Reviewerly/exHarmony huggingface dataset:
Paco and Paco-Dct: Patch Consensus and Its Application To Inpainting

Many signal processing methods break the target signal into overlapping patches, process them separately, and then stitch them back to produce an output. How to merge the resulting patches at the overlaps is central to such methods. We propose a novel framework for this type of problem based on the idea that estimated patches should coincide at the overlaps (consensus), and develop an algorithm for solving the general problem. In particular, an efficient method for projecting patches onto the consensus constraint is presented. Finally, a simple and effective audio/image/video inpainting algorithm is developed using this framework.
Hierarchical Clustering and Regression Classification based Review analysis on Movie based Applications

In recent year sentiment analysis plays an important role in the opinion mining where the research on the analysis is mainly focused on the extraction of opinions and their emotions towards a particular application in the web. This paper presents review analysis of movie from various movie based applications. This paper concentrates more on the fundamental aspects of sentimental analysis which includes different methods of analyzing the reviews including data extraction, clustering and classification. In this paper extraction of the reviews from various movie based applications like IMDB, Pay tm etc.,, can be taken and formed the Movie dataset, This Movie API consists of movie name, comments, reviews, rating, emoji's etc., In this paper first data pre-processing techniques is applied to clean the dataset and then apply the hierarchical clustering for the attributes taken and then regression based classification on the attributes to form a cluster based on negative and the positive reviews provided by the different users and finally determining the accuracy using proposed hierarchical clustering and regression technique with existing decision tree technique and the accuracy of proposed technique more accuracy is achieved.
Variants of the Epsilon Constrained Method in a Memetic Differential Evolution: A Comparative Study

Memetic approaches are composed of three general processes, a global optimizer, a set of local-search operators, and a coordination mechanism; which are defined depending on the problem to be optimized. For constrained optimization problems (COPs), memetic algorithms require the incorporation of a constraint handler that guides the search to the feasible regions of the search space. In this regard, the epsilon-constrained method has demonstrated to operate correctly in memetic approaches by transforming a COP into an unconstrained problem during a certain period of the search process. This constraint handler uses a tolerance level that promotes the exploration, mainly in COPs where there are disjoint feasible regions or equality constraints. Nevertheless, epsilon-constrained depends on a set of parameters that determine its behavior, so five variants have emerged in the control of its tolerance, (1) static, (2) dynamic, (3) truncated, (4) threshold, and (5) adaptive. This study focuses on determining the most appropriate control technique in a memetic approach and its relation to the performance and final results of the algorithm. For the study, a memetic differential evolution (MDE) is implemented, whose coordination mechanism controls the activation of three local search methods. Each epsilon-level control mechanism is incorporated separately within the MDE and is tested in eighteen well-known test problems. The results suggest that there is a benefit through the use of adaptive/dynamic mechanisms while reducing the budget for fitness evaluations. Likewise, its advantage is exhibited in functions with non-separable equality constraints. Finally, results determine that there is no benefit relationship between how to control the epsilon-level and the performance of the local optimizers used in this study.
Research on the Image Annotation Technology for Product Quality and Safety Inspection Data

Abstract In recent years, the vicious events about quality and safety in China have continued to bring serious impacts on people’s lives and property. The effective analysis and processing of product quality and safety inspection data will provide intellectual support for the overall improvement in product quality, and the effective control of prominent quality and safety problems in China. Aiming at the phenomenon that there is a large amount of image information in the quality detection data, this paper proposed an image annotation technology based on big data fusion, conducted weight fusion to image similarity and image user similarity, calculated the total similarity of images, and made denoising treatment. The experimental results showed that the method proposed in this study could annotate the quality test data well.
Application and Evaluation of Efficient Algorithm for Computing Product of Pairings to 256-bit Secure Pairing-friendly Curves

In some pairing-based primitives and protocols, evaluation of product of pairings is required. Granger et al proposed an efficient algorithm for computing product of pairings. Recently, Kim et al proposed an efficient algorithm, called extended tower number field sieve, for discrete logarithm problem in finite field, that reduces security levels of pairing friendly curves. Regarding this, Kiyomura et al proposed new 256-bit secure pairing friendly curves. In this paper, we apply the Granger's pairing product algorithm to the 256-bit secure pairing friendly curves called BLS-24 and BLS-48. Moreover, we estimate computational costs of the proposed application considering optimization techniques for pairing computation.
$ s $-PD-sets for codes from projective planes $ \mathrm{PG}(2,2^h) $, $ 5 \leq h\leq 9 $

