Reviewerly/exHarmony huggingface dataset:
GNet4FL: effective fault localization via graph convolutional neural network

Fault localization aims to efficiently locate faults when debugging programs, reducing software development and maintenance costs. Spectrum-based fault location (SBFL) is the most commonly used fault location technology, which calculates and ranks the suspicious value of each program entity with a specific formula by counting the coverage information of all the program entities and execution results of test cases. However, previous SBFL techniques suffered from low accuracy due to the sole use of execution coverage. This paper proposed an approach GNet4FL based on the graph convolutional neural network. GNet4FL first collects static features based on code structure and dynamic features based on test results. Then, GNet4FL uses GraphSAGE to obtain node representation of source codes and performs feature fusion on an entity consisting of multiple nodes, which preserves the topological information of the graph. Finally, the representation of each entity is input to the multi-layer perceptron for training and ranking entities. The results of the study showed that GNet4FL successfully located 160 out of 262 faults, outperforming the three state-of-the-art methods by 94, 42, and 14% in Top-1 accuracy, and having close results to Grace with less cost. Furthermore, we investigated the impact of each component (i.e., graph neural network, pruning, and dynamic features) of GNet4FL on the results. We found that all of these components had a positive impact on the proposed approach.
Team-based learning in online education: the development of students’ creative thinking skills in digital art

A person cultivates their creative skills influenced by various types of activities. The purpose of the work is to study the peculiarities of the development of students' creative thinking skills, which are interrelated with the development of appropriate stages of team teaching, as well as to determine the impact of creative thinking on the indicators of academic performance and motivation to learn. The authors used the methods of sociological survey, which allowed them to determine that the largest number of students had mastered the skills of discipline (27%) and emotionality (21%) to a greater extent at the initial stage of the study. The results showed that 11% of the students in creative subjects (painting, digital art) and 7% of students in general disciplines (history, sociology, pedagogy, mathematics, physics, Chinese, cultural studies) had their academic performance at a high level before the start of online learning. The process of online teaching to paint was based on teamwork using the technologies of online education through educational online platform in the context of digital art. The results of the survey showed that the students had developed their creative skills significantly after training. The development of creative manner (29%) and analytical thinking (28%) were most popular. The authors determined that 88% of the students had received high grades in creative disciplines and 83% of the students in general academic disciplines after training. Most students were characterized by a high level of knowledge. The results are valuable for researchers who study the relationship between the development of creative skills and knowledge in general academic subjects, as well as strive to develop new curricula.
Dual Stabilization of the Multidimensional Regression Object at the Given Level

The statement of the problem of the dual control of the regression object with multidimensional-matrix input and output variables and dynamic programming functional equations for its solution are given . The problem of the dual stabilization of the regression object at the given level is considered . The purpose of control is reaching the given value of the output variable by sequential control actions in production operation mode . In order to solve the problem , the regression function of the object is supposed to be affine in input variables , and the inner noise is supposed to be Gaussian . The sequential solution of the functional dynamic programming equations is performed . As a result , the optimal control action at the last control step is obtained . It is shown also that the obtaining of the optimal control actions at the other control steps is connected with big difficulties and impossible both a nalytically and numerically . The control action obtained at the last control step is proposed to be used at the arbitrary control step . This control action is called the control action with passive information accumulation . The dual control algorithm with passive information accumulation was programmed for numerical calculations and tested for a number of objects . It showed acceptable results for the practice . The advantages of the developed algorithm are theoretical and algorithmical generality .
Voice Detection Using Convolutional Neural Network

The article presents an approach , methodology , the software system based on a machine learning technologies for convolutional neural network and its use for voice ( cough ) recognition . Tasks of article are receiving evaluating a voice detection system with deep learning , the use of a convolutional neural network and Python language for patients with cough . The convolutional neural network has been developed , trained and tested using various datasets and Python libraries . Unlike the existing modern works related to this area , proposed system was evaluated using a real set of environmental sound data , and not only on filtered or separated voice audio tracks . The final compiled model showed a relatively high average accuracy of 85.37 %. Thus , the system is able to detect the sound of a voice in a crowded public place , and there is no need for a sound separation phase for pre-processing , as other modern systems require . Several volunteers recorded their voice sounds using microphones of their smartphones , and it was guaranteed that they would test their voices in public places to make noise , in addition to some audio files that were uploaded online . The results showed an average recognition accuracy – of 85.37 %, a minimum accuracy – of 78.8 % and a record – of 91.9 %.
An improved defocusing adaptive style transfer method based on a stroke pyramid

