Reviewerly/exHarmony huggingface dataset:
Research on Asymmetric Controlled Cyclic Joint Remote Quantum State Preparation in Noisy Environment

Artificial intelligence and quantum communication are two distinct but very far-reaching disciplines. In recent years, with the rapid development of these two disciplines, using artificial intelligence to solve the problem of transmission quality in quantum communication is also a hotspot of scientific research. In this paper, we propose the scheme for asymmetric controlled cyclic joint remote state preparation scheme in noisy environments to lay the foundation for the future use of artificial intelligence to solve the noise problem in the quantum communication process. In the scheme, under the control of supervisor David, several senders can jointly realize the preparation of quantum states for the remote receiver. We first performed the theoretical analysis of the scheme in the ideal environment, then studied the scheme in four different noise environments (amplitude damping noise, phase damping noise, bit-flip noise, depolarized noise). The results show that except in the bit-flip noise environment, the fidelity of the output state is only related to the decoherence rate, and in the other three noise environments, the fidelity is not only related to the decoherence rate but also the coefficients of the desired state. Finally, we also compared our scheme with other remote state preparation schemes, and the results show that our scheme has some superiority.
Research on Hybrid Intelligence Wargame Method

Wargame, as a tool to generate sample data for analysis and model training, has vast application in fields of training, command & control, and tactical research. Traditional wargame technologies greatly rely on human wisdom in the loop, impossible to generate large scale sample data. Reinforcement learning technology can generate large scale sample data, but it is not competent for the decision complexity above campaign level. This paper proposes a hybrid intelligence wargame method, which can generate large scale sample data using AI algorithms under the guidance of human wisdom. It has wide applications, which provides data analysis functions that existing wargame methods cannot provide. Prototype software has been developed based on the method, with feasibility and effectiveness verified through experiments, which has certain reference value.
Specify the Emotional Intensity Response Generation Model

Existing research on emtional response generation only considers the expression of emotion and does not consider the impact of different emotional intensities in responses. To do this end, we proposed a response generation model for specifying emotional intensity, which adds an gated decoding module to capture the changes in emotion intensity during the decoding process. The lexical type selection module is also added to generate different types of words at appropriate positions by controlling the weights of emotion words, degree adverbs, generic words and realize the expression of emotional intensity in text finally. The experiments were based on the open domain multi-round dialogue corpus of NLPCC 2018. The experimental system for generating specified emotion intensity responses was constructed, and experiments on emotional intensity consistency and content association were carried out. Experimental results show that the expression in terms of emotional intensity consistency can be achieved and has good performance in content generation compared to other baseline models. The model improved by 0.41 compared to the optimal model in terms of emotional intensity consistency, was able to generate responses with consistent emotional intensity according to the specified emotional intensity, and had some ability to generate emotional intensity responses.
Research on Deep Neural Networks for Real-Time Detection and Tracking of Highly Dynamic Targets

In recent years, artificial intelligence technology represented by deep learning has made major breakthroughs in the field of target recognition, and the application of intelligent target recognition capabilities to the aerospace field is an important direction for future development. However, artificial intelligence technology brings a huge amount of computation and high energy consumption to the aircraft while bringing the target perception capability. However, the aerospace field faces many challenges such as complex environmental interference conditions, lack of data samples, high dynamics, and strong real-time performance. In order to solve the problems above, we purpose a lightweight network which was realized by the separable convolution, ghost bottleneck and structures such as reverse residual modules with different lengths. We applied above purpose method to remote sensing image data. The results indicate that the accuracy of the algorithm is 4.7% higher than traditional lightweight algorithm. To validate the practicability under engineering background, we deployed purposed algorithm to Nvidia edge computing devices Jetson TX2. Experimental data show that our algorithm can achieve 10FPS. It meets the requirements of real-time and accuracy in aerospace applications and has high application value. The technologies described in this study can be used in radar image recognition, multi-modal image analysis, and aerospace intelligent target detection etc., which will effectively improve the intelligence level in the aerospace field and promote the development of equipment intelligence.
Multi-Grid Homography Estimation Based on Swin SC-Mixer

Homography estimation is an important and fundamental work in computer vision, and the accuracy of homography estimation has a very important impact on downstream tasks. Traditional homography methods are limited by weak texture images and are not robust enough in practical applications. As an alternative method, CNN can have better robustness. However, CNN cannot obtain global attention due to its limited receptive field, so the accuracy will be limited in the homography estimation task. This paper proposes a new model called Swin SC-Mixer. Swin SC-Mixer uses a shifted window mechanism to make the size of the model smaller, and the computational complexity is linear with the image. Applying the Swin SC-Mixer model to multi-grid homography estimation achieves higher accuracy than CNN-based and Transformer-based methods. And unlike previous methods, this paper estimates a multi-grid homography instead of single homography, so images with a certain parallax can be aligned. Experiments show that our method is fast and effective.
Knowledge-Enriched Domain Specific Chatbot on Low-resource Language

