Reviewerly/exHarmony huggingface dataset:
BreakBot: Analyzing the Impact of Breaking Changes to Assist Library Evolution

“If we make this change to our code, how will it impact our clients?” It is difficult for library maintainers to answer this simple—yet essential!—question when evolving their libraries. Library maintainers are constantly balancing between two opposing positions: make changes at the risk of breaking some of their clients, or avoid changes and maintain compatibility at the cost of immobility and growing technical debt. We argue that the lack of objective usage data and tool support leaves maintainers with their own subjective perception of their community to make these decisions.We introduce BreakBot, a bot that analyses the pull requests of Java libraries on GitHub to identify the breaking changes they introduce and their impact on client projects. Through static analysis of libraries and clients, it extracts and summarizes objective data that enrich the code review process by providing maintainers with the appropriate information to decide whether—and how—changes should be accepted, directly in the pull requests.
Optimal and Effective Resource Management in Edge Computing

Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency.The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers.Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy.Federated learning (FL) is a recently developed decentralized profound learning methodology, where customers train their localized neural network models independently using private data, and then combine a global model on the core server together.The models on the edge server use very little time since the edge server is highly calculated.But the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations.A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation, while recognising mobile appliance energy restrictions, is the focus of this study.To further speed up integration and reduce the amount of data, it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees' shared learners.The main solutions and lessons learnt along with the prospects are discussed.Experiments show that our method is superior in terms of the effective and elastic use of resources.
Comparative study of the electrical performance of three types of photovoltaic technologies in Rabat and Errachidia station

The production of electricity by the photovoltaic systems is an idea that has been applied for many years. In this paper, the annual AC power, total AC power of three kinds of photovoltaic systems (Monocrystalline, polycrystalline, amorphous silicon) were calculated and discussed in Rabat and Errachidia stations, located approximately 316.61 km apart, over the year 2018. The photovoltaic (PV) systems were installed in order to determine which technology produces the most clean electricity. It is based on a comparison of the monthly production of the three technologies in the two cities, and the justification of the behaviour of the three technologies using two similar weather stations installed near the solar panels. In such a way, the determination of the climatic conditions will justify the behaviour of the three technologies. The comparative analysis included annual energy efficiency, performance ratio, annual energy density and system efficiency. Thus, with these performances, we can observe that photovoltaic installations with polycrystalline are appropriate in Errachidia, whereas in Rabat they are monocrystalline. Also, the photovoltaic system of Errachidia stations is more efficient as compared with Rabat stations.
A Decision-Supportive Structured Light Monitoring System for Additive Manufacturing Part Surface Profiling

Additive manufacturing (AM) processes are rapidly maturing and being adopted in numerous industrial sectors. One of the big challenges with many AM processes is the need for part quality control, either in post-manufactured assessment or in-situ during the build. This paper presents a low-cost structured light system (using camera and projector) that exploits digital fringe projection to achieve surface profiling of AM parts. Additionally, a probability density function of the surface profile is derived, helping the measurement process to provide the probabilistic support required for AM part quality control decisions. Results from a prototype system on AM parts are demonstrated.
Balancing Bias and Variance for Active Weakly Supervised Learning

As a widely used weakly supervised learning scheme, modern multiple instance learning (MIL) models achieve competitive performance at the bag level. However, instance-level prediction, which is essential for many important applications, remains largely unsatisfactory. We propose to conduct novel active deep multiple instance learning that samples a small subset of informative instances for annotation, aiming to significantly boost the instance-level prediction. A variance regularized loss function is designed to properly balance the bias and variance of instance-level predictions, aiming to effectively accommodate the highly imbalanced instance distribution in MIL and other fundamental challenges. Instead of directly minimizing the variance regularized loss that is non-convex, we optimize a distributionally robust bag level likelihood as its convex surrogate. The robust bag likelihood provides a good approximation of the variance based MIL loss with a strong theoretical guarantee. It also automatically balances bias and variance, making it effective to identify the potentially positive instances to support active sampling. The robust bag likelihood can be naturally integrated with a deep architecture to support deep model training using mini-batches of positive-negative bag pairs. Finally, a novel P-F sampling function is developed that combines a probability vector and predicted instance scores, obtained by optimizing the robust bag likelihood. By leveraging the key MIL assumption, the sampling function can explore the most challenging bags and effectively detect their positive instances for annotation, which significantly improves the instance-level prediction. Experiments conducted over multiple real-world datasets clearly demonstrate the state-of-the-art instance-level prediction achieved by the proposed model.
Data Transmission over a Wireless Communication Channel of a Mobile Information Measurement System for the Study of a Person Kinematic Portrait

