Query: Customer Purchase Behavior Prediction from Payment Datasets

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Who, where, when and what: discover spatio-temporal topics for twitter users <=SEP=> Micro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a good opportunity to study the user's spatial-temporal behavior and preference. In this paper, we propose a probabilistic model W4 (short for Who+Where+When+What) to exploit such data to discover individual users' mobility behaviors from spatial, temporal and activity aspects. To the best of our knowledge, our work offers the first solution to jointly model individual user's mobility behavior from the three aspects. Our model has a variety of applications, such as user profiling and location prediction; it can be employed to answer questions such as ``Can we infer the location of a user given a tweet posted by the user and the posting time?" Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.

Modelling the scaling properties of human mobility <=SEP=> Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.

Lévy flights and random searches <=SEP=> In this work we discuss some recent contributions to the random search problem. Our analysis includes superdiffusive Lévy processes and correlated random walks in several regimes of target site density, mobility and revisitability. We present results in the context of mean-field-like and closed-form average calculations, as well as numerical simulations. We then consider random searches performed in regular lattices and lattices with defects, and we discuss a necessary criterion for distinguishing true superdiffusion from correlated random walk processes. We invoke energy considerations in relation to critical survival states on the edge of extinction, and we analyze the emergence of Lévy behavior in deterministic search walks. Finally, we comment on the random search problem in the context of biological foraging. PACS numbers: 05.50.Fb, 05.40.−a

Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks <=SEP=> Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.

The scaling laws of human travel <=SEP=> The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools. Human travel, for example, is responsible for the geographical spread of human infectious disease. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Lévy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.

Estimating surface normals in noisy point cloud data <=SEP=> In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in R2 or a smooth surface in R3 and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.

Big data: the next frontier for innovation in therapeutics and healthcare. <=SEP=> Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.

Forecasting Real Estate Prices ∗ <=SEP=> This chapter reviews the evidence of predictability in US residential and commercial real estate markets. First, we highlight the main methodologies used in the construction of real estate indices, their underlying assumptions and their impact on the stochastic properties of the resultant series. We then survey the key empirical findings in the academic literature, including short-run persistence and long-run reversals in the log changes of real estate prices. Next, we summarize the ability of local as well as aggregate variables to forecast real estate returns. We illustrate a number of these results by relying on six aggregate indexes of the prices of unsecuritized (residential and commercial) real estate and REITs. The effect of leverage and monetary policy is also discussed.

The Beta Generalized Exponential Distribution <=SEP=> We introduce the beta generalized exponential distribution that includes the beta exponential and generalized exponential distributions as special cases. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the rth moment thus generalizing some results in the literature. Expressions for the density, moment generating function and rth moment of the order statistics also are obtained. We discuss estimation of the parameters by maximum likelihood and provide the information matrix. We observe in one application to real data set that this model is quite flexible and can be used quite effectively in analyzing positive data in place of the beta exponential and generalized exponential distributions. keywords: Beta exponential distribution, Information matrix, Generalized exponential distribution, Maximum likelihood estimation.

Motivation and job satisfaction <=SEP=> The movement of workers to act in a desired manner has always consumed the thoughts of managers. In many ways, this goal has been reached through incentive programs, corporate pep talks, and other types of conditional administrative policy. However, as the workers adjust their behaviour in response to one of the aforementioned stimuli, is job satisfaction actualized? The instilling of satisfaction within workers is a crucial task of management. Satisfaction creates confidence, loyalty and ultimately improved quality in the output of the employed. Satisfaction, though, is not the simple result of an incentive program. Employees will most likely not take any more pride in their work even if they win the weekend getaway for having the highest sales. This paper reviews the literature of motivational theorists and draws from their approaches to job satisfaction and the role of motivation within job satisfaction. The theories of Frederick Herzberg and Edwin Locke are presented chronologically to show how Locke’s theory was a response to Herzberg’s theory. By understanding these theories, managers can focus on strategies of creating job satisfaction. This is followed by a brief examination of Kenneth Blanchard and Paul Hersey’s theory on leadership within management and how this art is changing through time. Herzberg and job satisfaction

Recognizing Objects in Range Data Using Regional Point Descriptors <=SEP=> Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.

How Smart is Smart Money ? An Empirical Two-Sided Matching Model of Venture Capital <=SEP=> In capital markets, top-tier investors may have better abilities to monitor and manage their investments. In addition, there may be sorting in these markets, with top-tier investors investing in the best deals, second-tier investors investing in the second-best deals, and so forth. To separate and quantify these two effects, a structural model of the market for venture capital is developed and estimated. The model is a two-sided matching model that allows for sorting in equilibrium. It is found that more experienced venture capitalists make more successful investments. This is explained both by their value-adding influence on their investments, and by their access to late stage and biotechnology companies, companies that are more successful on average. Sorting is found to be prevalent and has general implications for the interpretation of empirical evidence of the impact of investors on their investments.

