Reviewerly/exHarmony huggingface dataset:
Diffuse solar radiation models for different climate zones in China: Model evaluation and general model development

In this paper, nine new diffuse fraction and eight new diffuse coefficient models were developed by introducing precipitation as one of the model parameters. Concurrently, thirty-four existing diffuse fraction and eleven existing diffuse coefficient models were reviewed. The existing and proposed models were compared to assess their applicability in different climate zones in China. According to the best models and climate zones, general models were established. Overall, introducing precipitation could effectively improving the underestimation problems of daily diffuse solar radiation; for diffuse fraction models, precipitation had stronger correlation with diffuse radiation than relative humidity in humid areas and weaker correlation in arid areas. For diffuse coefficient models, the models incorporating precipitation could obtain better accuracy than temperature and relative humidity in all climate zones under similar meteorological parameters. Therefore, precipitation should be preferentially adopted, followed by temperature and relative humidity. The general models developed in this paper performed well in estimating the daily diffuse solar radiation. The average coefficient of determination, root mean square error and mean absolute bias error of diffuse fraction general models were 0.82, 1.44 MJm−2 d−1 and 1.04 MJm−2 d−1, respectively; while the corresponding values of diffuse coefficient general models were 0.69, 1.98 MJm−2 d−1 and 1.54 MJm−2 d−1, respectively. The general models could represent the daily diffuse solar radiation estimation of stations without diffuse solar radiation records.
Avoiding drill-down fallacies with <i>VisPilot</i>

As datasets continue to grow in size and complexity, exploring multi-dimensional datasets remain challenging for analysts. A common operation during this exploration is drill-down-understanding the behavior of data subsets by progressively adding filters. While widely used, in the absence of careful attention towards confounding factors, drill-downs could lead to inductive fallacies. Specifically, an analyst may end up being "deceived" into thinking that a deviation in trend is attributable to a local change, when in fact it is a more general phenomenon; we term this the drill-down fallacy. One way to avoid falling prey to drill-down fallacies is to exhaustively explore all potential drill-down paths, which quickly becomes infeasible on complex datasets with many attributes. We present VisPilot, an accelerated visual data exploration tool that guides analysts through the key insights in a dataset, while avoiding drill-down fallacies. Our user study results show that VisPilot helps analysts discover interesting visualizations, understand attribute importance, and predict unseen visualizations better than other multidimensional data analysis baselines.
Explaining recommendations in an interactive hybrid social recommender

Hybrid social recommender systems use social relevance from multiple sources to recommend relevant items or people to users. To make hybrid recommendations more transparent and controllable, several researchers have explored interactive hybrid recommender interfaces, which allow for a user-driven fusion of recommendation sources. In this field of work, the intelligent user interface has been investigated as an approach to increase transparency and improve the user experience. In this paper, we attempt to further promote the transparency of recommendations by augmenting an interactive hybrid recommender interface with several types of explanations. We evaluate user behavior patterns and subjective feedback by a within-subject study (N=33). Results from the evaluation show the effectiveness of the proposed explanation models. The result of post-treatment survey indicates a significant improvement in the perception of explainability, but such improvement comes with a lower degree of perceived controllability.
College-aged users behavioral strategies to reduce envy on social networking sites: A cross-cultural investigation

Social networking sites (SNSs) are central to social interaction and information sharing in the digital age. However, consuming social information on SNSs invites social upward comparisons with highly socially desirable profile representations, which easily elicits envy in users and leads to unfavorable behaviors on SNSs. This in turn can erode the subjective well-being of users and the sustainability of the SNS platform. Therefore, this paper seeks to develop a better theoretical understanding of how users respond to envy on SNSs. We review literature on envy in offline interactions to derive three behavioral strategies to reduce envy, which we then transfer to the SNS context (self-enhancement, gossiping, and discontinuous intention). Further, we propose a research model and examine how culture, specifically individualism-collectivism, affects the relationship between envy on an SNS and the three strategies. We empirically test the variance-based structural equation model through survey data collected of Facebook users from Germany and Hong Kong. Our findings provide first insights into the link between envy on SNSs, related behavioral strategies and the moderating role of individualism for self-enhancement.
Gesture Recognition Based on CNN and DCGAN for Calculation and Text Output

