Reviewerly/exHarmony huggingface dataset:
Medical Images Super Resolution Reconstruction based on Residual Network

Low-quality medical ultrasound images have serious spots, noise, and weak borders between similar tissues, which affect the accuracy of segmentation of human organs and lesions in the image to a certain extent. These problems seriously hinder the subjective diagnosis of doctors and intelligent healthcare assisted diagnosis. In this paper, a super-resolution approach is proposed based on deep learning that aims to upscale the prostate transrectal ultrasound (TRUS) image. The proposed method is based on the TRUS dataset, which is termed ultrasound image super-resolution (USSR). Firstly, this method comprises a deep residual network which through extracting feature to learns a complex mapping between low and high projections. Secondly, skip connection is introduced to enhance shallow information transmission. Thirdly, some typical ideas and sub-pixel convolution are adopted in the upsample part in order to ensure reconstruction speed. Experiments demonstrate that the USSR method proposed in this paper can reconstruct high-resolution medical images of different scales effectively, and reconstruct the internal texture and edge information of medical images accurately. Both the subjective qualitative results and the objective quantitative evaluation results show that the USSR method gives good reconstruction performance.
Arabic Semantic Textual Similarity Identification based on Convolutional Gated Recurrent Units

The augmentation of data exchanged on the internet has favored the practice of paraphrase. Its detection is one of the fundamental tasks of Natural Language Processing (NLP). It consists of identifying the degree of semantic similarity between sentences that convey the same meaning with different words. Although many researchers focused on this task on the English language, there are few works for other languages like the Arabic. It has presented important challenges because of its richness of features and processing complexities. Nowadays, deep neural networks have yielded immense success in most NLP applications. In this paper, a Siamese architecture is proposed for Arabic paraphrase detection. It has proven its relevance for semantic textual similarity. Indeed, the effectiveness of feed forward and recurrent neural networks models are studied. Experiments are conducted on the paraphrased Open-Source Arabic Corpora (OSAC). It is generated semi-automatically and validated using the benchmark SemEval. Evaluations demonstrated that Gated Recurrent Unit (GRU) outperformed Convolutional Neural Network (CNN) and other state-of-the-art methods.
SMartyPerspective: a perspective-based inspection technique for software product lines

Software Product Line (SPL) is an approach for reusing software artifacts for a specific domain. To improve products quality, verification and validation activities for SPL artifacts are necessary, thus defects are not propagated to derived products. The lack of techniques to exploit the inherited SPL reuse characteristics, mainly in early phases, provides an opportunity to investigate how to improve SPL quality. Perspective-Based Reading (PBR) has proven to be a feasible inspection technique, as it considers different scenarios and perspectives of reviewers of software artifacts. Therefore, in this paper, we specify and evaluate SMartyPerspective, a PBR technique to inspect UML-based SPL diagrams (use case, class, sequence, and component) and feature diagrams. SMartyPerspective comprises Domain Engineering perspectives: Product Manager, Domain Requirements Engineer, Domain Architect, Domain Developer, and Domain Asset Manager. We evaluated it by carrying out a TAM-based qualitative study with 19 participants with experience in SPL and software inspections. We also used coding to analyze the open questions. Obtained results provide initial evidence SMartyPerspective is feasible for inspecting its supported diagrams.
Non-parametric MRI Brain Atlas for the Polish Population

Introduction: The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology in vivo is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence. Material and Methods: The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed. Results: The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller. Discussion and Conclusion: To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.
A Study of Public Sentiment and Influence of Politics in COVID-19 Related Tweets

Mukherjee, Anjishnu Bhattacharyya, Souparno Ray, Kinjal Gupta, Balraj Das, Asit KumarAn increased usage of social media was observed as the Covid-19 pandemic progressed. We extract tweets relevant to the pandemic from publicly available Twitter data and analyse them to understand the change in public emotions over the year along with a detailed analysis of the topics of discussion. We find public health and politics to be the two most dominant topics. Hence, we perform a study where we compare the performance of existing unsupervised classification methods for the task of detecting whether a tweet is medically relevant or politically motivated.
Renewable Energy Systems and The Fourth Industrial Revolution

