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.

Web page clustering : a hyperlink-based similarity and matrix-based hierarchical algorithms

This paper proposes a hyperlink-based web page similarity measurement and two matrix-based hierarchical web page clustering algorithms. The web page similarity measurement incorporates hyperlink transitivity and page importance within the concerned web page space. One clustering algorithm takes cluster overlapping into account, another one does not. These algorithxms do not require predefined similarity thresholds for clustering, and are independent of the page order. The primary evaluations show the effectiveness of the proposed algorithms in clustering improvement.

Similarity solutions of partial differential equations using DESOLV

We present and describe new reduction routines included in DESOLV which, in many cases, may allow the complete automation of the determination of similarity solutions of partial differential equations.

Technology and teachers in rural schools : diversity and similarity

This paper reports on a project situated in regional areas of Victoria in which 16 primary and secondary teachers participated in an intensive professional development program designed to assist them in embedding Information Communications Technology (ICT) into their classroom practice. The project provides some insight into the availability and use of current technological resources in the rural schools and examines the impact of an intensive professional development program on the implementation of ICT into the curriculum.

The results identified a large diversity of circumstances experienced by the schools in the project, not only in terms of ICT availability and use, and teacher experience, but also in more general issues of cultures of curriculum planning and integration, size, communication, and pedagogical presumptions. The successful integration of ICT into their pedagogical practice was influenced by a complex of factors including the availability of ICT resources, the teachers’ ICT skill level, the teachers’ ability and opportunity to integrate ICT in classroom, the level of support provided, both technical and pedagogical, and the curriculum requirements.

The results of the project have been positive with evidence of increased networking among the teachers, changes in teaching practice and increased teacher proficiency and awareness of ICT resources. The project has highlighted common difficulties that teachers experienced including frustrations with the unreliability of technology and a lack of time for necessary training and preparation. In response to the constraints, teachers have been resourceful and inventive in developing pedagogical strategies to aid the integration of ICT into their classroom practice.

Similarity solution of axisymmetric flow in porous media

Applications of the axisymmetric Boussinesq equation to groundwater hydrology and reservoir engineering have long been recognised. An archetypal example is invasion by drilling fluid into a permeable bed where there is initially no such fluid present, a circumstance of some importance in the oil industry. It is well known that the governing Boussinesq model can be reduced to a nonlinear ordinary differential equation using a similarity variable, a transformation that is valid for a certain time-dependent flux at the origin. Here, a new analytical approximation is obtained for this case. The new solution,, which has a simple form, is demonstrated to be highly accurate.

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.

Video indexing and similarity retrieval by largest common subgraph detection using decision trees

While the largest common subgraph (LCSG) between a query and a database of models can provide an elegant and intuitive measure of similarity for many applications, it is computationally expensive to compute. Recently developed algorithms for subgraph isomorphism detection take advantage of prior knowledge of a database of models to improve the speed of on-line matching. This paper presents a new algorithm based on similar principles to solve the largest common subgraph problem. The new algorithm significantly reduces the computational complexity of detection of the LCSG between a known database of models, and a query given on-line.

Minimal similarity in songs suggests limited exchange between humpback whales (Megaptera novaeangliae) in the southern Indian ocean

Comparing humpback whale song from different breeding assemblages can reveal similarities in song due to acoustically interacting males, and therefore indirectly test whether males from different breeding sites are mixing. Northern Hemisphere song comparisons illustrated that whales within ocean basins share similar songs and are subpopulations within a larger population, whereas whales in different ocean basins are isolated populations and therefore do not share songs. During the 2006 breeding season, recordings were collected in Madagascar and Western Australia, and were compared visually plus aurally. Both regions shared one theme, whereas each region had four and six private themes, respectively. This study had a substantially low number of shared themes. The co-occurrence of one theme was interpreted as an indication of limited exchange between these breeding assemblages, and we speculate that limited song similarity is due to inter-oceanic interactions. Male(s) from an Indian Ocean breeding group could be exposed to novel song when they geographically overlap, and acoustically interact, with males from a different ocean basin. Novel song could induce rapid temporal changes as new song content is incorporated, thereby minimizing song similarities between that breeding group and other Indian Ocean breeding groups that were not exposed to the novel song.

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.

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.