OpenRaiser/Intern-Atlas huggingface dataset:
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function
Venue: arXiv cs.NE 2021
Year: 2021

The activation function plays a fundamental role in the artificial neural network learning process. However, there is no obvious choice or procedure to determine the best activation function, which depends on the problem. This study proposes a new artificial neuron, named global-local neuron, with a trainable activation function composed of two components, a global and a local. The global component term used here is relative to a mathematical function to describe a general feature present in all problem domain. The local component is a function that can represent a localized behavior, like a transient or a perturbation. This new neuron can define the importance of each activation function component in the learning phase. Depending on the problem, it results in a purely global, or purely local, or a mixed global and local activation function after the training phase. Here, the trigonometric sine function was employed for the global component and the hyperbolic tangent for the local component. The proposed neuron was tested for problems where the target was a purely global function, or purely local function, or a composition of two global and local functions. Two classes of test problems were investigated, regression problems and differential equations solving. The experimental tests demonstrated the Global-Local Neuron network's superior performance, compared with simple neural networks with sine or hyperbolic tangent activation function, and with a hybrid network that combines these two simple neural networks.
Data Science for Engineers: A Teaching Ecosystem
Venue: arXiv cs.CY 2021
Year: 2021

We describe an ecosystem for teaching data science (DS) to engineers which blends theory, methods, and applications, developed at the Faculty of Physical and Mathematical Sciences, Universidad de Chile, over the last three years. This initiative has been motivated by the increasing demand for DS qualifications both from academic and professional environments. The ecosystem is distributed in a collaborative fashion across three departments in the above Faculty and includes postgraduate programmes, courses, professional diplomas, data repositories, laboratories, trainee programmes, and internships. By sharing our teaching principles and the innovative components of our approach to teaching DS, we hope our experience can be useful to those developing their own DS programmes and ecosystems. The open challenges and future plans for our ecosystem are also discussed at the end of the article.
Ask Me or Tell Me? Enhancing the Effectiveness of Crowdsourced Design Feedback
Venue: arXiv cs.HC 2021
Year: 2021

Crowdsourced design feedback systems are emerging resources for getting large amounts of feedback in a short period of time. Traditionally, the feedback comes in the form of a declarative statement, which often contains positive or negative sentiment. Prior research has shown that overly negative or positive sentiment can strongly influence the perceived usefulness and acceptance of feedback and, subsequently, lead to ineffective design revisions. To enhance the effectiveness of crowdsourced design feedback, we investigate a new approach for mitigating the effects of negative or positive feedback by combining open-ended and thought-provoking questions with declarative feedback statements. We conducted two user studies to assess the effects of question-based feedback on the sentiment and quality of design revisions in the context of graphic design. We found that crowdsourced question-based feedback contains more neutral sentiment than statement-based feedback. Moreover, we provide evidence that presenting feedback as questions followed by statements leads to better design revisions than question- or statement-based feedback alone.
Learning Invariant Representation for Continual Learning
Venue: arXiv cs.LG 2021
Year: 2021

Continual learning aims to provide intelligent agents that are capable of learning continually a sequence of tasks, building on previously learned knowledge. A key challenge in this learning paradigm is catastrophically forgetting previously learned tasks when the agent faces a new one. Current rehearsal-based methods show their success in mitigating the catastrophic forgetting problem by replaying samples from previous tasks during learning a new one. However, these methods are infeasible when the data of previous tasks is not accessible. In this work, we propose a new pseudo-rehearsal-based method, named learning Invariant Representation for Continual Learning (IRCL), in which class-invariant representation is disentangled from a conditional generative model and jointly used with class-specific representation to learn the sequential tasks. Disentangling the shared invariant representation helps to learn continually a sequence of tasks, while being more robust to forgetting and having better knowledge transfer. We focus on class incremental learning where there is no knowledge about task identity during inference. We empirically evaluate our proposed method on two well-known benchmarks for continual learning: split MNIST and split Fashion MNIST. The experimental results show that our proposed method outperforms regularization-based methods by a big margin and is better than the state-of-the-art pseudo-rehearsal-based method. Finally, we analyze the role of the shared invariant representation in mitigating the forgetting problem especially when the number of replayed samples for each previous task is small.
Probabilistic Inference for Learning from Untrusted Sources
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning brings potential benefits of faster learning, better solutions, and a greater propensity to transfer when heterogeneous data from different parties increases diversity. However, because federated learning tasks tend to be large and complex, and training times non-negligible, it is important for the aggregation algorithm to be robust to non-IID data and corrupted parties. This robustness relies on the ability to identify, and appropriately weight, incompatible parties. Recent work assumes that a \textit{reference dataset} is available through which to perform the identification. We consider settings where no such reference dataset is available; rather, the quality and suitability of the parties needs to be \textit{inferred}. We do so by bringing ideas from crowdsourced predictions and collaborative filtering, where one must infer an unknown ground truth given proposals from participants with unknown quality. We propose novel federated learning aggregation algorithms based on Bayesian inference that adapt to the quality of the parties. Empirically, we show that the algorithms outperform standard and robust aggregation in federated learning on both synthetic and real data.
Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems
Venue: arXiv cs.CY 2021
Year: 2021

