Miracle-dz/UniFAR-FTUs huggingface dataset:
TOWARDS AUTOMATIC INDEXING: AUTOMATIC ASSIGNMENT OF CONTROLLED‐LANGUAGE INDEXING AND CLASSIFICATION FROM FREE INDEXING

A number of techniques have been studied for the automatic assignment of controlled subject headings and classifications from free indexing. These techniques involve the automatic manipulation and truncation of the free‐index phrases assigned to a document and the use of a manually‐constructed thesaurus and automatically‐generated dictionaries together with statistical ranking and weighting methods. These are based on the use of a statistically‐generated ‘adhesion coefficient’ which reflects the degree of association between the free‐indexing terms, the controlled subject headings, and the classifications. By the analysis of a large sample of manually‐indexed documents the system generates dictionaries of free‐language and controlled‐language terms together with their associated classifications and adhesion coefficients. Having learnt from the manually‐indexed documents the system uses these dictionaries in the subsequent automatic classification procedure. The accuracy and cost‐effectiveness of the automatically‐assigned subject headings and classifications has been compared with that of the manual system. The results were encouraging and the costs comparable to those of a manual system.
A multi-agent approach to involve multiple knowledge models and the case base reasoning approach in decision support systems

The building of Decision Support System (DSS) in complex domains like management, medicine, environment.requires to involve various data and knowledge models and to support the reasoning modes taking place in the professional cognitive process during his or her job practice. We propose a Multi-Agent Decision Support System (MADSS) architecture to integrate the different categories of knowledge representation. We describe the functionalities and capacities of cognitive agents implementing these different kinds of knowledge and bringing them to cooperate in elaborating the decision. Agents are able to commit to build sub-decisions and thus, the system provides a whole sequence of decisions fitting the user's usual cognitive scheme. We trust in a supervised architecture because such a MADSS must be goal driven by the user's requirements. However, the agents remain autonomous and keep the opportunity to commit or not to the task exhibited by the supervisor at the moment.
Knowledge-based system for analysis of aerial images

In this paper we describe a system for monitoring activities in aerial images. The system uses many knowledge sources and databases to guide the interpretation process. It has an innovative control scheme for managing the execution of the tasks utilizing of its knowledge sources and updating of its many databases. As a result the system not only is aware of the existence and usefulness of all knowledge present in its vanous databases it also knows how to use that knowledge and the processing modules in cooperation to achieve a common goal of detecting events of interests from a set of aerial photographs. Also to overcome the difficulties of the object recognition task a model-based technique is used to detect predict verify and recognize the objects of interests in the scene.
Implication of Cognitive Style in Designing Computer‐Based Procedure Interface

Different people have different models of mental perception, which form the bases of human reactions. The design of human‐computer interfaces should consider these differences in the cognitive models of users to achieve maximum benefits. The Myers‐Briggs Type Indicator (MBTI) is an accepted tool for measuring four dimensions of an individual's cognitive model. This research investigated the effects of two interface designs based on the characteristics of the Sensing/iNtuitive (S/N) and Thinking/Feeling (T/F) categories. These interfaces were designed for the visual information displays of computer‐based procedures at a nuclear power plant. Because of the nature of this industry, quicker reaction times are required and fewer mistakes are tolerated. A group of subjects were selected, and the subjects were assigned to either the ST or NT group before the experiment. The results, when compared with earlier statistics for a single design for all users, indicate that reaction times were reduced in some critical situations and the number of mistakes was reduced. © 2011 Wiley Periodicals, Inc.
LC ladder-based synthesis of log-domain bandpass filters

The design method of log-domain filters based on LC ladder synthesis is extended to cover a class of filter topologies which were not realizable otherwise. As an example, bandpass filters are considered. Known log-domain sub-circuits are combined together to realize a more versatile multiple-input log-domain integrator circuit. The realization of a second-order bandpass filter is discussed. A large signal analysis of the circuit shows how the gain, cutoff frequency, and Q-factor can be electronically and independently tuned. Experimental results are presented.
The Research and Application in Intelligent Document Retrieval Based on Text Quantification and Subject Mapping

