Miracle-dz/UniFAR-FTUs huggingface dataset:
Using high dimensional indexes to support relevance feedback based interactive images retrieval

Image retrieval has found more and more applications. Due to the well recognized semantic gap problem, the accuracy and the recall of image similarity search are often still low. As an effective method to improve the quality of image retrieval, the relevance feedback approach actively applies users' feedback to refine the search. As searching a large image database is often costly, to improve the efficiency, high dimensional indexes may help. However, many existing database indexes are not adaptive to updates of distance measures caused by users' feedback. In this paper, we propose a demo to illustrate the relevance feedback based interactive images retrieval procedure, and examine the effectiveness and the efficiency of various indexes. Particularly, audience can interactively investigate the effect of updated distance measures on the data space where the images are supposed to be indexed, and on the distributions of the similar images in the indexes. We also introduce our new B+-tree-like index method based on cluster splitting and iDistance.
Active fault-tolerant control design for Takagi-Sugeno fuzzy systems

In this paper, a virtual actuator-based active fault-tolerant control strategy is presented. After a short introduction to Takagi-Sugeno fuzzy systems, it is shown how to design a fault-tolerant control strategy for this particular class of non-linear systems. The key contribution of the proposed approach is an integrated fault-tolerant control design procedure of fault identification and control within an integrated fault-tolerant control scheme. In particular, fault identification is implemented with the suitable state observer. While, the controller is implemented in such a way that the state of the (possibly faulty) system tracks the state of a fault-free reference model. Consequently, the fault-tolerant control stabilizes the possibly faulty system taking into account the input constraints and some control objective function. Finally, the last part of the paper shows a comprehensive case study regarding the application of the proposed strategy to fault-tolerant control of a twin-rotor system.
PAPER Special Section on Papers Selected from the 20th Symposium on Signal Processing An Adaptive Penalty-Based Learning Extension for the Backpropagation Family

SUMMARY Over the years, many improvements and refinements to the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning extension for the backpropagation learning algorithm and its variants is proposed. The new method initially puts pressure on artificial neural networks in order to get all outputs for all training patterns into the correct half of the output range, instead of mainly focusing on minimizing the difference between the target and actual output values. The upper bound of the penalty values is also controlled. The technique is easy to implement and computationally inexpensive. In this study, the new approach is applied to the backpropagation learning algorithm as well as the RPROP learning algorithm. The superiority of the new proposed method is demonstrated though many simulations. By applying the extension, the percentage of successful runs can be greatly increased and the average number of epochs to convergence can be well reduced on various problem instances. The behavior of the penalty values during training is also analyzed and their active role within the learning
A Collaborative Code Review Platform for GitHub

The incorporation of peer code reviews as being part of a developer's work flow, and hence the software development lifecycle, has steadily grown in popularity over the past three decades. During the process of statically inspecting code, developers of a codebase are able to collaboratively detect possible code defects, as well as use code reviews as a means of transferring knowledge to improve the overall understanding of a system. The uptake of such practices is dependent on several factors, specifically the availability of tools that aid in providing an easy-to-use and intuitive platform to perform code reviews, and is readily accessible to members of a project. This paper briefly explores the act of code review, identifies the pitfalls of existing code review tools, and proposes the design and development of a web-based code review application, along with the evaluation of the prototype. The code review tool developed, Fistbump, targets the popular Git based repository hosting service, GitHub, and provides a versatile tool to coordinate and manage discussions between the owner of a pull request and the elected participants of a review.
Social support effects on diabetes management by South African emerging adults: A replication and extension study

This study aimed to explore and describe the role of social support in successful living with type 1 diabetes. Informants were eight emerging adults with well-controlled type 1 diabetes (females = 5; white = 7, African = 1; mean time since diagnosis = 8.5 years, SD time since diagnosis = 6.23 years). They completed semi-structured interviews on their types and sources of social support for successful diabetes management. Thematic analysis of the data revealed important social support agents, which included family members, friends, and their healthcare professionals; as well as the social support mechanisms these individuals provided. Social support appeared to help the participants with diabetes management through facilitation of shared knowledge regarding management of type 1 diabetes, fostering a sense of belonging and companionship, and practical assistance with daily regimen tasks.
Tradução e Adaptação Transcultural da Versão Brasileira do Schutte Self-Report Emotional Intelligence Test

