Multi-class pattern classification in imbalanced data

The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.

Microphone identification using one-class classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a subcategory of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. Research results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Reliability and validity of the modified Chinese version of the children's leisure activities study survey (CLASS) questionnaire in assessing physical activity among Hong Kong children

This study aimed to examine the reliability and validity of the modified Children’s Leisure Activities Study Survey (CLASS) Chinese-version questionnaire in assessing physical activity among Hong Kong Chinese Children. Test-retest reliability was examined in 84 boys and 136 girls aged 9–12 years by comparing data from two administrations of the survey conducted one week apart. Validity was determined by comparing data from the second administration with accelerometer estimates. The results suggested that the questionnaire provided reliable and valid estimates in overall physical activity patterns in Hong Kong Chinese children. However, substantial overestimation was observed in vigorous activity.

Identifying microphone from noisy recordings by using representative instance one class-classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. In addition, we propose a representative instance classification framework (RICF) that can effectively improve performance of OCC algorithms for recording signal with noise. Experiment results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Sesamin modulation of lipid class and fatty acid profile in early juvenile teleost, Lates calcarifer, fed different dietary oils

Sesamin, a major sesame seed lignan, has diverse biological functions including the modulation of molecular actions in lipid metabolic pathways and reducing cholesterol levels. Vertebrates have different capacities to biosynthesize long-chain PUFA from dietary precursors and sesamin can enhance the biosynthesis of ALA to EPA and DHA in marine teleost. Early juvenile barramundi, Lates calcarifer, were fed for two weeks on diets rich in ALA or SDA derived from linseed or Echium plantagineum, respectively. Both diets contained phytosterols and less cholesterol compared with a standard fish oil-based diet. The growth rates were reduced in the animals receiving sesamin regardless of the dietary oil. However, the relative levels of n-3 LC-PUFA in total lipid, but not the phospholipid, increased in the whole body by up to 25% in animals fed on sesamin with ALA or SDA. Sesamin reduced the relative levels of triacylglycerols and increased polar lipid, and did not affect the relative composition of phospholipid subclasses or sterols. Sesamin is a potent modulator for LC-PUFA biosynthesis in animals, but probably will have more effective impact at advanced ages. By modulating certain lipid metabolic pathways, sesamin has probably disrupted the body growth and development of organs and tissues in early juvenile barramundi.

Scandium-doped AlN 1D hexagonal nanoprisms : a class of room-temperature ferromagnetic materials

Pretty vacancy: The formation energy of Al vacancies in aluminum nitride is decreased by doping with nonmagnetic scandium ions. These vacancies are shown to be the cause of the room-temperature ferromagnetism in the resulting 1D hexagonal nanoprisms of AlN:Sc, a result that is confirmed by first-principles calculations. The doping approach provides a new route to dilute magnetic semiconductor materials.

Robust modular artmap for multi-class shape recognition

This paper presents a fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as modular adaptive resonance theory map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness of the proposed architecture is analyzed and compared with ARTMAP-FD network, FAM network, and One-Against-One Support Vector Machine (OAO-SVM). Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts.

Profiles of language development in pre-school children : a longitudinal latent class analysis of data from the early language in Victoria study

Background Pre-school language impairment is common and greatly reduces educational performance. Population attempts to identify children who would benefit from appropriately timed intervention might be improved by greater knowledge about the typical profiles of language development. Specifically, this could be used to help with the early identification of children who will be impaired on school entry.

Methods This study applied longitudinal latent class analysis to assessments at 8, 12, 24, 36 and 48 months on 1113 children from a population-based study, in order to identify classes exhibiting distinct communicative developmental profiles.

Results Five substantive classes were identified: Typical, i.e. development in the typical range at each age; Precocious (late), i.e. typical development in infancy followed by high probabilities of precocity from 24 months onwards; Impaired (early), i.e. high probabilities of impairment up to 12 months followed by typical language development thereafter; Impaired (late), i.e. typical development in infancy but impairment from 24 months onwards; Precocious (early), i.e. high probabilities of precocity in early life followed by typical language by 48 months. The entropy statistic (0.84) suggested classes were fairly well defined, although there was a non-trivial degree of uncertainty in classification of children. That half of the Impaired (late) class was expected to have typical language at 4 years and 6% of the numerically large Typical class was expected to be impaired at 4 years illustrates this. Characteristics indicative of social advantage were more commonly found in the classes with improving profiles.

