nils-herrmann/clean_scirepeval huggingface dataset:
Data management support via spectrum perturbation-based subspace classification in collaborative environments

Data management support to enable effective and efficient information sharing in collaborative environments is critical, especially in semantics based search and retrieval. In this paper, a novel spectrum perturbation-based subspace classification is proposed to mine semantics and other useful information from a large-scale dataset by utilizing a lower-dimensional subspace to discriminate different classes of the dataset. Among the existing subspace-based approaches, the principal component (PC) subspace is the most prevailing one and has been well studied. After investigating previous work related to PC subspace, we found that none of them had considered the perturbation on spectrum when building the subspace learning models. However, such perturbation is of certain importance and is able to provide discriminant information that helps improve classification performance by measuring the closeness of each testing data instance towards a subspace model by a closeness score based on the spectrum perturbation. Each testing data instance is assigned to its closest class by searching the smallest closeness score. Experiments are conducted to evaluate our proposed subspace classifier using data sets from three different sources, and the experimental results show that it achieves promising results and outperforms comparative subspace classifiers as well as some other commonly used classifiers.
Knowledge-Assisted Sequential Pattern Analysis With Heuristic Parameter Tuning for Labor Contraction Prediction

The optimal dosing regimen of remifentanil for relieving labor pain should achieve maximal efficacy during contractions and little effect between contractions. Toward such a need, we propose a knowledge-assisted sequential pattern analysis with heuristic parameter tuning to predict the changes in intrauterine pressure, which indicates the occurrence of labor contractions. This enables giving the drug shortly before each contraction starts. A sequential association rule mining based patient selection strategy is designed to dynamically select data for training regression models. A novel heuristic parameter tuning method is proposed to decide the appropriate value ranges and searching strategies for both the regularization factor and the Gaussian kernel parameter of least-squares support vector machine with radial basis function (RBF) kernel, which is used as the regression model for time series prediction. The parameter tuning method utilizes information extracted from the training dataset, and it is adaptive to the characteristics of time series. The promising experimental results show that the proposed framework is able to achieve the lowest prediction errors as compared to some existing methods.
Integration of global and local information in videos for key frame extraction

Key frame extraction methods aim to obtain a set of frames that can efficiently represent and summarize video contents and be reused in many video retrieval-related applications. An effective set of key frames, viewed as a high-quality summary of the video, should include the major objects and events of the video, and contain little redundancy and overlapped content. In this paper, a new key frame extraction method is presented, which not only is based on the traditional idea of clustering in the feature extraction phase but also effectively reduces redundant frames using the integration of local and global information in videos. Experimental results on the TRECVid 2007 test video dataset have demonstrated the effectiveness of our proposed key frame extraction method in terms of the compression rate and retrieval precision.
A data mining framework for building a Web-page recommender system

In this paper, we propose a new framework based on data mining algorithms for building a Web-page recommender system. A recommender system is an intermediary program (or an agent) with a user interface that automatically and intelligently generates a list of information, which suits an individual's needs. Two information filtering methods for providing the recommended information are considered: (1) by analyzing the information content, i.e., content-based filtering, and (2) by referencing other user access behaviors, i.e., collaborative filtering. By using the data mining algorithms, the information filtering processes can be performed prior to the actual recommending process. As a result, the system response time could be improved and thus, making the framework scalable.
Augmented transition networks as video browsing models for multimedia databases and multimedia information systems

In an interactive multimedia information system, users should have the flexibility to browse and choose various scenarios they want to see. This means that two-way communications should be captured by the conceptual model. Digital video has gained increasing popularity in many multimedia applications. Instead of sequential access to the video contents, the structuring and modeling of video data so that users can quickly and easily browse and retrieve interesting materials has become an important issue in designing multimedia information systems. An abstract semantic model called the augmented transition network (ATN), which can model video data and user interactions, is proposed in this paper. An ATN and its subnetworks can model video data based on different granularities, such as scenes, shots and key frames. Multimedia input strings are used as inputs for ATNs. The details of how to use multimedia input strings to model video data are also discussed. Key frame selection is based on the temporal and spatial relations of semantic objects in each shot. These relations are captured from our proposed unsupervised video segmentation method, which considers the problem of partitioning each frame as a joint estimation of the partition and class parameter variables. Unlike existing semantic models, which only model multimedia presentation, multimedia database searching or browsing, ATNs together with multimedia input strings can model these three in one framework.
Cyber-Secure UAV Communications Using Heuristically Inferred Stochastic Grammars and Hard Real-Time Adaptive Waveform Synthesis and Evolution