Image style transfer aims to assign a specified artist's style to a real image. However, most existing methods cannot generate textures of various thicknesses due to the rich semantic information of the input image. The image loses some semantic information through style transfer with a uniform stroke size. To address the above problems, we propose an improved multi-stroke defocus adaptive style transfer framework based on a stroke pyramid, which mainly fuses various stroke sizes in the image spatial dimension to enhance the image content interpretability. We expand the receptive field of each branch and then fuse the features generated by the multiple branches based on defocus degree. Finally, we add an additional loss term to enhance the structural features of the generated image. The proposed model is trained using the Common Objects in Context (COCO) and Synthetic Depth of Field (SYNDOF) datasets, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to evaluate the overall quality of the output image and its structural similarity with the content image, respectively. To validate the feasibility of the proposed algorithm, we compare the average PSNR and SSIM values of the output of the modified model and those of the original model. The experimental results show that the modified model improves the PSNR and SSIM values of the outputs by 1.43 and 0.12 on average, respectively. Compared with the single-stroke style transfer method, the framework proposed in this study improves the readability of the output images with more abundant visual expression.
Modelling our way out of a featureless correspondence problem for automatic calibration of laser stripe mapping systems

This paper discusses a problem in predicting observations for laser stripe mapping systems that is akin to the correspondence problem for stereo imagery and structure from motion. Solving this problem is essential for allowing robust, efficient, featureless calibration of laser stripe extrinsic parameters using ordinary mapping data, similar to a bundle adjustment and referred to here as an automatic calibration. Such a calibration is important for laser stripe systems to calibrate them in-situ using natural scenes without additional hardware or calibration routines. We propose a simple modelling approach that ensures single predictions are made per observation and we highlight the implications for the design of laser stripe systems that would make simple laser line extraction algorithms more robust. We present the results of an automatic calibration using this modelling approach for simulated observations of entirely featureless terrain by a laser stripe mapping system; proving the concept of a robust, efficient, featureless, automatic calibration of laser stripe extrinsics.
AMR Navigation Based on Dynamic Control Sampling Space

This paper addresses the problem of local navigation for autonomous mobile robots in partially observable dynamic environments. The main contribution of this work is the expansion of a sampling based approach to deal with the robot and environment dynamic constraints and integrate a safety distance to keep the robot away from collisions. The developed algorithm has been implemented on a robotic platform with a Robot Operating System based embedded architecture, where it has been tested and validated in an indoor environment.
Chaos based fingerprint image encryption in the wavelet domain

With the widespread of distributed biometric-based authentication systems, security of the biometric data transmitted over open networks has become a major concern, as there are higher risks of identity theft and replay attacks. In this paper, a new chaos-based encryption scheme that operates in the Integer Wavelet Transform domain is proposed to enhance the security of fingerprint images during transmission. The proposed scheme relies on combining the chaotic Lorenz system, blocks permutation and the Rubik’s cube principle to achieve confusion and diffusion operations. Experimental results show that the proposed technique provides high performances in terms of security, computation time and quality of encrypted images.
Blockchain-based Resource Registration in Constrained IoT Environments

The interconnection of devices in Internet of Things (IoT) networks results in a ubiquitous exchange of resources between clients and servers. This interaction involving multicast discovery requests in classical Service oriented Architectures is hardly applicable for IoT nodes that go through sleep phases to conserve energy. CoRE Resource Directory (RD) provides a CoAP-based solution, including a central node where the server registers its resources, and the client requests them. Nevertheless, the centralization of operations makes this node a point of failure vulnerable to DDoS attacks and causes a mistrusted authentication between RD and endpoint devices. The Blockchain (BC) is a valuable solution for building decentralized, trustless peer-to-peer networks that enable reliable and non-repudiable data exchange. However, its application in IoT environments is a real challenge due to the computing and storage constraints of the devices. In this paper, we propose a novel architecture based on a distributed network of RDs acting as validators in a permissioned blockchain. The aim is to safeguard the resource registration phase and define the type of data stored in the BC ledger. Three approaches are proposed, implemented, and evaluated by exploiting Hyperledger Iroha Blockchain and a CoAP library called CoAPthon. The differences between approaches regarding storage size, usability, and security are highlighted. The assessment results show the impact of each approach and discuss the possibility of a combined solution concluding with their applicability in the proposed architecture to further secure the CoAP resource registration.
Anonymous Traffic Detection and Identification

