Reviewerly/exHarmony huggingface dataset:
Continuous Adaptation in Nonstationary Environments Based on Actor-Critic Algorithm

In reinforcement learning, the training process for the agent is highly relevant to the dynamics, Agent's dynamics are generally considered to be parts of environments. When dynamics changed, the previous learning model may be unable to adapt to the new environment. In this paper, we propose a simple adaptive method based on the traditional actor-critic framework. A new component named Adaptor is added to the original model. The kernel of the Adaptor is a network which has the same structure as the Critic. The component can adaptively adjust the Actor's actions. Experiments show the agents pre-trained in different environments including Gym and MuJoCo achieve better performances in the tasks of adapting to the new dynamics-changed environments than the original methods. Moreover, the proposed method shows superior performance over the baseline method just learning form the scratch in some original tasks.
Underwater Corrosion Recognition using Colour Illumination Mapping

Visual-based corrosion detection is one of the techniques that can be utilised for performing inspection in areas where human access is impossible because of environment, safety and other concerns such as underwater. Image quality has become a great factor in underwater image analysis. Noticeable challenges are limited source of natural lighting, loss of colour underwater at depth, magnification of objects underwater and much more. These factors may affect the process of estimating the actual corroded area underwater. Due to that, this work is proposing a technique to recognize underwater corrosion based on colour illumination mapping.
Shadow Compensation for Aerial Images using Statistical Color Tuning

This study proposes a shadow compensation method for aerial images based on color feature tuning. The information from shadow-free regions is used to improve the image quality of the shadowed regions. The shadow compensation of a shadowed pixel is obtained by performing statistical feature correction on channel V of HSV color space. Considering the challenging aerial images used in the experiment, the qualitative results of the shadow removal are reasonably acceptable.
Towards More Stable LIME For Explainable AI

Although AI's development is remarkable, end users do not know how the AI has come to a specific conclusion due to the black-box nature of AI algorithms like deep learning. This has given rise to the field of explainable AI (XAI) where techniques are being developed to explain AI algorithms. One such technique is called Local Interpretable Model-Agnostic Explanations (LIME). LIME is popular because it is modelagnostic and works well with text, tabular and image data. While it has some good features, there are still room for improvements towards the original LIME algorithm especially it's stability. In this work, the LIME stability is being reviewed and three different approaches were investigated for its effectiveness in stability improvement which are; 1) using high sample size for stable ordering, 2) using an averaging method to reduce region flipping; and 3) to evaluate different super-pixels segmentation algorithms in generating stable LIME outcome. The experiment results shows a definite increase in the stability of the improved LIME compared to the baseline LIME and thus the reliability of using it practically.
TransUNet for Cross-Domain Semantic Segmentation of Urban Scenery

TransUNet is a hybrid architecture that combines a transformer-based encoder with a CNN-based UNet. Originally introduced for semantic segmentation of medical images, we show in our work that TransUNet can be successfully applied to urban scenery datasets commonly used for developing autonomous driving systems. We also explore the performance characteristics of training on multi-domain data from the real world and a simulator, and show that using simulated images to augment a live dataset can improve segmentation performance. Code will be made available at https://github.com/weiyuen.
Joint Channel Estimation and Signal Detection using Latent Space Representations in VAE

This paper presents a data-driven unsupervised Deep Learning-based joint channel estimation and signal detection method for a narrowband wireless communication system. Our proposed Deep Learning-based architecture uses a Variational Autoencoder (VAE) that can combat the effects of additive white Gaussian noise and Rayleigh fading by encoding the input into a lower dimensional representation as the latent space outputs. The lower dimensional representation is used to extract the symbol information and is classified to the corresponding symbols of the transmitted signal using a classifier. We propose two approaches for the VAE-based architecture by using a parallel 1-D VAE and a joint 2-D VAE that takes different signal representations. From our simulation results, the proposed VAE-based architectures can achieve BER performance improvements over a deep Convolutional Neural Network approach and corre-lator detector.
Achieving sustainable self-esteem as the basis of a musician’s productive activity

the article discusses the phenomenon of young musicians’ assessment of their achievements. The article studies a stable self-esteem, capable of fulfilling a constructive function as the basis of productive activity, and unstable, exposed to fluctuations depending on circumstances, which become false reference point of personality development. By this logic the conditions and mechanisms for the formation of a stable self-esteem are determined. Psychotechnologies of self-knowledge and self-esteem have been developed to ensure the musician’s readiness for continuous improvement of professionalism and personal growth. As an example, it has been proved the conditionality of creative stage well-being by achieving a stable self-esteem.
Secure Deduplication Against Frequency Analysis Attacks

