Reviewerly/exHarmony huggingface dataset:
Research on Digital Identity Technology and Application Based on Identification Code and Trusted Account Blockchain fusion

Digital identity is one of the key elements to build a trust system in the digital economy era. Digital identity has empowered social governance and public services in the new era in many fields, and promoted the rapid development of digital economy. In this paper, a trusted digital identity based on identification code and trusted account blockchain fusion is proposed. Through the unified standard, unified coding identifier, combined with identity-based cryptographic algorithm and blockchain, identification code and trusted account blockchain fusion is realized, and a real, effective, safe and credible digital identity technology system is constructed. Trusted digital identity is a bridge connecting the virtual world and the physical world. It plays a strong supporting role in realizing the identity fusion and ‘digital twin’ of the physical world offline and the virtual world. It is the core element of the construction of new smart cities and the development of digital economy.
Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.
Enhancing the Data Security by using RSA Algorithm with Application of Laplace Transform Cryptosystem

Encryption and Decryption schemes based on applications of Laplace Transforms are unable to provide more security to communicate the information. Rivest, Shemir, Adleman (RSA) Cryptosystem is popular public-key algorithm. This paper provides the conditions that give rise to the RSA Cryptosystem based on the Laplace Transform techniques. The modified RSA cryptosystem is explained with an algorithm. The proposed algorithm is implemented using a high level program and time complexity of the proposed algorithm is tested with RSA cryptosystem algorithms. The comparison reveals that the proposed algorithm enhances the data security as compare with RSA cryptosystem algorithms and application of Laplace Transform for cryptosystem scheme. The statistical analysis for the proposed and existing algorithms is provided
Classical and Physical Security of Symmetric Key Cryptographic Algorithms

Symmetric key cryptography is one of the cornerstones of security in the modern era of electronic communication. The symmetric key algorithms, known as the ciphers, are to satisfy certain requirements in order to be considered secure, which are broadly classified as classical attack and physical attack. We show new results in context of both the classical and physical attacks to advance the state-of-the-art [1].
Towards Tile Based Distribution Simulation in Immersive Video Streaming

There has been increasing attention to virtual reality applications in recent years, especially to immersive or 360-degree videos that typically consume much more bandwidth than traditional ones. Though all produced data is transferred, only a small part (denoted as Field of View or viewport) is watched by users due to the nature of immersive videos. Obviously, this causes a large waste of network resources. Hence, it is important to define a viewport-dependent streaming transmission strategy by detecting where the user is gazing and the movement of the user's head. Unfortunately, there are few datasets providing this information. In this paper, we propose a tile-based simulation approach to generate the distribution of the user's behavior and to provide information that can be used to optimize future view-dependent streaming protocols. We first characterize the users' viewport pattern from datasets gathered from real users by decomposing the 360-degree stream into tiles and analyzing the frequency and time-interval distribution for each tile. Then, we devise a hierarchical Markov model that incorporates the beta distribution of each tile time interval to predict tile transition. The results show that the simulation tool characterizes the tile sequences of users accurately, performing close to the empirical results.
To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.
Evolution of Cloud in Big Data With Hadoop on Docker Platform

Data can be anything but from a large data base extraction of useful information is known as data mining. Cloud computing is a term which represent a collection of huge amount of data. Cloud computing can be correlated with data mining and Big Data Hadoop. Big data is high volume, high velocity, and/or high variety information asset that require new form of processing to enable enhanced decision making, insight discovery and process optimization. Data growth, speed and complexity are being accompanied by deployment of smart sensors and devices that transmit data commonly called the Internet of Things, multimedia and by other sources of semi-structured and structured data. Big Data is defined as the core element of nearly every digital transformation today.
Reversible Data Hiding Based on Improved Block Selection Strategy and Pixel Value Ordering

Block selection is an effective strategy to improve the performance of pixel value ordering (PVO) based reversible data hiding since image blocks with higher smoothness are first employed to embed secret data, which means a better prediction accuracy could be achieved. However, in current solutions, the definition of smoothness cannot always reflect the true performance of prediction since the calculation only involves limited pixels. Even though more pixels are considered in the state-of-the art, the precision of prediction is still seriously affected by the fluctuating pixels. In this paper, we proposed an improved calculation method for smoothness, which takes enough pixels into account while eliminates the fluctuating pixels simultaneously. Experimental results show that our proposed scheme greatly improves the accuracy of prediction and achieves a better imperceptibility compared with the state-of-the art when the secret bits are not fully embedded.
FALIoTSE: Towards Federated Adversarial Learning for IoT Search Engine Resiliency

