Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Soft Computing and Decision Support Systems Hybrid Service for Business Contingency Plan and Recovery Service as a Disaster Recovery Framework for Cloud Computing

Cloud computing is the latest effort in delivering computing resources as a service to small and medium sized enterprises. These enterprise organizations require installing and maintaining expensive equipment to keep business up and running at all the times. Naturally this requires building an infrastructure flexible enough to respond to any threat under all circumstances. Any disaster may be considered to be a threat associated with the IT infrastructure in a data center. Disaster can occur either naturally or by humans. This paper is focused on how disaster may be controlled in a cloud computing data center which provides services to an organization and how to keep the organization business running while a disaster strikes. The availability and performance of any service is measured by its overall uptime. Recent recovery techniques that have been developed in cloud computing domain have several advantages and disadvantages. Therefore, researchers should conduct some investigations in this field. A hybrid service which utilize redundancy and fault tolerance techniques for providing more accurate recovery in cloud computing when disaster strikes is proposed in order to overcome these challenges in this paper. This hybrid service integrates the Infrastructure as a Service (IaaS) and Disaster Recovery as another Service (DRaaS). The proposed framework is formed by the integration of five essential types of proven redundancy techniques that have a major impact on the uptime of the services during disaster in cloud data centers. For evaluation of the proposed framework, a survey was conducted through a questionnaire presented to and filled by networking professionals and experts. The outcome of data analysis indicates that redundancybased disaster recovery framework improves the performance of data center recovery and results in a high level of availability of the restored enterprise when disaster strikes. A total of 59.4 % of survey respondents accepted the fact that this framework reduces more than 70 % of threats associated with disaster.
Optimizing networks of traffic signals in real time-the SCOOT method

The evolution of the SCOOT urban traffic control system from research by the British Government's Transport and Road Research Laboratory on the TRANSYT method of optimizing networks of fixed time signals is described. The key principles of the SCOOT online traffic model and real-time signal optimizers are explained. The results of surveys on the effectiveness of SCOOT are reviewed, applications are summarized, and some recent developments are outlined. >
Panning for gold: understanding students information searching experiences

When was the last time you observed someone searching the Internet or library databases and wondered, "Why on earth did they do it that way? " Searchers are prepared to sit for long periods trying to find the elusive and even rare item they require, yet spend little time learning how to search more effectively. Reflections of this nature led to the research reported in this paper. Observing students over a period of a few years, all studying within one unit in the Faculty of Information Technology (FIT), Queensland University of Technology (QUT), it became clear that efforts to understand what students were experiencing while web-based information searching would be valuable for both the students and ongoing curriculum design. This paper reports on research intended to discover both the student experiences and the likely ways to encourage students to pan for gold rather than junk when they search. The research was undertaken using a phenomenographic approach. The paper will outline the background to the study, explain the research method and objectives, and present findings. Both teaching and learning strategies and curriculum design have been strongly influenced by this project
Research on the Palm Print Recognition Algorithm Research Based on Improved Geometric Invariance Principle

The paper studies the problem of identity authentication system based on palm print recognition. It puts forwards the fusion algorithm combining the geometric rotational invariance characteristic with the straight line characteristic to solve the negative problems of deformation and interference in biometric identity authentication system based on non-contact pickup, which effectively improve the accuracy of palm recognition. First, it matches the two features—SIFT and LOFO extracted from the images of palm and gets the match value, and then uses the fusion algorithm to organically integrate these two matching results and gets the final match value. The advantage of this method is that it can more effectively afford the recognition in dealing with the negative factors such as rotation and scaling. And it uses the Gabor filtering algorithm to treat the original image adopted in the algorithm which can effectively extract the richer feature information and help a lot in the final recognition. The method can be used for personal identification and authentication.
Optimal target detection using one channel SAR complex imagery: application to ship detection

Target detection is a delicate process in SAR imagery. Single-point targets are often supposed to have a reflectivity level higher than the clutter mean power and one could think that a simple thresholding operation would permit their detection. Because of the speckle, however, the signal has a very high variability (especially in the one-look case) which may cause confusion between high speckle peaks and target points. The authors present optimal target detection based on radiometric criteria. Complex images permit optimal radiometric estimation by means of the spatial whitening filter, which takes spatial correlation into account. The application to ship detection on a complex fine mode Radarsat image is presented.
FHAMIPv6/AHRA vs FHAMIPV6/AODV

