HIERARCHICAL LEARNING OF DISCRIMINATIVE FEATURES AND CLASSIFIERS FOR LARGE-SCALE VISUAL RECOGNITION

Enabling computers to recognize objects present in images has been a long standing but tremendously challenging problem in the field of computer vision for decades. Beyond the difficulties resulting from huge appearance variations, large-scale visual recognition poses unprecedented challenges when the number of visual categories being considered becomes thousands, and the amount of images increases to millions. This dissertation contributes to addressing a number of the challenging issues in large-scale visual recognition. First, we develop an automatic image-text alignment method to collect massive amounts of labeled images from the Web for training visual concept classifiers. Specif- ically, we first crawl a large number of cross-media Web pages containing Web images and their auxiliary texts, and then segment them into a collection of image-text pairs. We then show that near-duplicate image clustering according to visual similarity can significantly reduce the uncertainty on the relatedness of Web images’ semantics to their auxiliary text terms or phrases. Finally, we empirically demonstrate that ran- dom walk over a newly proposed phrase correlation network can help to achieve more precise image-text alignment by refining the relevance scores between Web images and their auxiliary text terms. Second, we propose a visual tree model to reduce the computational complexity of a large-scale visual recognition system by hierarchically organizing and learning the classifiers for a large number of visual categories in a tree structure. Compared to previous tree models, such as the label tree, our visual tree model does not require training a huge amount of classifiers in advance which is computationally expensive. However, we experimentally show that the proposed visual tree achieves results that are comparable or even better to other tree models in terms of recognition accuracy and efficiency. Third, we present a joint dictionary learning (JDL) algorithm which exploits the inter-category visual correlations to learn more discriminative dictionaries for image content representation. Given a group of visually correlated categories, JDL simul- taneously learns one common dictionary and multiple category-specific dictionaries to explicitly separate the shared visual atoms from the category-specific ones. We accordingly develop three classification schemes to make full use of the dictionaries learned by JDL for visual content representation in the task of image categoriza- tion. Experiments on two image data sets which respectively contain 17 and 1,000 categories demonstrate the effectiveness of the proposed algorithm. In the last part of the dissertation, we develop a novel data-driven algorithm to quantitatively characterize the semantic gaps of different visual concepts for learning complexity estimation and inference model selection. The semantic gaps are estimated directly in the visual feature space since the visual feature space is the common space for concept classifier training and automatic concept detection. We show that the quantitative characterization of the semantic gaps helps to automatically select more effective inference models for classifier training, which further improves the recognition accuracy rates

