Reviewerly/exHarmony huggingface dataset:
A Real-time Smooth Lifting Path Planning for Tower Crane Based on TD3 with Discrete-Continuous Hybrid Action Space

Smoothness and rapidity are two important performance indexes for crane lifting path planning. Traditionally, cranes are modeled as multi-freedom robots and use robot path planning algorithms to plan path in continuous space. However, the paths planned by these methods are not smooth enough to operate. In addition, presently, most of proposed lifting path planning algorithms focus on static environments, requiring accurate environmental information to build maps, which is too computation expensive to meet the requirement of real-time path planning. In this paper, we propose a deep reinforcement learning-based lifting path planning algorithm for hybrid action spaces. The network structure is developed based on TD3. A new reward function is designed and hindsight experience replay is used to solve the reward sparsity problem in long distance path planning. The planning path is smooth and can achieve real-time path planning in unknown environments. The result of simulation experiments demonstrates the effectiveness of the proposed approach.
TCS Framework: Generating Text Summaries of Specified Style Using Classifier and Summarizer

There have been a lot of excellent researches in the field of natural language processing for text summarization tasks. However, there is almost no research on automatic text summarization with a specified style. Usually, people only need a piece of text in the style of content we want in many cases. Therefore, we propose an extended task of the traditional text summarization task: the task of generating text summaries with a specified style. We designed the TCS framework to accomplish this task. The TCS framework mainly includes classifiers, summarizers and data annotation processing rules. We trained the classifier by fine-tuning ALBERT. Fine-tune the T5 model to train the summarizer. By comparing the performance of T5 and other summary models, the PEGASUS model is also used as one of our classifiers. We randomly used a small number of samples in the XSUM dataset to verify the TCS framework. The results show that the TCS framework can generate text summaries with specified styles.
Evaluation of Typing Efficiency Using Language Model for the Chinese Typewriter

The predictive text with “Radiating style” refers to the personalized distribution of Chinese characters in the tray bed of the Chinese typewriter. With this arrangement, the typing speed reached 60- 80 words per minute (WPM). In the field of natural language processing (NLP), there is little research on this topic. To fill this gap in the literature, we identified some “Radiating style” patterns from the predicted text, and proposed a language model to evaluate the input efficiency of three different versions of the tray beds: Traditional Chinese tray bed (TCTB), Simplified Chinese tray bed (SCTB), and personalized setting panel. The research demonstrates that the tray bed setting and query of the Chinese typewriter inherit some Chinese language properties, and should be considered as one important step of Chinese natural language processing (CNLP). It is also valuable as a reference to improve the inputting panel of the future for the smart Chinese Language Information Processing Systems (CLIPS).
An Image Encryption Algorithm Based on a New Fractional Order Chaotic Neural Network

An image encryption algorithm based on a new fractional order chaotic neural network (CNN) is proposed. Firstly, a three-dimensional continuous integral order CNN is obtained by numerical simulation on the chaotic neuron model. Then, the integral order CNN is extended to fractional order, and the fractional order CNN iteratively generates pseudo-random sequences for subsequent encryption. In addition, the initial value of the fractional order CNN is generated from the hash value (secure hash algorithm: SHA-256) of the plain image. Finally, we apply the encryption structure of forward diffusion, scrambling and backward diffusion to the encryption algorithm, and diffusion is performed at the bit level, scrambling is performed at the pixel level and bit level. Experimental results and security analysis show that the algorithm has better performance and can resist typical attacks.
Experimental Implementation of Remote Attestation over OPC UA Protocol

We present design alternatives and a proof-of-concept demonstration realising the inclusion of remote attestation into Open Platform Communication - Unified Architecture (OPC UA), a modern Operational Technology (OT) protocol. This aspect is not yet covered by the OPC UA set of specifications. Remote attestation is about ascertaining the integrity of the software and the configuration of the attested devices, and is considered to be essential for safeguarding critical infrastructure. Remote attestation and OPC UA concepts are briefly summarized. We discuss alternatives for realising remote attestation over OPC UA, and, based on their relative merits, select one for experimentation. Related work is also discussed. Python-based proof-of-concept implementation was realised and experimented with for unearthing relevant issues that need to be resolved for actual OPC UA remote attestation. The findings are presented and the demonstrator is available as open source software as an example how to implement remote attestation over OPC UA protocol.
Compact Fuzzy BLS Driven Semi-Supervised Multi-Objective Evolutionary Adaptive Multiple-Kernel Fuzzy Clustering for Color Image Segmentation

