Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance evaluation of phasor estimator within IEC 61850-9-2 communication network

This paper presents performance evaluation of a new architecture for phasor estimation within IEC 61850 substation Process-Bus. To benefit from the raw IEC 61850-9-2 Sampled Measured Values streamed with high sampling rate from various Merging Units at substation bay level, Unsented Kalman Filter is proposed to be utilized in the computation of Phasor at substation process level to enhance the state estimation of dynamic phenomena. Effects of IEC 61850-9-2 communication network and the time errors due to the Asynchrony between Merging Units on the performance of the estimated Phasor are evaluated and presented in the paper.
Computerized multichannel lung sound analysis. Development of acoustic instruments for diagnosis and management of medical conditions.

T he stethoscope has been so widely used by clinicians in the diagnosis and management of patients' illnesses that it has virtually become a symbol of the medical profession. Indeed, as pointed out by Reiser in Scientific American, the invention of the stethoscope by Laënnec revolutionized medicine, changing it from an art to a science [1]. Another revolution is now going on. Auscultation of the chest is now being computerized. One of the forces driving this computerization is that it offers the promise of providing improved instruments for noninvasive diagnosis, i.e., tools that do not invade the body or involve the harmful effects of radiation. Comprehensive reviews of the stethoscope's use in aus-cultation of the lung and of the development of computerized analysis of lung sounds have been presented [2]–[4]. There are also some more recent publications that report on employing advanced signal processing techniques for the detection of adventitious sounds that have been developed by a few groups in the field of lung sounds research [6], [7]. This discussion will focus on clinically important applications of this type of investigation and, in particular, on the development of multi-channel lung sound analysis and its clinical importance. The invention of the chest X ray had a large impact on the clinical utility of the stethoscope. There was no doubt that chest X rays were more reliable than auscultation with an acoustic stethoscope in the detection of important lung diseases such as tuberculosis and cancer. Physical examination of the chest also suffers from a variety of problems. Observer variability is a particularly troublesome one as has been shown in numerous studies. There are differences between clinicians in their education and in their use of nomenclature for sounds. There are even differences in the importance they place on auscultation in the first place. Consequently, it is difficult to know when an observation is made whether it is accurate or not; thus, in the medical literature, a number of derogatory statements can be found about the stethoscope. In the 1961 edition of Rubin and Rubin's Textbook of Chest Medicine, the following quote can be found: " The stethoscope is largely a decorative instrument insofar as its value in diagnosis of pulmonary diseases is concerned. Nevertheless, it occupies an important role in the art of medicine. Apprehensive patients with functional complaints are often relieved when they feel the chest piece on their pec-toral muscles " …
A plagiarism detection procedure in three steps: Selection, matches and squares

We present a detailed description of an algorithm tailored to detect external plagiarism in PAN-09 competition. The algorithm is divided into three steps: a first reduction of the size of the problem by a selection of ten suspicious plagiarists using a n-gram distance on properly recoded texts. A search for matches after T9-like recoding. A “joining algorithm” that merges selected matches and is able to detect obfuscated plagiarism. The results are briefly discussed.
Does the real GDP per capita convergence hold in the Common Market for Eastern and Southern Africa

This article examines the convergence of real GDP per capita in the Common Market for Eastern and Southern Africa (COMESA) during the period 1950-2003. Income departures across countries were evaluated from several panel data unit root tests, especially we consider the absolute and conditional convergence. We find no evidence supporting the existence of convergence process for the income in the COMESA. Nevertheless, applying economic development criterion allows to identity two absolute convergence clubs into the COMESA, one for the most four developed countries (Egypt, Libya, Mauritius, Seychelles), and one other for the fourteen less developed ones. Thus, we show that most economies of COMESA are locked into a sustained poverty trap process.
Cohort Profile: the Corogene study.

Coronary artery disease (CAD) risk factor profiles change over time. The death rate from coronary disease has been decreasing mainly due to changes in levels of risk factors, and improvement in CAD treatments. However, there are risk factors, whose prevalence is increasing, such as diabetes and obesity. When the risk factor profile changes, we can assume that gene profiles and epigenetics also change. This has not been studied widely. The Corogene study was designed as a large cohort to study mainly CAD, but also other related heart diseases such as heart failure and aortic valve disease. We selected the patients from the CAD point of view, and decided to include over 5000 consecutive patients assigned for coronary angiogram. In Finland, coronary angiogram is performed to practically all patients assigned for invasive heart examination. Despite technical developments in diagnostics, coronary angiogram is still the gold standard for evaluating coronaries. The purpose of this study is to follow contemporary trends in coronary heart disease, and related heart disease risk factors, genetics and epigenetics by collecting cohorts referred to heart examination. New cohorts will be collected at 5-year intervals in order to see trends in CAD, its risk factors and epigenetics.
Modeling Preferences with Availability Constraints

