Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Missing Labels and Overlapping Sound Events on Multi-label Multi-instance Learning for Sound Event Classification

Automated analysis of complex scenes of everyday sounds might help us navigate within the enormous amount of data and help us make better decisions based on the sounds around us. For this purpose classiﬁcation models are required that translate raw audio to meaningful event labels. The speciﬁc task that this paper targets is that of learning sound event classiﬁer models by a set of example sound segments that contain multiple potentially overlapping sound events and that are labeled with multiple weak sound event class names. This involves a combination of both multi-label and multi-instance learning. This paper investigates two state-of-the-art methodologies that allow this type of learning, low-resolution multi-label non-negative matrix deconvolution (LRM-NMD) and CNN. Besides comparing the accuracy in terms of correct sound event classiﬁcations, also the robustness to missing labels and to overlap of the sound events in the sound segments is evaluated. For small training set sizes LRM-NMD clearly outperforms CNN with an accuracy that is 40 to 50% higher. LRM-NMD does only mi-norly suffer from overlapping sound events during training while CNN suffers a substantial drop in classiﬁcation accuracy, in the order of 10 to 20%, when sound events have a 100% overlap. Both methods show good robustness to missing labels. No matter how many labels are missing in a single segment (that contains multiple sound events) CNN converges to 97% accuracy when enough training data is available. LRM-NMD on the other hand shows a slight performance drop when the amount of missing labels increases.
Receiver Autonomous Integrity Monitoring Performance for Two- Satellites Simultaneous Fault of BeiDou

As the probability of two-satellites fault simultaneously increase, the difficulty of fault identification in the integrity monitoring become serious and the traditional Receiver Autonomous Integrity Monitoring (RAIM) may not be able to properly deal with this problem. This paper investigates the impact of two-satellites fault at the same time on integrity parameters. Two factors, two-satellites fault mode and range deviation combination, are introduced to simulate the condition of two satellites fault in BeiDou, and the change of integrity parameters (Horizontal Positioning Error (HPE) and Vertical Positioning Error (VPE)) is discussed. The experiment results show that: under two-satellites fault condition, with range deviation increase, HPE and VPE grow. However, HPE/VPE show little change if the faulty satellite mode contains MEO and the range deviation is less than 100 meters. KeywordsTwo-Satellites fault; HPE/VPE;Kruskal-Wallis test;Multiple Comparison.
An Implementation of Pixel Based Segmentation and Modified Linear Search Algorithm in Blood Typing Characterization

Our study implemented the process of image segmentation and the application of linear search algorithm to determine human blood type, which resulted in higher rate of efficiency in terms of time and accuracy. The proponent conducted a series of tests to validate that the system can give credible results compared to a series of tests done in the laboratory and performed by a medical practitioner. The researcher used purposive sampling and 30 respondents were clearly identified and chosen. Flowcharts were utilized in which shapes or blocks that represented the functions were connected by flow lines to show the flow and the design of the proposed system. The proposed system provides an alternative to blood typing characterization, which produces faster results of 10-15 minutes compared to the conventional method that produce results in 3-4 hours. The research also concluded that image processing could be used not only in remote sensing or geomantic but also in the medical field.
A study of online learning with multiple kernels and variable metric(本文)

In this thesis entitled “A Study of Online Learning with Multiple Kernels and Variable Metric”, online learning schemes are considered. One of the recent topics in signal processing is how to deal with big data. Batch processing of big data involves high computational complexity. When data arrive sequentially, the batch learning needs to recompute the solution at each time. The batch learning is thus inappropriate to process such sequential data. Online learning, of which the complexity is linear order, adaptively processes sequential data and reflects recently observed information to learning. The existing online learning schemes need a priori information to design a proper metric and/or kernel. This thesis presents effective online learning schemes based on a variable metric and multiple kernels. The proposed schemes estimate a proper metric and/or kernel, and, at the same time, learn an unknown system to be estimated. This thesis consists of six chapters. Chapter 1 introduces the background and the outline of this thesis. Chapter 2 introduces mathematical basics that will be used throughout this thesis, including proximity operator, variable-metric projection, and reproducing kernel. Chapter 3 introduces a novel adaptive filtering scheme named the metric-combining normalized least mean square (MC-NLMS) algorithm. The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metricmatrices convexly in an adaptive manner, thereby taking advantages of the metrics that rely on multiple pieces of information. This chapter compares the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, with the improved PNLMS (IPNLMS) algorithm and it is shown that the performance of NPNLMS is controllable with the convex combination coefficient as opposed to IPNLMS. This chapter also presents another example of MC-NLMS with its application to an acoustic echo cancellation problem and shows the efficacy of the proposed scheme. Chapter 4 investigates the use of compactly supported kernels (CSKs) for the kernel NLMS (KNLMS) algorithm. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. A simple two-step method to compute the kernelized input vectors efficiently is presented. In the first step, it computes an over-estimation of the support of the kernelized input vector based on a certain l1-ball. In the second step, it identifies the exact support by detailed inspections based on an l2-ball. Also, the proposed method employs the identified support given by the second step for dictionary construction, and then reduces the amount of l2-distance evaluations, leading to the complexity reduction. The nu-
More systematic than claimed: Insights on the SCAN tasks

