Reviewerly/exHarmony huggingface dataset:
Implicitly Localized MCMC Sampler to Cope With Non-local/Non-linear Data Constraints in Large-Size Inverse Problems

Many practical applications involve the resolution of large-size inverse problems, without providing more than a moderate-size sample to describe the prior probability distribution. In this situation, additional information must be supplied to augment the effective dimension of the available sample. This is the role played by covariance localization in the large-size applications of the ensemble Kalman filter. In this paper, it is suggested that covariance localization can also be efficiently applied to an approximate variant of the Metropolis/Hastings algorithm, by modulating the ensemble members by the large-scale patterns of other members. Modulation is used to design a (global) proposal probability distribution (i)~that can be sampled at a very low cost (proportional to the size of the state vector, with a small integer coefficient),(ii)~that automatically accounts for a localized prior covariance, and (iii)~that leads to an efficient sampler for the augmented prior probability distribution or for the posterior probability distribution. The resulting algorithm is applied to an academic example, illustrating (i)~the effectiveness of covariance localization, (ii)~the ability of the method to deal with nonlocal/nonlinear observation operators and non-Gaussian observation errors, (iii)~the possibility to deal with non-Gaussian (even discrete) prior marginal distributions, by including (stochastic) anamorphosis transformations, (iv)~the reliability, resolution and optimality of the updated ensemble, using probabilistic scores appropriate to a non-Gaussian posterior distribution, and (v)~the scalability of the algorithm as a function of the size of the problem. The evaluation of the computational complexity of the algorithm suggests that it could become numerically competitive with local ensemble Kalman filters, even in the absence of nonlocal constraints, especially if the localization radius is large. All codes necessary to reproduce the example application described in this paper are openly available from github.com/brankart/ensdam.
Predicting Video Saliency Using Crowdsourced Mouse-Tracking Data

This paper presents a new way of getting high-quality saliency maps for video, using a cheaper alternative to eye-tracking data. We designed a mouse-contingent video viewing system which simulates the viewers’ peripheral vision based on the position of the mouse cursor. The system enables the use of mouse-tracking data recorded from an ordinary computer mouse as an alternative to real gaze fixations recorded by a more expensive eye-tracker. We developed a crowdsourcing system that enables the collection of such mouse-tracking data at large scale. Using the collected mouse-tracking data we showed that it can serve as an approximation of eye-tracking data. Moreover, trying to increase the efficiency of collected mouse-tracking data we proposed a novel deep neural network algorithm that improves the quality of mouse-tracking saliency maps.
Obstacles avoidance for autonomous marine vehicles based on the model predictive control

In this paper, a new obstacle collision avoidance algorithm based on the model predictive control is proposed. The algorithm has the following advantages. First, the vehicle nonlinear dynamics as well as restrictions on the phase variables and the controls defining forbidden areas for the vehicle are taken into account. Second, an adaptation of the algorithm to the time-varying number of obstacles according to information coming in real-time from sensors is implemented. Third, the return to the prescribed trajectory after obstacles avoidance is provided. The ship collision avoidance on the sea surface is considered as an example.
Data-Driven Abnormal Behavior Identification System: A Survey for “In-out” in the Same Subway Station

With the development of subway transportation capacity, “stealing behavior”, “illegal advertising behavior” and “begging behavior” have also emerged in an endless stream. These behaviors have brought negative influence on subway operation and management. The paper will analyze the abnormal data of “In-out” in the same subway station (IOSSS) to explore the abnormal behavior of subway riders with Beijing' riding data. IOSSS refers to passengers' entering and leaving at the same subway station. The study collected half one year' passenger data, analyzed the characteristics of IOSSS, and carried out passenger cluster analysis and portrait analysis. The research can deepen the understanding of IOSSS, analyzed the purpose and characteristics of the abnormal riders, which is conducive to the safety of subway passengers.
Phenomenology of reduction of consciousness: comparativist approach

The article presents an analysis of the spiritual practices of self-knowledge, based on the method of phenomenological reduction. In the paper, the author describes the features of such practices as methods of "purification" (expansion) of consciousness, liberation from mental, speech and behavioral stereotypes. The phenomenological method, implemented in the spiritual practices of self-knowledge, is aimed at restructuring individual consciousness through the rejection of egocentrism, the elimination of subjective-evaluative activity in relation to the outside world and to oneself. Overcoming egocentrism allows going beyond social and cultural stereotypes, overcoming the disunity of consciousness and being that serves as an obstacle to the reality’s objective perception. The author notes that the phenomenological reduction practice requires constant tension of existential potential, and in the methodological plan for cognitive activity aimed at the study of consciousness, it is necessary, along with epistemological criteria, to develop a number of axiological criteria for this activity.
Mejora de contraste de imagenes de mamografía mediante procesamiento digital de imagen

