nils-herrmann/clean_scirepeval huggingface dataset:
Domain Knowledge Assisted Data Processing for Florida Public Hurricane Loss Model (Invited Paper)

Catastrophes have caused tremendous damages in human history and triggered record high post-disaster relief from the governments. The research of catastrophic modeling can help estimate the effects of natural disasters like hurricanes, floods, surges, and earthquakes. In every Atlantic hurricane season, the state of Florida in the United States has the potential to suffer economic and human losses from hurricanes. The Florida Public Hurricane Loss Model (FPHLM), funded by the Florida Office of Insurance Regulation, has assisted Florida and the residential insurance industry for more than a decade. How to process big data for historical hurricanes and insurance companies remains a challenging research topic for cat models. In this paper, the FPHLM's novel integrated domain knowledge assisted big data processing system is introduced and its effectiveness of data processing error prevention is presented.
Leveraging Concept Association Network for Multimedia Rare Concept Mining and Retrieval

Automatic high-level semantic concept detection is a crucial step for multimedia data management, indexing, and retrieval. It is well-acknowledged that semantic gap poses a great challenge in multimedia content-based research. It becomes even more challenging when the concept of interest is extremely rare in the training data sets because of the poor modeling for the positive instances. In this paper, a Concept Association Network (CAN) is trained by selecting significant links to capture the strong associations among different concepts using association rule mining (ARM). By taking into account of the correlations and credibilities of reference concept nodes, the advantages of the reference nodes are utilized. Experimental results using TRECVID 2010 data sets show that by utilizing the proposed framework, the Mean Average Precision (MAP) values of all the concepts are improved, and the significant improvement of the MAP values of the rare concepts further attests the promising results.
VideoTopic: Content-Based Video Recommendation Using a Topic Model

Most video recommender systems limit the content to the metadata associated with the videos, which could lead to poor results since metadata is not always available or correct. Meanwhile, the visual information of videos is typically not fully explored, which is especially important for recommending new items with limited metadata information. In this paper, a novel content-based video recommendation framework, called Video Topic, that utilizes a topic model is proposed. It decomposes the recommendation process into video representation and recommendation generation. It aims to capture user interests in videos by using a topic model to represent the videos, and then generates recommendations by finding those videos that most fit to the topic distribution of the user interests. Experimental results on the Movie Lens dataset validate the effectiveness of Video Topic by evaluating each of its components and the whole framework.
Exciting Event Detection Using Multi-level Multimodal Descriptors and Data Classification

Event detection is of great importance in high-level semantic indexing and selective browsing of video clips. However, the use of low-level visual-audio feature descriptors alone generally fails to yield satisfactory results in event identification due to the semantic gap issue. In this paper, we propose an advanced approach for exciting event detection in soccer video with the aid of multi-level descriptors and classification algorithm. Specifically, a set of algorithms are developed for efficient extraction of meaningful mid-level descriptors to bridge the semantic gap and to facilitate the comprehensive video content analysis. The data classification algorithm is then performed upon the combination of multimodal mid-level descriptors and low-level feature descriptors for event detection. The effectiveness and efficiency of the proposed framework are demonstrated over a large collection of soccer video data with different styles produced by different broadcasters
IF-MCA: Importance Factor-Based Multiple Correspondence Analysis for Multimedia Data Analytics

Multimedia concept detection is a challenging topic due to the well-known class imbalance issue, where the data instances are distributed unevenly across different classes. This problem becomes even more prominent when the minority class that contains an extremely small proportion of the data represents the concept of interest as has occurred in many real-world applications such as frauds in banking transactions and goal events in soccer videos. Traditional data mining approaches often have difficulty handling largely skewed data distributions. To address this issue, in this paper, an importance-factor (IF)-based multiple correspondence analysis (MCA) framework is proposed to deal with the imbalanced datasets. Specifically, a hierarchical information gain analysis method, which is inspired by the decision tree algorithm, is presented for critical feature selection and IF assignment. Then, the derived IF is incorporated with the MCA algorithm for effective concept detection and retrieval. The comparison results in video concept detection using the disaster dataset and the soccer dataset demonstrate the effectiveness of the proposed framework.
Damage pattern mining in hurricane image databases

