Reviewerly/exHarmony huggingface dataset:
On the development of a blockchain-implementable intermediation model for digital supply chains

For years, mass production built on the assumption that customer share preferences. Today, mass customization maximises customer satisfaction by product customization and quick, efficient service. According to recent advancements in Digital Supply Chains, information exchange between supply chain actors and levels is critical not only for proper Supply Chain Management, but also for achieving common objectives such as short time-to-market, high service level and high productivity. This work proposes a new intermediation model for digital supply chains allowing full exploitation of global market potentials by making use of the blockchain technology. The decentralised intermediation model allows all supply chain actors operating in the context of cloud manufacturing leveraging the benefits of blockchain. For this purpose, specific performance models for buyers and vendors are developed. Together with a privacy scheme for sensitive data they are integrated in a blockchain-based intermediation agent able to automatically seek for a suitable trade-off between profit and responsiveness. The paper concludes with some test scenarios to demonstrate the effectiveness of the logic behind proposed modelling as well as its capability to find optimum negotiation solutions.
Single-Phase Fault Identification in Distribution Network Based on Multi-feature and Bayesian Optimization

In order to overcome the limitations of traditional methods and single-feature identification of single-phase fault lines in distribution networks, this paper proposes a machine learning fault line selection method based on multi-features and Bayesian optimization. First, the characteristic changes of electrical quantities of the single-phase-to-ground fault in the distribution network are analyzed, and a feature engineering based on multiple steady-state and transient electrical quantities is constructed. Secondly, the principal component analysis (PCA) dimensionality reduction and synthetic minority over-sampling technique (SMOTE) are introduced to solve the problem of high feature correlation and unbalanced sample categories. Furthermore, aiming at the problem of hyperparameter tuning existing in the classifier, a Bayesian optimization-based support vector machine(SVM) model is proposed, which realizes the automatic optimization of hyperparameters. Finally, the model is trained with the experimental recorded wave data, and the overall performance of the model is evaluated by the learning curve and ROC curve, which verifies the satisfied fault line identification ability of the model in the face of complex situations and different environmental configurations.
DASRSNet: Multitask Domain Adaptation for Super-Resolution-Aided Semantic Segmentation of Remote Sensing Images

Unsupervised domain adaptation (UDA) has become an important technique for cross-domain semantic segmentation (SS) in the remote sensing community and obtained remarkable results. However, when transferring from high-resolution (HR) remote sensing images to low-resolution (LR) images, the existing UDA frameworks always fail to segment the LR target images, especially for small objects (e.g., cars), due to the severe spatial resolution shift problem. In this article, to improve the segmentation ability of UDA models for LR target images and small objects, we propose a novel multitask domain adaptation network (DASRSNet) for SS of remote sensing images with the aid of super-resolution (SR). The proposed DASRSNet contains domain adaptation for SS (DASS) branch, domain adaptation for SR (DASR) branch, and feature affinity (FA) module. Specifically, the DASS and DASR branches share the same encoder to extract the domain-invariant features for the target and source domains, and these two branches utilize different decoders and discriminators to conduct cross-domain SS task and SR task, which align the domain shift in output space and image space, respectively. Finally, the FA module, which involves the proposed FA loss, is applied to enhance the affinity of SS features and SR features for both source and target domains. The experimental results on the cross-city aerial datasets demonstrate the effectiveness and superiority of our DASRSNet against the recent UDA models.
SparkCAD

Developers of Apache Spark applications can accelerate their workloads by caching suitable intermediate results in memory and reusing them rather than recomputing them all over again every time they are needed. However, as scientific workflows are becoming more complex, application developers are becoming more prone to making wrong caching decisions, which we refer to as caching anomalies , that lead to poor performance. We present and give a demonstration of Spark Caching Anomalies Detector (SparkCAD) , a developer decision support tool that visualizes the logical plan of Spark applications and detects caching anomalies.
Research and Implementation of MVC Design Pattern on J2EE Platform

With the wide application of web technology, many enterprises urgently need to build their own web business system quickly and efficiently. Enterprise J2EE is a standard enterprise application development platform provided by sun. It provides us with rich technical options for developing enterprise web applications. However, a high-quality application depends on the support of a well-designed architecture. How to correctly apply J2EE core technology to design and build a robust, scalable and reusable web application architecture is the challenge we face. Based on the research of MVC design pattern on J2EE platform, this paper proves that using MVC pattern for development on J2EE platform can make the software structure clearer and shorten the software development cycle, Improve software maintainability and code reuse rate.
Adapting Agile Principles for Requirements Engineering in Automotive Software Development