Multi-objective evolutionary algorithms (MOEAs)-based fuzzy clustering have been successfully applied to image segmentation problems. However, these MOEAs require a lot of expensive fitness function evaluations. In order to make full use of image prior information and multi-kernel advantages as well as further improve the segmentation performance and time efficiency for image segmentation, a compact fuzzy broad learning system (CFBLS) surrogate-assisted semi-supervised multi-objective evolutionary adaptive multiple-kernel fuzzy clustering (CFBLS-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AMKFC) algorithm is proposed in this paper. The main contributions of CFBLS-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 2AMKFC are described as follows: first, a semi-supervised adaptive multiple-kernel weights fuzzy clustering fitness function is constructed to obtain satisfactory segmentation results; second, using CFBLS model predict the real objective function values in the MOEAS framework, thus reducing the time complexity of the algorithm, and third, constructing a semi-supervised multi-kernel clustering validity index to select the optimal solution from the final nondominated solution set. Experiment results on Berkeley images demonstrate that the proposed algorithm CFBLS-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AMKFC has good segmentation accuracy and low time cost.
Based on the improved YOLOv3 remote sensing target detection algorithm

At present, deep learning technology has achieved remarkable results in remote sensing target detection, but there are still great challenges to improve the detection accuracy for the complex and changeable background environment in remote sensing images. To solve the above problems, an improved YOLOv3 remote sensing image target detection algorithm is proposed. Firstly, K-means clustering algorithm is used to cluster the data set and cluster the anchors of complex remote sensing targets; Secondly, the loss function is optimized to solve the problem of uneven distribution of difficult and easy samples and improve the ability of the network to extract target features in complex scenes. Finally, the RFB module is introduced to expand the receptive field of the network and strive to improve the missed detection of small targets and dense targets.
SolChecker: A Practical Static Analysis Framework for Ethereum Smart Contract

Ethereum, a blockchain-based platform, provides a suitable environment for coding and executing smart contracts among trustless parties. However, smart contracts are exposed to inherent security risks due to manipulating digital units of value on a public decentralized blockchain. Even worse, vulnerabilities can hardly be fixed as the contracts are stored on the blockchain. Hence, there is a significant need to write secure and well-performing contracts and ensure their high reliability. To overcome these challenges, we propose an automated static framework SolChecker, for vulnerability detection on Solidity smart contracts. It works by analyzing information extracted from constructed abstract syntax tree of Solidity smart contracts, and developers can use the analyzed results to design detectors for specific vulnerabilities according to their needs. In this paper, we present an overview of SolChecker, provide the detailed design of each component, and evaluate the capabilities of the designed detectors on 47,037 real-world contracts. Furthermore, we clarify the effect of each extracted information by performing a comparative experiment on 500 contracts sampled from the dataset. Our tool reflects the current state of knowledge on Solidity vulnerabilities.
Application of Mobile Information Technology in Chinese International Education: A Case Study of Chinese Learning APP

With the initiative of China General Secretary Xi Jinping, Chinese international education has met new opportunities and challenges. With the development of mobile information technology in the new era, Chinese learning has become more convenient. Based on the theories of mobile learning and self-regulated learning, we investigated the use of Chinese learning APP by overseas students in China for 157 foreign students from universities in China as the subjects of investigation. Through the analysis of the data, it is concluded that the Chinese learning APP is conducive to the reform and innovation of Chinese international education because of its characteristics of informality, mobility, fragmentation, autonomy, and individualization. The app also meets the different needs of overseas students in China.
Online and Offline Experiential Teaching Reform Mode of Piano in Normal Colleges on Account of Improved Genetic Algorithm

Educational reform is ongoing, and the importance of music courses is becoming more important in education. We aim to find a way to improve the innovation of the online and offline experiential teaching reform model of the piano, by using genetic algorithms. After many tests and verifications, the improved online and offline experiential teaching reform model of gaoshi piano has an obvious improvement effect on improving the teaching quality. The use of genetic algorithms enhances the online and offline experience of gaoshi piano courses with the basic content and related theories. The data test analysis proves that the teaching reform model based on the improved genetic algorithm promotes the efficient improvement of teaching quality of the piano.
Research on Application Strategy of Confidentiality and Integrity Protection Algorithm on 5G Air Interface

In the evolution of mobile communications from 4G to 5G, the concept of security design and protection has been proposed. The wireless air interface may face risks, such as identity spoofing, service denial and replay attacks. It is crucial to protect the confidentiality and integrity of data. In this article, we analyze the performances for three kinds of confidentiality and integrity protection algorithms for 5G air interface, and put forward application strategies in different scenarios. It is beneficial to popularize various kinds of cryptographic algorithms and make the mechanism of the protection algorithms in the customized network perfect.
Unsupervised Title Generation from Unpaired Data with N-Gram Discriminators

