Perceiving Unknown in Dark from Perspective of Cell Vibration

Low light very likely leads to the degradation of image quality and even causes visual tasks' failure. Existing image enhancement technologies are prone to over-enhancement or color distortion, and their adaptability is fairly limited. In order to deal with these problems, we utilise the mechanism of biological cell vibration to interpret the formation of color images. In particular, we here propose a simple yet effective cell vibration energy (CVE) mapping method for image enhancement. Based on a hypothetical color-formation mechanism, our proposed method first uses cell vibration and photoreceptor correction to determine the photon flow energy for each color channel, and then reconstructs the color image with the maximum energy constraint of the visual system. Photoreceptor cells can adaptively adjust the feedback from the light intensity of the perceived environment. Based on this understanding, we here propose a new Gamma auto-adjustment method to modify Gamma values according to individual images. Finally, a fusion method, combining CVE and Gamma auto-adjustment (CVE-G), is proposed to reconstruct the color image under the constraint of lightness. Experimental results show that the proposed algorithm is superior to six state of the art methods in avoiding over-enhancement and color distortion, restoring the textures of dark areas and reproducing natural colors. The source code will be released at https://github.com/leixiaozhou/CVE-G-Resource-Base.Comment: 13 pages, 17 figure

Protecting information infrastructure from DDoS attacks by MADF

Distributed Denial of Service (DDoS) attacks have become one of the most serious threats to the information infrastructure. In this paper, we propose a new approach, Mark-Aided Distributed Filtering (MADF), to find the network anomalies by using a back-propagation neural network. The marks in the IP header that are generated by a group of IP traceback schemes called Deterministic Packet Marking (DPM)/Flexible Deterministic Packet Marking (FDPM) assist this process of identifying and filtering attack packets. MADF can detect and filter DDoS attack packets with high sensitivity and accuracy, thus providing high legitimate traffic throughput and low attack traffic throughput

Defending against Distributed Denial of Service

Distributed Denial of Service (DDoS) attack is currently a serious problem in the Internet. It is characterized by an explicit attempt by an attacker to prevent legitimate users of a service from using the desired resource. The counter measures have been researched for some years, which can be classified into two categories, one is passive, and the other is active. We conclude that most current defense measures are passive, that is, the defense actions are taken only after the DDoS attacks are launched. Therefore, more or less, the target host or network is harmed before the attack source(s) can be found and controlled. Current passive defense techniques and their limitations are analyzed. We discuss some passive mechanisms such as traffic monitoring, filtering, and congestion control. After that, we propose a novel concept of active defense against DDoS attacks. This is a new point of view to treat the problem of defeating the infamous DDoS attacks on the Internet. It has numerous of advantages over conventional passive defense mechanisms. As an example of active defense, we introduce the Distributed Active Defense System (DADS) project at Deakin University. Challenges and future work of active defense against DDoS is discussed in the later part

Using multi-core processors to support network security applications

Multi-core processors represent a major evolution in computing hardware technology. Multi-core provides a network security application with more processing power from the hardware perspective. However, there are still significant software design challenges that must be overcome. In this paper, we present new architecture for multi-core supported network security applications, which aims at providing network security processing without causing performance penalty to normal network operations. We also provide an instance of this architecture – a multi-core supported intrusion detection system based on neural network. While hardware-based parallelisms have shown their advantage on throughput performance, parallelisms based multi-core provides more flexible, high performance, comprehensive, intelligent, and scalable solutions to network security applications

Using multi-core to support security-related applications

This tutorial introduces the challenges of modern security-related applications and the opportunities that multi-core technology brings. We envision that multi-core supported security applications will become the killer applications for next generation personal computers

Pervasive computing at tableside : a wireless web-based ordering system

In this paper, we introduce a wireless web-based ordering system called iMenu in restaurant industry. By using wireless devices such as Personal Digital Assistants (PDAs) and WebPads, this system realizes the paradigm of pervasive computing at tableside. Detailed system requirement, design, implementation and evaluation of iMenu are presented. While many researchers have explored wireless web-based information systems in different industries, this paper presents a system that employs wireless multi-tiered web-based architecture to build pervasive systems. Instead of discussing theoretical issues on pervasive computing, we focus on practical issues of developing a real system, such as choosing of web-based architecture, design of input methods in small screens, and response time in wireless web-based systems. The evaluation of iMenu shows it increases productivity of restaurant staff. It also has other desirable features such as integration, interoperation and scalability. Compared to traditional restaurant ordering process, by using this system customers get faster and better services, restaurant staff cooperate more efficiently with less working mistakes, and enterprise owners thus receive more business profits

Protecting web services from DDoS attacks by SOTA

In the area of SOA and Web Service Security,many well defined security dimensions have been established. However, current Web Security Systems (WS-Security for example) are not equipped to handle Distributed Denial of Service (DDoS) attacks. In this paper we extend upon our previous work on, Service Oriented Traceback Architecture (SOTA), in order to defend WebServices against such attacks. SOTA’s main objective is to identify the true identity of forged messages, since an attacker tries to hide their identity, in which to avoid current defence systems and escape prosecution. To accomplish the main objective, SOTA should be attached as close to the source of the attack. When an incoming SOAP message comes into the router, it is tagged with our own SOAP header. The header can be used to traverse the network back to the true source of the attack. According to our experimental evaluations we find that SOTA is simple and effective to use against DDoS attacks

Protecting web services with service oriented traceback architecture

Service Oriented Architecture (SOA) is a way of reorganizing software infrastructure into a set of service abstracts. In the area of applying SOA to Web Service Security, there have been some well defined security dimensions. However, current Web Security Systems, like WS-Security are not efficient enough to handle Distributed Denial of Service (DDoS) attacks. Our new approach, Service Oriented Traceback Architecture (SOTA), provides a framework to be able to identify the source of an attack. This is accomplished by deploying our defence system at distributed routers, in order to examine the incoming SOAP messages and place our own SOAP header. By this method, we can then use the new SOAP header information, to traceback through the network the source of the attack. According to our experimental performance evaluations, we find that SOTA is quite scaleable, simple and quite effective at identifying the source

Managing email overload with an automatic nonparametric clustering approach

Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges

Video based Cross-modal Auxiliary Network for Multimodal Sentiment Analysis

Multimodal  sentiment  analysis  has  a  wide  range  of applications    due    to    its information    complementarityinmultimodal    interactions. Previous    works    focus more oninvestigating efficient   joint representations,but they rarelyconsidertheinsufficient  unimodal  features  extractionanddata redundancy ofmultimodal  fusion.In  this  paper,  a  Video-based Cross-modal  Auxiliary  Network  (VCAN)  is  proposed,  which  is comprised  of an  audio  features  map  module  and a  cross-modal selection  module.  The first  moduleis  designed  to substantiallyincreasefeaturediversityin  audio  feature  extraction, aiming  to improve classification accuracy by providing more comprehensive acoustic representations.To   empower   the   model   to   handle redundant  visual features,  thesecond  moduleis addressedto efficiently  filter  the  redundant  visual framesduring  integrating audiovisual data. Moreover, aclassifier group consisting of several image  classification  networks  is  introducedto  predict  sentiment polarities and emotion categories. Extensive experimental results on   RAVDESS, CMU-MOSI, andCMU-MOSEIbenchmarks indicate that VCAN issignificantly superior to the state-of-the-artmethodsforimproving the classificationaccuracy of multimodal sentiment analysis.</p