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Cardiovascular diseases are the global leading cause of death. Automated electrocardiogram (ECG) analysis can support clinicians to identify abnormal excitation of the heart and prevent premature cardiovascular death. An explainable classification is particularly important for support systems. Our contribution to the PhysioNet/CinC Challenge 2021 (team name: ibmtPeakyFinders) therefore pursues an approach that is based on interpretable features to be as explainable as possible. To meet the challenge goal of developing an algorithm that works for both 12-lead and reduced lead ECGs, we processed each lead separately. We focused on signal processing techniques based on template delineation that yield the template's fiducial points to take the ECG waveform morphology into account. In addition to beat intervals and amplitudes obtained from the template, various heart rate variability and QT interval variability features were extracted and supplemented by signal quality indices. Our classification approach utilized a decision tree ensemble in a one-vs-rest approach. The model parameters were determined using an extensive grid search. Our approach achieved challenge scores of 0.47, 0.47, 0.34, 0.40, and 0.41 on hidden 12-, 6-, 4-, 3-, and 2-lead test sets, respectively, which corresponds to the ranks 12, 10, 23, 18, and 16 out of 39 teams.

本論文では，計量的な手法を用いて川端康成の代筆問題と文体問題に取り組み，次に挙げたことを明らかにした。①小説『乙女の港』と『花日記』は川端康成と中里恒子の共同執筆である。②『コスモスの友』，『古都』，『眠れる美女』と『山の音』は代筆の可能性が低い。③泉鏡花，徳田秋聲と横光利一と比べ川端康成文体の存在が確認され，終戦の1945 年を境に川端康成の語彙の豊富さと，機能語の助詞，副詞，接続詞に変化が見られた。 / In this study, we revealed the following ghostwriting and writing style problem of Kawabata quantitatively. ①Otome no minato and Hana nikki were colloboratively written by Kawabata and Nakazato. ② Kosumosu no tomo, The Old Capital, House of the Sleeping Beauties, and The Sound of the Mountain were not written by the suspected ghostwriters. ③ Kawabata has his own writing style as compared to novels written by Izumi, Tokuda, and Yokomitsu. Changes were observed in vocabulary richness, postpositional particles, adverb, and conjunctions in Kawabata's novels after the second world war. / 博士(文化情報学) / Doctor of Culture and Information Science / 同志社大学 / Doshisha University

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[pt] Para o diagnóstico de distúrbios do sono, um dos exames mais usado é a polissonografia (PSG), na qual é registrada uma variedade de sinais fisiológicos. O exame de PSG é observado por um especialista do sono, processo que pode levar muito tempo e incorrer em erros de interpretação. O presente trabalho desenvolve e compara o desempenho de quatro sistemas baseados em arquiteturas de redes neurais de aprendizado profundo, mais especificamente, redes convolutivas (CNN) e redes recorrentes Long-Short Term Memory (LSTM), para a identificação de despertares relacionados ao esforço respiratório (Respiratory Effort-Related Arousal-RERA) e a eventos de despertar relacionados à apneia/hipopneia. Para o desenvolvimento desta pesquisa, foram usadas as informações de apenas seis canais eletroencefalográficos (EEG) provenientes de 994 registros de PSG noturna da base de dados PhysioNet CinC Challenge2018, além disso, foi considerado o uso de class weight e Focal Loss para lidar com o desbalanceamento de classes. Para a avaliação de cada um dos sistemas foram usadas a Accuracy, AUROC e AUPRC como métricas de desempenho. Os melhores resultados para o conjunto de teste foram obtidos com os modelos CNN1 obtendo-se uma Accuracy, AUROC e AUPRC de 0,8404, 0,8885 e 0,8141 respetivamente, e CNN2 obtendo-se uma Accuracy, AUROC e AUPRC de 0,8214, 0,8915 e 0,8097 respetivamente. Os resultados restantes confirmaram que as redes neurais de aprendizado profundo permitem lidar com dados temporais de EEG melhor que os algoritmos de aprendizado de máquina tradicional, e o uso de técnicas como class weight e Focal Loss melhoram o desempenho dos sistemas. / [en] For the diagnosis of sleep disorders, one of the most commonly used tests is polysomnography (PSG), in which a variety of physiological signs are recorded. The study of PSG is observed by a sleep therapist, This process may take a long time and may incur misinterpretation. This work develops and compares the performance of four classification systems based on deep learning neural networks, more specifically, convolutional neural networks (CNN) and recurrent networks Long-Short Term Memory (LSTM), for the identification of Respiratory Effort-Related Arousal (RERA) and to events related to apnea/hypopnea. For the development of this research, it was used the Electroencephalogram (EEG) data of six channels from 994 night polysomnography records from the database PhysioNet CinC Challenge2018, the use of class weight and Focal Loss was considered to deal with class unbalance. Accuracy, AUROC, and AUPRC were used as performance metrics for evaluating each system. The best results for the test set were obtained with the CNN1 models obtaining an accuracy, AUROC and AUPRC of 0.8404, 0.8885 and 0.8141 respectively, and RCNN2 obtaining an accuracy, AUROC and AUPRC of 0.8214, 0.8915 and 0.8097 respectively. The remaining results confirmed that deep learning neural networks allow dealing with EEG time data better than traditional machine learning algorithms, and the use of techniques such as class weight and Focal Loss improve system performance.