In the past few years, with the continuous improvement of hardware conditions, deep learning had performed well in solving many problems, such as visual recognition, speech recognition, and natural language processing.In recent years, human-computer interaction behavior has appeared more and more in daily life.Especially with the rapid development of computer vision technology, the human-centered human-computer interaction technology is bound to replace computer-centered human-computer interaction technology.The study of gesture recognition is in line with this trend, and gesture recognition provides a way for many devices to interact with humans.The traditional gesture recognition method requires manual extraction of feature values, which is a time-consuming and laborious method.In order to break through the bottleneck, we propose a new gesture recognition algorithm based on the convolutional neural network and deep convolution generative adversarial networks.We apply this method to expression recognition, calculation, and text output, and achieve good results.The experiments show that the proposed method can train the model to identify with fewer samples and achieve better gesture classification and detection effects.Moreover, this gesture recognition method is less susceptible to illumination and background interference.It also can achieve an efficient real-time recognition effect.
Deep ${Q}$ -Network-Based Route Scheduling for TNC Vehicles With Passengers’ Location Differential Privacy

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning-based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment, such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network-based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.
Synchronization and identification of nonlinear systems by using a novel self-evolving interval type-2 fuzzy LSTM-neural network

Nonlinear systems widely exist in the real world. Researches on the synchronization and identification of nonlinear systems have both theoretical and practical interests. However, since the pseudo-random and parameter sensitivity, some nonlinear dynamics, such as chaotic systems, are difficult to achieve the required regression approximation. By means of the fuzzy-neural structure and long short-term memory (LSTM) mechanism, this paper proposes a novel inference structure of the self-evolving interval type-2 fuzzy LSTM-neural network (eIT2FNN-LSTM) for the synchronization and identification of nonlinear dynamics. In order to tackle with the time-dependency data generated by nonlinear systems, recurrent neural fuzzy systems with LSTM structure is introduced into type-2 fuzzy neural networks, where, by means of gate mechanism, a recurrent structure in the time dimension by feeding the rule firing strength of each rule back to itself is implemented. Besides, an online rule generation algorithm based on dynamic density clustering is utilized to achieve structural updates, which can meet the need of high frequency data process in reality. By incorporating direct adaptive interval type-2 LSTM fuzzy control scheme and sliding mode approach, two chaotic systems with external disturbance noise or/and random-varying parameters can be synchronized based on Lyapunov stability criterion, where two methods, i.e., the gradient training and particle swarm optimization (PSO) algorithm are used. In order to further verify the universality of the proposed scheme, besides nonlinear system, real-life datasets are also utilized to verify the effectiveness of the proposed fuzzy-neural inference system.
Electric sail trajectory design with Bezier curve-based shaping approach

The aim of this paper is to propose a shape-based method in which the concept of Bezier curve is used to efficiently design the three-dimensional interplanetary trajectory of a spacecraft whose primary propulsion system is an Electric Solar Wind Sail. The latter is a propellantless propulsion concept that consists of a spinning grid of tethers, kept at a high positive potential by a power source and maintained stretched by the centrifugal force. The proposed approach approximates the time variation of the components of the spacecraft position vector using a Bezier curve function, whose geometric coefficients are calculated by optimizing the total flight time with standard numerical methods and enforcing the boundary conditions of a typical interplanetary rendezvous mission. The paper also discusses a geometrical approach to include, in the optimization process, the propulsive acceleration vector constraints obtained with the latest Electric Solar Wind Sail thrust model.
Correlation Filter-Based Visual Tracking for UAV with Online Multi-Feature Learning

