Reviewerly/exHarmony huggingface dataset:
Recognition of Plastic Bottle Using Improved U-Net

This paper focuses on separation rubbish, which is one of the causes of global warming. Currently, recyclable waste including garbage is sent to the waste disposal site in an unsorted state, and it is separated by human source. This is difficult work and a cause of manpower shortage. To overcome those problem, it is necessary to introduce automatic sorting algorithms on separation rubbish process. In this study, we focused on separation rubbish process especially plastic bottles based on image recognition technique using a deep learning approach. We implement an improved U-Net as the deep learning scheme to increase segmentation accuracy. As a result, we obtained the segmentation accuracy with 0.789 of cap part and 0.972 of body part on plastic bottle respectively.
A Mobile Robot Framework in Industrial Disaster for Human Rescue

In this paper, we develop a rescue robot framework for first-step rescue work that can help rescue teams. We model a disaster environment in detail and updated the layered map and divided the topography as per the reachability of the robot using memory-efficient 3D Octomap. And we construct an autonomous driving algorithm for efficient and fast calculation by projecting it into a 2D map. Also, unlike previous studies that detect a moving person with an onboard sensor, we propose a system that detects a person’s posture by converting the 2D keypoint coordinates extracted through pose estimation using a depth camera into 3D. Keypoints converted to 3D distinguish a person’s posture and show the current state with a 3D map. And a person’s pose is discriminated by using the coordinates and the posture is sent to the team. Therefore, the whole process was experimented, and we confirmed that we can provide the first stage of an efficient human structure framework.
GRU-Attention based TD3 Network for Mobile Robot Navigation

In this paper, we propose a goal-oriented navigation reinforcement learning network called GRU-Attention based TD3 network, which takes lidar measurements, the distance between agent and target, and yaw toward the target as state inputs. The policy in the network outputs continuous actions consisting of forward velocity and yaw angular velocity. Our proposed network can perform obstacle-avoidance navigation without prior knowledge of the environment. We train our network in a simulation environment. To show that our proposed network is better in navigation tasks, we compare its performance with two other networks: the pure TD3 network and the GRU-based TD3 network in several different simulation worlds. The experiments show that the mobile robot with our proposed network can bypass the obstacles safely and arrive at the goal positions as fast as possible. The supplementary video is given at: https:// youtu.be/HkqUZSsT5a0. The implementation is made open source at: https://github.com/Barry2333/ DRL_Navigation.git.
Edge Computational Offloading for Corrosion Inspection in Industry 4.0

The fifth generation (5G) mobile networks are a game changer for the 4th industrial revolution (Industry 4.0). The difficulty of developing vertical applications for mobile networks is however still challenging. This article proposes a new paradigm solution to significantly facilitate deploying network applications (NetApp) closer to the end user and improving the user experience, facilitated by 5G. This new paradigm leverages flexible service onboarding and composition end to end, and computation offloading to a Multi-access Edge Computing (MEC) platform to meet the demanding requirements of the NetApps. A functional prototype is developed and empirical comparison is conducted to validate and evaluate the performance of a corrosion detection NetApp using artificial intelligence (AI) in three different scenarios where the application is hosted. The prototype runs this NetApp over an Unmanned Aerial Vehicle (UAV) connected to a 5G network testbed, and the results offer insights into this performance improvement and elaborates how opting for this NetApp solution can help the industry. In this case, the use of this NetApp can achieve a 55.47 percent reduction in computation time and improve corrosion identification processing time by 25.5 percent.
FCPNet: A novel model to predict forward collision based-upon CNN

Predicting real-time collisions in unknown environments is a challenge especially for autonomous vehicles to avoid obstacles during missions. In this paper, we propose a novel approach called FCPNet which is a model based on Convolutional Neural Network (CNN) for a fast collision prediction. FCPNet is designed to run on CPU and embedded in an autonomous Unmanned Aerial Vehicle (UAV). Inspired by DroNet, FCPNet is composed of a ResNet-8 with three residual blocks, a dropout layer, and one output layer. Training and validation data are the Zurich images collected in an unstructured outdoor environment such as a city. This dataset consists of 31,972 color images in two classes: collision and no collision. FCPNet is compared to VGG-16, ResNet50V2, and MobileNetV2 models on CPU. The proposed model achieves the best F1-score and execution time on Raspberry Pi 4 thanks to the optimized parameters and class weights initialization to deal with an imbalanced dataset. The collision prediction probabilities as an alternative approach to ultrasonic sensors are presented in terms of confusion matrix showing the outstanding performance of FCPNet. We plan to use the proposed model with an obstacle avoidance policy to build robust and fast autonomous aerial vehicles.
Measurement and Evaluation Method of Gait Parameters for Timed Up and Go Test using an IoRT Care-Walker

