A similarity search algorithm to predict protein structures

Accurate prediction of protein structures is very important for many applications such as drug discovery and biotechnology. Building side chains is an essential to get any reliable prediction of the protein structure for any given a protein main chain conformation. Most of the methods that predict side chain conformations use statistically generated data from known protein structures. It is a computationally intractable problem to search suitable side chains from all possible rotamers simultaneously using information of known protein structures. Reducing the number of possibility is a main issue to predict side chain conformation. This paper proposes an enumeration based similarity search algorithm to predict side chain conformations. By introducing “beam search” technique, a significant number of unrelated side chain rotamers can easily be eliminated. As a result, we can search for suitable residue side chains from all possible side chain conformations.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

EST-PAC HPC - a web portal for high-throughput EST annotation and protein sequence prediction

Expressed Sequence Tags (ESTs) are short DNA sequences generated by sequencing the transcribed cDNAs coming from a gene expression. They can provide significant functional, structural and evolutionary information and thus are a primary resource for gene discovery. EST annotation basically refers to the analysis of unknown ESTs that can be performed by database similarity search for possible identities and database search for functional prediction of translation products. Such kind of annotation typically consists of a series of repetitive tasks which should be automated, and be customizable and amenable to using distributed computing resources. Furthermore, processing of EST data should be done efficiently using a high performance computing platform. In this paper, we describe an EST annotator, EST-PACHPC, which has been developed for harnessing HPC resources potentially from Grid and Cloud systems for high throughput EST annotations. The performance analysis of EST-PACHPC has shown that it provides substantial performance gain in EST annotation.

Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.

Improving P2P IPTV random peers search through user similarity

With internet services to the end users becoming more homogenous, thus providing high bandwidth for all users, multimedia services such as IPTV to the public as a whole will finally become a reality, but even given the more abundant resources, IPTV architecture is far from being highly available due to technical limitations, we aim to provide a meaningful optimization in the P2P distribution model, which is currently based on a random structure bounded by high delays and low performance, by using channel probability, user's habits studies and users' similarity, in order to optimize one of the key aspects of IPTV which is the peers management, which directly reflects on resources and user's Quality of Experience.

Exploiting monge structures in optimum subwindow search

Optimum subwindow search for object detection aims to find a subwindow so that the contained subimage is most similar to the query object. This problem can be formulated as a four dimensional (4D) maximum entry search problem wherein each entry corresponds to the quality score of the subimage contained in a subwindow. For n x n images, a naive exhaustive search requires O(n⁴) sequential computations of the quality scores for all subwindows. To reduce the time complexity, we prove that, for some typical similarity functions like Euclidian metric, χ² metric on image histograms, the associated 4D array carries some Monge structures and we utilise these properties to speed up the optimum subwindow search and the time complexity is reduced to O(n³). Furthermore, we propose a locally optimal alternating column and row search method with typical quadratic time complexity O(n²). Experiments on PASCAL VOC 2006 demonstrate that the alternating method is significantly faster than the well known efficient subwindow search (ESS) method whilst the performance loss due to local maxima problem is negligible.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

A new framework with similarity reasoning and monotone fuzzy rule relabeling for fuzzy inference systems

A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of a Fuzzy Inference System (FIS). In this paper, a new monotone fuzzy rule relabeling technique to relabel a non-monotone fuzzy rule base provided by domain experts is proposed. Even though the Genetic Algorithm (GA)-based monotone fuzzy rule relabeling technique has been investigated in our previous work [7], the optimality of the approach could not be guaranteed. The new fuzzy rule relabeling technique adopts a simple brute force search, and it can produce an optimal result. We also formulate a new two-stage framework that encompasses a GA-based rule selection scheme, the optimization based-Similarity Reasoning (SR) scheme, and the proposed monotone fuzzy rule relabeling technique for preserving the monotonicity property of the FIS model. Applicability of the two-stage framework to a real world problem, i.e., failure mode and effect analysis, is further demonstrated. The results clearly demonstrate the usefulness of the proposed framework.