This study presents architecture in learning human-machine conversation using a human language known as chatbots. Given an utterance, a chatbot attempt to reply using human language by mimicking human intelligence in communication. Previous works in this domain were mostly implemented in English, thus it remains a problem when bringing to a low-resource language, e.g., Indonesian. The limited availability of human-to-human dialog history led to difficulty in the learning process of machine learning algorithms. Therefore, this study proposed a Knowledgeable Chatbot (KC), an enhanced pipeline that enables to receiving of transferred knowledge from another task. A data augmentation pipeline is proposed to handle the limited number of available. To deal with the low-resource language, this study proposed to incorporate a pre-trained language model to gain contextualized language understanding. As this research can be categorized as preliminary, extensive experiments are required to prove the effectiveness of each part. Standard automatic metrics for information retrieval and classification prove that KC excels in the ablation study.
Context-Aware Semantic Matching with Self Attention Mechanism

As we know, semantic matching plays a very important role in the field of natural language processing, and is the basis of many other language applications, such as question answer matching, chat robots and customer service. Though large efforts have been made, they mainly focus on the semantic matching of a single sentence pair. However, in many real-world scenarios, a sentence tends to have semantic associations with multiple sentences. So these relational information between sentences has been largely ignored in previous research work. To this end, in this paper, we propose a novel Self-attention Relational Sentence Semantic Matching (SR-SSM) framework in order to jointly utilize the sentences and relational information to achieve better semantic matching. Specifically, we first utilize the LSTM network to obtain the original sentence representation. Then we incorporate the information of adjacent sentences to obtain context-aware sentence representation through the self-attention mechanism. Finally, we adopt a multi-layer perceptron to model interactions between sentence pairs and predict the semantic similarity. Experiments on public datasets have validated the effectiveness of SR-SSM with a significant margin compared with the state-of-the-art baselines.
Deep Wavelet Frequency Laplacian Pyramid Network for Vehicle Logo Super-Resolution

In intelligent transportation systems, generated high-resolution images promote high-level version tasks compared to captured low-resolution vehicle logo images, which lack adequate details. However, the resolution of vehicle logo images is restricted by factors like hardware. Few attempts have been made with the Laplacian pyramid structure, while images can be represented on multi-scale in the wavelet domain. This paper proposes a progressive reconstruction network with a Laplacian pyramid (WLapsrn) structure to predict the sub-bands of wavelet transformed images. Firstly, we adopted a suitable network architecture for feature extraction using residual blocks for the advanced reconstruction network in the wavelet domain. Then, the Texture-Charbonnier loss function is used to achieve highfrequency sub-bands reconstruction. Ultimately, the inverse 2D discrete stationary wavelet transform is applied to generate predicting HR images from mapped coefficients. Extensive objective and subjective evaluations using benchmark datasets demonstrate that the proposed algorithm achieves superior visual effects compared with the state-of-the-art Laplacian-based algorithms.
Multi-object Tracking with Noisy Labels

Multi-Object Tracking (MOT) has achieved tremendous progress in the past few years. However, almost all existing joint detection and embedding (JDE) based methods have overlooked the ground-truth annotation qualities affect by occlusion. In this paper, we argue that MOT annotations are often effect by noisy labels. In addition, these noisy labels are actually harmful for the training process of JDE based method. To address this challenge, we propose a novel Multi-Object Tracking method for Noisy Labels, named MOTNL. MOTNL can not only diagnose noisy labels but also choose more high-quality clean labels for model training. Specifically, we first analyze the properties of occluded samples in multi-object tracking video datasets. And design a simple but effective selection mechanism in the Re-ID branch of our MOTNL. Extensive results on different baseline show that training with carefully selected samples leads to significant improvements for different MOT methods.
Heterogeneous Network Building and Embedding for Efficient Skeleton-Based Human Action Recognition

