Reviewerly/exHarmony huggingface dataset:
Visual Tracking Via Multi-Layer Factorized Correlation Filter

Pruning the parameters of basis filters can effectively eliminate the negative effect of redundant deep features in discriminative correlation filter based trackers. However, traditional methods often treat feature maps in Convolutional Neural Networks (CNN) as isolate observations, ignore the intrinsic correlation between partially attentional feature maps in multiple convolutional layers, when basis filter pruning is pursued. In this letter, we propose a multi-layer factorized discriminant correlation filter (MLF-DCF) for visual tracking. By integrating the multi-view discriminant learning and the discriminative correlation filter into a unified optimization problem, we can explore the correlation between different target sub-regions from multi-layer viewpoint, thus can effectively prune multi-layer basis filters. To enhance the efficiency of MLF-DCF in terms of speed and accuracy, we not only adopt alternating direction method of multipliers (ADMM) to solve the unified optimization, but also employ a mask estimation strategy to eliminate the background noise in deep features. A large number of experiments on challenging video sequences are given to illustrate the superiority of our tracking method.
A robust matching method for fast 3D shape measurement using digital speckle correlation

Digital speckle correlation (DSC) solves the problem of searching corresponding points between two images, and it shows great application potential in pattern-projection based fast 3D shape measurement, because only one shot is enough to retrieve the 3D structure. As DSC relies on analyzing the spatial intensity distribution of a subset in image with a given point, it is likely to get false correspondences in low quality DSC area such as the background, because the searching range is hard to locate. So it is still hard to use DSC to realize fast 3D shape measurement. To solve this problem, the gray standard deviation of the subset is designed to recognize and remove the low-quality DSC area, and the principle of epipolar geometry and disparity constraint are utilized to determine the searching range, so the correspondences can be obtained. Moreover, in order to enhance the robustness of this method, a connected region method based on neighboring pixels possessing similar disparity is proposed to remove mismatched points after establishing initial disparity map by correspondences. Once the disparity map is obtained 3D structure can be retrieved based on the triangulation principle. The experiment on reconstructing Gorky plaster statue is performed, verifying that the proposed method can substantially reduce mismatched points and achieve robust single frame 3D shape measurement.
Analysis of Factors that affect Users’ Behavioral Changes in News Service

A variety of information is transmitted through websites, which often requires users to give immediate and appropriate reactions. Consequently, users must make ever greater efforts to select information themselves, and this is fueling further improvements in recommendation services that can reduce such burdens. On the other hand, filter bubbles that only provide biased information to users are generated due to redundant recommendations. Previous research has analyzed the behavioral changes of users on existing news sites, and findings have suggested that the index of diversity of browsed articles decreases over time. However, no consideration has been given to the change in the category diversity of articles across news media and the influence of the number of views. In this research, we propose a method to evaluate the relationship between the change in diversity and the period and number of views by dividing the target period at regular intervals and evaluating the diversity of article categories by considering the readability of each article based on the number of views. Then, we analyze the relationship between the change in diversity of the entire media and the change in the variety of articles viewed by users on the actual online media, as well as the influence of the period and number of views on the change in diversity. As a result, there is a correlation between changes in user behavior and changes in the number of browsing times. Furthermore, the results indicate that the change in the category diversity of articles across news media has a strong impact on the change in the diversity of users.
Bivariate Kolchin-type dimension polynomials of non-reflexive prime difference-differential ideals. The case of one translation

We use the method of characteristic sets with respect to two term orderings to prove the existence and obtain a method of computation of a bivariate Kolchin-type dimension polynomial associated with a non-reflexive difference-differential ideal in the algebra of difference-differential polynomials with several basic derivations and one translation. In particular, we obtain a new proof and a method of computation of the dimension polynomial of a non-reflexive prime difference ideal in the algebra of difference polynomials over an ordinary difference field. As a consequence, it is shown that the reflexive closure of a prime difference polynomial ideal is the inverse image of this ideal under a power of the basic translation. We also discuss applications of our results to the analysis of systems of algebraic difference-differential equations.
Recommendation System Based on Deep Learning

With the exponential growth of digital resource from Internet, search engines and recommendation systems have become the effective way to find relevant information in a short period of time. In recent years, advances in deep learning have received great attention in the fields of speech recognition, image processing, and natural language processing. The recommendation system is an important technology to alleviate information overload. How to integrate deep learning into the recommendation system, use the advantages of deep learning to learn the inherent essential characteristics of users and items from various complex multi-dimensional data, and build a model that more closely matches the user’s interest needs has become a hotpot in the research field. This paper reviews the research and application status of recommendation algorithms based on deep learning, and tries to discusses and forecasts the research trends of deep learning approaches applied to recommendation systems. proceedings.
Formalizing and Verifying UML Activity Diagrams

