Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Performance Gain of NOMA over OMA in Uplink Single-Cell Systems

In this paper, we investigate the performance gain of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink single- cell systems. In both single-antenna and multi- antenna scenarios, the performance gain of NOMA over OMA in terms of asymptotic ergodic sum-rate is analyzed for a sufficiently large number of users. In particular, in single-antenna systems, we identify two types of near-far gains brought by NOMA: 1) the large-scale near-far gain via exploiting the large-scale fading increases with the cell size; 2) the small-scale near-far gain via exploiting the small-scale fading is a constant given by &#947; = 0.57721 nat&#x002F;s&#x002F;Hz in Rayleigh fading channels. Furthermore, we have analyzed that the performance gain achieved by single- antenna NOMA can be amplified via increasing the number of antennas equipped at the base station due to the extra spatial degrees of freedom. The numerical results confirm the accuracy of the derived analyses and unveil the performance gains of NOMA over OMA in different scenarios.
Cloud Programming Simplified: A Berkeley View on Serverless Computing

Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming, and represents an evolution that parallels the transition from assembly language to high-level programming languages. This paper gives a quick history of cloud computing, including an accounting of the predictions of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing, describes applications that stretch the current limits of serverless, and then lists obstacles and research opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing.
Passing Tests without Memorizing: Two Models for Fooling Discriminators

We introduce two mathematical frameworks for foolability in the context of generative distribution learning. In a nuthsell, fooling is an algorithmic task in which the input sample is drawn from some target distribution and the goal is to output a synthetic distribution that is indistinguishable from the target w.r. t to some fixed class of tests. This framework received considerable attention in the context of Generative Adversarial Networks (GANs), a recently proposed approach which achieves impressive empirical results. From a theoretical viewpoint this problem seems difficult to model. This is due to the fact that in its basic form, the notion of foolability is susceptible to a type of overfitting called memorizing. This raises a challenge of devising notions and definitions that separate between fooling algorithms that generate new synthetic data vs. algorithms that merely memorize or copy the training set. The first model we consider is called GAM--Foolability and is inspired by GANs. Here the learner has only an indirect access to the target distribution via a discriminator. The second model, called DP--Foolability, exploits the notion of differential privacy as a candidate criterion for non-memorization. We proceed to characterize foolability within these two models and study their interrelations. We show that DP--Foolability implies GAM--Foolability and prove partial results with respect to the converse. It remains, though, an open question whether GAM--Foolability implies DP--Foolability. We also present an application in the context of differentially private PAC learning. We show that from a statistical perspective, for any class H, learnability by a private proper learner is equivalent to the existence of a private sanitizer for H. This can be seen as an analogue of the equivalence between uniform convergence and learnability in classical PAC learning.
COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring - Activity Report of Working Group 1

This report presents the activities of the first working group of the COST Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide an overview of some of the major core aspects involved in Runtime Verification. Runtime Verification is the field of research dedicated to the analysis of system executions. It is often seen as a discipline that studies how a system run satisfies or violates correctness properties. The report exposes a taxonomy of Runtime Verification (RV) presenting the terminology involved with the main concepts of the field. The report also develops the concept of instrumentation, the various ways to instrument systems, and the fundamental role of instrumentation in designing an RV framework. We also discuss how RV interplays with other verification techniques such as model-checking, deductive verification, model learning, testing, and runtime assertion checking. Finally, we propose challenges in monitoring quantitative and statistical data beyond detecting property violation.
La place du Rhinocéros dans le régime alimentaire des hyènes à Camiac (Gironde, France) et ses implications pour la compétition avec les derniers néandertaliens

Dans le Sud-Ouest de la France entre 60 et 40 ka cal. BP, hommes et hyenes cohabitent dans les memes ecosystemes. Neanmoins, si les deux predateurs exploitent pour partie les memes gibiers, les restes de rhinoceros laineux sont tres rares dans les sites anthropiques alors qu'ils abondent dans certains repaires d'hyenes. A Camiac, Coelodonta antiquitatis represente pres de 20 % des restes accumules par l'Hyene. De nouvelles donnees acquises sur ce gisement particulier permettent de mieux comprendre si cette surrepresentation du rhinoceros laineux est le reflet d'une reelle difference de regime alimentaire entre hyenes et derniers neandertaliens ou le simple artefact de biais taphonomiques.
Land Use Classification Using Multi-neighborhood LBPs

