Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Trust Aware Network Slice based End to End Services for Virtualised Infrastructures

Future communication networks such as 5G are expected to support end-to-end delivery of services for several vertical markets with diverging requirements. Network slicing is a key construct that is used to provide end to end logical virtual networks running on a common virtualised infrastructure, which are mutually isolated. Having different network slices operating over the same 5G infrastructure creates several challenges in security and trust. This paper addresses the fundamental issue of trust of a network slice. It presents a trust model and property-based trust attestation mechanisms which can be used to evaluate the trust of the virtual network functions that compose the network slice. The proposed model helps to determine the trust of the virtual network functions as well as the properties that should be satisfied by the virtual platforms (both at boot and run time) on which these network functions are deployed for them to be trusted. We present a logic-based language that defines simple rules for the specification of properties and the conditions under which these properties are evaluated to be satisfied for trusted virtualised platforms. The proposed trust model and mechanisms enable the service providers to determine the trustworthiness of the network services as well as the users to develop trustworthy applications.
Models of Adaptive Behaviour: An Approach Based on State

Models of Adaptive Behaviour: An Approach Based on State. --Alisdair I. Houston and John M. McNamara. 1999. Cambridge University Press, Cambridge, England. viii + 378 pp. [SBN 0 521-38480-X. Hardback, $80.00 ISBN 0-521-65539-0. Paper, $34.95. --There are two types of review book written by long-time experts in a field. The first emerges when the resident experts synthesize their accumulated (and typically prolific) work into a unified whole. That process involves cutting across domains of their original articles to create a new work in which all the relevant work is revisited and reworked
Scalable Plug-and-Play ADMM with Convergence Guarantees

Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the state-of-the-art performance of PnP algorithms using pre-trained deep neural nets as denoisers in a number of imaging applications. However, current PnP algorithms are impractical in large-scale settings due to their heavy computational and memory requirements. This work addresses this issue by proposing an incremental variant of the widely used PnP-ADMM algorithm, making it scalable to large-scale datasets. We theoretically analyze the convergence of the algorithm under a set of explicit assumptions, extending recent theoretical results in the area. Additionally, we show the effectiveness of our algorithm with nonsmooth data-fidelity terms and deep neural net priors, its fast convergence compared to existing PnP algorithms, and its scalability in terms of speed and memory.
Linear Programming and Community Detection

The problem of community detection with two equal-sized communities is closely related to the minimum graph bisection problem over certain random graph models. In the stochastic block model distribution over networks with community structure, a well-known semidefinite programming (SDP) relaxation of the minimum bisection problem recovers the underlying communities whenever possible. Motivated by their superior scalability, we study the theoretical performance of linear programming (LP) relaxations of the minimum bisection problem for the same random models. We show that, unlike the SDP relaxation that undergoes a phase transition in the logarithmic average degree regime, the LP relaxation fails in recovering the planted bisection with high probability in this regime. We show that the LP relaxation instead exhibits a transition from recovery to nonrecovery in the linear average degree regime. Finally, we present nonrecovery conditions for graphs with average degree strictly between linear and logarithmic.
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training

Modern deep neural network (DNN) training jobs use complex and heterogeneous software/hardware stacks. The efficacy of software-level optimizations can vary significantly when used in different deployment configurations. It is onerous and error-prone for ML practitioners and system developers to implement each optimization separately, and determine which ones will improve performance in their own configurations. Unfortunately, existing profiling tools do not aim to answer predictive questions such as "How will optimization X affect the performance of my model? ". We address this critical limitation, and proposes a new profiling tool, Daydream, to help programmers efficiently explore the efficacy of DNN optimizations. Daydream models DNN execution with a fine-grained dependency graph based on low-level traces collected by CUPTI, and predicts runtime by simulating execution based on the dependency graph. Daydream maps the low-level traces using DNN domain-specific knowledge, and introduces a set of graph-transformation primitives that can easily model a wide variety of optimizations. We show that Daydream is able to model most mainstream DNN optimization techniques, and accurately predict the efficacy of optimizations that will result in significant performance improvements.
Hierarchical Optimal Transport for Robust Multi-View Learning

