Reviewerly/exHarmony huggingface dataset:
GAN-based Style Transformation to Improve Gesture-recognition Accuracy

Gesture recognition and human-activity recognition from multi-channel sensory data are important tasks in wearable and ubiquitous computing. In these tasks, increasing both the number of recognizable activity classes and the recognition accuracy is essential. However, this is usually an ill-posed problem because individual differences in the same gesture class may affect the discrimination of different gesture classes. One promising solution is to use personal classifiers, but this requires personal gesture samples for re-training the classifiers. We propose a method of solving this issue that obtains personal gesture classifiers using few user gesture samples, thus, achieving accurate gesture recognition for an increased number of gesture classes without requiring extensive user calibration. The novelty of our method is introducing a generative adversarial network (GAN)-based style transformer to 'generate' a user's gesture data. The method synthesizes the gesture examples of the target class of a target user by transforming of a) gesture data into another class of the same user (intra-user transformation) or b) gesture data of the same class of another user (inter-user transformation). The synthesized data are then used to train the personal gesture classifier. We conducted comprehensive experiments using 1) different classifiers including SVM and CNN, 2) intra- and inter-user transformations, 3) various data-missing patterns, and 4) two different types of sensory data. Results showed that the proposed method had an increased performance. Specially, the CNN-based classifiers increased in average accuracy from 0.747 to 0.822 in the CheekInput dataset and from 0.856 to 0.899 in the USC-HAD dataset. Moreover, the experimental results with various data-missing conditions revealed a relation between the number of missing gesture classes and the accuracy of the existing and proposed methods, and we were able to clarify several advantages of the proposed method. These results indicate the potential of considerably reducing the number of required training samples of target users.
Unveiling geothermal potential sites along Gulf of Suez (Egypt) using an integrated geoscience approach

Abstract The appraisal of geothermal sites along Gulf of Suez, is important to develop the renewable energy resources in Egypt. An integrated approach based on remote sensing, Geographic Information System and geophysical methods were used to predict the ubication of promising geothermal sites. Multi‐source datasets were selected, analysed and integrated to develop a Geothermal Potential Model. Information‐Value and Weight‐of‐Evidence analyses were adopted to derive ranks and weights to spatial factors and their classes. The spatial factors were summarized in eight thematic maps indicating the possible sites of geothermal reservoirs. The developed geothermal potential model defines the promising sites, which are distributed in Ras‐Badran, Ras‐Matarma, Abu‐Durba, El‐Tur and some other locations in southwestern Gulf of Suez. Seismic interpretation is a fundamental tool for depicting subsurface major faults controlling the geothermal reservoir in the promising sites. The holistic integrated approach provides a powerful technique to design development plans in the study area.
Uncertainty in Bayesian deep label distribution learning

Though widely used, most deep convolutional neural networks fail to capture prediction uncertainty, which can be crucial in scenarios such as automotive applications and disease diagnosis. Aleatoric and epistemic uncertainties have been proposed in the Bayesian deep learning framework for regression and classification, which require training images with unambiguous labels for success. Some situations do not have precise labels by nature, such as age estimation or lesion contour annotation by different physicians in the real world. Label distribution learning (LDL) has been proposed to account for the label ambiguity. However, uncertainty estimation has not been studied for LDL. This study presents a Bayesian deep label distribution learning (BLDL) to obtain the uncertainties of LDL tasks. We define semantic uncertainty to account for the incompleteness of data acquisition and inaccurate data labeling and unify the three types of uncertainties' mathematical expressions for classification and LDL. We further demonstrate that applying the three types of uncertainties in the loss functions significantly improves model performance, and using semantic uncertainty as a criterion to select the training samples substantially promotes active learning.
An efficient attribute-based encryption scheme based on SM9 encryption algorithm for dispatching and control cloud

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Sketching an AI Marketplace: Tech, Economic, and Regulatory Aspects

Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.
Scalable event-triggered distributed extended Kalman filter for nonlinear systems subject to randomly delayed and lost measurements

