Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Multi-Lane Detection and Modeling for Embedded Platforms

Most Advanced Driver Assistance Systems (ADAS) or Autonomous Driving (AD) functions require the ability to perceive the road and its elements around the ego-vehicle. The precise localization of other road participants (e.g. vehicles, pedestrians, traffic signs) is demanded at lane level, to enable higher semantic analysis of the scene. This requires a lane detection and modeling stage able to provide the number of existing lanes, and their precise local geometry. The current trend in computer vision is to use the full power of GPU technology with deep learning-based detection methods, which requires costly high-end platforms, and difficult the co-existence with other heavy-processing functions (e.g. vehicle detection), specially critical when considering a single platform processing multiple cameras and Laser scanners. In this paper we propose an efficient lane detection and modeling pipeline, composed of optimized steps for segmentation, transformation, modeling, control and tracking. The method is able to detect multiple lanes and their curvature, in continuous function, with minimal processing power requirements, thus enabling its implementation into low-cost embedded platforms. Experimental results support our claims, and demonstrate that the proposed method outperforms other methods in the literature in computational cost, while keeping good accuracy results for a variety of road types.
Initial Coin Offerings (ICOs): The role of Social Media for Organizational Legitimacy and Underpricing

Recently, Initial Coin Offerings (ICOs) have developed substantial influence as a funding mechanism while being largely neglected by research. In parallel, microblogs were identified as a source of organizational legitimacy and underpricing in Initial Public Offerings. In this paper, we investigate the influence of different types of social media, specifically microblogs and discussion forums, on organizational legitimacy in the realm of ICOs. As legitimacy cannot be measured directly, we follow renowned scholars and use underpricing as the dependent variable and an established proxy for legitimacy. We analyze data of 95 ICOs and provide first results indicating that discussion forum activity confers organizational legitimacy. Additionally, we show that the influence of strategic twitter management by the legitimacy-accruing organization is mediated by the activity of independent users on discussion forums. Furthermore, we introduce the phenomenon ICO as a funding mechanism fundamentally based on information technology and motivate further research on this topic.
Joint Subcarrier Assignment and Downlink-Uplink Time-Power Allocation for Wireless Powered OFDM-NOMA Systems

In this paper, a wireless powered orthogonal frequency division multiplexing (OFDM)-non-orthogonal multiple access (NOMA) system is considered. The subcarrier assignment, energy harvesting/information transmission (EH/IT) time allocation, and downlink-uplink power allocation are jointly optimized to maximize the system sum-throughput while taking user fairness into account. Due to the intractability of the considered problem, we first decompose the original problem into two subproblems, namely, subcarrier assignment subproblem with fairness consideration and joint downlink-uplink time-power allocation subproblem. For subcarrier assignment sub-problem, with given joint downlink-uplink time-power allocation, a locally optimal subcarrier assignment algorithm is proposed based on the many-to-many matching theory. Then with given subcarrier assignment, an algorithm based on golden section method, iterative water-filling and projected subgradient method is proposed for achieving the optimal solution to joint downlink-uplink time-power allocation subproblem. An alternate iteration framework is further proposed to jointly optimize two subprob-lems for maximizing the system sum-throughput. Simulation results validate the effectiveness of the proposed algorithms, i.e., the proposed framework approaches to the joint downlink-uplink time-power allocation optimal solution with less than 5% throughput loss compared to the upper bound and with only about 40% complexity of the interior-point algorithm.
Morphing Detection Using a General- Purpose Face Recognition System

Image morphing has proven to be very successful at deceiving facial recognition systems. Such a vulnerability can be critical when exploited in an automatic border control scenario. Recent works on this topic rely on dedicated algorithms which require additional software modules deployed alongside an existing facial recognition system. In this work, we address the problem of morphing detection by using state-of-the-art facial recognition algorithms based on hand-crafted features and deep convolutional neural networks. We show that a general-purpose face recognition system combined with a simple linear classifier can be successfully used as a morphing detector. The proposed method reuses an existing feature extraction pipeline instead of introducing additional modules. It requires neither fine-tuning nor modifications to the existing recognition system and can be trained using only a small dataset. The proposed approach achieves state-of-the-art performance on our morphing datasets using a 5-fold cross-validation.
Removal of Insufficiently Informative Data to Support System Identification in MISO Processes

Model accuracy can be degraded by using insuf-ficiently informative data for parameter estimation. This effect can be observed in case of model structures where the input transfer function and the noise transfer function share the same denominator. Equation error models such as ARX, ARMAX or ARARX are examples of model structures exhibiting the latter characteristic. Removal of insufficiently informative data for parameter estimation may increase model accuracy as reported for SISO systems. In this paper, a search method to find informative intervals in MISO systems is introduced. The removal criteria are based on comparison between means of a regression matrix associated to a model class and a threshold related to the conditioning of the matrix. These matrix means are used as quality factor to discard insufficiently informative data. The performance of the search method is demonstrated in a case study.
Alexithymia Predicts Attrition and Outcome in Weight-Loss Obesity Treatment

