Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evolutionary Indicator for Monitoring Morbidity of Avian Influenza A Virus Subtypes

Background: Avian influenza A has been a major threat to worldwide public health. The evolutionary patterns of the pathogenic viruses between humans and birds are essential to effective monitoring of disease propagation. In this study, we focused on finding an appropriate evolutionary measure for avian influenza A to reflect its adaptation to human hosts and outbreaks in human population. We estimated the synonymous and nonsynonymous substitution rates (Ks and Ka) for two hemagglutinin domains (HA1 and HA2) and neuraminidase protein in five virus subtypes (H1N1, H2N2, H3N2, H5N1, and H9N2) from human and avian hosts. We analyzed the relation between Ka and Ks and detected meaningful evolutionary patterns. Results: Our results reveal that the slopes of Ka-Ks linear regression lines in HA1 are strongly indicative of virus subtypes. We also show that dramatically increased Ka-Ks regression slopes are associated with outbreaks of H5N1(year 1997 and 2003) related human infections, suggesting that the slope might be a good indicator for monitoring influenza propagation. Analysis of Ka/Ks ratios further suggests that HA1 in H5N1 is undergoing accelerated evolution. Furthermore, the distribution pattern of Ka vs. Ks in HA1 of H1N1 viruses indicates that the evolution of avian influenza virus antigenicity between humans and birds may have a “ceiling” of Ka = ~ 0.2 (Ka = ~ 0.1 in HA). In comparison, the majority of Ka values of HA1 in H5N1 and H9N2 are much smaller than 0.1, implying large room for changes at the amino acid level. Conclusions: In this study, we proposed a straightforward indicator, the Ka-Ks regression slopes between human and avian hosts, for monitoring influenza propagation to predict outbreaks. The indicator was highly correlated to avian influenza transmission and might provide a different direction for studying.
A personality-based adaptive system for visualizing classical music performances

To enhance the experience of listening to classical orchestra music, either in the concert hall or at home, we present a personalized system that integrates three visualization/interaction concepts: Score Follower (points to the current position in the score), Orchestra Layout (illustrates instruments that are currently playing and their dynamics), and Structure Visualization (visualizes structural elements such as themes or motifs). Motivated by previous literature that found evidence for connections between personality and music consumption and preference, we first assessed in a user study to which extent personality traits and music visualization preferences correlate. Measuring preference via pragmatic quality and personality traits according to the Big Five Inventory (BFI) questionnaire, we found substantial interconnections between them. These translate into rules relating certain personality traits (e.g., extraversion or agreeableness) to preference rankings of the visualizations. In the proposed personality-based system, users are grouped into four clusters according to their answers to the most significant personality questions determined in the study. The order of the visualizations for a given user is adapted with respect to the ranking preferred by other users in the same cluster. Evaluation of the system was carried out by a second user study that showed a significantly higher normalized discounted cumulative gain (NDCG) for the personalized system in comparison to a system with randomized order of the visualizations.
An Event-Triggered Smart Sensor Network Architecture

A smart transducer is the integration of a sensor/actuator element, a processing unit and a network interface. Smart sensor networks are composed of smart transducer nodes interconnected through a communication network. This paper proposes a new architecture for smart sensor networks, that is driven by events (asynchronous data). The events are derived from a data compression algorithm embedded in the smart sensor, which compresses data from the sensor. The proposed architecture also provides configuration and monitoring data to manage the distributed system.
Large Pages May Be Harmful on NUMA Systems

Application virtual address space is divided into pages, each requiring a virtual-to-physical translation in the page table and the TLB. Large working sets, common among modern applications, necessitate a lot of translations, which increases memory consumption and leads to high TLB and page fault rates. To address this problem, recent hardware introduced support for large pages. Large pages require fewer translations to cover the same address space, so the associated problems diminish. We discover, however, that on systems with nonuniform memory access times (NUMA) large pages may fail to deliver benefits or even cause performance degradation. On NUMA systems the memory is spread across several physical nodes; using large pages may contribute to the imbalance in the distribution of memory controller requests and reduced locality of accesses, both of which can drive up memory latencies. Our analysis concluded that: (a) on NUMA systems with large pages it is more crucial than ever to use memory placement algorithms that balance the load across memory controllers and maintain locality; (b) there are cases when NUMA-aware memory placement is not sufficient for optimal performance, and the only resort is to split the offending large pages. To address these challenges, we extend an existing NUMA page placement algorithm with support for large pages. We demonstrate that it recovers the performance lost due to the use of large pages and makes their benefits accessible to applications.
On the Design of the Huggable Robot Probo

