Geometrical features of lips using the properties of parabola for recognizing facial expression

Various real-time applications such as Human–Computer Interactions, Psychometric analysis, etc. use facial expressions as one of the important parameters. The researchers have used Action Units (AU) of the face as feature points and its deformation is compared with the reference points on the face to estimate the facial expressions. Among many parts of the face, features from the mouth contribute largely to all the well-known emotions. In this paper, the parabola theory is used to identify and mark various points on the lips. These points are considered as feature points to construct feature vectors. The Latus Rectum, Focal Point, Directrix, Vertex, etc. are also considered to identify the feature points of the lower lips and upper lips. The proposed approach is evaluated on benchmark datasets such as JAFFEE and Cohn–Kanade dataset and it is found that the performance is encouraging in understanding the facial expressions. The results are compared with contemporary methods and found that the proposed approach has given good classification accuracy in recognizing facial expressions.     

Image Registration Algorithm Using Mexican Hat Function-Based Operator and Grouped Feature Matching Strategy

Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods for feature detection and matching are proposed. A Mexican hat function-based operator is used for image feature detection, including the local area detection and the feature point detection. For the local area detection, we use the Mexican hat operator for image filtering, and then the zero-crossing points are extracted and merged into the area borders. For the feature point detection, the Mexican hat operator is performed in scale space to get the key points. After the feature detection, an image registration is achieved by using the two classes of image features. The feature points are grouped according to a standardized region that contains correspondence to the local area, precise registration is achieved eventually by the grouped points. An image transformation matrix is estimated by the feature points in a region and then the best one is chosen through competition of a set of the transformation matrices. This strategy has been named the Grouped Sample Consensus (GCS). The GCS has also ability for removing the outliers effectively. The experimental results show that the proposed algorithm has high registration accuracy and small computational volume.     

An Improvement of Robust Biometrics-Based Authentication and Key Agreement Scheme for Multi-Server Environments Using Smart Cards

In multi-server environments, user authentication is a very important issue because it provides the authorization that enables users to access their data and services; furthermore, remote user authentication schemes for multi-server environments have solved the problem that has arisen from user’s management of different identities and passwords. For this reason, numerous user authentication schemes that are designed for multi-server environments have been proposed over recent years. In 2015, Lu et al. improved upon Mishra et al.’s scheme, claiming that their remote user authentication scheme is more secure and practical; however, we found that Lu et al.’s scheme is still insecure and incorrect. In this paper, we demonstrate that Lu et al.’s scheme is vulnerable to outsider attack and user impersonation attack, and we propose a new biometrics-based scheme for authentication and key agreement that can be used in multi-server environments; then, we show that our proposed scheme is more secure and supports the required security properties.     

中国野生动物调查存在的问题与改进建议

一百余年前,人类就开始了相对系统的野生动物调查。目前已经建立了成熟的方法体系,并制定了相应的调查规范。最近几十年来,我国科研人员进行了大量的野生动物调查。但是,当前我国的野外调查规范还不够细致,调查者在调查时缺乏必要的约束,导致调查数据不规范、不可靠,很多重要信息缺失。突出问题有：样线信息不全,只有起点和终点的经纬度,没有自动记录的详细样线信息（如每秒记录一次的连续点位信息,含经纬度和时间）;动物位点信息缺乏可信度指标（如距观测者的距离）;调查时间不合理;调查地点的空间取样不均衡;记录的标准化不够（如每个观测点的观测时长不确定）等等。对此,我们参考国际通用的调查规范,提出了一些简单易行的调查要点,以便提高野外调查数据的质量。另外,我们提倡野外记录的无纸化,充分利用现有的手机应用软件（APP）和模型工具提高野外记录以及后期数据处理的效率。最后,我们提议建立固定的中国生物多样性监测样线体系,以便消除每年调查时空间取样的不确定性,更好地量化野生动物的时间动态,为野生动物的保护和管理提供依据。     

End-to-end security scheme for Machine Type Communication based on Generic Authentication Architecture

Machine Type Communication (MTC) and its related services have become the new business growth-point of mobile communication. However, application market with the size of about a hundred of million makes end-to-end security facing grand challenges, especially the repeated construction of authentication facilities for different applications. According to features of MTC, an end-to-end security scheme is proposed, which is based on the Generic Authentication Architecture (GAA), and is thus suitable for MTC communication in application layer. User authentication and session key agreement are achieved by the assistance of operator network in this scheme. Hence, the MTC server can only focus on service delivery. Performance of the proposal is also analyzed, and from this procedure, we obtain the function of lifetime of master session key against expected number of bootstrapping request. Finally, it is shown how the operator could set an optimized lifetime of master session key.     

