Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sketch-based Interface for Real-time Control of Crowd Simulations that Use Navigation Meshes

Controlling crowd simulations typically involves tweaking complex parameter sets to attempt to reach a desired outcome, which can be unintuitive for non-technical users. This paper presents an approach to control pedestrian simulations in real time via sketching. Most previous work has relied on grid-based navigation to support the sketching approach, however this does not scale well for large environments. In contrast, this paper makes use of a tiled navigation mesh (navmesh), based on the open source tool Recast, to support pedestrian navigation. The navmesh is updated in real time based on the user's sketches and the simulation updates accordingly. Users are able to create entrances/exits, barriers to block paths, flow lines to guide pedestrians, waypoint areas, and storyboards to specify the journeys of crowd subgroups. Additionally, a timeline interface can be used to control when simulation events occur. The effectiveness of the system is demonstrated with a set of scenarios which make use of a 3D model of an area of a UK city centre created using data from OpenStreetMap. This includes a comparison between the grid-based approach and our navmesh approach.
UItra-Wideband Communication and Relative Localisation for Swarming Robots: UItra-Wideband Communication and Relative Localisation for Swarming Robots

A robot swarm of zebros (Dutch: ZEs Benige RObots) is being developed at the TU Delft, with the purpose of forming a large self-deploying sensor network that survey remote locations without the need for any pre-existing infrastructure, or be used for evaluating swarming algorithms in the field. Towards this goal, zebros need to be able to communicate with and localise nearby neighbours. Existing localisation solutions showed to be inadequate for the task, because they generally do not allow for localisation without existing infrastructure, do not allow scaling to a large swarm or because they rely on robot behaviour, which introduces a dependency of localisation on a specific swarming algorithm. We present a fully-localised method of localisation called Tangolation, which estimates the location of a neighbour as a range and an angle, accompanied by a confidence value from 1 (low) to 5 (high). With Tangolation, two nodes estimate each other’s next location from a range measurement using Two-Way Ranging (TWR), and the exchange of the nodes’ displacement since the last range measurement. Therefore, the two nodes only need each other to determine each other’s location as long as at least one of the two is moving, allowing Tangolation to scale well to larger swarms. No knowledge about the network topology or control over robot behaviour is needed, and communication between localising nodes only needs to happen sparsely, at a frequency of once every five seconds per neighbour. A fully distributed Time Division Multiple Acces (TDMA) protocol named Anarchic TDMA (AN-TDMA) was devised to support communcation within the robot swarm, where nodes synchronise the slot start times without the need for a special coordinating node that indicates the start of slots or frames. AN-TDMA showed to increase the reachable channel utilisation ratios by 55% to 67% compared to the ALOHA method recommended by Decawave. For TWR, the Decawave DW1000 transceiver was used, and the BNO055 IMU aided in displacement measuring. Tangolation was tested in a simulation with realistic conservative estimates of the noise over the range and displacement measurements. Even using conservative noise estimates, at least 95% of the estimated location angles are within the required 22◦ of the true value. Depending on the confidence level that Tangolation reports, 65% to 84% of the angle estimates are within 10◦ of the true value, which shows that Tangolation more than meets the set requirements.
Stroke-based Character Recognition with Deep Reinforcement Learning

Background elimination for noisy character images or character images from real scene is still a challenging problem, due to the bewildering backgrounds, uneven illumination, low resolution and different distortions. We propose a stroke-based character reconstruction(SCR) method that use a weighted quadratic Bezier curve(WQBC) to represent strokes of a character. Only training on our synthetic data, our stroke extractor can achieve excellent reconstruction effect in real scenes. Meanwhile. It can also help achieve great ability in defending adversarial attacks of character recognizers.
Detect User Transition Areas on Community Structure with Noise in 5G Mobile Environment

Detecting communities problems have been studied under various background and networks in last two decades. This paper poses a new problem, named community detection with noise, to find communities where nodes connect densely to each other under the background of user movement in 5G. Because the prospective small cells in 5G would make data lengthy [1] [2], this paper aims to figure out meaningful areas without seldom visited cells named as “noise”. Two kinds of methods are proposed. One is based on the normal community detection problem. Noise nodes are picked and deleted by proper quantities. The other is a heuristic algorithm that starts with the most important node, adding closely connected nodes into the community. Compared with classic GN algorithm, these two methods outperform in quality metrics that focus on internal density. And a practical example that gives user movement areas is showed.
Application of Multichannel Electrical Stimulation of the Neck Nervous Structures in Patients with Depressive Disorders: An fMRI Case Study

