Reviewerly/exHarmony huggingface dataset:
TraceRefiner: An Automated Technique for Refining Coarse-Grained Requirement-to-Class Traces

Requirement-to-code traces reveal the code location(s) where a requirement is implemented. Traceability is essential for code evolution and understanding. However, creating and maintaining requirement-to-code traces is a tedious and costly process. In this paper, we introduce TraceRefiner, a novel technique for automatically refining coarse-grained requirement-to-class traces to fine-grained requirement-to-method traces. The inputs of TraceRefiner are (1) the set of requirement-to-class traces, which are easier to create as there are far fewer traces to capture, and (2) information about the code structure (i.e., method calls). The output of TraceRefiner is the set of requirement-to-method traces (providing additional, fine-grained information to the developer). We demonstrate the quality of TraceRefiner on four case study systems (7-72KLOC) and evaluated it on over 230,000 requirement-to-method predictions. The evaluation demonstrates TraceRefiner&#x0027;s ability to refine traces even if many requirement-to-class traces are undefined (incomplete input). The obtained results show that the proposed technique is fully automated, tool-supported, and scalable.
FDSNet: Disentangled Represent RF Fingerprints Characteristic Based on Deep Neural Networks

Specific emitter identification (SEI) is a non-password authentication method based on the intrinsic hardware characteristics carried by the received waveforms and widely adopted in military and civil applications. To date, deep learning-based methods have been generally performed to extract the Radio frequency fingerprints (RFFs) characteristic and identify the specific emitter. However, most works consider it as a common classification task and directly apply the existing deep learning method without considering its particularity. In this paper, we propose a novel RFFs characteristic extraction technique based on convolutional neural network (CNN). We design a novel deep neural network, named Feature Domain Separation Network (FDSNet), to extract the RFFs feature explicitly. Specifically, we introduce a specific mirror structure and optimization objective into FDSNet to separate the RFFs feature by embedding raw signal onto two feature spaces: a domain-private space capturing content information and a domain-shared space capturing RFFs information. The simulation result shows that the proposed method outperforms other algorithms in our nine signal-to-noise ratio (SNR) levels datasets.
A Similarity-based Framework for Classification Task

Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification task. Specifically, we unite similarity-based learning and generalized linear models to achieve the best of both worlds. This allows us to capture interdependencies between classes and prevent from impairing performance of noisy classes. Each learned parameter of the model can reveal the contribution of one class to another, providing interpretability to some extent. Experiment results show the effectiveness of the proposed approach on multi-class and multi-label data sets.
A Three-Dimensional Path Planning System for AUV Diving Process Considering Ocean Current and Energy Consumption

This paper provides a three-dimensional (3D) path planning system for autonomous underwater vehicles (AUVs) to accomplish the feasible diving process with less energy and restricted motion rotation angle in complex marine environment. Aiming at the limitation of traditional A* algorithm in high dimensional environment and local extremum, a constrained sampling A* (CSA*) algorithm with spatial boundary is developed to simplify the 3D environment modeling process and improve the search efficiency. The CSA* is combined with adaptive quantum-behaved particle swarm optimization (AQPSO) algorithm to reduce the energy consumption under the influence of ocean currents. Considering the kinematic limit, AQPSO is constrained by a series of proposed attitude correction laws to ensure the feasibility of the path result. The proposed method is tested in multiple numerical scenarios and compared with four typical evolutionary algorithms. The simulation results verify that the optimization ability and stability of the proposed planning system are the best among the methods compared in this paper, and it can well balance the relationship between route feasibility and energy consumption, and guide the vehicle to move towards underwater target points.
An Innovative Model-Based Approach for Credit Card Fraud Detection Using K-Nearest

Credit card is an important source in today’s business economy. With the increase in credit card users, many e-commerce sites have increased the online payment systems along with these online payments online frauds are also increasing. Many researchers are implementing different fraud-reducing techniques like machine learning, all these techniques aim to reduce frauds, and each one of these techniques has their own advantages and characteristics. In this paper, there is an intelligent approach which can improve the detection of frauds in credit card transactions utilizing machine learning methods like K-nearest neighbour stood best by giving accuracy of 94%. This large percentage is estimated to be due to the inconsistency in the number of legal and illegal transactions.
Geometric Inductive Matrix Completion

