Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Advanced Robotic Systems Mechanical Performance of a Cascaded V-shaped Electrothermal Actuator Regular Paper

The function and performance of MEMS are affected by the output ability of the displacement and force of microactuators. The lateral deflection of cascaded V-shaped electrothermal actuators is modelled as a triple, statically indeterminate problem. An iteration algorithm is set up to calculate the output displacement and typical forces in anchors. The calculated results of this model agree well with our own simulation results as well as with the experimental and calculated results in the literature, and we conclude that the model is credible. The paper also discusses the effects of the structure parameters and temperature increments on the displacement production ability. Clear differences were found between single and cascaded V-shaped electrothermal actuators. For the former, the maximum deflection takes place in a 0.04 rad beam angle, while for the latter the beam angle is 0.19 rad. For both types of actuators, the deflection is unrelated to the beam thickness. The displacement of the cascaded V-shaped electrothermal actuator is proportional to the temperature increment and beam length, but is inversely proportional to the beam width. Experiments on the output ability of cascaded V-shaped electrothermal actuators validated the calculated and simulation results.
Frege in Space: A Program for Composition Distributional Semantics

The lexicon of any natural language encodes a huge number of distinct word meanings. Just to understand this article, you will need to know what thousands of words mean. The space of possible sentential meanings is infinite: In this article alone, you will encounter many sentences that express ideas you have never heard before, we hope. Statistical semantics has addressed the issue of the vastness of word meaning by proposing methods to harvest meaning automatically from large collections of text (corpora). Formal semantics in the Fregean tradition has developed methods to account for the infinity of sentential meaning based on the crucial insight of compositionality, the idea that meaning of sentences is built incrementally by combining the meanings of their constituents. This article sketches a new approach to semantics that brings together ideas from statistical and formal semantics to account, in parallel, for the richness of lexical meaning and the combinatorial power of sentential semantics. We adopt, in particular, the idea that word meaning can be approximated by the patterns of co-occurrence of words in corpora from statistical semantics, and the idea that compositionality can be captured in terms of a syntax-driven calculus of function application from formal semantics.
Experimental and Comparative Analysis of Machine Learning Classifiers

Classification methods have been rapidly used for variety of the different fields including medical, banking and finance, social science, political and economic science and variety of other different fields to classify the available data, which may be containing many different attributes and it will be difficult to classify them manually. As people are generating more data everyday so there is a need for such a classifier which can classify those newly generated data accurately and efficiently. This paper mainly focuses on the supervised learning technique called the Random forests for classification of data by changing the values of different hyper parameters in Random Forests Classifier to get accurate classification results. This paper also focuses on experimental comparison of Random Forests classifier with some of the state of art supervised learning technique like NB (Naive Bayes), C4.5 and ID3 (Iterative Dichotomiser 3) with respect to their accuracy of correctly classified instances, incorrectly classified instances and very important ROC Area which helps in understanding the classification model and their results, which can also help other researchers in making decision for the selection in classification model based on their data and number of attributes.
A visual design support system for telecommunication services

The authors propose a visual design support system for telecommunication services that uses the state transition rule (STR) as specification knowledge and a description language. The system has excellent design support facilities, which are discussed. A designer, who has no knowledge of the professional aspects of network specifications, can describe various services from the user's viewpoint using the STR-GR (graphical representation) description method. Merely by synthesizing some service descriptions, the system can automatically validate the service interactions by means of rule conflict detection. A designer can verify a service specification by simulating a designed service description and displaying its execution processes in an animated form. The experimental evaluation of the design support system is presented. <<ETX>>
The digital information war

Purpose – The purpose of this Guest Editorial is to introduce the papers in this special issue.Design/methodology/approach – A brief summary of the main contributions of the papers included in this issue is provided.Findings – In order to combat the digital information war it was found that important work must be done to establish both users' and content providers' trust through fair e‐commerce/digital rights management (DRM). Originality/value – The paper provides an overview of the basic requirements of DRM systems.
Security Knowledge Classification Framework for Future Intelligent Environment

