Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Conceal: A Method for Preserving Privacy and Avoiding Prejudice in Images

We introduce a learning model able to conceal personal information (e.g. gender, age, ethnicity, etc.) from an image while maintaining any additional information present in the image (e.g. smile, hair-style, brightness). Our trained model is not provided the information that it is concealing, and does not try learning it either. Namely, we created a variational autoencoder (VAE) model that is trained on a dataset including labels of the information one would like to conceal (e.g. gender, ethnicity, age). These labels are directly added to the VAE's sampled latent vector. Due to the limited number of neurons in the latent vector and its appended noise, the VAE avoids learning any relation between the given images and the given labels, as those are given directly. Therefore, the encoded image lacks any of the information one wishes to conceal. The encoding may be decoded back into an image according to any provided properties (e.g. a 40-year old woman). Our method successfully conceals the private information; a convolutional neural network trained on the concealed images cannot restore the original private information. In contrast to the private information, a user study shows that the remaining properties of the original image carry-on to the concealed image. The proposed architecture can be used as a mean for privacy preserving and can serve as an input to systems, which will become unbiased and not suffer from prejudice.
WD3: Taming the Estimation Bias in Deep Reinforcement Learning

The overestimation phenomenon caused by function approximation is a well-known issue in value-based reinforcement learning algorithms such as deep Q-networks and DDPG, which could lead to suboptimal policies. To address this issue, TD3 takes the minimum value between a pair of critics, which introduces underestimation bias. By unifying these two opposites, we propose a novel Weighted Delayed Deep Deterministic Policy Gradient algorithm, which can reduce the estimation error and further improve the performance by weighting a pair of critics. We compare the learning process of value function between DDPG, TD3, and our proposed algorithm, which verifies that our algorithm could indeed eliminate the estimation error of value function. We evaluate our algorithm in the OpenAI Gym continuous control tasks, outperforming the state-of-the-art algorithms on every environment tested.
Multiple Sequential Constraint Removal Algorithm For Channel Estimation In Vehicular Environment

Since the vehicular environment is highly mobile, the transmitted messages are affected by the wireless channel effect. This makes channel estimation one of the important tasks in Vehicle-To-Everything (V2X) communications. In this paper, we propose a novel Multiple Sequential Constraint Removal (MSCR) algorithm of channel estimation, dedicated to V2X communications. This method is performed on a vehicular doubly selective channel, where the communication system is based on the Orthogonal Frequency Division Multiplexing (OFDM) waveform, with a high order modulation (64-QAM). The proposed approach is compared to state-of-the-art Least square (LS), minimum mean square error (MMSE) estimators and the iterative Sequential Constraint Removal (SCR) algorithms. It shows that it achieves better performance, where we can get a small symbol error rate (SER) on highly mobile scenarios (Highway with non-line of the sight), with a latency time that meets with the V2X communication requirements.
Analysis of IEEE 802.11p MAC Protocol for Safety Message Broadcast in V2V Communication

V2X communication technology is expected to improve road safety and optimize transportation efficiency. Safety message broadcasting is in need of high reliability and requires real-time communications between vehicles under adverse environments. In this study, we evaluate IEEE 802.11p MAC protocol performance in terms of packet delivery ratio (PDR), by means of simulation and analysis. Analytical expression of PDR is derived for packets with exponential and deterministic inter-arrival time by taking hidden terminal problem into account as the major cause of performance degradation. Furthermore, the effects of various data rate, packet arrival rate, and packet size towards PDR performance are evaluated. Numerical and simulation results show how packet delivery ratio (PDR) drops dramatically in cases with hidden problem nodes.
ST-MGAT: Spatial-Temporal Multi-Head Graph Attention Networks for Traffic Forecasting

Graph Neural Networks (GNNs) have attracted increasing attention due to the significant representation learning capacity for graphs. The traffic forecasting is a typical graph representation learning task, but it is challenging to model the complex spatial and temporal relationships in traffics. Traditional spectral approaches get filters based on the eigendecomposition, which depends on the Laplacian matrix of the graph. However, these approaches have expensive matrix operation on graph convolutions neural networks and are insufficient to tackle the spatial dependency. In this paper, we propose a novel graph neural network - Spatial-Temporal Multi-head Graph ATtention network (ST-MGAT), to deal with the traffic forecasting problem. We build convolutions on the graph directly. We consider the features of neighborhood nodes and the weights of the edges to generate new node representation. More specifically, there are two main modules: i) Temporal convolution blocks to capture the dynamic time correlations; ii) Graph attention networks to capture the dynamic spatial relations between nodes. Experimental results show that our model achieves up to 13% improvement over the state-of-the-art approaches in short-term, medium-term, and long-term highway traffic forecasting. 1Code is available at https://github.com/Kelang-Tian/ST-Mgat
Increasing the Capacity for Automated Valet Parking Using Variable Spot Width

