Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Machine Learning Approach to Popularity Prediction of Newly Released Contents for Online Video Streaming Service

In the industry of video content providers such as VOD and IPTV, predicting the popularity of video contents in advance is critical not only from a marketing perspective but also from a network optimization perspective. By predicting whether the content will be successful or not in advance, the content file, which is large, is efficiently deployed in the proper service providing server, leading to network cost optimization. Many previous studies have done view count prediction research to do this. However, the studies have been making predictions based on historical view count data from users. In this case, the contents had been published to the users and already deployed on a service server. These approaches make possible to efficiently deploy a content already published but are impossible to use for a content that is not be published. To address the problems, this research proposes a hybrid machine learning approach to the classification model for the popularity prediction of newly video contents which is not published. In this paper, we create a new variable based on the related content of the specific content and divide entire dataset by the characteristics of the contents. Next, the prediction is performed using XGBoosting and deep neural net based model according to the data characteristics of the cluster. Our model uses metadata for contents for prediction, so we use categorical embedding techniques to solve the sparsity of categorical variables and make them learn efficiently for the deep neural net model. As well, we use the FTRL-proximal algorithm to solve the problem of the view-count volatility of video content. We achieve overall better performance than the previous standalone method with a dataset from one of the top streaming service company.
Generalisation dynamics of online learning in over-parameterised neural networks

Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisation dynamics of two-layer neural networks in a teacher-student setup, where one network, the student, is trained using stochastic gradient descent (SGD) on data generated by another network, called the teacher. We show how for this problem, the dynamics of SGD are captured by a set of differential equations. In particular, we demonstrate analytically that the generalisation error of the student increases linearly with the network size, with other relevant parameters held constant. Our results indicate that achieving good generalisation in neural networks depends on the interplay of at least the algorithm, its learning rate, the model architecture, and the data set.
A Practical Bandit Method with Advantages in Neural Network Tuning

Stochastic bandit algorithms can be used for challenging non-convex optimization problems. Hyperparameter tuning of neural networks is particularly challenging, necessitating new approaches. To this end, we present a method that adaptively partitions the combined space of hyperparameters, context, and training resources (e.g., total number of training iterations). By adaptively partitioning the space, the algorithm is able to focus on the portions of the hyperparameter search space that are most relevant in a practical way. By including the resources in the combined space, the method tends to use fewer training resources overall. Our experiments show that this method can surpass state-of-the-art methods in tuning neural networks on benchmark datasets. In some cases, our implementations can achieve the same levels of accuracy on benchmark datasets as existing state-of-the-art approaches while saving over 50% of our computational resources (e.g. time, training iterations).
Embedding is not Cipher: Understanding the risk of embedding leakages

Machine Learning (ML) already has been integrated into all kinds of systems, helping developers to solve problems with even higher accuracy than human beings. However, when integrating ML models into a system, developers may accidentally take not enough care of the outputs of ML models, mainly because of their unfamiliarity with ML and AI, resulting in severe consequences like hurting data owners' privacy. In this work, we focus on understanding the risks of abusing embeddings of ML models, an important and popular way of using ML. To show the consequence, we reveal several kinds of channels in which embeddings are accidentally leaked. As our study shows, a face verification system deployed by a government organization leaking only distance to authentic users allows an attacker to exactly recover the embedding of the verifier's pre-installed photo. Further, as we discovered, with the leaked embedding, attackers can easily recover the input photo with negligible quality losses, indicating devastating consequences to users' privacy. This is achieved with our devised GAN-like structure model, which showed 93.65% success rate on popular face embedding model under black box assumption.
Physical Access Control Management System Based on Permissioned Blockchain

Using blockchain as a decentralized backend infrastructure has been attracting many startups entrepreneurs' and developers' attention. Blockchain records transactions permanently and protects them from undesirable tampering. It provides a reliable tamper-proof database which can be considered as a trustable resource for tracking previous system's state. In this paper, we present our access control application based on Hyperledger Fabric Blockchain and Hyperledger Composer to control access to physical places. The system components and modular architecture are illustrated, and we have extracted metadata include historian transactions' details arising from our demo test. Finally, the performance metrics and resources consumption are provided using Hyperledger Caliper, a benchmark framework for measuring Hyperledger blockchains performance.
Edge, Ridge, and Blob Detection with Symmetric Molecules

