Reviewerly/exHarmony huggingface dataset:
Co-Saliency Detection Based on Multi-Scale Feature Extraction and Feature Fusion

In this paper, we propose a co-saliency detection algorithm based on multi-scale feature extraction and feature fusion. The algorithm extracts multi-scale features of images based on image information and combines these multiscale features with single image saliency maps (SISMs) generated by the edge guidance network (EGNet) to obtain single image vectors (SIVs). Based on these features, self-correlated features (SCFs) and rearranged self-correlated features (RSCFs) are calculated, and co-saliency attention (CSA) maps are created by weighting. Finally, the decoder receives the rearranged self-correlation and co-saliency maps in order to generate the final prediction maps. It can effectively solve the problem of poor performance of current feature extraction and saliency detection algorithms in complex scenes with multiple saliency targets. The simulation results show that the proposed algorithm not only improves the accuracy of co-saliency detection of RGB images in complex scenes but also reduces the error, and its performance is better than other algorithms.
Advanced Quantum Inspired Evolutionary Algorithm for Multivariate Optimization

In real life, there are many applications where we need to take care of multiple parameters to get the optimized result. Similarly, many scientific and engineering problems require optimization of various parameters to get desired results. Many algorithms work well with a few variables to get optimized results, but on increasing the number of variables, they do not perform well. In this paper, we proposed an advanced quantum-inspired evolutionary algorithm (A-QEAM) to solve optimization problems where the tuning of multiple parameters or variables is required. A-QEAM is characterized by the principle of quantum computing such as superposition and qubit. This algorithm uses a qubit in place of the classical bit. The proposed algorithm is tested on mathematical functions consisting of 2 variables, 10 variables, 30 variables, and 50 variables. The result shows that the proposed algorithm performs well even on increasing the number of variables.
A Full-reference Video Quality Assessment Method for 4K UHD Video based on Multi-Feature Fusion

Video quality assessment plays an important role in the quality control of video transmission and the development of video processing equipment and algorithms. With the popularity of UHD TV, the demand for UHD video quality assessment is becoming more and more urgent. In this paper, we propose a method for 4K UHD video quality assessment based on multi-feature fusion (MFF- VQA). First, we select eight frame-level features which could better reflect the perceived video quality through a series of ablation experiments. Then, we present a scheme which can fuse the eight features into a quality score. Experimental results show that, compared with other similar methods, the proposed method can achieve better performance even with lower algorithm complexity and fewer video frames.
A Non-normal Warning System for Dam Operation Using Machine Learning

A country's critical infrastructures are heavily related to the quality of life and safety of the people. As a result, the security protection aspect of critical infrastructure has gained more and more attention nowadays, especially the security of its industrial control system (ICS). To avoid the abnormal condition happening in the critical infrastructure which could put people in great danger, a system that is capable of detecting any abnormal state of the ICS promptly is needed. Fortunately, due to the dramatic growth of the applications of machine learning in recent years, some researchers have already proposed anomaly detection methods with machine learning to provide instant warning and protection for ICS. However, most of the existing anomaly detection research tends to only target one cause that harms the system, such as attacks on the network or physical equipment failures. The ICS will be more comprehensively secured if the anomaly detection system can cover multiple aspects of the ICS. Therefore, we have established a non-normal warning system with the Generative Adversarial Network (GAN) for dam operations in this study, which can detect various types of non-normal operations and notify relevant personnel right away. Note that we use real historical data to make predictions and verify our warning system, and we improve it even more by implementing the visual analysis method, which makes up the indecipherable results often found in unsupervised learning.
Instructions with Complex Control-Flow Entailing Machine Learning

Reinforcement learning is when the system is allowed to make its own decisions based on what it learns. There are 2 types of observations, formative and summative. These observations have been identified as crucially important for neural network training of complicated tasks with conditional control flow. The central theme of this paper is applying reinforcement learning to follow instructions with complex control-flow. The authors study a special but important subset of multi- task reinforcement learning problems, namely instructions with complex control-flow in this work. They develop an encoding and attention architecture to achieve the research objective.
Research on Methods of Rake Suction and Obstacle Avoidance of Sewage Cleaning Robot for Aquaculture Pond