Obesity is a psychosomatic condition characterized by a complex interaction of biological and psychological factors and a large body of research has aimed to identify variables limiting efficacy and determining high attrition rates in weight loss programs. In this study, we used the Diagnostic Criteria for Psychosomatic Research (DCPR), designed to broaden the clinician’s perspective on patients’ problems by providing additional clinical information not found in the more traditional psychiatric classification, to predict psychosomatic variables that may limit efficacy and determine attrition in clinical interventions with people with obesity. We evaluated 82 consecutive participants with obesity at baseline for psychopathology, psychosomatic correlates, psychological distress, and eating-related symptoms before entering a weight loss program. Regression models were used to assess attrition and outcome at a 6-month follow-up and per-protocol and intention-to-treat analyses were performed. DPCR alexithymia significantly predicted attrition (OR = 6.9), and unsuccessful weight-loss (OR = 11.3). These findings suggest that the identification of psychosomatic factors, in addition to psychological and psychopathological factors, may predict adherence to weight-loss programs.
A Multi Input Converter for Interfacing Battery and Supercapacitor to the Load

Hybrid Energy Storage System (HESS), i.e. battery assisted by supercapacitor is used to safeguard the battery from power surges, which can be much greater than the steady state power requirement. These power surges severely deteriorate the battery lifetime. In a conventional HESS, two converters are used, one for battery and the other for supercapacitor. This could be expensive since one of the converters has to be rated for the surge power requirement. In this paper, a multi input converter has been proposed, in which the supercapacitor directly supplies the load without any power conversion stage during high power demand. To compensate for the changing supercapacitor voltage, a compensating voltage has been added to the supercapacitor output. This leads to reduced ratings for the required power electronic conversion stages for the HESS. Battery current is regulated to protect the battery from sudden current changes, thus increasing its lifetime. Simulation results have been presented for the proposed converter, which show that the proposed converter serves the purpose of regulating the load voltage and protecting the battery from power surges.
Multi-scale Semantic Segmentation Enriched Features for Pedestrian Detection

Pedestrian detection, as a branch of computer vision, has many significant real world applications such as autonomous driving or human behavior analysis. In this paper, we propose a convolutional neural network (CNN) based pedestrian detection framework which can be trained end-to-end. We design a feature enrichment unit to produce more representative features to improve detection performance. The feature enrichment units receive feature maps from the body network layer by layer and convey features in a backward manner. Together they produce multi-scale semantic segmentation results as extra features and merge them with feature maps of the body network. Then the merged feature maps will be fed into the detector to produce final predictions. The feature enrichment unit is easy to embed into existing convolutional neural networks based detection frameworks since it receives and produces feature maps. We use an alternating training strategy to train the network for detection and segmentation respectively and achieve considerable accuracy. The multi-scale feature enrichment units improve detection accuracy significantly as proven by the experiments.
UAV Aided Irrigation Using Object Detection Through Wireless Communication Technology

At a time when all sectors are diversifying the range of applications of unmanned aerial vehicles, one of the largest communities in India that is yet to reap the benefits of this advanced technology is that of the farmers. Survey of vast swathes of agricultural land to determine how well they are irrigated is often a laborious process. This procedure can be rendered simple using UAVs. The aim of this work is to assess whether a parcel of farmland is irrigated, and to what extent. The use of UAVs will enable survey of large tracts to be surveyed in a matter of days. Moreover, use of UAVs easily offsets the disadvantages of accessing real-time data from satellites, which normally takes months to obtain. Among the tools that will be used for this purpose include remote sensing technology, image processing software and bluetooth.
Optimal Design of Permanent Magnet Linear Synchronous Motors at Multispeed Based on Particle Swarm Optimization Combined With SN Ratio Method

