Miracle-dz/UniFAR-FTUs huggingface dataset:
GAT: A Unified GPU-Accelerated Framework for Processing Batch Trajectory Queries

The increasing amount of trajectory data facilitates a wide spectrum of practical applications in which large numbers of trajectory range and similarity queries are issued continuously. This calls for high-throughput trajectory query processing. Traditional in-memory databases lack considerations of the unique features of trajectories, while specialized trajectory query processing systems are typically designed for only one type of trajectory queries. This paper introduces GAT, a unified GPU-accelerated framework to process batch trajectory queries with the objective of high throughput. GAT follows the filtering-and-verification paradigm where we develop a novel index GTIDX for effectively filtering invalid trajectories on the CPU, and exploit the massive parallelism of the GPU for verification. To optimize the performance of GAT, we first greedily partition batch queries to reduce the amortized query processing latency. We then apply the Morton-based encoding method to coalesce data access requests from the GPU cores, and maintain a hash table to avoid redundant data transfer between CPU and GPU. To achieve load balance, we group size-varying cells into balanced blocks with similar numbers of trajectory points. Extensive experiments have been conducted over real-life trajectory datasets. The results show that GAT is efficient, scalable, and achieves high throughput with acceptable indexing cost.
An amoeboid approach for identifying optimal citation flow in big scholarly data network

Scholarly big data network is a complex network of citations from research community across the globe. An effective scholar assessment structure is essential for scholars, researchers, and universities. The research publications are an important factor in the university rankings. The fast growth of digital publishing and scholarly data is progressively challenging every day. These days, the scholarly data can be accessed effortlessly through various data analysis techniques. In this paper, a new framework is designed for big scholarly data, and an amoeboid approach article‐optimal citation flow (A‐OCF) is used to find the optimal flow of citations in the big scholarly data network. A novel modern metrics for article quality (MMAQ) metric is proposed to identify the quality of articles. The performance analysis uses different bibliometric measures, including the impact factor citations, conference proceedings citations, and other citations with the purpose of measuring the quality of cited articles. The scholar analytic results are equated with existing techniques. We have also analyzed central articles in a research area through the MMAQ metrics and tested it with benchmark data sets.
Design and Evaluation of Band-Pass Matching Coupler for Narrow-Band DC Power Line Communications

In power line communication (PLC), coupling transformers are usually required for coupling, band-pass filtering and impedance matching. However, coupling transformer design involves so many parameters that it is typically an imprecise and experimental procedure. In addition, the cost and size of transformers prevent them from being an economic and compact solution for PLC couplers. This paper first analyzes a simplified, distributed parameter model of the power line, which can be used to calculate power line impedance easily and accurately. Next, a low-cost, band-pass matching coupler with compact architecture is designed to replace the coupling transformer for direct current PLC (DC-PLC), which ensures impedance matching on the basis of an accurate power line impedance instead of using an average value. Finally, simulations as well as laboratory tests are conducted under 95–125[Formula: see text]kHz (CENELEC B-band), which confirm the new coupler’s excellent band-pass filtering and impedance matching performance.
Research on virtual simulation and control of robot

In order to realize the vivid simulation and the accurate feedback of the entity robot,a set of robot simulation system is designed,which is of generality.It uses classification strategy for virtual objects to combine the AABB algorithm with the SWIFT++ algorithm.Therefore,it meets the requirements of real-time and accurate toward collision detection algorithm in the simulation system and solves the paradox between efficiency and accuracy.In the stage of algorithm pretreatment,the surface convex decomposition method is taken so that the collision detection algorithm is able to deal with the non-convex objects.The experiments show that the system can be applied in service robot and it is of generality.The collision detection algorithm is more efficient than those which only use SWIFT++ algorithm.
Control of Stochastic Systems

Instructor: Serdar Yuksel, Jeffrey Hall 415, Phone: 533–2429, e-mail: yuksel@mast.queensu.ca Marker: Drew Steeves e-mail: 1ds19@queensu.ca Office Hours: Thu 10:30-11:20 Recommended Text: Discrete-Time Markov Control Processes, by O. Hernandez-Lerma and J. B. Lasserre, Supplemental Notes will be posted on the course web site References: Dynamic Programming and Optimal Control, by D. P. Bertsekas Markov Chains and Stochastic Stability, by S. P. Meyn, R. L. Tweedie Announcements: Visit http://www.mast.queensu.ca/ math472 for announcements, homeworks etc. Grading: Homework Assignments 15%, Project and Presentations 25%, maximum of(Midterm 25% + Final 35%, Midterm 15% + Final 45%)
A New Kind of Anti-fake Label Application Based on SIFT Algorithm