Subcellular Localization of Gram-Negative Bacterial Proteins Using Sparse Learning

One of the main challenges faced by biological applications is to predict protein subcellular localization in an automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological system into account. Traditionally such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressive Sensing), we propose a method which performs locality preserving projection with a sparseness criterion such that the feature selection and dimension reduction are merged into one analysis. The proposed sparse method decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse method is quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.     

Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.     

Cryptanalysis and Improvement of "A Secure Password Authentication Mechanism for Seamless Handover in Proxy Mobile IPv6 Networks"

Proxy Mobile IPv6 is a network-based localized mobility management protocol that supports mobility without mobile nodes’ participation in mobility signaling. The details of user authentication procedure are not specified in this standard, hence, many authentication schemes have been proposed for this standard. In 2013, Chuang et al., proposed an authentication method for PMIPv6, called SPAM. However, Chuang et al.’s Scheme protects the network against some security attacks, but it is still vulnerable to impersonation and password guessing attacks. In addition, we discuss other security drawbacks such as lack of revocation procedure in case of loss or stolen device, and anonymity issues of the Chuang et al.’s scheme. We further propose an enhanced authentication method to mitigate the security issues of SPAM method and evaluate our scheme using BAN logic.     

Compressed learning and its applications to subcellular localization

One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.     

Development of an Environmental Assessment Method for Consumer Electronics by combining Top-down and Bottom-up Approaches (11 pp)

Goal, Scope and Background Ecodesign requires environmental assessment methods, which are often time consuming and cost intensive. In this paper we proposed a method that combines top-down (e.g. LCA) and bottom-up (e.g. UNEP) approaches that allows one within short period of time to generate ecodesign ideas by identifying what to improve, how much to improve, and how to improve within a short period of time. The proposed method incorporates an environmental assessment method for use in the ecodesign of consumer electronics that employs the top-down and bottom-up approaches simultaneously. Method The proposed method consists of five modules: A. a life cycle thinking for a product, B. environmental benchmarking, C. checklist method, D. ecodesign strategies, and E. environmental design information. A key life cycle stage with significant environmental impact is identified in module A. When the identified key life cycle stage is not product manufacturing, environmental benchmarking is used; however, a checklist method is applied if product manufacturing is identified as the key life cycle stage. Ecodesign strategies for consumer electronics are obtained in module D. Environmental design information is produced by linking both the top-down and bottom-up information in module E. Results and Discussion The applicability of the proposed method was evaluated using mobile phones. First, the key life cycle stage of the mobile phone was identified as the raw material acquisition stage. Next, environmental benchmarking was carried out for 10 parameters belonging to the raw material acquisition stage. Environmental target specifications for the 10 parameters were set, ranging from 14% to 60%. Finally, environmental design information for the mobile phone was determined by linking the target specifications of the environmental benchmarking parameters and the corresponding ecodesign strategies. The proposed method was also compared with the LCA and the UNEP/promising approaches, which are representative examples of the top-down approach and the bottom-up approach, respectively. Based on the results of this comparison, the proposed method was judged to be an advanced method in facilitating the generation of ecodesign ideas. Environmentally significant benchmarking parameters correspond to what to improve, target specifications to on how much to improve, and ecodesign strategies ton how to improve. It was found that the use of the proposed method minimizes the time and money expenditure by confining the identification of environmental weak points within the key life cycle stage. Conclusion and Outlook An environmental assessment method for consumer electronics in ecodesign was proposed and applied to mobile phones. The advantages of the proposed method are as follows: it is efficient and cost-effective, and it allows designers to generate ecodesign ideas more easily and effectively by simultaneously identifying the specific environmental weak points of a product and corresponding ecodesign strategies. The proposed method can be envisaged as a useful ecodesign approach when electronic companies identify the environmental aspects of their products and integrate them into product design and development process.     

