最古老的识谎方法——人识法

"人识法",是不借助任何外在手段,只依靠人自身的经验、知识和智慧判断言语真假的方法。人识法一直是识别谎言的重要手段,它文明于刑识法,进步于神识法。但是,不管是哪一种人识法,目前都很难满足认定谎言所需要的必然性和确定性。其准确性与否,还要取决于识别人的人生阅历、个人经验与综合素质。

最古老的识谎方法——人识法

“人识法”，是不借助任何外在手段，只依靠人自身的经验、知识和智慧判断言语真假的方法。人识法一直是识别谎言的重要手段，它文明于刑识法，进步于神识法。但是，不管是哪一种人识法，目前都很难满足认定谎言所需要的必然性和确定性。其准确性与否，还要取决于识别人的人生阅历、个人经验与综合素质。

DUAL-SPARSITY PRESERVING PROJECTION

Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating information by preserving sparse reconstruction relationship of data sets. However, SPP suffers from the fact that every new feature learned from data sets is linear combinations of all the original features, which often makes it difficult to interpret the results. To address this issue, a novel DR method called dual-sparsity preserving projection （DSPP） is proposed to further impose sparsity constraints on the projection directions of SPP. Specifically, the proposed method casts the projection function learning of SPP into a regression-type optimization problem, and then the sparse projections can be efficiently computed by the related lasso algorithm. Experimental results from face databases demonstrate the effectiveness of the proposed algorithm.

Face Orientation Normalization Using Eye Positions

Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.

Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping

Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.

Interframe Variation Vector:A Novel Feature for Gait Recognition

Gait representation is an important issue in gait recognition. A simple yet efficient approach, called Interframe Variation Vector （IW）, is proposed. IW considers the spatiotemporal motion characteristic of gait, and uses the shape variation information between successive frames to represent gait signature. Different from other features, IVV rather than condenses a gait sequence into single image resulting in spatial sequence lost; it records the whole moving process in an IVV sequence. IVV can encode whole essential features of gait and preserve all the movements of limbs. Experimental results show that the proposed gait representation has a promising recognition performance.

Identification of abnormal movement state and avoidance strategy for mobile robots

Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence ot abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinematics of the normal and abnormal movement states were exploited, 8 kinds of features were extracted. Secondly, 4 multi-layer pereeptrons were employed to classify the features for four 4-driving wheels into 4 kinds of states, i.e. normal, blocked, deadly blocked, and slipping. Finally, avoidance strategies were designed based on this. Experiment results show that the methods can identify most abnormal movement states and avoid the abnormality correctly and timely.

An infrared human face recognition method based on 2DPCA

Aimed at the problems of infrared image recognition under varying illumination,face disguise,etc.,we bring out an infrared human face recognition algorithm based on 2DPCA.The proposed algorithm can work out the covariance matrix of the training sample easily and directly;at the same time,it costs less time to work out the eigenvector.Relevant experiments are carried out,and the result indicates that compared with the traditional recognition algorithm,the proposed recognition method is swift and has a good adaptability to the changes of human face posture.

Recognition of human face based on improved multi-sample

In order to solve the problem caused by variation illumination in human face recognition,we bring forward a face recognition algorithm based on the improved multi-sample. In this algorithm,the face image is processed with Retinex theory,meanwhile,the Gabor filter is adopted to perform the feature extraction. The experimental results show that the application of Retinex theory improves the recognition accuracy,and makes the algorithm more robust to the variation illumination. The Gabor filter is more effective and accurate for extracting more useable facial local features. It is proved that the proposed algorithm has good recognition accuracy and it is stable under variation illumination.

Decentralized Multiagent Task Planning for Heterogeneous UAV Swarm

A decentralized task planning algorithm is proposed for heterogeneous unmanned aerial vehicle(UAV)swarm with different capabilities.The algorithm extends the consensus-based bundle algorithm(CBBA)to account for a more realistic and complex environment.The extension of the algorithm includes handling multi-agent task that requires multiple UAVs collaboratively completed in coordination,and consideration of avoiding obstacles in task scenarios.We propose a new consensus algorithm to solve the multi-agent task allocation problem and use the Dubins algorithm to design feasible paths for UAVs to avoid obstacles and consider motion constraints.Experimental results show that the CBBA extension algorithm can converge to a conflict-free and feasible solution for multi-agent task planning problems.

Creatinine, urea, uric acid, water and electrolytes renal handling in the healthy oldest old

Renal physiology in the healthy oldest old has the fol-lowing characteristics, in comparison with the renal physiology in the young： a reduced creatinine clear-ance, tubular pattern of creatinine back-fltration, pre-served proximal tubule sodium reabsorption and uric acid secretion, reduced sodium reabsorption in the thick ascending loop of Henle, reduced free water clear-ance, increased urea excretion, presence of medulla hypotonicity, reduced urinary dilution and concentra-tion capabilities, and fnally a reduced collecting tubules response to furosemide which expresses a reduced potassium excretion in this segment due to a sort of aldosterone resistance. All physiological changes of the aged kidney are the same in both genders.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