The bottom sewage raking and suction obstacle avoidance robot for aquaculture pond is a sewage cleaning device that uses raking and suction technology and path planning as technical means to build a multi particle hybrid identification system, which can realize the effective cleaning of aquaculture pond. The team took turbot breeding and seedling raising as the research object, and carried out simulation tests on the decontamination robot. The test found that the path planning of GBNN algorithm in obstacle environment can achieve high-efficiency decontamination effect, and the total energy consumption and distance are small, which proved that the robot can use cbnn model to carry out adaptive cruise in static obstacle environment. At the same time, the raking and suction performance of the robot is tested, and through the range analysis method of orthogonal experiment, it is determined that the raking and suction efficiency and raking and suction coverage of the cleaning robot can reach 90%.
Combining OpenPose with BiLSTM for Violence Detection in Long-Term Care

The Ministry of Health and Welfare's Statistics reports present the incidence of domestic care violence becomes higher annually. However, there is no efficient method to get rid of physical abuse. After being ill-treated of violence, someone is assessed injure by official organization. Then, the victims take legal actions to damage after the events. The deep learning motion recognized violence to family care in advance. To analyst that the images from complex data sets on the internet is important. The key part of images that are recognized as physical abuse is ambiguous and distorted in many pictures. The solution of ambiguity is to label joint points of human skeleton by OpenPose, and to train the marked joint point features in Bi-directional Long Short-Term Memory (BiLSTM). The accuracy is about to 96%, that can effectively detect physical abuse in time in the experimental results.
Vision Technology in Underwater: Applications, Challenges and Perspectives

Vision technology is growing vigorously, but underwater vision remains challenges and problems, which is a field with application value and development prospects. The visual information obtained underwater is often color-biased and blurred, and it is highly susceptible to the influence of the surrounding environment and its stability cannot be controlled. Therefore, the current research and applications of vision technologies in underwater vision provide ways to solve the described problems, including image processing, target detection and recognition, localization and tracking technologies. The purpose of this review is to summarize the development of underwater vision technology and the results achieved. The mainstream underwater vision technologies are classified according to the different theories or algorithms used, and the current new research progress in each field is introduced in detail. By summarizing and analyzing the above research results, the application of each key technology of underwater vision is sorted out, and its further development direction is foreseen.
Real-Time Traffic Sign Detection for Self-Driving and Energy-Saving Driving Based on YOLOv4 Neural Network

With the booming development of autonomous vehicles (AV) in recent years, a vehicle needs to have the ability to detect changes in the environment in real-time. If the vehicle can be decelerated in advance according to the traffic signs, it can effectively reduce fuel consumption and improve overall comfort. This paper uses the Kaggle data set for training based on marking the common traffic signs in foreign countries, adds the local data set in Taiwan to the testing data set, and uses the You Only Look Once v4 (YOLOv4) neural network to detect the traffic signs in real time. The experimental results show that YOLOv4 still has a good generalization ability in the case of slight differences in different national sign types, and the mean Average Precision (mAP) can reach more than 87.6%.
Undeniable Vaccine Production Protocol with Blockchain and IPFS

Vaccination is the most effective way to prevent related diseases, and the age distribution ranges from infants to the elderly, so people are hesitant to get vaccinated when they are not sure whether the vaccine is safe, and due to the expiration of vaccines and vaccine fraud, people are concerned about the process of vaccine production. Concerned and distrustful of vaccine production companies, in order to prevent companies from modifying or falsifying the data in the vaccine production process without permission and making the data related to the production process correspond to the product. There is a secure and public data storage space as a traceable basis for problems can be used in the product in the future. This paper designs a vaccine product safety production process supervision system based on blockchain and IPFS, so that the current data of the vaccine production process is stored in a decentralized space, and the data of each machine in the production process of the vaccine enterprise is hashed and calculated. A digital signature together with the timestamp is used as the undeniable data of the machine to ensure the security and non-repudiation of the data generated by each machine during the vaccine production process, and the hash operation prevents the production data of the manufacturer from leaking. At the same time, it allows government examiners to track the production process of each batch of vaccine through encrypted data stored in the blockchain and IPFS to prevent companies from privately modifying or falsifying data, and allows examiners to pass certain data with a highly trusted Decentralized storage space to assist it in judging whether the enterprise has committed illegal activities, and use the above methods to implement a decentralized supervision system for the safety production process of vaccine products.
Goal-Driven Context-Aware Service Recommendation for Mashup Development

As service-oriented architecture becoming one prevalent technique to rapidly compose functionalities to customers, increasingly more reusable software components have been published online in the form of web services. To create a mashup, however, it gets not only time-consuming but also error-prone for developers to find suitable services components from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected up to the current step as well as the mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of this approach.
Fast Convergence Detection Algorithm of Image Small Object Based on Distance Intersection over Union

