Reviewerly/exHarmony huggingface dataset:
Using Machine Learning for Person Identification through Physical Activities

In this paper, the concept of utilizing machine learning algorithms for person identification through physical activity is proposed. Many previous machine learning research articles focused on building models to identify physical activities using a sensor fusion input. Nevertheless, there has been no focus on building models that can identify the activity performer as well. This paper will demonstrate that machine learning can be applied not only for the identification of physical activities but also for the identification of the activity performer as well. The paper will present the achieved accuracies for the person identification through physical activities using different machine learning algorithms. Additionally, a novel multi-label shared deep neural network (DNN) is proposed for identifying both the physical activity and the activity performer simultaneously. The proposed design allows for a single training/re-training which is advantageous over having to train two separate DNNs. Moreover, it is 30% smaller compared to a design that consists of two separate DNNs for identifying the physical activity and the activity performer.
Entropy-based skew detection and correction for printed Meitei/Meetei script OCR system

In this paper, we have proposed a technique based on Horizontal Projection Profile (HPP) and Entropy for skew detection and correction for the printed Meitei/Meetei script document image which is an important step in the development of Optical Character Recognition (OCR) system for any script. This technique can be used to any script and the orientation of the document. The algorithm is implemented via MATLAB. The precise results of transforming various images with different skew angles at the rage of ± 30° with precision of 0.5° and different levels of complexity is shown.
Generative Modeling of Environments with Scene Grammars and Variational Inference

In order to understand how a robot will perform in the open world, we aim to establish a quantitative understanding of the distribution of environments that a robot will face when when it is deployed. However, even restricting attention only to the distribution of objects in a scene, these distributions over environments are nontrivial: they describe mixtures of discrete and continuous variables related to the number, type, poses, and attributes of objects in the scene. We describe a probabilistic generative model that uses scene trees to capture hierarchical relationships between collections of objects, as well as a variational inference algorithm for tuning that model to best match a set of observed environments without any need for tediously labeled parse trees. We demonstrate that this model can accurately capture the distribution of a pair of nontrivial manipulation-relevant datasets and be deployed as a density estimator and outlier detector for novel environments.
Just Noticeable Difference for Machines to Generate Adversarial Images

One way of designing a robust machine learning algorithm is to generate authentic adversarial images that can trick the algorithms as much as possible. In this study, we propose a new method to generate adversarial images which are very similar to true images, yet, these images are discriminated from the original ones and are assigned into another category by the model. The proposed method is based on a popular concept of experimental psychology, called, Just Noticeable Difference. We define Just Noticeable Difference for a machine learning model and generate the least perceptible difference for adversarial images which can trick a model. The suggested model iteratively distorts a true image by gradient descent method until the machine learning algorithm outputs a false label. Deep Neural Networks are trained for object detection and classification tasks. The cost function includes regularization terms to generate just noticeably different adversarial images which can be detected by the model. The adversarial images generated in this study look more natural compared to the output of the state of the art adversarial image generators.
BikeVR

While becoming more and more aware of the ongoing climate change, eco-friendly means of transport for all citizens are moving further into focus. In order to be able to implement specific measures, it is necessary to better understand and emphasize sustainable transportation like walking and cycling through focused research. When developing novel traffic concepts and urban spaces for non-motorized traffic participants like bicycles and pedestrians, traffic and urban planning must be focused on their needs. To provide rare qualitative factors (such as stress, the perception of time and attractiveness of the environment) in this context, we present an audiovisual VR bicycle simulator which allows the user to cycle through a virtual urban environment by physically pedaling and also steering. Virtual Reality (VR) is a suitable tool in this context, as study participants find identical and almost freely definable (virtual) urban spaces with adjustable traffic scenarios. Our preliminary prototype proved to be promising and will be further optimized and evaluated.
GAPNet: Generic-Attribute-Pose Network For Fine-Grained Visual Categorization Using Multi-Attribute Attention Module

