6,209 research outputs found

    PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels

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    The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Scaling up integrated photonic reservoirs towards low-power high-bandwidth computing

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    Offline and Online Models for Learning Pairwise Relations in Data

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    Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting

    Advancing Model Pruning via Bi-level Optimization

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    The deployment constraints in practical applications necessitate the pruning of large-scale deep learning models, i.e., promoting their weight sparsity. As illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the potential of improving their generalization ability. At the core of LTH, iterative magnitude pruning (IMP) is the predominant pruning method to successfully find 'winning tickets'. Yet, the computation cost of IMP grows prohibitively as the targeted pruning ratio increases. To reduce the computation overhead, various efficient 'one-shot' pruning methods have been developed, but these schemes are usually unable to find winning tickets as good as IMP. This raises the question of how to close the gap between pruning accuracy and pruning efficiency? To tackle it, we pursue the algorithmic advancement of model pruning. Specifically, we formulate the pruning problem from a fresh and novel viewpoint, bi-level optimization (BLO). We show that the BLO interpretation provides a technically-grounded optimization base for an efficient implementation of the pruning-retraining learning paradigm used in IMP. We also show that the proposed bi-level optimization-oriented pruning method (termed BiP) is a special class of BLO problems with a bi-linear problem structure. By leveraging such bi-linearity, we theoretically show that BiP can be solved as easily as first-order optimization, thus inheriting the computation efficiency. Through extensive experiments on both structured and unstructured pruning with 5 model architectures and 4 data sets, we demonstrate that BiP can find better winning tickets than IMP in most cases, and is computationally as efficient as the one-shot pruning schemes, demonstrating 2-7 times speedup over IMP for the same level of model accuracy and sparsity.Comment: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

    Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond

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    [ES] Esta tesis se enmarca en la intersecciĂłn entre las tĂ©cnicas modernas de Machine Learning, como las Redes Neuronales Profundas, y el modelado probabilĂ­stico confiable. En muchas aplicaciones, no solo nos importa la predicciĂłn hecha por un modelo (por ejemplo esta imagen de pulmĂłn presenta cĂĄncer) sino tambiĂ©n la confianza que tiene el modelo para hacer esta predicciĂłn (por ejemplo esta imagen de pulmĂłn presenta cĂĄncer con 67% probabilidad). En tales aplicaciones, el modelo ayuda al tomador de decisiones (en este caso un mĂ©dico) a tomar la decisiĂłn final. Como consecuencia, es necesario que las probabilidades proporcionadas por un modelo reflejen las proporciones reales presentes en el conjunto al que se ha asignado dichas probabilidades; de lo contrario, el modelo es inĂștil en la prĂĄctica. Cuando esto sucede, decimos que un modelo estĂĄ perfectamente calibrado. En esta tesis se exploran tres vias para proveer modelos mĂĄs calibrados. Primero se muestra como calibrar modelos de manera implicita, que son descalibrados por tĂ©cnicas de aumentaciĂłn de datos. Se introduce una funciĂłn de coste que resuelve esta descalibraciĂłn tomando como partida las ideas derivadas de la toma de decisiones con la regla de Bayes. Segundo, se muestra como calibrar modelos utilizando una etapa de post calibraciĂłn implementada con una red neuronal Bayesiana. Finalmente, y en base a las limitaciones estudiadas en la red neuronal Bayesiana, que hipotetizamos que se basan en un prior mispecificado, se introduce un nuevo proceso estocĂĄstico que sirve como distribuciĂłn a priori en un problema de inferencia Bayesiana.[CA] Aquesta tesi s'emmarca en la intersecciĂł entre les tĂšcniques modernes de Machine Learning, com ara les Xarxes Neuronals Profundes, i el modelatge probabilĂ­stic fiable. En moltes aplicacions, no nomĂ©s ens importa la predicciĂł feta per un model (per ejemplem aquesta imatge de pulmĂł presenta cĂ ncer) sinĂł tambĂ© la confiança que tĂ© el model per fer aquesta predicciĂł (per exemple aquesta imatge de pulmĂł presenta cĂ ncer amb 67% probabilitat). En aquestes aplicacions, el model ajuda el prenedor de decisions (en aquest cas un metge) a prendre la decisiĂł final. Com a conseqĂŒĂšncia, cal que les probabilitats proporcionades per un model reflecteixin les proporcions reals presents en el conjunt a quĂš s'han assignat aquestes probabilitats; altrament, el model Ă©s inĂștil a la prĂ ctica. Quan aixĂČ passa, diem que un model estĂ  perfectament calibrat. En aquesta tesi s'exploren tres vies per proveir models mĂ©s calibrats. Primer es mostra com calibrar models de manera implĂ­cita, que sĂłn descalibrats per tĂšcniques d'augmentaciĂł de dades. S'introdueix una funciĂł de cost que resol aquesta descalibraciĂł prenent com a partida les idees derivades de la presa de decisions amb la regla de Bayes. Segon, es mostra com calibrar models utilitzant una etapa de post calibratge implementada amb una xarxa neuronal Bayesiana. Finalment, i segons les limitacions estudiades a la xarxa neuronal Bayesiana, que es basen en un prior mispecificat, s'introdueix un nou procĂ©s estocĂ stic que serveix com a distribuciĂł a priori en un problema d'inferĂšncia Bayesiana.[EN] This thesis is framed at the intersection between modern Machine Learning techniques, such as Deep Neural Networks, and reliable probabilistic modeling. In many machine learning applications, we do not only care about the prediction made by a model (e.g. this lung image presents cancer) but also in how confident is the model in making this prediction (e.g. this lung image presents cancer with 67% probability). In such applications, the model assists the decision-maker (in this case a doctor) towards making the final decision. As a consequence, one needs that the probabilities provided by a model reflects the true underlying set of outcomes, otherwise the model is useless in practice. When this happens, we say that a model is perfectly calibrated. In this thesis three ways are explored to provide more calibrated models. First, it is shown how to calibrate models implicitly, which are decalibrated by data augmentation techniques. A cost function is introduced that solves this decalibration taking as a starting point the ideas derived from decision making with Bayes' rule. Second, it shows how to calibrate models using a post-calibration stage implemented with a Bayesian neural network. Finally, and based on the limitations studied in the Bayesian neural network, which we hypothesize that came from a mispecified prior, a new stochastic process is introduced that serves as a priori distribution in a Bayesian inference problem.Maroñas Molano, J. (2022). Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond [Tesis doctoral]. Universitat PolitĂšcnica de ValĂšncia. https://doi.org/10.4995/Thesis/10251/181582TESI

