Miracle-dz/UniFAR-FTUs huggingface dataset:
Heuristic, Systematic, and Affective Components of Online Service Reviews : Impact on Intra-Organizational Adoption and Sharing

There are plenty of studies on how online service reviews (OSRs) influence customers’ behavior in the hospitality industry. However, little is known about how the reviews affect managers’ behavior. Drawing on the heuristic-systematic model (HSM), this study proposes a model that explains how the key attributes of OSRs affect managers’ intention to adoption and sharing. Further, building on the emotional contagion theory, this study demonstrates that the affective intensity (but not the valence) elicited by the reviews alters the likelihood of intra-organizational diffusion by moderating the relationship between adoption and sharing.
Epidemic Information Dissemination in Mobile Molecular Communication Networks for Target Detection Applications

Epidemic information dissemination is a promising technique for delivering messages in sparse mobile networks. This paper proposes an epidemic information dissemination method for mobile molecular communication networks. We first describe a model of the proposed epidemic information dissemination for mobile molecular communication networks. We then provide numerical experimental results showing the performance of the proposed method in terms of the fraction of bio-nanomachines that successfully receive information. Further, we apply the proposed information dissemination method to a target detection problem in mobile molecular communication networks. CCS Concepts •Applied computing → Health care information systems; •Hardware → Biology-related information processing;
Virtualization for Flexibility and Network-Aware on 5G Mobile Devices

The dawn of 5G is rising with emerging wireless and network technologies that promisingly transform various aspects of human lives. The 5G mobile devices are going to tightly engage with the 5G infrastructure for future applications such as virtual reality, AI-enabled applications, etc. However, we argue that the tight engagement with convenience may bring downside that is a user may not correctly control/own her/his device. In particular, the user may not have the flexibility in utilizing networks, as well as, be aware of the network selection. This paper introduces a novel networking stack for 5G mobile devices (namely 5GVir) that leverages virtualization techniques for flexibility and awareness. The 5GVir device can concurrently exploit surrounding wireless networks by using Software Defined Networking (SDN). SDN provides a rich set of flexibility feature that will meet user expectation. Moreover, 5GVir includes wireless virtualization that can relax the dependence on hardware as well as provides a wireless link for the SDN's control channel. Last but not least, the 5GVir device has an additional awareness feature aware that drives each application process to predetermined networks (i.e., applying network namespace). Our prototype shows the potential of realizing 5GVir. Moreover, the initial experiments show that the virtualization in 5GVir has negligible overhead.
Propagation of solutions to the Fisher-KPP equation with slowly decaying initial data

The Fisher-KPP equation is a model for population dynamics that has generated a huge amount of interest since its introduction in 1937. The speed with which a population spreads has been computed quite precisely when the initial data, u0, decays exponentially. More recently, though, the case when the initial data decays more slowly has been studied. In Hamel F and Roques L (2010 J. Differ. Equ. 249 1726–45), the authors show that the level sets of height of m of u move super-linearly and may be bounded above and below by expressions of the form u0−1(cme−t) when u0 decays algebraically of a small enough order. The constants cm for the upper and lower bounds that they obtain are not explicit and do not match. In this paper, we improve their precision for a broader class of initial data and for a broader class of equations. In particular, our approach yields the explicit highest order term in the location of the level sets, which in the most basic setting is given by u0−1(me−t/(1−m)) as long as u0 decays slower than e−x. We generalize this to the previously unstudied setting when the nonlinearity is periodic in space. In addition, for large times, we characterize the profile of the solution in terms of a generalized logistic equation.
Use of the theory of dynamical systems for transient problems: application to the switching-on problem

This paper addresses the problem of dynamical systems with parameters varying in time (transient systems). A method to predict their behaviour is proposed. This class of transient systems can be seen as the evolution of an ordinary steady system into another ordinary steady system, for both of which the classical theory of dynamical systems holds. The evolution from a steady system to the other is driven by a transient force, which is regarded as a map between the two steady systems. We apply our method to a system which is subjected to a transient excitation, that is neither constant nor periodic, to simulate the effect of switching-on procedures.
Visual Analytics of Stratigraphic Correlation for Multi-Attribute Well-Logging Data Exploration

