Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is based on the social value of Generation Z that online and offline selves are not different. With the technological development of deep learning-based high-precision recognition models and natural generation models, Metaverse is being strengthened with various factors, from mobile-based always-on access to connectivity with reality using virtual currency. The integration of enhanced social activities and neural-net methods requires a new definition of Metaverse suitable for the present, different from the previous Metaverse. This paper divides the concepts and essential techniques necessary for realizing the Metaverse into three components (i.e., hardware, software, and contents) and three approaches (i.e., user interaction, implementation, and application) rather than marketing or hardware approach to conduct a comprehensive analysis. Furthermore, we describe essential methods based on three components and techniques to Metaverse&#x2019;s representative Ready Player One, Roblox, and Facebook research in the domain of films, games, and studies. Finally, we summarize the limitations and directions for implementing the immersive Metaverse as social influences, constraints, and open challenges. 

/pdf/a-metaverse-taxonomy-components-applications-and-open-1ix4u7e7.pdf

A Metaverse: Taxonomy, Components, Applications, and Open Challenges

Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is based on the social value of Generation Z that online and offline selves are not different. With the technological development of deep learning-based high-precision recognition models and natural generation models, Metaverse is being strengthened with various factors, from mobile-based always-on access to connectivity with reality using virtual currency. The integration of enhanced social activities and neural-net methods requires a new definition of Metaverse suitable for the present, different from the previous Metaverse. This paper divides the concepts and essential techniques necessary for realizing the Metaverse into three components (i.e., hardware, software, and contents) and three approaches (i.e., user interaction, implementation, and application) rather than marketing or hardware approach to conduct a comprehensive analysis. Furthermore, we describe essential methods based on three components and techniques to Metaverse’s representative Ready Player One, Roblox, and Facebook research in the domain of films, games, and studies. Finally, we summarize the limitations and directions for implementing the immersive Metaverse as social influences, constraints, and open challenges.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09667507.pdf

Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network's recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN).

Electrocardiogram soft computing using hybrid deep learning CNN-ELM

Information security has become a key issue of public concern recently. In order to radically resist the decryption and analysis in the field of image information hiding and significantly improve the security of the secret information, a novel coverless information hiding approach based on deep learning is proposed in this paper. Deep learning can select the appropriate carrier according to requirements to achieve real-time image data hiding and the high-level semantic features extracted by CNN are more accurate than the low-level features. This method does not need to employ the designated image for embedding the secret data but transfer a set of real-time stego-images which share one or several visually similar blocks with the given secret image. In this approach, a group of real-time images searched online are segmented according to specific requirements. Then, the DenseNet is used to extract the high-level semantic features of each similar block. At the same time, a robust hash sequence with feature sequence, DC and location is generated by DCT. The inverted index structure based on the hash sequence is constructed to attain real-time image matching efficiently. At the sending end, the stego-images are matched and sent through feature retrieval. At the receiving end, the secret image can be recovered by extracting similar blocks through the received stego-images and stitching the image blocks according to the location information. Experimental results demonstrate that the proposed method without any modification traces provides better robustness and has higher retrieval accuracy and capacity when compared with some existing coverless image information hiding.

Coverless real-time image information hiding based on image block matching and dense convolutional network

For protecting the copyright of a text and recovering its original content harmlessly, this paper proposes a novel reversible natural language watermarking method by combining arithmetic coding and synonym substitution operations. By analyzing relative frequencies of synonymous words, synonyms employed for carrying payload are quantized into an unbalanced binary sequence, which is redundant. The quantized binary sequence is compressed by arithmetic coding losslessly to provide a spare for accommodating additional data. Then, the compressed data appended with the watermark are embedded into the cover text via synonym substitutions in an invertible manner. On the receiver side, the watermark and compressed data can be extracted by decoding the values of synonyms in the watermarked text, so that the original context can be perfectly recovered by employing arithmetic coding to decompress the extracted compressed data and substituting the replaced synonyms with their original synonyms. Experimental results illustrate that the proposed method can extract the watermark successfully and recover the original text losslessly. Additionally, it achieves a high embedding capacity.

/pdf/reversible-natural-language-watermarking-using-synonym-3f1choeexc.pdf

Reversible Natural Language Watermarking Using Synonym Substitution and Arithmetic Coding

Hashing has become a promising technique to be applied to the large-scale visual retrieval tasks. Multi-view data has multiple views, providing more comprehensive information. The challenges of using hashing to handle multi-view data lie in two aspects: (1) How to integrate multiple views effectively? (2) How to reduce the distortion error in the quantization stage? In this paper, we propose a novel hashing method, called discrete multi-graph hashing (DMGH), to address the above challenges. DMGH uses a multi-graph learning technique to fuse multiple views, and adaptively learns the weights of each view. In addition, DMGH explicitly minimizes the distortion errors by carefully designing a quantization regularization term. An alternative algorithm is developed to solve the proposed optimization problem. The optimization algorithm is very efficient due to the low-rank property of the anchor graph. The experiments on three large-scale datasets demonstrate the proposed method outperforms the existing multi-view hashing methods.

Discrete Multi-graph Hashing for Large-Scale Visual Search

Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data. Recently, many efforts of unsupervised learning have been done to effectively capture information from heterogeneous data. However, most of them are with huge time consumption, which obstructs their further application in the big data analytics scenarios, where an enormous amount of heterogeneous data are provided but real-time learning are strongly demanded. In this paper, we address this problem by proposing a fast unsupervised heterogeneous data learning algorithm, namely two-stage unsupervised multiple kernel extreme learning machine (TUMK-ELM). TUMK-ELM alternatively extracts information from multiple sources and learns the heterogeneous data representation with closed-form solutions, which enables its extremely fast speed. As justified by theoretical evidence, TUMK-ELM has low computational complexity at each stage, and the iteration of its two stages can be converged within finite steps. As experimentally demonstrated on 13 real-life data sets, TUMK-ELM gains a large efficiency improvement compared with three state-of-the-art unsupervised heterogeneous data learning methods (up to 140 000 times) while it achieves a comparable performance in terms of effectiveness.

/pdf/tumk-elm-a-fast-unsupervised-heterogeneous-data-learning-1h8yp3a5x4.pdf

TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach

With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text generation model. Through training, the best evaluated model is obtained as the prediction model of generating stego text. Then, we use the secret information as the control information to select the right character from predictions of the trained character-level text generation model. Thus, the secret information is hidden in the generated text as the predicted characters having different prediction probability values can be encoded into different secret bit values. For the same secret information, the generated stego texts vary with the starting strings of the text generation model, so we design a selection strategy to find the highest quality stego text from a number of candidate stego texts as the final stego text by changing the starting strings. The experimental results demonstrate that compared with other similar methods, the proposed method has the fastest running speed and highest embedding capacity. Moreover, extensive experiments are conducted to verify the effect of the number of candidate stego texts on the quality of the final stego text. The experimental results show that the quality of the final stego text increases with the number of candidate stego texts increasing, but the growth rate of the quality will slow down.

/pdf/novel-linguistic-steganography-based-on-character-level-text-16d2lqoua9.pdf

Lingyun Xiang

Papers

Coverless real-time image information hiding based on image block matching and dense convolutional network

Reversible Natural Language Watermarking Using Synonym Substitution and Arithmetic Coding

Discrete Multi-graph Hashing for Large-Scale Visual Search

TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach

Novel Linguistic Steganography Based on Character-Level Text Generation