A title provides readers a succinct representation of the whole document and consequently helps them get the gist without going through the details. Although the research of title generation has been investigated for decades, it still leaves much space to improve due to its requirements of complex tasks such as language understanding and text synthesis. To perform unsupervised learning of titles, we employ a generative adversarial network (GAN) without paired training data. A sequence-to-sequence model with an auto-encoder architecture is adapted to model the generator. The readability gap between human-written and machine-generated text still needs to reduce. As n-gram model has been successfully used as a language model for solving the language tasks, we present n-gram discriminators in the GAN model to increase the readability of machine-generated titles. The proposed model is tested on the English Gigaword dataset and the experimental results demonstrate that our approach obtains encouraging results.
Viewpoint Contrastive and Adversarial Learning for Unsupervised Domain Adaptive Person re-Identification

Unsupervised Domain Adaptive (UDA) Person reIDentification (re-ID) adapts the model learned from labeled source domain to unlabeled target domain and bridges the domain gap in between. Most of UDA re-ID methods utilize refined pseudo person ids to assign images, but neglect the effectiveness of auxiliary person information to rectify the noisy pseudo identities. We acknowledge that viewpoint-related variations led by changeable person viewpoints hamper the further improvement of performance. To handle these issues, this paper introduces viewpoint information as solid supervisions for each image. To fully exploit superiority of viewpoint, we propose viewpoint contrastive learning (VCL) and view point adversarial learning (VAL). The VCL enforces input image close to its one feature cluster consisting of samples with same person id and viewpoint id, and exploits the similarities among those samples with different person or viewpoint id. The VAL inducts viewpoint discriminator to ensure input image belong to its viewpoint id, but also learns view point-irrelevant representations. Besides, due to feeble discrimination of those images with same viewpoint id, we introduce Attentive Learning Module (ALM) into our framework, capturing pixel-level feature representations and improving entire method accuracies. Experimental results indicate that the proposed methods achieve state-of-the-art performance of $ 83.0\% \text{mAP},93.9\% $ Rank-1 on Duke$\rightarrow$Market, $71.1\% \text{mAP},\ 83.7\% $ Rank-1 on Market$\rightarrow$Duke, $ 30.8\% \text{mAP},62.1\% $ Rank-1 on Duke$\rightarrow$MSMT, 28.4% mAP, 58.6% Rank-1 on Market$\rightarrow$MSMT. The code will be public soon.
The Anti-Interference Algorithm of BPSK Spread Spectrum Signal Based on Generative Adversarial Networks

In order to avoid the problem of unsatisfactory anti-jamming effect when the interference feature extraction of traditional anti-jamming algorithm is inaccurate, this paper takes BPSK spread spectrum signal as the research object, and proposes an anti-jamming algorithm for BPSK spread spectrum signal based on generative adversarial network. The algorithm firstly despreads the spread spectrum signal, and then images the despread signal, and uses the known disturbed despread signal set and undisturbed signal set for network training. The mapping relationship between the disturbed despread signal image and the undisturbed despread signal image is constructed by generative adversarial network, so as to achieve the purpose of interference suppression. Experiments show that the signal anti-jamming algorithm based on generative adversarial network proposed in this paper has better interference suppression ability than the frequency-domain notch anti-jamming algorithm under multi-tone interference and narrow-band noise interference.
Research on Improvement of DES Encryption Algorithm

Modern cryptography is constantly developing based on classical cryptography. In order to improve the security of encryption algorithms and ensure the reliability of the operation of key systems, cryptography technology is still constantly innovating. This paper introduces the encryption principle and characteristics of DES encryption algorithm, analyzes the improved DES encryption algorithm proposed by W. Yihan and L. Yongzhen, and on this basis, further improved research, gives a new optimization encryption algorithm. The encryption algorithm extends the key from 64 bits to 128 bits by improving the original encryption algorithm. Meanwhile, the security of the new encryption algorithm is further strengthened through the exchange communication between left and right parts, and the possibility of effectively resisting the exhausted attack is improved. It also overcomes the shortcomings of the improved DES encryption algorithm proposed by W. Yihan and L. Yongzhen.
A Bit Allocation Method for Object-Based Audio Coding Based on G.719 Codec

