Reviewerly/exHarmony huggingface dataset:
Towards Modelling Acceptance Tests as a Support for Software Measurement

The DevOps paradigm emphasizes the need for a measurable feedback loop, starting from requirements and going as far as deployment in an automated way. In this context, a modelling challenge is to leverage the existing requirement engineering approaches to support measurements. Unfortunately, measurement methods are slow and costly by definition, preventing precisely measured requirements from being used in the DevOps loop. As a result, developers have to deal with grossly estimated elements, e.g., using story points promoted by agile methods. Thus, it is not possible to provide better support for the development team. We envision taking advantage of the artifacts that already exist in a DevOps context to provide better support for requirements measurement, making it available in an automated context such as the DevOps one. This paper focuses on the automated analysis of acceptance tests (e.g., expressed using the Gherkin language) to support functional measurement automation in a DevOps context. This proposition is illustrated by a scenario coming from an industrial partner, supporting the identification of four research challenges to be tackled.
Zero-Shot Learning for Skeleton-based Classroom Action Recognition

Classroom behaviours reflect teaching efficiency and performances significantly. Different from daily actions such as running and walking, most classroom behaviours are in sitting positions (e.g. listening, reading and playing mobile phones), which makes the action features of those behaviours relatively close along with occlusion problems. Recently, the graph convolution operation on human skeleton data can effectively extract the skeleton point information, which provides the possibility for the effective recognition of classroom action. Inspired by this, in our research paper, a kind of class action recognition method on the basis of graph convolution was proposed, which takes classroom video as input and recognizes student actions through a skeleton extraction module, followed by a feature extraction module based on graph convolution and a zero-shot feature classification module. Comprehensive experiments show that our model has achieved significant performance in classroom behaviour recognition tasks with an accuracy of 60.67% in collected classroom dataset. Simultaneously, the validity of the model is verified in the ablation experiment.
Using augmented reality WEB-application for providing virtual excursion tours in university campus

The paper describes the augmented reality information technology for the formation of excursion tours by Lviv Polytechnic. This research contains analysis of main modern tendencies for projects in the design and development of virtual and augmented reality technologies, developed a WEB-application that allows you to visit the main building of the National University &#x201C;Lviv Polytechnic&#x201D; online, learn interesting information about objects or people who studied or managed educational institution. The software was developed with the help of WebGL technologies, React360, MySQL database management system was used to store data on the server part. The system can be developed as a separate independent product or as a separate module within existing web applications. To use the software product, the user must follow the link in any browser convenient for him and start working with a virtual tour.
ORSUM 2021 - 4th Workshop on Online Recommender Systems and User Modeling

Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content – e.g. posts, news, products, comments –, but also user feedback – e.g. ratings, views, reads, clicks –, together with context data – user device, spacial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online.
Target feature extraction algorithm for SAR images of complex background based on corner estimation

Aiming at the problem that the Synthetic Aperture Radar (SAR) image is difficult to target detection for the speckle noise, this paper proposes a SAR image target feature extraction method based on the corner information estimation. Firstly, the image is preprocessed by spatial filtering technique. Then the target detection algorithm based on pulse coupled neural network (PCNN) is used to detect the target of SAR image, and the target in the image is better detected. Finally, through Harris operator extracts the target corner point and performs a least squares estimation to obtain the target corner size. The simulation results show that compared with the traditional angle estimation algorithm, the proposed SAR image feature extraction algorithm based on the rotation angle estimation has higher precision and less error.
Occluded Face Recognition Using Sparse Complex Matrix Factorization with Ridge Regularization

Matrix factorization is a method for dimensionality reduction which plays an important role in pattern recognition and data analysis. This work exploits the usefulness of our proposed complex matrix factorization (CMF) with ridge regularization (SCMF-L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) in occluded face recognition. Experiments on occluded face recognition reveal that the SCMF-L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> method provides the best recognition result among all the nonnegative matrix factorization (NMF) and CMF methods. The proposed method also reaches the stopping condition and converge much faster than the other NMF and CMF methods.
Infrared Image Edge Detection Based on Improved Canny Algorithm

