Miracle-dz/UniFAR-FTUs huggingface dataset:
The Science of Human Mating Strategies: An Historical Perspective

There is much that is admirable, scientifically accurate, and useful in “The Ape That Thought It Was a Peacock. ” It summarizes, albeit selectively, much empirical evidence that has cumulated about human mating strategies. It deftly dismisses blank slate theories. It correctly points out that social role theory (in its various new or old labels) is woefully inadequate, cannot explain the now massive panoply of empirical findings, and should properly be consigned to a historical footnote (see also Miller, this issue). It argues that men and women both have evolved mate preferences and engage in mutual mate choice, and that both sexes engage in intrasexual competition—precisely the theoretical stance my colleagues and I have long maintained, as the authors correctly acknowledge (e.g., Buss, 1985, 1988a, 1988b, 1989b; Buss & Dedden, 1990; Buss & Schmitt, 1993; Buss & Shackelford, 1997; Schmitt & Buss, 1996). Because most of this review of the science of human mating is accurate, it is perhaps useful to pan back and provide a historical perspective on the study of human mating. Subsequently, I note the few quibbles I have with the target article, most of which are matters of labeling and emphasis, and suggest a few directions for the future science of human mating.
Efficient convolution architectures for convolutional neural network

Convolutional Neural Network (CNN) is the state-of-the-art deep learning approach employed in various applications due to its remarkable performance. Convolutions in CNNs generally dominate the overall computation complexity and thus consume major computational power in real implementations. In this paper, efficient hardware architectures incorporating parallel fast finite impulse response (FIR) algorithm (FFA) for CNN convolution implementations are discussed. The theoretical derivation of 3 and 5 parallel FFAs is presented and the corresponding 3 and 5 parallel fast convolution units (FCUs) are proposed for most commonly used 3 × 3 and 5 × 5 convolutional kernels in CNNs, respectively. Compared to conventional CNN convolution architectures, the proposed FCUs reduce the number of multiplications used in convolutions significantly. Additionally, the FCUs minimize the number of reads from the feature map memory. Furthermore, a reconfigurable FCU architecture which suits the convolutions of both 3 × 3 and 5 × 5 kernels is proposed. Based on this, an efficient top-level architecture for processing a complete convolutional layer in a CNN is developed. To quantize the benefits of the proposed FCUs, the design of an FCU is coded with RTL and synthesized with TSMC 90nm CMOS technology. The implementation results demonstrate that 30% and 36% of the computational energy can be saved compared to conventional solutions with 3 × 3 and 5 × 5 kernels in CNN, respectively.
A Goal-Driven Measurement Model for Software Testing Process

In order to enhance the software quality, control and improve software testing process, the software testing process needs to be effectively measured. This paper presents a goal-driven measurement model for software testing process (MMSTP). Based on MMSTP, software organizations can deduce the appropriate measurement process according to the process goals they determine. At the same time, they also can get the direction of testing process, which will provide an effective support for make the correct decision to better the testing process.
Urban Terrain Multiple Target Tracking Using The Probability Hypothesis Density Particle Filter

The tracking of multiple targets becomes more challenging in complex environments due to the additional degrees of nonlinearity in the measurement model. In urban terrain, for example, there are multiple reflection path measurements that need to be exploited since line-of-sight observations are not always available. Multiple target tracking in urban terrain environments is traditionally implemented using sequential Monte Carlo filtering algorithms and data association techniques. However, data association techniques can be computationally intensive and require very strict conditions for efficient performance. This thesis investigates the probability hypothesis density (PHD) method for tracking multiple targets in urban environments. The PHD is based on the theory of random finite sets and it is implemented using the particle filter. Unlike data association methods, it can be used to estimate the number of targets as well as their corresponding tracks. A modified maximum-likelihood version of the PHD (MPHD) is proposed to automatically and adaptively estimate the measurement types available at each time step. Specifically, the MPHD allows measurement-to-nonlinearity associations such that the best matched measurement can be used at each time step, resulting in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the MPHD in improving tracking ability, both for tracking multiple targets and targets in clutter.
Evaluation Of Appearance Based Methods For Facial Expression Recognition. By

