Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordinate Systems and Bounded Isomorphisms for Triangular Algebras

For a Banach D-bimodule M over an abelian unital C*-algebra D, we define E(M) as the collection of norm-one eigenvectors for the dual action of D on the Banach space dual of M. Equip E(M) with the weak-* topology. We develop general properties of E(M). It is properly viewed as a coordinate system for M when M is a subset of C, where C is a unital C*-algebra containing D as a regular MASA with the extension property; moreover, E(C) coincides with Kumjian's twist in the context of C*-diagonals. We identify the C*-envelope of a subalgebra A of a C*-diagonal which contains D. For triangular subalgebras, each containing the MASA, a bounded isomorphism induces an algebraic isomorphism of the coordinate systems which can be shown to be continuous in certain cases. For subalgebras, each containing the MASA, a bounded isomorphism that maps one MASA to the other MASA induces an isomorphism of the coordinate systems. We show that the weak operator closure of the image of a triangular algebra in an appropriate representation is a CSL algebra and that bounded isomorphism of triangular algebras extends to an isomorphism of these CSL algebras. We prove that for triangular algebras in our context, any bounded isomorphism is completely bounded. Our methods simplify and extend various known results; for example, isometric isomorphisms of the triangular algebras extend to isometric isomorphisms of the C*-envelopes, and the conditional expectation E of C onto D is multiplicative when restricted to a triangular subalgebra. Also, we use our methods to prove that the inductive limit of C*-diagonals with regular connecting maps is again a C*-diagonal.
Chapter 1 On the Semantics of SPARQL

The Resource Description Framework (RDF) is the standard data model for representing information about World Wide Web resources. Jointly with its release as Recommendation of the W3C, the natural problem of querying RDF data was raised. In the last years, the language SPARQL has become the standard query language for RDF and, in fact, a W3C Recommendation since January 2008. In this chapter, we give a detailed description of the semantics of SPARQL. We start by focusing on the definition of a formal semantics for the core part of SPARQL, and then move to the definition for the entire language, including all the features in the specification of SPARQL by the W3C such as blank nodes in graph patterns and bag semantics for solutions.
A Primal-Dual Solution to Minimal Test Generation Problem

This newly proposed solution to the minimal test generation problem of combinational circuits is based on (1) identifying independent faults, (2) generating tests for them, and (3) minimizing the tests. The third part, test minimization, is accomplished by an already known solution using integer linear programming (ILP). Using the theory of primal-dual linear programs, we model the independent fault set identification as the dual of the test minimization problem. A solution of the dual problem, whose existence is guaranteed by the duality theorem, gives us a conditionally independent fault set (CIFS). We start with any (non-optimal but complete) vector set. Our CIFS is, therefore, not absolute but is specific to the starting vector set. Successively adding more vectors for the identified independent set and solving the dual problem, we bring the independent set closer to its minimal size. Finally, a primal solution minimizes the set of all accumulated vectors. Benchmark results show potential for both smaller test sets and lower CPU times.
Keep your triples together: modeling a RESTtful, layered linked data store

Linked data (LD) is an increasingly important way for publishing structured data on the Web. Applications that represent their (meta) data as LD benefit from the flexibility, the semantic elaborateness and the simplicity of this approach. There are, however, several issues with such a solution in real-world scenarios, e.g., (i) mechanisms for writing LD such as SPARQL UPDATE are still under development, (ii) the generic, flexible nature of the used RDF data model makes the development of data- and user interfaces complex, and, (iii) descriptions of LD resources are treated as "atomic" entities, no mechanisms for accessing or delivering partial resource descriptions via HTTP are available. In this work, we introduce a model for a LD store that enforces structuring of its data in two dimensions: named graphs are used to separate logical aspects of the contained data into "horizontal" layers. Extraction functions are used to decompose these graphs into "vertical" records, collections of sub-graphs "centered" around some resource of interest. This model can be implemented using established technologies and formats from (Semantic) Web research and we discuss a prototypical implementation in this paper. Our prototype implements a REST interface for read/write access to the stored records. Further, clients can access partial records based on HTTP header settings. The higher structuredness of the data in such a store, when compared to arbitrary RDF graphs, brings practical advantages for implementing data or user interfaces. The strong data compartmentalization can be exploited in many ways, e.g., for more fine-grained access control or for concurrent write access to various layers of the store.
Simultaneous localization and mapping using the Geometric Projection Filter and correspondence graph matching

