Reviewerly/exHarmony huggingface dataset:
Fault Coverage-Aware Metrics for Evaluating the Reliability Factor of Solar Tracking Systems

This paper presents a mathematical approach for determining the reliability of solar tracking systems based on three fault coverage-aware metrics which use system error data from hardware, software as well as in-circuit testing (ICT) techniques, to calculate a solar test factor (STF). Using Euler’s named constant, the solar reliability factor (SRF) is computed to define the robustness and availability of modern, high-performance solar tracking systems. The experimental cases which were run in the Mathcad software suite and the Python programming environment show that the fault coverage-aware metrics greatly change the test and reliability factor curve of solar tracking systems, achieving significantly reduced calculation steps and computation time.
Sequential Recommendations on GitHub Repository

The software development platform is an increasingly expanding industry. It is growing steadily due to the active research and sharing of artificial intelligence and deep learning. Further, predicting users’ propensity in this huge community and recommending a new repository is beneficial for researchers and users. Despite this, only a few researches have been done on the recommendation system of such platforms. In this study, we propose a method to model extensive user data of an online community with a deep learning-based recommendation system. This study shows that a new repository can be effectively recommended based on the accumulated big data from the user. Moreover, this study is the first study of the sequential recommendation system that provides a new dataset of a software development platform, which is as large as the prevailing datasets. The experiments show that the proposed dataset can be practiced in various recommendation tasks.
Interpreting Deep Neural Networks through Prototype Factorization

Typical deep neural networks (DNNs) are complex black-box models and their decision making process can be difficult to comprehend even for experienced machine learning practitioners. Therefore their use could be limited in mission-critical scenarios despite state-of-the-art performance on many challenging ML tasks. Through this work, we empower users to interpret DNNs with a post-hoc analysis protocol. We propose ProtoFac, an explainable matrix factorization technique that decomposes the latent representations at any selected layer in a pre-trained DNN as a collection of weighted prototypes, which are a small number of exemplars extracted from the original data (e.g. image patches, shapelets). Using the factorized weights and prototypes we build a surrogate model for interpretation by replacing the corresponding layer in the neural network. We identify a number of desired properties of ProtoFac including authenticity, interpretability, simplicity and propose the optimization objective and training procedure accordingly. The method is model-agnostic and can be applied to DNNs with varying architectures. It goes beyond per-sample feature-based explanation by providing prototypes as a condensed set of evidences used by the model for decision making. We applied ProtoFac to interpret pretrained DNNs for a variety of ML tasks including time series classification on electrocardiograms, and image classification. The result shows that ProtoFac is able to extract meaningful prototypes to explain the models' decisions while truthfully reflects the models' operation. We also evaluated human interpretability through Amazon Mechanical Turk (MTurk), showing that ProtoFac is able to produce interpretable and user-friendly explanations.
Edge Detection to Make Drawing Sketch using Laplacian Operator and Gabor Wavelet for Learning Devices

Abstract Image processing plays important role. Image processing is signal processing where the input is an image, while the output from image processing can be an image or a number of characteristics or parameters related to the image. So the image can be changed in such a way as needed. One of the goals of image processing is to make learning devices. Learning tools in the form of sketch drawings that are needed in the world of education. In this study Laplacian operators will use edge detection to produce sketches. Edge detection is a process to produce edges of an object image, the purpose is to mark the part that becomes the image details Laplacian Operators aim to trace points in the image that are considered as edges of an object, which limit an objects area to one another. Another goal of Laplacian operations is to increase the appearance of the boundary lines of an area or object in the image. With this combination good sketches will be produced and can be utilized in making learning tools.
Search &amp; Catch: Detecting Promotion Infection in the Underground through Search Engines