Nowadays, anonymous communication technology attracts a lot of attention because it can hide identity information to achieve secure communication. Invisible Internet Project (I2P) and The Onion Router (TOR) are the most widely used open-source anonymous communication tools. Based on the analysis of the implementation mechanism of I2P and TOR anonymous service, this paper aims to identify the use of this kind of traffic through the DPI’’ Deep Packet Inspection’’ method, in order to obtain the lowest level of false positives for the detection and identification of anonymous network traffic. An experimental study using Snort’’ Network Intrusion Detection System (NIDS)’’ allowed us to detect the use of Tor/I2P networks based on a fingerprint database based on a set of Snort rules were developed as a proof-of-concept for the proposed I2P and Tor detection approach.
Forecasting Underwater Positioning Base and Station for Coastal Construction and Inspection

This article forecasts about underwater utility base for coastal construction and inspection by AUV, ROV, and several machineries. Coastal engineering and construction for port and airport at the sea space will be transformed to the "Robotics Construction" because of lack of labors and skilled workers. It is important that the base provides information for real time positioning underwater as the GNSS satellite as a bench mark. Because the automated machines and robots can recognize their situation of the site only by the measured information, the base must sense hydrographic data and send it as the condition of the works. Additionally, the base should monitor the environment data during both of the construction and after operation. The bases will consist network system for coastal engineering and hazard attentions. Those underwater base and station will change the future coastal engineering as the infrastructure of the maritime state.
A Digital Signature Based on PKI To Authentication and Secure Exchanging data used in water boreholes intelligent Decision Support System

The Northern Sahara uses groundwater as the only source of drinking water, irrigation and industry. To achieve better exploitation, we suggest implementing a Decision Support System (DSS) to sustain the management of water boreholes in the south of Algeria while exposing the key characteristics of these settlements. Data exchanged in our system is critical and must be secured. This paper presents a digital signature system based on the Public Key Infrastructure (PKI) of the different documents exchanged in the DSS. The cryptographic system was developed with JAVA technology based on PKCS standards (Public Key Cryptographic Standards). To have a PKI server, we have configured the Linux server through an open SSL. The PKI is used for creating users’ certificates with their private and public keys.
Semantic Role Labeling of Arabic Emotional Text in Tweets

We propose an Arabic corpus annotated with semantic role labels and emotion to improve Arabic NLP tasks.Emotion analysis and semantic role labeling concern many areas of applications and represent a big challenge for NLP tasks. To the best of our knowledge,few studies in the literature have attempted to integrate semantic role labeling with emotion analysis. However, Arabic language suffers from a lack of such a relevant datasets and tools compared to English. In this research, we build a corpus of 3000 Arabic tweets annotated with emotion categories and their related arguments using a semi-automatic tool that supports Frame semantics based annotation. This ongoing effort represents a first stepto providinga sizeable annotated corpus for the Arabic Language.
Chaos-based Key Generator using Artificial Neural Networks Models

Because of the growing number of attacks on existing systems, it is crucial to design new cryptographic techniques to ensure confidentiality. Although, the chaotic systems and deep learning methods have been proven to be effective in cryptography because of the nonlinearity and the high degree of randomness [1] and due to this, we combined them to produce cryptographic keys. This paper presents the chaotic time series forecasting models in order to produce encryption keys applied in cryptographic applications. Our artificial neural network (ANN) is trained by the Unified chaotic system samples using the optimal layers design. For comparative purposes, we have tested the performances of the Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) models in terms of chaotic time series patterns prediction. The Mean Squared Error (MSE) of the model achieves a value of 3.2x1$0^{-3}$ and we discover that, despite the small differences between the predicted samples of LSTM and GRU models, they produce nearly identical results for the task of chaotic time series prediction when compared to the MLP.
Quality Metrics in Software Architecture

The importance of software architecture is largely recognized also in iterative and agile development settings. However, it is quite complex to provide evidence that an architecture is of good quality and that the architectural decisions are appropriate, correct, or optimal. Architecture evaluation aims at showing and providing confidence that design decisions contribute to fulfilling the stakeholder concerns. Some architecture evaluation methods are scenario-based and aim at balancing many potentially conflicting quality attributes. Other works focus on a specific quality attribute and provide metrics to measure it.In this paper we survey the state of the art in metrics for evaluating quality attributes of architectures. The elicited metrics are organized into a catalog, which associates them with the specific quality attributes they aim to measure. We contribute also an MDE framework that generates web views facilitating the analysis of architectures. In this way, researchers and practitioners can easily retrieve the metrics that are appropriate to their specific needs. The catalog of metrics and quality attributes is released to the research community and open to contributions from experts and practitioners.
Image Forest Fire Segmentation Using Dirichlet Process Mixture Model

