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"abstract": "Although a graph can be a useful device for visualizing complex relationships, drawings of large graphs can be difficult to comprehend. As one remedy, we formulated a novel generalized approach for generating fisheye views of nested graphs with multiple variable focal points, and devised an algorithm that creates fisheye views in the absence of application specific distance metrics. Previous solutions produced fisheye views by filtering or distorting drawings of graphs. Since these approaches relied on geometric notions of distance, they could only be applied effectively in limited cases. By contrast, our approach treats fisheye view generation as a phase that precedes graph layout, rather than as a technique that alters an existing drawing, and does not suffer these drawbacks.<>",
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"abstract": "Understanding the eye and body movements required to perform large vehicle assembly tasks is important for developing a mobile support system for mechanics, and tracking user movements with regard to the surrounding environment is critical for designing a wearable Augmented Reality (AR) systems. This poster summarizes a study measuring the eye and body movements of mechanics performing assembly activities in a live manufacturing environment. It reviews our quantitative analysis of eye movements and qualitative analysis of body movements and describes the implications of this data in terms of the feasibility and potential utility of using a mobile AR application to support manufacturing. We found that the mechanics' eye movements ranged over a slightly larger field than the eye movements reported in previous research because of constraints imposed by some body positions required in manufacturing tasks.",
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"abstract": "Augmented Reality is the combination of virtual scene generated from computer with the user's real world. Nowadays, many applications apply augmented reality technology in defense sector, traffic management, which uses real time system, where time is an important factor. In this paper, an efficient augmented reality system is suggested to compute the response time of machine to detect the marker. Luminous affecting computing of glyph recognition and detection of glyph by marker at different challenging distance (feet) has been processed. Glyph is created for augmented process through image processing and glyph matrix representation has been structured. Here, Posit algorithm is used to detect the position and orientation of marker with respect to camera. A prototype structure is created to compute the detection response time of machine under different challenging conditions. Our experimental result shows minimum computation Detection time is 5400 millisecond at lux 13490 (ambient light) and maximum computation detection time is 12000 millisecond at Lux 10.3 (no tube-light) to detect the fiducial marker.",
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"content": "Augmented Reality is the combination of virtual scene generated from computer with the user's real world. Nowadays, many applications apply augmented reality technology in defense sector, traffic management, which uses real time system, where time is an important factor. In this paper, an efficient augmented reality system is suggested to compute the response time of machine to detect the marker. Luminous affecting computing of glyph recognition and detection of glyph by marker at different challenging distance (feet) has been processed. Glyph is created for augmented process through image processing and glyph matrix representation has been structured. Here, Posit algorithm is used to detect the position and orientation of marker with respect to camera. A prototype structure is created to compute the detection response time of machine under different challenging conditions. Our experimental result shows minimum computation Detection time is 5400 millisecond at lux 13490 (ambient light) and maximum computation detection time is 12000 millisecond at Lux 10.3 (no tube-light) to detect the fiducial marker.",
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"affiliation": "Department of Computer Science, Manipal University, Jaipur, India",
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"affiliation": "Department of Computer Science, Manipal University, Jaipur, India",
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"abstract": "Aiming at the problem that the fuzzy neural network (FNN) technique itself does not provide the input matrix to the FNN prediction model, we present a prediction modeling methodology which combines the computation and analysis of condition number with FNN, and design the computation and analysis of analog deviation for the input matrix to choose samples close correlated with predictand as training samples, thus effectively reducing the scale of network and evidently enhancing the prediction ability of the FNN prediction model. Using the same CMA T213 and Japanese numerical prediction product (NPP) data, we performed the contrast experiments and analyses of the FNN prediction model for daily regional mean precipitation based on condition number and analog deviation against the condition number-FNN prediction model and the traditional stepwise regression prediction model, and results show that under the condition of the same number of selected predictors, the prediction accuracy of the FNN prediction model based on condition number and analog deviation is 12.6% higher than that of the stepwise regression model in the experiment of independent samples of 49 days.",
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"affiliation": "Guangxi Research Institute of Meteorological Disasters Mitigation, China",
"fullName": "Long Jin",
