Reviewerly/exHarmony huggingface dataset:
Structural Text Steganography Using Unseen Tag Attribute Values

Apart from an effective steganography scheme, it is vital to have an abundance of cover medium while considering the practicability of a stego-system. Aside from images, document files are one of the most exchanged attached content via electronic mailings. In this paper, we present a structural steganographic scheme based on unseen tag attribute values using office documents as the medium. Specifically, we use the XML file that builds the core of the file documents to vehiculate the message. The secret is not visible within the text content, and the stego file size is not far from the cover size. We are among the first to investigate the unseen tag identifier within the cover document to hide the secret message. We assess the performance of the proposed scheme in terms of the invisibility, embedding capacity, robustness, and security. The performance results show the advantage of a higher capacity embedding and a better flexibility while keeping high practicability in terms of accessibility and implementation.
The Model of How Languages will Develop

Abstract The total number of speakers of a language may increase or decrease over time because of various and complicated influences. In this paper, we build the quantitative prediction model based on gray estimation, FCM model and geographical distribution prediction model to predict the distribution of language users. In addition, we conducted a sensitivity analysis of the model to facilitate future research.
Combining Impression Feature Representation for Multi-turn Conversational Question Answering

Multi-turn conversational Question Answering (ConvQA) is a practical task that requires the understanding of conversation history, such as previous QA pairs, the passage context, and current question. It can be applied to a variety of scenarios with human-machine dialogue. The major challenge of this task is to require the model to consider the relevant conversation history while understanding the passage. Existing methods usually simply prepend the history to the current question, or use the complicated mechanism to model the history. This article proposes an impression feature, which use the word-level inter attention mechanism to learn multi-oriented information from conversation history to the input sequence, including attention from history tokens to each token of the input sequence, and history turn inter attention from different history turns to each token of the input sequence, and self-attention within input sequence, where the input sequence contains a current question and a passage. Then a feature selection method is designed to enhance the useful history turns of conversation and weaken the unnecessary information. Finally, we demonstrate the effectiveness of the proposed method on the QuAC dataset, analyze the impact of different feature selection methods, and verify the validity of the proposed features through visualization.
Comparison of Local Binary Pattern and Eigenfaces for Predict Suspect Positive Drugs

The current activity of drug inspection is usually carried out at school or university. This procedure, however, is less effective and efficient, as the urine samples are taken randomly. In many cases, the suspect student is not present or escapes the urine or hair inspection. A predictive drug user is needed, where only students suspected of positive drug use are selected for a urine test. To handle this problem, we need a system to predict suspect positive drugs. The dataset is generated from online sources by collecting and pre-processing 30 images of people before and after drug. We compare two algorithm local binary pattern and Eigenfaces for predicting suspect positive drugs based on face images. The experiment shows that the result of the prediction using Local binary pattern is better than the prediction using Eigenfaces. However, a higher accuracy of prediction reaches only 75 %.
Comparative Analysis of Cricket Games in VR and Other Traditional Display Environments

This research is study involves around a VR based cricket game. It uses the technology of VR to simulate the environment of a player playing cricket in the stadium. The research tries to state that how this type of game creates a more immersive and interactive experience compared to regular 2D/3D video games played in traditional display environments.
Performance Enhancement of Satellite FSO/QKD Systems using HAP-based Relaying and ARQ

This paper proposes to use high-altitude platform (HAP)-based relaying and link-layer automatic repeat request (ARQ) techniques to improve the performance of the satellite quantum key distribution (QKD) over free-space optics (FSO) systems. We also develop analytical frameworks based on two 3D Markov chain models allowing us to comprehensively analyze the performance of the proposed system in terms of key loss rate and delay outage rate. Our performance analysis takes into account the physical layer impairments induced by FSO channel and receiver noise. Numerical results quantitatively demonstrate that the satellite FSO/QKD system using HAP-based relaying and link-layer ARQ techniques can offer considerable performance improvement over the conventional ones.
Quantum simulations with multiphoton number states

We show the first quantum simulation involving genuine number (Fock) states with up to four photons. Already this resource-efficient basic demonstration reveals topological matter not known before, and that number-state interference can simulate nonlinear systems. It also elucidates perfect quantum wave packet transfer mechanism.
Single-shot fringe-pattern analysis algorithm robust against abrupt phase jumps

