Reviewerly/exHarmony huggingface dataset:
Towards Better Crowdsourced Software Testing Process

Crowdsourced software testing (CST) is an emerging trend in software testing. Companies and developers assign testing tasks through CST platforms to thousands of online testers. Currently, CST platform managers are trying to find and resolve challenges in order to reach the best CST practice. Many features have been applied by CST platforms to improve CST activities, including notification emails, online chat rooms, forums and, most importantly, a CST platform dashboard to view all testing projects and tasks; these features have enabled CST to operate efficiently. Still, CST users find it difficult to stay abreast of test project updates, maintain their motivation, and avoid frustration. This aligns with the increasing studies in the literature that call for more efficient solutions to support CST process. This research aims to support CST by searching for potential process limitations and overcoming them. In order to do so, a five-stage approach is used: first, the current process of CST is investigated through reviewing 15 CST platforms. Second, the review has resulted in identifying eight possible activities to improve CST; among them six have been selected after a survey distributed to 30 domain experts in Stage 3. In Stage 4, we have designed and implemented five process models in a web-based system that can fulfill the requirements of the six activities identified earlier. In Stage 5, we have evaluated these process models through interviews with representatives of two CST platforms and an expert tester, and through making scenario-based evaluation with 20 domain experts who used the system and rated the value of the processes. The results show that the new improvements are sound and can strengthen the CST practice.
A 2020 perspective on “A fair contract signing protocol with blockchain support”

The development of the blockchain with its different virtues (decentralized trust, auditability, accessibility, immutability of their operations, etc.) has allowed centralized applications to find a solution to their problems, such as trust, scalability, etc. One of these applications is the electronic signature of contracts which is a key point for the deployment of electronic transactions in the field of e-commerce. We have used the blockchain for contract-signing to avoid the use of centralized trusted third-parties, in a simple way, and where the use of any blockchain solution, allowing data to be published at a reasonable cost, can be easily integrated in our proposal, involving Bitcoin-based solutions (e.g., Litecoin or Dash) or Ethereum. Our results can help other authors to consider more factors, and not just the technological aspects, when selecting a blockchain solution.
Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns.Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation.Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn't happened.For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics.Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events.While ESM development is actually completely necessary, a parallel concentrate on utilizing ML and AI to determine as well as capitalize a great deal more on pre pre-existing simulations as well as info is suggested by us.
Design of Efficient AES Architecture for Secure ECG Signal Transmission for Low-power IoT Applications

Cryptography has an important role in securing information which has gained prominence due to the digitization. Exchange of sensitive personal data such as medical information tends to take place frequently throughout the globe; therefore, protecting data from unauthorized adversary access is imperative. Advanced encryption standard (AES) algorithm is one of the algorithms which is broadly employed because of its exemplary security and usage in extensive applications. This research proposes a modified folded pipelined architecture of an AES algorithm for resources constraint applications. The folding transformation controls the circuit functionalities by time-multiplexing operations to a single functional unit and reduces its area considerably. The proposed architecture is implemented using fewer resources, hence, improves area and the power requirements as compared to the conventional algorithm. The work presented in this paper is applicable for encrypting and decrypting personalized Electrocardiograph (ECG) signals for secure transmission. The architecture proposed in this paper reduces the area requirements by 83% and power by 96.8% enabling its hardware implementation more efficient than the conventional AES. Due to its low power and area requirements, the architecture presented in this paper can be used for portable Internet of things (IoT) applications as well.
Quantum Coin Method for Numerical Integration

Abstract Light transport simulation in rendering is formulated as a numerical integration problem in each pixel, which is commonly estimated by Monte Carlo integration. Monte Carlo integration approximates an integral of a black‐box function by taking the average of many evaluations (i.e. samples) of the function (integrand). For N queries of the integrand, Monte Carlo integration achieves the estimation error of . Recently, Johnston [Joh16] introduced quantum super‐sampling (QSS) into rendering as a numerical integration method that can run on quantum computers. QSS breaks the fundamental limitation of the convergence rate of Monte Carlo integration and achieves the faster convergence rate of approximately which is the best possible bound of any quantum algorithms we know today [NW99]. We introduce yet another quantum numerical integration algorithm, quantum coin (QCoin) [AW99], and provide numerical experiments that are unprecedented in the fields of both quantum computing and rendering. We show that QCoin's convergence rate is equivalent to QSS's. We additionally show that QCoin is fundamentally more robust under the presence of noise in actual quantum computers due to its simpler quantum circuit and the use of fewer qubits. Considering various aspects of quantum computers, we discuss how QCoin can be a more practical alternative to QSS if we were to run light transport simulation in quantum computers in the future.
Variational Bayesian adaptive high‐degree cubature Huber‐based filter for vision‐aided inertial navigation on asteroid missions

