Context-Aware Optimization Algorithm of RFID Event Detection

Runtime migration has been widely adopted to achieve several tasks such as load balancing, performance optimization, and fault-tolerance. However, existing migration techniques do not work for event detectors in distributed publish/subscribe systems that are used to analyze sensor data. Since low-latency time-constraints are no longer valid they reorder streams incorrectly and cause erroneous event detector states. This paper presents a safe runtime migration of stateful event detectors that respects low-latency time-constraints and seamlessly orders input events correctly on the migrated host. Event streams are only forwarded until timing delays are properly calibrated, the migrated event detector immediately stops processing after its state is transferred, and the processing overhead is negligible. On a Realtime Locating System (RTLS) we show that we can efficiently migrate event detectors at runtime between servers where other techniques would fail.

With the arrival of cloud computing and the internet of things, the difficulty of extracting data from the cloud services is preventing some organizations from adopting cloud computing. At the same time, the number of web services increases dramatically, and the granularity of service is different, so how to choose a suitable granularity service also becomes a problem that needs to be resolved. So a storage strategy for cloud computing services is given and the method based on event stream is used to construct the hierarchal model of services which can provide different functional services or different granularity services to meet the business requirements. Firstly, the storage strategy for cloud computing services is that, when registering the services, the meta-data of services will be extracted and stored into database according to certain structure. Secondly, the Event Cloud can be constructed as the one-way event processing, which provide the raw data for more-way event processing. Then the more-way event processing can construct the hierarchical model of services with certain composition goal. The prototype system has been developed in order to fulfill the application, both human events and RFID events can trigger other events in the cloud events, which can create new business models, improve business processes, and reduce costs and risks.

With the rapid development of the Internet, the number of Internet users has grown rapidly, and the Internet has become more and more influential on people's lives. As a result, the amount of network text is increasing rapidly, and it is difficult to extract interested event information from it only by manual reading. Therefore, event extraction technique automatically extracting useful information from a large amount of unstructured texts becomes increasingly important. Event detection is the first step of event extraction task and plays a vital role in it. However, current event detection research lacks comprehensive consideration of the context of the trigger words. A Chinese event detection method based on multi-feature fusion and BiLSTM is proposed in this paper. The contextual information of word is divided into sentence-level and document-level in the method. The contextual information is captured based on BiLSTM model. At the same time, a word representation method suitable for trigger word classification tasks is proposed in this paper. The word representation incorporates semantic information, grammar information, and document-level context information of word. The word vectors in the sentence are sequentially inputted into BiLSTM model to obtain output vectors containing sentence-level contextual information. Finally, output vectors of BiLSTM are inputted into the Softmax classifier to realize the identification of the trigger words. The experimental results show that Chinese Event Detection Based on Multi-feature Fusion and BiLSTM method proposed in this paper has high accuracy.

The task of event detection aims to find the event trigger for a sentence and identify the correct event type. Contextual information is crucial to event detection, which helps a model to identify the triggers better. Existing models utilizing contextual information only take document information as additional features for deep learning model, without considering the specific contribution of document information to trigger classification. In this paper, we propose a Document-Improved Hierarchical Modular Event Detection (DIHMED) model to extract hierarchical contextual information. Specifically, considering the relevance between event types, we build independent modules that combine the document-level information to express this relevance. Given the fact that events from the same document are often related, these modules can make better use of document-level information. We conduct several experiments on a Chinese political event dataset and the results show that our model can outperform the state-of-the-art models.

The fast spread of a worm is a great challenge to Internet security. Most current defense systems use a signature matching approach while most signatures are developed manually. It is difficult to catch a variety of new worms promptly. An efficient worm defense system is designed and implemented to provide early warning at the moment the worms start to spread in the network and to contain or slow down the spread of the worm by automatically extracting a signature that could be used by firewalls or Intrusion Prevention Systems. Several recent efforts to automatically extract worm signatures from Internet traffic have been done, but the efficiency is an unsolved problem especially in real high-speed network. In this paper, we proposed an efficient worm defense system based signature extraction. The input of the system is all traffic crossing an edge network and its output is a database of worm signatures which can be used by content-based defense. There are three main stages to extract signatures from network traffic. First, a clustering stage uses multidimensional traffic mining based IP header to identify significant traffic volume. We propose a binary clustering algorithm and this leaves a preferred policy to improve the front traffic filter, which can reduce the traffic to be processed and enhance its purity. After clustering, nonsignificant traffic volume is stored in an innocuous packet pool and significant traffic volume is further classified using address dispersion as suspicious or innocuous. After this stage, only a small portion of the packets captured from the edge network are analyzed in third stage, signature extraction. A position-aware signature generation method based bloom filter is proposed to extract more accurate signatures with less CPU time and memory consumption. To minimize false positives, the signatures will be verified based on the innocuous packet pool. Both trace data and tcpdump data are used to test the prototype system. Experiment results show that the system can efficiently filter through suspicious traffic with high purity and extract more accurate signature, which can well support popular content-based defense system such as Snort.

We develop a direct finite-element solver of linear (optimal) complexity to extract broadband circuit parameters such as S-parameters of arbitrarily shaped 3-D interconnects in inhomogeneous dielectrics. Numerical experiments demonstrate a clear advantage of the proposed solver as compared with existing finite-element solvers that employ state-of-the-art direct sparse matrix solutions. A linear complexity in both CPU time and memory consumption is achieved with prescribed accuracy satisfied. A finite-element matrix from the analysis of a large-scale 3-D circuit in multiple dielectrics having 5.643 million unknowns is directly factorized in less than 2 hours on a single core running at 2.8 GHz.

Context-aware vocabulary tree for mobile landmark recognition

First Story Detection is hard because the most accurate systems become progressively slower with each document processed.We present a novel approach to FSD, which operates in constant time/space and scales to very high volume streams.We show that when computing novelty over a large dataset of tweets, our method performs 192 times faster than a state-of-the-art baseline without sacrificing accuracy.Our method is capable of performing FSD on the full Twitter stream on a single core of modest hardware.

It is becoming increasingly crucial to integrate pattern matching functionality over live and archived event streams with hybrid event queries for various complex event processing(CEP) applications. As existing Stream Processing Engine(SPE) and DBMS alone can not accommodate hybrid event queries, we investigate system integration issues and scheduling algorithm optimizations of hybrid event processing based on discrete pattern in a single framework. First, hierarchical event stream storage is introduced to synchronize data access over live/archived event streams. Second, live and historical partial pattern matching caching mechanisms are proposed to provide effective partial pattern match search and update. Third, to reduce database access overhead, sub-window based event detect scheduling algorithms are proposed. Empirical performance study in a prototype hybrid event processing engine demonstrates effectiveness of our approaches.

FGCAN: Filter-based Gated Contextual Attention Network for event detection

A tunable hybrid memory allocator

To calculate scattering of a vegetation field consisting of a large number of plants, an improved fast version of the hybrid method (FHM) combining fast multiple scattering theory (FMST) and a numerical electromagnetic approach has been developed. In the HM, we use a library of T-matrices and the Foldy–Lax (FL) equation to calculate the electromagnetic field interactions outside of the cylinders enclosing the individual plants. To improve CPU time and memory requirements, the FL equation is solved by a triplet of fast Fourier transforms (FFTs) consisting of two FFTs solving 2-D FFTs of spatial distribution of plants and one 1-D FFT for solving the transformation of the order of cylindrical waves in the translation addition theorem. The triple FFTs minimize the CPU time and the memory requirement for computing translation addition matrix, which has been the bottleneck of large-scale simulations in the HM. To account for nonperiodic distribution of scatterers in a corn field, the premultiplication and the postmultiplication processes were applied to the FHM. The proposed method is validated at L-band (1.4 GHz) for 100 corn plants of height 1.25 m occupying an area of 9.54 by 9.54 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula> . On a standard laptop, the CPU time is less than 6 min and the memory consumption is less than 710 Mbytes. The CPU time and the memory requirements are orders of magnitude more efficient than those required for solving the problem with a commercial software or using the previous version of the HM.

Today automatic meshing of CAD geometry is the most common method of FEM mesh generation. However, to get results of acceptable accuracy with universal meshing algorithms it is necessary to use rather small-sized elements which leads to high memory and CPU time consumption. Furthermore, the irregularity of automatically generatated meshes makes it difficult to create well-defined local areas with different material properties. A solution for this problem is the application of predefined building blocks (FEM-Features) which contain algorithms for a problem-adapted mesh generation. These building blocks may be realised as in an object-oriented modelling system. By employing interface objects, it is easy to combine design objects to form complex geometries. The technique is explained by an example.

This paper describes the activities carried out for the development and the use of CRFD codes and related procedures for the design of industrial furnaces. The objective is to predict the emission of pollutants, such as nitrogen oxides, in combustion flue gases. This prediction may be correctly accomplished using a detailed kinetic mechanism, which cannot be directly implemented on CRFD codes due to actual computational limits (both in terms of memory and CPU time consumption). A new approach has been developed: analysing the 3D CRFD flow fields, an “equivalent” chemical reactors network model is extracted with corresponding residence time distributions and overall reactor properties, and the detailed kinetic calculation is performed on this simpler scheme. The approach has been successfully applied to different scale of furnaces such as pilot plants and industrial boilers, low- NOx burners and glass furnaces. In the papers a description of the CRFD codes is given, and the methodology to extract a chemical ideal reactor network from CFRD fields is presented. Finally, an application of the procedure on the Monfalcone #3 steam generator is discussed.

Currently, the internet serves as the predominant means of communication and is utilized by a vast number of individuals worldwide. Simultaneously, the commercial aspect of the internet is contributing to a rise in susceptibility to cybercrimes, leading to a significant surge in the occurrence of distributed Denial of Service (DDoS) assaults over the last decade. DoS/DDoS assaults primarily target network resources such as network bandwidth, CPU time, memory consumption, web servers, and network switches. Network security is an essential and crucial problem in the modern interconnected society. Numerous studies have been undertaken by multiple researchers thus far in order to identify this attack. However, there is still room for improvement in past investigations. This paper presents a novel approach for detecting and simulating DoS/DDoS attacks in modern networking environments, introducing a new paradigm. It is done in a controlled environment. The primary focus of this work is to simulate an attacker's perspective of a DoS/DDoS attack by repeatedly sending huge SYN flood packets to a specific target or network server using the hping3 tool. On the server side, the proposed attacker detector script continuously monitors incoming network connections on the network server using the netstat command. It identifies potential DoS/DDoS attacks by analyzing the connection count and comparing connections count with an assumed threshold. This experiment results in 61% CPU usage and 7.1% memory consumption while a DDoS attack triggers on the target server. Additionally, the proposed script performs statistical analysis and displays warning messages on the console when suspicious activity is detected on the network server. Wireshark is also utilized in this work to detect anomalous network traffic patterns in order to identify distributed denial-ofservice (DDoS) attacks that are targeting a network server. Additionally, it offers the capability to block the IP address of the attacker if the configuration allows for it. This proposed approach efficiently identifies DDoS activity in real-time network traffic, further helping to improve network security.