OpenRaiser/Intern-Atlas huggingface dataset:
The cross-sectional distribution of portfolio returns and applications
Venue: arXiv cs.CE 2021
Year: 2021

This paper aims to develop new mathematical and computational tools for modeling the distribution of portfolio returns across portfolios. We establish relevant mathematical formulas and propose efficient algorithms, drawing upon powerful techniques in computational geometry and the literature on splines, to compute the probability density function, the cumulative distribution function, and the k-th moment of the probability function. Our algorithmic tools and implementations efficiently handle portfolios with 10000 assets, and compute moments of order k up to 40 in a few seconds, thus handling real-life scenarios. We focus on the long-only strategy which is the most common type of investment, i.e. on portfolios whose weights are non-negative and sum up to 1; our approach is readily generalizable. Thus, we leverage a geometric representation of the stock market, where the investment set defines a simplex polytope. The cumulative distribution function corresponds to a portfolio score capturing the percentage of portfolios yielding a return not exceeding a given value. We introduce closed-form analytic formulas for the first 4 moments of the cross-sectional returns distribution, as well as a novel algorithm to compute all higher moments. We show that the first 4 moments are a direct mapping of the asset returns' moments. All of our algorithms and solutions are fully general and include the special case of equal asset returns, which was sometimes excluded in previous works. Finally, we apply our portfolio score in the design of new performance measures and asset management. We found our score-based optimal portfolios less concentrated than the mean-variance portfolio and much less risky in terms of ranking.
Merit and Blame Assignment with Kind 2
Venue: arXiv cs.LO 2021
Year: 2021

We introduce two new major features of the open-source model checker Kind 2 which provide traceability information between specification and design elements such as assumptions, guarantees, or other behavioral constraints in synchronous reactive system models. This new version of Kind 2 can identify minimal sets of design elements, known as Minimal Inductive Validity Cores, which are sufficient to prove a given set of safety properties, and also determine the set of MUST elements, design elements that are necessary to prove the given properties. In addition, Kind 2 is able to find minimal sets of design constraints, known as Minimal Cut Sets, whose violation leads the system to an unsafe state. The computed information can be used for several purposes, including assessing the quality of a system specification, tracking the safety impact of model changes, and analyzing the tolerance and resilience of a system against faults or cyber-attacks. We describe these new capabilities in some detail and report on an initial experimental evaluation of some of them.
Thirty years of TEFLIN Journal: A bibliometric portrait through the lens of Microsoft Academic
Venue: arXiv cs.DL 2021
Year: 2021

Bibliometric studies are a rare undertaking in the field of English language teaching, especially at a journal level. To celebrate the 30th anniversary of TEFLIN Journal, this study exhibits a bibliometric portrait of its publication, indexation, and citation from 1990 to 2019. Two pieces of free software were adopted to conduct the descriptive and network analyses of bibliographic data from Microsoft Academic. The publication and citation metrics have risen during its lifetime. The bibliographic network identifies the most productive authors, institutions, and countries along with the co-authorship pattern, type of top-cited articles, and top-used keywords. The articles relatedness is also sighted in terms of the citation frequency and number of shared references. Even though the analyses were complicated by some missing articles and improper indexation, this study could still take a full-length bibliometric portrait of the journal during its 30-year journey between the commitment to competence and the quest for higher impact.
Biometrics: Trust, but Verify
Venue: arXiv cs.CV 2021
Year: 2021

Over the past two decades, biometric recognition has exploded into a plethora of different applications around the globe. This proliferation can be attributed to the high levels of authentication accuracy and user convenience that biometric recognition systems afford end-users. However, in-spite of the success of biometric recognition systems, there are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems that create an element of mistrust in their use - both by the scientific community and also the public at large. Some of these problems include: i) questions related to system recognition performance, ii) security (spoof attacks, adversarial attacks, template reconstruction attacks and demographic information leakage), iii) uncertainty over the bias and fairness of the systems to all users, iv) explainability of the seemingly black-box decisions made by most recognition systems, and v) concerns over data centralization and user privacy. In this paper, we provide an overview of each of the aforementioned open-ended challenges. We survey work that has been conducted to address each of these concerns and highlight the issues requiring further attention. Finally, we provide insights into how the biometric community can address core biometric recognition systems design issues to better instill trust, fairness, and security for all.
Risk Model of German Corona Warning App - Reloaded
Venue: arXiv cs.SI 2021
Year: 2021