Traditional multi-view learning methods often rely on two assumptions: ($i$) the samples in different views are well-aligned, and ($ii$) their representations in latent space obey the same distribution. Unfortunately, these two assumptions may be questionable in practice, which limits the application of multi-view learning. In this work, we propose a hierarchical optimal transport (HOT) method to mitigate the dependency on these two assumptions. Given unaligned multi-view data, the HOT method penalizes the sliced Wasserstein distance between the distributions of different views. These sliced Wasserstein distances are used as the ground distance to calculate the entropic optimal transport across different views, which explicitly indicates the clustering structure of the views. The HOT method is applicable to both unsupervised and semi-supervised learning, and experimental results show that it performs robustly on both synthetic and real-world tasks.
Segmentation of Surgical Instruments for Minimally-Invasive Robot-Assisted Procedures Using Generative Deep Neural Networks

This work proves that semantic segmentation on minimally invasive surgical instruments can be improved by using training data that has been augmented through domain adaptation. The benefit of this method is twofold. Firstly, it suppresses the need of manually labeling thousands of images by transforming synthetic data into realistic-looking data. To achieve this, a CycleGAN model is used, which transforms a source dataset to approximate the domain distribution of a target dataset. Secondly, this newly generated data with perfect labels is utilized to train a semantic segmentation neural network, U-Net. This method shows generalization capabilities on data with variability regarding its rotation- position- and lighting conditions. Nevertheless, one of the caveats of this approach is that the model is unable to generalize well to other surgical instruments with a different shape from the one used for training. This is driven by the lack of a high variance in the geometric distribution of the training data. Future work will focus on making the model more scale-invariant and able to adapt to other types of surgical instruments previously unseen by the training.
PLANS: Robust Program Learning from Neurally Inferred Specifications

Recent years have seen the rise of statistical program learning based on neural models as an alternative to traditional rule-based systems for programming by example. Rule-based approaches offer correctness guarantees in an unsupervised way as they inherently capture logical rules, while neural models are more realistically scalable to raw, high-dimensional input, and provide resistance to noisy I/O specifications. We introduce PLANS (Program LeArning from Neurally inferred Specifications), a hybrid model for program synthesis from visual observations that gets the best of both worlds, relying on (i) a neural architecture trained to extract abstract, high-level information from each raw individual input (ii) a rule-based system using the extracted information as I/O specifications to synthesize a program capturing the different observations. In order to address the key challenge of making PLANS resistant to noise in the network's output, we introduce a filtering heuristic for I/O specifications based on selective classification techniques. We obtain state-of-the-art performance at program synthesis from diverse demonstration videos in the Karel and ViZDoom environments, while requiring no ground-truth program for training. We make our implementation available at this http URL.
DEPARTMENT OF ENGINEERING MANAGEMENT Classification over bipartite graphs through projection

Many real-world large datasets correspond to bipartite graph data settings; think for example of users rating movies or people visiting locations. Although some work exists over such bigraphs, no general network-oriented methodology has been proposed yet to perform node classification. In this paper we propose a three-stage classification framework that effectively deals with the typical very large size of such datasets. First, a weighting of the top nodes is defined. Secondly, the bigraph is projected into a unipartite (homogenous) graph among the bottom nodes, where the weights of the edges are a function of the weights of the top nodes in the bigraph. Finally, relational learners/classifiers are applied to the resulting weighted unigraph. This general framework allows us to explore the design space, by applying different choices for the three stages, introducing new alternatives and mixing-and-matching to create new techniques. We present an empirical study of the predictive and run-time performances for different combinations of functions in the three stages over a large collection of bipartite datasets. There are clear differences in predictive performance with different design choices. Based on these results, we propose several specific combinations that show good accuracy and also allow for easy and fast scaling to big datasets. A comparison with a linear SVM method on the adjacency matrix of the bigraph shows the superiority of the network-oriented approach.
An adaptive iterative/subdivision hybrid algorithm for curve/curve intersection

The behavior of the iterative/subdivision hybrid algorithm for curve/curve intersection proposed in [20] depends on the choice of the domain for their convergence test. Using either too small or too large test domain may cause the test to fail to detect cases where Newton's method in fact converges to a solution, which results in unnecessary additional subdivisions and consequently more computation time. We propose a modification to the algorithm to adaptively adjust the test domain size according to what happens during the test of the parent region. This is in contrast to the original algorithm whose test domain is always a fixed multiple of the input domain under consideration. Computational results show that the proposed algorithm is slightly more efficient than the original algorithm.
How individual behaviors drive inequality in online community sizes: an agent-based simulation