We show that some standard attention-based architectures widely used in Neural Machine Translation as well as a pointer-based variant achieve results on some of the compositional SCAN tasks that are far superior to those reported in Lake & Baroni (2018). We next show that there is high variance in the test accuracy across both random initialization and training duration. We show that ensembling can be used to take advantage of this variance and improve results but that, for many tasks, a large gap remains between ensemble performance and the performance of an oracularly selected single best model. Based on these insights, we suggest some possible directions for future research, emphasizing selection and regularization over the need for more compositional architectures
Underdiagnosis of Genital Herpes by Current Clinical and Viral-Isolation Procedures

Abstract Background The current clinical strategy for diagnosing genital herpes simplex virus (HSV) infection in women relies on clinical findings plus the selective use of viral culture. The effectiveness of this approach for identifying women with genital herpes is unknown. Methods We performed physical examinations, colposcopy, Pap smears, viral cultures, and HSV type-specific serologic assays of 779 randomly selected women attending a sexually transmitted disease clinic. Results Evidence of HSV type 2 infection was detected in 363 women (47 percent), and 9 others (1 percent) had positive cultures indicative of urogenital or anal infection with HSV type 1. Of these 372 women, only 82 (22 percent) had symptoms. Fourteen women (4 percent) had viral shedding without symptoms, 60 (16 percent) had formerly had symptomatic episodes, and 216 (58 percent) had antibodies to HSV-2 with neither viral shedding nor a history of clinical episodes. Characteristic ulcerations of the external genitalia were present in .
Asymptomatic Patient with Complex Exercise-induced Ventricular Tachycardia

We present a 58-year-old patient who had planned to start running and bicycling after the winter break. He takes no medications on a regular basis, had no known risk factors, and his blood pressure and cholesterol levels were normal. His GP carried out an exercise bicycle test. Ventricular ectopic beats started at 200 watts at increasing frequency. The exercise bicycle test had to be stopped at 225 watts because of a polytope ventricular tachycardia. A sophisticated diagnostic procedure including CMR enabled us to diagnose asymptomatic myocarditis with oedema as cause of the complex ventricular tachycardia in this patient. EPU ruled out other causes such as Brugada syndrome, ion channel diseases or QT syndromes. The patient had no structural heart disease such as previous myocardial infarction, dilated or hypertrophic cardiomyopathies or arrythmogenic right-heart cardiomyopathy. Coronary angiography ruled out coronary artery disease. This patient was at possible risk of sudden cardiac death while engaging in intensive sport activities. A diagnostic approach including cardiac CMR enabled this potentially life-saving diagnosis.
Using an Evaluator Fixed Structure Learning Automata in Sampling of Social Networks

Social networks are streaming, diverse and include a wide range of edges so that continuously evolves over time and formed by the activities among users (such as tweets, emails, etc.), where each activity among its users, adds an edge to the network graph. Despite their popularities, the dynamicity and large size of most social networks make it difficult or impossible to study the entire network. This paper proposes a sampling algorithm that equipped with an evaluator unit for analyzing the edges and a set of simple fixed structure learning automata. Evaluator unit evaluates each edge and then decides whether edge and corresponding node should be added to the sample set. In The proposed algorithm, each main activity graph node is equipped with a simple learning automaton. The proposed algorithm is compared with the best current sampling algorithm that was reported in the Kolmogorov-Smirnov test (KS) and normalized L1 and L2 distances in real networks and synthetic networks presented as a sequence of edges. Experimental results show the superiority of the proposed algorithm.
ANALYSIS OF ROUTING PROTOCOLS IN FLYING WIRELESS NETWORKS