UML (Unified Modelling Language) is the de facto standard for the development of software models. Static aspects of systems are mainly described with UML class diagrams. However, the behavioral aspects are often designed by UML state machine and activity diagrams. Due to the ambiguous semantics of UML diagrams, formal methods can be used to generate the corresponding formal specifications and then check their properties. In this paper, we opt for functional semantics of UML activity diagrams by means of FoCaLiZe, a proof based formal method. Thus, we generate formal specifications in order to detect eventual inconsistencies of UML activity diagrams using Zenon, the automatic theorem prover of FoCaLiZe. The proposed approach directly supports action constraints, activity partitions and the communication between structural (classes) and dynamic (activity diagrams) aspects.
Optimized OpenCL™ kernels for frequency domain image high-boost filters using image vectorization technique

Image high boost filtering uses high-boost filters to enhance the quality of an image, which has also seen in remote sensing, satellite broadcasting, classroom monitoring, and many more real-time video processing applications and requires its faster implementation. OpenCL is a widely adapted parallel programming framework that provides core level data parallelism, and dedicated for heterogeneous parallel devices like from low cost DSP to high-end CPU, GPU and FPGA. In this article, we have considered mostly used Ideal, Gaussian, Butterworth, and Laplacian of Gaussian frequency domain high-boost filters and implemented channelized OpenCL kernels for their rapid execution. In addition to that, these kernels are modified using image vectorization technique to optimize their time utilization by reducing the execution time of these OpenCL kernels to half. At last, performance analysis is carried out for these two types of OpenCL kernel implementations to determine their effectiveness with regard to time consumption and accuracy. Here, different image performance evaluation metrics like entropy, standard deviation, mean absolute error, percentage fit error, SSIM, correlation, and peak signal to noise ratio are applied to measure rightness of the above high-boost filters. From the results, we have concluded that a vectorized Butterworth high-boost filter kernel is the suitable one to provide better results among those filters, which might be highly adaptable in time bound real-time applications using various embedded devices.
Multidimensional assessment of virtual reality applications in clinical neuropsychology: The “VR-Check” protocol

The experimental features of Virtual Reality (VR) applications in clinical neuropsychology are only inadequately captured by classical psychometric quality criteria, warranting an extended paradigm evaluation framework. Here we propose such a framework by means of a multidimensional checklist (VR-Check). Evaluation dimensions encompass cognitive domain specificity, ecological relevance, technical feasibility, user feasibility, user motivation, task adaptability, performance quantification, immersive capacities, training feasibility, and predictable pitfalls. Assessment along these VR-Check dimensions enables a systematic and comparative evaluation of neuropsychological VR applications, as we illustrate here for the exemplary case of assessing spatial cognition with immersive VR.
On the Analysis of Byte-Granularity Heap Randomization

Heap randomization, in general, has been a well-trodden area; however, the efficacy of byte-granularity randomization has never been fully explored as misalignment raises various concerns. Modern heap exploits often abuse the determinism in word alignment, and modern CPU architecture better supports unaligned access (since Nehalem). Based on such new developments, we conduct an in-depth analysis of evaluating the efficacy of byte-granularity heap randomization in three folds: (i) security effectiveness, (ii) performance impact, and (iii) compatibility analysis to measure deployment cost. Security discussion is based on 20 CVE case studies. To measure performance details, we conduct cycle-level microbenchmarks and report that the performance cost is highly concentrated to edge cases depending on the L1-cache line. Based on such analysis, we design and implement an allocator suited for byte-granularity heap randomization. On the negative side, our analysis suggests that byte-granularity heap randomization has high deployment cost due to various implementation conflicts. We enumerate the problematic compatibility issues using Coreutils, Nginx, and ChakraCore benchmarks.
Early Discovery of Disaster Events from Sensor Data Using Fog Computing

