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"abstract": "Zoomable User Interfaces (ZUI) can help people perceive and interact with large collections of information. These large collections complicate the task of creating ZUIs. In this paper the ORRIL (Objects, Regions, Relations and Interface Logic) framework is defined and presented as a technique for aiding ZUI design and creation. ORRIL makes explicit the data that appears in a zoomable information space, while simultaneously emphasising the relationships between user actions and transforms of the data. This is important for modeling and clarifying the processes that occur within a ZUI application. From the perspective of an implementer it may be used as an underlying model when designing and implementing a framework for building ZUIs.",
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"abstract": "Information visualization faces challenges presented by the need to represent abstract data and the relationships within the data. Previously, we presented a system for visualizing similarities between a single DNA sequence and a large database of other DNA sequences [6]. Similarity algorithms generate similarity information in textual reports that can be hundreds or thousands of pages long. Our original system visualized the most important variables from these reports. However, the biologists we work with found this system so useful they requested visual representations of other variables. We present an enhanced system for interactive exploration of this multivariate data. We identify a larger set of useful variables in the information space. The new system involves more variables, so it focuses on exploring subsets of the data. We present an interactive system allowing mapping of different variables to different axes, incorporating animation using a time-axis, and providing tools for viewing subsets of the data. Detail-on-demand is preserved by hyperlinks to the analysis reports. We present three case studies illustrating the use of these techniques. The combined technique of applying a time axis with a 3D scatter plot and query filters to visualization of biological sequence similarity data is both powerful and novel.",
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"abstract": "In this paper we focus on the automatic identification of bird species from their audio recorded song. Bird monitoring is important to perform several tasks, such as to evaluate the quality of their living environment or to monitor dangerous situations to planes caused by birds near airports. We deal with the bird species identification problem using signal processing and machine learning techniques. First, features are extracted from the bird recorded songs using specific audio treatment, next the problem is performed according to a classical machine learning scenario, where a labeled database of previously known bird songs are employed to create a decision procedure that is used to predict the species of a new bird song. Experiments are conducted in a dataset of recorded songs of bird species which appear in a specific region. The experimental results compare the performance obtained in different situations, encompassing the complete audio signals, as recorded in the field, and short audio segments (pulses) obtained from the signals by a split procedure. The influence of the number of classes (bird species) in the identification accuracy is also evaluated.",
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"title": "Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling",
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"abstract": "Citizen scientists, who are volunteers from the community that participate as field assistants in scientific studies [3], enable research to be performed at much larger spatial and temporal scales than trained scientists can cover. Species distribution modeling [6], which involves understanding species-habitat relationships, is a research area that can benefit greatly from citizen science. The eBird project [16] is one of the largest citizen science programs in existence. By allowing birders to upload observations of bird species to an online database, eBird can provide useful data for species distribution modeling. However, since birders vary in their levels of expertise, the quality of data submitted to eBird is often questioned. In this paper, we develop a probabilistic model called the Occupancy-Detection-Expertise (ODE) model that incorporates the expertise of birders submitting data to eBird. We show that modeling the expertise of birders can improve the accuracy of predicting observations of a bird species at a site. In addition, we can use the ODE model for two other tasks: predicting birder expertise given their history of eBird checklists and identifying bird species that are difficult for novices to detect.",
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"abstract": "A key issue for any project that aims to study endangered animals is the counting process. In this work, we propose an algorithm for automatic counting of singing birds in their habitat by using wireless sensors fitted with microphone. Sensors are used to record audio samples of bird songs and samples are analyzed to extract fingerprints that permit to recognize bird species. This paper details the counting algorithm that uses graph theory and audio inputs comparison. Finally, we demonstrate our scheme efficiency through experimentations.",