In this paper, a distributed extended Kalman filter (EKF) is developed for a class of nonlinear systems, whose outputs are measured by multiple sensors which send data using an event triggered mechanism through a communication network subject to loss and latency. Random transmission delay and multiple dropouts are modelled by a Bernoulli random sequence. The filter gains are determined in each sensor node such that an upper bound on the cross covariance of the estimation error is minimized; so, less computational burden is required, even in the networks with the large number of nodes. To be specific, the scalability is the main feature of the proposed scheme. The boundedness of the filtering error is proved under some conditions. Finally, comparative simulation results are presented to illustrate the effectiveness and the applicability of the suggested filter.
Recurrent disease progression networks for modelling risk trajectory of heart failure

Motivation Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-dimensional data. However, very few studies have explored the ability of RNN in predicting long-term trajectories of recurrent events, which is more informative than predicting one single event in directing medical intervention. Methods In this study, we focus on heart failure (HF) which is the leading cause of death among cardiovascular diseases. We present a novel RNN framework named Deep Heart-failure Trajectory Model (DHTM) for modelling the long-term trajectories of recurrent HF. DHTM auto-regressively predicts the future HF onsets of each patient and uses the predicted HF as input to predict the HF event at the next time point. Furthermore, we propose an augmented DHTM named DHTM+C (where “C” stands for co-morbidities), which jointly predicts both the HF and a set of acute co-morbidities diagnoses. To efficiently train the DHTM+C model, we devised a novel RNN architecture to model disease progression implicated in the co-morbidities. Results Our deep learning models confers higher prediction accuracy for both the next-step HF prediction and the HF trajectory prediction compared to the baseline non-neural network models and the baseline RNN model. Compared to DHTM, DHTM+C is able to output higher probability of HF for high-risk patients, even in cases where it is only given less than 2 years of data to predict over 5 years of trajectory. We illustrated multiple non-trivial real patient examples of complex HF trajectories, indicating a promising path for creating highly accurate and scalable longitudinal deep learning models for modeling the chronic disease.
XCSF with Experience Replay for Automatic Test Case Prioritization

The verification of a new product is of major importance for companies. With the rise of test automation, companies start to rely on huge numbers of tests. Often, it is not feasible to run all available tests due to time constraints. Thus, a test suite of critical tests has to be determined. Recent research has shown that reinforcement learning is suitable for this prioritization problem: neural networks and XCS(F) learning classifier systems have been applied to this task. We extend the existing XCSF-based agent by incorporating experience replay (ER) in order to improve learning efficiency. In an experimental evaluation we show that this not only boosts performance but also enables our agent to exceed the aforementioned solutions. For XCSF without ER and neural networks, the most suitable reward function is strongly dependent on the underlying data set. This is in practice a downside as reward functions usually need to be chosen a priori in order to ensure the quality of the chosen test suite. However, for our improved agent this is not the case and we can give a clear recommendation for the reward function.
Forecasting Hourly Solar Irradiance Using Long Short-Term Memory (LSTM) Network

The increasing global demand for solar energy is a good indicator that it is a viable alternative to fossil energy. However, solar irradiance which is the principal component required for efficient power generation in solar plants is stochastic in nature. This is the reason why the prediction accuracy of solar irradiance for reliable electricity power output continues to be a very difficult task whether in the field of artificial intelligence (AI) or physical simulation. In this paper, Long Short-Term Memory (LSTM) Network, a variant of Recurrent Neural Network (RNN), was used to forecast hourly solar irradiance of Johannesburg city. LSTM is a deep learning network designed to overcome the vanishing and exploding gradient problems associated with a typical RNN. Ten years of historical meteorological data used in this work were obtained from Meteoblue. The simulation results obtained using the LSTM model were compared with the ones recorded using Support Vector Regression (SVR). From the results, it was observed that the LSTM network with normalized Root Mean Square Error (nRMSE) value of 3.2% performed much better than SVR using the same dataset.
A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates.
Optical Flow based Visual Potential Field for Autonomous Driving

