nils-herrmann/clean_scirepeval huggingface dataset:
Wavelet Analysis in Current Cancer Genome Research: A Survey

With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences.
MRBAC: Hierarchical Role Management and Security Access Control for Distributed Multimedia Systems

In this paper, a role-based access control (KBAC) model is applied and extended to a multimedia version called multi-role based access control (MRBAC), which can fully support the comprehensive and multilevel security control requirements of the distributed multimedia applications. The object-oriented concept is adopted in MRBAC to perform the hybrid role hierarchy management and security roles and rules administration. In summary, MRBAC can: 1) support the multi-level security protection for multimedia data; 2) provide access control by checking both the time constrains and IP addresses; and 3) decentralize the administration functions to make the access control management more efficient.
A live TV-quality distant learning multimedia presentation system for education

In this paper an abstract semantic model called multimedia augmented transition network (MATN) to model a live TV-like multimedia presentation system for distance learning education purpose is presented. Unlike the original design for CISCO IP/TV system that is built upon Microsoft ActiveMovie technology, a Java-based viewer called JTViewer is developed to provide the cross-platform capability for CISCO IP/TV system. In the JTViewer system, different media streams need to be synchronized and displayed in real-time. In order to meet this requirement. The MATN model is the underlying semantic model for the presentation in the proposed framework since it provides the visualization of control structure and a good programming data structure for the implementation. An example of distance learning via multimedia presentations is given to illustrate how the MATN model serves as the underlying semantic model for the JTViewer system.
A Classifier Ensemble Framework for Multimedia Big Data Classification

Numerous classification algorithms have been developed for a variety of data types. However, it is nearly impossible for one classifier to perform the best in all kinds of datasets. Therefore, ensemble learning models which aim to take advantages of different classifiers have received a lot of attentions recently. In this paper, a scalable classifier ensemble framework assisted by a set of judgers is proposed to integrate the outputs from multiple classifiers for multimedia big data classification. Specifically, based on the confusion matrices of different classifiers, a set of "judgers" are organized into a hierarchically structured decision model. A testing instance is first input to different classifiers, and then the classification results are passed to the proposed hierarchical structured decision model to derive the final result. The ensemble system can be run on Spark, which is designed for big data processing. Experimental results on multimedia data containing different actions demonstrate that the proposed classifier ensemble framework outperforms several state-of-the-art model fusion approaches.
A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval

A rapid increase in the amount of image data and the inefficiency of traditional text-based image retrieval systems have served to make content-based image retrieval an active research field. It is crucial to effectively discover users' concept patterns through an acquired understanding of the subjective role played by humans in the retrieval process for such systems. A learning and retrieval framework is used to achieve this. It seamlessly incorporates multiple instance learning for relevant feedback to discover users concept patterns-especially in the region of greatest user interest. It also maps the local feature vector of that region to the high-level concept pattern. This underlying mapping can be progressively discovered through feedback and learning. The user guides the retrieval systems learning process using his/her focus of attention. Retrieval performance is tested to establish the feasibility and effectiveness of the proposed learning and retrieval framework
Automated quality control of tropical cyclone winds through data mining

The analysis of tropical cyclones (TC) depends heavily on the quality of the incoming data set. With the advances in technology, the sizes of these data sets also increase. There is a great demand for an efficient and effective unsupervised quality control tool. Towards such a demand, data mining algorithms like spatial clustering and specialized distance measures can be applied to perform this task. This paper reports our findings on the studies on utilizing a density-based clustering algorithm with three different distance measures on a series of TC data sets.
Message from the WEDA 2016 Program Chairs

Welcome to the COMPSAC Symposium on Web, Big Data & Analytics (WEDA) 2016. WEDA 2016 Symposium is an integral part of the IEEE COMPSAC main conference. Complementing the COMPSAC theme, WEDA uniquely positions itself as a forum for both researchers and practitioners in Web Technologies, Big Data, Data Science, e-Science and Data Analytics. It aims to discern emerging and genuinely challenging issues and brainstorm about robust solution strategies as well as advocate novel applications. WEDA invites authors to present recent findings, innovations, theories, experiences, and ideas. Technical contributions accepted by WEDA will likely cover theory, applications, pragmatics, systems and services enabled by the Web, underlying technologies, big data, data science, e-science, and concomitant big data analytics. The goal of WEDA is to deepen the understanding of, fostering innovation in, and defining the future of its interest domain.
Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection

Semantic understanding of multimedia content has become a very popular research topic in recent years. Semantic concept detection algorithms face many challenges such as the semantic gap and imbalance data, among others. In this paper, we propose a novel algorithm using multiple correspondence analysis (MCA) to discover the correlation between features and classes to reduce the feature space and to bridge the semantic gap. Moreover, the proposed algorithm is able to explore the correlation between items (i.e., feature-value pairs generated for each of the features) and classes which expands its ability to handle imbalance data sets. To evaluate the proposed algorithm, we compare its performance on semantic concept detection with several existing feature selection methods under various well-known classifiers using some of the concepts and benchmark data available from the TRECVID project. The results demonstrate that our proposed algorithm achieves promising performance, and it performs significantly better than those feature selection methods in the comparison for the imbalanced data sets.
Correlation-Based Video Semantic Concept Detection Using Multiple Correspondence Analysis