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"abstract": "This study proposes an automatic bird sound identification system to help people learn to identify bird species. The system is built upon a two-stage identification framework. The first stage performs call/song classification. If an unknown sound clip is classified as a call, it is then handled by a call identifier in the second stage; otherwise, it is handled by a song identifier. Both identifiers use two acoustic features, timbre and pitch, to determine which of the bird species the sound clip belong to. In using timbre features, bird sounds are converted into MFCCs and their first derivatives and then analyzed using Gaussian mixture models. In using pitch feature, we convert bird sounds into MIDI note sequences and then use bigram models to analyze the dynamic change information of the notes. Our experiments, conducted using a database including twenty common bird species, show that the proposed system can achieve 90.4% accuracy.",
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"affiliation": "Department of Electronic Engineering & Graduate Institute of Computer and Communication Engineering, National Taipei University of Technology, Taiwan",
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"abstract": "Knowledge of wetland use of migratory bird species during the annual life circle is important to construct conservation strategy and explore the implication for avian influenza control. Biological scientists have used GPS satellite telemetry to determine the habitat of wild birds. However, because there is not an efficient method to process the location data sets, scientists have to devote themselves to calculating the scattering location points in the GIS and using MCP or Kernel method to find the approximate home range of bird species. This paper presents an alternative hierarchy clustering-based methodology to build a Spatial-Tree for discovering the habitat area that overcomes the limit of human observation ability. Preliminary results showed that some nodes of the Spatial-Tree correspond to core range area providing an illusive map to depict the breeding and wintering home range of Bar-headed Goose.",
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"abstract": "Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.",
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"title": "Discovering Abiotic Interactions between Bird Habitat and Water Quality through Ubiquitous Computing",
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"abstract": "Conventional water quality monitoring and bird species observation were recorded using pen and paper. Moreover, experiments can usually only be conducted in limited areas due to the high cost of the water monitoring equipment. Consequently, not enough data could be collected to find the a biotic interactions between water quality factors and bird habitat. To resolve this problem, the proposed smart phone based system can transmit the water quality measurements and observed quantity of each bird species to the back-end server via a 3G network. This server functions as a ubiquitous information center where all measured and observed data are stored. Fuzzy C-means is applied to cluster water quality factors and bird species. The clustering results of bird quantity are then used to calculate the Simpson¡¦s diversity indices. Based on the results from the water quality factors and biodiversity index pattern trees (PTs) are constructed to find the a biotic interactions between them. The PTs can be further transformed into IF-THEN rules to provide references for researchers to conduct environmental protection. Experimental results are given to verify the applicability of the proposed system in finding the a biotic interactions.",
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"abstract": "After identifying the function of a protein, biologists produce new useful proteins by substituting some residues of the identified protein. These new proteins have high sequence homology (similarity). We define a sequence cluster as a cluster that is constituted of similar sequences. As another example of a sequence cluster, we consider a SNP (single nucleotide polymorphism) cluster. A SNP is a DNA sequence variation occurring when a single nucleotide in the genome (or other shared sequence) differs between members of a species (or between paired chromosomes in an individual). We suggest a new compressing technique for these sequence clusters using a sequence alignment method. We select a representative sequence which has a minimum sequence distance in the cluster by scanning distances of all sequences. The distances are obtained by calculating a sequence alignment score. The result of this sequence alignment is utilized to author conversion information called an edit-script between the two sequences. We only stored representative sequences and edit-scripts of each cluster into a database. Member sequences of each cluster can then be easily created using representative sequences and edit-scripts.",
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"content": "After identifying the function of a protein, biologists produce new useful proteins by substituting some residues of the identified protein. These new proteins have high sequence homology (similarity). We define a sequence cluster as a cluster that is constituted of similar sequences. As another example of a sequence cluster, we consider a SNP (single nucleotide polymorphism) cluster. A SNP is a DNA sequence variation occurring when a single nucleotide in the genome (or other shared sequence) differs between members of a species (or between paired chromosomes in an individual). We suggest a new compressing technique for these sequence clusters using a sequence alignment method. We select a representative sequence which has a minimum sequence distance in the cluster by scanning distances of all sequences. The distances are obtained by calculating a sequence alignment score. The result of this sequence alignment is utilized to author conversion information called an edit-script between the two sequences. We only stored representative sequences and edit-scripts of each cluster into a database. Member sequences of each cluster can then be easily created using representative sequences and edit-scripts.",
