Miracle-dz/UniFAR-FTUs huggingface dataset:
Incremental Unsupervised Name Disambiguation in Cleaned Digital Libraries

Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the Digital Library (DL) community. Here we deal with the problem of disambiguating new citations records inserted into a cleaned DL, without the need to process the whole collection, which is usually necessary for unsupervised methods. Although supervised solutions can deal with this situation, there is the costly burden of generating training data besides the fact that these methods cannot handle well the insertion of records of new authors not already existent in the repository. In this article, we propose a new unsupervised method that identifies the correct authors of the new citation records to be inserted in a DL. The method is based on heuristics that are also used to identify whether the new records belong to authors already in the digital library or not, correctly identifying new authors in most cases. Our experimental evaluation, using synthetic and real datasets, shows gains of up to 19\% when compared to a state-of-the-art method without the cost of having to disambiguate the whole DL at each new load (as done by unsupervised methods) or the need for any training (as done by supervised methods).
Syntactic learning for ESEDA.1, a tool for enhanced speech emotion detection and analysis

This paper presents the second version of ESEDA, a speech emotion recognition tool. The current version of the tool has a number of novel capabilities as compared to the first version ESEDA.0 and other speech emotion recognition systems: firstly, it incorporates a novel classification method TGI+, and secondly, it includes a module for highlevel feature extraction. These two functionalities have the same underlying idea: calculate how consistent a given sample is with a set of rules derived for each recognition class, which is measured with edit distances to tree automata representing recognition classes. Then the sample is either classified with a decision tree working on these distance to automaton features (as in the case of TGI+) or we do early fusion of the low-level and the high-level features before the classification step (as in the case of high-level features).
Sparse BRDF approximation using compressive sensing

BRDF acquisition is a tedious operation, since it requires measuring 4D data. On one side of the spectrum lie explicit methods, which perform many measurements to potentially produce very accurate reectance data after interpolation [Matusik et al. 2003]. These methods are generic but practically difficult to setup and produce high volume data. On the other side, acquisition methods based on parametric models implicitly reduce the infinite dimensionality of the BRDF space to the number of parameters, allowing acquisition with few samples. However, parametric methods require non linear optimization. They become unstable when the number of parameters is large, with no guaranty that a given parametric model can ever fit particular measurements.
Optimization of Multi-Scenario Problems Using Multi-Criterion Methods : A Case Study on Byzantine Agreement Problem

In this paper, we address solution methodologies of an optimization problem under multiple scenarios. Often in practice, a problem needs to be considered for different scenarios, such as evaluating for different loading conditions, different blocks of data, multi-stage operations, etc. After reviewing various single-objective aggregate methods for handling objectives and constraints under multiple scenarios, we then suggest a multi-objective optimization approach for solving multi-scenario optimization problems. On a Byzantine agreement problem, we demonstrate the usefulness of the proposed multi-objective approach and explain the reasons for their superior behavior. The suggested procedure is generic and now awaits further applications to more challenging problems from engineering and computational fields.
Modified DSR (Preemptive) to reduce link breakage and routing overhead for MANET using Proactive Route Maintenance (PRM)

Dynamic Source Routing (DSR) algorithm is simple and best suited for high mobility nodes in wireless ad hoc networks. Due to high mobility in ad-hoc network, route may not exit for long time. Hence, DSR algorithm finds an alternative route when the existing communicating route goes down. It becomes a time consuming process if the communicating route fails frequently. In order to avoid this, we propose a modification to the existing DSR protocol. In this paper, we add a link breakage prediction algorithm to the Dynamic Source Routing (DSR) protocol. The mobile node uses signal power strength from the received packets to predict the link breakage time, and sends a warning to the source node of the packet if the link is soon-to-be-broken. The source node can perform a pro-active route rebuild to avoid disconnection. Intermediate nodes in the route continuously monitor the signal strength at the time of communication, based on a predefined threshold signal value. Intermediate node sends a message to the source node that the route is likely to be disconnected, if signal strength falls below the threshold value. If source receive this message it starts using backup route and if back route also fails then it finds alternative route. The backup route will minimize the time consuming process of finding an alternative route to some extent. Experiments demonstrate that adding link breakage prediction to DSR can significantly reduce the total number of dropped data packets (by at least 25%). Simulation results shows the probability of the communication breakage decreases when parallel routes are used and comparisons between DSR and Modified DSR(Preemptive Version) with respective to no of broken paths and routing overhead. KeyWords-Ad-Hoc Networks, Preemptive, Dynamic Source Routing, Proactive, age of path.
Recent progress in satellite cell/myoblast engraftment – relevance for therapy

