Reviewerly/exHarmony huggingface dataset:
Regularized Deep Signed Distance Fields for Reactive Motion Generation

Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting both humans and their hardware. However, different applications require different distance resolutions, leading to various heuristic approaches for measuring distance fields w.r.t. obstacles, which are computationally expensive and hinder their application in dynamic obstacle avoidance use-cases. We propose Regularized Deep Signed Distance Fields (ReDSDF), a single neural implicit function that can compute smooth distance fields at any scale, with fine-grained resolution over high-dimensional manifolds and articulated bodies like humans, thanks to our effective data generation and a simple inductive bias during training. We demonstrate the effectiveness of our approach in representative simulated tasks for whole-body control (WBC) and safe Human- Robot Interaction (HRI) in shared workspaces. Finally, we provide proof of concept of a real-world application in a HRI handover task with a mobile manipulator robot.
AI-Empowered Trajectory Anomaly Detection for Intelligent Transportation Systems: A Hierarchical Federated Learning Approach

The vigorous development of positioning technology and ubiquitous computing has spawned trajectory big data. By analyzing and processing the trajectory big data in the form of data streams in a timely and effective manner, anomalies hidden in the trajectory data can be found, thus serving urban planning, traffic management, safety control and other applications. Limited by the inherent uncertainty, infinity, time-varying evolution, sparsity and skewed distribution of trajectory big data, traditional anomaly detection techniques cannot be directly applied to anomaly detection in trajectory big data. To solve this problem, we propose a hierarchical trajectory anomaly detection scheme for Intelligent Transportation Systems (ITS) using both machine learning and blockchain technologies. To be specific, a hierarchical federated learning strategy is proposed to improve the generalization ability of the global trajectory anomaly detection model by secondary fusion of the multi-area trajectory anomaly detection model. Then, by integrating blockchain and federated learning, the iterative exchange and fusion of the global trajectory anomaly detection model can be realized by means of on-chain and off-chain coordinated data access. Experiments show that the proposed scheme can improve the generalization ability of the trajectory anomaly detection model in different areas, while ensuring its reliability.
Towards Wave Digital Modeling of Neural Pathways Using Two-Port Coupling Networks

Biological systems are a great source of inspiration for developing novel computing technologies, as they exhibit favourable properties such as self-organisation and energy efficiency. The sheer complexity of these systems, however, makes developing such technologies both difficult and time-consuming. Tools providing the ability of emulating the behavior of complex systems, therefore become very essential. In this work, we present a method for emulating the behavior of electrical networks containing a coupling network with two-port coupling elements. Here, we make use of the wave digital concept due to the robustness and parallelism of the associated algorithms, which are closely related to the underlying electrical circuit. This work should serve as a basis for future implementations of close-to-biology neural networks.
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network

In skeleton-based action recognition, Graph Convolutional Networks model human skeletal joints as vertices and connect them through an adjacency matrix, which can be seen as a local attention mask. However, in most existing Graph Convolutional Networks, the local attention mask is defined based on natural connections of human skeleton joints and ignores the dynamic relations for example between head, hands and feet joints. In addition, the attention mechanism has been proven effective in Natural Language Processing and image description, which is rarely investigated in existing methods. In this work, we proposed a new adaptive spatial attention layer that extends local attention map to global based on relative distance and relative angle information. Moreover, we design a new initial graph adjacency matrix that connects head, hands and feet, which shows visible improvement in terms of action recognition accuracy. The proposed model is evaluated on two large-scale and challenging datasets in the field of human activities in daily life: NTU-RGB+D and Kinetics skeleton. The results demonstrate that our model has strong performance on both dataset.
Particle Swarm Optimization for Compact Neural Architecture Search for Image Classification

Convolutional neural networks (CNNs) are a superb computing paradigm in deep learning, and their architectures are considered to be the key to performance breakthroughs in various tasks. Recently, neural architecture search (NAS) methods have been proposed to automate the process of network architecture design, many of which have discovered novel CNN architectures that are superior to human-designed ones. However, most of the current NAS methods suffer from either prohibitively high computational complexity of resulting architectures which have inadvertently affected the deep model deployment, or limitations which impede the flexibility of architecture design. To address these deficiencies, this work proposes an evolutionary computation (EC)-based method for compact and flexible NAS. A valuable search space with parameter-efficient mobile-inverted bottleneck convolution blocks as the primitive components is proposed to ensure the initial quality of the compact architectures. In addition, a two-level variable-length particle swarm optimization (PSO) approach is devised to evolve both the microarchitecture and macroarchitecture of CNNs. Furthermore, this study proposes an effective scheme by integrating multiple computationally reduced methods to greatly speed up the evaluation process. Experimental results on the CIFAR-10, CIFAR-100, and ImageNet datasets show the superiority of the proposed method against the state-of-the-art algorithms in terms of the classification performance, search cost, and resulting architecture complexity.
Real-time and Accurate Gesture Recognition with Commercial RFID Devices

