Reviewerly/exHarmony huggingface dataset:
The Design of Automated Validation System for Satellite Data Transmission System Based on AOS

In order to satisfy data transmission in space data system, advanced orbiting systems(AOS) is proposed by consultative committee for space data systems(CCSDS). According to AOS space data link protocol, data transmission system in satellite is used to transfer valid data of different type from space to ground. Although there exist some validation systems for data transmission system, they can be applied to some specifical satellites only. In this paper, based on space communications protocols reference model, we propose an automated extensible validation system for satellite data transmission system, while the valid data from different virtual channel are separated exclusively in real-time, to be read by different processing and analyzing software. After configuring instructions appropriately in control and analysis equipment, which acts as the interactive interface, the system checks the data automatically. Validation results demonstrate that, the proposed automated validation system works well when receiving AOS transfer frame at rate of 800Mbps with seventeen virtual channels.
Autonomous Mover with Social Distance Respect

Modern robots need to interact with human and move around human environment, in places such as museums, restaurants, or supermarkets. Therefore, robots should have social navigation capability. This article uses object detection to detect pedestrians, fuses object detection result with lidar information to obtain the state of the pedestrian, and then changes the navigation path according to the calculated pedestrian state. When there are people face-to-face and talking to each other, the autonomous mover bypasses instead of passing through them. When pedestrian in front of the autonomous mover is crossing the autonomous mover from left to right, the autonomous mover turns left to pass the other side instead of going straight and blocking the pedestrian. Therefore, the autonomous mover can navigate without disturbing pedestrians and respect social distance.Our approach uses a single RGB camera and a one-line lidar to detect pedestrian and accomplish the two specific goals in the real world. We fuse lidar information and object detection result to obtain the position and face orientation of the pedestrian. We add a customized social layer to the cost map of an existing navigation system, and thus, change the original shortest path algorithm. The face-to-face and crossing scenarios are verified in the hall of a university department building.
CRDet: An Object-Context-Aware Detection Network for Oriented Object in Aerial Images

With the increasing of aerial remote sensing imagery data, object detection in aerial images has become a specific and active topic in remote sensing and computer vision areas. Although great progress has been made, there still exists challenges for object detection of small size, arbitrary orientations, and dense distribution. To address these problems, we propose a novel detector named CRDet (object-Context-aware Rotated object Detector) in this paper. Our CRDet is mainly consists of two modules, RRGM (the Rotated Region of Interests Generation module) and OCIEM (Object Context Information Extraction Module). Specifically, the RRGM based on affine transformation is devised to improve the detection effect of objects with dense distribution and arbitrary orientations. The OCIEM is designed to improve the detection effect for small objects. The network with the two modules is designed with analysis. The proposed CRDet is tested on the challenging benchmark datasets and compared with several state-of-the-art methods. The analyzing and experimental results show that our proposed CRDet achieve superior performances, which clearly demonstrate its effectiveness.
C-CycleTransGAN: A Non-parallel Controllable Cross-gender Voice Conversion Model with CycleGAN and Transformer

In this study, we propose a conversion intensity controllable model for the cross-gender voice conversion (VC) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Demo page can be found at https://cz26.github.io/DemoPage-c-CycleTransGAN-VoiceConversion/. In particular, we combine the CycleGAN and transformer module, and build a condition embedding network as an intensity controller. The model is firstly pre-trained with self-supervised learning on the single-gender voice reconstruction task, with the condition set to male-to-male or female-to-female. Then, we fine-tune the model on the cross-gender voice conversion task after the pretraining is completed, with the condition set to male-to-female or female-to-male. In the testing procedure, the condition is expected to be employed as a controllable parameter (scale) to adjust the conversion intensity. The proposed method was evaluated on the Voice Conversion Challenge dataset and compared to two baselines (CycleGAN, CycleTransGAN) with objective and subjective evaluations. The results show that our proposed model is able to equip the model with an additional function of cross-gender controllability and without hurting the voice conversion performance.
Active and Passive Radar Target Fusion Recognition Method Based on Bayesian Network

Multi-sensor fusion recognition technology can make full use of the complementarity of information between sensors to reduce the influence of interference improves the success rate of target recognition, and has been widely used in the domain of radar target recognition. The multi-sensor fusion recognition methods that commonly used include Bayesian network, D-S evidence theory and so on, among which the Bayesian network has attracted extensive attention as not only it has a solid probability theory foundation but its structure and parameters can be learned. This paper proposes a fusion recognition method for active and passive radar target, the recognition results of active and passive radar targets are fused by the Bayesian network. The results show that the recognition success rate of using fusion recognition method based on Bayesian network is increased by 9.1%, 4.8% and 2.2% compared with that using recognition methods for only active radar target and only passive radar target and fusion recognition method based on D-S evidence theory, which proves the feasibility and effectiveness of the fusion recognition method based on Bayesian network.
ASGAN-VC: One-Shot Voice Conversion with Additional Style Embedding and Generative Adversarial Networks