Vision-aided inertial navigation (VAIN) is a prospective technique for determining the pose of the spacecraft during asteroid missions. The VAIN system can fuse the inertial and visual data by employing the high-degree cubature Kalman filter (HCKF) because it can accurately handle non-linear problems. However, the visual measurements can be corrupted by non-Gaussian noise with unknown time-varying covariance, resulting in severe degradation of the HCKF. To improve the navigational accuracy of the spacecraft in these situations, the authors propose a novel adaptive robust HCKF known as variational Bayesian (VB) adaptive high-degree cubature Huber-based filter (VB-AHCHF). In the novel algorithm, the fifth-degree cubature rule and VB theory are combined to estimate the state and track the non-stationary statistical characteristics of the measurement noise. In addition, utilising the M-estimation, which is defined as the Huber technique, it modifies the update step of the formal Bayesian filtering. Therefore, the VB-AHCHF can exhibit adaptability and robustness to the covariance uncertainty and non-Gaussianity of the measurement noise. Their simulation results show that the estimation accuracy of VB-AHCHF, as well as its adaptability and robustness, is superior to all state-of-the-art algorithms, e.g. HCKF, high-degree cubature Huber-based filter, and the VB adaptive HCKF.
Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search

Meta-learning techniques based on neural architecture search (NAS) show excellent performance in the design of learning models used in deep neural networks. In particular, when NAS is applied to design a convolutional neural network (CNN) for image recognition, the performance of the network when evaluating public benchmark datasets such as CIFAR10 and ImageNet exceeds that of hand-designed models. Nevertheless, there are very few cases wherein NAS has been applied to real-world problems, i.e. recognition problems with a limited dataset. We proposed a method in which the NAS technique does not require a proxy task for the scene text recognition (STR) framework to apply the NAS method to a new image recognition field. Therefore, we proposed an architecture space for CNN-based modules in the STR framework and applied the ProxylessNAS method, enabling end-to-end training while meta learners design a new model that requires only a single commonly used GPU (approximately 100 GPU hours). To evaluate the STR model obtained by the proposed NAS method, seven STR benchmark datasets were used. Finally, the obtained model could achieve a performance similar to that of the ideal model in terms of accuracy and number of parameters. We thus confirm that the model design based on NAS can be effectively applied to STR scenarios.
FADE: Feature Aggregation for Depth Estimation With Multi-View Stereo

Both structural and contextual information is essential and widely used in image analysis. However, current multi-view stereo (MVS) approaches usually use a single common pre-trained model as pixel descriptor to extract features, which mix structural and contextual information together and thus increase the difficulty of matching correspondence. In this paper, we propose FADE (feature aggregation for depth estimation), which treats spatial and context information separately and focuses on aggregating features for efficient learning of the MVS problem. Spatial information includes image details such as edges and corners, whereas context information comprises object features such as shapes and traits. To aggregate these multi-level features, we use an attention mechanism to select important features for matching. We then build a plane sweep volume by using a homography backward warping method to generate match candidates. Furthermore, we propose a novel cost volume regularization network aims to minimize the noise in the matching candidates. Finally, we take advantage of 3D stacked hourglass and regression to produces high-quality depth maps. With these well-aggregated features, FADE can efficiently perform dense depth reconstruction, achieving state-of-the-art performance in terms of accuracy and requiring the least amount of model parameters.
Real-time Detection of Activities in Untrimmed Videos

Real-time detection of spatio-temporal sparse activities in untrimmed videos is a challenging problem. In this work, we present the details of our proposed solution. We begin with a slow baseline implementation of a previously state-of-the-art system [13] and redesign it to achieve real-time performance for detecting 37 activities in the ActEV19 Sequestered Data Leaderboard [4]. This is primarily achieved by introducing speed related hyperparameters into the baseline approach. A tradeoff analysis is performed to assist in hyperparameter selection which results in a real-time, high quality action detection system. Our system achieves an AUDC score of 0.476 on the ActEV19 Sequestered Data Leaderboard.
Identifying <scp>density‐based</scp> local outliers in medical multivariate circular data