To improve efficiency and resource usage in data retrieval, an Internet of Things (IoT) search engine organizes a vast amount of scattered data and responds to client queries with processed results. Machine learning provides a deep understanding of complex patterns and enables enhanced feedback to users through well-trained models. Nonetheless, machine learning models are prone to adversarial attacks via the injection of elaborate perturbations, resulting in subverted outputs. Particularly, adversarial attacks on time-series data demand urgent attention, as sensors in IoT systems are collecting an increasing volume of sequential data. This paper investigates adversarial attacks on time-series analysis in an IoT search engine (IoTSE) system. Specifically, we consider the Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) as our base model, implemented in a simulated federated learning scheme. We propose the Federated Adversarial Learning for IoT Search Engine (FALIoTSE) that exploits the shared parameters of the federated model as the target for adversarial example generation and resiliency. Using a real-world smart parking garage dataset, the impact of an attack on FALIoTSE is demonstrated under various levels of perturbation. The experiments show that the training error increases significantly with noises from the gradient.
Prediction of High-Frequency Economic Data Based on Stochastic Fluctuation Model

In order to improve the effect of economic high-frequency data analysis, this paper combines the stochastic fluctuation model to carry out the forecast analysis of economic high-frequency data. Moreover, this paper uses the spider web model for data processing and makes a preliminary judgment on the extent to which futures/stock prices lead the spot through error correction model, impulse response, and variance decomposition. Furthermore, this paper studies the contribution of futures/stock price changes to the effective price through the common factor model so as to obtain its price discovery efficiency. In addition, this paper judges the efficiency of price discovery by studying the contribution of futures/stock price changes to the effective price. Finally, based on the research idea of futures/stock price discovery efficiency, an intelligent data analysis model is constructed. The experimental study shows that the economic high-frequency data prediction model based on the stochastic fluctuation model proposed in this paper has a good effect on the analysis and prediction of economic high-frequency data.
SCU-Net++: A Nested U-Net Based on Sharpening Filter and Channel Attention Mechanism

U-Net++ is one of the most prominent deep convolutional neural networks in the field of medical image segmentation after U-Net. However, the semantic gaps between the encoder and decoder subnets are still large, which will lead to fuzzy feature maps and even target regions of segmentation. To solve this problem, we propose an improved semantic segmentation model utilizing channel attention mechanism and Laplacian sharpening filter, SCU-Net++: dense skip connections are redesigned with sharpening filters to ease the semantic gaps, and channel attention modules are used to make the model pay more attention on the feature maps that are useful for our pixel-level classification task. Compared with U-Net++, the proposed model obtains a more competitive performance on the Pancreas Segmentation dataset and Liver Tumor Segmentation dataset, while increases a very small number of learnable parameters and thus almost does not make additional training and reasoning costs. The training of the proposed method is carried out in deep supervision mode, which alleviates the problem of gradient disappearance, and pruning mechanism can be activated to accelerate the reasoning speed.
Cross-document attention-based gated fusion network for automated medical licensing exam

One of the applications of machine-learning in the medical industry is to automatically learn knowledge from medical textbooks and transfer medical knowledge into diagnosis abilities. Because of complex nature of medical issues, the learning process usually requires multiple knowledge documents to form a comprehensive reasoning chain for diagnosis, which increases the difficulty of the automatic learning process. Existing models for multiple document comprehension either concatenate multiple documents together for inference or reason on every document independently. In this paper, we propose a Co-Attention-based Multi-document Inference (CAMI) framework for better reasoning over multiple documents. The proposed framework makes use of not only the attentional information among questions, answers and support documents but also the complementary attentional information across different documents. In addition, a gated fusion network is designed to fuse the cross-document information. The proposed model outperforms the state-of-the-art methods on Chinese National Medical Licensing Examination (CNMLE) dataset, ClinicQA, which contains 27,432 plain text documents and 13,827 CNMLE questions. We intend to make it publicly available as the first clinical OpenQA dataset. • A CAMI frame is proposed to tackle the OpenQA medical MRC tasks. • The proposed CDCA could extract the attentional information across documents. • The proposed HGFN could dynamically fuse information from multiple documents. • The proposed ClinicQA is the first public dataset to evaluate clinical diagnosis ability. • The proposed method greatly outperforms SOTA openQA medical MRC models.
The Journey to a Successful Illustrated Review

Illustrated review articles, rooted in scientific rigor, are made up of “capsules” or panels of visuals that together provide an up‐to‐date overview of a topic. Illustrated reviews aim to provide a more accessible format than traditional written reviews to facilitate more effective knowledge translation and dissemination. However, the novelty of this format can dissuade prospective authors due to uncertainty and lack of comfort. To remedy this uncertainty, we have summarized the journey of developing an illustrated review, from identifying an appropriate topic to submitting the final manuscript for peer review. We highlight the importance of approaching an illustrated review from a storytelling perspective, and encouraging authors to keep their audience in mind when picking a theme or characters. We provide storyboard considerations and simplify graphic design principles to develop an outline and line draft for the illustrated review. We list programs available to authors to demystify creating attractive and engaging scientific visuals. Finally, we provide information on choosing colors or fonts and where to find copyright‐free icons, graphics, illustrations, and pictures. This review provides prospective authors with the knowledge, tools, and resources to create an effective illustrated review article. If there is difficulty with the links embedded within the document please download the full PDF.
An associative knowledge network model for interpretable semantic representation of noun context