In this paper, we present a voice conversion system that improves the quality of generated voice and its similarity to the target voice style significantly. Many VC systems use feature-disentangle-based learning techniques to separate speakers' voices from their linguistic content in order to translate a voice into another style. This is the approach we are taking. To prevent speaker-style information from obscuring the content embedding, some previous works quantize or reduce the dimension of the embedding. However, an imperfect disentanglement would damage the quality and similarity of the sound. In this paper, to further improve quality and similarity in voice conversion, we propose a novel style transfer method within an autoencoder-based VC system that involves generative adversarial training. The conversion process was objectively evaluated using the fair third-party speaker verification system, the results shows that ASGAN-VC outperforms VQVC + and AGAINVC in terms of speaker similarity. A subjectively observing that our proposal outperformed the VQVC + and AGAINVC in terms of naturalness and speaker similarity.
Consistency Regularization for GAN-based Neural Vocoders

Neural vocoders based on generative adversarial networks (GANs) are very useful for enabling both high quality and fast inference speed. However, they are typically lower quality or take longer to learn than the neural vocoders of autoregressive models. In this work, we propose a method for improving GAN-based neural vocoders by means of consistency regularization with data augmentation. Specifically, we apply data augmentation to a natural speech and penalize the difference of the discriminator output with this augmentations. Consistency regularization demonstrated good results in semi-supervised learning and image-generation GANs. We investigated various data augmentations for speech waveforms used in the consistency regularization, including noise addition and lossy compression. Since the proposed method only adds a regularization to the discriminator, it can be applied to a wide range of GAN-based neural vocoders. The results of an objective evaluation experiment showed that MelGAN with our method outperformed the baseline. Moreover, the results of a subjective evaluation experiment showed that the mean opinion score for naturalness was improved from 3.23 to 3.85 for MelGAN.
MoCoVC: Non-parallel Voice Conversion with Momentum Contrastive Representation Learning

Non-parallel voice conversion with deep neural net-works often disentangle speaker individuality and speech content. However, these methods rely on external models, text data, or implicit constraints for ways to disentangle. They may require learning other models or annotating text, or may not understand how latent representations are acquired. Therefore, we pro-pose voice conversion with momentum contrastive representation learning (MoCo V C), a method of explicitly adding constraints to intermediate features using contrastive representation learning, which is a self-supervised learning method. Using contrastive rep-resentation learning with transformations that preserve utterance content allows us to explicitly constrain the intermediate features to preserve utterance content. We present transformations used for contrastive representation learning that could be used for voice conversion and verify the effectiveness of each in an exper-iment. Moreover, MoCoVC demonstrates a high or comparable performance to the vector quantization constrained method in terms of both naturalness and speaker individuality in subjective evaluation experiments.
Semi-supervised ASR based on Iterative Joint Training with Discrete Speech Synthesis

This paper proposes Iterative Joint Training (IJT) with discrete speech synthesis for semi-supervised ASR. Thanks to the recent advances in Text-to-Speech synthesis, synthesized speech has become near human-level. However, a large amount of paired speech and text is indispensable to training a high quality TTS model. That prevents low-resource ASR from using TTS for data augmentation. To overcome that problem, we propose to use discrete speech representations, which may be easier to synthesize than continuous representations. The proposed method trains two models from the initial paired data; a speech recognition model whose input is discrete speech representation and a speech synthesis model that generates discrete speech from texts. Both models are repeatedly updated by pseudo-pair data generated by unpaired data using the previous models. Experimental results showed the effectiveness of the proposed method in low-resource settings. It successfully improved recognition performance by using discrete speech representations instead of conventional acoustic features in IJT experiments with a single-speaker speech corpus. Furthermore, the method improved the performance of multi-speaker speech recognition that used only single speaker pair data, unpaired multi-speaker speech, and unpaired text data from the conventional IJT approach using the conventional acoustic features.
Evaluation of Cognitive Test Results Using Concentration Estimation from Facial Videos