Conclusions Developmental profiles show that some pre-schoolers' language is characterized by periods of accelerated development, slow development and catch-up growth. Given the uncertainty in classifying children into these profiles, use of this knowledge for identifying children who will be impaired on school entry is not straightforward. The findings do, however, indicate greater need for language enrichment programmes among disadvantaged children.

A comparative study of the effect of web-based versus in-class textbook ethics instruction on accounting students' propensity to whistle-blow

The authors examined whether accounting students’ propensity to whistle-blow differed between those instructed through a web-based teaching module and those exposed to a traditional in-class textbook-focused approach. A total of 156 students from a second-year financial accounting course participated in the study. Ninety students utilized the web-based module whereas 66 students were instructed through a traditional teaching approach based on ethical problems presented in the textbook. Subsequently, when presented with a whistle-blowing situation, it was found that students exposed to a web-based ethics instruction module were more likely to whistle-blow than those students exposed to a traditional in-class textbook ethics instruction approach.

A serverless ultra-lightweight secure search protocol for EPC Class-1 Gen-2 UHF RFID tags

Radio Frequency Identification (RFID) is a technology that enables the non-contact, automatic and unique identification of objects using radio waves. Its use for commercial applications has recently become attractive with RFID technology seen as the replacement for the optical barcode system that is currently in widespread use. RFID has many advantages over the traditional barcode and these advantages have the potential to significantly increase the efficiency of decentralised business environments such as logistics and supply chain management. One of the important features of an RFID system is its ability to search for a particular tag among a group of tags. In order to ensure the privacy and security of the tags, the search has to be conducted in a secure fashion. To our knowledge not much work has been done in this secure search area of RFID. The limited work that has been done does not comply with the EPC Class-1 Gen-2 standards since most of them use expensive hash operations or sophisticated encryption schemes that cannot be implemented on low-cost passive tags that are highly resource constrained. Our work aims to fill this gap by proposing a serverless ultra-lightweight secure search protocol that does not use the expensive hash functions or any complex encryption schemes but achieves compliance with EPC Class-1 Gen-2 standards while meeting the required security requirements. Our protocol is based on XOR encryption and random numbers - operations that are easily implemented on low-cost RFID tags. Our protocol also provides additional protection using a blind-factor to prevent tracking attacks. Since our protocol is EPC Class-1 Gen-2 compliant it makes it possible to implement it on low-cost passive RFID tags.

Isolation and characterisation via 454 sequencing of microsatellites from the tawny frogmouth, Podargus strigoides (Class Aves, Family Podargidae)

We isolated 24 novel polymorphic microsatellite markers from the tawny frogmouth, a nocturnal bird endemic to Australia, which has successfully adapted to urban environments. Initially, 454 shotgun sequencing was used to identify 733 loci with primers designed. Of these, we trialled 30 in the target species of which all amplified a product of expected size. Subsequently, all 30 of these loci were screened for variation in 25 individuals, from a single population in Melbourne, Victoria, Australia. Twenty-eight loci were polymorphic with observed heterozygosity ranging from 0.03 to 0.96 (mean 0.58) and the number of alleles per locus ranged from 2 to 18 (average of 6.5); we confirmed that 24 loci conformed to Hardy–Weinberg expectations. The 24 loci identified here will be sufficient to unequivocally identify individuals and will be useful in understanding the reproductive ecology, population genetics and the gene flow amongst localities in urban environments where this bird thrives.

Adaptive fast finite-time multiple-surface sliding control for a class of uncertain non-linear systems

This paper concerns the adaptive fast finite-time multiple-surface sliding control (AFFTMSSC) problem for a class of high-order uncertain non-linear systems of which the upper bounds of the system uncertainties are unknown. By using the fast control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite-time stability of the closed-loop system. Further, it is proved that the control input is bounded.