In order to support the FHMIPv6 protocol in an Ad-hoc network, we introduced a series of changes in the source code, we modified the ability to recognize and process messages of the intermediate nodes during the registration process, so that messages were successfully transmitted from source to destination. The process of implementing modifications at the source code level is complex. From this work, the protocol FHAMIPv6 came up as an extension of FHMIPv6 protocol for Ad-hoc networks and the AHRA routing protocol, which is the modification of the NOAH protocol to complement the FHAMIPv6 registration processes. However, AHRA after making all the changes and trying different types of traffic, communication from a source to a destination was only achieved with TCP traffic. Then, we tried the AODV routing protocol integrated with FHAMIPv6. The registration process and communication from source to
An explorative study of module coupling and hidden dependencies based on the normalized systems framework

—Achieving the property of evolvability is considered a major challenge of the current generation of large, compact, powerful, and complex systems. An important fa-cilitator to attain evolvability is the concept of modularity: the decomposition of a system into a set of collaborating subsystems. As such, the implementation details of the functionality in a module is hidden, and reduces complexity from the point of view of the user. However, some information should not be hidden if they hinder the (re)use of the module when the environment changes. More concretely, all collaborating modules must be available for each other. The way how a collaborating module is accessible is also called module coupling. In this paper, we examined a list of classiﬁcations of types of module couplings. In addition, we made a study on the implications of the used address space for both data and functional constructs, and the implications of how data is passed between modules in a local or remote address space. Several possibilities are evaluated based on the Normalized Systems Theory. Guidelines are derived to improve reusability.
Xperience of Programmable Network with OpenFlow

Today in the era of fast, modern and continuous changing world, we need to enhance and improve the existing routing and switching protocols for better performance. OpenFlow switches enable researchers to test and examine the behavior of newly designed protocol in their local environment without disturbing the existing production flow. This allows us to provide the minimize delay in production packet forwarding and maximum flexibility in controlling the flow of experimental packets through the network. In this paper, we have demonstrated to run a network topology on OpenFlow Virtual Machine Simulator (VMS) using NOX controller. Monitoring of flow tables on various switches has been analyzed. Later, we have developed a number of NOX controller components using C++ code to restrict Address resolution Protocol (ARP) on our network and to define dynamic programmable flows on OpenFlow switches.
Framework for Creating Intuitive Motion Content for Humanoid Robots Based on Programming by Demonstration

In this paper we propose an easy to use framework for creating motion content for humanoid robots based on user demonstrations. A wearable interface is proposed and a prototype of the wearable interface is implemented to provide an intuitive content creation method for users who lack expertise in robot programming. An algorithm for transferring human motion to a robot and creating editable motions is presented to improve the reusability of the demonstrated motions. For generating various motions from a limited number of demonstrations, two motion models are presented and diverse motions are generated from the motion models. A humanoid robot, AMIO, is used to validate the proposed motion generation framework.
Measurement and analysis of communication motion for nonverbal human interface

非言語ヒューマンインタフェースを構築することは, 人に優しい情報環境を実現する上で重要である. 将来的には人間の動作を理解し, CGの動作生成などによってインタラクティブに対応できる非言語ヒューマンインタフェースの構築を目指す. 本研究では, 非言語表現を, 実際の身体動作, 動作の計測データ, 物理的動作特徴, 言語的動作特徴, 人物の感情・意図の5段階の理解構造モデルとして捉えることを提案した. 本論文では頭部の動きがかかわるコミュニケーション動作に着目し, 動作計測実験を経て身体動作の時系列計測データを収集し, そこから物理的動作特徴の指標を構築することで, 質問紙調査によって抽出された人物の言語的動作特徴と感情・意図の関連構造を多変量解析手法によって分析した. 得られた分析結果等から, 構造モデル, 分析方法については, 数理的アプローチの有効性が確認され, 非言語ヒューマンインタフェース技術への展開の可能性が示唆された.
Natural image classification driven by human brain activity