In this paper we discuss the risk model of the German Corona Warning App (CWA) in two versions. Both are based on a general semi-quantitative risk approach that is not state of the art anymore and for some application domains even deprecated. However, it turns out that the CWA uses a much more limited model, that does not even assess risk, but relies only on one parameter, a weighted exposure time. It is shown that the CWA grossly underestimates even this parameter and so may reassure the users wrongly. As the CWA also has other systematic limitations and shortcomings it is advised not to rely on its results but rather on Covid testing and vaccination.
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL
Venue: arXiv cs.AI 2021
Year: 2021

There is considerable interest in designing meta-reinforcement learning (meta-RL) algorithms, which enable autonomous agents to adapt new tasks from small amount of experience. In meta-RL, the specification (such as reward function) of current task is hidden from the agent. In addition, states are hidden within each task owing to sensor noise or limitations in realistic environments. Therefore, the meta-RL agent faces the challenge of specifying both the hidden task and states based on small amount of experience. To address this, we propose estimating disentangled belief about task and states, leveraging an inductive bias that the task and states can be regarded as global and local features of each task. Specifically, we train a hierarchical state-space model (HSSM) parameterized by deep neural networks as an environment model, whose global and local latent variables correspond to task and states, respectively. Because the HSSM does not allow analytical computation of posterior distribution, i.e., belief, we employ amortized inference to approximate it. After the belief is obtained, we can augment observations of a model-free policy with the belief to efficiently train the policy. Moreover, because task and state information are factorized and interpretable, the downstream policy training is facilitated compared with the prior methods that did not consider the hierarchical nature. Empirical validations on a GridWorld environment confirm that the HSSM can separate the hidden task and states information. Then, we compare the meta-RL agent with the HSSM to prior meta-RL methods in MuJoCo environments, and confirm that our agent requires less training data and reaches higher final performance.
Fast Stencil Computations using Fast Fourier Transforms
Venue: arXiv cs.DS 2021
Year: 2021

Stencil computations are widely used to simulate the change of state of physical systems across a multidimensional grid over multiple timesteps. The state-of-the-art techniques in this area fall into three groups: cache-aware tiled looping algorithms, cache-oblivious divide-and-conquer trapezoidal algorithms, and Krylov subspace methods. In this paper, we present two efficient parallel algorithms for performing linear stencil computations. Current direct solvers in this domain are computationally inefficient, and Krylov methods require manual labor and mathematical training. We solve these problems for linear stencils by using DFT preconditioning on a Krylov method to achieve a direct solver which is both fast and general. Indeed, while all currently available algorithms for solving general linear stencils perform $Θ(NT)$ work, where $N$ is the size of the spatial grid and $T$ is the number of timesteps, our algorithms perform $o(NT)$ work. To the best of our knowledge, we give the first algorithms that use fast Fourier transforms to compute final grid data by evolving the initial data for many timesteps at once. Our algorithms handle both periodic and aperiodic boundary conditions, and achieve polynomially better performance bounds (i.e., computational complexity and parallel runtime) than all other existing solutions. Initial experimental results show that implementations of our algorithms that evolve grids of roughly $10^7$ cells for around $10^5$ timesteps run orders of magnitude faster than state-of-the-art implementations for periodic stencil problems, and 1.3$\times$ to 8.5$\times$ faster for aperiodic stencil problems.
DaLAJ - a dataset for linguistic acceptability judgments for Swedish: Format, baseline, sharing
Venue: arXiv cs.CL 2021
Year: 2021

We present DaLAJ 1.0, a Dataset for Linguistic Acceptability Judgments for Swedish, comprising 9 596 sentences in its first version; and the initial experiment using it for the binary classification task. DaLAJ is based on the SweLL second language learner data, consisting of essays at different levels of proficiency. To make sure the dataset can be freely available despite the GDPR regulations, we have sentence-scrambled learner essays and removed part of the metadata about learners, keeping for each sentence only information about the mother tongue and the level of the course where the essay has been written. We use the normalized version of learner language as the basis for the DaLAJ sentences, and keep only one error per sentence. We repeat the same sentence for each individual correction tag used in the sentence. For DaLAJ 1.0 we have used four error categories (out of 35 available in SweLL), all connected to lexical or word-building choices. Our baseline results for the binary classification show an accuracy of 58% for DaLAJ 1.0 using BERT embeddings. The dataset is included in the SwedishGlue (Swe. SuperLim) benchmark. Below, we describe the format of the dataset, first experiments, our insights and the motivation for the chosen approach to data sharing.
Simulating Social Acceptability With Agent-based Modeling
Venue: arXiv cs.HC 2021
Year: 2021