Gesture recognition based on radio frequency identification (RFID) has attracted much research attention in recent years. Most existing RFID-based gesture recognition approaches use signal profile matching to distinguish different gestures, which incur large recognition latency and fail to support real-time applications. In this paper, we design and implement ReActor, a real-time and accurate gesture recognition system that recognizes a user's gestures with low latency and high accuracy even when the gestures'speed varies. ReActor combines the time-domain statistical features and the frequency-domain features to precisely represent the signal profile corresponding to different gestures. To maintain high accuracy across different environments, we preprocess the signals to remove reflection signals from surrounding objects and use only the signals related to gestures to train the classifier. Moreover, we train a classifier to predict the speed of the gesture and feed the extracted features to different classifiers according to the speed. We implement ReActor and evaluate its performance in different scenarios. Experimental results show that ReActor achieves an average accuracy of 97.2% in recognizing 18 different gestures with an average latency of 72 ms, more than two orders of magnitude faster than approaches based on profile template matching.
A Game-Theoretic Federated Learning Framework for Data Quality Improvement

Federated learning is a promising distributed machine learning paradigm that has been playing a significant role in privacy-preserving machine learning tasks. However, alongside all its achievements, the framework has limitations. First, traditional frameworks assume that all clients want to improve model accuracy and so participation is voluntary. However, in reality, clients usually want to be appropriately compensated for the data and resources they will need to commit to the training process before contributing. Second, today's frameworks allow clients to perturb their parameter updates locally, which introduces a great deal of noise to the trained model and can seriously impact model accuracy. To address these concerns, we have developed a private reward game that incentivizes clients to contribute high-quality data to the training process. The game converges to a Nash equilibrium under the guarantee of joint differential privacy, and each client maximizes their reward following an equilibrium strategy. The noise injected into the model is reduced by introducing a centralized differential privacy model that aggregates the parameters and compensates clients via a data trading market. Experimental simulations show the rationales behind and effectiveness of the proposed game approach. Additionally, we present comparisons between different training models to demonstrate the performance of the proposed approach in real-world scenarios.
Jointformer: Single-Frame Lifting Transformer with Error Prediction and Refinement for 3D Human Pose Estimation

Monocular 3D human pose estimation technologies have the potential to greatly increase the availability of human movement data. The best-performing models for single-image 2D-3D lifting use graph convolutional networks (GCNs) that typically require some manual input to define the relationships between different body joints. We propose a novel transformer-based approach that uses the more generalised self-attention mechanism to learn these relationships within a sequence of tokens representing joints. We find that the use of intermediate supervision, as well as residual connections between the stacked encoders benefits performance. We also suggest that using error prediction as part of a multi-task learning framework improves performance by allowing the network to compensate for its confidence level. We perform extensive ablation studies to show that each of our contributions increases performance. Furthermore, we show that our approach outperforms the recent state of the art for single-frame 3D human pose estimation by a large margin. Our code and trained models are made publicly available on Github.
ODS-Bot: Mobile Robot Navigation for Outdoor Delivery Services

Autonomous mobile robots have been used in outdoor delivery services. Delivery robots have to cope with dynamic obstacles and various environmental conditions. Although several successful technological solutions are available for indoor applications, there are still plenty of unsolved problems in outdoor environments. In this study, we concentrate on three technological challenges hindering the development of campus delivery robots. The first challenge is robust localization in various dynamic outdoor environments. Localization results obtained using lidars and Global Navigation Satellite System (GNSS) sensors show complementary advantages and disadvantages. The proposed localization strategy efficiently combines information from multiple sensors. The second challenge is safe navigation based on the detection of the traversable region. The presented terrain traversability analysis provides clear, real-time positive and negative obstacle information. The third challenge is the requirement of an effective path planning strategy to reduce the risk of collisions or deadlocks. We collected the appearance history of regional dynamic obstacles in the map. The resultant path was carefully generated to avoid crowded areas. Experimental verifications have been successfully conducted on the Korea University campus. The presented results clearly indicate that the proposed methods are essential to achieve safe and reliable navigation in dynamic, real-world environments.
Graph-context Attention Networks for Size-varied Deep Graph Matching

