Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing criticality of proteins by system dynamics

Systems biology calls for studying system-level properties of genes and proteins rather than their individual chemical/biological properties. Up to date, most studies aiming at this goal are confined to topology-based approach. However, proteins have tertiary structures and specific functional roles, especially in metabolic systems. Thus topological properties such as connectivity, path length, etc., are not good surrogates for protein properties. In the present work, we developed a method to directly assess protein system-level properties based on system dynamics and in silico knockout tests. Applying the method to E. coli central carbon metabolic system, we found that transaldolase and transketolase-b had great impact on the system in terms of both system states and dynamical stability, while glucose-6-phosphate isomerase exerted very little influence. This finding is highly consistent with experimental characterization of metabolic essentiality. We also found that enzymes could affect a distant metabolite or enzyme even greater than a close neighbor. Our work may create a new angle for evaluating protein criticality in a system.
A maximum likelihood neural network based on a log-linearized Gaussian mixture model

The present paper proposes a new probabilistic neural network based on a log-linearized Gaussian mixture model, which can estimate a posteriori probability for pattern classification problems. Although a structure of the proposed network represents a statistic model, a forward calculation and a backward learning rule based on the maximum likelihood estimation can be defined in the same manner as the error back propagation neural network model. It is shown from experiments that considerably high classification performance for small sample size of training data can be realized and a structure of the network is easily determined by an incorporated statistical model.
CacOphoNy: Metamodel-Driven Architecture Recovery

Far too often, architecture descriptions of existing software systems are out of sync with the implementation. If they are, they must be reconstructed, but this is a very challenging task. The first problem to be solved is to define what "software architecture" means in the company. The answer can greatly vary, especially among the many stakeholders. In order to solve this problem, this paper presents CacOphoNy, a generic metamodel-driven process for reconstructing software architecture. This paper provides a methodological guide and shows how metamodels can be used (1) to define architectural viewpoints, (2) to link these viewpoints to existing metaware and (2) to drive architecture reconstruction processes. The concepts presented in this paper were identified over the last decade in the context of Dassault Systemes, one of the largest software companies in Europe, with more than 1200 developers. CacOphoNy is however a very generic process pattern, and as such it can be applied in many other contexts. This process pattern is in line with the MDA and ADM approaches from the OMG. It also complies with the IEEE Standard 1471 for software architecture. A megamodel integrating these standards is presented.
An Energy Efficient Protocol for Evaluating Performance of Cluster Based WSN

The wireless sensors networks is that the powerful space for providing all wireless networking support and fulfill the customers necessities. During this paper we tend to measure the performance of the WSNs and deploy the H-HEED protocol that supports the heterogeneity environment. The HHEED protocol is that the efficient protocol and run on base stations additionally as nodes communication. Sensor nodes could change their location once initial deployment. Quality may result from environmental influences such as wind or water, sensing element nodes could also be hooked up to or carried by mobile entities, and sensing element nodes could possess automotive capabilities. In other words, quality could be either associate incidental aspect impact, or it's going to be a desired property of the system. The clustering technique adapts by the on top of same protocol and reenergized the networks in order that the performance of this protocol was higher than alternative causing protocol. This paper calculates the general energy and delay performance using NS2 network machine. The detailed clarification given in the Experimental Result section.
Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data

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Synthesis of Finite State Machines: Functional Optimization

Synthesis of Finite State Machines: Functional Optimization is one of two monographs devoted to the synthesis of Finite State Machines (FSMs). This volume addresses functional optimization, whereas the second addresses logic optimization. By functional optimization here we mean the body of techniques that: compute all permissible sequential functions for a given topology of interconnected FSMs, and select a `best' sequential function out of the permissible ones. The result is a symbolic description of the FSM representing the chosen sequential function. By logic optimization here we mean the steps that convert a symbolic description of an FSM into a hardware implementation, with the goal to optimize objectives like area, testability, performance and so on. Synthesis of Finite State Machines: Functional Optimization is divided into three parts. The first part presents some preliminary definitions, theories and techniques related to the exploration of behaviors of FSMs. The second part presents an implicit algorithm for exact state minimization of incompletely specified finite state machines (ISFSMs), and an exhaustive presentation of explicit and implicit algorithms for the binate covering problem. The third part addresses the computation of permissible behaviors at a node of a network of FSMs and the related minimization problems of non-deterministic finite state machines (NDFSMs). Key themes running through the book are the exploration of behaviors contained in a non-deterministic FSM (NDFSM), and the representation of combinatorial problems arising in FSM synthesis by means of Binary Decision Diagrams (BDDs). Synthesis of Finite State Machines: Functional Optimization will be of interest to researchers and designers in logic synthesis, CAD and design automation.
Design, implementation, and testing of a real-time software system for a quaternion-based attitude estimation filter

