Reviewerly/exHarmony huggingface dataset:
3D object detection for autonomous driving: Methods, models, sensors, data, and challenges

Detection of the surrounding objects of a vehicle is the most crucial step in autonomous driving. Failure to identify those objects correctly in a timely manner can cause irreparable damage, impacting our safety and society. Several studies have been introduced to identify these objects in the two-dimensional (2D) and three-dimensional (3D) vector space. The 2D object detection method has achieved remarkable success; however, in the last few years, detecting objects in 3D have received more remarkable adoption. 3D object recognition has several advantages over 2D detection methods, as more accurate information about the environment is obtained for better detection. For example, the depth of the images is not considered in the 2D detection, which reduces the detection accuracy. Despite considerable efforts in 3D object detection, it has not yet reached the stage of maturity. Therefore, in this paper, we aim at providing a comprehensive overview of the state-of-the-art 3D object detection methods, with a focus on 1) identifying advantages and limitations, 2) revelling a novel categorization of the literature, 3) outlying the various training procedures, 4) highlighting the research gap in the existing methods and 5) building a road map for future directions.
Exploring Immersive Learning Experiences: A Survey

Immersive technologies have been shown to significantly improve learning as they can simplify and simulate complicated concepts in various fields. However, there is a lack of studies that analyze the recent evidence-based immersive learning experiences applied in a classroom setting or offered to the public. This study presents a systematic review of 42 papers to understand, compare, and reflect on recent attempts to integrate immersive technologies in education using seven dimensions: application field, the technology used, educational role, interaction techniques, evaluation methods, and challenges. The results show that most studies covered STEM (science, technology, engineering, math) topics and mostly used head-mounted display (HMD) virtual reality in addition to marker-based augmented reality, while mixed reality was only represented in two studies. Further, the studies mostly used a form of active learning, and highlighted touch and hardware-based interactions enabling viewpoint and select tasks. Moreover, the studies utilized experiments, questionnaires, and evaluation studies for evaluating the immersive experiences. The evaluations show improved performance and engagement, but also point to various usability issues. Finally, we discuss implications and future research directions, and compare our findings with related review studies.
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints

Backdoor attack is a type of serious security threat to deep learning models. An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern. Most existing backdoor attacks focus on manually defining imperceptible triggers in input space without considering the abnormality of triggers' latent representations in the poisoned model. These attacks are susceptible to backdoor detection algorithms and even visual inspection. In this paper, We propose a novel and stealthy backdoor attack - DEFEAT. It poisons the clean data using adaptive imperceptible perturbation and restricts latent representation during training process to strengthen our attack's stealthiness and resistance to defense algorithms. We conduct extensive experiments on multiple image classifiers using real-world datasets to demonstrate that our attack can 1) hold against the state-of-the-art defenses, 2) deceive the victim model with high attack success without jeopardizing model utility, and 3) provide practical stealthiness on image data.
Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.
One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging contrast may vary amongst patients, which poses challenges to radiologists and automated image analysis algorithms. A general approach for tackling this problem is missing data imputation, which aims to synthesize the missing contrasts from existing ones. While several convolutional neural networks (CNN) based algorithms have been proposed, they suffer from the fundamental limitations of CNN models, such as the requirement for fixed numbers of input and output channels, the inability to capture long-range dependencies, and the lack of interpretability. In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing. MMT consists of a multi-scale Transformer encoder that builds hierarchical representations of inputs combined with a multi-scale Transformer decoder that generates the outputs in a coarse-to-fine fashion. The proposed multi-contrast Swin Transformer blocks can efficiently capture intra- and inter-contrast dependencies for accurate image synthesis. Moreover, MMT is inherently interpretable as it allows us to understand the importance of each input contrast in different regions by analyzing the in-built attention maps of Transformer blocks in the decoder. Extensive experiments on two large-scale multi-contrast MRI datasets demonstrate that MMT outperforms the state-of-the-art methods quantitatively and qualitatively.
A modified Adam algorithm for deep neural network optimization

