Reviewerly/exHarmony huggingface dataset:
Path Planning for Robotic Manipulator in Complex Multi-Obstacle Environment Based on Improved_RRT

To plan a successful and practically executable path for a manipulator in a complex obstacle environment, the Improved_RRT method is proposed. In this article, we develop the collision detection model between the manipulator and obstacle based on the cylinder and sphere bounding box, and the shortest distance between the link and obstacle at each moment is obtained. Afterward, hybrid constrained sampling is used instead of original random sampling. The distribution of nodes obtained by sampling is close to the passages between obstacles. During the sampling process, the forward search process is accelerated. In the expansion, we propose a novel approach of RRT_Connect in collaboration with the artificial potential field method. Meanwhile, the adaptive gravitational field, dynamic step size, and new node constraint strategy are adopted. In the process of expansion, the algorithm uses the information of the environment and nodes to expand to the target area in a short time, which reduces the excessive exploration and collision area expansion. Considering the low success rate of algorithm planning in complex, multiobstacle and dynamic environments, local minimum processing and a dynamic path planning method are proposed, which generate double trees in the Cartesian and configuration spaces, respectively, for local path replanning. The improved algorithm is more robust to the singular problems of the manipulator, and the path is smoother after pruning optimization. Simulation results show that, compared with other algorithms, the proposed method can effectively plan a safe and optimal obstacle avoidance path with the best comprehensive performance. Experiments on the actual robotic manipulator platform further prove the efficiency of our method.
CartoonLossGAN: Learning Surface and Coloring of Images for Cartoonization

Cartoonization as a special type of artistic style transfer is a difficult image processing task. The current existing artistic style transfer methods cannot generate satisfactory cartoon-style images due to that artistic style images often have delicate strokes and rich hierarchical color changes while cartoon-style images have smooth surfaces without obvious color changes, and sharp edges. To this end, we propose a cartoon loss based generative adversarial network (CartoonLossGAN) for cartoonization. Particularly, we first reuse the encoder part of the discriminator to build a compact generative adversarial network (GAN) based cartoonization architecture. Then we propose a novel cartoon loss function for the architecture. It can imitate the process of sketching to learn the smooth surface of the cartoon image, and imitate the coloring process to learn the coloring of the cartoon image. Furthermore, we also propose an initialization strategy, which is used in the scenario of reusing the discriminator to make our model training easier and more stable. Extensive experimental results demonstrate that our proposed CartoonLossGAN can generate fantastic cartoon-style images, and outperforms four representative methods.
DTA: Physical Camouflage Attacks using Differentiable Transformation Network

To perform adversarial attacks in the physical world, many studies have proposed adversarial camouflage, a method to hide a target object by applying camouflage patterns on 3D object surfaces. For obtaining optimal physical adversarial camouflage, previous studies have utilized the so-called neural renderer, as it supports differentiability. However, existing neural renderers cannot fully represent various real-world transformations due to a lack of control of scene parameters compared to the legacy photo-realistic renderers. In this paper, we propose the Differentiable Transformation Attack (DTA), a framework for generating a robust physical adversarial pattern on a target object to camouflage it against object detection models with a wide range of transformations. It utilizes our novel Differentiable Transformation Network (DTN), which learns the expected transformation of a rendered object when the texture is changed while preserving the original properties of the target object. Using our attack framework, an adversary can gain both the advantages of the legacy photo-realistic renderers including various physical-world transformations and the benefit of white-box access by offering differentiability. Our experiments show that our camouflaged 3D vehicles can successfully evade state-of-the-art object detection models in the photo-realistic environment (i.e., CARLA on Unreal Engine). Furthermore, our demonstration on a scaled Tesla Model 3 proves the applicability and transferability of our method to the real world.
Critical Analysis of Genetic Algorithm under Crossover and Mutation Rate

