Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphical Feedback System to Effectively Support User's Task

The book metaphor approach was created to simplify database access. The intent was to improve access speed and comfort. In 1990, we created an advanced book metaphor interface (BMI) to a set of about 300 design guidelines. The user interacts with the guidelines through a ‘book like’ screen and operations such as the table of contents, the index, or browsing. Experiments revealed, however, that the BMI did not offer a significant improvement over the equivalent printed version of the guidelines. The BMI was more comfortable but users did not perform the task, correcting an example of a bad data input screen, any more rapidly. The problem was that novice users did not understand the tools offered by the BMI and so failed to use them in the optimum manner. An agent was added to the BMI that monitors the user's commands and, when the user deviates from the optimum procedure, graphically suggests what the more correct procedure would be. The subjects of the usability analysis were software designers who did not have background in human factors. They were instructed to design screens through the use of the guidelines using the BMI. The agent was provided to only half of them. The usability analysis finds that the new BMI realizes higher productivity and increased user acceptance.
Highly-resilient, energy-efficient multipath routing in wireless sensor networks

Previously proposed sensor network data dissemination schemes require periodic low-rate flooding of data in order to allow recovery from failure. We consider constructing two kinds of multipaths to enable energy efficient recovery from failure of the shortest path between source and sink. Disjoint multipath has been studied in the liteature. w propose a model braided multipath scheme, which results in several partially disjoint multipath schemes. We find that braided multipaths are a viable alternative for energy-efficient recovery from isolated and patterned failures
Wi-Fi (802.11b) and Bluetooth: enabling coexistence

This article provides an introduction to issues of coexistence between Bluetooth and Wi-Fi/sup TM/ (IEEE 802.11b), with particular attention to scenarios requiring simultaneous operation, or Sim-OP/sup TM/, of both systems in very close proximity. The article explains basic interference mechanisms and quantifies their impact through both actual measurements and simulation. We have developed a detailed simulator that fully models behavior of the physical layer (PHY) and media access control (MAC) of both Bluetooth and 802.11b; it is used to expand the analysis and project the mutual impact of collocated Bluetooth and 802.11b systems across a number of geometries, system parameter settings, and design choices, complementing efforts within the IEEE 802.15. 2 Task Group, which are also discussed. The article concludes with a discussion of techniques with the potential to greatly improve the performance of collocated Bluetooth and Wi-Fi systems. A key result of this investigation is that while performance of both systems can degrade when they are collocated, a number of techniques can be employed to virtually eliminate the problems.
Bayesian Multi-instance Learning for Image Retrieval with Unlabeled Data

To deal with the two problems in image retrieval, i.e., the small number of query images, the ambiguity of an image - the image consists of many regions with different semantic meaning, in this paper, we proposed a novel method for image retrieval based on Bayesian multi-instance learning using unlabeled data, termed as Bayesian-MIL method, which treats the image retrieval as a binary classification problem. In this method, to obtain an approximate estimation of the class- conditional probability of positive images, a multi-instance learning algorithm is adopted to filter out background regions in positive images, and then a Bayesian classifier is constructed to rank the images from a large digital repository according to their score of posterior probability. Finally, the ranking top k images will be returned to users. Experimental results on COREL image data set have demonstrated the effectiveness and efficiency of the proposed approach.
Picturing short-period diachronic phenomena in specialised corpora: a textual terminology description of the dynamics of knowledge in space technologies

This article presents a first description and a proposal for the classification of the evolution phenomena involved in short-term diachrony in the field of space. It is based on the principles of Textual Terminology and relies on a tool-based analysis of two diachronic corpora. The linguistic methodology is briefly described but the emphasis is on the list of evolution phenomena revealed through our analysis. These results present an original description of knowledge evolution: 17 types of evolution are listed, revealing the heterogeneity and richness of terminology dynamics and offering a descriptive basis to start new subsequent research that would complete this typology
Rack‐Level DC Power Solution for Volume Servers

Rack‐level DC power supply is the optimal technology for providing DC power to a volume server without any power infrastructure changes in an existing AC data center. In this paper, we propose a smartly controllable and monitorable DC rack power system. The proposed system improves power efficiency by changing the power distribution architecture of a conventional method in the rack. We developed an optimal power control method in multipower modules to provide high efficiency at low loads. In addition, the proposed system provides real‐time web monitoring of the rack power and environment around a rack. In our experiments, the proposed system improved the power efficiency by over 10% compared to an AC power system providing N+1 redundant power and power monitoring.
An Evaluation of Computer-Assisted Instruction in Phonological Awareness with below Average Readers

