Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots

The behavior-based approach has been successfully applied to designing robot control systems. This paper presents our work, based on evolutionary algorithms, to program behavior-based robots automatically. Instead of hand-coding all the behavior controllers or evolving an entire control system for an overall task, we suggest our approach at the intermediate level: it includes evolving behavior primitives and behavior arbitrators for a mobile robot to achieve the specified tasks. To examine the developed approach, we evolve a control system for a moderately complicated box-pushing task as an example. We first evolved the controllers in a simulation and then transferred them to the Khepera miniature robot. Experimental results show the promise of our approach, and the evolved controllers are transferred to the real robot without loss of performance.
CDMA coding and decoding methods for space-time block codes

Space-time block code design and decoder design are addressed for code division multiple access (CDMA) systems. Optimal code designs are found by optimizing the Chernoff bound of the probability of decoding error. From this, the diversity gain and the coding gain are determined for the CDMA scenario. The resultant optimal code designs are classified and analyzed. Both optimal and moderate complexity suboptimal decoding algorithms are proposed and evaluated.
Learning Object Metadata for Learning Content Accessibility

Accessibility metadata for learning content is identified as an interesting topic in educational metadata authoring. In this paper, we address learning content accessibility proposing an accessibility application profile of the IEEE LOM based on IMS Accessibility for LIP recommendations. The result is a metadata model that allows learning content selection according to accessibility requirements of learners, providing the necessary learning object metadata information for matching learner accessibility characteristics to learning object accessibility characteristics.
Concurrent error-detectable butterfly chip for real-time FFT processing through time redundancy

The chip design for a fast Fourier transform (FFT) butterfly module using a novel concurrent error detection (CED) technique is presented. It is a time-redundant realization based on the direct complex computation approach. By the use of symmetry and the exchanging design strategy, the recomputation step can be performed by interleaving the circuits for the real and imaginary parts in a complex function. It leads to a lower hardware overhead (about 7/(4n+8), where n is the word length), and the error detection capability is as robust as that of the duplicated module technique. The CED butterfly is designed in 1.2- mu m CMOS technology, using the structural silicon complier. The theoretical analysis and experimental results are presented. It is shown that the design is very attractive for real-time high-reliability DSP systems. Its regular structure make the proposed algorithm and architecture easy to implement in VLSI or WSI. >
E-Journal Usage and Impact in Scholarly Research: A Review of the Literature

This article reviews the literature dealing with scholarly information behavior around the use of e-journals. Its aims are to examine the use and impact the availability of e-journals has had on the community of scholars, mainly from the UK, but looking also at literature from other countries. Results demonstrated the huge rise in availability and take-up of e-journals, although there are mixed findings regarding the fate of the print format. Access to e-literature is dominated by keyword searching, with subject-specific (e.g., chemical abstracts); or general academic (Web of Knowledge) gateways and search engines (typically Google) all used above publishers platforms, alerts, and other ways to find literature. The value of e-journals has been shown to be high, both in terms of in gaining new insights and helping with teaching, and in measure of “Contingent valuation”: the time or cost incurred by not having provision. Barriers to e-journal use included non-purchase of titles by the library and years or volumes not available electronically. Although many disciplinary differences exist, due to their differing natures and means of scholarly communication, nevertheless, the review concludes that it is now unthinkable for researchers to work without the convenience and comprehensiveness that e-journals provide them.
A Lightweight Secure Solution for RFID.

Radio Frequency Identification (RFID) systems have provided promising solutions to effective identification of a large number of tagged objects. However, RFID systems suffer from unauthorized tag reading and potential eavesdropping, which becomes a challenging issue because of the shared radio medium and limited size and cost considerations in RFID. In this paper, based on a Linear Congruential Generator (LCG), we propose a lightweight block cipher that can meet the security and performance requirement of RFID systems. The trade-off between the security and overhead is discussed. Based on the proposed block cipher, we further propose a secure protocol for RFID that can provide data confidentiality and mutual authentication between the reader and the tag. We also provide performance analysis of our proposed block cipher. There is always a good trade-off between complexity and security. In this paper, based on a Linear Congruential Gen- erator (LCG) (6), we propose a lightweight block cipher that can meet the security and performance requirement of RFID systems. We are motivated by the fact that a good balance between security and efficiency can be achieved after properly arranging the numbers generated by the LCG. By adding random noise generated by a LCG and random permutations to RFID data, we demonstrate that our proposed cipher is secure enough for RFID. We further analyze that the security of our proposed cipher can be adjusted with the input length of the data. Armed with the lightweight block cipher, we demonstrate its usage in the mutual authentication between the reader and the tag. Security and performance of the proposed block cipher are also analyzed.
Predictable architecture for real-time systems