During recent years, much focus have been put on replacing time consuming manual mappingand classification tasks with automatic methods, having minimal human interaction. Now it ispossible to quickly classify land cover and terrain features covering large areas to a digital formatand with a high accuracy. This can be achieved using nothing but remote sensing techniques,which provide a far more sustainable process and product. Still, some terrain features do not havean established methodology for high quality automatic mapping.The Scandinavian Mountains contain several terrain features with low passability, such asmires, shrub and stony ground. It would be of interest to anyone passing the land to avoid theseareas. However, they are not sufficiently mapped in current map products.The aim of this thesis was to find a methodology to classify and map these terrain featuresin the Scandinavian Mountains with high accuracy and minimal human interaction, using remotesensing techniques. The study area chosen for the analysis is a large valley and mountain sidesouth-east of the small town Abisko in northern Sweden, which contain clearly visible samplesof the targeted terrain features. The methodology was based on training a Fuzzy Logic classifierusing labeled training samples and descriptors derived from ortophotos, LiDAR data and currentmap products, chosen to separate the classes from each other by their characteristics. Firstly,a set of candidate descriptors were chosen, from which the final descriptors were obtained byimplementing a Fisher score filter. Secondly a Fuzzy Inference System was constructed usinglabeled training data from the descriptors, created by the user. Finally the entire study area wasclassified pixel-by-pixel by using the trained classifier and a majority filter was used to cluster theoutputs. The result was validated by visual inspection, comparison to the current map productsand by constructing Confusion Matrices, both for the training data and validation samples as wellas for the clustered- and non-clustered results.The results showed that / De senaste åren har mycket fokus lagts på att ersätta tidskrävande manuella karterings- och klassificeringsmetodermed automatiserade lösningar med minimal mänsklig inverkan. Det är numeramöjligt att digitalt klassificera marktäcket och terrängföremål över stora områden, snabbt och medhög noggrannhet. Detta med hjälp av enbart fjärranalys, vilket medför en betydligt mer hållbarprocess och slutprodukt. Trots det finns det fortfarande terrängföremål som inte har en etableradmetod för noggrann automatisk kartering.Den skandinaviska fjällkedjan består till stor del av svårpasserade terrängföremål som sankmarker,videsnår och stenig mark. Alla som tar sig fram i terrängen obanat skulle ha nytta av attkunna undvika dessa områden men de är i nuläget inte karterade med önskvärd noggrannhet.Målet med denna analys var att utforma en metod för att klassificera och kartera dessa terrängföremåli Skanderna, med hög noggrannhet och minimal mänsklig inverkan med hjälp avfjärranalys. Valet av testområde för analysen är en större dal och bergssida sydost om Abisko inorra Sverige som innehåller tydliga exemplar av alla berörda terrängföremål. Metoden baseradespå att träna en Fuzzy Logic classifier med manuellt utvald träningsdata och deskriptorer,valda för att bäst separera klasserna utifrån deras karaktärsdrag. Inledningsvis valdes en uppsättningav kandidatdeskriptorer som sedan filtrerades till den slutgiltiga uppsättningen med hjälp avett Fisher score filter. Ett Fuzzy Inference System byggdes och tränades med träningsdata fråndeskriptorerna vilket slutligen användes för att klassificera hela testområdet pixelvis. Det klassificeraderesultatet klustrades därefter med hjälp av ett majoritetsfilter. Resultatet validerades genomvisuell inspektion, jämförelse med befintliga kartprodukter och genom confusion matriser, vilkaberäknades både för träningsdata och valideringsdata samt för det klustrade och icke-klustraderesultatet.Resultatet visade att de svårpasserade terrängföremålen sankmark, videsnår och stenig markkan karteras med hög noggrannhet med hjälp denna metod och att resultaten generellt är tydligtbättre än nuvarande kartprodukter. Däremot kan metoden finjusteras på flera plan för att optimeras.Bland annat genom att implementera deskriptorer för markvattenrörelser och användandeav LiDAR med högre spatial upplösning, samt med ett mer fulltäckande och spritt val av klasser.

