Miracle-dz/UniFAR-FTUs huggingface dataset:
Building a Foundation System for Producing Short Answers to Factual Questions

In this paper we describe question answering research being pursued as a joint project between Columbia University and the University of Colorado at Boulder as part of ARDA’s AQUAINT program. As a foundation for targeting complex questions involving opinions, events, and paragraph-length answers, we recently built two systems for answering short factual questions. We submitted results from the two systems to TREC’s Q&A track, and the bulk of this paper describes the methods used in building each system and the results obtained. We conclude by discussing current work aiming at combining modules from the two systems in a unified, more accurate system and adding capabilities for producing complex answers in addition to short ones.
Mitigation strategies in epidemics: evolutionary optimization using a hierarchy of objective functions

In this paper, we describe a multi-objective approach to determine an effective mitigation strategy during an outbreak of foot and mouth disease in cattle. The goal of the approach is to minimize the total number of infections. In addition, the direct costs must also be minimized as a secondary objective. The animals culled and vaccinations may also be minimized, as long as the number of infections is not compromised. Thus a three-tier hierarchy of objectives exists. A framework that considers objective hierarchies during a multi-objective optimization algorithm is suggested. Results show that this produces better results than a conventional multi-objective optimization approach.
Musical instrument sound classification with deep convolutional neural network using feature fusion approach

A new musical instrument classification method using convolutional neural networks (CNNs) is presented in this paper. Unlike the traditional methods, we investigated a scheme for classifying musical instruments using the learned features from CNNs. To create the learned features from CNNs, we not only used a conventional spectrogram image, but also proposed multiresolution recurrence plots (MRPs) that contain the phase information of a raw input signal. Consequently, we fed the characteristic timbre of the particular instrument into a neural network, which cannot be extracted using a phase-blinded representations such as a spectrogram. By combining our proposed MRPs and spectrogram images with a multi-column network, the performance of our proposed classifier system improves over a system that uses only a spectrogram. Furthermore, the proposed classifier also outperforms the baseline result from traditional handcrafted features and classifiers.
Making Customer Intention Tactics with Network Value and Churn Rate

Nowadays, customer churn is one of the toughest problems. Thanks for the development of database and data mining, the customer churn rate can be predicted exactly. But is the higher churn rate customer should be the most urgent to retain? The customer intention tactics is becoming the core problem for the customer churn management. This paper aimed to introduce the network value to the customer intention tactic of the SCMDA. First, the call information of SCDMA in a city branch of China Telecom was collected, then the customer churn rate is predicted by a Decision Tree Model via SAS8.50, and finally, intention tactic is made based on the network value of complex network and customer churn rate.
Assist-Me - A Volunteer Mobile Emergency System to Assist Elderly People

Assisting elderly people in emergency situations is very important to improve their quality of life. This assistance can be done with the help of volunteers using technology and in particular pervasive and mobile computing. We propose in this paper Assist-Me, a smart system aimed at assisting elderly people in case of emergency. The goal is to enhance elderly people daily life independence and autonomy, in indoor and outdoor environments, by offering flexible and faster assistance in emergency situations via social intervention offered by volunteers. We believe that our solution can alleviate the unnecessary intervention of emergency center, provide a flexible volunteering intervention and better serve elderly people in needs. Assist-Me includes two smartphone applications: one for elderly people that require emergency assistance and the second for volunteers who receive the request. Assist-Me manages emergency situations by automatically selecting appropriate volunteers based on fuzzy-logic engine. The implementation of the engine is also presented in this paper.
New single-stage PFC regulator using the Sheppard-Taylor topology

This paper describes a new usage of the DC/DC converter developed by D.I. Sheppard and B.E. Taylor in 1983 for achieving high power factor and output regulation. This converter may be viewed as a cascade of a modified boost stage and a buck stage, with the two stages sharing the same active switch. Two possible operation regimes are described. In the first regime, the converter's input part, which is a modified boost converter, operates in discontinuous mode, and the output part, which is a buck converter, operates in continuous mode. In this regime, high power factor is naturally achieved, and the output voltage is regulated by duty-cycle modulation via a simple output feedback. In the second regime, the input part operates in continuous mode, and the output part operates in discontinuous mode, with duty-cycle modulation maintaining a high power factor and frequency modulation regulating the output. Some comparisons between the Sheppard-Taylor converter and conventional boost and buck cascade are given in the paper.
Online social network to engage students meaning making in Shakespearean drama