High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN

Recent efforts to reduce the measurement time for multidimensional NMR experiments have fostered the development of a variety of new procedures for sampling and data processing. We recently described concentric ring sampling for 3-D NMR experiments, which is superior to radial sampling as input for processing by a multidimensional discrete Fourier transform. Here, we report the extension of this approach to 4-D spectroscopy as Randomized Concentric Shell Sampling (RCSS), where sampling points for the indirect dimensions are positioned on concentric shells, and where random rotations in the angular space are used to avoid coherent artifacts. With simulations, we show that RCSS produces a very low level of artifacts, even with a very limited number of sampling points. The RCSS sampling patterns can be adapted to fine rectangular grids to permit use of the Fast Fourier Transform in data processing, without an apparent increase in the artifact level. These artifacts can be further reduced to the noise level using the iterative CLEAN algorithm developed in radioastronomy. We demonstrate these methods on the high resolution 4-D HCCH-TOCSY spectrum of protein G's B1 domain, using only 1.2% of the sampling that would be needed conventionally for this resolution. The use of a multidimensional FFT instead of the slow DFT for initial data processing and for subsequent CLEAN significantly reduces the calculation time, yielding an artifact level that is on par with the level of the true spectral noise.     

A multipurpose user-friendly tool for voice analysis: Application to pathological adult voices

Assessing voice quality objectively is of great relevance to clinicians, both for quantifying surgical or pharmacological effectiveness and for detecting and classifying voice pathology. A large number of objective indexes have been proposed in literature and implemented in commercially available software tools. However, clinicians commonly resort to a small subset of these indexes since they may be difficult to set up or understand.This paper presents a new user-friendly voice analysis tool named BioVoice. At present, BioVoice allows for the evaluation of few but important indexes, devoting great effort to their robust and automatic evaluation, although extensions are foreseeable. Specifically, fundamental frequency, along with irregularity (jitter, relative average perturbation), noise, and formant frequencies, is tracked on voiced parts of the signal only. Mean and standard deviation values are also calculated and displayed. This high-resolution estimation procedure is further strengthened by an adaptive estimation of the optimal length of signal frames for analysis, linked to varying signal characteristics.Moreover, BioVoice allows automatic analysis of any kind of voice signal as far as F₀ range and sampling frequency are concerned, with no manual setting required. This new tool is thus feasible for use by non-experts from different scientific fields, thanks to its simple interface.Here, the proposed approach is applied to patients who underwent micro-laryngoscopic direct exeresis to remove cysts and polyps. Pre- and post-surgical indexes were estimated using BioVoice and then compared with the output of one of the most common commercial software tools to both assess voice quality recovery and to evaluate the new method’s capabilities.     

Basics of voice dysfunction--etiology and prevention of voice damage

Voice is one of the most important means of communication and as such should be taken care of. The etiology of voice disorders is diverse. Due to the development of the society we live in, way of life, environmental factors, and exposure to pharmacological agents as well as demands we make towards our voice, there is a substantial growth in the number of people with voice disorders. We tasked ourselves to find out if it is possible to enlighten people on the importance of voice, to motivate them to take care of it, to notice the changes in its quality and eventually ask for help. We assessed in which measure do we understand the importance of a healthy voice, and do we know which is the most important factor that adds to its decline. For a long number of years voice therapists and other experts in the voice disorder field have been discussing the optimal voice impostation as well as vocal exercises and methods behind voice recovery. They have all come to the same conclusion that phonation is dependant on the sort of the voice disorder and the patient motivation. We wanted to go one step further and investigate, dependence of voice quality and the damage etiology (organic - functional), which are the predominant causes, what are the factors that account for the damage and how the disorder motivates the patient and therefore influences the rehabilitation success rate.     

