Miracle-dz/UniFAR-FTUs huggingface dataset:
The Networked Minds Measure of Social Presence : Pilot Test of the Factor Structure and Concurrent Validity

A within-subjects experiment was conducted to explore the factor structure and concurrent validity of the Net-worked Minds measure of social presence. A set of eight factors consistent with the Networked Minds theory of social presence emerged. These were grouped under three areas: (1) co-presence, with the factor, mutual awareness; (2) psychological involvement including the factors: mutual attention, empathy, mutual understanding; and (3) behavioral engagement, including the factors: behavioral interaction, mutual assistance, and dependent action. The concurrent validity of the scales was largely supported. All scales were higher in the face-to-face than mediated interactions. As predicted the co-presence, and two of the three psychological involvement factors, mutual attention and mutual understanding, were significantly different in the face-to-face interaction. There was not any significant different in empathy. Among the three behavioral engagement scales, mutual assistance scale was significant. Reliabilities were satisfactory with an average of ? =. 77. The pilot provides preliminary support for the reliability, validity, and sensitivity of the Networked Minds measure of social presence.
Value Reuse Potential in ARM Architectures

Code execution in modern superscalar processors is inherently redundant. Many instructions execute repeatedly with the same inputs, producing the same outputs, thus wasting resources in the process. Value reuse techniques memorize previous executions of instructions, blocks or traces which may be reused if they appear again with the same input contexts. Although trace reuse techniques show great potential for both performance and energy consumption improvement, they have not been studied yet in one of the most widely available computer architectures - the ARM architecture. In this paper, the main issues with reusing traces in instruction sets with conditional execution are revisited. Afterwards, the reuse potential in the benchmark suite MiBench is analyzed varying (i) how traces are generated, and (ii) the size of reuse tables. Our results show that a memoization table of 32 KiB allows to reuse 18.36% of the total instructions on average.
An Adaptive Approximation Algorithm for Community Detection in Social Network

Social network is one of the most important complex networks, which aims to describe the interactive relationship among a group of active actors that represent different kind of structure. Many systems in the real world such as human societies and different types of components can be modeled as social networks. We can represent such a network in terms of graphical community. Social Network Analysis provides inherent research due to success of social media sites and social content sharing facility. Social Network Analysis provides key terms to provide platform for industry to generate survey of product and facilitate to introduce new innovation ideas to public entity. Now a day, as increase the use of social media sites provide the entrepreneurs and user to define new concept of community creation that represents the relationship of users that might be interested in same kind of activity. To create such communities introduce new research area for researcher. This community detection is different from traditional clustering. In This paper, we propose new algorithm for community detection in social network to get some meaningful and important information.
Broadband plasmon induced transparency in terahertz metamaterials

Plasmon induced transparency (PIT) could be realized in metamaterials via interference between different resonance modes. Within the sharp transparency window, the high dispersion of the medium may lead to remarkable slow light phenomena and an enhanced nonlinear effect. However, the transparency mode is normally localized in a narrow frequency band, which thus restricts many of its applications. Here we present the simulation, implementation, and measurement of a broadband PIT metamaterial functioning in the terahertz regime. By integrating four U-shape resonators around a central bar resonator, a broad transparency window across a frequency range greater than 0.40 THz is obtained, with a central resonance frequency located at 1.01 THz. Such PIT metamaterials are promising candidates for designing slow light devices, highly sensitive sensors, and nonlinear elements operating over a broad frequency range.
A Carpooling Recommendation System for Taxicab Services

Carpooling taxicab services hold the promise of providing additional transportation supply, especially in the extreme weather or rush hour when regular taxicab services are insufficient. Although many recommendation systems about regular taxicab services have been proposed recently, little research, if any, has been done to assist passengers to find a successful taxicab ride with carpooling. In this paper, we present the first systematic work to design a unified recommendation system for both the regular and carpooling services, called CallCab, based on a data-driven approach. In response to a passenger's real-time request, CallCab aims to recommend either: 1) a vacant taxicab for a regular service with no detour or 2) an occupied taxicab heading to the similar direction for a carpooling service with the minimum detour, yet without assuming any knowledge of destinations of passengers already in taxicabs. To analyze these unknown destinations of occupied taxicabs, CallCab generates and refines taxicab trip distributions based on GPS data sets and context information collected in the existing taxicab infrastructure. To improve CallCab's efficiency to process such a big data set, we augment the efficient MapReduce model with a Measure phase tailored for our CallCab. Finally, we design a reciprocal price mechanism to facilitate the taxicab carpooling implementation in the real world. We evaluate CallCab with a real-world data set of 14000 taxicabs, and results show that compared with the ground truth, CallCab reduces 60% of the total mileage to deliver all passengers and 41% of passenger's waiting time. Our price mechanism reduces 23% of passengers' fares and increases 28% of drivers' profits simultaneously.
RF characterization of 3D printed flexible materials - NinjaFlex Filaments