근래에 들어 정보보안 환경이 ICT와 융합됨에 따라 새로운 취약성 지속적으로 증가하고 있다. 이에 따라 새로운 유형의 사이버범죄가 대두되고 있으며 사이버 공격, 내부자 유출 등 보안문제로 인하여 보안사고 사례가 급증하고 있다. 또한 기존의 기술적인 보안위협처럼 취약점을 악용한 외부의 해킹이 아닌 내부직원에 의한 정보유출 등의 신종 보안위협이 등장하고 있으며 산업과 기술이 융합되는 새로운 환경으로 발전함에 따라 그 위협은 더욱 증가하고 있는 실정이다. 따라서, 본 논문에서는 고도화된 정보위협에 능동적으로 대처하기 위한 전문보안관리 인재를 양성하기 위해 균형있는 정보보호 교과목을 설계하여 정보보호 교과목 분류체계를 도출하고자 하였다. 이를 위해 선행연구조사 분석과 전문가 자문위원회의 회의를 통해 기술적인 교육과 경영·관리적인 교육이 적절히 배분된 정보보호 교과목을 도출하고 국내 실정에 적합한 형태로 분류된 정보보호 직업분류체계와의 연결을 통해 균형감있는 정보보호교과목 분류체계를 도출하였다. 본 연구결과는 미래 산업융합 환경의 신종 보안위협을 막아낼 수 있는 지능형 보안인재를 양성하는데 긍정적인 효과를 미칠 것으로 기대된다.
A learning methodology in uncertain and imprecise environments

A step‐by‐step methodology for learning fuzzy rules is presented. This methodology tries to be general enough to give a framework within which different learning methods in an environment of uncertainty and imprecision could be developed. the final product will always be an uncertainty distribution on the different rules representing the behavior of the system. the particular uncertainty distribution depends on the concrete functions selected in each step of the process. the learning approach can also be considered as a way to construct uncertainty measures from data. © 1995 John Wiley & Sons, Inc.
A FORMAL FRAMEWORK TO SUPPORT WORKFLOW ADAPTATION

A workflow system automates a business process, enabling documents, information and tasks to be passed among participants following a set of defined rules. The system needs to address changes that are frequently encountered in a complex heterogeneous environment. In such scenarios, a formal description of an adaptive workflow system will lead to greater reliability and clearer understanding of the entire system. Previous studies on workflow formalization have focused on specific perspectives, for example, deadlock and reachability in a workflow process. However, there is a close relationship between workflow components. Therefore a unifying description is an aid to understanding workflow behaviour. In this paper, the main workflow components are described in a unifying framework WfSL (Workflow Specification Language) through a combination of process algebra and Z specification. The former is used to describe workflow processes and the latter to specify the workflow information model and workflow organization model. An integration semantics is presented for data state transition and process state transition. The framework can be used for verifying workflow correctness when changes are made to the workflow.
The Management of Diversity in a College of Business Environment.

Abstract The term “diversity” encompasses a broad range of human qualities/characteristics that make individuals and groups unique (e.g. age, gender, ethnicity, race, culture, physical abilities, nationality, and sexual orientation). The responses of government, educational institutions, and private industry to an increasingly diverse population have been as diverse as the changes themselves. This article describes these changes and presents implementation strategies to accommodate and instill diversity in education, particularly business schools.
RATATOSKR: WIDE-AREA ACTUATOR RPC OVER GRIDSTAT WITH TIMEL INESS, REDUNDANCY, AND SAFETY

