Miracle-dz/UniFAR-FTUs huggingface dataset:
The Fingerprint of a Traffic Situation: A Semantic Relationship Tensor for Situation Description and Awareness

Many modern approaches for autonomous vehicles are still limited to a low-level data representation without considering complex relational information about their environment. This often leads to a generalization problem in complex situations where algorithms only perform well in tailored scenes. The general challenge lies in combining probabilistic information about partially observable traffic participants with a semantic description of the environment to reduce the complexity of a scene making it manageable. In this work, we present a novel approach for considering high-level information in traffic situations with respect to modeling a semantic representation of the vehicles' environment as well as uncertainties respecting their probabilities through relations between entities. The knowledge is described by first-order logic. Environmental information is stored in a multi-dimensional tensor which will be called Semantic Tensor. It allows to model complex scenes and functions as a very fast data structure. A novel similarity measure allows comparing any two Semantic Tensors to extract information about the spatiotemporal differences.
An Accurate AoA Estimation Approach for Indoor Localization Using Commodity Wi-Fi Devices

Angle-of-Arrival (AoA) is one of the important indoor localization techniques. In this paper, we study on the AoA-based indoor localization by using commodity Wi-Fi devices. However, most existing AoA-based localization approaches face a critical challenge, those accuracys of AoA estimation are degraded when the Signal-to-Noise Ratio (SNR) becomes low. To solve this problem, we propose CSI forward/backward smoothing algorithm to improve the performance of AoA estimation. Experimental results show that our approach can efficiently mitigate AoA estimation accuracy deterioration at low SNRs and achieve higher localization accuracy compared with existing AoA-based localization approach in reality.
Mobile Robot Navigation Based on Turning Point Algorithm and Sliding Mode Controller

The design of an efficient navigation approach for the mobile robot and the insurance of its security are important tasks to achieve, when the robot goes from an initial position to the desired one. The aim of the present work is to develop an algorithm using a turning point in order to find the shortest path and help the robot to reach the target without hitting obstacles. In this manuscript, we developed an algorithm in a static environment where the positions of obstacles are known at first. In addition, a sliding mode controller is used to achieve robustness, stability and reactivity. Finally, simulation results are presented to illustrate the utility and the validity of this approach.
Framework for Evaluating Vision-based Autonomous Steering Control Model

Recent advances in deep learning methods for visual perception tasks have contributed greatly to the development of algorithm models for autonomous driving. However, there is a lack of efforts to construct a dataset or to provide a fair evaluation method specialized for autonomous driving tasks. In this study, we constructed a dataset for the training and evaluation of an algorithm model for vision-based autonomous steering control (V-ASC). In addition, we developed a benchmark environment to analyze and provide qualitative and quantitative evaluation results. In particular, considering the characteristics of the V-ASC evaluation, it is possible to evaluate the accuracy of not only the prediction result of the steering value of each frame but also the autonomous driving result after the continuous frame change. We implemented a software-in-the-loop simulator (S-ILS) that provides a view-transformed image frame corresponding to the steering value change based on the actual vehicle's dynamic model and the camera sensor model. We also developed a baseline V-ASC model based on the handcrafted feature and the newly proposed convolutional neural network (CNN) based end-to-end driving model to verify the evaluation environment of the constructed dataset and simulator. The comparison between the two methods confirmed that the end-to-end CNN technique exhibits superior accuracy in tracking the ground-truth (GT) result based on the human driving result; further, this technique is also superior in terms of the autonomy result of the test-driving scenario.
[Regular Paper] Stochastic Non-minimal State Space Control with Application to Automated Drug Delivery

This paper shows how a standard proportional-integral-plus controller, based on a non-minimal state space (NMSS) design, can be extended to reduce the effects of measurement noise and so yield smoother control inputs for automated drug delivery control applications. Use of a NMSS model for state variable feedback control design, in which all the states are based on the directly measured input and output variables, removes the need for state estimation. Nonetheless, a stochastic NMSS form, with a Kalman filter, can optionally be introduced to reduce the effect of measurement noise and so yield smoother control inputs. In this case, the Kalman filter attenuates measurement noise but does not address state disturbances. In this article, we propose a modification to the stochastic NMSS control system to enable the elimination of such state disturbances. This involves further extending the non–minimal state vector to include additional terms based on the innovations. We compare the deterministic, stochastic and extended stochastic NMSS controllers via a simulation of the control of anaesthesia using propofol.
Unsupervised Incident Detection Model in Urban and Freeway Networks

