Reviewerly/exHarmony huggingface dataset:
Densenet-Based Multi-scale Recurrent Network for Video Restoration with Gaussian Blur

Video cameras are now commonplace and available, and videos can be obtained almost everywhere at anytime. However, due to turbulence or thermal effects of air, blurring occurs during image acquisition. Removing these artifacts from the blurry recordings is a highly ill-posed problem as neither the sharp image nor the blur kernel is known. Propagating information between multiple consecutive blurry observations can help restore the desired sharp video. In this work, we propose an efficient approach to produce a significant amount of realistic training data and introduce a novel multi-scale recurrent network architecture to deblur frames taking temporal information into account. The experimental results demonstrate the effectiveness of the proposed method.
A spatial multivariable SVR method for spatiotemporal fuzzy modeling with applications to rapid thermal processing

Many industrial processes have significant spatiotemporal dynamics and they are usually called distributed parameter systems (DPSs). Modeling such system is challenging due to its nonlinearity, time-varying dynamics, and spatiotemporal coupling. Using model reduction techniques, traditional DPS modeling methods usually reduce an infinite-dimensional system to a finite-dimensional system, which leads to unknown nonlinearity and unmodeled dynamics. The modeling method and the established model are hard to understand. Here, we propose a spatial multivariable support vector regression (SVR) based three-domain (3-D) fuzzy modeling method for complex nonlinear DPSs. The proposed 3-D modeling method integrates the time-space separation and time-space synthesis into a 3-D fuzzy model. Therefore, it does not require model reduction and owns the capability of linguistic interpretability. A spatial multivariable SVR with spatial kernel functions is proposed to deal with spatiotemporal data. The spatial fuzzy basis functions from a 3-D fuzzy model are spatial kernel functions for a spatial multivariable SVR, which satisfy Mercy theorem. Hence, the spatial multivariable SVR can be directly employed to build up a complete 3-D fuzzy rule-base of the 3-D fuzzy model. The proposed modeling method integrates the merits of learning ability from a spatial multivariable SVR and fuzzy space processing and fuzzy linguistic expression from a 3-D fuzzy model. The proposed 3-D fuzzy modeling method is successful applied to a simulated rapid thermal processing system. In comparison with several newly developed modeling methods for DPSs, the simulation results validate the superiority of the proposed modeling method.
Coupled Squeeze-and-Excitation Blocks Based CNN for Image Compression

Recent researches have shown that deep convolutional neural networks (CNN) have achieved promising results in the field of image compression. In this paper, we propose an end-to-end image compression framework based on effective attention modules. In the proposed method, two channel attention mechanisms are employed jointly. The first is the Squeeze-and-Excitation block (SEblock) in the encoder. The other is the novel inversed SEblock (ISEblock) placed in decoder. These blocks, named coupled SEblocks, are placed behind the convolutional layer in both encoder and decoder. By using SEblocks, the encoder learns the interdependencies between different channels and the feature maps can be better distributed after entropy coding. In decoder, the inversed SEblock is employed which adaptively learns the weights and divides weights between the channels to supplement information compressed from the encoder. The whole network is trained as a joint rate-distortion optimization by using a subset of the ImageNet dataset. We evaluate our method on public Kodak test set. At low bit rates, our approach outperforms the existing Ballè’s, JPEG, JPEG2000 and WebP on multi-scale structural similarity (MS-SSIM) and gets good visual qualities for all images at test set.
SecHadoop: A Privacy Preserving Hadoop

With the generation of vast amounts of data, there has been a tremendous need for processing the same in an economical way. MapReduce paradigm provides an economical processing of huge datasets in an effective way. Hadoop is a framework for managing huge amounts of data, and facilitates parallel computations on data using commodity hardware, through an integration of MapReduce paradigm with the HDFS file system. Due to intrinsic data divisions during parallel processing, there is a possibility of data leaks. Thus, in the context of Hadoop, if processing has to keep the privacy invariant over the computation, it is necessary to guarantee privacy not only of the MapReduce process but also assure that the HDFS file system does leak any information. The focus of our work is on data security and privacy in such cloud environments. Our main thrust is to preserve data confidentiality and privacy as per specifications notwithstanding data divisions or scheduling for fault tolerance. We realise privacy invariance on Hadoop by monitoring the information flow from subjects to objects created in Hadoop using the readers writers flow model (RWFM). In this paper, we describe the design, implementation and performance of a security enhanced Hadoop, called SecHadoop. We illustrate our approach with various case studies corresponding to infection of map/reduce tasks, failure of nodes etc., and demonstrate how end-to-end security of programs is realised. It is further shown that the overall overhead is less than 5% on single/multi-node setup.
Analysis of the satisfaction degree of students participating in international educational projects