Stratigraphic correlation based on well-logging data is able to help geological interpreters analogize and deduce underground sedimentary morphology. A great deal of traditional methods has been studied to automatically or manually achieve stratigraphic correlation, the courses of which are usually time-consuming and not intuitive. Many uncertainties are easily generated to reduce the effectiveness of stratigraphic correlation and the accuracy of geological interpretation. To address this issue, this paper introduces an interactive visual analytics system for identifying correlation patterns and improving correlation accuracies using large-scale well-logging data. First, we propose a novel stratigraphic correlation model with the composition of multi-log curve integration, layer identification, and layer matching. Then, a visualization framework is designed by working closely with domain experts in an iterative manner to get deeper insights into the course of stratigraphic correlation based on a few visual interfaces such as map view, correlation view, matrix view and attribute view. Also, a rich set of interactions is provided allowing interpreters to refine the results of stratigraphic correlation according to domain knowledge and user requirements. Furthermore, case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system for the stratigraphic correlation and geological interpretation.
A Comparative Study of Switched-Tank Converter and Cascaded Voltage Divider for 48-V Data Center Application

This paper presents a comparative study of the switched-tank converter (STC) and the cascaded voltage divider (CVD). The operating principles of the STC and the CVD with four times input-to-output conversion ratio (<inline-formula> <tex-math notation="LaTeX">$4\times$ </tex-math></inline-formula>) are discussed. Two design approaches of the CVD are considered, including the high-density CVD (CVD-HD) and the high-efficiency low-density CVD (CVD-LD). The power loss breakdown analysis and system efficiency evaluation are provided for both converters. From these results, it is found that the STC is more efficient and smaller in size than the CVD converter. Gallium nitride (GaN)-based hardware prototypes of both converters are built and tested. The <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>-STC can achieve ~1500-W/in<sup>3</sup> power density and 98.79% efficiency. The <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>-CVD-LD can achieve 1061-W/in<sup>3</sup> power density and 98.41% efficiency. Therefore, the <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>-STC has 41% higher power density and 24% less power loss than the <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>-CVD-LD.
Generalization of Quantum Search Algorithm

Grover's quantum search algorithm is an important landmark discovery in the area of quantum computing that drives a quantum computer from a known initial state(source state) to an unknown final state(target state) by using selective phase inversions of these states. The algorithm is simply a successive iteration of the Grover's search operator, which is a product of selective inversion operators of source and target states. In its original form, Grover's operator performs the selective inversions only on a unique source state and a unique target state. In the generalized version, we make our search operator to perform selective inversions on more than one source and target states. First, we find that the case of selective inversions of two source and two target states can be easily analysed somewhat similar to the case of Grover's algorithm. But unlike Grover's operator, the selective inversions of three source and three target states is not so easy to study analytically and hence we perform numerical calculations on some specific cases. We find that these generalized search operators also give us a successful quantum search as expected.
Condition monitoring and fault diagnosis of power equipment

With the rapid progress of modernization, power equipment can smooth and safe operation, relates to the enterprise’s production level, production safety, product quality assurance and other aspects of the smooth development of the electric power equipment fault are often referred to in a specific time and condition, the equipment does not have the specified function, touch the occurrence of this situation is mostly due to a component. The fault diagnosis of power equipment is actually the analysis of the specific location and nature of the fault. This paper is to detect the state of electric power equipment and to find fault, and gives some suggestions for the power equipment fault diagnosis.
Multi-feature analysis fatigue driving based on Conditional Local Neural Fields algorithm detection method

In order to effectively strengthen the monitoring technology of fatigue driving and reduce the incidence of traffic accidents, this paper proposes a new method based on conditional local neural fields algorithm for comprehensive evaluation of fatigue driving state. At present, most of the fatigue driving detection algorithms are based on extracting a single characteristic index, which is strict in environmental requirements and not high in detection. In this paper, the HOG feature is combined with the CLNF algorithm to implement face detection and feature point localization. Then, the EPnP algorithm is used to estimate the head anomaly frequency based on the feature point information, and the blink frequency is calculated according to the EAR eye length aspect ratio concept according to the feature points around the eye. Finally the threshold set by the P80 fatigue detection standard in the PERCLOS method is integrated, and the distributed information fusion strategy is used for fatigue evaluation. Experimental results confirm the effectiveness of the method.
A framework for generating informative answers for Question Answering systems

