Miracle-dz/UniFAR-FTUs huggingface dataset:
A secure ubiquitous healthcare system based on IMS and the HL7 standards

Body sensors are small devices close or attached to the human body that gather information regarding vital signs (e.g. body temperature, blood pressure, heartbeat rate etc.) or other physiological data. The Health Level Seven (HL7) has published the Clinical Document Architecture (CDA) which is a document markup standard that specifies the structure and semantics of a clinical document. CDA documents are encoded in Extensible Markup Language (XML) and are used for exchanging documents in heterogeneous environments. In this paper we are introducing an architecture to enable the collection of medical data in real time from body sensors, with the use of a hand held device (e.g. PDA) and their secure transmition to a central party, essentially a hospital, from where medical personnel will be able to access it, and assess the patients health status. When it comes to exchanging critical medical information the need for QoS (Quality of Service) is imperative. The IP (Internet Protocol) Multimedia Subsystem (IMS) can be used to provide Internet services (including realtime multimedia services) with QoS, and integrate different services as well. The data gathered by the sensors are transmitted to the PDA. They are converted to CDA format, digitally signed, encrypted and then transmitted over IMS to the hospital from where data can be accessed by doctors using their own PDAs'.
Vehicle Make and Model classification system using bag of SIFT features

Vehicle Make and Model classification is an important part of Intelligent Transportation System. In this work, we have proposed a method based on Linear Support vector machine to solve this problem. Scale Invariant Transform Feature (SIFT) algorithm is used in this work to extract and represent local interest points. Bag of words model is used to represent the local features as fixed length vector to represent an image. The proposed method is evaluated on a publicly available vehicle make and model dataset and promising results are achieved with this method.
Efficient localisation of sensor networks with noisy distance measurements

The localization of sensor networks using positioning systems rather than a GPS device is of great interest in many real world applications. One of the systems involves a circular type localization using distance measurements, where the problem of localizing a free sensor with knowledge of three anchors in a 2D plane is formulated using the Cayley-Menger determinant with no closed-form solution. Under some mild assumptions on the free sensor distribution, we propose a linear closed-formed algorithm to approximately solve the sensor localization problem. Analysis and discussion are presented to justify the proposed method and its localization error performance is demonstrated via simulation.
On-line voltage stability monitoring

This paper presents a new methodology for online voltage stability assessment, consisting of two steps. Firstly, the time evolution of the power system operating state is modeled with the help of a forecasting-aided state estimator; secondly the voltage collapse point is determined through an extrapolation technique based on tangent vector behavior. Tests are carried out using the IEEE-14 bus system, where different operating scenarios are considered. The results show that monitoring system load trends may improve the assessment of voltage stability.
Detecting Depression in Patients with Coronary Heart Disease: a Diagnostic Evaluation of the PHQ-9 and HADS-D in Primary Care, Findings From the UPBEAT-UK Study

Objective People with coronary heart disease (CHD) are at heightened risk of depression, and this co-occurrence of conditions is associated with poorer outcomes including raised mortality. This study compares the diagnostic accuracy of two depression case finding instruments in CHD patients relative to a diagnostic standard, the revised Clinical Interview Schedule (CIS-R). Methods The Patient Health Questionnaire (PHQ-9), the Hospital Anxiety and Depression Scale depression subscale (HADS-D) and the CIS-R depression module were administered to 803 patients identified from the CHD registers of GP practices in Greater London. Results Of 730 recruited patients without previously identified depression, 32 (4.4%) met ICD-10 depressive episode criteria according to the CIS-R. For the PHQ-9 and HADS-D lower cut-points than those routinely recommended were associated with improved case identifying properties. The PHQ-9 appeared the superior instrument using a cut-point of ≥8 (sensitivity=94%; specificity=84%). Using categorical scoring the PHQ-9 was 59% sensitive and 95% specific. For the HADS-D using cut-point ≥5, sensitivity was 81% and specificity was 77%. Areas under the curves (AUC) (standard error) were 0.95 (0.01) and 0.88 (0.02) for the PHQ-9 and HADS-D, and 0.91 (0.02) for PHQ-9 using the categorical algorithm. Statistically significant differences between AUCs of the PHQ-9 and the HADS-D favoured the former. Severity ratings compared across measures indicated inconsistency between recommended bandings: the PHQ-9 categorised a larger proportion of participants with mild and moderate depression. Conclusion This is the first large-scale investigation of the accuracy of these commonly used measures within a primary care CHD population. Our results suggest that although both scales have acceptable abilities and can be used as case identification instruments for depression in patients with CHD, the PHQ-9 appeared diagnostically superior. Importantly, optimal cut-off points for depression identification in this population appear to differ from standard values, and severity ratings differ between these measures.
Microcontroller implementation of a voice command recognition system for human-machine interface in embedded systems