Deep learning for graph matching has received growing interest and developed rapidly in the past decade. Although recent deep graph matching methods have shown excellent performance on matching between graphs of equal size in the computer vision area, the size-varied graph matching problem, where the number of keypoints in the images of the same category may vary due to occlusion, is still an open and challenging problem. To tackle this, we firstly propose to formulate the combinatorial problem of graph matching as an Integer Linear Programming (ILP) problem, which is more flexible and efficient to facilitate comparing graphs of varied sizes. A novel Graph-context Attention Network (GCAN), which jointly capture intrinsic graph structure and cross-graph information for improving the discrimination of node features, is then proposed and trained to resolve this ILP problem with node correspondence supervision. We further show that the proposed GCAN model is efficient to resolve the graph-level matching problem and is able to automatically learn node-to-node similarity via graph-level matching. The proposed approach is evaluated on three public keypoint-matching datasets and one graph-matching dataset for blood vessel patterns, with experimental results showing its superior performance over existing state-of-the-art algorithms for keypoint and graph-level matching.
Design of Distributed Rule-Based Models in the Presence of Large Data

Generally, fuzzy models, especially rule-based models, are designed in a monolithic manner, meaning that all data are used <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">en bloc</i> to design the model. At the same time, there is a visible need to cope with the ever-increasing volumes of data (both in terms of the number of data and their dimensionality) as well as being faced with distributed data located at various locations. The objective of this article is to develop a concept and provide a design framework as well as assess its performance for constructing a collection of rule-based models on a basis of a randomly sampled repository of data and then realize their aggregation. More specifically, for the sampled data, the design of each model is carried out in a standard way as commonly encountered in the case of Takagi–Sugeno (TS) rule-based models and next augmented by gradient boosting. The aggregation is realized by optimizing a weighting scheme applied to the results of the individual models. Our intent is also to carefully demonstrate the performance offered by the mechanisms of machine learning applied in the setting of rule-based models, which is an original task completed before. A number of high-dimensional data are used in the experimental studies to complete a thorough assessment. A comparative performance analysis is reported with respect to the monolithically developed TS models.
A Machine Learning Study to Enhance Project Cost Forecasting

In project management it is critical to obtain accurate cost forecasts using effective methods. This study presents a Machine Learning model based on Long-Short Term Memory to forecast the project cost. The model uses the seven-dimensional feature vector, including schedule and cost performance factors and their moving averages as a predictor. Based on the cost variation patterns from the training phase, we validate the model using three hundred experiments in the testing phase. Overall, the proposed model produces more accurate cost estimates when compared to the traditional Earned Value Management index-based model.
FileInsurer: A Scalable and Reliable Protocol for Decentralized File Storage in Blockchain

With the development of blockchain applications, the requirements for file storage in blockchain are increasing rapidly. Many protocols, including Filecoin, Arweave, and Sia, have been proposed to provide scalable decentralized file storage for blockchain applications. However, the reliability is not well promised by existing protocols. Inspired by the idea of insurance, we innovatively propose a decentralized file storage protocol in blockchain, named as FileInsurer, to achieve both scalability and reliability. While ensuring scalability by distributed storage, FileInsurer guarantees reliability by enhancing robustness and fully compensating for the file loss. Specifically, under mild conditions, we prove that no more than 0.1% value of all files should be compensated even if half of the storage collapses. Therefore, only a relatively small deposit needs to be pledged by storage providers to cover the potential file loss. Because of lower burdens of deposit, storage providers have more incentives to participate in the storage network. FileInsurer can run in the top layer of the InterPlanetary File System (IPFS), and thus it can be directly applied in Web 3.0, Non-Fungible Tokens, and Metaverse.
Cryptocurrency Price Prediction using Social Media Sentiment Analysis

