Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision Anticipation for Driving Assistance Systems

Anticipating the correctness of imminent driver decisions is a crucial challenge in advanced driving assistance systems and has the potential to lead to more reliable and safer human-robot interactions. In this paper, we address the task of decision correctness prediction in a driver-in-the-loop simulated environment using unobtrusive physiological signals, namely, eye gaze and head pose. We introduce a sequence-to-sequence based deep learning model to infer the driver’s likelihood of making correct/wrong decisions based on the corresponding cognitive state. We provide extensive experimental studies over multiple baseline classification models on an eye gaze pattern and head pose dataset collected from simulated driving. Our results show strong correlates between the physiological data and decision correctness, and that the proposed sequential model reliably predicts decision correctness from the driver with 80% precision and 72% recall. We also demonstrate that our sequential model performs well in scenarios where early anticipation of correctness is critical, with accurate predictions up to two seconds before a decision is performed.
Movie Success Prediction Using ML

Movies continue to be a major source of entertainment in any country. However, this industry also incurs a lot of losses when the movie does not perform at the Box Office. Our solution will try to predict the success rate of a movie by doing predictive analysis on the various features of the movie. Our model will predict the Success, based on different attributes / features of the movie. i.e. Movie crew (including director producer, music director), Movie plot (Storyline), Box-Office revenue, Audience and Critics reviews / ratings. In this paper a detailed study of machine learning algorithms such as Random Forest, DecisionTree, K-NearestNeighbours (KNN), NLP, XGBoost Classifier and Deep Neural Network were done and were implemented on IMDB dataset for predicting Success of movies. Based on the results, XGBoost Classifier gave best accuracy.
Customer-Centric Vehicle Usage Profiling Considering Driving, Parking, and Charging Behavior

We introduce a novel method to generate customer vehicle usage profiles, representing driving, parking, and charging behavior. Synthesizing usage profiles is the key to support decision-making for customer-centric vehicle development. So far, current methods for vehicle development focus exclusively on driving cycles, whereas the representability of parking and charging behavior, which is essential for electromobility, remains neglected. In this paper, we perform vehicle usage profiling by (i) allocating time spans for driving and parking sections, (ii) optimally selecting driving sections from a trip library established from testing fleets, (iii) rearranging driving sections with parking sections into e.g. week profiles, and (iv) integrating the inferred charging behavior of the profiles together with simulation. Using a model of an exemplary plugin hybrid electric vehicle and given raw data from our testing fleets, we demonstrate that our method is capable of estimating the influence of driving, parking and charging behavior on vehicle loads.
The Visual Quality of Teleoperated Driving Scenarios How good is good enough?

Teleoperated Driving can be a solution for situations that can not be solved by automated vehicles on their own. A human driver resolves the situation by controlling the vehicle remotely. Thus, drivers need to get an impression of the vehicle’s environment. This is typically achieved by streaming video data through existing cellular networks. However, the available bitrate and the respective stream quality can vary drastically. This paper utilizes an user study to investigate, which video qualities are sufficient for different driving scenarios and how they are correlated with the required bitrates. Results show, that scenarios have a strong influence on the users’ perceived quality ratings.
Cycle-level vs. Second-by-Second Adaptive Traffic Signal Control using Deep Reinforcement Learning

With unrelenting growth in population and urbanization, cities face escalating challenges in providing fast and reliable mobility. A significant source of delay for urban commuters is related to signalized intersections. optimizing traffic signals to maximize the capacity of intersections is of critical importance for cities. Although this topic has been around for decades, recently, Deep Reinforcement Learning (DRL) approaches have begun to be used for intelligent traffic signal control. The result is a promising new generation of traffic signal controllers (TSCs). They are model-free, adaptive, and capable of exploiting newer pervasive sensory technologies (e.g., connected vehicles). While most of the RL-based TSCs focus on second-by-second level decision making, the industry favors controllers that manipulate signals less frequently. This changes the RL control problem from being in a discrete to a continuous action space. With the lack of RL-based TSCs that can handle a continuous action space, we propose a novel RL-based cycle-level TSC that determines the phase timings once every cycle. Our controller uses Proximal Policy optimization (PPO) as one of the most promising continuous action RL algorithms to produce signal timings in a cycle. We test our proposed controller against one RL-based second-level controller as well as an optimized fixed-time traffic signal controller and compare the results.
Quantifying the impact of connected and autonomous vehicles on traffic efficiency and safety in mixed traffic