Recent developments in signal processing, pattern recognition, neural networks and powerful (low-cost) embedded processors allow including speech processing for human-machine interaction in consumer equipment. In this paper we present a compact system for vowels and small vocabulary recognition, its orientation being consumer applications, where cost is of paramount importance. In this case, the speech recognition method must be as simple as possible, in such a way that could be programmed onto a standard microcontroller device, with the typical requirements of low range embedded application, where memory (kilobytes of RAM) and computer power (some MHz) are very limited. In this work we show both, algorithms and their implementation onto a low-cost microcontroller, and we study the final system performance
Research Statement Research Focus

In recent years, the topic of object detection/recognition has rapidly gained in popularity and is now perhaps the most actively researched topic in computer vision. Object detection algorithms are becoming prevalent in consumer devices such as digital cameras (real-time face detection) and automobiles (pedestrian detection systems for collision avoidance are already available and will be a standard feature on new cars within a few years). Object recognition technology is quickly becoming widespread in smartphone apps; examples include Google Goggles, Amazon Flow and Leafsnap. I believe we are at a ‘tipping point’ towards the impending ubiquity of computer vision, specifically object recognition, in our everyday lives.
Vehicle detection with image conversion into point representation

The paper presents a novel image data-based method of vehicle detection. A sequence of input images consists of consecutive frames taken from the video-stream. The video stream is obtained from the camera placed over a road. Images from the sequence of input images are converted into point representation separately. Conversion of images into point representation is performed using analysis of small image gradients. Point representation of images consists of edge points whose layout is in accordance with edge of objects contained in images before conversion. For all input images the same detection field is defined. The state of the detection field is determined on the basis of sums of the edge points calculated within the detection field. A vehicle driving through the detection field changes its state. Detection of vehicles is carried out by analysis of the state of the detection field. The experimental results are provided.
What does convergent evolution mean? The interpretation of convergence and its implications in the search for limits to evolution

Convergent evolution is central to the study of life's evolutionary history. Researchers have documented the ubiquity of convergence and have used this ubiquity to make inferences about the nature of limits on evolution. However, these inferences are compromised by unrecognized inconsistencies in the definitions, measures, significance tests and inferred causes of convergent evolution. I review these inconsistencies and provide recommendations for standardizing studies of convergence. A fundamental dichotomy exists between definitions that describe convergence as a pattern and those that describe it as a pattern caused by a particular process. When this distinction is not acknowledged it becomes easy to assume that a pattern of convergence indicates that a particular process has been active, leading researchers away from alternative explanations. Convergence is not necessarily caused by limits to evolution, either adaptation or constraint; even substantial amounts of convergent evolution can be generated by a purely stochastic process. In the absence of null models, long lists of examples of convergent events do not necessarily indicate that convergence or any evolutionary process is ubiquitous throughout the history of life. Pattern-based definitions of convergence, coupled with quantitative measures and null models, must be applied before drawing inferences regarding large-scale limits to evolution.
Combining Cochlear Implants and Hearing Instruments

Recently, it has been observed, that “ . an increasing number of people with unilateral cochlear implants have residual hearing in the non-implanted ear that is usable with acoustic amplification. Nevertheless, many adults who receive a unilateral cochlear implant do not continue to wear a hearing aid in the non-implanted ear” (Ching, Incerti and Hill 2004). In order to explore ways of improving this situation and to allow more hearing impaired subjects to make better use of modern technology, we should first have a closer look into the differences and similarities between hearing instruments (HI) and cochlear implants (CI). As shown in table 1, a CI is the appropriate choice for a patient with total deafness, whereas in cases of profound deafness either a HI or a CI may be the better solution, depending on the severity of the hearing loss. In many instances however, it may be advantageous to provide both devices to either the same or opposite ears. The first condition has become known as electroacoustic stimulation (EAS) whereas the second condition is called bimodal stimulation. Signal processing methods for cochlear implants were originally designed for optimal transmission of speech information over a very limited communication channel and made use of a variety of coding strategies which had been developed for vocoder systems. While the direct stimulation of neural elements poses specific challenges and problems for the CI devices, the principles of signal analysis used in CIs and HIs have become more similar in recent years. Both types of systems now rely on compression of the
Can HIV reverse transcriptase activity assay be a low-cost alternative for viral load monitoring in resource-limited settings?