Why are online community sizes so extremely unequal? Most answers to this question have pointed to general mathematical processes drawn from physics like cumulative advantage. These explanations provide little insight into specific social dynamics or decisions that individuals make when joining and leaving communities. In addition, explanations in terms of cumulative advantage do not draw from the enormous body of social computing research that studies individual behavior. Our work bridges this divide by testing whether two influential social mechanisms used to explain community joining can also explain the distribution of community sizes. Using agent-based simulations, we evaluate how well individual-level processes of social exposure and decisions based on individual expected benefits reproduce empirical community size data from Reddit. Our simulations contribute to social computing theory by providing evidence that both processes together---but neither alone---generate realistic distributions of community sizes. Our results also illustrate the potential value of agent-based simulation to online community researchers to both evaluate and bridge individual and group-level theories.
V363 Cassiopeiae: a new lithium-rich Galactic Cepheid

Context. Classical Cepheids (DCEPs) are important astrophysical objects not only as standard candles in the determination of the cosmic distance ladder, but also as a testbed for the stellar evolution theory. This is based on the strict connection between their pulsation (period(s) and amplitudes) and stellar parameters (luminosity, mass, effective temperature, and metallicity). Aims. We examine the nature of the Galactic DCEP V363 Cas and other DCEPs that show cosmic abundances of lithium in their atmospheres. Methods. We collected three epochs of high-resolution spectroscopy for V363 Cas with HARPS-N at the TNG. We measured accurate stellar parameters: the effective temperatures, gravities, microturbulences, radial velocities, and metal abundances. Results. We detected a lithium abundance of A(Li) = 2.86 ± 0.10 dex, along with iron, carbon, and oxygen abundances of [Fe/H] = −0.30 ± 0.12 dex, [C/H] = −0.06 ± 0.15 dex, and [O/H] = 0.00 ± 0.12 dex. V363 Cas is the fifth of the Milky Way DCEPs to exhibit a Li-rich feature. An analysis of historical time-series spanning a 100-year interval shows that the period of V363 Cas is increasing, with a sharp acceleration after HJD = 2 453 000 days. This is a clear indication of a first crossing of the instability strip. Conclusions. Our results favour the scenario in which the five Galactic Li-rich DCEPs are on their first crossing of the instability strip and have had slowly rotating progenitors during their main-sequence phase.
On the Scaling Properties of Magnetic-field Fluctuations through the Inner Heliosphere

Although the interplanetary magnetic-field variability has been extensively investigated in situ using data from several space missions, newly launched missions providing high-resolution measures and approaching the Sun offer the possibility to study the multiscale variability in the innermost solar system. Here, using Parker Solar Probe measurements, we investigate the scaling properties of solar wind magnetic-field fluctuations at different heliocentric distances. The results show a clear transition at distances close to say 0.4 au. Closer to the Sun fluctuations show a f−3/2 frequency power spectra and regular scaling properties, while for distances larger than 0.4 au fluctuations show a Kolmogorov spectrum f−5/3 and are characterized by anomalous scalings. The observed statistical properties of turbulence suggest that the solar wind magnetic fluctuations, in the late stage far from the Sun, show a multifractal behavior typical of turbulence and described by intermittency, while in the early stage, when leaving the solar corona, a breakdown of these properties is observed, thus showing a statistical monofractal global self-similarity. Physically, the breakdown observed close to the Sun should be due either to a turbulence with regular statistics or to the presence of intense stochastic fluctuations able to cancel out the correlations necessary for the presence of anomalous scaling.
Can the Multi-Incoming Smart Meter Compressed Streams be Re-Compressed?

Smart meters have currently attracted attention because of their high efficiency and throughput performance. They transmit a massive volume of continuously collected waveform readings (e.g. monitoring). Although many compression models are proposed, the unexpected size of these compressed streams required endless storage and management space which poses a unique challenge. Therefore, this paper explores the question of can the compressed smart meter readings be re-compressed? We first investigate the applicability of re-applying general compression algorithms directly on compressed streams. The results were poor due to the lack of redundancy. We further propose a novel technique to enhance the theoretical entropy and exploit that to re-compress. This is successfully achieved by using unsupervised learning as a similarity measurement to cluster the compressed streams into subgroups. The streams in every subgroup have been interleaved, followed by the first derivative to minimize the values and increase the redundancy. After that, two rotation steps have been applied to rearrange the readings in a more consecutive format before applying a developed dynamic run length. Finally, entropy coding is performed. Both mathematical and empirical experiments proved the significant improvement of the compressed streams entropy (i.e. almost reduced by half) and the resultant compression ratio (i.e. up to 50%).
FaaSdom: a benchmark suite for serverless computing