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.
Eigen-based DOA estimators for non-linear array configurations

A novel form of steering vector is proposed for eigen-based direction-of-arrival estimators. This steering vector permits the processing of data from arrays which are configured as the arc of a circle of unknown radius. Techniques for estimating the new unknown parameters, the circle radius, are discussed. Results from computer simulations are presented, confirming that the circularly configured arrays the model outperforms the conventional MUSIC algorithm. <<ETX>>
Short Term Course Assessment, Improvement, And Verification Feedback Loop

An assessment-improvement feedback process is presented for improving the students’ classroom learning experience. The new process uses multiple short surveys during the term to identify strengths and weaknesses in a course curriculum and in an instructors teaching style. The surveys questions used in the process are derived directly from course educational objectives. The advantage of the new process is that it reduces the assessment-improvementverification turn around time; addresses the problem of varying class dynamics; and thoroughly identifies class problems. The paper also presents and discusses the results of applying this new process to three freshman engineering classes.
Introducing the IMS-Wrocław-Szeged-CIS entry at the SPMRL 2014 Shared Task: Reranking and Morpho-syntax meet Unlabeled Data

We summarize our approach taken in the SPMRL 2014 Shared Task on parsing morphologically rich languages. Our approach builds upon our contribution from last year, with a number of modiﬁcations and extensions. Though this paper summarizes our contribution, a more detailed description and evaluation will be presented in the accompanying volume containing notes from the SPMRL 2014 Shared Task
A Modified Particle Swarm Optimization Based on Selective-Dimension Learning Model

In this paper, a selective-dimension learning model is introduced in PSO to improve its comprehensive performance including maintaining diversity of swarm and accelerating convergence speed. In the novel learning model, PSO dynamic adjust every particle's learning model. To keep the diversity of PSO at the initial stage of the evolution, each particle random selects major dimensions of the personal-best to learn from. On the contrary, to improve the convergence speed of PSO, each particle learns more information from the global-best than from the personal-best position at the end stage of evolution. Simulation results substantiate the effectiveness of the proposed algorithm to solve unimodal and multimodal functions.
Robust asymmetric 6T-SRAM cell for low-power operation in nano-CMOS technologies

An asymmetric 6T-SRAM cell design is presented for reliable low-power circuit operation under large variability. A low overhead write assist circuit is added to increase the write-noise-margin (WNM) and improve the write speed/power. Sizing is used to strengthen the pull-down transistor of the feedback inverter of the single ended read circuit to enhance the static-noise-margin (SNM). Monte Carlo simulations indicate a 90% improvement in SNM and a boost in the WNM of 108% compared to the conventional 6T-SRAM design. Comparative analysis of a 65nm 64×32 bit SRAM designed using both SRAM cells (Symmetric-6T and Asymmetric-6T) shows the write delay and power decrease by 46% and 35%, respectively, while total power decreases by 52% using the proposed design.
Automated detection of reflection in texts : a machine learning based approach

Promoting reflective thinking is an important educational goal. A common educational practice is to provide opportunities for learners to express their reflective thoughts in writing. The analysis of such text with regard to reflection is mainly a manual task that employs the principles of content analysis. Considering the amount of text produced by online learning systems, tools that automatically analyse text with regard to reflection would greatly benefit research and practice. Previous research has explored the potential of dictionary-based approaches that automatically map keywords to categories associated with reflection. Other automated methods use manually constructed rules to gauge insight from text. Machine learning has shown potential for classifying text with regard to reflection-related constructs. However, not much is known of whether machine learning can be used to reliably analyse text with regard to the categories of reflective writing models. This thesis investigates the reliability of machine learning algorithms to detect reflective thinking in text. In particular, it studies whether text segments from student writings can be analysed automatically to detect the presence (or absence) of reflective writing model categories. A synthesis of the models of reflective writing is performed to determine the categories frequently used to analyse reflective writing. For each of these categories, several machine learning algorithms are evaluated with regard to their ability to reliably detect reflective writing categories. The evaluation finds that many of the categories can be predicted reliably. The automated method, however, does not achieve the same level of reliability as humans do.
Aligning sequence from molecular inversion probes