This article is considered to be the first to deal with the problem of outlier‐detection in multivariate circular data. The proposed algorithm is an extension of the Local Outlier Factor (LOF) method. Two different circular distances are used; taking into account the close bounded range of circular variables, and testing all possible permutations. The performance of the algorithm is investigated via an extensive simulation study. The performance of the LOF algorithm has a direct relationship with concentration parameter, while it has an inverse relationship with the sample size. For illustrative purposes, the algorithm has been implemented on two medical multivariate circular data, namely, X‐ray beam projectors data and eye data. The extension of the LOF algorithm for other types of directional data such as spherical and cylindrical datasets is worth to be investigated.
Histopathologic Cancer Detection by Dense-Attention Network with Incorporation of Prior Knowledge

To identify the cancerous region in histology Whole-Slide Images (WSI), the common approach is to apply a patch-level classifier. Appending surrounding tissues could improve the accuracy of patch-wise classification and maintain consistency of WSI. However, the rule that surrounding tissues play a supporting role rather than a decisive one is difficult to be learned directly by a Convolutional Neural Networks (CNN). In this paper, we propose Dense-Attention Network (DAN) for cancerous patch classification, where the attention mechanism is further developed to incorporate prior knowledge about the surrounding tissue. Moreover, the effectiveness of Data Augmentation in Inference stage (DAI) is further validated. The proposed method is evaluated on the PatchCamelyon dataset, where images with tumor tissues in the center are labeled positive, and those in the outer regions do not influence the label. Compared with other competitive deep-learning methods the proposed method has achieved better performance in terms of AUC.
Region of Interest Identification for Cervical Cancer Images

Every two minutes one woman dies of cervical cancer globally, due to lack of sufficient screening. Given a whole slide image (WSI) obtained by scanning a microscope glass slide for a Liquid Based Cytology (LBC) based Pap test, our goal is to assist the pathologist to determine presence of precancerous or cancerous cervical anomalies. Inter-annotator variation, large image sizes, data imbalance, stain variations, and lack of good annotation tools make this problem challenging. Existing related work has focused on sub-problems like cell segmentation and cervical cell classification but does not provide a practically feasible holistic solution. We propose a practical system architecture which is based on displaying regions of interest on WSIs containing potential anomaly for review by pathologists to increase productivity. We build multiple deep learning classifiers as part of the proposed architecture. Our experiments with a dataset of ~19000 regions of interest provides an accuracy of ~89% for a balanced dataset both for binary as well as 6-class classification settings. Our deployed system provides a top-5 accuracy of ~94%.
Standard Dropout as Remedy for Training Deep Neural Networks with Label Noise

Deep neural networks, trained on large annotated datasets, are often considered as universal and easy-to-use tools for obtaining top performance on many computer vision, speech understanding, or language processing tasks. Unfortunately, these data-driven classifiers strongly depend on the quality of training patterns. Since large datasets often suffer from label noise, the results of training deep neural structures can be unreliable. In this paper, we present experimental study showing that simple regularization technique, namely dropout, improves robustness to mislabeled training data, and even in its standard version can be considered as a remedy for label noise. We demonstrate it on popular MNIST and CIFAR-10 datasets, presenting results obtained for several probabilities of noisy labels and dropout levels.
Estimating VR Sickness Caused By Camera Shake in VR Videography

Recent development of Virtual Reality (VR) technology provides more realistic experience for viewers with a variety of contents. While the viewing safety of the viewers is one of the important issues in VR industry, the necessity of VR sickness estimation has been drawing attentions. Inspired by the observations that camera shake in VR videography is one of the major causes of VR sickness, we propose a novel deep network that predicts VR sickness level of individuals caused by camera shake. The proposed method is designed to comprehensively identify changes in direction and speed of the VR video scenes with camera shake. Sparse selection of optical flow maps with different intervals allows the proposed network to efficiently extract stimulus features with a variety of camera shake patterns. We built a new benchmark database for the evaluation of the proposed method that consists of 360-degree videos including various camera shake movements, physiological signals, and Simulation Sickness Questionnaires (SSQ) scores of the experimental participants. Experimental results of the sickness prediction show the effectiveness of the proposed method on the built benchmark database.
User Preference Based Weighted Page Ranking Algorithm