For many years, wireless data transmission has posed a significant challenge to medical sensor developers, where instrument cabling is a major inconvenience for physicians and patients alike. This area of research requires modern data transmission technologies to be used to improve equipment that allows assessing and measuring the kinematic characteristics of the patient. In this paper, data transmission software from an inertial measurement system to an iOS mobile application using Bluetooth and Wi-Fi is described along with the sensor calibration procedure of the measurement system, and the main metrological characteristics are determined.
Neuro-fuzzy Model for In-circuit Control Systems

Embedded systems development is a very important area. Devices must provide specific electrical signals. In-circuit control system is able to increase the quality of output or in-circuit signals, which is essential for devices in specific areas. Neuro-fuzzy systems provide an intellectual baseline for developing effective subsystems for signals’ control. Neuro-fizzy model for establishing in-circuit control is provided.
Stacker Crane Position System Development

A stacker crane for warehouses is under consideration. The structure of the stacker crane's electric drive power part is discussed in detail. Advantages and disadvantages of each considered systems are highlighted. The stacker crane position system is under consideration as well. Variants of positioning systems are discussed as a result of significant warehouse problems. A computer vision-based system for positioning hoisting-and-transport machines is presented to overcome the aforementioned issue. Common problems that are often faced in the computer vision systems area are discussed. Computer vision software and hardware are considered in the paper. The example of finding blobs that is used in the first version of the stacker crane is shown in the paper.
Artificially Generated Facial Images for Gender Classification Using Deep Learning

Given the current expansion of the computer vision field, several applications that rely on extracting biometric information like facial gender for access control, security or marketing purposes are becoming more common.A typical gender classifier requires many training samples to learn as many distinguishable features as possible.However, collecting facial images from individuals is usually a sensitive task, and it might violate either an individual's privacy or a specific data privacy law.In order to bridge the gap between privacy and the need for many facial images for deep learning training, an artificially generated dataset of facial images is proposed.We acquire a pre-trained Style-Generative Adversarial Networks (StyleGAN) generator and use it to create a dataset of facial images.We label the images according to the observed gender using a set of criteria that differentiate the facial features of males and females apart.We use this manuallylabelled dataset to train three facial gender classifiers, a custom-designed network, and two pre-trained networks based on the Visual Geometry Group designs (VGG16) and (VGG19).We cross-validate these three classifiers on two separate datasets containing labelled images of actual subjects.For testing, we use the UTKFace and the Kaggle gender dataset.Our experimental results suggest that using a set of artificial images for training produces a comparable performance with accuracies similar to existing state-of-the-art methods, which uses actual images of individuals.The average classification accuracy of each classifier is between 94% and 95%, which is similar to existing proposed methods.
Developing a Collaborative Diversity, Equity, and Inclusion (DEI) Guide: A Library-Department Partnership in the Earth Sciences

Earth Science is one of the least diverse scientific fields, but libraries can play a role in assisting their liaison departments’ diversity, equity, and inclusion (DEI) efforts by diversifying our collections and supporting research practices that promote their use. In 2020, the University of California, Berkeley’s Earth & Planetary Science Department graduate students created an impressive Call to Action directed toward the faculty of the department. The Call to Action included a plan for advancing the department’s diversity, equity, inclusion, and accessibility, particularly important in a department with only 17% of students from underserved backgrounds enrolled in the graduate program. Stemming from this effort, the Earth Sciences & Map Library at UC Berkeley, developed a guide (https://guides.lib.berkeley.edu/geo_dei) to provide resources to further educate departmental faculty, staff, and students on the topic of DEI. The guide encourages researchers to expand their engagement with scholarship and citation practices intentionally while working on these DEI efforts. Although Earth Science is highlighted in this particular LibGuide, the strategies are universal, especially as applied to science, technology, engineering, math (STEM) fields. The development of this guide, as well as partnerships with active student groups, can serve as a tool for other academic departments creating and promoting DEI efforts.
IMPLEMENTASI TEKNOLOGI PETA VIRTUAL 3D GEDUNG E TEKNIK SIPIL DAN GEDUNG F LABORATORIUM HIDROLIKA FAKUTAS TEKNIK UNIVERSITAS LAMPUNG