<p style='text-indent:20px;'>In this paper we construct <inline-formula><tex-math id="M4">\begin{document}$ 2 $\end{document}</tex-math></inline-formula>-PD-sets of <inline-formula><tex-math id="M5">\begin{document}$ 16 $\end{document}</tex-math></inline-formula> elements for codes from the Desarguesian projective planes <inline-formula><tex-math id="M6">\begin{document}$ \mathrm{PG}(2,q) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M7">\begin{document}$ q = 2^h $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M8">\begin{document}$ 5\leq h \leq 9 $\end{document}</tex-math></inline-formula>. We also construct <inline-formula><tex-math id="M9">\begin{document}$ 3 $\end{document}</tex-math></inline-formula>-PD-sets of <inline-formula><tex-math id="M10">\begin{document}$ 75 $\end{document}</tex-math></inline-formula> elements for the code from the Desarguesian projective plane <inline-formula><tex-math id="M11">\begin{document}$ \mathrm{PG}(2,q) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M12">\begin{document}$ q = 2^9 $\end{document}</tex-math></inline-formula>. These <inline-formula><tex-math id="M13">\begin{document}$ 2 $\end{document}</tex-math></inline-formula>-PD-sets and <inline-formula><tex-math id="M14">\begin{document}$ 3 $\end{document}</tex-math></inline-formula>-PD-sets can be used for partial permutation decoding of codes obtained from the Desarguesian projective planes.
Polar Coding with Chaos and Frozen Bits Operation for Wiretap Channel

A chaos stream cipher embedded polar coding structure with frozen bits operation scheme is presented in this paper. Several solutions are proposed for solving the remaining problem of former encrypted polar coding scheme for symmetric and degraded wiretap channel. In the proposed structure, a chaos stream cipher is embedded for secure encryption. A solution for long key transmission over the wiretap channel is presented. A multi-block chaining structure is employed for achieving the secure and reliable transmission under the strong security criterion. And a frozen bits operation is constructed with the chaining structure to increase the error probability for unauthorized decoding. And our proposed scheme can also achieve an additional secrecy capacity for the symmetric and degraded wiretap channel.
Substate Detection Based Confidence Scoring in Speech Recognition

This paper discusses an approach for confidence scoring at the phoneme level. Various features derived from multi layer perceptron (MLP) posteriors that indicates the strength of a phoneme detection are introduced. The capability of these features to discriminate between true positive and false positive phoneme detection is demonstrated. Appropriate distributions are fit on these features. These distributions are combined to derive the posterior odds ratio, which signals the confidence of a phoneme detection. Finally, simple thresholding on the posterior odds ratio is used to classify a detected phoneme as true/false positive. Relevant real world datasets are used to benchmark the proposed approach.
An Easy-to-Implement Framework of Fast Subspace Clustering For Big Data Sets

Subspace clustering has attracted much attention due to its successful application on many data mining and computer vision tasks. However, most subspace clustering algorithms suffer from the scalability and the curse of dimensionality problems. When the volume or the dimension of the datasets becomes high, these algorithms are infeasible for the high computational complexity and large memory requirement. To enable the fast implementation of subspace clustering on big datasets, this paper proposes a simple but effective subspace clustering framework called Fast Subspace Clustering (FSC), which adopts a "sampling, random projecting, clustering, and classifying" strategy. We prove that under certain conditions on the subspace and the original subspace clustering algorithm, both the time and space complexity of FSC is O(MN) for M samples in N-dimensional space. Experimental results on several real-world datasets demonstrate the effectiveness and efficiency of the proposed framework.
Image Super-Resolution based on Multi-Convolution Neural Network

In recent years, convolutional neural network method has been widely and successfully applied in the field of image super-resolution. With the development of CNN structure, the reconstruction algorithm based on CNN has also been developed. However, in these reconstruction models, the scale of convolution operator is single. This will greatly limit the model's learning ability about input image and impact the reconstruction effect. In order to improve the accuracy of the convolution network to the input image, a reconstruction method based on multi-scale convolution operator is proposed. In this method, multi-scale convolution operators are set up in each layer network to calculate multi-scale features of the input image. Experiments show that this method can effectively improve the accuracy of the reconstructed image in detail.
Object Extraction Algorithm Based on Saliency Prior Information