Image analysis in the detection of forest fires aims to minimize damage by timely intervention to limit the damage. In this work, we will focus on image segmentation nonparametric Bayesian models used in forest fire detection. This model defines a probability distribution over functional spaces (of infinite dimension). We have therefore chosen to study the processes of Chinese restaurants in what is called mixture models. We used Gibbs sampling methods to generate the weights used in the representation of the Dirichlet process. Simulations were performed on signals, synthetic and real images, the results are promising.
Real-time tsunami damage prediction using DONET and the implementation

In Japan, hazard maps for tsunami risks are shown by many sites of organizations for disaster prevention, and known well for public, recently. The truly meaning and reality of tsunami damages, however, do not seem to be recognized correctly. In particular, second damages by tsunami inundation, which are occurrence of debris, the drift, the fire and so on, could be severe for local residents. Therefore, we have considered the tsunami debris as one of important factors for evaluation of the disaster and reconstruction. We combined our technologies for real-time tsunami prediction system showing the arrival time, height and inundation depth using Dense Oceanfloor Network system for Earthquake and Tsunamis (DONET) and evaluation of tsunami debris based on motion equations proposed by Kozono et al. (2016). To improve inundation depth prediction, diversification for introduction of real-time observed data and multifunctionalization for selection of fault models. Then, we developed a real-time tsunami damage prediction system and installed it in Owase city. This system visualizes behavior of tsunami debris with the inundation map and users can recognize tsunami damages, which are healthy of infrastructure and access from the sea to transport materials. Here, we summarized the details of technology and implementation.
Towards the Assisted Decomposition of Large-Active Files

Tightly coupled and interdependent systems inhibit productivity by requiring developers to carefully coordinate their changes, even when modifying subsystems that should be independent of one another. Poor architectural decisions frequently lead to the presence of large, change-prone source files that are at the center of complexes of technical debt [11], [6]. This kind of technical debt quickly incurs interest both through coordination costs and error-proneness. In this paper, we present a new approach for decomposing these "large-active" files to pay down critical technical debt. We package our approach as a refactoring recommendation system. Each recommendation is determined by analyzing patterns of co-change and mutual dependency among files. Each recommendation corresponds to a responsibility the large-active file has in relation to the rest of the system. By moving recommended functionality from the large-active file into smaller files, developers can reduce the impact of a debt-laden file and clarify its essential responsibilities. A key advantage of this approach over prior work is that we better focus effort; we avoid spending developer effort refactoring code that is only superficially problematic. We achieve this by incorporating revision history into both determining and ranking recommendations. Each recommendation corresponds to some change-prone responsibility. We present some examples of this approach in action and outline our future plans.
Safety-Aware Deployment Synthesis and Trade-Off Analysis of Apollo Autonomous Driving Platform

The adoption of autonomous cars requires operational critical functions even in the event of HW faults and/or SW defects, and protection of safety-critical functions against security threats. Defining appropriate safe and secure architectures is challenging and costly. In previous work, we have proposed tools to automate the recommendation of safety and security patterns for safety-critical systems. However, safety and security measures may (negatively) influence system performance, besides introducing additional development effort. We present a design space exploration approach, a model-based engineering workflow and tool prototype for automated guidance on trade-off decisions when applying safety and security patterns on a given (unsafe) baseline architecture. Based on models that abstract the vehicle’s functionality and its software and hardware components, as well as an engine for the automated pattern recommendation, we investigate the optimization of HW/SW deployments, and provide a trade-off analysis for different architecture candidates. We implemented our approach in an open-source tool and evaluate it with a model of the Apollo autonomous driving platform.
A Blockchain Design Supporting Verifiable Reputation-based Selection of Committee Members and IPFS for Storage

We present a prototype for a novel blockchain architecture which incorporates two significant changes from existing models. First, it strongly integrates the InterPlanetary File System (IPFS) for data storage. Secondly, it incorporates a multiple block structures which segregate raw block data, validation data and meta data to support a verifiable reputation-based committee selection mechanism. In our experiments, we demonstrate that the duplication of data across multiple nodes for some nodes to prune block or transaction data without impact to the network as long as at least one node maintains a copy of the data, which is enforced through careful block and transaction storage requirements for miners and wallets. We also show that by segregating validation data and raw block data, a verifiable reputation score can be used reducing dishonest member selection over time.