This paper shows the obtained results of applying image enhancement techniques in regions of interest extracted of mamographies. The objective is to improve the detection of breast lesions, such as the microcalcifications and thus contribute to the early detection of breast cancer. Usually, the quality of the region of interest images is not better, and they have some deficiencies that difficult the microcalcifications detection. Due to this, the approach in this paper is to increase the quality of the region of interest image by means of edge and contour enhancement, modifying the contrast and noise reduction and its possible suppression of the background of the image. The techniques that were implemented are the modification of the contrast by lineal and non-lineal transformations, the histogram equalization technique, spatial filtering technique and a technique based on mathematical morphology operations. Finally, a contrast improvement index was applied in order to evaluate in a quantitative way each of the contrast enhancement technique implemented.
Technique to model the movement of the scene using image sequence

Abstract The paper proposes a technique to model the movement of a scene based on backward stochastic gradient pixel-by-pixel estimation of the deformation field. Calculation of the moving object’s area is considered as the task of testing the hypothesis that the image grid nodes belong to the area of motion. The paper presents the estimated two kinds of errors: false positive and false negative. The obtained results are compared with the results of the MVFAST algorithm. The increase in the signal-to-noise ratio of the image of a moving object is achieved by combining the frames of the video sequence under study. To combine conjugate points, high-speed recurrent algorithms are used that do not require a priori information. The paper presents an example of estimation of object’s trajectory parameters using the technique. The interframe geometric deformations of the video sequence are used as intermediate parameters of the trajectory.
Abnormality Detection using Graph Matching for Multi-Task Dynamics of Autonomous Systems

Self-learning abilities in autonomous systems are essential to improve their situational awareness and detection of normal/abnormal situations. In this work, we propose a graph matching technique for activity detection in autonomous agents by using the Gromov-Wasserstein framework. A clustering approach is used to discretise continuous agents' states related to a specific task into a set of nodes with similar objectives. Additionally, a probabilistic transition matrix between nodes is used as edges weights to build a graph. In this paper, we extract an abnormal area based on a sub-graph that encodes the differences between coupled of activities. Such sub-graph is obtained by applying a threshold on the optimal transport matrix, which is obtained through the graph matching procedure. The obtained results are evaluated through experiments performed by a robot in a simulated environment and by a real autonomous vehicle moving within a University Campus.
Weakly-Supervised Semantic Segmentation with Mean Teacher Learning

Weakly-supervised semantic segmentation with image-level labels is a important task as it directly associates high-level semantic to low-level appearance, which can significantly reduce human efforts. Despite the remarkable progress, it is still not as good as fully supervised segmentation methods. To improve the accuracy, in this paper, we proposed a novel framework of weakly-supervised semantic segmentation with mean teacher (WSSS-MT) learning to advance the class estimation of image pixels. More specifically, our proposed framework includes a student network and a teacher network in the segmentation module, which aims to effectively utilize information of the training process. The student learns the semantic segmentation network with an updated supervision, while the teacher uses the exponential moving average of the student to achieve a more accurate estimation of supervision. WSSS-MT employs the trained teacher as final segmentation network. Experimental results on the PASCAL VOC 2012 dataset show that the performance of our framework is better than the competing methods.
Method for the Improvement of Knee Angle Accuracy Based on Kinect and IMU: Preliminary Results

One way to identify musculoskeletal disorders in the lower limb is through the functional examination where the ranges of normality of the joints are evaluated. Currently, this test can be performed with technological support, with optical sensors and inertial measurement sensors (IMU) being the most used. Kinect has been widely used for the functional evaluation of the human body, however, there are some limits to the movements made in the depth plane and when there is occlusion of the limbs. Inertial measurement sensors (IMU) allow orientation and acceleration measurements to be obtained with a high sampling rate, with some restrictions associated with drift. This article proposes a methodology that combines the acceleration measures of the IMU and kinect sensors in two planes of movement (Frontal and sagittal). These measurements are filtered in the preprocessing stage according to a Kalman filter and are obtained from a mathematical equation that allows them to be merged. The fusion system data obtains acceptable RMS error values of 5.5$$^{\circ }$$ and an average consistency of 92.5% for the sagittal plane with respect to the goniometer technique. The data is shown through an interface that allows the visualization of knee joint kinematic data, as well as tools for the analysis of signals by the health professional.
Image clustering by autoencoders

