Miracle-dz/UniFAR-FTUs huggingface dataset:
Using the Stator Current Ripple Model for Real-Time Estimation of Full Parameters of a Permanent Magnet Synchronous Motor

In this paper, a new method is proposed for the real-time estimation of the full parameters of a permanent magnet synchronous motor (PMSM) that is based on the stator current ripple model. By introducing the stator current ripple model together with the known two state equations for the <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-axes currents, we resolve the rank deficiency problem, thus enabling real-time full parameter estimation even in the steady state. A signal processing technique for removing adverse effects from noises and uncertainties existing in the stator current ripple measurement is also presented. The efficacy of the proposed method is verified by simulation on a 15-kW PMSM, and both the proposed and conventional methods are compared.
Application of Fuzzy Decision Tree for Signal Classification

A typical algorithm for signal classification consists of two steps: signal preliminary transformation and classification itself. The procedures of preliminary transformation are used to extract specific features of the initial signal and reduce its dimension for effective classification. The result of this transformation is information loss of initial signal, which implies uncertainty of data used in classification. This uncertainty can be taken into account by the application of fuzzy classifiers. In this paper, a new algorithm with application of fuzzy classifier is proposed for signal classification. A new procedure of fuzzification is added into the preliminary transformation and fuzzy decision tree is used for classification. The efficiency of this algorithm is examined based on the problem of detection of defective blades of an aircraft engine gas turbine. The experiments showed that the accuracy of the classification for the considered example is 0.989. This is the best result in comparison with other classification methods used to solve this problem.
Using Bayesian Networks for Web Effort Estimation

The Web is used as a delivery platform for numerous types of Web applications and services, ranging from complex e-commerce solutions with back-end databases to on-line personal static Web pages, blogs and wikis. We currently have available a sheer diversity of Web application types, Web technologies and services, and such diversity is likely to continue growing. However, such diversity also entails many challenges to those who develop/propose such applications, technologies and services. One of these challenges relates to the management of Web projects, where the accurate estimation of effort for new projects is paramount to the proper allocation of resources (Mendes, 2007d). Complementary to the diversity of Web technologies, application types, and services, there is also the issue associated with the characteristics of the companies that generally develop Web applications. They are mostly small to medium, and generally bid for as many Web projects as they can accommodate, delivering applications in domains often unfamiliar to developers (e.g. social networking applications, aggregation services, data ‘mash-ups’), and that use technologies and services with which these companies had no previous experience. This scenario ABSTRACT
AccAnn: A New Subjective Assessment Methodology for Measuring Acceptability and Annoyance of Quality of Experience

User expectations have a crucial impact on the levels of quality of experience (QoE) that they consider acceptable or satisfying. Measuring acceptability and annoyance has mainly been performed in separate or multi-step experiments without any control over participants’ expectations. This paper introduces a simple methodology to obtain the information about both of the entities in a single step and compares several data processing strategies useful for results interpretation. A specifically designed subjective experiment, conducted on compressed videos, has shown that the multi-step procedures could be replaced by our proposed single-step approach, regardless of the viewing conditions, while the novel approach is significantly preferred by observers for its low time requirements and higher intuitiveness. The test has simultaneously proven that user expectations can be altered by the instructions and it is, therefore, possible to simulate different user profiles regardless of the participants’ real habits. The acceptability/annoyance experimental results are also used to benchmark the state-of-the-art objective video quality metrics in predicting acceptability/annoyance of QoE. A case study on the determination of the threshold of acceptability/annoyance for objective quality metrics is conducted, which can be served as a guideline for video streaming service providers.
A Belief Approach for Detecting Spammed Links in Social Networks

Nowadays, we are interconnected with people whether professionally or personally using different social networks. However, we sometimes receive messages or advertisements that are not correlated to the nature of the relation established between the persons. Therefore, it became important to be able to sort out our relationships. Thus, based on the type of links that connect us, we can decide if this last is spammed and should be deleted. Thereby, we propose in this paper a belief approach in order to detect the spammed links. Our method consists on modelling the belief that a link is perceived as spammed by taking into account the prior information of the nodes, the links and the messages that pass through them. To evaluate our method, we first add some noise to the messages, then to both links and messages in order to distinguish the spammed links in the network. Second, we select randomly spammed links of the network and observe if our model is able to detect them. The results of the proposed approach are compared with those of the baseline and to the k-nn algorithm. The experiments indicate the efficiency of the proposed model.
Addressing Big data