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"abstract": "Bioinformatics is a quickly emerging area of science with many important applications to human life. Sequence alignment in various forms is one of the main instruments used in bioinformatics. This work is motivated by the ever-increasing amount of sequence data that requires more and more computation power for its processing. This task calls for new GPU-based systems and their higher computational potential and energy efficiency as compared to CPUs. We address the problem of facilitating faster sequence alignment using modern multi-GPU clusters. Our initial step was to develop a fast and scalable GPU exact short sequence aligner. We used matching algorithm with small memory footprint based on Burrows-Wheeler transform. We developed a mathematical model of computation and communication costs to find optimal memory partitioning strategy for index and queries. Our solution achieves 10 times speedup over previous implementation based on suffix array on one GPU and scales to multiple GPUs. Our next step will be to adapt the suggested data structure and performance model for multi-node multi-GPU approximate sequence alignment. It is also planned to use exact matching to detect common regions in large sequences and use it as an intermediate step in full-scale genome comparison.",
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"content": "Bioinformatics is a quickly emerging area of science with many important applications to human life. Sequence alignment in various forms is one of the main instruments used in bioinformatics. This work is motivated by the ever-increasing amount of sequence data that requires more and more computation power for its processing. This task calls for new GPU-based systems and their higher computational potential and energy efficiency as compared to CPUs. We address the problem of facilitating faster sequence alignment using modern multi-GPU clusters. Our initial step was to develop a fast and scalable GPU exact short sequence aligner. We used matching algorithm with small memory footprint based on Burrows-Wheeler transform. We developed a mathematical model of computation and communication costs to find optimal memory partitioning strategy for index and queries. Our solution achieves 10 times speedup over previous implementation based on suffix array on one GPU and scales to multiple GPUs. Our next step will be to adapt the suggested data structure and performance model for multi-node multi-GPU approximate sequence alignment. It is also planned to use exact matching to detect common regions in large sequences and use it as an intermediate step in full-scale genome comparison.",
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"abstract": "The performance of most image denoising systems depends on some parameters which should be set carefully based on noise distribution and its variance. As in some applications noise characteristics are unknown, in this research, a criterion which its minimization leads to the best parameter set up is introduced. The proposed criterion is evaluated for the wavelet shrinkage image denoising algorithm using the cross validation procedure. The criterion is tested for some different values of thresholds, and the output leading to the minimum criterion value is selected as the final denoised output. The resulting outputs of our method and the previous threshold selection scheme for the wavelet shrinkage, i.e. the median absolute difference (MAD), are compared. The objective and subjective test results show the improved efficiency of the proposed denoising algorithm.",
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"abstract": "To relieve \"News Information Overload\", classification, summarization and recommendation techniques have been proposed. However, these techniques fail to provide sufficient semantic information about news events. In this paper, considering5W1H (Who, What, Whom, When, Where and How), the full list of elements of a news article, we propose a novel approach to extract event semantic elements. The approach comprises a key event identification step and an event element extraction step. We first use machine learning method to identify the key events of Chinese news stories. Then we employ semantic role labeling (SRL) enhanced by heuristic rules to extract event 5W1Helements. A prototype system is implemented based on proposed approach. Extensive experiments on real online news data sets confirm the reasonability and feasibility of our approach.",
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"abstract": "Abstract—This paper presents new approaches for analyzing Egyptian Newspaper websites and presenting the most interesting topics that attract the largest portion of reading population to their newspaper website. The paper also presents techniques used for text and news mining. We first start with topic detection and tracking techniques to collect the reader website navigation data for the various topics then we use the proposed incremental algorithm (PIA), our previously published algorithm that was implemented in our previous researches, for mining of the news database. The paper concludes with significant results for identifying the readers trends, thus can dynamically display the most expected and required headline topics to the reader. The paper discovers also the most important topics for the users. Moreover, the paper had a significant effect for guiding the newspaper for the appropriate method for modifying its website to be dynamic according to the user interests specified by the association rules algorithm. We believe that this work had high impact in increasing the number of visitors for the newspaper website and attracting the reader's attention.",
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