nils-herrmann/clean_scirepeval huggingface dataset:
UNPCC: A Novel Unsupervised Classification Scheme for Network Intrusion Detection

The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (unsupervised principal component classifier) algorithm is a multiclass unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms
Unit detection from American football TV broadcast using multimodal content analysis

In this paper, a multimodal unit detection framework to detect and extract units, a novel concept towards event detection and extraction in sports TV broadcasts, is proposed. The proposed unit is defined to be a segment of a sports TV broadcast that describes a potentially interesting event, which possesses the potential of attracting the attention of the observer and satisfy his/her need of viewing the more interesting segments of the broadcast. A number of events that are considered as the unit target events are game impacting events such as score, missed score, penalties, and special game inserts, such as highlights and statistics clips. The proposed framework serves as an efficient data preprocessing procedure that can reduce the amount of data by ridding off the irrelevant data and prepare the remaining data in an efficient way for future event detection and extraction. Several experiments are conducted on various football games from different TV broadcasts, including college football and professional football. The experimental results demonstrate that the proposed framework effectively achieves the goal of data reduction, which is expected to increase the accuracy of event detection and extraction from the American football TV broadcast.
Correlation-based interestingness measure for video semantic concept detection

The technique of performing classification using association rule mining (ARM) has been adopted to bridge the multimedia semantic gap between low-level features and high-level concepts of interest, taking advantages of both classification and association rule mining. One of the most important research approaches in ARM is to investigate the interestingness measure which plays a key role in association rule discovery stage and rule selection stage. In this paper, a new correlation-based interestingness measure that is used at both stages is proposed. The association rules are generated by a novel interestingness measure obtained from applying multiple correspondence analysis (MCA) to explore the correlation between two feature-value pairs and concept classes. Then the correlation-based interestingness measure is reused and aggregated with the inter-similarity and intra-similarity values to rank the final rule set for classification. Detecting the concepts from the benchmark data provided by the TRECVID project, we have shown that our proposed framework achieves higher accuracy than the classifiers that are commonly applied to multimedia retrieval.
Utilizing concept correlations for effective imbalanced data classification

Data imbalance is a challenging and common problem in data mining and machine learning areas, and has attracted significant research efforts. A data set is considered imbalanced when the data instances (samples) are not close to uniformly distributed across different classes/categories, which is very common in real-world data sets. It is likely to result in biased classification results. In this paper, a two-phase classification framework is proposed to make the classification of imbalanced data more accurate and stable. The proposed framework is based on the correlations generated between concepts. The general idea is to identify negative data instances which have certain positive correlations with data instances in the target concept to facilitate the classification task. The experimental results show that our framework is effective in imbalanced data classification and is robust to feature descriptors by comparing it with four existing approaches using four different kinds of feature representations.
Comparison of Visual Datasets for Machine Learning

One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new approach creating datasets using real-time, geo-tagged visual data, greatly improving the contextual information of the data. The data could be automatically labeled by cross-referencing information from other sources (such as weather).
Video Semantic Concept Discovery using Multimodal-Based Association Classification

Digital audio and video have recently taken a center stage in the communication world, which highlights the importance of digital media information management and indexing. It is of great interest for the multimedia research community to find methods and solutions that could help bridge the semantic gap that exists between the low-level features extracted from the audio or video data and the actual semantics of the data. In this paper, we propose a novel framework that works towards reducing this semantic gap. The proposed framework uses the a priori algorithm and association rule mining to find frequent itemsets in the feature data set and generate classification rules to classify video shots to different concepts (semantics). We also introduce a novel pre-filtering architecture which reduces the high positive to negative instances ratio in the classifier training step. This helps reduce the amount of misclassification errors. Our proposed framework shows promising results in classifying multiple concepts.
Automatic Discovery of Bioluminescent Proteins from Large Protein Databases

Accurate annotation of different protein features becomes increasingly important in enriching gene ontology databases. In this work, we present a framework to predict the bioluminescence of any given protein sequence. Bioluminescent proteins are produced by living organisms and emit light naturally. Bioluminescence is deemed to have different functions in living organisms including camouflage, attraction to prey, communication, etc. In addition, bioluminescent proteins are also widely used as labels in assay development, reporters of gene expression, and imaging agents in biotechnology. Currently, bioluminescent proteins are mainly curated by researchers through experimental analysis, which is a time consuming process. However, the data mining based algorithms provide an efficient way to detect candidate bioluminescent proteins and suggest prioritization of the experimental work. While traditional alignment based algorithms (such as BLAST) show promising results in terms of sequence analysis, it suffers from the limitation that the testing sequence should show homology to the sequences in the available training data sets. In order to overcome such a limitation, our proposed framework uses a set of homology-independent features that are extracted directly from the primary sequences to represent the global physicochemical properties as well as the sequence order characteristics of proteins. In addition, a novel subspace-based data filtering algorithm is proposed to eliminate noise from the training data. One existing framework addressing the same problem was implemented and compared with our proposed framework. The experimental results indicate that our proposed framework shows promising performance. In addition, the proposed framework is generic and could easily be applied to annotations of other protein properties.
Field-effect natural language semantic mapping

