Reviewerly/exHarmony huggingface dataset:
High-accuracy real-time monitoring of solar radiation attenuation in commercial solar towers

The main objectives of this work are twofold, the immediate objective is to provide current commercial solar towers with a system able to monitor accurately the solar radiation attenuation, the midterm objective is to acquire, with the adequate and precise measurements, the necessary knowledge about the different phenomena involved in the atmospheric attenuation in order to correlate it with meteorological and weather conditions when possible. The purpose of this paper is to describe the basic design features and operational capabilities of a new system designed by CENER and the University of Zaragoza (patent P201830758) for measuring real-time atmospheric attenuation of solar radiation at surface level. It performs a direct measurement without needing any previous assumption about atmospheric conditions. The work will be complemented with the analysis of the influence of atmospheric aerosols and meteorological conditions at surface level in atmospheric attenuation (at the lower ∼300 m atmospheric layer).
Three-stream Very Deep Neural Network for Video Action Recognition

The purpose of this study is to determine whether fine-tuning very deep three-dimensional Convolutional Neural Network (3D CNN) that already pre-trained on an adequately large video dataset will give sufficient motion information for action recognition or still need to have supplementary information. We introduce a three-stream CNN that is based on two-dimensional (2D) and 3D kernels while leveraging successful pre-trained networks on ImageNet and Kinetics datasets. In order to analyze these streams, we fine-tune each on the HMDB-51 challenging dataset and show that supplementary motion information (optical flow and the proposed sparse trajectory image) are critical to action recognition despite using 3D CNN. Experimental outcomes determine that our network reaches 80.92% accuracy on the HMDB-51 dataset and its performance is comparable with the performance of state-of-the-art networks on this dataset.
Joint modeling for longitudinal set-inflated continuous and count responses

A joint mixture model for analyzing mixed longitudinal continuous and count data is presented. The continuous response is inflated in a set A, and a set-inflated normal (SIN) distribution is used as its distribution. The count response is inflated in a set B. B includes one or more points of sample space and a set-inflated power series (SIPS) distribution is used as its distribution. A full likelihood-based approach is used to obtain the maximum likelihood estimates of parameters via the EM algorithm. A random effects approach is applied to investigate the correlated longitudinal responses and correlated inflation mechanisms of each subject through time. Also, to consider the correlation between the mixed continuous and count responses of each individual at each time, the correlated random effects are used. In order to assess the performance of the model, some simulation studies are performed. An application of our models is illustrated for joint analysis of (1) number of days in the last month that the individual drank alcohol, and (2) weight of respondent for the first two waves of the American's Changing Lives survey.
Targeted Adaptable Sample for Accurate and Efficient Quantile Estimation in Non-Stationary Data Streams

The need to detect outliers or otherwise unusual data, which can be formalized as the estimation a particular quantile of a distribution, is an important problem that frequently arises in a variety of applications of pattern recognition, computer vision and signal processing. For example, our work was most proximally motivated by the practical limitations and requirements of many semi-automatic surveillance analytics systems that detect abnormalities in closed-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper, we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the absolute (rather than asymptotic) memory for storing observations is severely limited. We make several major contributions: (i) we derive an important theoretical result that shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data; (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings; (iii) we introduce a novel algorithm that implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity; and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic datasets and three large “real-world” streams acquired in the course of operation of an existing commercial surveillance system. Our results and their detailed analysis convincingly and comprehensively demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high-valued and the available buffer capacity severely limited.
Intelligent Vehicles in Urban Traffic – Communication Based Cooperation

One of the major problem in the cities nowadays is the constantly congested road network. In the future new technologies and opportunities will appear to solve this problem. One of the anticipated solutions may be the communication based co-operation of the autonomous driver-less vehicles.In the paper we propose such solution for the efficient control of the intersections in an urban environment. We define suitable communication protocols and algorithms to establish a cooperative intelligent agent organization of self-driving vehicles and traffic controllers.We verify its merits with simulations based on real network models. The results show great improvement in American-like regular urban road networks and also in the irregular European-like intersections in case of the extraordinary traffic situations.
Tilt-Scrolling: A Comparative Study of Scrolling Techniques for Mobile Devices