The additive manufacturing technique of 3D printing has become increasingly popular for designs that have been previously unachievable due to cost and design complexity. Due to the special mechanical properties of NinjaFlex [1], there is great potential for its use in the 3D printed fabrications of numerous RF applications, such as strain sensors and wearable RF devices. This paper investigates for the first time the RF properties of various NinjaFlex filaments of varying densities utilizing the ring resonator approach, while these properties are verified on a 3D printed patch antenna topology.
Python in neuroscience

EDITORIALpublished:14April2015doi:10.3389/fninf.2015. 00011FrontiersinNeuroinformatics|www.frontiersin. org 1 April2015|Volume9|Article11Editedandreviewedby:Sean L. Hill,International NeuroinformaticsCoordinating Facility, Sweden*Correspondence:Andrew P. Davison,andrew.davison@unic.cnrs-gif.frReceived:20 March 2015Accepted:28 March 2015Published:14 April 2015Citation:Muller E, Bednar JA, Diesmann M,Gewaltig M-O, Hines M and DavisonAP (2015) Python in neuroscience.Front. Neuroinform. 9:11.doi: 10.3389/fninf.2015. 00011
Telephone based speaker recognition using multiple binary classifier and Gaussian mixture models

The present study evaluates multiple binary classifier model (MBCM) and Gaussian mixture model (GMM) solutions for both automatic speaker verification (ASV) and automatic speaker identification (ASI) problems involving text-independent telephone speech from the King speech database. The MBCM's accuracy is enhanced by selectively removing those classifiers within the model which perform worst (pruning). An unpruned MBCM outperforms a GMM for ASV and speakers taken from within the same dialectic region (San Diego, CA). Once pruned, the MBCM is found to be 2.6 times more accurate than the GMM. For closed set ASI, based on the same data, the MBCM is roughly twice as accurate as the GMM but only after pruning.
AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.
Interstellar Gas in Low Mass Virgo Cluster Spiral Galaxies

We have measured the strengths of the [C II] 158 micron, [N II] 122 micron, and CO (1 - 0) lines from five low blue luminosity spiral galaxies in the Virgo Cluster, using the Infrared Space Observatory and the NRAO 12m millimeter telescope. Two of the five galaxies have high L([C II)]/L(CO) and L(FIR)/L(CO) ratios compared to higher mass spirals. These two galaxies, NGC 4294 and NGC 4299, have L([C II])/L(CO) ratios of >14,300 and 15,600, respectively, which are similar to values found in dwarf irregular galaxies. This is the first time that such enhanced L([C II])/L(CO) ratios have been found in spiral galaxies. This result may be due to low abundances of dust and heavy elements, which can cause the CO (1 - 0) measurements to underestimate the molecular gas content. Another possibility is that radiation from diffuse HI clouds may dominate the [C II] emission from these galaxies. Less than a third of the observed [C II] emission arises from HII regions.
Option Pricing in Multivariate Stochastic Volatility Models of OU Type

We present a multivariate stochastic volatility model with leverage, which is flexible enough to recapture the individual dynamics as well as the interdependencies between several assets while still being highly analytically tractable. First we derive the characteristic function and give conditions that ensure its analyticity and absolute integrability in some open complex strip around zero. Therefore we can use Fourier methods to compute the prices of multi-asset options efficiently. To show the applicability of our results, we propose a concrete specification, the OU-Wishart model, where the dynamics of each individual asset coincide with the popular Gamma-OU BNS model. This model can be well calibrated to market prices, which we illustrate with an example using options on the exchange rates of some major currencies. Finally, we show that covariance swaps can also be priced in closed form.
A 2.6psrms-period-jitter 900MHz all-digital fractional-N PLL built with standard cells

An all-digital fractional-N Phase-Locked Loop (PLL) built with standard cells and digital synthesis tools allows easier integration with digital blocks and portability to different processes or technologies. This paper presents a PLL built with standard cells and digital synthesis tools and achieves good jitter performance. It uses an embedded time-to-digital converter (TDC) with multipath to increase TDC resolution, and includes digital correction circuitry to resolve issues from clock skew. A . 18μm CMOS prototype occupies 500μm × 500μm of area, generates a 900MHz clock from a 10MHz reference, has phase noise of −90dBc/Hz at 1MHz offset and 2.62psrms jitter while consuming 4.2mA from a 1.8V supply.
Formal Analysis of an Authentication Protocol Against External Cloud-Based Denial-of-Service (DoS) Attack