Semantic concept detection has emerged as an intriguing topic in multimedia research recently. The ability to interpret high-level semantics from low-level features has been the long desired goal of many researchers. In this paper, we propose a novel framework that utilizes the ability of multiple correspondence analysis (MCA) to explore the correlation between different items (feature-value pairs) and classes (concepts) to bridge the gap between the extracted low-level features and high-level semantic concepts. Using the concepts and benchmark data identified and provided by the TRECVID project, we have shown that our proposed framework demonstrates promising results and performs better than the decision tree (DT),support vector machine (SVM), and naive Bayesian (NB) classifiers that are commonly applied to the TRECVID datasets.
Temporal Multiple Correspondence Analysis for Big Data Mining in Soccer Videos

A multimedia big data mining framework consisting of two phases for interesting event detection in soccer videos has been proposed in this paper. In the pre-processing phase, it utilizes the multi-modal multi-filtering content analysis techniques for shot boundary detection and feature extraction. A pre-filtering process based on domain knowledge analysis is then applied to clean the noise and obtain a candidate set. In the event detection phase, a temporal multiple correspondence analysis (TMCA) algorithm that adopts an indicator weighting scheme is proposed to efficiently and effectively incorporate the temporal semantic information for improving the detection results. Furthermore, another enhanced MCA (EN-MCA) approach is presented to better capture the correspondence between feature items and classes by thoroughly utilizing the pair-wise principal components. Finally, a re-ranking procedure is performed to retrieve the missed interesting event. Our proposed semantic re-ranking framework is evaluated on a large collection of soccer videos for interesting event detection. The experimental results demonstrate the effectiveness of the proposed framework.
Prediction of Uterine Contractions Using Knowledge-Assisted Sequential Pattern Analysis

The usage of the systemic opioid remifentanil in relieving the labor pain has attracted much attention recently. An optimal dosing regimen for administration of remifentanil during labor relies on anticipating the timing of uterine contractions. These predictions should be made early enough to maximize analgesia efficacy during contractions and minimize the impact of the medication between contractions. We have designed a knowledge-assisted sequential pattern analysis framework to 1) predict the intrauterine pressure in real time; 2) anticipate the next contraction; and 3) develop a sequential association rule mining approach to identify the patterns of the contractions from historical patient tracings (HT).
Model-driven collaboration and information integration for enhancing video semantic concept detection

With the fast development and popularity of digital cameras, smart phones, and video surveillance devices, the amount of video data increases dramatically. Accordingly, automatically mining and annotating high-level concepts for video indexing and management become imperative research tasks in both multimedia research and data mining research. The mainstream content-based semantic concept mining approaches suffer from the notorious semantic gap problem, which is the difficulty of associating the low-level features to high-level concepts directly. Recently, the utilization of concept-concept association has been proven to be effective to address the semantic gap problem. In this paper, a framework based on multi-model collaboration and information integration is proposed to integrate the association among concepts to enhance the high-level concept detection by reusing the information outputs from the primary concept detectors. The experimental results show that the proposed framework outperforms the other approaches in the comparison and therefore is promising.
Handling nominal features in anomaly intrusion detection problems

Computer network data stream used in intrusion detection usually involve many data types. A common data type is that of symbolic or nominal features. Whether being coded into numerical values or not, nominal features need to be treated differently from numeric features. This paper studies the effectiveness of two approaches in handling nominal features: a simple coding scheme via the use of indicator variables and a scaling method based on multiple correspondence analysis (MCA). In particular, we apply the techniques with two anomaly detection methods: the principal component classifier (PCC) and the Canberra metric. The experiments with KDD 1999 data demonstrate that MCA works better than the indicator variable approach for both detection methods with the PCC coming much ahead of the Canberra metric.
An open multiple instance learning framework and its application in drug activity prediction problems

In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Square Error (MSE) criterion, and then the framework can be constructed with an open form of hypothesis and gradient search method. The proposed Multiple Instance Learning framework is applied to the drug activity problems in bioinformatics applications. Specifically, experiments are conducted on the Musk-I dataset to predict the binding activity of drug molecules. In the experiments, an algorithm with the exponential hypothesis model and the Quasi-Newton method is embedded into our proposed framework. We compare our proposed framework with other existing algorithms and the experimental results show that our proposed framework yields a good accuracy of classification, which demonstrates the feasibility and effectiveness of our framework.
A Scalable and Automatic Validation Process for Florida Public Hurricane Loss Model (Invited Paper)