Object-based audio coding method is one of the most important technologies of 3D audio since it can effectively reconstruct each audio object while making the interaction between users and audio scene more flexible and personalized. A major challenge of object-based audio coding is the higher bit rate with the increase of the number of objects. Most of the existing object-based audio coding methods use downmixing to achieve a lower bit rate, while there is few research on how to encode each audio object individually and effectively. A bit allocation method for object audio coding based on G.719 is proposed, which encodes each audio object individually while allocating different bit rate to each object adaptively according to their acoustic importance. Firstly, the objects are attenuated and combined so as to allocate the total bits among the sub-vectors; Secondly, the attenuated objects are decomposed by HRTF to obtain their number of bits within each sub-vector. Experimental results show that our method can obviously improve the reconstructed audio quality at the same coding rate compared with the equal bit allocation method.
Research on Evaluation Method of Aerial Image Segmentation Algorithm

Aerial image segmentation algorithms are used in many scenarios such as traffic warning, road assistance, terrain mapping and military reconnaissance. It has been widely applied in both military and civilian applications. A lot of researches on target detection and recognition technology in aerial images have been carried out. In addition, technologies such as computer vision and machine learning have been introduced. Compared with the research on image segmentation algorithms, there are relatively few studies on evaluation method segmentation algorithms. In view of the above situation, the evaluation method of aerial image segmentation is established, and the performance of comprehensive evaluation of the medhod for image segmentation algorithms is verified. Specifically, it includes similarity index, misidentification similarity index and distance index. Selecting image data sources with different degrees of complexity are selected in order to verify the effectiveness of the above image segmentation evaluation method. The results show that the evaluation indicators prove that the algorithms can achieve effective target segmentation to a certain extent. The segmentation accuracy rate is above 95%. In view of the accuracy and effectiveness of the aerial image segmentation algorithm, it is expected to be further applied in the field of aerial images.
Text-independent Speaker Recognition Based on X-vector

Speaker recognition is also called voiceprint recognition. The current state-of-the-art technology for speaker recognition is to use deep neural networks to extract features of the speaker's speech. This embedded feature extracted by DNN is generally called x-vector. Recently, resnet-based structures have received extensive attention and have gradually become the basis for speaker recognition research. In terms of model input, the most commonly used features include Linear Prediction Coefficient, Mel Frequency Cepstral Coefficient, Mel Filter Bank, and Spectrogram. However, a single feature cannot reveal all the features of speech. In this paper, we propose a text-independent speaker recognition algorithm based on fused features and x-vector architecture, in which we use LPC, F-bank and Spectrogram for acoustic features and fuse them at frame level, we use the currently popular ResNet as model for training and modify its structure, we use the additive angular margin loss for classification loss function. The experiments show that our proposed fusion feature and modified ResNet achieves remarkable Equal Error Rate of 0.9 for the VTCK dataset, which greatly improves the accuracy of speaker recognition.
Permissionless Blockchain-Based Sybil-Resistant Self-Sovereign Identity Utilizing Attested Execution Secure Processors

The current circumstance that requires people more online has encouraged us to address digital identity preserving privacy. There is a momentum of research addressing Self-Sovereign Identity (SSI); many research approach blockchain technology as a foundation. SSI brings natural persons various benefits such as their owning controls; on the other side, digital identity systems in the real world require Sybil-resistance to comply with Anti-Money-Laundering (AML) and other needs. Our proposal in this paper is to build a secure SSI system by utilizing permissionless blockchain and Rafael Pass et al.'s contribution of the formal abstraction of Attested Execution Secure Processors (AESPs). Our proposal of the AESP-based SSI architecture and system protocols, ∏ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gatt</sup> , demonstrates the powerfulness of hardware-assisted security and the formal abstraction of AESPs, fitting into building a proper SSI system that satisfies Sybil-resistance. Assuming AESPs and G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">att</inf> , the protocols may eliminate the online distributed committee assumed in other research such as CanDID; thus, ∏ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gatt</sup> allows not to rely on multiparty computation (MPC), and it brings drastic flexibility and efficiency compared with the existing SSI systems.
Research on Space Multitargets Precise Tracking and Trajectory Prediction Algorithm

One of the key problems that must be solved in space target monitoring is the accurate trajectory prediction, which needs high precision initial value as the premise. In order to improve the accuracy of the initial prediction value, space targets precise tracking algorithm based on dynamic equation is studied in this paper. In this method the state equation constrained by dynamic model is establishes firstly, and secondly the real-time estimated acceleration and covariance feedback are used to improve the tracking accuracy of space targets. Then the target trajectory is predicted by Euler equation in Earth-Centered-Inertial (ECI) coordinate system (CS). Finally, the feasibility and validity of the algorithm are verified by simulation experiments.