The Denial-of-service (DoS) attack is considered one of the largest threats to the availability of cloud-computing services. Due to the unique architecture of cloud-computing systems, the methods for detecting and preventing DoS attacks are quite different from those used in traditional network systems. A main target for DoS attackers is the authentication protocol because it is considered a gateway to accessing cloud resources. In this work, we propose a cloud-based authentication protocol - one that securely authenticates the cloud user and effectively prevents DoS attack on the cloud-computing system-by involving the user in a high computation process. Then, we analyze the protocol via Syverson and Van Oorschot (SVO) logic to verify the authentication process of the protocol in a cloud-computing system.
Parallel computing techniques for sequential and multi-core sorting

We adapt techniques employed in the design of parallel sorting algorithms to propose new sequential and multi-core sorting operations. The proposed approach is used to develop asymptotically efficient deterministic and randomized sorting operations whose practical sequential and multi-core performance, as witnessed by an experimental study, matches or surpasses existing optimized sorting algorithm implementations. We utilize techniques employed in parallel sorting such as deterministic regular sampling and random oversampling. We extend the notion of deterministic regular sampling into deterministic regular oversampling for sequential and multi-core sorting and demonstrate its potential along with the previously used technique of random oversampling. We then show how our techniques can not only utilize but also potentially speedup several existing sequential sorting algorithms and also lead to better multi-core sorting algorithm performance. Experimental results based on an implementation of our two operations support our claims on sequential and multi-core processors.
Performance Analysis of Energy Efficient Two-Way Relay in Presence of Interferers

In this paper, we analysed the outage performance of energy efficient two way relay in presence of interferers. Here, a single relay is selected from multiple relays, which consume less power. Unlike opportunistic scheme, single relay selection scheme is based on two power minimization scheme. First scheme is to reduce the total system power which includes relay and transceivers power and the second approach is to reduce the relay power, which subject to some predetermined constraints on the transceiver. From this minimization approach, only few parameters are needed to be broadcasted to the relays. Closed-form solution is also obtained which shows that the relay selection is totally based on the channel coefficients and the values of interferers. Outage performance is evaluated for different number of relays and different distribution of interferers power. Results show that as the number of relays increases the outage improves.
Tweet Contextualization (Answering Tweet Question) - the Role of Multi-document Summarization

The article presents the experiments carried out as part of the participation in the Tweet Contextualization (TC) track of INEX 2013. In our system there are three major sub-systems; i) Offline multi-document summarization, ii) Focused IR and iii) online multi-document Summarization. The Offline multi-document summarization system is based on document graph, clustering and sentence compression. In the Focused IR system, Wikipedia documents are indexed using Lucene with NE field. The most relevant documents are retrieved using the tweet. Online multi-document summary are generated from the most relevant Wikipedia documents and the offline summary of entity (if any). Most relevant sentences are retrieved and each retrieved sentence is assigned a ranking score in the online summary with a limit of 500 words. The three unique runs differ in the way of how many documents are retrieved per tweet. The evaluation score of informativeness is 0.9397 and Readability is 46.72% and both of which achieved 7 rank among the automatic runs.
Older people's experiences of therapeutic exercise as part of a falls prevention service: survey findings from England, Wales and Northern Ireland.

INTRODUCTION falling, and fear of falling, significantly affect older people and their lifestyle resulting in loss of confidence, restriction of activity and deteriorating quality of life. Multi-factorial assessment and active participation in an evidence-based exercise programme are key interventions to prevent and manage falls. OBJECTIVE to examine older people's experiences of therapeutic exercise as part of a falls prevention service in NHS Trusts in England, Wales and Northern Ireland. METHODS a cross-sectional survey targeted patients and staff members delivering exercise interventions for reducing falls. A multi-disciplinary group including patient and staff representatives developed a 20-item patient questionnaire and a 12-item staff questionnaire that were distributed to 94 NHS Trusts (113 participating sites within the NHS Trusts) in October 2011. RESULTS response was 57% for the patient sample and 88% for the staff sample. The median (IQR) age of patients was 82 (77-86) years. 72% were women. Two-thirds reported attending group-based therapeutic exercise classes generally of short duration (80% <12 weeks) and low intensity (85% one class per week) at hospitals and community venues. Balance and strength exercises were prescribed; 68% reported using resistance equipment such as ankle weights and/or exercise band. Only 52% reported exercises were made more difficult as they improved. However, patient satisfaction levels were high (95% satisfied or very satisfied). Patients and staff reported limited availability of strength and balance follow-up classes. CONCLUSION despite high levels of patient satisfaction therapeutic exercise provision was limited and implementation of evidence-based exercise interventions by healthcare providers is incomplete and varies widely. Patients and staff wanted greater availability of long-term exercise services for falls prevention.
Handwritten Arabic text line segmentation using affinity propagation

