Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Alternative Solid Dielectrics of Power Transformers with a Blend of Mineral and Silicon Oils

ABSTRACT In the present paper, the influence of accelerated thermal ageing on the performance of thermally upgraded Kraft, Nomex 910 and Nomex 410 alternative solid dielectrics of transformers in conjunction with mineral oil has been investigated. Subsequently, the characteristics of the solid dielectrics which are impregnated in a blend of 80% mineral and 20% silicon oils are also studied. To investigate these properties, accelerated ageing tests have been carried out at different temperatures ranging from 100 to 220 °C. Furthermore, several electrical and thermal properties of transformer oil which include breakdown voltage, interfacial tension, water content, absorption coefficient and colour factor have been determined. It has been revealed from the test results that Nomex oil-impregnated samples have better thermal characteristics and oxidation stability than the thermally upgraded Kraft oil-immersed samples. Moreover, it is also found from the diagnostic test results that a combination of mineral and silicon oils in conjunction with Nomex insulations has improved characteristics than the samples which are composed of these dielectrics and normal mineral oil. Therefore, Nomex insulating materials and a mix of mineral and silicon oils are suggested as alternative solid and liquid dielectrics for power transformers in order to achieve better oxidation stability, improved thermal characteristics for long service run, reduced operating and maintenance costs. It is envisioned that the present experimental study shall be very beneficial for the utility managers and the end-users of transformers.
Map Reduce and Match Aggregate Pipeline Performance Analysis in Metadata Identification and Analysis for Document, Audio, Image, and Video

The study observes the metadata identification and analysis for Document, Audio, Image, and Videos. The process uses MapReduce and Match Aggregate Pipeline to identify, classify, and categories for identification purposes. The inputs are FITS array results and processed in form of XML. The works consist of the extraction process, identification and analysis, classification, and metadata information. The objective is establishing the file information based on volume, variety, veracity, and velocity criteria as part of task identification component in Self-Assignment Data Management. Testing is done for all file types with the number of files and the size of the file according to the grouping. The results show that there is a pattern where the matchaggregate-pipeline has a longer processing time than MapReduce on a small block size, shown in a block size of 64 Mb, 128 Mb, and 256 Mb. But once the block size is magnified the match-aggregate-pipeline has faster processing time at 1024 Mb and 2048 Mb. The results have a contribution in the metadata processing for large files can be done by arranging the block sizes in Match Aggregate Pipeline.
Evaluation of decentralized email architecture and social network analysis based on email attachment sharing

Present day email is provided by centralized services running in the cloud. The services transparently connect users behind middleboxes and provide backup, redundancy, and high availability at the expense of user privacy. In present day mobile environments, users can access and modify email from multiple devices with updates reconciled on the central server. Prioritizing updates is difficult and may be undesirable. Moreover, legacy email protocols do not provide optimal email synchronization and access. Recent phenomena of the Internet of Things (IoT) will see the number of interconnected devices grow to 27 billion by 2021. In the first part of my dissertation I am proposing a decentralized email architecture which takes advantage of user’s a IoT devices to maintain a complete email history. This addresses the email reconciliation issue and places data under user control. I replace legacy email protocols with a synchronization protocol to achieve eventual consistency of email and optimize bandwidth and energy usage. The architecture is evaluated on a Raspberry Pi computer. There is an extensive body of research on Social Network Analysis (SNA) based on email archives. Typically, the analyzed network reflects either communication between users or a relationship between the email and the information found in the email’s header and the body. This approach discards either all or some email attachments that cannot be converted to text; for instance, images. Yet attachments may use up to 90% of an email archive size. In the second part of my dissertation I suggest extracting the network from email attachments shared between users. I hypothesize that the network extracted from shared email attachments might provide more insight into the social structure of the email archive. I evaluate communication and shared email attachments networks by analyzing common centrality measures and classification and clustering algorithms. I further demonstrate how the analysis of the shared attachments network can be used to optimize the proposed decentralized email architecture.
A New Speed Control Technique for a Separately Excited Direct Current Motor by PID Controller

In this study, by deriving electro-mechanic mathematical model of separately excited direct current motor (SEDCM) via equivalent circuit, a new control block diagram has been formed. By using this block diagram, state space model of SEDCM has been created and block diagram of motor has been formed in Matlab Simulink environment. Steady state and transient values of armature current, emf, produced torque, speed and position angle have been obtained using this new model. By adding PID controller to block diagram, SEDCM has been remodeled. Finally, results of steady state and transient analysis of SEDCM have examined through speed control of the proposed model. It is reported that the suggested modeling technique is able to analyze the speed control of the SEDCM.
Active noise systems for reducing outdoor noise