Social acceptability is an important consideration for HCI designers who develop technologies for social contexts. However, the current theoretical foundations of social acceptability research do not account for the complex interactions among the actors in social situations and the specific role of technology. In order to improve the understanding of how context shapes and is shaped by situated technology interactions, we suggest to reframe the social space as a dynamic bundle of social practices and explore it with simulation studies using agent-based modeling. We outline possible research directions that focus on specific interactions among practices as well as regularities in emerging patterns.
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement
Venue: arXiv cs.NI 2021
Year: 2021

Network Slice placement with the problem of allocation of resources from a virtualized substrate network is an optimization problem which can be formulated as a multiobjective Integer Linear Programming (ILP) problem. However, to cope with the complexity of such a continuous task and seeking for optimality and automation, the use of Machine Learning (ML) techniques appear as a promising approach. We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization heuristic based on the Power of Two Choices principle. The DRL algorithm uses the so-called Asynchronous Advantage Actor Critic (A3C) algorithm for fast learning, and Graph Convolutional Networks (GCN) to automate feature extraction from the physical substrate network. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches as the evaluation results evidence.
Cybersecurity Anomaly Detection in Adversarial Environments
Venue: arXiv cs.CR 2021
Year: 2021

The proliferation of interconnected battlefield information-sharing devices, known as the Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to the IoBT operating environment is the practice of adversarial machine learning, which attempts to circumvent machine learning models. This work examines the feasibility of cost-effective unsupervised learning and graph-based methods for anomaly detection in the network intrusion detection system setting, and also leverages an ensemble approach to supervised learning of the anomaly detection problem. We incorporate a realistic adversarial training mechanism when training supervised models to enable strong classification performance in adversarial environments. The results indicate that the unsupervised and graph-based methods were outperformed in detecting anomalies (malicious activity) by the supervised stacking ensemble method with two levels. This model consists of three different classifiers in the first level, followed by either a Naive Bayes or Decision Tree classifier for the second level. The model maintains an F1-score above 0.97 for malicious samples across all tested level two classifiers. Notably, Naive Bayes is the fastest level two classifier averaging 1.12 seconds while Decision Tree maintains the highest AUC score of 0.98.
Facial Age Estimation using Convolutional Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

This paper is a part of a student project in Machine Learning at the Norwegian University of Science and Technology. In this paper, a deep convolutional neural network with five convolutional layers and three fully-connected layers is presented to estimate the ages of individuals based on images. The model is in its entirety trained from scratch, where a combination of three different datasets is used as training data. These datasets are the APPA dataset, UTK dataset, and the IMDB dataset. The images were preprocessed using a proprietary face-recognition software. Our model is evaluated on both a held-out test set, and on the Adience benchmark. On the test set, our model achieves a categorical accuracy of 52%. On the Adience benchmark, our model proves inferior compared with other leading models, with an exact accuray of 30%, and an one-off accuracy of 46%. Furthermore, a script was created, allowing users to estimate their age directly using their web camera. The script, alongside all other code, is located in our GitHub repository: AgeNet.
3.5 GHz Coverage Assessment with a 5G Testbed
Venue: arXiv cs.NI 2021
Year: 2021

Today, cellular networks have saturated frequencies below 3\,GHz. Because of increasing capacity requirements, 5th generation (5G) mobile networks target the 3.5\,GHz band (3.4 to 3.8\,GHz). Despite its expected wide usage, there is little empirical path loss data and mobile radio network planning experience for the 3.5\,GHz band available. This paper presents the results of rural, suburban, and urban measurement campaigns using a pre-standard 5G prototype testbed operating at 3.5\,GHz, with outdoor as well as outdoor-to-indoor scenarios. Based on the measurement results, path loss models are evaluated, which are essential for network planning.
Empathetic Dialog Generation with Fine-Grained Intents
Venue: arXiv cs.CL 2021
Year: 2021

Empathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help. Existing models either rely on pre-defined emotion labels to guide the response generation, or use deterministic rules to decide the emotion of the response. With the advent of advanced language models, it is possible to learn subtle interactions directly from the dataset, providing that the emotion categories offer sufficient nuances and other non-emotional but emotional regulating intents are included. In this paper, we describe how to incorporate a taxonomy of 32 emotion categories and 8 additional emotion regulating intents to succeed the task of empathetic response generation. To facilitate the training, we also curated a large-scale emotional dialog dataset from movie subtitles. Through a carefully designed crowdsourcing experiment, we evaluated and demonstrated how our model produces more empathetic dialogs compared with its baselines.
Automatically Assessing and Extending Code Coverage for NPM Packages
Venue: arXiv cs.SE 2021
Year: 2021

Typical Node.js applications extensively rely on packages hosted in the npm registry. As such packages may be used by thousands of other packages or applications, it is important to assess their code coverage. Moreover, increasing code coverage may help detect previously unknown issues. In this paper, we introduce TESA, a new tool that automatically assembles a test suite for any package in the npm registry. The test suite includes 1) tests written for the target package and usually hosted in its development repository, and 2) tests selected from dependent packages. The former tests allow assessing the code coverage of the target package, while the latter ones can increase code coverage by exploiting third-party tests that also exercise code in the target package. We use TESA to assess the code coverage of 500 popular npm packages. Then, we demonstrate that TESA can significantly increase code coverage by including tests from dependent packages. Finally, we show that the test suites assembled by TESA increase the effectiveness of existing dynamic program analyses to identify performance issues that are not detectable when only executing the developer's tests.
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE
Venue: arXiv cs.LG 2021
Year: 2021

Affine rank minimization problem is the generalized version of low rank matrix completion problem where linear combinations of the entries of a low rank matrix are observed and the matrix is estimated from these measurements. We propose a trainable deep neural network by unrolling a popular iterative algorithm called the singular value thresholding (SVT) algorithm to perform this generalized matrix completion which we call Learned SVT (LSVT). We show that our proposed LSVT with fixed layers (say T) reconstructs the matrix with lesser mean squared error (MSE) compared with that incurred by SVT with fixed (same T) number of iterations and our method is much more robust to the parameters which need to be carefully chosen in SVT algorithm.
Multiparty Session Types for Safe Runtime Adaptation in an Actor Language (Extended version)
Venue: arXiv cs.PL 2021
Year: 2021

Human fallibility, unpredictable operating environments, and the heterogeneity of hardware devices are driving the need for software to be able to adapt as seen in the Internet of Things or telecommunication networks. Unfortunately, mainstream programming languages do not readily allow a software component to sense and respond to its operating environment, by discovering, replacing, and communicating with components that are not part of the original system design, while maintaining static correctness guarantees. In particular, if a new component is discovered at runtime, there is no guarantee that its communication behaviour is compatible with existing components. We address this problem by using multiparty session types with explicit connection actions, a type formalism used to model distributed communication protocols. By associating session types with software components, the discovery process can check protocol compatibility and, when required, correctly replace components without jeapordising safety. We present the design and implementation of EnsembleS, the first actor-based language with adaptive features and a static session type system, and apply it to a case study based on an adaptive DNS server. We formalise the type system of EnsembleS and prove the safety of well-typed programs, making essential use of recent advances in non-classical multiparty session types.
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Venue: arXiv cs.CV 2021
Year: 2021

Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pretext tasks to learn meaningful and reusable representations for the target task, these efforts obtain marginal performance gains compared to fully-supervised learning. Meanwhile, little attention has been given to study the robustness of networks trained in a self-supervised manner. In this work, we demonstrate that networks trained via self-supervised learning have superior robustness and generalizability compared to fully-supervised learning in the context of medical imaging. Our experiments on pneumonia detection in X-rays and multi-organ segmentation in CT yield consistent results exposing the hidden benefits of self-supervision for learning robust feature representations.
Automatic Non-Linear Video Editing Transfer
Venue: arXiv cs.CV 2021
Year: 2021

We propose an automatic approach that extracts editing styles in a source video and applies the edits to matched footage for video creation. Our Computer Vision based techniques considers framing, content type, playback speed, and lighting of each input video segment. By applying a combination of these features, we demonstrate an effective method that automatically transfers the visual and temporal styles from professionally edited videos to unseen raw footage. We evaluated our approach with real-world videos that contained a total of 3872 video shots of a variety of editing styles, including different subjects, camera motions, and lighting. We reported feedback from survey participants who reviewed a set of our results.