Architecture used to support real-time applications must have the ability to achieve predictability. This paper surveys the problems and solutions related to conventional architectures for predictable real-time systems. Issues covered include caches, pipelines, context switches, real-time clock and hardware queues. This paper, also proposes the approaches in a constructive way that it is possible to design computer architectures that are apt for real-time control. Such architectures avoid the harmful features of conventional computers and allow worst-case timing analyses.
A conceptual framework for the assessment of EDI

The assessment of EDI impact is confronted with two problems: on the one hand it is difficult to evaluate the contribution of EDI as it cannot be fully distinguished from its context, namely the information system, the organization and the strategy (internal perspective), on the other hand most of the effects have an impact only in the context of inter-organizational transactions (external perspective). In response to these problems, the paper develops and applies two conceptual frameworks to elaborate the economic understanding of EDI: contingency theory and information logistics. On this theoretical background propositions concerning the context and impact of EDI are formulated which are then illustrated by the findings of an evaluation study of 12 EDI Pilot Projects, funded by the Commission of the European Communities in the context of the TEDIS Programme. <<ETX>>
Research on the Pre-warning Model of Enterprise Financial Crisis Based on the Information Entropy and PSO-ANN

The pre-warning of enterprise financial crisis is a hot studying spot in current theoretical field. This paper firstly overviews the present research conditions of enterprise financial crisis pre-warning, sets up a more perfect pre-warning indicators system. Then accomplishes dynamic pre-warning for the financial risk based on the great nonlinear function approaching capability of artificial nerve network(ANN). To simplify the space dimension of input information and reduce the complexity of network structure, the information entropy reduction theory is brought in. At the same time, aim at the main shortage of ANN (the converging speed is often slow and the network is easily involved in local optimum), this paper introduces the Particle Swarm Optimization(PSO). Thus establishing a pre-warning system to enterprise financial crisis based on the information entropy and the PSO-ANN, making the information reduction, the dynamic study and induction of ANN, the particle swarm optimization, the forecast and evaluation of enterprise financial crisis organically combined. At last, an actual example is given to justify the validity and efficiency of this model. The study can supplies a new means for the dynamic pre-warning of enterprise financial crisis.
Music notation;: A manual of modern practice

A mass of diamond crystals in contact with a mass of eutectiferous silicon-rich alloy and a silicon nitride ceramic substrate are disposed in a container and placed within a pressure transmitting powder medium. Pressure is applied to the powder medium resulting in substantially isostatic pressure being applied to the container and its contents sufficient to dimensionally stabilize the container and its contents. The resulting shaped substantially isostatic system of powder-enveloped container is hot-pressed whereby fluid eutectiferous silicon-rich alloy is produced and infiltrated through the interstices between the diamond crystals and contacts the contacting face of the silicon nitride substrate sufficiently producing, upon cooling, an adherently bonded integral composite.
A Novel Technique for Asymmetric Group Key Agreement for Dynamic Open Group Connection Networks

In this piece of work, we proposed an authenticated asymmetric group key agreement (A 2 GKA) protocol using bilinear mapping for dynamic networks. This protocol is mostly suitable for multicasting (or broadcasting) system without relying on the group controller to distribute secret keys to the group members. Our proposed protocol is equipped with various security issues including perfect forward security. Also, we revised the group key agreement definition and distinguished the symmetric key agreement from AGKA protocol. The protocol A 2 GKA enables the group members to negotiate a common encryption key which is accessible to the public, by holding their secret decryption key. Conventional AGKA is secured against passive attacks only, whereas our proposed one is impregnable against both active as well as passive attacks.
Electromechanical response of cellular electret films

Charged films with a cellular structure show pseudo-piezoelectric properties with high transducer constants. The electromechanical response equations of such films are derived for their operation as sensors and actuators. Measurements of the direct and inverse transducer constants, the thermal stability of the charge, and Young's modulus are presented. By assuming reasonable charge distributions and charge densities, the calculated transducer constants are in good agreement with the measured values. Both the theoretical model and the measurements show the reciprocity of the transducer constants.
A distributed approach to 3D road scene recognition