Requirements engineering plays a significant role in successfully executing automotive projects. Due to critical factors like the high system complexity, the hardware-software-dependency and safety criticality, automotive software development is very different from traditional software engineering. It has long development cycles with late system integration and test phase, which leads to late customer feedback. This makes the process vulnerable to requirements engineering issues, e.g. insufficient or unclear requirements, late requirements changes, and in general, the high number and high complexity of requirements. The automotive industry is slowly adopting agile practices to encounter such issues, e.g. by introducing iterative, incremental development cycles. This contribution investigates the issues related to requirements engineering in automotive software development and proposes agile principles as a possible solution. Literature-based research identifies relevant root causes and process deficits regarding requirement-related issues. The paper also provides recommendations and solutions from agile methods for the identified issues and respective cause-and-effect chains. A survey with practitioners from five automotive companies has been conducted to check the validity of hypotheses about the issues and improvements. Literature findings and survey results conclude that adapting agile principles is a potential way to address requirement-related issues in automotive software development.
A Live Environment to Improve the Refactoring Experience

Refactoring helps improve the design of software systems, making them more understandable, readable, maintainable, cleaner, and self-explanatory.Many refactoring tools allow developers to select and execute the best refactorings for their code.However, most of them lack quick and continuous feedback, support, and guidance, leading to a poor refactoring experience.To fill this gap, we are researching ways to increase liveness in refactoring.Live Refactoring consists of continuously knowing, in real-time, what and why to refactor.To explore the concept of Live Refactoring and its main components -recommendation, visualization, and application, we prototyped a Live Refactoring Environment focused on the Extract Method refactoring.With it, developers can receive recommendations about the best refactoring options and have support to apply them automatically.This work helped us reinforce the hypothesis that early and continuous refactoring feedback helps to shorten the time needed to create high-quality systems.
On Stream Face Mask and helmet detection System Using Machine Learning Techniques

Within the context of the current day, safety and cleanliness are the most important factors to consider in order to guarantee a healthy and risk-free human existence. Research has constantly proven that basic hygiene is vital, and that wearing a mask in public areas is crucial, in light of the terrible epidemic caused by the Coronavirus. Consider mandating the use of helmets as another precautionary safety measure for users of two-wheeled vehicles. A real-time Yolov3 object detector is used to train the model to recognise face masks and helmets in video footages, live feeds, or photos in order to search for public hygiene and safety. This is done in order to ensure that people are not exposed to potentially hazardous conditions. A neural network is used for the prediction of bounding boxes and the possibilities associated with those boxes over the whole picture. The dataset was gathered via the use of the internet. The model has been taught with the use of internet photos for several classes, such as wearing a mask but not a helmet; not wearing a mask or helmet; wearing a mask but not a helmet; wearing a helmet but not a mask; and wearing both a mask and a helmet. The trained dataset is used mostly for the purposes of classifying items and locating them within the aforementioned categories. In the event that neither the helmet nor the mask is located, the SMTP library module that is supplied by Python specifies an SMTP that may deliver mail to any internet device using SMTP or ESMTP as a client session object. The suggested approach was evaluated using a dataset that included photographs of persons adhering to COVID-19 safety regulations as well as photographs of people adhering to road safety recommendations while wearing helmets. Accuracy and precision are both quite good in the outcomes that were achieved.
Deep MMSE Estimation for Data Detection

This letter develops minimum mean-squared error (MMSE) estimators based on deep neural networks for data detection. Since the optimal MMSE is analytically intractable, researchers usually resort to linear MMSE approximations, which often incur performance degradation. To overcome this loss, we develop a near-optimal estimator by exploiting the Donsker-Varadhan representation of mutual information (MI) and the recently discovered derivative relationship between MI and MMSE. The near-optimal MMSE estimator can be computed with a deep neural network (NN). We can train the NN using the mini-batches of input and output samples. We validate this estimator using two examples where the closed-from MMSE is available. We then use this estimator to design an end-to-end communication system. We compare this setup with several conventional techniques and show promising performance.
SMART APPROACH TO RECOGNIZE PUBLIC GRIEVANCE FROM MICROBLOGS