Fringe-analysis methods, with fine carrier fringes, allow us to extract phase signals using a single-shot imaging measurement, enabling real-time observation of dynamic phenomena. However, abrupt phase jumps produced by the structural boundaries (or edges) in an object and/or object surfaces occasionally generate significant errors in the resulting images. In this paper, a new algorithm consisting of two steps is proposed to address this problem. In the first step, we computed an approximate phase map by ignoring abrupt phase jumps. In the second step, we solved a weighted least-squares problem to recover the abrupt phase jumps using the approximate phase map. The influence of several factors, such as carrier-fringe frequency and noise, was examined through simulation and experiment, and the effectiveness of the proposed method was demonstrated.
A Text-Independent Speaker Verification for SdSV Challenge 2020

In this paper, we present an intelligent system for text-independent speaker verification. The system has been constructed based on the X-vector framework by expanding some TDNN layers to the baseline system. The work has been open evaluated by Short-Duration Speaker Verification (SdSV) Challenge 2020 for the task of speaker recognition. From the evaluaton, a fixed training condition of the dataset is provided to build the speaker verification (SV) system. In the end, the result can achieve the Equal Error Rate (EER) and minimum Detection Cost Function (minDCF) of 16.26% and 0.4794, respectively.
A Framework for Customer-Oriented Solution Applied to the Tourism Sector

Tourism is a very sensitive economic sector and depends heavily on the stability of market variables such as the political and social conditions of regions, as well as terrorism threats, natural disasters, nuclear or industrial risks (pollution) and epidemics or pandemics. In times of great uncertainty and volatility in the market, when the customer quickly demands new services or products, it is essential that tourism operators use information platforms that enable and agile these new responses to the market. New models of cross-organizational business collaborations have emerged to allow the integration of companies in a consortium to rapidly seize market opportunities. Service-Oriented Computing and Cross-organizational Business Processes provide the means to create and execute dynamic business environments, meeting constantly evolving customer requirements and support for current challenges in a turbulent market. This paper describes the basis of a conceptual adaptive framework that supports a method of service selection and ranking applied to Tourism sector relying on a set of services that satisfies the customer specific requests. The framework will provide the means to engage a collaborative network of tourism partners, taking into consideration mechanisms to deal with business process constraints and the dynamic environment. In a future work, a prototype oriented to Tourism sector will be developed and implemented.
An Approach for Diagnostically Lossless Coding of Volumetric Medical Data Based on Wavelet and Just-Noticeable-Distortion Model

This paper explores a technique for visually/diagnostically lossless coding in the wavelet domain to effectively compress the three-dimensional medical image data. The quantisation module based on Just Noticeable Distortion (JND) for wavelets guarantees the visual quality in the reconstructed data. This method has been further extended to present the Volume of Interest (VOI) based technique that enables to preserve the quality of the diagnostically significant VOI region. The proposed method tested on several datasets outperforms the state-of-the-art methods. Apart from the conventional quality metric, Human Visual System (HVS) based quality metrics are also used to evaluate the visual quality of the reconstructed image. A subjective and objective evaluation carried out for VOI based coder shows that the quality-compression needs of the medical community are well addressed.
Measures to compare the shape of objects in remote sensing images

We consider the urgent task of recognizing and classifying objects in remote sensing images by comparing their shapes with reference images. Measures of similarity of the image shapes based on a comparison of the outlines and medial representations of the object silhouettes are proposed. The solution to the problem is considered on the example of classification of images of aircraft. The study of the proposed approach was performed for images of aircraft of more than 40 types obtained as a result of segmentation of real remote sensing images. Computational experiments demonstrate a rather high level of recognition quality (Top1-71%, Top3-90%, Top5-95%), which confirms the possibility of practical use of the proposed solution.
Web Application Based on PHP for Farmers

This paper demonstrates the Web application called Kisan Bank designed especially for the farmers to aid them to have the connection directly with the government for exchange of their stock without interference of any intermediate parties. The application was designed using PHP [1–3] and HTML [4] for secure and clean Web site. For having better user interface JavaScript [5] and CSS [6] are used. The Web site also uses the MySQL [7] database, jQuery [8] and Google API [9].
A Statistical Foray into Contextual Aspects of Intertextuality