This paper describes an approach to solving the problem of finding similar images by visual similarity using neural networks on previously unmarked data. We propose to build special architecture of the neural network - autoencoder, through which high-level features are extracted from images. The search for the nearest elements is realized by the Euclidean metric in the generated feature space, after a preliminary decomposition into two-dimensional space. Proposed approach of generate feature space can be applied to the classification task using pre-clustering.
A trustable platform for exchange of resources across organizations and their customers

Collaborations across companies can improve revenue, productivity and customer relationship building. Therefore, it could be desirable to allow customers to exchange digital resources across organizations, e.g. to let customers collect and spend loyalty points across stores. In order to allow such exchange of resources, a secure and trustable environment, where customers are in full control of their resources, is required. In this PhD work, we will develop a generic framework for supporting the set-up of such exchange platforms by providing core functionality in combination with components that can be configured to the specific needs of the use cases.
An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment

The main aim of the proposed methodology is to design a multi-objective function for replica management system using oppositional gravitational search algorithm (OGSA), in which we analyze the various factors influencing replication decisions such as mean service time, mean file availability, energy consumption, load variance, and mean access latency. The OGSA algorithm is hybridization of oppositional-based learning (OBL) and gravitational search algorithm (GSA), which is change existing solution, and to adopt a new good solution based on objective function. Here, firstly we create a set of files and data node to generate a population by assigning the file to data node randomly and evaluate the fitness which is minimizing the objective function. Secondly, we regenerate the population to produce optimal or suboptimal population using OGSA. The experimental results show that the performance of the proposed methods is better than the other methods of data replication problem.
nagoy Team’s Summarization System at the NTCIR-14 QA Lab-PoliInfo

The nagoy team participated in the NTCIR-14 QA Lab-PoliInfo’s summarization subtask. This paper describes our summarization system for assembly member speeches using random forest classifiers. Since we encountered an imbalance in the data, we were unable to achieve good results in this subtask when training on all data. To solve this problem, we developed a new summarization system that applies multiple random forest classifiers training on different-sized data sets step by step. As a result, our system achieved good performance, especially in the evaluation by ROUGE scores. In this paper, we also compare our system with a single random forest classifier using probability.
Network Elastic Net for Identifying Smoking specific gene expression for lung cancer

Survival month for non-small lung cancer patients depend upon which stage of lung cancer is present. Our aim is to identify smoking specific gene expression biomarkers in prognosis of lung cancer patients. In this paper, we introduce the network elastic net, a generalization of network lasso that allows for simultaneous clustering and regression on graphs. In network elastic net, we consider similar patients based on smoking cigarettes per year to form the network. We then further find the suitable cluster among patients based on coefficients of genes having different survival month structures and showed the efficacy of the clusters using stage enrichment. This can be used to identify the stage of cancer using gene expression and smoking behavior of patients without doing any tests.
Super-Resolution for Imagery Enhancement Using Variational Quantum Eigensolver

Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling prospect to offer exponential speedup to process computationally expensive operations, such as the ones verified in SR imaging. Envisioning the design of a quantum-ready method for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation using Variational Quantum Eigensolver (VQE) is demonstrated. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and VQE, a hybrid classical-quantum algorithm, to conduct SR with the support of quantum computation, preserving quantitative performance in terms of Image Quality Assessment (IQA).
Study on two-dimensional code recognition algorithm in non-uniform illumination based on digital images processing technology

Abstract It is possible that the shaded area was mistakenly identified based on digital images processing technology with non-uniform illumination. The binarization algorithm of two-dimensional code was optimized to this problem. The local area threshold of the image and the pixel-by pixel sliding calculation are used to eliminate the influence of the illumination on the image, thereby improving the recognition rate of the two-dimensional code image which contains the shadow. Experimental result indicate that the method has a stronger adaptability to images with shaded areas, especially for unilateral shadows which are less ideal for block resolution. Compared with the classical global Ostu algorithm, the recognition rate of the image is increased by about 3 times.
Research on Multidimensional Information Security Assessment Based on Big Data

In order to ensure the security of multi-dimensional information, this paper proposes a research on key technologies of multi-dimensional information security risk assessment based on big data framework. According to the characteristics of multi-dimensional information security data, security division is carried out, intrusion data impact parameters are collected, and intrusion risk budget is carried out according to different impact parameter values and divided security levels, so as to realize accurate evaluation of multi-dimensional information security risks. Finally, the experiment proves that the key technology of multidimensional information security risk assessment based on big data framework has higher practicability and accuracy, and meets the research requirements.
An odometry free automatic perpendicular parking strategy for a light urban vehicle based on a low resolution lidar