Due to low resolution or few features, small object detection has become a difficult problem in the field of image recognition. This paper proposes a fast convergence detection algorithm for small image objects based on distance intersection over union. First of all, EnlightenGAN is used to enhance the image, reduce image noise, and highlight the detection object features. Then, a loss function design of YOLOv5 network based on distance intersection over union is proposed. This method speeds up the gradient regression of the network, greatly shortens the training time of the YOLOv5 network, and improves the detection accuracy. The experimental results using the WiderPerson dataset and the VOC07++12 dataset show that, compared with the traditional YOLOv5 network image detection results, the method proposed in this paper improves AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> by 4.4% and 3.3%, and AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> by 6.8% and 2.5%, respectively, which verifies the effectiveness of this method.
Long-Tailed Object Mining Based on CLIP Model for Autonomous Driving

The long-tailed object distribution poses great challenges for autonomous driving. And the field collection of long-tailed objects is difficult and high-cost. In this paper, we propose a novel data mining approach for those long-tailed objects. The softmax distribution produced by CLIP model is adopted as the representation of cropped objects in the image. Then for each long-tailed classification, the category grouping is performed to divide the text concepts into three sets. Finally, combining the softmax representation with the grouped categories, we develop an effective softmax mining algorithm to search and identify the long-tailed objects from the large database. Experiments demonstrate that the proposed method outperforms the baseline results and accurately finds the long-tailed data.
A Vehicle Information Authentication Method of Expressway Based on Heterogeneous Blockchain Technology

With the continuous development of expressways' convenient and unconscious toll collection, it is crucial to improve the accuracy of highway vehicle authentication while reducing the time delay of highway vehicle charging. Based on the characteristics of multi-body and multi-stage operation status of highway vehicles, starting from the dynamic authentication of highway vehicle driving process, we propose the highway reliable driving trajectory authentication based on heterogeneous blockchain technology. It realized the real-time inspection of vehicle information and the reliable track authentication of expressway vehicles based on track information chain association.
Gradient Guided Sampling Method for Imbalanced Learning

Training Deep Neural Networks (DNN) with imbalanced data is a challenging task. The categories with small amounts of training data often encounter poor representation, which leads to low recognition accuracy. To address this problem, we proposed a gradient-guided sampling method during the training progress. By employing the average gradients on each category of the validated set, the training data is sampled for updating DNN's parameters. The category with a larger gradient is selected more frequently. The experiments are conducted on imbalanced CIFAR-10 and CIFAR-100 data sets. The results show that our proposed method can surpass the compared methods.
LSBO-NAS: Latent Space Bayesian Optimization for Neural Architecture Search

From the perspective of data stream, neural architecture search (NAS) can be formulated as a graph optimization problem. However, many state-of-the-art black-box optimization algorithms, such as Bayesian optimization and simulated annealing, operate in continuous space primarily, which does not match the NAS optimization due to the discreteness of graph structures. To tackle this problem, the latent space Bayesian optimization NAS (LSBO-NAS) algorithm is developed in this paper. In LSBO-NAS, the neural architectures are represented as sequences, and a variational auto-encoder (VAE) is trained to convert the discrete search space of NAS into a continuous latent space by learning the continuous representation of neural architectures. Hereafter, a Bayesian optimization (BO) algorithm, i.e., the tree-structure parzen estimator (TPE) algorithm, is developed to obtain admirable neural architectures. The optimization loop of LSBO-NAS consists of two stages. In the first stage, the BO algorithm generates a preferable architecture representation according to its search strategy. In the second stage, the decoder of VAE decodes the representation into a discrete neural architecture, whose performance evaluation is regarded as the feedback signal for the BO algorithm. The effectiveness of the developed LSBO-NAS is demonstrated on the NAS-Bench-301 benchmark, where the LSBO-NAS achieves a better performance than several NAS baselines.
An Automated Statistical Evaluation Framework of Rapidly-Exploring Random Tree Frontier Detector for Indoor Space Exploration