Message-locked Encryption (MLE) is the most common approach used in encrypted deduplication systems. However, the systems based on MLE are vulnerable to frequency analysis attacks, because MLE encrypts the identical plain texts into the identical ciphertexts, which is deterministic. The state-of-the- art defense scheme, which named TED, lacks key verification and uses a single key server to record frequency information. Once the key server is compromised, TED will be vulnerable to brute-force attacks. In addition, TED's key generation algorithm needs to be designed more exquisitely to strengthen protection, and its security indicator is not comprehensive. We propose SDAF, which supports key verification and enhanced protection against frequency analysis attacks. Based on chameleon hash, SDAF realizes key verification to prevent malicious key servers from generating fake encryption keys. In order to disturb the frequency information, SDAF introduces reservoir sample to generate uniformly distributed encryption keys, and uses multiple key servers, which interact with each other via multi-party PSI and rotate spontaneously to avoid the single point of failure. Moreover, a new indicator Kurtosis is pointed out to evaluate the security against frequency analysis attacks. We implement the prototypes of SDAF. The experiments of the real-world data sets show that, compared with the existing schemes, SDAF can better resist frequency analysis attacks with lower time overheads.
Application Identification under Multi-Service Integration Platform

Multi-service integration platform (MIP) is becoming a new way for mobile applications to provide services, such as the ChatBot of Facebook and the applet of WeChat. However, currently there are no special means and filtering strategies to supervise the services running on various MIPs. Existing solutions for program detection and traffic analysis are not suitable for MIP scenarios, which creates favorable conditions for the dissemination of illegal content through MIP. To address this issue, in this work we propose a new approach to identify mobile applications running on MIP platforms. The proposed approach uses IP flow to reconstruct data units of both transport and ap-plication layers respectively. By this way, we can capture the data transmission behavior of multi-protocol layers and obtain richer semantic features for application identification. Then, multi-kernel convolutional neural networks (CNN s) and long short term memory (LSTM) neural networks are employed to extract and aggregate the multi-scale features from the perspective of both protocol layer and time series. Finally, the fused features generated by the models are used to identify the category of the pending applications by a classifier composed of a fully connected neural network. We validate the proposed approach by three real datasets. The experimental results show that the proposed approach outperforms most existing benchmark methods in performance.
An Efficient and Secure Node-sampling Consensus Mechanism for Blockchain Systems

The consensus mechanism plays a pivotal role in guaranteeing the security and consistency of blockchain systems and substantially affects system performance. However, an increasing number of blockchain nodes degrade the consensus performance dramatically because of the high communication complexity in traditional consensus mechanisms. In this paper, we propose NS-consensus, a secure node-sampling blockchain consensus mechanism reducing the communication complexity significantly. The key novelty lies in the sampling of blockchain nodes so that the leader only needs to interact with the sampling nodes in each consensus epoch. However, NS-consensus imposes two challenges in determining an optimal sample size and denying malicious proposals. To address the challenges, we determine the sample size under the constraints of a confidence level and a margin of error to enhance communication efficiency without compromising system security. Furthermore, we design a mechanism to enable the leader to interact with all blockchain nodes in the last consensus phase, ensuring the denial of malicious proposals. The extensive experimental results indicate that NS-consensus outperforms the state-of-the-art with up to 175.1% higher system throughput and 79.9% lower time overhead in the sampling phases.
Semantic Image Synthesis via Location Aware Generative Adversarial Network

Semantic image synthesis aims to synthesize photo-realistic images through the given semantic segmentation masks. Most existing models use conditional batch normalization (CBN) to regulate normalization activation by spatially varying modulation parameters. It can prevent semantic information from being eliminated during normalization. But the modulation parameters in CBN lack location constraint, resulting in the lack of structural information in the synthetic image. And CBN is highly dependent on the batch size. To address these limitations, we propose location aware conditional group normalization (LACGN) and construct a location aware generative adversarial network (LAGAN) based on this method. LACGN can learn spatial location aware information in a weakly supervised manner that relies on the current image synthesis process to guide transformations spatially. It allows the synthetic image to have more structural information and detailed features. At the same time, group normalization(GN) replace the traditional BN to eliminate the dependence on batch size. Extensive experiments show that LAGAN is better than other methods.
Image Style Transfer–A Critical Review

Style Transfer refers to the conversion of images into two different domains. Specifically, it provides a style image, converts any image into this style, and preserves the content of the original image as much as possible. Image style transfer is now often employed in image processing applications. It offers a number of evaluation techniques for contrasting numerous prevalent and significant applications of image style transfer algorithms on various level. The examination of numerous Neural Style Transfer applications and unresolved issues for further study comes to a close in this paper.
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition

The recent emergence of joint CTC-Attention model shows significant improvement in automatic speech recognition (ASR). The improvement largely lies in the modeling of linguistic information by decoder. The decoder joint-optimized with an acoustic encoder renders the language model from ground-truth sequences in an auto-regressive manner during training. However, the training corpus of the decoder is limited to the speech transcriptions, which is far less than the corpus needed to train an acceptable language model. This leads to poor robustness of decoder. To alleviate this problem, we propose linguistic-enhanced transformer, which introduces refined CTC information to decoder during training process, so that the decoder can be more robust. Our experiments on AISHELL-1 speech corpus show that the character error rate (CER) is relatively reduced by up to 7 %. We also find that in joint CTC-Attention ASR model, decoder is more sensitive to linguistic information than acoustic information.
RDP-WGAN: Image Data Privacy Protection Based on Rényi Differential Privacy

In recent years, artificial intelligence technology based on image data has been widely used in various industries. Rational analysis and mining of image data can not only promote the development of the technology field but also become a new engine to drive economic development. However, the privacy leakage problem has become more and more serious. To solve the privacy leakage problem of image data, this paper proposes the RDP-WGAN privacy protection framework, which deploys the Rényi differential privacy (RDP) protection techniques in the training process of generative adversarial networks to obtain a generative model with differential privacy. This generative model is used to generate an unlimited number of synthetic datasets to complete various data analysis tasks instead of sensitive datasets. Experimental results demonstrate that the RDP-WGAN privacy protection framework provides privacy protection for sensitive image datasets while ensuring the usefulness of the synthetic datasets.
Throughput Prediction-Enhanced RL for Low-Delay Video Application

Maximizing user quality of experience (QoE) is the ultimate goal of video players, and adaptive bitrate (ABR) is recognized as one of the most effective solutions. Approaches employing reinforcement learning (RL) have performed well as hybrid ABR algorithms, due to the ability to learn autonomously. However, throughput, which plays a crucial role in low-delay video streaming, is difficult to predict simply in mobile and wireless networks, and the inaccurately predicted throughput can lead to the wrong selection of bitrates. Worse, the general RL approaches are prone to frequent bitrate switching due to bandwidth fluctuation. These obstacles make the RL-based ABR approach unable to truly reflect the user QoE. We propose TP-RL, an application that makes ongoing decisions to maximize user QoE. To realize this, TP-RL adopts three ideas: (i) It takes the RL neural network as the main body of decision-making, which will inherit the advantages of RL and improve on this basis; (ii) Explore Mogrifier LSTM for throughput prediction, and replace the throughput part in the state space of the original RL neural network with a prediction module; (iii) The decided bitrate is further processed to achieve better smoothness when the bandwidth fluctuates. The performance of TP-RL is evaluated in different experimental environments, and experiments show that it can improve QoE by about 14% to 20.7% compared with the best baseline.
Automatic Spraying of Insulator Persistent Room Temperature Vulcanized Coating Based on Digital Image Processing

Pollution flashover prevention has always been the focus of external insulation design and renovation. Reducing pollution flashover accidents is of great significance to reducing power grid failures. Persistent Room Temperature Vulcanized (PRTV) coating is a permanent anti-pollution flashover composite material for external insulation, which is an important material to prevent insulators from pollution flashover at present. In this paper, the method of digital image processing for insulator PRTV coating is adopted for coating spraying. Firstly, the technical characteristics of PRTV coating are studied, and the quality problems of common coatings are analyzed. Then the sprayed PRTV coating is processed by image processing. Finally, the defects are detected through image recognition to achieve effective spraying of PRTV.
Data Encryption Algorithm Based on Chaos Sequence in Computer Network Security

Information security is related to personal interests, enterprise survival, social stability, state secrets, etc., and is closely related to my country's economic security, social security and national security. Therefore, research on reliable and secure information transmission encryption technology has important academic and practical value. The purpose of this paper is to study the application of data encryption algorithm in computer network security based on chaotic sequence. The related theoretical knowledge of chaotic image encryption algorithm is studied, including chaotic cryptography and chaotic time series; a data encryption algorithm based on chaotic sequence is designed and applied in image encryption; the realization of the whole image encryption is completed. In addition, the performance of the designed image encryption algorithm is analyzed in various aspects, including the correlation of adjacent pixels and the analysis of security performance. The experimental results show that this paper designs an image encryption algorithm with better performance.
Face tracking by fusing convolutional neural networks and particle filtering