This paper focuses on the optimal design of permanent magnet linear synchronous motor (PMLSM) with trapezoidal permanent magnets (TPMs) at multispeed that are applied in laser-engraving machines. Particle swarm optimization (PSO) and signal-to-noise (SN) ratio method are proposed to minimize the thrust ripple and maintain average thrust. First, an analytical model based on equivalent magnetic charge method is presented to analyze the PMLSM with TMPs, and the accuracy of the proposed analytical method is validated by finite-element method. Then, the PSO is applied to optimize the pole pitch <inline-formula><tex-math notation="LaTeX">$(\tau)$</tex-math> </inline-formula>, air gap length <inline-formula><tex-math notation="LaTeX">$(\delta)$</tex-math></inline-formula>, bottom angle of TPM <inline-formula><tex-math notation="LaTeX">$(\theta)$</tex-math></inline-formula>, and magnet height <inline-formula><tex-math notation="LaTeX">$(h)$</tex-math></inline-formula> to obtain the optimized structure parameters at a single speed. Application of PSO is repeated at different speeds to obtain their optimal structure parameters. Finally, the SN ratio method is used to synthetically optimize the parameters obtained at different speeds to acquire the optimized parameters at multispeed. Experimental results of prototype can confirm the validity and superiority of the method.
Macro action selection with deep reinforcement learning in StarCraft

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also widely accepted as a challenging testbed for AI research because of its enormous state space, partially observed information, multi-agent collaboration, and so on. With the help of annual AIIDE and CIG competitions, a growing number of SC bots are proposed and continuously improved. However, a large gap remains between the top-level bot and the professional human player. One vital reason is that current SC bots mainly rely on predefined rules to select macro actions during their games. These rules are not scalable and efficient enough to cope with the enormous yet partially observed state space in the game. In this paper, we propose a deep reinforcement learning (DRL) framework to improve the selection of macro actions. Our framework is based on the combination of the Ape-X DQN and the Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as LastOrder. Our evaluation, based on training against all bots from the AIIDE 2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning rate, outperforming 26 bots in total 28 entrants.
Delving into the Synthesizability of Dynamic Texture Samples

The example-based dynamic texture synthesis (EDTS) methods have emerged in multitude, dedicated to generating new dynamic textures (DTs) of high quality from an input exemplar. The problem of EDTS has been studied for several decades, but none of the existing synthesis methods are able to tackle all kinds of dynamic textures equally well. Rather than focus on new synthesis methods, we turn to another way to help EDTS by investigating dynamic texture synthesizability - how synthesizable a specific dynamic texture sample is by EDTS. We propose to predict synthesizability score of a given dynamic texture sample, and suggest which EDTS method is best suited to synthesize it. To this end, we compiled a dynamic texture dataset and annotated each DT in terms of synthesizability. We address the problem of learning dynamic texture synthesizability by using regression model to train a predictor on the data collection. More precisely, we first characterize DT samples by a set of spatiotemporal features. Then, based on dynamic texture descriptors, we train regression models to estimate synthesizability scores and use an additional classifier to choose the optimal EDTS methods. The experiments demonstrate that our method can predict the synthesizability of DT samples effectively.
ADS-B Signal Signature Extraction for Intrusion Detection in the Air Traffic Surveillance System

Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance system used in Air Traffic Control. In this system aircraft transmit their own information (identity, position, velocity etc.) to any equipped listener for surveillance scope. ADS-B is based on a very simple protocol and doesn't provide any kind of authentication and encryption, making it vulnerable to many types of cyber-attacks. In the paper, it is proposed the use of airplane/transmitter RF level features to perform a test to distinguish legitimate messages from fake ones. The received signal features extraction process is described and an intrusion detection algorithm is proposed and evaluated by the use of real data. The results show that by a simple signal processing add-on on a classical (and low cost) ADS- B receivers, it is possible to detect if the ADS- B messages are sent using the expected hardware or not in the 85% of the case.
Adaptive Locality Preserving based Discriminative Regression

Classical linear regression not only lacks of the flexibility in fitting the label, but also ignores to preserve the intrinsic local geometric structure of data, which leads to overfitting. In this paper, we propose a novel discriminative regression method, called adaptive locality preserving based discriminative regression (ALPDR), to address these problems. Firstly, a locality preserving constraint regularized by the adaptive weight is introduced to preserve the intrinsic geometric structures of data, in which the similar points of the same class are adaptively pulled together by the projection. Secondly, ALPDR directly learns the discriminative target matrix from data based on the given label information, which allows more freedom in label fitting and simultaneously enlarges the margins between different classes. Thirdly, ALPDR imposes a row-sparsity constraint on the projection, which enables the method to adaptively select the most discriminative features from data such that the negative influence of noises and redundant features can be eliminated. Finally, an efficient iterative algorithm is provided to optimize the model. Extensive experiments show that the proposed method outperforms the other state-of-art methods, which proves the effectiveness of the proposed method.
An HTTP Adaptive Streaming Method Considering Motion Intensity

