Reviewerly/exHarmony huggingface dataset:
A new quantitative non-destructive testing approach of broken wires for steel wire rope

In order to solve the problems including poor signal denoising effect, low recognition rate, and poor real-time performance in wire rope magnetic flux leakage (MFL) testing, this paper proposes a new algorithm combining kernel extreme learning machine (KELM) with compressed sensing wavelet (CSW). F irstly, we consider a new mechanism and regularized orthogonal matching pursuit (ROMP) into CSW, and combine double-density wavelet transform (DD-DWT) to improve the result of wire rope signal noise reduction; Then, an effective normalization method is developed to improve the accuracy of classification. Finally, the detection accuracy and efficiency in wire rope quantitative identification are ameliorated through KELM. The effectiveness and novelties of the proposed algorithms are verified by the experimental platform based on unsaturated magnetic excitation non-destructive testing (NDT) device.
Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. 1
Building Confidence not to be Phished Through a Gamified Approach: Conceptualising User's Self-Efficacy in Phishing Threat Avoidance Behaviour

Phishing attacks are prevalent and humans are central to this online identity theft attack, which aims to steal victims' sensitive and personal information such as username, password, and online banking details. There are many antiphishing tools developed to thwart against phishing attacks. Since humans are the weakest link in phishing, it is important to educate them to detect and avoid phishing attacks. One can argue self-efficacy is one of the most important determinants of individual's motivation in phishing threat avoidance behaviour, which has co-relation with knowledge. The proposed research endeavours on the user's self-efficacy in order to enhance the individual's phishing threat avoidance behaviour through their motivation. Using social cognitive theory, we explored that various knowledge attributes such as observational (vicarious) knowledge, heuristic knowledge and structural knowledge contributes immensely towards the individual's self-efficacy to enhance phishing threat prevention behaviour. A theoretical framework is then developed depicting the mechanism that links knowledge attributes, self-efficacy, threat avoidance motivation that leads to users' threat avoidance behaviour. Finally, a gaming prototype is designed incorporating the knowledge elements identified in this research that aimed to enhance individual's self-efficacy in phishing threat avoidance behaviour.
Digital Image Correlation Analysis of Displacement Based on Corrected Three Surface Fitting Algorithm

SFA (Surface Fitting Algorithm) for continuous displacement is an important method for digital image correlation with antinoise ability and computational efficiency advantages in practical applications. In order to improve the algorithm accuracy and expand its application range, this paper tries to improve the SFA and studies the modified cubic surface fitting algorithm CTSFA (Corrected Three Surface Fitting Algorithm), which is suitable for solving the initial value of continuous displacement. Bilinear interpolation and adjacent interpolation are used to analyze the gray level at any integer-pixel position in the displacement matrix and the weight coefficient is given. The distance-weighted method is used to approximate the true initial displacement value of the continuum, and the algorithm suitable for digital image processing is extended to the continuum displacement solution. The cubic surface expression of the CTSFA programmatic application is solved by the least squares method, and the correlation coefficient of the power basis function is calculated. In the computer simulation of speckle test, the comparison between CTSFA and SFA on the calculation results of linear and nonlinear displacement fields shows that the calculated amount of CTSFA is basically the same as that of SFA, but the calculation accuracy is doubled. The study of analysing the Brazilian splitting test using CTSFA and SFA reveals that CTSFA is better than SFA in observing the development of cracks.
Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multiorganizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a realtime manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.
On the Investigation of Essential Diversities for Deep Learning Testing Criteria

Recent years, more and more testing criteria for deep learning systems has been proposed to ensure system robustness and reliability. These criteria were defined based on different perspectives of diversity. However, there lacks comprehensive investigation on what are the most essential diversities that should be considered by a testing criteria for deep learning systems. Therefore, in this paper, we conduct an empirical study to investigate the relation between test diversities and erroneous behaviors of deep learning models. We define five metrics to reflect diversities in neuron activities, and leverage metamorphic testing to detect erroneous behaviors. We investigate the correlation between metrics and erroneous behaviors. We also go further step to measure the quality of test suites under the guidance of defined metrics. Our results provided comprehensive insights on the essential diversities for testing criteria to exhibit good fault detection ability.
Accelerating the Deep Reinforcement Learning with Neural Network Compression

Acceleration in deep reinforcement learning has attracted the attention of researchers. Many parallel training frameworks have been proposed to speed up the sampling and the training by running multiple agents simultaneously. However, the bottleneck of performance in a single agent is the neural network prediction which takes a lot of time as compared to the rapid environmental changes. Different from these parallel frameworks, we try to speed up the prediction of the agent to accelerate the entire training process. As far as we know, this is the first time to accelerate the training from this perspective. We propose a novel training framework NNC-DRL to accelerate the whole training process of deep reinforcement learning. NNC-DRL uses different neural networks to represent target policy and behavior policy. The behavior policy network is a smaller neural network derived from the target policy network, which could speed up the prediction. The target policy network will transfer its latest policy to the behavior policy network. The inconsistent policy distribution between behavior network and target network will degrade the convergence of the training. In NNC-DRL, we introduce the Important Sampling technique to estimate policy gradient, which could improve the convergence of the training. The experiments show that our approach NNC-DRL can speed up the whole training process by about 10-20% on Atari 2600 games with little performance loss.
A Gaussian Process Regression Approach for Point Target Tracking