Nowadays, document retrieval was an important way of academic exchange and achieving new knowledge. Choosing corresponding category of database and matching the input key words was the traditional document retrieval method. Using the method, a mass of documents would be got and it was hard for users to find the most relevant document. The paper put forward text quantification method. That was mining the features of each element in some document, which including word concept, weight value for position function, improved weights characteristic value, text distribution function weights value and text element length. Then the word’ contributions to this document would be got from the combination of five elements characteristics. Every document in database was stored digitally by the contribution of elements. And a subject mapping scheme was designed in the paper, which the similarity calculation method based on contribution and association rule was firstly designed, according to the method, the documents in the database would be conducted text clustering, and then feature extraction method was used to find class subject. When searching some document, the description which users input would be quantified and mapped to some class automatically by subject mapping, then the document sequences would be retrieved by computing the similarity between the description and the other documents’ features in the class. Experiment shows that the scheme has many merits such as intelligence, accuracy as well as improving retrieval speed.
Head-mounted computer interface based on eye tracking

A head-mounted computer interface using eye-tracking as a `pointer' was demonstrated at the Third Annual Weapons Technology Review and Training Exposition at San Diego in January 1992, and is under further development for military and civilian applications. in this interface, the computer interprets eye gaze direction just as it would input from a mouse or trackball. This technology makes possible computer-human interaction with many ideal aspects: complete portability, hands-free operation, silent, secure operation anywhere and any time. Commercial systems using control inputs from the tracked eye are available and effective within their limits, but size, weight, and cost have been barriers to wider use. This paper describes the latest head-mounted eye tracking display (HETD) development prototype. We are exploring the technological limits of current and near-term lighting, detector, and lens technology to project the feasibility of this device.
Simultaneous and Proportional Bandwidth, Delay, and Loss Differentiation

Abstract A robust adaptive scheduler for proportional delay differentiation services is presented. Proportional services are further policed by a class-based packet dropper. Our proposed combination of the adaptive scheduler and the packet dropper treats the traffic classes proportionally in terms of three quality of service (QoS) metrics: bandwidth, delay, and packet loss, simultaneously. Traffic types used to study the performance of the scheme ranges from ordinary FTP data to bursty Pareto traffic. Numerical results validate our claim that regardless of the network traffic characteristics, especially burstiness, the adaptive delay scheduler combined with the packet dropper can effectively differentiate services in terms of delay, bandwidth, and loss simultaneously. In addition, our proposed scheme showed on an average 11% improvement in performance when compared to the scheme proposed in C. Dovrolis, D. Stiliadis, & P. Ramanathan, Proportional differentiated services: Delay differentiation and packet scheduling, IEEE/ACM Transactions on Networking, 10(1), 2002, 12–26.
High performance Discrete Cosine Transform (DCT) operator using multimedia oriented subword parallelism (SWP)

In this paper an efficient two-dimensional discrete cosine transform (DCT) operator is proposed for multimedia applications. It is based on the DCT operator proposed in Kovac and Ranganathan, 1995. Speed-up is obtained by using multimedia oriented subword parallelism (SWP). Rather than operating on a single pixel, the SWP-based DCT operator performs parallel computations on multiple pixels packed in word size input registers so that the performance of the operator is increased. Special emphasis is made to increase the coordination between pixel sizes and subword sizes to maximize resource utilization rate. Rather than using classical subword sizes (8, 16, and 32 bits), multimedia oriented subword sizes (8, 10, 12, and 16 bits) are used in the proposed DCT operator. The proposed SWP DCT operator unit can be used as a coprocessor for multimedia applications.
Organizational Models for Big Data and Analytics

In this article, we introduce a framework for determining how analytics capability should be distributed within an organization. Our framework stresses the importance of building a critical mass of analytics staff, centralizing or decentralizing the analytics staff to support business processes, and establishing an analytics governance structure to ensure that analytics processes are supported by the organization as a whole.
Modular data-flow analysis of statically typed object-oriented programming languages