We present a novel approach to the detection and characterization of edges, ridges, and blobs in two-dimensional images which exploits the symmetry properties of directionally sensitive analyzing functions in multiscale systems that are constructed in the framework of alpha-molecules. The proposed feature detectors are inspired by the notion of phase congruency, stable in the presence of noise, and by definition invariant to changes in contrast. We also show how the behavior of coefficients corresponding to differently scaled and oriented analyzing functions can be used to obtain a comprehensive characterization of the geometry of features in terms of local tangent directions, widths, and heights. The accuracy and robustness of the proposed measures are validated and compared to various state-of-the-art algorithms in extensive numerical experiments in which we consider sets of clean and distorted synthetic images that are associated with reliable ground truths. To further demonstrate the applicability, we show how the proposed ridge measure can be used to detect and characterize blood vessels in digital retinal images and how the proposed blob measure can be applied to automatically count the number of cell colonies in a Petri dish.
Best Arm Identification in Linked Bandits.

We consider the problem of best arm identification in a variant of multi-armed bandits called linked bandits. In a single interaction with linked bandits, multiple arms are played sequentially until one of them receives a positive reward. Since each interaction provides feedback about more than one arm, the sample complexity can be much lower than in the regular bandit setting. We propose an algorithm for linked bandits, that combines a novel subroutine to perform uniform sampling with a known optimal algorithm for regular bandits. We prove almost matching upper and lower bounds on the sample complexity of best arm identification in linked bandits. These bounds have an interesting structure, with an explicit dependence on the mean rewards of the arms, not just the gaps. We also corroborate our theoretical results with experiments.
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging

We propose a novel automatic method for accurate segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI). Our method is based on convolutional neural networks (CNNs). Because of the large variability in the shape, size, and appearance of the prostate and the scarcity of annotated training data, we suggest training two separate CNNs. A global CNN will determine a prostate bounding box, which is then resampled and sent to a local CNN for accurate delineation of the prostate boundary. This way, the local CNN can effectively learn to segment the fine details that distinguish the prostate from the surrounding tissue using the small amount of available training data. To fully exploit the training data, we synthesize additional data by deforming the training images and segmentations using a learned shape model. We apply the proposed method on the PROMISE12 challenge dataset and achieve state of the art results. Our proposed method generates accurate, smooth, and artifact-free segmentations. On the test images, we achieve an average Dice score of 90.6 with a small standard deviation of 2.2, which is superior to all previous methods. Our two-step segmentation approach and data augmentation strategy may be highly effective in segmentation of other organs from small amounts of annotated medical images.
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks

Capsule Networks preserve the hierarchical spatial relationships between objects, and thereby bears a potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. A large body of work has explored adversarial examples for CNNs, but their effectiveness on Capsule Networks has not yet been well studied. In our work, we perform an analysis to study the vulnerabilities in Capsule Networks to adversarial attacks. These perturbations, added to the test inputs, are small and imperceptible to humans, but can fool the network to mispredict. We propose a greedy algorithm to automatically generate targeted imperceptible adversarial examples in a black-box attack scenario. We show that this kind of attacks, when applied to the German Traffic Sign Recognition Benchmark (GTSRB), mislead Capsule Networks. Moreover, we apply the same kind of adversarial attacks to a 5-layer CNN and a 9-layer CNN, and analyze the outcome, compared to the Capsule Networks to study differences in their behavior.
Detecting multiple communities using quantum annealing on the D-Wave system

A very important problem in combinatorial optimization is the partitioning of a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences. The problem of community detection is a twofold problem that consists of determining the number of communities and, at the same time, finding those communities. This drastically increases the solution space for heuristics to work on, compared to traditional graph partitioning problems. In many of the scientific domains in which graphs are used, there is the need to have the ability to partition a graph into communities with the “highest quality” possible since the presence of even small isolated communities can become crucial to explain a particular phenomenon. We have explored community detection using the power of quantum annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out that the problem of detecting at most two communities naturally fits into the architecture of a quantum annealer with almost no need of reformulation. This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer.
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals

Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to multidimensional data. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. As estimated patterns can be positioned anywhere in signals or images, optimization techniques face the difficulty of working in extremely high dimensions with millions of pixels or time samples, contrarily to standard patch-based dictionary learning. To address this optimization problem, this work proposes a distributed and asynchronous algorithm, employing locally greedy coordinate descent and an asynchronous locking mechanism that does not require a central server. This algorithm can be used to distribute the computation on a number of workers which scales linearly with the encoded signal's size. Experiments confirm the scaling properties which allows us to learn patterns on large scales images from the Hubble Space Telescope.
Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory

In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an anomaly as a localized increase in temporal activity in a cluster of nodes. The algorithm is unsupervised. It is able to detect and track anomalous activity in a dynamic network despite the noise from multiple interfering sources. We use the Hopfield network model of memory to combine the graph and time information. We show that anomalies can be spotted with good precision using a memory network. The presented approach is scalable and we provide a distributed implementation of the algorithm. To demonstrate its efficiency, we apply it to two datasets: Enron Email dataset and Wikipedia page views. We show that the anomalous spikes are triggered by the real-world events that impact the network dynamics. Besides, the structure of the clusters and the analysis of the time evolution associated with the detected events reveals interesting facts on how humans interact, exchange and search for information, opening the door to new quantitative studies on collective and social behavior on large and dynamic datasets.
Functional Gaussian Distribution Modelling of Mobility Prediction Accuracy for Wireless Users

Mobility entropy is proposed to measure predictability of human movements, based on which, the upper and lower bound of prediction accuracy is deduced, but corresponding mathematical expressions of prediction accuracy keeps yet open. In this work, we try to analyze and model prediction accuracy in terms of entropy based on the 2-order Markov chain model empirical results on a large scale CDR data set, which demonstrates the observation that users with the same level of entropy achieve different levels of accuracy\cite{Empirical}. After dividing entropy into intervals, we fit the probability density distributions of accuracy in each entropy interval with Gaussian distribution and then we estimate the corresponding mean and standard deviation of these distributions. After observing that the parameters vary with increasing entropy, we then model the relationship between parameters and entropy using least squares method. The mean can be modelled as a linear function, while the standard deviation can be modelled as a Gaussian distribution. Thus based on the above analysis, the probability density function of accuracy given entropy can be expressed by functional Gaussian distribution. The insights from our work is the first step to model the correlation prediction accuracy and predictability entropy, thus shed light on the further work in this direction.
Word Embeddings: A Survey

This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra features in many downstream NLP tasks.
Toward Unsupervised Text Content Manipulation

Controlled generation of text is of high practical use. Recent efforts have made impressive progress in generating or editing sentences with given textual attributes (e.g., sentiment). This work studies a new practical setting of text content manipulation. Given a structured record, such as `(PLAYER: Lebron, POINTS: 20, ASSISTS: 10)', and a reference sentence, such as `Kobe easily dropped 30 points', we aim to generate a sentence that accurately describes the full content in the record, with the same writing style (e.g., wording, transitions) of the reference. The problem is unsupervised due to lack of parallel data in practice, and is challenging to minimally yet effectively manipulate the text (by rewriting/adding/deleting text portions) to ensure fidelity to the structured content. We derive a dataset from a basketball game report corpus as our testbed, and develop a neural method with unsupervised competing objectives and explicit content coverage constraints. Automatic and human evaluations show superiority of our approach over competitive methods including a strong rule-based baseline and prior approaches designed for style transfer.
Virtual Conditional Generative Adversarial Networks