Connected and Autonomous Vehicles (CAVs) are expected to bring major transformations to transport efficiency and safety. Studies show a range of possible impacts, from worse efficiency of CAVs at low penetration rates, to significant improvements in both efficiency and safety at high penetration rates and loads. However, these studies tend to explore efficiency and safety separately, focus on one type of a road network, and include only cars rather than other vehicle types. This paper presents a comprehensive study on impact of CAVs on both efficiency and safety, in three types of networks (urban, national, motorway), simulating different penetration rates of vehicles with multiple levels of automation, using historical traffic data captured on Irish roads. Our study confirms existing results that near-maximum efficiency improvements are observed at relatively low penetration rates, but reveals further insights that the exact penetration ranges between 20% and 40% depending on the network type and traffic conditions. Safety results show a 30% increase of conflicts at lower penetration rates, but 50-80% reduction at higher ones, with consistent improvement for increased penetration. We further show that congestion has a higher impact on conflicts than penetration rates, highlighting the importance of unified evaluation of efficiency and safety.
Self-supervised Deep Learning for Flower Image Segmentation

Segmentation plays an important role in imagebased plant phenotyping applications. Deep learning has led to a dramatic improvement in segmentation performance. Most deep learning-based methods are supervised and require abundant application-specific training data. Considering the wide range of plant phenotyping applications, such data may not be always available. To mitigate this problem, we introduce a segmentation method that exploits the power of deep learning without using any prior training. In this paper, we specifically focus on flower segmentation. Recurrence of information inside a flower image is used to train an image-specific deep network that is subsequently used for segmentation. The proposed method is self-supervised as it exploits the internal statistics of input image without using any prior labeled data. To the best of our knowledge, this is the first unsupervised deep learning-based method proposed for single-image flower segmentation.
Relay Selection Based on Location Prediction in Collaborative Communication

This paper studies the relay selection in wireless cooperative communication systems. Since the high mobility of Unmanned Aerial Vehicles (UAVs) will lead to the change of link state, a relay selection algorithm based on position prediction is proposed in this paper to better study this problem. First, the Unscented Kalman Filter (UKF) algorithm is used to predict the real-time location information of each UAV, and based on the location information, it is determined whether the current UAV relay node needs to be replaced. The real-time location information of each UAV is predicted by UKF algorithm, which is the basis for determining whether the current UAV relay node needs to be replaced. Then, the outage probability of each standby UAV relay node is calculated, and the standby UAV relay node is sorted according to the link outage probability. Finally, the greedy algorithm is used to find the node with the smallest interruption probability among the backup UAV relay nodes as the next UAV relay node. The simulation results show that in terms of position prediction, the UKF algorithm is more accurate than the Kalman Filtering (KF) algorithm, and the error is smaller. In terms of interruption probability, the relay selection algorithm proposed in this paper reduces the interruption probability of the link, and the interruption probability of the link is more stable.
From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)

The validation and verification (V&V) of highly automated driving depends on reference data in the different steps of the development process. While there exist a variety of datasets to optimize and benchmark perception algorithms, large trajectory datasets with complex interactions between traffic participants are quite rare. Such data offers the opportunity to derive virtual traffic scenarios and road user behaviour models that can be used to evaluate in-vehicle perception systems as well as decision making modules.We present the Test Area Autonomous Driving BadenWürttemberg (TAF-BW) Dataset that provides globally referenced road user trajectories extended with context information like traffic light signals and high definition map data recorded in the test area. We point out an exemplary application that becomes possible with the dataset: building semantic scene models that can be used for criticality assessment in traffic scenes. We conclude with a short outlook on future extensions.
Determination of the Effect of Glycosaminoglycan Mimetic Peptide Hydrogels on Cell Viability for Cartilage Tissue Engineering Applications

Cartilage is a tissue type that doesn’t have blood vessels, neural networks and lymphatic vessels. Regeneration of the cartilage tissue is limited due to the small number of cells in the articular cartilage, low vascularization and low cell migration to the damage site. Biomaterial scaffolds are used for regeneration of cartilage since the cartilage needs structural and metabolic support in case of any damage. Mimicking the network structure of the natural cartilage is extremely important and hydrogels are good candidates for cartilage tissue engineering due to 3-D structure and the high-water holding capacity similar to the natural tissue. Also, biomimetic self-assembling peptides (SAP) can self-assemble with physiological conditions and form SAP hydrogels. Glycosaminoglycan (GAG) is crucial components of natural cartilage matrix, they are negatively charged chains, and they maintain the mechanical properties of tissue. In this study, it was develop GAG mimetic SAP hydrogels that can mimic original cartilage, and to determine the effect of these peptide hydrogels on cell viability and cell proliferation. SAP hydrogel structures were successfully produced by functionalization of SAP with GAG mimetic peptide epitope, and effect of these hydrogels on cell viability was evaluated by cell culture methods in this work.
Scaling Client-Specific Equivalence Checking via Impact Boundary Search