Recent trends in Question Answering (QA) systems have led to a proliferation of studies which have focused on building advanced QA systems which are able to compete with the QA ability of humans. To this effect, a large number of these systems have shifted to Question Answering over Linked Data (QALD). The use of Linked Data as the basis of knowledge representation by the QA systems has led to noticeable improvements in both recall and precision compared to the conventional, unstructured text based systems. However, answers from these systems are still not able to mimic human generated answers, which has been an ambition for Artificial Intelligence (AI) researchers for more than a decade. One of the two main reasons for the “machine feel” of the answers has been the inability of QA systems to present the answer as a fully constructed, natural language sentence. The second reason is that humans generally answer a question with elaboration containing additional contextual information, apart from the specific answer to the question. This aspect has been especially challenging for QA systems as it is difficult to source the contextual information, rank them and then formulate the information as multiple sentences in a form that emanates human generated text. Previous research has investigated answer presentation by summarizing unstructured text, selecting contextual information from a closed domain ontology, and using cooperative and user tailored answers, however, these studies have not dealt with the generation of an answer in natural language with additional contextual information.
Partial discharge source classification using pattern recognition algorithms

Design, development, and testing of a comprehensive and automated classification system for single and multiple partial discharge (PD) source identification based on the relationship between the variation of phase resolved partial discharge (PRPD) patterns and the sources of PD is proposed. The proposed system consists of feature extraction methods and classifier algorithms that are implemented for recognition of partial discharge patterns. Once the PRPD patterns are recorded, features generation algorithms are applied on the collected data. For single PD source identification, twelve high performance, applicable feature extraction techniques on PRPD patterns are employed to extract features. In order to present a comprehensive classification system, 10 well-known algorithms for the classification of PD sources have then been used. To evaluate the performance of the classification system, three laboratory test setups are designed and built to simulate various types of PD activities. The first test setup includes test cells which are designed to model common sources of PD in air, oil, and SF6. Using this setup, the application of automated classification system on different sources of PD in different HV insulation media is investigated. The second and third test setups are designed to test the classification system on identification of different sources of PD in oil-immersed insulation and power transformer cellulose insulation under both electrical and thermal stresses, respectively. In many practical situations, the interest lies in the identification of multiple, simultaneously activated PD sources in insulation. Multi-source PDs sometimes results in partially overlapped patterns, which makes them hard to be identified by single source identification techniques. To further enhance the proposed classification system, a novel algorithm to identify Multi-source PDs is developed and appended to the system. To evaluate the performance of this
Generating Emoji with Conditional Variational Autoencoders and Word Embedding

Emoji are among the most widely used communication tools worldwide. Because the number of emoji increases every year and there are 82 face emoji, it might be difficult for users to select an appropriate emoji immediately. Moreover, it is troublesome to continue designing new emoji. Therefore, the aim of the present study is to generate an emoji based on input text automatically to facilitate easier communication and eliminate the process of designing new emoji. The proposed model employs conditional variational autoencoders (CVAE), quasi-recurrent neural networks (QRNN) as the text encoder, and the pre-trained word vector GloVe to the embedding layer connected to the text encoder. In the experiments described herein, it will be observed that the proposed method can generate an emoji that corresponds to an input caption, and output image quality is improved using GloVe.
Personalized Ranking Algorithm Based on User Interest Modeling

It’s convenient for Internet users to access to web resources with a search engine. However, most traditional search engines can’t provide personalized search results for users. In order to overcome this limit, we adopt a combination method of the explicit and implicit user modeling to build and update a user interest model. To be specific, we first build the user interest model with a topic-based representation method by the information offered by users. Then we update the model by considering the time factor and the user browsing behavior. As nouns are obviously more distinctive than other words, we give a greater weight for them. Based on this, we have improved the BM25 algorithm. Finally, a personalized ranking algorithm combining topic ranking and BM25 ranking is proposed. The experiments show that the personalized ranking algorithm based on user interest modeling can provide personalized search service for users.
A new biometric system based on inner-knuckle-print recognition

