Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

FLIP: a targetable mediator of resistance to radiation in non-small cell lung cancer

Resistance to radiotherapy due to insufficient cancer cell death is a significant cause of treatment failure in non-small cell lung cancer (NSCLC). The endogenous caspase-8 inhibitor, FLIP, is a critical regulator of cell death that is frequently overexpressed in NSCLC and is an established inhibitor of apoptotic cell death induced via the extrinsic death receptor pathway. Apoptosis induced by ionizing radiation (IR) has been considered to be mediated predominantly via the intrinsic apoptotic pathway; however, we found that IR-induced apoptosis was significantly attenuated in NSCLC cells when caspase-8 was depleted using RNA interference (RNAi), suggesting involvement of the extrinsic apoptosis pathway. Moreover, overexpression of wild-type FLIP, but not a mutant form that cannot bind the critical death receptor adaptor protein FADD, also attenuated IR-induced apoptosis, confirming the importance of the extrinsic apoptotic pathway as a determinant of response to IR in NSCLC. Importantly, when FLIP protein levels were down-regulated by RNAi, IR-induced cell death was significantly enhanced. The clinically relevant histone deacetylase (HDAC) inhibitors vorinostat and entinostat were subsequently found to sensitize a subset of NSCLC cell lines to IR in a manner that was dependent on their ability to suppress FLIP expression and promote activation of caspase-8. Entinostat also enhanced the anti-tumor activity of IR in vivo. Therefore, FLIP down-regulation induced by HDAC inhibitors is a potential clinical strategy to radio-sensitize NSCLC and thereby improve response to radiotherapy. Overall, this study provides the first evidence that pharmacological inhibition of FLIP may improve response of NCSLC to IR.

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.

The governance of adaptation: choices, reasons, and effects. Introduction to the Special Feature

The governance of climate adaptation involves the collective efforts of multiple societal actors to address problems, or to reap the benefits, associated with impacts of climate change. Governing involves the creation of institutions, rules and organizations, and the selection of normative principles to guide problem solution and institution building. We argue that actors involved in governing climate change adaptation, as climate change governance regimes evolve, inevitably must engage in making choices, for instance on problem definitions, jurisdictional levels, on modes of governance and policy instruments, and on the timing of interventions. Yet little is known about how and why these choices are made in practice, and how such choices affect the outcomes of our efforts to govern adaptation. In this introduction we review the current state of evidence and the specific contribution of the articles published in this Special Feature, which are aimed at bringing greater clarity in these matters, and thereby informing both governance theory and practice. Collectively, the contributing papers suggest that the way issues are defined has important consequences for the support for governance interventions, and their effectiveness. The articles suggest that currently the emphasis in adaptation governance is on the local and regional levels, while underscoring the benefits of interventions and governance at higher jurisdictional levels in terms of visioning and scaling-up effective approaches. The articles suggest that there is a central role of government agencies in leading governance interventions to address spillover effects, to provide public goods, and to promote the long-term perspectives for planning. They highlight the issue of justice in the governance of adaptation showing how governance measures have wide distributional consequences, including the potential to amplify existing inequalities, access to resources, or generating new injustices through distribution of risks. For several of these findings, future research directions are suggested.

Feature-based camera pose estimation

The estimating of the relative orientation and position of a camera is one of the integral topics in the field of computer vision. The accuracy of a certain Finnish technology company’s traffic sign inventory and localization process can be improved by utilizing the aforementioned concept. The company’s localization process uses video data produced by a vehicle installed camera. The accuracy of estimated traffic sign locations depends on the relative orientation between the camera and the vehicle. This thesis proposes a computer vision based software solution which can estimate a camera’s orientation relative to the movement direction of the vehicle by utilizing video data. The task was solved by using feature-based methods and open source software. When using simulated data sets, the camera orientation estimates had an absolute error of 0.31 degrees on average. The software solution can be integrated to be a part of the traffic sign localization pipeline of the company in question.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Evolution feature oriented model driven product line engineering approach for synergistic and dynamic service evolution in clouds: A04BPEL3.0 proposal.

Model Driven based approach for Service Evolution in Clouds will mainly focus on the reusable evolution patterns' advantage to solve evolution problems. During the process, evolution pattern will be driven by MDA models to pattern aspects. Weaving the aspects into service based process by using Aspect-Oriented extended BPEL engine at runtime will be the dynamic feature of the evolution.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

ALCOHOL AS A SOCIAL MEDIATOR AMONG UNIVERSITY STUDENTS

Objective: To identify the prevalence of alcohol consumption in Psychology students of a higher education institution in the city of Montes Claros, MG. Methods: Quantitative crosssectional descriptive research conducted from September to October 2014. The population consisted of 116 Psychology students from the city of Montes Claros, MG. Data were collected using the Alcohol Use Disorders Identification Test (AUDIT), the Inventário de Expectativas e Crenças Pessoais Acerca do Álcool – IECPA (Inventory of Expectations and Personal Beliefs about Alcohol), the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) and the Escala de Satisfação com o Suporte Social – ESSS (Social Support Satisfaction Scale). Descriptive analysis of data was performed using SPSS 19.0. Results: The sample had a predominance of female gender (82.75%, n=96), pardos (65.51%, n=76) and single (60.34%, n=70) individuals. Regarding the AUDIT risk classification, it was found that 49.13% (n=57) of the respondents were in the level 4, considered alcohol dependence. They reported occasional use of alcohol, smoking and other substances, which refer to ASSIST level 1 classification, with 94.82% (n=110). Regarding the IECPA, 87.06% (n=101) of the individuals were classified as level 1, with low vulnerability to the effects of alcohol. As to the ESSS, 68.10% (n=79) of the students showed high social support. Conclusion: Regarding the sample studied, it was found a high prevalence of dependence on alcohol and other legal and illegal drugs.

Investigating the Mediator role of Social Safeness on the Relationship between Forgiveness and Life Satisfaction

The aim of the present study is to examine the mediating effect of social safeness on the relationship between forgiveness and life satisfaction. Participants were 311 university students who completed a questionnaire package that included the Trait Forgiveness Scale, the Social Safeness and Pleasure Scale, and the Life Satisfaction Scale. According to the results, social safeness and life satisfaction were predicted positively by forgiveness. On the other hand, life satisfaction was predicted positively by social safeness. In addition, social safeness mediated on the relationship between forgiveness and life satisfaction. The results were discussed in the light of the related literature and dependent recommendations to the area were given.

Mediator framework for inserting xDRs into hadoop

During the last decades, we assisted to what is called “information explosion”. With the advent of the new technologies and new contexts, the volume, velocity and variety of data has increased exponentially, becoming what is known today as big data. Among them, we emphasize telecommunications operators, which gather, using network monitoring equipment, millions of network event records, the Call Detail Records (CDRs) and the Event Detail Records (EDRs), commonly known as xDRs. These records are stored and later processed to compute network performance and quality of service metrics. With the ever increasing number of collected xDRs, its generated volume needing to be stored has increased exponentially, making the current solutions based on relational databases not suited anymore. To tackle this problem, the relational data store can be replaced by Hadoop File System (HDFS). However, HDFS is simply a distributed file system, this way not supporting any aspect of the relational paradigm. To overcome this difficulty, this paper presents a framework that enables the current systems inserting data into relational databases, to keep doing it transparently when migrating to Hadoop. As proof of concept, the developed platform was integrated with the Altaia - a performance and QoS management of telecommunications networks and services.