The new wave of technology and digitalization worldwide has transformed almost every sphere of day-to-day activities. This technological transformation has seen a massive revolution in digital systems and has been referred as to “Industry 4.0 or the fourth industrial revolution (4IR)”. The energy sector is not left out in this digitalization. The growing energy demand has seen an increase in the transition from conventional fossil plants to more sustainable green or renewable energy sources (RES). The transition to RES has led to a corresponding complexity in providing reliable energy from RES as new methods emerge. Therefore, the need for technological transformation and digitalization in renewable energy systems cannot be ignored. This research, therefore, discusses the role of digitalization and technological transformation in providing reliable and efficient energy from RES.
Detection of Weapons in Surveillance Scenes Using Masked R-CNN

Weapon detection in surveillance videos is one of the major research topics in computer vision and machine learning. Current techniques require human intervention to identify the weapons in the scenes by monitoring the videos continuously. To reduce the human monitoring, an automated weapon detection framework is defined. In the proposed method, Masked R-CNN is implemented to detect the weapons in the surveillance scenes. A background network called Resnet50 is combined with feature pyramid network to extract the features. For every feature map, region proposals are created using region proposal network (RPN). Proposed method is experimented on dataset containing guns and knives. Experimental results in real-time environment show that Masked R-CNN technique performs well for weapon detection without human intervention.
Similarity quantification of 3D surface topography measurements

3D surface topography provides critical information about surface textures and has begun to be used in additive manufacturing applications such as in-situ 3D monitoring and sample porosity comparisons, etc. In this research, we establish a thorough framework to quantify the similarity of 3D surface topography measurements and determine whether they are from the same surface or not based on the frequency domain representations after 2D Fourier transformation. Two measurements portraying the same surface are defined as a matched pair while the others are unmatched. This framework quantifies the similarity effectively, provides a new perspective for surface topography similarity evaluation, and serves as a benchmark work in 3D surface topography feature extraction in the frequency domain. Our work has a great potential to benefit not only the quality assurance of AM but also many other communities where surface topography data is useful.
Research on SQL Injection Vulnerabilities and Its Detection Methods

SQL injection is a typical kind of Web vulnerability, and it is also the most common method used by attackers to attack databases. Attackers usually detect and use this vulnerability to access the back-end database of target website, and illegally obtain confidential information in the database through a series of injection methods, thereby causing unpredictable damage and loss. This paper studies the attack principle, detection technologies and preventive measures of SQL injection, and proposes an approach and a tool named SQLIiscan. The tool is tested by detecting a popular project WAVSEP1.5 which includes many test cases of different vulnerabilities, and the test results show that it can detect SQL injection cases efficiently and accurately.
Towards estimating global probabilities of evaluation in English based on automatic extraction of least delicate Appraisal in large corpora

Systemic Functional Linguistics (hereafter SFL) hypothesizes that language systems globally follow two probability distributions (equiprobable or maximally skewed). Accordingly, a particular register, corpus or text is defined probabilistically as a local resetting of the global probabilities; or in other words, a statistical deviation of the local probabilities from the two global distributions of language. However, the original context in which this hypothesis has been proposed and, later, tested is the lexico-grammar. The current study addresses this hypothesis, both computationally and statistically, with respect to the discourse semantics system of appraisal. Through the implementation of automatic classification of least delicate engagement and attitude polarity in fifteen English corpora, this paper approaches three questions concerning the global patterns of appraisal choices in English, the intrastratal conditioning between the two appraisal subsystems, and the local, register-specific deviations of local probabilities. The computationally-based statistical analysis indicates that, globally, least delicate engagement and attitude polarity are equiprobable, whereas, within the polarity subsystem itself, choices are skewed in favor of the positive. The findings of this paper also show how local deviations of appraisal probabilities in the corpora cluster the relevant registers topologically in terms of what is contextually expected and what is contextually divergent. A major implication of this paper is that computational methods to large-scale corpora can substantiate the theoretical premises of SFL.
Analysis and Prediction of Bitcoin Price using Bernoulli RBM-based Deep Belief Networks