This paper addresses the problem of mapping natural language to its semantics. It presupposes that the input is in random (compressed) form and proceeds to detail a methodology for extracting the semantics from that normal form. The idea is to enumerate contextual cues and learn to associate those cues with meaning. The process is inherently fuzzy and for this reason is also inherently adaptive in nature. It is shown that the influence of context on meaning grows exponentially with the length of a word sequence. This suggests that rule-based randomization plays a key role in rendering a field-effect natural language semantic mapping tractable. An example of rule-based randomization for semantic normalization is as follows. Suppose that two commands to a robot are deemed to be equivalent; namely, "Grasp and pick up the glass" and "Hold the cup and raise it". Their mutual normalization might then be, "Grab container. Lift container." Clearly, the randomization process can be effected by rules. Also, the normalized syntax makes the result of any semantic mapping process-such as detailed herein-more efficient. A natural language front-end is described, which is designed to reduce the impedance mismatch between the human and the machine. Most significantly, the effective translation of natural language semantics is shown to critically depend on an accelerated capability for learning.
An efficient deep residual-inception network for multimedia classification

Deep learning has led to many breakthroughs in machine perception and data mining. Although there are many substantial advances of deep learning in the applications of image recognition and natural language processing, very few work has been done in video analysis and semantic event detection. Very deep inception and residual networks have yielded promising results in the 2014 and 2015 ILSVRC challenges, respectively. Now the question is whether these architectures are applicable to and computationally reasonable in a variety of multimedia datasets. To answer this question, an efficient and lightweight deep convolutional network is proposed in this paper. This network is carefully designed to decrease the depth and width of the state-of-the-art networks while maintaining the high-performance. The proposed deep network includes the traditional convolutional architecture in conjunction with residual connections and very light inception modules. Experimental results demonstrate that the proposed network not only accelerates the training procedure, but also improves the performance in different multimedia classification tasks.
A Hybrid Layered Multiagent Architecture with Low Cost and Low Response Time Communication Protocol for Network Intrusion Detection Systems

Applying multiagent technology to the management of network security is a challenging task since it requires the management on different time instances and has many interactions. This paper aims at utilizing potential benefits from applying distributed multiagent technology to network security. To facilitate information exchange between different agents in our proposed hybrid layered multiagent architecture, a low-cost and low response time agent communication protocol is developed to tackle the issues typically associated with a distributed multiagent system, such as poor system performance, excessive processing power requirement, and long delays. The bandwidth and response time performance of the proposed end-to-end system is investigated through the simulation of the proposed agent communication protocol on our private LAN testbed called Hierarchical Agent Network for Intrusion Detection Systems (HAN-IDS). The simulation results show that our proposed system is efficient and extensible since it consumes negligible bandwidth with low cost and low response time.
Neural network based framework for goal event detection in soccer videos

In this paper, a neural network based framework for semantic event detection in soccer videos is proposed. The framework provides a robust solution for soccer goal event detection by combining the strength of multimodal analysis and the ability of neural network ensembles to reduce the generalization error. Due to the rareness of the goal events, the bootstrapped sampling method on the training set is utilized to enhance the recall of goal event detection. Then a group of component networks are trained using all the available training data. The precision of the detection is greatly improved via the following two steps. First, a pre-filtering step is employed on the test set to reduce the noisy and inconsistent data, and then an advanced weighting scheme is proposed to intelligently traverse and combine the component network predictions by taking into consideration the prediction performance of each network. A set of experiments are designed to compare the performance of different bootstrapped sampling schemes, to present the strength of the proposed weighting scheme in event detection, and to demonstrate the effectiveness of our framework for soccer goal event detection.
Unit detection in American football TV broadcasts using average energy of audio track

In this paper, we explore the domain of American Football TV broadcasting with respect to interesting event detection. Most of the existing methods achieve the goal of event detection in sports video using some type of shot or scene based approaches. Many problems arise when it comes to work in the shot or scene level in the American Football domain. The big amount of camera movement, such as tilting, zooming, panning, and the large number of different angles used by different broadcasting networks make it very difficult to work in the shot/scene level. We show how in any of these two levels, over segmentation is almost inevitable. We also propose a novel concept that answers most of the problems of the domain we work on. This is the concept of "unit". A unit is defined as good workable data, which is not necessary to be very accurate. The main purpose of the unit detection process we present is to locate these potentially interesting segments in the video, and separate them from the rest of the redundant data.
Message from the Program Co-Chairs

Welcome to the proceedings of the 12th IEEE International Conference on Information Reuse and Integration (IEEE IRI 2011) in Las Vegas, Nevada, USA. Information Reuse and Integration (IRI) aims at maximizing the reuse of information by creating simple, rich, and reusable knowledge representations and consequently explores strategies for integrating this knowledge into legacy systems. IRI plays a pivotal role in the capture, representation, maintenance, integration, validation, and extrapolation of information; and applies both information and knowledge for enhancing decision-making in various application domains. During more than a decade of conferences, IRI has established itself as an internationally renowned forum for researchers and practitioners to exchange ideas, connect with colleagues, and advance the state of the art and practice of current and future research in information reuse and integration.
Video Database Modeling and Temporal Pattern Retrieval using Hierarchical Markov Model Mediator