Promotion infection is an attack where adversaries exploit website weakness to inject illicit content into web pages for promoting illicit products, e.g., gambling and unlicensed drugs, etc. As the development of online underground economy, promotion infection is widely utilized by adversaries. To escape legal supervision, adversaries have exploited ingenious anti-regulatory techniques, making it challenging to access the infected webpage, let alone the collection of a large scale corpus. Limited by the small size of corpus, the effectiveness of the literature in detecting promotion infection is not proven yet. Towards this end, we propose a novel and efficient approach to (semi-)automatically collect infected webpages. Our insight is that the goal of infection is to promote the underground business, mainly through search engines. Exploiting this observation, we propose the search-engine dork technique to crawl the infected webpages by querying underground advertisings (i.e., a list of black keywords) on search engines. Running with an initial 58 seed black keywords, we have collected 22,939 infected pages ranging 2,563 domains, and along the way automatically gathered 8,374 new black keywords. This large scale dataset thereby enables us to build a machine-learning model to distinguish infected webpages from normal ones. The experimental results show that the model, thanks to the large-scale and high-quality dataset, can provide high detection rate and low false alarm rate.
Improved Image Security in Internet of Thing (IOT) Using Multiple Key AES

Image is an important digital information that used in many internet of things (IoT) applications such as transport, healthcare, agriculture, military, vehicles and wildlife. etc. Also, any image has very important characteristic such as large size, strong correlation and huge redundancy, therefore, encrypting it by using single key Advanced Encryption Standard (AES) through IoT communication technologies makes it vulnerable to many threats, thus, the pixels that have the same values will be encrypted to another pixels that have same values when they use the same key. The contribution of this work is to increase the security of transferred image. This paper proposed multiple key AES algorithm (MECCAES) to improve the security of the transmitted image through IoT. This approach is evaluated via applying it on RGB bmp images and analyzing the results using standard metrics such as entropy, histogram, correlation, Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MES) metrics. Also, the time for encryption and decryption for the proposed MECCAES is the same time consumed by original single key AES is 12 second(the used image size is 12.1MB therefore time is long). The performance experiments show that this scheme achieves confidentiality also it encourages to use effectively in a wide IoTs fields to secure transmitted image.
Physical Computing Strategy to Support Students’ Coding Literacy: An Educational Experiment with Arduino Boards

The purpose of this study is to explore the influence of introduction of the physical computing strategy of Arduino Boards in a program design course on coding literacy and the effectiveness of the application in technical high school students. This study selected two classes of twelfth-grade students enrolled in a program design course at a technical high school in Southern Taiwan as the samples. One class was the control group (43 students), and the other was the experimental group (42 students). During the 18-week course, the control group carried out a DBL (design-based learning) programming project, and the experimental group carried out the DBL programming project using the physical computing strategy of Arduino boards. Pre- and posttests and a questionnaire survey were carried out, while ANCOVA (analysis of covariance) was used for evaluation purposes. In the course, students in the experimental group were randomly selected for semi-structured interviews to understand their learning status and to perform qualitative analysis and summarization. This study proposed the physical computing strategy of Arduino boards, featuring staged teaching content, practical teaching activities, and real themes and problem-solving tasks. The results show that the coding literacy of students in the different teaching strategy groups was significantly improved. However, in the Arduino course on DBL programming, the students in the experimental group had a significantly higher learning efficiency in coding literacy than those in the control group. Moreover, according to the qualitative analysis using student interviews, Arduino boards were found to improve students’ motivation to learn coding and to aid in systematically guiding students toward improving their coding literacy by combining their learning with DBL theory. Thus, Arduino technology can be effectively used to improve students’ programming abilities and their operational thinking in practically applying programming theories.
Content-Enhanced Network Embedding for Academic Collaborator Recommendation

It is meaningful for a researcher to find some proper collaborators in complex academic tasks. Academic collaborator recommendation models are always based on the network embedding of academic collaborator networks. Most of them focus on the network structure, text information, and the combination of them. The latent semantic relationships exist according to the text information of nodes in the academic collaborator network. However, these relationships are often ignored, which implies the similarity of the researchers. How to capture the latent semantic relationships among researchers in the academic collaborator network is a challenge. In this paper, we propose a content-enhanced network embedding model for academic collaborator recommendation, namely, CNEacR. We build a content-enhanced academic collaborator network based on the weighted text representation of each researcher. The content-enhanced academic collaborator network contains intrinsic collaboration relationships and latent semantic relationships. Firstly, the weighted text representation of each researcher is obtained according to its text information. Secondly, a content-enhanced academic collaborator network is built via the similarity of the weighted text representation of researchers and intrinsic collaboration relationships. Thirdly, each researcher is represented as a latent vector using network representation learning. Finally, top- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> similar researchers are recommended for each target researcher. Experiment results on the real-world datasets show that CNEacR achieves better performance than academic collaborator recommendation baselines.
Development of hybrid ARQ protocol for the quantum communication system on stabilizer codes