M.M. who have provided guidance me, advice and the outlook that were needed for my development as a master student. I would also like to thank TU/e, Manipal University and Philips (Eindhoven) for giving me this opportunity to do my master program and the thesis. I consider my self lucky to have worked with such great persons whose knowledge and experience in the field of computer vision has benefited me in my work and has helped me grow in understanding the field and made my approach towards the field more broader. I was also encouraged and supported to continue and perform good work by my friends at the university and at work and for this I am really grateful to them. Last but not the least I would like to thanks my parents for their love, encouragement and support. I will always respect and love them. Abstract Analyzing the valence of emotion in human face images although is a trivial task for humans, the same process involves a lot of constraints for computer programs. Some of these constraints are due to the noise introduced by digital capturing devices such as cameras, while others include illumination variations and pose variations in the captured images. A number of approaches have been suggested [1],[2],[3] for detecting the presence of human faces in images which is also the first step in the process of analyzing the valence of emotions in the detected face images. The most important problem here is one that involves a way to represent most important characteristics that go into defining the face image. These important characteristics are defined as the feature vectors of the image. Most of these approaches present a good representation of face images and few have adopted a learning procedure to classify these features into face and non face classifications [1],[4]. Most of these approaches classify a single image and use the information extracted from single image (feature extraction) for classification purposes. Similar kind of work has been done in the area of speech recognition. A major difference in the approaches in two areas is that speech recognition includes temporal changes in the classification of feature vectors. One such significant approach is the one involving Hidden Markov Models. In the current work we investigate the couple of appearance based approaches namely the Local Binary patterns [2], and the optical flow motion estimation [5] as a part of …
Investigation of the Communication Skills Levels of Football Trainers According to University Football Team Sports’ Perceptions

This study’s purpose is to prove the communication skills levels of football trainers according to perceptions of football players. The universe of the research is formed by 178 football players who participated in the football tournament amongst the second league universities in Şanlıurfa between 30 th April and 5 th May in 2013. The sample of the research is composed of 117 football players who took part in the research voluntarily. The data of the study were collected with ‘Trainer Communication Skills Scale’ ’in the type of 5-Likert, consisting of 48 items and one size which was developed by Yılmaz and his colleagues. Arithmetic mean and Anova tests were used in statistical analysis of the study. According to the results obtained in this descriptive study, the level of communication skills of the trainers was adequate but at the level required to be developed (X=3, 96± .523). In addition, according to the results of the research, the communication skills levels of the coaches of ages between 18-21 years were found to be higher than the ages of 22-25 and 26-and above age groups.
Dissociation of Exact and Approximate Calculation in Severe Global Aphasia

We report a 68-year-old patient with severe global aphasia secondary to a large left hemisphere infarction including the parietal lobe. In addition to language and neuroradiological evaluation, the patient was given specifically designed arithmetic and clock tasks requiring either exact calculation or approximate calculation. Despite severe language impairment, the patient showed relatively well-preserved abilities for numerical comprehension and arithmetic operations. Further analyses using specifically designed arithmetic and clock tasks demonstrated a clear dissociation of the patient’s abilities between impaired exact calculation and well-preserved approximate calculation. The results support the notion that numerical and arithmetic abilities are heterogeneous in that rote verbal arithmetic facts and quantitative numerical knowledge can be separable. Implications of the present findings for neural correlates of numerical and arithmetic processing suggest that the right hemisphere plays a crucial role in approximate calculation.
Additive controlled crystallization

Additives play a decisive role in crystallization processes. We survey the ongoing efforts in the realm of additive-controlled crystallization. Emphasis is placed on the commonly used and most studied types of additives and their influences on prenucleation, stabilization of amorphous precursors, nucleation, crystallization, chiral resolution, nanocrystal assembly, polymorph, and thus morphologies. We will highlight three types of nonclassical trajectories adopted by additive controlled crystallization, including oriented attachment, mesocrystals, and biomorphs.
C18unsaturated fatty acid hydrogenation patterns of some rumen bacteria and their ability to hydrolyse exogenous phospholipid

1. A number of rumen bacteria isolated because of their ability to deacylate phosphatidyl-choline, were found, in addition, to hydrogenate polyunsaturated fatty acids. 2. The most active lipolytic organisms had an unusual pattern of hydrogenation of dietary fatty acids in that α-linolenic acid was hydrogenated only as far as trans-11,cis-15-octadeca-dienoic acid.
Threaded Dynamic Memory Management in Many-Core Processors