The maintenance and improvement Quality of Life (QOL) in elderly people is a pressing issue in Japan, which has entered the “super-aging society”. Fall risk associated with poor gait function and health-related QOL are closely related, and therefore, it is important to measure and evaluate gait parameters focusing on balance function. In this study, we investigated measurement and evaluation method for TUG using an Internet of Robotic Things (IoRT) care-walker. We can measure gait parameters such as upper weight and center of weight, walking speed, and walking cycle, during TUG. We proposed an evaluation method using IoRT walker to improve the usual TUG, automatic detection gait parameters and separation of subcomponents, namely: sit to stand, walk1, turn around, walk2, turn and stand to sit; detailed analysis of each subcomponent.
FedTeams: Towards Trust-Based and Resource-Aware Federated Learning

Federated Learning (FL) has enabled Machine Learning (ML) applications to capture a larger spectrum of data by allowing such data to remain on-device, a desirable privacy guarantee in many applications. However, the highly iterative nature of FL optimization algorithms requires low-latency and high-throughput connections to clients. Unfortunately, realistic FL training scenarios include heterogeneous clients that are restricted by computation and communication, thereby slowing down or even failing FL training. In this paper, we propose FedTeams; a trust-based and resource-aware FL system that minimizes training latency, while improving accuracy. To achieve this, we mitigate the risk of straggling and weakly-connected clients by leveraging social trust and allowing these clients to offload their data to more powerful trusted peers that can train on their behalf. In specific, we formulate and solve an optimization problem that leverages the FedTeam’s trust graph and client resource information to optimize the distribution of training and minimize training latency. We evaluate FedTeams in a simulated environment, demonstrating up to a 81.6% decrease in training latency and 11.2% increase in global model accuracy when compared to existing state-of-the-art solutions.
Effective Local-Global Transformer for Natural Image Matting

Learning-based matting methods have been dominated by convolution neural networks for a long time. These methods mainly propagate the alpha matte according to the similarity between unknown and known regions. However, correlations between pixels in unknown and known regions are limited due to the insufficient receptive fields of common convolution neural networks, which leads to inaccurate estimation for pixels in unknown regions that are far away from known regions. In this paper, we propose an Effective Local-Global Transformer for natural image matting (ELGT-Matting), which can further expand receptive fields to establish a wide range of correlations between unknown and known regions. The kernel module is the effective local-global transformer block, and each block consists of two modules: 1) A Window-Level Global MSA (Multi-head Self-Attention) module, which learns global context features among windows. 2) A Local-Global Window MSA, which combines coarse global context features and corresponding fine local window features to help local window self-attention capture both local and context information. Experiments demonstrate that our ELGT-Matting performs outstandingly against other competitive approaches on Composition-1K, Distinctions-646, and real-world AIM-500 datasets. In particular, we achieve a new SOTA result on Composition-1K with MSE 0.00374.
Complementarity-aware Space Learning for Video-Text Retrieval

In general, videos are powerful at recording physical patterns ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., spatial layout) while texts are great at describing abstract symbols ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., emotion). When video and text are used in multi-modal tasks, they are claimed to be complementary and their distinct information is crucial. However, when it comes to cross-modal tasks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., retrieval), existing works usually use their common part in the form of common space learning while their distinct information is abandoned. In this paper, we argue that distinct information is also beneficial for cross-modal retrieval. To address this problem, we propose a divide-and-conquer learning approach, namely Complementarity-aware Space Learning (CSL), by recasting this challenge into learning of two spaces ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., latent and symbolic spaces) to simultaneously explore their common and distinct information by considering multi-modal complementary character. Specifically, we first propose to learn a symbolic space from video with a memory-based video encoder and a symbolic generator. In contrast, we also introduce learning a latent space from text with a text encoder and a memory-based latent feature selector. Finally, we propose a complementarity-aware loss by integrating two spaces to facilitate video-text retrieval tasks. Extensive experiments show that our approach outperforms existing state-of-the-art methods by 5.1%, 2.1% and 0.9% of R@10 for text-to-video retrieval on three benchmarks, respectively. Ablation study also verifies that the distinct information from video and text improves the retrieval performance. Trained models and source code have been released at https://github.com/NovaMind-Z/CSL.
ViViD: View Prediction of Online Video Through Deep Neural Network-Based Analysis of Subjective Video Attributes