In this paper, we develop a novel hybrid automatic-repeat-request (ARQ) protocol for the quantum communication system using quantum stabilizer codes. The quantum information is encoded by stabilizer codes to against the channel noise. The two-photon entangled state is prepared for codeword secure transmission. Hybrid ARQ protocol rules the recognition and retransmission of error codewords. In this protocol, the property of quantum entangled state ensures the security of information, the theory of hybrid ARQ system improves the reliability of transmission, the theory of quantum stabilizer codes corrects the flipping errors of codewords. Finally, we verify the security and throughput efficiency of this protocol.
Two Stage Continuous Gesture Recognition Based on Deep Learning

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance.
Optimal Device Selection for Federated Learning over Mobile Edge Networks

Data privacy preservation has drawn much attention with emerging machine learning applications. Federated Learning is thus developed to offer decentralized learning on user devices. However, it is difficult to jointly address multiple issues such as device selection, upload scheduling, and payment minimization. To jointly optimize the issues above, we first formulate a new optimization problem, named TRAIN, to minimize the training cost (including incentive payment and upload time) while ensuring the data requirement. We then prove the NP-hardness and propose a 3-approximation algorithm, named DETECT to obtain a near-optimal solution. Simulation results manifest that DETECT reduces the training cost by 50% compared with other traditional methods and achieves high accuracy and short convergence time.
Group Equivariant Generative Adversarial Networks

Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity. However, these approaches typically involve image augmentations via additional regularizers in the GAN objective and thus spend valuable network capacity towards approximating transformation equivariance instead of their desired task. In this work, we explicitly incorporate inductive symmetry priors into the network architectures via group-equivariant convolutional networks. Group-convolutions have higher expressive power with fewer samples and lead to better gradient feedback between generator and discriminator. Further, group-representations are especially relevant for modalities such as medical imaging, where image labels are typically invariant under rotation/reflection. We show that group-equivariance integrates seamlessly with recent techniques for GAN training across regularizers, architectures, and loss functions. We demonstrate the utility of our methods for conditional synthesis by improving generation in the limited data regime across globally-symmetric imaging datasets and even finding benefits for natural images with preferred orientation.
A Probabilistic Analysis of Cyber Risks

Cyber risk affects all organizations. Cyber risk management has generally been based on the heuristics and the availability of protective tools, such as firewalls and passwords. Only recently have there been quantitative analyses of these tools’ costs and benefits. This article presents a probabilistic method based on the existing data in an organization and on their extension to assess the probabilities of new attack scenarios. The objective is to set priorities among risk management measures and to optimize the allocation of limited resources. The model is illustrated first by a statistical analysis of 60 000 incidents, such as lost or stolen laptops, over six years in a specific organization. This analysis is then expanded to the probabilistic domain to cover threats that have not occurred yet. This requires a systematic construction of new attack scenarios and an assessment both of their probability of success and of subsequent losses. The conjunction of statistics and probabilities of more extreme scenarios yields full risk curves. These curves represent the overall cyber risk for the organization and its insurers and unable assessment of the benefits of a spectrum of protective options.
Transparency, provenance and collections as data

