Miracle-dz/UniFAR-FTUs huggingface dataset:
Satisfiability and Model Checking of CTL* with Graded Path Modalities

Graded path modalities count the number of paths satisfying a property, and generalize the existential (E) and universal (A) path modalities of CTL*. The resulting logic is called GCTL*. We settle the complexity of satisfiability of GCTL*, i.e., 2ExpTime-Complete, and the complexity of the model checking problem for GCTL*, i.e., PSpace-Complete. The lower bounds already hold for CTL*, and so, using the automata-theoretic approach we supply the upper bounds. The significance of this work is two-fold: GCTL* is more expressive than CTL* at no extra cost in computational complexity, and GCTL* has all the advantages over GCTL (CTL with graded path modalities) that CTL* has over CTL, e.g., the ability to express fairness.
Radix – 4 Implementation of a Montgomery Multiplier for a RSA Cryptosystem

As cryptosystems become more important to computing, so does the need to make the encryption and decryption of larger and stronger keys faster. The RSA algorithm presents one of these problems specifically in performing modular exponentiation. A popular method to implement the necessary modular multiplication and squaring required for exponentiation is the Montgomery Multiplication (MM) algorithm. This project is an extension of a thesis done by Allen Michalski that explores the use of Montgomery Multiplication algorithms and looks to include a radix-4 implementation of the multiplications done in the Montgomery domain using FPGAs, optimizing for speed and area. It then shows the additions necessary for a radix-4 Montgomery Multiplier implantation, as well as the potential benefits of the architecture.
GAP/D: VLSI Hardware for Parallel and Adaptive Distributed Genetic Algorithms

This paper presents GAP/D, a VLSI implementation of a dynamic adaptation scheme for the frequency of inter-deme migration in distributed genetic algorithms (GA). Distributed GA, or multi-deme-based GA, uses multiple populations which evolve concurrently. The purpose of dynamic adaptation is to improve convergence performance so as to obtain better solutions. Through simulation experiments, we proved that our scheme achieves better performance than fixed frequency migration schemes.
Received Signal Strength Indicator-Based Adaptive Localization Algorithm for Indoor Wireless Sensor Networks

Solutions for indoor localization have become more critical with recent advancement in context and location-aware technologies. When wireless sensor network (WSN) used in complex indoor environment, great propagation loss will be caused and it is very difficult to estimate adaptively the location of target nodes when environment changed. In this paper, an indoor adaptive localization algorithm based on received signal strength indication (RSSI) for wireless sensor networks is proposed. The algorithm utilizes the RSSI of radio signals radiating from two other fixed nodes to generate the local parameters of signal propagation model for each fixed node, and the parameters are updated online according to environmental variation. According to the estimated parameters of the signal propagation model, iteration method is applied to estimate the position of target node. Through actual experimental tests, the validity of the proposed algorithm is demonstrated. Copyright © 2013 IFSA.
The Research and Practice of Mobile IP Position Management Based on Virtual Private Network

Based on virtual private network technology, a method of IP mobile position management is presented. Each mobile agent (MA) is set up corresponding to a respective autonomy network on the Internet. Once MA is connected to the local ISP, a VPN that is used to manage the binding information of mobile nodal position is formed, which manages the position of mobile nodes. The memory structure of the binding information table and the algorithm used to manage the binding information of mobile position is designed. The simulative test shows that the design can solve the problem of triangular route, obtain an optimized route, and greatly increase the speed of receiving data compared with the standard mobile IP
A new design approach for Kalman filter system based on model to hardware

Kalman filter is widely applied in engineering as its realtime recursive theory. Traditional design flow to implement the filter has problems of repetive description and hidden mistakes, because system is programmed again after the simulation and verification in Matlab environment. The paper proposes a novel approach to realize Kalman filtered system, in which designs system from Matlab model directly to FPGA hardware. That is, model and simulate the filter system in Matlab, and generate hardware description code after the verification without need of hardware program manually. This approach has the advantages in the flexibility of the FPGA, and in the efficiency of the system implement, moreover in the consistency between results and simulation. The approach is suitable for any algorithm and models which can be described in Matlab, and will be applied widely.
Extraction of Frequent Association Patterns Co-occurring across Multi-sequence Data