In this paper, an automatic perpendicular parking algorithm for a light urban vehicle with active steer assembly is presented, designed to help the user when the manoeuvre has to be performed in a narrow space between two obstacles. The parking strategy is odometry free and based only on the measurements from a limited FOV low resolution Lidar, resulting in a simple and cost effective solution. In the initial phase of the manoeuvre, the algorithm exploits the Lidar information - combined with the a priori knowledge of the park structure - to localize the vehicle in the parking environment and to generate the steering action accordingly, so as to approach the parking space. Then, as the vehicle approaches the parking spot entrance, an obstacle avoidance logic is applied to end the manoeuvre without crashes. The performance of the proposed strategy has been assessed through an extensive simulation campaign and validated by experimental tests using a real instrumented vehicle.
The Concept and Development Application Prospect of Intelligent Sports in China

The concept of intelligent sports is along with the Internet, big data, cloud computing, VR, AR, 5G communications, artificial intelligence and this new concepts in the field of sports have been put forward with the development of science and technology. Under the background of China's policy of invigorating sports by science and technology, promoting sports industry in an all-round way and building a strong sports country, this paper takes intelligent sports as the research object. By using the methods of documentation, case study, interview and investigation, and mathematical statistics, the paper puts forward are analyzed the concept of smart sports, produces the background and changes of intelligent sports, the future development, research field, application field and future prospect of sports. Finally, the countermeasures and suggestions for the development of wisdom sports are put forward in order to provide theoretical reference for the development and construction of sports science and technology and intelligent sports in China.
An Efficient Minimum-Time Cooperative Task Planning Algorithm for Serving Robots and Operators

SUMMARY We establish an efficient minimum-time cooperative task planning algorithm for robots and operators to serve clients. This problem is an extension of the multiple traveling salesman problem and the vehicle routing problem with synchronization constraints, but it is more difficult due to the cooperative tasks of the robots and operators. To find the exact minimum-time task plan with a small tree size, we propose an efficient branch-and-bound with a good initial tree and keen complementary heuristics. Our algorithm is also effective as an approximate algorithm for the many clients problem within the capacity of the computer used. The efficiency of our algorithm is revealed via case studies: telemedical service robots and planetary exploration robots.
Scene Classification via Learning a Multi-Branch Convolutional Network

Efficient and accurate classification of high-resolution scene remains a challenge of within-class diversity and between-class similarity due to rich image variations in viewpoint, object pose, spatial resolution and background. To address these issues, we propose a multi-branch convolutional neural network (MB-CNN), which focuses on tackling the problem of learning the appropriate representation of a high-resolution scene that is rich enough to discriminate between different semantic classes. First, pyramid scene parsing network (PSPNet) with minor modification is introduced to gather global object information. Then, an attention net is proposed to highlight transformation invariance and key regions for attention feature extraction. Finally, above two branches are fused with original input branch to learn consistently semantic class information and generate powerful predictions. Our approach achieves better performance favorably against state-of-the-arts on two publicly available scene datasets.
Bitcoin influence on E-commerce

Abstract The purpose of this research is to identify the development of e-commerce technology in the business world, and the benefits of the application of e-commerce in the online business. The method used in this research was the descriptive method to present a complete overview of the situation related to some variable situations examined. The result of this research is to give clear identification of how far the development of e-commerce technology in the world of online business, as well as the benefits provided by the application of e-commerce in the world of online business. In the end, bitcoin gives mutual connection to expand the business of E-commerce.
Modified Cuckoo Search Algorithm (MCSA) For Extracting the ODF Maxima

In this section, the modified CSA (MCSA) is presented for extracting the ODF maxima (MCSA-ODF). CSA was proposed by Yang and Deb in 2009. To date, work on this algorithm has significantly increased, and CSA currently has its rightful place among other optimization methodologies (Shehab et al. 2017). MCSA is based on replacing the random selection process with the tournament selection scheme. Thus, the probability of achieving better results is increased, thereby avoiding premature convergence. Performance is validated by applying several benchmarks (Shehab and Khader 2018). The results of the experimental indicate that MCSA performs better than the standard CSA and the other compared methods. Subsequently, MCSA is applied to extract the ODF maxima, namely, MCSA-ODF.