A distributed 3D scene recognition system based on a multilevel representation of object models and signals is described. The solution to a recognition problem is obtained through a set of object-observation couples at the different abstraction levels. The various system modules exchange two kinds of information: (1) top-down messages, which are used to communicate to lower modules the predictions made on the basis of a priori knowledge on the application domain, (2) bottom-up messages, which are used to communicate to higher modules the evidence supporting possible local solutions. A local scheme for the combination of message flows is defined, and messages are interpreted by using a probabilistic network of estimators of random variables. The proposed model is suitable for addressing the problem of distributed geometric reasoning aimed at 3D road scene recognition by an autonomous vehicle. Recognition results include road detection and obstacle localization, together with a study of the relative computational load required by different modules of the system. The proposed approach is currently simulated on a workstation, while an effective implementation on board of an autonomous vehicle is under development in the contest of the CEC-EUREKA Prometheus project. >
Design and Implementation of Improved Attenuation CIC Decimator and Interpolator in FPGA

Cascaded Integrator Comb (CIC) filters are widely used in Multirate signal processing as a filter in both decimator (decrease in the sampling rate) and interpolator (increase in the sampling rate). This paper discusses the architecture, design and implementation aspects of decimator and interpolator using CIC filter and comparison between the results in hardware and simulations. The hardware is implemented in FPGA and verified with Modelsim & Lab VIEW simulation results. CIC filters serve as powerful anti aliasing agents before decimation and anti imaging agents for interpolated signals. This paper also discusses about the method to improve the CIC attenuation. CIC Decimator and Interpolator were coded in Verilog, simulated using Modelsim simulator and Lab VIEW and implemented using Xilinx FPGA device.
The Reload Learning Design Tools

Commentary on: Chapter7: Learning Design Tools. (Griffiths, Blat, GarcA­a, Vogten and Kwong,2005) Abstract: The Reload Learning Design Editor (LDE) is an Open Source, close-to-specification, tree-based Learning Design (LD) editor written in Java using the Eclipse platform. The editor tools are complemented by a LD Player, which provides a familiar and user-friendly interface to the CopperCore LD runtime engine. This paper will describe the history and design rationale underpinning the tools, show how they fit into the LD authoring tools framework devised by Griffiths et al. (2005) and consider their suitability to various user roles and design approaches. The paper will conclude by outlining future versions of the software and how these new developments should facilitate the creation and manipulation of Units of Learning by staff in all user roles. Editors: Colin Tattersall and Rob Koper. Interactive demonstrations: The software tools described in this paper (the Reload Learning Design Editor and Reload Learning Design Player, both at version 2.0 as of May 27 2005) are available from the Reload project web site at www.reload. ac.uk
A constraint-based genetic algorithm for concurrent engineering

Concurrent engineering is a complex problem in that a large number of considerations have to be brought to bear during the design stage. Compounding this complexity is that concurrent engineering problems are also likely to encompass both numeric and symbolic variables, and constraints that range from simple algebraic constraints, through conditional constraints, to potentially very complex database constraints. Previous approaches have concentrated on finding good solutions to simplified problems or, alternatively, to finding a feasible solution which may not be close to optimal. This paper presents an approach to concurrent engineering problems that extends the conventional genetic algorithm approach to handle the complex variety of variables and constraints that are inherent in a typical concurrent engineering problem. This poses some severe research challenges and this paper describes three genetic operators (the constraint-based initialization, crossover, and mutation operators) that preserve feasibi.
Visual Recognition System for Cleaning Tasks by Humanoid Robots

In this study, we present a visual recognition system that enables a robot to clean a tabletop. The proposed system comprises object recognition, material recognition and ungraspable object detection using information acquired from a visual sensor. Multiple cues such as colour, texture and three-dimensional point-clouds are incorporated adaptively for achieving object recognition. Moreover, near-infrared (NIR) reflection intensities captured by the visual sensor are used for realizing material recognition. The Gaussian mixture model (GMM) is employed for modelling the tabletop surface that is used for detecting ungraspable objects. The proposed system was implemented in a humanoid robot, and tasks such as object and material recognition were performed in various environments. In addition, we evaluated ungraspable object detection using various objects such as dust, grains and paper waste. Finally, we executed the cleaning task to evaluate the proposed system's performance. The results revealed that the proposed system affords high recognition rates and enables humanoid robots to perform domestic service tasks such as cleaning.
Efficient compression from non-ergodic sources with genetic algorithms

