Reviewerly/exHarmony huggingface dataset:
Federated Learning for Object Detection in Autonomous Vehicles

With the recent proliferation of Artificial Intelligence (AI), object detection is becoming increasingly ubiquitous. It is one of the key features of Autonomous Driving Systems. In current applications, object detection models are usually trained at a centralized location by collecting data from multiple sources. This raises concerns about data privacy among other issues. This paper addresses data privacy through a Federated Learning (FL) approach. FL architecture aims at preserving data privacy while maintaining performance by training the model in a decentralized manner. In this paper, we analyze how FL impacts the performance of object detection in a real-world traffic environment. We have constructed a prototype FL system and evaluated it on the KITTI Vision Benchmark 2D image dataset. In our prototype, object detection models are trained locally on a vehicle’s dataset, and the resultant weights are securely aggregated, using symmetric encryption techniques during data transfer, at the global server to yield an improved model. The FL model converged at 68% mean average precision. We compared the performance of object detection using FL to the traditional deep learning approach and noticed significant difference between the two models.
A Novel Grid-Based Clustering Algorithm

Abstract Data clustering is an important method used to discover naturally occurring structures in datasets. One of the most popular approaches is the grid-based concept of clustering algorithms. This kind of method is characterized by a fast processing time and it can also discover clusters of arbitrary shapes in datasets. These properties allow these methods to be used in many different applications. Researchers have created many versions of the clustering method using the grid-based approach. However, the key issue is the right choice of the number of grid cells. This paper proposes a novel grid-based algorithm which uses a method for an automatic determining of the number of grid cells. This method is based on the k dist function which computes the distance between each element of a dataset and its k th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
Progressive structure network-based multiscale feature fusion for object detection in real-time application

Deep learning-based target detection techniques have already made a wide-range impact on our daily life. Currently, a feature pyramid is a widely utilized technique for multiscale target detection, the effectiveness of the technique has already been proved. Nevertheless, in the pyramid structure, problems, such as multiscale feature alignment, model turmoil after fusion, feature redundancy, and no-local feature fusion, exist. In this paper, we propose a novel progressive structure network to solve the aforementioned problems. The proposed structure contains three modules: multiscale feature alignment fusion, different scale channels & spatial location adaptive weighted fusion, and multiscale global and local feature fusion. The proposed structure is capable of fusing information from different feature layers more effectively. Subsequently, the semantic gaps among different scales can be reduced. Furthermore, the proposed structure can maintain the stability of the detection network and its performance has been proved by comparing with other state-of-art feature fusion method. The proposed progressive network structure has also been applied to actual target detection tasks and the practical application effectiveness of our method has been verified.
Machine learning with big data analytics for cloud security

The amount of data generated and transmitted more quickly, particularly with the demand for action in real-time, has greatly increased with the growing number of internet-connected devices. With the rising diversity of data and need for data integrity, it is more challenging to accomplish this processing on time. However, cloud and edge computing pose security problems and time delays, as computing models are being applied rapidly. Hence in this paper, a Machine Learning-Assisted Cloud Computing Model (ML-CCM) with big data analytics has been proposed to increase security and improve data transmission rates. The simplest approach for storing large volumes of data is cloud storage. Big data can manage or store large amounts of distributed data in clouds. The ML algorithms analyze supervised and unsupervised training used to solve cloud protection problems. The experimental results show that ML-CCM has a data transmission rate of 96.4%, effective data management of 94.3%, computational time of 35.2%, accuracy of 91.7%, and performance of 95.2%.
Structural Evolution of the Central Kiruna Area, Northern Norrbotten, Sweden: Implications on the Geologic Setting Generating Iron Oxide-Apatite and Epigenetic Iron and Copper Sulfides

