Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted Appeals for Participation in Letters to Panel Survey Members

Previous research into survey invitation letters and prenotification letters has sought to identify optimal features of a standard letter that can be sent to all sample members. In the spirit of adaptive design, this study seeks to establish whether it can be advantageous to target different versions of a letter to different sample subgroups. Specifically, a paragraph intended to heighten perceptions of relevance of the survey is varied among six subgroups in a panel survey. Random allocation to control and treatment groups is crossed with variants of two other design features: time in sample and data collection mode. This enables analysis of the effects of the targeted letter in different survey contexts. The targeted version of the letter improved response rates for two operationally important low-response propensity groups, but only in certain survey design contexts.
Image Ownership Verification via Private Pattern and Watermarking Wavelet Filters

In this paper, we propose a watermarking scheme for the image owner- ship verification in terms of a private key pattern and wavelet filters. The water- marking is mainly achieved within the process of decomposition and reconstruc- tion by forging watermark-carrying wavelet filters. This scheme improves the wa- termark robustness and invisibility since it maximally avoids inserting the water- mark into the images directly. It also allows the flexibility to optimize the wavelet filters for better performance. The private key pattern is free from watermark re- striction and is associated with individual images. It adds an additional layer of security in a different dimension. The detection of our watermarks, can moreover be achieved without the aid of the original images, and will also be broadly dis- cussed in regard to cropped images or images of certain other distortions.
Smile Veracity Recognition Using LBP Features for Image Sequence Processing

Correct recognition of emotion veracity exhibited in facial gestures is troublesome for people. Yet, there is a belief that computer systems are able to perceive some tiny changes correlated to veracity expression, invisible for people, and therefore are able to improve proper perception of emotions. This work addresses the problem of spontaneous and posed smile recognition and suggests two approaches. The first one uses the visual cues, in which the feature vector describes the content of evenly sampled frames in the movie by applying uniform local binary patterns. The second one, describes the video sequence with smile intensity information derived from information extracted from each frame, where the feature vector is built using simple statistical measures calculated from this data. These two systems and a combination of them are tested on UVA-NEMO database and proved to deliver encouraging results.
MTT : A hardware-based Memory Trace Tool for real systems

for real systems Yungang Bao, Mingyu Chen, Yuan Ruan, Jianping Fan {baoyg,cmy,ry}@ncic.ac. cn fan@ict.ac. cn 1. Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences 2. Graduate School of Chinese Academy of Sciences Abstract Program behavior analysis is one of the most important methods for architecture design, system software (i.e. OS, compiler) optimization and application performance improving. As “Memory Wall” is becoming more and more significant, analysis of memory behavior is also becoming the critical point of program behavior analysis. In this paper, we propose a new memory trace approach and present a hardware-based Memory Trace Tool for real systems (MTT). MTT has several features: (1) be transparent to applications and system software; (2) be nearly zero-overhead and no memory pollutions; (3) be available to obtain full-system trace on real systems; (4) provide various online analysis approaches, such as memory access frequency statistic, page reuse distance calculation, hot pages collection and so on; and (5) be independent of system platform, support different CPU and OS platform. MTT is an FPGA extension board plugged in a DDR SDRAM DIMM slot on PC mainboard directly. It is able to acquire and analyze memory bus behaviors of real computer systems, and output data through Gigabit Ethernet. With MTT, we obtained different granularities of memory trace, memory access frequency, address bits change frequency, page reuse distance, and hot pages of SPEC CPU2000 benchmark, office and multimedia
Digitization and paperless processing through the use of mobile imaging technology

Even today, a large number of organizations collect data using paper forms. However, it can be difficult to aggregate, and analyze the data collected using paper forms. Better management and processing of forms and applications is indispensable to improving customer experience. But, typing the form data into a spreadsheet is time-consuming, mundane and may result in errors. Various attempts have been made to automate the process but the solutions require the use of expensive specialized hardware. This paper presents ACE, a system that uses computer vision and mobile imaging technology to capture data from any paper form using inexpensive and readily available smartphones. ACE is not restricted to a specific type of form, it is designed to dynamically adapt to any type of paper form presented to it. It generates template of the form, which the user wishes to extract data from. Later, this template is used to extract data from the filled form. The extracted data from filled forms is stored in a structured format, exportable as a CSV file.
Journal of Internet Banking and Commerce Moderating Role of Product Involvement on the Relationship between Brand Personality and Brand Loyalty

