Searching surveillance video contents using convolutional neural network

Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification

A New Multi-threaded and Interleaving Approach to Enhance String Matching for Intrusion Detection Systems

String matching algorithms are computationally intensive operations in computer science. The algorithms find the occurrences of one or more strings patterns in a larger string or text. String matching algorithms are important for network security, biomedical applications, Web search, and social networks. Nowadays, the high network speeds and large storage capacity put a high requirement on string matching methods to perform the task in a short time. Traditionally, Aho-Corasick algorithm, which is used to find the string matches, is executed sequentially. In this paper, a new multi-threaded and interleaving approach of Aho-Corasick using graphics processing units (GPUs) is designed and implemented to achieve high-speed string matching. Compute Unified Device Architecture (CUDA) programming language is used to implement the proposed parallel version. Experimental results show that our approach achieves more than 5X speedup over the sequential and other parallel implementations. Hence, a wide range of applications can benefit from our solution to perform string matching faster than ever before

KPN-based parallelization of Wu–Manber algorithm on multi-core machines

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Pattern matching is the most time consuming task in many cybersecurity, bioinformatics and computational biological applications. Speeding up the pattern matching task is an essential step for the success of the aforementioned applications. Wu–Manber algorithm is one of the fastest and most widely used algorithms for multi-pattern matching. Many researchers focused on improving the performance of Wu–Manber algorithm and this work presents a novel attempt parallelize Wu–Manber and make it suitable for multi-core machines. This paper uses Kahn processing network (KPN) model to effectively parallelize data and functional tasks. KPN suggests a parallel programming model that can be utilized in today’s multi-core machines. Hence, we use the KPN model to tailor the execution of Wu–Manber algorithm by breaking down the complexity of data sharing and task processing. The data parallelization is implemented using concurrent executions of multiple KPNs. In addition, task parallelization is achieved within each executing KPN. A single KPN consists of two threads, a producer thread and a consumer thread. The proposed KPN-based parallelization achieves up to 4× speedup over the serial implementation of the algorithm. Finally, the algorithm performance scales well with increasing workloads and the speedup up remains almost constant with increasing number of attack signatures

AFND: Arabic fake news dataset for the detection and classification of articles credibility

The news credibility detection task has started to gain more attention recently due to the rapid increase of news on different social media platforms. This article provides a large, labeled, and diverse Arabic Fake News Dataset (AFND) that is collected from public Arabic news websites. This dataset enables the research community to use supervised and unsupervised machine learning algorithms to classify the credibility of Arabic news articles. AFND consists of 606912 public news articles that were scraped from 134 public news websites of 19 different Arab countries over a 6-month period using Python scripts. The Arabic fact-check platform, Misbar, is used manually to classify each public news source into credible, not credible, or undecided. Weak supervision is applied to label news articles with the same label as the public source. AFND is imbalanced in the number of articles in each class. Hence, it is useful for researchers who focus on finding solutions for imbalanced datasets. The dataset is available in JSON format and can be accessed from Mendeley Data repository

Graph-based data management system for efficient information storage, retrieval and processing

Data management systems rely on a correct design of data representation and software components. The data representation scheme plays a vital role in how the data are stored, which influences the efficiency of its processing and retrieval. The system components design realizes software engineering concepts to enable performance metrics such as scalability, efficiency, flexibility, maintainability, and extendibility. This paper presents a data management system that uses a graph-based data representation scheme to achieve an efficient data retrieval when using graph-based databases. Input data are transformed into vertices, edges, and labels while inserting them into the database. The proposed system consists of three layers which are: system beans layer, data access layer, and the database engine. Healthcare data are used to evaluate the system in comparison with resource description framework (RDF) semantics. Extensive experiments are conducted to compare different scenarios of data storage and retrieval using Neo4J, OrientDB, and RDF4J. Experimental results show that the performance of the proposed graph-based approach outperforms RDF4J framework in terms of insertion and retrieval time

An Automated Methodology For Negotiation Behavior in Multi-Agent Engineering Applications

The ability to manage and exploit geographically distributed systems of service providers is rather limited in today engineering solutions. Existing techniques suffer from three main problems: first, current techniques cannot provide brokering in managing loosely coupled service providers. Second, the engineering design of existing management tools does not provide enough expressive capabilities for varying user behaviors or when different domains are encountered. Third, lack of interaction between different requestors and providers yields inefficient and very costly agreements. In this thesis, we will present an automated Domain-Independent Marketplace architecture that allows user agents to interact with provider agents using two simple and yet powerful negotiation protocols which define the rules of interactions in multi-agent environments. Having a trusted third party marketplace supports privacy and transparency among collaborative agents and service providers. Service providers have different capabilities depending on the domain of interest. Such providers can be radar sensors as in oceanography surveillance systems, print servers in distributed printing jobs community, or they can be online stores providing products on the web in the E-commerce domain. In order to provide negotiation in different domains, a dynamic message structuring capability is needed. A key role to support such an expressive power is to design an ontology that contains specialization relations between the different domains of interest. The automation of integrating the Domain-Dependent message structure Ontology with the Domain-Independent marketplace architecture gives the designer a powerful tool in which systems can be tailored based on the operational purposes and objectives.The System Entity Structure (SES) methodology, which is a formalism to define hierarchical relations among entities, is used to build the required message structures Ontology automatically through the creation of SES natural language. The architecture design of the Marketplace suggests different phases and functionalities which are mapped and implemented using the Discrete Event System Specifications (DEVS). DEVS/Service Oriented Architecture (DEVS/SOA) is used to validate our system and show a proof of the concept by deploying models of printing jobs in a web-services multi-server environment for printing server domain

Trusted Mediator Agents to Better Manage Complex and Competitive Supply Chains (Extended Abstract)

ABSTRACT Competitive markets in supply chains choose not to share their inventory, backlog, and revenue costs and hence global information is not available. In this paper, we propose a new framework for supply chain management based on trusted mediator agents. A mediator agent places an order on behalf of its customer to a corresponding supplier. The agents use local information and apply adaptive heuristic rules in order to enhance the performance of the entire supply chain. We have evaluated our framework through conducting extensive experiments in an agent-based modeling and simulation environment. The results show a consistent improvement in all the cases that were considered in the literature. We show that local information can in fact lead artificial mediator agents to discover effective ordering strategies

MultiPLZW: A novel multiple pattern matching search in LZW-compressed data

© 2019 Searching encrypted or compressed data provides security and privacy without sacrificing efficiency. It has many applications in cloud storage, bioinformatics, IoT, unmanned aerial vehicles and drones. This paper introduces a novel, simple, and efficient algorithm to locate all occurrences of a set of patterns in LZW-compressed data, in a single pass. The algorithm comprises a preprocessing phase and a subsequent search phase. It uses a modified version of the generalized suffix tree, a lookup table, a mapping table, and a history tree. The proposed algorithm is superior in terms of the time complexity, while maintaining a space complexity of the same order as the best of existing algorithms. The time complexity is O(n+m+r), which is proportional to the length of the LZW-compressed data, where n is the length of the compressed data, m is the total size of the patterns, and r is the number of pattern occurrences in the compressed data. The space complexity is O(m2+t+r), where t is the size of the dictionary table that is used during compression. Experimental results show a significant improvement in search time, approximately twice as fast, compared to decompressing and then searching using Aho–Corasick algorithm. Also, results on various dataset sizes, demonstrate the algorithm\u27s superior scalability, which improves as the size of the dataset increases

Detecting Arabic Fake News Using Machine Learning

The rise of fake news has been the subject of several research studies in the last decade. This is due to the increasing number of Internet users and the simplicity in posting news over platforms and websites. Hence, researchers have been developing machine learning (ML) models to detect fake contents and warn readers. However, there is a limited number of Arabic fake news datasets in terms of articles and news sources. This paper aims at introducing the first large Arabic fake news corpus which consists of 606912 articles collected from 134 Arabic online news sources. An Arabic fact-check platform is used to annotate news sources as credible, not-credible, and undecided. Moreover, different ML algorithms are used for the detection task. Experiments show that deep learning models perform better than traditional ML models. Models training showed underfitting and overfitting problems which indicate that the corpus is noisy and challenging