In this paper we propose the use of multiple local binary patterns(LBPs) to effectively classify land use images. We use the UC Merced 21 class land use image dataset. Task is challenging for classification as the dataset contains intra class variability and inter class similarities. Our proposed method of using multi-neighborhood LBPs combined with nearest neighbor classifier is able to achieve an accuracy of 77.76%. Further class wise analysis is conducted and suitable suggestion are made for further improvements to classification accuracy. Keywords— Computer Vision, Pattern Recognition, Remote Sensing, Land Use Classification.
All those EPPA classes (Strengthenings of the Herwig-Lascar theorem)

In this paper we prove a general theorem showing the extension property for partial automorphisms (EPPA, also called the Hrushovski property) for classes of structures containing relations and unary functions, optionally equipped with a permutation group of the language. The proof is elementary, combinatorial and fully self-contained. Our result is a common strengthening of the Herwig–Lascar theorem on EPPA for relational classes with forbidden homomorphisms, the Hodkinson–Otto theorem on EPPA for relational free amalgamation classes, its strengthening for unary functions by Evans, Hubička and Nešetřil and their coherent variants by Siniora and Solecki. We also prove an EPPA analogue of the main results of J. Hubička and J. Nešetřil: All those Ramsey classes (Ramsey classes with closures and forbidden homomorphisms), thereby establishing a common framework for proving EPPA and the Ramsey property. Our results have numerous applications, we include a solution of a problem related to a class constructed by the Hrushovski predimension construction.
A Novel Channel Reservation Mechanism for Next Generation Cellular Networks

The increase in the number of mobile users increases in the requirement of the spectrum. When effective and efficient channel allocation procedures are introduced, the requirement can be reduced. As the users move from one location to the other and wish to continue their communication without any interruption, handoff process needs to be handled transparently such that the user is not aware of the change in the control of base stations. Hence, a deep learning-based channel reservation mechanism is proposed in this paper along with a channel allocation procedure.
A Blockchain Framework for Managing and Monitoring Data in Multi-Site Clinical Trials

The cost of conducting multi-site clinical trials has significantly increased over time, with site monitoring, data management, and amendments being key drivers. Clinical trial data management approaches typically rely on a central database, and require manual efforts to encode and maintain data capture and reporting requirements. To reduce the administrative burden, time, and effort of ensuring data integrity and privacy in multi-site trials, we propose a novel data management framework based on permissioned blockchain technology. We demonstrate how our framework, which uses smart contracts and private channels, enables confidential data communication, protocol enforcement, and and an automated audit trail. We compare this framework with the traditional data management approach and evaluate its effectiveness in satisfying the major requirements of multi-site clinical trials. We show that our framework ensures enforcement of IRB-related regulatory requirements across multiple sites and stakeholders.
MISO: Mutual Information Loss with Stochastic Style Representations for Multimodal Image-to-Image Translation

Unpaired multimodal image-to-image translation is a task of translating a given image in a source domain into diverse images in the target domain, overcoming the limitation of one-to-one mapping. Existing multimodal translation models are mainly based on the disentangled representations with an image reconstruction loss. We propose two approaches to improve multimodal translation quality. First, we use a content representation from the source domain conditioned on a style representation from the target domain. Second, rather than using a typical image reconstruction loss, we design MILO (Mutual Information LOss), a new stochastically-defined loss function based on information theory. This loss function directly reflects the interpretation of latent variables as a random variable. We show that our proposed model Mutual Information with StOchastic Style Representation(MISO) achieves state-of-the-art performance through extensive experiments on various real-world datasets.
Lossless Source Coding in the Point-to-Point, Multiple Access, and Random Access Scenarios

This paper treats point-to-point, multiple access and random access lossless source coding in the finite-blocklength regime. A random coding technique is developed, and its power in analyzing the third-order coding performance is demonstrated in all three scenarios. Results include a third-order-optimal characterization of the Slepian-Wolf rate region and a proof showing that for dependent sources, the independent encoders used by Slepian-Wolf codes can achieve the same third-order- optimal performance as a single joint encoder. The concept of random access source coding, which generalizes the multiple access scenario to allow for a subset of participating encoders that is unknown a priori to both the encoders and the decoder, is introduced. Contributions include a new definition of the probabilistic model for a random access-discrete multiple source, a general random access source coding scheme that employs a rateless code with sporadic feedback, and an analysis demonstrating via a random coding argument that there exists a deterministic code of the proposed structure that simultaneously achieves the third- order-optimal performance of Slepian-Wolf codes for all possible subsets of encoders.
Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification

We develop efficient algorithms to train $\ell_1$-regularized linear classifiers with large dimensionality $d$ of the feature space, number of classes $k$, and sample size $n$. Our focus is on a special class of losses that includes, in particular, the multiclass hinge and logistic losses. Our approach combines several ideas: (i) passing to the equivalent saddle-point problem with a quasi-bilinear objective; (ii) applying stochastic mirror descent with a proper choice of geometry which guarantees a favorable accuracy bound; (iii) devising non-uniform sampling schemes to approximate the matrix products. In particular, for the multiclass hinge loss we propose a \textit{sublinear} algorithm with iterations performed in $O(d+n+k)$ arithmetic operations.
Speech Enhancement with Variational Autoencoders and Alpha-stable Distributions

This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders. The noise model remains unsupervised because we do not assume prior knowledge of the noisy recording environment. In this context, our contribution is to propose a noise model based on alpha-stable distributions, instead of the more conventional Gaussian non-negative matrix factorization approach found in previous studies. We develop a Monte Carlo expectation-maximization algorithm for estimating the model parameters at test time. Experimental results show the superiority of the proposed approach both in terms of perceptual quality and intelligibility of the enhanced speech signal.
Context-Aware Visual Compatibility Prediction

How do we determine whether two or more clothing items are compatible or visually appealing? Part of the answer lies in understanding of visual aesthetics, and is biased by personal preferences shaped by social attitudes, time, and place. In this work we propose a method that predicts compatibility between two items based on their visual features, as well as their context. We define context as the products that are known to be compatible with each of these item. Our model is in contrast to other metric learning approaches that rely on pairwise comparisons between item features alone. We address the compatibility prediction problem using a graph neural network that learns to generate product embeddings conditioned on their context. We present results for two prediction tasks (fill in the blank and outfit compatibility) tested on two fashion datasets Polyvore and Fashion-Gen, and on a subset of the Amazon dataset; we achieve state of the art results when using context information and show how test performance improves as more context is used.
Radiation Constrained Fair Charging for Wireless Power Transfer

Recently, wireless power transfer technology (WPT) has attracted considerable attention and become a promising technology to prolong the lifetime of wireless sensor networks (WSNs) by providing perpetual energy to sensors. However, electromagnetic radiation (EMR) incurred by WPT is largely overlooked in most of the existing literature. In this article, we first propose and study the radiation constrained fair charging problem for WPT, i.e., maximizing the minimum utility of sensors by adjusting the power of wireless chargers with no EMR intensity at any location in the field exceeding a given threshold Rt. To address this problem, we first adopt an area discretization method to transform it from nonlinear to linear. Then, we propose four algorithms to deal with the reformulated problem, i.e., 1/3 and 1/4 Approximation Algorithms, Primal-Dual algorithm, and area division algorithm. In particular, the area division algorithm is not only fully distributed but also provably achieves an approximation ratio of (1 − ε). Further, we conduct extensive simulations and build a field testbed to verify our theoretical findings. Our simulation results show that the approximation ratios of the proposed algorithms hold; the Primal-Dual and area division algorithms have comparable performance of the optimal results and outperform baseline algorithms obviously.
Chinese Short Text Multi-Classification Based on Word and Part-of-Speech Tagging Embedding

In this paper, a convolutional neural network model with part-of-speech tagging and word double embedding is proposed to deal with text multi-classification problem. The chunk max pooling is added in the sampling layer for down sampling to enhance the ability of feature extraction. And in the text preprocessing, the word segmentation knowledge base is expanded according to the content of the data set to improve the accuracy of the text preprocessing model. In order to verify the accuracy of the model, 8000 enterprise text profiles were used to classify the business categories. The experimental results show that compared with the traditional machine learning model and the standard convolutional neural network model, the accuracy of the proposed model is improved.
Speech Emotion Recognition Based on LSTM and Mel Scale Wavelet Packet Decomposition

The traditional Mel Frequency Cepstral Coefficient (MFCC) feature can only reflect low frequency information, ignoring high frequency signals. In order to solve this problem, this paper uses the Mel frequency decomposition to decompose the speech signal in the wavelet packet, and then obtains many valuable feature information in the high frequency signal, such as short-time energy, fundamental frequency, formant, MFCC, sub-band energy. These features are extremely important parameters in speech signals. In this paper, the short-time energy is obtained by the energy formula, the pitch period and the fundamental frequency are obtained from the autocorrelation function, and the formant frequency characteristics are characterized by Hilbert-Huang transform. The speech signal is continuous in the time domain, so the features extracted from each frame reflect only the emotional features in a single frame. In order for features to better reflect temporal continuity, LSTM is used to increase the information between adjacent frames. LSTM is naturally suitable for speech recognition due to its ability to take advantage of dynamically changing time information.
Prediction of Passenger Flow in the Associated Region outside Subway Station

