Reviewerly/exHarmony huggingface dataset:
Observation Capability for Distributed Multi-Sensor Information Fusion

In distributed multi-sensor system, many information fusion rules have been investigated to process the information data. And, it is a primary issue to study better fusion rules. In this paper, a statistical viewpoint that multi-sensor information fusion rules based on Fisher information are investigated. And we use them to analyse the observation capability. Firstly, a distributed multi-sensor observation model from the viewpoint of Fisher information is established. Then, three fusion rules represented by the amount of information, i.e., independent observation, information fusion and signal fusion, are proposed. In particular, we prove their ralations in detail and apply them to fuse multisensor information to obtain different observation range. Finally, the observation range obtained by signal fusion and information fusion are compared with that obtained by conventional coherent integration and non-coherent integration. Simulation results show their consistency.
G-Model: A Novel Approach to Privacy-Preserving 1:M Microdata Publication

Public availability of electronic health records raises major privacy concerns, as that data contains confidential personal information of individuals. Publishing such data must be accompanied by appropriate privacy-preserving techniques to avoid or at least minimize privacy breaches. The task of privacy preservation becomes even more challenging when the data have multiple sensitive attributes (SAs). Privacy risks increase even further when an individual has multiple records (1:M) in a dataset, a rather typical situation with electronic health records (EHRs). To overcome these privacy issues, the methodologies known as 1:M generalization and l-anatomy have been proposed by the research community. However, these models fail to provide optimal privacy protection, data utility and security against certain types of attacks, such as gender-specific SA attacks. In this paper, we propose a generic 1:M data privacy model, called G-model, which provides guaranteed data privacy with high data utility and no information loss. Our G-model maintains separate groups and caches of male and female SAs, thus protecting privacy against gender-specific SA attacks. Furthermore, G-model avoids generalization, thus providing high data utility with no information loss. Experiments performed on three real-world datasets (Adult, Informs, and YouTube datasets) have shown that the proposed model is more efficient and better at privacy protection than the existing models from the literature.
MLCE: A Multi-Label Crotch Ensemble Method for Multi-Label Classification

Multi-label classification addresses the problem that each instance is associated with multiple labels simultaneously. In this paper, we propose a multi-label crotch ensemble (MLCE) model for multi-label classification, which takes label correlations into consideration. In MLCE, a multi-label cluster tree is first constructed. Then, we incorporate all multi-label crotch predictors of the tree into a classifier, where the multi-label crotch predictor is the crotch formed by an inner node of the tree and its children. Finally, a flexible weighted voting scheme is designed to produce the classification output. We perform experiments on 11 benchmark datasets. Experimental results clearly demonstrate the MLCE significantly outperforms six well-established multi-label classification approaches, in terms of the widely used evaluation metrics.
Data stream processing in HPC systems: New frameworks and architectures for high-frequency streaming

Many research works deal with big data platforms looking forward to data science and analytics. These are complex and usually distributed environments, composed of several systems and tools. As expected, there is a need for a closer look at performance issues.In this work, we review performance tuning strategies in the big data environment. We focus on data-driven tuning techniques, discussing the use of database inspired approaches. Concerning big data and NoSQL stores, performance tuning issues are quite different from the so-called conventional systems. Many existing solutions are mostly ad-hoc activities that do not fit for multiple situations. But there are some categories of data-driven solutions that can be taken as guidelines and incorporated into general-purpose auto-tuning modules for big data systems.We examine typical performance tuning actions, discussing available solutions to support some of the tuning process's primary activities. We also discuss recent implementations of data-driven performance tuning solutions for big data platforms. We propose an initial classification based on the domain state-of-the-art and present selected tuning actions for large-scale data processing systems. Finally, we organized existing works towards self-tuning big data systems based on this classification and presented general and system-specific tuning recommendations. We found that most of the literature pieces evaluate the use of tuning actions at the physical design perspective, and there is a lack of self-tuning machine-learning-based solutions for big data systems.
Collaborative Filtering and Leaders' Advice Based Recommendation System for Cold Start Users