RESUMO INTRODUÇÃO Estudos recentes demonstram que a inteligência emocional (IE) pode melhorar a educação médica e a prática profissional. Não existem instrumentos de avaliação da IE validados para o português (Brasil). OBJETIVO Realizar tradução e adaptação transcultural do Schutte Self Report Emotional Intelligence Test (SSEIT) do inglês para o português. MATERIAIS E MÉTODOS A tradução e adaptação transcultural foi realizada em seis etapas: tradução, síntese, tradução reversa, revisão por comitê, pré-teste e confecção da versão final. As traduções do inglês para o português foram realizadas por um psicólogo e um professor de inglês brasileiros e fluentes em inglês. A retrotradução foi realizada por duas pessoas nativas de língua inglesa que não conheciam o questionário original. O Comitê de Revisão foi formado pelos autores do estudo. A versão final foi submetida ao teste de Cronbach alfa (Ca) para avaliação da consistência e confiabilidade interna. Foram considerados aceitáveis valores ≥ 0,70. O pré-teste foi realizado em estudantes de Medicina e médicos residentes, que, além de responderem ao questionário, reescreveram as perguntas em suas próprias palavras. RESULTADOS A versão traduzida do SSEIT para o português apresentou 100% de equivalência semântica e idiomática. A comparação das versões retrotraduzidas com o questionário original em inglês apresentou discrepâncias semânticas discretas em quatro questões, que tiveram seus textos ajustados. A versão pré-teste foi aplicada em 41 voluntários, após assinatura do Termo de Consentimento Livre e Esclarecido. Sete questionários foram eliminados por preenchimento incompleto. A análise da transcrição mostrou pequenas discrepâncias nas mesmas questões da retrotradução, que foram novamente ajustadas. O OCa foi de 0,786 para o questionário completo e variou de 0,763 a 0,798 entre as questões. CONCLUSÃO O SSEIT foi traduzido e adaptado para o português com sucesso e apresentou consistência e validade internas aceitáveis de acordo com o teste de Cronbach alfa.
A Kalman filter-based stable dynamic inversion for discrete-time, linear, time-varying systems

Abstract In this paper the problem of estimating an unknown input for discrete-time, non-minimum phase, multivariable, linear time-varying systems (LTV) is considered. The initial condition of the plant may be unknown and stochastic process and measurement noise are included. The input signal is modelled as a random walk with drifts. Then it is estimated using a Kalman filter for a uniformly detectable augmented system. A necessary and sufficient condition for the detectability of the augmented system is provided. A Kalman filter-based stable dynamic inversion (SDI) for LTV systems is obtained as a consequence of our solution to the proposed problem. The inversion technique can be applied to achieve output tracking for LTV systems in the presence of non-minimum phase zeros and measurement and/or system noise. We are mainly motivated by typical need to replicate time signals in the automobile industry. Similar problems appear also in other fields as machine tool applications, aeronautic industry a.o.
Leveraging Formal Concept Analysis with Topic Correlation for Service Clustering and Discovery

With a growing number of web services, discovering services that can match with a user's query becomes a challenging task. It's very tedious for a service consumer to select the appropriate one according to her/his needs. In this paper, we propose a non-logic-based matchmaking approach that uses the Correlated Topic Model (CTM) to extract topic from semantic service descriptions and model the correlation between the extracted topics. Based on the topic correlation, service descriptions can be grouped into hierarchical clusters. In our approach, we use the Formal Concept Analysis (FCA) formalism to organize the constructed hierarchical clusters into concept lattices according to their topics. Thus, service discovery may be achieved more easily using the concept lattice. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. In our experiment, we compared the accuracy of the our hierarchical clustering algorithm with that of a classical hierarchical agglomerative clustering. The comparisons of Precision@n and Normalised Discounted Cumulative Gain (NDCGn) values for our approach, Apache lucene and SAWSDL-MX2 Matchmaker indicate that the method based on CTM presented in this paper outperform all the others matchmakers in terms of ranking of the most relevant services.
The Induction and Analysis of Gene Networks 1 1 To appear in the proceedings of CAMDA 2003

We report on the use of programs based upon rule induction for the induction of gene influence networks from microarray data and for analyzing the resulting networks. We use these programs to induce gene networks from a dataset created by merging normalized versions of the Beer et al. and Battacharjee et al. datasets made available for the 2003 CAMDA conference. We verified our findings by comparing them to lists of known oncogenes. Our results suggest that (1) normalization can be used to combine microarray datasets to yield a dataset with a larger number of cases, which facilitates discovery from the cases, and (2) our programs can be used to induce gene influence networks from microarray datasets and to use the induced networks to identify statistically significant differences in subgroups of the datasets. For example, our programs have rediscovered many known oncogenes from the combined dataset. General Terms Algorithms, experimentation, evaluation
Testbed Design for Cognitive Radio Spectrum Sensing Experiments