GridStat is a QoS-managed middleware framework designed to replace the power grid’s aging, inflexible, and slow data communications system. GridStat is a specialization of the publish-subscribe paradigm that takes advantage of the semantics of periodic status data updates to provide data delivery with a per-subscriber rate, latency, and redundant paths. While GridStat is well-suited for delivery of sensor data over a wide-area network, its baseline one-way mechanisms are not suitable for round-trip invocations such as setting an acutator or calling between control centers. In this paper we present the design, implementation and experimental evaluation of Ratatoskr, a tunable RPC mechanism that builds on the QoS semantics of GridStat and supports three kinds of redundancy. Additionally, user-defined preand post-condition predicates over GridStat status variables are built into the call semantics. Preconditions can abort an RPC call if the measured conditions dictate, while post-conditions provide additional physical assurance the call succeeded.
A Power-Efficient Propulsion Method for Magnetic Microrobots

Current magnetic systems for microrobotic navigation consist of assemblies of electromagnets, which allow for the wireless accurate steering and propulsion of sub-millimetric bodies. However, large numbers of windings and/or high currents are needed in order to generate suitable magnetic fields and gradients. This means that magnetic navigation systems are typically cumbersome and require a lot of power, thus limiting their application fields. In this paper, we propose a novel propulsion method that is able to dramatically reduce the power demand of such systems. This propulsion method was conceived for navigation systems that achieve propulsion by pulling microrobots with magnetic gradients. We compare this power-efficient propulsion method with the traditional pulling propulsion, in the case of a microrobot swimming in a micro-structured confined liquid environment. Results show that both methods are equivalent in terms of accuracy and the velocity of the motion of the microrobots, while the new approach requires only one ninth of the power needed to generate the magnetic gradients. Substantial equivalence is demonstrated also in terms of the manoeuvrability of user-controlled microrobots along a complex path.
Generating guidelines for choosing appropriate metaphors in GUIs through the analysis of cross-cultural understanding of metaphorical icons: an explorative study with French and Turkish users on an e-learning site

This paper investigates the influence of culture on understanding metaphors in GUIs and aims to generate guidelines for choosing appropriate metaphors for users from different cultures. Considering the dual-coded structure of compound metaphorical icons – composed of two major units: image and label – this study evaluates the main hypothesis that understanding of graphical and textual elements of the metaphors differs due to real world and language experiences of the users. A study on a French e-learning site – based on a spatial ‘Campus’ metaphor – was conducted with sixty-eight Turkish and French students. The qualitative study applied a multi-method approach including data collection instruments like background and icon recognition questionnaires and interview. Findings suggest differences in understanding across the two cultural groups and provide an in-depth analysis on the process of cross-cultural understanding of metaphors by focusing on the metaphorical inconsistencies. Through this analysis, guidelines are proposed for choosing metaphors in GUIs.
Enhancing%' or paper_id like 'brand equity through flow and telepresence: a comparison of 2D and 3D virtual worlds

This research uses theories of flow, telepresence, positive emotions, and brand equity to examine the effect of using two-dimensional versus three-dimensional virtual world environments on telepresence, enjoyment, brand equity, and behavioral intention. The findings suggest that the 3D virtual world environment produces both positive and negative effects on brand equity when compared to the 2D environment. The positive effect of the 3D virtual world environment on brand equity occurs through telepresence, a specific aspect of flow, as well as enjoyment. The negative effect on brand equity can be explained using distraction-conflict theory in which attentional conflicts faced by users of a highly interactive and rich medium resulted in distractions from attending to the brand. Brand equity, in turn, has a positive effect on behavioral intention. The results suggest that although the 3D virtual world environment has the potential to increase brand equity by offering an immersive and enjoyable virtual product experience, the rich environment can also be a distraction. Therefore, developers of virtual world branding sites need to take into account limitations in the information processing capacity and attention span of users when designing their sites in order to avoid cognitive overload, which can lead to users being distracted from branding information. This paper not only provides a theoretical foundation for explaining users' experience with 2D versus 3D virtual world branding sites, but also provides insights to practitioners for designing 3D virtual world sites to enhance brand equity and intentions through user engagement.
On balancing sorting on a linear array