When trained on multimodal image datasets, normal Generative Adversarial Networks (GANs) are usually outperformed by class-conditional GANs and ensemble GANs, but conditional GANs is restricted to labeled datasets and ensemble GANs lack efficiency. We propose a novel GAN variant called virtual conditional GAN (vcGAN) which is not only an ensemble GAN with multiple generative paths while adding almost zero network parameters, but also a conditional GAN that can be trained on unlabeled datasets without explicit clustering steps or objectives other than the adversary loss. Inside the vcGAN's generator, a learnable ``analog-to-digital converter (ADC)" module maps a slice of the inputted multivariate Gaussian noise to discrete/digital noise (virtual label), according to which a selector selects the corresponding generative path to produce the sample. All the generative paths share the same decoder network while in each path the decoder network is fed with a concatenation of a different pre-computed amplified one-hot vector and the inputted Gaussian noise. We conducted a lot of experiments on several balanced/imbalanced image datasets to demonstrate that vcGAN converges faster and achieves improved Frechet Inception Distance (FID). In addition, we show the training byproduct that the ADC in vcGAN learned the categorical probability of each mode and that each generative path generates samples of specific mode, which enables class-conditional sampling. Codes are available at \url{this https URL}
A Practical Scheme for Two-Party Private Linear Least Squares

Privacy-preserving machine learning is learning from sensitive datasets that are typically distributed across multiple data owners. Private machine learning is a remarkable challenge in a large number of realistic scenarios where no trusted third party can play the role of a mediator. The strong decentralization aspect of these scenarios requires tools from cryptography as well as from distributed systems communities. In this paper, we present a practical scheme that is suitable for a subclass of machine learning algorithms and investigate the possibility of conducting future research. We present a scheme to learn a linear least squares model across two parties using a gradient descent approach and additive homomorphic encryption. The protocol requires two rounds of communication per step of gradient descent. We detail our approach including a fixed point encoding scheme, and one time random pads for hiding intermediate results.
User Donations in a Crowdsourced Video System

Crowdsourced video systems like YouTube and Twitch.tv have been a major internet phenomenon and are nowadays entertaining over a billion users. In addition to video sharing and viewing, over the years they have developed new features to boost the community engagement and some managed to attract users to donate, to the community as well as to other users. User donation directly reflects and influences user engagement in the community, and has a great impact on the success of such systems. Nevertheless, user donations in crowdsourced video systems remain trade secrets for most companies and to date are still unexplored. In this work, we attempt to fill this gap, and we obtain and provide a publicly available dataset on user donations in one crowdsourced video system named BiliBili. Based on information on nearly 40 thousand donators, we examine the dynamics of user donations and their social relationships, we quantitively reveal the factors that potentially impact user donation, and we adopt machine-learned classifiers and network representation learning models to timely and accurately predict the destinations of the majority and the individual donations.
Wavelets and Multiwavelets

Although some wavelet books have a short section on multiwavelets, to date none offer a detailed treatment. Pulling together information scattered throughout the literature of mathematics and engineering, this book offers the first full exposition of wavelet and multiwavelet theory. Maintaining the delicate balance between mathematical rigor and readability, the author presents the theory of classical wavelets and general m-band multiwavelets in parallel. This provides a more coherent approach and yields alternative proofs and new insights even for standard wavelets. MATLAB routines that allow readers to implement and experiment with multiwavelet algorithms are available from the CRC Press Web site.
Enzyme replacement therapy: lessons learned and emerging questions

Introduction: Over the past two decades, enzyme therapy for lysosomal storage disorders (LSDs) has become integral to the specific treatment paradigms of monogenic disorders. As enzyme replacement therapy (ERT) has been successfully introduced for patients with Gaucher disease type 1, this treatment principle has been taken into consideration for other LSDs as well. However, repeating this spectacular success in the mucopolysaccharidoses and Fabry or Pompe disease proved to be impossible. Areas covered: This article is an overview of lessons learned during the last 20 years since ERT was first introduced. It highlights some emerging questions and potential reasons why ERT has not been as universally successful as hoped. Expert opinion: The usefulness of ERT is limited due to the fact that it does not have beneficial effects on all aspects of a disorder to the same degree. Even with an early introduction of treatment there are still several limitations of this therapy: delivering therapeutic levels of lysosomal enzymes across the blood–brain barrier or effect on dysostosis multiplex, joint disease or cardiac valve disease. In fact, despite efforts, ERT mostly slows down disease progression and attenuates symptom expression.