— The progress in computer performance and ubiquitous sensor technology has made it possible to yield a large amount of data such as vital data and earthquake data. In general, sensor data are measured from multiple observation points. Extracting correlation or causal association from such huge multi-sequences is a challenging issue in the field of data-mining technology. Development of high-performance sequence mining tools will make great contributions for analyzing various real data such as disease risk prediction or earthquake prediction. In this paper, we propose a new method for extracting sets of patterns (called association patterns) that co-occur repeatedly across multiple sequences. Extraction of association patterns is performed by a combination of frequent pattern extraction and interval graph mining. Our method is different from traditional multi-sequence mining methods in that it does not assume similarity of patterns among different sequences. Namely, even if frequent patterns in different sequences show no similarity, our method extracts them as an association pattern if these patterns exhibit a frequent co-occurrence relation along a time-sequence. In this paper, we evaluate the usefulness of our method using synthetic datasets.
Successful Treatment of Disseminated Acanthamoeba sp. Infection with Miltefosine

We report on an HIV-negative but immunocompromised patient with disseminated acanthamoebiasis, granulomatous amoebic encephalitis, and underlying miliary tuberculosis and tuberculous meningitis. The patient responded favorably to treatment with miltefosine, an alkylphosphocholine. The patient remained well with no signs of infection 2 years after treatment cessation.
Global Network Interference Detection Over the RIPE Atlas Network

Existing censorship measurement platforms frequentlysuffer from poor adoption, insufficient geographic coverage, and scalability problems. In order to outline ananalytical framework and data collection needs for futureubiquitous measurements initiatives, we build on top ofthe existent and widely-deployed RIPE Atlas platform.In particular, we propose methods for monitoring thereachability of vital services through an algorithm thatbalances timeliness, diversity, and cost. We then use Atlas to investigate blocking events in Turkey and Russia.Our measurements identify under-examined forms of interference and provide evidence of cooperation betweena well-known blogging platform and government authorities for purposes of blocking hosted content.
Exploring Uncoupling Proteins and Antioxidant Mechanisms under Acute Cold Exposure in Brains of Fish

Exposure to fluctuating temperatures accelerates the mitochondrial respiration and increases the formation of mitochondrial reactive oxygen species (ROS) in ectothermic vertebrates including fish. To date, little is known on potential oxidative damage and on protective antioxidative defense mechanisms in the brain of fish under cold shock. In this study, the concentration of cellular protein carbonyls in brain was significantly increased by 38% within 1 h after cold exposure (from 28°C to 18°C) of zebrafish (Danio rerio). In addition, the specific activity of superoxide dismutase (SOD) and the mRNA level of catalase (CAT) were increased after cold exposure by about 60% (6 h) and by 60%–90% (1 and 24 h), respectively, while the specific glutathione content as well as the ratio of glutathione disulfide to glutathione remained constant and at a very low level. In addition, cold exposure increased the protein level of hypoxia-inducible factor (HIF) by about 50% and the mRNA level of the glucose transporter zglut3 in brain by 50%–100%. To test for an involvement of uncoupling proteins (UCPs) in the cold adaptation of zebrafish, five UCP members were annotated and identified (zucp1-5). With the exception of zucp1, the mRNA levels of the other four zucps were significantly increased after cold exposure. In addition, the mRNA levels of four of the fish homologs (zppar) of the peroxisome proliferator-activated receptor (PPAR) were increased after cold exposure. These data suggest that PPARs and UCPs are involved in the alterations observed in zebrafish brain after exposure to 18°C. The observed stimulation of the PPAR-UCP axis may help to prevent oxidative damage and to maintain metabolic balance and cellular homeostasis in the brains of ectothermic zebrafish upon cold exposure.
Energy effciency and performance of cloud data centers: which role can modeling play?

Resource Virtualization and Load Balancing are main objectives to reduce the power consumption and to improve the performance of large data centers (DC). The management of Cloud Data Centers (CDC) requires an accurate planning and an efficient use of system resources in order to save energy consumption ("greening"), to provide Quality of Service (QoS), and to meet negotiated Service Level Agreements (SLA). This contribution addresses the question of modeling and the development of generic queuing models for energy-efficient use of resources for dynamic load balancing in virtualized CDCs. Performance models are developed for energy efficiency through automatic Server Consolidation, Dynamic Voltage and Frequency Scaling (DVFS) under Static Load Balancing; Dynamic Load Balancing can be achieved through Virtual Machine (VM) migrations. The analysis of such models provides quantitative performance figures upon which the system operation can be optimized with respect to guaranteed real-time performance and energy efficiency under prescribed SLAs.
Online dating recommender systems: the split-complex number approach

A typical recommender setting is based on two kinds of relations: similarity between users (or between objects) and the taste of users towards certain objects. In environments such as online dating websites, these two relations are difficult to separate, as the users can be similar to each other, but also have preferences towards other users, i.e., rate other users. In this paper, we present a novel and unified way to model this duality of the relations by using split-complex numbers, a number system related to the complex numbers that is used in mathematics, physics and other fields. We show that this unified representation is capable of modeling both notions of relations between users in a joint expression and apply it for recommending potential partners. In experiments with the Czech dating website Libimseti.cz we show that our modeling approach leads to an improvement over baseline recommendation methods in this scenario.
Examining the Satisfaction of Educational Leaders and Their Intent to Pursue Career Advancement in Public School Administration

