Reviewerly/exHarmony huggingface dataset:
A Utility Game Driven QoS Optimization for Cloud Services

Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode. Current enterprises compact their business software as services into cloud platform to users. In the cloud services market, service providers attempt to make more profits from their services, while users hope to choose low-cost services with high-quality. The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This article characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties from an economic point of view. By analyzing the behaviors of users and service providers, we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions. In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method. The results are applicable to win-win service applications between service providers and users.
Sports-Net18: Various Sports Classification using Transfer Learning

With the advancement of Computer Vision (CV) classification of sports images is very popular nowadays. Deep learning techniques have become feasible to use and see what happens inside the contents of an image. As videos are the collection of continuous images, or `frames', this technique can be also used in videos. Computer Vision tends to reduce the complexities in the human visualization system and understanding by applying convolutional neural network models to accurately recognize and classify objects from the potential and asymmetrical physical world. Classification of images is now very popular in recent years as the emergence of computer vision technology but it is quite challenging. In this paper, we proposed a VGG16 transfer learning model to classify eighteen categories of various sports and we have created our sports dataset which contains 9000 images. Our proposed model has shown a promising result which is 93%.
Use of Wearable Technologies for Analysis of Activity recognition for sports

Activity recognition and enhance performance are the major issues that are faced by wearable technology devices. Therefore, in this paper, we present an exploratory study to examine and evaluate the current issues with wearable technology for analysis of activity recognition for sports. These issues can be controlled by implementing a high sensor and WSN technologies using components such as- Stimuli signal, Data recognition and analysis, and Physical testing. The study utilised the verified taxonomy based on wearable technology, which aims to use effective technology. Raw and processed data has been gathered from different sources. The study shows that there is a major gap in the previous research studies and most of the studies only investigated some of the component of the Wearable Technologies for Analysis of Activity recognition for sports which make it inefficiency for current researchers.
A practical analysis of sample complexity for structure learning of discrete dynamic Bayesian networks

Discrete Dynamic Bayesian Network (dDBN) is used in many challenging causal modelling applications, such as human brain connectivity, due to its multivariate, non-deterministic, and nonlinear capability. Since there is not a ground truth for brain connectivity, the resulting model cannot be evaluated quantitatively. However, we should at least make sure that the best structure results for the used modelling approach and the data. Later, this result can be appreciated by further correlated literature of anatomy and physiology. Nearly all of the previously published studies rest on limited data, which brings doubt to the resulting structures. In theory, an immense number of samples is required, which is impossible to collect in practice. In this study, the appropriate number of data which makes a dDBN modelling trustable is searched by practical experiments and found to be O(Kp+1) for binary and ternary-valued networks, where K is the cardinality of the random variables and p is the maximum number of parents possibly present in the network. If a modelling approach satisfies this amount of data, we can at least say that the resulting structure is trustable.
A Sensitive Data Access Model in Support of Learning Health Systems

Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR.
M-Universities: Critical Sustainability Factors

This study intends to investigate the factors which can affect the sustainability of mobile learning (m-learning) from many perspectives. Current studies on the sustainability of m-learning are reviewed in the context of organizational, financial, legal and ethical, pedagogical, assessment, psychological, and social factors. In addition to the literature review, an investigation based on formal interviews is conducted with 11 heads in university centers for distance education (UCDE) to obtain additional sustainability factors. To test the impacts of the factors on sustainability in m-learning, another investigation which bases on survey research is conducted with administrative personnel in UCDE, and instructors and students in the distance education universities. A total of 667 survey responses are analyzed, and a model for the sustainability of m-learning is developed. This article concludes that some factors have significant effects on sustainability in m-learning and some of them are critically important for m-learning initiatives. M-learning sustainability is studied rarely in some contexts such as organizational, psychological, and social context. Therefore, this study can bridge this gap, and provide a guideline to m-learning initiatives for sustainable education.
A fast explicit diffusion algorithm of fractional order anisotropic diffusion for image denoising