Collaborative filtering is a central task in a broad range of recommender systems. As traditional methods train latent variables for user/item individuals under a transductive setting, it requires re-training for out-of-sample inferences. Inductive matrix completion (IMC) solves this problem by learning transformation functions upon engineered features, but it sacrifices model expressiveness and highly depends on feature qualities. In this paper, we propose Geometric Inductive Matrix Completion (GIMC) by introducing hyperbolic geometry and a unified message passing scheme into this generic task. The proposed method is the earliest attempt utilizing capacious hyperbolic space to enhance the capacity of IMC. It is the first work defining continuous explicit feedback prediction within non-Euclidean space by introducing hyperbolic regression for vertex interactions. This is also the first to provide comprehensive evidence that edge semantics can significantly improve recommendations, which is ignored by previous works. The proposed method outperforms the state-of-the-art algorithms with less than 1% parameters compared to its transductive counterparts. Extensive analysis and ablation studies are conducted to reveal the design considerations and practicability for a positive impact to the research community.
Utilizing Network Features to Detect Erroneous Inputs

Neural networks are vulnerable to a wide range of erroneous inputs such as corrupted, out-of-distribution, misclassified, and adversarial examples. Previously, separate solutions have been proposed for each of these faulty data types, however, in this work we show that a collective set of inputs with variegated data quality issues can be jointly identified with a single model. Specifically, we train a linear SVM classifier to detect four types of erroneous data using the hidden and softmax feature vectors of pre-trained neural networks. Our results indicate that these faulty data types generally exhibit linearly separable activation properties from correctly processed examples. We are able to identify erroneous inputs with an AUROC of 0.973 on CIFAR10, 0.957 on Tiny ImageNet, and 0.941 on ImageNet. We experimentally validate our findings across a diverse range of datasets, domains, and pre-trained models.
The Reform of Engineering Professional Online Education Courses by Artificial Intelligence and Wireless Network Technology in the Context of Engineering Certification

The problems existing in the online education courses of engineering majors in colleges and universities are studied, and the online education platform under the background of engineering certification is designed and researched. Artificial intelligence (AI) technology and wireless network technology are used in the in-depth study of online education courses. In terms of AI, the storage of data is specifically divided into three levels, namely, the source layer (operational data layer), the historical storage layer, and the data model layer. Data analysis includes the total number of data, spatial scope, and period, etc. The results show that the overall response time of the designed platform to the operation of university teachers and students is controlled within 3 s. The test results of two online education platforms show that the performance of the designed online education platform is relatively stable. It provides hardware support for online curriculum reform. In addition, in terms of the platform test pass rate, after many tests, the online education platform security test pass rate is above 98%. In the end, these conclusions are drawn: AI technology and wireless network technology can effectively improve the performance of online education platforms. Meanwhile, this can also further improve the learning efficiency of online education courses for college engineering students.
Complex shearlets and rotary phase congruence tensor for corner detection

Corner detection algorithms based on multi-scale analysis attract more attention due to their promising performance. However, they only consider amplitude information, neglect phase information and partially utilize multi-scale decomposition coefficients to detect corners. This limits their detection accuracy, repeatability and localization ability. This paper describes a new multi-scale analysis based corner detector. To overcome the problems of bilateral margin responses, edge extension and lack of phase information in traditional shearlets, a novel complex shearlet transform is proposed to better localize distributed discontinuities and especially to extract phase information from geometrical features. Moreover, a new rotary phase congruence tensor is proposed to utilize all amplitude and phase information for corner detection. Its tolerances to noise and ability for corner localization are improved further by screening and normalizing the amplitude information. Experimental results demonstrate that the localization ability and detection accuracy of the proposed method are superior to current detectors, and its repeatability is generally higher than current detectors and recent machine learning based interest point detectors.
A Data Pipeline to Preserve Privacy in Educational Settings

In the sensitive field of distance learning data handling should lead to actionable knowledge and, at the same time, ought to respect the privacy of the students. The hype of online learning led to a plethora of data but also raised ethical issues regarding privacy protection which is mainly addressed in the GDPR. There is an optimum equilibrium between making out the most of available data and protecting the individual freedom of the participants. In this paper, we propose a data pipeline that could be incorporated in a Learning Analytics cycle and provide anonymous, low-risk data.
A novel hyperbolic intuitionistic fuzzy divergence measure based mammogram enhancement for visual elucidation of breast lesions