Abstract : Human limb segment angle tracking requires a system which can track through all orientations. The major problem addressed by this research was to develop a real time inertial motion tracking system based on quaternions to overcome the singularities of Euler angle filters. This work involves clarification of the theory behind quaternion attitude estimation and development of a system capable of determining the orientation of an object in world coordinates. System sensors were built using miniature accelerometers, rate sensors, and magnetometers. The software system was designed by using Unified Modeling Language (UML) with object oriented design techniques. The actual implementation created a real time orientation tracking system. The system was tested with dynamic tilt table experiments. Test results showed that the quaternion attitude estimation filter system can track human limb segments in real time within 1 degree of accuracy in an orientation and with a 55 Hz update rate.
A computational model for generating and interpreting indirect answers

This dissertation provides a computational model for generating and interpreting indirect answers. An indirect answer conversationally implicates the requested information, i.e., it licenses a pragmatic inference about the intended meaning of the response. In my model, pragmatic knowledge about full answers, consisting of direct answers and extra appropriate information, is encoded in discourse plan operators. In appropriate contexts, the extra information in a full answer may be used as an indirect answer. Interpretation is modeled as discourse plan recognition. The inputs to interpretation include the discourse expectation that the speaker S will inform the hearer H if the questioned proposition is true, H's beliefs, the semantic interpretation of the utterances given by S during S's turn, and pragmatic knowledge. The output is a ranked set of discourse plans which it is plausible to ascribe to S. During discourse plan recognition, logical inference plays a role: (1) in verifying that the applicability conditions and goals of a candidate plan are consistent with H's beliefs about S's beliefs and goals, (2) in proving that the coherence, relations specified by the plan are plausible, and (3) in hypothesizing missing parts of the plan based upon assumptions about discourse coherence. The inputs to generation include the same discourse expectation as above, S's beliefs, and pragmatic knowledge. The output is a discourse plan. The plan is constructed in two steps: content planning and plan pruning. Content planning is performed by top-down construction of a hierarchical discourse plan for a full answer. S's motivation for including subactions providing extra information is modeled by means of stimulus conditions which reflect general concerns of politeness and cooperativity. During plan pruning, the generator determines what parts of the full answer are inferable and thus do not need to be given explicitly by S. This model handles a wider variety of types of indirect answers than previous models, provides new techniques for discourse plan inference and content planning, and provides aIl approach for other types of conversational implicature.
Reconocimiento y anotación de nombres de fármacos genéricos en la literatura biomédica

espanolEste trabajo propone un sistema para la identificacion y anotacion de nombres de farmacos genericos en textos biomedicos basado en modelos de estado -finito. El procedimiento presentado utiliza reglas de nomenclatura para farmacos genericos, recomendadas por el Consejo United States Adoptated Names (USAN), que van a permitir la clasificacion de los farmacos en familias farmacologicas, y una herramienta de ingenieria linguistica basada en tecnologia de estado -finito. Por medio de una interfaz grafica, se han construido analizadores capaces de identificar, clasificar y etiquetar nombres de farmacos genericos, utilizando los afijos recomendados por USAN. El sistema consigue un 99,8 % de precision y un 92 % de exhaustividad sobre una coleccion de 259 resumenes de articulos cientificos extraidos de la base de datos Medline. La combinacion de reglas USAN y tecnologia de estado-finito constituye un procedimiento eficaz para la deteccion, clasificacion y etiquetado de nombres de farmacos genericos. EnglishThis paper proposes a system for identification and annotation of generic drug names in biomedical te xts based on finite-state models. The proposed procedure uses naming rules for generic drugs, recommended by the United States Adoptated Names (USAN) Council, allow the classification of drugs in drug families, and linguistic engine based on finite -state techniques. Through a graphical interface, we have built analyzers able to identify to identify, classify and assign annotations to generic drug names, using affixes recommended by USAN. The evaluation corpus consists of 256 Medline abstracts. The system ac hieves a 99.8% precision and 92% recall. The combination of rules USAN and finite -state technology is an effective procedure for the detection, classification and tagging of generic drug names.
A P2P File Sharing Technique by Indexed-Priority Metric