Disaster event such as hurricane, blizzard, and winter storm always demand an early response. The lesser the time it takes to respond, the more damage can be prevented. In a disaster event, predicting the happening and alerting the concerned authorities should be done with a minimal latency. Today’s existing technologies highly rely on information disposal to a far away control station. Hence, we aim at achieving an almost zero latency in Natural Disaster discovery. In this paper, the early discovery of disaster events are achieved with the help of Fog Computing infrastructure. Here, we have proposed a machine learning based prediction with Weather sensors. Machine Learning as a tool offers quick and highly reliable predictive models. These models once trained, it will make use of the basic computational operations. Hence they are perfectly suitable for various emergency situations. With Fog computing, the latency in data upload has been minimized. Also, for prediction purpose we have used data from over 5 years by using a Weather API. Multiple machine learning models were trained on this data, and the best model in terms of computation time has been deployed for evaluation. Our evaluation metrics show an impressive 96% accuracy of the deployed model and the response time remains as less as milliseconds.
Fringe projection based visual servoing for cylindrical surface positioning task

Cylindrical surface positioning task is defined as moving a camera to a certain pose with respect to cylindrical surface. To achieve this task, a fringe projection-based visual servoing method is proposed. First of all, phase shiftingbased fringe projection profilometry (FPP) is used to obtain 3D point cloud of the cylindrical surface. Then, the cylindrical axis is extracted by solving a least square optimization problem without constraints. Afterwards, a visual feature selection method is proposed to construct four features of six axis parameters and corresponding interaction matrix and control law are derived to move the camera to desired pose. Special circumstances when interaction matrix is highly decoupled is also discussed. Experiments on cylindrical axis fitting and cylindrical surface positioning task are performed. The results show the visual servo control method is feasible.
Fast and Robust RGB-D Multiple Human Tracking Based on Part Model for Mobile Robots

In this paper, we address the problem of multiple human tracking for mobile robots and proposed a fast and robust RGB-D multiple human tracking approach based on part model. Firstly, a simplified deformable part model method is used for fast human detection using RGB-D information. Then the partial occlusion is detected with depth information. Finally, part model and partial occlusion information are combined into data association process and an on-line appearance classification method with dynamic occlusion handling is proposed for robust tracking. The performance of the proposed method is evaluated on the public dataset, and experiment results have demonstrated the effectiveness of the proposed method.
Product Demand Forecasting Based on Reservoir Computing

Common forecasting methods fail to accurately model the nonlinear and time-varying fluctuations of product demand. Reservoir computing (RC) utilizes a dynamical system to project time-series data to a higher-dimensional state representation extracting mathematical relations within complex demand functions. We demonstrate forecasting accuracy of RC on a multivariate product demand dataset.
Robotic Fish Path Planning in Complex Environment

In this paper, ant colony (ACO) algorithm and steering model of robotic fish are combined for path planning. Firstly, an environmental model with multiple obstacles is established. Then, the ACO algorithm is used to generate feasible path. An optimal path is obtained which satisfies the principle of shortest path and least energy consumption. Sharp turns in the generated optimal path are replaced by small arcs that allow the robotic fish to move normally. Finally, a series of simulation experiments are carried out to verify the excellent performance and effectiveness of proposed algorithm.
Enhancing BMI-Based Student Clustering by Considering Fitness as Key Attribute

The purpose of this study was to redefine health and fitness categories of students, which were defined based on body mass index (BMI). BMI enables identifying overweight and obese persons, however, it inappropriately classifies overweight-and-fit and normal-weight-and-non-fit persons. Such a classification is required when personalized advice on healthy life style and exercises is provided to students. To overcome this issue, we introduced a clustering-based approach that takes into account a fitness score of students. This approach identifies fit and not-fit students, and in combination with BMI, students that are overweight-and-fit and those that are normal-weight-and-non-fit. These results enable us to better target students with personalized advice based on their actual physical characteristics.
Improving Anaphora Resolution by Animacy Identification

Identification of entities' attributes plays a crucial role in anaphora resolution which will influence the final resolution result. This paper focuses on animacy identification and propose a method based on sequence label and it can improve anaphora resolution's performance effectively. This method built a bidirectional LSTM network to capture the information contained in the long text sequence effectively. Taking the output of the network as an intermediate result, then input it to the CRF structure, the animacy identification model was obtained through the organic combination of the two methods. We design two experiments in Chinese corpus. Experiments show that this method can effectively improve the performance of anaphora resolution.
Experimental Demonstration of Entropic Steering

Quantum entanglement is a key resource for foundational quantum information and communication tasks, such as teleportation, entanglement swapping and quantum key distribution [1]. Quantum steering is a quantum correlation that is distinct from other nonclassical correlations such as Bell nonlocality [2] and entanglement. Steering describes the effect of measuring one system, let's say Alice, and affecting the measurement results on the other system, let's say Bob. Moving through the hierarchy of nonlocal classes, from Bell nonlocality towards nonsepa-rability, requires an increasing number of parties and apparatus that need to be trusted but gives access to protocols which are more robust to noise for projective measurements [3,4]. In a quantum steering task [5] the correlations of Alice and Bob's measurement outcomes are tested against a classical probability bound which can be only violated if the state shared between the parties is quantum steerable.
FCA-based Energy Aware-data Placement Strategy for Intensive Workflow in Cloud Computing.