The foremost priority for the government of any country is to resolve the citizen's problems. Indian government provides various ICT (Information Communication Technology) based plat-forms such as website and mobile application for the Indian citizens to lodge their grievance against ministries and departments. Due to the easy access and transparency of social media, people prefer to raise their voices there. The dissatisfaction and anger that they express through microblogging is also a kind of complaint. This paper discusses the various categories of public grievance, their importance and its redressal mechanism in India. The main aim of this research is to take these social media complaints into consideration and solve them rapidly with the help of Artificial Intelligence where, it’s one branch Natural Language Processing, processes the text to retrieve the information from the piece of text and another branch Machine Learning helps to know the priority and severity of the grievance.
The Influence of Local Search on Genetic Algorithms with Balanced Representations

Certain combinatorial optimization problems with binary representation require the candidate solutions to satisfy a balancedness constraint (e.g., being composed of the same number of 0s and 1s). A common strategy when using Genetic Algorithms (GA) to solve these problems is to use crossoveer and mutation operators that preserve balancedness in the offspring. However, it has been observed that the reduction of the search space size granted by such tailored variation operators does not usually translate to a substantial improvement of the GA performance. There is still no clear explanation of this phenomenon, although it is suspected that a balanced representation might yield a more irregular fitness landscape, where it could be more difficult for GA to converge to a global optimum. In this paper, we investigate this issue by adding a local search step to a GA with balanced operators, and use it to evolve highly nonlinear balanced Boolean functions. We organize our experiments around two research questions, namely if local search (1) improves the convergence speed of GA, and (2) decreases the population diversity. Surprisingly, while our results answer affirmatively the first question, they also show that adding local search actually increases the diversity among the individuals. We link these findings to some recent results on fitness landscape analysis for problems on Boolean functions.
Expressing the adaptation intent as a sustainability goal

Adaptation and sustainability are two key challenges leading the development of software-systems nowadays. Adaptation denotes the capacity of a system to cope with variations and uncertainties at runtime in order to continue providing its functionalities with certain quality levels, notwithstanding change. But how can adaptation and its intent be expressed at design time so that to analyze its possible impact at runtime over a long period of time? To answer this question we look at adaptation from the sustainability point of view. SustainabiKty denotes the capacity of a system to both endure and preserve its function over time. We propose an approach which uses decision maps to make sustainability-driven decisions for adaptation in a systematic way. The proposed approach is illustrated through two self-adaptive exemplars as illustrative cases.
Federated Learning for Physical Violence Detection in Videos

Domestic violence has increased globally as the COVID-19 pandemic combines with economic and social stresses. Some works have used traditional feature extractors to identify body positions to detect physical violence. Besides, the use of Machine Learning is limited by the trade-off between collecting more data while keeping users' privacy. Federated Learning (FL) is a technique that allows the creation of client-server networks, in which anonymized training data can be uploaded to a central model, responsible for aggregating and keeping the model up to date, and then distributing the updated model to the clients' nodes. This paper proposed an FL approach to the violence detection problem in video. The framework was evaluated on AIRTLab Dataset, in which frames were extracted. It used pretrained Convolutional Neural Networks (CNN) to address the image classification problem. Inception v3, MobileNet v2, ResNet-152 v2, and VGG-16 architectures were evaluated, with the MobileNet architecture presenting the best performance, in terms of accuracy (99.4%), with a loss of 0.5% when compared to the non-FL setting.
Deep Learning-based human activity recognition using RGB images in Human-robot collaboration

In human-robot interaction, to ensure safety and effectiveness, robots need to be able to accurately predict human intentions. Hidden Markov Model, Bayesian Filtering, and deep learning methods have been used to predict human intentions. However, few studies have explored deep learning methods to predict variant human intention. Our study aims to evaluate the performance of the human intent recognition inference algorithm, and its impact on the human-robot team for collaborative tasks. Two deep learning algorithms ConvLSTM and LRCN were used to predict human intention. A dataset of 10 participants performing Pick, Throw, Wave, and Carry actions was used. The ConvLSTM method had a prediction accuracy of 74%. The LRCN method had a lower prediction accuracy of 25% compared to ConvLSTM. This result shows that deep learning methods using RGB images can predict human intent with high accuracy. The proposed method is successful in predicting human intents underlying human behavior.
Multi-Label Feature Selection Based on Min-Relevance Label