Abstract To guide future exploration, this predominantly field based study has investigated the structural evolution of the central Kiruna area, the type locality for iron oxide-apatite deposits that stands for a significant amount of the European iron ore production. Using a combination of geologic mapping focusing on structures and stratigraphy, petrography with focus on microstructures, X-ray computed tomography imaging of sulfide-structure relationships, and structural 2D-forward modeling, a structural framework is provided including spatial-temporal relationships between iron oxide-apatite emplacement, subeconomic Fe and Cu sulfide mineralization, and deformation. These relationships are important to constrain as a guidance for exploration in iron oxide-apatite and iron oxide copper-gold prospective terrains and may help to understand the genesis of these deposit types. Results suggest that the iron oxide-apatite deposits were emplaced in an intracontinental back-arc basin, and they formed precrustal shortening under shallow crustal conditions. Subsequent east-west crustal shortening under greenschist facies metamorphism inverted the basin along steep to moderately steep E-dipping structures, often subparallel with bedding and lithological contacts, with reverse, oblique to dip-slip, east-block-up sense of shears. Fe and Cu sulfides associated with Fe oxides are hosted by structures formed during the basin inversion and are spatially related to the iron oxide-apatite deposits but formed in fundamentally different structural settings and are separated in time. The inverted basin was gently refolded and later affected by hydraulic fracturing, which represent the last recorded deformation-hydrothermal events affecting the crustal architecture of central Kiruna.
Generating Large-Scale Trajectories Efficiently using Double Descriptions of Polynomials

For quadrotor trajectory planning, describing a polynomial trajectory through coefficients and end-derivatives both enjoy their own convenience in energy minimization. We name them double descriptions of polynomial trajectories. The transformation between them, causing most of the inefficiency and instability, is formally analyzed in this paper. Leveraging its analytic structure, we design a linear-complexity scheme for both jerk/snap minimization and parameter gradient evaluation, which possesses efficiency, stability, flexibility, and scalability. With the help of our scheme, generating an energy optimal (minimum snap) trajectory only costs 1 µs per piece at the scale up to 1,000,000 pieces. Moreover, generating large-scale energy-time optimal trajectories is also accelerated by an order of magnitude against conventional methods.
Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes

In contrast to Connectionist Temporal Classification (CTC) approaches, Sequence-To-Sequence (S2S) models for Handwritten Text Recognition (HTR) suffer from errors such as skipped or repeated words which often occur at the end of a sequence. In this paper, to combine the best of both approaches, we propose to use the CTC-Prefix-Score during S2S decoding. Hereby, during beam search, paths that are invalid according to the CTC confidence matrix are penalised. Our network architecture is composed of a Convolutional Neural Network (CNN) as visual backbone, bidirectional Long-Short-Term-Memory-Cells (LSTMs) as encoder, and a decoder which is a Transformer with inserted mutual attention layers. The CTC confidences are computed on the encoder while the Transformer is only used for character-wise S2S decoding. We evaluate this setup on three HTR data sets: IAM, Rimes, and StAZH. On IAM, we achieve a competitive Character Error Rate (CER) of 2.95% when pretraining our model on synthetic data and including a character-based language model for contemporary English. Compared to other state-of-the-art approaches, our model requires about 10–20 times less parameters. Access our shared implementations via this link to GitHub .
Fiat–Shamir Bulletproofs are Non-Malleable (in the Algebraic Group Model)

Bulletproofs (Bünz et al. IEEE S&P 2018) are a celebrated ZK proof system that allows for short and efficient proofs, and have been implemented and deployed in several real-world systems. In practice, they are most often implemented in their non-interactive version obtained using the Fiat-Shamir transform, despite the lack of a formal proof of security for this setting. Prior to this work, there was no evidence that malleability attacks were not possible against Fiat-Shamir Bulletproofs. Malleability attacks can lead to very severe vulnerabilities, as they allow an adversary to forge proofs re-using or modifying parts of the proofs provided by the honest parties. In this paper, we show for the first time that Bulletproofs (or any other similar multi-round proof system satisfying some form of weak unique response property) achieve simulation-extractability in the algebraic group model. This implies that Fiat-Shamir Bulletproofs are non-malleable.
FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection

In this paper, we investigate the problem of weakly supervised 3D vehicle detection. Conventional methods for 3D object detection usually require vast amounts of manually labelled 3D data as supervision signals. However, annotating large datasets needs huge human efforts, especially for 3D area. To tackle this problem, we propose a frustum-aware geometric reasoning (FGR) method to detect vehicles in point clouds without any 3D annotations. Our method consists of two stages: coarse 3D segmentation and 3D bounding box estimation. For the first stage, a context-aware adaptive region growing algorithm is designed to segment objects based on 2D bounding boxes. Leveraging predicted segmentation masks, we develop an anti-noise approach to estimate 3D bounding boxes in the second stage. Finally 3D pseudo labels generated by our method are utilized to train a 3D detector. Independent of any 3D groundtruth, FGR reaches comparable performance with fully supervised methods on the KITTI dataset. The findings indicate that it is able to accurately detect objects in 3D space with only 2D bounding boxes and sparse point clouds.
Machine Learning Algorithms In User Authentication Schemes