&lt;p style='text-indent:20px;'&gt;In this paper, we mainly show a novel fast fractional order anisotropic diffusion algorithm for noise removal based on the recent numerical scheme called the Fast Explicit Diffusion. To balance the efficiency and accuracy of the algorithm, the truncated matrix method is used to deal with the iterative matrix in the model and its error is also estimated. In particular, we obtain the stability condition of the iteration by the spectrum analysis method. Through implementing the fast explicit format iteration algorithm with periodic change of time step size, the efficiency of the algorithm is greatly improved. At last, we show some numerical results on denoising tasks. Many experimental results confirm that the algorithm can more quickly achieve satisfactory denoising results.&lt;/p&gt;
Analysis of risk factors affecting delay of high-speed railway in China based on Bayesian network modeling

This study aims to investigate the effects of risk factors affecting high-speed railway delay using Bayesian networks. At first, we determined risk factors based on real time data about high-speed railway delay in China. Then, the Bayesian network structure model of the delay of high-speed railway was established according to expert experience and Dempster-Shafer evidence theory. The established Bayesian network structure model was modified by test for conditional independence in the next step. Finally, the posterior probability of each contributing factors in the Bayesian network was calculated based on the real time data. Compared to other statistical analysis methods, Bayesian network took interaction of risk factors into consideration by analyzing the correlation and influence degree of influencing factors under incomplete information. It has been found that the device failure is the most significant factor leading to the delay of the train, among which, failure of automatic train protection, platform door and catenary are the three specific factors that affect the delay most. The findings in this study provide useful and valuable information for high-speed railway operation managers to take effective countermeasures to reduce the delay rate of high-speed railway.
Review and analysis of data security using image steganography

Development innovation and speedy Network make it simple and monetary to spread data across the globe. This leads people to think about their safety and their jobs. Steganography is a methodology that stops unwanted consumers from going towards simple information. Steganography and sophisticated watermarking offer procedures for consumers to hide and blend their data with other details that makes it impossible for attackers to see them. In this paper the main purpose is to research a couple of steganography and mechanized watermarking systems in both spatial and repeat spaces.
A Secure and Efficient Query Processing Algorithm Over Encrypted Database in Cloud Computing

Since studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Therefore, a couple of privacy-preserving kNN query processing techniques have been proposed over the encrypted database. However, the existing schemes are either insecure or inefficient. Therefore, in this paper we propose a secure and efficient kNN query processing algorithm. Our algorithm preserves both data privacy and query privacy while hiding data access patterns. To achieve a high efficiency in query processing, we also propose efficient and secure protocols using Yao's garbled circuit and a data packing technique. Through our performance analysis, we verify that our proposed scheme outperforms the existing schemes in terms of a query processing cost.
Development and Evaluation of a Rehabilitation Wheelchair with Multiposture Transformation and Smart Control

Stroke and other neurological disorders have an effect on mobility which has a significant impact on independence and quality of life. The core rehabilitation requirements for patients with lower limb motor dysfunction are gait training, restand, and mobility. In this work, we introduce a newly developed multifunctional wheelchair that we call “ReChair” and evaluated its performance preliminarily. ReChair seamlessly integrates the mobility, gait training, and multiposture transformation. ReChair driving and multiposture transformation are done using the voice, button, and mobile terminal control. This work describes the functional requirements, mechanical structure, and control system and the overall evaluation of ReChair including the kinematic simulation of the multiposture transformation and passive lower limb rehabilitation training to quantitatively verify the motion capability of ReChair, the voice control system evaluation that shows how the voice recognition system is suitable for home environment, the sensorless speed detection test results that indicate how the wheel speeds measured by sensorless method are appropriate for travelling control, and the passive and balance training test results that show how the lower limb rehabilitation training in daily life by ReChair is convenient. Finally, the experimental results show that ReChair meets the patients’ requirements and has practical significance. It is cost-effective, easy to use, and supports multiple control modes to adapt to different rehabilitation phases.
Efficient Implementation for PRINCE Algorithm in FPGA Based on the BB84 Protocol