Supervised Topic Models <=SEP=> We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.

A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors <=SEP=> This paper presents a circuit-compatible compact model for short channel length (5 nm~100 nm), quasi-ballistic single wall carbon nanotube field-effect transistors (CNFETs). For the first time, a universal circuit-compatible CNFET model was implemented with HSPICE. This model includes practical device non-idealities, e.g. the quantum confinement effects in both circumferential and channel length direction, the acoustical/optical phonon scattering in channel region and the resistive source/drain, as well as the real time dynamic response with a transcapacitance array. This model is valid for CNFET for a wide diameter range and various chiralities as long as the carbon nanotube (CNT) is semiconducting

Fear bradycardia and activation of the human periaqueductal grey <=SEP=> Animal models of predator defense distinguish qualitatively different behavioral modes that are activated at increasing levels of predation threat. A defense mode observed at intermediate threat levels is freezing: a cessation of locomotion that is characterized by a parasympathetically dominated autonomic nervous system response that causes heart rate deceleration, or fear bradycardia. Studies in rodents have shown that freezing depends on amygdalar projections to the periaqueductal grey (PAG). In humans, freezing-like behaviors are implicated in development and maintenance of psychopathology, but neural mechanisms underlying freezing or its characteristic autonomic response profile have not been identified. Here, we combined event-related blood oxygenation level-dependent functional MRI (BOLD-fMRI) with autonomic response measures in a picture viewing paradigm to probe activity and interconnectivity within the amygdala-PAG pathway and test for an association with parasympathetic as opposed to sympathetic activation. In response to negatively arousing pictures, we observed parasympathetic (bradycardia) and sympathetic (pupil dilation) autonomic responses, BOLD responses in the amygdala and PAG, and effective connectivity between these regions. Critically, BOLD responses in the PAG to negative pictures correlated on a trial-by-trial basis with bradycardia but not pupil dilation. This correlation with bradycardia remained significant when partialling out pupil dilation. Additionally, activity in regions associated with motor planning and inhibition mirrored the PAG response. Thus, our findings implicate the human PAG in a parasympathetically dominated defense mode that subserves a state of attentive immobility. Mechanistic insight into this qualitatively distinct defense mode may importantly advance translational models of anxiety disorders.

RSSI informed phase method for distance calculations <=SEP=> In an attempt to find an accurate, environmentally robust, and fast process for RFID distance estimations, a method was developed called RSSI Informed Phase. This method uses both phase and RSSI measurements to estimate the distance, and potentially location, of a moving RFID tag. RSSI is initially used to find an approximate reader to tag separation distance. This distance value is applied to find an approximate slope of the phase angle vs. frequency curve. The estimated slope can inform the phase distance calculation, meaning fewer reads are required to find the actual phase angle vs. frequency slope. The reduction in the number of necessary reads accelerates the localization process and makes this method more robust for dynamic environments.

Teleoperated robot writing using EMG signals <=SEP=> In this paper, we have developed a method to tele-control a robot arm to imitate human writing skills using electromyography (EMG) signals. The control method is implemented on the Baxter® robot with a brush attached on the endpoint of its arm to imitate human writing skills, and a MYO sensor is used to measure the surface electromyographic (sEMG) signals generated by contractions of human muscles involved in writing tasks. A haptic device Sensable® Omni is used to control the motion of the Baxter® robot arm, and V-Rep® is employed to simulate the movement of the arm and to calculate the inverse kinematic of Baxter® robot arm. All the communications for Baxter® robot, V-Rep simulator, Omni device and MYO are integrated in MATLAB®/Simulink®. The main test is to make the Baxter® arm following the movement of a human subject when writing with Omni stylus, and the EMG signals are processed using low pass filter and moving average technique to extract the smoothed envelope which is utilised to control the variation of position instead of variation of force along the vertical Z axis, so when the operator writes with force variations, the Baxter® can draw lines with variable thickness. This imitation system is successfully tested to make the Baxter® arm to follow the writing movement of the operator's hand and would have potential applications in more complicated human robot teleoperation tasks such as tele-rehabilitation and tele-surgery.

Parallel computations for controlling an arm. <=SEP=> In order to control a reaching movement of the arm and body, several different computational problems must be solved. Some parallel methods that could be implemented in networks of neuron-like processors are described. Each method solves a different part of the overall task. First, a method is described for finding the torques necessary to follow a desired trajectory. The methods is more economical and more versatile than table look-up and requires very few sequential steps. Then a way of generating an internal representation of a desired trajectory is described. This method shows the trajectory one piece at a time by applying a large set of heuristic rules to a "motion blackboard" that represents the static and dynamic parameters of the state of the body at the current point in the trajectory. The computations are simplified by expressing the positions, orientations, and motions of parts of the body in terms of a single, non-accelerating, world-based frame of reference, rather than in terms of the joint-angles or an egocentric frame based on the body itself.