Object extraction provides the basis for subsequent tasks such as object recognition, and its importance is self-evident. For this purpose, this paper proposes an object extraction algorithm based on saliency prior information. Firstly, the SIFT operator and the oriented edge forest method are used to extract the saliency points and the saliency edges respectively. Then construct a simple small convolutional neural network for the saliency fusion task, and fuse the saliency point and the saliency edge to obtain the saliency fusion map. Then the fusion map is added to the object extraction network structure as a priori information. In this paper, the residual network is used for the object extraction task, and finally the high-quality object extraction work is realized. The algorithm uses IOU parameters and Precision as the quantitative evaluation index, and uses qualitative analysis to comprehensively analyse the experimental results. Experiments show that the proposed algorithm has good results in both image quality and visual, and has good robustness.
Testing Hybrid Computational Intelligence Algorithms for General Game Playing

General Videogame Playing is one of the hottest topics in the research field of AI in videogames. It aims at the implementation of algorithms or autonomous agents able to play a set of unknown games efficiently, just receiving the set of rules to play in real time. Thus, this work presents the implementation of eight approaches based on the main techniques applied in the literature to face this problem, including two different hybrid implementations combining Montecarlo Tree Search and Genetic Algorithms. They have been created within the General Video Game Artificial Intelligence (GVGAI) Competition platform. Then, the algorithms have been tested in a set of 20 games from that competition, analyzing its performance. According to the obtained results, we can conclude that the proposed hybrid approaches are the best approaches, and they would be a very competitive entry for the competition.
Research on Technology Related to Intelligent Producing of Globe Based on User Customization

Not only the producing technique and the function of the globe has changed with the changing of the users' requirement and the artificial intelligence's permeating, but also the requirement of the users' for the globe is different and complex. For this situation, this article has brought forward the intelligent globe producing technique basing on the user customization, expounded the producing flow, and testified this technique having the excellence of customizing easily, making fast, low cost, high satisfaction and some intelligent assistant deciding ability by the experiment basing on analyzing the two key factors of the globe producing technique and the advantage and disadvantage.
Variation Method Overview of Image Segmentation

Abstract With the research and development of image segmentation methods for several decades, the various theories and methods have been applied to the image segmentation. Among the many methods of image segmentation, the variation method is applied widely, because its model process is easier, the expansibility of the method is better and the implementation process is simple. On the basics of many achievements of variation method research of image segmentation recently, the variation method of image segmentation is divided into the model based on the boundary and the field according to various structural driving force based on energy functional. In this paper, we analyze these models based on these two methods and discuss their advantages and disadvantages. Relevant innovation algorithms and breakthrough progresses recently have been researched. Some development directions of image segmentation variation method have been given. The application field and development of variation image segmentation still have great research value.
Influence of a detuning and Kerr nonlinearity on atom-atom entanglement in a double two-photon Jaynes-Cummings model

In this paper, we have investigated the entanglement dynamics between two identical two-level atoms in the framework of two-photon double Jaynes-Cummings model taking into account the detuning between atoms and twice cavities frequencies and Kerr nonlinearities in both cavities. We have carried out the temporal dependence of the atom-atom negativity for Bell’s initial entangled atomic states. The results show that these parameters have great impact on the amplitudes of the atom-atom entanglement oscillations and that presence of detuning and Kerr nonlinearity lead to stabilization of entanglement for all Bell-types initial atomic states. For a certain initial atomic entangled states, there is an entanglement sudden death effect between the two atoms. The detuning and Kerr medium in the cavities can prevent the undesirable entanglement sudden death from occurring.
Development of AR Learning Assistance Tool for Clay-Sculpting 3D Model

Clay sculpting is one of the skills that can be acquired through learning and training. In a traditional method for learning to sculpt a clay model, learners tend to use 2D images as references for sculpting their works. In this paper, we propose an alternative method for learning to sculpt a clay model using the AR technology to provide different views of a clay-sculpting 3D model. It can increase learners' perception, attention, and understanding of proportional details of reference models since they could interactively view a reference model in 3D. There are 2 modes for viewing a 3D model which are a normal 3D and AR mode. The proposed tool is mainly developed with Unity, Autodesk Maya, and Clip Studio Paint. The evaluation of this tool is done on 30 university students who are currently studying in the multimedia and animation field. Participants are divided into 2 groups. Each group is further split into small teams of 3 persons. In each round during an experiment, we swap a turn of using the application in each group. A questionnaire is used to obtain feedback on our tool after experiments are finished. The result shows that participants have positive opinions about our tool. They tend to prefer viewing a 3D model in a normal 3D mode than an AR mode. In conclusion, our proposed application is a great learning assistance tool for clay sculpting.
Study on Image Encryption Algorithm Based on Logistic Chaotic System

