Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet Neural Network Based on Modified PSO and Its Application in Pattern Recognition

A modified particle swarm optimization--compound model PSO with stochastic inertia weigh is put forward and used to optimize the parameters of wavelet neural network. The trained wavelet neural-network is applied to the Iris classification experiment. The experimental result indicates that the wavelet neural-network training method based on the modified PSO is effective. This is an available approach to solve some problems, such as the pattern recognition, condition monitoring and fault diagnosis, etc.
A Patent Document Retrieval System Addressing Both Semantic and Syntactic Properties

Combining the principle of Differential Latent Semantic Index (DLSI) (Chen et al., 2001) and the Template Matching Technique (Tokuda and Chen, 2001), we propose a new user queries-based patent document retrieval system by NLP technology. The DLSI method first narrows down the search space of a sought-after patent document by content search and the template matching technique then pins down the documents by exploiting the words-based template matching scheme by syntactic search. Compared with the synonymous search scheme by thesaurus dictionaries, the new method results in an improved overall retrieval efficiency of patent documents.
Final Report and Findings of the Safe School Initiative: Implications for the Prevention of School Attacks in the United States

This publication results from on ongoing collaboration between the U.S. Secret Service and the U.S. Department of Education. Its goals are to determine whether it could have been known that incidents of targeted violence at schools were being planned and whether anything could have been done to prevent them from occurring. Results from the Secret Service's Exceptional Case Study Project (ECSP) are used to organize planning. This report describes the Safe School Initiative, defines "targeted" school violence, and discusses the prevalence of school violence in American schools. The methodology of this study, sources of information, and an analysis of survey responses are also discussed. Incidents of targeted school violence are characterized, including characteristics of the-attacker, conceptualization of the attack, and signaling, advancing, and resolving the attack. Implications of study findings and the use of threat assessment as a strategy to prevent school violence are presented. Appendix A lists incidents of targeted school violence by state; Appendix B lists these incidents by year; and Appendix C is a list of resources. Contact information follows the appendices. (Contains 13 references and 2 Web site resources.) (RT) Reproductions supplied by EDRS are the best that can be made from the original document. mmmumppm.00WASWItSIIM-1114e-e. %i THE FINAL REPORT AND FINDINGS OF THE. SAFE SCHOOL INITIATIVE: IMPLICATIONS. FOR THE PREVENTION OF SCHOOL ATTACKS IN THE UNITED STATES ONITEDSTATES SEC! ET, SEIIVICE 31, N D ONITED STATES DEM FITO4 INT OF EDOCATION U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 'This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. WASHINCT'ON, D.C. May 2002 2 BEST COPY AVA1 BLE ge)
Issues and Challenges in Content-Based Media Retargeting

Smartphones are rapidly increasing in this technological world, in that visual media is the most important thing in mobile arena and due to visual graphical world everyone wants high graphical visual media. Now a day demands of videos and images are very high, some devices support HD images and HD videos and some does not. It is due to the low resolution support of the device like some small screened phones which comes with low screen supporting only one or two aspect ratios. So media retargeting is the technique to resize visual media like images or videos and fit it to different resolution displays and aspect ratios while by keeping important regions as it is in that visual media. There are media retargeting like image retargeting and video retargeting to resize the media and fit to appropriate display without changing any content of media it is content-aware retargeting. By removing visual salient objects from media and resizing by image warping can be suitable for streaming online videos.
Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

Many real-world optimization problems have more than three objectives, which has triggered increasing research interest in developing efficient and effective evolutionary algorithms for solving many-objective optimization problems. However, most many-objective evolutionary algorithms have only been evaluated on benchmark test functions and few applied to real-world optimization problems. To move a step forward, this paper presents a case study of solving a many-objective hybrid electric vehicle controller design problem using three state-of-the-art algorithms, namely, a decomposition based evolutionary algorithm (MOEA/D), a non-dominated sorting based genetic algorithm (NSGA-III), and a reference vector guided evolutionary algorithm (RVEA). We start with a typical setting aimed at approximating the Pareto front without introducing any user preferences. Based on the analyses of the approximated Pareto front, we introduce a preference articulation method and embed it in the three evolutionary algorithms for identifying solutions that the decision-maker prefers. Our experimental results demonstrate that by incorporating user preferences into many-objective evolutionary algorithms, we are not only able to gain deep insight into the trade-off relationships between the objectives, but also to achieve high-quality solutions reflecting the decision-maker's preferences. In addition, our experimental results indicate that each of the three algorithms examined in this work has its unique advantages that can be exploited when applied to the optimization of real-world problems.
Strategies for Migrating from DCE to CORBA