This paper determines whether the two core data protection principles of data minimisation and purpose limitation can be meaningfully implemented in data-driven systems. While contemporary data processing practices appear to stand at odds with these principles, we demonstrate that systems could technically use much less data than they currently do. This observation is a starting point for our detailed techno-legal analysis uncovering obstacles that stand in the way of meaningful implementation and compliance as well as exemplifying unexpected trade-offs which emerge where data protection law is applied in practice. Our analysis seeks to inform debates about the impact of data protection on the development of artificial intelligence in the European Union, offering practical action points for data controllers, regulators, and researchers.
The Eye of Horus: Spotting and Analyzing Attacks on Ethereum Smart Contracts
Venue: arXiv cs.CR 2021
Year: 2021

In recent years, Ethereum gained tremendously in popularity, growing from a daily transaction average of 10K in January 2016 to an average of 500K in January 2020. Similarly, smart contracts began to carry more value, making them appealing targets for attackers. As a result, they started to become victims of attacks, costing millions of dollars. In response to these attacks, both academia and industry proposed a plethora of tools to scan smart contracts for vulnerabilities before deploying them on the blockchain. However, most of these tools solely focus on detecting vulnerabilities and not attacks, let alone quantifying or tracing the number of stolen assets. In this paper, we present Horus, a framework that empowers the automated detection and investigation of smart contract attacks based on logic-driven and graph-driven analysis of transactions. Horus provides quick means to quantify and trace the flow of stolen assets across the Ethereum blockchain. We perform a large-scale analysis of all the smart contracts deployed on Ethereum until May 2020. We identified 1,888 attacked smart contracts and 8,095 adversarial transactions in the wild. Our investigation shows that the number of attacks did not necessarily decrease over the past few years, but for some vulnerabilities remained constant. Finally, we also demonstrate the practicality of our framework via an in-depth analysis on the recent Uniswap and Lendf.me attacks.
Deep Reinforcement Learning for Haptic Shared Control in Unknown Tasks
Venue: arXiv cs.RO 2021
Year: 2021

Recent years have shown a growing interest in using haptic shared control (HSC) in teleoperated systems. In HSC, the application of virtual guiding forces decreases the user's control effort and improves execution time in various tasks, presenting a good alternative in comparison with direct teleoperation. HSC, despite demonstrating good performance, opens a new gap: how to design the guiding forces. For this reason, the challenge lies in developing controllers to provide the optimal guiding forces for the tasks that are being performed. This work addresses this challenge by designing a controller based on the deep deterministic policy gradient (DDPG) algorithm to provide the assistance, and a convolutional neural network (CNN) to perform the task detection, called TAHSC (Task Agnostic Haptic Shared Controller). The agent learns to minimize the time it takes the human to execute the desired task, while simultaneously minimizing their resistance to the provided feedback. This resistance thus provides the learning algorithm with information about which direction the human is trying to follow, in this case, the pick-and-place task. Diverse results demonstrate the successful application of the proposed approach by learning custom policies for each user who was asked to test the system. It exhibits stable convergence and aids the user in completing the task with the least amount of time possible.
Towards Approximate Query Enumeration with Sublinear Preprocessing Time
Venue: arXiv cs.DB 2021
Year: 2021

