Web-based Biometric Computer Mouse Advisory System to Analyze a User's Emotions and Work Productivity

This article describes the analysis of emotional state and work productivity using a Web-based Biometric Computer Mouse Advisory System to Analyze a User's Emotions and Work Productivity (Advisory system hereafter) developed by this paper's authors. The Advisory system determines the level of emotional state and work productivity integrally by employing three main biometric techniques (physiological, psychological and behavioral). By using these three biometric techniques, the Advisory system can analyze a person's eleven states of being (stress, work productivity, mood, interest in work) and seven emotions (self-control, happiness, anger, fear, sadness, surprise and anxiety) during a realistic timeframe. Furthermore, to raise the reliability of the Advisory system even more, it also integrated the data supplied by the Biometric Finger (blood pressure and pulse rates). Worldwide research includes various scientists who conducted in-depth studies on the different and very important areas of biometric mouse systems. However, biometric mouse systems cannot generate recommendations. The Advisory system determines a user's physiological, psychological and behavioral/movement parameters based on that user's real-time needs and existing situation. It then generates thousands of alternative stress management recommendations based on the compiled Maslow's Pyramid Tables and selects out the most rational of these for the user's specific situation. The information compiled for Maslow's Pyramid Tables consists of a collection of respondent surveys and analyses of the best global practices. Maslow's Pyramid Tables were developed for an employee working with a computer in a typical organization. The Advisory system provides a user with a real-time assessment of his/her own productivity and emotional state. This article presents the Advisory system, a case study and a scenario used to test and validate the developed Advisory system and its composite parts to demonstrate its validity, efficiency and usefulness.     

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Purpose

Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence.

Method

Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function.

Results

The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers.     

Introducing dynamic diversity into a discrete particle swarm optimization

Particle swarm optimization (PSO) is an evolutionary metaheuristic inspired by the flocking behaviour of birds, which has successfully been used to solve several kinds of problems, although there are few studies aimed at solving discrete optimization problems. One disadvantage of PSO is the risk of a premature search convergence. To prevent this, we propose to introduce diversity into a discrete PSO by adding a random velocity. The degree of the introduced diversity is not static (i.e. preset before running PSO) but instead changes dynamically according to the heterogeneity of the population (i.e. if the search has converged or not). We solve the response time variability problem (RTVP) to test these two new ideas. The RTVP is an NP-hard combinatorial scheduling problem that has recently appeared in the literature. It occurs whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the instants at which they receive the necessary resources is minimized. The most efficient algorithm for solving non-small instances of the RTVP published to date is a classical PSO algorithm, referred to by the authors as PSO-M1F. In this paper, we propose 10 discrete PSO algorithms for solving the RTVP: one based on the ideas described above (PSO  -_c3dyn$c_3\text{<italic>dyn</italic>}$) and nine based on strategies proposed in the literature and adapted for solving a discrete optimization problem such as the RTVP. We compare all 11 PSO algorithms and the computational experiment shows that, on average, the best results obtained are due to our proposal of dynamic control mechanism for introducing diversity.     

BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks

Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.     

Immune-based algorithms for dynamic optimization

The main problem with biologically inspired algorithms (like evolutionary algorithms or particle swarm optimization) when applied to dynamic optimization is to force their readiness for continuous search for new optima occurring in changing locations. Immune-based algorithm, being an instance of an algorithm that adapt by innovation seem to be a perfect candidate for continuous exploration of a search space. In this paper we describe various implementations of the immune principles and we compare these instantiations on complex environments.     

The subsequence composition of a string

Words that appear as constrained subsequences in a text-string are considered as possible indicators of the host string structure, hence also as a possible means of sequence comparison and classification. The constraint consists of imposing a bound on the number ω$\omega$ of positions in the text that may intervene between any two consecutive characters of a subsequence. A subset of such ω$\omega$-sequences is then characterized that consists, in intuitive terms, of sequences that could not be enriched with more characters without losing some occurrence in the text. A compact spatial representation is then proposed for these representative sequences, within which a number of parameters can be defined and measured. In the final part of the paper, such parameters are empirically analyzed on a small collection of text-strings endowed with various degrees of structure.     

<Emphasis Type="Italic">New-Age</Emphasis>: A Negative Bias Temperature Instability-Estimation Framework for Microarchitectural Components

Degradation of device parameters over the lifetime of a system is emerging as a significant threat to system reliability. Among the aging mechanisms, wearout resulting from Negative Bias Temperature Instability (NBTI) is of particular concern in deep submicron technology generations. While there has been significant effort at the device and circuit level to model and characterize the impact of NBTI, the analysis of NBTI’s impact at the architectural level is still at its infancy. To facilitate architectural level aging analysis, a tool capable of evaluating NBTI vulnerabilities early in the design cycle has been developed that evaluates timing degradation due to NBTI. The tool includes workload-based temperature and performance degradation analysis across a variety of technologies and operating conditions, revealing a complex interplay between factors influencing NBTI timing degradation.