Lampung University doing out routine activities every new academic year for students especially for new students college who majoring in Civil Engineering, Faculty of Engineering, Introduction about Campus Life for New Students (PKKMB), one of the main agendas of PKKMB is introducing about everyvbuildings and public facilities in Civil Engineering also every building and the room in it. The current state of the COVID-19 pandemic make the University of Lampung conduct learning activy by network (online). So, it's make every students activities not possible to perform direct tracing. Serving method information follows technological developments, one of which is 3D visualization techniques. With using the Multimedia Development Life Cycle (MDLC) system development method, 3D virtual map applications can be developed that can display building layouts and facilities in 3D. This research is Alpha Testing and Customer satisfaction. Alpha Testing provides test results The application can operate on versions in operation android system 7.1 to 10. The application can operate on smartphone with screen specifications from 5.0 inch to 6.5 inches. The application can operate on smartphones from minimum 3GB RAM to 8GB RAM. Customer Satisfaction Respondents stated that the 3D Virtual Map Application of Building E Civil Engineering and Building F of the Hydraulics Laboratory get good results with a percentage index between 87.5% to 95%.
A Hybrid Encoder Transformer Network for Video Inpainting

Video inpainting is to fill the missing areas in the video frame with reasonable content based on the information of the known areas in the video. Although deep learning has achieved very good results in image processing, it is still very challenging in video inpainting. Recently, some studies have introduced the Transformer module into the problem of video inpainting, and achieved good results. However, the speed and effect cannot be guaranteed at the same time. Therefore, in order to solve the speed problem of video inpainting, we propose an improved 3D and 2D hybrid encoder network. At the same time, the Transformer module of other studies is introduced to extract the reference frame information far away from the target frame, so as to meet the time consistency and obtain a better video inpainting effect. In the test video sequences doped with static and moving masks, we made qualitative and quantitative evaluations respectively. Compared with recent research, our proposed video inpainting framework can further improve the speed of video inpainting while ensuring the same inpainting effect.
Fuzzy Logic in PRO-networks in the System of Simulation Modeling of Production Processes

The paper deals with the mathematical apparatus of PRO-nets - modifications of Petri nets, modified by fuzzy calculations. The device is used as a basis for a system of simulation modeling of technological processes of production. The paper describes how to use fuzzy logic to organize the transition process between net states.
Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like, fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users' identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However, divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called, Gaussian Weighted Cauchy Krigingbased Continuous Czekanowski's (GWCK-CC).First, a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise present in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally, Continuous Czekanowski's Classification is utilized for proficient classification between the genuine user and attacker.By this way, the proposed GWCK-CC method achieves accurate authentication with minimum error rate and time.Experimental assessment of the proposed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy, by 9%, reduces the authentication time, and error rate by 44%, and 43% as compared to the existing methods.
Predicting risk of cardiovascular death in the high-dimensional cohort follow-up data in the presence of competing events: a guide for building a modeling pipeline

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Proposed Application for an Entity Component System in an Energy Services Interface

An Entity Component System is a data-oriented architecture originally developed to streamline video game performance. Despite being quite new, Entity Component Systems are relatively well established within the video game industry due to the cutting edge nature of research into performance, especially around graphics. However, Entity Component Systems have not been widely examined or adopted outside of that industry. We propose adopting an Entity Component Systems framework to serve the needs of an Energy Service Interfaces. We examine the needs of an Energy Service Interface, give an overview of open-source Entity Component Systems (ECSs) libraries, examine some preliminary performance results for ECSs, and explore the traditional approach to fulfilling the needs of an Energy Service Interface (ESI) with database architectures.
A new discrete information model and its applications for the study of contingency tables