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First, when no identifying marks are set on the landmarks, their identification by a robust algorithm is a complex problem which may be solved using correspondence graphs. Secondly, when the localization system has no a priori information about its environment, it has to build its own map in parallel with estimating its position, a problem known as simultaneous localization and mapping (SLAM). Recent works have proposed to solve this problem based on building a map made of invariant features. This paper describes the algorithms and data structure needed to deal with landmark matching, robot localization and map building in a single efficient process, unifying the previous approaches. Experimental results are presented using an outdoor robot car equipped with a two-dimensional scanning laser sensor.
Throughput Analysis of Stop-and-Wait Automatic Repeat Request Scheme for Network Coding Nodes

This contribution analyzes the steady-state throughput of general network coding nodes, when data is transmitted based on stop-and-wait automatic repeat request (SW-ARQ) scheme. The state transition of network coding nodes employing SW-ARQ is analyzed, which shows that the operations of network coding nodes can be modelled by a finite state machine. Therefore, the throughput expressions of general network coding nodes can be derived based on the properties of finite state machines. Furthermore, the throughput performance of network coding nodes is investigated either by simulations or by evaluation of the expressions obtained. It can be shown that the simulation results converge closely to the numerical results and justify the effectiveness of our analytical expressions obtained.
Simplified adaptive path planning for percutaneous needle insertions

Needle placement errors can mitigate the effectiveness of the diagnosis or the therapy, sometimes with catastrophic outcomes. Previous design of a simplified model for needle deflection estimation was motivated by the clinical constraints of ARCS (Abdomino-pelvic Robotic-driven slightly flexible needle insertion performed in CT/MRI-guided Scenario). We present in this work, the validation results for the needle deflection prediction model. Its robustness is evaluated under an unknown context such as a different robotic platform, facing uncertainties conditions not conceived previously in the model's confection. In addition, the work presents the development and validation experiments of an adaptive path planner that uses the model as predictor's strategy. It provides pre-operative planning assistance, as well as intra-operative decision-making support. The experiments results showed average error around 1mm for the pre-operative planning and the intra-operative replanning approach showed to be very robust to correct the initial predictions, showing average error smaller than 1 mm.
Towards LarKC: A Platform for Web-Scale Reasoning

Current semantic Web reasoning systems do not scale to the requirements of their hottest applications, such as analyzing data from millions of mobile devices, dealing with terabytes of scientific data, and content management in enterprises with thousands of knowledge workers. In this paper, we present our plan of building the large knowledge collider, a platform for massive distributed incomplete reasoning that will remove these scalability barriers. This is achieved by (i) enriching the current logic-based semantic Web reasoning methods, (ii) employing cognitively inspired approaches and techniques, and (iii) building a distributed reasoning platform and realizing it both on a high-performance computing cluster and via "computing at home". In this paper, we will discuss how the technologies of LarKC would move beyond the state-of-the-art of Web scale reasoning.
ADAPTIVE DECISION MAKING IN MENU SEARCH : THE ROLE OF INTERDEPENDENCE AND PAST EXPERIENCE ON LINK SELECTION

When searching a web page for information that is relevant to some search goal, people focus on labelled links and estimate the likelihood that these links will lead to the goal. Evidence presented in this paper supports the idea that people adapt their search strategy to the statistics of the environment and that assessments of menu items are interdependent. The work contributes to the development of a cognitive model of how people search web sites, which may serve as an engineering tool to support web design.
Protein Structures and Information Extraction from Biological Texts: The PASTA System

MOTIVATION The rapid increase in volume of protein structure literature means useful information may be hidden or lost in the published literature and the process of finding relevant material, sometimes the rate-determining factor in new research, may be arduous and slow. RESULTS We describe the Protein Active Site Template Acquisition (PASTA) system, which addresses these problems by performing automatic extraction of information relating to the roles of specific amino acid residues in protein molecules from online scientific articles and abstracts. Both the terminology recognition and extraction capabilities of the system have been extensively evaluated against manually annotated data and the results compare favourably with state-of-the-art results obtained in less challenging domains. PASTA is the first information extraction (IE) system developed for the protein structure domain and one of the most thoroughly evaluated IE system operating on biological scientific text to date. AVAILABILITY PASTA makes its extraction results available via a browser-based front end: http://www.dcs. shef.ac. uk/nlp/pasta/. The evaluation resources (manually annotated corpora) are also available through the website: http://www.dcs. shef.ac. uk/nlp/pasta/results.html.
Spectral Methods for Cancer Classification Using Microarray Data

In this paper, we present a novel method based on spectral bipartitioning, traditionally used for finding min-cuts in graphs, for classification of cancer using microarray data. Our method is applied to five publicly available datasets of acute leukemia, colon cancer, ovarian cancer, prostate cancer and diffuse large B-cell lymphoma, and is shown to have classification accuracy comparable to that of some of the currently known best classification methods for microarray data.
Adoption and Implementation of Balanced Scorecard in Malaysia.

