Reviewerly/exHarmony huggingface dataset:
Cheating Detection in (k, n) Secret Image Sharing Scheme

Secret image sharing will inevitably be threatened by various types of attacks in practical applications, of which cheating behavior is the most prone. In this paper, in order to prevent deceivers from threatening the security of secret image transmission process, a polynomial based cheating detection scheme is proposed. The (k, n) secret image sharing scheme must ensure that any k or more participants can restore the secret image, and less than k participants cannot get any information of the secret image. In our scheme, only one honest shareholder can detect the collusion from other k − 1 deceivers. Furthermore, our scheme reduces the size of shadow images thus saving storage space and transmission time. Theory and experiments show that our scheme is effective and feasible in cheating detection.
Intelligent Human Age Estimation Based Machine Learning Techniques

Automatic age estimation system is a challengeable filed for researchers since different face characteristics are founded for people, never the less aging variation still provide some unique characteristics to deal with. In this paper, the proposed system for age estimation is used to classify the face image to different classes (three age classes were suggested), the face image is preprocessed to keep only valuable information within the system and the rest will be discarded. The resulted image was then being used to extract the feature using Linear Discriminant Analysis (LDA) feature extraction to be optimized later using LDA feature extraction which will later be optimized using hybrid feature optimization algorithms (FIREFLY-BAT) the resulted features are classified using the J48 classification algorithm and the results showed that the suggested system has given precision 90.45% and MAE by 1.14.
Automatic Text Summarization Based on Transformer and Switchable Normalization

With the development of text summarization research, the methods based on RNN with the Encoder-Decoder model gradually become the mainstream. However, RNN tends to forget previous context information and leads to the lack of original information. That will reduce the accuracy of the generated summarizations. The Transformer model uses the self-attention mechanism to encode and decode historical information so that it can achieve better performance than RNN in learning context information. In this paper, a text summarization model based on transformer and switchable normalization is proposed. The accuracy of the model is improved by optimizing the normalization layer. Compared with other models, the new model has a great advantage in understanding words' semantics and associations. Experimental results in English Gigaword dataset show that the proposed model can achieve high ROUGE values and make the summarization more readable.
Convolutional Neural Network for Automatic MeSH Indexing

MEDLINE is the indexed subset of the National Library of Medicine’s (NLM) journal citation database. It currently contains over 25 million biomedical citations, each indexed with a controlled vocabulary called MeSH. Since 1990, there has been a sizable increase in the number of articles indexed each year for MEDLINE, and since 2002, the NLM has been using automatic MeSH indexing systems to assist indexers with their increasing workload. This paper explores a deep learning approach to the automatic MeSH indexing problem. We present a Convolutional Neural Network (CNN) for automatic MeSH indexing and evaluate its performance by participating in the BioASQ 2019 task on large-scale online biomedical semantic indexing. The CNN model demonstrates competitive performance and outperforms the NLM’s Medical Text Indexer (MTI) by about 3%. The paper presents a preliminary analysis comparing the results of the CNN model to MTI and also outlines the advantages of end-to-end deep learning approaches to automatic MeSH indexing.
MEDA: Multi-output Encoder-Decoder for Spatial Attention in Convolutional Neural Networks

Utilizing channel-wise spatial attention mechanisms to emphasize special parts of an input image is an effective method to improve the performance of convolutional neural networks (CNNs). There are multiple effective implementations of attention mechanism. One is adding squeeze-and-excitation (SE) blocks to the CNN structure that selectively emphasize the most informative channels and suppress the relatively less informative channels by taking advantage of channel dependence. Another method is adding convolutional block attention module (CBAM) to implement both channel-wise and spatial attention mechanisms to select important pixels of the feature maps while emphasizing informative channels. In this paper, we propose an encoder-decoder architecture based on the idea of letting the channel-wise and spatial attention blocks share the same latent space representation. Instead of separating the channel-wise and spatial attention modules into two independent parts in CBAM, we combine them into one encoder-decoder architecture with two outputs. To evaluate the performance of the proposed algorithm, we apply it to different CNN architectures and test it on image classification and semantic segmentation. Through comparing the resulting structure equipped with MEDA blocks against other attention module, we show that the proposed method achieves better performance across different test scenarios.
Design and Implementation of Web-based Teacher Remote Training Platform