Due to the huge number of information on the internet, users use search engines to fetch the relevant pages, which include the information that meet users' needs. Search engines encountered some challenges in the process of retrieving pages matching user queries. To improve search results and how the user navigates the results of pages, search engines applied ranking method on the obtained search results. In this paper, we discussed the main Page Ranking algorithms including PageRank, Weighted Page Rank and Hyperlink- Induced Topic Search algorithms. we presented a comparative study of the latest improvements on the page ranking algorithms focusing on the algorithms that are related to user preference and user behavior. The main contribution of this paper is the proposal of an algorithm called User Preference Based Weighted Page Ranking Algorithm (UPWPR) which is an enhancement for existing ranking algorithms. UPWPR algorithm uses web content mining and web usage mining in order to rank the search results based on user preferences. A numerical case study was used to validate and compare UPWPR proposed algorithm. Results showed better ranking output based on different parameters such as the Content Weight, the User Activities Time, Page Reading Time, and the number of visits.
User attention and behaviour in virtual reality art encounter

Abstract With the proliferation of consumer virtual reality (VR) headsets and creative tools, content creators are experimenting with new forms of interactive audience experience using immersive media. Understanding user attention and behaviours in virtual environment can greatly inform the creative processes in VR. We developed an abstract VR painting and an experimentation system to study audience art encounters through eye gaze and movement tracking. The data from a user experiment with 35 participants reveal a range of user activity patterns in art exploration. Deep learning models are used to study the connections between the behavioural data and the audience’s background. The work also introduced new integrated methods to visualise user attention for content creators.
Development of a clear-sky model to determine circumsolar irradiance using widely available solar radiation data

Accurate assessment of the solar irradiance reaching the absorber of concentrating solar power (CSP) systems is crucial for accurate energy generation estimates as well as for power plants design and operation. Due to a larger aperture angle, direct normal irradiance (DNI) measurements taken by pyrheliometers include circumsolar normal irradiance (CSNI), i.e., the diffuse radiance from the vicinity of the sun disk, which in some cases is not totally captured by the CSP systems because of their lower aperture angles. This work reports the modelling of DNI and CSNI using a radiative transfer model and atmospheric measurements and aims to develop a fast and simple model to estimate the CSNI based on more common solar irradiance measurements such as the DNI, global horizontal irradiance (GHI) and diffuse horizontal irradiance (DHI). The proposed model performs better than other models available in the literature for the six locations analysed (scattered around the globe), thus contributing to a more accurate solar resource assessment and CSP systems design and operation.
Language contact through translation

Abstract This article investigates the role of translations from English in language change in Chinese. It employs a new corpus, the Chinese Diachronic Composite Corpus (CDCC), which incorporates a parallel corpus and comparable corpus in three sampling periods in the twentieth century, and a refe­rence corpus as a starting point in the timeframe. We examine whether explicitness in English–Chinese translations has exerted an impact on the target language, focusing on adversative conjunctions as a measure of explicitness. The results of the study demonstrate that: (1) translated Chinese texts have changed in step with original Chinese texts in the frequency of adversative conjunctions; (2) translated Chinese texts and original Chinese texts are interrelated throughout the three periods, but the correlation between them has changed perceptibly over the three sample points; and (3) source language interference found in translated Chinese texts increases over the three periods.
Switching Perfect Control Algorithm

The application of the switching control framework to the perfect control algorithm is presented in this paper. Employing the nonunique matrix inverses, the different closed-loop properties are obtained and further enhanced with possible switching methodology implementation. Simulation examples performed in the MATLAB/Simulink environment clearly show that the new framework can lead to benefits in terms of the control energy, speed, and robustness of the perfect control law. The possibility of transferring the new obtained results to the symmetrical nonlinear plants seems to be immediate.
Distributed Server Allocation Model with Preventive Start-Time Optimization against Single Failure

This paper proposes a distributed server allocation model with the preventive start-time optimization against a single server failure. The proposed model preventively determines the assignment of servers to users under each failure pattern to minimize the largest maximum delay among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove the NP-completeness for the considered problem. The numerical results reveal that the proposed model reduces the largest maximum delay compared to one baseline; it avoids instability caused by the unnecessary disconnection, which frequently occurs in the other baseline.
Understanding Users’ Engagement and Responses in 3D Virtual Reality: The Influence of Presence on User Value

Abstract Virtual reality (VR) provides new opportunities for businesses to gain competitive advantages by enabling them to innovatively engage customers. Based on presence theory, this study aims to test the influences of two major components of presence, social presence and spatial presence, on users’ perceptions of hedonic value, utilitarian value and engagement in the VR environment. An experiment was conducted on two conditions of a VR application (low vs. high social presence) to test the hypotheses proposed in the research model. The results reveal that social presence and spatial presence can improve the hedonic value of VR. However, inconsistent with previous studies, our findings reveal a negative relationship between spatial presence and engagement. Theoretical and practical implications and future research directions are subsequently discussed.