There is currently no cure for muscular dystrophies, although several promising strategies are in basic and clinical research. One such strategy is cell transplantation with satellite cells (or their myoblast progeny) to repair damaged muscle and provide dystrophin protein with the aim of preventing subsequent myofibre degeneration and repopulating the stem cell niche for future use. The present review aims to cover recent advances in satellite cell/myoblast therapy and to discuss the challenges that remain for it to become a realistic therapy.
A Dynamic Image Matching Model and Architecture for Smart Devices

The aim of this research is on developing a dynamic image matching model (DIMM) for smart devices. People with no knowledge on searching keywords can see the information and make good use of it in their daily life. Although existing search engines (Google, Yahoo etc.) have this systems, but it is necessary to get the information of any objects from anywhere on any platform. To fulfill this requirement a unique model and architecture is proposed for ending users thirsts in both offline and online. The proposed model has four planes. The users are sited in the top plane. In the second plane, object attributes are checked automatically by the system administrator. The core system is in the third plane which is responsible for extended feature extraction, object matching and object categorization etc. Finally, the core system retrieves the information from the fourth plane where object information stored in different databases. The output of DIMM is: a user can enjoy the object information from anywhere, such as, home, office, train station, Himalaya, etc. Any platform indicates that the user can enjoy the image information from smart phones, PDA, palmtop, home appliances, personal computer, etc. that means, the system is not limited to hardware and software. Open CV 2.2 is used for both the Android and iPhone for the DIMM experiment.
Modelling and simulation of a thermoelectric generator for waste heat energy recovery in Low Carbon Vehicles

This paper details the development of a thermoelectric generator (TEG) model undertaken within the Low Carbon Vehicle Technology Project (LCVTP*). The model has been developed as a tool for investigating the application of using a TEG for waste energy recovery from an automotive engine exhaust and converting it to electrical energy to offset the electrical demand on the 12V battery. The model features three main subsystems that make up the TEG system; these are the heat exchanger, the thermoelectric material and the power conditioning unit. Particular attention has been given to the power conditioning unit where two different DC-DC converter topologies namely; buck-boost and single ended primary inductor capacitor (SEPIC) have been compared for best performance. In addition, the Perturb and Observe maximum power point tracking algorithm has also been implemented and compared with a standard fixed duty cycle pulse width modulator (PWM) control. The process of developing the subsystems are briefly explained and the advantages of using the maximum power point system is demonstrated. The simulation results demonstrate that a power conditioning unit with a buck-boost converter and Perturb and Observe control is suitable for TEG systems. MATLAB/Simulink has been used for modelling and simulation of the system as well as implementation of the control strategy.
Effect of Age on the Wage Distribution: A Quantitative Evaluation Using US Data

The distribution of wages varies with workers'age. In this article we build a model able to explain this evolution by taking into account two channels: the evolution of the wage game described in Burdett and Mortensen (1998), and the evolution of the workers'productivity within a match. The model considers three age groups, the juniors, the adults and the seniors. Using US data, we show that these two channels allow to reproduce quite well the aggregated wage distribution as well as the evolution of it over the workers life-cycle. The channel of the evolution of the wage game accounts for the lower density of low wages in the seniors'wage distribution. Yet ﬁrms are naturally induced to create lower quality jobs to seniors because of their short working horizon. In order to ﬁt the data, we show that it is necessary to assume learning by doing of workers. The ﬁrst aspect of learning by doing is to increase the workers'speciﬁc productivity, the second is to improve the adaptation to a new jobs of older workers. These two aspects explain diﬀerent features of the wage distribution. The ﬁrst one explains why the mode of the wage distribution is translated over the workers'life-cycle. The second accounts for the existence of high wages within the distribution of older workers.
Targeting SR-BI for Cancer Diagnostics, Imaging and Therapy

Scavenger receptor class B type I (SR-BI) plays an important role in trafficking cholesteryl esters between the core of high density lipoprotein and the liver. Interestingly, this integral membrane protein receptor is also implicated in the metabolism of cholesterol by cancer cells, whereby overexpression of SR-BI has been observed in a number of tumors and cancer cell lines, including breast and prostate cancers. Consequently, SR-BI has recently gained attention as a cancer biomarker and exciting target for the direct cytosolic delivery of therapeutic agents. This brief review highlights these key developments in SR-BI-targeted cancer therapies and imaging probes. Special attention is given to the exploration of high density lipoprotein nanomimetic platforms that take advantage of upregulated SR-BI expression to facilitate targeted drug-delivery and cancer diagnostics, and promising future directions in the development of these agents.
Inter-vivos Transfers and Exchange

