通讯机构：
[Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词：
area-optimized;high throughput;Internet of Things;lightweight;LILLIPUT block cipher
摘要：
The relationship between encryption algorithm and key scheduling algorithm is utilized to achieve optimal sharing among components, which significantly reduces hardware area. The number of XOR gates and S‐boxes required for low area optimization is reduced by 52 and 8, respectively. Summary The widespread use of Internet of Things devices has increased the demand for lower cost and more efficient lightweight ciphers. However, there is a difficult trade‐off between cost and efficiency for lightweight block ciphers. The optimizations of area and throughput are important for some constrained environments. This paper proposes two novel hardware architectures for the LILLIPUT cipher. In the novel low area structure, a new permutation layer is provided for LILLIPUT. The relationship between encryption algorithm and key scheduling algorithm is utilized to achieve optimal sharing among components, which significantly reduces hardware area. The experimental results show that the number of XOR gates and S‐boxes required for low area optimization is reduced by 52 and 8, respectively. The total area is reduced by about 18%. For high throughput structure, this paper provides 2‐round, 5‐round, and 15‐round loop unrolling designs for LILLIPUT to improve throughput. The experimental results show that the throughput of the 5‐round loop unrolling structure reaches a good level, which is relatively the most cost‐effective. In practical application, ciphers can be unrolled implementations according to the needs of devices to improve the execution speed, which can greatly reduce the execution time and complexity of the algorithm.
摘要：
The number of industrial Internet of Things (IoT) users is increasing rapidly. Lightweight block ciphers have started to be used to protect the privacy of users. Hardware-oriented security design should fully consider the use of fewer hardware devices when the function is fully realized. Thus, this paper designs a lightweight block cipher IIoTBC for industrial IoT security. IIoTBC system structure is variable and flexibly adapts to nodes with different security requirements. This paper proposes a 4x4 S-box that achieves a good balance between area overhead and cryptographic properties. In addition, this paper proposes a preprocessing method for 4x4 S-box logic gate expressions, which makes it easier to obtain better area, running time, and power data in ASIC implementation. Applying it to 14 classic lightweight block cipher S-boxes, the results show that is feasible. A series of performance tests and security evaluations were performed on the IIoTBC. As shown by experiments and data comparisons, IIoTBC is compact and secure in industrial IoT sensor nodes. Finally, IIoTBC has been implemented on a temperature state acquisition platform to simulate encrypted transmission of temperature in an industrial environment.
通讯机构：
[Li, QP ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
摘要：
The graph G of order n is an L-borderenergetic graph which means it has the same Laplacian energy as the complete graph Kn. In this paper, we find that the combination of complete bi-partite graphs and stars can construct infinite numbers of infinite classes L-borderenergetic graphs. We give two infinite numbers of infinite classes L-borderenergetic graphs and two infinite classes L-borderenergetic graphs under the operators union, join and their mixed. This research could provide experience for further study the structural characteristics of L-borderenergetic graphs.
通讯机构：
[Lang Li] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang 421002, China
关键词：
ARX-Based lightweight block cipher;High-diffusion architecture;Mixed integer linear programming;SAND
通讯机构：
[Huihuang Zhao] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
通讯机构：
[Mugang Lin] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
style transfer;generative adversarial networks;deformable convolutional networks;artistic font generation
摘要：
The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effectively recognized. In this paper, a deep deformable style transfer network is proposed for artistic font style transfer, which can adjust the degree of font deformation according to the style and realize the multiscale artistic style transfer of text. The new model consists of a sketch module for learning glyph mapping, a glyph module for learning style features, and a transfer module for a fusion of style textures. In the glyph module, the Deform-Resblock encoder is designed to extract glyph features, in which a deformable convolution is introduced and the size of the residual module is changed to achieve a fusion of feature information at different scales, preserve the font structure better, and enhance the controllability of text deformation. Therefore, our network has greater control over text, processes image feature information better, and can produce more exquisite artistic fonts.
摘要：
Researches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases. In this manuscript, we proposed a novel prediction model named MADGAN to infer potential microbe-disease associations by combining biological information of microbes and diseases with the generative adversarial networks. To our knowledge, it is the first attempt to use the generative adversarial network to complete this important task. In MADGAN, we firstly constructed different features for microbes and diseases based on multiple similarity metrics. And then, we further adopted graph convolution neural network (GCN) to derive different features for microbes and diseases automatically. Finally, we trained MADGAN to identify latent microbe-disease associations by games between the generation network and the decision network. Especially, in order to prevent over-smoothing during the model training process, we introduced the cross-level weight distribution structure to enhance the depth of the network based on the idea of residual network. Moreover, in order to validate the performance of MADGAN, we conducted comprehensive experiments and case studies based on databases of HMDAD and Disbiome respectively, and experimental results demonstrated that MADGAN not only achieved satisfactory prediction performances, but also outperformed existing state-of-the-art prediction models.
作者机构：
[Zhu, Xianyou; Hu, Weixin] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
通讯机构：
[Zhu, XY ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
摘要：
With the development of computer technology, speech synthesis techniques are becoming increasingly sophisticated. Speech cloning can be performed as a subtask of speech synthesis technology by using deep learning techniques to extract acoustic information from human voices and combine it with text to output a natural human voice. However, traditional speech cloning technology still has certain limitations; excessively large text inputs cannot be adequately processed, and the synthesized audio may include noise artifacts like breaks and unclear phrases. In this study, we add a text determination module to a synthesizer module to process words the model has not included. The original model uses fuzzy pronunciation for such words, which is not only meaningless but also affects the entire sentence. Thus, we improve the model by splitting the letters and pronouncing them separately. Finally, we also improved the preprocessing and waveform conversion modules of the synthesizer. We replace the pre-net module of the synthesizer and use an upgraded noise reduction algorithm combined with the SV2TTS framework to achieve a system with superior speech synthesis performance. Here, we focus on improving the performance of the synthesizer module to achieve higher-quality speech synthesis audio output.