Abstract The current study introduces a hardware-implemented PRINCE block cipher within Field Programmable Gate Array (FPGA) determined by the quantum cryptography protocol (BB84). Most security-related software applications of cryptographic algorithms tend to be rather slow and of no efficiency. So as to present a solution to this issue, a new hardware architecture is suggested for speeding up the execution of the PRINCE algorithm and increasing its flexibility, yet with more security. Concurrent computing designs allow an encryption block data of 64 bits during a single clock cycle, resulting in the reduction of hardware area and the production of a higher throughput and relatively lower latency. Higher speed processing and lower power consumption are other features that have been observed. This could be achieved by means of implementing the encrypting, decrypting and quantum key schedule using little hardware sources, followed by the development of a sufficient hardware architecture model for the PRINCE algorithm through very high speed integrated circuit hardware description language (VHDL). The synthetization of this VHDL design is eventually performed in FPGA boards. As for the present study, two FPGA boards have been employed, namely Virtex-4 and Kintex-7. The resulting data indicates the throughput and efficiency values to be (2.029 Gbps) and (1.9 Mbps/slice) for Virtex-4, and (3.931 Gbps) and (7.290 Mbps/slice) for Kintex-7, all respectively.
Creating Forecast Maps of the Spatial Distribution of Dangerous Geodynamic Phenomena Based on the Principal Component Method

Abstract Subject matter of the research is mining and geological subsurface objects use in the conditions of the DGDE possibility. Scope of research is the regularities of spatial distribution of geological objects properties, causing DGDE in the mining enterprise. The purpose of research is to map the mining enterprise territory subject to DGDE using the method of objective determination of weight coefficients and influencing factors. The main tasks: establish the size of the operational territorial units (OTU) and discretization of the research area; determination of the weight coefficients of the factors influencing the process of the DGDE occurrence formation on the subsurface territory; create a mathematical expression for a generalizing value determining that characterizes the degree of adverse factors coincidence, and therefore the risk rank of the DGDE occurrence.
Gait and Full Body Movement Dataset of Autistic Children Classified by Rough Set Classifier

Abstract Gait and body movement are a window to human brain which make these activities unique for each person. These activities are used to diagnose some disorders related to parts of brain which causes have not been known such as Autism Disorders (AD). The traditional diagnostic methods of AD are time-consuming and highly dependent on clinician’s judgment which is based on behaviour assessment. This approach leads to subjective interpretations that differ from doctor to another and affect by strengths and weaknesses of patient. Therefore this paper aims to diagnosis of AD based on gait and body movement analysis. At first, Kinect v2 uses to create a 3D dataset, which includes three dimensional joints positions, joints trajectories video, skeleton movement video captured by Kinect v2, and color videos captured by Samsung Note 9 camera. This paper also aims to classify children with autism from normal children by proposed system based on four stages: Augmentation of the database by using seven transformations to deal with small number of autism cases; Extracting features that we think play an important role in classification; Reducing data dimensions using Principal Component Analysis; using Rough Set to classify dataset. Results show that classification is 92% after augmentation.
On-Device Extractive Text Summarization

With increasing connectivity, there has been an exponential surge in the creation and availability of textual content in the form of news articles, blogs, social media posts and product reviews. A large portion of this data is consumed on mobile devices, and more recently, through wearables and smart speakers. Text summarization involves generating a brief description of a text, which captures the overall intention and the vital information being conveyed in its content. Common techniques for automatic text summarization follow extractive or abstractive approaches and involve large scale models with millions of parameters. While such models can be utilized in web or cloud-based applications, they are impractical for deployment on devices with limited storage and computational capabilities. In this paper, we propose a novel character-level neural architecture for extractive text summarization, with the model size reduced by 99.64% to 97.98% from existing methods, thus making it suitable for deployment on-device such as in mobiles, tabs and smart speakers. We tested the performance of our model on various benchmark datasets and compared it with several strong baselines and models. Despite using only a fraction of the space, our model outperformed the baselines and several state-of-the-art models, while coming close in performance with others. On-device text summarization remains largely an unexplored area, and our model's results show a promising approach towards building summarization models suitable for a constrained environment.
Steganalytic feature based adversarial embedding for adaptive JPEG steganography