Serverless computing has become a major trend among cloud providers. With serverless computing, developers fully delegate the task of managing the servers, dynamically allocating the required resources, as well as handling availability and fault-tolerance matters to the cloud provider. In doing so, developers can solely focus on the application logic of their software, which is then deployed and completely managed in the cloud. Despite its increasing popularity, not much is known regarding the actual system performance achievable on the currently available serverless platforms. Specifically, it is cumbersome to benchmark such systems in a language- or runtime-independent manner. Instead, one must resort to a full application deployment, to later take informed decisions on the most convenient solution along several dimensions, including performance and economic costs. FaaSdom is a modular architecture and proof-of-concept implementation of a benchmark suite for serverless computing platforms. It currently supports the current mainstream serverless cloud providers (i.e., AWS, Azure, Google, IBM), a large set of benchmark tests and a variety of implementation languages. The suite fully automatizes the deployment, execution and clean-up of such tests, providing insights (including historical) on the performance observed by serverless applications. FaaSdom also integrates a model to estimate budget costs for deployments across the supported providers. FaaSdom is open-source and available at https://github.com/faas-benchmarking/faasdom.
"To Target or Not to Target": Identification and Analysis of Abusive Text Using Ensemble of Classifiers

With rising concern around abusive and hateful behavior on social media platforms, we present an ensemble learning method to identify and analyze the linguistic properties of such content. Our stacked ensemble comprises of three machine learning models that capture different aspects of language and provide diverse and coherent insights about inappropriate language. The proposed approach provides comparable results to the existing state-of-the-art on the Twitter Abusive Behavior dataset (Founta et al. 2018) without using any user or network-related information; solely relying on textual properties. We believe that the presented insights and discussion of shortcomings of current approaches will highlight potential directions for future research.
Adversarial Image Generation and Training for Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have achieved great success in image classification, but they may be very vulnerable to adversarial attacks with small perturbations to images. Moreover, the adversarial training based on adversarial image samples has been shown to improve the robustness and generalization of DCNNs. The aim of this paper is to develop a novel framework based on information-geometry sensitivity analysis and the particle swarm optimization to improve two aspects of adversarial image generation and training for DCNNs. The first one is customized generation of adversarial examples. It can design adversarial attacks from options of the number of perturbed pixels, the misclassification probability, and the targeted incorrect class, and hence it is more flexible and effective to locate vulnerable pixels and also enjoys certain adversarial universality. The other is targeted adversarial training. DCNN models can be improved in training with the adversarial information using a manifold-based influence measure effective in vulnerable image/pixel detection as well as allowing for targeted attacks, thereby exhibiting an enhanced adversarial defense in testing.
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus

The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide. To help tackle this, we developed computational methods to support COVID-19 disinformation debunking and social impacts research. This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework. We demonstrate that CANTM efficiently improves classification performance with low resources, and is scalable. In addition, the classification-aware topics help researchers and end-users to better understand the classification results.
Alessandro Jaker

1.0 Introduction This paper re-visits the issue of tone reversal in Dene languages—that is, the problem of why some Dene languages with tone have the opposite tones of other languages—especially in cases where two or more languages are very closely related and to a large extent mutually intelligible, despite having opposite tones. The languages Dogrib, Slave, and úáòe S òé, all spoken in Canada’s Northwest Territories, present just such a situation. Through a careful analysis of the prosody and morphophonemics of the conjugation marker the in these languages, and both internal and comparative reconstruction, I will demonstrate that the most recent common ancestor of these three languages, which I call Proto Northeast Dene (PNED), was tonal and High-marked, with a mobile pitch accent. It follows that Dogrib, which is presently Lowmarked, has undergone tone reversal. In treating these three languages, Dogrib, Slave, and úáòe S òé, as a group, to the exclusion of, for example, Sekani and Beaver, I am following earlier work by Howren (1971), Ackroyd (1976), and Marinakis (2004). While this classification may not be universally accepted (cf. Mithun 1999, Hargus & Tuttle 2004), I believe that the existence of a mobile pitch accent in these languages, to be described in §6.0, is itself an important isogloss which supports treating these languages as their own subgroup.