Abstract Deep Neural Networks (DNNs) are widely regarded as the most effective learning tool for dealing with large datasets, and they have been successfully used in thousands of applications in a variety of fields. Based on these large datasets, they are trained to learn the relationships between various variables. The adaptive moment estimation (Adam) algorithm, a highly efficient adaptive optimization algorithm, is widely used as a learning algorithm in various fields for training DNN models. However, it needs to improve its generalization performance, especially when training with large-scale datasets. Therefore, in this paper, we propose HN Adam, a modified version of the Adam Algorithm, to improve its accuracy and convergence speed. The HN_Adam algorithm is modified by automatically adjusting the step size of the parameter updates over the training epochs. This automatic adjustment is based on the norm value of the parameter update formula according to the gradient values obtained during the training epochs. Furthermore, a hybrid mechanism was created by combining the standard Adam algorithm and the AMSGrad algorithm. As a result of these changes, the HN_Adam algorithm, like the stochastic gradient descent (SGD) algorithm, has good generalization performance and achieves fast convergence like other adaptive algorithms. To test the proposed HN_Adam algorithm performance, it is evaluated to train a deep convolutional neural network (CNN) model that classifies images using two different standard datasets: MNIST and CIFAR-10. The algorithm results are compared to the basic Adam algorithm and the SGD algorithm, in addition to other five recent SGD adaptive algorithms. In most comparisons, the HN Adam algorithm outperforms the compared algorithms in terms of accuracy and convergence speed. AdaBelief is the most competitive of the compared algorithms. In terms of testing accuracy and convergence speed (represented by the consumed training time), the HN-Adam algorithm outperforms the AdaBelief algorithm by an improvement of 1.0% and 0.29% for the MNIST dataset, and 0.93% and 1.68% for the CIFAR-10 dataset, respectively.
A new SAR image despeckling using correlation based fusion and method noise thresholding

This paper presents a new technique for despeckling of Synthetic Aperture Radar (SAR) images using a local correlation based fusion of high-frequency coefficients in Discrete Wavelet Transform (DWT) with method noise thresholding. The decomposition level is decided by analyzing the texture of the input image at each level by calculating entropy. The core idea of the proposed technique lies in the selection of decomposition level in 2D-DWT based on entropy parameter and on the fusion of high-frequency coefficients. On decomposition, the low-frequency coefficients remain untouched and the high-frequency coefficients are thresholded using two different shrinkage rules. Therefore the Bayesian and Bivariate shrinkage methods are applied to the high-frequency coefficients. After performing two different thresholding methods, the improved high-frequency coefficients are fused using local correlation based strategy. The threshold value is calculated by correlation strategy. Later the correlation coefficient (CC) is evaluated between the two improved high-frequency coefficients. The CC is now compared with the threshold value for the fusion purpose. On the basis of defined fusion strategy, the average and maximum operation are applied to perform the fusion of high-frequency coefficients. The despeckling scheme is followed by method noise thresholding in order to preserve the fine details of the image. The performance of the proposed method is assessed using metrics such as Signal-to-Noise Ratio (SNR), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM) and visual appearance of the despeckled image. The experimental results demonstrate the effectiveness of proposed work over prior works on SAR image despeckling.
Navigation Functions With Time-Varying Destination Manifolds in Star Worlds

This paper formally constructs navigation functions with time-varying destinations on star worlds. The construction is based on appropriate diffeomorphic transformations and extends an earlier sphere-world formulation. A new obstacle modeling method is also introduced, reducing analytical complexity and offering unified expressions of common classes of n-dimensional obstacles. The method allows for dynamic target tracking and is validated through simulations and experiments.
A Smartphone-Based Application Using Machine Learning for Gesture Recognition: Using Feature Extraction and Template Matching via Hu Image Moments to Recognize Gestures