Objective To evaluate the performance and cost of an HIV reverse transcriptase-enzyme activity (HIV-RT) assay in comparison to an HIV-1 RNA assay for routine viral load monitoring in resource limited settings. Design A cohort-based longitudinal study. Setting Two antiretroviral therapy (ART) centres in Karnataka state, South India, providing treatment under the Indian AIDS control programme. Participants A cohort of 327 HIV-1-infected Indian adult patients initiating first-line ART. Outcome measures Performance and cost of an HIV-RT assay (ExaVir Load V3) in comparison to a gold standard HIV-1 RNA assay (Abbott m2000rt) in a cohort of 327 Indian patients before (WK00) and 4 weeks (WK04) after initiation of first-line therapy. Results Plasma viral load was determined by an HIV-1 RNA assay and an HIV-RT assay in 629 samples (302 paired samples and 25 single time point samples at WK00) obtained from 327 patients. Overall, a strong correlation of r=0.96 was observed, with good correlation at WK00 (r=0.84) and at WK04 (r=0.77). Bland-Altman analysis of all samples showed a good level of agreement with a mean difference (bias) of 0.22 log10copies/mL. The performance of ExaVir Load V3 was not negatively affected by a nevirapine/efavirenz based antiretroviral regimen. The per test cost of measuring plasma viral load by the Abbott m2000rt and ExaVir Load V3 assays in a basic lab setting was $36.4 and $16.8, respectively. Conclusions The strong correlation between the HIV-RT and HIV-1 RNA assays suggests that the HIV-RT assay can be an affordable alternative option for monitoring patients on antiretroviral therapy in resource-limited settings. Trial registration number ISRCTN79261738.
A survey of P2P multidimensional indexing structures

Traditional databases have long since reaped the benefits of multidimensional indexes. Numerous proposals in the literature describe multidimensional index designs for P2P systems. However, none of these designs have had real world implementations. Several proposals for P2P multidimensional indexes are reviewed and analyzed. Znet and VBI-tree are the most promising from a technical standpoint. All of the proposed designs assume honest nodes and are thus open to abuse. This is a critical flaw that must be solved before any of the proposed systems can be used.
Success in the DREAM3 Signaling Response Challenge Using Simple Weighted-Average Imputation: Lessons for Community-Wide Experiments in Systems Biology

Our group produced the best predictions overall in the DREAM3 signaling response challenge, being tops by a substantial margin in the cytokine sub-challenge and nearly tied for best in the phosphoprotein sub-challenge. We achieved this success using a simple interpolation strategy. For each combination of a stimulus and inhibitor for which predictions were required, we had noted there were six other datasets using the same stimulus (but different inhibitor treatments) and six other datasets using the same inhibitor (but different stimuli). Therefore, for each treatment combination for which values were to be predicted, we calculated rank correlations for the data that were in common between the treatment combination and each of the 12 related combinations. The data from the 12 related combinations were then used to calculate missing values, weighting the contributions from each experiment based on the rank correlation coefficients. The success of this simple method suggests that the missing data were largely over-determined by similarities in the treatments. We offer some thoughts on the current state and future development of DREAM that are based on our success in this challenge, our success in the earlier DREAM2 transcription factor target challenge, and our experience as the data provider for the gene expression challenge in DREAM3.
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization

We demonstrate SODA (Service Oriented Domain Adaptation) for efﬁcient and scalable cross-domain microblog categorization which works on the principle of transfer learning. It is developed on a novel similarity-based iterative domain adaptation algorithm while extended with features such as active learning and interactive GUI to be used by business professionals. SODA demonstrates ef-ﬁcient classiﬁcation accuracy on new collections while minimizing and sometimes eliminating the need for expensive data labeling efforts. SODA also implements an active learning (AL) technique to select informative instances from the new collection to seek annotations, if a small amount of labeled data is required by the adaptation algorithm.
Predicting susceptibility to social bots on Twitter