Technology has become an integral part of everyone’s life. Students nowadays are adept at using technology especially online social network such as Facebook, Twitter, MySpace and so on in their everyday life. This has helped them to connect and communicate with friends and relatives easily. It also helps them to develop and connect meaning from their inner world to the outside world. It is important for teachers to take this opportunity to integrate online social network in the teaching and learning processes as it may encourage engagement and enjoyment among students. In the case of introducing Shakespearean drama in literature lessons, technology in the form of social media can become an effective platform for students to become active meaning makers. Through a case study of twenty teacher trainees, this paper uncovers how Shakespearean drama can be taught and learnt through online social network in particular Facebook. The findings generally show that when students got to express their thoughts and feelings through Facebook they were able to develop their linguistic, cultural and personal competence as they connect and communicate personal meaning openly which in turn encouraged participation in classroom learning.
Complete Genome of Acute Rheumatic Fever-Associated Serotype M5 Streptococcus pyogenes Strain Manfredo

ABSTRACT Comparisons of the 1.84-Mb genome of serotype M5 Streptococcus pyogenes strain Manfredo with previously sequenced genomes emphasized the role of prophages in diversification of S. pyogenes and the close relationship between strain Manfredo and MGAS8232, another acute rheumatic fever-associated strain.
Evaluation of a computer-assisted 2D interactive virtual reality system in training street survival skills of people with stroke

The rationale, procedures and results of pilot study of the VR system in training street survival skills of people with stroke were presented and discussed. The following main study was also refined from the outcomes to make it more feasible and potentially beneficial to the patients. Stroke is a focal or global neurological impairment of sudden onset, and lasting more than 24 hours (or leading to death) and of presumed vascular etiology (WHO, 1978). Resulted cognitive deficiencies represent substantial sources of disability. The functional limitations in the activities of daily living, work and leisure could be resulted from those impaired functioning. Therefore, a systematic functionally oriented services of cognitive rehabilitation based on assessment and understanding of individual deficits were used to compensate for the cognitive deficits and promote functional adaptation in daily living (Gontkovsky et al, 2002). The traditional methods have involved the various paper-and-pencil tasks, manipulation of blocks and real-life activities. Learning theory, cognitive theory and neuropsychology are used to direct the development of the treatment and explain the results. Since the technological developments over the past few years had led to the use of computers in the rehabilitation, there are numerous computer-based cognitive rehabilitation programs for different purpose. Particularly, using Virtual Reality (VR) as a medium in rehabilitation has becoming more popular and was progressing in a rapid way.
Similarity of Attributes by External Probes

In data mining, similarity or distance between attributes is one of the central notions. Such a notion can be used to build attribute hierarchies etc. Similarity metrics can be user-defined, but an important problem is defining similarity on the basis of data. Several methods based on statistical techniques exist. For defining the similarity between two attributes A and B they typically consider only the values of A and B, not the other attributes. We describe how a similarity notion between attributes can be defined by considering the values of other attributes. The basic idea is that in a 0/1 relation r, two attributes A and B are similar if the subrelations σA=1(r) and σB=1(r) are similar. Similarity between the two relations is defined by considering the marginal frequencies of a selected subset of other attributes. We show that the framework produces natural notions of similarity. Empirical results on the Reuters-21578 document dataset show, for example, how natural classifications for countries can be discovered from keyword distributions in documents. The similarity notion is easily computable with scalable algorithms.
A method for pico-specific upper bound CRE bias setting in HetNet

Heterogeneous Networks (HetNet) consisting of Macrocells deployment and overlaying Picos provide an attractive way to improving the Macrocell capacity and coverage. However, the inter-tier interference from Macrocell to Pico in such two-tiers system can significantly reduce the coverage of Pico and cause the system capacity loss. So, the Cell Range Expansion (CRE) for Pico with Almost Blank Subframe (ABS) configuration for Macrocell is proposed in 3GPP to improve the coverage of Pico. Based on this background, we proposed an estimation algorithm for Pico-specific upper bound CRE bias calculation which can ensure the SINR requirement for Pico User Equipments (PUEs).
Lifted Cover Inequalities for 0-1 Integer Programs: Complexity

We investigate several complexity issues related to branchand-cut algorithms for 0-1 integer programming based on lifted cover inequalities (LCIs). We show that given a fractional point, determining a violated LCI over all minimal covers is NP-hard. The main result is that there exists a class of 0-1 knapsack instances for which any branch-and-cut algorithm based on LCIs has to evaluate an exponential number of nodes to prove optimality.
Approche centrée sur la famille: perception parentale des pratiques d'intervention à domicile des pédagogues en éducation précoce spécialisée