An ANC system prototype designed for stationary noi se control linked to traditional noise barriers was initially developed by authors in the past year s. The encouraging results initially obtained have shown that ANC systems are feasible and should be further investigated to improve their performances. Nevertheless, FXLMS algorithm, consid ere in the first prototype architecture, is known to be affected by convergence problems that g enerally arise when a system fails to properly identify the noise to be cancelled; this process ma y (and often does) yield an instable system, where the control algorithm tries to catch-up its o wn sound, increasing the overall sound pressure level on the targets as in “avalanche” effect, pote ntially leading to damage fragile instrumentation. Hence, instability is unacceptable in practical app lications and must be avoided or prevented. With this respect, in this manuscript some more rob ust alternative ANC algorithms have been investigated and experimentally verified.
Security and routing issues in wireless networks

Wireless networks have been widely used in industries, military and education. We usually represent a wireless network as a Unit Disk Graph (UDG) G = (V, E). There are many issues considered in wireless networks. In this dissertation, I mainly focused on the security and routing issues which are two of the most important issues in wireless networks. Security is one of the important issues in wireless networks. Secure clustering problem plays an important role in distributed sensor networks. Weakly Connected Dominating Set (WCDS) is used for solving this problem. Therefore, computing a minimum WCDS becomes an important topic of this research. I compared the size of Maximal Independent Set (MIS) and minimum WCDS in unit disk graph. The analysis showed that five is the least upper bound for this ratio. I also presented a distributed algorithm to produce a weakly connected MIS within a factor 5 from the minimum WCDS. DENIAL-OF-SERVICE (DoS) attack, which aims to make a service unavailable to legitimate clients, is another threat to the internet security. I made a connection between group testing and internet security. Group testing is a technique to detect the defective items with minimum number of tests. It tests the items by group instead of one by one to save the testing times. I used group testing technique to test which server or application is being attacked. Group testing is a technique to detect the defective items with minimum number of tests. It tests the items by group instead of one by one to save the testing times. Routing is another most important issues in wireless networks. A good routing path can save much energy. Energy consumption is always a big concern in wireless networks. Connected Dominating Set (CDS) is a popular way to construct the virtual backbone in wireless network. I showed that a minimum nonsubmodular cover problem could be reduced into a problem of minimum submodular cover with submodular cost. And I approached to the solution of MOC-CDS problem by showing a little weaker result using Generalized Hitting Set.
Diversity-Ensured Semantic Movie Recommendation by Applying Linked Open Data

The recommender system becomes a significant research area due to the popularity of the social web. Traditional semantic recommender systems deliver poor performance when balancing the recommendation accuracy and diversity. Also, the rank-based recommendation methods lack to obtain the coverage of the entire preferences of the user in the top-N recommendation list. Thus, this paper presents the Diversity-Ensured Semantic-aware Item REcommendation (DESIRE) that deals with the consistent and reliable knowledge source to significantly improve the quality and provide the diversity-ensured top-N recommendation list. The DESIRE approach builds the semantically relevant graphs such as movie-centric and user rating-centric graph with the help of both the Linked Open Data (LOD) and the explicit ratings of the users. By extracting the semantic-path based features from the user rating-centric graph, it executes the ranking algorithm for the top-N movie recommendation. Moreover, the diversity-aware re-ranking tends to maintain the trade-off between the diversity and accuracy in the top-N recommendation.
Multitask saliency detection model for synthetic aperture radar (SAR) image and its application in SAR and optical image fusion

Abstract. Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR images. We extract four features of the SAR image, which include the intensity, orientation, uniqueness, and global contrast, as the input of the MSD model. The saliency map is generated by the multitask sparsity pursuit, which integrates the multiple features collaboratively. Detection of different scale features is also taken into consideration. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps obtained by the MSD model, we apply the saliency map of the SAR image to the SAR and color optical image fusion. The experimental results of real data show that the saliency map obtained by the MSD model helps to improve the fusion effect, and the salient areas in the SAR image can be highlighted in the fusion results.
A subaperture based approach for SAR moving target imaging by low-rank and sparse decomposition

In this paper, we propose a synthetic aperture radar (SAR) moving-target imaging approach that exploits the low-rank and sparse decomposition (LRSD) of subaperture data. The low-rank component consists of the static background whereas the sparse component captures the moving targets. This allows the reconstruction of a full resolution moving target image separate from the static background image after LRSD. Furthermore, it facilitates the applicability of sparsity-driven moving target imaging in low signal to clutter ratio (SCR) scenarios. We demonstrate the effectiveness of our approach with experiments on synthetic as well as real SAR data.
Personalized Affective Feedback to Address Students’ Frustration in ITS