Intertextuality is a highly productive concept in literary theory. The pervasiveness of intertextuality in literary texts has lead simultaneously to a proliferation of applications with often divergent interpretations of the concept of intertextuality, as well as a recurrent interest in studying it from a computational point of view. Despite the potential of data-driven, bottom-up approaches, most computational research into intertextuality has focused on the matter of text reuse detection, exploiting surface-level properties to improve the performance of retrieval systems. In the present study, we utilize the Patrologia Latina -- a substantial collection of religious texts spanning over a millennium of Latin writing (3rd to 13th centuries) -- to provide a large-scale systematic study of biblical intertexts. On the basis of multi-level statistical models, we investigate two axes of intertexts: the degree of lexical similarity, and the degree to which intertexts are thematically embedded in the context. Furthermore, we investigate the extent to which the following contextual sources of variation help explain the distribution of intertexts along the aforementioned axes: first, we analyze the effect of authorship: do authors differ in the way they compose their intertexts? Secondly, we inspect factors related to the source collection (i.e. the Bible) to elucidate whether the authority and tradition of particular books exert an influence on the observed intertexts: do certain books trigger a more allusive or quotational intertext type? Finally, we take into account the dominant topic surrounding the intertext location and examine associations between the distribution of dominant topics and intertext types. On the one hand, our analysis indicates that both axes (lexical similarity and thematic embedding) play partially complementary roles in our computational account of intertextual types. On the other hand, we find that biblical books and, more strongly, dominant topics constitute important factors of variation, while the authorial signal remains comparatively weak.
Decision Support for Voyaging

This chapter provides a ship perspective on digitalisation and maritime informatics in relation to decision support for navigation and voyage monitoring. The rapid technological development in the world will continue to affect the development of information systems for decision-making used both on-board and ashore. The chapter gives a general introduction where the ship as an information source is introduced, before moving on to ship navigation, followed by the monitoring and management of a ship’s voyages and port calls. A ship is both a consumer of data originating from different sources and a provider of data. Most ships are now sources of some level of information that can be remotely monitored, collected, analysed, and acted upon. Many on-board information systems support decisions for the officer on watch during a voyage. The presentation of the information is important, and successful standardisation has been made in several areas.
WITHDRAWN: Innovation Capability of Smart Entrepreneurial Park Based on FPGA and Wireless Sensors

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Visual Computing-based Perception System for Small Autonomous Vehicles: Development on a Lighter Computing Platform

Recently, perception system for autonomous vehicle has seen a tremendous growth. Most of the recent works employ sensor fusion with complementary properties to produce a robust and accurate perceptive system for vehicle. However, this comes at a high price, requires high computing power and consumes more energy. In this study a perceptive system is designed to tackle the above issues while maintaining its accuracy and robustness. The proposed perceptive system is using only a pair of vision sensors. A Convolution Neural Network is used to detect and identify objects in the field of vision. A pair of cameras are then used to form a stereovision which is used to measure the distance of the objects detected. A disparity map from stereovision images was constructed first, then from the region of interest, a single disparity value was extracted to calculate the distance. The system is employed on a single board computer system StereoPi with the help of Intel Neural Compute Stick 2 to run deep neural network inference. An experiment was then conducted to test the perceptive system's robustness, accuracy, and runtime. Results show that the proposed system is capable of a detection accuracy of 71.7% with an average error of 0.37% up to a distance of 1.3m.
An instance-based algorithm for deciding the bias of a coin

Let [Formula: see text] and [Formula: see text] be real numbers, and let C be a coin that comes up heads with an unknown probability p, such that [Formula: see text]. We present an algorithm that, on input C, q, and [Formula: see text], decides, with probability at least [Formula: see text], whether [Formula: see text] or [Formula: see text]. The expected number of coin flips made by this algorithm is [Formula: see text], where [Formula: see text].
Migration-Related Semantic Concepts for the Retrieval of Relevant Video Content