Cette etude vise a connaitre l’avis de familles, suivies au sein d'un SEI vaudois, sur les pratiques centrees sur la famille utilisees par les pedagogues avec lesquels elles sont en lien afin d’identifier leur adequation a une approche centree sur la famille, a sa philosophie, a ses principes, tels que developpes dans le chapitre theorique. La participation des parents a ete sollicitee par le biais de l’outil Family-Centered Practices Scale (Extended Version) de Dunst & Trivette (2004), adapte et traduit en 3 langues. Sur 181 familles suivies par les SEI-Verdeil ; 77 d’entre elles ont accepte de participer a cette etude.
A New Approach for Deep Gray Matter Analysis Using Partial-Volume Estimation

Introduction The existence of partial volume effects in brain MR images makes it challenging to understand physio-pathological alterations underlying signal changes due to pathology across groups of healthy subjects and patients. In this study, we implement a new approach to disentangle gray and white matter alterations in the thalamus and the basal ganglia. The proposed method was applied to a cohort of early multiple sclerosis (MS) patients and healthy subjects to evaluate tissue-specific alterations related to diffuse inflammatory or neurodegenerative processes. Method Forty-three relapsing-remitting MS patients and nineteen healthy controls underwent 3T MRI including: (i) fluid-attenuated inversion recovery, double inversion recovery, magnetization-prepared gradient echo for lesion count, and (ii) T1 relaxometry. We applied a partial volume estimation algorithm to T1 relaxometry maps to gray and white matter local concentrations as well as T1 values characteristic of gray and white matter in the thalamus and the basal ganglia. Statistical tests were performed to compare groups in terms of both global T1 values, tissue characteristic T1 values, and tissue concentrations. Results Significant increases in global T1 values were observed in the thalamus (p = 0.038) and the putamen (p = 0.026) in RRMS patients compared to HC. In the Thalamus, the T1 increase was associated with a significant increase in gray matter characteristic T1 (p = 0.0016) with no significant effect in white matter. Conclusion The presented methodology provides additional information to standard MR signal averaging approaches that holds promise to identify the presence and nature of diffuse pathology in neuro-inflammatory and neurodegenerative diseases.
Gaussian mixture model speaker recognition system experiments with CORPORA database

In this paper current state-of-the-art speaker recognition system was tested with Polish Corpora database. System based on MFCC feature extraction method and Gaussian mixture model were used in several experiments. The minimum amount of data to train a reliable model has been determined. In the second experiment minimum length of a signal to recognize speakers has been determined. In the last part the speaker discriminative properties of Polish phonemes were investigated. The phonemes with the best speaker discriminative properties were determined.
ELA MINIMUM NUMBER OF DISTINCT EIGENVALUES OF GRAPHS

The minimum number of distinct eigenvalues, taken over all real symmetric matrices compatible with a given graph G, is denoted by q(G). Using other parameters related to G, bounds for q(G) are proven and then applied to deduce further properties of q(G). It is shown that there is a great number of graphs G for which q(G) = 2. For some families of graphs, such as the join of a graph with itself, complete bipartite graphs, and cycles, this minimum value is obtained. Moreover, examples of graphs G are provided to show that adding and deleting edges or vertices can dramatically change the value of q(G). Finally, the set of graphs G with q(G) near the number of vertices is shown to be a subset of known families of graphs with small maximum multiplicity.
Observations of Co^{4+} in a Higher Spin State and the Increase in the Seebeck Coefficient of Thermoelectric Ca_{3}Co_{4}O_{9}

Ca3Co4O9 has a unique structure that leads to exceptionally high thermoelectric transport. Here we report the achievement of a 27% increase in the room-temperature in-plane Seebeck coefficient of Ca3Co4O9 thin films. We combine aberration-corrected Z-contrast imaging, atomic-column resolved electron energy-loss spectroscopy, and density-functional calculations to show that the increase is caused by stacking faults with Co-ions in a higher spin state compared to that of bulk Ca3Co4O9. The higher Seebeck coefficient makes the Ca3Co4O9 system suitable for many high-temperature waste-heat-recovery applications. PACS numbers: 68.37. Lp, 79.20. Uv, 75.50. Pp
Efficient Incremental Sensor Network Deployment Algorithm

We propose an efficient algorithm for incremental deployment of nodes in a wireless sensor network. By examining the distribution of node density, its energy level, and the sensing cover area, the algorithm indicates which position should have more nodes deployed and how many new nodes are necessary to cover the desired monitoring area. Our approach uses the largest empty circle problem, a well-known computational geometry problem which is Θ(n logn), to incrementally deploy sensors in a stressed wireless sensor network. Experiments show that our algorithm is very close to the upper limit and performance 2.5 times better than the random algorithm.
A stochastic model of ant trail following with two pheromones