This paper focuses on the design of an automated statistical evaluation framework for mapping generation of Rapidly-Exploring Random Tree (RRT) frontier detectors. By evaluating the run time and distance traveled of the simulated Kobuki robot agent in a Gazebo environment, the designed framework can automatically evaluate the process on a user-defined Gazebo map for a large number of repeated simulations. We also expanded the experiment platform into customized maps with complex layouts and trial schemes. The key formulas and parameters are provided with different trial settings. During the development of this framework, we have added functions that allow the user to choose among the maps we have designed, and the initial positions of the simulated robots for each map at the beginning of each trial. We have also modified the modules developed by Umari et al. so that the RRT frontier detection process can be started automatically with pre-defined exploration area in place. Modules have also been added so that the run time and distance traveled by the simulated robot for each trial can be measured and saved to the respective CSV files for further statistical analysis. We have created additional procedures that ensure the consistency of each trial. The results show that our designed automated evaluation framework is reliable and suitable for use as a fully automated research platform for robot exploration.
Detection of Material on a Tray in an Automatic Assembly Line Based on Convolution Attention and Multitask Loss

This paper proposes an end-to-end first-stage pallet detection algorithm with short training time and high detection accuracy based on the pre-detection staged material detection algorithm. Not only can it detect known materials, blank areas and fixed material areas on pallets, but also unknown and unwanted materials that are mixed and misplaced on pallets on automated assembly lines. It employs ResNet18 as the backbone network, incorporates the Convolutional Block Attention Module (CBAM) to improve model stability and accuracy, and optimizes the detection model using the multitask loss function based on Complete-IoU(CIoU) and cross entropy. The experimental results show that when compared to the original phased detection algorithm using YOLOv5s trained on four NVIDIA GeForce RTX 2080 Ti for 18 h, the phased detection algorithm used in this study's first stage material detection algorithm achieves 98% overall recognition accuracy, which is 7% higher than the original phased algorithm (91 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). It also greatly reduces the model training time and allows rapid model deployment.
A Multi-AUV Bearings-Only Multi-target Tracking Method Based on the Fast LMB Filter

Autonomous underwater vehicles (AUVs) with bearing target tracking capability are fundamental and essential. However, they face the problems of high nonlinearity, difficult target trajectory initialization, and poor multi-target tracking (MTT) performance. Consequently, we adopt a novel MTT method for multi-AUV based on the fast Labeled Multi-Bernoulli (LMB) filter. In this method, the LMB filter uses belief propagation (BP) to solve the data association problem quickly and effectively approximate the LMB during the update step. And a Gaussian mixture approximation is used to determine the new potential target trajectory based on individual AUV-bearing measurements. Furthermore, we employ the iterator-corrector strategy to perform the fast LMB filter for multi-AUV. The simulation results show that the method performs well in MTT for multi-AUV using bearing-only measurements.
Lossless Image Compression with Learned Local and Global Features

Estimating the probability distribution of an image is the key issue in lossless image compression. Though image compression can benefit from both global and local information, few works have been proposed to utilize both in lossless image compression. In this work, we propose to use a neural network for multiscale feature learning, the learned features are used to estimate the distribution of the image in a chain rule. In a further step, we utilize a context model to learn local features from the image. Finally, we combine the multiscale features with local features for image distribution learning. Our work surpasses state-of-the-art learning algorithms and several traditional codecs in several challenging datasets.
Application of Small Animals Control in Substation Based on GBDT and LR Fusion Algorithm

Animals control is an important task for the safe operation of substations. Aiming at the problems existing in the control of small animals, with the superior prediction ability of machine learning, a prediction model of small animals hazard grade is proposed, which combines gradient boosting decision (GBDT) and logistic regression (LR) algorithm. The model combined substation operation and maintenance data with local meteorological data, performs features screening by calculating the variance value, and achieves classes balance by using sampling technology. And finally the model achieves the prediction of small animals hazard grade in substation. By using different data sets and not using GBDT algorithm to train the model, the prediction results are compared and analyzed. The proposed model is better in all prediction performance indicators, which verifies the validity of the method.
Speech Synthesis for Speaker Timbre Translation Across Languages

We propose a cross-lingual TTS model based on the neural network. The model is capable of synthesizing speech across languages and translating the speaker's timbre. It uses a few seconds of untranscribed reference audio of the target speaker to synthesize the new speech of that speaker. The model consists of a separate speaker encoder, STT Translator, synthesizer, and vocoder. We decouple speaker information and speech to build a speaker recognition network. Our synthesizer is mainly built based on the Tacotron model and is divided into three parts: encoder, attention mechanism and decoder. The vocoder, on the other hand, is based on two methods, WaveRNN and HiFi-GAN, and serves to predict the synthesized waveform using the Mel spectrum. We conducted experiments to analyze the effectiveness of our approach. Besides, we also analyzed the effect of different datasets on the training effect.