    A novel Auto-ML Framework for Sarcasm Detection

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    Many domains have sarcasm or verbal irony presented in the text of reviews, tweets, comments, and dialog discussions. The purpose of this research is to classify sarcasm for multiple domains using the deep learning based AutoML framework. The proposed AutoML framework has five models in the model search pipeline, these five models are the combination of convolutional neural network (CNN), Long Short-Term Memory (LSTM), deep neural network (DNN), and Bidirectional Long Short-Term Memory (BiLSTM). The hybrid combination of CNN, LSTM, and DNN models are presented as CNN-LSTM-DNN, LSTM-DNN, BiLSTM-DNN, and CNN-BiLSTM-DNN. This work has proposed the algorithms that contrast polarities between terms and phrases, which are categorized into implicit and explicit incongruity categories. The incongruity and pragmatic features like punctuation, exclamation marks, and others integrated into the AutoML DeepConcat framework models. That integration was possible when the DeepConcat AutoML framework initiate a model search pipeline for five models to achieve better performance. Conceptually, DeepConcat means that model will integrate with generalized features. It was evident that the pretrain model BiLSTM achieved a better performance of 0.98 F1 when compared with the other five model performances. Similarly, the AutoML based BiLSTM-DNN model achieved the best performance of 0.98 F1, which is better than core approaches and existing state-of-the-art Tweeter tweet dataset, Amazon reviews, and dialog discussion comments. The proposed AutoML framework has compared performance metrics F1 and AUC and discovered that F1 is better than AUC. The integration of all feature categories achieved a better performance than the individual category of pragmatic and incongruity features. This research also evaluated the performance of the dropout layer hyperparameter and it achieved better performance than the fixed percentage like 10% of dropout parameter of the AutoML based Bayesian optimization. Proposed AutoML framework DeepConcat evaluated best pretrain models BiLSTM-DNN and CNN-CNN-DNN to transfer knowledge across domains like Amazon reviews and Dialog discussion comments (text) using the last strategy, full layer, and our fade-out freezing strategies. In the transfer learning fade-out strategy outperformed the existing state-of-the-art model BiLSTM-DNN, the performance is 0.98 F1 on tweets, 0.85 F1 on Amazon reviews, and 0.87 F1 on the dialog discussion SCV2-Gen dataset. Further, all strategies with various domains can be compared for the best model selection

    FiabilitĂ© de l’underfill et estimation de la durĂ©e de vie d’assemblages microĂ©lectroniques