Nowadays robots are being created that interact with human beings in order to satisfy certain social needs. Following this trend, the development of the social robot Probo has started. The robot will be used in hospitals, as a tele-interface for entertainment, communication and medical assistance. Therefore, it requires the ability to express emotions. In order to do so, an emotional interface is developed to fully configure the display of emotions. These emotions -represented as a vector in an emotion space- are mapped to the degrees of freedom used in the robot. Besides emotions, the interface includes a control for the point of attention and a module to create and store animations. A 3D virtual model is created, acting as a virtual replica of the robot, providing realistic visual feedback to evaluate the design choices for the facial expressions. This paper presents the objectives of this new robot and describe the concepts and design of the first prototype.
A Decade of Innovation in Particleboard and Composite Materials : a content analysis of Washington State

The authors investigated the typology and characteristics of recent technology innovation in particleboard and composite materials. Conducting a cluster analysis of data derived from a content analysis of the International Particleboard/Composite Materials Symposium Proceedings, they identified four major clusters of like-type technology innovations – manufacturing, high technology, materials processing, and new products. Equipment makers dominated innovation in all four clusters, and “improved product quality” was the predominant source of economic benefits. Innovations in the manufacturing cluster are characterized as process innovations originated by an equipment maker. Equipment makers provided specific technology, but participation by manufacturers was not unusual. There was no participation by endusers. After “improved product quality,” “reduced energy consumption” was most often cited as a source of economic benefits. High technology innovations are characterized as process innovations originated by an equipment maker that provided specific technology. There was no participation by a manufacturer or end-user. In general, equipment makers were technology leaders, sometimes involving themselves in particleboard and composite materials production processes for the first time. “Improved product quality” was the predominant source of economic benefits. Materials processing innovations are characterized as process innovations originated by an equipment maker. There was significant manufacturer participation and no participation by end-users. After “Improved product quality,” “substitution of inexpensive for expensive raw materials” and “reduced environmental impacts,” were jointly cited as sources of economic benefit. New products innovations are characterized as product or combination product/process innovations originated by an equipment maker or jointly with an equipment maker and a Deleted: e
TAHES: Truthful double Auction for Heterogeneous Spectrums

Auction is widely applied in wireless communication for spectrum allocation. Most of prior works have assumed that spectrums are identical. In reality, however, spectrums provided by different owners have distinctive characteristics in both spacial and frequency domains. Spectrum availability also varies in different geo-locations. Furthermore, frequency diversity may cause non-identical conflicts among spectrum buyers since different frequencies have distinct communication ranges. Under such realistic scenario, existing spectrum auction schemes cannot provide truthfulness or efficiency. In this paper, we propose a Truthful double Auction for HEterogeneous Spectrum, called TAHES. TAHES allows buyers to explicitly express their personalized preferences for heterogeneous spectrums and also addresses the problem of interference graph variation. We prove that TAHES has nice economic properties including truthfulness, individual rationality and budget balance.
A Term-Based Genetic Code for Artificial Neural Networks

We developed a well-structured term-based language for the structural speciication of artii-cial neural networks. The language achieves an intuitive and compact representation even for very large networks, making it interesting as an input language for network simulators. Since it describes neural networks on a logical level, it is very well suited as a \genetic code" for the optimization of network architectures by genetic algorithms, allowing well-controllable mutation operators and a powerful crossover operation that is able to recombine functional blocks of any shape instead of destroying them. We de-ne the language formally, give examples of its application and present some results of its use as a genetic code for nding network architec-tures.
Time-frequency feature extraction from spectrograms and wavelet packets with application to automatic stress and emotion classification in speech

Three new methods of feature extraction based on time-frequency analysis of speech are presented and compared. In the first approach, speech spectrograms were passed through a bank of 12 log-Gabor filters and the outputs are averaged. In the second approach, the spectrograms were sub-divided into ERB frequency bands and the average energy for each band is calculated. In the third approach, wavelet packet arrays were calculated and passed through a bank of 12 log-Gabor filters and averaged. The feature extraction methods were tested in the process of automatic stress and emotion classification. The feature distributions were modeled and classified using a Gaussian mixture model. The test samples included single vowels, words and sentences from the SUSAS data base with 3 classes of stress, and spontaneous speech recordings with 5 emotional classes from the ORI data base. The classification results showed correct classification rates ranging from 64.70% to 84.85%, for different SUSAS data sets and from 39.6% to 53.4% for the ORI data base.
Bradley-Terry Models in R: The BradleyTerry2 Package