With the rapid development of computer and network technology, a lot of information is transmitted through the computer and network, but there are lots of online digital image hiding information extraction software and hacker's attacks, even if the information is hidden and not visible, the hackers still have the possibility of extracted by software calculation. Therefore, choosing suitable digital image encryption algorithm before information hiding processing becomes an ideal choice. This paper proposed a new binarization algorithm of chaotic sequences based on Logistic mapping, and this algorithm could keep the good randomness and initial sensitivity of the original chaotic sequence, using the obtained binary sequence to encrypt the image achieved good security.
Composite Dynamic Texture Synthesis Using Hierarchical Linear Dynamical System

We demonstrate that a systematic inclusion of prior structural constraints on the states of a linear dynamical system significantly improves its ability to model complex multidimensional sequences. This constrained LDS, typically termed as the hierarchical linear dynamical system (HLDS), is a Kalman filter based topology that extracts relevant self-segmenting information from the input signal in an unsupervised manner by hierarchically constraining its information representing state subspaces thereby slowing down the signal dynamics. We highlight some of its practical advantages over the existing methods in real-world video applications. As a concrete application, we show that the HLDS, despite being a linear model trained in an unsupervised setting, is able to capture the dynamics of complex texture sequences consisting of multiple co-occurring textures. We compare its performance with a similarly trained LDS model in the reconstruction and synthesis of such signals.
Practical forgeries for ORANGE

We analyze the authenticated encryption algorithm of ORANGE, a submission to the NIST lightweight cryptography standardization process. We show that it is practically possible to craft forgeries out of two observed transmitted messages that encrypt the same plaintext. The authors of ORANGE have confirmed the attack, and they discuss a fix for this attack in their second-round submission of ORANGE to the NIST lightweight cryptography competition.
Study on Gesture Recognition Method for Special Effects

Visual-based gesture recognition products for special effects at the moment are of poor gesture diversity and low recognition accuracy. Given the above problems, this study proposes a gesture recognition method based on positioning of key points of the hand. The method realizes the recognition of specific gestures by discriminating the five-finger state, and can feedback different special effects according to the recognition results of different gestures in real time. Problems of weak anti-interference ability and self-occlusion of key points in gesture recognition technology are solved through the method, this method also effectively improves the gesture recognition accuracy and diversity.
Design and Development of Campus Environment Display System Based on Augmented Reality Technology

Abstract With the rapid development of augmented reality technology and the continuous improvement of people’s needs in various aspects, people are more looking forward to interactive, real-time augmented reality systems. In the field of physical display, Augmented Reality Technology provides a more convenient channel for people to experience various objects at zero distance, and reforms a new form of physical display. Including campus display, AR augmented reality technology creates a new display experience for users. This project uses Beijing Institute of Graphic Communication as the research model. In the early stage, the scene was modeled using Cinema 4D software. In the later stage, the scene was interactively designed using Unity 3D. The Vuforia engine was used to detect and track feature points and match them. After superimposing on the scene, we explored the optimization strategy of AR augmented reality technology in campus display applications.
Research on Information Hiding Method Based on Encrypted Image

Aiming at the problems of low security and poor robustness of traditional information hiding algorithms, a chaotic theory and Arnold transform double-encrypted information hiding algorithm are proposed. The binary secret image is scrambled by chaos and Arnold transform, and the carrier image is subjected to one-stage discrete wavelet transform, the low-frequency domain is selected to use singular value decomposition. By modifying the singular value, the double-encrypted secret image is embedded. Then, the transformed diagonal matrix is subjected to singular value decomposition to calculate the intermediate matrix. Finally, the first-order discrete wavelet inverse transform is applied to obtain the carrier image containing the secret image. The experimental simulation shows that the algorithm can restore the secret image well and withstand various kinds of noise, geometric transformation and shearing attacks, and has strong security and robustness.
Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.