The article is based on the results of joint project between the University of Southern Denmark, Denmark, Southern Federal University, Russia and University of Østfold, Norway, “Cooperation active student learning, NCM-RU-2015/10060”, an international educational project aimed at intercultural skills development on the basis of a specific discipline “Innovation Management” at higher education level. Project training simulates real professional research situations in order to solve complex tasks. At the final stage of the project a student satisfaction survey was conducted in order to obtain some information about strengths and weaknesses of the present project. Our customized student feedback survey requires Danish, Norwegian and Russian students who have participated in the project to make an overall statement about their satisfaction with the participation in the project. Students’ satisfaction is a multidimensional process which is influenced by different factors, so survey respondents were asked to rate aspects of their educational, research and communication experience, from skill development and personal growth to quality of curriculum and instruction. Determining which features of the student experience in international teamwork are most closely related to satisfaction may provide information about actions that can be taken to maintain high level of project planning and implementation in the future.
A Fast Neural Network Multi-label Classification Algorithm Based on Approximate Extreme Points

In order to solve the problem that the training time of existing neural network multi-label classification algorithms is too long, we propose a new BP neural network multi-label classification (BP-AEPML) algorithm based on approximate extreme points. Firstly, the original multi-label data set is transformed into multiple separate binary sub data sets using binary relevance transformation strategy. And then the representative data set of each binary sub data set is extracted using approximate extreme points. Finally, the representative data set is trained with BP neural network. To validate and evaluate the classification results and training time of the proposed algorithm, three public data sets are used to compared with traditional BP neural network multi-label classification algorithm, basic SVM multi-label classification algorithm and ML-BVM algorithm. The experiment results shows that the classification results of BPAEPML is similar to that of the traditional BP neural network multi-label classification algorithm and a little better than that of basic SVM multi-label classification algorithm and ML-BVM algorithm. At the same time, the training time is greatly reduced.
Analyzing Tagore's Emotion With the Passage of Time in Song-Offerings

The emotions of humans can be observed through tears, smiles, etc. The emotion of poets is reflected through poetry/songs. The works of a poet give philosophical insights about the beauty and mystery of nature, socio-economic conditions of that era, besides his personal state of mind. In the proposed work ‘Song- Offerings': A collection of poems and songs composed by Rabindranath Tagore, for which, Tagore received the Nobel Prize for literature in 1913, has been analyzed. Earlier, most of the research work on Song-Offerings was based on Zipf's law or bibliometric laws. This article analyzes the changes in Tagore's emotion in Song-Offerings with the passage of time (1895-1912). Emotions are analyzed based on the Arousal-Valence Model. To analyze the arousal state, ‘Plutchik's' emotion model has been employed and to find the valence, a Fuzzy-based model has been engaged. The work reveals that the emotions of the poet gradually mellows with the passage of time barring some transitional time, nevertheless, poet submission towards almighty remains unchanged during this period.
Classification of Daily Activities Using an Intelligent Tip for Crutches

Individualization of rehabilitation therapies for gait recovery is usually guided by subjective assessments based on the perception of the patient or the therapist. The use of sensorized devices can provide more objective data to adapt the therapy to each patient. In this work, an approach to recognise different daily activities is proposed based on the data captured by an instrumented tip for crutches. The developed approach is based on two steps: a pre-processing based on obtaining a set of statistical indicators, and an artificial neural network that process them. The proposed methodology has been tested in a group of 13 volunteers, allowing to recognize three critical daily activities (walking, standing still and going up/down stairs) with a success rate of 88%.
Overcoming Limitations of GPGPU-Computing in Scientific Applications