Scrolling is a frequent operation on a mobile screen. The current scrolling method for a mobile device is both time-consuming and fatigue-prone, especially for one-handed interaction mode. In this study, we systematically evaluated six tilt-based scrolling techniques together with the base line through a repeated measure experiment participated by twenty-one subjects. The experimental results revealed that tilt-based scrolling techniques were more suitable for one-handed interaction with higher interaction performance and were less fatigue-prone than the base line. Tilt scrolling techniques with adaptive reference points outperformed those with the horizontal reference point in terms of scrolling speed and accuracy. The Step control function had higher interaction efficiency than the other two functions but was more error-prone. The results of our study have some implications for the design of scrolling techniques on mobile screens.
Architecture challenges in the Android 3D graphics stack

The increasing traction of high-fidelity games on mobile devices is highlighting the challenges game developers have to face in order to optimize their content within the Android ecosystem.
Fuzzy based approach for diet prediction

In the current era, people are too busy to think about what they are eating and its effects on their health. Over the years there has been an accretion of such diseases due to the loss of nutrition owing to unhealthy diet followed on an everyday basis and motionless life. In this work a method is devised for creating a proper diet plan for people of each age group using data collected from many different sources. A diet list will be proposed to the user by calculating the necessary amount of carbohydrates, vitamins and minerals, milk protein, meat protein, fat and sugar according to the age group they fall in. Fuzzy logic is many-valued logic in which there are various input and output along with the member function. The work will propose a fuzzy logic technique which shows the accurately balanced diet for each age group.
Deployment and navigation of multiple robots using a self-clustering method and type-2 fuzzy controller in dynamic environments

In recent years, multiple robots have been successfully applied in various fields, including the handling of logistics factories, agriculture, and disaster relief. This study proposes a novel method for multi-robot deployment and navigation in dynamic environments. To address the problem of locatio n deployment, a grid-based method was used to simplify environmental input information, and a self-clustering method was used to adjust location deployment. To address the problem of navigation, a behavior manager was used as a navigation strategy to control the towards-goal behavior and wall-following behavior (WFB) of mobile robots. An interval type-2 fuzzy controller based on improved particle swarm optimization (IPSO) was proposed to implement the WFB control. The proposed IPSO improved the search ability and enhanced the convergence speed of traditional PSO. Additionally, an escape mechanism was proposed to avoid a dead cycle. Experimental results show that the proposed IPSO is superior to other methods used for WFB and navigation control.
Aspect Based Hierarchical System: A Fine-grained Sentiment Analysis System in Edge Computing

Aspect based sentiment analysis (ABSA) has been an important field due to its practicality that can dig out the sentiment polarities of different aspects in a sentence. For a sentence, "The service is nice while the food is bad", ABSA can obtain positive with aspect service and negative with aspect food. With this feature, ABSA related services can help people understand product reviews and community status in a more convenient way. However, we have analyzed deploying ABSA models in cloud computing is not a good choice due to the network resource consumption, central server load and the security consideration. We develop a system called aspect based hierarchical system (ABHS) whose basis is edge computing. ABHS can mitigate mentioned disadvantages of cloud computing and effectively provide services like recognizing the aspects in reviews, figuring out the sentiment polarity of each aspect in a review as well as generating the overall sentiment polarity of an aspect in all reviews. We also propose a scheme to deploy ABHS in edge computing, which can take the advantages of hierarchy, distribution, low latency, and heterogeneity. Experiments show our ABHS can effectively recognize the aspects and obtain useful results.
Forecasting Solar Potential Using Support Vector Regression

Solar energy is one of the most commonly used renewable energy resources. To obtain reliable output from solar energy, prediction of solar radiation is necessary. In this paper, a solar radiation prediction model has been developed for New Alipore, Kolkata. Easily available meteorological parameters like temperature, pressure and humidity have been utilized as inputs, to build the prediction model. Two years data (2011-2012) have been used to develop the Support Vector Regression (SVR) based solar radiation prediction model. The results obtained from the prediction model have been validated with the help of statistical metrics, Root-Mean-Square Error (RMSE) and Coefficient of Determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The results signifies that the performance of the developed model is better in comparison with the models existing in the literature.
Short Text Clustering Algorithm Based on Frequent Closed Word Sets