In this article, we have proposed a novel mammogram image enhancement approach for visual interpretation of breast masses using an adaptive intuitionistic fuzzy divergent measure under hyperbolic regularization. The proposed scheme pursuits to raise the underexposed and abnormal growth of cells such as breast masses, deformity tissues, nodules, and lumps in mammogram images. In the first step, a complementary image has been generated from the source mammogram to separate object and background area. Thereafter, both source and background images are mapped into Intuitionistic Fuzzy Set (IFS) plane under Hyperbolic Regularization (HR). A novel entropy based intuitionistic fuzzy divergence measure has been designed using hyperbolic function for the membership modification in this work. In addition, fuzzy ambiguity correction distance function has been obtained from the hesitant score vectors from both source and complementary images. Werner's AND-OR has been utilized on both of them to generate modified membership function. Finally, we have achieved enhanced mammogram with visually improved breast masses through defuzzification process. It has been shown that the proposed method provides better result in comparison with the existing methods in terms of qualitative and higher score of image quantity measurement metrics such as SSIM (0.83), FQI (0.85), and IFQI (0.88).
Extractive Knowledge Distillation

Knowledge distillation (KD) transfers knowledge of a teacher model to improve performance of a student model which is usually equipped with lower capacity. In the KD framework, however, it is unclear what kind of knowledge is effective and how it is transferred. This paper analyzes a KD process to explore the key factors. In a KD formulation, softmax temperature entangles three main components of student and teacher probabilities and a weight for KD, making it hard to analyze contributions of those factors separately. We disentangle those components so as to further analyze especially the temperature and improve the components respectively. Based on the analysis about temperature and uniformity of the teacher probability, we propose a method, called extractive distillation, for extracting effective knowledge from the teacher model. The extractive KD touches only teacher knowledge, thus being applicable to various KD methods. In the experiments on image classification tasks using Cifar-100 and TinyImageNet datasets, we demonstrate that the proposed method outperforms the other KD methods and analyze feature representation to show its effectiveness in the framework of transfer learning.
Modified deep attractor neural networks for variability compensation in recognition tasks

Some brain functions can be described with attractor formation in the human neural system. Various biologically-inspired models have employed this characteristic for memory representation, pattern completion and, noise reduction. Attractor neural networks are amongst these models capable of settling to stable patterns called attractors using their recurrent connections. Deep attractor neural networks (DANets) are relatively new models that utilize their attractors to separate the sources in the mixtures. In this paper, a modified version of DANets, variability compensator attractor neural network (VCANets); is proposed. This model compensates variabilities which are a common source of performance degradation in recognition tasks. For this purpose, significant modifications were applied to the structure and training procedure of DANets, including attractors' calculation, multi-task learning, curriculum learning, mining of challenging samples, and special mini-batch training procedure. Experiments on the MNIST and MNIST-C datasets for handwritten digit recognition and FARSDAT dataset for acoustic landmark recognition show that VCANet can effectively improve recognition accuracy and reduce unwanted variabilities.
A RRT path planning algorithm based on A* for UAV

Rapid-exploration Random Tree (RRT) is an efficient algorithm to search non-convex and high-dimensional spaces via randomly constructing spatial filling trees. This algorithm has been widely used in autonomous robot path planning. However, the basic RRT algorithm has some shortcomings. In order to improve the defects of low search efficiency and poor path quality of the RRT algorithm, this paper proposes an A* based RRT path planning algorithm with the advantages of completeness and optimality of the A* algorithm and fast extensibility of the RRT algorithm. During the procedure of random node sampling of the RRT algorithm, A* path is used to formulate the sampling strategy. Meanwhile, the constraint of the path turning angle is added to the nearest neighboring search of the RRT algorithm, which can enhance the rationality of the search tree node selection and improve the obtained path quality. Simulation experiments have been performed to verify the effectiveness of the proposed method for unmanned aerial vehicle path planning.
Effect of Cryptocurrency Mining on Semiconductor Industry

The cryptocurrency mining business imposes a strong demand for the semiconductor industry products. I find a positive association between the MSCI worldwide semiconductor index return and crash periods of the cryptocurrency market. This association is absent for the boom periods and other MSCI worldwide technology sector indices. I observe a similar impact of the cryptocurrency market on the top 64 semiconductor firms globally which brings forth stronger evidence in the support of the negative impact of the cryptocurrency crash on the semiconductor industry. The impact of crash period due to natural calamities is the highest and most significant than other events. I find a bilateral Granger Causal relationship between daily returns of Bitcoin and MSCI worldwide semiconductor index returns at lag 1. The stronger impact of the cryptocurrency bust cycle on the volatile technology sector specifically during a natural calamity period implies a lower diversification effect by including semiconductor index returns in a portfolio.
High-performance Detection and Predication Safety System using HUAWEI Atlas 200 DK AI Developer Kit