User preferences are commonly learned from historical data whereby users express preferences for items, e.g., through consumption of products or services. Most work assumes that a user is not constrained in their selection of items. This assumption does not take into account the availability constraint, whereby users could only access some items, but not others. For example, in subscription-based systems, we can observe only those historical preferences on subscribed (available) items. However, the objective is to predict preferences on unsubscribed (unavailable) items, which do not appear in the historical observations due to their (lack of) availability. To model preferences in a probabilistic manner and address the issue of availability constraint, we develop a graphical model, called Latent Transition Model (LTM) to discover users' latent interests. LTM is novel in incorporating transitions in interests when certain items are not available to the user. Experiments on a real-life implicit feedback dataset demonstrate that LTM is effective in discovering customers' latent interests, and it achieves significant improvements in prediction accuracy over baselines that do not model transitions.
Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment

Objectives: Alarm fatigue from high false alarm rate is a well described phenomenon in the intensive care unit (ICU). Progress to further reduce false alarms must employ a new strategy. Highly sensitive alarms invariably have a very high false alarm rate. Clinically useful alarms have a high Positive-Predictive Value. Our goal is to demonstrate one approach to suppressing false alarms using an algorithm that correlates information across sensors and replicates the ways that human evaluators discriminate artifact from real signal. Methods: After obtaining IRB approval and waiver of informed consent, a set of definitions, (hypovolemia, left ventricular shock, tamponade, hemodynamically significant ventricular tachycardia, and hemodynamically significant supraventricular tachycardia), were installed in the monitors in a 10 bed cardiothoracic ICU and evaluated over an 85 day study period. The logic of the algorithms was intended to replicate the logic of practitioners, and correlated information across sensors in a way similar to that used by practitioners. The performance of the alarms was evaluated via a daily interview with the ICU attending and review of the tracings recorded over the previous 24 hours in the monitor. True alarms and false alarms were identified by an expert clinician, and the performance of the algorithms evaluated using the standard definitions of sensitivity, specificity, positive predictive value, and negative predictive value. Results: Between 1 and 221 instances of defined events occurred over the duration of the study, and the positive predictive value of the definitions varied between 4.1% and 84%. Conclusions: Correlation of information across alarms can suppress artifact, increase the positive predictive value of alarms, and can employ more sophisticated definitions of alarm events than present single-sensor based systems.
Proposing Research at the Intersection of Social Media and Collective Intelligence

The objective of this commentary is to propose some fruitful research direction built upon the reciprocal interplay of social media and collective intelligence. We focus on “wicked problems” — a class of what Introne et al. 2013 call “problems for which no single computational formulation of the problem is sufficient, for which different stakeholders do not even agree on what the problem really is, and for which there are no right or wrong answers, only answers that are better or worse from different points of view”. We argue that information systems research in particular can aid in designing appropriate systems due to benefits derived from the combined perspectives of both social media and collective intelligence. We document the relevance and timeliness of social media and collective intelligence for business and information systems engineering, pinpoint needed functionality of information systems for wicked problems, describe related research challenges, highlight prospective suitable methods to tackle those challenges, and review examples of initial results.
Compressive sensing over strongly connected digraph and its application in traffic monitoring

Compressive sensing over graphs has recently attracted great research attentions, which takes limited number end-to-end measurements along paths (walks) to recover sparse vectors representing link/node properties. Unlike traditional compressive sensing, the along-path measurements rule out the freedom of random sampling, which introduces path constraints to the measurement matrix. The constraint makes explicit analysis of recovery performance difficult Only for undirected graphs, early results showed that O(klog(n)) end-to-end measurements taken by random walks are sufficient to recover k-sparse edge vector. However, the problem becomes more difficult when directed graphs are considered, because of the easy state absorbing and the difficulty of evaluating the stationary distribution. But digraphs inherently model many network systems. In this paper, particularly for strongly connected digraphs with low node degrees, we presents bounds for the stationary distribution of random walks, and present deliberative proofs which put forward that O(klog(n)) path measurements are sufficient to recover k-sparse edge vectors. Further more, because urban road networks are exactly strongly connected, low degree digraphs, we designed efficient recovery methods to estimate road delays by a small number of probing cars. Although the road delay vector is actually not sparse, we leverage the empirical non-congested road delays as references and develop an algorithm which divide the problem to iteratively recover several k-sparse vectors. Simulation results show that when less than 10% edges are congested, more than 90% congestion states can be recovered correctly by 10% measurements.
Improving rating estimation in recommender using demographic data and expert opinions

Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, however the rating data are very sparse, which affects prediction accuracy greatly. To solve this problem, we use expert opinions to improve prediction accuracy. Firstly, we propose a novel similarity measure in order to highlight users' background. Then, combining users' ratings with expert opinions, the prediction get a right balance in both expert professional opinions and similar users. Finally, since SVD-based collaborative filtering algorithms shows good performance on the prevention of noise, we use it to smooth the prediction. Experiments of MovieLens have shown that our proposed method improves recommendation quality obviously.
Does Celebrity Image Congruence Influences Brand Attitude and Purchase Intention?

ABSTRACT We examine whether image congruence (IC) of celebrities have significant effect on purchase intention (PI) and whether attitude towards brand and attitude towards advertisement mediate the relationships in a developing country, based on sample data from India. The data was collected using a Likert Scale of 1 to 7. Structural Equation Modeling (SEM) was employed to test the recursive model incorporating endorser-brand IC, advertising effectiveness (AE), attitude toward advertisement (ATA), attitude towards brand (ATB), and purchase intention (PI). It was found that the celebrity image congruence (IC) has a negative significant effect on ATB and positive significant effect on ATA whereas advertising effectiveness (AE) had significant effect on both attitude towards brand and attitude towards advertisement. Finally AE and ATA both had significant positive effect on PI. Drawing on research on moral reasoning associated with celebrity endorsement, we provide strong theoretical as well as empirical evidence that celebrity (endorser-brand) IC is not a strong predictor of PI in a developing country. Spending huge resources on endorsers for IC does not always provide the desired benefits in countries like India. Findings would be useful for both multinational firms engaged in international marketing as well as local firms.
Robust beamforming for coherent signals in the presence of steering vector and correlation matrix errors

Based on the uniform linear array structure and the spatial-smoothing technique, a robust beamformer for the reception of coherent signals is proposed by considering both the steering vector error and the sample correlation matrix error. The resultant constrained optimisation problem is transformed into a generalised eigenvector problem and three implementations are studied, including forward-only, forward-backward, and real-valued cases, supported by simulation results.
Strong typing of object-oriented languages revisited

This paper is concerned with the relation between <italic>subtyping</italic> and <italic>subclassing</italic> and their influence on programming language design. Traditionally subclassing as introduced by Simula has also been used for defining a hierarchical type system. The type system of a language can be characterized as <italic>strong</italic> or <italic>weak</italic> and the type checking mechanism as <italic>static</italic> or <italic>dynamic</italic>. Parameterized classes in combination with a hierarchical type-system is an example of a language construct that is known to create complicated type checking situations. In this paper these situations are analyzed and several different solutions are found. It is argued that an approach with a combination of static and dynamic type checking gives a reasonable balance also here. It is also concluded that this approach makes it possible to base the type system on the class/subclass mechanism.
Simulation on Semiconductor Device Based on CPSO Algorithm

An culture particle swarm optimization algorithm (CPSO) for solving the large and serious ill-conditioned matrix equation in semiconductor simulation exactly and quickly is presented. This algorithm is constructed by the population space based on particle swarm and the knowledge space. By the example of solving the potential in linearly graded p-n junctions, the method is compared against Newton algorithm and traditional PSO and can achieve high accuracy, velocity of convergence and saving memory. In simulation of diodes, the simulation results are accord with that of theoretical analysis, which shows the CPSO algorithm is applicable and veracious.
Poxvirus Immunomodulatory Strategies: Current Perspectives

Successful transmission by viruses in the face of vigorous innate and acquired host immunity requires the ability to evade, obstruct, or subvert critical elements that mediate host antiviral responses. To that end, viruses with larger genomes, such as poxviruses, encode multiple classes of immunomodulatory proteins that have evolved specifically to inhibit such diverse processes as apoptosis, the production of interferons, chemokines, and inflammatory cytokines, and the activity of cytotoxic T lymphocytes (CTLs), natural killer (NK) cells, complement, and antibodies. Often, the evolutionary origins of these virus-encoded immunomodulatory proteins are difficult to trace. The obvious sequence similarity between some immunomodulatory poxvirus genes and the cDNA versions of related cellular counterparts suggests that they were once captured by ancestral retrotranscription and/or recombination events and then reassorted into individual virus isolates during coevolution with vertebrate hosts. However, other poxviral immunomodulators have no known cellular counterpart or have putative functions that cannot be predicted based on similarity to known cellular proteins. The origins of these orphan regulators may be obscure, but their potential for immune subversion can be profound.
Lack of strong ellipticity in Euclidean quantum gravity