The text mining of micro-blog topic information can effectively obtain the attention degree of internet users for news events. It is of great significance in the field of public opinion monitoring and analysis. At the situation of the algorithm of traditional frequent word set is suitable for long text information clustering, this paper proposes to mine top-K frequent corpus in short text database and then to divide micro-blog topic texts covering the same frequent word sets into the same cluster. Combined with the largest frequent word-sets for similarity calculation, the overlapped document is re-divided to achieve micro-blog short text clustering. The experimental results of micro-blog topic dataset and the comparison with K-means clustering algorithm show that the proposed algorithm can effectively solve the sparseness and high-dimension problem of micro-blog topic short text clustering and greatly improve the micro-blog short text clustering effect.
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification

Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration of their computational algorithms due to their highly dynamic operation. Developing a personalized and adaptive machine learning model requires real-time reconfiguration of the model. Due to stringent computation and memory constraints of these embedded sensors, the training/re-training of the computational algorithms need to be memory- and computation-efficient. In this paper, we propose a framework, based on the notion of online learning, for real-time and on-device machine learning training. We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers. Our results, based on Pegasos online learning, demonstrate that the proposed approach achieves 97% accuracy in detecting activities of varying intensities using a limited memory while power usages of the system is reduced by more than 40%.
Performance Evaluation of Faster R-CNN for On-Road Object Detection on Graphical Processing Unit and Central Processing Unit

On road object detection is very active research area for autonomous cars driving, pedestrian detection etc. Despite recent momentous enhancements, on road object detection is still a challenge that calls for more accuracy. In this study, we present the implementation of Faster R-CNN training for on road object detection and recognition. We have trained the model with our own dataset categorized into three classes such as car, cycle, pedestrian and test against three different datasets, such as KITTI dataset, video of Beijing road and also tested on our dataset to check the performance of the Faster R-CNN on GPU and CPU. we used this data to utilize Faster R-CNN and to analyze the impact of several factors like training datasets size, pre training model, iteration time, and training methods on the detection results of vehicle and pedestrians. Training the Faster R-CNN model by our own dataset on GPU has took 12 h while CPU took 11 days and 9 h to complete 50000 iterations. The GPU processed the video with a frame rate of 8 fps while CPU processed with 4 fps. The result shows that Faster R-CNN on GPU has higher mean Average Precision than CPU.
Organizational and Technical Conditions for the Implementation of Educational Programs of the University Using Open Online Courses of the National Platform for Open Education

The article considers the current state of open online courses in the educational process. The organizational and technical conditions for the implementation of the educational program in a network form have been developed using the online course of the National Platform for Open Education. The rights and duties of the consumer institution on the use of open online courses are defined. The administrative-legal field for all participants of the educational process, organized in a network form, is concretized. The necessity of creating a coordinating group on open education in the university is substantiated. Requirements for online courses are revealed. The results of a pedagogical experiment on the organization of training and the conduct of reflexively evaluative activities of students after completing the online course are described. The evaluation of the adequacy of the proposed organizational and technical conditions for the implementation of the educational program in a network form was carried out using an online course.
Mask-Most Net: Mask Approximation Based Multi-oriented Scene Text Detection Network

In this paper, a novel multi-task cascade framework, which jointly takes the detection and the segmentation into account, is presented for the scene text detection. To address the issue of multi-oriented scene text detection, we propose an instance-level mask approximation method through the auxiliary regression task on center and corner points. Specifically, the text instance in the image is first coarsely detected, followed by a contextual module which can capture more accurate instances. To cope with the scale variation existing in these detected instances, a combination of high-level semantic and low-level features is further exploited, achieving more robust and better performance. A series of experiments conducted on different benchmark datasets demonstrate the effectiveness of the proposed method.
Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space