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    Abstract : In order to protect the interconnections in flip-chip packages, an underfill material layer is used to fill the volumes and provide mechanical support between the silicon chip and the substrate. Due to the chip corner geometry and the mismatch of coefficient of thermal expansion (CTE), the underfill suffers from a stress concentration at the chip corners when the temperature is lower than the curing temperature. This stress concentration leads to subsequent mechanical failures in flip-chip packages, such as chip-underfill interfacial delamination and underfill cracking. Local stresses and strains are the most important parameters for understanding the mechanism of underfill failures. As a result, the industry currently relies on the finite element method (FEM) to calculate the stress components, but the FEM may not be accurate enough compared to the actual stresses in underfill. FEM simulations require a careful consideration of important geometrical details and material properties. This thesis proposes a modeling approach that can accurately estimate the underfill delamination areas and crack trajectories, with the following three objectives. The first objective was to develop an experimental technique capable of measuring underfill deformations around the chip corner region. This technique combined confocal microscopy and the digital image correlation (DIC) method to enable tri-dimensional strain measurements at different temperatures, and was named the confocal-DIC technique. This techique was first validated by a theoretical analysis on thermal strains. In a test component similar to a flip-chip package, the strain distribution obtained by the FEM model was in good agreement with the results measured by the confocal-DIC technique, with relative errors less than 20% at chip corners. Then, the second objective was to measure the strain near a crack in underfills. Artificial cracks with lengths of 160 ÎŒm and 640 ÎŒm were fabricated from the chip corner along the 45° diagonal direction. The confocal-DIC-measured maximum hoop strains and first principal strains were located at the crack front area for both the 160 ÎŒm and 640 ÎŒm cracks. A crack model was developed using the extended finite element method (XFEM), and the strain distribution in the simulation had the same trend as the experimental results. The distribution of hoop strains were in good agreement with the measured values, when the model element size was smaller than 22 ÎŒm to capture the strong strain gradient near the crack tip. The third objective was to propose a modeling approach for underfill delamination and cracking with the effects of manufacturing variables. A deep thermal cycling test was performed on 13 test cells to obtain the reference chip-underfill delamination areas and crack profiles. An artificial neural network (ANN) was trained to relate the effects of manufacturing variables and the number of cycles to first delamination of each cell. The predicted numbers of cycles for all 6 cells in the test dataset were located in the intervals of experimental observations. The growth of delamination was carried out on FEM by evaluating the strain energy amplitude at the interface elements between the chip and underfill. For 5 out of 6 cells in validation, the delamination growth model was consistent with the experimental observations. The cracks in bulk underfill were modelled by XFEM without predefined paths. The directions of edge cracks were in good agreement with the experimental observations, with an error of less than 2.5°. This approach met the goal of the thesis of estimating the underfill initial delamination, areas of delamination and crack paths in actual industrial flip-chip assemblies.Afin de protĂ©ger les interconnexions dans les assemblages, une couche de matĂ©riau d’underfill est utilisĂ©e pour remplir le volume et fournir un support mĂ©canique entre la puce de silicium et le substrat. En raison de la gĂ©omĂ©trie du coin de puce et de l’écart du coefficient de dilatation thermique (CTE), l’underfill souffre d’une concentration de contraintes dans les coins lorsque la tempĂ©rature est infĂ©rieure Ă  la tempĂ©rature de cuisson. Cette concentration de contraintes conduit Ă  des dĂ©faillances mĂ©caniques dans les encapsulations de flip-chip, telles que la dĂ©lamination interfaciale puce-underfill et la fissuration d’underfill. Les contraintes et dĂ©formations locales sont les paramĂštres les plus importants pour comprendre le mĂ©canisme des ruptures de l’underfill. En consĂ©quent, l’industrie utilise actuellement la mĂ©thode des Ă©lĂ©ments finis (EF) pour calculer les composantes de la contrainte, qui ne sont pas assez prĂ©cises par rapport aux contraintes actuelles dans l’underfill. Ces simulations nĂ©cessitent un examen minutieux de dĂ©tails gĂ©omĂ©triques importants et des propriĂ©tĂ©s des matĂ©riaux. Cette thĂšse vise Ă  proposer une approche de modĂ©lisation permettant d’estimer avec prĂ©cision les zones de dĂ©lamination et les trajectoires des fissures dans l’underfill, avec les trois objectifs suivants. Le premier objectif est de mettre au point une technique expĂ©rimentale capable de mesurer la dĂ©formation de l’underfill dans la rĂ©gion du coin de puce. Cette technique, combine la microscopie confocale et la mĂ©thode de corrĂ©lation des images numĂ©riques (DIC) pour permettre des mesures tridimensionnelles des dĂ©formations Ă  diffĂ©rentes tempĂ©ratures, et a Ă©tĂ© nommĂ©e le technique confocale-DIC. Cette technique a d’abord Ă©tĂ© validĂ©e par une analyse thĂ©orique en dĂ©formation thermique. Dans un Ă©chantillon similaire Ă  un flip-chip, la distribution de la dĂ©formation obtenues par le modĂšle EF Ă©tait en bon accord avec les rĂ©sultats de la technique confocal-DIC, avec des erreurs relatives infĂ©rieures Ă  20% au coin de puce. Ensuite, le second objectif est de mesurer la dĂ©formation autour d’une fissure dans l’underfill. Des fissures artificielles d’une longueuer de 160 ÎŒm et 640 ÎŒm ont Ă©tĂ© fabriquĂ©es dans l’underfill vers la direction diagonale de 45°. Les dĂ©formations circonfĂ©rentielles maximales et principale maximale Ă©taient situĂ©es aux pointes des fissures correspondantes. Un modĂšle de fissure a Ă©tĂ© dĂ©veloppĂ© en utilisant la mĂ©thode des Ă©lĂ©ments finis Ă©tendue (XFEM), et la distribution des contraintes dans la simuation a montrĂ© la mĂȘme tendance que les rĂ©sultats expĂ©rimentaux. La distribution des dĂ©formations circonfĂ©rentielles maximales Ă©tait en bon accord avec les valeurs mesurĂ©es lorsque la taille des Ă©lĂ©ments Ă©tait plus petite que 22 ÎŒm, assez petit pour capturer le grand gradient de dĂ©formation prĂšs de la pointe de fissure. Le troisiĂšme objectif Ă©tait d’apporter une approche de modĂ©lisation de la dĂ©lamination et de la fissuration de l’underfill avec les effets des variables de fabrication. Un test de cyclage thermique a d’abord Ă©tĂ© effectuĂ© sur 13 cellules pour obtenir les zones dĂ©laminĂ©es entre la puce et l’underfill, et les profils de fissures dans l’underfill, comme rĂ©fĂ©rence. Un rĂ©seau neuronal artificiel (ANN) a Ă©tĂ© formĂ© pour Ă©tablir une liaison entre les effets des variables de fabrication et le nombre de cycles Ă  la dĂ©lamination pour chaque cellule. Les nombres de cycles prĂ©dits pour les 6 cellules de l’ensemble de test Ă©taient situĂ©s dans les intervalles d’observations expĂ©rimentaux. La croissance de la dĂ©lamination a Ă©tĂ© rĂ©alisĂ©e par l’EF en Ă©valuant l’énergie de la dĂ©formation au niveau des Ă©lĂ©ments interfaciaux entre la puce et l’underfill. Pour 5 des 6 cellules de la validation, le modĂšle de croissance du dĂ©laminage Ă©tait conforme aux observations expĂ©rimentales. Les fissures dans l’underfill ont Ă©tĂ© modĂ©lisĂ©es par XFEM sans chemins prĂ©dĂ©finis. Les directions des fissures de bord Ă©taient en bon accord avec les observations expĂ©rimentales, avec une erreur infĂ©rieure Ă  2,5°. Cette approche a rĂ©pondu Ă  la problĂ©matique qui consiste Ă  estimer l’initiation des dĂ©lamination, les zones de dĂ©lamination et les trajectoires de fissures dans l’underfill pour des flip-chips industriels