Client-specific equivalence checking (CSEC) is a technique proposed previously to perform impact analysis of changes to downstream components (libraries) from the perspective of an unchanged system (client). Existing analysis techniques, whether general (re-gression verification, equivalence checking) or special-purpose, when applied to CSEC, either require users to provide specifications, or do not scale. We propose a novel solution to the CSEC problem, called 2clever, that is based on searching the control-flow of a program for impact boundaries. We evaluate a prototype implementation of 2clever on a comprehensive set of benchmarks and conclude that our prototype performs well compared to the state-of-the-art.
Automatic YARA Rule Generation

Since 2010, the number of new malware released daily have become so high, that manual analysis is not an option anymore. The purpose of this work is to focus on the increased modern cyber-attacks and malware campaigns. This work devises a framework that automates the process of generating high quality, effective and efficient malware signatures in considerably less amount of time and effort. It also facilitates in the tedious task of malware analysis. The proposed framework presents a generic approach to automatic YARA rule-based signature generation. This approach is based upon cherry-picking the most promising core ideas of the related work. The testing of the prototype shows that it is capable of detecting samples with an average precision of 0.95.
Improved Prediction of High Taxi Demand: A Deep Spatiotemporal Network for Hyper-imbalanced Data

Taxi demand prediction is an intensively studied topic in intelligent transportation research. Recently, deep learning models have been widely applied and have shown good performances. However, these methods overlook the existence of hyper-imbalanced taxi demand, which may result in good indicators in numerical experiments but weak performance in real scenarios. In this paper, we focus on the hyper-imbalance data and improve deep learning abilities for taxi demand prediction. To accomplish this task, slice indicators are introduced to fairly evaluate prediction performance at each taxi demand level. Then, through the lens of the slice indicators, a new form of loss called slice-weighted loss (SWL) is developed to improve the prediction of high taxi demand. Combining the SWL with an improved convolutional long short-term memory (Conv-LSTM) model, a spatiotemporal network called slice-wighted-Conv-LSTM (SW-CLSTM) is proposed. It can overcome the problem of data hyper-imbalance and make considerable improvements in taxi demand prediction. By conducting extensive experiments on large-scale TLC trips, we validate the power of sliceindicators and demonstrate the effectiveness of our approach over state-of-the-art methods.
Double deep multiagent reinforcement learning for autonomous driving in traffic maps with road segments and unsignaled intersections

In this work we present an advanced deep multiagent reinforcement learning scheme for autonomous driving of multiple vehicles in traffic networks with road segments and unsignaled intersections. The key aspect of the proposed method lies on the definition of the possible routes one vehicle can follow as agents, called route-agents, that allows the transfer learning and the reuse of their learned policies. An informative state space is constructed by introducing a collision matrix that “displays” a predictive traffic map of the intersections that the vehicles traverse. Also, an efficient reward function is designed that aims at providing optimal driving policies in order to navigate vehicles safely and rapidly to their destinations, avoiding collisions. A combined scheme of a Deep Q-Network (DQN) and a feedforward neural network along with the Double Q-leaning technique is employed that offers more stable solutions. Several experiments were made in simulated road traffic maps that empirically illustrate the efficiency of the proposed deep multiagent framework.
Managing App Testing Device Clouds: Issues and Opportunities

Because creating and maintaining an in-house test lab is expensive and time-consuming, companies and app developers often use device clouds to test their apps. Because quality-assurance activities depend on such device clouds, it is important to understand possible issues related to their use. To this end, in this paper we present a preliminary study that investigates issues and highlights research opportunities in the context of managing and maintaining device clouds. In the study, we analyzed over 12 million test executions on 110 devices. We found that the management software of the cloud infrastructure we considered affected some test executions, and almost all the cloud devices had at least one security-related issue.
Weakly-supervised Road Condition Classification Using Automatically Generated Labels