Image processing is a very important technical support in robotic vision. As a preprocessing step for image processing, superpixel segmentation is one of the significant branches of image segmentation. Simple linear iterative clustering (SLIC) algorithm, as a widely used superpixel segmentation algorithm, can help to deal with boundary adherence and reduce computational cost for image segmentation. However, the segmentation result of the original SLIC algorithm fails to adhere well to boundary of object, causing undersegmentation sometimes. Therefore, the work proposed an improved method based on SLIC to address the problem of undersegmentation. First, HSL color space was introduced to have a better recognition and processing of color instead of CIELAB color space. In addition, adding flexible combinations of weight coefficient for HSL can achieve different results. Finally, an edge detection strategy was used to enhance the accuracy of superpixel segmentation. The quantification effect of the proposed method was verified using the dataset BSDS500. The experimental results show that the improved algorithm has better accuracy and efficiency.
Open Compass

Artificial intelligence (AI) has immense potential spanning research and industry. AI applications abound and are expanding rapidly, yet the methods, performance, and understanding of AI are in their infancy. Researchers face vexing issues such as how to improve performance, transferability, reliability, comprehensibility, and how better to train AI models with only limited data. Future progress depends on advances in hardware accelerators, software frameworks, system and architectures, and creating cross-cutting expertise between scientific and AI domains. Open Compass is an exploratory research project to conduct academic pilot studies on an advanced engineering testbed for artificial intelligence, the Compass Lab, culminating in the development and publication of best practices for the benefit of the broad scientific community. Open Compass includes the development of an ontology to describe the complex range of existing and emerging AI hardware technologies and the identification of benchmark problems that represent different challenges in training deep learning models. These benchmarks are then used to execute experiments in alternative advanced hardware solution architectures. Here we present the methodology of Open Compass and some preliminary results on analyzing the effects of different GPU types, memory, and topologies for popular deep learning models applicable to image processing.
Biorthogonal Wavelet Filtration of Signals Used in the Industrial Automation Systems

Compared to orthogonal wavelets, biorthogonal wavelet functions allow more degrees of freedom. In the present work the application of biorthogonal and reverse biorthogonal functions for signal filtration is analyzed. Signals with high notch noise-to-signal ratio are filtered. The obtained results are compared based on different criteria, and corresponding conclusions are made.
Quadratic residues and related permutations

Let p be an odd prime. For any p-adic integer a we let a‾ denote the unique integer x with −p/2<x<p/2 and x−a divisible by p. In this paper we study some permutations involving quadratic residues modulo p. For instance, we consider the following three sequences.A0:12‾,22‾,⋯,((p−1)/2)2‾,A1:a1‾,a2‾,⋯,a(p−1)/2‾,A2:g2‾,g4‾,⋯,gp−1‾, where g∈Z is a primitive root modulo p and 1≤a1<a2<⋯<a(p−1)/2≤p−1 are all quadratic residues modulo p. Obviously Ai is a permutation of Aj and we call this permutation σi,j. Sun obtained the sign of σ0,1 when p≡3(mod4). In this paper we give the sign of σ0,1 and determine the sign σ0,2 when p≡1(mod4).
Hardware Implementation of High Speed Bartlett Spectral Density Estimator Based on R4MDC FFT

Power spectrum analysis is a crucial matter in signal processing. In communication and biomedical signal processing, designing appropriate custom hardware for estimating signal is under everyday research. High frequency power spectrum estimator with embedded systems capabilities and for biomedical applications is the purpose of the current research. Nonparametric power spectral density estimation is the approach of this research in which no specific model is assumed in prior. This paper proposes a low power, high speed hardware with 125 MHz clock frequency rate, which its fast Fourier transform (FFT) engine is implemented with a radix-4 MDC structure. Bartlett method has been implemented in this research. The Artix-7 is the utilized FPGA in this research and VHDL is the used hardware design language. The proposed design computes Bartlett Power Spectral Density (PSD) estimation with only two added hardware units to a modified FFT engine. The offered limited power hardware performs well in small Word Length and high clock frequency.