    Walking with the Earth: Intercultural Perspectives on Ethics of Ecological Caring

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    It is commonly believed that considering nature different from us, human beings (qua rational, cultural, religious and social actors), is detrimental to our engagement for the preservation of nature. An obvious example is animal rights, a deep concern for all living beings, including non-human living creatures, which is understandable only if we approach nature, without fearing it, as something which should remain outside of our true home. “Walking with the earth” aims at questioning any similar preconceptions in the wide sense, including allegoric-poetic contributions. We invited 14 authors from 4 continents to express all sorts of ways of saying why caring is so important, why togetherness, being-with each others, as a spiritual but also embodied ethics is important in a divided world

    Accelerated Federated Learning with Decoupled Adaptive Optimization

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    The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuristics efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings for improving convergence and accuracy. However, there is still a paucity of theoretical principles on where to and how to design and utilize adaptive optimization methods in federated settings. This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs). First, an analytic framework is established to build a connection between federated optimization methods and decompositions of ODEs of corresponding centralized optimizers. Second, based on this analytic framework, a momentum decoupling adaptive optimization method, FedDA, is developed to fully utilize the global momentum on each local iteration and accelerate the training convergence. Last but not least, full batch gradients are utilized to mimic centralized optimization in the end of the training process to ensure the convergence and overcome the possible inconsistency caused by adaptive optimization methods
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