This paper aims to accurately predict the passenger flow in the associated region outside subway station. For this purpose, the concept of passenger pool was introduced to abstract the associated region outside the subway station. Considering the features of passenger flow, a nonstationary time series model was established to determine the input and output of the pool. Then, the cumulative residual error was controlled within the confidence interval by the ARIMA model and the deep residual network, aiming to enhance the accuracy and reduce the cumulative error of the model during the calculation of the passenger flow in the associated region. Finally, the proposed model was verified through a case study on Bajiao Amusement Park Station of Beijing Subway Line 1. The results demonstrate the accuracy of the model in predicting the passenger flow aggregation in the associated region outside the station. The research findings provide a decisive basis for warning against abnormal passenger flows, making it possible to realize efficient control of passenger flow.
Research on Chinese Named Entity Recognition in the Marine Field

Named entity recognition is a basic problem in natural language processing, and also an indispensable part of many natural language processing technologies such as information extraction and information retrieval. There are a large number of proprietary entities in the marine field. In this paper, we propose a method based on user dictionary and conditional random field, which recognize energy, organisms and drugs named entities in the marine filed. Analyzing the characteristics of marine named entities and successively adding different features to raise the recognition rate. Ultimately, the accuracy rate reaches over 96.95% and the recalling rate reaches over 96.41%. In addition, the method of combining neural network with statistical model (BiLSTM-CRF) is also used in this paper. In order to reduce the influence of inaccurate word segmentation on named entity recognition in the marine field, we use character vectors instead of word vectors as input of neural network layer. Finally, the accuracy rate reaches over 72.23% and the recalling rate reaches over 66.76%. The experimental results confirm that the method based on user dictionary and conditional random field is feasible for named entity recognition in the marine field.
Visual Tracking via Multi-view Semi-supervised Learning

In this paper, we present a novel visual object tracking model via multi-view semi-supervised learning. Instead of concatenating multiple views into a single view directly to adapt to conventional machine learning algorithms, the combination of views is learned by exploiting the consensus of distinct views in the entire tracking. Besides, semi-supervised learning alleviates the lack of sufficient labeled samples in the tracking task, resulting in significant improvement in generalization performance. By showing that the sample data is block-circulant, we diagonalize it with the Discrete Fourier Transform to keep the tracking at high speed. Using features extracted by the VGG-19 network and in a 1:1 ratio of the labeled samples to the unlabeled, the experiment results on the CVPR2013 Online Object Tracking Benchmark show the effectiveness of our multi-view semi-supervised tracking model.
Movie Short-Text Reviews Sentiment Analysis Based on Multi-Feature Fusion

The sentiment analysis of film reviews is the basis for obtaining the opinions of the audience, and has a very important influence on the movie public opinion control and the stimulation of potential consumers. This paper focuses on the short-text movie reviews, in which the Chinese emotion feature database and theme feature set are constructed according to the important features in the short-text film review corpus. A sentiment analysis method of short-text movie reviews based on multi-feature fusion (ETAT-LSTM) is proposed to analyze emotional orientation of short-text movie reviews in this paper. This method uses LSTM to obtain the semantic features of short-text movie reviews, and uses attention mechanism to fuse subject features and emotion features. Experimental results show that for emotional classification of movie short-text reviews, our proposed model ETAT-LSTM performance better in precision, call rate and F-value, when compared to basic LSTM, CNN model and the traditional deep learning model based on Attention mechanism.
Extracting Aspects Hierarchies using Rhetorical Structure Theory

We propose a novel approach to generate aspect hierarchies that proved to be consistently correct compared with human-generated hierarchies. We present an unsupervised technique using Rhetorical Structure Theory and graph analysis. We evaluated our approach based on 100,000 reviews from Amazon and achieved an astonishing 80% coverage compared with human-generated hierarchies coded in ConceptNet. The method could be easily extended with a sentiment analysis model and used to describe sentiment on different levels of aspect granularity. Hence, besides the flat aspect structure, we can differentiate between aspects and describe if the charging aspect is related to battery or price.