In this paper, a novel online learning-based tracker is presented for the unmanned aerial vehicle (UAV) in different types of tracking applications, such as pedestrian following, automotive chasing, and building inspection. The presented tracker uses novel features, i.e., intensity, color names, and saliency, to respectively represent both the tracking object and its background information in a background-aware correlation filter (BACF) framework instead of only using the histogram of oriented gradient (HOG) feature. In other words, four different voters, which combine the aforementioned four features with the BACF framework, are used to locate the object independently. After obtaining the response maps generated by aforementioned voters, a new strategy is proposed to fuse these response maps effectively. In the proposed response map fusion strategy, the peak-to-sidelobe ratio, which measures the peak strength of the response, is utilized to weight each response, thereby filtering the noise for each response and improving final fusion map. Eventually, the fused response map is used to accurately locate the object. Qualitative and quantitative experiments on 123 challenging UAV image sequences, i.e., UAV123, show that the novel tracking approach, i.e., OMFL tracker, performs favorably against 13 state-of-the-art trackers in terms of accuracy, robustness, and efficiency. In addition, the multi-feature learning approach is able to improve the object tracking performance compared to the tracking method with single-feature learning applied in literature.
Personal Data Trading Scheme for Data Brokers in IoT Data Marketplaces

With the widespread use of the Internet of Things, data-driven services take the lead of both online and off-line businesses. Especially, personal data draw heavy attention of service providers because of the usefulness in value-added services. With the emerging big-data technology, a data broker appears, which exploits and sells personal data about individuals to other third parties. Due to little transparency between providers and brokers/consumers, people think that the current ecosystem is not trustworthy, and new regulations with strengthening the rights of individuals were introduced. Therefore, people have an interest in their privacy valuation. In this sense, the willingness-to-sell (WTS) of providers becomes one of the important aspects for data brokers; however, conventional studies have mainly focused on the willingness-to-buy (WTB) of consumers. Therefore, this paper proposes an optimized trading model for data brokers who buy personal data with proper incentives based on the WTS, and they sell valuable information from the refined dataset by considering the WTB and the dataset quality. This paper shows that the proposed model has a global optimal point by the convex optimization technique and proposes a gradient ascent-based algorithm. Consequently, it shows that the proposed model is feasible even if the data brokers spend costs to gather personal data.
Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection

Feature selection is a process that provides model extraction by specifying necessary or related features and improves generalization. The Artificial Bee Colony (ABC) algorithm is one of the most popular optimization algorithms inspired on swarm intelligence developed by simulating the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently proposed high level automatic programming technique for a Symbolic Regression (SR) problem based on the ABC algorithm. In this paper, a new feature selection method based on ABCP is proposed, Multi Hive ABCP (MHABCP) for high-dimensional SR problems. The learning ability and generalization performance of the proposed MHABCP is investigated using synthetic and real high-dimensional SR datasets and is compared with basic ABCP and GP automatic programming methods. Experimental results show that MHABCP has better performance choosing relevant features in high dimensional SR problems and generalization than other methods.
Deploying a Novel 5G-Enabled Architecture on City Infrastructure for Ultra-High Definition and Immersive Media Production and Broadcasting

With the massive growth of cutting-edge media services, such as ultra-high definition video and immersive media (i.e., virtual and augmented reality), demand for large investments in a scalable, ubiquitous, and robust communication infrastructure and services increases enormously. The H2020 5GCity project aims to provide a solution for such issues by designing, developing, and deploying a sliceable, distributed cloud/edge and radio platform with neutral hosting capability to support the sharing between information technology infrastructure owners and media service providers (i.e., vertical media actors). In this paper, we initially introduce the essential benefits of the 5GCity technology and neutral host model to facilitate the rise of highly demanding media use cases (UCs) and its implication on how service providers typically operate (in terms of business model). Then, we show how the 5GCity architecture and infrastructure, in light of certain key performance indicators, address this demand through three media UCs (namely related to “video acquisition and production at the edge,” “immersive services,” and “mobile production and transmission”) and we explain how they are implemented and deployed in real citywide pilots (in Bristol, Lucca, and Barcelona) to demonstrate the benefits for infrastructure owners and media service providers.
Cuteness as a ‘Dark Pattern’ in Home Robots