A balanced parallel algorithm to sort a sequence of items on a linear array of processors is presented. The length of the sequence may be small to arbitrarily large. For a short sequence, the output of the sorted sequence begins at the step following the last input of the whole sequence. For an arbitrarily long sequence, the time complexity is optimal under realistic hardware conditions. A variation of the algorithm is also introduced. Both algorithms require far less local memory than that required by a different approach of balanced computation. Any number of balanced processors can be connected to deliver more computing power without increasing the memory size of each processor. >
Straightforward design of robust cellular neural networks for image processing

The analytical design of cellular neural network (CNN) templates for image processing often goes through the resolution of pixel level analytical rule-based task descriptions involving ideal CNN models. Due to nonideal analog implementations of CNN, recent issues have addressed the template robustness in order to achieve fault-tolerant processing. However, besides their efficiency and usefulness for the definition of coupled operators, rule-based approaches can make CNN templates design appear to be an intricate art reserved for initiated CNN specialists rather than for image processing scientists. An alternative straightforward analytical design method for uncoupled CNNs, which is until now the only unified approach to the design of both gray and binary output operators, has already been presented, and is now extended to the design of robust binary operators.
An impressive variety of systems have been designed with capabilities such as forming, growing, reconfiguring, repairing and replicating themselves, based on information coded in their components.

In this paper, we review half a century of research on the design of systems displaying (physical) self-assembly of macroscopic components. We report on the experience gained in thedesignof 21suchsystems,exhibitingcomponents ranging from passive mechanical parts to mobile robots. We present a taxonomy of the systems and discuss design principles and functions. Finally, we summarize the main achievements and indicate potential directions for future research.
Iterative Bilateral Filter and Non-Local Mean

Using local or non-local features has proven to be a competent approach for denoising images. As noise and edges have similar effect of changes in gradient in many cases, noise allocation for denoising is still significant challenge. This work addresses the classic problem but introducing the combination concept of local and non-local factors with deviation refinement procedure. A new algorithm of the concept is proposed to ameliorate noise reduction. Sensitivity of noise detection is examined by iterative non-local mean and bilateral filter with refinement of range deviation. The final methodology is tested with Gaussian noise and compared with both non-local mean, bilateral filter. Experiment demonstrates improvement of denoising level in the new algorithm.
A 2.5 volt 6 bit 600MS/s flash ADC in 0.25µm CMOS

A 2.5V high speed 6 bits flash analog-to-digital converter for storage read channels is presented. Offset voltages in the distributed amplifiers are averaged to improve DNL and INL performance. An end-load circuit is designed to act as a characteristic termination, to prevent the bending of the output curve. The ADC is implemented in a single supply voltage, 0. 25µm standard digital CMOS process, and consumes 193mW.
Co-registration and fusion of CT and SPECT images using mutual information

We present an automatic and accurate technique for 3D co-registration of Single Photon Emission Computed Tomography (SPECT) and Computed Tomography (CT) in order to allow for the attenuation correction of SPECT images and for the fusion of the anatomic details from CT and the functional information from SPECT. Methods: The goal of medical image registration is to find the spatial transformation that maximizes the similarity between two images and brings their corresponding anatomical landmarks into alignment. Within the registration framework provided by Insight Toolkit, we used the mutual information similarity metric to evaluate the accuracy of the alignment. Before registration, an initial translation was applied to the SPECT image to align the centers of gravity of both images. To improve the robustness and accuracy of the registration algorithm, a multiresolution approach was used in which registration is performed in a coarse-to-fine manner. During registration, the metric value is optimized over the parameter space defined by the translation parameters using a gradient-descent minimization scheme. Results: we applied the algorithm to four clinical data sets originated from two pelvic studies, one cardiac study, and one thoracic study. Validation was done by inspecting the 2D and 3D fusions of the registered images and by observing the convergence in the similarity metric and the transformation parameters. We also evaluated quantitatively the effect of initial misalignment of the paired volumes and the reproducibility of the registration. In all studies, registration with multi-resolution converged close to a final alignment with translational error 0.512mm /1.91 mm. When multi-resolution is used, an initial alignment was not needed for all cases except for the cardiac study. The average computation time (on a 3.0GHz PC) was < 4 minutes for the entire registration procedure. Conclusion: we have developed an automatic technique for the linear registration of CT and SPECT images.
Study on Compressive Sensing in the Application of Wireless Localization