In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different Machine Learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning

A limitation of model-based reinforcement learning (MBRL) is the exploitation of errors in the learned models. Blackbox models can fit complex dynamics with high fidelity, but their behavior is undefined outside of the data distribution. Physics-based models are better at extrapolating, due to the general validity of their informed structure, but underfit in the real world due to the presence of unmodeled phenomena. In this work, we demonstrate experimentally that for the offline model-based reinforcement learning setting, physics-based models can be beneficial compared to high-capacity function approximators if the mechanical structure is known. Physics-based models can learn to perform the ball in a cup (BiC) task on a physical manipulator using only 4 minutes of sampled data using offline MBRL. We find that black-box models consistently produce unviable policies for BiC as all predicted trajectories diverge to physically impossible state, despite having access to more data than the physics-based model. In addition, we generalize the approach of physics parameter identification from modeling holonomic multi-body systems to systems with nonholonomic dynamics using end-to-end automatic differentiation.Videos: https://sites.google.com/view/ball-in-a-cup-in-4-minutes/
Hierarchical Classification and Transfer Learning to Recognize Head Gestures and Facial Expressions Using Earbuds

Head gestures and facial expressions – like, e.g., nodding or smiling – are important indicators of the quality of human interactions in physical meetings as well as in computer-mediated settings. Computer systems able to recognize such behavioral cues can support and improve human interactions. Several researchers have thus tackled the problem of automatically recognizing head gestures and facial expressions, mainly leveraging video data. In this paper, we instead consider inertial signals collected from unobtrusive, ear-mounted devices. We focus on typical activities performed during social interactions – head shaking, nodding, smiling, talking and yawning – and propose a hierarchical classification approach to discriminate them from each other. Further, we investigate whether the transfer of knowledge learned from publicly available datasets leads to further performance improvements. Our results show that the combined use of our hierarchical approach and transfer learning allows the classifier to discriminate head and mouth activities with an F1 score of 84.79, smile, talk and yawn with an F1 score of 45.42, and nodding and head shaking with an F1 score of 88.24, outperforming shallow classifiers by 2-9 percentage points.
Visual-Saliency-Based Abnormality Detection for MRI Brain Images—Alzheimer’s Disease Analysis

In recent years, medical image analysis has played a vital role in detecting diseases in their early stages. Medical images are rapidly becoming available for various applications to solve human problems. Therefore, complex medical features are needed to develop a diagnostic system for physicians to provide better treatment. Traditional methods of abnormality detection suffer from misidentification of abnormal regions in the given data. Visual-saliency detection methods are used to locate abnormalities to improve the accuracy of the proposed work. This study explores the role of a visual saliency map in the classification of Alzheimer’s disease (AD). Bottom-up saliency corresponds to image features, whereas top-down saliency uses domain knowledge in magnetic resonance imaging (MRI) brain images. The novelty of the proposed method lies in the use of an elliptical local binary pattern descriptor for low-level MRI characterization. Ellipse-like topologies help to obtain feature information from different orientations. Extensively directional features at different orientations cover the micro patterns. The brain regions of the Alzheimer’s disease stages were classified from the saliency maps. Multiple-kernel learning (MKL) and simple and efficient MKL (SEMKL) were used to classify Alzheimer’s disease from normal controls. The proposed method used the OASIS dataset and experimental results were compared with eight state-of-the-art methods. The proposed visual saliency-based abnormality detection produces reliable results in terms of accuracy, sensitivity, specificity, and f-measure.
SiteForge: Detecting and localizing forged images on microblogging platforms using deep convolutional neural network