We present a damage pattern mining framework for hurricane data on residential houses using aerial photographs with 1:3000 based scale. The vertical photographs are normally collected for an overview of a disaster area or for more detailed assessment. Damage on roof, especially for shingles torn off, is expected to be discovered in a more efficient and automatic way instead of going through the high-resolution aerial photograph for numerous details. The discovered damage patterns can then be used for hurricane damage assessment and impacts on different geographical areas.  applying a novel and effective segmentation method on each residential house on the aerial photograph of one community, (2) using the segmentation results to obtain a set of indexing parameters for each house representing the damage level of roof cladding as well as the patterns of damage, (3) using these parameters to select several templates representing the damage patterns so that users can issue query-by-example (QBE) queries. The proposed segmentation method is an unsupervised simultaneous partition and class parameter estimation algorithm that considers the problem of segmentation as a joint estimation of the partition and class parameter variables. By utilizing this segmentation method, the indexing parameters can be obtained automatically. The QBE capability can assist in finding similar damage patterns on the roof of the residential houses in different locations in the image databases. Experiments based on the aerial photographs of Hurricane Andrew in 1992 are conducted and analyzed to show the effectiveness of the proposed hurricane damage pattern mining framework.
Integration of Visual Temporal Information and Textual Distribution Information for News Web Video Event Mining

News web videos exhibit several characteristics, including a limited number of features, noisy text information, and error in near-duplicate keyframes (NDK) detection. Such characteristics have made the mining of the events from news web videos a challenging task. In this paper, a novel framework is proposed to better group the associated web videos to events. First, the data preprocessing stage performs feature selection and tag relevance learning. Next, multiple correspondence analysis is applied to explore the correlations between terms and events with the assistance of visual information. Cooccurrence and visual near-duplicate feature trajectory induced from NDKs are combined to calculate the similarity between NDKs and events. Finally, a probabilistic model is proposed for news web video event mining, where both visual temporal information and textual distribution information are integrated. Experiments on the news web videos from YouTube demonstrate that the integration of visual temporal information and textual distribution information outperforms the existing methods in the news web video event mining.
Component-based design and integration of a distributed multimedia management system

In order to fulfill the functional requirements of multimedia management systems while achieving the efficiency and reliability at the same time, we adopt the component-based design to develop a complicated distributed multimedia management system called DMMManager in this paper. DMMManager integrates a set of core components for managing multimedia data, such as content-based retrieval for image and video data, and multimedia presentation design and rendering. Component-based design makes the reuse and share of the components easy and reliable, and thus avoids the waste of both time and man power in the development cycle. Moreover, new applications can be easily integrated into this system as a new development and utilize the existing components. Besides component reuse, the system is also designed to facilitate information reuse. For example, the output of the image/video retrieval component can be taken as the input for the multimedia presentation component. In addition, the use of subnetworks in our multimedia presentation design also enables the modularized reuse of the existing presentation models that are previously created.
An intelligent framework for spatio-temporal vehicle tracking

Recently, the Intelligent Transportation Systems (ITS) have been identified as the new paradigm to address the growing mobility problems, and to alleviate congestion and augment the quality of vehicular flow. This paper presents an intelligent and automatic framework for spatio-temporal vehicle tracking in video sequences in order to provide useful information to the traffic control center. The spatio-temporal relationships of the objects are captured automatically via a proposed unsupervised image/video segmentation method and multimedia input strings. A traffic video sequence is used to demonstrate the effectiveness of the proposed framework to identify and track the vehicle objects in the video sequence.
Optimal bandwidth allocation scheme with delay awareness in multimedia transmission

Recently, efficient network resource management and quality-of-service (QoS) guarantee become more and more important for multimedia applications and services, especially when considering network delays. An optimal bandwidth allocation scheme is introduced. It achieves maximal utilization of the client buffer and minimal allocation of bandwidth for each client. The proposed scheme allocates bandwidth to multiple clients requesting services from a server by adjusting the transmission rates based on the client buffer occupancy, the playback requirements of the individual client and the network delays. Simulations for the single client and multiple client scenarios are conducted under different network congestion levels. The simulation results show that our approach performs better in comparison with the fixed rate allocation approach and the rate by playback requirement approach, since it avoids underflows and overflows efficiently and provides QoS for more clients with limited available bandwidth in the network.
Sparse Linear Integration of Content and Context Modalities for Semantic Concept Retrieval