Drawing from observations by consumer behavior theorists and product involvement studies, the present study seeks to investigate the moderating role of product involvement on the relationship between brand personality and brand loyalty. A total of 958 respondents participated in this study. They completed a 14-items questionnaire to measure product involvement, and a 32-items and 16-items questionnaire to measure brand personality and brand loyalty respectively. Results suggest that the effect of product involvement related to brand personality and brand loyalty is partly moderated, where the impact is greater for low involvement consumer groups than for moderate and high involvement consumer groups for both dimensions of 'excitement' and " ruggedness'. The present study has also revealed that product involvement increases the direct effect of excitement and ruggedness of brand personality dimension on brand loyalty. However, product involvement decreases the direct effect of sincerity, competence, and sophistication of brand personality dimension on brand loyalty. The study provides support for the value of measuring product involvement in the sportswear industry. Furthermore, marketing efforts for sportswear companies need to distinguish between the elements of involvement that are more significant to brand personality and bran loyalty. The study also expands the body of knowledge by providing empirical verification of the link among product involvement profile (i.e., pleasure, interest, sign, risk importance, and risk probability), brand personality, and brand loyalty.
On the Ricci flow on the rotationally symmetric two-ball

In this paper we study a boundary value problem for the Ricci flow in the two dimensional ball endowed with a rotationally symmetric metric. We show short and long time existence results. We construct families of metrics for which the flow uniformizes the curvature along a sequence of times. Finally we show that if the initial metric has positive Gaussian curvature and the boundary positive geodesic curvature then the flow uniformizes de curvature along a sequence of times.
Performance metrics and methods for tracking in surveillance

Performance evaluation has become an increasingly important feature of video surveillance systems, as researchers attempt to assess the reliability and robustness of their operation. Although many algorithms and systems have been developed to address the problem of detecting and tracking moving objects in the image, few systems have been tested in anything other than fairly ideal conditions. In order to satisfy the requirements of a real video surveillance task, the algorithms will need to be assessed over a wide range of conditions. The aim of this paper is to examine some of the main requirements for effective performance analysis and to examine methods for characterising video datasets.
An Estimation Theoretic Approach for Sparsity Pattern Recovery in the Noisy Setting

Compressed sensing deals with the reconstruction of sparse signals using a small number of linear measurements. One of the main challenges in compressed sensing is to find the support of a sparse signal. In the literature, several bounds on the scaling law of the number of measurements for successful support recovery have been derived where the main focus is on random Gaussian measurement matrices. In this paper, we investigate the noisy support recovery problem from an estimation theoretic point of view, where no specific assumption is made on the underlying measurement matrix. The linear measurements are perturbed by additive white Gaussian noise. We define the output of a support estimator to be a set of position values in increasing order. We set the error between the true and estimated supports as the $\ell_2$-norm of their difference. On the one hand, this choice allows us to use the machinery behind the $\ell_2$-norm error metric and on the other hand, converts the support recovery into a more intuitive and geometrical problem. First, by using the Hammersley-Chapman-Robbins (HCR) bound, we derive a fundamental lower bound on the performance of any \emph{unbiased} estimator of the support set. This lower bound provides us with necessary conditions on the number of measurements for reliable $\ell_2$-norm support recovery, which we specifically evaluate for uniform Gaussian measurement matrices. Then, we analyze the maximum likelihood estimator and derive conditions under which the HCR bound is achievable. This leads us to the number of measurements for the optimum decoder which is sufficient for reliable $\ell_2$-norm support recovery. Using this framework, we specifically evaluate sufficient conditions for uniform Gaussian measurement matrices.
Empowering Fast Incremental Computation over Large Scale Dynamic Graphs

Unprecedented growth of online social networks, communication networks and internet of things have given birth to large volume, fast changing datasets. Data generated from such systems have an inherent graph structure in it. Updates in staggering frequencies (e.g. edges created by message exchanges in online social media) impose a fundamental requirement for real-time processing of unruly yet highly interconnected data. As a result, large-scale dynamic graph processing has become a new research frontier in computer science. In this paper, we present a new vertex-centric hierarchical bulk synchronous parallel model for distributed processing of dynamic graphs. Our model allows users to easily compose static graph algorithms similar to the widely used vertex-centric model. It also enables incremental processing of dynamic graphs by automatically executing user composed static graph algorithms in an incremental manner. We map widely used single source shortest path and connected component algorithms to this model and empirically analyze the performance on real-world large scale graphs. Experimental results show that our model improves the performance of both static and dynamic graph computation compared to the vertex-centric model by reducing the global synchronization overhead.
Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption

Due to imprecision and uncertainties in predicting real world problems, artifi- cial neural network (ANN) techniques have become increasingly useful for modeling and optimization. This paper presents an artificial neural network approach for forecasting elec- tric energy consumption. For effective planning and operation of power systems, optimal forecasting tools are needed for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers. Monthly data for electric energy consumed in the Gaza strip was collected from year 1994 to 2013. Data was trained and the proposed model was validated using 2-Fold and K-Fold cross validation techniques. The model has been tested with actual energy consumption data and yields satisfactory performance.
Avoidance of external fixation pin induced rotational stiffness in the forearm; a cadaver study of soft tissue displacement relative to the varying position of radius and ulna fixation

Introduction: Stiffness of forearm rotation secondary to transfixion pin sites is a frequent complication of external fixation. Conventional surgical atlases do not consider the effect of rotation on skin displacement and thus do not provide a comprehensive answer. We asked: (1) in what locations in the forearm is soft tissue displacement relative to the ulna and radius least during rotation; (2) in what positions are major neurovascular structures absent; and (3) what maximal range of rotation can be expected in forearm external fixation. Methods: Thirty-four matched cadaver arms were used to assess displacement of soft tissues at 10°, 30° and 70° of pronation and supination in relation to a testing frame. The results of these were correlated with positions in which neurovascular structures were absent and deemed insertional “Reference Positions (RP)”. Results: Expected range of rotation in diaphyseal fractures of different levels of both forearm bones was found with RP for the ulna occurring along the length of the forearm. Reference positions for the radius which provide full forearm rotation are situated only in the distal third; positions which provide partial rotation are located in the proximal and middle third. Discussion: Full range of rotation may be maintained in the case of isolated external fixation of ulnar diaphyseal fractures. In isolated external fixation of the radius a reduced range of forearm rotation may be expected.
Nitric Oxide Is a Physiological Inhibitor of Neurogenesis in the Adult Mouse Subventricular Zone and Olfactory Bulb

The subventricular zone of the rodent brain retains the capacity of generating new neurons in adulthood. The newly formed neuroblasts migrate rostrally toward the olfactory bulb, where they differentiate as granular and periglomerular interneurons. The reported presence of differentiated neurons expressing the neuronal isoform of nitric oxide synthase (NOS) in the periphery of the neurogenic region and the organization of their varicose axons as a network in which the precursors are immersed raised the hypothesis that endogenous nitric oxide (NO) may participate in the control of neurogenesis in the subventricular zone. Systemic administration of the NOS inhibitors Nω-nitro-l-arginine methyl ester or 7-nitroindazole to adult mice produced a dose- and time-dependent increase in the number of mitotic cells in the subventricular zone, rostral migratory stream, and olfactory bulb, but not in the dentate gyrus of the hippocampus, without affecting apoptosis. In the subventricular zone, this effect was exerted selectively on a precursor subpopulation expressing nestin but not neuronal or glial cell-specific proteins. In addition, in the olfactory bulb, analysis of maturation markers in the newly generated neurons indicated that chronic NOS inhibition caused a delay in neuronal differentiation. Postmitotic cell survival and migration were not affected when NO production was impaired. Our results suggest that NO, produced by nitrergic neurons in the adult mouse subventricular zone and olfactory bulb, exerts a negative control on the size of the undifferentiated precursor pool and promotes neuronal differentiation.
Understanding Academic Persistence through the Theory of Planned Behavior

High drop-out rates among first-year university students have led many researchers to attempt to gain a better understanding of academic persistence. However, despite this extensive literature, only a few studies have taken normative factors into account. These normative factors may be an essential factor of influence for persistence as it is already the case for other behaviors. We therefore decided to study whether including injunctive and descriptive norms in the investigation might improve the understanding of persistence. To this end, we focused on the theory of planned behaviour which considers for background, motivational, and normative factors. Seven hundred and twenty-seven first-year college students were asked to complete a self-report questionnaire. A structural equation modeling technique revealed that the model including injunctive norms fits better with the data than the model without norms. However, this was not the case with descriptive norms. These results are examined in the Discussion section. Finally, directions for future research and practical implications are suggested.
Comparative Analysis of Human Gut Microbiota by Barcoded Pyrosequencing