The solution of data-flow analysis of object-oriented programming languages such as C++/Java is needed for many important applications: aggressive code optimization, side-effect analysis, program specialization, program slicing and data-flow-based testing. However, data-flow analysis of object-oriented programming languages is difficult due to a large number of heap-allocated objects whose fields point to other heap-allocated objects (recursive structures), dynamic dispatch, frequent method invocations, a large number of methods, many invocation contexts per method and exceptions. In this thesis we present a new data-flow analysis technique called Relevant Context Inference (RCI) for modular, flow- and context-sensitive data-flow analysis of statically typed object-oriented programming languages such as C++ and Java. This technique has been designed to overcome the above difficulties. RCI has several long sought-after characteristics: (1) It can analyze programs by keeping only a part of the programs in memory at a time, with a constant bound on the number of times a procedure needs to be in memory. (2) It can analyze incomplete programs such as libraries. (3) It can analyze programs that have exceptions. We have built a prototype of RCI for points-to analysis of C++ programs. The empirical results obtained using this prototype and presented in this thesis show that RCI is effective in practice. We present several new complexity characterizations of points-to analysis in the presence of object-oriented language constructs: exceptions and dynamic dispatch. Our results clearly identify the difficult features and indicate approximations that any efficient algorithm has to make. We also present a new approach to data-flow-based testing of object-oriented libraries using RCI. We show how the information computed by RCI can be used for generating relevant test cases.
Model-based fault diagnosis of an actuator system driven by the brushless DC motor

A fault detection and isolation scheme for a brushless DC motor driving a mechanical actuation system is presented. First, a detailed mathematical model of the drive is derived. The residual generator is based on parity relations while the fault isolation part is implemented by means of the transferable belief model. The problem of distinguishing between faults with the same fault signatures is addressed. It is shown that additional improvements in terms of resolution can be achieved by employing parameter estimation. The proposed FDI scheme is tested on the actual drive under various faults. The achieved performance features the highest resolution, diagnostic stability and accuracy.
Collocational frameworks in Spanish

Collocational frameworks are discontinuous combinations of grammatical items which enclose lexical words (e.g., a_of in English). In this paper, the concept of collocational framework is applied to the analysis of five corpora of Spanish which allow comparisons across a range of communication channel types. Twenty-eight frameworks, consisting of articles and/or prepositions, are investigated in terms of their quantitative importance in texts and their selectivity for the lexical words they enclose. Although measures of quantitative significance and selectivity show strong correlations across all types of text, these correlations are to some extent sensitive to the spoken/written dimension. The lexical collocates also show clear semantic groupings. Implications of the work for linguistic theory and description and for language teaching and learning are briefly discussed.
Study of optimal importance sampling in Monte Carlo estimation of average quality index

In this paper, the optimal importance sampling, which is a variance reduction technique in Monte Carlo estimation, is theoretically studied in the case of electronic circuits. To the best of our knowledge, this problem has not previously been studied in electronic circuits. In this study, the theoretical basis of the optimal importance sampling is developed in the case where no approximate model of the circuit responses is given. Simulation results show good agreement with the theoretical basis.
Term Weighting Revisited

Term weighting is an essential part of the modern information retrieval systems. Out of the three main components of a term weighting strategy --term frequency, inverse document frequency, and document length normalization --the term frequency factor has been investigated recently by researchers. In this work, we study the inverse document frequency, and document length normalization components of term weights. We observe that a document length normalization scheme that retrieves documents of all lengths with similar chances as their likelihood of relevance will outperform another scheme which retrieves documents with chances very different from their likelihood of relevance. We present {\em pivoted normalization\/}, a technique that can be used to modify normalization functions to reduce the gap between the relevance and the retrieval probabilities. We present two new normalization functions --{\em pivoted unique normalization\/} and {\em pivoted byte size normalization}, both of which yield significant improvements over the previous state of the art normalization functions. When optical character recognition is used to create large information bases, term weighting schemes can be highly sensitive to the errors in the input text, introduced by the OCR process. This work examines the effects of the well known {\em cosine normalization\/} method in the presence of OCR errors, and proposes a new, more robust, normalization method. Experiments show that the new scheme is less sensitive to OCR errors and facilitates the use of more diverse basic weighting schemes. This study also explains why the use of cosine normalization in presence of the inverse document frequency factor is not advisable in large document collections. When a user types a natural language query for an IR system, certain keywords in the query are more pertinent to the user''s information need than others. Most modern IR systems incorporate these distinctions by using an inverse document frequency ({\em idf\/}) factor in term weighting. Preliminary experiments show that the usefulness of an {\em idf\/} type function is high at low ranks. We observe that the main reason for this effect is the widened gap between the weights of the rare terms and the non-rare query terms. The standard {\em idf\/} function works very well across query sets. Experiments show that there is room for improvement in the {\em idf\/} function. Further studies are needed to discover a better replacement for the standard {\em idf\/} function.
Linking medical records: a machine learning approach