The article presents the pilot functional neuroimaging study results of the treatment process by means neuro-electrostimulator SYMPATHOCOR-01 in patients with depressive disorder. The study involved three patients. The changes in the brain's default mode network (DMN) as a result of neuro-electrostimulation course are demonstrated. Along with clinical improvement (HDRS-21 and BDI-II scales), changes in DMN's performance from neuroimaging data are shown. An increase in the medial prefrontal cortex and cingulate gyrus functional connection activity with various parts of the brain was noted. These changes indicate the activation of neuroplasticity processes and restoring the work of DMN in patients with depression.
The promise of natural language processing in healthcare

The healthcare industry generates data at a rapid rate, with no signs of slowing down. A large portion of this information takes the form of unstructured narrative text, making it difficult for computer systems to analyze the data in a usable format. However, automated analysis of this information could be incredibly useful in daily practice. This could be accomplished with natural language processing, an area of artificial intelligence and computational linguistics that is used to analyze and process large sets of unstructured data, namely spoken or written communication. Natural language processing has already been implemented in many sectors, and the industry is projected to be worth US$16 billion by 2021. Natural language processing could take unstructured patient data and interpret meaning from the text, allowing that information to inform healthcare delivery. Natural language processing can also enable intelligent chatbots, interacting and providing medical support to patients. It has the potential to aid physicians by efficiently summarizing patient charts and predicting patient outcomes. In hospitals, it has the ability to analyze patient satisfaction and facilitate quality improvement. Despite current technical limitations, natural language processing is a rapidly developing technology that promises to improve the quality and efficiency of healthcare delivery.
Real-time Fatigue Driving Recognition System Based on Deep Learning and Embedded Platform

The frequent occurrence of automobile traffic accidents seriously threatens the safety of human life and property. Therefore, fatigue driving detection has important social value and research significance. In consideration of the market demand of intelligent assistant driving system, we design a real-time driver fatigue detection system based on deep learning and ARM platform, which uses Samsung 6818A53 series ARM as the driver fatigue real-time detection platform. In order to reduce the interference caused by the change of light and the occlusion of sunglasses in the actual driving environment, the driver's face image is captured by USB infrared camera. Firstly, face detection and alignment are carried out by multi-task cascaded convolutional neural network; Then the eye region is obtained according to geometric relationship between the feature points; Moreover, the driver's eye state is identified by Convolutional Neural Network (CNN); Finally, fatigue judgment is made based on PERCLOS criterion. The system has been tested in the experimental simulation environment and the actual driving environment. The experimental results show that detection speed of the system can reach more than 20 frames per second, which meets the requirement of real-time detection.
GSBDTRUST: GROUP STRUCTURE-BASED DYNAMIC TRUST MODEL FOR PEER-TO-PEER NETWORKS

The trust model is one of the most important technologies of Peer-to-Peer (P2P) security. The popular EigenTrust reputation system can well resist the attack of malicious peers, which aggregates the extensive view of all other peers in the system to compute the global trust values for the peers, and relies on a set of pre-trusted peers heavily to ensure that the malicious peers do not subvert the system. Nevertheless, the EigenTrust model, which designates early participants or creators of a P2P network as pre-trusted peers statically, poses a high security risk, especially for the targeted attacks. To solve this problem, it is an inevitable trend to select pre-trusted peers dynamically according to the interaction between peers. And thus, this paper selects pre-trusted peers dynamically based on the time-interactivity of peers in the network, which can avoid a series of problems caused by statically specified pre-trusted peers. Meanwhile, the peers can not be effectively organized together for network models with community structure, which increases the unnecessary interaction between peers greatly, and aggravates the load rate of the system heavily. In order to better organize peers together in the distributed network, this paper proposes a P2P network trust model based on group structure based on the interaction characteristics of peers. To ensure the high credibility of P2P network, we choose the peers with high global trust values as pre-trusted peers in each group. The final simulation experiments show that the GSBDTrust model not only has strong robustness against malicious peer attacks with different thread models, but also has low query overhead and failure rate in resource search.
Vector Representation of Words for Detecting Topic Trends over Short Texts

It is a critical task to infer discriminative and coherent topics from short texts. Furthermore, people not only want to know what kinds of topics can be extract from these short texts, but also desire to obtain the temporal dynamic evolution of these topics. In this paper, we present a novel model for short texts, referred as topic trend detection (TTD) model. Based on an optimized topic model we proposed, TTD model derives more typical terms and itemsets to represent topics of short texts and improves the coherence of topic representations. Ultimately, we extend the topic itemsets obtained from the optimized topic model by vector space representations of words to detect topic trends. Through extensive experiments on several real-world short text collections in Sina Microblog, the results show our method achieves comparable topic representations than state-of-the-art models, measured by topic coherence, and then show its application in identifying topic trends in Sina Microblog. Keywords—topic model; short text; vector space representations; trend detection
Experimental Evaluation of SAFEPOWER Architecture for Safe and Power-Efficient Mixed-Criticality Systems