Intensive workflows require a lot of computational resources and massive data movement between Storage and Computing servers hosting these datasets and tasks. Moving these datasets among these servers may increase the execution time, generate a high energy consumption by communication devices and a significant data movement cost. Thus, we need a good data placement strategy (DPS) to minimize the data movement between these servers, the communication energy consumption and the workflow execution time and cost. In this paper, we propose a data placement strategy based on the Formal Concept Analysis approach that considers the original datasets, the different communication levels (switches, routers) and the granularity of used resources in data center. It aims at grouping the maximum of datasets and tasks in a minimal number of Storage computing Servers (SC) as close as possible to each other. Simulations show that our strategy can greatly reduce the energy consumed by communication devices, the volume of data movement as well as the execution time and cost of the workflow.
A moving time node method for solving robot optimal control problem

Many motion control problems of robots are typical Bang-Bang optimal control problems with multi-control variables. In order to solve the contradiction between discrete approximation accuracy and computational time, and the inaccurate location of time-switching points caused by fixed time grids, a method of moving time nodes is proposed for more reasonably dividing the discrete-time grids of optimal control problems. In this method, each component of the control variable is taken as the parameter to be optimized, and the time grid distribution of each control variable is obtained. Unlike the Time-Scaling method, this method not only can effectively move the time grid nodes and obtain good time grid distribution, but also has stronger approximation ability and more flexibility. The effectiveness of the proposed method is verified by a robot optimal control example, and compared with the traditional control vector parameterization method and Time-Scaling method. The results show that the proposed method has low computational cost, accurate time-switching point location and high accuracy.
Fast meteor tracking in noisy video sequences

Abstract Meteor recording, detection, and analysis is one of the traditional topics of astronomy. This paper deals with fast‐tracking of faint meteors and meteor showers in video sequences with high frame rates. Since meteors are fast optical events, the utilization of cameras with high frame rates and short exposure times is the standard technique of meteor tracks capture. Low efficiency of such camera systems is typically compensated with image intensifiers, mostly based on the microchannel plate (MCP). In this work, we use image data captured with the meteor automatic imager and analyzer, double‐station system dedicated for observing faint meteor showers. The system works with minimal human interaction, and it is necessary to automatically process (i.e., identify all meteor candidates) all captured data as fast as possible. Results show possible speed‐up of the image processing method when fluctuations of MCP are reduced.
How Safe Is Artificial Intelligence?

Machine learning dramatically changes our civilization. We rely more and more on efficient algorithms, because otherwise the complexity of our civilizing infrastructure would not be manageable: Our brains are too slow and hopelessly overwhelmed by the amount of data we have to deal with. But how secure are AI algorithms? In practical applications, learning algorithms refer to models of neural networks, which themselves are extremely complex. They are fed and trained with huge amounts of data. The number of necessary parameters explodes exponentially. Nobody knows exactly what happens in these “black boxes” in detail. A statistical trial-and-error procedure often remains. But how should questions of responsibility be decided in, e.g., autonomous driving or in medicine, if the methodological basics remain dark?In machine learning with neural networks, we need more explainability and accountability of causes and effects in order to be able to decide ethical and legal questions of responsibility!
Disjunctive Sets of Phrase Queries for Diverse Query Suggestion

This paper proposes a method of suggesting expanded queries that disambiguate the original Web query which has multiple interpretations. In order to produce a diverse set of queries including those corresponding to infrequent query intents, our method produces queries by extracting phrases connecting given query terms from a corpus. We use a corpus because infrequent query intents may not appear in query logs. We use phrase queries because we need sufficiently specific queries for retrieving pages corresponding to infrequent query intents out of many pages corresponding to popular query intents. Phrase queries usually have high accuracy but low recall. In order to also achieve high recall, we use a disjunction of many phrase queries as a query. Our method first produces many phrase queries by using term expansion and phrase extraction from a corpus, then group semantically similar phrases into clusters, and use each cluster as a disjunctive set of phrase queries.