Miracle-dz/UniFAR-FTUs huggingface dataset:
Creating a Secured Cloud Based Data Center Using Billboard Manager (BM) and Secuare Co- Processor

Cloud computing is fast becoming a reality, combining IT on-demand with pay-per-use flexibility. But cloud computing not only cuts costs - it also provides increased resilience for critical applications. Cloud computing has become one of the most significant issues in recently. Those associative applications and services based on cloud computing are dramatically emerging. However, in order to enjoy the widely utiliza- tion of cloud computing through wired/wireless networking, providing sufficient assurance of information security such as confidentiality, authenti- cation, non-repudiation, and integrity is the critical factor of success . As in most other streams of computers, security is a major obstacle for cloud computing. In this paper, we proposed secure cloud data center architecture. We discuss a secure cloud data center architecture where end users can connect over a secure channel and store their encrypted data in suitable storage area with the help of secure co-processor and Billboard Manager. Billboard Manager (21) helps to choose the appropriate location of storage. Key woard— cloud data center, Authentication,secure channel, secure co-processor, Billboard Manager,storage, ——————————  ——————————
A Novel Hyperspectral Endoscopic Imaging Method Based on Broad- and Overlapped-Band Filters

In medical domain, hyperspectral imaging (HSI) [1] offers a hybrid modality of optical diagnostics that obtains spectral information and renders the information in image form, thus it has great potential for noninvasive disease diagnosis and surgical guidance [2]. Masood [3] explored a series of research problems for classification of hyperspectral (HS) colon biopsy images, and concluded that HSI has enough discriminatory power to distinguish normal and malignant biopsy tissues. However, HS illumination is always with low intensity and signal-to-noise (SNR) ratio is low for HS images. The reason is that sensors of endoscopic systems, such as chargedcouple-device (CCD), work by converting photons into electrons which are then stored in each pixel. The number of electrons stored in each pixel well is proportional to the number of photons that struck that pixel, although in actual pixel well, electrons are not only generated when receiving photons, but also due to physical processes within CCD itself, such as thermal noise which generates additional electrons dependent to temperature [4,5]. At the same time, dispersive device such as monochromators (such as prism and grating), and optical filters (including interference filters and tunable filters) are commonly used for HS imaging. Using these traditional filtering approaches, only a low fractions of photons are transmitted into the sensor. Moreover, due to the requirements of safety and keeping the working time per frame as short as possible for in-vivo and real-time application in medical domain, common methods which could improve SNR, such as using cooled sensor or high exposure time, are not applicable for HS endoscopy systems. To overcome this drawback, we proposed this novel HS imaging method based on broadand overlapped-band filters [6].
Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information

Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scene spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, in order to cope with the problem of data sparsity, we propose to comprehensively describe POI using various types of user generated content (UGC) (e.g., text and image) from multiple sources. Then, we devise an effective POI modelling method in a hierarchical manner, which simultaneously injects semantic knowledge as well as multi-clue representative power into POIs. For evaluation, we construct a multi-source POI dataset by collecting all the textual and visual content of several specific provinces in China from four main-stream tourism platforms during 2006 to 2017. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions’ popularity as compared to several baseline methods.
Project Learning and Virtual Collaboration--A Master Program in ICT and Learning.

: This paper will introduce a master program in ICT and Learning (MIL) and present some of the experiences we have gained so far. MIL is a result of a collaborative initiative taken by five Danish universities, and it is an accredited part-time 2-year master program. It is unique in the sense that it builds on the pedagogical framework of project pedagogy and is based on online collaboration. The paper will describe MIL, the universities involved, the administrative organization, and the profile of the students. We will discuss the pedagogical framework and the project collaboration in relation to the modularity and flexibility that characterize the study and allow admission of part-time students, full-time students and students who only sign up for one accredited module. The methodology will be illustrated through empirical snapshots from selected modules in the start-up phase, and the focus will be directed towards problems experienced by the students. From an analytical perspective, the paper will identify and discuss fundamental problems related to the organization, flexibility, and implementation of project pedagogy online. MIL is organized around ICT and Learning and the study theme focuses on ICT and Learning. In addition, MIL provides a learning space where practice is under constant negotiation and reconstruction as an inherent, integrated part of the learning process. Consequently, we argue that MIL may be seen as an example of best practice in blended learning.
Hybrid Mppt Applied To Boost Zvt Converter And H6 Inverter Association For Photovoltaic Applications