Target tracking performance relies on the match between the tracker motion model and the unknown target dynamics. The performance of these model-based trackers degrades when there is a mismatch between the model and the target motion. In this paper, a Gaussian process based approach, namely, Gaussian process motion tracker (GPMT) is proposed. The Gaussian process framework is flexible and can represent an infinite number of motion modes. The evaluation of the proposed approach is performed on challenging scenarios and is compared with popular single and multiple-model based approaches. The results show high accuracy of the predicted and estimated target position and velocity over challenging maneuver scenarios.
Analysis on Characteristics of Multi-Step Learning Echo State Networks for Nonlinear Time Series Prediction

Reservoir Computing (RC) is a framework based on recurrent neural networks for high-speed learning and has attracted much attention. RC has been applied to a variety of temporal recognition tasks. Especially, Jaeger showed that the Echo State Network (ESN), which is one of the RC models, was effective for chaotic time series prediction tasks. However, there are two inevitable problems in nonlinear time series prediction using the standard ESN. One is that its prediction ability reaches a saturation point as the reservoir size is increased. The other is that its prediction ability depends heavily on hyperparameter values. In this paper, we propose a multi-step learning ESN to solve these problems. The proposed system has multiple reservoirs and the prediction error of one ESN-based predictor is corrected by another subsequent predictor. We demonstrate the effectiveness of the proposed method in two nonlinear time series prediction tasks. Another experiment using Lyapunov exponents suggests that the performance of the proposed method is robust against changes in hyperparameter values. In addition, we clarify the characteristic of the proposed method with regard to nonlinearity and memory using simple function approximation tasks.
On the efficiency of software implementations of lightweight block ciphers from the perspective of programming languages

Lightweight block ciphers are primarily designed for resource constrained devices. However, due to service requirements of large-scale IoT networks and systems, the need for efficient software implementations cannot be ruled out. A number of studies have compared software implementations of different lightweight block ciphers on a specific platform but to the best of our knowledge, this is the first attempt to benchmark various software implementations of a single lightweight block cipher across different programming languages and platforms in the cloud architecture. In this paper, we defined six lookup-table based software implementations for lightweight block ciphers with their characteristics ranging from memory to throughput optimized variants. We carried out a thorough analysis of the two costs associated with each implementation (memory and operations) and discussed possible trade-offs in detail. We coded all six types of implementations for three key settings (64, 80, 128 bits) of LED (a lightweight block cipher) in four programming languages (Java, C#, C++, Python). We highlighted the impact of choice relating to implementation type, programming language, and platform by benchmarking the seventy-two implementations for throughput and software efficiency on 32 & 64-bit platforms for two major operating systems (Windows & Linux) on Amazon Web Services Cloud. The results showed that these choices can affect the efficiency of a cryptographic primitive by a factor as high as 400.
What are Good Discussions Within Bug Report Comments for Shortening Bug Fixing Time?

Bugs must be resolved efficiently for developers' limited resources. Bug reports are necessary for the bug modification process. Service user reports a bug as a bug report. Developer read bug reports and fix bugs. The developer can make discussion by posting comments on reported bug reports. There are several researches on the initial report of the bug report so that bugs can be fixed efficiently. But there are few researches on bug report comments. We focus on comments on bug reports. Currently, everyone is free to comment, but the modification time may be affected by how to comment. We investigate the topic of comments of the bug report. As a result of the investigation, the fact that the topics are mixed does not affect the modification time, however we found a tendency to shorten the modification time when the topic of the solution started early.
Türkçe Metinlerde Duygu Analizi için Farklı Makine Öğrenmesi Yöntemlerinin Karşılaştırılması

In this research, the classification results of different Machine Learning Algorithms were compared on the validated TREMO data set used in the field of emotion extraction from Turkish texts.Emotion analysis was considered as text classification problem and four different machine algorithms, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) have been investigated.The categories provided by the data set, which are happiness, fear, anger, sadness, disgust and surprise, were used as emotion categories.In the preprocessing phase, stemming process was performed using the truncate at five (F5) method.After stemming process, the data set was modeled using the Vector Space Model.After that, the first 500 words for each emotion in the data set were identified by the Mutual Information (MI) formula.The comparison of classification results was based on accuracy metric.According to experimental study results, the ANN classifier was performed best, and SVM, RF and KNN performed, in descending order.
A Hierarchical Clustering Strategy of Processing Class Imbalance and Its Application in Fraud Detection