The bias against attractiveness is fairly implicit and furthermore, powerfully impacts people’s subsequent impressions of and behaviors toward others (Cash, Gillen, & Burns, 1977; Dion et al., 1972). Pallet, Link and Lee (2010) examined the effect of various facial spatial configurations on attractiveness and found that raters rated faces as most attractive when the eyeto-mouth ratio approximated 36% of the face length (the "golden ratio"), which coincides with the measurements of an average and thus more attractive face. The present study examined the extent to which the distance of these objectively measured facial features affected mentors’ perceptions of their protégés, the subsequent mentoring given to them, and the protégés’ own behavior (e.g. seek feedback, request specific information).The gender composition of the mentor-protégé dyad was expected to moderate these relationships. I also examined whether, given the expected effects of facial measurements, withholding access to visual cues would affect mentor perceptions and behavior. Participants were 118 mentor/protégé dyads from a large Southeastern university who volunteered to participate in a formal online peer mentoring program. After seeing their protégés’ profiles (and for those in the experimental condition, a picture), mentors chatted with their protégés once a week for 30 minutes for a total of 4 weeks. Results indicated that protégés with facial features moderately distant from the golden ratio were perceived as more similar by mentors in same-gender dyads and received greater mentoring than did protégés closest and farthest from the golden ratio. In opposite-gender dyads, however, mentors reported greater similarity toward those that were farthest from the golden ratio but provided the greatest mentoring to those closest to the golden ratio. The relationship iv between facial measurements and protégé proactivity was moderated by whether or not their mentor had access to their picture. While protégés closest to the ratio were more proactive in the picture condition, those that were farthest from it were more proactive in the non-picture condition. Proactivity was as expected associated with greater levels of mentoring, which was ultimately related to a more fulfilled and beneficial relationship for protégés (i.e. less stress, greater self-efficacy and satisfaction). The results of this study indicate that facial measurements are associated with both differences in mentor and in protégé behavior and that the specific nature of these relationships differs as a function of gender composition. Implications for practice and theory will be discussed

A new paradigm of electronic devices with bio-inspired features is aiming to mimic the brain’s fundamental mechanisms to achieve recognition of very complex patterns and more efficient computational tasks. Networks of electropolymerized dendritic fibers are attracting much interest because of their ability to achieve advanced learning capabilities, form neural networks, and emulate synaptic and plastic processes typical of human neurons. Despite their potential for braininspired computation, the roles of the single parameters associated with the growth of the fiber are still unclear, and the intrinsic randomness governing the growth of the dendrites prevents the development of devices with stable and reproducible properties. In this manuscript, we provide a systematic study on the physical parameters influencing the growth, defining cause-effect relationships for direction, symmetry, thickness, and branching of the fibers. We build an electrochemical model of the phenomenon and we validate it in silico using Montecarlo simulations. This work shows the possibility of designing dendritic polymer fibers with controllable physical properties, providing a tool to engineer polymeric networks with desired neuromorphic features.