The importance of affective states in learning has led many Intelligent Tutoring Systems (ITS) to include students’ affective states in their learner models. The adaptation and hence the benefits of an ITS can be improved by detecting and responding to students’ affective states. In prior work, we have created and validated a theory-driven model for detecting students’ frustration, as well as identifying its causes as students interact with the ITS. In this paper, we present a strategy to respond to students’ frustration by offering motivational messages that address different causes of frustration. Based on attribution theory, these messages are created to praise the student's effort, attribute the results to the identified cause, show sympathy for failure or obtain feedback from the students. We implemented our approach in three schools where students interacted with the ITS. Data from 188 students from the three schools collected across two weeks was used for our analysis. The results suggest that the frustration instances reduced significantly statistically ($p < 0.05$), due to the motivational messages. This study suggests that motivational messages that use attribution theory and address the reason for frustration reduce the number of frustration instances per session.
On The Feasibility of Polar Code as Channel Code Candidate for the 5G-IoT Scenarios

5G is on the horizon and internet of things (IoT) is anticipated as an eminent scenario for the 5G network paradigm. The emerging 5G-IoT scenarios are required to potentially support and encourage the massive number of devices of low cost, low latency, long battery life with high reliability. IoT is envisioned to have billions of simultaneous connections over next few years that will completely change the characteristics of mobile network. M2M communication is prime enabling technology of the IoT which is much related to the 5G’s Machine type Communication (MTC). Therefore, 5G’s machine type Communication between massive numbers of devices with mission critical services is become very popular now days and named as 5G-IoT scenario. Channel Coding for the 5G-IoT scenarios is facing novel challenges as to support its various requirements on latency, reliability, scalability and energy consumption. Hence, an FEC code needs to endorse diverse requirements of code lengths and rates, as well as high throughput with low decoding complexity. This paper compares various candidate channel coding methods for 5G-IoT scenario. Polar code is believed as dominant advancement in channel coding theory and guarantees for apical performance. And hence, polar code is considered as promising candidate for the 5G-IoT scenario. This paper put an emphasis on the suitability of polar codes for 5G-IoT scenario .
Desenvolvimento de Uma Solução Computacional Aplicada no Acompanhamento do Processo de Reabilitação Física em Pacientes Amputados de Membros Inferiores

O objetivo deste artigo e apresentar uma solucao baseada na integracao de sensores, da plataforma de prototipagem hardware Arduino e de um software, que monitora sessoes de exercicios fisicos realizados por pacientes amputados em uma bicicleta ergometrica. Assim sendo, como resultado, espera-se que o sistema possa auxiliar os fisioterapeutas na avaliacao do progresso do paciente ao longo do tratamento, de maneira eficiente.
Visualizing and Exploring Highly Processed Information Spaces in Tactical Decision Making

Tactical decision makers rely on highly processed information to make decisions and solve problems. Such highly processed information spaces place unique demands on visualization and information management technologies. The purpose of this paper is to describe our use of a visualization tool, called the Reasoning Systems and Information Space (RSIS) Navigator, which is being used to represent the information associated with a tactical scene for Navy command decision makers. The paper begins with a discussion of the rationale for our approach, shows how a cognitive metaphor is being used to construct visualizations, and concludes with a discussion of relevant research and development issues, and future work.
Improvements to the complex question answering models

In recent years the amount of information on the web has increased dramatically. As a result, it has become a challenge for the researchers to find effective ways that can help us query and extract meaning from these large repositories. Standard document search engines try to address the problem by presenting the users a ranked list of relevant documents. In most cases, this is not enough as the end-user has to go through the entire document to find out the answer he is looking for. Question answering, which is the retrieving of answers to natural language questions from a document collection, tries to remove the onus on the end-user by providing direct access to relevant information. This thesis is concerned with open-domain complex question answering. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Hence, we considered the task of complex question answering as query-focused multi-document summarization. In this thesis, to improve complex question answering we experimented with both empirical and machine learning approaches. We extracted several features of different types (i.e. lexical, lexical semantic, syntactic and semantic) for each of the sentences in the document collection in order to measure its relevancy to the user query. We have formulated the task of complex question answering using reinforcement framework, which to our best knowledge has not been applied for this task before and has the potential to improve itself by fine-tuning the feature weights from user feedback. We have also used unsupervised machine learning techniques (random walk, manifold ranking) and augmented semantic and syntactic information to improve them. Finally we experimented with question decomposition where instead of trying to find the answer of the complex question directly, we decomposed the complex question into a set of simple questions and synthesized the answers to get our final result.
Interactive Image Recognition of Space Target Objects