Many evolutionary algorithms have been developed by the researcher. Genetic algorithms and memetic algorithms are most commonly used by researchers to solve various optimization problems. Genetic and Memetic algorithms have certain parameters that need to be modified so that the algorithm can produce better results. In the present paper, we are working on a genetic algorithm. Crossover operators and mutation operators play a very important role in the development of an efficient genetic algorithm (GA). The performance of the genetic algorithm depends upon its parameter setting. In the present paper, the basic conceptual features and unique characteristics of the different rates of crossover and mutation operators are discussed. The simulation environment was created to study the combinatorial problems, i.e., scheduling and sequencing problems. Currently, the researcher is focusing on the scheduling problem which is a NP-Hard problem. In the future study, the traveling salesman problem could be considered to investigate the additional parameters of GA. The current empirical study shows that the crossover and mutation rate have an impact on the GA's performance. This research helps us to understand how to choose the right crossover and mutation rate so that the GA's performance can be improved.
EESANet: edge-enhanced self-attention network for two-dimensional phase unwrapping

In this paper, we first propose a quantitative indicator to measure the amount of prior information contained in the wrapped phase map. Then, Edge-Enhanced Self-Attention Network is proposed for two-dimensional phase unwrapping. EESANet adopts a symmetrical en-decoder architecture and uses self-designed Serried Residual Blocks as its basic block. We add Atrous Spatial Pyramid Pooling and Positional Self-Attention to the network to obtain the long-distance dependency in phase unwrapping, and we further propose Edge-Enhanced Block to enhance the effective edge features of the wrapped phase map. In addition, weighted cross-entropy loss function is employed to overcome the category imbalance problem. Experiments show that our method has higher precision, stronger robustness and better generalization than the state-of-the-art.
Application of Augmented Intelligence Technology with Human Body Tracking for Human Anatomy Education

Technological developments have a positive impact on humans to improve their abilities in line with innovations in the field of education which are increasing day by day. One of them is by increasing intelligence through collaboration between humans and technology called augmented intelligence. In this study augmented intelligence is applied to assist humans in studying human anatomy by utilizing augmented reality technology to perform motion tracking that can control 3D assets to follow it. The anatomy learning platform that was built was named AIVE (Artificial Intelligence on Virtual Education) with an architectural system consisting of a frontend for interface needs and AR platform development, there is also a backend for streaming databases and AI algorithms. Measurement of satisfaction and interest based on the PIECES framework is also carried out to determine the user’s response to the platform being built. The results show a satisfaction level of 4.12 and an interest of 4.10 which means that users are satisfied and interested in the human anatomy tracking platform that is accessed via smartphones.
Focal Stack Image Compression Based on Basis-Quadtree Representation

In this paper, we propose an efficient compression scheme for focal stack images (FoSIs) based on a new basis-quadtree representation. In the new basis-quadtree representation, FoSIs are initially reorganized as co-located block groups in the depth dimension. In each group, selective basis blocks and adaptive quadtree partition are optimized to predict the focused or defocused co-located blocks by intra-group approximation. By solving a joint optimization problem, FoSIs can be efficiently represented by the optimal basis blocks, corresponding quadtree partition and approximation parameters, which will be compressed separately. Then, these basis blocks are stitched into several new frames (basis frames) according to their original locations and partition modes. Basis frames are compressed by our designed encoder, where the intra-group approximation is embedded into the high efficiency video coding (HEVC) encoder. Thus, the redundancies of basis blocks can be further eliminated. Finally, the approximation parameters are refined to suppress the amplified errors caused by introduced compression blur after basis frame coding. The refined parameters are compressed losslessly and multiplexed with the bitstream of the basis frames to ensure the reconstruction quality of FoSIs. Experiments on 12 test sequences demonstrate that the proposed scheme can obtain higher coding performance than the state-of-the-art comparison schemes. Specifically, the proposed scheme achieves up to 5.23 dB PSNR gains and 71.59% bitrate savings over the HEVC baseline scheme on sequences I03 and I05, respectively.
Covert Channel Detection: Machine Learning Approaches