This is a short overview of the R add-on package BradleyTerry2, which facilitates the specification and fitting of Bradley-Terry logit, probit or cauchit models to paircomparison data. Included are the standard ‘unstructured’ Bradley-Terry model, structured versions in which the parameters are related through a linear predictor to explanatory variables, and the possibility of an order or ‘home advantage’ effect or other ‘contest-specific’ effects. Model fitting is either by maximum likelihood, by penalized quasi-likelihood (for models which involve a random effect), or by bias-reduced maximum likelihood in which the first-order asymptotic bias of parameter estimates is eliminated. Also provided are a simple and efficient approach to handling missing covariate data, and suitably-defined residuals for diagnostic checking of the linear predictor.
Knowledge element analogy relation recognition using text and graph structure

Knowledge element analogy relation is a corresponding relationship in content, function or other aspects between two knowledge elements. This paper proposes a framework of relation Gaussian processes-based learning for knowledge element analogy relation recognition, which can integrate information from text and relation graph structure. Based on terms or core terms co-occurrence and type compatibility, two rules are first developed to construct candidate analogy relation instances from knowledge element set. Next, three kernels are devised to capture information of terms, semantic types and relative positions of two knowledge elements, and graph Laplacian and expectation propagation algorithm are employed to approximate the relation graph structure. Then, these two types of information are integrated to predict analogy relation. Experimental evaluation on four data sets related to “computer” discipline demonstrates that the rules are effective and integrating three text kernels with relation graph structure can achieve better performance than only text kernels.
Pinwheels in the Quintuplet Cluster

The five enigmatic cocoon stars, after which the Quintuplet cluster was christened, have puzzled astronomers since their discovery. Their extraordinary cool, featureless thermal spectra have been attributed to various stellar types from young to highly evolved, whereas their absolute luminosities place them among the supergiants. We present diffraction-limited images from the Keck 1 telescope that resolve this debate with the identification of rotating spiral plumes characteristic of colliding-wind binary "pinwheel" nebulae. Such elegant spiral structures, found around high-luminosity Wolf-Rayet stars, have recently been implicated in the behavior of supernovae light curves in the radio and optical.
Transient and limit performance of distributed relative localization

In this paper, we consider the problem of relative localization in a network of sensors, according to the formulation of Barooah and Hespanha. We introduce a distributed algorithm for its solution, and we study the algorithm performance by evaluating a suitable performance metric as a function of the network eigenvalues. Remarkably, the performance analysis indicates that it is preferable to stop the algorithm before convergence is reached: an estimate of the optimal stopping time is provided.
Using Photo-Consistency to Register 2D Optical Images of the Human Face to a 3D Surface Model

The authors propose a novel method to register two or more optical images to a 3D surface model. The potential applications of such a registration method could be in medicine for example, in image guided interventions, surveillance and identification, industrial inspection, or telemanipulation in remote or hostile environments. Registration is performed by optimizing a similarity measure with respect to the transformation parameters. We propose a novel similarity measure based on "photo-consistency. " For each surface point, the similarity measure computes how consistent the corresponding optical image information in each view is with a lighting model. The relative pose of the optical images must be known. We validate the system using data from an optical-based surface reconstruction system and surfaces derived from magnetic resonance (MR) images of the human face. We test the accuracy and robustness of the system with respect to the number of video images, video image noise, errors in surface location and area, and complexity of the matched surfaces. We demonstrate the algorithm working on 10 further optical-based reconstructions of the human head and skin surfaces derived from MR images of the heads of five volunteers. Matching four optical images to a surface model produced a 3D error of between 1.45 and 1.59 mm, at a success rate of 100 percent, where the initial misregistration was up to 16 mm or degrees from the registration position.
Two-layer generative models for estimating unknown gait kinematics

We propose a two-layer gait modeling framework for estimating unknown gait kinematics from a monocular camera. Dual gait generative models are introduced to represent a human gait both visually and kinematically via a few latent variables. A new manifold learning method is developed to create two sets of gait manifolds that capture the gait variability among different individuals at both whole and part levels and by which the two generative models can be integrated together for video-based gait estimation. A two-stage statistical inference algorithm is employed for whole-part gait estimation. The proposed algorithm was trained on the CMU Mocap data and tested on the HumanEva data, and the experiments show very promising results on estimating the kinematics of unknown gaits.
Effects of Voluntary Product Recall on Consumer's Trust