In recent years, the infrared image has been used frequently in medical, military, and industrial fields, and it has become increasingly important to extract a good target contour from the infrared image. Because of the imaging mechanism of the infrared image, there is a lot of noise in the image, which leads to the difficulty of edge extraction. By analyzing the application of the Canny edge detection algorithm in the infrared image, it is found that the detection results have a poor noise filtering effect and the loss of edge details. To solve this problem, this paper improves the Canny algorithm. The Gaussian filter is replaced with the bilateral filter for smoothing noise filtering, and the double global threshold segmentation algorithm is used to select adaptively the high and low thresholds to overcome the error caused by artificial experience setting thresholds. The experimental results show that compared with the traditional Canny algorithm, the improved algorithm can suppress noise better and retain more edge details in the process of infrared image edge detection.
DWD-net: Cascaded local and global deep learning network for brain MR registration

At present, non-rigid registration of adult brain magnetic resonance (MR) images has been widely applied. Both traditional registration methods and those based on deep learning can achieve preferable registration effect. However, low registration accuracy and poor registration effect may occur in local areas and complex anatomical structures. Therefore, dynamically and weakly supervised method is proposed in this paper, which can dynamically learn different local regions. In order to achieve the method, a local loss function is designed. The Dual Weakly and Dynamically supervised deep learning registration network (DWD-net) is proposed. The DWD-net is divided into two parts: Local net (Lnet) and Global net (Gnet). Lnet will dynamically align difficultly-aligned local regions by the proposed method and loss function. Gnet can align the details of the whole image. Experimental results are presented on the OASIS-1 dataset show that the proposed DWD-net realizes higher accuracy on both difficultly-aligned local regions and global regions than state-of-the-art registration methods.
Lightweight frame scrambling mechanisms for end‐to‐end privacy in edge smart surveillance

As smart surveillance has become popular in today's smart cities, millions of closed circuit television cameras are ubiquitously deployed that collect huge amount of visual information. All these raw visual data are often transported over a public network to distant video analytic centres. This increases the risk of interception and the spill of individuals' information into the wider cyberspace that risks privacy breaches. The edge computing paradigm allows the enforcement of privacy protection mechanisms at the point where the video frames are created. Nonetheless, existing cryptographic schemes are computationally unaffordable at the resource-constrained network edge. Based on chaotic methods, three lightweight end-to-end privacy-protection mechanisms are proposed: (1) a novel lightweight Sine-cosine Chaotic Map, which is a robust and efficient solution for enciphering frames at edge cameras; (2) Dynamic Chaotic Image Enciphering scheme that can run in real time at the edge; (3) a lightweight Regions of Interest Masking scheme that ensures the privacy of sensitive attributes like face on video frames. Design rationales are discussed and extensive experimental analyses substantiate the feasibility and security of the proposed schemes.
Design and Implementation of User Interface and User Experience in Online Sales Applications At Sugosha Pharmacy With User Centered Design Method

The health sector, such as pharmacies, is currently growing in line with technological developments from year to year. The existence of online drug distribution proves the development of the health sector, especially pharmacies. Circular Letter Number HK.02.01/MENKES/303/2020 to prevent the spread of COVID-19 from supporting online drug distribution, increase sales and prevent the spread of COVID-19, of course, needs to be done in all pharmacies, including Sugosha Pharmacy, which currently does not have a drug sales application. Based on these problems, Sugosha Pharmacy requires an online sales application, namely an e-commerce website. User interface and user experience are needed to design an e-commerce website to look attractive and easy to use. To design and implement the design, user-centred design is used. User-centered design is an iterative design process that requires designers to focus on users and their needs at every phase of the design process. This research produces the user interface and user experience of the Sugosha Pharmacy e-commerce website for sellers and buyers and a front-end for the Sugosha Pharmacy e-commerce website for sellers. System Usability Scale used to test the user interface design and user experience of the Sugosha Pharmacy e-commerce website. The Usability Scale system results in the seller's part design being used well and the buyer's part design being used very well. Then for the front-end of the e-commerce website, black box testing is carried out, producing features that run according to the design.
M2OVQA: Multi-space signal characterization and multi-channel information aggregation for quality assessment of compressed omnidirectional videos