The rapid development of smart devices, such as smartphones and tablets, leads to new challenges and ushers in a new stage of human-computer interaction. In this context, it becomes essential to develop methods and techniques for a better and more natural interaction with these devices. In this article, we address the problem of gesture segmentation and recognition, taking into account the limited computational resources of smartphone devices. We introduce a methodology for designing efficient and useful applications that, by using low-cost and widely diffused technologies, can be used in telemedicine, home-based rehabilitation, and other biomedical applications for patients with specific disabilities. To this end, we have designed a new machine-learning algorithm that is able to identify hand gestures through the use of Hu image moments, due to their invariance to rotation, translation, scaling, and their low computational cost. The experimental results collected from a case study show an excellent gesture recognition performance and an affordable real-time execution speed on smartphones and other mobile devices.
Augmented Reality Based on SLAM to Assess Spatial Short-Term Memory

Spatial short-term memory is defined as the limited ability of people to retain and remember the location of elements for short periods of time. In this paper, we present the first AR app based on SLAM (Simultaneous Localization and Mapping) to assess spatial short-term memory. A total of 55 participants were involved in a study for remembering the real place where four virtual objects were located in the real environment. The participants were divided into two groups: the ARGroup (the participants learned the location of the virtual objects in the real environment in an adaptation phase using AR) and the NoARGroup (the participants learned the location of the objects by looking at photographs). The results indicated that the performance outcomes in remembering objects and their location for the participants in the ARGroup were statistically significantly greater than those obtained by the participants in the NoARGroup. From this result and our observations, we can conclude that touring the augmented environment helped the participants to better remember the location of virtual objects added to the real scene compared to looking at photographs of the environment. Furthermore, statistically significant differences were not found in relation to gender or age. Finally, our app has several advantages: 1) Our app works in any environment and does not require adding real elements to the environment; 2) the evaluators can select any real environment and place the virtual elements where they want and even change them between sessions; and 3) our app could work similar to the way spatial memory does in everyday life.
Camouflage pattern features interact with movement speed to determine human target detectability

Applied ergonomics research examines not only the fit, form and function of military uniforms, but also their ability to effectively camouflage personnel as they perform job-related tasks. Many of these job-related tasks involve moving through environments, but existing literature examining camouflage effectiveness often assumes that movement effectively “breaks” even the best camouflage patterns, rendering them of limited utility for reducing the visual signature of a moving target. However, recent research demonstrates that animals equipped with adaptive camouflage change their patterning in predictable ways during movement and this adaptation decreases detectability, suggesting that uniform patterning may still hold value for reducing conspicuity during movement. The present experiment examined whether three visual pattern characteristics, local contrast, orientation, and spatial frequency, would influence the detectability of a moving human target. Participants attempted to detect and localize a simulated human target moving across a background scene, and a factorial design varied target movement speed, and the local contrast, spatial frequency, and orientation of its camouflage patterning. Results showed that target detectability was strongly influenced by target movement rate, pattern local contrast, and pattern spatial frequency, and these effects persisted even under conditions of very fast movement. Importantly, we found that the effect of local contrast was most robust under conditions of movement, suggesting its importance for reducing detectability of moving personnel. We conclude that movement is not always sufficient to break the concealment offered by a pattern with low contrast and a spatial frequency match with its background. Results are discussed in the context of visual processing theories and the application of these findings to the design and development of static and adaptive camouflage patterns for military personnel.
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering

This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only requiring access to the token-level hidden representations of the reader. The retriever uses fast nearest neighbor search to scale to corpora containing millions of paragraphs. A gated recurrent unit updates the query at each step conditioned on the state of the reader and the reformulated query is used to re-rank the paragraphs by the retriever. We conduct analysis and show that iterative interaction helps in retrieving informative paragraphs from the corpus. Finally, we show that our multi-step-reasoning framework brings consistent improvement when applied to two widely used reader architectures DrQA and BiDAF on various large open-domain datasets --- TriviaQA-unfiltered, QuasarT, SearchQA, and SQuAD-Open.
A Grounded Interaction Protocol for Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an interactive explanation to propose an interaction protocol. We follow a bottom-up approach to derive the model by analysing transcripts of different explanation dialogue types with 398 explanation dialogues. We use grounded theory to code and identify key components of an explanation dialogue. We formalize the model using the agent dialogue framework (ADF) as a new dialogue type and then evaluate it in a human-agent interaction study with 101 dialogues from 14 participants. Our results show that the proposed model can closely follow the explanation dialogues of human-agent conversations.
14.1 A 65nm 1.1-to-9.1TOPS/W Hybrid-Digital-Mixed-Signal Computing Platform for Accelerating Model-Based and Model-Free Swarm Robotics

Artificial swarm intelligence, inspired by biological studies of insects, ants and other organisms, present an emerging computing paradigm, where seemingly simple elements interact with each other to collectively solve challenging problems. In particular, swarm robotics, where multiple robots co-ordinate in real-time to solve diverse problems such as pattern-formation, cooperative reinforcement learning (RL), path-planning etc. [1], find extensive uses in exploration, reconnaissance and disaster relief. This is partly motivated by the robustness of swarm dynamics to failures and malfunctions of individual robots. Successful hardware demonstrations of neuro-inspired algorithms on edge-devices [2]-[6] is now leading to the emergence of intelligence and control in swarms as the next frontier. Although certain swarm algorithms rely on real-time learning (e.g., cooperative RL) representing a model-free approach, many powerful algorithms that have been developed over the past two decades (e.g., pattern formation) rely on a mathematical structure and represent a more traditional model-based approach. The next generation of swarm hardware needs to support both of these approaches. In this paper, we identify the commonalities and shared compute primitives across a variety of model-based and model-free swarm algorithms and present a unified, fully-programmable, energy-efficient and scalable platform capable of real-time swarm intelligence.
Continuous Authentication Using Mouse Clickstream Data Analysis

Biometrics is used to authenticate an individual based on physiological or behavioral traits. Mouse dynamics is an example of a behavioral biometric that can be used to perform continuous authentication as protection against security breaches. Recent research on mouse dynamics has shown promising results in identifying users; however, it has not yet reached an acceptable level of accuracy. In this paper, an empirical evaluation of different classification techniques is conducted on a mouse dynamics dataset, the Balabit Mouse Challenge dataset. User identification is carried out using three mouse actions: mouse move, point and click, and drag and drop. Verification and authentication methods are conducted using three machine-learning classifiers: the Decision Tree classifier, the K-Nearest Neighbors classifier, and the Random Forest classifier. The results show that the three classifiers can distinguish between a genuine user and an impostor with a relatively high degree of accuracy. In the verification mode, all the classifiers achieve a perfect accuracy of 100%. In authentication mode, all three classifiers achieved the highest accuracy (ACC) and Area Under Curve (AUC) from scenario B using the point and click action data: (Decision Tree - ACC: 87.6%, AUC: 90.3%), (K-Nearest Neighbors - ACC: 99.3%, AUC: 99.9%), and (Random Forest - ACC: 89.9%, AUC: 92.5%).
The deformable most-likely-point paradigm

In this paper, we present three deformable registration algorithms designed within a paradigm that uses 3D statistical shape models to accomplish two tasks simultaneously: 1) register point features from previously unseen data to a statistically derived shape (e.g., mean shape), and 2) deform the statistically derived shape to estimate the shape represented by the point features. This paradigm, called the deformable most-likely-point paradigm, is motivated by the idea that generative shape models built from available data can be used to estimate previously unseen data. We developed three deformable registration algorithms within this paradigm using statistical shape models built from reliably segmented objects with correspondences. Results from several experiments show that our algorithms produce accurate registrations and reconstructions in a variety of applications with errors up to CT resolution on medical datasets. Our code is available at https://github.com/AyushiSinha/cisstICP.