The wireless network that are formed on the fly, without relying on the previous infrastructure, including the autonomous devices that lie nearby are called the flying wireless networks. Despite of the networks, self-configuring and self-healing nature, it incurs many challenges due to its mobile nature, limited battery span and the lack of the centralized control, so these wireless network requires a perfect routing protocol addressing the challenges and improving the performance of the network, so the paper presents the survey on the various routing protocols that enhances the performance of the flying wireless network. The survey is further continued with the analysis of the routing protocols to evince the capability of the same on the grounds of network life time, energy consumption and the quality of service.
Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios

Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches.
Études génétiques de familles récessives d’ataxies et de paraplégies spastiques

i Université de Montréal Faculté des Études Supérieures et Postdoctorales Cette thèse intitulée: Études génétiques de familles récessives d'ataxies et de paraplégies spastiques Présentée par : Anne Noreau a été évaluée par un jury composé des personnes suivantes : l'implication du gène SYNE1 dans les cas d'ataxie cérébelleuse à travers le monde. Le second chapitre fera la démonstration qu'il est souhaitable d'utiliser le séquençage de l'exome entier dans le but de poser un diagnostic clinique. En effet, il a été possible de trouver la cause génétique d'une famille comportant deux membres atteints d'atrophie congénitale du cervelet, où le symptôme prédominant est l'ataxie. Le séquençage de l'exome a permis la mise en évidence de mutations dans le gène PMM2, déjà connues pour cause le syndrome des glycoprotéines déficientes en hydrates de carbone. Dans un second temps, il sera question d'un autre désordre du mouvement la paraplégie spastique familiale (PSF). Le chapitre 3 relatera les mutations trouvées dans le gène CYP7B1 dans notre cohorte de patients PSF. Abstract Over the past years, genetics has undergone a phenomenal growth due to the development of new sequencing technologies. Indeed, whole exome sequencing in families led to the identification of new genes involved in many diseases. Neurosciences were also able to benefit from these discoveries, where several new genes have been identified in several neurological diseases. This thesis will covered two different movement disorders for which we have used traditional sequencing technology, in this case by Sanger sequencing, combined with whole exome sequencing for new gene discovery. The first movement disorder that is described is ataxia, which will be focused on autosomal recessive mode of inheritance. The first chapter will relate the new mutations found in French-Canadian with ataxia of Beauce. We will also discuss new mutations found in two other populations, confirming the involvement of the gene SYNE1 in worldwide cases of cerebellar ataxia. The second chapter will demonstrate that it is desirable to use whole exome sequencing in order to make a clinical diagnosis. Indeed, it has been possible to find the genetic cause for a family with two members with congenital cerebellar atrophy, which the predominant symptom is ataxia. Exome sequencing allowed the identification of mutations in the PMM2 gene, already known to cause a syndrome leading to glycosylation deficit of glycoprotein. For the second part, we will cover another movement disorder call hereditary spastic paraplegia (HSP). Chapter 3 relates the mutations …
Method for determining elements of urban infrastructure objects based on the results from air monitoring

The study proposes a two-stage method for determination of elements of urban infrastructure objects in images made by air monitoring systems. The first stage implies determining the contours of objects in images. The advanced Canny method was selected as the contour determination method. We considered the main stages of the advanced Canny method for determination of contours of objects in images made by air monitoring systems. The application of the Hough transform at the second stage was proposed. The paper reports features in the method for determination of elements of urban infrastructure in color images made by air monitoring systems. In contrast to known methods, the method takes into account features of formation of images made by air monitoring systems. It highlights color channels and marks out contours and geometric primitives in each color channel; it re-integrated color channels and determines elements of urban infrastructure objects in the space of an output image. The study presents the results of applying the method for determination of elements of urban infrastructure objects in a standard color image acquired from an air monitoring system. We defined elements of urban infrastructure objects, such as roads, houses, streets, building elements and others, as an example. A visual evaluation of the quality of processing of a typical color image made by an air monitoring system was performed. We calculated errors of the first kind and the second kind. It was established that application of a two-stage method for determination of elements of urban infrastructure objects in an image made by an air monitoring system improves the quality of processing of optoelectronic images. Moreover, errors of the first kind and the second kind in determination of elements of urban infrastructure objects reduced by 13 % on average.
Formulation: An investigation into perspectives of non-psychologists within a Child and Adolescent Mental Health Service