Current trends in desktop processor design have been toward many-core solutions with increased parallelism. As the number of supported threads grows in these processors, it may prove difficult to exploit them on the commodity desktop. This paper presents a study that explores the spawning of the dynamic memory management activities into a separately executing thread that runs concurrently with the main program thread. Our approach works without requiring modifications to the original source program by redefining the dynamic link path to capture malloc and free calls in a threading dynamic memory management library. The routines of this library are setup so that the initial call to malloc triggers the creation of a thread for dynamic memory management; successive calls to malloc and free will trigger coordination with this thread for dynamic memory management activities. Our preliminary studies show that we can transparently redefine the dynamic memory management activities and we have successfully done so for numerous test programs including most of the SPEC CPU2006 benchmarks, Firefox, and other unix utilities. The results of our experiments show that it is possible to achieve 2-3% performance gains in the three most memory-intensive SPEC CPU2006 benchmarks without requiring recompilation of the benchmark source code. We were also able to achieve a 3-4% speedup when using our library with the gcc and llvm compilers.
On rank aggregation for face recognition from videos

Face recognition from still face images suffers due to intrapersonal variations caused by pose, illumination, and expression that degrade the performance. On the other hand, videos provide abundant information that can be leveraged to compensate the limitations of still face images and enhance face recognition performance. This paper presents a video based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images. The video signature embeds diverse intra-personal and temporal variations across multiple frames, thus facilitates matching two videos with large variations. Two videos are matched by comparing their discriminative signatures using the Kendall tau similarity distance measure. Performance comparison with the benchmark results and a commercial face recognition system on the publicly available YouTube faces database show the efficacy of the proposed video based face recognition algorithm.
Promoting Uncommon Knowledge Use within is Department: Human Resource Management Perspective

Employing new IT to react to external changes and developing IS effectively and efficiently are critical for IS department in contemporary business environment. In order to solve wicked problems that are never faced before, IS department needs to use knowledge uncommonly through twisting current knowledge slightly or analogizing from other areas. In addition to understand its impact on performance, this study also explores the antecedents of uncommon knowledge use from a human resource management perspective. Specifically, we hypothesize that uncertainty avoidance culture, communication, shared decision making, team style work design, and human resource policies are critical antecedents of knowledge augmentation and adaptation, which in turns lead to better IS performance. The results show that while knowledge adaptation can be enhanced by uncertainty avoidance culture and communication, knowledge augmentation is a function of shared decisionmaking, use of team, and innovation-based policies. The results serve as guidance for managers to choose needed HRM practices to boost desired uncommon knowledge use style.
On latent fingerprint minutiae extraction using stacked denoising sparse AutoEncoders

Latent fingerprint identification is of critical importance in criminal investigation. FBI's Next Generation Identification program demands latent fingerprint identification to be performed in lights-out mode, with very little or no human intervention. However, the performance of an automated latent fingerprint identification is limited due to imprecise automated feature (minutiae) extraction, specifically due to noisy ridge pattern and presence of background noise. In this paper, we propose a novel descriptor based minutiae detection algorithm for latent fingerprints. Minutia and non-minutia descriptors are learnt from a large number of tenprint fingerprint patches using stacked denoising sparse autoencoders. Latent fingerprint minutiae extraction is then posed as a binary classification problem to classify patches as minutia or non-minutia patch. Experiments performed on the NIST SD-27 database shows promising results on latent fingerprint matching.
DYNAMIC ANALYSIS AND CONTROL OF ACTIVE ENGINE MOUNT SYSTEM

Dynamic characteristics of a prototype AEM, designed based on a hydraulic engine mount, has been investigated and an adaptive controller for the AEM has been designed. An equivalent mass-spring-damper AEM model is proposed, and the transfer function that describes dynamic characteristics of the AEM is deduced from mathematical analysis on the model. The damping coefficient of the model is derived by considering nonlinear flow effect in the inertia track. Experiments confirmed that the model precisely describes dynamic characteristics of the AEM. An adaptive controller using the filtered-X LMS algorithm is designed to cancel the force transmitted through the AEM. The stability of the LMS algorithm is guaranteed by using the secondary path transfer function derived based on the dynamic model of the AEM. The performance test in the laboratory shows that the AEM system is capable of significantly reducing the force transmitted through the AEM.
QUEVICO QA Model for Video-based Interactive Media

This paper introduces the basic idea of QUEVICO, its tagset, answering process, and our prototype system. QUEVICO is a question-based video composition scheme in which video indexing and editing is designed from the viewpoint of “question and answer”, and in which multi-view videos can be effectively used. Based on the tagset in this framework, we can index a video in a suitable form for answering typical questions. The system retrieves a relevant video portion by this framework, even if a complete set of indices are not given. By editing and arranging the retrieved portion, a smart answer will be given to the user.
Limits on Reacquisition of Song in Adult Zebra Finches Exposed to White Noise