Humans host complex microbial communities believed to contribute to health maintenance and, when in imbalance, to the development of diseases. Determining the microbial composition in patients and healthy controls may thus provide novel therapeutic targets. For this purpose, high-throughput, cost-effective methods for microbiota characterization are needed. We have employed 454-pyrosequencing of a hyper-variable region of the 16S rRNA gene in combination with sample-specific barcode sequences which enables parallel in-depth analysis of hundreds of samples with limited sample processing. In silico modeling demonstrated that the method correctly describes microbial communities down to phylotypes below the genus level. Here we applied the technique to analyze microbial communities in throat, stomach and fecal samples. Our results demonstrate the applicability of barcoded pyrosequencing as a high-throughput method for comparative microbial ecology.
The Effect of Noise Correlation in Amplify-and-Forward Relay Networks

In wireless relay networks, noise at the relays can be correlated possibly due to common interference or noise propagation from preceding hops. A parallel relay network with noise correlation is considered in this network. For the relay strategy of amplify and forward (AF), the optimal rate maximizing relay gains when correlation knowledge is available at the relays are determined. Interestingly, it is shown that, on average, noise correlation is beneficial regardless of whether the relays know the noise covariance matrix. However, the knowledge of correlation can greatly improve the performance. Typically, the performance improvement from correlation knowledge increases with the relay power and the number of relays. With perfect correlation knowledge the system is capable of canceling interference if the number of interferers is less than the number of relays. For a two-hop multiple-access parallel network, closed-form expressions for the maximum sum rate and the optimal relay strategy are determined. Relay optimization for networks with three hops is also considered. Based on the result of two-hop network with noise correlation, an iterative algorithm is proposed for solving the relay optimization problem for three-hop networks.
Recognizing Stress Using Semantics and Modulation of Speech and Gestures

This paper investigates how speech and gestures convey stress, and how they can be used for automatic stress recognition. As a first step, we look into how humans use speech and gestures to convey stress. In particular, for both speech and gestures, we distinguish between stress conveyed by the intended semantic message (e.g. spoken words for speech, symbolic meaning for gestures), and stress conveyed by the modulation of either speech and gestures (e.g. intonation for speech, speed and rhythm for gestures). As a second step, we use this decomposition of stress as an approach for automatic stress prediction. The considered components provide an intermediate representation with intrinsic meaning, which helps bridging the semantic gap between the low level sensor representation and the high level context sensitive interpretation of behavior. Our experiments are run on an audiovisual dataset with service-desk interactions. The final goal is having a surveillance system that would notify when the stress level is high and extra assistance is needed. We find that speech modulation is the best performing intermediate level variable for automatic stress prediction. Using gestures increases the performance and is mostly beneficial when speech is lacking. The two-stage approach with intermediate variables performs better than baseline feature level or decision level fusion.
Preferred speeds in terrestrial vertebrates: are they equivalent?

Terrestrial animals have 'preferred speeds' within each gait, that are used much more frequently than others for moving along the ground. Energy costs reach minimal values at these speeds within each gait. In this study we asked whether these speeds are mechanically equivalent among different animals (i.e. speeds where the same levels of peak muscle stress occur). If so, this would help in establishing a link between the energetics and the mechanics of the active muscles at these speeds, providing a first step in understanding why energy costs are minimal. We also asked whether peak muscle stress reaches a similar fraction of the maximal isometric stress at these speeds. If so, this would suggest that muscles are structured so that a similar reserve capacity remains, with a similar safety factor for avoidance of injury in response to prolonged repetitive loading. We compared two species that use quite different locomotory methods at their preferred speeds: white rats that gallop and kangaroo rats that hop. We measured peak stress in the ankle extensor muscles of these two species, as they moved at their preferred speeds, using a force platform/ciné analysis technique. We also measured the maximum isometric force that this muscle group could develop in situ in the same individuals. We found the ankle extensors of white rats and kangaroo rats developed virtually identical levels of peak stress at their preferred speeds (70 +/- 6 kPa and 69 +/- 6 kPa, respectively, mean +/- S.E.), despite a fourfold difference in peak ground reaction force per unit body mass exerted on each limb. The values of peak isometric stress in situ were also virtually identical (206 +/- 17 kPa and 200 +/- 9 kPa, respectively). Our finding that the peak muscle stress is about one-third of maximum isometric stress at the preferred speeds is consistent with the idea that these are mechanically equivalent speeds, where the same fraction of available muscle fibres is recruited. Finding nearly identical values in two species that move in such different ways (galloping vs hopping), and have such large differences in ground reaction force exerted by each limb, suggests this may be true more generally for terrestrial vertebrates.