The advanced development of computer networks and communication technologies has made covert communications easier to construct, faster, undetectable and more secure than ever.A covert channel is a path through which secret messages can be leaked by violating a system security policy.The detection of such dangerous, unwatchable, and hidden threats is still one of the most challenging aspects.This threat exploits methods that are not dedicated to communication purposes, meaning that traditional security measures fail to detect its existence.This review has introduced a brief introduction of covert channel definitions, types and developments, with a particular focus on detection techniques using machine learning (ML) approaches.It provides a thorough review of the most common covert channels and ML techniques that are used to counter them, as well as addressing their achievements and limitations.In addition, this paper introduces a comparative experimental study for some common ML approaches that are commonly used in this field.Accordingly, the performance of these classifiers was evaluated and reported.The paper concludes that our information is still at risk, nothing is said to be secured and more work on the detection of covert channels is required.
Machine learning for cardiology

This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in machine learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying machine learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while neural networks are slowly being incorporated in cardiovascular research, other important techniques such as semi-supervised learning and federated learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.
An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.
Spatiotemporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks either fail at modeling collisions or are too expensive/slow, making them less suitable for deployment on AV edge hardware. To address these limitations, we propose SG2VEC, a spatiotemporal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scene-graph</i> embedding methodology that uses the graph neural network (GNN) and long short-term memory (LSTM) layers to predict future collisions via visual scene perception. We demonstrate that SG2VEC predicts collisions 8.11% more accurately and 39.07% earlier than the state-of-the-art method on synthesized data sets, and 29.47% more accurately on a challenging real-world collision data set. We also show that SG2VEC is better than the state of the art at transferring knowledge from synthetic data sets to real-world driving data sets. Finally, we demonstrate that SG2VEC performs inference <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.3\times $ </tex-math></inline-formula> faster with an 88.0% smaller model, 32.4% less power, and 92.8% less energy than the state-of-the-art method on the industry-standard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.
SLUE: New Benchmark Tasks For Spoken Language Understanding Evaluation on Natural Speech

Progress in speech processing has been facilitated by shared datasets and benchmarks. Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks. Interest has been growing in higher-level spoken language understanding tasks, including using end-to-end models, but there are fewer annotated datasets for such tasks. At the same time, recent work shows the possibility of pre-training generic representations and then fine-tuning for several tasks using relatively little labeled data. We propose to create a suite of benchmark tasks for Spoken Language Understanding Evaluation (SLUE) consisting of limited-size labeled training sets and corresponding evaluation sets. This resource would allow the research community to track progress, evaluate pre-trained representations for higher-level tasks, and study open questions such as the utility of pipeline versus end-to-end approaches. We present the first phase of the SLUE benchmark suite, consisting of named entity recognition, sentiment analysis, and ASR on the corresponding datasets. We focus on naturally produced (not read or synthesized) speech, and freely available datasets. We pro-vide new transcriptions and annotations on subsets of the VoxCeleb and VoxPopuli datasets, evaluation metrics and results for baseline models, and an open-source toolkit to reproduce the baselines and evaluate new models.
Broad-spectrum diffractive network via ensemble learning

We propose a broad-spectrum diffractive deep neural network (BS-D2NN) framework, which incorporates multiwavelength channels of input lightfields and performs a parallel phase-only modulation using a layered passive mask architecture. A complementary multichannel base learner cluster is formed in a homogeneous ensemble framework based on the diffractive dispersion during lightwave modulation. In addition, both the optical sum operation and the hybrid (optical-electronic) maxout operation are performed for motivating the BS-D2NN to learn and construct a mapping between input lightfields and truth labels under heterochromatic ambient lighting. The BS-D2NN can be trained using deep learning algorithms to perform a kind of wavelength-insensitive high-accuracy object classification.
Light Field Reconstruction Using Efficient Pseudo 4D Epipolar-Aware Structure