Most parents divide their bequests equally among their children, whereas inter-vivos transfers are usually unequal. We propose that exchange is better for inducing inter-vivos transfers than bequests. Inter-vivos transfers can be adjusted quickly to the amount of care, are less costly than writing a will, and can be kept secret from other family members and the public. The results from national longitudinal data show that, as expected, if a parent gives any inter-vivos transfers, she is more likely to give to children who provide informal care. Informal care has no effect on the equality of intended bequests.
A combinatorial data model for representing topological relations among 3D geographical features in micro‐spatial environments

This research is motivated by the need for 3D GIS data models that allow for 3D spatial query, analysis and visualization of the subunits and internal network structure of ‘micro‐spatial environments’ (the 3D spatial structure within buildings). It explores a new way of representing the topological relationships among 3D geographical features such as buildings and their internal partitions or subunits. The 3D topological data model is called the combinatorial data model (CDM). It is a logical data model that simplifies and abstracts the complex topological relationships among 3D features through a hierarchical network structure called the node‐relation structure (NRS). This logical network structure is abstracted by using the property of Poincaré duality. It is modelled and presented in the paper using graph‐theoretic formalisms. The model was implemented with real data for evaluating its effectiveness for performing 3D spatial queries and visualization.
A 3.84 Gbits/s AES Crypto Coprocessor with Modes of Operation in a 0.18-(cid:255) m CMOS Technology

In this paper an AES crypto coprocessor that is fabricated using a 0.18-(cid:255) m CMOS technology is presented. This crypto coprocessor performs the AES-128 encryption in both feedback and non-feedback modes of operation. A maximum throughput of 3.84 Gbits/s is achieved at a 330 MHz clock frequency for ECB, OFB, and CBC modes of operation. This crypto coprocessor can be programmed using the memory-mapped interface of an embedded CPU core and is tested using a LEON 32-bit (SPARC V8) processor in the ThumbPod secure system-on-chip.
The Caenorhabditis elegans gene lin-26 can trigger epithelial differentiation without conferring tissue specificity.

How epithelial cell fates become specified is poorly understood. We have previously shown that the putative C2H2 zinc-finger transcription factor LIN-26 is required for the differentiation of ectodermal and mesodermal epithelial cells in Caenorhabditis elegans. Here, we report that ectopic LIN-26 expression during early gastrulation transforms most blastomeres into epithelial-like cells. Specifically, LIN-26 induced the expression of three epithelial markers: the adherens junction protein JAM-1; DLG-1, which is essential for the assembly of JAM-1 at junctions; and CHE-14, which is involved in apical trafficking. Furthermore, ultrastructural studies revealed that ectopic LIN-26 expression induced the formation of adherens-like junctions. However, ectopic lin-26 expression did not confer any tissue-specific cell fate, such as the epidermal cell fate, as evidenced from the observation that several epidermal-specific genes were not induced. Conversely, we show that epidermal cells displayed some polarity defects in lin-26 mutants. We conclude that lin-26 can induce epithelial differentiation and that epitheliogenesis is not a default pathway in C. elegans.
Networked estimation under information constraints

In this paper, we study estimation of potentially unstable linear dynamical systems when the observations are distributed over a network. We are interested in scenarios when the information exchange among the agents is restricted. In particular, we consider that each agent can exchange information with its neighbors only once per dynamical system evolution-step. Existing work with similar information-constraints is restricted to static parameter estimation, whereas, the work on dynamical systems assumes large number of information exchange iterations between every two consecutive system evolution steps. We show that when the agent communication network is sparely-connected, the sparsity of the network plays a key role in the stability and performance of the underlying estimation algorithm. To this end, we introduce the notion of \emph{Network Tracing Capacity} (NTC), which is defined as the largest two-norm of the system matrix that can be estimated with bounded error. Extending this to fully-connected networks or infinite information exchanges (per dynamical system evolution-step), we note that the NTC is infinite, i.e., any dynamical system can be estimated with bounded error. In short, the NTC characterizes the estimation capability of a sparse network by relating it to the evolution of the underlying dynamical system.
Globalization of Financial Markets and Islamic Financial Institutions