Recommendation system is a specific type of intelligent systems, which exploits historical user ratings on items and auxiliary information to make recommendations on items to the users. It plays a critical role in a wide rang of online shopping, e-commercial services and social networking applications. Collaborative Filtering(CF) is the most popular approaches used for recommendation systems, but it suffers from cold start problem where no rating data is available for new users or items in the system. In this paper, we propose a novel approach to improve cold start problem for new users. The approach is named Leaders Advice Model(LAM). We use it to finish recommendation task. In our approach, we don't make use of private data sources of users, because it is difficult to get and involved in legal problem. Firstly, we use CF approach to generate candidate leaders. Secondly, to identify these candidate leaders, we need to train them. Then, when one new user enters the system, we can interview the user to get his/her interesting trend. Finally, according to new user's interesting trend, our approach can recommend corresponding items to the new user. Some experiments on a MovieLens rating data set of movies are performed, which show that our proposed recommendation approach can improve the cold start problem for new users. The design can be applied to many other recommendation systems for online shopping and social networking applications. The solution of cold start user problem can improve user experience and trust of recommendation systems.
Anime-to-real clothing: Cosplay costume generation via image-to-image translation

Abstract Cosplay has grown from its origins at fan conventions into a billion-dollar global dress phenomenon. To facilitate the imagination and reinterpretation of animated images as real garments, this paper presents an automatic costume-image generation method based on image-to-image translation. Cosplay items can be significantly diverse in their styles and shapes, and conventional methods cannot be directly applied to the wide variety of clothing images that are the focus of this study. To solve this problem, our method starts by collecting and preprocessing web images to prepare a cleaned, paired dataset of the anime and real domains. Then, we present a novel architecture for generative adversarial networks (GANs) to facilitate high-quality cosplay image generation. Our GAN consists of several effective techniques to bridge the two domains and improve both the global and local consistency of generated images. Experiments demonstrated that, with quantitative evaluation metrics, the proposed GAN performs better and produces more realistic images than conventional methods. Our codes and pretrained model are available on the web.
A Text Classification Model Base On Region Embedding AND LSTM

In the field of natural language processing, recurrent neural networks are good at capturing long-range dependent information and can effectively complete text classification tasks. However, Recurrent neural network is model the entire sentence in the process of text feature extraction, which easily ignores the deep semantic information of the local phrase of the text. To further enhance the expressiveness of text features, we propose a text classification model base on region embedding and LSTM (RELSTM). RELSTM first divides regions for text and then generates region embedding. We introduce the learnable local context unit(LCU) to calculate the relative position information of the middle word and its influence on the context words in the region, and obtain a region matrix representation. In order to reduce the complexity of the model, the max pooling operation is applied to the region matrix and we obtain a dense region embedding. Then, we use LSTM's long-term memory of text information to extract the global characteristics. The model is verified on public data sets, and the results are compared using 5 benchmark models. Experimental results on three dataset show that RELSTM has better overall performance and is effective in improving the accuracy of text classification compared with traditional deep learning models.
Blending Technology-based Teacher-led and Student-centered Approaches in STEM Education

The paper describes an extensive study of the teachers' views on technology use in teaching strategies in a way to build on the students' knowledge acquisition. This research exploits the findings from a targeted survey among STEM teachers of different educational levels, types of schools, and regions in Bulgaria. The usefulness and the usage frequency of various types of ICT resources are investigated. Two main technology-enhanced teaching approaches are considered -- a teacher-led and a student-centered. The goal is to outline an appropriate ratio between them. The solution is given by a precise formal description and evaluation of the teacher's subjective preferences concerning these teaching approaches. For this purpose, the mathematical approach of decision-making that involves utility and probability theory, and a stochastic approximation is applied. As a result, the utility functions of teacher-led and student-centered teaching are constructed thus, enabling determination of their optimal proportion.
Based on the construction of financial management information platform of colleges and universities in 5G environment——Take Guangzhou Nanyang Polytechnic Vocational College as an example