Reducing the effects of incidents by their early detection is one of a crucial requirements for incident management. This paper presents automated incident detection model based on unsupervised approach that uses only traffic observations as a model inputs. First, a novel self-tuning statistic is introduced as a feature generation function to capture spatio-temporal relationship of traffic data in both, urban and freeway networks. Next, these features are used as input in the segment-based mixture model that learns complex data distributions and their parameters. Then, we use the Mahalanobis distance to determine whether the traffic observation corresponds to an incident or recurrent traffic state. The model performance is demonstrated for two network examples, freeway with real data and urban with simulated data. Results show that the developed method achieves high accuracy rates and early incident detection compared to widely used approaches, such as California algorithm series and their extensions.
Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain

As an effective way to integrate the information contained in multiple medical images with different modalities, medical image fusion has emerged as a powerful technique in various clinical applications such as disease diagnosis and treatment planning. In this paper, a new multimodal medical image fusion method in nonsubsampled shearlet transform (NSST) domain is proposed. In the proposed method, the NSST decomposition is first performed on the source images to obtain their multiscale and multidirection representations. The high-frequency bands are fused by a parameter-adaptive pulse-coupled neural network (PA-PCNN) model, in which all the PCNN parameters can be adaptively estimated by the input band. The low-frequency bands are merged by a novel strategy that simultaneously addresses two crucial issues in medical image fusion, namely, energy preservation and detail extraction. Finally, the fused image is reconstructed by performing inverse NSST on the fused high-frequency and low-frequency bands. The effectiveness of the proposed method is verified by four different categories of medical image fusion problems [computed tomography (CT) and magnetic resonance (MR), MR-T1 and MR-T2, MR and positron emission tomography, and MR and single-photon emission CT] with more than 80 pairs of source images in total. Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.
Research on Safety Analysis Method of Functional Integrated Avionics Systems

Confronted with difficulties in the safety status analysis of avionics systems attributed to functional integration, a new safety analysis method based on a denoising-improved deep belief network, a deep learning algorithm, is proposed, realizing the mapping of system status parameters to overall system safety status within the context of multiple dimensions. By setting the aircraft environment surveillance system (AESS) as an instance, the engineering data was utilized to verify the feasibility of the method, yielding high classification performance. Comparative experiments with original DBN, DSAE, and NN demonstrated that the proposed method can achieve safety status identification directly from complex original data, and possesses strong classification robustness.
Enhancing GLSLIM Using User Preference Change Marking Algorithm

In this paper, we proposed a data preparation method, User Preference Change Marking (UPCM) algorithm, which aims to boost the performance of top-N recommendation. Integrating with a global-local recommendation, our method shows that the prepared datasets favor the recommendation method significantly. Global-and-local recommendation method not only considers the item-item similarity but also the user cluster one. The method uses two important characteristics of data to discover the trend of user preferences: (i) categorical dimension of the data, such as movie genre, product catalog, etc, and (ii) timestamp of the relation, such as timestamp of when a user rated a movie. The categorical dimension is useful in projecting additional information about the rating instead of focusing on the item relation solely. The timestamp is used to calculate rating frequencies and then activity trend. These two combined help our algorithm to remove inactive users from the dataset. As a result, the prediction performance is improved especially for the users who provide sufficient rating data.
Research of Classical Machine Learning Methods and Deep Learning Models Effectiveness in Detecting Anomalies of Industrial Control System