Compressive sensing is a revolutional theorem beyond Nyquist sampling theorem which is emerged recently. This paper aims to present the key ideas underlying the CS theorem as well as show the novel applications of CS in the area of wireless localization. By using this new theorem, some wireless localization problems can be better solved than the conventional methods.
Induced specialization of context units for temporal pattern recognition and reproduction

Additional inputs to a feedforward network, derived from the output of the hidden layer neurons, allow a feedforward network to deal with temporal pattern recognition and reproduction tasks. These 'network derived' or 'context' inputs augment the 'true' inputs to the network and allow the network to retain past information necessary for temporal sequence processing. The choice of which hidden neurons to retain to provide the context inputs is difficult. Use of all the hidden neurons increases the size of the overall network resulting in poorer generalization performance. The problem is complicated due to difficulty in choosing the number of hidden layer neurons in the first place. In this paper, we propose the use of regularization terms in the sum-of-squared error cost function. Assuming the hidden layer neurons are indexed 1,2,. ,m, the regularization terms force the differentiation of hidden neurons 1 through m/sub 1/, and m/sub 2/ through m (where 1<m/sub 1/<m/sub 2/<m). Both m/sub 1/ and m/sub 2/ are controllable and allow fringe neurons to be used to provide the context inputs if the number of context units to use is known. When the number of context neurons to use cannot be determined, the regularization terms minimize m/sub 1/, and maximize m/sub 2/, while hidden neurons m/sub 1/ through m/sub 2/ are penalized for differentiation. An amplitude detection simulation is used to evaluate the efficacy of the proposed paradigm.
Network analysis of tagging structure

Our purpose is to discuss tagging as a personal information management mechanism. Personal tagging behaviors influence the content and structure of tagging and associated resource networks. To understand structural differences in how tags are used to organize resources, we designed an information organization scenario and used it to compare four network indicators: connectedness, centrality, clustering coefficient, and average path length. Our results indicate strong correlations between the repeated use of certain tag terms and centrality, connectedness, and the clustering coefficients of the network structures produced by the study participants. Resource structures employed by users with library and information science backgrounds were significantly different from those used by other participants.
A Likelihood Robust Method for Water Allocation under Uncertainty

We adapt and extend the likelihood robust optimization method recently proposed by Wang, Glynn, and Ye to examine water allocation under the ambiguous distribution of future available supply and demand in a two-stage setting. We examine the value of collecting additional data and the cost of finding a solution robust to an ambiguous probability distribution. A decomposition-based solution algorithm to solve the resulting model is given. Computational results are presented on a long-term water allocation problem in southeast area of Tucson.
A new approach towards secure IFF technique

Identification between friend and foe (IFF) is a technique for recognising a platform, be it ship, aircraft or helicopter. It is widely used in both civil and military worlds. In the case of the former, aircraft coming in to land at airports are recognised, while various units/platforms are accessed for multiple applications in the case of the latter. The new technique being proposed here is based on the concept of public-key cryptography. Security rests in exploiting the difficulty of finding discrete logarithms over an elliptic curve (EC), where the elliptic curve itself is defined over a binary finite field. The proposed scheme has significant merits of using insecure channels, compact implementation and speed suitable for real-time applications without prejudice to confidentiality and integrity. A "challenge-response" type of protocol, typical of IFF, is being adhered to.