Big data is defined as large volume of data which requires new technologies and architecture to process the data and extract significant result from it. The creation and aggregation of data are increasing and will approach the zetta byte range within a few years. Because of this huge size of data it is very difficult to perform effective analysis using the existing traditional techniques. Volume is one of the main aspect of Big data, others being variety, velocity, value, and veracity. The challenge is, how to operate this volume of data in a proper way. Since Big data is a recent booming technology in the business and scientific environment, it is necessary that various challenges and issues associated with it should be brought into light. This paper gives a brief overview of Big data, its challenges, and also analyses in the perspective ofHadoop , MapReduce and MongoDB.
Hallucinating A Cleanly Labeled Augmented Dataset from A Noisy Labeled Dataset Using GAN

Noisy labeled learning methods deal with training datasets containing corrupted labels. However, prediction performances of existing methods on small datasets still leave room for improvements. With this objective, in this paper we present a GAN-based method to generate a clean augmented training dataset from a small and noisy labeled dataset. The proposed approach combines noisy labeled learning principles with GAN state-of-the-art techniques. We demonstrate the usefulness of the proposed approach through an empirical study on simple and complex image datasets.
Biometric Face Recognition Payment System

Use of payment cards in various places such as shopping, restaurants, lodges and online payment for booking hotels, movie tickets, flight and train tickets etc are increasing day by day. So the problem is that a person has to carry payment cards along with him and keep the cards secure to use it all the time. This also lacked security. In the present work the biometric face recognition payments is used in all kinds of payments. Thus it avoids the need to memorize different passwords. Face recognition payment system is safe, secure and even easy to use. It is reliable and more efficient compared to other payment technologies. A general design of online payment system using face recognition is proposed. The methods adopted for face recognition are by finding the eigenfaces and Euclidean distance. Keywords—Face recognition; eigenfaces; cardless payment; normalized histogram; authentication; biometric
Modeling Multi-User WLANs Under Closed-Loop Traffic

In this paper, we present the first cross-layer analysis of wireless LANs operating under downlink multi-user multi-in multi-out (MU-MIMO), considering the fundamental role played by the closed-loop (TCP) traffic. In particular, we consider a scenario in which the access point transmits on the downlink via MU-MIMO, whereas stations must employ single-user transmissions on the uplink, as is the case in IEEE 802.11ac. With the help of analytical models built for different regimes that can occur in the considered system, we identify and explain crucial performance anomalies that can result in very low throughput in some scenarios, completely offsetting the theoretical gains achievable by MU-MIMO. We discuss solutions to mitigate the risk of this performance degradation and alternative uplink strategies allowing WLANs to approach their maximum theoretical capacity under MU-MIMO.
Power Allocation in Cognitive Satellite-Vehicular Networks From Energy-Spectral Efficiency Tradeoff Perspective

With great potential to support multitudinous services and applications in intelligent transportation systems, cognitive satellite-vehicular networks, an emerging paradigm of cognitive satellite-terrestrial networks, are attracting increasing attentions. To realize friendly coexistence of satellite and vehicular networks as well as efficient resource utilization, we investigate power allocation for the cognitive satellite-vehicular network under a realistic 3-D vehicle-to-vehicle channel model. Specifically, by analyzing the characteristics of energy efficiency (EE) and spectral efficiency (SE) performance in different vehicular environments, we first develop an EE–SE tradeoff metric with a preference factor. Based on the developed metric, we formulate and analyze a power allocation strategy from the EE–SE tradeoff perspective while guaranteeing the interference power constraint imposed by satellite communications. Further, utilizing the obtained optimal transmit power, we derive a closed-form expression of the outage probability, through which the impacts of the preference parameter and interference constraints on the performance of vehicular communications can be theoretically analyzed. Finally, numerical results are provided to demonstrate the viability of the EE–SE tradeoff metric and the validity of theoretical analyses.
Supporting Reuse of EHR Data in Healthcare Organizations: The CARED Research Infrastructure Framework