The semantic gap between low-level visual features and high-level semantics is a well-known challenge in content-based multimedia information retrieval. With the rapid popularization of social media, which allows users to assign tags to describe images and videos, attention is naturally drawn to take advantage of these metadata in order to bridge the semantic gap. This paper proposes a sparse linear integration (SLI) model that focuses on integrating visual content and its associated metadata, which are referred to as the content and the context modalities, respectively, for semantic concept retrieval. An optimization problem is formulated to approximate an instance using a sparse linear combination of other instances and minimize the difference between them. The prediction score of a concept for a test instance measures how well it can be reconstructed by the positive instances of that concept. Two benchmark image data sets and their associated tags are used to evaluate the SLI model. Experimental results show promising performance by comparing with the approaches based on a single modality and approaches based on popular fusion methods.
Optimal resource utilization in multimedia transmission

Efficient utilization of network resources is essential in the provision of quality multimedia services. Different approaches have been proposed to shape the multimedia streams as a transmission schedule with smoothed traffic burst. In this paper, we concentrate on the problem of efficient use of bandwidth and client buffer. For this purpose, an optimal transmission schedule is proposed to provide the minimal allocation of the bandwidth and maximal utilization of the client buffer. Simulation results show that the shaping results obtained can dynamically adjust the transmission rate according to the buffer packet sizes and playback rates at the client to avoid the loss of packets, and at the same time achieve the minimal bandwidth allocation and maximal utilization of the client buffer.
Effective supervised discretization for classification based on correlation maximization

In many real-world applications, there are features (or attributes) that are continuous or numerical in the data. However, many classification models only take nominal features as the inputs. Therefore, it is necessary to apply discretization as a pre-processing step to transform numerical data into nominal data for such models. Well-discretized data should not only characterize the original data to produce a concise summarization, but also improve the classification performance. In this paper, a novel and effective supervised discretization algorithm based on correlation maximization (CM) is proposed by using multiple correspondence analysis (MCA) which is a technique to capture the correlations between multiple variables. For each numeric feature, the correlation information generated from MCA is used to build the discretization algorithm that maximizes the correlations between feature intervals/items and classes. Empirical comparisons with four other commonly used discretization algorithms are conducted using six well-known classifiers. Results on five UCI datasets and five TRECVID datasets demonstrate that our proposed discretization algorithm can automatically generate a better set of features (feature intervals) by maximizing their correlations with the classes and thus improve the classification performance.
PixSO: a system for video shot detection

In this paper, a system called PixSO (pixel-level comparison, segmentation and object tracking) is presented for effective shot change detection using an unsupervised object segmentation algorithm and the technique of object tracking based on the segmentation mask maps. The detection method was tested on TV news, commercial, sports and documentary video sequences which contain different types of shots having different object and camera motions. The results have shown that the PixSO system can not only produce accurate shot change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis in multimedia databases.
Adaptive association rule mining for web video event classification

Due to the popularity and development of social networks and web video sites, we have witnessed an exponential growth in the volumes of web videos in the last decade. This prompts an urgent demand for efficiently grasping the major events. Nevertheless, the insufficient and noisy text information has made it difficult and challenging to mine the events based on the initial keywords and visual features. In this paper, we propose an adaptive semantic association rule mining method in the NDK (Near-Duplicate Keyframes) level to enrich the keyword information and to remove the words without any semantic relationship. Moreover, both textual and visual information are employed for event classification, targeting for bridging the gap between NDKs and the high-level semantic concepts. Experimental results on large scale web videos from YouTube demonstrate that our proposed method achieves good performance and outperforms the selected baseline methods.
Self-adjusted network transmission for multimedia data

High bandwidth requirements in multimedia transmission make the efficient use of limited network resource a challenging task, especially when multiple clients make their requests to the server simultaneously. We propose a self-adjusted network transmission mechanism for multiple clients. Instead of assigning a fixed bandwidth for each client, the server determines the transmission rate for each client connection according to the buffer packets and playback rate at each client. Transmission rates are adjusted when the total requested bandwidth is larger than the network bandwidth. In addition, the proposed mechanism can minimize the bandwidth allocation and maximize the client buffer utilization. A simulation is performed and the simulation results show that the proposed mechanism can dynamically change the transmission rate for each client to avoid overflow of the client buffer, and achieve the optimal utilization of the limited network resource in multiple client network environments.
Multiple object retrieval for image databases using multiple instance learning and relevance feedback

The paper proposes a method to discover effectively users' concept patterns when multiple objects of interest (e.g., foreground and background objects) are involved in content-based image retrieval. The proposed method incorporates multiple instance learning into the user relevance feedback in a seamless way to discover where the user's objects/regions of most interest are and how to map the local features of that(those) region(s) to the user's high-level concepts. A three-layer neural network is used to model the underlying mapping progressively through the feedback and learning procedure
Data integration for capital projects via community-specific conceptual representations

Although data integration has been a research subject for decades in the AEC (architecture engineering construction) industry whose data is usually highly fragmented, nowadays we still see problem statements regarding data integration, which are often very similar to those discussed in articles dated long time ago. Representing a departure from existing methods, this paper suggests exploring a novel method, the community-specific conceptual representation, based on the Markov model framework and the mediator concept. The community-specific conceptual representation provides a de facto standard for collaborating systems in order to share data; while the Markov model framework and the mediator concept provide a theoretic foundation for the generation of the conceptual representation with expert assistances. This paper offers a theoretic discussion on the relevant theories and concepts. Further studies are needed to validate the feasibility and the practicality of this approach in the context of the AEC industry.
Collaborative Filtering by Mining Association Rules from User Access Sequences

Recent research in mining user access patterns for predicting Web page requests focuses only on consecutive sequential Web page accesses, i.e., pages which are accessed by following the hyperlinks. In this paper, we propose a new method for mining user access patterns that allows the prediction of multiple non-consecutive Web pages, i.e., any pages within the Web site. Our approach consists of two major steps. First, the shortest path algorithm in graph theory is applied to find the distances between Web pages. In order to capture user access behavior on the Web, the distances are derived from user access sequences, as opposed to static structural hyperlinks. We refer to these distances as minimum reaching distance (MRD) information. The association rule mining (ARM) technique is then applied to form a set of predictive rules which are further refined and pruned by using the MRD information. The proposed approach is applied as a collaborative filtering technique to recommend Web pages within a Web site. Experimental results demonstrate that our approach improves performance over the existing Markov model approach in terms of precision and recall, and also has a better potential of reducing the user access time on the Web
Negative-Based Sampling for Multimedia Retrieval

Nowadays, in such a high-tech living lifestyle, profusion of multimedia data are produced and propagated around the world. To identify meaningful semantic concepts from the large amount of data, one of the major challenges is called the data imbalance problem. Data imbalance occurs when the number of positive instances (i.e., instances which contain the target concept) is greatly less than the number of negative instances (i.e., instances which do not contain the target concept). In other words, the ratio between positive and negative instances is extremely low. Rebalancing the dataset is usually proposed to resolve the problem by sampling or data pruning. In this paper, we propose a sampling method which consists of three stages, namely selecting features to identify the negative instances, producing negative ranking scores, and performing sampling. The method is compared with some other existing methods on the TRECVID dataset and is demonstrated to have better performance.
An enhanced query model for soccer video retrieval using temporal relationships

The focal goal of our research is to develop a general framework which can automatically analyze the sports video, detect the sports events, and finally offer an efficient and user-friendly system for sports video retrieval. In our earlier work, a novel multimedia data mining technique was proposed for automatic soccer event extraction by adopting multimodal feature analysis. Until now, this framework has been performed on the detection of goal and corner kick events and the results are quite impressive. Correspondingly, in this work, the detected video events are modeled and effectively stored in the database. A temporal query model is designed to satisfy the comprehensive temporal query requirements, and the corresponding graphical query language is developed. The advanced characteristics make our model particularly well suited for searching events in a large scale video database.
Gaussian Mixture Model-Based Subspace Modeling for Semantic Concept Retrieval

Data mining and machine learning methods have been playing an important role in searching and retrieving multimedia information from all kinds of multimedia repositories. Although some of these methods have been proven to be useful, it is still an interesting and active research area to effectively and efficiently retrieve multimedia information under difficult scenarios, i.e., detecting rare events or learning from imbalanced datasets. In this paper, we propose a novel subspace modeling framework that is able to effectively retrieve semantic concepts from highly imbalanced datasets. The proposed framework builds positive subspace models on a set of positive training sets, each of which is generated by a Gaussian Mixture Model (GMM) that partitions the data instances of a target concept (i.e., the original positive set of the target concept) into several subsets and later merges each subset with the original positive data instances. Afterwards, a joint-scoring method is proposed to fuse the final ranking scores from all such positive subspace models and the negative subspace model. Experimental results evaluated on a public-available benchmark dataset show that the proposed subspace modeling framework is able to outperform peer methods commonly used for semantic concept retrieval.