Abstract
"Taking less treating better" has been one of the major improvements of breast cancer surgery in the last four decades. The application of this principle translates into equivalent survival of breast cancer conserving treatment (BCT) when compared to mastectomy, with a better cosmetic outcome. While it is relatively easy to evaluate the oncological results of BCT, the cosmetic outcome is more difficult to measure due to the lack of an effective and consensual procedure. The assessment of cosmetic outcome has been mainly subjective, undertaken by a panel of expert observers or/and by patient self-assessment. Unfortunately, the reproducibility of these methods is low. Objective methods have higher values of reproducibility but still lack the inclusion of several features considered by specialists in BCT to be fundamental for cosmetic outcome. The recent addition of volume information obtained with 3D images seems promising. Until now, unfortunately, no method is considered to be the standard of care. This paper revises the history of cosmetic evaluation and guides us into the future aiming at a method that can easily be used and accepted by all, caregivers and caretakers, allowing not only the comparison of results but the improvement of performance. (C) 2014 Wiley Periodicals, Inc.

Abstract
In this paper, a new method for music symbol classification named Combined Neural Network (CNN) is proposed. Tests are conducted on more than 9000 music symbols from both real and scanned music sheets, which show that the proposed technique offers superior classification capability. At the same time, the performance of the new network is compared with the single Neural Network (NN) classifier using the same music scores. The average classification accuracy increased more than ten percent, reaching 98.82%.

Abstract
Animal behavior assessment plays an important role in basic and clinical neuroscience. Although assessing the higher functional level of the nervous system is already possible, behavioral tests are extremely complex to design and analyze. Animal's responses are often evaluated manually, making it subjective, extremely time consuming, poorly reproducible and potentially fallible. The main goal of the present work is to evaluate the use of consumer depth cameras, such as the Microsoft's Kinect, for detection of behavioral patterns of mice. The hypothesis is that the depth information, should enable a more feasible and robust method for automatic behavior recognition. Thus, we introduce our depth-map based approach comprising mouse segmentation, body-like per-frame feature extraction and per-frame classification given temporal context, to prove the usability of this methodology.

Abstract
The increasing demand for human activity analysis on surveillance scenarios has been provoking the emerging of new features and concepts that could help to identify the activities of interest. In this paper, we present a context-based descriptor to identify individual profiles. It accounts with a multi-scale histogram representation of position-based and attention-based features that follow a key-point trajectory sampling. The notion of profile is expressed by a new semantic concept introduced as an adjective for action recognition. We also identify a very rich dataset, in terms of intensity and variability of human activity, and extended it by manual annotation to validate the introduced concept of profile and test the descriptor's discriminative power. High rates of recognition were achieved.

Abstract

Abstract
In 802.11 Wireless Networks, detecting faulty equipment, poor radio conditions, and changes in user behavior through anomaly detection techniques is of great importance in network management. The traffic load and user movement on different access points (APs) in a wireless covered area vary with time, making these network management tasks harder. We intend to inspect the evolving structure of wireless networks and their inherent dynamics in order to provide models for anomaly detection. For this purpose we explore the temporal usage behavior of the network by applying various types of Hidden Markov Models. We observe the usage pattern of up to 100 APs in one week period in 2011 at the Faculty of Engineering of the University of Porto. The first step of this study consists of constructing various Hidden Markov Models from 802.11 AP usage data. We then apply statistical techniques for outlier detection and justify the presented outliers by inspecting the models' parameters and a set of HMM indicators. We finally introduce examples of wireless networks anomalous patterns based on the transitions between HMM states and provide an analysis of the entire set of APs under study.