This paper proposes an HTTP adaptive streaming method considering motion intensity of video scenes for improving quality of experience (QoE) performance. Motion score is used as one of the metrics for video multimethod assessment fusion (VMAF), which represents motion intensity. The proposed method calculates the motion score of each two-frame difference in advance. Based on the motion scores, a client adaptively selects the quality of the next video segment. The proposed method selects a high-quality segment when the motion intensity of the scene is high in order to improve QoE performance, whereas in order to prevent stalling, the proposed method selects a low-quality segment for a low-motion segment. We implement the proposed method on a real system to evaluate the performance, and show the effectiveness of the proposed method.
Joint Denoising and Super-Resolution via Generative Adversarial Training

Single image denoising and super-resolution are sitting in the core of various image processing and pattern recognition applications. Typically, these two tasks are handled separately, without regarding to joint reinforcement and learning. The former deals with equal-size pixel-to-pixel translation, while the latter deals with scaling up amount of input pixels. In this paper, we propose a Generative Adversarial Network(GAN) towards joint learning of single image denoising and super-resolution. In principle, our design allows both tasks to share several common building blocks, with the linking between both outputs to reinforce each other. Such a reinforcement is accomplished via designing a novel generative network through optimizing a novel loss function to achieve both denoising and super-resolution. Quantitatively comparing to a set of alternative approaches and baselines, the experiment demonstrated superior performance our method in denoising and super-resolution with high upscaling factors.
SmartQ: A Question and Answer System for Supplying High-Quality and Trustworthy Answers

Question and Answer (Q&A) systems aggregate the collected intelligence of all users to provide satisfying answers for questions. A well-developed Q&A system should incorporate features such as high question response rate, high answer quality, a spam-free environment for users and bridging disjoint social clusters. Previous works use reputation systems to achieve the goals. However, these reputation systems evaluate a user with an overall rating for all questions the user has answered regardless of the question categories, thus the reputation score does not accurately reflect the user's ability to answer a question in a specific category. We propose SmartQ: a reputation based Q&A System. SmartQ employs a category and theme based reputation management system to evaluate users’ willingness and capability to answer various kinds of questions. The reputation system facilitates the forwarding of a question to favorable experts, which improves the question response rate and answer quality. SmartQ bridges disjoint social clusters by calculating reputation scores for each cluster on each question theme; SmartQ incorporates a lightweight spammer detection method to identify potential spammers. In order to reduce the loads of experts, we propose a strategy to recommend suggested answers from similar questions to each new question. Our trace-driven simulation on PeerSim demonstrates the effectiveness of SmartQ in providing good user experience. We then develop a real application of SmartQ and deploy it for use in a student group in Clemson University. The user feedback shows that SmartQ can provide high-quality answers for users in a community.
NFV-Inspector: A Systematic Approach to Profile and Analyze Virtual Network Functions

Network Function Virtualization (NFV) focuses on decoupling network functions from proprietary hardware (i.e., middleboxes) by leveraging virtualization technology. Combining it with Software Defined Networking (SDN) enables us to chain network services much easier and faster. The main idea of using these technologies is to consolidate several Virtual Network Functions (VNFs) into a fewer number of commodity servers to reduce costs, increase VNFs fluidity and improve resource efficiency. However, the resource allocation and placement of VNFs in the network is a multifaceted decision problem that depends on many factors, including VNFs resource demand characteristics, arrival rate, configuration of underlying infrastructure, available resources and agreed Quality of Services (QoS) in Service Level Agreements (SLAs). This paper presents a bottom-up open-source NFV analysis platform (NFV-Inspector) to (1) systematically profile and classify VNFs based on resource capacities, traffic demand rate, underlying system properties, placement of VNFs in the network, etc. and (2) extract/calculate the correlation among the QoS metrics and resource utilization of VNFs. We evaluated our approach using an emulated virtual Evolved Packet Core platform (Open5GCore) to showcase how complex relation among various NFV service chains can be systematically profiled and analyzed.
Semi-classical Green functions

Let H(x, p) ∼ H<inf>0</inf>(x, p) + hH<inf>1</inf>(x, p) + … be a semi-classical Hamiltonian on T<sup>∗</sup>R<sup>n</sup>, and Σ<inf>E</inf> = {H<inf>0</inf>(x, p) = E} be a non-critical energy surface. Consider f<inf>h</inf>, a semi-classical distribution (the "source") microlocalized on a Lagrangian manifold Λ which intersects cleanly the flow-out Λ<inf>+</inf> of the Hamilton vector field ${X_{{H_0}}}$ in Σ. Using Maslov’s canonical operator, we look for a semi-classical distribution u<inf>h</inf> satisfying the limiting absorption principle and H<sup>w</sup>(x, hD<inf>x</inf>)u<inf>h</inf> = f<inf>h</inf> (semi-classical Green function). In this report, we elaborate (still at an early stage) on some results announced in [1] and provide some examples, in particular, from the theory of wave beams.