The performance of discrete general purpose graphics processing units (GPGPUs) has been improving at a rapid pace. The PCIe interconnect that controls the communication of data between the system host memory and the GPU has not improved as quickly, leaving a gap in performance due to GPU downtime while waiting for PCIe data transfer. In this article, we explore two alternatives to the limited PCIe bandwidth, NVIDIA NVLink interconnect, and zero-copy algorithms for shared memory Heterogeneous System Architecture (HSA) devices. The OpenCL SHOC benchmark suite is used to measure the performance of each device on various scientific application kernels.
Modified FP-Growth: An Efficient Frequent Pattern Mining Approach from FP-Tree

Prefix-tree based FP-growth algorithm is a two step process: construction of frequent pattern tree (FP-tree) and then generates the frequent patterns from the tree. After constructing the FP-tree, if we merely use the conditional FP-trees (CFP-tree) to generate the patterns of frequent items, we may encounter the problem of recursive CFP-tree construction and a huge number of redundant itemset generation. Which also leads to huge search space and massive memory requirement. In this paper, we have proposed a new data structure layout called Modified Conditional FP-tree (MCFP-tree). Moreover, we have proposed a new pattern growth algorithm called Modified FP-Growth (MFP-Growth), which uses both top-down and bottom-up approaches to efficiently generate the frequent patterns without recursively constructing the MCFP-tree. During mining phase only one MCFP-tree is maintained in main memory at any instance and immediately deleted or discarded from the memory after performing the mining. From the experimental analysis, it is noticed that the proposed MFP-Growth algorithm requires less memory to construct the MCFP-tree as compared to conditional FP-tree. Moreover, the execution of the MFP-Growth method is found significantly faster than the traditional FP-Growth as it does not generate redundant patterns.
Joint Instance Segmentation of Obstacles and Lanes Using Convolutional Neural Networks

Autonomous vehicles aim at higher levels of intelligence to recognize all the elements in the surrounding environment; in order to be able to make decisions efficiently and in real time. For this reason, a convolutional neural networks capable of perform semantic segmentation of these elements have been implemented. In this work it is proposed to use the ERFNet architecture to segment the main obstacles and lanes in a road environment. One of the requirements for training this type of networks is to have a complete and large dataset with these two types of labels. In order to avoid manual labeling, an automatic way of carrying out this process is proposed, using convolutional neural networks and different dataset already labeled. The generated dataset contains 19000 images tagged with obstacles and lanes, to be used to train a network of ERFnet architecture. From the experiment, the obtained results show the performance of the proposed approach providing accuracy of 74.42%.
Creating Figures in R that Meet the AFS Style Guide: Standardization and Supporting Script

FisheriesVolume 44, Issue 11 p. 539-544 Essay Creating Figures in R that Meet the AFS Style Guide: Standardization and Supporting Script Hayley C. Glassic, Corresponding Author Hayley C. Glassic hcg0509@gmail.com Montana Cooperative Fishery Research Unit, Department of Ecology, Montana State University, Bozeman, MT, 59717Search for more papers by this authorKurt C. Heim, Kurt C. Heim Department of Ecology, Montana State University, Bozeman, MTSearch for more papers by this authorChristopher S. Guy, Christopher S. Guy U.S. Geological Survey, Montana Cooperative Fishery Research Unit, Department of Ecology, Montana State University, Bozeman, MTSearch for more papers by this author Hayley C. Glassic, Corresponding Author Hayley C. Glassic hcg0509@gmail.com Montana Cooperative Fishery Research Unit, Department of Ecology, Montana State University, Bozeman, MT, 59717Search for more papers by this authorKurt C. Heim, Kurt C. Heim Department of Ecology, Montana State University, Bozeman, MTSearch for more papers by this authorChristopher S. Guy, Christopher S. Guy U.S. Geological Survey, Montana Cooperative Fishery Research Unit, Department of Ecology, Montana State University, Bozeman, MTSearch for more papers by this author First published: 20 November 2019 https://doi.org/10.1002/fsh.10272Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume44, Issue11November 2019Pages 539-544 RelatedInformation
Composition of Boolean functions: An application to the secondary constructions of bent functions