This paper presents a single-phase inverter applied to photovoltaic distributed generation, based on the association of a Boost ZVT converter and a H6 type inverter intended for grid connection. The designed 4.8 kW rated power prototype, which is fed by PV arrays, may demand boost voltage during operation. However, due to its nominal current values the resonant Boost ZVT converter has advantages, considering its low electromagnetic interference, as well as high efficiency. The H6-type inverter allows the design of grid connecting elements with reduced inductance, providing low common mode voltage. A hybrid MPPT system that can act depending on the panel association input voltage is presented which may increase efficiency. Finally, analysis and experimental results are presented considering the main control algorithms for the application. 
A Quantitative Evalution of the Enhanced Topic-based Vector Space Model

This contribution presents a quantitative evaluation procedure for Information Retrieval models and the results of this procedure applied on the enhanced Topic-based Vector Space Model (eTVSM). Since the eTVSM is an ontology-based model, its effectiveness heavily depends on the quality of the underlaying ontology. Therefore the model has been tested with different ontologies to evaluate the impact of those ontologies on the effectiveness of the eTVSM. On the highest level of abstraction, the following results have been observed during our evaluation: First, the theoretically deduced statement that the eTVSM has a similar effecitivity like the classic Vector Space Model if a trivial ontology (every term is a concept and it is independet of any other concepts) is used has been approved. Second, we were able to show that the effectiveness of the eTVSM raises if an ontology is used which is only able to resolve synonyms. We were able to derive such kind of ontology automatically from the WordNet ontology. Third, we observed that more powerful ontologies automatically derived from the WordNet, dramatically dropped the effectiveness of the eTVSM model even clearly below the effectiveness level of the Vector Space Model. Fourth, we were able to show that a manually created and optimized ontology is able to raise the effectiveness of the eTVSM to a level which is clearly above the best effectiveness levels we have found in the literature for the Latent Semantic Index model with compareable document sets.
Discourse with Visual Health Data: Design of Human-Data Interaction

Previous work has suggested that large repositories of data can revolutionize healthcare activities; however, there remains a disconnection between data collection and its effective usage. The way in which users interact with data strongly impacts their ability to not only complete tasks but also capitalize on the purported benefits of such data. Interactive visualizations can provide a means by which many data-driven tasks can be performed. Recent surveys, however, suggest that many visualizations mostly enable users to perform simple manipulations, thus limiting their ability to complete tasks. Researchers have called for tools that allow for richer discourse with data. Nonetheless, systematic design of human-data interaction for visualization tools is a non-trivial task. It requires taking into consideration a myriad of issues. Creation of visualization tools that incorporate rich human-data discourse would benefit from the use of design frameworks. In this paper, we examine and present a design process that is based on a conceptual human-data interaction framework. We discuss and describe the design of interaction for a visualization tool intended for sensemaking of public health data. We demonstrate the utility of systematic interaction design in two ways. First, we use scenarios to highlight how our design approach supports a rich and meaningful discourse with data. Second, we present results from a study that details how users were able to perform various tasks with health data and learn about global health trends.
A three dimensional modeling approach to groundwater management in Paharpur Canal Command Area , Dera

The main aim of this study was to analyze the groundwater flow system in Paharpur Canal Command Area, Dera Ismail Khan using 3D numerical groundwater flow modeling. The groundwater flow system in the area was modeled using MODFLOW 4.2. The model was run for both steady and non-steady state conditions. The area was modeled with a grid of 171 columns and 399 rows with three layers assigned on the basis of the varying hydraulic conductivity values. The upper 100 meter of the unconfined aquifer was modeled. The steady state model was simulated and then calibrated for the year 1980 using PEST. Non-steady state simulation was carried out from the year 1980 to 2010 to evaluate the effects of groundwater abstraction on the water table depths and flow direction. The groundwater model developed has resulted in better understanding of the groundwater flow of the study area. Water logged zones have also been identified with the aid of the model. Groundwater management of the area can be effectively carried out with the support of the model developed.
Écologie et régulation des relations homme-faune : repenser la conservation de la biodiversité par les Communs

La maniere dont l’Homme pense et represente ses relations avec l’animal sauvage a ete profondement marquee d’une part par la maniere dont les ecologues ont decrit, organise et explique le monde vivant, et d’autre part, par la maniere dont les juristes ont elabore et interprete les regles de droit. Les modes de classification, de quantification et de fonctionnalisation du vivant etabli par la science ecologique ont contribue a la formulation du droit autour de categories assorties d’une finalite anthropocentree de la regulation des relations entre humains et non-humains. Cette regulation a principalement consiste a attenuer les impacts du developpement economique et a conserver une biodiversite minimum. Dans cette histoire, la place et le role des animaux sauvages comme acteurs des systemes naturels ont ete oublies. Nous proposons ici de questionner la structure du droit en la confrontant aux pistes ouvertes par le renouveau des sciences ecologiques, des debats et des dispositifs de politiques publiques pour depasser la vision reductrice et anthropocentree de l’enjeu de l’erosion de la biodiversite. L’approche par les Communs contribue aujourd’hui a remettre en question les modeles dominants de regulation des societes humaines en privilegiant le « faire commun ». Nous analysons les perspectives qu’offre cette approche pour restituer les notions d’interdependance, de responsabilite, et de solidarite entre humains et non-humains dans l’usage des ressources.
What works in fraud prevention: a review of real-world intervention projects

Purpose The purpose of this paper is to report on a search of criminology and related databases to identify fraud prevention intervention projects that showed promising achievements in reducing offences. Design/methodology/approach A keyword search of Criminal Justice Abstracts was supplemented by searches of related databases and case study repositories – conducted up to 31 May 2019. The paper includes some attention to indicators of “what doesn’t work” as well as “what might work” or “what should work”. Selection criteria were open about evidence, rather than exclusively applying strict scientific standards. Findings Overall, the literature is marked by inferences regarding what should work, based on diagnostics about opportunity factors in fraud and victim characteristics. In terms of evaluated intervention projects, only two were identified that met best practice scientific standards. However, a further 17 projects were included where there was some evidence of a likely benefit. Research limitations/implications Available evaluations of intervention projects are predominantly within-in group studies. Pre- and post-intervention time frames are often short, limited to one year or less; with only one follow-up study. Practical implications The findings demonstrate the need for applied experiments to build knowledge about effective fraud reduction strategies. The main lesson from the content of the 19 projects concerns the value of a situational crime prevention framework for designing and testing interventions, especially through enhancing rule setting, reducing anonymity, extending guardianship and formal surveillance, and facilitating compliance. Originality/value This appears to be the first systematic literature review of applied counter-fraud intervention projects.
Szenarien modellbasierten Testens

Zusammenfassung Modellbasiertes Testen ist eine Technik zur¨Uberprüfung des Verhal-tens eines Systems, die auf dem Einsatz von Modellen, d.h. Abstrak-tionen, des Systems beruht. Verschiedene Einsatzmöglichkeiten von Modellen werden untersucht. Darunter fallen die Reihenfolge der Ent-wicklung von Modell und System (Modell-Code, Code-Modell oder unabhängige Entwicklung), die Rolle von Modellen bei der Testfall-generierung sowie als Orakel bei der Testdurchführung und schließlich der Einsatz von Modellen bei Systementwicklung,-verifikation und-validierung.
A Review of the Chemistry of Anaerobic Digestion: Methods of Accelerating and Optimizing Process Efficiency

The anaerobic digestion technology has been in existence for centuries and its underlying theory established for decades. It is considered a useful technology for the generation of renewable energy, and provides means to alleviate problems associated with low access to energy. However, a great deal of current research is targeted towards the optimization of this technology under diverse digestion process conditions. This review presents an in-depth analysis of the chemistry of anaerobic digestion and discusses how process chemistry can be used to optimize system performance through identification of methods that can accelerate syntrophic interactions of different microorganisms for improved methanogenic reactions. Recent advances in addition to old research are discussed in order to offer a general but comprehensive synopsis of accumulated knowledge in the theory of anaerobic digestion, as well as an overview of previous research and future directions and opportunities of the AD technology. Achieving a sustainable energy system requires comprehensive reforms in not just economic, social and policy aspects, but also in all technical aspects, which represents one of the most crucial future investments for anaerobic digestion systems.
A Spatial Collaboration: Building a Multi-Institution Geospatial Data Discovery Portal

As academic education and research increasingly take advantage of geospatial data and methodologies, we see a corresponding exponential growth in the number of available geospatial resources in the form of GIS datasets and scanned historical maps. However, users can experience difficulty finding these resources due to the unconnected multitude of platforms and clearinghouses that host them. Additionally, the resources are not always well described with web semantic metadata that facilitates discovery. In response to this challenge, The Big Ten Academic Alliance Geospatial Data Project began in 2015 to provide discoverability, facilitate access, and connect scholars to geospatial resources. Our project leverages a multi-institutional collaboration and open source technologies to improve discovery for users of geospatial data and scanned maps. We outline collaborative workflows and strategies for a successful multi-institution collaboration.
Modelado del sistema cardiovascular para evaluación de técnicas de estimación de reactividad vascular basadas en hiperemia reactiva