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"abstract": "A mathematical data model for scientific visualization that is based on the mathematics of fiber bundles is presented. Previous results are extended to the case of piecewise field representations (associated with grid-based data representations), and a general mathematical model for piecewise representations of fields on irregular grids is presented. The various types of regularity that can be found in computational grids and techniques for compact field representation based on each form of regularity are discussed. These techniques can be combined to obtain efficient methods for representing fields on grids with various regular or partially regular structures.<>",
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"content": "Driven by economic benefits, the number of malware attacks is increasing significantly on a daily basis. Malware Detection Systems (MDS) is the first line of defense against malicious attacks, thus it is important for malware detection systems to accurately and efficiently detect malware. Traditional MDS typically utilizes traditional machine learning algorithms that require feature selection and extraction, which are time-consuming and error-prone. Conventional deep learning based approaches typically use Recurrent Neural Network (RNN) which can be vulnerable to redundant API injection. Thus, we investigate the effectiveness of Convolutional Neural Networks (CNN) against redundant API injection. We designed a malware detection system that transforms malware files into image representations and classifies the image representation with CNN. The CNN is implemented with spatial pyramid pooling layers (SPP) to deal with varying size input. We evaluate the effectiveness of SPP and image color space (greyscale/RGB) by measuring the performance of our system on both unaltered data and adversarial data with redundant API injected. Results show that naive SPP implementation is impractical due to memory constraints and greyscale imaging is effective against redundant API injection.",
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"affiliation": "University of Canterbury",
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"affiliation": "University of Queensland",
"fullName": "Dong-Seong Kim",
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"abstract": "We recognize actions and activities in video sequences as distinguishing patterns in the 3D spatiotemporal volume of motion energy. Local motion descriptors, which capture highly discriminative invariant motion characteristics in a spherical neighborhood, are computed in the 3D volume at points of salient motion to represent actions or activities in video sequences. Two actions are then matched based on the similarity between their representing motion descriptors. Our action recognition algorithm using the new motion descriptors has achieved an accuracy rate of 98.6% on the Weizmann action dataset.",
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"fullName": "Yu Zhong",
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"abstract": "In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency based pruning and the supersequence frequency based pruning are applied. In MSPS, sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better performance. This method makes sampling more efficient when the minimum support is small. A signature technique is utilized for the subsequence infrequency based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms.",
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"content": "In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency based pruning and the supersequence frequency based pruning are applied. In MSPS, sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better performance. This method makes sampling more efficient when the minimum support is small. A signature technique is utilized for the subsequence infrequency based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms.",
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"affiliation": "Wright State University",
"fullName": "Congnan Luo",
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"affiliation": "Wright State University",
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"abstract": "A new representation of dynamic scene objects to facilitate the compression and indexing of image sequences is presented. Exploiting the spatiotemporal (ST) piecewise-continuity of visual cues, the dynamic scene objects are represented as a collection of motion discontinuities. Each discontinuity signals either the start of a different motion or the occurrence of occlusion. It is shown that the sign of the Gaussian and mean curvature of the ST surfaces is sufficient for qualitatively representing the motion discontinuities. An estimation technique is developed to compute the sign of surface curvature from ST volume data, and the transformation rules that relate the image motion to the type of surface patch are described.<>",
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"abstract": "We give efficient algorithms for volume sampling, i.e., for picking Z_$k$_Z-subsets of the rows of any given matrix with probabilities proportional to the squared volumes of the simplices defined by them and the origin (or the squared volumes of the parallelepipeds defined by these subsets of rows). %In other words, we can efficiently sample Z_$k$_Z-subsets of Z_$[m]$_Z with probabilities proportional to the corresponding Z_$k$_Z by Z_$k$_Z principal minors of any given Z_$m$_Z by Z_$m$_Z positive semi definite matrix. This solves an open problem from the monograph on spectral algorithms by Kannan and Vempala (see Section Z_$7.4$_Z of \\cite{KV}, also implicit in \\cite{BDM, DRVW}). Our first algorithm for volume sampling Z_$k$_Z-subsets of rows from an Z_$m$_Z-by-Z_$n$_Z matrix runs in Z_$O(kmn^\\omega \\log n)$_Z arithmetic operations (where Z_$\\omega$_Z is the exponent of matrix multiplication) and a second variant of it for Z_$(1+\\eps)$_Z-approximate volume sampling runs in Z_$O(mn \\log m \\cdot k^{2}/\\eps^{2} + m \\log^{\\omega} m \\cdot k^{2\\omega+1}/\\eps^{2\\omega} \\cdot \\log(k \\eps^{-1} \\log m))$_Z arithmetic operations, which is almost linear in the size of the input (i.e., the number of entries) for small Z_$k$_Z. Our efficient volume sampling algorithms imply the following results for low-rank matrix approximation: (1) Given Z_$A \\in \\reals^{m \\times n}$_Z, in Z_$O(kmn^{\\omega} \\log n)$_Z arithmetic operations we can find Z_$k$_Z of its rows such that projecting onto their span gives a Z_$\\sqrt{k+1}$_Z-approximation to the matrix of rank Z_$k$_Z closest to Z_$A$_Z under the Frobenius norm. This improves the Z_$O(k \\sqrt{\\log k})$_Z-approximation of Boutsidis, Drineas and Mahoney \\cite{BDM} and matches the lower bound shown in \\cite{DRVW}. The method of conditional expectations gives a \\emph{deterministic} algorithm with the same complexity. The running time can be improved to Z_$O(mn \\log m \\cdot k^{2}/\\eps^{2} + m \\log^{\\omega} m \\cdot k^{2\\omega+1}/\\eps^{2\\omega} \\cdot \\log(k \\eps^{-1} \\log m))$_Z at the cost of losing an extra Z_$(1+\\eps)$_Z in the approximation factor. (2) The same rows and projection as in the previous point give a Z_$\\sqrt{(k+1)(n-k)}$_Z-approximation to the matrix of rank Z_$k$_Z closest to Z_$A$_Z under the spectral norm. In this paper, we show an almost matching lower bound of Z_$\\sqrt{n}$_Z, even for Z_$k=1$_Z.",