The traditional anti-fake label is easy to fabricated because it has not a unique feature. To satisfy the trace-to-source requirement of nowadays, a new printed label was proposed which combined the advantages of unique QR code and physically unique texture feature of traditional texture label. Adopting SIFT algorithm, the texture feature of this kind of label was extracted and stored in database, and then labels were matched by using minimum Euclidean-distance method. Experimental Results showed that a good identification and matching accuracy can be obtained even labels’ image were deformed. Finally, a new kind of anti-fake label system were completely conformed by texture paper and QR code, SIFT texture feature recognizing module, texture matching module, database and mobile phone App. In this paper, the emphasis was focused on the middle two parts.
ECG Signal Acquisition and Preprocessing System based on Wavelet Transforms

The ECG signal (Electro-Cardiogram) is an essential means for the detection of cardiac pathologies. The measurement of an ECG signal is often subject to different types of noise. Filtering of these noises is essential for better accuracy in the analysis of ECG parameters. In this work, our contribution consisted to design and implement a device for acquiring and filtering ECG signals based on the wavelet transformations.
Development and application of software product for speed perception test

To measure the speed perception ability of a person more accurately and efficiently,a software product for speed perception test is designed and developed.The software is based on the psychological paradigm for speed perception test,and is developed in C++. Several parameters in the software is set flexibly,and the test results of the same person are relatively stable.Sixteen male subjects are selected to take the speed perception tests and the simulated mannally controlled rendezvous and docking(MCRVD) tasks.Sinificant correlation is found between the subjects' performance in the the speed perception tests and their performance in the MCRVD tasks.These data indicate that the software developed can measure people's speed perception ability sensitively,and the speed perception ability of a person may inflence his performance in the MCRVD tasks.
Mid-infrared Chalcogenide Suspended Slot Waveguide for Gas Sensing

A chalcogenide (ChG) vertical slot waveguide and a ChG horizontal slot waveguide racetrack resonator were proposed for gas sensing. The vertical slot waveguide and the upper strip waveguide of the horizontal slot waveguide are suspended to enhance light-gas interaction. The waveguide sensors were optimized and the sensing performances were studied numerically.
Identify Sentiment-Objects from Chinese Sentences Based on Cascaded Conditional Random Fields

Sentiment-objects extraction aims to identify the targets of opinion described in sentiment sentences.However,previous researches fail to extract compound targets and unknown words.In this paper,the cascaded CRFs model is presented to deal with the problem.The method first acquires opinion target set using lower-lever CRFs model.then,middle-lever models is employed to get candidate set by filtering noise,complementing missing candidate targets,and merging compound noun phrases.Finally,opinion targets set is extract from the higher-lever model using middle-lever model candidate set as input.Experiments show that our method outperforms linear chain CRFs by 1.62% in precision,5.75% in recall,and 4.17% in F1 measure.Meanwhile,the method is also effective to identify the compound targets and unknown targets.
Exploring Bitcoin Cryptocurrency: An Analysis, Applications and its Market Influence

Being in this digital era, technology continuously updates with new variants in providing security while performing the financial transactions. It is a mandated task for the user for conducting secure transactions in order to defend the intruders to restrict the interference. This paper deals with digital currency-Cryptocurrency, that provides a medium of secure exchange between the peers during financial management. Authors explored the internal scheme of bitcoin and the abstract view of cryptocurrency to know how the bitcoin is providing a shield during financial negotiation between peers with various application in security point of view.
Sim-to-real reinforcement learning applied to end-to-end vehicle control

In this work, we study vision-based end-to-end reinforcement learning on vehicle control problems, such as lane following and collision avoidance. Our controller policy is able to control a small-scale robot to follow the right-hand lane of a real two-lane road, while its training was solely carried out in a simulation. Our model, realized by a simple, convolutional network, only relies on images of a forward-facing monocular camera and generates continuous actions that directly control the vehicle. To train this policy we used Proximal Policy Optimization, and to achieve the generalization capability required for real performance we used domain randomization. We carried out thorough analysis of the trained policy, by measuring multiple performance metrics and comparing these to baselines that rely on other methods. To assess the quality of the simulation-to-reality transfer learning process and the performance of the controller in the real world, we measured simple metrics on a real track and compared these with results from a matching simulation. Further analysis was carried out by visualizing salient object maps.
Standardising the Capture and Processing of Custody Images