With the ever-increasing industrial demand for bigger, faster and more efficient systems, a growing number of cores is integrated on a single chip. Additionally, their performance is further maximized by simultaneously executing as many processes as possible. Even in safety-critical domains like railway and avionics, multicore processors are introduced, but under strict certification regulations. As the number of cores is continuously expanding, the importance of cost-effectiveness grows. One way to increase the cost-efficiency of such a System on Chip (SoC) is to enhance the way the SoC handles its power consumption. By increasing the power efficiency, the reliability of the SoC is raised because the lifetime of the battery lengthens. Secondly, by having less energy consumed, the emitted heat is reduced in the SoC, which translates into fewer cooling devices. Though energy efficiency has been thoroughly researched, there is no application of those power-saving methods in safety-critical domains yet. The EU project SAFEPOWER (Safe and secure mixed-criticality systems with low power requirements) targets this research gap and aims to introduce certifiable methods to improve the power efficiency of mixed-criticality systems. This article provides an overview of the SAFEPOWER reference architecture for low-power mixed-criticality systems, which is the most important outcome of the project. Furthermore, the application of this reference architecture in novel railway interlocking and flight controller avionic systems was demonstrated, showing the capability to achieve power savings up to 37%, while still guaranteeing time-triggered task execution and time-triggered NoC-based communication.
Optimization of Line Extraction Based on an Affine Camera Model

Line feature constitutes important geometric structure information in image processing and is significant in visual navigation and 3-D structure contour extraction of 3-D objects. The current state-of-the-art line extraction methods exhibit fast extracting speed and good extracting results. However, most line extraction methods face a problem, wherein a long line is easily fragmented. Thus, a long line is broken into several short segments due to local small changes or disturbances. To obtain longer and more useful line segments without the aid of external information, this paper proposes an improved line extraction method called affine-lines, which is based on an affine camera model. First, the affine camera model is used to simulate affine projections from different viewing angles, and a sequence of affine simulated images is obtained via affine transformation matrices. Subsequently, line segments are extracted from the original image and each simulated image. Each set of line segments on the simulated image is back-projected on the original image based on its corresponding inversely affine transformation matrix. Finally, the line segments on the original image are used as references to sequentially purify and optimize subsequent lines. Several defined geometric constraints are used to eliminate pseudo lines and combine short lines. Thus, fragmented lines are connected, and short lines are converted into long lines. Given several sets of close-range and aerial images, experiments are performed and compared via a state-of-the-art line extraction algorithm, called the line segment detector (LSD). The results indicate that the proposed method significantly increases the lengths of the obtained line segments, significantly reduces the fragmentation effect, and obtains more useful line segments. The extracted improved line segments are applied to line matching for building structures, and promising results are obtained.
A Survey on Online Social Voting Using Collaborative Filtering Based Recommendation Systems

Superb improvement inside the idea of online informal communities (OSNs) of late. The greater part of existing on the web interpersonal organizations like Face book and Twitter district unit planned to slant towards data talk act to an outsized assembling of individuals and in addition raises grouping of assurance and security issues. Regardless of the way that OSNs licenses one customer to limit access to her/his knowledge, eventually they are doing not give any instrument to maintain security thoughts over data related to various customers. In the midst of this paper, we tend to propose relate approach to manage empower pleasing security organization of shared learning in OSNs. We tend to expand and detail a multiparty get to organization show; named Evidence based aggregate system to discover the substance of voting in OSNs, nearby a multiparty approach assurance point and a course of action social control instrument. We tend to besides demonstrate the relevance of our approach by executing a proof-of-thought case encouraged in Face book.
Current Trend in Wireless Networks

In this study, a general overview of the current trend wireless network has been presented the explosive parallel growth of both the Internet and cellular telephone services are becoming the two most important phenomena that impact the required modern feasible and secure telecommunications. A wireless service technology has become well-known in technology markets. WLAN implemented the development of the wired LAN. Industries produce the required components, which lead to the fast developments in designing and implementing of such networks. The merging of wireless network deemed to be a solution to some of the problems of the wired networks. In this survey study, we will try to discuss the advantages of wireless devices and indicate the challenges involved in this technology from the point of view of its foundation, architecture, requirements, its components, and protocols.
IMPLEMENTASI ALGORITMA FUZZY LOGIC PADA ROBOT ARM DENGAN MEMANFAATKAN ACCELEROMETER SMARTPHONE ANDROID