Limited by sensor resolution, Light Field (LF) images often suffer from an inherent trade-off between angular resolution and spatial resolution. LF reconstruction, including Spatial Super-Resolution (LFSSR) and Angular Super-Resolution (LFASR), upsamples LF in spatial and angular domain based on the complementary information in different views. In this paper, we propose efficient pseudo-4D end-to-end frameworks for LFSSR and LFASR, respectively. Since the epipolar information and spatial-angular information reflect the relationship between views, we propose to fully consider both of them to exploit complementary information thoroughly. Specifically, we first extract epipolar information from horizontally and vertically stacked views separately. Then an Epipolar-Aware Grid (EAG) network composed of Dual Interactive Blocks (DIBs) fully interacts the epipolar information, characterizing the grid-like LF parallax structure. In order to effectively achieve dense interaction between the spatial and angular domain to extract complementary information, several Parallel Spatial-Angular Integration Blocks (PSAIBs) are further introduced. For LFs with large baselines, we also propose a generic shear attention network, which generalizes the model designed for small baselines to large baselines without depth estimation. As compared to the state-of-the-art methods, extensive experiments conducted on both synthetic and real-world datasets demonstrate our superiority in LFSSR tasks and LFASR tasks.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations

Deep learning-based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimension of categorical variables (e.g., user/item identifiers) and meaningfully transform them in the low-dimensional space. The majority of existing DLRSs empirically pre-define a fixed and unified dimension for all user/item embeddings. It is evident from recent researches that different embedding sizes are highly desired for different users/items according to their frequency. However, manually selecting embedding sizes in recommender systems can be very challenging due to a large number of users/items and the dynamic nature of their frequency. Thus, in this paper, we propose an AutoML based end-to-end framework (AutoEmb), enabling various embedding dimensions according to the frequency in an automated and dynamic manner. To be specific, we first enhance a typical DLRS to allow various embedding dimensions; then, we propose an end-to-end differentiable framework that can automatically select different embedding dimensions according to user/item frequency; finally, we propose an AutoML based optimization algorithm in a streaming recommendation setting. The experimental results based on widely used benchmark datasets demonstrate the effectiveness of the AutoEmb framework.
Towards Private Learning on Decentralized Graphs With Local Differential Privacy

Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profile. It is typical to collect these local views of social graphs and conduct graph learning tasks. However, learning over graphs can raise privacy concerns as these local views often contain sensitive information. In this paper, we seek to ensure private graph learning on a decentralized network graph. Towards this objective, we propose <i>Solitude</i>, a new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees based on edge local differential privacy. The crux of <i>Solitude</i> is a set of new delicate mechanisms that can calibrate the introduced noise in the decentralized graph collected from the users. The principle behind the calibration is the intrinsic properties shared by many real-world graphs, such as sparsity. Unlike existing work on locally private GNNs, our new framework can simultaneously protect node feature privacy and edge privacy, and can seamlessly incorporate with any GNN with privacy-utility guarantees. Extensive experiments on benchmarking datasets show that <i>Solitude</i> can retain the generalization capability of the learned GNN while preserving the users&#x2019; data privacy under given privacy budgets.
Sequential Diagnosis Prediction with Transformer and Ontological Representation

Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient’s interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some recent works train healthcare predictive models by making use of sequential information in EHR data, but they are vulnerable to irregular, temporal EHR data with the states of admission/discharge from hospital, and insufficient data. To mitigate this, we propose an end-to-end robust transformer-based model called SETOR, which exploits neural ordinary differential equation to handle both irregular intervals between a patient’s visits with admitted timestamps and length of stay in each visit, to alleviate the limitation of insufficient data by integrating medical ontology, and to capture the dependencies between the patient’s visits by employing multi-layer transformer blocks. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR not only achieves better predictive results than previous state-of-the-art approaches, irrespective of sufficient or insufficient training data, but also derives more interpretable embeddings of medical codes. The experimental codes are available at the GitHub repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Github repository: https://github.com/Xueping/SETOR