Modern industrial control systems (ICS) act as victims of cyber attacks more often in last years. These attacks are hard to detect and their consequences can be catastrophic. Cyber attacks can cause anomalies in the work of the ICS and its technological equipment. The presence of mutual interference and noises in this equipment significantly complicates anomaly detection. Moreover, the traditional means of protection, which used in corporate solutions, require updating with each change in the structure of the industrial process. An approach based on the machine learning for anomaly detection was used to overcome these problems. It complements traditional methods and allows one to detect signal correlations and use them for anomaly detection. Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation dataset was analyzed as example of industrial process. In the course of the research, correlations between the signals of the sensors were detected and preliminary data processing was carried out. Algorithms from the most common techniques of machine learning (decision trees, linear algorithms, support vector machines) and deep learning models (neural networks) were investigated for industrial process anomaly detection task. It's shown that linear algorithms are least demanding on computational resources, but they don't achieve an acceptable result and allow a significant number of errors. Decision tree-based algorithms provided an acceptable accuracy, but the amount of RAM, required for their operations, relates polynomially with the training sample volume. The deep neural networks provided the greatest accuracy, but they require considerable computing power for internal calculations.
A 1- $\mu$ s Ramp Time 12-bit Column-Parallel Flash TDC-Interpolated Single-Slope ADC With Digital Delay-Element Calibration

This work presents a hybrid column-parallel time-to-digital-converter interpolated (TDC) single-slope (SS) ADC with a digital delay element feedback. The proposed scheme solves the multiphase clock period matching problem in flash TDC-interpolation of SS ADCs without the use of a delay-locked-loop. The architecture employs open-loop delay elements for multiphase clock generation forming a lowered TDC radix of <2 adding redundancy and providing ability for gain calibration using a per-column digital multiplication operation. The presented digital feedback correction scheme is applied online and in-column after the end of each conversion, occupying less than 5% of the ramp time. The architecture is verified on a <inline-formula> <tex-math notation="LaTeX">$1024\times128$ </tex-math></inline-formula> linescan image sensor testchip manufactured in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P3M CMOS process. The ADC operates at 250 MHz and achieves a 1-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> ramp time and 4.4-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> digital correlated double sampling row time for a 12-bit linear A/D conversion.
Human-Agent Interaction Model Learning based on Crowdsourcing

Missions involving humans interacting with automated systems become increasingly common. Due to the non-deterministic behavior of the human and possibly high risk of failing due to human factors, such an integrated system should react smartly by adapting its behavior when necessary. A promise avenue to design an efficient interaction-driven system is the mixed-initiative paradigm. In this context, this paper proposes a method to learn the model of a mixed-initiative human-robot mission. The first step to set up a reliable model is to acquire enough data. For this aim a crowdsourcing campaign was conducted and learning algorithms were trained on the collected data in order to model the human-robot mission and to optimize a supervision policy with a Markov Decision Process (MDP). This model takes into account the actions of the human operator during the interaction as well as the state of the robot and the mission. Once such a model has been learned, the supervision strategy can be optimized according to a criterion representing the goal of the mission. In this paper, the supervision strategy concerns the robot's operating mode. Simulations based on the MDP model show that planning under uncertainty solvers can be used to adapt robot's mode according to the state of the human-robot system. The optimization of the robot's operation mode seems to be able to improve the team's performance. The dataset that comes from crowdsourcing is therefore a material that can be useful for research in human-machine interaction, that is why it has been made available on our web site.
Scaling-Up In-Memory Datalog Processing: Observations and Techniques

Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, program analysis, networking and decision making. Due to the large volumes of data being processed, several research efforts across multiple communities have explored how to scale up recursive queries, typically expressed in Datalog. Our experience with these tools indicate that their performance does not translate across domains---e.g., a tool designed for large-scale graph analytics does not exhibit the same performance on program-analysis tasks, and vice versa. Starting from the above observation, we make the following two contributions. First, we perform a detailed experimental evaluation comparing a number of state-of-the-art Datalog systems on a wide spectrum of graph analytics and program-analysis tasks, and summarize the pros and cons of existing techniques. Second, we design and implement our own general-purpose Datalog engine, called RecStep, on top of a parallel single-node relational system. We outline the techniques we applied on RecStep, as well as the contribution of each technique to the overall performance. Using RecStep as a baseline, we demonstrate that it generally out-performs state-of-the-art parallel Datalog engines on complex and large-scale Datalog evaluation, by a 4-6X margin. An additional insight from our work is that it is possible to build a high-performance Datalog system on top of a relational engine, an idea that has been dismissed in past work.
Towards Predictive Driving through Blind Intersections