Automated vehicles allow to adapt new parking concepts, which have the potential to increase the parking capacity significantly. Thereby, the abilities to assign optimal parking spaces to the vehicles and to perform shunting operations are systematically utilised. In this paper, the typical parking order with spots of constant width is replaced by a loose parking order with variable width. In this way, the width of each individual vehicle can be considered, which leads to an improved utilisation of the available space. Despite this improvement, first simulation studies without shunting operations under realistic conditions did not show the expected results. Therefore, an optimum strategy for shunting operations is deployed. As the optimisation is computationally challenging, a heuristic method is proposed which shows similar results as the optimal solution. A substantial increase in the parking capacity of up to 12% for a commercial parking garage is achieved, where no constructional modifications are necessary.
Pre-Development Analysis of Truther Framework: A Platform to Verify Information Authenticity

Today, an enormous information stream rapidly floods from various channels and media, such as websites, blogs, WhatsApp, Facebook, etc. This condition makes us incapable of reading all the content, let alone verify the context. To overcome this fuzzy problem, we introduce Truther, a lightweight on-demand app and web browser plugin, to verify digital information. Truther can verify text and images from websites, blogs, WhatsApp, Facebook, and more. It is mainly based on text input, so the usage is very open to any possibility. Truther is backing up by three central back-end systems such as Truther Validator (the cross-language text semantic verifier for validating a post), Truther Debunker Aggregator (aggregator of many hoax-debunker websites), and third-party API (Google, DuckDuckGo, and Microsoft Cognitive Service API). This paper mainly discusses our proposed framework’s pre-development analysis, its cross-language text semantic verifier, and MVP experiment result. As an additional finding, we also formalize the proposed business model of Truther.
Sample-efficient learning of soft priorities for safe control with constrained Bayesian optimization

A complex motion can be achieved by executing multiple tasks simultaneously, where the key is tuning the task priorities. Generally, task priorities are predefined manually. In order to generate task priorities automatically, different frameworks have been proposed. In this paper, we employed a black-box optimization method, i.e. a variant of constrained Bayesian optimization to learn the soft task priorities, guaranteeing that the robot motion is optimized with high efficiency and no constraints violations occur during the whole learning process.
Extending Conductive Thread Use to Unintended Microcontrollers

Most microcontrollers are not designed for use with wearable thread. Of those that are, the smallest as of this writing is the Adafruit Gemma M0, which is still 27.95mm in diameter and 6.4 mm thick, designed primarily to control LEDs. Microcontroller boards designed for use with wearable thread and external sensors are being outstripped in the size race by competitors which do not consider that users might attempt replacing wires with conductive thread. In this paper, we explore the technical requirements for safely replacing wires with conductive thread in the Raspberry Pi Zero W. Based on this work, we extended a costume to include an LED which was configurable from the audience without the use of wires. Our Raspberry Pi Zero W prototype costume was worn by an Improv dancer, Vaval Victor, for his performance during the show "Evening of Readings and Dance" at the Denison Fringe Festival 2019 [1]. An additional prototype costume retrieves data from an audience-accessible cloud-based application to allow an Arduino Nano IoT 33 to change its lights during remote performances.
A Fully Integrated X-Band Multifunction Core Chip in 0.25 µm GaN HEMT Technology

This paper presents the design of a wide band and highly compact multifuction core chip Monolithic Microwave Integrated Circuit (MMIC) using WIN Semiconductor’s 0.25 µm Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) process design kit for the first time. The core chip is designed for use in GaN based X-band Transmit Receive (T/R) modules of Active Electronically Scanned Antenna (AESA) radars. Core chip operates in the frequency band of 9 to 11 GHz and provides multiple functions that includes 6-bit Digital Phase Shifter (DPS) for beam steering, 6-bit Digital Step Attenuator (DSA) for amplitude correction, interstage Low Noise Amplifiers (LNAs) and Single Pole Double Throw (SPDT) T/R switches. The Electromagnetic Simulation (EM) shows that MMIC exhibits a transmit gain of 19 dB and receive gain of 17 dB at 9 GHz. Transmit path Root Mean Square (RMS) gain and phase error are better than 1.15 dB and 4.7◦ respectively. The RMS attenuation and insertion phase shift error are also better than 0.75 dB and 7.25◦ respectively. The receive path has a maximum noise figure of 5.8 dB at 11 GHz. This core chip occupies die area of 15.36 mm2 (6.65 mm x 2.31 mm). The compact size, low receiver noise figure and high transmit/receive gain makes this core chip suitable for integration in T/R modules of X-band AESA radars.
Time series prediction & generation from disentangled latent factors: new opportunities for smart cities

