. Advantages of the approach compared to other solutions, e.g., based on cameras, are related to the low level of invasiveness and flexibility of the adopted technology. A major contribution of this paper is related to the wide investigation of system behavior, which is aimed to define the optimal working conditions of the system, with regards to the power budget, user (antenna)-tag reading range, and the optimal inter-tag distance. To investigate the performance of the system in tag detection, experiments were performed in a scenario replicating a home environment. To achieve this aim, specificity and sensitivity indexes were computed to provide an objective evaluation of the system performance. For the case considered, if proper conditions are meet, a specificity value of 0.9 and a sensitivity value of 1 were estimated.
Comparisons of Facial Recognition Algorithms Through a Case Study Application

&lt;p class="affiliations"&gt;&lt;strong&gt;Abstract— &lt;/strong&gt;Computer visions and their applications have become important in contemporary life. Hence, researches on facial and object recognition have become increasingly important both from academicians and practitioners. Smart gadgets such as smartphones are nowadays capable of high processing power, memory capacity, along with high resolutions camera. Furthermore, the connectivity bandwidth and the speed of the interaction have significantly impacted the popularity of mobile object recognition applications. These developments in addition to computer vision’s algorithms advancement have transferred object’s recognitions from desktop environments to the mobile world. The aim of this paper to reveal the efficiency and accuracy of the existing open-source facial recognition algorithms in real-life settings. We use the following popular open-source algorithms for efficiency evaluations: Eigenfaces, Fisherfaces, Local Binary Pattern Histogram, the deep convolutional neural network algorithm, and OpenFace. The evaluations of the test cases indicate that among the compared facial recognition algorithms the OpenFace algorithm has the highest accuracy to identify faces. The findings of this study help the practitioner on their decision of the algorithm selections and the academician on how to improve the accuracy of the current algorithms even further.&lt;/p&gt;
RGB-D Image Inpainting Using Generative Adversarial Network with a Late Fusion Approach

Diminished reality is a technology that aims to remove objects from video images and fills in the missing region with plausible pixels. Most conventional methods utilize the different cameras that capture the same scene from different viewpoints to allow regions to be removed and restored. In this paper, we propose an RGB-D image inpainting method using generative adversarial network, which does not require multiple cameras. Recently, an RGB image inpainting method has achieved outstanding results by employing a generative adversarial network. However, RGB inpainting methods aim to restore only the texture of the missing region and, therefore, does not recover geometric information (i.e, 3D structure of the scene). We expand conventional image inpainting method to RGB-D image inpainting to jointly restore the texture and geometry of missing regions from a pair of RGB and depth images. Inspired by other tasks that use RGB and depth images (e.g., semantic segmentation and object detection), we propose late fusion approach that exploits the advantage of RGB and depth information each other. The experimental results verify the effectiveness of our proposed method.
Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil

Several studies show the effects of lake breezes on cloudiness over natural lakes and large rivers, but only few contain information regarding large flooded areas of hydroelectric dams. Most Brazilian hydropower plants have large water reservoirs that may induce significant changes in the local environment. In this work, we describe the prevailing breeze mechanism in a Brazilian tropical hydropower reservoir to assess its impacts on local cloudiness and incoming surface solar irradiation. GOES-16 visible imagery, ISCCP database products, and ground measurement sites operated by INMET and LABREN/INPE provided data for the statistical analysis. We evaluate the cloudiness frequency assuming two distinct perspectives: spatial distribution by comparing cloudiness over the water surface and areas nearby its shores, and time analysis by comparing cloudiness prior and after reservoir completion. We also evaluated the solar irradiance enhancement over the water surface compared to the border and land areas surrounding the hydropower reservoir. The results pointed out daily average cloudiness increases moving away from the reservoir in any of the four cardinal directions. When looking at the afternoon-only cloudiness (14 h to 16 h local time), 4% fewer clouds were observed over the flooded area during summer (DJF). This difference reaches 8% during autumn (MAM) and spring (SON). Consequently, the irradiance enhancement at the water surface compared to external areas was around 1.75% for daily average and 4.59% for the afternoon-only average. Our results suggest that floating solar PV power plants in hydropower reservoirs can be an excellent option to integrate both renewable energy resources into a hybrid power generation due to the high solar irradiance in Brazilian territory combined with the prevailing breeze mechanism in large tropical water reservoirs.
Multiclass Recognition of Offline Handwritten Devanagari Characters using CNN