This study analyzed the impact of voluntary product recall due to product failure on consumer’s trust. Since trust is composed by integrity, competence and benevolence, this study verified the mediator effect of these constructs on the relation between recall and trust. Once trust is essential for the establishment of a relationship between companies and consumers, this research also evaluated the effects of relational and transactional purchase situations on consumer’s trust and its constructs. We conducted an experimental study with a 2 (recall: control; voluntary recall) x 2 (purchase situation: relational; transactional) between subjects design. The results indicated that voluntary recall has a positive effect on the variables analyzed. Integrity and competence fully mediate the relation between voluntary recall and trust. Purchase situation moderates the effect of voluntary recall on competence.
Extended GM-PHD filter for multitarget tracking in nonlinear/non-Gaussian system

The Gaussian mixture probability hypothesis density (GM-PHD) filter involves the joint estimation of number of targets as well as their individual states in linear/nonlinear Gaussian system, however theoretically not suit for dynamics with non-Gaussian noise. In this paper we show that for the state transition and likelihood both with non-Gaussian distribution, the prior and posterior PHD still can be formulated by the weighted Gaussian sum and propagating the Gaussian mixtures separately over time, in this way, state estimation in a nonlinear/non-Gaussian system can be approximately recast as state estimation in a set of parallel nonlinear/Gaussian systems, moreover, the reduced rank scaled unscented/ensemble transform variational (RSEV) filtering [11] is applied to each individual nonlinear/Gaussian system for an improved accuracy of estimation of Gaussian pdf. In addition, an implementation of the proposed algorithm is proposed by combining the closed-form recursions with a strategy for pruning/merging to the number of Gaussian components to increase efficiency.
Geometric Calibration of a Structured Light System Using Circular Control Points

We present a new geometric calibration method for a structured light system combining a projector with a camera, using a planar target with circular control points. By solely exploiting the mapping between projected conics, the proposed method is strictly geometric and provides unbiased camera to projector correspondences during its application. Such a geometric method does not rely on radiometric calibration. Moreover, the method consistently ensures uniform coverage of the working volume and automatically avoids interference between both the projected and the printed patterns on the calibration target.
MapReduce Job Optimization: A Mapping Study

MapReduce has become the standard model for supporting big data analytics. In particular, MapReduce job optimization has been widely considered to be crucial in the implementations of big data analytics. However, there is still a lack of guidelines especially for practitioners to understand how the MapReduce jobs can be optimized. This paper aims to systematic identify and taxonomically classify the existing work on job optimization. We conducted a mapping study on 47 selected papers that were published between 2004 and 2014. We classified and compared the selected papers based on a 5WH-based characterization framework. This study generates a knowledge base of current job optimization solutions and also identifies a set of research gaps and opportunities. This study concludes that job optimization is still in an early stage of maturity. More attentions need to be paid to the cross-data center, cluster or rack job optimization to improve communication efficiency.
Adaptive coding of DCT coefficients by Golomb-Rice codes

In this paper we apply some of the Golomb-Rice coding techniques that emerged in JPEG-LS, a new standard for lossless image compression, and apply them towards coding DCT coefficients in the lossy JPEG baseline algorithm. We show that this results in significant improvements in performance with limited impact on computational complexity. In fact, one significant reduction in complexity provided by the proposed techniques is the complete elimination of the Huffman tables in JPEG baseline which can be a bottleneck in hardware implementations. We give simulation results, comparing the performance of the proposed technique to JPEG baseline, JPEG baseline with optimal Huffman coding (two pass) and JPEG arithmetic.
Extending Order Preserving Encryption for Multi-User Systems

Several order preserving encryption (OPE) algorithms have been developed in the literature to support search on encrypted data. However, existing OPE schemes only consider a single encryption key, which is infeasible for a practical system with multiple users (implying that all users should have the single encryption key in order to encrypt or decrypt confidential data). In this paper, we develop the first protocols, DOPE and OE-DOPE, to support the use of OPE in multi-user systems. First, we introduce a group of key agents into the system and invent the DOPE protocol to enable “distributed encryption” to assure that the OPE encryption key is not known by any entity in the system. However, in DOPE, if a key agent is compromised, the share of the secret data that is sent to this key agent is compromised. To solve the problem, we developed a novel oblivious encryption (OE) protocol based on the oblivious transfer concept to deliver and encrypt the shares obliviously. Then, we integrate it with DOPE to obtain the OEDOPE protocol. Security of OE-DOPE is further enhanced with additional techniques. Both DOPE and OE-DOPE can be used with any existing OPE algorithms while retaining all the advantages of OPE without requiring the users to share the single encryption key, making the OPE approach feasible in practical systems.