TimeMachine: Timeline Generation for Knowledge-Base Entities <=SEP=> We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.

Associations among Wine Grape Microbiome, Metabolome, and Fermentation Behavior Suggest Microbial Contribution to Regional Wine Characteristics <=SEP=> UNLABELLED
Regionally distinct wine characteristics (terroir) are an important aspect of wine production and consumer appreciation. Microbial activity is an integral part of wine production, and grape and wine microbiota present regionally defined patterns associated with vineyard and climatic conditions, but the degree to which these microbial patterns associate with the chemical composition of wine is unclear. Through a longitudinal survey of over 200 commercial wine fermentations, we demonstrate that both grape microbiota and wine metabolite profiles distinguish viticultural area designations and individual vineyards within Napa and Sonoma Counties, California. Associations among wine microbiota and fermentation characteristics suggest new links between microbiota, fermentation performance, and wine properties. The bacterial and fungal consortia of wine fermentations, composed from vineyard and winery sources, correlate with the chemical composition of the finished wines and predict metabolite abundances in finished wines using machine learning models. The use of postharvest microbiota as an early predictor of wine chemical composition is unprecedented and potentially poses a new paradigm for quality control of agricultural products. These findings add further evidence that microbial activity is associated with wine terroir


IMPORTANCE
Wine production is a multi-billion-dollar global industry for which microbial control and wine chemical composition are crucial aspects of quality. Terroir is an important feature of consumer appreciation and wine culture, but the many factors that contribute to terroir are nebulous. We show that grape and wine microbiota exhibit regional patterns that correlate with wine chemical composition, suggesting that the grape microbiome may influence terroir In addition to enriching our understanding of how growing region and wine properties interact, this may provide further economic incentive for agricultural and enological practices that maintain regional microbial biodiversity.

Sustainable construction: construction and demolition waste reconsidered. <=SEP=> Construction activity in Europe has increased substantially in the past decade. Likewise, there has also been a commensurate rise in the generation of construction and demolition waste (C&DW). This, together with the fact that in many European countries the rate of recycling and reuse of C&DW is still quite low has engendered a serious environmental problem and a motivation to develop strategies and management plans to solve it. Due to its composition, there is a significant potential to reuse and/or recycle C&DW, and thereby, contribute to improving the sustainability of construction and development, but practical procedures are not yet widely known or practiced in the construction industry. This article (a) summarizes the different applications that are presently practiced to optimize the recovery and/or application of C&DW for reuse, and (b) proposes various measures and strategies to improve the processing of this waste. The authors suggest that to enhance environmental effectiveness, a conscious and comprehensive C&DW management plan should be implemented in each jurisdiction. More precisely, this study presents a holistic approach towards C&DW management, through which environmental benefits can be achieved through the application of new construction methods that can contribute to sustainable growth.

Wireless image communication system for fire-fighting robots <=SEP=> Recently, it has sometimes been impossible for fire-fighting personnel to access the site of a fire, even as the fire causes tremendous property damage and loss of human life, due to high temperatures or the presence of explosive materials. In such environments, fire-fighting robots can be useful for extinguishing a fire, and they should be controlled by remote operators. In order to help a remote operator who is located far away from the fire-fighting robot, wireless image communication systems have been investigated in this study. The developed system uses a High-Speed Downlink Packet Access (HSDPA) network to transmit images, showing the view from a fire-fighting robot. The fire-fighting robots and remote controllers are equipped with industrial computers, and image-transmitting and image-receiving programs function according to various protocols to allow communication between the robots and controllers

Low voltage low power analog circuit design OTA using signal attenuation technique in universal filter application <=SEP=> This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18μm. The simulation results of this circuit showed a high DC gain of 73.6dB with a unity frequency of 50.19MHz and a total harmonic distortion of -60.81dB at 100 kHz for an input voltage of 1Vpp. Based on this circuit, a voltage mode universal filter has been implemented. The simulation results are in a good agreement with the theoretical calculations.

Risk Factors in Adolescence: The Case of Gambling, Videogame Playing, and the Internet <=SEP=> It has been noted that adolescents may be more susceptible to pathological gambling. Not only is it usually illegal, but it appears to be related to high levels of problem gambling and other delinquent activities such as illicit drug taking and alcohol abuse. This paper examines risk factors not only in adolescent gambling but also in videogame playing (which shares many similarities with gambling). There appear to be three main forms of adolescent gambling that have been widely researched. Adolescent gambling activities and general risk factors in adolescent gambling are provided. As well, the influence of technology on adolescents in the form of both videogames and the Internet are examined. It is argued that technologically advanced forms of gambling may be highly appealing to adolescents.