This statewide study examined selected demographic characteristics, worklife experiences, and personal issues that influence the satisfaction of administrators (assistant principals, principals, assistant superintendents) and their intent to leave their positions and careers or pursue career advancement in public school administration. The results of this study suggest that this group of school administrators is satisfied with their worklife experiences; however, personal issues and individual demographic characteristics have a major influence on their intent to leave their careers and professions or pursue advancement. Moreover, few intend to pursue public school advancement, with the exception of assistant principals, who are the least qualified and experienced but most willing.
Improving the performance of translation process in a statistical machine translator using sequence IRSTLM translation parameters and pruning

A translation process, as a critical part of a machine translation, can be put simply as a process of decoding the meaning of the source text and re-encoding the meaning into the target language. Unfortunately, most of the translation process requires complex operations and in-depth knowledge of the languages in order to give a good quality translation. This study proposes a better approach, which does not require in-depth knowledge of the linguistic properties of the languages, but it produces a good quality translation. This statistical machine translation uses parallel corpus and statistical machine learning for English and Bahasa Indonesia. This study also evaluated 28 different parameters in IRSTLM language modeling and proposes a sequence evaluation mechanism based on a maximum evaluation of each parameter in producing a good quality translation based on NIST and BLEU. The pruning process, user interface, and the personalization of translation have a very important role in implementing of this machine translation. The result is quite promising. It shows that pruning process increases of the translation process time. The particular sequence value parameter in translation process, it also has a better performance than the other method using in-depth linguistic knowledge approaches. All these processes, including the process of parsing from a stand-alone mode to an online mode, are also discussed in detail.
Supporting Heterogeneous Architecture Descriptions in an Extensible Toolset

Many architecture description languages (ADLs) have been proposed to model, analyze, configure, and deploy complex software systems. To face this diversity, extensible ADLs (or ADL interchange formats) have been proposed. These ADLs provide linguistic support for integrating various architectural aspects within the same description. Nevertheless, they do not support extensibility at the tool level, i.e. they do not provide an extensible toolset for processing ADL descriptions. In this paper, we present an extensible toolset for easing the development of architecture-based software systems. This toolset is not bound to a specific ADL, but rather uses a grammar description mechanism to accept various input languages, e.g. ADLs, interface definition languages (IDLs), domain specific languages (DSLs). Moreover, it can easily be extended to implement many different features, such as behavioral analysis, code generation, deployment, etc. Its extensibility is obtained by designing its core functionalities using fine-grained components that implement flexible design patterns. Experiments are presented to illustrate both the functionalities implemented by the toolset and the way it can be extended.
Resource Reservation for Voice Support in MANETs

We propose an adaptive reservation protocol for voice and data support. Proposed scheme allocates slots to voice sources each time they wake up and gives them high priority to send their reservation requests over data sources. Bandwidth unused by idle voice sources is rendered temporarily available for reservation. Simulation results show that the proposed scheme improves performance of voice traffic in terms of dropping rate.
Knowledge and attitudes of nurses in community health centres about electronic medical records.

BACKGROUND Nurses in primary healthcare record data for the monitoring and evaluation of diseases and services. Information and communications technology (ICT) can improve quality in healthcare by providing quality medical records. However, worldwide, the majority of health ICT projects have failed. Individual user acceptance is a crucial factor in successful ICT implementation. OBJECTIVES The aim of this study is to explore nurses' knowledge, attitudes and perceptions regarding ICT so as to inform the future implementation of electronic medical record (EMR) systems. METHODS A qualitative design was used. Semi-structured interviews were undertaken with nurses at three community health centres (CHCs) in the King Sabata Dalyindyebo Local Municipality. The interview guide was informed by the literature on user acceptance of ICT. Interviews were recorded and analysed using content analysis. RESULTS Many nurses knew about health ICT and articulated clearly the potential benefits of an EMR such as fewer errors, more complete records, easier reporting and access to information. They thought that an EMR system would solve the challenges they identified with the current paper-based record system, including duplication of data, misfiling, lack of a chronological patient record, excessive time in recording and reduced time for patient care. For personal ICT needs, approximately half used cellphone Internet-based services and computers. CONCLUSIONS In this study, nurses identified many challenges with the current recording methods. They thought that an EMR should be installed at CHCs. Their knowledge about EMR, positive attitudes to ICT and personal use of ICT devices increase the likelihood of successful EMR implementation at CHCs.
Identification and Accessioning of Individuals for the Officer Candidate School (OCS)