The dream of pervasive multimedia retrieval and reuse will not be realized without incorporating semantics in the multimedia database. As video data is penetrating many information systems, the need for database support for video data evolves. Hence, we propose an innovative database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) which integrates lowlevel features, semantic concepts, and high-level user perceptions for modeling and indexing multiple-level video objects to facilitate temporal pattern retrieval. Different from the existing database modeling methods, our approach carries a stochastic and dynamic process in both search and similarity calculation. In the retrieval of semantic event patterns, HMMM always tries to traverse the right path and therefore it can assist in retrieving more accurate patterns quickly with lower computational costs. Moreover, HMMM supports feedbacks and learning strategies, which can proficiently assure the continuous improvements of the overall performance.
Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests

Recent developments in social media and cloud storage lead to an exponential growth in the amount of multimedia data, which increases the complexity of managing, storing, indexing, and retrieving information from such big data. Many current content-based concept detection approaches lag from successfully bridging the semantic gap. To solve this problem, a multi-stage random forest framework is proposed to generate predictor variables based on multivariate regressions using variable importance (VIMP). By fine tuning the forests and significantly reducing the predictor variables, the concept detection scores are evaluated when the concept of interest is rare and imbalanced, i.e., having little collaboration with other high level concepts. Using classical multivariate statistics, estimating the value of one coordinate using other coordinates standardizes the covariates and it depends upon the variance of the correlations instead of the mean. Thus, conditional dependence on the data being normally distributed is eliminated. Experimental results demonstrate that the proposed framework outperforms those approaches in the comparison in terms of the Mean Average Precision (MAP) values.
A decision tree-based multimodal data mining framework for soccer goal detection

We propose a new multimedia data mining framework for the extraction of soccer goal events in soccer videos by using combined multimodal analysis and decision tree logic. The extracted events can be used to index the soccer videos. We first adopt an advanced video shot detection method to produce shot boundaries and some important visual features. Then, the visual/audio features are extracted for each shot at different granularities. This rich multimodal feature set is filtered by a pre-filtering step to clean the noise as well as to reduce the irrelevant data. A decision tree model is built upon the cleaned data set and is used to classify the goal shots. Finally, the experimental results demonstrate the effectiveness of our framework for soccer goal extraction.
Rule Mining and Classification in a Situation Assessment Application: A Belief-Theoretic Approach for Handling Data Imperfections

Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB) that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.
An Optimized Scheduling Scheme to Provide Quality of Service in 802.11e Wireless LAN

802.11 WLAN technology has been widely used recently to transmit heterogeneous data. The transmission of multimedia data has its quality of service (QoS) requirements. Hybrid Coordination Function (HCF) is proposed to provide service differentiation to support real-time transmission by 802.11e working group. HCF is composed of connection-based Enhanced Distributed Coordination Access (EDCA) and contention-free control-based HCF Controlled Channel Access (HCCA). The simple scheduler in HCCA proposed by 802.11e working group generates constant bit rate service, which is not efficient for the multimedia data with variable bit rates. An efficient scheduling scheme for the 802.11e wireless LAN is proposed in this paper. The allocation of the transmission opportunity to each wireless station is based on the optimization performance index and uses the queue length of the wireless station as feedback information. Simulation results demonstrate that the proposed scheduling can achieve a better QoS for different traffic class under heavy traffic load.
Message from the ICSC-SMM'15 program chairs

ICSC-SMM'15 is the fourth International Workshop on Semantic Multimedia. The aim of the workshop is to promote the state-of-the-art research regarding semantic multimedia. The ICSC-SMM' 15 workshop provides a forum for researchers, engineers, and scientists in affiliated disciplines to present their latest research progress, exchange brilliant ideas/thoughts, and explore new research directions. We continue seeking high quality research contributions and encouraging significant work that addresses the major challenges in the semantic multimedia domain. Topics of interest include but are not limited to practical areas that span a variety of aspects of semantic multimedia including: • Multimedia big data • Multimedia search and recommendation • Multimedia social networks • Multimedia data mining and retrieval • Media transport and delivery • Semantic multimedia metadata ontology and management • Multimedia content analysis and processing • Social media and presence • Semantics-enabled networking and middleware for multimedia applications • Multimedia security, content protection and forensics • Multi-modal integration, human-machine interaction and applications • Semantic-based QoS control and scheduling • Semantic-based Internet data streaming and delivery • Multimedia-enriched semantic web • Multimedia event detection and understanding • Semantic multimedia demonstrations and applications • Multimedia information fusion for semantics mining • Multimedia near duplication detection • Multimedia mobile applications • Semantic multimedia web interfaces and applications • Semantic web annotations and recommendations • Other related areas