Colonies of ants are systems of interacting living organisms in which interactions between individuals and their environment can produce a reliable performance of a complex tasks without the need for centralised control. Particularly remarkable is the process of formation of refined paths between the nest and food sources that is essential for successful foraging. We have designed a simple stochastic off-lattice model of ant foraging in the absence of direct communication. The motion of ants is governed by two components - a random change in direction of motion that improves ability to explore the environment (facilitating food discovery), and a non-random global indirect interaction component based on pheromone signalling. Using numerical simulations we have studied the model behaviour in different parameter regimes and tested the ability of our model ants to adapt to changes in the external environment. The simulated behaviour of ants in the model recapitulated the experimentally observed behaviours of real ants. Furthermore we observe interesting synchronization in ant movement patterns which vary with the diffusion properties of the pheromones.
GridQTC: A Desktop Client for the Computational Chemistry Grid Infrastructure

The "Grupo de Quimica Teorica y Computacional de la Universitat de Valencia" (GQTC/UVEG) Computational Chemistry Grid is a virtual organization that provides access to high performance computing resources for computational chemistry through a desktop client application (GridQTC). The GQTC/UVEG environment is based on a three-tiered architecture that includes a client, a grid middleware server and a set of distributed, high-end computational resources. The GridQTC desktop client is an open source application that allows the researcher to submit jobs to high performance compute resources, without learning the intricacies of the different operating systems, environments or resources. It presents what is available on the chosen system to the user and provides a simple and intuitive interface incorporating chemistry functionality that computational chemists need to conduct their work, including pre- and post-processing tools. We show with a use case how GridQTC provides an easy-to-use integrated computing environment for academic software packages such as NWChem.
Usability of Visual Evoked Potentials as Behavioral Characteristics for Biometric Authentication

Biometric authentication methods are one of three approaches currently used. They offer a lot of benefits as well as they have few disadvantages. One of these disadvantages is low level of flexibility. It’s not possible to change your biometric characteristic or even to increase number of your characteristics. This could be problem if we consider many systems with different level of security. Corruption of system with low level of security could help attacker to gain access to system with higher level of security. Solution of this problem could lie in use of behavioral biometric. The article introduces challenge-response approach in this area. We discuss possibilities of challenge-response biometric authentication and show new behavioral biometric suitable for this approach – visual evoked potentials. We give description of physiological features of this characteristic, discuss this properties and usability. We try to answer the question if it’s suitable only for liveness testing or it’s possible to use it for full authentication. Further we present design of prototype challenge-response biometric authentication system which takes advantage of visual evoked potentials.
Automatic expression recognition and expertise prediction in Bharatnatyam

Bharatnatyam is an ancient Indian Classical Dance form consisting of complex postures and expressions. One of the main challenges in this dance form is to perform expression recognition and use the resulting data to predict the expertise of a test dancer. In this paper, expression recognition is carried out for the 6 basic expressions in Bharatnatyam using iMotions tool. The intensity values obtained from this tool for 4 distinct expressions - Joy, Surprise, Sad and Disgust are being used as our feature set for classification and predictive analysis. The recognition was performed on our own dataset consisting of 50 dancers with varied expertise ratings. Logistic Regression performed the best for Joy, Surprise and Disgust expressions giving an average accuracy of 80.78% whereas Support Vector Machine classifier with Radial Basis kernel function performed best for Sad expression giving an accuracy of 71.36%. A separate analysis on positive and negative emotions is carried out to determine the expertise of each rating on the basis of these emotions.
On 2-D non-adjacent-error channel models

In this work, we consider two-dimensional (2-D) binary channels in which the 2-D error patterns are constrained so that errors cannot occur in adjacent horizontal or vertical positions. We consider probabilistic and combinatorial models for such channels. A probabilistic model is obtained from a 2-D random field defined by Roth, Siegel and Wolf (2001). Based on the conjectured ergodicity of this random field, we obtain an expression for the capacity of the 2-D non-adjacent-errors channel. We also derive an upper bound for the asymptotic coding rate in the combinatorial model.
Secure Transmission in Amplify and Forward Networks for Multiple Degraded Eavesdroppers

We have evaluated the optimal secrecy rate for Amplify-and-Forward (AF) relay networks with multiple eavesdroppers. Assuming i.i. d. Gaussian noise at the destination and the eavesdroppers, we have devised technique to calculate optimal scaling factor for relay nodes to obtain optimal secrecy rate under both sum power constraint and individual power constraint. Initially, we have considered special channel conditions for both destination and eavesdroppers, which led us to analytical solution of the problem. Contrarily, the general scenario being a non-convex optimization problem, not only lacks an analytical solution, but also is hard to solve. Therefore, we have proposed an efficiently solvable quadratic program (QP) which provides a sub-optimal solution to the original problem. Then, we have devised an iterative scheme for calculating optimal scaling factor efficiently for both the sum power and individual power constraint scenario. Necessary figures are provided in result section to affirm the validity of our proposed solution.