This paper presents an approach for predictive driving when facing blind intersections based on expert data. Expert drivers anticipate and avoid potential dangerous situations. In most cases these complex behaviors cannot be reproduced by state of the art planning approaches. We present an analysis of expert drivers while passing through blind intersections, we extract useful driving features to model the intersection and propose a cost function based on those features. We use inverse reinforcement learning to extract a feature-based cost function and learn its parameters from driver data. Finally, feature weights are computed based on collected expert driving data (using 211 trajectories). Evaluation was performed in terms of trajectory and speed using modified Hausdorff distance. Experimental results show that the planner is capable of computing trajectories comparable to those ones of the expert drivers when facing blind intersections.
Vision-Based Gait Recognition: A Survey

In the digital world of today, global security issues have given rise to video surveillance devices. Gait-based human recognition is an emerging behavioral biometric trait for intelligent surveillance monitoring because of its non-contact and non-cooperation with subjects. Other benefits of gait recognition in video surveillance are that it can be acquired at a distance and help to identify an object under low-resolution videos. This paper surveys extensively the current progress made towards vision-based human gait recognition. This paper discusses historical research that performs analysis of gait locomotion and provides information on how gait recognition can be performed. This paper describes measuring metrics that can be used to measure the performance of gait recognition model under verification and identification mode. This paper also provides an up-to-date review of existing studies on gait recognition representations (model based and model free). We also provide an extensive survey of available gait databases used in state-of-art gait recognition models, created since 1998. Furthermore, it offers insight into open research problems that help researchers to explore unripe areas in gait analysis, such as occlusion, view variations, and appearance changes in gait recognition. This paper also identifies the future perspectives in gait recognition and also outlines the proposed work.
Designing Expressive Lights and In-Situ Motions for Robots to Express Emotions

In this paper, we explore how a utility robot might express emotions via expressive lights and in-situ motions. In most previous work, methods for either modality were investigated alone, leaving a huge potential to improve the expression of emotions by combining the two modalities. We present a series of three studies, one for investigating how well people might recognize emotions on the basis of expressive light cues alone, one for exploring how people might perceive affect towards in-situ motion characteristics, and one for further combining the two modalities and studying whether multi-modal expressions could be better recognized by people. Results from the first study show participants were not able to recognize target emotions with high accuracy. Results from the second suggest a relationship between the in-situ motion characteristics of a robot and perceived affect. Results from the third suggest that expressions that combine in-situ motions with expressive lights were better able to convey many emotions but not all. We conclude that adding in-situ motions to affective expressive lights appears to be better able to help convey emotions. These findings are important for designing affective behaviors for future utility robots that need to possess certain social abilities.
Multi-expertise Aware Participant Selection in Mobile Crowd Sensing via Online Learning

With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of appropriate participants from the huge user pool to perform the tasks. However, the capabilities of individual participants are usually unknown by the selection mechanism, which leads to the most challenging issue of participant selection. While online learning techniques can be used to learn the participant's capability, the diverse expertise of each individual makes a single capability metric is not sufficient. To address the multi-expertise of participants, in this paper we introduce a new self-learning architecture which leverages the historical performing records of participants to learn the different capabilities (both sensing probability and time delay) of participants. Formulating the participant selection problem as a combinational multi-armed bandit problem, we present an online participant selection algorithm with both performance guarantee and bounded regret. Extensive simulations with a real-world mobile dataset demonstrate the efficiency of the proposed solution.
Filtering-Based Layer-Wise Parameter Update Method for Training a Neural Network

Aiming at the difficulties in modeling variable nonlinear systems with noise interference, a network model with optimal generalization ability is established to realize the identification of the system. Traditional network parameter training method, like gradient descent method and least squares are centralized, and it is difficult to adaptively update the model parameters according to changes in the system. Firstly, in order to adaptively update the network parameters and quickly reflect the changes in the input and output of the system, the network weights are used as time-varying parameters, and some parameters in the network are updated by Kalman filtering algorithm. Then, in order to further improve the generalization ability of the network, EKF is used to update all the parameters in the network. Finally, the effectiveness of the algorithm is verified by an example of the standard data set UCI-ccpp.