In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Our school prides itself on providing the best opportunities for each child. The academic achievements have been and continue to be well above Western Australian and National averages, while the pastoral care programs that operate in the school teach children the specific social and emotional skills they need in order to develop and maintain friendships and a strong sense of selfworth. Students have a strong sense of pride in the school and display significant support for each other. Student success is valued, supported and celebrated by all.

Annual Report 2012 -個人別教育研究報告-

[기술해설]암호법(Cryptography)

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.

Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey

The unpredictable cyber attackers and threats have to be detected in order to determine the outcome of risk in a network environment. This work develops a Bayesian network classifier to analyse the network traffic in a cyber situation. It is a tool that aids reasoning under uncertainty to determine certainty. It further analyze the level of risk using a modified risk matrix criteria. The classifier developed was experimented with various records extracted from the KDD Cup'99 dataset with 490,021 records. The evaluations showed that the Bayesian Network classifier is a suitable model which resulted in same performance level for classifying the Denial of Service (DoS) attacks with Association Rule Mining while as well as Genetic Algorithm, the Bayesian Network classifier performed better in classifying probe and User to Root (U2R) attacks and classified DoS equally. The result of the classification showed that Bayesian network classifier is a classification model that thrives well in network security. Also, the level of risk analysed from the adapted risk matrix showed that DoS attack has the most frequent occurrence and falls in the generally unacceptable risk zone.

Risk analysis in cyber situation awareness using Bayesian approach

The rate of students entering inappropriate disciplines in our tertiary institutions, especially Nigeria, is soaring. This invariably has caused the nation many unproductive and frustrated graduates. In view of this, there is a need to enhance the quality of service of the Guidance and Counseling Unit in Nigeria. This paper presents the development of a knowledge assistant (Decision Support System DSS) for counselors to assist students in selecting the right discipline as they enter tertiary institutions, having considered the appropriate factors necessary for taking such decisions. Thus, the work which was carried out at the Department of Computer Science, Federal University of Technology, Akure, Nigeria was proposed for Secondary School “leaving students” who are likely to have a problem with their choice of carriers as they intend to study in at tertiary institutions of their choice. The deployment of this work was discovered to have a high correlation with that of the conventional method of (using a human counselor) career counseling. It has the prospects of creating an unlimited accessibility to career guides for students and validated information on career suggestion by human counselors. (

http://www.akamaiuniversity.us/PJST10_2_337.pdf

Career Master: A Decision Support System (DSS) for Guidance and Counseling in Nigeria.

Biometrics technology is evolving. Thus, researches have been done and implemented on many biometrics systems such as hand geometric, palm print, iris, voice gait. All these are sensitive to a number of factors such as background noise, high cost of deployment, inappropriate for large-scale identification, and finally gait-based system by the choice of footwear, nature of clothing, affliction of the legs, walking surface. However, the use of other characteristic feature such as the nose which is not bogged down with the aforementioned factors has been largely unexplored. In this work, geometry is employed for nose identification; the nose alae are measured. The curvature points of the nose alae are selected as the nasal feature using linear object class and triangulation technique. Our experiment was carried out with 100 dataset, the result showed 87% recognition; this shows that nose biometrics, if well explored could be a valuable biometrics traits.

https://ieeexplore.ieee.org/iel7/7048126/7055074/07055111.pdf

Nose biometrics verification using linear object technique

A risk is the possibility that an undesirable event could happen. Several risk management software tools have been developed in the past to address pressing industrial concerns such as risk identification and estimation, risk exposure, mitigation and keep track of risk positions and respective management plans. Several methods have been developed and used in risk assessments. Two specific methods of interest in this work are: "Risk Matrices" and "Risk Registers". A generic Risk Register application module and an updatable Risk Matrix module was designed. This work studies risk management techniques and employs a custom model for the automated assessment of IS risks. This model was implemented in phases corresponding to its aspects. The "Assessment methods" of interest to this work are Risk Registers, Risk Matrices and the Scenario Geek". What-if analysis is a data-intensive simulation whose goal is to inspect the behavior of a complex system under some given hypotheses called scenarios. What-ifs are used to generate qualitative descriptions of potential problems in the form of questions and responses lists of recommendations for preventing problems. The Risk Assessor was developed using Microsoft's Visual Basic .Net with Active Server Pages (ASP.Net) Technologies on .Net Framework 4.0. This work, if adopted will help keep track of the basic sources which can hamper the operations of the information technology organizations.

Evaluation of information security risks using hybrid assessment model

Long Term Evolution (LTE) network is a wireless network developed for fast and effective communication among internet users for resource accessing and sharing. Several scheduling algorithms have been proposed in attempts by researchers to solve the problem of scarcity of radio resources available to network users. Determining the best scheduling algorithm based on chosen metrics is an interesting issue of research. In this paper thus, we present a comparative analysis of these scheduling algorithms for resource management in Long Term Evolution (LTE) network. The algorithms; [Proportional Fair (PF), Exaggerated Earliest Deadline First (E2DF), Modified-Largest Weighted Delay First (M-LWDF) and Exponential Proportional Fair (EXP-PF)], were simulated, then compared using standard metrics such as; Block Error Rate (BLER), Spectral Efficiency, Throughput and Fairness with the aim of determining the best performing scheduling algorithm with respect to each of the metrics.Keywords: Wireless Network, Long Time Evolution Network, Scheduling, Computer Network.

A. F. Thompson

Papers

Risk analysis in cyber situation awareness using Bayesian approach

Career Master: A Decision Support System (DSS) for Guidance and Counseling in Nigeria.

Nose biometrics verification using linear object technique

Evaluation of information security risks using hybrid assessment model

Analysis of algorithms in long term evolution (LTE) network