This paper reports the results of an empirical study on the adoption and implementation of Balanced Scorecard in Malaysia. More specifically, it investigates the sequential dependency among the four balances Scorecard (BSC) perspectives, namely learning and growth, internal business process, customer value and financial performance. The findings indicate that organizations responded positively to BSC measures and believed the causeand-effect relationship of BSC will lead to improved business efficiency and profitability.
PERFORMANCE MEASUREMENT IN HUMANITARIAN LOGISTICS : A LITERATURE REVIEW

Purpose Performance measurement in humanitarian logistics is crucial and is a critical element of successful humanitarian relief operations on strategic, tactical and operational levels. The purpose of this paper is to provide a literature review on performance measurement and key performance measures in humanitarian logistics and to outline future research opportunities in performance measurement in humanitarian logistics. Design/methodology/approach For the literature review a qualitative content analysis is used to determine main managerial problems and major findings in this area. The existing literature is categorized in several attributes like general trends of performance measurement in humanitarian logistics, stage of disaster, research methodologies, key performance measures, performance measurement systems and the relationship with internal and external stakeholders. Findings Findings reveal that performance measurement in the humanitarian logistics is still a gap within logistics research, especially compared to the commercial logistics sector. However, the major findings from existing literature are presented and recommendations for further research are proposed. Practical implications The result helps the humanitarian logistics community to conduct further research in this area and to develop key performance measures that suit the humanitarian logistics sector. Original/value The research paper shows how essential performance measurement in humanitarian logistics is and what the state-of-the-art in this area includes.
Accuracy of Transcutaneous Carbon Dioxide Measurement in Premature Infants

Background. In premature infants, maintaining blood partial pressure of carbon dioxide (pCO2) value within a narrow range is important to avoid cerebral lesions. The aim of this study was to assess the accuracy of a noninvasive transcutaneous method (TcpCO2), compared to blood partial pressure of carbon dioxide (pCO2). Methods. Retrospective observational study in a tertiary neonatal intensive care unit. We analyzed the correlation between blood pCO2 and transcutaneous values and the accuracy between the trends of blood pCO2 and TcpCO2 in all consecutive premature infants born at <33 weeks' gestational age. Results. 248 infants were included (median gestational age: 29 + 5 weeks and median birth weight: 1250 g), providing 1365 pairs of TcpCO2 and blood pCO2 values. Pearson's R correlation between these values was 0.58. The mean bias was −0.93 kPa with a 95% confidence limit of agreement of −4.05 to +2.16 kPa. Correlation between the trends of TcpCO2 and blood pCO2 values was good in only 39.6%. Conclusions. In premature infants, TcpCO2 was poorly correlated to blood pCO2, with a wide limit of agreement. Furthermore, concordance between trends was equally low. We warn about clinical decision-making on TcpCO2 alone when used as continuous monitoring.
A Hybrid Information Filtering Algorithm Based on Distributed Web Log Mining

For distributed large commercial mirror sites, this paper presents a hybrid information filtering algorithm based on distributed web log mining. Based on multi-agent technology, this algorithm preprocesses the web logs of mirror sites, in which the web page's manual rating is replaced by user browsing preference, and then user access matrix is constructed and standardized. On this basis, this paper proposes utilizing web page similarities to predict the rating for pages not having been rated, thus increasing the pages that have been jointly rated among users. This method could effectively solve the sparsity of user ratings in collaborative filtering. Eventually, a hybrid-filtering model is proposed to overcome the drawbacks of the content-based filtering and the collaborative filtering models. The experimental results show that this algorithm is applicable to distributed web server clustering architecture, avoids the inaccuracy and complexity of web page's manual ratings, effectively solves the faults of traditional filtering models and greatly improve the recommendation quality.
A learning environment for mobile computing integrating J2ME and software components