In this paper, we present a novel adversarial embedding scheme named Steganalytic Feature based Adversarial Embedding (SFAE), which is elaborately designed in a non-data-driven style. Firstly, a novel DCTR based adversary is designed to generate adversarial stego images which can not only resist feature based steganalysis but also deep learning based steganalysis. Specifically, our adversary consists of an end-to-end neural network structure, while its inner weights are set according to DCTR rather than learned from datasets. Secondly, we use the minimum distance to the cover in steganalytic space as the criterion to select the optimal adversarial stego image, rather than fooling the adversary. Last but not least, we present two SFAE implementations to adapt to different cases. One is Iterative SFAE, which needs to calculate gradients multiple times. Iterative SFAE is more secure but has higher complexity. It fits the case that the steganographer has adequate computing resources. Another implementation is Oneshot SFAE, which can calculate gradients once. Oneshot SFAE trades the security for lower complexity. It fits the steganographer that has stricter requirements for running time. Experiments demonstrate that SFAE is effective to improve the security of conventional steganographic schemes against the state-of-the-art steganalysis including both feature based steganalysis and deep learning based steganalysis.
An Executable Axiomatization of the REA2 Ontology

ABSTRACT The formalization of the REA2 ontology presented in this paper offers a minimal set of operationalized semantics for a single white-box model relevant to all business stakeholders independent of their role or involvement in economic activities. This paper's theoretical innovations are the use of MERODE to model increment and decrement semantics as fundamental stand-alone concepts that simultaneously affect economic resources, event, agents, and the semantics of the stock-flow, participation, and ownership associations and the formalization of the REA axioms as executable finite state machines. MERODE's possibilities for model execution through fast prototyping allowed validation through the modeling of an archetypical exchange scenario. Both innovations contribute to the reliability of a generic semantic model for finance and logistics in both the traditional, as well as the sharing, economy, thus promoting traceability and accountability in value networks and supply chains supported by both centralized and decentralized ledger technologies.
Autonomous Driving Technology through Image Classfication and Object Recognition based on CNN

The time has come for humans that the cars themselves think and judge rather than driving by humans, and has already reached a certain level. It hasn't reached the commercialization stage yet, but it's not a story of the future anymore, and even now, a great deal of technological development is taking place every day. However, since this autonomous driving technology does not have a driver when an accident occurs, it can cause problems in terms of responsibility and ethics, so it has to be possible to make the best choice at anytime and anywhere without accident. It requires a high level of technology on the software and the hardware. Most autonomous vehicles currently on the market do not stay or exceed the level of 2 to 2.5. The level of 2~2.5 is a partial automation step, and in a stable environment, autonomous driving is possible in part, but it must be possible for the driver to immediately take over control of the driving of the vehicle.
Intent Classification from Code Mixed Input for Virtual Assistants

Virtual Assistants like Bixby, Google Assistant, Cortana & Alexa are making life easier by understanding intent from user's input utterance. Adoption of Virtual Assistants in multilingual society like India is harder than in monolingual communities. People in multilingual societies tend to use linguistic elements from multiple languages in a single sentence. This phenomenon produces code-mixing, which lacks standard syntactic & semantic linguistic properties unlike monolingual input. It is a challenging NLP Task to understand user's intent from code-mixed input. There is a lack of relevant corpus & researches for Intent Classification on Code-mixed text. In this paper, we introduce first of its kind a Hindi-English Code-Mixed dataset for Intent Classification (CoMTIC), covering 10 most preferred features for a Virtual Assistant. We also introduce a novel deep learning based method of Intent classification from Code-Mixed Text. We conduct empirical analysis by comparing the suitability & performance with various state-of-the-art methods. Our method attains 96.68% accuracy on our dataset.