Summary Molecular inversion probes (MIP’s) allow efficient enrichment of genomic regions of interest for the purpose of targeted sequencing. To date, there is a paucity of simple-to-use software to align sequences derived from this method. Here, we describe a single program that performs mapping, arm removal, and deduplication before outputting alignments in SAM format. Availability bwa-mips is available at https://github.com/brentp/bwa-mips under the MIT license. Contact bpederse@gmail.com
GPU accelerated Monte-Carlo simulation of SEM images for metrology

In this work we address the computation times of numerical studies in dimensional metrology. In particular, full Monte-Carlo simulation programs for scanning electron microscopy (SEM) image acquisition are known to be notoriously slow. Our quest in reducing the computation time of SEM image simulation has led us to investigate the use of graphics processing units (GPUs) for metrology. We have succeeded in creating a full Monte-Carlo simulation program for SEM images, which runs entirely on a GPU. The physical scattering models of this GPU simulator are identical to a previous CPU-based simulator, which includes the dielectric function model for inelastic scattering and also refinements for low-voltage SEM applications. As a case study for the performance, we considered the simulated exposure of a complex feature: an isolated silicon line with rough sidewalls located on a at silicon substrate. The surface of the rough feature is decomposed into 408 012 triangles. We have used an exposure dose of 6 mC/cm2, which corresponds to 6 553 600 primary electrons on average (Poisson distributed). We repeat the simulation for various primary electron energies, 300 eV, 500 eV, 800 eV, 1 keV, 3 keV and 5 keV. At first we run the simulation on a GeForce GTX480 from NVIDIA. The very same simulation is duplicated on our CPU-based program, for which we have used an Intel Xeon X5650. Apart from statistics in the simulation, no difference is found between the CPU and GPU simulated results. The GTX480 generates the images (depending on the primary electron energy) 350 to 425 times faster than a single threaded Intel X5650 CPU. Although this is a tremendous speedup, we actually have not reached the maximum throughput because of the limited amount of available memory on the GTX480. Nevertheless, the speedup enables the fast acquisition of simulated SEM images for metrology. We now have the potential to investigate case studies in CD-SEM metrology, which otherwise would take unreasonable amounts of computation time.
Recognizing Comparative Sentences from Chinese Review Texts

Comparisons play an important role in making decisions by referring to the comparative opinions of opinion holders in earlier customer reviews. Recognizing comparative sentences from review texts contributes to opinion mining and information recommendation. Our objective is to automatically recognize comparative sentences from Chinese text documents. In this paper, an effective approach is proposed based on comparative patterns to recognize comparative sentences in Chinese. Our experiments on customer-generated product reviews show that the proposed approach is effective.
Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning

Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the capacity of the battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the capacity of a Li-ion battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the battery capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online capacity estimation computationally efficient. 10 years’ continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.Copyright © 2015 by ASME
Broadband beamforming optimization for speech enhancement in noisy environments

We have developed a method to optimize the directional sensitivity of a filter-and-sum beamformer. The directivity of the broadband microphone array is optimized by adjusting the spatial transducer positions and the impulse response of the beamformer. We focus on the optimization of 1-dimensional arrays consisting of M omnidirectional microphones and M FIR filters each of length L. The signal sources are assumed to be point sources evenly distributed over a sphere of radius r or over a set of concentric spheres. The target beam pattern for optimization is defined in terms of the desired source signal directions (mainlobe) and the angles for background noise attenuation.
Performance Comparison of Ad-hoc Network Routing Protocols Using NS-2 Simulator

Ad-hoc networks are basically self organizing, self configuring and peer to peer multi-hop mobile wireless networks in which the information packets are transmitted in a 'store and forward' manner from a source to an arbitrary destination via intermediate nodes. The main objective of this paper is to evaluate the performance of various ad-hoc networks routing protocols viz. DSDV (Destination Sequence Distance Vector), DSR (Dynamic Source Routing) and AODV (Ad-hoc On Demand Distance Vector). The comparison of these protocols is based on different performance metrics, which are throughput, packet delivery ratio, routing overheads, packet drop. The simulation is performed through the simulation tool NS-2 (Network Simulator- 2) due to its open source simplicity and free availability.