This paper aims at providing extremely efficient algorithms for approximate query enumeration on sparse databases, that come with performance and accuracy guarantees. We introduce a new model for approximate query enumeration on classes of relational databases of bounded degree. We first prove that on databases of bounded degree any local first-order definable query can be enumerated approximately with constant delay after a constant time preprocessing phase. We extend this, showing that on databases of bounded tree-width and bounded degree, every query that is expressible in first-order logic can be enumerated approximately with constant delay after a sublinear (more precisely, polylogarithmic) time preprocessing phase. Durand and Grandjean (ACM Transactions on Computational Logic 2007) proved that exact enumeration of first-order queries on databases of bounded degree can be done with constant delay after a linear time preprocessing phase. Hence we achieve a significant speed-up in the preprocessing phase. Since sublinear running time does not allow reading the whole input database even once, sacrificing some accuracy is inevitable for our speed-up. Nevertheless, our enumeration algorithms come with guarantees: With high probability, (1) only tuples are enumerated that are answers to the query or `close' to being answers to the query, and (2) if the proportion of tuples that are answers to the query is sufficiently large, then all answers will be enumerated. Here the notion of `closeness' is a tuple edit distance in the input database. For local first-order queries, only actual answers are enumerated, strengthening (1). Moreover, both the `closeness' and the proportion required in (2) are controllable. We combine methods from property testing of bounded degree graphs with logic and query enumeration, which we believe can inspire further research.
Lissy: Experimenting with on-chain order books
Venue: arXiv cs.CR 2021
Year: 2021

Financial regulators have long-standing concerns about fully decentralized exchanges that run 'on-chain' without any obvious regulatory hooks. The popularity of Uniswap, an automated market makers (AMM), made these concerns a reality. AMMs implement a lightweight dealer-based trading system, but they are unlike anything on Wall Street, require fees intrinsically, and are susceptible to front-running attacks. This leaves the following research questions we address in this paper: (1) are conventional (i.e., order books), secure (i.e., resistant to front-running and price manipulation) and fully decentralized exchanges feasible on a public blockchain like Ethereum, (2) what is the performance profile, and (3) how much do Layer 2 techniques (e.g., Arbitrum) increase performance? To answer these questions, we implement, benchmark, and experiment with an Ethereum-based call market exchange called Lissy. We confirm the functionality is too heavy for Ethereum today (you cannot expect to exceed a few hundred trade executions per block) but show it scales dramatically (99.88% gas cost reduction) on Arbitrum.
A Multi-Platform Study of Crowd Signals Associated with Successful Online Fundraising
Venue: arXiv cs.HC 2021
Year: 2021

The growing popularity of online fundraising (aka "crowdfunding") has attracted significant research on the subject. In contrast to previous studies that attempt to predict the success of crowdfunded projects based on specific characteristics of the projects and their creators, we present a more general approach that focuses on crowd dynamics and is robust to the particularities of different crowdfunding platforms. We rely on a multi-method analysis to investigate the correlates, predictive importance, and quasi-causal effects of features that describe crowd dynamics in determining the success of crowdfunded projects. By applying a multi-method analysis to a study of fundraising in three different online markets, we uncover general crowd dynamics that ultimately decide which projects will succeed. In all analyses and across the three different platforms, we consistently find that funders' behavioural signals (1) are significantly correlated with fundraising success; (2) approximate fundraising outcomes better than the characteristics of projects and their creators such as credit grade, company valuation, and subject domain; and (3) have significant quasi-causal effects on fundraising outcomes while controlling for potentially confounding project variables. By showing that universal features deduced from crowd behaviour are predictive of fundraising success on different crowdfunding platforms, our work provides design-relevant insights about novel types of collective decision-making online. This research inspires thus potential ways to leverage cues from the crowd and catalyses research into crowd-aware system design.
Optical Flow Estimation via Motion Feature Recovery
Venue: arXiv cs.CV 2021
Year: 2021