This paper initially describes how an inferred context-free (stochastic) grammar can be used to verify command transmissions and serve as a hedge against a successful cyber-attack. The remainder of the paper addresses a computational problem not amenable to closed-form solution; namely, the hard real-time (~57 usec) synthesis of a desired waveform through the adaptive modification of a carrier wave. This effectively increases the signal to noise ratio – ensuring better UAV communications. Here, the modulation of the primary waveform is under user control and is of strictly positive amplitude. The primary waveform induces a secondary waveform having delayed leading and trailing edges and expanded rise and fall times. There is, in general, a direct relation between the period of the primary waveform and the amplitude of the secondary waveform. The relation between the primary and secondary waveforms may be characterized by trigonometric functions or even interpolating polynomials. However, response time will be minimized where the primary waveforms are discretized and stored in the form of array-based cases. The tertiary (target) wave may be any periodic trigonometric function, but is taken to be a simple sine wave without loss of generality. The task of the adaptive program is to minimize ||s(t) – g(t)||2, where f(t) „³ g(t) and f(t) is the primary waveform at time t, g(t) is the secondary waveform at time t, and s(t) is the tertiary waveform at time t. A computationally efficient algorithm is provided for solving this task in real time. Moreover, an evolutionary program (EP) is provided for automatic case acquisition. Primary waveforms are mutated in accordance with a normal distribution.
Defense against Bandwidth Attacks with Traffic Resource Management

In this paper, a framework is proposed to defend against Internet bandwidth attacks with traffic resource management to provide service for legitimate users. Denial of service (DoS) is one of the major bandwidth attacks in the Internet. A DoS attack generates a large volume of traffic to consume the network bandwidth and degrade the service that legitimate users can obtain. Incoming traffic to the server is monitored and features are extracted for each connection. Anomaly detection technique is used to detect the abnormal traffic. Based on the outcome of the anomaly detection technique, the proposed resource management approach allocates suitable bandwidth. With the early detection of DoS, the attack traffic can be isolated. The bandwidth occupied by the attack can be reduced and protected for the legitimate users. Performances are compared under different attack loads with and without resource management. Simulation results show that bandwidth can be greatly saved from an attack and the service for the legitimate users can be protected during an attack.
Semantic Retrieval for Videos in Non-static Background Using Motion Saliency and Global Features

In this paper, a video semantic retrieval framework is proposed based on a novel unsupervised motion region detection algorithm which works reasonably well with dynamic background and camera motion. The proposed framework is inspired by biological mechanisms of human vision which make motion salience (defined as attention due to motion) is more "attractive" than some other low-level visual features to people while watching videos. Under this biological observation, motion vectors in frame sequences are calculated using the optical flow algorithm to estimate the movement of a block from one frame to another. Next, a center-surround coherency evaluation model is proposed to compute the local motion saliency in a completely unsupervised manner. The integral density algorithm is employed to search the globally optimal solution of the minimum coherency region as the motion region which is then integrated into the video semantic retrieval framework to enhance the performance of video semantic analysis and understanding. Our proposed framework is evaluated using video sequences in non-static background, and the promising experimental results reveal that the semantic retrieval performance can be improved by integrating the global texture and local motion information.
A multimedia semantic model for RTSP-based multimedia presentation systems

An abstract multimedia semantic model called the multimedia augmented transition network (MATN) model is proposed to model the RTSP-based (real-time streaming protocol) multimedia presentation systems. RTSP provides a set of methods to support the functionality of "VCR-like" remote control on the client side so that users are allowed to control playback of the streaming media. The MATN model is used to model RTSP actions as well as temporal relations and synchronization control of the media streams. The advantages of using the MATN model are its simplicity, effectiveness, and ease of modification. Furthermore, a formal definition of the MATN model and modeling of RTSP actions using the MATN model are presented in detail.
Negative Correlation Discovery for Big Multimedia Data Semantic Concept Mining and Retrieval