The use of computer-assisted instruction (CAI) to train phonological awareness skills in at-risk first graders was evaluated. Fifty-four children ranging in age from six years two months to seven years eight months participated in an eight-week training study. There were three experimental conditions. The first group received approximately twenty-five minutes a day, four days a week with two phonological awareness training programs. The second group received the same amount of training with a program designed to train alphabetic decoding skills. The third group served as an attentional control group and spent equal time on the computer with several programs designed to provide practice on basic math skills. The children exposed to the phonological awareness training programs made significantly greater improvements on several measures of phonological awareness and on a measure of word recognition, when compared to children in the other two groups. Tentative conclusions were drawn about the use of CAI as means of training phonological awareness skills with at-risk students.
Expert-based multi-mode switching control for the temperature and humidity control system of archive establishment

An expert-based multi-mode switching control for the temperature and humidity control system of archive establishment is proposed in this paper. This system possesses a two-level structure including: multi-mode switching controllers (MSC) in the lower level for basic control, and an expert supervisory system in the higher level for performance enhancement and energy saving. Due to its simple structure, the multi-mode switching controller is very easy to be designed or tuned. Moreover, it is very suitable for the temperature and humidity control system for its strong capacity to handle time-delays. The expert supervisory system implements an operation mode recognition and control approach selection to optimise the overall control and obtains an enhanced and robust performance in different operation modes. The performance analysis and simulation results show the practicability of the proposed system and its viability in the real archive establishment environment.
Performance evaluation of power level division multiple access (PDMA) scheme

The authors present a new multiple access method, PDMA (power level division multiple access), for sharing a common channel among stations by dividing the channel into several power levels. By this method, the capture effect among packets sent at different power levels can be exploited. The authors propose two access schemes employing PDMA and show that the schemes improve the throughput-delay characteristics of the conventional schemes. An approximate analysis of the performance of each scheme is presented. The analytical results are in agreement with the simulation results. <<ETX>>
Common and dedicated rate control with variable QoS support for reverse-link high-speed packet data service in 1xEV-DV

This paper examines variable QoS support in the recently standardized cdma2000 Revision D which provides high-speed packet data support over the reverse link (RL). Among the several medium access control (MAC) techniques defined by the standard, the paper considers two of the simpler techniques: common rate control (CRC) and dedicated rate control (DRC) given that initial 1xEV-DV deployments are likely to support only these techniques. A simple algorithm to provide differential throughput and delay performance (based on the user classes) for CRC and another for providing RL video streaming service (i.e., low jitter) to all simulated MS for DRC are discussed. Performance results show their effectiveness in supporting different QoS requirements.
Fast algorithms for block FIR adaptive digital filtering

Two fast gradient algorithms for block FIR (finite impulse response) adaptive digital filtering are presented in this paper. The proposed algorithms employ a time-varying convergence factor \mu_B(j) which is optimized in a least-squares (LS) sense. In the first algorithm, the optimum block adaptive (OBA) algorithm, the processed signal blocks are disjointed. In the second algorithm, the optimum block adaptive shifting (OBAS) algorithm, the signal blocks are overlapping. Computer simulations and analysis of the computational complexity of the algorithms are given. It is shown that, although OBA and OBAS require a relatively modest increase in computation for each block iteration compared to the existing block least-mean-square (BLMS) algorithm, OBA and OBAS may be found in some applications more computationally efficient due to the considerable reduction in the number of iterations required for a given adaptation accuracy. A comparison between the OBAS algorithm and the recently proposed fast a posteriori error sequential technique (FAEST) is also conducted for adapting to time-varying (unknown) systems, and the results show that OBAS is superior to the FAEST algorithm with respect to speed and accuracy of adaptation, at least for the variety of filters used in the simulations.
Extracting fuzzy rules for modeling of complex processes using neural networks

Modeling of complex processes often leads to complex mathematical relationships between inputs and outputs, which do not reflect the influence of the independent variables on the output parameters. In this article, an innovative technique based on neural networks is presented to extract fuzzy linguistic rules for modeling some processes using some input–output data. In this way, genetic algorithm is used both for optimal structure design of those group method of data handling-type neural networks and for subsequent optimization of sub-bounds of fuzzy singleton antecedents to further optimize the obtained fuzzy rule base. Three different input–output data tables related to some complex problems of a nonlinear mathematical system, an explosive cutting process and the probability of failure estimation of a two mass-spring system are modeled by some fuzzy rules, using the technique discussed in this article.
Effect of Synthesis Parameters on the Magnetic Properties of Cobalt Ferrite Prepared by Glow Discharge Treatment