Biometrics is a highly efficient method of personal identification in civil fields. In this paper, we present a new biometrics system based on inner-knuckle-print (IKP). A new IKP image acquisition device with a finger guide is built for restricting the finger posture in a friendly way. A modified maximum curvature point method is proposed for feature extraction; it can extract the central region of the IKP line pattern robustly against illumination variation. The experimental results show that the proposed method achieves the equal error rate of 0.36%, performing better other traditional methods. In addition, it works in real-time.
Cognitive Radio Networking Model Using Intelligent Receptors Based Rewrite Techniques

—The objective of the research work is to propose a cognitive radio networking model using intelligent receptors (IR) based rewrite techniques. The set of special function receptors in the cognitive engine within the radio framework senses the status of the bombarding channels and collects all the relevant details within the system and the outside environment. The proposed IR technique declares and defines a specific control path among number of choices meeting the requirements of the spectrum users and allocator. A formal model for cognitive radio with extended adaptability and control action has been proposed. The computational rule rewriting techniques to decide the best reaction to be taken by the cognitive engine among the prevailing issues like spectrum sharing, leasing and identification of hidden nodes and peak spectrum are used. The work improvises the performance, reliability and cost factors of the software defined network in a cognitive radio framework through receptor modules.
Artificial Neural Network for Facial Feature Extraction System

: This paper aims to develop face detection system using Artificial Neural Network (ANN). Face detection is one of the most important in security system. In this paper, an input image of face is acquired firstly by using a digital camera (model - Canon PowerShotA3100). The image is segmented by using threshold function and is needed to perform image processing techniques such as converting gray scale, noise removal image, edge image and changing to digited image. The important features of the image are needed to be extracted according to morphological features. A complete set of 12 features are used for the classification of final outcomes. Feature vectors obtained from feature extraction are used as input to neural networks. The system trains the Neural Network using Back-propagation algorithm. MATLAB 2009a programming language is used to implement this system.
APPLICATION OF FMEA-FTA IN RELIABILITY-CENTERED MAINTENANCE PLANNING

The failure mode and effects analysis (FMEA) has been used in identifying and reducing risks of failures in systems, equipment, and components following a bottom-up approach. The technique has been criticized for being unintuitive and cumbersome. The fault tree analysis (FTA) follows a top-down approach to identifying the root causes of failures. We apply FMEA to a large-sized axial plunger pump in an Egyptian fertilizers production plant. We extend the analysis to using the FMEA outcomes to launch the FTA to support the evaluation of the potential failures modes. We argue that the two techniques; FMEA and FTA, can complement each other in support of equipment reliability and availability studies. Together, they can offer as the basis of a reliability-centered maintenance planning, a systematic means of cataloguing information about potential failures, and accumulation of better knowledge of potential problems and improvement actions besides possible maintenance cost reductions.
Optimizing management of emergency gas leaks: a case study in business analytics

ABSTRACT Managing the response to reported gas leaks is of significant importance to both utilities and regulators. This paper utilizes a business analytics approach to investigate strategies for managing gas leak response while balancing the objectives of both utilities and regulators. The approach integrates the translation of the business issue into a quantitative framework by which actual gas leak data can be analysed and modelled to measure the performance of different gas leak response strategies. An agent-based simulation model is utilized to provide a decision support platform that translates the analytic results into a visualization tool to assist stakeholders and decision makers in evaluating the impact of different response strategies. The paper highlights both the analytic methods and the related “soft skills” that must be managed in the business analytics context to ensure an outcome that is acceptable for all stakeholders.
Detection and Feature Extraction for Images Signatures

The signing process is one of the most important processes used by organizations to ensure the confidentiality of information and to protect it against any unauthorized penetration or access to such information. As organizations and individuals enter the digital world, there is an urgent need for a digital system capable of distinguishing between the original and fraud signature, in order to ensure individuals authorization and determine the powers allowed to them. In this paper, three widely used feature detection algorithms, HARRIS, BRISK (Binary Robust Invariant Scalable Keypoints) and FAST (Features from Accelerated Segment), these algorithms are compared to calculate the run time and accuracy for set of signature images. Three techniques have been applied using (UTSig) dataset; the experiment consisted of four phases: first, applying the techniques on one image, then on four images, then on eight images, finally applying the techniques on ten images where time and accuracy were calculated for each algorithm in the all phases. The results showed that the BRISK algorithm got the best result among the feature detection algorithm in terms of accuracy and the FAST algorithm got the best result among the feature detection algorithm in terms of run time.