Miracle-dz/UniFAR-FTUs huggingface dataset:
Negative Impact of Social Network Services Based on Stressor-Stress-Outcome: The Role of Experience of Privacy Violations

Social network service (SNS) information has benefited many individuals. However, as such information has increased exponentially, the number of SNS users has increased dramatically and negative effects of SNSs on users have emerged. Many SNS users experience negative psychological conditions such as fatigue, burnout, and stress. Thus, in this study, we investigated the SNS and user characteristics that affect SNS fatigue, living disorder, and reduced SNS use intention. We developed a research model to examine the impact of two SNS characteristics (irrelevant information overload and open reachability) and two user characteristics (engagement and maintaining self-reputation) on SNS fatigue. We also examined the role of the experience of privacy violations in the relationship between living disorder and reduced SNS use intention. We collected data from 579 SNS users and created a partial least squares structural equation model to test the hypotheses. The results of the analysis showed that three factors, other than open reachability, positively affected SNS fatigue. Furthermore, we found that SNS fatigue significantly affected living disorder and reduced SNS use intention, and that experience of privacy violations significantly affected the relationship between living disorder and reduced SNS use intention. These results expand our understanding of SNS fatigue and users’ negative behaviors.
Research on Spatial Statistical Data Compression Algorithm Based on Point Cloud Clustering

The spatial information service system have the characteristics of a large amount of massive data and a large number of users, so the compression algorithm for statistical data will affects the system performance of spatial data services. A compression algorithm for spatial statistical data based on point cloud clustering is proposed in this paper to solve the above-mentioned problems. The access rule of the spatial data will be mapped into a point cloud. And the part of points will be deleted according to the clustering gradient of point cloud. Then the statistical data is compressed by run-length coding and spatial clustering extraction. The experimental results show that the algorithm have a high compression efficiency.
Explaining the Mobile Health Services Acceptance from Different Age Groups: A Protection Motivation Theory Perspective

With the rapid development of the mobile devices, there arouses great interest in mobile health service. As a new phenomenon, previous studies tend to investigate the adoption behavior in terms of the general population or one special population. In order to target more populations as potential customers, our study will use protection motivation theory (PMT) to explain the mobile health services acceptance of difference age groups. During the research, the hypothesized model is tested using data collected from a field survey of 492 potential customers of three difference age groups. The results revealed that response efficacy and self efficacy had a significant impact on adoption intention for the group of young adults and middle age people, while for the group of the elderly, only self efficacy determined the intention to use mobile health service. Theoretical and practical implications are discussed.
Cloud-based Methods and Architectures for Robot Grasping

The Cloud has the potential to enhance a broad range of robotics and automation systems. Cloud Robotics and Automation systems can be broadly defined as follows: Any robotic or automation system that relies on either data or code from a network to support its operation, i.e., where not all sensing, computation, and memory is integrated into a single standalone system. We identify four potential benefits of Cloud Robotics and Automation: 1) Big Data: access to remote libraries of images, maps, trajectories, and object data, 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning, 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes, and 4) Human computation: using crowdsourcing access to remote human expertise for analyzing images, classification, learning, and error recovery.We present four Cloud Robotics and Automation systems in this dissertation. First, we develop a system for Cloud-based grasping of 2D polygonal objects with uncertainty in shape using an analytic conservative estimate of the probability of force closure. Second, we develop a system for Cloud-based grasping of 2D polygonal objects with uncertainty in pose, using a quasi-static simulation that is less conservative than the approach for the first system. These two systems demonstrate the usefulness of Cloud-based parallelism for handling uncertainty. Third, we develop a system for recognizing and grasping household objects using the Google Object Recognition Engine as a web service and using Cloud storage of object and grasp information. Finally, we develop a system for providing algorithms as web services and integrating datasets with these services. These systems advance the understanding of the benefits the Cloud can provide for Robotics and Automation.
A Fingerprint Representation Technique based on Minutiae Quadrilateral Structure

— Fingerprint is a popular biometric trait which is used extensively in several applications for person authentication, providing high uniqueness and acceptable performance. The goal of any fingerprint representation is to capture as much of the unique information available in a fingerprint while discarding the variations between multiple impressions of the same finger. In this paper, we propose a new alignment-free fingerprint representation using the quadrilateral structure. We construct the feature set from the quadrilateral structure. The constructed feature set is quantised and mapped to a pre-defined 3D array. By sequentially visiting the cells of the 3D array, a fixed length 1D bit string is generated. This bit string is applied with DFT to generate a complex vector and the complex vector is multiplied with a random matrix generated by user’s key, to generate a final cancellable template. The proposed method FS_QUAD is tested on FVC 2002 databases and results show satisfactory performance.
Validator – a Web-Based Interactive Tool for Validation of Ocean Models at Oceanographic Stations