In this paper, I reflect on the implications of financial globalization for Islamic financial institutions in terms of coordinates selected from both history and theory. I present in outline the 18th century case for and against commerce, the 19th century case for and against a central institution acting as a lender of last resort, and modern theoretical developments in finance and insurance based on the law of large numbers and centered around the notions of arbitrage, naive and efficient diversification of risk, moral hazard and adverse selection. I argue that an informed understanding of the processes of globalization is a necessary prerequisite for charting out the impact on current financial institutions, and perhaps more importantly, on the development of future institutions based on Islamic values and assumptions. At the same time, I argue that this prerequisite also deepens and enriches our understanding of the values themselves, and thereby, through their derivation, leads to a closer and more-informed reading of the canonical text itself. As such, I question the extent to which the financial aspects can be bracketed, and argue for a lack of closure in what must be a mutually illuminating and necessarily interdisciplinary enquiry.
Behavourial Modeling in Mammography

An evolutionary autonomous agent approach to image feature extraction does provide a more effective means of searching an image environment, overcoming the potential combinatorial explosion experienced by more traditional image processing techniques. This approach is also suited to more difficult image processing tasks where the features being searched for are either not visible or not easily distinguishable by the human vision system. This paper proposes the development of an evolutionary based image processing application for mammography.
Prediction of call arrival traffic for Spectrum Sharing using Cognitive Radio

Spectrum sharing between service providers improves the spectral efficiency, probability efficiency of sensing and reduces the call blockage. If the number of service providers is increased to share the spectrum, this may reduce the high traffic patterns of the calls. In this paper, we present an approach for channel availability and call arrival rate. The results are used to evaluate the probability of the channel availability of a frequency band within a time period. In this work, we propose an algorithm for predicting the call arrival rate which used to predict the traffic process.
Volatile and Persistent Real Exchange Rates Without the Contrivance of Sticky Prices

The flexible-price two-country monetary model is extended to include a consumption externality with habit persistence. The model is simulated using the artificial economy methodology. It successfully explains (i) the high volatility of nominal and real exchange rates, (ii) the high correlation between real and nominal rates, and (iii) the persistence of real exchange rates. It offers a neo-classical explanation for the Meese-Rogoff exchange rate forecasting puzzle.
Persuasion in Contemporary Presidential Campaigns

As the Presidential Election of 2008 approaches its apex, the citizens of the United States of America will be increasingly bombarded with information and images of the presidential candidates. However, most individuals have never stopped to ask themselves, why and for what purpose they are having this experience. This analysis of the 2004 and 2008 Presidential Election Campaign will answer these unasked questions. Before I delve into the complexities of these contemporary campaigns, it is important to discuss the roots of political campaigns, their history, and components.
Machine Learning Based Cross-Site Scripting Detection in Online Social Network

Nowadays online social network (OSN) is one of the most popular internet services in the world. It allows us to communicate with others and share knowledge. However, from the security's point of view, OSN is becoming the favorite target for the attackers, and is under a lot of threats such as cross-site scripting (XSS) attacks. In this paper, we present a novel approach using machine learning to do XSS detection in OSN. Firstly, we leverage a new method to capture identified features from web pages and then establish classification models which can be used in XSS detection. Secondly, we propose a novel method to simulate XSS worm spreading and build our webpage database. Finally, we set up experiments to verify the classification models using our test database. Our experiment results demonstrate that our approach is an effective countermeasure to detect the XSS attack.
Establishing continuous assessment using measurements

Software process assessments have become commonplace in the software industry. Assessments are sometimes regarded, however, as too infrequent, expensive and disruptive. Hence, there is a clear need for alternative ways to assess the current status of software processes and monitor the implementation of improvement activities. An assessment of software processes is based on finding indicators for establishing whether certain processes exist and how well they are performed. Based on the assessment outcome, improvement actions will be identified and guided with appropriate measurements. The software process (re-) assessment can be supported and made more frequent by using data from these measurements, and thus reducing the cost of the assessment. In the European project PROFES (PROduct-Focused Improvement of Embedded Software processes) an integrated, product-focused process improvement methodology has been developed and tested in the industrial partner sites. Among other things, PROFES integrates process assessment and software measurement to enable continuous assessment. This paper shows how this combination of assessment and measurement was done in practice. It also describes examples of measurements and their connection to the assessment method.
Determining Type, Part, Color, and Time Relationships

The development of a simple question-answering system is considered. In particular, methods are described that are designed to supplement a deductive question-answering algorithm that is now operational. The algorithm draws on a base of logical propositions organized as a semantic net. The net permits selective access to the contents of individual mental worlds and narratives, to sets of entities of any specified type, and to propositions involving any specified entity and classified under any specified topic. The problems involved in determining type, part-of, color, and time relationships are discussed. It is shown that much combinatory reasoning in a question-answering system can be short-circuited by the use of special graphical and geometrical methods. 13 references.