Abstract Uninterpretability has become the biggest obstacle to the wider application of deep neural network, especially in most human–machine interaction scenes. Inspired by the powerful associative computing ability of human brain neural system, a novel interpretable semantic representation model of noun context, associative knowledge network model, is proposed. The proposed network structure is composed of only pure associative relationships without relation label and is dynamically generated by analysing neighbour relationships between noun words in text, in which incremental updating and reduction reconstruction strategies can be naturally introduced. Furthermore, a novel interpretable method is designed for the practical problem of checking the semantic coherence of noun context. In proposed method, the associative knowledge network learned from the text corpus is first regarded as a background knowledge network, and then the multilevel contextual associative coupling degree features of noun words in given detection document are computed. Finally, contextual coherence detection and the location of those inconsistent noun words can be realized by using an interpretable classification method such as decision tree. Our sufficient experimental results show that above proposed method can obtain excellent performance and completely reach or even partially exceed the performance obtained by the latest deep neural network methods especially in F1 score metric. In addition, the natural interpretability and incremental learning ability of our proposed method should be extremely valuable than deep neural network methods. So, this study provides a very enlightening idea for developing interpretable machine learning methods, especially for the tasks of text semantic representation and writing error detection.
Natural Language Processing to Extract Contextual Structure from Requirements

The automatic extraction of structure from text can be difficult for machines. Yet, the elicitation of this information can provide many benefits and opportunities for various applications. Such benefits have been identified amongst others for the area of Requirements Engineering. By assessing the Natural Language Processing for Requirement Engineering status quo and literature, a necessity for an automatic and universal approach to elicit structure from requirement and specification documents was identified. This paper outlines the first steps and results towards a modularized approach that splits the core algorithm from the text corpus as an input and underlying rule/knowledge base. This separation of functions allows for individual modification of the included parts and eases or potentially removes restrictions as well as limitations, such as input rules or the necessity for human supervision. Furthermore, contextual information and links via ontology inference can be considered that are not explicit on a textual level. The initial results of the approach show the successful extraction of structural information from requirement text, which was validated by comparing the results to human interpretations for small and public sample sets. In addition, the contextual consideration and inference via ontologies is described conceptually. At the current stage, limitations still exist regarding scalability and handling of text ambiguities, but solutions for these caveats have been developed and are being tested. Overall, the approach and results presented will be integrated and are part of a novel requirement complexity assessment framework.
Positioning of aircraft relative to unknown runway with delayed image data, airdata and inertial measurement fusion

This work addresses the challenge of providing precise runway-relative position, velocity and orientation reference to a landing aircraft based on monocular camera and low-cost inertial sensor data complemented by barometric sensor readings. GPS information is excluded from this sensor set because it is intended to use the developed estimator for GPS and Instrumental Landing System fault detection. The characteristic size of the runway is assumed to be unknown and it is estimated run-time after verifying global observability of the nonlinear system. The delay caused by image processing is dealt with a delayed-Error-State Kalman Filter (ESKF). This algorithm considers dynamic propagation of the image information between acquisition and application forward in time thus the delay does not appear in the system dynamics. The first evaluation of the estimator is done for ideal simulated data to verify applicability of the delayed-ESKF and its flawless implementation. Then more realistic simulated data with sensor biases and noise is considered to verify closer to realistic performance and bias estimation precision. Finally, the estimator is tested with real flight data collected in the VISION EU H2020 research project. Estimation results are compared to GPS SBAS-based data and Airbus precision tolerances showing satisfactory performance. The methodological contribution of the paper is the unique combination of existing methods and ideas leading to a new solution proven to work satisfactorily even with real flight data.
Document forgery detection using source printer identification: A comparative study of <scp>text‐dependent</scp> versus <scp>text‐independent</scp> analysis

Abstract Source printer identification represents an interesting modality for document forgery detection. Establishing the identity of the printer that was employed to print a questioned document allows concluding its authenticity. This paper investigates the effectiveness of deep visual features (learned using convolutional neural networks) in characterization of the source printer. Images of printed documents are divided into small patches as well as characters for extraction of features. An off‐the‐shelf recognition engine is also integrated, allowing experiments in text‐dependent as well as text‐independent modes. Experiments are carried out on a standard data set of documents from 20 different printers and identification rates of 95.52% and 98.06% are reported using patches and characters, respectively. Furthermore, the discriminating power of different characters, as well as their combinations, is also being studied. Unlike many existing techniques, which rely on pre‐segmented characters and report results by comparing same characters only, the proposed technique works on complete images of printed documents and reports high identification rates.