Bent functions are optimal combinatorial objects and have been attracted their research for four decades. Secondary constructions play a central role in constructing bent functions since a complete classification of this class of functions seems to be elusive. This paper is devoted to establishing a relationship between the secondary constructions and the composition of Boolean functions. We firstly prove that some well-known secondary constructions of bent functions, can be described by the composition of a plateaued Boolean function and some bent functions. Then their dual functions can be calculated by the Lagrange interpolation formula. By following this observation, two secondary constructions of bent functions are presented. We show that they are inequivalent to the known ones, and may generate bent functions outside the primary classes M and PS. These results show that the method we present in this paper is genetic and unified and therefore can be applied to the constructions of Boolean functions with other cryptographical criteria.
THE COMPETENCY BASED APPROACH IN PROFESSIONAL EDUCATION, OR IN CONTRADICTION WITH LABOR SCIENCE

The content and existing practice, the approach and provisions adopted in official methodological documents for assessing the qualifications of personnel, as well as the content and parameters of qualification requirements for employees during their certification have been considered. In light of this and in the light of the ongoing work on the development of professional standards in all sectors of production and the economy, the competency-based approach implemented by the Ministry of Science and Higher Education of the Russian Federation in higher professional education has been analyzed. As a result of this analysis, the shortcomings of conceptual provisions and an approach to assessing the level of professional preparedness of university graduates, to determining and accepting the general economic and professional competencies of graduates of the bachelor and master level have been revealed. Suggestions have been made for adjusting the content of the methodological support of the educational process in terms of assessing knowledge and preparedness for the practical work of graduates of vocational education institutions.
EAAT: Environment-Aware Adaptive Transmission for Split-Screen Video Streaming

With the tremendous growth of video contents and mobility demands, there is a need to develop more personalized video services. Split-screen services, such as picture in picture become more and more popular. Furthermore, the user's viewing environment affects the user's quality of experience (QoE). Therefore, video transmission of split-screen services face several major challenges, such as to quantify the impact of environmental factors on user's QoE; how to assess the user's QoE of the split-screen services; how to choose the bit-rate of each video stream to maximize user's QoE of the split-screen services. To address these challenges, in the paper, an environment-aware adaptive transmission (EAAT) scheme for split-screen video streaming is first presented. Then, we introduce a mathematical model for characterizing user's QoE to be affected by environmental factors in the proposed EAAT. In the model, the QoE of user's relationship with the viewing environment is proposed. Based on the model, a problem of maximizing user's QoE is formulated, and we develop a heuristic algorithm to solve the optimization problem. In addition, we conduct various trace-bandwidth experiments to rigorously evaluate the proposed EAAT scheme in different network environments, and show that EAAT can enrich the video quality while saving network resources.
A novel parallel learning algorithm for pattern classification

In today’s data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.
Trusting Algorithms in Society 5.0

Society 5.0 will rest fundamentally on advanced algorithms, but will people trust them? This brief essay examines some factors that may influence future acceptance or rejection of a cybernetically-integrated society. These include algorithmic honesty, competence, transparency, and flexibility, as well as our willingness to relate appropriately to nonhuman intelligent agents.
Typical dynamic peculiarities of the spheres of manifestation of social activity among student youth

The purpose of the study presented in the article is an empirical study of typical dynamic peculiarities spheres of young people’ social activity manifestation. The study sample consisted of 240 participants (Saratov region, Russia), including: university students (n = 120), age M = 18.22 (SD = 0.87) and high school students (n = 120), age M = 16.43; SD = 0.53. We used a questionnaire (R. M. Shamionov, I. V. Arendachiuk, E. E. Bocharova et al.) to register various forms of social activity and the degree of their manifestation, and the “Morphological Test of Life Values” technique (V. F. Sopov, L. V. Karpushina) to study various spheres of life. In the sample of students we have recorded a trend towards negative dynamics in the range of typical spheres of social activity manifestation, which, moreover, differs in its substantive multidirectionality. The study has shown that manifestation of various forms of social activity in the typical spheres of life is characterized by multidirectional dynamics of their motives’ actualization, depending on the person’s social and age-related status. The applied aspect of the problem under study can be implemented in the development of youth policy programs.