Communication software middleware and distributed services for next-generation applications must be reliable, flexible, reusable, and capable of providing the necessary quality of service to applications like multimedia and telecommunication systems running over high-speed networks. Requirements for reliability, flexibility, and reusability motivate the use of object-oriented middleware like the Common Object Request Broker Architecture (CORBA). This document provides an indepth analysis of DCE and CORBA in terms of their key similarities and differences. It evaluates the advantages of porting DCE applications to CORBA, and describes the key areas where the porting effort is most likely to face the hardest problems.
A Triadic Neurocognitive Approach to Addiction for Clinical Interventions

According to the triadic neurocognitive model of addiction to drugs (e.g., cocaine) and non-drugs (e.g., gambling), weakened “willpower” associated with these behaviors is the product of an abnormal functioning in one or more of three key neural and cognitive systems: (1) an amygdala-striatum dependent system mediating automatic, habitual, and salient behaviors; (2) a prefrontal cortex dependent system important for self-regulation and forecasting the future consequences of a behavior; and (3) an insula dependent system for the reception of interoceptive signals and their translation into feeling states (such as urge and craving), which in turn plays a strong influential role in decision-making and impulse control processes related to uncertainty, risk, and reward. The described three-systems account for poor decision-making (i.e., prioritizing short-term consequences of a decisional option) and stimulus-driven actions, thus leading to a more elevated risk for relapse. Finally, this article elaborates on the need for “personalized” clinical model-based interventions targeting interactions between implicit processes, interoceptive signaling, and supervisory function aimed at helping individuals become less governed by immediate situations and automatic pre-potent responses, and more influenced by systems involved in the pursuit of future valued goals.
Title Effect of Biofield Treatment on Structural and Morphological Properties of Silicon Carbide Permalink

Ceramics have been used for many years in structural, abrasive and electronics devices; and mostly are metal oxides. However, nowadays new ceramics like nitrides, carbides also grab significant attention due to their unique characteristics, such as high melting point, hardness and excellent mechanical and electronic properties. The silicon carbide (SiC), naturally exists in about 250 crystalline forms [1]. Out of them, two most commonly recognized forms of SiC are: cubic (β-SiC) and hexagonal (α-SiC) (Figure 1). The α-SiC crystalline form exists : 2H-SiC, 4H-SiC, and 6H-SiC as per number of hexagonal layer present in a unit cell; for instance 6H-SiC have six layers [2,3]. In microelectronic devices, and high temperature applications, the energy band gap and electron mobility of SiC plays a crucial role, which are closely associated with its crystal structure, lattice strain, dislocations density and crystallinity [4]. The SiC powders can be synthesized via various processes such as Acheson process (solid state reaction between sand and coke at 2500oC), liquid phase reactions, carbothermal reduction method, physical vapour deposition, pyrolysis, and chemical vapor deposition [5,6]. Most of the above processes require a very high temperature (>1500oC) for better control over particle size, surface area and crystal structure of SiC. Recently, some other techniques such as high energy milling, mechanical alloying (or ball milling), reaction milling also have been employed to control the particle size, surface area and crystal structure parameters [7-9].
CFASST Implementation and Reflective Practice: The Interplay of Structures and Perceptions.

What does it take to implement an innovative teacher induction program? A history of research on the implementation of educational innovations has illustrated the need for mutual adaptation of programs (Berman & McLaughlin, 1977) and highlighted the importance of local context (Fullan, 1991). This work raises a number of important considerations for those who would undertake large-scale program implementation. To what extent can adaptation occur without undermining the purpose of a particular improvement effort? What factors in the local context are most relevant in supporting effective implementation of a “proven practice”?
Looking for the path : image segmentation

Processing of the images acquired from the camera attached to the mobile robot in outdoor environment can be used for feature extraction or to distinguish the path the surrounding in the environment. Such information is further used in the planner and/or position estimator. The paper gives the overview of image segmentation method used on real data gathered by mobile robot Bender II during the tests for Robotour 2009 competition.
A New Minimally-Supervised Framework for Domain Word Sense Disambiguation

We present a new minimally-supervised framework for performing domain-driven Word Sense Disambiguation (WSD). Glossaries for several domains are iteratively acquired from the Web by means of a bootstrapping technique. The acquired glosses are then used as the sense inventory for fully-unsupervised domain WSD. Our experiments, on new and gold-standard datasets, show that our wide-coverage framework enables high-performance results on dozens of domains at a coarse and fine-grained level.
Collaborative goal and policy learning from human operators of construction co-robots

Human operators of real-world co-robots, such as excavator, require extensive experience to skillfully handle these complicated machines in uncertain safetycritical environments. We consider the problem of human-robot collaborative learning and task execution, where efficient human-robot interaction is critical to safely and efficiently accomplish complex tasks in uncertain environments. Our collaborative learning algorithm enables a construction co-robot to learn latent task subgoals from the demonstrations of skilled human operators which can then be used to guide novice human operators in completing complex tasks under uncertainty. The effectiveness our algorithm is demonstrated through experimentation on a scaled model of an excavator with guided and unguided human operators. Our results demonstrate that when the co-robot’s inferred subgoals are communicated back to the novice human operator, task performance significantly improves.
Informative Motif Detection Using Data Mining