In this paper, we propose a method of discriminating between concentration and non-concentration on the basis of facial videos, and we confirm the usefulness of excluding cognitive test results when a user has not been concentrating. In a preliminary experiment, we have confirmed that level of concentration has a strong impact on correct answer rates in memory tests. Our proposed concentration/non-concentration discrimination method uses 15 features extracted from facial videos, including blinking, gazing, and facial expressions (Action Units), and discriminates between concentration and non-concentration, which are reflected in terms of a binary correct answer label set based on subjectively rated concentration levels. In the preliminary experiment, memory test scores during non-concentration states were lower than those during concentration states by an average of 18%. This has usually been included as measurement error, and, by excluding scores during non-concentration states using the proposed method, measurement error was reduced to 4%. The proposed method is shown to be capable of obtaining test results that indicate true cognitive functions when people are concentrating, making possible a more accurate understanding of cognitive functions.
Direct speech-reply generation from text-dialogue context

Natural speech-dialogue generation has been achieved with cascade systems combining automatic speech recog-nition, text-dialogue, and text-to-speech models. However, it is still challenging to generate expressive speech-replies depending on context because text-replies could lead to information loss in estimating appropriate expressions for speech generation. One promising approach is generating speech without requiring text. Direct speech generation from a dialogue context has never been achieved because it is difficult to learn the semantically one- to-many relationship between context and reply. This paper proposes a direct speech-reply generation model from the text-dialogue context in the same manner as the text-dialogue model. We focus on two challenges: an insufficient number of training dialogue pairs of text-context and speech-reply, and the difference between continuous speech signals and discrete text sequences. For the former, we applied text- to-speech to a text-dialogue dataset to acquire huge-scale training pairs. For the latter, we introduced the vector quantization on acoustic features to convert them into discrete sequences. The results indicate that the proposed model can successfully generate speech-reply directly from text-dialogue contexts, although a quality gap still exists with the text-dialogue model.
A Restructable Strategy of Information Transmission Security Architecture in Edge Computing

The design process of security architecture in edge computing is of high complexity. Manual design is always inefficient and error prone, while automatic design cannot be widely used due to calculation explosion. Combining the goodness of these two ways, we propose a new restructable strategy for security architecture in edge computing. Firstly, we leverage automatic tool to generate critical security activities for information transmission, including challenge-response, creation of nonce and secret, encryption and decryption, addition of attribute set, message sending and receiving. These primitives can be specified according to various security requirements. Secondly, according to the five kinds of restructable rules, we construct security modules of identity authentication, information secrecy, information integrity, non-repudiation and user privacy. At last, according to security requirements in edge computing, we can obtain security architecture and verify security modules with composition rules. In the application field of information transmission, security goals can be realized, even for multiple interactions between different pairs of users and service providers. Compared with manual method from security and efficiency, the new strategy provides a more convenient and secure way for security architecture design.
Encrypted JPEG Image Retrieval via Huffman-code Based Self-Attention Networks

Image retrieval has been widely used in daily life. In recent years, with the increasing awareness of privacy protection, encrypted image retrieval has also been gradually developed. In this paper, we propose a new encrypted JPEG image retrieval scheme, named Huffman-code Based Self-Attention Networks (HBSAN), which could conduct image retrieval and protect image privacy effectively. To be specific, we first extract Huffman-code histograms directly from cipher-images which are encrypted by jointly using new orthogonal transformation, permutation cipher and stream cipher during JPEG compression. Then we employ the self-attention neural networks to mine the deep relations and retrieve the cipher-images. In our retrieval model, we design a self-attention multi-layer perceptron module, called SAMLP, to effectively learn global dependencies within representations of cipher-images. Extensive experiments present our encryption algorithm is compression-friendly, ensures no information leakage, and HBSAN significantly outperforms other state-of-the-art models in retrieval performance.
An Application Of Knowledge Map In Intelligent Education

“Rita” is short for “right teacher AI” which is a learning assistant app developed for college teachers and students, aiming to provide an intelligent platform to assist teachers and students in learning and teaching. The system includes subject content tag graphic database, intelligent article push module, intelligent Q&A module, user service module, etc. This paper studies the structure, classification and application of knowledge map in the field of intelligent education, points out the practical efficacy of knowledge map in mobile teaching assistant system, and establishes a subject tree relationship model, which provides a basis for intelligent recommendation and subject analysis.
LightGBM Model based Parkinson’s Disease Detection by using Spiral Drawings

