Computer Science

In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-Intron (5' and 3') splice sites. We present the use of Markov based statistical methods, in a log likelihood discriminator framework, to create a non-summed, fixed-length, feature vector for SVM-based classification. We also explore the use of Shannon-entropy based analysis for automated identification of minimal-size models (where smaller models have known information loss according to the specified Shannon entropy representation). We evaluate a variety of kernels and kernel parameters in the classification effort. We present results of the algorithms for splice-site datasets consisting of sequences from a variety of species for comparison.

A mini-talk talking (in general) about how avenues of digital forensics or computer security research can have unanticipated legal problems, and (in specific example) how the recent lawsuit involving typosquatting to intercept emails illustrates this concept.

This was a department talk at the IA center examining the legal charges against Aaron Swartz and what technical issues could be discerned based on how the legal text was phrased. It also includes some timeline information on the incident. I originally did not intend to post these slides given the talk was less research oriented and more current events, but given the passing of Aaron Swartz, I thought it appropriate to do so for anyone looking for more information.

In this paper, we employ Extended Cognition as a background for a series of thought experiments about privacy and common used information technology devices. Laptops and smart phones are now widely used devices, but current privacy standards do not adequately address the relationship between the owners of these devices and the information stored on them. Law enforcement treats laptops and smart phones are potential sources of information about criminal activity, but this treatment ignores the use of smart devices as extensions of users’ cognitive capability. In Philosophy of Mind, Extended Cognition is a metaphysical theory about the relationship between consciousness or cognitive activity and various external tools or aids that agents employ in the service of cognition. Supporters of Extended Cognition argue that mental activity must be understood as taking place both within the brain and by way of tools such as a logician’s pen and paper, a mathematician’s calculator, or a writer’s word processing program. While Extended Cognition does not have universal support among philosophers of mind, the theory nevertheless describes how agents interact with their “smart devices.” We explore the the implications of taking Extended Cognition seriously with regard to privacy concerns by way of a series of thought experiments. By comparing the differences in expectations of privacy between a citizen and the government, between an employee of a corporate firm, and between citizens alone, we show that expectations of privacy and injury are significantly affected by taking the cognitive role of smart devices into account.

This paper presents a new model and solution for the multi-objective Vehicle Routing Problem with Soft Time Windows (VRPSTW) using a hybrid metaheuristic technique. The proposed methodology is developed on the basics of a new swarm based Artificial Bee Colony (ABC) algorithm combined with twostep constrained local search for neighborhood selection. VRPSTW involves computing the routes of a set of vehicles with fixed capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. Here, the time window constraints are relaxed into "soft", that is penalty terms are added to the solution cost whenever a vehicle serves a customer outside of his time window. The solution of routing problems with soft time windows has valuable practical applications. This paper uses a direct interpretation of the VRPSTW as a multi-objective optimization problem where the total traveling distance, number of window violations and number of required vehicles are minimized while capacity and time window constraints are met. Our work aims at using ABC inspired foraging behavior of honey bees which balances exploration and exploitation to avoid local optima and reach the global optima. The algorithm is applied to solve the well known benchmark Solomon's problem instances. Experimental results show that our suggested approach is quite effective, as it provides solutions that are competitive with the best known results in the literature. Finally, we present an analysis of our proposed algorithm in terms of computational time.

The longest common subsequence (LCS) problem is a classic and well-studied problem in computer science. Palindrome is a word which reads the same forward as it does backward. The longest common palindromic subsequence (LCPS) problem is an interesting variant of the classic LCS problem which finds the longest common subsequence between two given strings such that the computed subsequence is also a palindrome. In this paper, we study the LCPS problem and give efficient algorithms to solve this problem. To the best of our knowledge, this is the first attempt to study and solve this interesting problem.

The Vehicle Routing Problem with Time Windows (VRPTW) is to serve a set of customer demands with time constraints and vehicle with limited capacity. This has practical applications in container truck routing, delivery service scheduling, garbage collection, fleet network design etc. In this paper, we consider one of its variants where the time constraint is 'soft', that is it can be violated with a penalty cost. In this paper, we present the Artificial Bee Colony (ABC) metaheuristic based approach for solving the VRPTW problem with soft timing constraint. We have shown our experimental results for different parameters, compared with previous results and shown that our algorithm gives better result for many instances of the problem.

and Intelligent Transportation Systems (ITS) are one of the most attractive and challenging topics in recent days since a well efficient protocol for vehicular communication can facilitate the reduction of traffic congestion and can provide us with many more promising applications. In this paper, we propose a protocol for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. As one of the challenging parts of this paper, we present an experimental testbed in which two major applications of V2I & V2V communication (i.e. traffic congestion detection and emergency warning) is implemented. Based on careful analysis, we also calculate some key system parameters which reflect the efficiency of the protocol in different applications.