Abstract Nanoparticles of cobalt ferrite (CoFe2O4) have been successfully synthesized by glow discharge treatment. The effect of the initial pH of the suspension (8-12), the processing temperature (20-40 °C), and the duration of the glow discharge treatment on the average crystallite size, coercivity, saturation magnetization were studied using an experimental design based on X-ray, magnetometer. A mathematical models adequately describes the process are constructed. The effect of the synthesis parameters on the magnetic properties of the samples is established. The results showed that the pH of the reaction medium is a parameter that has the greatest effect on crystallite growth, coercive force, saturation magnetization for powders obtained by glow discharge treatment. A conclusion is drawn that the saturation magnetization depends on the crystallite size.
An Investigation on the Effect of Migration Strategy on Parallel GA-Based Shortest Path Routing Algorithm

Genetic algorithm (GA) is one of the alternative approaches for solving the shortest path routing problem. In previous work, we have developed a coarse-grained parallel GA-based shortest path routing algorithm. With parallel GA, there is a GA operator called migration, where a chromosome is taken from one sub-population to replace a chromosome in another sub-population. Which chromosome to be taken and replaced is subjected to the migration strategy used. There are four different migration strategies that can be employed: best replace worst, best replace random, random replace worst, and random replace random. In this paper, we are going to evaluate the effect of different migration strategies on the parallel GA-based routing algorithm that has been developed in the previous work. Theoretically, the migration strategy best replace worst should perform better than the other strategies. However, result from simulation shows that even though the migration strategy best replace worst performs better most of the time, there are situations when one of the other strategies can perform just as well, or sometimes better.
Thread Synchronization: From Mutual Exclusion to Transactional Memory

Abstract Transactional memory (TM) is being viewed by researchers as a suitable mechanism to perform shared-memory synchronization on upcoming many-core systems. This paper provides an introductory material on TM, followed by a background of important historical work on synchronization leading to current TM research. The paper reviews recent pure hardware and hardware-software TM design proposals, and finally brings out a list of interesting open problems in this field.
Development and evaluation of a Minspeak™ application using Blissymbols: Experiences from two case studies

This article describes the development and structure of two Minspeak™ applications based on Blissymbols. The work was carried out in the form of case studies, and a qualitative evaluation of the two applications was completed. Based on previous findings in the literature and the results of this case study, we concluded that there are positive outcomes associated with the combining of Blissymbols and Minspeak. The study found that a phrase-based application was not sufficient for the communicatively advanced study participants, who required a wordbased vocabulary of their Blissymbol boards to maximize voice output communication aid (VOCA) use. These results, along with other findings relevant to the introduction of VOCAs, are discussed.
A hybrid intelligent algorithm for grain logistics vehicle routing problem

Grain logistics vehicle routing problem is derived from vehicle routing problem, they have been a focus of research in the grain logistics managements recently, which the aim is to use the limited vehicles to a large number of jobs so that the maximum number of jobs can be completed with minimum cost. Aiming at the characteristics of the large batch and multi-point to multi-point transportation of grain logistics, a hybrid particle swarm optimization (PSO) with simulated annealing (SA) algorithm is proposed to solve grain logistics vehicle routing problem (VRP) in this paper. The experimental results manifest that the hybrid algorithm of PSO can solve grain logistics VRPTM quickly, the proposed algorithm is effective, and can reduce the cost of distribution.
Combination of radial basis function neural networks with optimized learning vector quantization

Randomly initialized radial basis function neural networks are compared to networks whose centers are obtained by using vector quantization. It is shown that the error rate for small networks can be decreased by about 28%. To achieve the same performance with a trained network as with a randomly initialized network, only half of the number of hidden neurons is needed. This may be important for time critical applications. The time used for the training and initialization of a smaller network is comparable to the time used for the initialization of a larger network. <<ETX>>
A user's guide to the Gottman-Williams time-series analysis computer programs for social scientists