Introduction: The Evaluation of Vascular reactivity (VR) is done by the hyperemic response after ischemia produced by arterial occlusion. There are VR measurement techniques that allow the evaluation of vascular function at lower cost and without dependence on the operator, but they are in development and require validation and clinical acceptance. Objective: To model vascular mechanics computationally in order to evaluate the performance of a VR technique. Materials and methods: The electrical model of the vasculature of the arm was modified, obtaining the peripheral volume with and without brachial artery occlusion. A computational identification, which relates the peripheral volume to the results of a VR evaluation technique and presents color changes in the occluded hand during reactive hyperemia, was performed. The software used was Matlab ®. Results: The modified model allowed to obtain the peripheral volume with and without occlusion, representing the perfusion in the microvasculature. The Hammerstein-Weiner non-linear model was the best descriptor of color changes depending on the dynamics of the vascular system and it presented an average adjustment percentage of 95.69%. Conclusions: It is possible to model computationally the technique of evaluation of vascular function using nonlinear identification.
GRAS: a Research and Development Framework for Grid and P2P Infrastructures

Distributed service architectures are mandatory to handle the platform scale and dynamicity hindering the development of grid and P2P applications. These large-scaled distributed applications are difficult to design, develop and tune because of both theoretical and practical issues. This paper presents the GRAS framework that allows developers to first implement and experiment with such an infrastructure in simulation, benefiting from a controlled environment. The infrastructure can then be deployed in-situ without code modification. We detail our design goals, and contrast them with the state of the art. We study the exchange of a message (from the Pastry protocol) using either GRAS or several other solutions. We quantify both the code complexity and the performance and find that GRAS performs better according to both metrics.
Phrase-Based Language Model in Statistical Machine Translation

As one of the most important modules in statistical machine translation (SMT), language model measures whether one translation hypothesis is more grammatically correct than other hypotheses. Currently the state-of-the-art SMT systems use standard word n-gram models, whereas the translation model is phrase-based. In this paper, the idea is to use a phrase-based language model. For that, target portion of the translation table are retrieved and used to rewrite the training corpus and to calculate a phrase n-gram language model. In this work, we perform experiments with two language models word-based (WBLM) and phrase-based (PBLM). The different SMT are trained with three optimization algorithms MERT, MIRA and PRO. Thus, the PBLM systems are compared to the baseline system in terms of BLUE and TER. The experimental results show that the use of a phrase-based language model in SMT can improve results and is especially able to reduce the error rate.
P300 Tabanlı Beyin Bilgisayar Arayüzü Sistemlerinde Uyaranlar Arası Sürenin ve Uyaran Yapısının Performansa Etkisi

Electroencephalography based brain computer interface systems provide communication with the environmental devices for users who cannot use neuromuscular system. There are various brain signal activities for EEG-based BCIs. P300 is a type of event related potential and is a convenient signal type for BCI systems. One of the most important performance parameters is the classification accuracy rate for a P300-based BCI. In this study we aimed to obtain a higher accuracy rate with the changes of row column based P300 speller structure. Changes were made to the matrix structure of P300 speller and inter stimulus interval duration. our new approach region based P300 speller. In most of previous studies are about changes of stimlus color and shapes in row column based P300 speller. In this research experiments with 4 different modes P300 speller were used for character selection. The EEG recordings that collected in the experiments were pre-processed and then classified by stepwise linear discriminant analysis. Acording to the classification result the highest average of the classification accuracy was reached to 84.76% in the experiments with 4x4 matrix based P300 speller with 150 ms inter stimulus interval duration. In the contrary of this the lowest classification accuracy was observed with 6x6 matrix based P300 speller with 300 ms inter stimulus interval duration. This study showed that the row column based P300 speller can be achieved to high accuracy rate with changes in stimulus matrix structure and inter stimulus interval duration.
Thermal Modelling for Permanent Magnet Synchronous Machine (PMSM)

It is important to predict the temperature in some of the essential parts in machine design to prevent the machine from any damage. Most researchers usually not given much attention on thermal analysis during machine design as much as the electromagnet design. Therefore, in this study, two types of thermal modelling were proposed to predict the temperature of a permanent magnet synchronous machine (PMSM). Infolytica ThermNet software has been used since the software is related to Infolytica Magnet and the losses obtained from the electromagnetic field analysis are used as the heat source in the thermal analysis. Comparison has been done between the predicted temperature from lumped parameter model and finite element analysis. The results also will be validated by using experimental setup. Since Infolytica Magnet has been used to analyse the electromagnetic output therefore finite element analysis model is the better choice.