In a paper that was anonymously published and signed by the pseudonym Satoshi Nakamoto, Bitcoin was introduced to the world. Due to its enormous success, a great number of cryptocurrencies were created in the upcoming years. This exponential growth relies mostly on the extreme volatility of the market, which led many people to become interested and get involved, primarily for profit. Cryptocurrency enthusiasts tend to share and learn news and opinions on social media platforms, one of the most popular being Twitter. In this paper, we study the extent to which Twitter sentiment analysis can be used to predict price fluctuations for cryptocurrencies. Initially, we gathered tweets and price data of seven of the most popular cryptocurrencies, which were processed to perform sentiment analysis using Valence Aware Dictionary for Sentiment Reasoning (VADER). The time-series stationarity was determined with Augmented Dicky Fuller (ADF) Kwiatkowski Phillips Schmidt Shin (KPSS) tests and then Granger Causality testing took place. While price fluctuations seem to cause sentiment for Bitcoin, Cardano, XRP and Doge, predictability was found for Ethereum and Polkadot, based on a bullishness ratio. Finally, predictability of price returns is examined with Vector Autoregression (VAR) and highly accurate forecasts for two of the seven cryptocurrencies were achieved. More specifically, price forecasts of Ethereum’s and Polkadot’s prices reached 99.67% and 99.17% accuracy, respectively.
A Style-Based GAN Encoder for High Fidelity Reconstruction of Images and Videos

We present a new encoder architecture for the inversion of Generative Adversarial Networks (GAN). The task is to reconstruct a real image from the latent space of a pre-trained GAN. Unlike previous encoder-based methods which predict only a latent code from a real image, the proposed encoder maps the given image to both a latent code and a feature tensor, simultaneously. The feature tensor is key for accurate inversion, which helps to obtain better perceptual quality and lower reconstruction error. We conduct extensive experiments for several style-based generators pre-trained on different data domains. Our method is the first feed-forward encoder to include the feature tensor in the inversion, outperforming the state-of-the-art encoder-based methods for GAN inversion. Additionally, experiments on video inversion show that our method yields a more accurate and stable inversion for videos. This offers the possibility to perform real-time editing in videos. Code is available at https://github.com/InterDigitalInc/FeatureStyleEncoder .
Multi-modal Extreme Classification

This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors. Applications of MUFIN to product-to-product recommendation and bid query prediction over several millions of products are presented. Contemporary multi-modal methods frequently rely on purely embedding-based methods. On the other hand, XC methods utilize classifier architectures to offer superior accuracies than embedding only methods but mostly focus on text-based categorization tasks. MUFIN bridges this gap by reformulating multi-modal categorization as an XC problem with several millions of labels. This presents the twin challenges of developing multi-modal architectures that can offer embeddings sufficiently expressive to allow accurate categorization over millions of labels; and training and inference routines that scale logarithmically in the number of labels. MUFIN develops an architecture based on cross-modal attention and trains it in a modular fashion using pre-training and positive and negative mining. A novel product-to-product recommendation dataset MM-AmazonTitles-300K containing over 300K products was curated from publicly available amazon.com listings with each product endowed with a title and multiple images. On the all datasets MUFIN offered at least 3% higher accuracy than leading text-based, image-based and multi-modal techniques. Code for MUFIN is available at https://github.com/Extreme-classification/MUFIN
Applying Machine Learning Techniques To Predict Breast Cancer

Breast cancer is most occurring leading cancer amongst women in the world after lung cancer. The doctors are facing problems to prepare a medication treatment owing to a deficiency of the greatest diagnosis results that may prolong patient survival time. Breast cancer was scary and risky before the 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century. A machine learning algorithm plays a substantial role in early-stage breast cancer prediction. The main objective of a research paper is to find the best classification algorithm. In this paper, the Wisconsin dataset has used. First, it predicts on data set of breast cancer and finds whether it is Malignant(M) or Benign(B). Second, it analyses theperformance and accuracy of various Machine learning algorithms, and third, it compares various machine learning algorithms. The research paper proposed an approach that improves accuracy and enhances the performance of algorithms. The efficient Machine learning algorithms are used such as Naïve Bayes, J48, Sequential Minimal Optimization (SMO) and Instance- Based for K-Nearest neighbor (IBK). The investigational results illustration in the proposed IBK algorithm gives the maximum accuracy of 100% when differentiate from the other three algorithms, this result will benefit in selecting the best classification algorithm for the breast cancer prediction and can be used for detection and treatment