The handwriting style of every writer consists of variations, skewness and slanting nature and therefore, it is a stimulating task to recognise these handwritten documents. This article presents a study on various methods available in literature for Devanagari handwritten character recognition and performs its implementation using Convolutional neural network (CNN). Available methods are studied on different parameters and a tabular comparison is also presented which concludes superiority of CNN model in character recognition task. The proposed CNN model results in well acceptable accuracy using dropout and stochastic gradient descent (SGD) optimizer.
Penerapan Model Make A Match Berbantuan Media Permainan Ular Tangga Untuk Meningkatkan Hasil Belajar Siswa

The study aims to improve student learning outcomes in sloving problems with the material by implementing the Make A Match model assisted by snake and ladder media. The subjects of this study were 26 grade IV students with the location of the research carried out in SD 1 Gamong, Kaliwungu District, Kudus Regency. This research lasted for 2 cycles with 2 meatings in each cycle. Data collection techniques are carried out in the form of test and observation sheets.Beston the results of the analysis obtained, it shows that the application of the Make A Match model with the assistance of snake and ladder media on theme 7 especially in social studies content and indonesian can improve student learning outcomes. This research is expected that other researchers can develop learning using the Make A Match model to be more effective by paying more attention to learning syntax and paying attention to the characteristics of the subjects and materials to be used.
Evolving Cyber Threats, Combating Techniques, and Open Issues in Online Social Networks

Online social networks (OSNs) are renowned powerful web tools that allow worldwide users to connect with their friends, families, professional groups, and social circle through social interaction for sharing common interests. With the proliferation, ease, and efficacy of OSNs, these are becoming an essential tool for communication. But many OSN users innocently uncover their personal information such as their home address, phone numbers, email id, etc. that can be used by the malicious user to perform various kind for cyber-crimes like cyber-bullying, spamming, click-jacking, identity theft, phishing, distrust, fake profiles, spreading malicious content, etc. This chapter presents a review of various privacy and security threats/attacks associated with OSN users and recommended combating techniques based on data-mining and machine learning algorithms. Also, the future directions for upcoming researchers in this field are suggested.
The Use of Electronic Educational Resources and Innovative Educational Technologies in University Education

The penetration of digital technologies in the field of human sciences requires the use of more diverse teaching methods and techniques. Electronic educational resources (EER), which include a whole range of educational materials, allow varying the forms of teaching and contribute to improving the quality of education. This study analyses international experience in the development and implementation of EER and offers a practical description for organizing electronic teacher education with the bachelor's degree programmes. We tried to consider the specificity of using e-learning in the field of human sciences, specifically history. The study describes original development of electronic educational resources that have effectively proven themselves when working with students: the Eureka forum, creating a database project, text analysis of academic articles and creating a digest of scientific research on the problem. The study offers e-learning techniques that allow in a digital environment to preserve the traditional learning model – lecture-and-book model. To determine the impact of the e-learning, we conducted a survey among 98 students to evaluate the degree of involvement in the e-learning process and to obtain an assessment of its use in teaching practice. The study results can be practically used for creating new electronic educational resources focusing on distance education.
Experimental Applications of Virtual Reality in Design Education

By introducing rapid reproduction, algorithms, and complex formal configurations, the digital era of architecture began a revolution. Architects incorporated the computational capacity of the computer into the design process both as a tool and as a critical component of the theories and practice of architecture as a whole. As we move into what has been coined Âthe second digital turn,Â a period in which digital integration is considered ubiquitous, how can we consider, prepare, and propel towards the next technological innovation to significantly inform design thinking, representation, and manifestation? What tools are available to investigate this speculative design future and how can they be implemented? If the integration of technology in architecture is now a given, perhaps the next digital design era is not just digital but virtual. As new technologies emerge the potential for integrating the virtual design world with our physical senses affords novel possibilities for interactive design, simulation, analysis and construction. Hybrid reality technologies including virtual reality (VR) and augmented reality (AR), embody the potential to supersede conventional representation methodologies such as drawing, rendering, physical modeling, and animation. As they become increasingly pervasive, they will transform how we communicate ideas and data as spatial concepts. Further, they will reform the construct of the built environment when applied to both materiality and fabrication. This paper will describe the incorporation of VR as a tool in various classroom and laboratory settings, recognize the educational outcomes of this incorporation, and identify the potential relationship of these technologies to future academic exploration and application to practice.