Healthcare organizations have in recent years started assembling their Electronic Health Record (EHR) data in data repositories to unlock their value using data analysis techniques. There are however a number of technical, organizational and ethical challenges that should be considered when reusing EHR data, which infrastructure technology consisting of appropriate software and hardware components can address. In a case study in the University Medical Center Utrecht (UMCU) in the Netherlands, we identified nine requirements of a modern technical infrastructure for reusing EHR data: (1) integrate data sources, (2) preprocess data, (3) store data, (4) support collaboration and documentation, (5) support various software and tooling packages, (6) enhance repeatability, (7) enhance privacy and security, (8) automate data process and (9) support analysis applications. We propose the CApable Reuse of EHR Data (CARED) framework for infrastructure that addresses these requirements, which consists of five consecutive data processing layers, and a control layer that governs the data processing. We then evaluate the framework with respect to the requirements, and finally describe its successful implementation in the Psychiatry Department of the UMCU along with three analysis cases. Our CARED research infrastructure framework can support healthcare organizations that aim to successfully reuse their EHR data.
Measuring Task Learning Curve with Usage Graph Eccentricity Distribution Peaks

Interaction logs (or usage data) are abundant in the era of Big Data, but making sense of these data having Human-Computer Interaction (HCI) in mind is becoming a bigger challenge. Interaction Log Analysis involves tackling problems as automatic task identification, modeling task deviation, and computing task learning curve. In this work, we propose a way of measuring task learning curve empirically, based on how task deviations (represented as eccentricity distribution peaks) decrease over time. From the analysis of 427 event-by-event logged sessions (captured under users’ consent) of a technical reference website, this work shows the different types of learning curves obtained through the computation of how deviations decrease over time. The proposed technique supported the identification of 6 different task learning curves in the set of 17 tasks, allowing differentiating tasks easy to perform (e.g., view content and login) from tasks users face more difficulties (e.g., register user and delete content). With such results, HCI specialists can focus on reviewing specific tasks users faced difficulties during real interaction, from large datasets.
Sharing Secure Data in The Cloud To Multiple users Among a Dynamic Group

Cloud computing provides an economical and efficient solution for sharing group resources among cloud users. When sharing the data in a group while preserving data, identity privacy is still a challenge because of frequent changes in the membership. To overcome this problem, a sharing secure data scheme among multiple users among a dynamic group is proposed so that any user within a group can share the data in a secure manner by leveraging both the group signature and dynamic broadcast encryption techniques. It enables authorized users to anonymously share data with others within the group. It supports efficient member revocation and new member joining the group. User revocation list is performed by group manager and it is given to the cloud service provider to check the active users within the group before giving access to the cloud.
Classification of EEG and fNIRS signals from Completely Locked-in State Patients for a Brain-Computer Interface communication system

People suffering from complete motor paralysis with no severe deficiency in cognitive abilities, syndrome called Completely Locked in State (CLIS), remain aware of their surroundings without being able to interact and communicate in any way. In this context, the only possibility of communicating is by the techniques of Brain-Computer Interface. In this work, the focus is on the features extraction and selection on EEG and fNIRS signals and, finally, on the combination of the two to develop a system capable of classifying affirmative and negative answers from users in CLIS. The analysis considers the data collected in 4 visits to one patient. The choice to focus on a single case was made because the psychophysical considerations on the state of the patient are fundamental interpreting the results and the author of this work had the opportunity to participate directly in some acquisition. Offline analysis led to good results in the classification of fNIRS signals. Once again, using EEG signals it was not possible to successfully classify yes/no answers. Finally, the combination of EEG and fNIRS features did not improve the performance of the system.
Working engineers’ multimedia type preferences

Abstract While the continuing education of working engineers is important, relatively few recommendations are given in the literature for how to design effective instructional materials for those engineers. The study reported here addressed this gap by investigating working engineers’ multimedia type preferences. Four media types in four different categories (in parentheses) were compared: text (verbal), drawing (static graphic), animation (dynamic non-interactive graphic) and simulated virtual reality (dynamic interactive graphic). The results showed working engineers strongly preferred graphics (drawing, animation and simulated virtual reality) over verbal (text) multimedia. There was relatively little difference in preferences among the graphic types. These results suggest a variety of graphic multimedia types should be used in the instructional design of continuing engineering education content and text should not be solely relied on to carry information content.
Operator, number please : mediating access to shared resources for efficiency and isolation