Monocular vision based navigation for automated driving is a challenging task due to the lack of enough information to compute temporal relationships among objects on the road. Optical flow is an option to obtain temporal information from monocular camera images, and has been used widely with the purpose of identifying objects and their relative motion. This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow, which is used together with a gradient tracking sliding mode controller to navigate the vehicle to destination without collision with obstacles. The angular reference for the vehicle is computed online. This work considers that the vehicle does not require to have a priori information from the map or obstacles to navigate successfully. The proposed technique is tested both in synthetic and real images.
An Improvised Model for Securing Cloud-Based E-Healthcare Systems

Healthcare frameworks can be improved inconceivably with the utilization of present day data innovation. Still currently in immature and creating nations, the conventional paper-based framework is being utilized in human services. The concept of cloud computing is advancing and thus the innovation that can be utilized to build up a diverse set of system to convalesce the framework. In this chapter, a three-level cloud-based application “e-Health Cloud” has been analyzed and an improvised model is suggested. This model will be inclusive of distinct gatherings to improve antiquated medicinal services framework. This chapter analyses and proposes an improvised the model of planning an adaptable e-healthcare management system to support Google Cloud Healthcare API including HL7.FHIR and DICOM. Furthermore, the chapter provides the artifacts to resolve the existing faults and suggests recommendations to enhance security in the e-healthcare system. Furthermore, the model helps in tracking the location of patients through GPS. The authors also highlight the Cloud Healthcare API that gives a completely oversaw, exceptionally adaptable, endeavor grade advancement condition for building clinical and investigation arrangements safely on Google Cloud. The Cloud Healthcare API likewise incorporates extra worth included abilities, for example, robotized DICOM and FHIR de-ID to more readily plan data for these arrangements.
Spectral Effects in Albedo and Rearside Irradiance Measurement for Bifacial Performance Estimation

We investigate the impact of spectral dependence of ground surface reflectivity on albedo and rearside irradiance measurements necessary for bifacial photovoltaic (PV) module performance estimation and monitoring. Because PV modules are spectrally selective, albedo and irradiance measurements performed with common irradiance sensors may require spectral mismatch corrections when used for performance prediction. We investigate via simulation the differences in spectrally responsive albedo measured with thermopile pyranometers and crystalline silicon PV reference cells in comparison to a typical crystalline-silicon bifacial PV module. Simulations are performed for nine different representative ground surface materials using simulated solar spectra together with spectral reflectivity data distributed with the SMARTS simulation software. For the materials considered, the results show that albedo spectral mismatch relative to the bifacial module is distributed over a range of ±9.2% for thermopile pyranometers versus only ±3.7% for a typical PV reference cell. We consider the impact of this spectrally-responsive albedo mismatch on bifacial PV module rearside irradiance measurements. Using synthesized rearside spectral irradiance distributions, we find that for the nine different ground surface materials the predicted rearside irradiance measurement deviates from the effective irradiance observed by the PV module by on the order of 16.5 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the pyranometer and 3.6 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the PV reference cell. We discuss the implications for bifacial albedo and irradiance measurement.
Covid-19 and the decolonisation of education in Palestinian universities

Despite the severe social, health, political and economic impacts of the outbreak of Covid-19 on Palestinians, we contend that one positive aspect of this pandemic is that it has revealed the perils and shortcomings of the teacher-centered, traditional education which colonizes students' minds, compromises their analytical abilities and, paradoxically, places them in a system of oppression which audits their ideas, limits their freedoms, and curtails their creativity. While Israeli occupation has proven to be an obstacle in the face of the Palestinian government's attempt to combat and contain the Corona crisis, on-line education, the sole arena that escapes this colonial system, has forced many instructors to give up their domination over the process of education and to create a more collaborative atmosphere of education that is based on dialogue, research and flexibility of the curriculum content. This study is designed to gauge English literature students' responses to this mode of digital learning. We interviewed a hundred students from six English literature programs between March and August, 2020. Thus, through critically examining students' answers, and by drawing on Freire's concepts of banking education, consciousness and dialogue, we propose that online education is an important step towards the decolonization of education and a call for a paradigm shift on the account that the existing paradigm of traditional education is stifling students' creativity and critical thinking.
Constructing undesirables: A critical discourse analysis of othering of Fulani nomads in the Ghanaian news media

