Miracle-dz/UniFAR-FTUs huggingface dataset:
Content and Location Preferences Documents for Personalized Results

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to search results for a particular user based on that user’s interests and preferences. Therefore, a new web search personalization approach has been proposed that captures the user's interests and preferences in the form of concepts by mining search results and their click through on the client side. To improve the personalized search, the ontological profile was created which will be very helpful in obtaining most relevant results. In personalized search system, ranking method is used which employs semantic similarity to improve the quality of search results. Architecture and design for implementation of PMSE can be described by using client-server model. In PMSE, client collects and stores locally the click through data or Queries, whereas server different operation such as extraction, training and re-ranking on the click through data. In PMSE, local click through data Provides privacy. By introducing an association rule mining algorithm collect the different travel patterns by original search engine result in each and every query of user from the original personal mobile search engine profile. Association rule learning is used for finding the interesting query travel pattern results from each user query in PMSE search engine. From this query related patterns of the user to identify strong rules discovered in databases using different measures of interestingness.
Boost Integrated High Frequency Isolated Half-Bridge DC-DC Converter: Analysis, Design, Simulation and Experimental Results

Recently, there has been a growing interest in alternative energy sources because world energy crisis intensified and the growing demand of energy globally. Among the various alternative source of energy, solar power stands apart as it is a clean, abundant and unlimited source of energy. Photovoltaic (PV) systems generally use DC-DC boost converter structure to step-up the low voltage to a higher voltage level. This DC-DC converter will form the front-end of utility interfaced PV array converter system. The performance of DC-DC converter has a direct impact on the conversion efficiency of PV system. This project report presents design, analysis, simulation and experimental results for a step-up dc-to-dc converter with high-frequency transformer isolation for use with photovoltaic array output. After reviewing the literature and discussing pros and cons of the existing topologies we select a configuration that has maximum efficiency, minimum number of switches and simple structure. This converter has the advantages such as high-voltage conversion ratio, low input current ripple and soft switching for all switches. Then we analyze the selected converter and design it for the required specifications (rated power 400 Watts, 40 to 80 V input, 200 V output). In the next step we simulate the designed converter with PSIM simulation package. An experimental circuit is also built to verify the analysis and simulation. The simulation and experimental results show that for the whole input voltage range the converter works in ZVS from full load to light load.
Combination of multisource information to ground target tracking

Ground targets can be detected by multiple classes of sources in military surveillance. There are two main challenges for the acquisition of ground situation picture from data collected by multiple sources. First, different sources provide different information that describes military entities at different granularities and accuracies. This makes processing of data in one unified tracker difficult. Secondly, the data update rates of these sources vary, some update rates could be very low (such as hours), leading to greater difficulty for data association. This paper presents our attempt in multi-source ground target tracking, taking the above two issues into consideration. Targets are tracked in groups, and multiple trackers are designed so that data of different granularities are processed by the respective trackers. Tracks from these trackers are then correlated to form the common picture. Two strategies are proposed to handle the problem of varying data update rate. The first strategy is to exploit different approaches to calculate the beliefs of data association according to update rates. When update rate is high, the belief is calculated by a distance function based on estimated kinematical states. When update rate is low, the belief of data association is computed using Bayesian network. Bayesian network infers the beliefs based on observed information and domain knowledge. The second strategy is to exploit the complementary information in different trackers to improve data association. The first step is to find the correlation among tracks from different trackers. This track-track correlation information is fed back to modify the beliefs of data associations in the tracks. Experiments demonstrated that such combination of multi-source information not only produces more complete ground picture, but also helps to improve the data association accuracy in the respective trackers.
De la pertinence à l'utilité en recherche d'information : le cas du Web

Les paradigmes mis en œuvre par les systemes de recherche documentaire traditionnels se retrouvent dans la conception des moteurs de recherche sur le Web. Cependant, les concepteurs doivent innover pour s'adapter a ce contexte particulier, qui a un impact important sur le fonctionnement des outils. A travers une analyse de la litterature scientifique et des observations realisees durant des sessions de formation a la recherche, nous analysons dans quelle mesure l'usager est pris en compte dans la conception meme de ces systemes, en comparant les implantations des paradigmes aux recherches sur le besoin d'information et le processus informationnel. Une analyse des differentes etapes du processus de recherche d'information souligne la complexite et les nombreuses dimensions du concept de pertinence en recherche documentaire.
A Graph Clustering Algorithm for the Homology Detection

In order to detect a large number of source program samples which are homologous files (files with plagiarism), a new graph-based cluster detection algorithm is proposed，the algorithm is divided into two phases, in the first phase, proposed algorithm based on the keyword program to calculate pairwise similarity in the detected sample program files,in the second stage,by means of graph clustering algorithm, the results of the first phase is dectected, homologous files (files with plagiarism) will form a cluster. The simulation results shows that the algorithm improved detection rate compare with the traditional homologous files detection algorithm and can determine which files are homologous.
Bio4j: a high-performance cloud-enabled graph-based data platform

Background Next Generation Sequencing and other high-throughput technologies have brought a revolution to the bioinformatics landscape, by offering sheer amounts of data about previously unaccessible domains in a cheap and scalable way. However, fast, reproducible, and cost-effective data analysis at such scale remains elusive. A key need for achieving it is being able to access and query the vast amount of publicly available data, specially so in the case of knowledge-intensive, semantically rich data: incredibly valuable information about proteins and their functions, genes, pathways, or all sort of biological knowledge encoded in ontologies remains scattered, semantically and physically fragmented. Methods and Results Guided by this, we have designed and developed Bio4j. It aims to offer a platform for the integration of semantically rich biological data using typed graph models. We have modeled and integrated most publicly available data linked with proteins into a set of interdependent graphs. Data querying is possible through a data model aware Domain Specific Language implemented in Java, letting the user write typed graph traversals over the integrated data. A ready to use cloud-based data distribution, based on the Titan graph database engine is provided; generic data import code can also be used for in-house deployment. Conclusion Bio4j represents a unique resource for the current Bioinformatician, providing at once a solution for several key problems: data integration; expressive, high performance data access; and a cost-effective scalable cloud deployment model.
Image Retrieval Using A New Fractal Feature and Robust Structural Information

The rapid increase in the volume of electronically archived images and video materials has given rise to a need for new methods of image retrieval. Therefore, various approaches have been proposed. Most conventional image retrieval methods are based on the content of objects or on features describing only the partial properties of images. But the diverse images we must deal with have induced a necessity to retrieve images more flexibly and effectively, focusing on the multiple properties of images. In this paper, we describe a new method of image retrieval based on two new ideas. One is adopting a fractal sequence designed to include multiple information of texture and to relate one's first impression of an image. The other is structural information of images by allowing rectangular regions, which are set to extract features in multiple points on the image, to be located at slightly varied positions dependent on the image features. Additionally, we show with the results of some experiments that image retrieval that widely perceives the information of images more variously can be achieved because of our applying the new method.
OPEN EDUCATIONAL RESOURCES (OER): OPPORTUNITIES AND CHALLENGES FOR INDIAN HIGHER EDUCATION

Creation of knowledge workers holds key for success of a country. Unfortunately, many of the countries though having chain of human resources yet are unable to transform human resources to their advantage as they face multiple challenges like poverty, poor economy, poor infrastructure, limited access to education and inadequate technological growth. Fortunately, India is one of among such countries in the recent past which has improved its position in the world forum and has taken a big stride by transforming human resource into knowledge workers. The entire credit goes to government of India, their schemes and most importantly the people working tirelessly in the higher education institutions. But, the percentage is very low.The reason is higher education of India is plagued with several challenges like poor quality of teachers, poor infrastructure, poor libraries and poor educational resources. Unless high quality of education both in terms of infrastructure and academic is not provided and sustained in all higher education institutes, it will be difficult to match with global world. One way it could be done at least in academic front is developing and disseminating quality educational material among the institutes of higher education. National Knowledge Commission has recommended that problems of educational material to a large extent can be reduced by Open Educational Resources (OER) and Open Access (OA). The easy and widespread availability of high quality educational material will change the paradigm of teaching and learning and thus improve the quality of education. Government of India has started several innovative programs and schemes like SHAKSHAT, NMEICT, NPTEL, OSCAR, E-grid etc. related to developing and disseminating educational resources. The national and global level are tremendous but it comes with many challenges specifically in a country like India which has a diversified population.The present paper will try to focus on the opportunities and challenges with respect to OER in Indian higher education.
Tackle 'em bugs! Managing the issue overflow in large-scale software engineering