People with Parkinson’s disease are typically diagnosed with a long-term prognosis. It is caused by the lack of the chemical dopamine, which is responsible for regulating various activities in the body. Voice is one of the symptoms of the disease, and researchers have identified it as an underlying cause. The disease can also affect the movement of people. Some of the other symptoms of Parkinson’s include rigidity, tremor, and micrographia. This condition is often one of the first signs of the disease. People with this condition often have small, cramped handwriting. People with Parkinson’s disease may eventually start writing letters in their regular handwriting. However, they may also start writing in smaller font. This condition can also affect the hand movements of those with it. To help diagnose the disease, researchers use the drawings of patients to determine the deviance of the picture in its shape. A model that is used to diagnose Parkinson’s disease takes into account the data collected by patients and spiral drawings to come up with a diagnosis. It can then classify the person as either a healthy or a Parkinson’s patient. The LightGBM algorithm, which is a better boost compared to other methods, can help the model clearly identify the healthy and the disease-affected individuals. To use the LightGBM algorithm, you need to create a window that will allow you to paste in more than one hundred words. For the best predictions, you should create at least five paragraphs.
An artificial bee colony algorithm with elite memory strategy

In recently years, due to its power global search ability, artificial bee colony (ABC) has been successfully applied in many real-world problems. But its shortcoming of slow convergence speed still constraints the further applications. In this paper, for further enhancing its merits and conquering this shortcoming, we propose three improved strategies into ABC algorithm. First, we introduce a new array to preserve some elites of population ever achieved. Based on this, the new updating equations are proposed in our paper. Finally, a new updating mechanism for scout bee is proposed to learn from the defined array for further accelerating the convergence rate of population. Compared with the compared modern evolutionary algorithms, experimental results verify our proposed algorithm achieve better performance especially on the accuracy and stability of solutions.
Text-dominated multimodal text classification with quadruplet attention

Visual-linguistic interaction is a problem of addressing the obstacles of Information Deficiency and Weight Deviation in text classification. Information deficiency usually occurs in vision-dominated or modality-balanced tasks, and many multimodal fusion approaches have been proposed (e.g. Gated-based and Contextualized-based methods). However, there is still no remarkable solution to the weight deviation of irrelevant descriptions in textdominated tasks. To solve it, we introduce a novel Quadruplet Attention to adjust the textual-visual weight distribution, where visual-linguistic information interacts with each other and dot product can be represented as a 2 × 2 matrix, referred to as quadruplet. Then, a Multimodal Architecture is further proposed to enhance text-dominated classification. Extensive experiments on Daily Mail have proved the effectiveness of our method, which achieves significant improvements of 1.42 and 3.4 respectively in ROUGE-L F1 and Marco F1.
Scalable and adjustable self-agent pseudonym management scheme for intelligent transportation systems

A pseudonym is the basic method of intelligent transportation system privacy protection. Using replaceable multi-pseudonym scheme can better protect the location privacy of vehicles. At present, pseudonym supply methods mainly include preloading and on-demand. The validity period of preloading scheme is fixed, the replacement is limited, the storage overhead is huge and the pseudonym waste is serious. The on-demand scheme needs roadside unit or pseudonym certificate authority assistance, with large delay, hidden dangers of Sybil attack and other problems. This paper proposes a massive pseudonym management scheme based on certificates signature and pseudonym self-agent generated by the vehicle. The scheme meets all the requirements of conditional pseudonym authentication. Through the self-agent generation of short-term pseudonyms, the system can realize the efficient management of the pseudonyms of millions of vehicles. The supply of pseudonym needs to be authorized by the pseudonym management authority, avoiding the abuse of pseudonym and Sybil attack. In this scheme, the pseudonym log server is used to provide the transparency of the management. For pseudonym revocation, we adopt the whitelist-like method without using the traditional certificate revocation list. We have carried out security analysis and performance evaluation of this scheme, and found that this scheme has good scalability and supports the transparency of management.
Copy and restricted paste: data augmentation for small object detection in specific scenes

Small object detection has been a difficult task because of small area, low resolution, few available features and many other problems. In order to improve the performance of small object detection, a classical augmentation method, which copies and pastes small objects to the image, is usually adopted. However, in some specific scenes, small objects cannot be pasted completely randomly on the picture without any area restriction. In this paper, to solve the task of small object detection in specific scenes, on the basis of the copy-paste augmentation method, we further design three strategies to restrict the paste position of the copied object to the target area of the image. In this way, the augmented image is more realistic to the scenario, which can improve the performance of small object detection. We conduct experiments on different object detection methods, and validate that in contrast to two-stage object detection methods, our copy-and-restricted-paste augmentation strategy is more suitable for one-stage object detection methods.