This paper proposes a multi-task learning framework for fine-grained visual categorization (FGVC) referred to as Generic-Attribute-Pose Network (GAPNet) that is capable of attending discriminating parts depending on the pose and part-attribute of an object using multi-attribute attention. FGVC is a challenging task that involves categorical data with small inter-class variation and large intra-class variation. Multi-Attribute Attention Module (MAAM) guides the GAPNet to focus on multiple parts of the image feature by emphasizing appropriate feature channels given both pose and part-attribute features. Experiments on Caltech-UCSD Birds and NABirds datasets demonstrate that GAPNet is competitive with other state-of-the-art methods, and ablation study on GAPNet conditioned on pose and part-attribute feature shows that GAPNet performs best when conditioned on both pose and part-attribute features.
Deepcabac: Plug &amp; Play Compression of Neural Network Weights and Weight Updates

An increasing number of distributed machine learning applications require efficient communication of neural network parameterizations. DeepCABAC, an algorithm in the current working draft of the emerging MPEG-7 part 17 standard for compression of neural networks for multimedia content description and analysis, has demonstrated high compression gains for a variety of neural network models. In this paper we propose a method for employing DeepCABAC in a Federated Learning scenario for the exchange of intermediate differential parameterizations. Furthermore, we discuss the efficiency of DeepCABAC when compressing trained neural networks. Our experiments on large neural networks show that in both scenarios, DeepCABAC achieves competitive compression rates, without degrading the network accuracy.
Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification

Depth and thermal sensors are well-suited for computer vision applications that involve the continuous monitoring of people, particularly in combination. Yet there is limited research in this field and a lack of available datasets. We present a method for creating synthetic but realistic depth and thermal images that include sensor noise. We utilize this method to create the SDT dataset, which contains 40k image pairs including labels for person detection and pose classification, and is publicly available. To assess the quality our image synthesis method and the utility of SDT, we train CNNs for classification on the SDT dataset and evaluate them on real data. The CNNs achieve accuracies up to 98%, highlighting their ability to generalize from synthetic to real data.
Multi-modal Information Extraction and Fusion with Convolutional Neural Networks

Developing computational algorithms to model the biological vision system has challenged researchers in the computer vision field for several decades. As a result, state-of-the-art algorithms such as the Convolutional Neural Network (CNN) have emerged for image classification and recognition tasks with promising results. CNNs however remain view-specific, producing good results when the variation between test and train data is small. Making CNNs learn invariant features to effectively recognise objects that undergo appearance changes as a result of transformations such as scaling remains a technical challenge. Recent physiological studies of the visual system are suggesting new paradigms. Firstly, our visual system uses both local features and global features in its recognition function. Secondly, cells tuned to global features respond quickly to visual stimuli for recognising objects. Thirdly, information from modalities that handle local features, global features and color are integrated in the brain for performing recognition tasks. While CNNs rely on aggregation of local features for recognition, these theories provide the potential for using global features to solve transformation invariance problems in CNNs. In this paper we realise these paradigms into a computational model, named as global features improved CNN (GCNN), and test it on classification of scaled images. We experiment combining Histogram of Gradients (HOG) global features, CNN local features and color information and test our technique on benchmark data sets. Our results show GCNN outperforms traditional CNN on classification of scaled images indicating potential effectiveness of our model towards improving scale-invariance in CNN based networks.
Online calibration of exterior orientations of a vehicle-mounted surround-view camera system

The increasing availability of surround-view camera systems in passenger vehicles motivates their use as an exterior perception modality for intelligent vehicle behaviour. An important problem within this context is the extrinsic calibration between the cameras, which is challenging due to the often reduced overlap between the fields of view of neighbouring views. Our work is motivated by two insights. First, we argue that the accuracy of vision-based vehicle motion estimation depends crucially on the quality of exterior orientation calibration, while design parameters for camera positions typically provide sufficient accuracy. Second, we demonstrate how planar vehicle motion related direction vectors can be used to accurately identify individual camera-to-vehicle rotations, which are more useful than the commonly and tediously derived camera-to-camera transformations. We present a complete and highly practicable online optimisation strategy to obtain the exterior orientation parameters and conclude with successful tests on simulated, indoor, and large-scale outdoor experiments.
MultiLevel Feature Pooling Network for Uncalibrated Stereo Rectification in Autonomous Vehicles