Numerical ocean models, like other geoscientific models, are a strongly simplified representation of real oceans. They are used as tools to answer research questions about the real-world systems. Therefore, their thorough validation is essential to ensure that the conclusions drawn from the model experiment are valid in reality. We demonstrate a software which allows an interactive model validation through a web interface based on the R Shiny framework. At pre-defined stations, different kinds of plots can be rendered within a few seconds, according to the user’s choice, allowing a live validation of different model parameters even in model simulations which are still running. This makes it different from validation approaches which generate a pre-defined set of plots after the calculations have finished and make it particularly useful for model tuning purposes. Observation data can be read in from text files or can be extracted from a database. Once set up, the validation tool requires no technical skills to use. It can be used for single- or multi-model validation and allows saving the generated plots as high-resolution images suitable for use in scientific publications. A Linux operating system is required for the Validator app, but via a virtual machine, the software can run on Windows or MacOS hosts as well. A Dockerfile is supplied which allows to test the software with example data without installation. Funding statement: This software was developed at the Leibniz Institute for Baltic Sea Research Warnemunde (IOW) on institutional funding.
Employee’s Performance Analysis and Prediction Using K-means Clustering & Decision Tree Algorithm

-Employee is the key element of the organization. The success or failure of an organization depends on the employee performance. Hybrid procedure based on Data Clustering and Decision Tree of Data mining method may be used by the authority to predict the employees’ performance for the next year. This paper presents how data clustering method can be applied for evaluating the employee’s performance as well in decision making process. Different performance evaluation factors like personality, punctuality, tact oral expression etc has been studied. The result of this paper predicts the number of employee those are selected for promotion or designation and discharged according to their performance. This study help to find out the inefficient employee, magnitude of inefficiency and aids to eliminate inefficiencies with a relatively easy to employ framework.
A novel MAC scheme for prioritized services in IEEE 802.11a wireless LAN

We propose a novel medium access control (MAC) employing two fundamental accesses for priority services-a distributed co-ordination function with short contention-window (DCF/SC) and DCF. Three kinds of prioritized services are applied for the enhanced QoS of multimedia services in a wireless local area network (WLAN). The proposed MAC scheme is shown to process all real-time traffics, MAC and satisfy the maximum tolerable latency and jitter bounds under the test environments recommended by IEEE 802.11 Task Group-E.
Blurring Political Leanings of Messages on Social Networks Using Deniable Steganography

While social network services have become popular platforms where large amounts of user-generated content are published, individuals, election campaigns, and political organizations leverage the services to publish, promote, and exchange political messages. However, sometimes, political discussions resulted in heated debates, doxing, and even cyberbullying. These conflicts may decrease the engagement of users and prevent them from publishing articles with apparent political orientation. To help users to post political opinion among the Internet freely, in this paper, we employ the concept of deniable encryption to generate neutral-like articles composed of two critics toward both sides of the discussion, while one of the critics is the actual message from the author to particular recipients. For receivers who know the credential information (e.g., key and codebook), they can communicate with each other by extracting the real information from the neutral-like articles. For people with the opposite stand, they cannot claim the message that is supporting specific political entity, as it contains disapproval to both stands; besides, they cannot prove that which part of the message is the plaintext even if they intercept the messages. We provide a generalized process of our proposal and present a practical scenario using transcripts, Facebook posts of Donald Trump, and posts of Hillary Clinton to demonstrate the usability of our scheme.
ARCHITECTURE OF A SENTIMENT ANALYSIS PLATFORM

A new domain of research evolved in the last decade, called sentiment analysis that tries to extract knowledge from opinionated text documents. The article presents an overview of the domain and present an architecture of a system that could perform sentiment analysis processes. Based on previous researches are presented two methods for performing classification and the results obtained.
Wavelets for texture analysis, an overview

Texture is an important cue for the analysis of many types of images. The term is used to point to intrinsic properties of surfaces, especially those that don't have a smoothly varying intensity. It includes intuitive properties like roughness, granulation and regularity. More formally, texture can be defined as the set of local neighbourhood properties of the gray levels of an image region. Texture analysis is considered a challenging task. The ability to effectively classify and segment images based on textural features is of key importance in scene analysis, medical image analysis, remote sensing and many other application areas.
Uncertain reasoning in an ID3 machine learning framework