Abstract With the rapid development of wireless technology, mobile communication service has experienced the development from voice service to mobile broadband data service, mobile communication network has gradually evolved from 3G, 4G to 5G, namely the fifth generation of mobile communication network.With the advent of 5G technology, the new generation of information technology has accelerated the reform of higher education system, and the construction of an information platform for financial management in colleges and universities has become an important research topic for the information construction and financial work in colleges and universities.Under the background of “Internet +", the construction target of “financial management informatization platform” from the construction stage of “smart campus” is being promoted to the informatization application service of “smart campus” and “Internet of Things +", and the development of financial management informatization in colleges and universities is changing from business process-oriented to service informatization.Financial resources are transformed from digitization to digitization, and the integration, analysis and utilization of data are emphasized to provide personalized services. The main network scenarios of 5G can be summarized as continuous wide area coverage, high capacity of hot spots, large connection with low power consumption and high reliability with low delay. Now that 5G technology is on the way, in the connected society of the future, everyone and everything will be connected to each other, thus achieving the so-called “Internet of everything”. In this paper, using the method of literature research, field survey and data analysis of guangzhou nanyang technological vocational college financial management informationization development research and analysis, combining the present situation of the financial management informatization, with the help of a financial management concepts and network information technology, knowledge, financial software system using financial management information platform for the school financial management software for scientific and standardized management, based on a student online payment platform, financial reimbursement platform, campus all-in-one-card platform, improve the service efficiency of funds, the school to further enhance the level of school financial management.
A maneuvering tracking method based on LSTM and CS model

Maneuvering target tracking is an important research field in radar tracking. In recent years, the development of machine learning provides a new idea for maneuvering target tracking. This paper presents a trajectory recognition method based on LSTM (Long Short-Term Memory) and a maneuvering tracking method using matched CS (current statistics) model parameter. This method makes use of the characteristics of LSTM that can effectively combine the above information to realize the recognition of target motion states. Then, clustering analysis is used to obtain the optimal filtering parameters of each motion mode in the statistical sense, and filtering is carried out according to the recognition results of LSTM. Compared with the traditional maneuvering tracking method, this method can maintain stable filtering gain in the duration of maneuvering, and the filtering accuracy is improved.
Reconstruction and Decomposition of High-Dimensional Landscapes via Unsupervised Learning

Uncovering the organization of a landscape that encapsulates all states of a dynamic system is a central task in many domains, as it promises to reveal, in an unsupervised manner, a system's inner working. One domain where this task is crucial is in bioinformatics, where the energy landscape that organizes three-dimensional structures of a molecule by their energetics is a powerful construct. The landscape can be leveraged, among other things, to reveal macrostates where a molecule is biologically-active. This is a daunting task, as landscapes of complex actuated systems, such as molecules, are inherently high-dimensional. Nonetheless, our laboratories have made some progress via topological and statistical analysis of spatial data over the recent years. We have proposed what is essentially a dichotomy, methods that are more pertinent for visualization-driven discovery, and methods that are more pertinent for discovery of the biologically-active macrostates but not amenable to visualization. In this paper, we present a novel, hybrid method that combines strengths of these methods, allowing both visualization of the landscape and discovery of macrostates. We demonstrate what the method is capable of uncovering in comparison with existing methods over structure spaces sampled with conformational sampling algorithms. Though the direct evaluation in this paper is on protein energy landscapes, the proposed method is of broad interest in cross-cutting problems that necessitate characterization of fitness and optimization landscapes.
An Approach to Decompose and Evaluate a Complex GIS-Application Design to a Simple, Lightweight, User-Centered App-Based Design Using User Experience Evaluation

Working with contemporary, monolithic desktop Geographical Information Systems can be laborious and confusing, especially for non-experts. An alternative for a more user friendly approach to spatial data edition and spatial analysis may be an “app-based” web application that is offering single tasks for the user—“micro applications”. In this paper, a method for determining such “micro applications” based on user stories is presented and applied to a specific use case—the case is centered around an Austrian Governmental Institution that digitizes and edits infrastructure data. The results of this process are implemented in an app-based web application. To measure the impacts of the app-based approach, we evaluate the user experience of the app-based approach in comparison to a desktop Geographical Information System offering the same functionality. For the measurement of the user experience, we used a focus group. The group had to accomplish tasks with the desktop Geographical Information System and with the app-based web application. To measure their user experience, we employed the tool AttrakDiff. The app-based web application achieves significantly better results in terms of user experience. This result is confirmed in a discussion panel carried out afterwards. Test persons responded that positive aspects of the app-based web application were the easy and intuitive handling and reduced user interface that helps the users to focus on their tasks without any distraction.
An Improved Harmonic Suppression Method Based on Adaptive Compensation Algorithm for Monobit Receiver