In the methods of human action recognition, modeling the human skeleton as a dual-stream adaptive graph convolutional network has achieved remarkable results. However, based on unimodal data, they cannot exploit the potential semantic correlation between different modalities. In the existing knowledge distillation methods, the model independently extracts the characteristics of each modality, and there are no rich information interactions between different modalities. Moreover, most models use the same network architecture to extract features of different modalities, which largely limits the distinguishability between modalities. In this work, we propose a novel model based on heterogeneous network building and embedding. We build a heterogeneous network to unify features of different modalities which process by different networks. Moreover, we also proposed a multimodal similarity loss function based on deep metric learning, which closely links the samples of the same kind and stays away from the samples of different kinds. Extensive experiments on the two datasets, NTU RGB+D and UTD-MHAD, demonstrate that our model effectively improves the performance of unimodal methods, and is also comparable to other multimodal methods.
A Majorization-Minimization Approach for Edge-Aware Image Smoothing

Edge-aware image smoothing plays an important part in practical applications of both computer vision and graphics. In this paper, we propose a majorization-minimization (MM) approach for edge-preserving image smoothing based on the edge-stopping function. With a given edge-stopping function, our theoretical results show that the differences between neighboring pixels in homogeneous regions of the output smoothing image are smaller than a small threshold, while they are larger than a large threshold at edges. We give the process of the MM algorithm solving the image smoothing problem in detail and discuss the convergence of the MM algorithm. Several experiments such as artifact removal, detail enhancement, texture smoothing and tone mapping are given to demonstrate the effectiveness of the proposed approach.
Comparison of Ensemble and Federal Learning for Secure Data Collaboration in Satellite Networks

Satellite network is becoming an important component of 6G and future networks for seamless coverage. Each satellite is generating a large amount of data based on their rich sensors, e.g. images, but the data from different satellite may not be gathered in some cases for performance improvement of deep learning task due to processing capability and privacy problems. Federal learning (FL) is a decentralized data collaboration solution without data exposure. But exchange of model parameters poses heavy network traffic between satellites. Ensemble learning (EL) is a popular method to improve task performance by collaborating diverse base leaners, and it has been initially proved to be effective for data collaboration. Since only the inference result is exchanged between clients, EL enables easier deployment of data collaboration than FL with dramatic decreasing of network traffic. At present, the performance of EL based data collaboration has not been well studied. In this paper, we compare the performance of FL and EL based data collaboration systems for different data division scenarios. Experiment results show that EL based scheme can achieve close or higher performance than FL based scheme for small number of clients with some data sharing.
An Efficient Human-Computer Interaction in Battlefield Environment via Multi-stream Learning

Human-computer interaction is fundamental for the increasingly complicating battlefield intelligent and automotive. This task, however, is challenging owing to the vibration and uneven illumination environment of weapon equipment. The traditional interaction methods cannot meet the needs of fast-paced, high flexibility and long-distance operation. In this paper, we propose a multi-stream learning algorithm that uses gesture recognition to realize human-computer interaction. Our method utilizes Time Domain Aggregation and Domain Adaption Fusion modules to solve the error-prone identification problem under uneven illumination and vibration environment respectively. Experiments using 10 types of gestures and more than 3000 infrared and visible light images under the real mobile vehicle environment datasets demonstrate the robustness, accuracy and efficiency of our method compared to previous state-of-the-art gesture recognition methods.
A Novel Spherical Video Quality Metric for 360° Video Coding

Video Quality Assessment (VQA) is an integral part of any video coding processes. The Versatile Video Coding (VVC) applies Peak Signal-To-Noise (PSNR) spherical objective quality metrics in the VQA of 360° videos. However, these metrics fail to display compatibility to be integrated with the rectilinear functionalities of VVC. In response, this paper proposes Weighted Craster Parabolic Projection Peak Signal-to Noise Ratio (WCPPPSNR), a novel spherical objective quality metric for 360° video that improves the existing CPPPSNR by providing rectilinear functionalities to support VVC. The subjective experiments conducted, validate the proposed metric to be a good candidate for VQA of 360° video while the use case test reports that the integration of the proposed metric with VVC can improve the coding efficiency up to 2.43% with reference to the anchor implementation of VVC Test Model (VTM) 2.2.
On the Number of Subjects Needed for 360° Video Quality Experiments: An SOS Based Analysis

Subjective 360° video quality experiments provide the ground truth for developing related systems such that acceptable quality of experience is offered to the users. To obtain statistically meaningful insights from the data recorded in quality experiments, engaging a sufficiently large number of subjects is essential. In contrast to conventional videos, little has been reported on statistical measures that may guide the choice of the number of subjects required for 360° video quality experiments. In this paper, the standard deviation of opinion score (SOS) hypothesis, measuring the diversity among the opinion scores given by a panel of subjects, is used to reveal the number of subjects needed. It is shown that the number of subjects needed varies depending on a threshold on SOS characteristics in terms of diversity among opinion scores.
Compensating for Latency in Cloud-based Game Streaming using Attribute Scaling