Abstract With the development of computer and network technology, teachers’ continuing education has also developed from traditional training to social networked training. The large-scale, open and online education in online training has transformed teacher training activities from a single form to a complex one. Higher requirements have been put forward on the construction of network training platforms and the setting and design of courses. According to the current status and development trend of computer technology, the development of research platforms is mainly based on the open technology and high integration, and the most advanced and mature mainstream technologies and products based on high-level application development. Under the premise of high security and reliability, the research platform is easy to use, maintainable and scalable. The purpose of this article is to design and implement a web-based teacher distance learning platform. This article uses .NET technology as the site development technology to develop the website, and uses Oraele9i database and object web dynamic web technology to build the database, and builds three modules of system management, teacher management, and student training. Experiments were conducted on a certain school through the establishment of a platform and the setting of school curriculums. After learning through the training, it was found that after participating in the training, students were satisfied with “the students are satisfied with the network training process”, “whether the training content meets the needs of the students”, “network training platform” Satisfaction of resources “and” Whether the process of activities in the training process are satisfactory “are all above 88%.
Sentiment Classification of Online Mobile Reviews Using Combination of Word2vec and Bag-of-Centroids

Sentiment classification is a technique to understand the feeling/attitude/sentiment toward a written piece of text by analyzing and then classifying the text as positive, negative, or neutral. One of the important aspects of classification is data that should be handled and represented carefully in the classification process, which affects the performance of the classifier. In the sentiment classification process, feature vector is used as the representation of data to work on. In the paper, we have experimented with the combination of Word2vec and Bag-of-Centroids’ feature vector in the sentiment classification process of online consumer reviews about different mobile brands. The feature vector is tested on different well-known machine learning classifiers used for sentiment analysis and compared with Word2vec feature vector. We also investigated the performance of a feature vector as the size of the dataset is increased. We found that the proposed feature vector performed well in comparison with Word2vec feature vector.
Gyro-stabilized Platform in Ambulance

The objective of this process is to develop a platform which is installed in an ambulance that can maintain its position perpendicular to gravitational force, irrespective of the position of the ambulance using servo motors which are placed perpendicular to each other. In this prototype, Arduino Mega 2560 Rev3 microcontroller is used to process the input from MPU-6050 inertial measurement unit. The code in Arduino 2560 Mega controller Rev3 analysis the input data, and when the C code receives the input from MPU and analyzes the angle and gives an output to the MG995 servo motor, it receives the signals and moves to the corresponding coordinates, so the movement of the vehicle will be nullified, for example, if the vehicle tilts 20 degrees with respect to x-axis, the servo motor moves −20 degrees with respect to x-axis.
Meaning Guided Video Captioning

Current video captioning approaches often suffer from problems of missing objects in the video to be described, while generating captions semantically similar with ground truth sentences. In this paper, we propose a new approach to video captioning that can describe objects detected by object detection, and generate captions having similar meaning with correct captions. Our model relies on S2VT, a sequence-to-sequence model for video captioning. Given a sequence of video frames, the encoding RNN takes a frame as well as detected objects in the frame in order to incorporate the information of the objects in the scene. The following decoding RNN outputs are then fed into an attention layer and then to a decoder for generating captions. The caption is compared with the ground truth by learning metric so that vector representations of generated captions are semantically similar to those of ground truth. Experimental results with the MSDV dataset demonstrate that the performance of the proposed approach is much better than the model without the proposed meaning-guided framework, showing the effectiveness of the proposed model. Code are publicly available at https://github.com/captanlevi/Meaning-guided-video-captioning- .
Comparative Assessment of Reinforcement Learning Algorithms in the Taskof Robotic Manipulation of Deformable Linear Objects

Reinforcement learning systems in robotics are still limited in their number of practical applications. They are often considered as unstable and difficult to implement. Moreover, very often, they demand a significant number of trials to the convergence, which may often be treated as a critical challenge in their application. However, gathering the data from the simulation can be the solution to that problem. In our paper, we are providing a comparative assessment of reinforcement learning algorithms in the task of robotic manipulation of Deformable Linear Objects (DLOs). We provide a comparison of four methods that work on the simulated robot. The tests were performed for two tasks - one is reaching, and the other is the folding of the DLO to the predefined, sinusoidal shape. The obtained results could be treated as a guideline for other researchers on the performance of RL methods in robotic manipulation tasks.
Internet-Based Researcher Interest Mining