Intrinsically disorder regions (IDRs) or, proteins (IDPs) are associated with important biological functions, while lacking stable structure in their native state. The phenomena of disordered proteins or residues are abundant in nature and are extensively involved in critical human diseases and hence impacting drug discovery. Thus, the study using disorder prediction is becoming crucial in the proteomic research. The large scale growth of genome database demands high performance computational methods for identification of protein disorder. We developed a canonical support vector machine based disorder predictor, DisPredict by integrating RBF kernel. It employs novel feature set for accurate characterization of disorder which outperformed two leading predictors: the neural network based SPINE-D and Meta predictor MFDp based on ten-fold cross validation. We propose a post processing of probabilities to further improve the accuracy, named DisPredict1.1 which yields outstanding performance further both in binary annotation and real valued probability prediction per residue in both short and long disordered regions. It provides highest Mathews Correlation Coefficient (MCC), competitive Area Under receiver operating characteristic Curve (AUC) and lowest Mean Absolute Error (MAE) when compared with twenty existing predictors of several kinds on independent benchmark dataset. DisPredict is available online.

Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution.

This paper describes a detailed investigation of a lattice-based HP (hydrophobic-hydrophilic) model for ab initio protein structure prediction (PSP). The outcome of the simplified HP lattice model has high degeneracy, which could mislead the prediction. The HPNX model was proposed to address the degeneracy problem as well as to avoid the conformational deformity with the hydrophilic (P) residues. We have experimentally shown that it is necessary to further improve the existing HPNX model. We have found and solved the critical error of another existing YhHX model. By extracting the significant features from the YhHX for the HPNX model, we have proposed a novel hHPNX model. Hybrid Genetic Algorithm (HGA) has been used to compare the predictability of these models and hHPNX outperformed other models. We preferred 3D face-centered-cube (FCC) lattice configuration to have closest resemblance to the real folded 3D protein.

Genetic Algorithms (GAs), as nondeterministic conformational search techniques, are promising for solving protein structure prediction (PSP) problems. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations. However, as the optimum PSP conformation is usually compact, the crossover operation may result in many invalid conformations (by having non-self-avoiding-walk). Although a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially reveal potential pathways and make an invalid crossover valid and successful. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA applications. The random-move-only-based conformation generator has exponential time complexity in generating random conformations, whereas the DFS-based random conformation generator has linear time complexity and performs relatively faster. We have performed extensive experiments using popular 2D, as well as useful 3D, models to justify our hypothesis empirically.

Proteins carry out the majority of functionality on a cellular level. Computational protein structure prediction (PSP) methods have been introduced to speed up the PSP process due to manual methods, like nuclear magnetic resonance (NMR) and x-ray crystallography (XC) taking numerous months even years to produce a predicted structure for a target protein.

Genetic Algorithms (GAs), as nondeterministic conformational search techniques, are promising for solving protein structure prediction (PSP) problems. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations. However, as the optimum PSP conformation is usually compact, the crossover operation may result in many invalid conformations (by having non-self-avoiding-walk). Although a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially reveal potential pathways and make an invalid crossover valid and successful. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA applications. Random-move-only-based conformation generator has exponential time complexity in generating random conformations, whereas the DFS based random conformation generator has linear time complexity and performs relatively faster. We have done extensive experiments using popular 2D as well as useful 3D models to justify our hypothesis empirically.

Traditional encodings for Hydrophobic (H) -Hydrophilic (P) model or HP lattice models is isomorphic, which adds unwanted variations for the same solution, thereby slowing convergence. In this paper a novel non-isomorphic encoding scheme is presented for HP lattice model, which constrains the search space. In addition, similarity comparisons are made easier and more consistent and it will be shown that non-deterministic search approach such as Genetic Algorithm (GA) converges faster when non-isomorphic encoding is employed. Fig. 1. Conformation in 2D HP Model shown by solid line. Dotted line indicates TN. Fitness = -(TN Count) = -9.

Protein Folding Prediction (PFP) is essentially an energy minimization problem formalised by the definition of a fitness function. Several PFP models have been proposed including the Hydrophobic-Hydrophilic (HP) model, which is widely used as a test-bed for evaluating new algorithms. The calculation of the fitness is the major computational task in determining the native conformation of a protein in the