Zebra finches (Taeniopygia guttata) learn a specific song pattern during a sensitive period of development, after which song changes little or not at all. However, recent studies have demonstrated substantial behavioral plasticity in song behavior during adulthood under a range of conditions. The current experiment examined song behavior of adult zebra finches temporarily deprived of auditory feedback by chronic exposure to loud white noise (WN). Long-term exposure to continuous WN resulted in disruption of song similar to that observed after deafening. When auditory feedback was restored by discontinuing WN, birds were either tutored using tape-recorded playback or housed with adult conspecific tutors. No evidence of learning new tutor syllables was observed, and recovery of pre-WN song patterns was very limited after restoration of hearing. However, many birds did reacquire some aspects of their pretreatment song, suggesting an adult form of learning that may retain some of the initial aspects of sensorimotor acquisition of song in which vocalizations are shaped to match a stored template representation. The failure to learn novel song elements and the modest degree of recovery observed overall suggest a limit on plasticity in adult birds that have acquired species-typical song patterns and may reflect an important species difference between zebra finches and Bengalese finches.
Adapting Cloud-based Applications through a Coordinated and Optimized Resource Allocation Approach

Cloud computing is getting an enormous popularity for software companies as a way to save and optimize the cost of large hardware and software infrastructure organizations demand. Also, the cooperation between cloud layers constitutes a timely research challenge as allocation and optimization of (often virtualized) resources is many times done in isolation or with poor interaction. In this paper we propose a framework that adapts a cloud-based software application by providing an enhanced assembly of resources using the Pareto-optimal solution to optimize the resource allocation with tight cooperation between the cloud layers.
Estimation and Variable Selection in Additive Nonparametric Regression Models

Additive regression models have been shown to be useful in many situations. Numerical estimation of these models is usually done using the back-tting technique. This iterative numerical procedure converges very fast but has the disadvantage of a complicated`hat matrix. ' This paper proposes an estimator with an explicit`hat matrix' which does not use backktting. The asymptotic normality of the estimator is proved. We also investigate a variable selection procedure using the proposed estimator and prove that asymptotically the procedure nds the correct variable set with probability 1. A simulation study is presented investigating the practical performance of the procedure.
An Effective On-line Polymer Characterization Technique by Using SALS Image Processing Software and Wavelet Analysis

This paper describes an effective on-line polymer characterization technique by using small-angle light-scattering (SALS) image processing software and wavelet analysis. The phenomenon of small-angle light scattering has been applied to give information about transparent structures on morphology. Real-time visualization of various scattered light image and light intensity matrices is performed by the optical image real-time processing software for SALS. The software can measure the signal intensity of light scattering images, draw the frequency-intensity curves and the amplitude-intensity curves to indicate the variation of the intensity of scattered light in different processing conditions, and estimate the parameters. The current study utilizes a one-dimensional wavelet to delete noise from the original SALS signal and estimate the variation trend of maximum intensity area of the scattered light. So, the system brought the qualitative analysis of the structural information of transparent film success.
Wenn-Sätze als propositionale Argumente

It is well known that in German (and many other languages) clauses headed by a conditional complementizer can appear as (apparent) arguments of some verbs, including preference predicates and factive verbs. In most of the recent literature, indirect analyses are advocated according to which in such cases the clauses headed by wenn (‘if’) are adverbial clauses with a conditional interpretation and not real argument-clauses of the embedding verb. I argue in this paper that an argument-analysis is more advantageous for a systematic group of cases. I will propose a slight modification of the semantics of the standard conditional complementizer which will allow to capture its natural distribution between the two other main argument complementizers: dass (‘that’) and ob (‘whether’). The paper strongly focuses on German but also discusses some distributional facts from other European languages such as English, French, Romanian and Hungarian.
Bidding for Highly Available Services with Low Price in Spot Instance Market

Amazon EC2 has built the Spot Instance Marketplace and offers a new type of virtual machine instances called as spot instances. These instances are less expensive but considered failure-prone. Despite the underlying hardware status, if the bidding price is lower than the market price, such an instance will be terminated. Distributed systems can be built from the spot instances to reduce the cost while still tolerating instance failures. For example, embarrassingly parallel jobs can use the spot instances by re-executing failed tasks. The bidding framework for such jobs simply selects the spot price as the bid. However, highly available services like lock service or storage service cannot use the similar techniques for availability consideration. The spot instance failure model is different to that of normal instances (fixed failure probability in traditional distributed model). This makes the bidding strategy more complex to keep service availability for such systems. We formalize this problem and propose an availability and cost aware bidding framework. Experiment results show that our bidding framework can reduce the costs of a distributed lock service and a distributed storage service by 81.23% and 85.32% respectively while still keeping availability level the same as it is by using on-demand instances.