Recently, the improvements in processing performance of a computer and in high speed communication of an optical fiber have been achieved, so that the amount of data which are processed by a computer and flowed on a network has been increasing greatly. However, in a client-server system, since the server receives and processes the amount of data from the clients through the network, a load on the server is increasing. Thus, there are needed to introduce a server with high processing ability and to have a line with high bandwidth. In this paper, concerning to P2P networks to resolve the load on a specific server, a criterion called an Indexed-Priority Metric is proposed and its performance is evaluated. The proposed metric is to allocate some files to each node. As a result, the load on a specific server can distribute them to each node equally well. A P2P file sharing system using the proposed metric is implemented. Simulation results show that the proposed metric can make it distribute files on the specific server. Keywords—peer-to-peer, file-sharing system, load-balancing, dependability.
Human Factors: Understanding People-System Relationships

A general, introductory, up-to-date text on the interface between people and systems, geared for the advanced undergraduate or graduate major in psychology, industrial engineering, computer science, and business. Integrates theory and practical implications of human behavior in terms of theoretical models. Covers non-traditional contemporary topics and uses a detailed description of human capabilities as a necessary precursor to the study of human-machine systems. Emphasizes the cognitive aspects of the person-system relationship.
Interfaces to encourage look-ahead : impact on problem solving and performance

The experiments reported in this thesis attempted to directly study the process of look-ahead during problem solving. Recent work has suggested that interface manipulations that increase look-ahead during problem solving lead to improvements in performance. However, evidence has been indirect, and there have been few attempts specifically made to quantify look-ahead span, changes that may occur over time and possible interactions with the task environment. An initial experiment required users to specify 3 moves in advance while solving the 8-puzzle. The strict enforcing of look-ahead by even a small number of moves was unsuccessful in terms of improving problem performance. In fact, results indicated that such move enforcement may negatively affect performance. Subsequent experiments, using both the 8-puzzle and Water Jars problems, provided participants with a motivation to plan using a Scoreboard system that rewarded greater planning and look-ahead. Results found this approach to be more viable, as the interface appeared to support the opportunistic planning behaviour frequently undertaken by participants. Across a series of experiments, increased look-ahead led to more efficient problem solving performance compared to controls, while leaving total time to solution unaffected. Look-ahead span increased to approximately 11 steps when transforming the same start-state to a goal-state over trials on the 8-puzzle. When a new solution path had to be generated for each new problem start-state, look-ahead still increased over trials, but only to a span of approximately 4 steps. This look-ahead span was also observed during Water Jars performance when the Scoreboard manipulation was present. A manipulation of 'system response time' (SRT) on Water Jars problems also led to improved performance but indicated an adaptation to the manipulation, leading to a lesser impact of SRT than previous manipulations. The results are discussed in relation to existing studies of planning, performance and the role that look-ahead may have in future studies of problem solving.
Technology transfer materials — Are we learning the technology of transfer?