Padel is one of the fastest growing sports in Sweden. Its popularity rose significantly during the Covid-19 pandemic in 2020, as many other types of sport facilities closed, and people had more flexible work schedules due to remote work. This paper is an analysis on the monthly occupancy of indoor padel facilities in Sweden between January 2018 and April 2022. It aims to answer to what degree a machine learning algorithm can predict the occupancy for a given padel facility and which key features have the largest impact on the occupancy. With these findings, it is possible to estimate the revenue for a given padel facility and therefore be used to identify which type of padel facilities have the biggest opportunity to succeed from an economical perspective. This article reviews the literature regarding different methods of machine learning, in this case, applied to booking systems and occupancy estimations. The reviewed literature also presents the most common evaluation metrics used for comparing different machine learning models. This study analyses the relationship between the occupancy level of a given padel facility and 12 input features, related to the padel facility in question, with a random forest regression model. This work results in a model that achieved a R2 score of 49% and a mean absolute error of 11%. The input features ranked according to the largest impact on the model’s estimation are (with the mean of all absolute SHAP values written in parentheses): Year (7.71), Month (5.23), Average Income in municipality (4.13), Driving Time from municipality Centre (2.35), Population of municipality (1.97), Padel Slots in municipality (1.27), Padel Slots in facility (1.27), Average Court Price (1.12), Tennis Slots in municipality (0.73), Badminton Slots in municipality (0.55), Squash Slots in municipality (0.44) and Golf Slots in municipality (0.26). Padel facilities had the highest average occupancy in 2020. The Covid-19 pandemic is likely a significant contributor to this, due to the shutdown of offices and many types of training venues. Therefore, Year has the largest impact on the model’s estimation. Occupancy of indoor facilities follows a seasonal trend, where it tends to be highest in December and January and lowest in June and July. This trend can partly be explained by a larger demand for indoor sport activities during winter and increased competition from outside padel facilities and other activities during summer. Because of this, Month had the second largest impact on the model’s estimation. / Padel är en av de snabbast växande sporterna i Sverige. Dess popularitet ökade avsevärt under Covid-19-pandemin i 2020, främst på grund av att många andra typer av sportanläggningar stängdes ner och människor hade mer flexibla arbetsscheman på grund av distansarbete. Den här uppsatsen är en analys av den månatliga beläggningen av inomhuspadelanläggningar i Sverige mellan januari 2018 och april 2022. Studien syftar till att svara på i vilken grad en maskininlärningsalgoritm kan förutsäga beläggningen för en given padelanläggning och vilka nyckelfunktioner som har störst inverkan på beläggningen. Med dessa insikter är det möjligt att uppskatta intäkterna för en given padelanläggning och kan därför användas vilka typer av padelanläggningar som har störst möjlighet att vara framgångsrika ur ett ekonomiskt perspektiv. Den granskade litteraturen studerar olika maskininlärningsmetoder tillämpad i områden som bokningssystemsanalys och beläggningsgradsstudier, samt presenterar de vanligaste utvärderingsmåtten som används för att jämföra metoderna. Denna studie analyserar sambandet mellan beläggningsgraden för en given padelanläggning och 12 inputparametrar, relaterade till padelanläggningen i fråga med hjälp av en random forest regressionsalgoritm. Detta arbete resulterar i en modell som uppnådde ett R2 värde på 49% och en genomsnittlig absolut avvikelse på 11 %. Inputparametrarna rangordnade enligt den största påverkan på modellens uppskattning är (med medelvärdet av alla absoluta SHAP-värden skrivna inom parentes): År (7.71), Månad (5.23), Genomsnittlig Inkomst i kommunen (4.13), Körtid mellan anläggning och kommunens centrum (2.35), Kommunens befolkningsmängd (1.97), Antal padeltider i kommunen (1.27), Padeltider i anläggningen(1.27), Genomsnittlig pris för bana(1.12), Tennistider i kommunen (0.73), Badmintontider i kommunen (0.55), Squashtider i kommunen (0.44) och Golftider i kommunen (0.26). Padelanläggningar hade högsta genomsnittliga beläggningsgraden under 2020. Covid-19-pandemin är sannolikt en betydande bidragande orsak till detta på grund av nedläggningen av kontor och andra sportanläggningar. Därför har inputparametern År den största inverkan på modellens uppskattning. Beläggningen av inomhusanläggningar följer en säsongsmässig trend, där den tenderar att vara högst i januari och lägst i juli. Denna trend kan delvis förklaras av en större efterfrågan på inomhussportaktiviteter under vintern och ökad konkurrens från utomstående padelanläggningar och andra aktiviteter under sommaren. På grund av detta hade Månad den näst största påverkan på modellens uppskattning.

With the availability of inexpensive hardware and software, digital imaging has become an important medium of communication in our daily lives. A huge amount of digital images are being collected and become available through the internet and stored in various fields such as personal image collections, medical imaging, digital arts etc. Therefore, it is important to make sure that images are stored, searched and accessed in an efficient manner. The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a standard choice for many computer vision tasks. Based on this promising model, this thesis investigates three main problems: natural scene classification, annotation and retrieval. Given an image, the task is to design a system that can determine to which class that image belongs to (classification), what semantic concepts it contain (annotation) and what images are most similar to (retrieval). This thesis contributes to scene classification by proposing a weighting approach, named keypoints density-based weighting method (KDW), to control the fusion of colour information and bag of visual words on spatial pyramid layout in a unified framework. Different configurations of BOW, integrated visual vocabularies and multiple image descriptors are investigated and analyzed. The proposed approaches are extensively evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories using 10-fold cross validation. The second contribution in this thesis, the scene annotation task, is to explore whether the integrated visual vocabularies generated for scene classification can be used to model the local semantic information of natural scenes. In this direction, image annotation is considered as a classification problem where images are partitioned into 10x10 fixed grid and each block, represented by BOW and different image descriptors, is classified into one of predefined semantic classes. An image is then represented by counting the percentage of every semantic concept detected in the image. Experimental results on 6 scene categories demonstrate the effectiveness of the proposed approach. Finally, this thesis further explores, with an extensive experimental work, the use of different configurations of the BOW for natural scene retrieval. / Applied Science University in Jordan