The popularity of the Twitter social networking site has made it a target for social bots, which use increasingly-complex algorithms to engage users and pretend to be humans. While much research has studied how to identify such bots in the process of spam detection, little research has looked at the other side of the question - detecting users likely to be fooled by bots. In this paper, we examine a dataset consisting of 610 users who were messaged by Twitter bots, and determine which features describing these users were most helpful in predicting whether or not they would interact with the bots (through replies or following the bot). We then use six classifiers to build models for predicting whether a given user will interact with the bot, both using the selected features and using all features. We find that a users' Klout score, friends count, and followers count are most predictive of whether a user will interact with a bot, and that the Random Forest algorithm produces the best classifier, when used in conjunction with one of the better feature ranking algorithms (although poor feature ranking can actually make performance worse than no feature ranking). Overall, these results show promise for helping understand which users are most vulnerable to social bots.
Combined-field solution of composite geometries involving open and closed conducting surfaces

Combined-field integral equation (CFIE) is modified and generalized to formulate the electromagnetic problems of composite geometries involving both open and closed conducting surfaces. These problems are customarily formulated with the electric-field integral equation (EFIE) due to the presence of the open surfaces. With the new definition and application of the CFIE, iterative solutions of these problems are now achieved with significantly improved efficiency compared to the EFIE solution, without sacrificing the accuracy.
Emergent polyethism as a consequence of increased colony size in insect societies.

A threshold reinforcement model in insect societies is explored over a range of colony sizes and levels of task demand to examine their effects upon worker polyethism. We find that increasing colony size while keeping the demand proportional to the colony size causes an increase in the differentiation among individuals in their activity levels, thus explaining the occurrence of elitism (individuals that do a disproportionately large proportion of work) in insect societies. Similar results were obtained when the overall work demand is increased while keeping the colony size constant. Our model can reproduce a whole suite of distributions of the activity levels among colony members that have been found in empirical studies. When there are two tasks, we demonstrate that increasing demand and colony size generates highly specialized individuals, but without invoking any strict assumptions about spatial organization of work or any inherent abilities of individuals to tackle different tasks. Importantly, such specialization only occurs above a critical colony size such that smaller colonies contain a set of undifferentiated equally inactive individuals while larger colonies contain both active specialists and inactive generalists, as has been found in empirical studies and is predicted from other theoretical considerations.
The Impact of Emotional Intelligence in the Banking Sector

Emotional Intelligence (El) is arguably one of the most passionately debated constructs in the field of social sciences. Yet to date, there has been a very little consensus on what it is, what it measures and its unique contribution to the prediction of meaningful outcomes. The banking industry plays the role as a financial supplier to enterprises in the entire economy. Therefore, when ever changes occur in political or economic environment the industry is bound to get affected to some extent. Especially nowadays were the economic recession effects are making everyone's life more difficult it would withstand much higher risk on business operation than in any other industries. This paper focuses on the impact of Emotional Intelligence in banking sector. Emotional intelligence is very crucial across all service sectors, particularly banks. Banks compete in the market place with undifferentiated products and the quality of service encounter is the fact that gives it a distinct marketing edge, resulting in better financial performance, competitive advantage, customer satisfaction, market share and most significant customer loyalty in a small (or close) society like Cyprus with various cultural particularities. Our approach is to identify the level of emotional intelligence among branch mangers' of a commercial bank in Cyprus examining possible gaps between managers performance and employees priority on several emotional intelligence competencies. The rather new concept of Emotional Intelligence encloses many of the key competencies that is required for a favorable service encounter but also can be a key for excellence in leadership and management. The concept of managers emotional intelligence is a critical aspect to an organization that is often overlooked as it is difficult to determine on a firms performance. Worldwide more and more companies have come to the realization of how critical it is and have invested resources to ensure that they have managers at workplaces, who are emotionally intelligent.
PERFORMANCE OPTIMIZATION OF SYMMETRIC FACTORIZATION ALGORITHMS

Nonlinear optimization algorithms that use Newton’s method to determine the search direction exhibit quadratic convergence locally. In the predominant case where the Hessian is positive definite, Cholesky factorization is a computationally efficient algorithm for evaluating the Newton search direction −∇2f(x(k))−1∇f(x(k)). If the Hessian is indefinite, then modified Cholesky algorithms make use of symmetric indefinite factorization to perturb the Hessian such that it is sufficiently positive definite and reasonably well-conditioned, while preserving as much as possible the information contained in the Hessian. This paper measures and compares the performance of algorithms implementing Cholesky factorization, symmetric indefinite factorization and modified Cholesky factorization. From these performance data we estimate the work (runtime) involved in symmetric pivoting and modifying the symmetric indefinite factorization. Furthermore, we evaluate the effect of the degree of indefiniteness of the symmetric matrix on performance. For each of these matrix factorizations we developed routines that implement a variety of performance optimization techniques including loop reordering, blocking, and the use of tuned Basic Linear Algebra Subroutines.