Stereo vision systems in autonomous vehicles can come with camera sensors with different models and lenses to detect objects at long and short ranges. The intrinsic/extrinsic parameters of the camera sensors, such as the focal length and distortion parameters used for calibration, are not provided. In this article, we present a novel rectification method for uncalibrated stereo cameras based on their corresponding feature points. To obtain reliable key/feature points and descriptors from stereo images, we propose a deep neural network, called a multilevel feature pooling network. The model processes images individually to learn both local and semiglobal features, from which the location and descriptor of each pixel in the image are estimated. We also introduce a method to balance the zoom effect caused by the different focal lengths of the camera lenses and rectify the images by estimating homography matrices for stereo images based on their refined corresponding feature points.
Tent Mode Interactions: Exploring Collocated Multi-User Interaction on a Foldable Device

Foldable handheld displays have the potential to offer a rich interaction space, particularly as they fold into a convex form factor, for collocated multi-user interactions. In this paper, we explore Tent mode, a convex configuration of a foldable device partitioned into a primary and a secondary display, as well as a tertiary, Edge display that sits at the intersection of the two. We specifically explore the design space for a wide range of scenarios, such as co-browsing a gallery or co-planning a trip. Through a first collection of interviews, end-users identified a suite of apps that could leverage Tent mode for multi-user interactions. Based on these results we propose an interaction design space that builds on unique Tent mode properties, such as folding, flattening or tilting the device, and the interplay between the three sub-displays. We examine how end-users exploit this rich interaction space when presented with a set of collaborative tasks through a user study, and elicit potential interaction techniques. We implemented these interaction techniques and report on the preliminary user feedback we collected. Finally, we discuss the design implications for collocated interaction in Tent mode configurations.
Analog Circuits to Accelerate the Relaxation Process in the Equilibrium Propagation Algorithm

Equilibrium Propagation (EP) is a novel biologically plausible algorithm for training deep neural networks. However, discrete time implementations of EP could not exploit the full potential of its proposed framework, because the relaxation step is slow on digital architectures. Here, we propose an analog circuit implementation for the relaxation process to accelerate its convergence. In this implementation, the optimization process is executed based on the continuous-time dynamics of EP. Our circuit is validated using a Continuous Hopfield Network prototype emulating the XOR function. This implementation improved the convergence time of EP by a factor of 250 compared to its Python counterpart. As this implementation is scalable to learn more complex functions, it has the potential to be applied on higher-dimensionality datasets.
Automated Detection of Racial Microaggressions using Machine Learning

Microaggressions describe subtle often offensive comments or actions made by one individual to another. Typically, such comments or actions are made subconsciously with the offender potentially unaware of the impacts on the recipient. Currently, machine learning methods for racial microaggression detection are sparse with no, one, comparable approach to the one we propose further on. Automated detection in this work describes the method of finding microaggressions through the use of machine-learning algorithms. Efforts have been made for the detection of hate speech and harassment; providing us with a rather humble place to begin, as we explore further, such methods are proven ineffective. We propose a step forward in solving this problem with the demonstration of an automated racial microaggression detection method. Whilst racial hatespeech detection method provides us with an idea as to where we can start, we find further on, that microaggressions and hatespeech use very different features to portray their sentiment. This work aims to provide a technical review which explores the understanding of the automated racial microaggression detection, outlining the definitions of microaggressions currently described in the literature, with the presentation and assessment (through precision, callback and F-measure) of a promising approach in regards to racial microaggression detection. We also intend to analyse a case study in which we detect the presence of racial microaggressions within new reports with the intention of laying a foundation for the potential security-related applications of this work. Our further works begin to discuss this notion in a higher level of detail.
Feature Bagging and Extreme Learning Machines: Machine Learning with Severe Memory Constraints