Testbed Design for Cognitive Radio Spectrum Sensing Experiments by Artem Tkachenko Master of Science in Engineering Electrical Engineering and Computer Sciences University of California, Berkeley Professor Robert Brodersen, Chair In this research project, requirements for cognitive radio testbed have been analyzed. A testbed, based on Berkeley Emulation Engine (BEE2), has been developed to study spectrum sensing issues for dynamic spectrum access networks. An extensive hardware and software environment has been integrated to optimize the experimentation process. Energy, pilot, cyclostationary feature, and network detector algorithms have been implemented and analyzed. This document presents necessary steps of implementing other algorithms and simulation environments on the testbed. Professor Robert Brodersen Thesis Committee Chair
TWO UPPER LIMITS ON THE ROSSITER–MCLAUGHLIN EFFECT, WITH DIFFERING IMPLICATIONS: WASP-1 HAS A HIGH OBLIQUITY AND WASP-2 IS INDETERMINATE

We present precise radial-velocity (RV) measurements of WASP-1 and WASP-2 throughout transits of their giant planets. Our goal was to detect the Rossiter–McLaughlin (RM) effect, the anomalous RV observed during eclipses of rotating stars, which can be used to study the obliquities of planet-hosting stars. For WASP-1, a weak signal of a prograde orbit was detected with ≈2σ confidence, and for WASP-2 no signal was detected. The resulting upper bounds on the RM amplitude have different implications for these two systems because of the contrasting transit geometries and the stellar types. Because WASP-1 is an F7V star, and such stars are typically rapid rotators, the most probable reason for the suppression of the RM effect is that the star is viewed nearly pole-on. This implies that the WASP-1 star has a high obliquity with respect to the edge-on planetary orbit. Because WASP-2 is a K1V star, and is expected to be a slow rotator, no firm conclusion can be drawn about the stellar obliquity. Our data and our analysis contradict an earlier claim that WASP-2b has a retrograde orbit, thereby revoking this system's status as an exception to the pattern that cool stars have low obliquities.
e2-Diagnoser: A System for Monitoring, Forecasting and Diagnosing Energy Usage

We propose e2-Diagnoser, a real-time data mining system for the energy management of smart, sensor-equipped buildings. The main features of e2-Diagnoser are: (i) fast extraction of a large portfolio of buildings' benchmarks at multiple places, and (ii) accurate prediction of buildings' energy usage down to sub meter level to detect and diagnose abnormal energy consumptions. Fundamentally, the e2-Diagnoser system is built on a novel statistical learning algorithm using the Generalized Additive Model (GAM) to simultaneously monitor the mean and variation of the energy usage as well as identify the influencing factors such as weather conditions. Its implementation is based on stream processing platform that integrates data from various sources using semantic web technologies and provides an interactive user interface to visualize results. The platform is scalable and can be easily adapted to other applications such as smart-grid networks. Here we describe the architecture, methodology, and show the web-interface to demonstrate the main functions in the e2-Diagnoser.
Greenhouse-Selected Resistance to Cry3Bb1-Producing Corn in Three Western Corn Rootworm Populations

Transgenic corn producing the Bacillus thuringiensis (Bt) toxin Cry3Bb1 has been useful for controlling western corn rootworm, Diabrotica virgifera virgifera LeConte, one of the most economically important crop pests in the United States. However, rapid evolution of resistance by this beetle to Bt corn producing Cry3Bb1 has been reported previously from the laboratory, greenhouse, and field. Here we selected in the greenhouse for resistance to Cry3Bb1 corn in three colonies of WCR derived from Kansas, Minnesota, and Wisconsin, respectively. Three generations of rearing on Cry3Bb1 corn significantly increased larval survival on Cry3Bb1 corn, resulting in similar survival in the greenhouse for selected colonies on Cry3Bb1 corn and isoline corn that does not produce Bt toxin. After four to seven generations of rearing on Cry3Bb1 corn, survival in the field on Cry3Bb1 corn relative to isoline corn more than doubled for selected colonies (72%) compared with control colonies (33%). For both selected and control colonies, survival in the field was significantly lower on Cry3Bb1 corn than on isoline corn. On isoline corn, most fitness components were similar for selected colonies and control colonies. However, fecundity was significantly lower for selected colonies than control colonies, indicating a fitness cost associated with resistance. The rapid evolution of resistance by western corn rootworm to Bt corn reported here and previously underlines the importance of effective resistance management for this pest.
Failure of the Collection Volume Method and Attempts of the ESE Lightning ROD Industry to Resurrect it

To facilitate the marketing of their devices, the vendors of Early Streamer Emission (ESE) lightning rods are attempting to get the Collection Volume Method (CVM) in IEEE Standard 998 which deals with the Shielding of Substations. This attempt follows their failures before IEC, Standards Australia and the NFPA (National Fire Protection Association). Voting on the standards of the IEEE is done by a pool of persons, most of whom are not lightning experts. The same applies to potential buyers of ESE devices who are being wooed by use of the CVM. This paper seeks to enlighten those persons by using indisputable field observations to prove invalidity of the CVM. The paper also shows that development of the CVM rested on a false perception that discrepancies existed between field observations and predictions of the Electrogeometric Model (EGM), and that the CVM failed to address those claimed discrepancies.
The Application of Dominant-Recessive Diploid Codes in MOGA