The performance density of modern hardware has forced the sharing of hardware resources across applications for better utilization and efficiency. Shared infrastructure, however, weakens isolation and risks interference, which can result in degraded performance and security breaches. This thesis explores the tension between isolation and sharing with three prototype systems: Xoar, Plastic, and Decibel. All three of these systems demonstrate the value of software mediation in providing isolation on shared hardware without sacrificing either hardware resource utilization or the performance of the underlying devices. Xoar, Plastic, and Decibel provide isolation for different hardware resources: Xoar strengthens isolation between virtual machines, thereby allowing underutilized processors to be shared; Plastic transparently mitigates poor cache utilization and the performance artifacts caused by insufficient cache line isolation across cores; and Decibel provides isolation in shared non-volatile storage and guarantees throughput, even in the face of competing workloads.
Disasters Tracing with Occurred Events and Unexpected Ones

Disasters occur and are broadcasted by social media in our daily life. Retrieving the user-oriented relevant events in an aggregated results list through meta-search engine is a useful application. The definition and detection of events has been a hot topic in the study of natural language processing. Then gathering the disaster event information via different search interface for an event chain releasing is important. This paper designed an event-triggered model for events detection. As the emergency requirement of government as well as individual activities, disaster events detection is discussed as an example in this paper. A meta-search engine interface model is constructed with java based on the open source project Carrot2. And a H-T-E (Hazard, Trigger and Event) query expansion approach is applied in our event triggered model. The experimental results show that the proposed method can bring about increased accuracy and extra clues not supplied by commercial search engines. The solution we applied can be useful for other information supervision tasks and some issues like expecting the unexpected crisis etc. also triggered by the project for further study.
Tracy: Tracing Facts over Knowledge Graphs and Text

In order to accurately populate and curate Knowledge Graphs (KGs), it is important to distinguish ? s? p? o? facts that can be traced back to sources from facts that cannot be verified. Manually validating each fact is time-consuming. Prior work on automating this task relied on numerical confidence scores which might not be easily interpreted. To overcome this limitation, we present Tracy, a novel tool that generates human-comprehensible explanations for candidate facts. Our tool relies on background knowledge in the form of rules to rewrite the fact in question into other easier-to-spot facts. These rewritings are then used to reason over the candidate fact creating semantic traces that can aid KG curators. The goal of our demonstration is to illustrate the main features of our system and to show how the semantic traces can be computed over both text and knowledge graphs with a simple and intuitive user interface.
RNN-Assisted Network Coding for Secure Heterogeneous Internet of Things With Unreliable Storage

With the rapid growth of Internet of Things (IoT), integrating a variety of IoT can result in novel applications. However, IoT devices are often deployed in an open environment where IoT are inclined to be malfunctioned. Although data reliability can be achieved by data recovery with conventional replication, the communication between IoT is susceptible to eavesdropping. Therefore, in this paper, we study the eavesdropping prevention of data repair in IoT environments based on network coding. We theoretically derive the relation between security level and storage in heterogeneous IoT systems. To further reduce the repair bandwidth, we exploit recurrent neural network for the storage failure prediction. Under the condition when failure probability and workloads of storage devices are considered, two allocation algorithms are proposed to avoid data repair. Finally, we show the relation between storage cost and reliability with different numbers of IoT devices. Experimental results manifest that the proposed allocation algorithms can outperform the baseline case by 18.4% in terms of the security level.
Visual measurement system for chosen parameters of current collectors

A current collector is an element in a traction vehicle, used for movable, contact con-nection between the main circuit of this vehicle and the contact line. Bad technical condi-tion of these important elements in the main circuit of the vehicle result in its incorrect cooperation with contact line. Periodic control of technical condition of current collectors ensures infallible current collection. The pantograph diagnostics consists of measurements of those parameters which influence correct cooperation of the collector with contact line. Technical diagnostics of locomotives comprises, among others, research of pantograph air driver. In norms and regulations which define the requirements regarding current collec-tors and methodology of their research, it is determined that the vertical movement of the contact slipper while it is being raised and lowered with the usage of air drive should be smooth, without stops and sudden changes of speed. Also, it should touch the contact line and then drop without any impact.In order to make an objective assessment of collector driver possible, we define meas-urements of time parameters determined as raising time, lowering time and the slipper con-tact detachment time. The diagnosis considering these parameters is performed during peri-odical inspections of locomotives. The measurement methods employed nowadays lack accuracy and are time-consuming. Therefore, it has become necessary to design and realise an automatic measurement stand which would guarantee a higher level of accuracy and shorten the time of diagnosis.