Recent work in Euclidean quantum gravity has studied boundary conditions which are completely invariant under infinitesimal diffeomorphisms on metric perturbations. On using the de Donder gauge-averaging functional, this scheme leads to both normal and tangential derivatives in the boundary conditions. In the present paper, it is proved that the corresponding boundary value problem fails to be strongly elliptic. The result raises deep interpretative issues for Euclidean quantum gravity on manifolds with boundary.
AN EXTESIBLE FRAMEWORK AMASTER’STHESIS BY DENNISDOUBOVSKI

We propose an open extensible framework for automatic music transcription (AMT) based on black board architecture. A basic AMT is implemented with a pitch detector and an onset detector modules. The framework is capable of containing low level sensory data as well high level knowledge about music, such as chord structures and meter information. The pitch detector is based on two methods for scoring pitches: Partial Overlap and Harmonic Sum. The first method makes use of partial distributions and using that tries to discard notes which are likely to be result of overtones produced by other notes. The most genuine notes are thus retained. The second method makes use of partial magnitudes. Note’s total harmonic strength is computed by adding the magnitude of all partials which are part of the note using a weighting function for each partial. Partial cancellation is used to discourage reuse of partials and thus ignoring ghost notes. The onset detector is based on two detection functions: Spectral Flux and High Frequency Content. Both methods operate in the frequency domain and are capable of detecting onsets in pitch and non pitched instruments. Several transcription problems are addressed in detail. These include octave ambiguity errors, duration estimation errors and splitting of notes during multiple subsequent onsets. We propose a solution to some extent for all three problems. We also include analysis of harmonic properties of notes for live pianos and piano synthesizers. Our system is tested on a wide array of data, both synthesized MIDI files and live recorded sounds with polyphony level up to 8. We achieved an overall performance of 0.45 (F-measure), measured using three properties of notes: pitch, duration and onset time.
Learning to integrate data from different sources and tasks

Supervised learning aims at developing models with good generalization properties using input/output empirical data. Methods which use linear functions and especially kernel methods, such as ridge regression, support vector machines and logistic regression, have been extensively applied for this purpose. The first question we study deals with selecting kernels appropriate for a specific supervised task. To this end we formulate a methodology for learning combinations of prescribed basic kernels, which can be applied to a variety of kernel methods. Unlike previous approaches, it can address cases in which the set of basic kernels is infinite and even uncountable, like the set of all Gaussian kernels. We also propose an algorithm which is conceptually simple and is based on existing kernel methods. Secondly, we address the problem of learning common feature representations across multiple tasks. It has been empirically and theoretically shown that, when different tasks are related, it is possible to exploit task relatedness in order to improve prediction on each task---as opposed to treating each task in isolation. We propose a framework which is based on learning a common set of features jointly for a number of tasks. This framework favors sparse solutions, in the sense that only a small number of features are involved. We show that the problem can be reformulated as a convex one and can be solved with a conceptually simple alternating algorithm, which is guaranteed to converge to an optimal solution. Moreover, the formulation and algorithm we propose can be phrased in terms of kernels and hence can incorporate nonlinear feature maps. Finally, we connect the two main questions explored in this thesis by demonstrating the analogy between learning combinations of kernels and learning common feature representations across multiple tasks.
A ‘Learning conversation’ as a style of feedback

This article was migrated. The article was marked as recommended. This paper explores the history behind the introduction and use of a ‘learning conversation’ as a mechanism of providing effective feedback to students on resuscitation courses. The authors hope the use of this style of feedback may useful in many other contexts to provide authentic feedback. The central aim of a learning conversation is to promote and support greater self-awareness of the individual student in order to develop competence and team leadership through critical inquiry ( Harri-Augstein & Thomas 1991). Learning, particularly in the context of resuscitation is demonstrated by the utilisation of “planned experience which brings about a change of behaviour” and the process is facilitated by feedback whether during skills teaching, in workshops or in simulation. The learning conversation uses empathic, active respectful listening and discussion shared between a small group of participants, facilitated by an instructor to ensure that key learning emerges from this process duly informing future practice and behaviours. A mnemonic has been developed to facilitate acquisition of the skills involved in this feedback. Faculty also require feedback to fully develop their feedback skills but once established the process makes both faculty and students share their feelings, frustrations and learning in a very positive learning climate. The learning conversation has been in use in resuscitation courses for almost six years and the authors feel there is a wealth of literature available to support this approach which can be usefully applied to facilitate learning in many small group teaching settings and the process of delivering a Learning conversation is detailed within the paper.