An interactive image pre-processing algorithm is proposed to solve the problem that the target object is not accurate and the calculation is time consuming when the space robot is in orbit. Firstly, the Grabcut algorithm is improved to identify the space object. The Gaussian filtering, edge detection, morphological processing and other methods are combined with Grabcut algorithm. Then the Hough transform algorithm is improved, and the straight line with similar slope is fitted, and the intersection of the straight line is obtained, which is the key point of the object to describe the spatial coordinates of the object. Through the experimental verification, the method proposed in this paper can meet the task of identifying the object of the space object, which can improve the accuracy and recognition speed of the object recognition and reduce the time taken to identify the space object.
Selfies em memoriais: uma análise do luto na era dos smartphones

Este artigo busca através das selfies - retiradas do aplicativo Instagram com o auxílio da hashtag #Holocaustmonument - clicadas no Memorial do Holocausto (Holocaust-Mahnmal) analisar como as pessoas estão se relacionando com os espaços de luto e silêncio dentro do contexto dos smartphones e suas câmeras. O objetivo é fazer uma breve contextualização da selfie e sua historiografia dentro da História da Fotografia e do Retrato, bem como mostrar como elas têm funcionando neste lugar de experimentação do espaço de luto e silêncio. O uso deste tipo de registro em memoriais traz uma nova forma de presentifi­cação do sujeito no contemporâneo, colocando em questão os significados que o passado dos memoriais pode evocar dentro de um contexto de produção de memo­rialização na Contemporaneidade. Além disto, selfies ajudam a localizar como o ocidente tem pensado o luto e comportamento esperado em locais específicos.
Challenge-Response Authentication Using In-Air Handwriting Style Verification

Challenge-response (CR) is an effective way to authenticate users even if the communication channel is insecure. Traditionally CR authentication relies on one-way hashes and shared secrets to verify the identities of users. Such a method cannot cope with an insider attack, where a user can obtained the secret (i.e., the response) from a legitimate user. To cope with it, we design a biometric-based CR authentication scheme (hereafter <monospace>MoCRA</monospace>), which is derived from the motions as a user operates emerging depth-sensor- based input devices, such as a Leap Motion controller. We envision that to authenticate a user, <monospace>MoCRA</monospace> randomly chooses a string (e.g., a few words), and the user has to write the string in the air. Using Leap Motion, <monospace>MoCRA</monospace> captures the user's writing movements and then extracts his / her handwriting style. After verifying that what the user writes matches what is asked for, <monospace>MoCRA</monospace> leverages a Support Vecter Machine (SVM) with co-occurrence matrices to model the handwriting styles and can reliably authenticate users, even if what they write is completely different every time. Evaluated on data from 24 subjects over 7 months, <monospace>MoCRA</monospace> managed to verify a user with an average of <inline-formula><tex-math notation="LaTeX">$1.18\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>18</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="tian-ieq1-2752164.gif"/></alternatives></inline-formula> (Equal Error Rate) EER and to reject impostors with <inline-formula><tex-math notation="LaTeX">$2.45\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>. </mml:mo><mml:mn>45</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="tian-ieq2-2752164.gif"/></alternatives></inline-formula> EER.
Scalable biometric travel token without barriers to access

Congestion at ticket gatelines is a growing problem. According to travel projections in the UK, the number of journeys for rail passengers is likely to double over the next 30 years. This creates the need to improve passenger throughput while still maintaining revenue protection. The proposed solution is to create a ‘Gateless Gateline’: a concept in which a user can seamlessly authenticate the intent to travel in train stations using current ticketing media ranging from barcode and Bluetooth low energy to radiofrequency identification, as well as new and emerging ticketing media, specifically related to biometrics. This article focuses on utilizing three-dimensional (3D) face recognition based on photometric stereo using near-infrared (NIR) light to illuminate a face for a 3D construction and tracking for the purpose of associating the correct biometric identifier with the correct person in a high-throughput environment. Other biometric systems have also been investigated as part of this project, i.e. palm vein scanning based on passing NIR light through the palm. There are challenges when it comes to associating passengers with valid travel tokens in a system which is free flowing and does not have a modus operandi of a standard access gate where validation occurs one at a time, normally signalled by paddles opening and closing. The proposed solution to the above, alongside the known issues associated with scaling biometric solutions, is reported in this article.