摘要：
Recently, there has been renewed interest in the combination of deep learning and side-channel analysis (SCA). Many previous studies have transformed the traditional SCA into a classification problem in deep learning. This paper considers it as a regression problem based on the principle that the changes of some circuit states are related to the special operation in cipher. We proposed a regression model which consists of an initial layer, a deep feature mining dense layer, and a regression layer. In the term of dataset, there are two sources of data: the raw ASCAD power traces and the data sampled from FPGA implementation of AES and PRESENT. The mainly advantages of this model and regression task processing method is that it can adapt to different cryptographic algorithms on the same hardware device. Moreover, the experimental result that the model can significantly improve the attack accuracy of SCA. In ASCAD, its prediction accuracy achieves 2.90% and 3.63% for two different intermediate values, and their correlation coefficient evaluation 0.873, 0.840. In FPGA power dataset, their prediction and correlation coefficient are 3%, 4%, and 0.963, 0.987 respectively.
通讯机构：
[Lu, H.] K;Kyushu Institute of Technology, Japan
关键词：
Point cloud compression;Three-dimensional displays;Robustness;Feature extraction;Deep learning;Training;Space exploration;Registration;point cloud data processing;PointNet;branch-and-bound
摘要：
Registration performs an individual and deciding role in multiple intelligent transport systems. The advancement of deep-learning-based methods enhances the robustness and effectiveness of the preliminary registration stage, although the algorithm will effortlessly fall into local optima when improving the ultimate exactitude. Similarly, traditional method based on optimization has a more reliable performance in terms of precision. However, its performance still counts on the quality of initialization. In order to solve the above problems, we propose a PBNet that combines a point cloud network with a global optimization method. This framework uses the feature information of objects to perform high-precision rough registration and then searches the entire 3D motion space to implement branch-and-bound and iterative nearest point methods. The evaluation results show that PBNet significantly reduce the influence of initial values on registration and has good robustness against noise and outliers.
摘要：
In recent years, chaotic image encryption algorithms with key and plaintext association have been developed, which are essentially similar to a one-time pad at a time because each encryption requires the transmission of the key. However, some existing schemes cannot uniquely map the seed key to the initial value of the chaotic system, which leads to the reduction of the key space of the encryption system. In addition, some schemes use the same key to encrypt the same image, which does not conform to the one-time pad strategy. This paper solves these problems from two aspects. On the one hand, random pixels are inserted into a plain image and then a hash value is generated using SHA-256. Different seed keys can be obtained even if the same image is encrypted. On the other hand, the Sequential Expansion Algorithm (SEA) and Feedback Iterative Piece-Wise Linear Chaotic Mapping (FI-PWLCM) are proposed to realize the one-to-one correspondence between the seed key and the encrypted key stream. SEA can quickly generate seed key sensitive and random sequences. FI-PWLCM achieves one-to-one correspondence with the seed key through feedback iteration with more control parameters. The mapping not only has the rapidity of PWLCM, but also can produce more complex chaotic sequences. Besides, this paper proposes a Segmented Coordinate Descent (SCD) method for histogram statistical optimization of images to improve the ability of cryptosystems against statistical attacks. Experiments and security analysis show that the algorithm can resist chosen-plaintext (chosen-ciphertext) attacks, brute force attacks, statistical attacks and so on. Compared with most current algorithms, it achieves the best performance in the statistical properties of histogram and entropy.
期刊：
Journal of Electronic Imaging,2022年31(5) ISSN：1017-9909
通讯作者：
Ge Jiao
作者机构：
[Li, Chen; Jiao, Ge] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;[Jiao, Ge] Hunan Prov Key Lab Intelligent Informat Proc & A, Hengyang, Peoples R China.
通讯机构：
[Ge Jiao] H;Hengyang Normal Univ.<&wdkj&>Provincial Key Lab. of Intelligent Information Processing and Application
关键词：
Transformers;Performance modeling;Data modeling;Visual process modeling;Computer programming;Camouflage;Convolution;Feature extraction;Visualization;Transmission electron microscopy
摘要：
Camouflaged object detection (COD) is a new computer vision challenge for locating and identifying camouflaged objects in complex situations. Camouflaged objects are more similar to their surroundings than conventional objects, and their appearance in terms of size and shape is also considerably different, making accurate identification of the COD tasks difficult. As a result, we propose an enhanced identification network (EINet) to strengthen the COD task's identification capabilities. First, the pyramid vision transformer is used as an encoder for extracting more robust multiscale features. Second, the multiple texture refinement modules are exploited to refine the multiscale features. Third, an improved neighbor and hop connection decoder is designed to produce a coarse estimation map for guiding the detailed identification of camouflaged objects backward. Finally, numerous new reverse criss-cross block attention modules that gradually recognize fine-grained features at various scales is designed to allow for the accurate recognition of camouflaged objects. Extensive experiments have been conducted on four benchmarked datasets of camouflaged objects. The results of the experiments reveal that our EINet is a powerful COD model that outperforms current state-of-the-art models. (c) 2022 SPIE and IS&T