Microblogging applications are currently used to disseminate information with concise text and images. Nevertheless, they are also the largest platform for circulating forged images. Forged images are digital photographs that have been modified to deceive or distort the information they communicate. These manipulated images posted on microblogging apps like Twitter often create biased user emotions leading to harmful consequences like religious feuds or riots. Microblogging platforms and fact-checking industry are investing in artificial intelligence solutions to detect these forged images on time. Many image forensic techniques are proposed earlier, but their effectiveness falls short on real-world images shared over microblogging sites. As forged images shared over these platforms are typically altered using multiple manipulation techniques, it is hard for forensic techniques to detect them. This paper proposes a customized convolutional neural network with an attention mechanism to spot fake images shared over microblogging platforms. Deep learning convolutional networks learn the intrinsic feature set of images and can detect the forged images. To handle multiple manipulations in an image, the applied attention mechanism focuses on the most relevant image region to learn the inherent feature sets. The model utilizes High-pass filters from the image processing domain to initialize kernel weights of the neural network. This supports the proposed model to converge faster and achieve better accuracy. The pooling layers are designed to specifically handle images from microblogging sites. The solution is universal and can detect complex tampering scenarios like text-editing, face-swapping, copy-move, splicing and mirroring. Local Interpretable Model-agnostic Explanations (LIME) is utilized to localize the manipulated region in a forged image. LIME also adds interpretability and confidence to the proposed model, a common concern in deep learning models. The model is verified against the publicly available CASIA 2.0 dataset. An accuracy score of 94.7% is achieved, which is better than the previous state-of-art papers in fake image detection. In order to test the model on real-world images published on Twitter, a recent dataset is built from an Indian viewpoint. The model achieves a modest accuracy of 83.2% over the real-world Twitter dataset. The experiment proves that the proposed model can accurately detect the forged images over social platforms. It can be utilized in the fact-checking field to improve manual efforts. It will also support manual fact-checkers in swift decision making.
ADCNN: Towards learning adaptive dilation for convolutional neural networks

Dilated convolution kernels are constrained by their shared dilation, keeping them from being aware of diverse spatial contents at different locations. We address such limitations by formulating the dilation as trainable weights with respect to individual positions. We propose Adaptive Dilation Convolutional Neural Networks (ADCNN), a light-weighted extension that allows convolutional kernels to adjust their dilation value based on different contents at the pixel level. Unlike previous content-adaptive models, ADCNN dynamically infers pixel-wise dilation via modeling feed-forward inter-patterns, which provides a new perspective for developing adaptive network structures other than sampling kernel spaces. Our evaluation results indicate ADCNNs can be easily integrated into various backbone networks and consistently outperform their regular counterparts on various visual tasks.
Learning automata based particle swarm optimization for solving class imbalance problem

Class imbalance is an important problem in many domains such as disease classification, network intrusion detection, fraud detection, and spam filtering. While dealing with imbalanced datasets, traditional supervised machine learning algorithms do not often provide acceptable results. Several approaches are used to handle the class imbalance problem. Of these, undersampling approaches are mostly followed by the researchers in which the number of instances in the majority class gets reduced. Selection of instances from the majority class can be considered as an optimization problem. To this end, in this paper, we present an undersampling approach based on widely-used Particle Swarm Optimization (PSO). The majority class samples are first clustered to form the initial undersampled set. The samples to be selected are then optimized using PSO to give the best model. The parameters of PSO are fine tuned using Learning Automata. Appropriate metrics suitable for class imbalance problems have been used to construct the fitness function for optimizing the undersampled training set. The proposed method has achieved 2% to 10% performance improvement over most of the contemporary methods on various datasets with imbalance ratios ranging from 5 to 130, thus showing that the method is robust and useful in practical scenarios. The code of the proposed method can be accessed via https://github.com/kkg1999/Undersampling . • A modified PSO, iPSO, has been used for undersampling imbalanced datasets. • The initial population of PSO is generated via clustering. • Learning automata is used for tuning the parameters. • The method is robust as it performs well on datasets of varying imbalance ratio. • The proposed method outperforms many state-of-the-art methods on the datasets.
Real-time Optimal Navigation Planning Using Learned Motion Costs

Navigation on challenging terrain topographies requires the understanding of robots&#x2019; locomotion capabilities to produce optimal solutions. We present an integrated framework for real-time autonomous navigation of mobile robots based on elevation maps. The framework performs rapid global path planning and optimization that is aware of the locomotion capabilities of the robot. A GPU-aided, sampling-based path planner combined with a gradient-based path optimizer provides optimal paths by using a neural network-based locomotion cost predictor which is trained in simulation. We show that our approach is capable of planning and optimizing paths three orders of magnitude faster than RRT* on GPU-enabled hardware, enabling real-time deployment on mobile platforms. We successfully evaluate the framework on the ANYmal C quadrupedal robot in both simulations and real-world environments for path planning tasks on multiple complex terrains.