Competitive intelligence: construct exploration, validation and equivalence <=SEP=> Purpose – Little empirical research has been conducted on competitive intelligence (CI). This paper aims to contribute to the quantitative strand of the CI literature by exploring and validating the theoretical constructs of the CI process. Design/methodology/approach – Data from 601 questionnaires filled out by South African and Flemish exporters were subjected to exploratory factor analysis and construct equivalence analysis between the sub-samples. Findings – The results showed that the CI process consists of three constructs, while the context in which CI takes place consists of four constructs. This agrees to some extent with the literature. When verifying the constructs for both cultures it was found that all but one CI context construct can be viewed as equivalent in both groups. Bias analysis identified one item in the questionnaire that was biased. Via regression analysis it was also indicated that the context in which CI takes place influences the CI process to a large extent. The research identified size as an important influencing factor in a business’ CI process. Practical implications – Businesses involved in CI should take note that an improvement in their formal infrastructure, employee involvement and internal information processes could enhance their CI capability. Originality/value – This paper contributes towards the formalising of the constructs of competitive intelligence.

Empirical studies of software engineering: a roadmap <=SEP=> In this article we summarize the strengths and weaknesses of empirical research in software engineering. We argue that in order to improve the current situation we must create better studies and draw more credible interpretations from them. We finally present a roadmap for this improvement, which includes a general structure for software empirical studies and concrete steps for achieving these goals: designing better studies, collecting data more effectively, and involving others in our empirical enterprises.

Hidost: a static machine-learning-based detector of malicious files <=SEP=> Malicious software, i.e., malware, has been a persistent threat in the information security landscape since the early days of personal computing. The recent targeted attacks extensively use non-executable malware as a stealthy attack vector. There exists a substantial body of previous work on the detection of non-executable malware, including static, dynamic, and combined methods. While static methods perform orders of magnitude faster, their applicability has been hitherto limited to specific file formats. This paper introduces Hidost, the first static machine-learning-based malware detection system designed to operate onmultiple file formats. Extending a previously published, highly effective method, it combines the logical structure of files with their content for even better detection accuracy. Our system has been implemented and evaluated on two formats, PDF and SWF (Flash). Thanks to its modular design and general feature set, it is extensible to other formats whose logical structure is organized as a hierarchy. Evaluated in realistic experiments on timestamped datasets comprising 440,000 PDF and 40,000 SWF files collected during several months, Hidost outperformed all antivirus engines deployed by the website VirusTotal to detect the highest number of malicious PDF files and ranked among the best on SWF malware.

Thinking in circuits: toward neurobiological explanation in cognitive neuroscience <=SEP=> Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.

Face, Age and Gender Recognition using Local Descriptors <=SEP=> This thesis focuses on the area of face processing and aims at designing a reliable framework to facilitate face, age, and gender recognition. A Bag-of-Words framework has been optimized for the task of face recognition by evaluating different feature descriptors and different bag-of-words configurations. More specifically, we choose a compact set of features (e.g., descriptors, window locations, window sizes, dictionary sizes, etc.) in order to produce the highest possible rate of accuracy. Experiments on a challenging dataset shows that our framework achieves a better level of accuracy when compared to other popular approaches such as dimension reduction techniques, edge detection operators, and texture and shape feature extractors. The second contribution of this thesis is the proposition of a general framework for age and gender classification. Although the vast majority of the existing solutions focus on a single visual descriptor that often only encodes a certain characteristic of the image regions, this thesis aims at integrating multiple feature types. For this purpose, feature selection is employed to obtain more accurate and robust facial descriptors. Once descriptors have been computed, a compact set of features is chosen, which facilitates facial image processing for age and gender analysis. In addition to this, a new color descriptor (CLR-LBP) is proposed and the results obtained is shown to be comparable to those of other pre-existing color descriptors. The experimental results indicates that our age and gender framework outperforms other proposed methods when examined on two challenging databases, where face objects are present with different expressions and levels of illumination. This achievement demonstrates the effectiveness of our proposed solution and allows us to achieve a higher accuracy over the existing state-of-the-art methods.

Mate choice decisions: the role of facial beauty <=SEP=> For most people, facial beauty appears to play a prominent role in choosing a mate. Evidence from research on facial attractiveness indicates that physical beauty is a sexually selected trait mediated, in part, by pubertal facial hormone markers that signal important biological information about the displayer. Such signals would be ineffective if they did not elicit appropriate cognitive and/or emotional responses in members of the opposite sex. In this article, I argue that the effectiveness of these hormonal displays varies with perceivers' brains, which have been organized by the degree of steroid hormone exposure in the uterus, and activated by varying levels of circulating steroids following puberty. I further propose that the methodology used for examining mate choice decisions has general applicability for determining how cognitive and emotional evaluations enter into decision processes.