Motif finding in biological sequences is a fundamental problem in computational biology with important applications in understanding gene regulation, protein family identification and determination of functionally and structurally important identities. The large amounts of biological data let us solve the problem of discovering patterns in biological sequences computationally. In this research, we have developed an approach using a method of data mining to detect frequent residue informative motifs that are high in information content. The proposed approach modifies an existing method based on Apriori algorithm by using the Frequent Pattern tree (FP-tree) algorithm of data mining method. This method can efficiently detect novel motifs in biological sequences based on information content of the motifs and shows better performance than the existing method. Experiments on real biological sequence data sets demonstrate the effectiveness of the method.
Membership and inference rule generation for fuzzy-neural MIMO channel modeling

Membership and inference rule generation plays a critical role in performance enhancement of fuzzy-neural (FN) modeling of Multi-Input-Multi-Output (MIMO) wireless channels. This work proposes certain methods of the formation of multiple membership generation as well as some inference rule, that determine the performance of FN-MIMO modeling in terms of processing speed and precision. Experimental results establish the enhanced performance of the proposed system in comparison to statistical and other methods.
Bases for Object Individuation in Infancy: Evidence From Manual Search

Two studies exploited a new manual search methodology to assess the bases on which 10- to 12-month-olds individuate objects. Infants saw 1 or 2 objects placed inside an opaque box, into which they could reach. Across conditions, the information specifying 2 objects differed. The dependent measures reflected persistence of reaching into a box that was empty regardless of whether an object should have remained. Success consists of little reaching after all objects are removed and persistent reaching for an object not yet retrieved. Given spatiotemporal information for 2 objects, both age groups succeeded. Given only property or kind information, only 12-month-olds succeeded. Despite disparate information-processing demands, this pattern converges with looking time data (Xu & Carey, 1996; Xu, Carey, & Welch, 1999), suggesting a developmental change orthogonal to that of executive function. This change may reflect the emergence of kind representations.
Increasing Reliability for IEEE 802.16j Mobile Multi-hop Relay Networks Planning

IEEE 802.16 network is a new generation of broadband networks. Recently, relay stations in IEEE 802.16j are proposed to perform as lightweight base station solutions to extend coverage and/or increase throughput. However, the 802.16j networks are tree-based structure which suffers from the single point of failure problem. In this paper, we define the coverage problem to place the base stations (BSs) and relay stations (RSs) at the lowest cost to support desired utilities and constrained by reliability limit. We develop two heuristic algorithms to solve coverage problems. We decide the proposed algorithms whose performance is quite near to the optimal selection by implementing a number of randomly selected graphs.
User-based Clustering with Top-N Recommendation on Cold-Start Problem

Recommender system has been recognized as the most effective method for information overload problem. Although many efforts have been done on the "Cold-Start" problem, it is still an open problem and has become a very emergent issue in social network analysis. In this paper, we propose a novel approach, which applies the character capture and clustering methods to address the cold-user problem (producing recommendations to new users who have no preference on any item). We use the vector cosine method to obtain the user's similarity matrix and clustering users into different groups. For each group, we produce the top-N recommendation by averaging ratings of every item and choosing the top N items on the list. The experimental results on MovieLens-1M data demonstrate that our approach achieve a remarkable and consistent improvements in overcoming the cold-start problem.
Looking Good? Appearance Preferences and Robot Personality Inferences at Zero Acquaintance

The study presented in this paper explored the relationships between participant personality, perceived robot personality and preferences for particular robot appearances. The participants (N=77) watched 3 videos of a HRI situation in which the appearance of the robot was altered to appear more or less anthropomorphic. Participant personality was assessed using the Big Five Domain Scale, while Robot Personality was measured using 5 items based on the traits from the Big Five Model. The results reveal that low Emotional Stability and Extraversion scores are related to preferences for mechanical robot appearances. Results for perceived robot personality suggest that participants clearly differentiated between the different robots on the dimensions of Extraversion, Agreeableness and Intelligence, but did not differentiate strongly between them on the Emotional Stability dimension. Index Terms – Human robot interaction, video trials, social robotics, personality, robot personality, anthropomorphism, robot appearance
Nanoethosomal transdermal delivery of vardenafil for treatment of erectile dysfunction: optimization, characterization, and in vivo evaluation