Predicting the condition of the road is an important task for autonomous vehicles to make driving decisions. Vehicles are expected to slow down or stop for potential road risks such as road cracks, bumps and potholes. Vision systems are widely used to provide such information given the rich colours and textures carried by images. This paper presents a weakly-supervised deep learning method to classify road images into two category sets. The first category identifies the existence of bumps or ramps in the image. The second category determines the road roughness given an input image. These two outputs are combined into a single convolutional neural network (CNN) to classify the camera image simultaneously. As a supervised learning method, deep learning algorithms normally require a large amount of training data with manually annotated labels. The annotation process is, however, very time-consuming and labour-intensive. This paper presents a method to avoid this costly process using a pipeline to automatically generate ground-truth labels by incorporating IMU and wheel encoder data. This automated pipeline does not require human effort to label images and will not be impeded by adverse environmental or illumination conditions. The experimental results presented show that after training the model using the automatically generated labels, the two-output CNN is capable to achieve good accuracy for classifying road conditions.
Portable ECG Monitoring Device Design Based on ARDUINO

Biomedical signals are the primary data source for diagnosis of diseases and monitoring of patients. These kinds of operations are carried out in hospital environment with huge devices. Today, due to rapid development of embedded systems, sizes of these devices have been reduced to small dimensions. In this way, while the patients continue their daily lives, the small sized devices make it easier to record biomedical signs related to their diseases. In this study, the electrocardiogram (ECG) sign, one of the most basic signs for heart diseases, is detected and displayed on the graphic display. To determined for this goal, ECG sensor card, wireless communication module, ARDUINO nano and TFT LCD screen are used. The idea is designed independently in two circuits and all circuits are supplied with small batteries for compact size. In this way, the circuits are realized in small sizes. According to the results, the system proposed can detect heart signals and heart beats and the system can show results to users.
A Deep Convolutional Neural Network Based Metro Passenger Flow Forecasting System Using a Fusion of Time and Space

Urban rail transit systems produce big automatic fare collection (AFC) data. With the support of big data analytics, more accurate forecasting of passenger flow in the urban rail transit system can optimize the train operation timetable and alleviate urban traffic congestion. Lacking in adequate data, most of the existing works study the urban rail passenger forecasting using time series analysis. The historical passenger data at one station is used to predict its future passenger flow, where the passenger flow correlation between time and station is largely ignored. In this paper, we use the data fusion of time and space to predict the passenger flow. We obtain the historical pristine passenger flow data from the real automatic fare collection system. Teradata big data platform is used to process the data and obtain the spatiotemporal fusion data. Spatiotemporal passenger flow dynamics is converted to a two-dimensional time-space matrix describing the time and space relations of passenger flow. A convolutional neural network model is established under the two-dimensional time-space matrix. We obtain the optimal hyperparameter combinations of CNN models using the grid search algorithm. The performance of the CNN model is evaluated using real metro data. The simulation results demonstrate the high efficiency and accuracy of the proposed method.
Re-localization for Self-Driving Cars using Semantic Maps

Localization of vehicle in adverse conditions such as in dense traffic conditions is a challenging problem and state-of-the-art techniques often make the vehicle get lost, requiring a re-localization technique to correctly reset the vehicle pose. The visual place recognition and loop-closure based re-localization techniques need to store a very large map and take a lot of time to re-localize the vehicle. We solve the problem by making a semantic map which is used to re-localize the vehicle, if and once it gets lost by conventional localization techniques. The semantic map is created using a test vehicle with sophisticated sensors, and the map can be used by any vehicle with a stereo camera for re-localization. It is assumed that the test vehicle has a budget stereo camera which produces numerous false positives to be rejected by the re-localizer; while the vehicle also misses many key landmarks during the run due to heavy traffic. These are the challenges which are overcome by the designed re-localization algorithm. The vehicle is tested on a highway scenario in Bengaluru, India for multiple runs in a highway segment. Results confirm accurate re-localization on a semantic map generated from road-signs.
Feedforward Transient Control Under Varying Coupling Condition for In-motion Wireless Power Transfer Using Envelope Model

In-motion wireless power transfer (WPT) is expected to solve many problems of electric vehicles (EVs). In order to transfer enough energy to vehicles running at high speed, the transient characteristics of the current must be improved with the precise circuit model. Particularly, the coupling coefficient between the transmitting-side coil and the receiving-side coil dramatically varies during the charging, thus the variation has to be considered in the control model. Here we show a novel control method using the envelope model of the transient response, where the coupling coefficient is a time variable. The feasibility of the proposed method is verified by the simulation and the experiment using the in-motion WPT bench.