Linking medical records across multiple databases is a very important task, since medical errors, uncompensated care and medical costs are rising at a rapid rate. However, inconsistencies in data records, caused primarily by errors in data entry, make matching of records and satisfactory data linkage difficult. This paper presents and assesses the performance of an approximate matching methodology developed utilising an algorithm for machine learning. The results indicate that this approach to record linkage is promising.
Heuristic algorithms for simulation-based dynamic traffic assignment

This study reviews and compares two heuristic algorithms: methods of successive averages and route swapping, which are commonly used in simulation-based dynamic traffic assignment models. A new hybrid algorithm that has the advantages of both algorithms is introduced. As finding a unique solution cannot be guaranteed, the concept of solution reliability is suggested for the formulation of convergence criterion. An example network is used to illustrate the method.
Design and implementation of a timeline for the Web

Visuzalizing time information using a timeline has been done for over 250 years.Using a timeline can assist users in making sense of time data which could otherwise be hard to understand.A timeline has been implemented at Ida Infront, and its purpose is to help managers understand how cases are progressing in a case management system created by Ida Infront. The timeline is designed using usability methods and has been evaluated with regards to its usability and how it interacts.Results show that there are a number of things that can increase usability and add meaningful interaction, such as using icons to collect similar data into one point and adding filtering capabilities so that users can find interesting data faster.
Biological event recognition with textual induction

This paper describes a supervised approach to th e recognition of biological events, which combines statistical sequential labelling and symbolic event extraction rules. Bottom-up textual induction has been applied to generating event extraction rules. As an evaluation data set, we use a corpus of biomedical abstracts, in which biological events concerning gene regulation in E. coli and H. Sapiens have been annotated by a group of biologists. The event instance extraction performance has been evaluated using 10fold cross validation. The experimental results show that named entity recognition (NER) and semantic role labelling (SRL) performance are close to annotator performance, as indicated by the interannotator agreement (IAA) scores, whereas automatic event extraction performance is around 28%, as compared to 40% IAA for exact manual event extraction.
Optical-code-based IP longest prefix match with variable-length optical gates

A bottleneck arises in backbone electronic Internet Protocol (IP) routers because the lookup in a routing table is performed according to the longest prefix match. To resolve this problem, a versatile optical-code- (OC-) based longest prefix match that uses variable-length optical gates for photonic IP packet routing has been developed. The developed OC-based IP routing with the longest prefix match will reduce the number of routing entries so that the matching entry can be found more quickly. Moreover, the new routing procedure will help make photonic routers more compact and cost effective.
High level modeling of Partially Dynamically Reconfigurable FPGAs based on MDE and MARTE

System-on-Chip (SoC) architectures are becoming the preferred solution for implementing modern embedded systems. However their design complexity continues to augment due to the increase in integrated hardware resources requiring new design methodologies and tools. In this paper we present a novel SoC co-design methodology based on aModel Driven Engineering framework while utilizing the MARTE (Modeling and Analysis of Real-time and Embedded Systems) standard. This methodology permits us to model fine grain reconfigurable architectures such as FPGAs and allows to extend the standard for integrating new features such as Partial Dynamic Reconfiguration supported by modern FPGAs. The overall objective is to carry out modeling at a high abstraction level expressed in a graphical language like UML (Unified Modeling Language) and afterwards transformations of these models, automatically generate the necessary specifications required for FPGA implementation.
Solution algorithms for the project selection and scheduling problem with resource constraints and time dependent returns

This paper considers the problem of resource constrained project selection and scheduling with a limited planning horizon and time–dependent return. The objective is to select a set of projects that maximise the present worth of profit. We first mathematically formulate the problem and compare the model with the adaptation of an available model for a similar problem. To solve the problem, we employ five novel metaheuristics based on genetic and iterated greedy algorithms. We propose a novel effective constructive heuristic to produce initial solution for the algorithm and a novel crossover operator. A comprehensive experiment is established with small and large sized instances to compare and evaluate the performance of the proposed model and algorithms. All the results show the effectiveness of the model and algorithms.
Building Ontologies with Basic Formal Ontology

In the era of "big data," science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use.After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies.