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A new learning framework is proposed for multivariate chaotic system modeling. In order to construct suitable input variables, we put forward a scheme of input variable selection based on nonuniform state space reconstruction. A new criteria based on low dimensional approximation of joint mutual information is derived, which is solved by evolutionary computation approach efficiently with low computation...
High power dissipation can occur by high launch-induced switching activity when the response to a test pattern is captured by flip-flops (FFs) in at-speed scan testing, resulting in excessive IR drop. IR drop may cause significant capture-induced yield loss in the deep-submicron era. It is known that test modification methods using X-identification and X-filling are effective to reduce power dissipation...
Performance Measurement Systems (PMS) have long captured the attention of organizational behavior and information systems (IS) research. The PMS in the study was implemented by public police forces, using advanced Business Intelligence (BI) technologies. The study examines the impact of enhancing that PMS, through analysis of the metric results over an 8-year time period that covered a transition...
In order to reduce students' test anxiety, collaborative testing was suggested as an evaluation strategy. However, few studies have focused on testing group construction, especially when an important factor, i.e., group diversity is taken into consideration. In this paper we conducted a case study to assess the association between group diversity and test anxiety in collaborative testing. The results...
We study the problem of multi-class image classification with large number of classes, of which the one-vs-all based approach is prohibitive in practical applications. Recent state-of-the-art approaches rely on label tree to reduce classification complexity. However, building optimal tree structures and learning precise classifiers to optimize tree loss is challenging. In this paper, we introduce...
The number of bumps that are touched during probe is a key factor in the cost of a probe card. DFT techniques have been commonly used to reduce the number of signal bumps that need to be probed, but the vast majority of bumps in a typical SoC are allocated to core power and ground. In this paper we describe a technique using power supply noise simulations to develop special bump masks that reduce...
Trauma patients suffer from a wide range of injuries, including vascular injuries. Such injuries can be difficult to immediately identify, only becoming detectable after repeated examinations and procedures. Large data sets of Shock Trauma patient treatment and care exist, spanning thousands to millions of patients, but machine learning techniques are needed to analyze this data and build appropriate...
The term of “World Englishes” describes the current and real state of English and one of their main characteristics is a large diversity of pronunciation, called accents. We have developed two techniques of individual-based clustering of the diversity [1, 2] and educationally-effective visualization of the diversity [3]. Accent clustering requires a technique to quantify the accent gap between any...
In this paper, a neural model intended to efficiently determine the number of moving electromagnetic sources of stochastic radiation in the monitoring space sector is presented. Neural model is based on a probabilistic neural network. As an illustration, one-dimensional case is considered in which the noisy sources are moving only in the azimuth plane.
In this paper, the problem of detecting correlated components in a p-dimensional Gaussian vector is considered. In the setup considered, s unknown components are correlated with a known covariance structure. Hence, there are equation possible hypotheses for the unknown set of correlated components. Instead of taking a full-vector observation at each time index, in this paper we assume that the observer...
Keeping in mind technological trends and student behaviour, higher educational institutes are adopting major changes in content delivery. The objective is to keep students engaged in the courses that they learn, which in turn would help them perform better academically. This paper describes students' online engagement in terms of their learning, participation and academic performance after teaching...
Currently, microalgae cultivation is one of the most promising alternative solutions to alleviate the value of CO2 concentration. Microalgae growth rate is convinced to be the indicator to measure the effectiveness in capturing CO2. In this paper, the microalgal growth behavior by means of various pH concentrations is observed. From the observation data, the growth behavior is modeled by regression...
Machine-learning test strategy has been developed in the last decade as an alternative to costly specification-driven tests for Analog, Mixed-Signal and RF circuits (AMS-RF). The concept is simple: powerful algorithms are used to map simple measurements onto specifications. But the proper execution requires an information-rich input space. This paper presents an efficient hybrid algorithm to select...
Due to changes in the development practices at Axis Communications, towards continuous integration, faster regression testing feedback is needed. The current automated regression test suite takes approximately seven hours to run which prevents developers from integrating code changes several times a day as preferred. Therefore we want to implement a highly selective yet accurate regression testing...
The problem of quickest data-adaptive and sequential search for clusters in a Gauss-Markov random field is considered. In the existing literature, such search for clusters is often performed using fixed sample size and non-adaptive strategies. In order to accommodate large networks, in which data adaptivity leads to significant gains in detection quality and agility, in this paper sequential and data-adaptive...
Software bugs contribute to the cost of ownership for consumers in a software-driven society and can potentially lead to devastating failures. Software testing, including functional testing and structural testing, remains a common method for uncovering faults and assessing dependability of software systems. To enhance testing effectiveness, the developed artifacts (requirements, code) must be designed...
In this paper, a global algorithm for human action, facial and gesture recognition is presented. The proposed algorithm depends on the extraction of multiple transform domain features and Canonical Correlation Analysis (CCA) for features fusion and classification. The proposed algorithm achieved the best reported results for facial and facial expression recognition. Excellent comparable results were...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
Feature selection algorithm in intrusion detection, data mining and pattern recognition plays a crucial role, it deletes unrelated and redundant features of the original data set to the optimal feature subset which are applied to some evaluation criteria. Due to the low accuracy, the high false positive rate and the long detection time of the existing feature selection algorithm, in the paper, we...
Data mining is to extract the potentially useful knowledge and information from large amounts of data. How to dig up effective, reliable, understandable, and interesting association rules from vast amounts of information to help people make decisions has become an urgent problem to be solved. People want to use a reasonable evaluation method to measure reliability or validity of association rules,...
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