With massive amounts of data producing each day in almost every field, traditional data processing techniques have become more and more inadequate. However, the research of effectively managing and retrieving these big data is still under development. Multimedia high-level semantic concept mining and retrieval in big data is one of the most challenging research topics, which requires joint efforts from researchers in both big data mining and multimedia domains. In order to bridge the semantic gap between high-level concepts and low-level visual features, correlation discovery in semantic concept mining is worth exploring. Meanwhile, correlation discovery is a computationally intensive task in the sense that it requires a deep analysis of very large and growing repositories. This paper presents a novel system of discovering negative correlation for semantic concept mining and retrieval. It is designed to adapt to Hadoop MapReduce framework, which is further extended to utilize Spark, a more efficient and general cluster computing engine. The experimental results demonstrate the feasibility of utilizing big data technologies in negative correlation discovery.
Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification

Many multimedia systems stream real-time visual data continuously for a wide variety of applications. These systems can produce vast amounts of data, but few studies take advantage of the versatile and real-time data. This paper presents a novel model based on the Convolutional Neural Networks (CNNs) to handle such imbalanced and heterogeneous data and successfully identifies the semantic concepts in these multimedia systems. The proposed model can discover the semantic concepts from the data with a skewed distribution using a dynamic sampling technique. The paper also presents a system that can retrieve real-time visual data from heterogeneous cameras, and the run-time environment allows the analysis programs to process the data from thousands of cameras simultaneously. The evaluation results in comparison with several state-of-the-art methods demonstrate the ability and effectiveness of the proposed model on visual data captured by public network cameras.
Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework

In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.
A Bayesian network-based expert query system for a distributed database system

A distributed database is a collection of data sources distributed across many computers. It is a single logical and centrally managed database stored in multiple physical locations. In order to access data locally and manage data globally, a good distributed database system (DDBS) that can supply reliable and up-to-date information is critical. A good DDBS should have functionalities such as providing users with timely and flexible access to information, providing DBA (database administrator) personnel with tools to analyze the data in a meaningful manner, and allowing the personnel to control the safety and integrity of the data. However, query processing in such a distributed database system is complicated. For example, some important issues are how to keep data up-to-date when it is physically dispersed, and how to propagate information to the whole DDBS when an update occurs. In response to these issues, we propose an expert query system that uses a Bayesian network as its framework to propagate information and to keep data up-to-date for a DDBS. The goal of this query system is to assist the DBA personnel in maintaining the data dynamically and to assist the users in accessing reliable and timely information.
Enhancing Multimedia Semantic Concept Mining and Retrieval by Incorporating Negative Correlations

In recent years, we have witnessed a deluge of multimedia data such as texts, images, and videos. However, the research of managing and retrieving these data efficiently is still in the development stage. The conventional tag-based searching approaches suffer from noisy or incomplete tag issues. As a result, the content-based multimedia data management framework has become increasingly popular. In this research direction, multimedia high-level semantic concept mining and retrieval is one of the fastest developing research topics requesting joint efforts from researchers in both data mining and multimedia domains. To solve this problem, one great challenge is to bridge the semantic gap which is the gap between high-level concepts and low-level features. Recently, positive inter-concept correlations have been utilized to capture the context of a concept to bridge the gap. However, negative correlations have rarely been studied because of the difficulty to mine and utilize them. In this paper, a concept mining and retrieval framework utilizing negative inter-concept correlations is proposed. Several research problems such as negative correlation selection, weight estimation, and score integration are addressed. Experimental results on TRECVID 2010 benchmark data set demonstrate that the proposed framework gives promising performance.
Web document classification based on fuzzy association

In this paper, a method of automatically classifying web documents into a set of categories using the fuzzy association concept is proposed. Using the same word or vocabulary to describe different entities creates ambiguity, especially in the web environment where the user population is large. To solve this problem, fuzzy association is used to capture the relationships among different index terms or keywords in the documents, i.e., each pair of words has an associated value to distinguish itself from the others. Therefore, the ambiguity in word usage is avoided. Experiments using data sets collected from two web portals: Yahoo! and Open Directory Project are conducted. We compare our approach to the vector space model with the cosine coefficient. The results show that our approach yields higher accuracy compared to the vector space model.
A probabilistic-based mechanism for video database management systems