Considering the high requirements for omnidirectional video compression, we propose an objective quality evaluation method to assess quality loss in encoding omnidirectional videos. According to characteristics of 360° videos, we consider multi-space signal characterization (MSSC) to fully characterize the distortions of video signals from spatial/image domains to frequency domains and from image content to motion information, and further consider multi-channel information aggregation (MCIA) to fuse scores from multiple projection planes and temporal divided groups. The main innovation of our method is to establish a universal framework in bridging the connection between typical quality assessment and 360° quality assessment to measure 360° video quality effectively and efficiently. Experimental results show that our method outperforms state-of-the-art 2D quality metrics and quality metrics for omnidirectional images.
Advance Driving Assistance Systems: Object Detection and Distance Estimation Using Deep Learning

Most of the traffic accident was caused by human error. Vehicle collision accident may happen due to the driver miscalculating the distance between other vehicles. To prevent this type of accident, we implemented an Advanced Driving Assistance System to estimate distance objects and Object detection. The architecture implemented for object detection is MobileNetV2, EfficientNet, and VGGNet16. The localization method uses Single Shot Detector (SSD). Distance Estimation method applies Depth prediction approaches using Deep Learning, with DenseDepth and MonoDepth2 as deep learning architectures. In the object detection experiment test using KITTI and PASCAL Datasets, the highest score was achieved by MobileNetV2 architecture with mean Average Precision of 75%. In terms of Deep Learning Architecture for distance estimation, comparison of prediction depth and actual distance shows that Densedepth have the lowest error with average error 3.6043 meters during the cloudy weather, and 4.0565 meters during the sunny weather.
Dual Watermarking Technique for Image Authentication using Biometrics

In this online generation of cyberspace, protection of crucial documents is highly significant. To achieve this goal, digital watermarking with biometric features plays an important part. It's the technology which hides secret data into other digital formats. It uses modern technology of hiding information inside some digital media i.e text, image, video, or audio files. The key purpose of this paper is to implement the technique of information hiding inside image using biometric feature. To improve the security, robustness and provide authenticity for the image, a two step watermarking method is demonstrated. To achieve this, Redundant Discrete Wavelet Transform ie. RDWT along with SVD ie. singular value decomposition is preferred. For two step watermarking, major and minor watermarks are used. The process of embedding minor into the major watermark is achieved in all sub bands produced after applying RDWT, thereby resulting in the transformed watermark. The cover image is divided into blocks which are not overlapped by one another for embedding the transformed watermark by changing the singular values to get the resultant watermark. Finally, to obtain the Major and Minor watermarks a reverse algorithm is applied. The efficacy of this method in terms of digital attacks has exhibited good experimental outcomes.
A Weight-Sharing Autoencoder with Dynamic Quantization for Efficient Feature Compression

Collaborative inference (CI) enhances the inference efficiency of deep neural networks (DNNs) by partitioning a computational workload between an edge device and a cloud platform. Efficient inference using CI requires searching for the optimal partition layer that minimizes the end-to-end inference latency. In addition, the intermediate feature at the partitioned layer should be effectively compressed. However, recent DNN-based feature compression methods require independent models dedicated for each partition point, resulting in significant storage overhead. In this paper, we propose a novel method that efficiently compresses the features from variable partition layers using a single autoencoder. The proposed method incorporates a weight-sharing technique that shares the weights of autoencoders that compress each partition layer. In addition, dynamic bitwidths quantization is supported for flexibility in compression ratio. The experimental results show that the proposed method reduced the required parameter size by 4× compared to the existing independent model based method, while maintaining the accuracy loss within 0.5%.
Self-Shape-Sensing Device with Flexible Mechanical Axes for Deformable Input Interface

We propose a novel device that is capable of sensing its own shape, structured around a flexible mechanical axis that allows for its deformation within a wider degree of freedom and enables its tangible and intuitive control by hand. This device has a 5 x 5 axis structure consisting of a spherical joint that rotates between ± 45° and a slide shaft whose length can be varied from 50 to 71 mm. We developed a signal-processing algorithm which can work on the micro controller unit (MCU) of the device and use built-in sensors to not only reconstruct deformations in real-time, but also directly use such reconstructions in 3D graphics applications. As a result, we achieved an intuitive and interactive 3D experience using volumetric displays.
Reducing Compound Degree for Optimum Linear Decomposition of Symmetric Index Generation Function