The problem that the world has faced in 2020 is adapting to the current situations. A sense of safety is compromised with our inclusion in public places and social activities. People&#x0027;s participation in activities has decreased, while students and workers have learned how to work from a distance, allowing obstacles to arise. With the movement towards life before COVID-19 comes a challenge, which is being reassured that going back to our older habits keeps us safe. The proposed system provides a real-time video feed captured by a connected camera used to monitor people in a given environment. If the distance between any two detected people is below the safe distance limit, it recognizes a violation. The system also checks whether any person is not wearing a face mask or has it lowered. To alert the responsible person with any of the previous scenarios, a notification is sent to a webpage with the image of the violation. Closed places like universities, schools, factories, and offices can make use of this real-time solution to make sure people are complying and to offer assurance of the safety of the place. This can improve their participation in activities such as resuming their work.
Bismuth—Telluride-Gold Mineralisation from Parsoi Area in Eastern Mahakoshal Belt, Sonbhadra District, UP, India

The Palaeoproterozoic eastern Mahakoshal belt of Central India Tectonic Zone (CITZ) is known for gold occurrences. Gold occurs predominantly in quartz veins and quartz-carbonate veins. It is associated with pyrite, pyrrhotite and arsenopyrite mineral assemblages. The secondary alteration products such as 'scorodite' and 'mansfieldite' are also associated with gold mineralisation. Recent investigations carried out in Parsoi area, have indicated the association of gold with bismuth, bismuthinite and telluride. This is the first record of bismuth from the Mahakoshal belt. The studies of the bismuth tellurides contribute to the assessment and evaluation of concentrations of gold resources.
Immunity Passport Ledger

The global outbreak of Coronavirus (SARS-CoV-2) which in 2020 reached pandemic scale, has been a central topic of debate in our society. Concerns over the ease of transmission of the infection led to the imposition of measures restricting freedom such as curfews, lockdown, general confinement, and closure of trade. Technology was one of the tools used to resist to the spread of the disease using applications that, on one hand, track contacts to warn users that were close to someone infected and, on the other hand, provide immunity digital certification. Despite the relevance of these options, end users have no confidence, transparency, and responsibility that the registration and use of their health data are ethical, secure, anonymous, and available through verifiable credentials and, most importantly, is being used for its main purpose. Consequently, a solution based on a distributed ledger technology, such as blockchain, is introduced to assure the trustworthiness and integrity of user's data. Since the proposed application embraced user privacy, we conducted a comparative study between permissioned blockchains, that includes an authorization abstraction layer and ensures that certain actions can only be performed by identifiable participants. We concluded that Hyperledger Fabric was an option that fulfilled all the requirements to develop a platform for the immunity passport ledger. Its modularity and versatility accommodates the needs that were initially proposed for the development of a proof of concept. The work leads us to propose that further research be conducted regarding scalability and performance evaluation.
Learning to Adapt – Software Engineering for Uncertainty

Modern businesses are being subjected to an unprecedented variety of change drivers that cannot be predicted such as new regulations, emerging business models, and changing needs of stakeholders. This creates new demands on enterprises to meet stated goals in a dynamic and uncertain environment that translate to demands on the enterprise’s software systems. Software systems however are currently designed to deliver a fixed set of goals and assumed to operate in a static environment, falling short in addressing the need for continuous adaptation under uncertainty. State-of-the-art adaptation architectures like MAPE-K have been applied to meeting non-functional requirements in a dynamic environment using a static repository of knowledge. This paper articulates the need for architecting software systems that learn from their own operation to dynamically extend existing knowledge, and utilize the knowledge to meet stated functional goals in an uncertain environment.
A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Abstract Document summarization is an important task in natural language processing that helps deal with the problem of information overload occurring due to the existence of redundant content. Summary generation with highly relevant contents and maximum coverage is particularly challenging which can only be achieved when redundancy is minimized. This article introduces a novel approach for automatic text summarization based on sentence scoring and collaborative ranking to produce summaries with minimal redundancy and improved overall performance of summarization. The proposed model is a fusion of weighted and unweighted features‐based sentence scoring methods. To learn optimal weights of text features, it has been modelled as an optimization problem. Moreover, the proposed model exploits the strength of collaborative ranking to generate the summary of a given document. Three similarity factors (proximity, significance and singularity)‐based models have been employed to find the similarity between weighted and unweighted sentence scores. The results of the comparison experiment demonstrate that the proposed (PS + Jac) method generates a closer summary to the reference summary with minimal redundant contents. On average, the proposed (PS + Jac) method generates the summaries with 61% accurate contents with greater improved rates up to 40%. The statistical testing also confirms that the performance improvement is significant at a 5% level of significance.
Video games and gamification against online hate speech?

The project "Play Your Role: Gamification against Hate Speech", funded by the "Rights, Equality and Citizenship" programme, proposes the creation of spaces for dialogue, debate and awareness-raising on hate speech through games.