Monobit receiver is widely used in electronic warfare due to its large instantaneous receiving bandwidth, high sensitivity and high dynamic range. However, in the procedure of signal processing, monobit receiver generates high harmonics because of nonlinear quantization operations. These harmonics greatly reduce the instantaneous dynamic range (IDR) of the simultaneous signal, thereby reducing the performance of the monobit receiver. Meanwhile, the existing suppression methods are of great storage space and computational complexity. Aiming at the disadvantages in suppression efficiency, storage space, computational complexity and the balance between them of existing harmonic suppression methods, this paper proposes an improved harmonics suppression method based on adaptive compensation algorithm (ACA). Firstly, the peak frequency of signal spectrum is detected by monobit DFT, and the positions of the harmonics are located according to the peak frequency. Secondly, the sidelobe-to-maximum adjacent unit ratio (SMR) criterion is proposed, and each harmonic in compensation vector from the pre-stored compensation matrix is evaluated whether it needs to be compensated according to this criterion. Then, the adaptive compensation vector of each harmonic is used to suppress the harmonic components in the signal spectrum selectively. Finally, Updating the signal spectrum and performing the simultaneous signal detection based on proportional threshold. The simulation results show that the proposed method effectively suppresses the harmonics under different signal-to-noise ratios (SNRs) when compared to the traditional compensation algorithm and its improved algorithms. Meanwhile, the proposed method greatly improves IDR with less computational complexity and is easier to implement in engineering.
Understanding and Analysis of Enhanced COVID-19 Chest X-Ray Images

The 2019 coronavirus disease (COVID-19) with its origin in China has spread rapidly to other nations and infected millions of people. In this context, this paper proposes the development of algorithm that enhances the details of images and assists the doctors in knowing the exact location of affected area. The proposed technique improvises the most popular image enhancement algorithm, namely, multiscale retinex and adjusts the parameters to intensify the details of chest X-ray/CT images of COVID-19 patients. Multiscale retinex (MSR) is human perception-related enhancement algorithm which improves intensity, contrast, and sharpness in medical image through dynamic range compression. The proposed scheme improves the details of images and validates the resulting images using novel metric called wavelet energy. The proposed study is evaluated on images of COVID-19 patients have been obtained from the open-source GitHub repository. Considering the experimental result presented and performance metric, the proposed algorithm has provided important details to doctors in making right decision.
Learning Compact Networks via Similarity-Aware Channel Pruning

In this paper, we introduce a new channel pruning method called Similarity-aware Channel Pruning to simultaneously accelerate and compress CNNs. Most existing channel pruning methods focus on pruning channels by the filter saliency. However, small magnitude doesn't necessarily lead to low saliency to the outputs (influenced by the values of the inputs and the cumulative calculation). Hence, we propose to determine the pruning channels by first comparing the similarity of output feature maps of a layer. Based on the similarity, we reversely find all the corresponding weight groups (filters, mean, variance, bias, etc.) and decide which channels can be removed. Then, we generate the new parameters of the next layer by Weight Combination strategy so as to reconstruct the outputs with fewer channels. The Weight Combination strategy also makes it easier to recover the accuracy through fine-tuning. The key of the proposed method is to focus on the redundancy of feature maps directly. We seek to reduce the redundancy by removing the redundant weight groups of the current layer that generate the similar outputs and discarding the related channels of the next layer that take the removed feature maps as inputs. Extensive experiments using several advanced CNN architecures have verified the effectiveness of our approach.
Real-time Remote Sensing Video Compression for On-orbit Heterogeneous Platform

With the rapid advancement of optical remote sensing (RS) satellites, RS video has replaced RS image as the main way to obtain information in orbit environment. However, the storage and transmission of on-orbit RS video faces enormous challenges: the contradiction between limited power, space and bandwidth and massive video data volume is becoming increasingly acute. To solve this problem, this paper proposes an effective implementation of spaceborne real-time RS video compression based on CPU+GPU heterogeneous framework. First, we propose a new rate control method based on HEVC for on-orbit RS video compression, which allocates less bitrate for low complexity code blocks (CBs) and more bitrate for high complexity CBs, which can improve the accuracy of rate control algorithm while ensuring compression quality. Then this paper makes full use of the CPU + GPU heterogeneous framework to speed up the whole compression processing. The experimental results show that the proposed compression framework realizes 200x real-time compression of 1080p spaceborne RS video with a PSNR greater than 38.7 dB.
Analysis of K Nearest Neighbor KL Divergence Estimation for Continuous Distributions

Estimating Kullback-Leibler divergence from identically and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the k nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator.