Dynamic shot suggestion filtering for home video based on user performance

This paper presents novel additions to our existing amateur media creation framework. The framework provides at-capture guidance to enable the home movie maker to realize their aesthetic and narrative goals and automation of post-production editing. A common problem with the amateur filming context is its contingent nature, which often results in the failure to gain footage vital to the user's goals, even with at-capture software embedding. Accordingly, we have modelled minimizing the difference between target and captured footage at a given time during filming as a probability distribution divergence problem. We apply two policies of feedback to the user on their performance, passive communication via a suggestion desirability measure, and active filtering of undesirable suggestions. We demonstrate the framework using each policy with a simulation of various user and filming situations with promising results.

CBF : A packet filtering method for DDoS attack defense in cloud environment

Distributed Denial-of-Service attack (DDoS) is a major threat for cloud environment. Traditional defending approaches cannot be easily applied in cloud security due to their relatively low efficiency, large storage, to name a few. In view of this challenge, a Confidence-Based Filtering method, named CBF, is investigated for cloud computing environment, in this paper. Concretely speaking, the method is deployed by two periods, i.e., non-attack period and attack period. More specially, legitimate packets are collected at non-attack period, for extracting attribute pairs to generate a nominal profile. With the nominal profile, the CBF method is promoted by calculating the score of a particular packet at attack period, to determine whether to discard it or not. At last, extensive simulations are conducted to evaluate the feasibility of the CBF method. The result shows that CBF has a high scoring speed, a small storage requirement and an acceptable filtering accuracy, making it suitable for real-time filtering in cloud environment.

Robust finite-horizon Kalman filtering for uncertain discrete-time systems

In this note, we propose a design for a robust finite-horizon Kalman filtering for discrete-time systems suffering from uncertainties in the modeling parameters and uncertainties in the observations process (missing measurements). The system parameter uncertainties are expected in the state, output and white noise covariance matrices. We find the upper-bound on the estimation error covariance and we minimize the proposed upper-bound.

Design, development, and evaluation of a pinch-grasp haptic interface

Interaction with virtual or teleoperated environments requires contact with objects on a multipoint level. We describe the design of a pinch--grasp hand interface device for use as a grasping mechanism to complement haptic interfaces. To preserve a suitable level of transparency for human--computer interaction, this novel interface is designed for high-resolution contact forces, while centered around a lightweight structure. This functionality renders the device scalable and adaptable to a wide range of haptic interface structures and force level requirements. We present an optimal configuration for a pinch--grasp interface, which produces bidirectional forces to an operator's fingers and a rotational force to the wrist through a cable drive system. The device is characterized for use on a commercial haptic interface through demonstration of sustained peak performance and also workspace utilization. The dynamic performance of the pinch--grasp interface is experimentally determined, and the frequency response is identified to illustrate its contact force resolution.

The efficient imputation method for neighborhood-based collaborative filtering

As each user tends to rate a small proportion of available items, the resulted Data Sparsity issue brings significant challenges to the research of recommender systems. This issue becomes even more severe for neighborhood-based collaborative filtering methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In this paper, we aim to address the Data Sparsity issue in the context of the neighborhood-based collaborative filtering. Given the (user, item) query, a set of key ratings are identified, and an auto-adaptive imputation method is proposed to fill the missing values in the set of key ratings. The proposed method can be used with any similarity metrics, such as the Pearson Correlation Coefficient and Cosine-based similarity, and it is theoretically guaranteed to outperform the neighborhood-based collaborative filtering approaches. Results from experiments prove that the proposed method could significantly improve the accuracy of recommendations for neighborhood-based Collaborative Filtering algorithms. © 2012 ACM.

Embedded vision-based autonomous move-to-grasp approach for a mobile manipulator

This paper proposes a vision‐based autonomous move‐to‐grasp approach for a compact mobile manipulator under some low and small environments. The visual information of specified object with a radial symbol and an overhead colour block is extracted from two CMOS cameras in an embedded way. Furthermore, the mobile platform and the postures of the manipulator are adjusted continuously by vision‐based control, which drives the mobile manipulator approaching the object. When the mobile manipulator is sufficiently close to the object, only the manipulator moves to grasp the object based on the incremental movement with its head end centre of the end‐effector conforming to a Bezier curve. The effectiveness of the proposed approach is verified by experiments.

A multi-tier ensemble construction of classifiers for phishing email detection and filtering

This paper is devoted to multi-tier ensemble classifiers for the detection and filtering of phishing emails. We introduce a new construction of ensemble classifiers, based on the well known and productive multi-tier approach. Our experiments evaluate their performance for the detection and filtering of phishing emails. The multi-tier constructions are well known and have been used to design effective classifiers for email classification and other applications previously. We investigate new multi-tier ensemble classifiers, where diverse ensemble methods are combined in a unified system by incorporating different ensembles at a lower tier as an integral part of another ensemble at the top tier. Our novel contribution is to investigate the possibility and effectiveness of combining diverse ensemble methods into one large multi-tier ensemble for the example of detection and filtering of phishing emails. Our study handled a few essential ensemble methods and more recent approaches incorporated into a combined multi-tier ensemble classifier. The results show that new large multi-tier ensemble classifiers achieved better performance compared with the outcomes of the base classifiers and ensemble classifiers incorporated in the multi-tier system. This demonstrates that the new method of combining diverse ensembles into one unified multi-tier ensemble can be applied to increase the performance of classifiers if diverse ensembles are incorporated in the system.

AdaM : adaptive-maximum imputation for neighborhood-based collaborative filtering

In the context of collaborative filtering, the well known data sparsity issue makes two like-minded users have little similarity, and consequently renders the k nearest neighbour rule inapplicable. In this paper, we address the data sparsity problem in the neighbourhood-based CF methods by proposing an Adaptive-Maximum imputation method (AdaM). The basic idea is to identify an imputation area that can maximize the imputation benefit for recommendation purposes, while minimizing the imputation error brought in. To achieve the maximum imputation benefit, the imputation area is determined from both the user and the item perspectives; to minimize the imputation error, there is at least one real rating preserved for each item in the identified imputation area. A theoretical analysis is provided to prove that the proposed imputation method outperforms the conventional neighbourhood-based CF methods through more accurate neighbour identification. Experiment results on benchmark datasets show that the proposed method significantly outperforms the other related state-of-the-art imputation-based methods in terms of accuracy.

Differential privacy for neighborhood-based collaborative filtering

As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.

A new look at filtering techniques for illumination invariance in automatic face recognition

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we propose a novel, general recognition framework for efficient matching of individual face images, sets or sequences. The framework is based on simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between individuals. It is shown how the discrepancy between illumination conditions between novel input and the training data set can be estimated and used to weigh the contribution of two competing representations. We describe an extensive empirical evaluation of the proposed method on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50-75% in recognition error rates, the best performing method-filter combination correctly recognizing 96% of the individuals.