In today's knowledge-based economy society, the rapid spread of a trend, personalized knowledge service has become the mainstream of development, so that the Web information users can meet the variety, high level of precision and content requirements of the high-level service form. Through analysis, we can find that the mining of researchers' interest can serve as the main learning content of personalized knowledge services. So the realization of research interest mining based on Internet is to meet the special information needs of the users, which can be called an effective method to optimize the content of personalized business. Therefore, a more effident algorithm is used to optimize the search search program for Internet information and improve the search efficiency. This optimization can become the research direction of the present age of all things in the world. The information data mining strategy to the simple to can improve the information retrieval efficiency of the network resources, and can provide the accurate, reliable summary of the Internet information resources, meet the convenience and precision of the researchers to retrieve the Internet information resources, thus can be accurate and high. The research interest mining based on the Internet includes the use of search engines to obtain the relevant information of the researchers. Through data mining and machine learning algorithms, the results information returned by the search engine are analyzed, and the research interests of the researchers are obtained from the contents of these results.
Logics for Knowability Paradox with a Non-normal Possibility Operator

This paper studies a Glivenko–Kuroda translation in logics for knowability paradox in terms of sequent calculi. Hilbert-style axiomatization and Gentzen-style sequent calculus are proposed for an intuitionistic modal logic with and without the knowability axiom. When the system does not contain the knowability axiom, it is shown to be sound and complete for an intuitionistic version of a combination of Kripke and neighborhood semantics. We define a double negation translation from our intuitionistic modal logics to the corresponding classical modal logics to establish syntactic proofs of Glivenko-style embedding results with and without the knowability axiom. In the setting without the knowability axiom, our embedding result enables us to reduce the cut-elimination theorem, Craig interpolation theorem, and decidability from the classical setting to the intuitionistic setting.
A New JPEG Image Watermarking Method Exploiting Spatial JND Model

For JPEG images, the current digital watermarking methods mainly embed the watermark by modifying the DCT coefficients, in which the spatial visual performance isn’t well taken into account. As a result, significant visual distortion can be observed in smooth and boundary regions of the marked image. Then, to improve the visual quality of the marked image, a new JPEG image watermarking method exploiting the spatial just-noticeable-difference (JND) model is proposed in this paper. In the proposed method, the watermark is embedded into the DCT domain, but the embedding strength is directly determined by a spatial JND model. Moreover, for each DCT block, the variances calculated from the difference blocks along four different directions are controlled to further enhance the imperceptibility. Finally, an optimization problem is summarized and developed to obtain the optimal embedding strength that meets the above requirements. Experimental results show that, with the same PSNR and robustness, the proposed method has better imperceptibility than the prior art.
Seeing Things in Random-Dot Videos

The human visual system correctly groups features and can even interpret random-dot videos induced by imaging natural dynamic scenes. Remarkably, this happens even if perception completely fails when the same information is presented frame by frame. We study this property of surprising dynamic perception with the first goal of proposing a new detection and spatio-temporal grouping algorithm for such signals when, per frame, the information on objects is both random and sparse. The algorithm is based on temporal integration and statistical tests of unlikeliness, the a contrario framework. The striking similarity in performance of the algorithm to the perception by human observers, as witnessed by a series of psychophysical experiments, leads us to see in it a simple computational Gestalt model of human perception.
Stata Center Frame: A Novel World Assumption for Self-Localization

Map matching is a commonly applied localization method to mobile robots. Due to the complexity of building maps and the matching task itself, many studies have adopted simplifying assumptions (geometrical and directional) of the world, such as the Manhattan World, the Atlanta World, a Mixture of Manhattan Frames and the Stata Center World. Even though the latter has flexibility to represent several environments, it has been so far limited to scene segmentation and has not yet been applied to self localization. This work explores the capabilities of the Stata Center World for self localization and further proposes a novel concept of Stata Center Frame. This assumption permits orientation estimation with one single line and position estimation by visual and positional patterns from the scene using only one monocular camera. The results show that self-localization can be achieved online with higher accuracy (average 0.12 m error) comparing to traditional techniques (0.13 m, offline).
Hybrid Features Extraction for Adaptive Face Images Retrieval

Existing methods of face emotion recognition have been limited in performance in terms of recognition accuracy and execution time. It is highly important to use efficient techniques for improving this performance. In this article, the authors present an automatic facial image retrieval combining the advantages of color normalization by texture estimators with the gradient vector. Starting from a query face image, an efficient algorithm for human face by hybrid feature extraction provides very interesting results.
Optimized CNN with Point-Wise Parametric Rectified Linear Unit for Spatial Image Steganalysis