The acceleration of urbanisation has brought many new challenges to cities around the world. Application range is wide, from air pollution to public transportation modelling. The availability of data pertaining to these issues has been growing fast in the last years, offering many opportunities to tackle those applications with machine learning algorithms. We propose an elegant and general architecture that is able to provide state of the art forecasting in several different domains. Our idea is the following: for many time-series, a number of factors, that often relate to the context they were created in, can influence the observed values, such as day or location. In this paper, we present a machine learning model that learns to represent and disentangle such factors. Our contribution is to provide an approach that works at different scales: on a short term basis (30 minutes to few hours) our deep neural network architecture delivers competitive forecasting in a classical setting; at the day/week/month level, we show that we can generate relevant time series associated with unknown contexts. To the best of our knowledge, this ambitious application has not been investigated until now.
Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data

Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers’ route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.
Detection of Parkinson’s Disease at The Level of Motor Experiences of Daily Living Using Spiral Handwriting

Parkinson’s disease (PD) is a neurological disease that gradually worsens and affects the brain’s part that functions to coordinate body movements. As a result, sufferers have difficulty regulating body movements, including when talking, walking, and writing. The diagnosis of PD patients can be analyzed through handwriting. Measurement of Parkinson’s disease at the level of motor experiences of daily living uses handwriting tasks. This paper aims to evaluate various image feature extraction techniques from handwriting. Handwriting were collected from 102 subjects (51 PD and 51 healthy control (HC)) The proposed method uses feature extraction of Histogram of Gradient (HOG), Oriented FAST and Rotated BRIEF (ORB), Speed-Up Robust Feature (SURF), Scale Invariant Feature Transform (SIFT), Color Gradient Histogram (CGH) and KAZE. Classifiers based on Random Forest (RF). The analysis shows that the extraction features of HOG and RF classification have the best accuracy 0.8167.
Gesture Recognition by Machine Learning Combined with Geometric Calculation

Human Machine Interface (HMI) in the vehicle environment is becoming more complex due to an increased number of desired user functions. These added functions increase the driver’s distraction since the driver must visually identify a particular physical interface that they want to control. A gesture recognition system is one possible method for controlling HMI functionalities without visual cues. Some technical approaches to gesture recognition that exploit deep learning have been proposed. However, these methods require increased computational cost in relation to processing time. Ideally, a gesture recognition system should enable fast and accurate driver action to avoid a distracted driver and maximize safety. In this study, we propose an algorithm that combines geometric calculation and machine learning techniques to estimate gestures with low computational overhead. The algorithm is explained in detail, and results are reported including a gesture recognition accuracy of 85% across three gestures for a moving hand from eight people.
Using Sum-Product Networks for the Generation of Vehicle Populations On Highway Sections

The importance of simulation-based approaches for the development of automated driving functions has strongly increased in recent years. Since current validation methods probably will not be feasible for highly automated driving functions, the simulation-based techniques’ relevance for the validation procedure is also increasing. In this context, it is a large challenge to simulate the huge if not infinitely large parameter space of an automated vehicle’s (AV) surrounding. Therefore, this paper presents a model which allows the modeling of the statistics of traffic scenes on straight highway sections. The model is based upon recent approaches using Bayesian networks and is adapted to the usage of sum-product networks (SPNs). It is shown, that the use of SPNs can enhance the sampling speed up to 36 times compared to the Bayesian network baseline.
SIC-Based Baseband Block Diagonalization for MmWave Massive Multi-User MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system is a key technology of 5G communications and a research focus of 6G communications in the future. It can be widely used in communication, radar, power and transmission systems. In the mmWave MIMO system, hybrid beamforming (HBF) is regarded as a potentially promising technology, because it keeps a good balance between hardware complexity and system performance. In this paper, we propose a new hybrid beamforming scheme for the multi-user MIMO (MU-MIMO) scenario. We first design the analog beamforming by trying to avoid the loss of information to maximize the sum-rate of the communication system. Then based on the idea of successive interference cancellation (SIC), we develop the successive block diagonalization (SBD) method to eliminate interference between two adjacent users, which can avoid the computation caused by the influence between users that are far apart. Unlike the baseband BD technology, the proposed scheme has low complexity. The simulation results are shown to validate the proposed scheme.
Abnormal ECG Beat Detection Based on Convolutional Neural Networks