Dark patterns are a recent phenomenon in the field of interaction design, where design patterns and behavioral psychology are deployed in ways that deceive the user. However, the current corpus of dark patterns literature focuses largely on screen-based digital interactions and should be expanded to include home robots. In this paper, we apply the concept of dark patterns to the `cute' aesthetic of home robots and suggest that their design constitutes a dark pattern in HRI by (1) emphasizing short-term gains over long-term decisions; (2) depriving users of some degree of conscious agency at the site of interaction; and (3) creating an affective response in the user for the purpose of collecting emotional data. This exploratory paper expands the current library of dark patterns and their application to new technological interfaces into the domain of home robotics in order to establish the grounds for an ethical design practice in HRI.
Quality Assessment for Video With Degradation Along Salient Trajectories

With the rapid growth of digital video through the Internet, a reliable objective video-quality assessment (VQA) algorithm is in great demand for video management. Motion information plays a dominant role for video perception, and the human visual system (HVS) is able to track moving objects effectively with eye movement. Moreover, the middle temporal area of the brain is selective for moving objects with particular velocities. In other words, visual contents that are along the motion trajectories will automatically attract our attention for dedicated processing. Inspired by the motion-related process in the HVS, we suggest analyzing the degradation along attended motion trajectories for VQA. The characteristic of motion velocity along each trajectory is analyzed for temporal quality measurement. Meanwhile, visual information along each trajectory is extracted for joint spatial-temporal quality measurement. Finally, considering the spatial-quality degradation from each frame, a novel full-reference assessor along salient trajectories (FAST) for VQA (which combines the spatial, temporal, and joint spatial-temporal quality degradations) is introduced. Experimental results on five publicly available VQA databases demonstrate that the proposed FAST VQA model performs consistently with the subjective perception. The source code of the proposed method is available at http://web.xidian.edu.cn/wjj/paper.html.
Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human-machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.
New fruit fly optimization algorithm with joint search strategies for function optimization problems

An enhanced fruit fly optimization algorithm (FOA) with joint search strategies named JS-FOA is proposed to optimize continuous function problems. First, a collaborative group search, which includes a new parameter, is conducted to obtain the critical value. Second, a new search strategy similar to biological memory, namely, memory move direction, is proposed to improve solution accuracy. Third, a gradient descent search is used in the collaborative group search to ensure that it does not fall into a local optimum. Finally, a new function, which is similar to the excitation function in a neural network, is proposed to combine the three search strategies. To test the robustness and convergence of the proposed JS-FOA, we used 29 complex continuous benchmark functions. Results show that the proposed JS-FOA outperforms other heuristic algorithms for most functions. The performance of JS-FOA is also evaluated for different parameter values and the results show that parameter values affect convergence speed within a certain range, but do not change the convergence accuracy for the continuous benchmark functions. The proposed JS-FOA may potentially solve high-dimensional optimization problems.
Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms

An algorithm of k-means hybridized with support vector regression (SVR) has been developed and utilised for the prediction of daily global solar radiation. The k-means was used to cluster the seasonal data into separate clusters independent of the seasons. Two observations, each from the group of wet (rainy) seasons and dry seasons, were used as the initial centroids for the clustering process. Subsequently, the clustered data were subdivided into four sky conditions as each serve as input to seven corresponding SVR models developed. The proficiency of this model was verified using six years of meteorological data of Ibadan (2010–2015) to predict daily global solar radiation for the future years (2016–2017). The predicted values were also compared with the forecasted values obtained from three established models i.e. ANN, Angstrom–Prescott and ARMA models. The performance of the model was investigated using different statistical test of fits (i.e. RMSE, RRMSE, MAPE and R2). Some of the key results revealed that the proposed k-means-SVR has the capability to predict the global solar irradiation for the year 2016 with overall accuracy of R2 of 0.9816, RMSE of 0.4275 MJ/m2/day, RRMSE 2.6515% and MAPE of 1.7928%. Similarly, the model was able to predict the 2017 global solar irradiation with the overall values of R2, RMSE, RRMSE and MAPE of 0.9842, 0.434 MJ/m2/day, 2.7498% and 1.795% respectively. The proposed model was also found to possess better predictive capability compared to the existing models, thus making it applicable in energy planning.