Migration, and especially irregular migration, is a critical issue for border agencies and society in general. Migration-related situations and decisions are influenced by various factors, including the perceptions about migration routes and target countries. An improved understanding of such factors can be achieved by systematic automated analyses of media and social media channels, and the videos and images published in them. However, the multifaceted nature of migration and the variety of ways migration-related aspects are expressed in images and videos make the finding and automated analysis of migration-related multimedia content a challenging task. We propose a novel approach that effectively bridges the gap between a substantiated domain understanding - encapsulated into a set of Migration-related semantic concepts - and the expression of such concepts in a video, by introducing an advanced video analysis and retrieval method for this purpose.
The Effect of Overlap Value and Grid Coefficient on Yang Method: Super-Resolution Image Reconstruction Using Random Raw Patches

Super-resolution images are often needed in computer vision applications because having high pixel density can provide more detailed information. Super-resolution images can be obtained by interpolation-based methods and training-reconstruction based methods. Although needs more processing time, the image quality of the training-reconstruction based method is better than the interpolation-based method. This paper contributes to investigating the effect of overlap value and grid coefficient on the Yang method. This Yang method works based on random raw patches obtained through the sampling process. The overlap value and the grid coefficient are set by the user in the sampling process. In testing, we use a grayscale image with a size of 60 × 80. The image is reconstructed with magnification factor 3. The result shows that the bigger overlap value produces a smaller RMSE and a longer processing time. While grid coefficient = 1 produces a big RMSE. This is because the grid coefficient gives a few sampling points.
Design and Application of Rehabilitation AIDS Based on User Experience

Sports rehabilitation is an important branch of rehabilitation medicine and the most widely received project in the social security system. In the process of continuous improvement of rehabilitation services, the sports rehabilitation aids industry is developing towards product, industrialization and individuation. With the increasing number of rehabilitation patients and the development trend of the Internet, Traditional therapist-assisted training has been difficult to meet patients’ rehabilitation needs, and more rehabilitation training equipment and service design are needed to meet modern technology and user experience. Modern rehabilitation training equipment has greatly improved the rehabilitation efficiency and curative effect than traditional methods, but it lacks the psychological and emotional satisfaction to the rehabilitation patients. This is also an important problem to be solved in the focus of rehabilitation auxiliary man-machine interface. Through the investigation and analysis of sports rehabilitation field, rehabilitation accessories industry and products, this paper clarifies the subject direction. The process of designing and developing rehabilitation accessory products based on user research is discussed in this paper. Under the guidance of the design concept, the design and development of rehabilitation accessory products and more humanized rehabilitation treatment service flow were carried out.
Evaluation of Edge Platforms for Deep Learning in Computer Vision

In recent years, companies, such as Intel and Google, have brought onto the market small low-power platforms that can be used to deploy and run inference of Deep Neural Networks at a low cost. These platforms can process data at the edge, such as images from a camera, to avoid transfer of large amount of data across a network. To determine which platform to use for a specific task, practitioners usually compare parameters, such as inference time and power consumption. However, to provide a better incentive on platform selection based on requirements, it is important to also consider the platform price. In this paper, we explore platform/model trade-offs, by providing benchmarks of state-of-the-art platforms within three common computer vision tasks; classification, detection and segmentation. By also considering the price of each platform, we provide a comparison of price versus inference time, to aid quick decision making in regard to platform and model selection. Finally, by analysing the operation allocation of models for each platform, we identify operations that should be optimised, based on platform/model selection.
Refined Urdu Lexicon Development K-Means Clustering Based Computational Model Using Colloquial Romanized Urdu Dataset

Urdu is among the most widely used languages in the world for verbal and written communication. Due to lack of optimized and user friendly native Urdu-script support on various platforms, it is mostly written in Romanized script in soft form. In our research, we have developed a refined Urdu lexicon using tokens with the highest frequency of occurrence in the data set. This data set is basically a raw corpus of colloquial Urdu written in Romanized script. The corpus was collected from volunteer participants who used this language as a mode of communication on the Internet and text massaging. The raw corpus is passed through a series of steps such as Prepossessing, Tokenization and Annotation before passing it to computationally extensive subsequent steps. Edit Distance and K-means Clustering techniques are used for identification of candidate tokens and their potential selection/ inclusion in the refined lexicon. We have also identified most commonly used tokens, candidate tokens and other lingual attributes from the data collected. Based on analysis, we have proposed a computational model for refined colloquial Romanized Urdu lexicon development.