Now the development of smart phone technology has evolved rapid, especially Smartphone Android. Where multiple sensors embedded in a mobile phone, primarily an accelerometer sensor support be used of smartphone phone. Accelerometer is used to measure the acceleration reviews Angle X, Y and Z smartphone. So that the sensor is used to review the Movement Robot Arm matching is done with corner acceleration motion X, Y and Z smartphone Method on the system is backed with Fuzzy Logic the which serves to Determine the movement of the robot arm that is sent to the microcontroller. Arm robot system is processed using Matlab 6.1 and using the Software Arduino, and then implanted into the microcontroller, so that the robot goes According movement. Keywords : Android, Accelerometer, Robot Arm, Fuzzy Logic
Enhanced Vehicle Detection and Identification Using Histogram of Oriented Gradients

Object detection and classification are necessary components in an artificially intelligent autonomous system. It is expected that these artificially intelligent autonomous system venture on to the street of the world, thus requiring detection and classification of car objects commonly found on the street. The identification and classification of object in an image should be faster and accurate. The aim of the proposed system is to detect the object as soon as possible with better accuracy and improved performance even if the object varies in appearance. Object identification and classification is a challenging process when the object of same category with large variation appears. Though number of papers deal with appearance variation, object detection process is considered to be slower. In proposed work, the detection speed is improved by using the optimized features, the object is detected and the object type was identified. Keywords— Detection, Classification, Multi-posed vehicle
A Framework for Exploring Finite Models

This thesis presents a framework for understanding first-order theories by investigating their models. A common application is to help users, who are not necessarily experts in formal methods, analyze software artifacts, such as access-control policies, system configurations, protocol specifications, and software designs. The framework suggests a strategy for exploring the space of finite models of a theory via augmentation. Also, it introduces a notion of provenance information for understanding the elements and facts in models with respect to the statements of the theory. The primary mathematical tool is an information-preserving preorder, induced by the homomorphism on models, defining paths along which models are explored. The central algorithmic ideas consists of a controlled construction of the Herbrand base of the input theory followed by utilizing SMT-solving for generating models that are minimal under the homomorphism preorder. Our framework for modelexploration is realized in Razor, a model-finding assistant that provides the user with a read-eval-print loop for investigating models.
A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil

The following dissertation aims to show the benefits of a forecast combination between an econometric and a deep learning approach. On one side, a Factor Augmented Vector Autoregressive Model (FAVAR) with naming variables identification following Stock and Watson (2016) 1 ; on the other side, a Stacked De-noising Auto-Encoder with Bagging (SDAE-B) following Zhao, Li and Yu (2017) 2 are implemented. From January 2010 to September 2018 Two-hundred-eighty-one monthly series are used to predict the price of the West Texas Intermediate (WTI). The model performance is analysed by Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Directional Accuracy (DA). The combination benefits from both SDAE-B’s high accuracy and FAVAR’s interpretation features through impulse response functions (IRFs) and forecast error variance decomposition (FEVD).
A Video-texture based Approach for Realistic Avatars of Co-located Users in Immersive Virtual Environments using Low-cost Hardware

Representing users within an immersive virtual environment is an essential functionality of a multi-person virtual reality system. Especially when communicative or collaborative tasks must be performed, there exist challenges about realistic embodying and integrating such avatar representations. A shared comprehension of local space and non-verbal communication (like gesture, posture or self-expressive cues) can support these tasks. In this paper, we introduce a novel approach to create realistic, video-texture based avatars of co-located users in real-time and integrate them in an immersive virtual environment. We show a straight forward and low-cost hard- and software solution to do so. We discuss technical design problems that arose during implementation and present a qualitative analysis on the usability of the concept from a user study, applying it to a training scenario in the automotive sector.
Dual-Channel Attention Model for Text Sentiment Analysis

Focused on the issue that text information cannot be fully extracted by the single-channel neural network model, the Dual-Channel Attention Model (DCAM) is proposed for text sentiment analysis. Firstly, text is represented in the form of a matrix using a word vector trained by Word2Vec. Secondly, the matrix is used as input data and sent to Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks for feature extraction. Thirdly, an attention model is introduced to extract important feature information. Finally, the text features are merged, and the classification layer is used to classify the sentiment. The model is evaluated on a Chinese corpus. According to the experimental results, the accuracy of the proposed model can reach 92.7%, which is obviously superior to other single-channel neural network models.