With the Internet and mobile communications becoming an indispensable part of people's daily lives, online transactions have become one of the most common payment methods. However, transaction fraud incidents also occur frequently, causing a larger number of economic losses. Therefore, transaction fraud detection is significant. The methods of machine learning are often used to detect fraudulent transactions from a larger amount of transaction data. However, the class imbalance problem reduces the performance of these methods. There are mainly four factors causing this problem: imbalanced class distribution, sample size, class separability and within-class concept. The existing improvement strategies for class imbalance mainly focus on the first factor but omit other three ones. This paper considers the four factors to propose a comprehensive model called clustering tree. Constructing a clustering tree includes two steps: 1) we first select a clustering algorithm considering the class separability; and then 2) we use this clustering algorithm to construct a tree that can be used to determine if an incoming transaction is illegal. The root node of this tree contains all samples, and we consider both the imbalanced class distribution and the within-class concept when samples are hierarchically divided into sub-nodes during the constructing process. We compare the proposed method with five state-of-the-art ones on two real transaction datasets, and the experimental results show that our method works better.
Generating Actionable Interpretations from Ensembles of Decision Trees

Machine-learned models are often perceived as “black boxes”: they are given inputs and hopefully produce desired outputs. There are many circumstances, however, where human-interpretability is crucial to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the feedback loop originated from the internals of any ensemble of decision trees to offer recommendations for transforming a k-labelled predicted instance into a k'-labelled one (for any possible pair of class labels k, k'). Our proposed algorithm perturbs individual feature values of an instance, so as to change the original prediction output by the ensemble on the so-transformed instance. This is also achieved under two constraints: the cost- and tolerance of transformation. Finally, we evaluate our approach on four distinct application domains: online advertising, healthcare, spam filtering, and handwritten digit recognition. Experiments confirm that our solution is able to suggest changes to feature values that help interpreting the rationale of model predictions, making it indeed useful in practice especially if implemented efficiently.
Attributed network representation learning via DeepWalk

Network representation learning aims at learning a low-dimensional vector for each node in a network, which has attracted increasing research interests recently. However, most existing approaches only use topology information of each node and ignore
Accelerating Distributed Training in Heterogeneous Clusters via a Straggler-Aware Parameter Server

Different from homogeneous clusters, when distributed training is performed in heterogeneous clusters, there will be great performance degradation due to the effect of stragglers. Instead of the synchronous stochastic optimization commonly used in homogeneous clusters, we choose an asynchronous approach, which does not require waiting for stragglers but has the problem of using stale parameters. To solve this problem, we design a straggler-aware parameter server (SaPS), which can detect stragglers through the version of parameters and mitigate their effect by a coordinator which can limit the staleness of parameters without waiting for stragglers. Experimental results show that SaPS can converge faster than fully synchronous, fully asynchronous and some SGD variants.
Focus+Context Exploration of Hierarchical Embeddings

Abstract Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level‐of‐detail‐hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi‐dimensional images.
TEACHER’S COMPETITIVENESS AT DIFFERENT STAGES OF PROFESSIONAL DEVELOPMENT

Purpose of the study: The research is aimed at determination of the competitiveness level of teachers with different experience in pedagogy and different level of education, and at establishing of a connection between content components of competitiveness and full teacher’s self-realization in his/her professional activity.&#x0D; Methodology: The main methods include experimental research which allows comprehensive analysis of the issue of pre-school teachers competitiveness improvement; theoretical and methodological analysis of the subject and the problem of the research that is based on the studying of scientific literature and its logical compilation; empirical methods (analyses of documents, testing), plus mathematical-statistical and interpretative methods.&#x0D; Results: The article presents the empirical research of the competitiveness of pre-school teachers and the system for improvement of their competitiveness. The adequacy of psycho-diagnostic methodologies for research of competitiveness levels of pre-school teachers is proved, the interconnection between the competitiveness level of teachers and various experiences of pedagogical activity and the level of education is established.&#x0D; Applications of this study: The results of the study will be useful for developing educational strategies in the preschool education system, they can help pre-school education leaders creating social-psychological-pedagogical conditions for the effective interaction of all subjects of the educational process as well as for the full realization of the individual in the modern educational process and improving individual efficiency.&#x0D; Novelty/Originality of this study: The results of the empirical study show that the applied set of psychodiagnostic methods turned out to be reliable and valid tools that ensured the goals achievement: the level of competitiveness of teachers with different lengths of pedagogical activity and level of education was determined, the relationship between the content components of competitiveness and full self-realization of a teacher in professional activities was determined based on prioritizing social and psycho-pedagogical characteristics of the person.
ROYALE: A Framework for Universally Composable Card Games with Financial Rewards and Penalties Enforcement

While many tailor made card game protocols are known, the vast majority of those lack three important features: mechanisms for distributing financial rewards and punishing cheaters, composability guarantees and flexibility, focusing on the specific game of poker. Even though folklore holds that poker protocols can be used to play any card game, this conjecture remains unproven and, in fact, does not hold for a number of protocols (including recent results). We both tackle the problem of constructing protocols for general card games and initiate a treatment of such protocols in the Universal Composability (UC) framework, introducing an ideal functionality that captures card games that use a set of core card operations. Based on this formalism, we introduce Royale, the first UC-secure general card games which supports financial rewards/penalties enforcement. We remark that Royale also yields the first UC-secure poker protocol. Interestingly, Royale performs better than most previous works (that do not have composability guarantees), which we highlight through a detailed concrete complexity analysis and benchmarks from a prototype implementation.