In this paper, we present a novel graph-based method for extracting handwritten text lines in monochromatic Arabic document images. Our approach consists of two steps - Coarse text line estimation using primary components which define the line and assignment of diacritic components which are more difficult to associate with a given line. We first estimate local orientation at each primary component to build a sparse similarity graph. We then, use a shortest path algorithm to compute similarities between non-neighboring components. From this graph, we obtain coarse text lines using two estimates obtained from Affinity propagation and Breadth-first search. In the second step, we assign secondary components to each text line. The proposed method is very fast and robust to non-uniform skew and character size variations, normally present in handwritten text lines. We evaluate our method using a pixel-matching criteria, and report 96% accuracy on a dataset of 125 Arabic document images. We also present a proximity analysis on datasets generated by artificially decreasing the spacings between text lines to demonstrate the robustness of our approach.
The Packing Server for real-time scheduling of MapReduce workflows

This paper develops new schedulability bounds for a simplified MapReduce workflow model. MapReduce is a distributed computing paradigm, deployed in industry for over a decade. Different from conventional multiprocessor platforms, MapReduce deployments usually span thousands of machines, and a MapReduce job may contain as many as tens of thousands of parallel segments. State-of-the-art MapReduce workflow schedulers operate in a best-effort fashion, but the need for real-time operation has grown with the emergence of real-time analytic applications. MapReduce workflow details can be captured by the generalized parallel task model from recent real-time literature. Under this model, the best-known result guarantees schedulability if the task set utilization stays below 50% of total capacity, and the deadline to critical path length ratio, which we call the stretch φ, surpasses 2. This paper improves this bound further by introducing a hierarchical scheduling scheme based on the novel notion of a Packing Server, inspired by servers for aperiodic tasks. The Packing Server consists of multiple periodically replenished budgets that can execute in parallel and that appear as independent tasks to the underlying scheduler. Hence, the original problem of scheduling MapReduce workflows reduces to that of scheduling independent tasks. We prove that the utilization bound for schedulability of MapReduce workflows is UB · φ-β/φ , where UB is the utilization bound of the underlying independent task scheduling policy, and β is a tunable parameter that controls the maximum individual budget utilization. By leveraging past schedulability results for independent tasks on multiprocessors, we improve schedulable utilization of DAG workflows above 50% of total capacity, when the number of processors is large and the largest server budget is (sufficiently) smaller than its deadline. This surpasses the best known bounds for the generalized parallel task model. Our evaluation using a Yahoo! MapReduce trace as well as a physical cluster of 46 machines confirms the validity of the new utilization bound for MapReduce workflows.
Functional Correlations of Pathogenesis-Driven Gene Expression Signatures in Tuberculosis

Tuberculosis remains a major health threat and its control depends on improved measures of prevention, diagnosis and treatment. Biosignatures can play a significant role in the development of novel intervention measures against TB and blood transcriptional profiling is increasingly exploited for their rational design. Such profiles also reveal fundamental biological mechanisms associated with the pathology of the disease. We have compared whole blood gene expression in TB patients, as well as in healthy infected and uninfected individuals in a cohort in The Gambia, West Africa and validated previously identified signatures showing high similarities of expression profiles among different cohorts. In this study, we applied a unique combination of classical gene expression analysis with pathway and functional association analysis integrated with intra-individual expression correlations. These analyses were employed for identification of new disease-associated gene signatures, identifying a network of Fc gamma receptor 1 signaling with correlating transcriptional activity as hallmark of gene expression in TB. Remarkable similarities to characteristic signatures in the autoimmune disease systemic lupus erythematosus (SLE) were observed. Functional gene clusters of immunoregulatory interactions involving the JAK-STAT pathway; sensing of microbial patterns by Toll-like receptors and IFN-signaling provide detailed insights into the dysregulation of critical immune processes in TB, involving active expression of both pro-inflammatory and immunoregulatory systems. We conclude that transcriptomics (i) provides a robust system for identification and validation of biosignatures for TB and (ii) application of integrated analysis tools yields novel insights into functional networks underlying TB pathogenesis.