Custody images are a standard feature of everyday Policing and are commonly used during investigative work to establish whether the perpetrator and the suspect are the same. The process of identification relies heavily on the quality of a custody image because a low-quality image may mask identifying features. With an increased demand for high quality facial images and the requirement to integrate biometrics and machine vision technology to the field of face identification, this research presents an innovative image capture and biometric recording system called the Halo. Halo is a pioneering system which (1) uses machine vision cameras to capture high quality facial images from 8 planes of view (including CCTV simulated), (2) uses high quality video technology to Original Research Article
The Reality of Virtual Reality: A Comparison of Pedestrian Behavior in Real and Virtual Environments

Virtual reality (VR) can be a very effective tool to evaluate built environment to support improvement of pedestrian and other vulnerable road user safety. However, in order to draw actionable conclusions from VR it is important to understand the degree to which pedestrians’ perceptions and behaviors match across real and virtual environments. In this study, participants experienced equivalent real and virtual environments and performed similar tasks in each. Tasks included pedestrian’ intention to cross, estimation of speed and distance of an approaching vehicle, and the perceived safety and risk of crossing a road. Pedestrians’ presence was also measured in all environments. Result showed that there were no differences between the real and virtual environments for most of the tasks. Significant differences between real and virtual environments were observed in the estimation of speed and measures of presence. These results have important implications for using VR as tool to evaluate pedestrian safety in built environments.
Collaborative Filtering Recommendation Algorithm Based on Attention GRU and Adversarial Learning

Aiming at the problem that the traditional collaborative filtering algorithm using shallow models cannot learn the deep features of users and items, and the recommendation model is very susceptible to the counter-interference of its parameters; this paper proposes a matrix-factorization recommendation model that combines adversarial learning and attention-gated recurrent units (AGAMF). Firstly, the gated recurrent unit based on the attention mechanism is used to extract the user’s latent vector from the user’s auxiliary side information. Secondly, the convolutional neural network is used to extract the item’s latent vector from the item’s auxiliary side information. Finally, adversarial disturbances are introduced on the latent factors of users and items to quantify the loss of the model under parameter disturbances, and the latent vectors of users and items are integrated into the probability matrix factorization to predict the user’s rating of the item. Experiments were performed on two real data sets MovieLens-1M and MovieLens-10M, and the RMSE, MAE and Recall indicators were used for evaluation. Experiments prove that the model proposed in this paper is robust and can effectively alleviate the problem of data sparsity. Compared with other related recommendation algorithms, our model has a significant improvement in recommendation performance.
Design of River Monitoring System Based on Video Recognition and Deep Learning Technologies

In recent years, there have been frequent rains and typhoons in Taiwan. Continuous heavy rains can cause river rushed and soared. In addition, terrain problems have caused floods to occur frequently, which will cause flooding, landslides, destroying many facilities and many casualties. In this paper, we proposed a remote video-based river monitoring system that uses some traditional image recognition techniques to obtain river regions. In addition, we also use a deep learning method to find water level to ultimately assess river conditions. The experimental results show that the recall of the water level marker is 94.5%.
Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures With Application to Blind Audio Source Separation

I Nonnegative Matrix Factorization (NMF) is usually used for single- channel audio signal power spectrogram decomposition. I We propose a multichannel NMF framework in a general case of convolutive mixtures of sources. I Possible applications: source separation (blind or supervised) and information retrieval from audio (e.g., music transcription). I Here we apply multichannel NMF to difierent stereo audio source separation tasks, and obtain very promising results.
Analysis of Feature Categories for Malware Visualization

It is important to know which features are more effective for certain visualization types. Furthermore, selecting an appropriate visualization tool plays a key role in descriptive, diagnostic, predictive and prescriptive analytics. Moreover, analyzing the activities of malicious scripts or codes is dependent on the extracted features. In this paper, the authors focused on reviewing and classifying the most common extracted features that have been used for malware visualization based on specified categories. This study examines the features categories and its usefulness for effective malware visualization. Additionally, it focuses on the common extracted features that have been used in the malware visualization domain. Therefore, the conducted literature review finding revealed that the features could be categorized into four main categories, namely, static, dynamic, hybrid, and application metadata. The contribution of this research paper is about feature selection for illustrating which features are effective with which visualization tools for malware visualization.
Force-Sensor-Based Walking-Environment Recognition of Biped Robots