When solving an optimal problem, different encoding method has an important effect on the performance of multi-objective genetic algorithm. This paper breaks the traditional binary coding ideas, introduces a new dominant-recessive diploid codes which applied in the MOGA. we analyses the impact on solution space by the binary multi-objective genetic algorithm and dominant-recessive diploid codes multi-objective genetic algorithm, which is operated by three basic operators of tournament selection, two-point crossing, and the basic bit mutation. Furthermore, by using the Numerical experiments of three Classic multi-objective optimization test functions, this algorithms and the efficient binary multi-objective algorithms named niched pare to genetic algorithms are compared. From the solution, we know that this paper’s algorithm is obviously superiors to the niched pare to genetic algorithm about the distribution, convergence of solution and the capability of anti-prematurity. Thus, it is interpreted that the algorithm is feasible from the aspects of theoretic analysis and numerical experiments, and the pare to solutions can be came to.
A widely linear affine projection algorithm

A widely linear affine projection algorithm (WL-APA) utilising the full second order statistical information in the complex domain ℂ is proposed. It is based on recent developments in augmented complex statistics, ℂℝ calculus, and the widely linear modelling in ℂ, making it suitable for the processing of noncircular complex valued signals. Simulations on circular and noncircular benchmark and real-world noncircular wind signals show the effectiveness of WL-APA as compared to standard APA.
Information theoretic reductions among disclosure problems

Alice disposes of some number of secrets. She is willing to disclose one of them to Bob. Although she agrees to let him choose which secret he wants, she is not willing to allow him to gain any information on more than one secret. On the other hand, Bob does not want Alice to know which secret he wishes. An all-or-nothing disclosure is one by which, as soon as Bob has gained any information whatsoever on one of Alice's secrets, he has wasted his chances to learn anything about the other secrets. We assume that Alice is honest when she claims to be willing to disclose one secret to Bob (i.e. she is not about to send junk). The only cheating Alice is susceptible of trying is to figure out which secret is of interest to Bob. We address the following question from an information theoretic point of view: what is the most elementary disclosure problem? The main result is that the general all-or-nothing disclosure of secrets is equivalent to a much simpler problem, which we call the two-bit problem.
Sodium Channel Nav1.7 Is Essential for Lowering Heat Pain Threshold after Burn Injury

Marked hypersensitivity to heat and mechanical (pressure) stimuli develop after a burn injury, but the neural mechanisms underlying these effects are poorly understood. In this study, we establish a new mouse model of focal second-degree burn injury to investigate the molecular and cellular basis for burn injury-induced pain. This model features robust injury-induced behavioral effects and tissue-specific altered cytokine profile, but absence of glial activation in spinal dorsal horn. Three voltage-gated sodium channels, Nav1.7, Nav1.8, and Nav1.9, are preferentially expressed in peripheral somatosensory neurons of the dorsal root ganglia (DRGs) and have been implicated in injury-induced neuronal hyperexcitability. Using knock-out mice, we provide evidence that Nav1.7 selectively contributes to burn-induced hypersensitivity to heat, but not mechanical, stimuli. After burn model injury, wild-type mice display increased sensitivity to heat stimuli, and a normally non-noxious warm stimulus induces activity-dependent Fos expression in spinal dorsal horn neurons. Strikingly, both effects are absent in Nav1.7 conditional knock-out (cKO) mice. Furthermore, burn injury increases density and shifts activation of tetrodotoxin-sensitive currents in a hyperpolarized direction, both pro-excitatory properties, in DRG neurons from wild-type but not Nav1.7 cKO mice. We propose that, in sensory neurons damaged by burn injury to the hindpaw, Nav1.7 currents contribute to the hyperexcitability of sensory neurons, their communication with postsynaptic spinal pain pathways, and behavioral thresholds to heat stimuli. Our results offer insights into the molecular and cellular mechanisms of modality-specific pain signaling, and suggest Nav1.7-blocking drugs may be effective in burn patients.
Opus: University of Bath Online Publication Store Robot Transparency, Trust and Utility

This version is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract. As robot reasoning becomes more complex, debugging becomes increasingly hard based solely on observable behaviour, even for robot designers and technical specialists. Similarly, non-specialist users find it hard to create useful mental models of robot reasoning solely from observed behaviour. The EPSRC Principles of Robotics mandate that our artefacts should be transparent, but what does this mean in practice, and how does transparency affect both trust and utility? We investigate this relationship in the literature and find it to be complex, particularly in non industrial environments where transparency may have a wider range of effects on trust and utility depending on the application and purpose of the robot. We outline our programme of research to support our assertion that it is nevertheless possible to create transparent agents that are emotionally engaging despite having a transparent machine nature.