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"content": "We give efficient algorithms for volume sampling, i.e., for picking $k$-subsets of the rows of any given matrix with probabilities proportional to the squared volumes of the simplices defined by them and the origin (or the squared volumes of the parallelepipeds defined by these subsets of rows). %In other words, we can efficiently sample $k$-subsets of $[m]$ with probabilities proportional to the corresponding $k$ by $k$ principal minors of any given $m$ by $m$ positive semi definite matrix. This solves an open problem from the monograph on spectral algorithms by Kannan and Vempala (see Section $7.4$ of \\cite{KV}, also implicit in \\cite{BDM, DRVW}). Our first algorithm for volume sampling $k$-subsets of rows from an $m$-by-$n$ matrix runs in $O(kmn^\\omega \\log n)$ arithmetic operations (where $\\omega$ is the exponent of matrix multiplication) and a second variant of it for $(1+\\eps)$-approximate volume sampling runs in $O(mn \\log m \\cdot k^{2}/\\eps^{2} + m \\log^{\\omega} m \\cdot k^{2\\omega+1}/\\eps^{2\\omega} \\cdot \\log(k \\eps^{-1} \\log m))$ arithmetic operations, which is almost linear in the size of the input (i.e., the number of entries) for small $k$. Our efficient volume sampling algorithms imply the following results for low-rank matrix approximation: (1) Given $A \\in \\reals^{m \\times n}$, in $O(kmn^{\\omega} \\log n)$ arithmetic operations we can find $k$ of its rows such that projecting onto their span gives a $\\sqrt{k+1}$-approximation to the matrix of rank $k$ closest to $A$ under the Frobenius norm. This improves the $O(k \\sqrt{\\log k})$-approximation of Boutsidis, Drineas and Mahoney \\cite{BDM} and matches the lower bound shown in \\cite{DRVW}. The method of conditional expectations gives a \\emph{deterministic} algorithm with the same complexity. The running time can be improved to $O(mn \\log m \\cdot k^{2}/\\eps^{2} + m \\log^{\\omega} m \\cdot k^{2\\omega+1}/\\eps^{2\\omega} \\cdot \\log(k \\eps^{-1} \\log m))$ at the cost of losing an extra $(1+\\eps)$ in the approximation factor. (2) The same rows and projection as in the previous point give a $\\sqrt{(k+1)(n-k)}$-approximation to the matrix of rank $k$ closest to $A$ under the spectral norm. In this paper, we show an almost matching lower bound of $\\sqrt{n}$, even for $k=1$.",
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"abstract": "Alignment-free sequence comparison methods can compute the similarity between a large number of sequences much faster than methods that depend on sequence alignment. We propose a new alignment-free sequence comparison method, called K2, based on the non-parametric Kendall statistic. Compared with the state-of-the-art alignment-free comparison methods (e.g., D2, D2*, D2sh, and Chisquare(χ2) statistic), K2 showed comparative power, demonstrating similar or better performance in computing the edit distance (similarity/dissimilarity) among a huge number of sequences. The K2 approach was much faster than each of the other methods, especialy, with long sequence lengths.",
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"abstract": "The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important, the challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as multi-domain, circular-permutation and tandem-repeats protein sequences, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-independent algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families.",
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"content": "The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important, the challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as multi-domain, circular-permutation and tandem-repeats protein sequences, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-independent algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families.",
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"abstract": "Animated movies are a popular way to communicate complex phenomena in cell biology to the broad audience. Animation artists apply sophisticated illustration techniques to communicate a story, while trying to maintain a realistic representation of a complex dynamic environment. Since such hand-crafted animations are time-consuming and cost-intensive to create, our goal is to formalize illustration techniques used by artists to facilitate the automatic creation of visualizations generated from mesoscale particle-based molecular simulations. Our technique Illustrative Timelapse supports visual exploration of complex biochemical processes in dynamic environments by (1) seamless temporal zooming to observe phenomena in different temporal resolutions, (2) visual abstraction of molecular trajectories to ensure that observers are able to visually follow the main actors, (3) increased visual focus on events of interest, and (4) lens effects to preserve a realistic representation of the environment in the context. Results from a first user study indicate that visual abstraction of trajectories improves the ability to follow a story and is also appreciated by users. Lens effects increased the perceived amount of molecular motion in the environment while trading off traceability of individual molecules.",
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