As more information sources become available in multimedia systems, the development of multimedia database management systems (MDBMSs) to efficiently model and search multimedia data, especially video data, becomes very crucial for multimedia applications. In response to such a demand, a probabilistic-based mechanism called the Markov Model Mediator (MMM) to facilitate an MDBMS for video database systems is presented. In our previous studies, the spatial relations of the semantic objects in the image/video data modeled by the MMM mechanism are assumed given by image processing/computer vision techniques or by human annotations. In this paper, an unsupervised video segmentation method that can identify objects with their corresponding spatial relations automatically is incorporated into the MMM mechanism. Based on the information obtained, users can retrieve video materials from video databases via database queries. Hence, both multimedia data modeling and image processing capabilities are integrated into the MMM mechanism.
Integration of hurricane wind analysis and multimedia semantic content analysis for public outreach

Natural disasters, such as hurricanes, could have an enormous impact on society. The level of the public's preparedness could make a significant difference in the severity of casualty and damage inflicted by such storms. We present a prototype system to reach out to the public and improve their awareness of the potential dangers involved with such weather events. This web-based system aggregates H*Wind storm track and wind fields data along with relevant videos extracted from YouTube and displays it to the user using Google Earth. A content-based concept detection algorithm is used to extract the videos, which may describe the impact of the storm in relevant geographic locations. Using Hurricane Ike as a case study, the result demonstrates how some of the information collected and displayed by the system could have increased the awareness of the public and potentially helped prepare them better to the devastating storm.
Video semantic concept detection via associative classification

Associative classification (AC) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high accuracy. The traditional AC algorithm discovers the association rules with the frequency count (minimum support) and ranking threshold (minimum confidence) while restricted to the concepts (class labels). In this paper, we propose a novel framework with a new associative classification algorithm which generates the classification rules based on the correlation between different feature-value pairs and the concept classes by using Multiple Correspondence Analysis (MCA). Experimenting with the high-level features and benchmark data sets from TRECVID, our proposed algorithm achieves promising performance and outperforms three well-known classifiers which are commonly used for performance comparison in the TRECVID community.
An Effective Multi-concept Classifier for Video Streams

In this paper, an effective multi-concept classifier is proposed for video semantic concept detection. The core of the proposed classifier is a supervised classification approach called C-RSPM (collateral representative subspace projection modeling) which is applied to a set of multimodal video features for knowledge discovery. It adaptively selects non-consecutive principal dimensions to form an accurate modeling of a representative subspace based on the statistical information analysis and thus achieves both promising classification accuracy and operational merits. Its effectiveness is demonstrated by the comparative experiment, as opposed to several well-known supervised classification approaches including SVM, Decision Trees, Neural Network, Multinomial Logistic Regression Model, and One Rule Classifier, on goal/corner event detection and sports/commercials concepts extraction from soccer videos and TRECVID news collections.
Scene change detection by audio and video clues

Automatic video scene change detection is a challenging task. Using audio or visual information alone often cannot provide a satisfactory solution. However, how to combine audio and visual information efficiently still remains a difficult issue since there are various cases in their relationship due to the versatility of videos. We present an effective scene change detection method that adopts the joint evaluation of the audio and visual features. First, video information is used to find the shot boundaries. Second, the audio features for each video shot can be extracted. Lastly, an audio-video combination schema is proposed to detect the video scene boundaries.
ICSC 2015 message from the general chairs

Welcome to the Ninth IEEE International Conference on Semantic Computing (ICSC 2015). Building on the success of its predecessors, ICSC 2015 continues to be a major international interdisciplinary forum for information exchange and discussion on topics related to major advances in semantic computing, an emerging field that addresses technologies which facilitate activities that allow users to create, manipulate, or retrieve computational content based on semantics (“meaning”, “context”, and “intention”), where “content” may be text, video, audio, software, hardware, network, environment, process, etc. The technical program of ICSC 2015 consists of invited talks and paper presentations.