Usability of biped robots in real applications depends on the robot capability of stable and robust walking with high efficiency. To cope with the various practical challenges, the robot must therefore be able to recognize its environment properties. We report a system for indoor-surface detection based on force sensors attached below the feet of the robot. To verify the recognition performance, indoor evaluation surfaces with 5 different properties are used. The capability of fast surface-property recognition is realized by processing the stream of force-sensor data according to the method of overlapping sliding windows, in order to generate 4 different features in a dynamic way. A k-nearest-neighbor (kNN) classifier with multiple classes is applied for real-time high- accuracy recognition of the surface-specific robot-walking characteristics. In particular, recognition performance can be increased by combining the studied features into a single feature descriptor, instead of using each feature separately. Achievability of an overall accuracy of 90.4% and an average precision of 91.49% is verified. Thus, a favorable trade-off between cost and performance is realized. The developed method is useful for optimized dynamic robot-body balancing and walking-speed adjustment, according to the recognized surface properties.
Multibiometric System for Internet of Things using Trust Management

Biometric-based authentication systems are prone to spoofing attacks, errors due to noisy data, intra- and inter-class variations. Combining multiple biometric traits (multibiometric system) promises more accuracy. However, this leads to overhead due to an increase in complexity, form factor, energy and latency in the system. In this paper, a trust management system is used together with a decision level multibiometric system to improve the accuracy and lower the energy consumption of the proposed system. The proposed system is found to drop the false positive rate value by a factor of 4 and the energy consumption was reduced by a factor of 7.
Multi-Target Detection and Tracking with Semantic Segmentation by Using LiDAR Sensor

This paper constructs a 3D multi-target semantic segmentation, detection and tracking framework by using the point cloud data captured from a LiDAR sensor in the outdoor traffic scene. This paper proposes the 3D multi-target detection by using scene segmentation results of PointNet++ to improve the loss of point cloud data when extracting the spatial features of point cloud data after it converting to the voxel space. The spatial features and the scene segmentation results are also represented as a 2D data by bird's-eye view in which the objects does not overlap in the traffic scene. Since the processing time of detection system is much slower than the data acquisition time. The detected target in the point clouds at each time instant are directly tracked by utilizing the local detection with the intersection-over-union ratio in a prediction region.
Trajectory-Based Analysis of Flow Simulation Data

The simulation of physical phenomena involving the dynamic behavior of fluids and gases has numerous applications in various fields of science and engineering. Of particular interest is the material transport behavior, the tendency of a flow field to displace parts of the medium. Therefore, many visualization techniques rely on particle trajectories. Lagrangian Flow Field Representation. In typical Eulerian settings, trajectories are computed from the simulation output using numerical integration schemes. Accuracy concerns arise because, due to limitations of storage space and bandwidth, often only a fraction of the computed simulation time steps are available. Prior work has shown empirically that a Lagrangian, trajectory-based representation can improve accuracy [Agr+14]. Determining the parameters of such a representation in advance is difficult; a relationship between the temporal and spatial resolution and the accuracy of resulting trajectories needs to be established. We provide an error measure for upper bounds of the error of individual trajectories. We show how areas at risk for high errors can be identified, thereby making it possible to prioritize areas in time and space to allocate scarce storage resources. Comparative Visual Analysis of Flow Field Ensembles. Independent of the representation, errors of the simulation itself are often caused by inaccurate initial conditions, limitations of the chosen simulation model, and numerical errors. To gain a better understanding of the possible outcomes, multiple simulation runs can be calculated, resulting in sets of simulation output referred to as ensembles. Of particular interest when studying the material transport behavior of ensembles is the identification of areas where the simulation runs agree or disagree. We introduce and evaluate an interactive method that enables application scientists to reliably identify and examine regions of agreement and disagreement, while taking into account the local transport behavior within individual simulation runs. Particle-Based Representation and Visualization of Uncertain Flow Data Sets. Unlike simulation ensembles, where uncertainty of the solution appears in the form of different simulation runs, moment-based Eulerian multi-phase fluid simulations are probabilistic in nature. These simulations, used in process engineering to simulate the behavior of bubbles in liquid media, are aimed toward reducing the need for real-world experiments. The locations of individual bubbles are not modeled explicitly, but stochastically through the properties of locally defined bubble populations. Comparisons between simulation results and physical experiments are difficult. We describe and analyze an approach that generates representative sets of bubbles for moment-based simulation data. Using our approach, application scientists can directly, visually compare simulation results and physical experiments.
Multi-Pattern Matching Algorithms and Hardware Based Implementation

This paper surveys the algorithms and hardware implementations of the multi-pattern matching. Firstly, two commonly used multi-pattern algorithms, Aho-Corasick’s automata based algorithm and Wu-Manber’s hash based suffix matching with skipping algorithm are introduced. And then, some improving ways are referred. Next, time and space complexity of these algorithms are analyzed, and the experimental results show their performances. Further, several hardware based implementations are taken as examples to demonstrate the general methods and strategies for the implementation on hardware. The developing trend is predicted in the end.