Digital currency aims to decentralize online transactions with high efficiency and increased security. These operations are carried out using blockchain based systems to eliminate the need for centralized verification and authorization. With the emergence of these systems come a vast number of cryptocurrencies such as bitcoins and ether that are widely used. The need for the analysis of the trend of digital currency arises because of the frequent fluctuation of their value in the markets. In this paper, the prediction of the trend of one such cryptocurrency, bitcoins, is performed using a Deep Belief Network model that is pre-trained using Restricted Boltzmann Machines for studying the data for 2019 with different time intervals: minute-by-minute, hour-by-hour and day-by-day. This would help to identify the trend of the cryptocurrency bitcoin for analyzing the demand supply dynamics of their market capital. The trained model was evaluated with the measures of MAE, MSE and compared with the existing state-of-the-art models. The minute-by-minute prediction model performed best with a RMSE of 25.87 and a MAE of 14.83.
A plate-tectonics based neighborhood search optimizer and its application for fault monitoring in IoT systems

Plate tectonics is a scientific theory that describes large scale motions of plates in Earth’s lithosphere. This paper presents a new optimization technique inspired by the tectonic forces that drive continental drift: Plate Tectonics based Neighborhood Search Optimization (PBO). The proposed evolutionary algorithm simulates tectonic forces on different points of the plate using nearest neighborhood search to exploit local information available in the objective function’s gradients while simultaneously exploring for better solutions throughout the search space. PBO is a versatile algorithm whose behavior can be altered in accordance to domain specific requirements on the objective function. Such tuning can lead to higher accuracy and faster convergence rate than other optimization algorithms. According to the CEC 2018 benchmark, it performs better than other top-performing evolutionary optimization methods in the CEC 2018 special session in all dimensions (D) where D = 10, 30, 50, and 100. It is resistant to getting stuck at local-minima due to increased sampling density of stable points even in vanishingly small gradients. This property, along with its customizability in search behavior, make it a compelling choice for adaptation in industrial uses such as those in mechanical engineering, communication, and aviation. Towards the end, this paper discusses and adapts PBO for application in one such industrial domain: Fault Monitoring in IoT systems. It also compares the results with other evolutionary algorithms like, GA, ACO, PSO, Simulated Annealing, etc. From the results, it can be seen that PBO outperforms other contemporary algorithms for the same application on standard datasets of different sizes.
Comprehensive Evaluation of Teaching Ability Based on Network Communication Environment

With the continuous development of school teaching quality, the measurement of the current teaching ability level has become a concern of people. This research mainly discusses the comprehensive evaluation of the teaching ability level based on the network communication environment. This study uses fuzzy comprehensive evaluation method to comprehensively evaluate the system. First, obtain the representative evaluation standards of education informatization and a series of indicators related to informatization teaching at home and abroad; secondly, conduct a comprehensive analysis of the various evaluation standards and related indicators of informatization teaching that have been obtained; finally, the process in each indicator is discussed and revised by experts and relevant information technology teachers, and a number of schools are selected to conduct small-scale experimental evaluation experiments to form information-based teaching evaluation indicators. In this study, the proportion method is used to calculate the membership degree of each observation index based on the recovered data and establish the fuzzy relationship matrix of each evaluation factor. Paying attention to the level of index weights can guide the development direction of informatization teaching and promote the healthy development of informatization teaching. Therefore, this article uses the expert scoring method combined with the analytic hierarchy process to determine the weights in the evaluation index system. There is a significant difference between teachers in urban areas and teachers in rural areas ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The ratio of each grade of the teacher’s informatization teaching ability comment collection is “bad 0.5%,” “relatively poor 5.7%,” “normal 35.3%,” “good 53.1%,” and “excellent 5.3%.” This research will help promote the improvement of school teaching quality.
Spatiotemporal just noticeable difference modeling with heterogeneous temporal visual features