Linear decomposition of Index Generation Functions attracted a lot of interest in the past decade. Typically, the researchers focus on the minimization of the number of variables. In this paper, we use an SMT (Satisfiability Modulo Theories) solver to reduce the compound degree of a linear transformation that provides the best solution in terms of the number of variables. Results obtained for symmetric Index Generation Functions prove the efficiency of our approach, i.e. the compound degree is significantly reduced for most of the analyzed functions, compared to the previously published results.
An Enhanced Metaheuristic Based Cuckoo Search Algorithm for Software Size Estimation

In software development, size estimation is a very difficult task because it depends on the other estimation of software like effort, cost, and time required for the completion of the software project. A huge number of techniques are implemented for software effort estimation preferred by researchers. In the current scenario, many researchers applied metaheuristic algorithms due to better performance as compared to previous approaches. The paper also focused upon the implementation of the Cuckoo Search (CS) metaheuristic algorithm to improve the prediction analysis through COCOMO II. For size and effort estimation, the correlation is calculated between the MSE and MAE parameters. The proposed work provides better results as compere to existing work.
Irradiation-Driven Dynamic Path-Planning of Moving Airborne Solar Farms Using Reinforcement Learning

This paper frames itself in the area of solar energy where harvesting is conducted with airborne moving farms. The goal of these farms is to harvest radiation to reach the storage capacity in the shortest time possible. To that end, we propose the use of reinforcement learning (RL) as a means to optimally plan the path of an airborne solar farm. In particular, the airborne solar farm dynamically plans its path driven by the forecasted irradiation; this schema allows minimization of the collection time by maximizing the irradiance of the farm and taking into consideration the costs of airship movement. The use of reinforcement learning promotes the quick adaptation of the airborne farm to the dynamically changing ambient weather conditions leading to increased radiation harvest yield. The proposed irradiation-driven RL planning method is tested on a set of various locations that exhibit characteristically different weather patterns. Our results demonstrate that the benefit of using RL is maximized as compared to static solar farms.
UAV Path Planning of Combining Ant Colony and Beetle Antennae Algorithm Using Intelligent Wireless Communication

Abstract In order to settle the problem of UAV path planning under mountain, an algorithm which based on the combination of ant colony algorithm and beetle antennae algorithm is proposed. Three dimensional environment model is established and objective function is constructed. It used ant colony algorithm to initialize the search path and the particle coordinates of all the next steps are updated by the beetle antennae algorithm. The improved algorithm adopted a new step update rule to speed up the convergence of the algorithm and used third-order B-spline interpolation method to smooth the path. Simulation results show that improved fusion algorithm has faster convergence speed and high stability by comparing with other algorithms under the same conditions, which verifies its effectiveness.
On How To Combine Image Segmentation Algorithms Using Entropy

Image segmentation is one of the most frequently used computer vision techniques. Whether we talk about medical imaging or autonomous driving, image segmentation algorithms are required to obtain the desired result. Therefore, a variety of algorithms have been implemented so far. Being based on different approaches, each of these algorithms has its own advantages and disadvantages. No algorithm can perform the same regardless of input data, with algorithms yielding better or worse results depending on the characteristics of the image. Some may accurately preserve the borders between large regions while clustering small details together (under-segmentation), while others can correctly delimit details while at the same time splitting large regions in multiple clusters (over-segmentation). Moreover, some algorithms might have a natural tendency in over-segmentation or under-segmentation, independent of input. This paper proposes a voting method which combines the results of some notable segmentation algorithms. The aim of this method is to limit the downsides of the used algorithms and to obtain, if possible, more accurate results. The results show that, in most cases, the proposed method offers both improvements in the quality of the provided output and more overall confidence in its usage.
Truncated Models for Probabilistic Weighted Retrieval

Existing probabilistic retrieval models do not restrict the domain of the random variables that they deal with. In this article, we show that the upper bound of the normalized term frequency ( tf ) from the relevant documents is much smaller than the upper bound of the normalized tf from the whole collection. As a result, the existing models suffer from two major problems: (i) the domain mismatch causes data modeling error, (ii) since the outliers have very large magnitude and the retrieval models follow tf hypothesis, the combination of these two factors tends to overestimate the relevance score. In an attempt to address these problems, we propose novel weighted probabilistic models based on truncated distributions. We evaluate our models on a set of large document collections. Significant performance improvement over six existing probabilistic models is demonstrated.