Popularity of a video in an online platform may be defined by its number of views. The total view count of a video may change throughout its presence in an online platform. However, in most cases the view count tends to saturate after a certain time. We propose a method to predict the total view of a video at saturation. We have modeled the task of finding the view count of a video at saturation as a joint classification and regression problem, which is solved via a deep neural network. The network has a classification and a regression head. The classification head decides the view band among a set of available bands, whereas the regression head outputs a tolerance view count within each band. We consider four video attributes as the input to the network, namely, the thumbnail associated with the video, the title, the audio and the video itself for the view prediction task. The attributes are fused in a hierarchical fashion in the deep neural network. We propose a custom mismatch loss function and a penalty loss function for the joint training of the classification and regression heads of the network. Experimental results show that our method is 6.47% better in view prediction than the competitive methods.
Adversarial Autoencoder Data Synthesis for Enhancing Machine Learning-Based Phishing Detection Algorithms

Supervised machine learning is often used to detect phishing websites. However, the scarcity of phishing data for training purposes limits the classifier's performance. Further, machine learning algorithms are prone to adversarial attacks: small perturbations on attack data can bypass the classifier. These problems make machine learning less effective for phishing detection. We propose two Generative Adversarial Network (GAN) based approaches that synthesize phishing and legitimate samples to mimic real-world websites. Information about real-world datasets is obtained from ten publicly available phishing datasets which are used by the AAE (Adversarial Autoencoder) and WGAN (Wasserstein GAN) for generating synthetic data. Using both real and synthesized data, we demonstrate how to implement classifiers with higher performance and more resistance to adversarial attacks. We propose a set of hypotheses and validate them through experiments to demonstrate: (i) indistinguishability of synthesized samples from actual ones, (ii) susceptibility of classifiers to adversarial attacks, (iii) mitigating adversarial attacks by training on larger datasets that include correctly labeled synthesized samples, and (iv) better performance of classifiers trained on large datasets. Our AAE and WGAN have been trained on a wide range of datasets, making us optimistic about its widespread applicability.
EyeDrive: A Deep Learning Model for Continuous Driver Authentication

Eye movement (EM) is considerably a new behavioral modality for biometric authentication. In this work, we use this modality in the context of continuous driver authentication. Existing models rely on different modalities that limit their usage or are inconvenient to drivers. We propose an end-to-end learning model that takes the remote eye movement profiles solely and produces embeddings for driver authentication scenarios. The model is based on Long short-term memory (LSTM) and dense networks to learn temporal characteristics from the EM profiles. We focus on low-rate devices because of their affordability. Yet, they present a challenge because of their limited ability to capture quality measurements. To evaluate our model, two low frame-rate devices are used to build our datasets, which are Autocruis and GazePoint. The authentication performance outperforms state-of-the-art with as low as 30 seconds frame length with both devices. The best authentication performances for the identification/verification modes are 92.38/0.76% and 91.05/0.11% for the first and the second datasets, respectively.
Hierarchical Reinforcement Learning With Guidance for Multi-Domain Dialogue Policy

Achieving high performance in a multi-domain dialogue system with low computation is undoubtedly challenging. Previous works applying an end-to-end approach have been very successful. However, the computational cost remains a major issue since the large-sized language model using GPT-2 is required. Meanwhile, the optimization for individual components in the dialogue system has not shown promising result, especially for the component of dialogue management due to the complexity of multi-domain state and action representation. To cope with these issues, this article presents an efficient guidance learning where the imitation learning and the hierarchical reinforcement learning (HRL) with human-in-the-loop are performed to achieve high performance via an inexpensive dialogue agent. The behavior cloning with auxiliary tasks is exploited to identify the important features in latent representation. In particular, the proposed HRL is designed to treat each goal of a dialogue with the corresponding sub-policy so as to provide efficient dialogue policy learning by utilizing the guidance from human through action pruning and action evaluation, as well as the reward obtained from the interaction with the simulated user in the environment. Experimental results on ConvLab-2 framework show that the proposed method achieves state-of-the-art performance in dialogue policy optimization and outperforms the GPT-2 based solutions in end-to-end system evaluation.
6Scan: A High-Efficiency Dynamic Internet-Wide IPv6 Scanner With Regional Encoding