&#x0D; ‘Collections as data’ has become a core activity for libraries in recent years: it is important that we make collections available in machine-readable formats to enable and encourage computational research. However, while this is a necessary output, discussion around the processes and workflows required to turn collections into data, and to make collections data available openly, are just as valuable. With libraries increasingly becoming producers of their own collections – presenting data from digitisation and digital production tools as part of datasets, for example – and making collections available at scale through mass-digitisation programmes, the trustworthiness of our processes comes into question. In a world of big data, often of unclear origins, how can libraries be transparent about the ways in which collections are turned into data, how do we ensure that biases in our collections are recognised and not amplified, and how do we make these datasets available openly for reuse? This paper presents a case study of work underway at the National Library of Scotland to present collections as data in an open and transparent way – from establishing a new Digital Scholarship Service, to workflows and online presentation of datasets. It considers the changes to existing processes needed to produce the Data Foundry, the National Library of Scotland's open data delivery platform, and explores the practical challenges of presenting collections as data online in an open, transparent and coherent manner.&#x0D;
NPQ-RRT<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mi>∗</mi> </msup> </math>: An Improved RRT<math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mi>∗</mi> </msup> </math> Approach to Hybrid Path Planning

In recent years, the path planning of robot has been a hot research direction, and multirobot formation has practical application prospect in our life. This article proposes a hybrid path planning algorithm applied to robot formation. The improved Rapidly Exploring Random Trees algorithm PQ-RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow /> <mi>∗</mi> </msup> </math> with new distance evaluation function is used as a global planning algorithm to generate the initial global path. The determined parent nodes and child nodes are used as the starting points and target points of the local planning algorithm, respectively. The dynamic window approach is used as the local planning algorithm to avoid dynamic obstacles. At the same time, the algorithm restricts the movement of robots inside the formation to avoid internal collisions. The local optimal path is selected by the evaluation function containing the possibility of formation collision. Therefore, multiple mobile robots can quickly and safely reach the global target point in a complex environment with dynamic and static obstacles through the hybrid path planning algorithm. Numerical simulations are given to verify the effectiveness and superiority of the proposed hybrid path planning algorithm.
Transaction Prediction in Blockchain: A Negative Link Prediction Algorithm Based on the Sentiment Analysis and Balance Theory

User relationship prediction in the transaction of Blockchain is to predict whether a transaction will occur between two users in the future, which can be abstracted into the link prediction problem. The link prediction can be categorized into the positive one and the negative one. However, the existing negative link prediction algorithms mainly consider the number of negative user interactions and lack the full use of emotion characteristics in user interactions. To solve this problem, this paper proposes a negative link prediction algorithm based on the sentiment analysis and balance theory. Firstly, the user interaction matrix is constructed based on calculating the intensity of emotion polarity for social network texts, and a reliability weight matrix (noted as RW-matrix) is constructed based on the user interaction matrix to measure the reliability of negative links. Secondly, with the RW-matrix, a negative link prediction algorithm is proposed based on the structural balance theory by constructing negative link sample sets and extracting sample features. To evaluate the performance of the negative link prediction algorithm proposed, the variable management method is used to analyze the influence of negative sample control error and other parameters on the accuracy of it. Compared with the existing prediction benchmark algorithms, the experimental results demonstrate that the proposed negative link prediction algorithm can improve the accuracy of prediction significantly and deliver good performances.
Earthquake Early Warning Using Low-Cost MEMS Sensors

Over the past several years, low-cost micro-electro-mechanical systems (MEMS) sensors have been actively used for detecting earthquakes in real time. To utilize the low-cost MEMS sensors for earthquake early warning (EEW), it is crucial to construct a high-density seismic network of the sensors along with a server system to process a large volume of acceleration data transmitted from the sensors. It is also of vital importance to design an effective and efficient detection algorithm for the networked system in order to ensure realtime earthquake detection with high accuracy and low false alarms. In this paper, we introduce our recent effort in South Korea to build an EEW system based on several thousands of low-cost MEMS sensors and a deep-learning-based detection algorithm, which is currently being developed and deployed nationwide. Its initial version of a networked system has been in operation for over a year and has been able to detect a few small-magnitude earthquakes. While the early results have been positive and promising, we envision to ultimately integrate the networked system of low-cost MEMS sensors with the existing network of traditional seismic stations so as to build a unified EEW system that has better coverage and detection performance.
Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.