Esther Williams and John Gottman describe a complete set of programs they have written in Fortran IV to enable even beginners to use all the techniques presented in John Gottman's Time-Series Analysis: A Comprehensive Introduction for Social Scientists. There are three packages, available on IBM card source desks from the authors, for (1) time and frequency domain model fitting, including detecting cycles, (2) forecasting and interrupted time-series analysis, and (3) multivariate time-series analysis, in both time and frequency domains. The packages have been tested for portability. Modification specific computers are noted in the guide.
An Automated Virtualization Performance Analysis Platform

This research compares three traditional categories of virtualization to a technique known as hybrid virtualization. Each technique is evaluated in terms of both capability and performance. The traditional methods of platform virtualization such as full virtualization, paravirtualization and operating system virtualization each comes with its own set of capabilities and engineering trade-offs. Hybrid virtualization attempts to leverage the benefits of full and operating system virtualization by allowing virtual machines of each type of virtualization to run simultaneously on the same host machine. This research measures the time required for each virtualization technique to perform a workload inside virtual machines as the number of virtual machines running the workload scales. This performance data will help determine the usefulness of the hybrid technique in building a military cyber warfare training simulation environment based on virtualization. The goal is to determine which technique is capable of supporting large-scale environments required by realistic network training scenarios. The capability evaluation results indicate that hybrid virtualization successfully leverages the benefits of its two virtualization components while minimizing the trade-offs of each individual technique. The performance results indicate that the performance of each virtualization technique differs significantly relative to the workload applied. Some workloads saw no significant differences in performance between techniques. However, in the workloads that did show significant difference, the hybrid technique performed as well as or better than full virtualization or operating system virtualization alone. This leads to the conclusion that hybrid virtualization is a viable candidate as the basis for a military cyber warfare simulation and training environment.
LMIK - learning medical image knowledge: an Internet-based medical image knowledge acquisition framework

As part of the Learning Medical Imaging Knowledge project, we are developing a knowledge-based, machine learning and knowledge acquisition framework for systematic feature extraction and recognition of a range of lung diseases from High Resolution Computed Tomography (HRCT) images. This framework allows radiologists to remotely diagnose and share expert knowledge about lung HRCT interpretation, which is then used to develop a Computer Aided Diagnosis (CAD) system for lung disease. In this paper, we describe the knowledge acquisition system LMIK, which is Internet-based and platform-independent. The LMIK utilises the Internet to provide users with secure access to patient and research data and facilitates communication among highly qualified radiologists and researchers. It is currently used by five radiologists and over 20 researchers and has proved to be an invaluable research tool. Research is underway to develop computer algorithms for automatic diagnosis of lung diseases. In future, these algorithms will be integrated into LMIK to equip it with CAD capabilities to improve diagnostic accuracy of radiologists and extend availability of expert clinical knowledge to wider communities.
Inverting sampled traffic

Routers have the ability to output statistics about packets and flows of packets that traverse them. Since however the generation of detailed traffic statistics does not scale well with link speed, increasingly routers and measurement boxes implement sampling strategies at the packet level. In this paper we study both theoretically and practically what information about the original traffic can be inferred when sampling, or `thinning', is performed at the packet level. While basic packet level characteristics such as first order statistics can be fairly directly recovered, other aspects require more attention. We focus mainly on the spectral density, a second order statistic, and the distribution of the number of packets per flow, showing how both can be exactly recovered, in theory. We then show in detail why in practice this cannot be done using the traditional packet based sampling, even for high sampling rate. We introduce an alternative flow based thinning, where practical inversion is possible even at arbitrarily low sampling rate. We also investigate the theory and practice of fitting the parameters of a Poisson cluster process, modelling the full packet traffic, from sampled data.
Arch-AdSenNets: an architecture for inter-working MANET with wireless sensor and actor networks

This paper proposes an architecture, namely Arch-AdSenNets, for inter-working mobile ad-hoc networks (MANETs) with wireless sensor and actor networks. We lay out requirements, discuss possible solutions and propose a network model within Arch-AdSenNets for the collaboration of MANET nodes and actors. It is formulated as a multi-objective optimisation problem, solving theoretically and centrally by the micro-genetic algorithm. Distributed and heuristic algorithms have also been developed, consisting of two phases: set-up and negotiation. The former is used to initially distribute MANET nodes over event areas, while the latter is used to redistribute MANET nodes depending on the existing and new coming events. We have implemented the distributed algorithm into ns-2, piggy backing over the optimised link-state routing protocol. Simulation results show that resources of MANET nodes, spending on processing detected events (utility), and their resources, spending on moving to event areas (cost), converge to optimal results found by the micro-genetic algorithm.