Efficient Internet-wide scanning plays a vital role in network measurement and cybersecurity analysis. While Internet-wide IPv4 scanning is a solved problem, Internet-wide scanning for IPv6 is still a mission yet to be accomplished due to its vast address space. To tackle this challenge, IPv6 scanning generally needs to use pre-defined seed addresses to guide further IPv6 scanning directions. Under this general principle, various solutions have been developed, but all suffer from two primary pitfalls, low hit rate and low probing speed, caused by the inherent sparse distribution of active IPv6 addresses and the high computational complexity of the search algorithms, respectively. We develop 6Scan, a novel asynchronous IPv6 scanner that effectively addresses the above two problems. To increase the hit rate, 6Scan infers the promising search directions by encoding the regional identifiers of the target addresses within the probing packets and recording the regional activities from the asynchronously arrived replies. It then dynamically adjusts the search directions according to the scanning result of the previous steps. To speed up the search algorithm, 6Scan leverages the regional identifier encoding to quickly adjust search direction without excessive computation. Real-world experiments over the IPv6 Internet in a billion-scale probing budget show that compared with the state-of-the-art solutions, on average 6Scan can discover <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\%$</tex-math> </inline-formula> more active addresses with nearly the same scanning time.
GENEREIT: generating multi-talented reinforcement learning agents

Abstract Creating an intelligent system that is able to generalize and reach human or above-human performance in a variety of tasks will be part of the crowning achievement of Artificial General Intelligence. However, even though many steps have been taken towards this direction, they have critical shortcomings that prevent the research community from drawing a clear path towards that goal, such as limited learning capacity of a model, sample-inefficiency or low overall performance. In this paper, we propose GENEREIT, a meta-Reinforcement Learning model in which a single Deep Reinforcement Learning agent (meta-learner) is able to produce high-performance agents (inner-learners) for solving different environments under a single training session, in a sample-efficient way, as shown by primary results in a set of various toy-like environments. This is partially due to the fixed subset selection strategy implementation that allows the meta-learner to focus on tuning specific traits of the generated agents rather than tuning them completely. This, combined with our system’s modular design for introducing higher levels in the meta-learning hierarchy, can also be potentially immune to catastrophic forgetting and provide ample learning capacity.
LFM Signal Parameter Estimation via FTD-FRFT in Impulse Noise

LFM signals are widely applied in radar, communication, sonar and many other fields. LFM signals received by these systems contain a lot of noise and outliers. In order to suppress the interference of strong impulse noise on target signals and realize the accurate estimation of LFM signal parameters, the impulse noise of echo signals need to be filtered. In this paper, to solve the problem of poor performance of LFM signal parameter estimation based on fractional Fourier transform in impulse noise, alpha stable distribution is used to establish the mathematical model of impulse noise. The proposed fastest tracking differentiator with an adaptive tracking factor is used to suppress the strong impulse noise, and fractional Fourier transform is used to estimate the parameter of the LFM signals. The experimental results show that the proposed fastest tracking differentiator with an adaptive tracking factor has a good filtering performance. It can effectively filter the impulse noise in the echo signal and allows the FrFT method to accurate estimate the parameters of the LFM signals in strong impulse noise.
Blockchain in the Healthcare Industry

Blockchain was first conceptualized in 2008, but became all the rage with Bitcoin. Blockchain is a digital ledger that records the transactions, excluding any third party. It has many features like transparency, security, privacy, accuracy, reliability, and decentralization. In the present study, the process and applications of blockchain in healthcare, and how and where blockchain is helpful for the healthcare industry, are discussed. The study shows that blockchain helps in generating a private key for its use, which can be accessed by that key only, ensuring privacy and security. Blockchain can record and manage the data of patients, which improves doctor-patient interaction with accuracy. It can also keep track of any upcoming outbreak of disease by helping epidemiologists and can help in claiming health insurance, which is helpful for both parties, i.e., insurer and insured. Lastly, it can help trace and secure the medical supplies without any fraud or manipulation, which can increase the product lifecycle by protecting the rights.
Fully Linear Graph Convolutional Networks for Semi-Supervised and Unsupervised Classification

This article presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closed-form solution with a decoupled procedure, resulting in a generalized linear framework and making it easier to implement, train, and apply. We show that (1) FLGC is powerful to deal with both graph-structured data and regular data, (2) training graph convolutional models with closed-form solutions improve computational efficiency without degrading performance, and (3) FLGC acts as a natural generalization of classic linear models in the non-Euclidean domain (e.g., ridge regression and subspace clustering). Furthermore, we implement a semi-supervised FLGC and an unsupervised FLGC by introducing an initial residual strategy, enabling FLGC to aggregate long-range neighborhoods and alleviate over-smoothing. We compare our semi-supervised and unsupervised FLGCs against many state-of-the-art methods on a variety of classification and clustering benchmarks, demonstrating that the proposed FLGC models consistently outperform previous methods in terms of accuracy, robustness, and learning efficiency. The core code of our FLGC is released at https://github.com/AngryCai/FLGC .