Quinlan's ID3 is a symbolic machine learning algorithm which uses training examples as input and constructs a decision tree as output. One problem with the standard decision tree approach to machine learning is that uncertain data, either in training and/or testing, often produces poor classification accuracies. The UR-ID3 algorithm described combines uncertain reasoning with the rule set produced by ID3 to create a machine learning algorithm which is robust in the presence of uncertain training and testing data. Experimental results are presented which compare the new algorithm's performance with that of ID3 and backpropagation neural networks. <<ETX>>
Get the Picture? Goodness of Image Organization Contributes to Image Memorability

According to Gestalt psychologists, goodness is a crucial variable for image organization. We hypothesized that these differences in goodness contribute to variability in image memorability. Building on this, we predicted that two characteristics of good organizations, (i) fast, efficient processing and (ii) robustness against transformations (e.g., shrinking), would be characteristic of memorable images. Two planned (Study 1, Study 2) and one follow-up (Study 3) study were conducted to test this. Study 1 operationalized fast processing as accuracy in a rapid-scene categorization task (“categorizability”). Study 2 operationalized robustness against shrinking as reaction time in a thumbnail search task (“shrinkability”). We used 44 real-life scene images of 14 semantic categories from a previous memorability study. Each image was assigned a categorizability and shrinkability score. The predicted positive relation between categorizability and memorability was not observed in Study 1. A post-hoc explanation attributed this null result to a masking role of image distinctiveness. Furthermore, memorable images were located faster in the thumbnail search task, as predicted, but Study 2 could not rule whether this was merely a result of their distinctiveness. To elucidate these results, Study 3 quantified the images on distinctiveness and statistically controlled for this variable in a reanalysis of Study 1 and Study 2. When distinctiveness was controlled for, categorizability and memorability did show a significant positive correlation. Moreover, the results also argued against the alternative explanation of the results of Study 2. Taken together, the results support the hypothesis that goodness of organization contributes to image memorability.
Adaptive optimization for self: reconciling high performance with exploratory programming

Crossing abstraction boundaries often incurs a substantial run-time overhead in the form of frequent procedure calls. Thus, pervasive use of abstraction, while desirable from a design standpoint, may lead to very inefficient programs. Aggressively optimizing compilers can reduce this overhead but conflict with interactive programming environments because they introduce long compilation pauses and often preclude source-level debugging. Thus, programmers are caught on the horns of two dilemmas: they have to choose between abstraction and efficiency, and between responsive programming environments and efficiency. This dissertation shows how to reconcile these seemingly contradictory goals. Four new techniques work together to achieve this: - Type feedback achieves high performance by allowing the compiler to inline message sends based on information extracted from the runtime system. - Adaptive optimization achieves high responsiveness without sacrificing performance by using a fast compiler to generate initial code while automatically recompiling heavily used program parts with an optimizing compiler. - Dynamic deoptimization allows source-level debugging of optimized code by transparently recreating non-optimized code as needed. - Polymorphic inline caching speeds up message dispatch and, more significantly, collects concrete type information for the compiler. With better performance yet good interactive behavior, these techniques reconcile exploratory programming, ubiquitous abstraction, and high performance.
An Intelligent Synchronous E-Learning Management System Based on Multi-Agents of Linked Data, Ontology, and Semantic Service

: In the definition of the term e-learning, the researchers considered that the use of electronic media as a means of distance learning is in a way that enables both instructors and learners to interact through content on the web. So, e-learning in higher education worldwide has an important role in the activities of teaching and learning. Web technology for the construction of synchronous e-learning has been successfully developed to provide automatic educational content. The most important developments in this area are the semantic web that provides distributed information with a specific meaning precisely so that e-learning becomes an efficient relevant task that grows in a new dynamically changing learning requirements and needs of the learner. This paper has contributed to this goal by a system known as Intelligent Synchronous E-Learning Management System (ISEMS) that based on multi-agent such as semantic service, ontology, and linked data. Each agent represents the basis for organizing content resources, sequence learning, and adaptation. ISEMS hypertext structures from distributed metadata are utilized from the semantic web resource description formats. The description is based primarily on the content, learning material content's structure, and context so then provides adaptable and personalized access to these materials objects.
OPTIMIZED IMAGE RETRIEVAL SYSTEM USING MULTIPLE THREADS