The convolutional neural network (CNN) based image steganalyzers have evolved remarkably over the past few years, and designing suitable CNN structures has been currently the fundamental method to improve the detection accuracy. However, CNN-based steganalyzer with the universal activation functions of the computer vision (CV) field barely achieves significant performance improvement. Therefore, a dedicated activation function is required to improve the detection performance for image steganalysis. In this paper, we propose a point-wise parametric rectified unit (PW-PReLU) which has different adaptively learnable parameters for each pixel of the negative inputs to facilitate the representation capacity of the activated feature maps. Then, in order to further boost the detection accuracy, the feature fusion is realized by the concatenation operation in the first layer. Based on the above components, an optimized CNN-based steganalyzer is proposed for spatial image steganalysis. The results of comparative experiments demonstrate that the proposed network can detect the state-of-the-art spatial steganographic algorithms with better performance than the previous steganalyzers on the 512 $$\times $$ 512 BOSSbase_1.01 dataset and the resized 256 $$\times $$ 256 union dataset of BOSSbase_1.01 and BOWS2.
Efficient Subquadratic Space Complexity Digit-Serial Multipliers over GF(2<sup>m</sup>) based on Bivariate Polynomial Basis Representation

Digit-serial finite field multipliers over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) with subquadratic space complexity are critical components to many applications such as elliptic curve cryptography. In this paper, we propose a pair of novel digit-serial multipliers based on bivariate polynomial basis (BPB). Firstly, we have proposed a novel digit-serial BPB multiplication algorithm based on a new decomposition strategy. Secondly, the proposed algorithm is properly mapped into a pair of pipelined and non-pipelined digit-serial multipliers. Lastly, through the detailed complexity analysis and comparison, the proposed designs are found to have less area-time complexities than the competing ones.
Using Transfer Learning to Classify Pornographic Images

Along with the growth of technology and social media, need to control and isolate people, especially children, from pornography has become quintessential. The existing Convolutional Neural Networks provide ways to distinguish between adult and non-adult images, but they are far too complex to use them. We use Transfer Learning as a simple approach to prove that we can easily classify adult images using simple models rather than existing complex image classification models or CNNs. The ease is that we will not build a complete architecture from scratch, rather, we will apply Transfer Learning to make use of complex architectures of existing CNNs.
Deep Learning Based Identification of Wireless Protocols in the PHY layer

The need for Artificial Intelligence algorithms for future Cognitive Radio (CR) systems is unavoidable. For a CR to operate as best as possible it must identify who is present in spectrum of interest, and what they are doing (jamming, communicating, rogue transmission, etc.). Using this information, a CR can accordingly decide what to do next. Furthermore, being able to determine which wireless protocols are occupying spectrum is an important ability in heterogeneous wireless networks. In this work, we investigate the robustness of various Neural Network (NN) algorithms for classification of wireless protocols when looking at base-band In-phase/Quadrature (IQ) data without needing to decode. We propose a spectrum sensing algorithm based on NNs or other similarly behaved classification algorithms for identifying wireless technologies occupying spectrum. In previous literature, using base-band IQ data, researchers have shown that NN models can classify different modulation formats with promising accuracy. This work explores the potentials, usage, and limitations of using base-band IQ data for classifying various wireless network protocols that employ the same modulation format.
Multi-player games with LDL goals over finite traces

Linear Dynamic Logic on finite traces (LDLF) is a powerful logic for reasoning about the behaviour of concurrent and multi-agent systems. In this paper, we investigate techniques for both the characterisation and verification of equilibria in multi-player games with goals/objectives expressed using logics based on LDLF. This study builds upon a generalisation of Boolean games, a logic-based game model of multi-agent systems where players have goals succinctly represented in a logical way. Because LDLF goals are considered, in the settings we study—Reactive Modules games and iterated Boolean games with goals over finite traces—players' goals can be defined to be regular properties while achieved in a finite, but arbitrarily large, trace. In particular, using alternating automata, the paper investigates automata-theoretic approaches to the characterisation and verification of (pure strategy Nash) equilibria, shows that the set of Nash equilibria in multi-player games with LDLF objectives is regular, and provides complexity results for the associated automata constructions.