Towards an agent-based framework for online after-sales services

The multi-agent paradigm for building intelligent systems has gradually been accepted by researchers and practitioners in the research field of artificial intelligence. There are also attempts of adapting agents and agent-based systems for creating industrial applications and providing e-services. In this paper, we present an attempt to use agents for constructing an online after-sale services system. The system is decomposed into four major cooperative agents, and in which each agent concentrates on particular aspects in the system and expresses intelligence by using various techniques. The proposed agent-based framework for the system is presented at both the micro-level and the macro-level according to the Gaia methodology. UML notations are also used to represent some software design models. As the result of this, agents are implemented into a framework for which exploits Case-Based Reasoning (CBR) technique to fulfil real life on-line services' diagnoses and tasks

Superconductivity under pressure in the Dirac semimetal PdTe2

The Dirac semimetal PdTe$_2$ was recently reported to be a type-I superconductor ($T_c =$1.64 K, $\mu_0 H_c (0) = 13.6$ mT) with unusual superconductivity of the surface sheath. We here report a high-pressure study, $p \leq 2.5$ GPa, of the superconducting phase diagram extracted from ac-susceptibility and transport measurements on single crystalline samples. $T_c (p)$ shows a pronounced non-monotonous variation with a maximum $T_c =$1.91 K around 0.91 GPa, followed by a gradual decrease to 1.27 K at 2.5 GPa. The critical field of bulk superconductivity in the limit $T \rightarrow 0$, $H_c(0,p)$, follows a similar trend and consequently the $H_c(T,p)$-curves under pressure collapse on a single curve: $H_c(T,p)=H_c(0,p)[1-(T/T_c(p))^2]$. Surface superconductivity is robust under pressure as demonstrated by the large superconducting screening signal that persists for applied dc-fields $H_a > H_c$. Surprisingly, for $p \geq 1.41$ GPa the superconducting transition temperature at the surface $T_c^S$ is larger than $T_c$ of the bulk. Therefore surface superconductivity may possibly have a non-trivial nature and is connected to the topological surface states detected by ARPES. We compare the measured pressure variation of $T_c$ with recent results from band structure calculations and discuss the importance of a Van Hove singularity.Comment: manuscript 9 pages with 8 figures + supplemental material 3 pages with 6 figure

Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them

Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have certain percentage of overlaps, and so the overlap classification problem is an important part of audio classification. Nowadays, the work on overlapped audio event classification is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innovatively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic Latent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in classifying isolated audio events

A genetic Algorithm-Based feature selection

This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy