nils-herrmann/clean_scirepeval huggingface dataset:
Provision of Quality of Service with Router Support

The current Internet servers are susceptible to network attacks. The DDoS attacks consume the network bandwidth and degrade the services provided by the servers. This paper proposed a mechanism to provide security and quality of service (QoS) for a server pool with the support at the edge router. It focuses on the protection of the services based on the priority of different traffic flows and the anomaly degrees of the traffic. Anomaly traffic will be detected using a lightweight anomaly detection method. The result of anomaly detection is sent to the queue management component for resource allocation. Traffic flows are treated with different priorities. The multimedia flows are guaranteed the bandwidth allocation; while the bandwidth allocated for anomaly traffic flows are restricted according to the degrees of the anomaly. Simulation results demonstrate the improvement of service provision for the legitimate traffic flows under a DDoS attack.
Differentiated Service Protection of Multimedia Transmission via Detection of Traffic Anomalies

Multimedia transmission over the Internet has its quality of service (QoS) requirement. However, denial-of-service (DoS) attacks launch large volumes of traffic and consume network bandwidth, thus degrading the quality of the delivered multimedia service. In this paper, we present a differentiated service protection framework consisting of anomaly traffic detection and resource management. Data mining based anomaly traffic detection is implemented at the host; whereas resource management is responsible for the allocation of network resources to the applications. Based on the deviation of the monitored traffic to the normal traffic, differentiated resources are allocated to the traffic. When suspicious traffic is detected, multimedia transmission reacts proactively via adjusting the transmission rate, which can avoid possible packet loss resulting from DoS bandwidth consumption, and thus provide better QoS. The impact of the traffic anomalies on the legitimate services can be mitigated, and the legitimate services can be protected. Simulation results show that better PSNR can be achieved for multimedia transmission during an attack in our proposed framework.
Handling missing values via decomposition of the conditioned set

In this paper, a framework for replacing missing values in a database is proposed since a real-world database is seldom complete. Good data quality in a database can directly improve the performance of any data mining algorithm in various applications. Our proposed framework adopts the basic concepts from conditional probability theories and further develops an algorithm to facilitate the capability of handling both nominal and numerical values, which addresses the problem of the inability of handling both nominal and numerical values with a high degree of accuracy in the existing algorithms. Several experiments are conducted and the experimental results demonstrate that our framework provides a high accuracy when compared with most of the commonly used algorithms such as using the average value, using the maximum value, and using the minimum value to replace missing values.
Utilizing Indirect Associations in Multimedia Semantic Retrieval

Technological developments have lead to the propagation of massive amounts of data in the form of text, image, audio, and video. The unstoppable trend draws researchers' attention to develop approaches to efficiently retrieve and manage multimedia data. The inadequacy of keyword-based search in multimedia data retrieval due to non-existent or incomplete text annotations has called for the development of a contentbased multimedia data management framework. Specifically, detecting high-level semantic concepts is one of the rapidly growing topics in this regard. In order to thoroughly identify semantic concepts in data which have different representations and are derived from different modalities, both positive and negative inter-concept correlations have been recently studied and explored to enhance the re-ranking performance. In this paper, an indirect association rule mining (IARM) approach is introduced to reveal the hidden correlation among semantic concepts. The effectiveness of IARM is evaluated by Multiple Correspondence Analysis (MCA). Furthermore, normalization and score integration are performed to achieve the optimal classification results. The TRECVID 2011 benchmark dataset is used to show the effectiveness of the proposed IARM factor in the re-ranking process.
Automatic annotation of drosophila developmental stages using association classification and information integration

In current developmental research, one of the challenging tasks is to understand the spatio-temporal gene expression patterns and the relationships among different genes. In situ hybridization (ISH) assay which shows mRNA spatio-temporal expression patterns in cells and tissues directly is currently widely utilized in the bench work. With the increasing of available ISH images, automatic annotation systems are highly demanded. In this paper, an automatic classification system is proposed for annotating the in situ hybridization images with respect to the developmental stages. The embryo is first segmented from the original image, registered and normalized. The segmented embryo image is then divided into 100 blocks from which the pixel intensity and texture features are extracted and discretized. The multiple correspondence analysis (MCA) based association classification approach is proposed to generate classification rules for different stages based on the training data set. The testing instance is classified by applying the rules generated in the training process and a classification coordination module is incorporated to resolve the conflicts utilizing the weights derived from angle values in the MCA procedure. Experimental results show that our proposed method achieves promising results and outperforms other state-of-the-art algorithms.
Video Event Detection with Combined Distance-Based and Rule-Based Data Mining Techniques

In this paper, the rare event detection issue in video event detection is addressed through the proposed data mining framework which can be generalized to be domain independent. The fully automatic process via the combination of distance-based and rule-based data mining techniques can greatly reduce the number of negative (non-event) instances and the feature dimension to facilitate the final event detection, without pruning away any positive (event) testing instance along the process. The effectiveness and efficiency of the proposed framework are demonstrated over the goal event detection application based on a large collection of soccer videos with different styles.
Deep Learning for Imbalanced Multimedia Data Classification

Classification of imbalanced data is an important research problem as lots of real-world data sets have skewed class distributions in which the majority of data instances (examples) belong to one class and far fewer instances belong to others. While in many applications, the minority instances actually represent the concept of interest (e.g., fraud in banking operations, abnormal cell in medical data, etc.), a classifier induced from an imbalanced data set is more likely to be biased towards the majority class and show very poor classification accuracy on the minority class. Despite extensive research efforts, imbalanced data classification remains one of the most challenging problems in data mining and machine learning, especially for multimedia data. To tackle this challenge, in this paper, we propose an extended deep learning approach to achieve promising performance in classifying skewed multimedia data sets. Specifically, we investigate the integration of bootstrapping methods and a state-of-the-art deep learning approach, Convolutional Neural Networks (CNNs), with extensive empirical studies. Considering the fact that deep learning approaches such as CNNs are usually computationally expensive, we propose to feed low-level features to CNNs and prove its feasibility in achieving promising performance while saving a lot of training time. The experimental results show the effectiveness of our framework in classifying severely imbalanced data in the TRECVID data set.
Moving Object Detection under Object Occlusion Situations in Video Sequences

It is a great challenge to detect an object that is overlapped or occluded by other objects in images. For moving objects in a video sequence, their movements can bring extra spatio-temporal information of successive frames, which helps object detection, especially for occluded objects. This paper proposes a moving object detection approach for occluded objects in a video sequence with the assist of the SPCPE (Simultaneous Partition and Class Parameter Estimation) unsupervised video segmentation method. Based on the preliminary foreground estimation result from SPCPE and object detection information from the previous frame, an n-steps search (NSS) method is utilized to identify the location of the moving objects, followed by a size-adjustment method that adjusts the bounding boxes of the objects. Several experimental results show that our proposed approach achieves good detection performance under object occlusion situations in serial frames of a video sequence.
An effective content-based visual image retrieval system

An effective content-based visual image retrieval system is presented. This system consists of two main components: visual content extraction and indexing, and query engine. Each image in the image database is represented by its visual features: color and spatial information. The system uses a color label histogram with only thirteen bins to extract the color information from an image in the image database. A unique unsupervised segmentation algorithm combined with the wavelet technique generates the spatial feature of an image automatically. The resulting feature vectors are relatively low in dimensions compared to those in other systems. The query engine employs a color filter and a spatial filter to dramatically reduce the search range. As a result, queue processing is speeded up. The experimental results demonstrate that our system is capable of retrieving images that belong to the same category.
Effective Moving Object Detection and Retrieval via Integrating Spatial-Temporal Multimedia Information

In the area of multimedia semantic analysis and video retrieval, automatic object detection techniques play an important role. Without the analysis of the object-level features, it is hard to achieve high performance on semantic retrieval. As a branch of object detection study, moving object detection also becomes a hot research field and gets a great amount of progress recently. This paper proposes a moving object detection and retrieval model that integrates the spatial and temporal information in video sequences and uses the proposed integral density method (adopted from the idea of integral images) to quickly identify the motion regions in an unsupervised way. First, key information locations on video frames are achieved as maxima and minima of the result of Difference of Gaussian (DoG) function. On the other hand, a motion map of adjacent frames is obtained from the diversity of the outcomes from Simultaneous Partition and Class Parameter Estimation (SPCPE) framework. The motion map filters key information locations into key motion locations (KMLs) where the existence of moving objects is implied. Besides showing the motion zones, the motion map also indicates the motion direction which guides the proposed integral density approach to quickly and accurately locate the motion regions. The detection results are not only illustrated visually, but also verified by the promising experimental results which show the concept retrieval performance can be improved by integrating the global and local visual information.
Enhancing Rare Class Mining in Multimedia Big Data by Concept Correlation

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. However, the detection of these events is such an important research problem and has attracted significant research efforts as lots of real-world big data sets have skewed class distributions. Despite extensive research efforts, rare class mining remains one of the most challenging problems in information science, especially for multimedia big data. Though inter-concept correlations have been utilized to address this issue recently, the very small number of instances in the minority class often lead to the detection of imprecise correlations and unsatisfactory classification results. This paper proposes a novel concept correlation analysis strategy framework using the correlations between the retrieval scores and labels. By integrating the correlation information, the proposed framework can help imbalance data classification and enhance rare class (or concept) mining even with trivial scores from the minority class. Experimental results on the TRECVID multimedia big benchmark data set demonstrate the effectiveness of the proposed framework with promising performance.
A conflict-based confidence measure for associative classification

Associative classification has aroused significant attention recently and achieved promising results. In the rule ranking process, the confidence measure is usually used to sort the class association rules (CARs). However, it may be not good enough for a classification task due to a low discrimination power to instances in the other classes. In this paper, we propose a novel conflict-based confidence measure with an interleaving ranking strategy for re-ranking CARs in an associative classification framework, which better captures the conflict between a rule and a training data instance. In the experiments, the traditional confidence measure and our proposed conflict-based confidence measure with the interleaving ranking strategy are applied as the primary sorting criterion for CARs. The experimental results show that the proposed associative classification framework achieves promising classification accuracy with the use of the conflict-based confidence measure, particularly for an imbalanced data set.
The protection of QoS for multimedia transmission against denial of service attacks

In this paper, a secure and adaptive multimedia transmission framework is proposed to maintain the quality of service (QoS) of the multimedia streams during the denial-of service (DoS) attacks. A DoS attack generates a large amount of traffic, which will occupy the bandwidth and degrade the quality of video. The proposed framework consists of two components: intrusion detection and adaptive transmission management. The intrusion detection component monitors the incoming traffic to the server, detects the attacks, and interacts with the adaptive transmission management component; while the adaptive transmission management component is designed to improve QoS of the video via the efficient utilization of the network resources. With the detection of the DoS attacks, the bandwidth occupied by the attacks can be reduced and protected for video transmission. The results of our preliminary simulations in NS2 show that the quality of the multimedia stream can still be maintained during an attack.
Per-class queue management and adaptive packet drop mechanism for multimedia networking

In this paper, we propose a per-class queue management and adaptive packet drop mechanism in the routers for Internet congestion control. We model active queue management as an optimization problem and our proposed mechanism provides congestion control and fairness for different types of traffic flows. An optimal packet drop rate is obtained to maintain a relatively small queue occupancy, which provides a less queue delay delivery of packets. Simulations are conducted to compare our approach with the fixed packet drop rate approach. Moreover, the queue occupancy and the packet drop rates obtained are both upper bounded, which is meaningful for providing the class-based guaranteed delay services for real-time multimedia applications.
Histology Image Classification Using Supervised Classification and Multimodal Fusion

The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.
Organizing a network of databases using probabilistic reasoning

Due to the complexity of real-world applications, the number of databases and the volumes of data in databases have increased tremendously. With the explosive growth in the amount and complexity of data, how to effectively organize the databases and utilize the huge amount of data becomes important. For this purpose, a probabilistic network that organizes a network of databases and manages the data in the databases is proposed. Each database is represented as a node in the probabilistic network and the affinity relations of the databases are embedded in the proposed Markov model mediator (MMM) mechanism. Probabilistic reasoning is used to formulate and derive the probability distributions for an MMM. Once the probability distributions of each MMM are generated, a stochastic process is conducted to calculate the similarity measures for pairs of databases. The similarity measures are transformed into the branch probabilities of the probabilistic network. Then, the data in the database can be managed and utilized to allow user queries for database searching and information retrieval. An example is included to illustrate how to model each database into an MMM and how to organize the network of databases into a probabilistic network.
Rule mining and classification in imperfect databases

A rule-based classifier learns rules from a set of training data instances with assigned class labels and then uses those rules to assign a class label for a new incoming data instance. To accommodate data imperfections, a probabilistic relational model would represent the attributes by probabilistic functions. One extension to this model uses belief functions instead. Such an approach can represent a wider range of data imperfections. However, the task of extracting frequent patterns and rules from such a "belief theoretic" relational database has to overcome a potentially enormous computational burden. In this work, we present a data structure that is an alternate representation of a belief theoretic relational database. We then develop efficient algorithms to query for belief of item sets, extract frequent item sets and generate corresponding association rules from this representation. This set of rules is then used as the basis on which an unknown data instance, whose attributes are represented via belief functions, is classified. These algorithms are tested on a data set collected from a test bed that mimics airport threat detection and classification scenario where both data attributes and threat class labels may possess imperfections.
Ensuring fairness in multimedia multicast streaming with optimal rate allocation and client buffer utilization

In this paper, we propose a rate control mechanism for multimedia multicast streaming in the Internet. A single and optimal transmission rate that is adaptive to the network congestion, buffer occupancies, and the playback requirements of the member clients in the multicast group is allocated. We optimize the multicasting delivery in the Internet by improving the buffer occupancy of all clients for on-time presentation while using the minimal transmission rate. Scalable playback is used for the clients with low bandwidth capabilities in a heterogeneous environment. Simulation results show that the buffer capacity can be efficiently improved and fairness is ensured among the member clients.
Deep Learning with MCA-based Instance Selection and Bootstrapping for Imbalanced Data Classification

In this paper, we propose an extended deep learning approach that incorporates instance selection and bootstrapping techniques for imbalanced data classification. In supervised learning, classification performance often deteriorates when the training set is imbalanced where at least one of the classes has a substantially fewer number of instances than the others. We propose to use adaptive synthetic sampling approach (ADASYN) to generate synthetic instances for the minority class. A data pruning process based on multiple correspondence analysis (MCA) is then performed to identify a sub-set of synthetic instances that are most suitable to supplement the existing minority instances. This results in a relatively more balanced training dataset which is then bootstrapped and fed into the convolutional neural networks (CNNs) for classification. Furthermore, we propose to use low-level features pre-processed by principal component analysis (PCA), instead of the commonly used raw signal data, as the input to CNNs to reduce the computational time. The experimental results show the effectiveness of our framework in classifying 54 TRECVID concepts with different imbalanced levels by comparing with other state-of-the-art methods.
Multimodal Sparse Linear Integration for Content-Based Item Recommendation

Most content-based recommender systems focus on analyzing the textual information of items. For items with images, the images can be treated as another information modality. In this paper, an effective method called MSLIM is proposed to integrate multimodal information for content-based item recommendation. It formalizes the probelm into a regularized optimization problem in the least-squares sense and the coordinate gradient descent is applied to solve the problem. The aggregation coefficients of the items are learned in an unsupervised manner during this process, based on which the k-nearest neighbor (k-NN) algorithm is used to generate the top-N recommendations of each item by finding its k nearest neighbors. A framework of using MSLIM for item recommendation is proposed accordingly. The experimental results on a self-collected handbag dataset show that MSLIM outperforms the selected comparison methods and show how the model parameters affect the final recommendation results.