This thesis describes the process of exploring the understanding of non-psychologists around the use of formulation, and alternative approaches to explaining a service user’s mental health difficulties. This includes uncovering perspectives about the role of the clinical psychologist within the team. Formulation is seen as a key concept in mainstream psychotherapeutic approaches, but it is not widely understood whether psychiatry or mental health nurses use this approach in understanding a service user’s difficulties, or whether they use diagnosis and classification as the mainstay methodology. Current literature suggests that the use of formulation in psychiatry is constrained to being a part of an overall medical summary, but that mental health nurses can use an approach similar to formulation, undertaking comprehensive assessments, and identifying difficulties through alternative therapeutic interventions. Clinical psychologists report sharing formulations informally within MDTs, helping increase staff cohesion, improve team dynamics, and improve relationships with service users. However, literature suggests that service users may feel more confident receiving a medical diagnosis as it provides a stronger justification and validation for their difficulties. To better understand the role that formulation takes within mental health working, a study was designed to explore these areas further. Using a qualitative approach called Template Analysis (TA), which utilises hierarchical coding to extract key themes from the interview transcripts, the researcher was able to find four themes: ‘level of understanding of formulation’, ‘level of benefit of formulation within the team’, ‘limitations of using formulation within the team’, and ‘Role of clinical psychologist’. The researcher used these themes and resultant codes to discuss levels of understanding of non-psychologists working within the teams, around formulation and the role of the clinical psychologist, and areas for development and future research.
Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction

In the process of software defect prediction dataset construction, there are problems such as high labeling costs. Active learning can reduce labeling costs when using uncertainty sampling. Samples with the most uncertainty will be labeled, but samples with the highest certainty will always be discarded. According to cognitive theory, easy samples can promote the performance of the model. Therefore, a hybrid active learning query strategy is proposed. For the sample with lowest information entropy, query-by-committee will analyze it again using vote entropy. Empirical studies show that the proposed HIVE approach outperforms several state-of-the-art active learning approaches.
Toward Incentivizing Fog-Based Privacy-Preserving Mobile Crowdsensing in the Internet of Vehicles

In the face of a massive number of vehicular users, a data collection paradigm based on vehicular crowdsensing requires an effective means of attracting participants. Thus, the incentive mechanisms play a key role in crowdsensing procedure design, inevitably leading to problems related to user privacy leakage. In this article, to reduce the risk of privacy leakage in the implementation of incentive mechanisms, we propose a fog computing-based crowdsensing architecture specialized for vehicular crowdsensing and corresponding privacy-preserving solutions for the processes of data reporting, reward issuing, and trust management. Authentication and encryption technologies, such as zero-knowledge verification, one-way hashing, partially blind signature authentication, and homomorphic encryption, are utilized to achieve our goals. Finally, efficiency improvements in both privacy preservation and network response are proven through analysis and simulations.
A streaming multi-class support vector machine classification architecture for embedded systems

Pedestrian detection, face detection, speech recognition and object detection are some of the applications that have benefited from hardware-accelerated SVM. SVM classification computational complexity makes it challenging for designing hardware architecture with real-time performance and low power consumption. On an embedded streaming architecture, test data are stored on external memory and transferred in streams to the FPGA device. The hardwareimplementation for SVM classification needs to be fast enough to keep up with the data transfer speed. Prior implementation throttles data input to avoid overwhelming the computational unit. This results in a bottleneck in overall streaming architecture as maximum throughput could not be obtained. In this work, we propose a streaming architecture multi-class SVM classification for embedded system that is fully pipelined and able to process data continuously with out any need to throttle data stream input. The proposed design is targeted for embedded platform where test data is transferred in streams from an external memory. The architecture was implemented on Altera Cyclone IV platform. Performance analysis on our proposed architecture is done with regards to the number of features and support vectors. For validation, the results obtained is compared with LibSVM. The proposed architecture is able to produce output rate identical to test data input rate.