Multi-label feature selection has been widely adopted to address multi-label data with highdimension features.It is critical to calculate label correlations for multi-label feature selection.Existing methods adopt different schemes to calculate label correlations, which obtain different importance of labels.However, there exist two issues regarding these schemes calculating label importance: first, previous schemes cannot predict the whole labels well because they only focus on the most important labels; second, most of important labels have similar classification information corresponding to redundant features.To this end, we use the mutual information metric to obtain different cores regarding label set rather than calculating the importance of labels.Afterwards, we capture features with respect to each core label, finally, obtaining an optimal feature subset.To verify the effectiveness, our method is compared to state-of-the-art multi-label methods on 16 real-world data sets with several evaluating metrics.The results of experiment proves that the proposed method has achieves the best classification performance among all multi-label feature selection methods.
VR Educational Teaching Tools for Viewing 3D View in Interior Design Education at Higher Institution

The art and science of analyzing the interior of a structure to improve and make it more visually beautiful is known as interior design. The main aim of this paper is to improve the learning experience of Interior Design students, to understand their lessons in more effective and interactive ways. In order to accomplish this objective, a survey was conducted among first semester students in order to identify the most suitable techniques. From the survey, the best technique is using technology in a more interactively and effectively way for students to understand the lessons that will use in this design. This Virtual Reality (VR) Educational tool is focusing on year one in Diploma Interior Design (ID) students. The new design of the 3D environment is proposed and most of the respondents believed that VR could help them to understand their lessons in the most effective and much easier.
Research on automatic text summarization technology based on ALBERT-TextRank

In order to solve the problems of insufficient understanding of the original text and easy generation of repeated texts in the automatic summarization task with the TextRank algorithm, a text summarization extraction model ALBERT-TextRank based on improved TextRank was proposed. The model uses the word vector model ALBERT to obtain dynamic word vectors to replace the traditional BERT benchmark model to extract word vectors to calculate the similarity between sentences. And comprehensively consider the sentence position, the similarity between the sentence and the title, key sentences and keywords that affect the sentence weight, so as to optimize the sentence weight. Then use TextRank iteratively to score sentences and select the sentence with the highest score as the text summary. After that, related experiments are designed on the NLPCC2018 Chinese abstract dataset combined with the ROUGE evaluation index to verify the effectiveness of the automatic abstracting algorithm proposed in this paper.
An iterative technique for solving path planning in identified environments by using a skewed block accelerated algorithm

&lt;abstract&gt; &lt;p&gt;Currently, designing path-planning concepts for autonomous robot systems remains a topic of high interest. This work applies computational analysis through a numerical approach to deal with the path-planning problem with obstacle avoidance over a robot simulation. Based on the potential field produced by Laplace's equation, the formation of a potential function throughout the simulation configuration regions is obtained. This potential field is typically employed as a guide in the global approach of robot path-planning. An extended variant of the over-relaxation technique, namely the skewed block two-parameter over relaxation (SBTOR), otherwise known as the explicit decoupled group two-parameter over relaxation method, is presented to obtain the potential field that will be used for solving the path-planning problem. Experimental results with a robot simulator are presented to demonstrate the performance of the proposed approach on computing the harmonic potential for solving the path-planning problem. In addition to successfully validating pathways generated from various locations, it is also demonstrated that SBTOR outperforms existing over-relaxation algorithms in terms of the number of iterations, as well as the execution time.&lt;/p&gt; &lt;/abstract&gt;
User Alignment Across Social Networks Based On ego-Network Embedding

Cross-social network user alignment is to find users with the same identity in multiple social networks. It has important applications in natural and scientific fields, such as link prediction and personality recommendation, and has certain research value in the field of data mining. Most current approaches embed social networks in a low-dimensional vector space and then align users in the low-dimensional space. However, because the social network is extremely complex and large, it is easy to be affected by error propagation and noise of different neighbors in the process of network embedding. Therefore, to obtain better embedding, we first form the user's EGO network, then use the random walk to extract the user node sequence, then use the framework of the natural language model to learn the low-dimensional vector representation of the user, and finally train a matrix to map the two social networks into the same feature space for alignment. Our experiments on real-world data set Foursquare-Twitter and Livejournal-myspace show some improvement over several baseline results.
SEbox4DL

Deep learning (DL) models are widely used in software applications. Novel DL models and datasets are published from time to time. Developers may also tempt to apply new software engineering (SE) techniques on their DL models. However, no existing tool supports the applications of software testing and debugging techniques on new DL models and their datasets without modifying the code. Developers should manually write code to glue every combination of models, datasets, and SE technique and chain them together.