Objective To address the problem of target loss due to the change of color, shape, texture and other features in face tracking, this paper proposes a face tracking algorithm integrating convolutional neural network and particle filtering. Methods Firstly, a large number of face images with different poses, different lighting and occlusion conditions are trained in the convolutional neural network to get the face feature maps under different conditions, then the trained network weights are used as the weight coefficients in the particle filtering algorithm, and finally the particle filtering algorithm is used for face tracking. Results In this paper, the OPE (one pass evaluation) evaluation criterion in OTB2015 database is used, and the area under the line (AUC) and the center point position error of the success rate graph are used as the evaluation criteria, and by doing comparison experiments with classical KCF, CSK, SRDCF, ECO-HC and other algorithms, the algorithm proposed in this paper achieves The proposed algorithm achieves a tracking success rate of 0.978 and a tracking accuracy rate of 0.97 on the test data. Conclusion In this paper, a face tracking model incorporating convolutional neural network and particle filtering is proposed, which can automatically track regions with large face differentiation and effectively improve the tracking accuracy of the target. Experimental results show that the algorithm in this paper can adapt to face tracking in complex situations such as different postures, different lighting and occlusion, with good robustness and real-time performance.
EAQ: Enabling Authenticated Complex Query Services in Sustainable-Storage Blockchain

The data query service is urgently required in sustainable-storage blockchain, where full nodes store the entire transaction data while light nodes only store block headers. Queries invariably seek data with multiple attributes. However, no existing method provides a unified authenticated data structure (ADS) to support complex query operators (e.g., range queries and data object queries) on multiattribute blockchain data. In this paper, we propose a framework EAQ that effectively supports both fast data queries on multiple attributes and authentication of the query result. We propose a new ADS, called the MR <inline-formula><tex-math notation="LaTeX">$^{Bloom}$</tex-math></inline-formula> -tree, based on the Bloom filter (BF) and Merkle R-tree. We prove the decomposability of BFs, which enables the BF to be seamlessly incorporated with the Merkle R-tree. This ADS enables range-level search using multidimensional attribute ranges and object-level search using BFs. This ADS also supports querying and proving inexistent data objects. To reduce storage overhead, we improve the MR <inline-formula><tex-math notation="LaTeX">$^{Bloom}$</tex-math></inline-formula> -tree using the suppressed BF structure, which constructs only one BF independent of the number of attributes. To manage string attributes, we transform them into discrete numerical attributes using density-based clustering to represent similar items with close numerical values. Experiments show that the proposed framework achieves promising results.
Build a real-time building segmentation network

Recently, most existing building segmentation networks have high memory usage and computational resource. And a large number of methods of the lightweight model have problems, such as the sharp decrease in segmentation performance due to parameter reduction, limited arithmetic power and real-time. Therefore, in this paper, we propose a new lightweight network based on the SGUnet segmentation network. Our network is a hourglass-like-depth-separable-convolution-based sgbunet segmentation network, pruning the sgnet and using residual connections. The core idea is that, shallow features are more important in lightweight networks. According to this point of view, increasing the proportion of features in the shallow part of the model successfully improved the accuracy of the model by 1%. Finally, the Cheap operation module is reused to further reduce the amount of network parameters. In the end, the parameter amount of the network is reduced from the original 260k to 70k, which greatly reduces the amount of model parameters.The sgbunet segmentation network have a reletive improvement, achieving 97.329 and 80.823 for Accuracy and Miou respectively in the building segmentation.
Attention Based End-to-End Network for Short Video Classification

It has been proved that three-dimensional (3D) convolutional kernel can effectively capture local features in the spatiotemporal range of videos, leading to impressive results of various models in video-related tasks. With the introduction of Transformer and the rise of self-attention mechanism, more self-attention models have been used on video representation learning recently. However, there exist limitations of local perception and self-attention operations respectively in both two types of models. Inspired by the global context network (GCNet), we take advantages of both 3D convolution and self-attention mechanism to design a novel operator called the GC-Conv block. The block performs local feature extraction and global context modeling with channel-level concatenation similarly to the dense connectivity pattern in DenseNet, which maintains the lightweight property at the same time. Furthermore, we apply it for multiple layers of our proposed end-to-end network in short video classification task while the temporal dependency is captured via dilated convolutions and bidirectional GRU for better representation. Finally, our model outperforms both state-of-the-art convolutional models and self-attention models on three human action recognition datasets with considerably fewer parameters, which demonstrates the effectiveness.