Abstract The process of producing written documents for use in technology transfer or extension is reviewed. Despite a well documented understanding of this process, when recent examples of extension materials are tested against a checklist of best practice of physical attributes (a technology of transfer), a departure is evident. A series of recommendations are made for improving the process. These emphasize (1) focusing upon the purpose of each document, (2) redressing the perceptions of the technologists involved and raising their awareness of the design aspects of good communication by using the checklist, and (3) engaging in evaluation at all stages of the production process with the participation of the target group.
Przegląd metod i technik eksploracji danych tekstowych

This article describes the author's classification of the methods and techniques of textual data mining. In this article also describes the currently available methods and sauces representation of textual data and their processing techniques. Also conducted a discussion on the processing of text documents using the presented methods. This paper also discussed the possibilities and limitations of individual methods to process the presented text documents.
Lazy learner text categorization algorithm based on embedded feature selection

To avoid the curse of dimensionality, text categorization (TC) algorithms based on machine learning (ML) have to use an feature selection (FS) method to reduce the dimensionality of feature space. Although having been widely used, FS process will generally cause information losing and then have much side-effect on the whole performance of TC algorithms. On the basis of the sparsity characteristic of text vectors, a new TC algorithm based on lazy feature selection (LFS) is presented. As a new type of embedded feature selection approach, the LFS method can greatly reduce the dimension of features without any information losing, which can improve both efficiency and performance of algorithms greatly. The experiments show the new algorithm can simultaneously achieve much higher both performance and efficiency than some of other classical TC algorithms.
BICLUSTERING GENE EXPRESSION DATASET USING ENHANCED BARYCENTER CROSSING MINIMIZATION

There are two main categories of bi-clustering approaches graph-based bi-clustering, and non-graph based bi-clustering. In graph-based bi-clustering algorithm the input dataset is converted into bigraph such as bipartite graph, and apply some heuristic local searching techniques to minimize the number of crossings between edges in the Bigraph such as BaryCenter (BC) used in SPHier algorithm. The main problem of graph-based bi-clustering algorithm is to extract the best bi-clusters, and this leads to the ordering of gene expression dataset before we apply biclustering algorithm. In bipartite graph this is achieved through minimizing the number of crossings in bipartite graph. As we minimize the number of crossings in the bipartite graph, the gene expression dataset becomes more ordered, and this enhances the results of biclustering algorithm. The main goal of our proposed algorithm is the enhancement of graph-based biclustering algorithm by enhancing BaryCenter crossing minimization heuristics of bipartite graph. In the proposed algorithm we add the rank of each node to the rank of its neighbors, and using the position of each node in the calculations to give a new rank to each node, and using this rank for reordering the nodes of each layer.
A paradigm for asynchronous communication and its application to concurrent constraint programming

We develop a general semantic theory of asynchronous communication in concurrent logic and concurrent constraint languages. The main characteristic of these languages, from the point of view of the communication mechanism, is that processes interact by querying and updating some common data structure. We abstract from the speci(cid:12)c features of the underlying data structure by means of a uniform language of which the actions are interpreted as transformations on an abstract set of states. This approach shows that there exists a basic similarity between concurrent logic (constraint) languages and other languages based on asynchrononous communication, like data(cid:13)ow and asynchronous CSP. Actually, our intention is to capture languages based on asynchronous communication as instances of this uniform language, such an instance being determined by a speci(cid:12)c set of states and interpretation of the actions. The computational model of our paradigm is described by a transition system in the style of Plotkin's SOS. A compositional model is presented that is based on reactive sequences, i.e., sequences of pairs of states.
Use of lidar-derived NDTI and intensity for rule-based object-oriented extraction of building footprints

Buildings play an essential role in urban intra-construction, planning, and climate. The precise knowledge of building footprints not only serves as a primary source for interpreting complex urban characteristics, but also provides regional planners with more realistic and multidimensional scenarios for urban management. The recently developed airborne light detection and ranging (lidar) technology provides a very promising alternative for building-footprint measurement. In this study, lidar intensity data, a normalized digital surface model (nDSM) of the first and last returns, and the normalized difference tree index (NDTI) derived from the two returns are used to extract building footprints using rule-based object-oriented classification. The study area is chosen in London, Ontario, based on the various types of buildings surrounded by trees. An integrated segmentation approach and a hierarchical rule-based classification strategy are proposed during the process. The results indicate that the proposed object-based classification is a very effective semi-automatic method for building-footprint extraction, with buildings and trees successfully separated. An overall accuracy of 94.0% and a commission error of 6.3% with a kappa value of 0.84 are achieved. Lidar-derived NDTI and intensity data are of great importance in object-based building extraction, and the kappa value of the proposed method is double that of the object-based method without NDTI or intensity.