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Analysis of virtualized cloud server together with shared storage and estimation of consolidation ratio and TCO/ROI

Purpose – The physical server transition to virtualized infrastructure server have encountered crucial problems such as server consolidation, virtual machine (VM) performance, workload density, total cost of ownership (TCO), and return on investments (ROIs). In order to solve the problems mentioned above, the purpose of this paper is to perform the analysis of virtualized cloud server together with shared storage as well as the estimation of consolidation ratio and TCO/ROI in server virtualization. Design/methodology/approach – This paper introduces five distinct virtualized cloud computing servers (VCCSs), and provides the appropriate assessment to five well-known hypervisors built in VCCSs. The methodology the authors proposed in this paper will gives people an insight into the problem of physical server transition to virtualized infrastructure server. Findings – As a matter of fact, VM performance seems almost to achieve the same level, but the estimation of VM density and TCO/ROI are totally different.
Identifying Memory Address Disclosures

Software is still being produced and used that is vulnerable to exploitation. As well as being in devices in the homes of many people around the world, programs with these vulnerabilities are maintaining life-critical systems such as power-stations, aircraft and medical devices and are managing the creation and distribution of billions of pounds every year. These systems are actively being exploited by governments, criminals and opportunists and have led to loss of life and a loss of wealth. This dependence on software that is vulnerable to exploitation has led to a society with tangible concerns over cyber-crime, cyber-terrorism and cyber-warfare. As well as attempts to eliminate these vulnerabilities, techniques have been developed to mitigate their effects; these prophylactic techniques do not eliminate the vulnerabilities but make them harder to exploit. As software exploitation is an ever evolving battle between the attackers and the defenders, identifying methods to bypass these mitigations has become a new battlefield in this struggle and the techniques that are used to do this require vulnerabilities of their own. As many of the mitigation techniques are dependent upon secrecy of one form or another, vulnerabilities which allow an attacker to view those secrets are now of importance to attackers and defenders. Leaking of the contents of computer memory has always been considered a vulnerability, but until recently it has not typically been considered a serious one. As this can be used to bypass key mitigation techniques, these vulnerabilities are now considered critical to preventing whole classes of software exploitation. This thesis is about detecting these types of leaks and the information they disclose. It discusses the importance of these disclosures, both currently and in the future. It then introduces the first published technique to be able to reliably identify specific classes of these leaks, particularly address disclosures and canary-disclosures. The technique is tested against a series of applications, across multiple operating systems, using both artificial examples and software that is critical, commonplace and complex.
Virtual reality and project management for astronomy

Over the years astronomical instrumentation projects are becoming increasingly complex making necessary to find efficient ways for project communication management. While all projects share the need to communicate project information, the required information and the methods of distribution vary widely between projects and project staff. A particular problem experienced on many projects regardless of their size, is related to the amount of design, planning information and how that is distributed among the project stakeholders. One way to improve project communications management is to use a workflow that offers a predefined way to share information in a project. Virtual Reality (VR) offers the possibility to get a visual feedback of designed components without the expenses of prototype building, giving an experience that mimics real life situations using a computer. In this contribution we explore VR as a communication technology that helps to manage instrumentation projects by means of a workflow implemented on a software package called Discut designed at Universidad Nacional Autónoma de Mexico (UNAM). The workflow can integrate VR environments generated as CAD models.
An investigation of streams of irregular particles in free-fall