Abstract : The "AccessOCS" project was a qualitative study designed to (a) identify and describe Officer Candidate School (OCS) applicants in terms of motivations, backgrounds, and incentives; (b) identify how the OCS selection and application process works, and (c) develop recommendations for improving the OCS accessioning process. The research approach was to conduct focus groups and one-on-one interviews with a number of personnel, e.g., sponsors, applicants, government officials, to learn about the OCS accessioning process from several vantage points. These individuals included: OCS candidates; OCS instructors, cadre, and Company Commanders; Basic Officer Leadership Course B (BOLC B) lieutenants; Captain's Career Course (CCC) officers; recruiters; and policy-level government officials. Content analysis of the interviews and focus groups revealed the following key findings: findings substantiated the fact that OCS candidates express a desire to serve and a commitment to the Army; OCS application procedures can be difficult for applicants to understand and utilize; a standardized, stored database management system for tracking officer candidates and their performance is lacking; officer candidates could be better prepared for OCS; and the review board process is decentralized. Issues surrounding the branch assignment are discussed. Several recommendations for improvement are presented.
A Parallel Layered Decoding Algorithm for LDPC Codes in WiMax System

WiMax presents a quasi-cyclic LDPC codes with excellent performance in decoding, which is suitable to parallel decoding for its structure. And the flexible matrix structure can support more implementation of code with different code-length. To decrease the iterative times of the decoding algorithm and save the hardware resource of the decoder for LDPC codes, the paper presents a parallel layered decoding algorithm based on OMS, which doesn't reduce the decoding performance. This algorithm proposes a novel scheduling to improve the updating of message. Simulation results show that, for a 2304 bits length of code, the parallel layered decoding algorithm exhibits a better convergent performance than a traditional algorithm. The SNR of parallel layered OMS decreases about 0.05 dB than that of traditional OMS, when the BER is 10-3.
Design and Implementation of a Performance-Based Assessment System for the Preparation of Effective Teachers for a Diverse Society.

This document describes Harris-Stowe State College's (HSSC's) Performance-Based Design System for the Preparation of Effective Teachers for a Diverse Society. The program prepares teachers who possess subject matter, sociocultural, instructional, and personal-professional competencies that mirror effective teaching practices for enhancing P-12 student achievement. HSSC's curriculum framework makes explicit connections to the enhancement of student learning within an emerging multicultural dynamic that shape the context of P-12 education. Its program design strategy enables prospective teachers to address continuing problems attached to achieving social justice and to clarify the central role of teaching for educating a diverse student body. An overview of HSSC's performance-based assessment system illustrates the process by which student teachers' multiple types of performance-based assessment artifacts are evaluated throughout a five core curriculum sequence. Examples of student teachers' evidence of performance, consistent with the program's conceptual framework, represent candidates' intellectual development for effective teaching. The report presents an analysis of HSSC candidates' performance-based assessment artifacts data. Faculty identified 342 artifacts, sequenced across the five core curriculum and documented in the curriculum syllabi. Results highlighted the program's success in preparing effective teachers for a diverse society. Student teachers' attainment of effective teaching competence illustrated a developmental trajectory based on program matriculation status. (Contains 91 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document.
An Optimization Based Framework for Dynamic Batch Mode Active Learning

Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled data, such algorithms automatically select the salient and representative samples for manual annotation. Batch mode active learning schemes have been recently proposed to select a batch of data instances simultaneously, rather than updating the classifier after every single query. While numerical optimization strategies seem a natural choice to address this problem (by selecting a batch of points to ensure that a given objective criterion is optimized), many of the proposed approaches are based on greedy heuristics. Also, all the existing work on batch mode active learning assume that the batch size is given as an input to the problem. In this work, we propose a novel optimization based strategy to dynamically decide the batch size as well as the specific points to be queried, based on the particular data stream in question. Our results on the widely used VidTIMIT and the MBGC biometric datasets corroborate the efficacy of the framework to adaptively identify the batch size and the particular data points to be selected for manual annotation, in any batch mode active learning application.
A comparison of artificial neural networks and cluster analysis for typing biometrics authentication

Password authentication is the most commonly used identification system in today's computer world. Its security can be enhanced using typing biometrics as a transparent layer of user authentication. Our research focuses on using the time period between keystrokes as the measure of the individual's typing pattern. The typing pattern of a particular individual can be represented by the weights of a fully trained multilayer perceptron. Alternatively, each user's typing pattern can be viewed as a cluster of measurements that can be differentiated from clusters of other users.