Several lossless data compression schemes have been proposed over the past years. Since Shannon developed information theory in his seminal paper, however, the problem of data compression has hinged (even though not always explicitly) on the consideration of an ergodic source. In dealing with such sources one has to cope with the problem, of defining a priori the minimum sized symbol. The designer, therefore, is faced with the necessity of choosing beforehand the characteristics of the basic underlying element with which he or she is to attempt data compression. In this paper we address the problem of finding the characteristics of the basic symbols to consider in information treatment without assuming the form of such symbols in the same data source. In doing so, we expect to achieve a pseudo-ergodic behavior of the source. Then we are able to exploit the characteristics of such sources. Finding the basic elements (which we call "metasymbols") is a complex (NP complete) optimization task. Therefore, we make use of a non-traditional genetic algorithm (Vasconcelos genetic algorithm) which has been shown to have excellent performance, to find the metasymbols. In this paper we discuss the problem, the proposed methodology, some of the results obtained so far and point to future lines of research.
Pipelined arrays for modular multiplication

New arrays performing the modular multiplication are presented; they operate on a bit-serial basis for both inputs and outputs. The distinctive characteristic of the arrays presented is the possibility to start a new operation, as soon as the cells in the array are no longer used for the previous one, thus allowing bit-level pipelining of the different multiplications. A first array presented has a short clock cycle and long latency, so that it is more suited for applications dealing with long blocks of data to be processed. A second one has a longer clock cycle and shorter latency, and it is more suited for repeated operation on the same block of data.
An Attribute Reduction Method Based on Rough Set and SVM and with Application in Oil-Gas Prediction

With greater generalization performance support vector machine (SVM) is a new machine learning method. Rough set theory is a new powerful tool h dealing with vagueness and uncertainty information. By combining the advantages of two approaches, an original attribute reduction method is proposed in the paper. Moreover, it is applied into oil-gas prediction to solve the problems when support vector machine is directly employed. Experiments and results show the validity and feasibility of the algorithm suggested in the paper.
Photonic neurocomputers and learning machines

Efforts and progress made towards achieving desirable attributes in analog photonic (optoelectronic and/or electron optical) hardware that utilizes primarily incoherent light are reviewed. A hardware implementation of a stochastic Boltzmann learning machine is used as a vehicle for identifying generic issues and clarifying research and development areas for further advancement of the field. The development of architectures and methodologies for learning in self-organizing networks that employ a type of quasi-nonvolatile storage medium called electron trapping material is discussed. <<ETX>>
Multimodal Deixis: Nearby Reality or Still Science Fiction?

of research has already been done in the field of multimodal or natural interaction with computer systems. Present day interaction with a computer system must be learned on top of communication skills humans already possess. Studying new forms of interaction with computers might overcome this shortcoming, thus making human computer interaction more robust and fast. For this kind of interaction we need a computer that can see gestures, hear spoken words and make sense of them. This article will review the possibilities and problems regarding the different aspects of making computers see, hear and reason about multimodal deixis. We will see that though research in this field has come a long way, there are still some difficult obstacles on the road ahead.
Multi-Issue Negotiation Research Based On Niched Co-evolutionary Genetic Algorithm

This paper presents a simulation algorithm called SANCGA for bilateral multi-issue negotiation based on co-evolutionary genetic algorithm and isolated niche technique, and designs an experiment to simulate the co-evolutionary strategy learning process in an alternating offer negotiation scenario. The experiment results validate that SANCGA algorithm can form local niches in the strategy populations and generate an approximate Pareto optimal strategy set.
Performance of CSMA/CD Networks Under Combined Voice and Data Loads

This paper describes a simulation study of a class of carrier sense multiaccess buses with collision detection, similar to the Xerox Ethernet. The driving loads for the network models are similar to ones that might be found in an office information system which integrates data and voice communication. Various backoff algorithms are considered, to investigate the suitability of the approach and to suggest an appropriate family of architectures for communication within this medium.
A Survey on Recommendation System Approaches

In the current age of information overloading, it is very hard to find out relevant information. Innovations of search engine which helped users to find out relative information. But, the information could not be personalized by these engines. So, Recommendation System introduced for solving for this problem. The main aim of recommendation system is providing suggestions to a user. The suggestions is based on the user’s choices like what items to buy, what music to listen to, what online news to read, or which is the best movie. Recommendation system is mainly used for to find out accuracy, diversity; flexibility.Diversity is divided into two parts. Aggregate diversity and individual diversity. Aggregate diversity is the diversity which provides the number of items because of the many of users like it. Individual diversity is the diversity which provide the accurate result based on the user's choice. Recommendation systems are usually divided into three categories: Content based method, collaborative method and hybrid method