This paper describes a dual -module application that allows educational content to be created for Mobile Computing environments, offering the student mobility in the educational process. One of the modules of this application, developed for use with conventional desktops, allows for the creation of Exercise Lists with Multiple Choice and True or False questions. The other module allows Exercise Lists generated by the application to be viewed and the exercises solved through devices that use Wireless Communication to access the World Wide Web, namely Palmtops. The application enables Palmtop users to access educational content regardless of their physical location, using the advantages inherent to Wireless Networks, such as independence from conventional cabled connections and mobility. The second module communicates over the air with a Web Server and with Java Servlets, which are able to correct the student's answers and send him/her a performance report indicating the correct and incorrect answers given. The module developed for desktops also generates statistical reports about the student's performance in solvin g the exercises of the Exercise Lists, enabling teachers to evaluate students and improve the educational process. The application described in this paper was implemented using the Software Component-Based technique, exploiting this development technique together with the Java 2 Micro Edition, a version of the Java technology developed specifically for the creation of mobile device applications.
Fractional-pel motion compensation interpolation architecture based on parallel FIR systolic arrays for H.264/AVC

A new architecture based on parallel FIR systolic arrays for motion compensation interpolation in H.264/AVC is presented in this paper. Unlike other interpolation architectures based on traditional adder tree or one systolic FIR, this design has advantages of both the pipeline property of systolic FIR filter and high parallel property. It has following characteristics: First, it uses several strategies to reduce the number of memory access. For example, the design fully uses the recursive relation between the fractional-pel samples, the appropriate interpolation orders for different situations are adopted, and two buffers are designed for storing immediate values. Second, it can increase the system clock frequency by using the systolic FIR filter to replace the traditional adder tree. Third, it can enhance the interpolation throughput by generating four fractional-pel samples in parallel. Fourth, it doesnpsilat need high memory bandwidth and can work under different bus-width by changing the number of systolic FIR filters. The design is synthesized with synopsys design compiler by using TSMC 0.18 um standard cell CMOS technology. The synthesis result shows that this architecture can achieve 230 MHz and meet the need for interpolation of the H.264 decoder for SDTV or HDTV.
Journal of Theoretical and Applied Information Technology Improving Diversity in Video Recommender Systems and the Discovery of Long Tail

The sharp growth of online-systems and vast availability of high quality data lead to information overload, increasingly very difficult for the online users to find most relevant content. When looking for information about any movie, music, video, the internet users come across a bewildering number of options to fetch precise data from the recommended list. The main goal of the recommender system is to suggest high quality and top rated videos to the user. However there exist thousands of video items said to be Long Tail (videos with least rating) that stagnate idle on the web server for years that are unrevealed by users because of its least rating. The new recommender system introduced in this paper uses rating based binning technique that favors not only top rated videos to get recommended, but also recommend Long-Tail videos. This can improve diversity on recommendation and suggest best long tail videos to the user. This implies that a least popular video has a high probability to become more popular when it is placed on the related video recommendation lists of popular videos. In order to evaluate the proposed video recommender system, the datasets are crawled from YouTube©, a well-liked online video community to suggest videos with high rating along with less rated long tail videos.
A new prototype of smart parking using wireless sensor networks

In this paper we present an outdoor smart parking prototype using wireless sensor networks as a possible solution to solve the problem of parking, through an application that present the information in order to find free parking spaces in real time, this system can help to improve the quality of life in smart cities, because the time for finding a free parking space could be reduced, as well as help to reduce pollution and to improve vehicular traffic. The use of a wireless sensor networks allow for easy installation outdoor, the system could use a solar panel to charge the batteries and being independent of an external power source wired or constantly replacing batteries. The vacancies spaces are presented to users through a smart device. The main results show a reduction of 50% of time to obtain a free parking space, a reduction in fuel consumption, and an estimated reduction of vehicle pollution. Finally, we determine for our campus university the fuel saved were 2,71 galons/year/vehicle.
Optimal Diversification: Is It Really Worthwhile?

Executive Summary. Recent research has demonstrated that the Markowitz efficient frontier is fuzzy and may consist of many statistically indistinguishable frontiers. Therefore, it opens the possibility that an efficient portfolio developed by mean-variance analysis may not be any more efficient than a naively diversified portfolio. Using an efficiency test developed by Gibbons, Ross, and Shanken (1989), we find evidence to support that an efficient portfolio is statistically more efficient than a corresponding naively diversified portfolio when the portfolio formation period is the same as the period used for testing the efficiency difference. However, no evidence is found to support that an efficient portfolio is statistically more efficient than a corresponding naively diversified portfolio when the portfolio formation period differs from the test period. Thus, in practical terms, efficient portfolios may not be superior to naively diversified portfolios in a statistical sense.