There exists an assortment of parametric and non-parametric discrete and continuous information models but still inevitability arises to enlarge supplementary parametric models to induce flexibility in the system under study. Moreover, there appears well-built relation between information entropy and the field of statistics. The present communiqué deals with the proposal of a new discrete information entropy model along with its applications in statistics. By employing the newly developed entropy model, we have enriched the application region of maximum entropy principle after the learning of contingency tables recurrently employed in statistics.
Real-Time Face Tracking Based on the Kalman Filter

Face tracking is not only one of the most interesting tasks in the fields of computer vision, but also an important part of practical applications such as surveillance systems, fault face detection systems, artificial intelligence, etc. Face tracking systems can be difficult to track the human face when face detection has too low of an accuracy rate. This paper proposes a method for real-time face tracking based on the Kalman filter. The proposed method has two main parts, such as face detection by using the YOLOv3 (You Only Look Once) algorithm and face tracking by using the Kalman filter. The face detection system is difficult to detect a human face when the face is occluded or other problems are affected (poor lighting conditions, motion blur, etc.), then the Kalman filter predicts the face's future location. The experimental results show that the proposed method can track the human face not only in different face position but also at high speed in real-time. Testing for this work used the Image Processing Toolbox and the Deep Learning Toolbox in MATLAB application 2021a.
Cluster-Based Authentication Process in a Smart City

The widespread deployment of the Internet of Things in any smart city provides a regular flow of huge amount of data in server(s) that poses challenges for effective and efficient management to improve the quality of citizens’ life. To maintain the privacy and security of these data, a proper and secured identification and authentication process is very essential. In this paper, we propose a cluster-based identification and authentication process for the users, edge servers, and service servers, which are engaged in storing, processing, and accessing data. The proposed identification and authentication process is secured due to some codes (values), which are not possible to compute except by the concerned entities. For the proposed trust evaluation method (which actually strengthens the proposed authentication process), we consider major components and their integration in the model very carefully so that the simulation results become credible. Hence, we hope that the simulation results will be useful for the readership. As a whole, the proposed approach has potentials of being implemented in real-time applications.
Predictive maintenance of infrastructure code using “fluid” datasets: An exploratory study on Ansible defect proneness

Abstract This work consolidates and compounds previous investigations in recognizing defects for infrastructure‐as‐code (IaC) scripts using general software development quality metrics with a focus on defect severity but adding to previous work an explorative look at creating datasets, which may boost the predictive power of provided models—we call this notion a fluid dataset . More specifically, we experiment with 50 different metrics harnessing a multiple dataset creation process whereby different versions of the same datasets are rigged with auto‐training facilities for model retraining and redeployment in a DataOps fashion. At this point, with a focus on the Ansible infrastructure code language—as a de facto standard for industrial‐strength infrastructure code—we build defect prediction models and manage to improve on the state of the art by finding an F1 score of 0.52 and a recall of 0.57 using a Naive–Bayes classifier. On the one hand, by improving state‐of‐the‐art defect prediction models using metrics generalizable for different IaC languages, we provide interesting leads for the future of infrastructure‐as‐code. On the other hand, we have barely scratched the surface on the novel approach of fluid‐datasets creation and automated retraining of Machine Learning (ML) defect prediction models, warranting for more research on the same direction in the future.
The Method for End-to-end Automatic Test Generation from Natural Language Test Scenarios Based on Pretrained OpenIE Model

The practice of automatic test covering is widespread now. Usually, framework and tests are separately developed, and framework functions are used in tests. We proposed the method to generate E2E tests from functional specification. The method includes the following main steps: test scenarios forming from specification; test scenarios splitting to sentences that will be translated to the one final code line; sentences transformation to syntax tree using pretrained OpenIE model; test steps comparison with testing functions using Word2Vec model; given semantic tree transformation to the Kotlin language code. The method feature is an application of syntax tree to generate tests and framework interfaces. The paper contains the description of protype of system automatically generating Kotlin language tests from natural language specification.