Content-Based Image Retrieval is a technique used to retrieve similar images where the most challenging aspect is to bridge the gap between low level feature layout and high level semantic concepts. Efficient and effective retrieval techniques of images are desired to work out a certain image on the condition that the result would be more suitable than the input image. A novel method is proposed, which links various images as threads; where the texture features and shape features of the images are extracted and stored. The minimum distance between the query image and the thread provides the output. These threads are based upon the query result. The resultant retrieval system is found to be beneficial and interactive.
Survey of Data Mining Techniques on Crime Data Analysis

Data mining is a process of extracting knowledge from huge amount of data stored in databases, data warehouses and data repositories. Crime is an interesting application where data mining plays an important role in terms of prediction and analysis. Clustering is the process of combining data objects into groups. The data objects within the group are very similar and very dissimilar as well when compared to objects of other groups. This paper presents detailed study on clustering techniques and its role on crime applications. This study also helps crime branch for better prediction and classification of crimes.
Autonomous Driving using Deep Reinforcement Learning in Urban Environment

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/ by/4.0) ABSTRACT Deep Reinforcement Learning has led us to newer possibilities in solving complex control and navigation related tasks. The paper presents Deep Reinforcement Learning autonomous navigation and obstacle avoidance of selfdriving cars, applied with Deep Q Network to a simulated car an urban environment. “The car, using a variety of sensors will be easily able to detect pedestrians, objects will allow the car to slow or stop suddenly. As a computer is far more precise and subject to fewer errors than a human, accident rates may reduce when these vehicles become available to consumers. This autonomous technology would lead to fewer traffic jams and safe road”.
Operation of Accumulator-Bank Serving Agent System Using Machine Learning

Advancements in on-demand power management of renewable energy can be achieved by multi-agent systems. This paper proposes an innovative approach where a population of autonomous agents are able to cooperate in managing an accumulator-bank in order to effectively deliver energy in places where it is required. The distributed and adaptive multi-agent approach is able to decrease the interferences by avoiding the negative interactions and conflicts, using the cooperation among agents. Our method uses the learning ability of agents to minimize the number of communications among agents and the central unit. This adaptive behavior lets the agents minimize the time to find the optimal routes during the search. A simulation env i- ronment has also been developed for visualizing the movements of the agents and the conflict situations. The operation and t he efficiency of the algorithm have been investigated using sim ple case studies. Keywords-renewable energy; agent; genetic algorithm (GA); cooperation
A hybrid fuzzy current regulator for three-phase voltage source PWM-inverter

This paper presents a fuzzy logic strategy for the current control of a three-phase voltage source power PWM-inverter. The base of this regulator is a direct digital predictive loop which has a very good dynamic performance, but the steady state performance will heavily depend on the accuracy of the model structure and plant's parameters. To reduce the steady errors and compensate the system uncertainties, a feedforward control loop and a fuzzy logic algorithm are introduced. The direct digital predictive loop with the feedforward loop, which can be seen as a hybrid current regulator, generates the command voltage based on the nominal system conditions, guarantees the system with a very fast dynamic response and a smaller system error. The fuzzy logic algorithm, by measuring the amplitude and phase angle of the system error, generates a modifying signal to correct the gains of the direct digital predictive loop and the feedforward loop. When the difference between the reference and actual signals is larger than a given threshold, therefore it minimizes the effects of system uncertainties. Simulation results are presented to verify the effectiveness of the current regulation algorithm.
Speech Reverberation: A Review

The aim of thispaper is to study the degrading effects of reverberation on the speech signal. Reverberation is one of the most important feature existing in a closed room. By estimating the reverberation time, the speech enhancement technique aims toimprove the speech. Speech reverberation is the total decay in the actual signal which is in the form of reflections of the target signal which degrades the quality of the speech.The objective of this review paper is to give an overview of reverberation, its effects on speech signal and estimation of the same. Reverberation is typically represented by the parameter 60 RT Section I gives the introduction. Section 2 gives a brief overview of reverberation and its types. Section 3 describes the various techniques to measure reverberation. Section 4 is the speech models used for the estimation of spectral parameters. Section 5 deals with the estimation of blind 60 RT reverberation. Section 6 gives the summary and conclusion IndexTerms— Reverberation, Reverberation time and its estimation, speech system modeling, Blind 60 RT estimation