The activities of Fulani nomads in Ghana have gained considerable media attention and engendered continuing public debate. In this paper, we analyze the prejudiced portrayals of the nomads in the Ghanaian news media, and how these contribute to an exclusionist and a discriminatory discourse that puts the nomads at the margins of Ghanaian society. The study employs a critical discourse analysis framework and draws on a dataset of 160 articles, including news stories, editorials and op-ed pieces. The analysis reveals that the nomads are discursively constructed as undesirables through an othering process that centers on three discourses: a discourse of dangerousness/criminalization, a discourse of alienization, and a discourse of stigmatization. This anti-nomad/Fulani rhetoric is evident in the choice of sensational headlines, alarmist news content, organization of arguments, and use of quotations. The paper concludes with a call for more balanced and critical news reporting on the nomads, especially since issues surrounding them border on national cohesion and security.
Unequal Impacts of Augmented Reality on Learning and Collaboration During Robot Programming with Peers

Augmented reality (AR) applications are growing in popularity in educational settings. While the effects of AR experiences on learning have been widely studied, there is relatively less research on understanding the impact of AR on the dynamics of co-located collaborative learning, specifically in the context of novices programming robots. Educational robotics are a powerful learning context because they engage students with problem solving, critical thinking, STEM (Science, Technology, Engineering, Mathematics) concepts, and collaboration skills. However, such collaborations can suffer due to students having unequal access to resources or dominant peers. In this research we investigate how augmented reality impacts learning and collaboration while peers engage in robot programming activities. We use a mixed methods approach to measure how participants are learning, manipulating resources, and engaging in problem solving activities with peers. We investigate how these behaviors are impacted by the presence of augmented reality visualizations, and by participants? proximity to resources. We find that augmented reality improved overall group learning and collaboration. Detailed analysis shows that AR strongly helps one participant more than the other, by improving their ability to learn and contribute while remaining engaged with the robot. Furthermore, augmented reality helps both participants maintain a common ground and balance contributions during problem solving activities. We discuss the implications of these results for designing AR and non-AR collaborative interfaces.
Transforming Digital Employee Experience with Artificial Intelligence

Employee expectations are changing significantly due to the increased digitalization and remote work in the last couple of years. This study emphasizes the growing role of Artificial Intelligence (AI) in Human Resources (HR) to design enhanced digital employee experience. A set of popular AI applications such as chatbots and virtual assistants in recruitment, career development, and employee engagement are provided along with their definitions. Considerations regarding their organizational implementation, including related concerns and potential benefits, are listed.
Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.
DynaVSR: Dynamic Adaptive Blind Video Super-Resolution

Most conventional supervised super-resolution (SR) algorithms assume that low-resolution (LR) data is obtained by downscaling high-resolution (HR) data with a fixed known kernel, but such an assumption often does not hold in real scenarios. Some recent blind SR algorithms have been proposed to estimate different downscaling kernels for each input LR image. However, they suffer from heavy computational overhead, making them infeasible for direct application to videos. In this work, we present DynaVSR, a novel meta-learning-based framework for real-world video SR that enables efficient downscaling model estimation and adaptation to the current input. Specifically, we train a multi-frame downscaling module with various types of synthetic blur kernels, which is seamlessly combined with a video SR network for input-aware adaptation. Experimental results show that DynaVSR consistently improves the performance of the state-of-the-art video SR models by a large margin, with an order of magnitude faster inference time compared to the existing blind SR approaches.