With the onset of easy access to supercomputers with high amounts of memory available, machine learning algorithms have continued to increase the resources necessary to perform their data analysis. This paper aims to show development in the other direction, by showing that through the use of a combination of feature bagging and ensembles of Extreme Learning Machines (ELMs) it is possible to leverage machine learning, without loss of accuracy, on devices where Flash memory is very scarce, and Random-access memory (RAM) is even scarcer, such as on embedded systems. This novel strategy is called Feature Bagged Extreme Learning Machines (FB-ELMs).
Autonomous Parallel Car Parking

As the number of drivers is directly correlated to the population growth, the problems of parallel parking will eventually rise as the number of drivers on the road increases. Hence, possible accidents might occur at parking spots that could lead to road traffic or blockage, especially in certain occasions. For example, in national festivals, people tend to drive to shopping malls and other gathering places. Since it is time consuming for a driver to find a suitable parking spot due to highly-condensed parking spaces that sometimes causes frustration to drivers, it is more efficient and easier to use parallel parking system and allow the car for self-parking. Also, autonomous parallel parking system decreases traffic casualties and optimizes fuel consumption of cars. The scope of this paper is to present our work in a project which implements autonomous parallel car parking, where a car finds an empty suitable parking slot and then parallel-park itself into it. Then, we write the algorithm that achieves the parallel parking system needed. After that, we implement and run the code onto the car circuit. However, implementing this system needs modification in a radio-controlled car by adding to it some hardware components such as a microcontroller, DC motor, servo motor and multiple of infra-red and ultra-sonic sensors. Besides that, we add some safety features to this prototype, such as open door and alcohol detection. Finally, we evaluate the performance of our system, where results show that the car is capable of parking itself after performing all the steps specified by the algorithm.
Improving orbital uncertainty realism through covariance determination

Classical Orbit Determination algorithms provide an estimation of the state vector and its associated uncertainty assuming Gaussian processes and linear dynamics. However, a common problem of Orbit Determination processes is the misrepresentation of the Resident Space Object uncertainty through the estimated and predicted covariance. Ultimately, this causes a great impact in the quality and accuracy of Space Surveillance and Tracking products as the covariance is overly optimistic (too small) and the true uncertainty of the object is not properly captured. One of the causes for the unrealism of the covariance is found in the uncertainty of the dynamical and measurement models used to describe the motion of the objects. The aim of this work is to devise a novel methodology to improve the covariance realism of Orbit Determination and Orbit Propagation processes through the classical theory of consider parameters in batch least-squares estimators. The devised methodology uses the theory of consider parameters to add the uncertainty of dynamic and measurement models to the estimated covariance. The magnitude of the consider parameters is weighted using statistical inference techniques, proposing an innovative method to derive its contribution to the covariance. The conceived methodology is suitable for any type of measurement or object, although its primary goal is to correct unrealistic covariance of non-cooperative targets. Among the wide variety of uncertainty sources affecting covariance realism, the influence and effect of two relevant modelling uncertainties, the atmospheric drag force and the range bias, is discussed during this publication. The proposed methodology is first applied to a simulated scenario of tracking measurements, using a Monte Carlo approach. A case involving real radar observations is presented as well, where the uncertainty realism of the Sentinel-3A orbit determination and propagation is assessed and improved. Using the novel methodology of Covariance Determination, covariance realism improvement of a real tracking campaign is achieved and the uncertainty of the atmospheric drag force model quantified.
Deep Learning Towards Intelligent Vehicle Fault Diagnosis

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT.
Employing Contribution and Quality Metrics for Quantifying the Software Development Process

The full integration of online repositories in contemporary software development promotes remote work and collaboration. Apart from the apparent benefits, online repositories offer a deluge of data that can be utilized to monitor and improve the software development process. Towards this direction, we have designed and implemented a platform that analyzes data from GitHub in order to compute a series of metrics that quantify the contributions of project collaborators, both from a development as well as an operations (communication) perspective. We analyze contributions throughout the projects' lifecycle and track the number of coding violations, this way aspiring to identify cases of software development that need closer monitoring and (possibly) further actions to be taken. In this context, we have analyzed the 3000 most popular GitHub Java projects and provide the data to the community.