In coal-fired power plants, pulverised coal is conveyed pneumatically to the burners through a system of pipes and undergoes complex particle-particle and air-particle interactions. Research on gas-solid flow has been directed mainly at spherical particles and in some cases to regular non-spherical particles. However, coal particles are irregularly shaped. Understanding the behaviour of real particles is necessary to develop effective modelling of complex gas-solid flow, control hazardous emissions, and for the pulverised coal plants to remain competitive. This research aimed to investigate experimentally the effect of particle shape on airparticle flow fields. The dynamics of streams of free falling irregular particles was observed by means of Particle Image Velocimetry and Laser Doppler Anemometry. Glass crushed beads and glass spherical particles with mean diameters varying from 60 to 300 j.tm were employed in the measurements, yielding particle Reynolds numbers between 1 and 70. Results on the particle-particle interaction and air-particle interaction mechanisms have been attained for both dilute and dense particle streams. Significant differences between the behaviour of irregular particles and spherical particles have been found in terms of spatial velocity distribution, terminal velocity, fluctuation velocity and turbulent energy content of the gas phase. The velocity of the particles in the stream was higher than the predicted single particle velocity at the flow centreline and converged towards it at the stream edges in all experimental conditions. Irregular particles exhibited a velocity consistently lower than that of spherical particles and the stream of irregular particles was characterised by a larger radius. The velocity profile of the particle stream was load-dependent with the maximum velocity moving outwards from the stream centreline with increasing mass flow rate. The particle concentration was revealed to have an important effect on the velocity of the particle stream and the generation of turbulence. In the case of a dense particle stream, the turbulent energy content of the gas phase in the presence of irregular particles was significantly reduced than in the presence of spherical particles at both low and high frequencies.
Self-organizing neural architectures and multi-agent cooperative reinforcement learning

Multi-agent system, wherein multiple agents work to perform tasks jointly through their interaction, is a fairly well studied problem. Many approaches to multi-agent learning exist, among which, reinforcement learning is widely used, as it does not require an explicit model of the environment. However, limitations remain in current multi-agent reinforcement learning approaches, including adaptability and scalability in complex and specialized multi-agent domains. In any multi-agent reinforcement learning system, two major considerations are the reinforcement learning methods used and the cooperative strategies among agents. In this research work, we propose to adopt a self-organizing neural network model, named Temporal Difference Fusion Architecture for Learning, COgnition, and Navigation (TD-FALCON), for multi-agent reinforcement learning. TD-FALCON performs online and incremental learning in real-time with and without immediate reward signals. It thus enables an agent to learn effectively in a dynamic environment. Based on the TD-FALCON model, we develop several cooperative reinforcement learning strategies, investigating how a team of FALCON agents may cooperate to learn and function in a dynamic multi-agent environment. We first experiment TD-FALCON in minefield navigation domain, which does not require an explicit mechanism of collaboration. Our experiments show that TD-FALCON agent teams are able to adapt and function well, comparing with the backpropagation (BP) based as well as the resilient propogation (RPROP) based reinforcement learners. In multi-agent tasks requiring explicit collaboration, scalability becomes an issue as each agent needs to communicate and share information with the other agents. To address the scalability issue, we propose the center of agent team ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Growth of aromatic molecules on solid substrates for applications in organic electronics

The growth of molecular adlayers on solid substrates is reviewed with aspecial emphasis on molecules of relevance for organic electronics. In particular,we will consider planar molecules with extended π-systems, namely acenes (anthracene, tetracene, pentacene), perylene, coronenes, diindenoperylene, 3,4,9,10-perylene-tetracarboxylicacid-dianhydride, poly-phenylenes, oligothiophenes, and phthalocyanines. Special consideration is given to the importance of the formation of ordered molecular overlayers, which are compared with the structure of the corresponding bulk crystals. Whenever possible, aspects relevant for device fabrication (morphology of deposited films, mobilities of charge carriers) will be addressed.