The Florida Public Hurricane Loss Model (FPHLM) is a public catastrophe model that integrates and regulates all key components, such as meteorology, engineering, and actuarial components, by following a certain workflow in the execution phase. The validation phase governed by an Automatic Data Validation (ADV) program simulates each modeled execution component with a large number of historical insurance data of specific hurricane events. The differences between the actual losses and the modeled losses of the insurance portfolios are evaluated to validate the model. The original validation process is time-consuming and error-prone when handling large data sets. This paper presents how the automated computer program efficiently and correctly incorporates the key components and produces useful reports for the validation purposes. By considering sixty-six combinations (i.e., the combination of one company and one hurricane) of the claim data, the FPHLM model adopts the largest set of portfolios comparing to the other four private models, which makes the validation process more challenging.
Collateral Representative Subspace Projection Modeling for Supervised Classification

In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, decision table (DT), nearest neighbor (NN), KNN, support vector machine (SVM), I-NN best warping window DTW, I-NN DTW with no warping window, and the well-known classifier boosting method AdaBoost with SVM
Adaptive background learning for vehicle detection and spatio-temporal tracking

Traffic video analysis can provide a wide range of useful information such as vehicle identification, traffic flow, to traffic planners. In this paper, a framework is proposed to analyze the traffic video sequence using unsupervised vehicle detection and spatio-temporal tracking that includes an image/video segmentation method, a background learning/subtraction method and an object tracking algorithm. A real-life traffic video sequence from a road intersection is used in our study and the experimental results show that our proposed unsupervised framework is effective in vehicle tracking for complex traffic situations.
Labor contraction prediction via demographic and obstetrical information analysis

Designing an optimal dosing regimen for the systemic opioid remifentanil during labor necessitates the prediction of the pace of contractions, so that the drug can be given shortly before the pain of the contraction begins. The prediction and drug administration should be made early enough to allow for the administration of intravenous analgesia that will have maximal efficacy during contractions and little effect between contractions. Towards such a need, we propose a knowledge-assisted sequential pattern analysis framework to predict the changes in intrauterine pressure, which indicate the occurrence of labor contractions. In particular, a patient selection strategy is proposed to select a group of patients, from the stored record, who share similar demographic and obstetrical information with the current patient of interest. A sequential association rule mining approach is designed to learn the patterns of the contractions from the historical patient tracings, and to determine which demographic and obstetrical features have an impact on the contraction patterns. The promising experimental results show that the proposed framework is effective, robust, and efficient in predicting the labor contraction patterns.
Semantic event detection via multimodal data mining

This paper presents a novel framework for video event detection. The core of the framework is an advanced temporal analysis and multimodal data mining method that consists of three major components: low-level feature extraction, temporal pattern analysis, and multimodal data mining. One of the unique characteristics of this framework is that it offers strong generality and extensibility with the capability of exploring representative event patterns with little human interference. The framework is presented with its application to the detection of the soccer goal events over a large collection of soccer video data with various production styles
An unsupervised segmentation framework for texture image queries

In this paper a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upon these parameters, an effective multi-filter query strategy which allows the users to issue texture-based image queries is developed The test results of the proposed framework on 318 texture images obtained from the MIT VisTex and Brodatz database are presented to show its effectiveness.
MMM: a stochastic mechanism for image database queries

We present a mechanism called the Markov model mediator (MMM) to facilitate the effective retrieval for content-based image retrieval (CBIR). Different from the common methods in content-based image retrieval, our stochastic mechanism not only takes into consideration the low-level image content features, but also learns high-level concepts from a set of training data, such as access frequencies and access patterns of the images. The advantage of our proposed mechanism is that it exploits the structured description of visual contents as well as the relative affinity measurements among the images. Consequently, it provides the capability to bridge the gap between the low-level features and high-level concepts. Our experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results for user queries.
A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine

Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm has been shown through comparative studies.
Feature Selection Using Correlation and Reliability Based Scoring Metric for Video Semantic Detection

Content-based multimedia retrieval faces many challenges such as semantic gap, imbalanced data, and varied qualities of the media. Feature selection as a component of the retrieval process plays an important role. The aim of feature selection is to identify a subset of features by removing irrelevant or redundant features. An effective subset of features can not only improve model performance and reduce computational complexity, but also enhance semantic interpretability. To achieve these objectives, in this paper, a novel metric that integrates the correlation and reliability information between each feature and each class obtained from Multiple Correspondence Analysis (MCA) is proposed to score the features for feature selection. Based on these scores, a ranked list of features can be generated and different selection criteria can be adopted to select a subset of features. To evaluate the proposed framework, four other well-known feature selection methods, namely information gain, chi-square measure, correlation-based feature selection, and relief are compared with the proposed method over five popular classifiers using the benchmark data from TRECVID 2009 high-level feature extraction task. The results show that the proposed method outperforms the other methods in terms of classification accuracy, the size of feature subspace, and the ability to capture the semantic information.