Cloud-based game streaming has the disadvantage of added latency from the thin client to the cloud-based server and back, decreasing player performance and degrading their experience. Attribute scaling can make the game eas-ier, potentially exactly counteracting the difficulty added by the latency. We incorporate attribute scaling models into two different games, deploy them on a commercial cloud-based game streaming system and evaluate their efficacy by measuring impact on player performance and Quality of Experience (QoE). Analysis through a user study shows that our compensation methods improve player performance and may improve QoE compared to no latency compensation.
Quadcopter Design With Waypoint Mission Using PID Control System

The quadcopter is one of the Unmanned Aerial Vehicles that can fly vertically. Problems with the drone such as unresponsive controls and choosing the right control system which has an impact on the instability of the drone. Proportional, Integral, Derivative (PID) has better performance to be applied to the quadcopter. This study applies PID control to the quadcopter control which is tested with a waypoint system. Quadcopter design uses various components such as flight controller, brushless motor 920 kv, electronic speed controller. The results of the calculation of the constant PID value are applied to the drone flight controller and the test is carried out by carrying out a waypoint tracking mission with the PID controller. Testing with a rectangular route waypoint shows that the quadcopter can fly according to the predetermined waypoint with the largest roll deviation value is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$20.93^{o}$</tex> , at pitch by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$24.01^{\mathrm{o}}$</tex> , and the largest deviation change at yaw is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$205^{\mathrm{o}}$</tex> which means the quadcopter can carry out waypoint tracking missions using PID control with high stability.
Identification of AI-Related Topic Evolution Based on Extraction of International Conference Proceedings Titles

International conference proceedings are a forum for researchers in various parts of the world to communicate their research findings to the scientific community and to the public in general. International conference proceedings published by globally recognized institutions are of unquestionable quality. Every conference must have a title that describes the theme or topic being discussed. This study wants to reveal the topic evolution by extracting conference titles. This study collects conference titles that contain terms related to artificial intelligence (AI) in them. Then the titles that have been collected are extracted to obtain a base noun phrase and filtered to eliminate uninformative terms. Of the 139,495 conference titles, it was obtained 8,133 titles containing topics related to AI. The results of the title extraction are 4,601 terms which are dominated by the exact term ‘artificial intelligence’, followed by the terms ‘machine learning’, ‘applications’, ‘intelligent systems’, ‘semantic’, ‘pattern recognition’ and other important related terms. Based on the glossary, frequency of occurrence, and year of publication, further identification of the evolution of a more specific topic is presented in this study. This study identified the early emergence and evolutionary phases of topics for specific terms such as natural language processing, cloud computing, and cybernetics or cyber security. Using the model proposed in this study, the process of identifying the evolution of specific topic can be extracted to get new insights.
Tuning LQR Parameters using Neuro Evolution of Augmenting Topologies (NEAT) on a Double Pendulum Cart

The double pendulum cart is an example of an unstable and nonlinear system. The modeling of this system can be done using the Euler-Lagrange method with linearization using Taylor Series expansion. Linear Quadratic Regulator (LQR) is one of the optimal control techniques applied to this problem. However, the process of tuning the Q and R matrices on the LQR is often made by design. This paper proposes LQR tuning method using Neuro Evolution of Augmenting Topologies (NEAT) to ensure that the system response has good stability, cost and gain feedback that is adaptive to the state value of the system. In this paper, the best genome produced through the NEAT process scored 31.1 from the designed objective function.
Parameters and Decoding Performance of Subfield Subcodes of One-Point Elliptic Codes

It is known that subfield subcodes of Reed-Solomon (RS) codes include many good codes, such as BCH codes and classical Goppa codes. Extending the subfield subcode discovery from RS codes to the more general algebraic-geometric (AG) codes, this paper investigates the subfield subcodes of one-point elliptic codes, in order to provide an alternative to BCH codes. Lower bounds on the dimension and the minimum distance of these codes are characterized, which behave tight for the medium-to-high rate subfield subcodes of differential elliptic codes. The work will show that subfield subcodes of differential elliptic codes are superior to those of the evaluation ones. Many good codes and even the best known linear codes can be found in the family of these codes. With both the elliptic codes and the RS codes defined over the same field, near maximum likelihood (ML) decoding results show that some binary subfield subcodes of the elliptic codes outperform the similar rate BCH codes.