The heart is the most critical organ for the sustainability of life. Arrhythmia is any irregularity of heart rate that causes an abnormality in your heart rhythm. Clinical analysis of Electrocardiogram (ECG) signals is not enough to quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning method for the accurate detection of abnormal and normal heartbeats based on 2-D Convolutional Neural Network (CNN) architecture. Two channels of ECG signals were obtained from the MIT-BIH arrhythmia dataset. Each ECG signal is segmented into heartbeats, and each heartbeat is transformed into a 2-D grayscale heartbeat image as an input for CNN structure. Due to the success of image recognition, CNN architecture is utilized for binary classification of the 2-D image matrix. In this study, the effect of different CNN architectures is compared based on the classification rate. The accuracies of training and test data are found as 100.00% and 99.10%, respectively for the best CNN model. Experimental results demonstrate that CNN with ECG image representation yields the highest success rate for the binary classification of ECG beats compared to the traditional machine learning methods, and one-dimensional deep learning classifiers.
Improving Road Detection Results Based on Ensemble Learning and Key Samples Focusing

Road detection is fundamental for many applications, especially vision-based autonomous driving systems. To improve the accuracy of the detection results, most of previous research focus on designing feature encoders and classifiers. In this paper, a road detection method is proposed based on ensemble learning and key samples focusing. A road detection network is designed, which integrates classification results based on different feature combinations by weighted voting. The outputs of the network are further processed by morphological transformation. To focus on key samples, a novel loss function is proposed. The loss function can attach importance to hard samples and pay different attention to missed detection and false detection. The method is evaluated on KITTI dataset, and its effectiveness is verified.
Simple MyUnet3D for BraTS Segmentation

The deep learning architectures that have been used for brain tumor segmentation in the BraTS challenge have performed well for the WT, TC, and ET segmentations. However, these architectures generally have many parameters and require large storage capacity for the model. In this paper, we propose a Simple MyUnet3D to do segmentation on BraTS 2018 dataset. This proposed architecture was inspired by 2D U-Net and modified to do 3D image segmentation. Dataset divides into 2 parts, one part of training and the other for validation. From 285 data, 213 for training, and 72 for validating the model. The segmentation consists of 3 parts, whole tumor(WT), tumor core(TC), and enhanced tumor(ET). Even its simplicity, it produces a dice coefficient of 0.8269 at segmenting the whole tumor. Nevertheless, its performance in tumor core and enhanced tumor need to be developed. The simplicity and its result in segmenting the whole tumor have great potential to be better developed.
Mitigation of Odometer Fraud for In-Vehicle Security Using the Discrete Hartley Transform

Odometer fraud is a serious offense in the automotive sector and indicates the disconnection, resetting, or alteration of a vehicle’s odometer and the related sensor with the intent to change the number of miles/Kms indicated or recorded to report false information. This paper focuses specifically on the threat scenario where the odometer sensor (i.e., Hall Sensor) is manipulated or replaced to implement an odometer fraud. This paper proposes a technique to mitigate odometer fraud by performing a physical layer authentication of the Hall Sensor, which takes in consideration the limitation of the in-vehicle networks and microprocessors. In particular, the Discrete Hartley Transform (DHT) in combination with machine learning algorithms is used to perform the authentication on an experimental data set of 12 Hall Sensors, which has been collected by the authors. The results shows that features extracted with DHT have more discriminating power than the original time domain and the frequency domain representations based on the Fast Fourier Transform (FFT) especially in presence of noise.
On Using Declarative Generation Rules To Deliver Linked Biodiversity Data

In the last decade, our capability to collect data has been improved significantly. A new era of big data has emerged as indicated by five characteristics of data: volume, variety, veracity, velocity, and value. The adoption of open-science approach is important in order to manage and exploit the available data appropriately. An open science approach enables curation, discovery, linking, and reusability of data across the globe. The challenge lies in the data heterogeneity, limitation of data interface, and conventional data visualization practices. In this work, we introduce a solution to overcome the challenges by using the Linked Data approach. The solution enables data to be represented in a machine-readable format and linked to or from external data sets, in a way that can be easily integrated, allow search optimization, as well as open the possibility to obtain new knowledge. Our solution consists of a schema construction to uniformly represent biodiversity data (mostly biological specimen data). After that, mapping rules were defined to align data from multiple biodiversity information systems that are available on the Web to the constructed schema. Finally, an engine will consume the mapping rules and generate linked data in a common format. Our results indicate that despite multiple data structures have been utilized by multiple systems, the mapping rules provide flexibility to accommodate every one of them. Further, we successfully demonstrated the possibility to deliver linked biodiversity data across multiple sources as our first step to harness big data biodiversity.