Recipe popularity prediction in Finnish social media by machine learning models

Abstract. In recent times, the internet has emerged as a primary source of cooking inspiration, eating experiences and food social gathering with a majority of individuals turning to online recipes, surpassing the usage of traditional cookbooks. However, there is a growing concern about the healthiness of online recipes. This thesis focuses on unraveling the determinants of online recipe popularity by analyzing a dataset comprising more than 5000 recipes from Valio, one of Finland’s leading corporations. Valio’s website serves as a representation of diverse cooking preferences among users in Finland. Through examination of recipe attributes such as nutritional content (energy, fat, salt, etc.), food preparation complexity (cooking time, number of steps, required ingredients, etc.), and user engagement (the number of comments, ratings, sentiment of comments, etc.), we aim to pinpoint the critical elements influencing the popularity of online recipes. Our predictive model-Logistic Regression (classification accuracy and F1 score are 0.93 and 0.9 respectively)- substantiates the existence of pertinent recipe characteristics that significantly influence their rates. The dataset we employ is notably influenced by user engagement features, particularly the number of received ratings and comments. In other words, recipes that garner more attention in terms of comments and ratings tend to have higher rates values (i.e., more popular). Additionally, our findings reveal that a substantial portion of Valio’s recipes falls within the medium health Food Standards Agency (FSA) score range, and intriguingly, recipes deemed less healthy tend to receive higher average ratings from users. This study advances our comprehension of the factors contributing to the popularity of online recipes, providing valuable insights into contemporary cooking preferences in Finland as well as guiding future dietary policy shift.Reseptin suosion ennustaminen suomalaisessa sosiaalisessa mediassa koneoppimismalleilla. Tiivistelmä. Internet on viime aikoina noussut ensisijaiseksi inspiraation lähteeksi ruoanlaitossa, ja suurin osa ihmisistä on siirtynyt käyttämään verkkoreseptejä perinteisten keittokirjojen sijaan. Huoli verkkoreseptien terveellisyydestä on kuitenkin kasvava. Tämä opinnäytetyö keskittyy verkkoreseptien suosioon vaikuttavien tekijöiden selvittämiseen analysoimalla yli 5000 reseptistä koostuvaa aineistoa Suomen johtavalta maitotuoteyritykseltä, Valiolta. Valion verkkosivujen reseptit edustavat monipuolisesti suomalaisten käyttäjien ruoanlaittotottumuksia. Tarkastelemalla reseptin ominaisuuksia, kuten ravintoarvoa (energia, rasva, suola, jne.), valmistuksen monimutkaisuutta (keittoaika, vaiheiden määrä, tarvittavat ainesosat, jne.) ja käyttäjien sitoutumista (kommenttien määrä, arviot, kommenttien mieliala, jne.), pyrimme paikantamaan kriittiset tekijät, jotka vaikuttavat verkkoreseptien suosioon. Ennustava mallimme — Logistic Regression (luokituksen tarkkuus 0,93 ja F1-pisteet 0,9 ) — osoitti merkitsevien reseptiominaisuuksien olemassaolon. Ne vaikuttivat merkittävästi reseptien suosioon. Käyttämiimme tietojoukkoihin vaikuttivat merkittävästi käyttäjien sitoutumisominaisuudet, erityisesti vastaanotettujen arvioiden ja kommenttien määrä. Toisin sanoen reseptit, jotka saivat enemmän huomiota kommenteissa ja arvioissa, olivat yleensä suositumpia. Lisäksi selvisi, että huomattava osa Valion resepteistä kuuluu keskitason terveyspisteiden alueelle (arvioituna FSA Scorella), ja mielenkiintoisesti, vähemmän terveellisiksi katsotut reseptit saavat käyttäjiltä yleensä korkeamman keskiarvon. Tämä tutkimus edistää ymmärrystämme verkkoreseptien suosioon vaikuttavista tekijöistä ja tarjoaa arvokasta näkemystä nykypäivän ruoanlaittotottumuksista Suomessa

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

A Comparison of Paper Sketch and Interactive Wireframe by Eye Movements Analysis, Survey, and Interview

Eye movement-based analyses have been extensively performed on graphical user interface designs, mainly on high-fidelity prototypes such as coded prototypes. However, practitioners usually initiate the development life cycle with low-fidelity prototypes, such as mock-ups or sketches. Since little or no eye movement analysis has been performed on the latter, would eye tracking transpose its benefits from high- to low-fidelity prototypes and produce different results? To bridge this gap, we performed an eye movement-based analysis that compares gaze point indexes, gaze event types and durations, fixation, and saccade indexes produced by $N{=}8$ participants between two treatments, a paper prototype vs. a wireframe. The paper also reports a qualitative analysis based on the answers provided by these participants in a semi-directed interview and on a perceived usability questionnaire with 14 items. Due to its interactivity, the wireframe seems to foster a more exploratory approach to design (e.g., testing and navigating more extensively) than the paper prototype

AI in Education

Artificial intelligence (AI) is changing the world as we know it. Recent advances are enabling people, companies, and governments to envision and experiment with new methods of interacting with computers and modifying how virtual and physical processes are carried out. One of the fields in which this transformation is taking place is education. After years of witnessing the incorporation of technological innovations into learning/teaching processes, we can currently observe many new research works involving AI. Moreover, there has been increasing interest in this research area after the COVID-19 pandemic, driven toward fostering digital education. Among recent research in this field, AI applications have been applied to enhance educational experiences, studies have considered the interaction between AI and humans while learning, analyses of educational data have been conducted, including using machine learning techniques, and proposals have been presented for new paradigms mediated by intelligent agents. This book, entitled “AI in Education”, aims to highlight recent research in the field of AI and education. The included works discuss new advances in methods, applications, and procedures to enhance educational processes via artificial intelligence and its subfields (machine learning, neural networks, deep learning, cognitive computing, natural language processing, computer vision, etc.)