Learning preferences of TAFE and university students

Using Entwistle and Ramsden's (1983) Approaches to Studying Inventory, Fuller and Chalmers showed little difference between TAFE students and university students both in the factor structures derived through factor analysis, and in a comparison of individual subscale through analysis of variance. Fuller and Chalmers had hypothesised that differences in approaches to study may be identified as a result of the different learning and teaching contexts experienced by the two groups of students. The current study has examined the similarities and differences between students in the two sectors using a learning preferences inventory. Learning preferences were chosen for investigation on the basis of previous theoretical work which suggests that it is preferences that are most influenced by environmental factors. The present study has shown significant similarity between the learning preferences of the two groups of students. However, there were also significant differences which indicate that the Fuller and Chalmers hypothesised differences may be more observable at a preferences level than at an approaches to study level.

In search of peaceful playgrounds

Recess is often thought of as a time to escape from the classroom, from work and from the constant gaze of the teacher. It was a peaceful and joyous time when most children occupied themselves in active play and the odd incident was quickly resolved with the help of the teacher on 'yard duty'. Not so any more. The playground is now a 'problem' in many schools largely because of the bullying behaviour occurring there. This paper discusses the relative merits of the strategies schools are implementing in an effort to deal with the bullying behaviour and suggests that more thought might be given to improving the playground environment itself as a way of addressing the problems.

Adolescent parenthood : a costly mistake or a search for love?

Family life is changing worldwide and an increasing number of women are choosing single parenthood. Adolescents who become pregnant and early childbearers do not always become pregnant unintentionally; some actively plan pregnancy while others are ambivalent mainly about the timing. This paper reports on a study using an ethnographic approach that explored the mothering experiences of five sole-supporting Australian teenage mothers who had a child over six months of age. It focuses on the story of one of them, a young woman who gave birth at 16 and set up home for herself and her son. Early childbearing is often a response to adverse social conditions such as poverty or homelessness and is not uncommonly chosen by teenage girls from socially deprived backgrounds. Educational and employment opportunities may be limited, whilst motherhood may provide a purpose in life when few other options are possible. Young women who make this choice need comprehensive services to support them in the parenting role, including appropriate health care, welfare and housing benefits, and support in dealing with parenting, a role which they may greatly desire but are not automatically well prepared for.

Visual perception of movement kinematics and the acquisition of "action prototypes"

Recognizing a class of movements as belonging to a "nominal" action category, such as walking, running, or throwing, is a fundamental human ability. Three experiments were undertaken to test the hypothesis that common ("prototypical") features of moving displays could be learned by observation. Participants viewed moving stick-figure displays resembling forearm flexion movements in the saggital plane. Four displays (presentation displays) were first presented in which one or more movement dimensions were combined with 2 respective cues: direction (up, down), speed (fast, slow), and extent (long, short). Eight test displays were then shown, and the observer indicated whether each test display was like or unlike those previously seen. The results showed that without corrective feedback, a single cue (e.g., up or down) could be correctly recognized, on average, with the proportion correct between .66 and .87. When two cues were manipulated (e.g., up and slow), recognition accuracy remained high, ranging between .72 and .89. Three-cue displays were also easily identified. These results provide the first empirical demonstration of action-prototype learning for categories of human action and show how apparently complex kinematic patterns can be categorized in terms of common features or cues. It was also shown that probability of correct recognition of kinematic properties was reduced when the set of 4 presentation displays were more variable with respect to their shared kinematic property, such as speed or amplitude. Finally, while not conclusive, the results (from 2 of the 3 experiments) did suggest that similarity (or "likeness") with respect to a common kinematic property (or properties) is more easily recognized than dissimilarity.

Black and white is always grey-the power of the police to conduct a strip search in Victoria

The performance of a strip search by a police officer is a serious interference with the liberty and dignity of an individual. However, it is considered by police to be an important part of their law enforcement armory and one that is increasingly necessary to utilise to assist in the investigation and prosecution of drug-related crimes. This article considers the troublesome issue of whether and in what circumstances the common law may extend to police the power to conduct a strip search. In addition, there is an examination of the statutes and regulations that purportedly give police in Victoria the power to strip search with particular attention given to ss 81 and 82 of the Drugs, Poisons and Controlled Substances Act 1981 (Vic).

A grid-based index method for time warping distance

Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree structures are quite weak, resulting in inefficient search. In this paper, we propose a dimensionality reduction method motivated by observations about the inherent character of each time series. A very compact index file is constructed. By scanning the index file, we can get a very small candidate set, so that the number of page access is dramatically reduced. We demonstrate the effectiveness of our approach on real and synthetic datasets.