• This paper extracts content-aware temporal feature of video for JND modeling. • We propose statistical probability models to quantitatively depict feature parameters. • We quantify saliency and uncertainty to relieve the heterogeneous fusion difficulty. Developing accurate Just-noticeable difference (JND) models are challenged by complicated HVS characteristics and nonstationary features of video sequence. Great efforts have been devoted to JND modeling, and inspiring performance improvements are witnessed in the literature, especially spatial JND models. However, there are not only urgent requirement but also technical potentiality for improving temporal JND models fully accounting for the temporal perception characteristics. In terms of temporal JND modeling, there are two challenges, one is how to extract perceptual feature parameters of source video, and the other is how to quantitatively characterize the interaction relationship between feature parameters and HVS characteristics? Firstly, this work extracts content-aware temporal feature parameters having predominate impacts on vision perception, including motion (foreground/background), pixel-correspondence duration and inter-frame residue fluctuation intensity along temporal trajectory, and investigates the HVS responses to these four heterogeneous feature parameters. Secondly, this work proposes respective probability density functions (PDF) in the perception sense to quantitatively depict the attention and suppression perception responses of feature parameters, accounting for the temporal perception characteristics. Using these PDF models, we fuse the heterogeneous feature parameters from the viewpoint of uniform dimension,i.e. self-information measured visual attention and information entropy measured masking uncertainty, achieving heterogeneous parameter homogenization. Thirdly, with self-information and entropy results, this work then proposes a temporal weight model, by striking the balance between visual attention and masking suppression, to adjust the spatial JND threshold, and then develops the improved spatiotemporal JND model. Intensive simulation results verity the effectiveness of the proposed spatiotemporal JND profile, with competitive model accuracy compared with the-state-of-the-art candidate models.
Ontology development for measurement process and uncertainty of results

In future manufacturing and metrology, there is increasing demand to organize relevant metadata and knowledge to present information in semantically meaningful, reusable, easily accessible, and interoperable form.Up-to-date information on measurement uncertainty is key to interpretation of measurement results and to assessment of the quality of the measurement process.Although various technologies from knowledge engineering have been proposed to fulfil this requirement, previous work has not fully addressed the uncertainty during the measurement process.This paper presents the method to develop an ontology of the measurement process and the uncertainty of results on the example of coordinate measurements.The resulting ontology model based on a set of competency questions, including key concepts and relationships between them, is presented and discussed.The consistency of the ontology model is verified by inferencing rules and answering competency questions in Protégé software.The presented ontology will find wide applications in metrology and Industry 4.0.
An Unsupervised Approach for Detecting Group Shilling Attacks in Recommender Systems Based on Topological Potential and Group Behaviour Features

To protect recommender systems against shilling attacks, a variety of detection methods have been proposed over the past decade. However, these methods focus mainly on individual features and rarely consider the lockstep behaviours among attack users, which suffer from low precision in detecting group shilling attacks. In this work, we propose a three-stage detection method based on strong lockstep behaviours among group members and group behaviour features for detecting group shilling attacks. First, we construct a weighted user relationship graph by combining direct and indirect collusive degrees between users. Second, we find all dense subgraphs in the user relationship graph to generate a set of suspicious groups by introducing a topological potential method. Finally, we use a clustering method to detect shilling groups by extracting group behaviour features. Extensive experiments on the Netflix and sampled Amazon review datasets show that the proposed approach is effective for detecting group shilling attacks in recommender systems, and the F1-measure on two datasets can reach over 99 percent and 76 percent, respectively.
An environmental monitoring data sharing scheme based on attribute encryption in cloud-fog computing

Environmental monitoring plays a vital role in environmental protection, especially for the management and conservation of natural resources. However, environmental monitoring data is usually difficult to resist malicious attacks because it is transmitted in an open and insecure channel. In our paper, a new data sharing scheme is proposed by using attribute-based encryption, identity-based signature and cloud computing technology to meet the requirements of confidentiality, integrity, verifiability, and unforgerability of environmental monitoring data. The monitoring equipment encrypts the monitored environmental data and uploads it to the environmental cloud server. Then, monitoring users can request access to the environmental cloud server. If the monitoring user meets the access policy, the plaintext is finally obtained through the fog node decryption. Our proposal mainly uses attribute-based encryption technology to realize the privacy protection and fine-grained access control of monitoring data. The integrity and unforgeability of the monitoring data are ensured by the digital signature. In addition, outsourcing computing technology saves the computing overhead of monitoring equipment and monitoring users. The security analysis illustrates that our proposal can achieve security purposes. Finally, the performance of our proposal and related schemes is evaluated from the aspects of communication overhead and computing overhead. The results indicate that our proposal is secure and efficient in environmental monitoring.