A new method for nonhomogeneous time course expression analysis

Time course expression analysis constitutes a large portion of applications of microarray experiments. One primary goal of such experiments is to detect genes with the temporal changes over a period of time or at some interested time points. Difficulties arising from data with small number of replicates over only a few unaligned time points in multiple groups pose challenges for efficient statistical analysis. Some known methods are limited by the unverifiable assumptions or by the scope of applications for only two groups. We present a new method for detecting differentially expressed genes under nonhomogeneous time course experiments in multiple groups. The new method first models the time course curve of one gene by a Gaussian process to align the nonhomogeneous time course data and to compute the gradient of the time course curve as well, the latter of which is used as directional information to enhance the sensitivity of detection for temporal changes. Second, we adopt a nonparametric method to test a surrogate hypothesis based on the augmented data from the Gaussian process model. The proposed method is robust in terms of model fitting and testing. It does not require any distributional assumption for the observations or the test statistic and the method works for the case with as few as triplicate samples over four or five time points under multiple groups. We show the effectiveness and superiority of the new method in comparison with some existing methods using simulated models and two real data sets.     

Point Transect Sampling Along Linear Features

Summary Distance sampling is a widely used methodology for assessing animal abundance. A key requirement of distance sampling is that samplers (lines or points) are placed according to a randomized design, which ensures that samplers are positioned independently of animals. Often samplers are placed along linear features such as roads, so that bias is expected if animals are not uniformly distributed with respect to distance from the linear feature. We present an approach for analyzing distance data from a survey when the samplers are points placed along a linear feature. Based on results from a simulation study and from a survey of Irish hares in Northern Ireland conducted from roads, we conclude that large bias may result if the position of samplers is not randomized, and analysis methods fail to account for nonuniformity.     

Target speech feature extraction using non-parametric correlation coefficient

Speech recognition systems for the automobile have a few weaknesses, including failure to recognize speech due to the mixing of environment noise from inside and outside the car and from other voices. Therefore, this paper features a technique for extracting only the selected target voice from input sound that is a mixture of voices and noises. The feature for selective speech extraction composes a correlation map of auditory elements by using similarity between channels and continuity of time, and utilizes a method of extracting speech features by using a non-parametric correlation coefficient. This proposed method was validated by showing that the average distortion of separation of the technique decreased by 0.8630 dB. It was shown that the performance of the selective feature extraction utilizing a cross correlation is good, but overall, the selective feature extraction utilizing a non-parametric correlation is better.     

Proportionate spatial sampling and equal‐time sampling of mobile animals: A dilemma for inferring areal dependence

Abstract Patch or island area is one of the most frequently used variables for inference in conservation biology and biogeography, and is often used in ecological applications. Given that all of these disciplines deal with large spatial scales, exhaustive censusing is not often possible, especially when there are large numbers of patches (e.g. for replication and control purposes). Therefore, data for patches or islands are usually collected by sampling. We argue that if area is to be used as an inferential factor, then the objects under study (i.e. the patches) must be characterized on an areal basis. This necessarily means that fixed‐area sampling is inadequate (e.g. a single standard quadrat or transect set within patches irrespective of the patch area) and that some form of area‐proportionate sampling is needed (e.g. a fixed areal proportion of each patch is surveyed by random allocation of standard quadrats across each patch). However, use of area‐proportionate sampling is not usually dissociated from the increased temporal intensity of sampling that arises from using this approach. The dilemma we see is deciding how much of the area‐specificity of variables such as species richness, rare‐species indices or probabilities of occurrence of individual species is related to the area‐proportionate survey protocol and how much is due to the temporal intensity of surveys. We undertook a study in which we balanced temporal and spatial effects by increasing the time spent surveying smaller patches of vegetation to account for the area‐ratio difference. The estimated species richness of birds of the box–ironbark system of central Victoria, Australia, was found to depend strongly upon area when area‐proportionate sampling alone was performed. When time‐balancing was imposed upon area‐proportionate sampling, the differences between smaller (10‐ha) and larger (40‐ha) areas were much reduced or effectively disappeared. We show that species found in the additional surveys used to conduct the time‐balancing were significantly less abundant than species recorded in area‐proportionate sampling. This effect is probably most severe for mobile animals, but may emerge in other forms of sampling.