Facial Expression Recognition by Split Rectangle Based Adaboost

The facial expression recognition systn using the Ariaboost based on the Split Rectangle feature is proposed in this paper. This system provides more various featmes in increasing speed and accuracy than the Haarolike featrue of Viola, which is commonly used for the Adaboost training algorithm. The Split Rectangle feature uses the nmsk-like shape composed with 2 independent rectangles, instead of using mask-like shape of Haar-like feature, which is composed of 2 --4 adhered rectangles of Viola. Split Rectangle feature has less di- verged operation than the Haar-like feaze. It also requires less oper- ation because the stun of pixels requires ordy two rectangles. Split Rectangle feature provides various and fast features to the Adaboost, which produrces the strong classifier with increased accuracy and speed. In the experiment, the system had 5.92 ms performance speed and 84 %--94 % accuracy by leaming 5 facial expressions, neutral, happiness, sadness, anger and surprise with the use of the Adaboost based on the Split Rectangle feature.

A Method of Identifying Electromagnetic Radiation Sources by Using Support Vector Machines

Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.

AN LBP-BASED MULTI-SCALE ILLUMINATION PREPROCESSING METHOD FOR FACE RECOGNITION

It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination pre- processing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry restoration/neighborhood replacement. Our experiment results show that the proposed method performs better than the existing LBP-based methods at the point of illumination preprocessing. Moreover, compared with some face image preprocessing methods, such as histogram equalization, Gamma transformation, Retinex, and simplified LBP operator, our method can effectively improve the robustness for face recognition against illumination variation, and achieve higher recog- nition rate.

A closed-loop algorithm to detect human face using color and reinforcement learning

A closed-loop algorithm to detect human face using color information and reinforcement learning is presented in this paper. By using a skin-color selector, the regions with color "like" that of human skin are selected as candidates for human face. In the next stage, the candidates are matched with a face model and given an evaluation of the match degree by the matching module. And if the evaluation of the match result is too low, a reinforcement learning stage will start to search the best parameters of the skin-color selector. It has been tested using many photos of various ethnic groups under various lighting conditions, such as different light source, high light and shadow. And the experiment result proved that this algorithm is robust to the vary-ing lighting conditions and personal conditions.

Cognitive Emotion Model for Eldercare Robot in Smart Home

Based on the smart home and facial expression recognition, this paper presents a cognitive emotional model for eldercare robot. By combining with Gabor filter, Local Binary Pattern algorithm(LBP) and k-Nearest Neighbor algorithm(KNN) are facial emotional features extracted and recognized. Meanwhile, facial emotional features put influence on robot's emotion state, which is described in AVS emotion space. Then the optimization of smart home environment on the cognitive emotional model is specially analyzed using simulated annealing algorithm(SA). Finally, transition probability from any emotional state to a state of basic emotions is obtained based on the cognitive reappraisal strategy and Euclidean distance. The simulation and experiment have tested and verified the effective in reducing negative emotional state.

Narrative Strategies and Aesthetic Ethics of Nabokovian Otherworld in Lolita and Pale Fire

This paper demonstrates the rhetoric technique, narrative function and sign decoding of Nabokocian otherworld in LoBta and Pale Fire to detect not only the presence of the metaphysical otherworld, but also the faint influence upon the living. Under the narrative strategies of the otherworld lies Nabokov＇s aesthetic ethics, which make those who label Nabokov pessimistic and indifferent to be aware of his persistent attempt to push beyond the boundaries of human consciousness and his sincere concern and deep sympathy for the misery of men.

Digital Image Processing Methods for Bridge Coating Management and Their Limitations

Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust defect recognition, digital image recognition methods have been developed for the past few years and they are expected to replace or complement conventional painting inspection methods. Efficient image processing methods are also essential for the successful implementation of steel bridge coating warranty contracting where the owner, usually a state agency, and the contractor inspect steel bridge coating conditions regularly and decide whether additional maintenance actions are needed based on the processed data. There are two approaches to develop automated rust defect recognition methods： applying a statistical method or an artificial intelligence technique. This paper presents the application of previously developed image processing methods for defect evaluations on a bridge coating surface and discusses their limitations under three environmental conditions which are often encountered while acquiring digital images.

Cosmopolitan Democracy and Democratic Public Law

Cosmopolitan democracy model is presented by David Held, beyond idealistic perspectives of left and right ideologies, mainly with a legal view, and found a global impact. As a prominent theorist in the field of democracy studies, David Held, by integrating the principle of autonomy in the model of constitutional democracy with the principle of participation in the model of participatory democracy, introduces a novel composition named ＂cosmopolitan democracy＂ that is a conception of democratic legal relations. Held is the first man who seeks to investigate democracy separated from the ideological models in relation to general human rights and identifies main areas of power in human life. He considers totally seven sets of rights necessarily enabling people to enjoy a free and equal participation in setting their communities. These rights include： right to health, welfare rights, cultural rights, civil rights, economic rights, political rights, and the right to enjoy a peaceful livelihood. Held＇s ultimate desire is to realize ideals of cosmopolitan democracy model in the global sphere, beyond the lessons of the West and the East. Through rethinking the theoretical and practical frameworks of this theory in today＇s world, the current paper seeks to study its role in reproducing democratic realism so that it would prepare the ground for the global consensus far from the ideal models.