Vesicular drug delivery systems have recently gained attention as a way of improving dosing accuracy for drugs with poor transdermal permeation. The current study focuses on utilization of the natural biocompatible vesicles to formulate vardenafil nanoethosomes (VRD-NE), for the enhancement of their transdermal permeation and bioavailability. Fifteen formulations were prepared by thin-layer evaporation technique according to Box–Behnken design to optimize formulation variables. The effects of lipid composition, sonication time, and ethanol concentration on particle size and encapsulation efficiency were studied. The diffusion of vardenafil (VRD) from the prepared nanoethosomes specified by the design was carried out using automated Franz diffusion cell apparatus. The optimized formula was investigated for in vivo pharmacokinetic parameters compared with oral VRD suspension. Confocal laser scanning microscopy images were used to confirm enhanced diffusion release of VRD in rat skin. The results showed that the optimized formula produced nanoethosomes with an average size of 128 nm and an entrapment efficiency of 76.23%. VRD-NE provided a significant improvement in permeation with an enhancement ratio of 3.05-fold for a film made with optimally formulated VRD-NE compared with a film made with VRD powder. The transdermal bioavailability of VRD from the nanoethosome film was approximately twofold higher than the oral bioavailability from an aqueous suspension. VRD-NE thus provide a promising transdermal drug delivery system. As a result, management of impotence for a longer duration could be achieved with a reduced dosage rate that improves patient tolerability and compliance for the treatment of erectile dysfunction.
Ciblage de l'inflation : 20 ans déjà

L y a 20 ans, la Nouvelle-Zélande adopta une conception de la politique monétaire axée sur la réalisation d’un objectif d’inflation. La nouveauté résidait dans l’engagement explicite de considérer la maîtrise de l’inflation comme le but principal de la politique monétaire et dans l’accent qui était mis sur la transparence et la responsabilité. Aujourd’hui, 26 pays ont une cible d’inflation, dont près de la moitié sont des économies émergentes ou à bas revenu (voir tableau). En outre, un certain nombre de banques centrales des pays les plus avancés — dont la BCE, la Réserve fédérale, la Banque du Japon et la Banque nationale suisse — ont intégré beaucoup des aspects essentiels du ciblage de l’inflation, et plusieurs autres se préparent à le faire. Cet article présente les résultats obtenus depuis 20 ans par les pays ciblant l’inflation, notamment pendant la flambée des cours des matières premières de 2006–08 et la crise financière mondiale qui a débuté en 2007. Il évoque aussi des problèmes très importants auxquels ils vont sans doute être confrontés dans le proche avenir.
Improving Internet multicast with routing labels

The IP-multicast architecture is extended with addressing information along multicast routing trees that permits more efficient and sophisticated multicast routing options and encourages communication and cooperation between IP and higher-layer protocols. The Addressable Internet Multicast (AIM) architecture is introduced that enables sources to restrict the delivery of packets to a subset of the receivers in a multicast group on a per-pocket basis, permits receivers to listen to subsets of sources on a subscription basis, provides nearest-host routing, and allows higher-layer protocols to place packets into application-defined logical streams, so that hosts may direct the multicast routing of packets based on application-defined contexts. In addition, the Reliable Multicast Architecture (RMA) is introduced to support end-to-end reliable multicasting using heterogeneous reliable multicast protocols and providing acknowledgment trees implicitly, thereby eliminating the ACK implosion problem and allowing NAK-avoidance algorithms to work within local groups.
Oral inoculation of live or dead third-stage larvae of Anisakis simplex in rats suggests that only live larvae induce production of antibody specific to A. simplex

Live Anisakis simplex third-stage larvae (L3) penetrate gastrointestinal mucosa after they are ingested in raw or undercooked seafood, thereafter causing gastrointestinal manifestations and allergic manifestations such as urticaria and anaphylaxis. These allergic reactions are mediated by specific IgE to L3 allergens, especially excretory-secretory (ES) allergens. Recent evidences suggest that only live larvae can cause allergic reactions, although cases attributable to ingestion of cooked, frozen seafood have been reported. Therefore the risk of Anisakis-associated hypersensitivity by ingestion of properly cooked and frozen fish remains controversial. No prior report describes the kinetics of antibody production in experimental animals after oral inoculation with dead L3. This study used ELISA to assess antibody production in rats inoculated orally with dead L3. Positive absorbance value in IgG, IgM, and IgE specific to ES antigen from L3 were found in rats inoculated with live L3 but not with dead L3 (frozen, heated, cut, or homogenized). At one week post re-inoculation with live or frozen L3 to the initially sensitized rats, the absorbance value of the specific IgM and IgE to ES antigen elevated quickly and highly in rats that had been re-inoculated with live L3, but they decreased slightly or did not change in rats inoculated with frozen L3. These results suggest that only ingestion of live L3 can produce the specific antibody and induce initial and secondary sensitizations to L3.