Optical flow estimation with occlusion or large displacement is a problematic challenge due to the lost of corresponding pixels between consecutive frames. In this paper, we discover that the lost information is related to a large quantity of motion features (more than 40%) computed from the popular discriminative cost-volume feature would completely vanish due to invalid sampling, leading to the low efficiency of optical flow learning. We call this phenomenon the Vanishing Cost Volume Problem. Inspired by the fact that local motion tends to be highly consistent within a short temporal window, we propose a novel iterative Motion Feature Recovery (MFR) method to address the vanishing cost volume via modeling motion consistency across multiple frames. In each MFR iteration, invalid entries from original motion features are first determined based on the current flow. Then, an efficient network is designed to adaptively learn the motion correlation to recover invalid features for lost-information restoration. The final optical flow is then decoded from the recovered motion features. Experimental results on Sintel and KITTI show that our method achieves state-of-the-art performances. In fact, MFR currently ranks second on Sintel public website.
Strings-and-Coins and Nimstring are PSPACE-complete
Venue: arXiv cs.CC 2021
Year: 2021

We prove that Strings-and-Coins -- the combinatorial two-player game generalizing the dual of Dots-and-Boxes -- is strongly PSPACE-complete on multigraphs. This result improves the best previous result, NP-hardness, argued in Winning Ways. Our result also applies to the Nimstring variant, where the winner is determined by normal play; indeed, one step in our reduction is the standard reduction (also from Winning Ways) from Nimstring to Strings-and-Coins.
An Empirical Comparison of Deep Learning Models for Knowledge Tracing on Large-Scale Dataset
Venue: arXiv cs.AI 2021
Year: 2021

Knowledge tracing (KT) is the problem of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. It is an active research area to help provide learners with personalized feedback and materials. Various deep learning techniques have been proposed for solving KT. Recent release of large-scale student performance dataset \cite{choi2019ednet} motivates the analysis of performance of deep learning approaches that have been proposed to solve KT. Our analysis can help understand which method to adopt when large dataset related to student performance is available. We also show that incorporating contextual information such as relation between exercises and student forget behavior further improves the performance of deep learning models.
Dynamic Ternary Content-Addressable Memory Is Indeed Promising: Design and Benchmarking Using Nanoelectromechanical Relays
Venue: arXiv cs.ET 2021
Year: 2021

Ternary content addressable memory (TCAM) has been a critical component in caches, routers, etc., in which density, speed, power efficiency, and reliability are the major design targets. There have been the conventional low-write-power but bulky SRAM-based TCAM design, and also denser but less reliable or higher-write-power TCAM designs using nonvolatile memory (NVM) devices. Meanwhile, some TCAM designs using dynamic memories have been also proposed. Although dynamic design TCAM is denser than CMOS SRAM TCAM and more reliable than NVM TCAM, the conventional row-by-row refresh operations land up with a bottleneck of interference with normal TCAM activities. Therefore, this paper proposes a custom low-power dynamic TCAM using nanoelectromechanical (NEM) relay devices utilizing one-shot refresh to solve the memory refresh problem. By harnessing the unique NEM relay characteristics with a proposed novel cell structure, the proposed TCAM occupies a small footprint of only 3 transistors (with two NEM relays integrated on the top through the back-end-of-line process), which significantly outperforms the density of 16-transistor SRAM-based TCAM. In addition, evaluations show that the proposed TCAM improves the write energy efficiency by 2.31x, 131x, and 13.5x over SRAM, RRAM, and FeFET TCAMs, respectively; The search energy-delay-product is improved by 12.7x, 1.30x, and 2.83x over SRAM, RRAM, and FeFET TCAMs, respectively.
Robustness to Augmentations as a Generalization metric
Venue: arXiv cs.LG 2021
Year: 2021

Generalization is the ability of a model to predict on unseen domains and is a fundamental task in machine learning. Several generalization bounds, both theoretical and empirical have been proposed but they do not provide tight bounds .In this work, we propose a simple yet effective method to predict the generalization performance of a model by using the concept that models that are robust to augmentations are more generalizable than those which are not. We experiment with several augmentations and composition of augmentations to check the generalization capacity of a model. We also provide a detailed motivation behind the proposed method. The proposed generalization metric is calculated based on the change in the output of the model after augmenting the input. The proposed method was the first runner up solution for the NeurIPS competition on Predicting Generalization in Deep Learning.
On linear codes with one-dimensional Euclidean hull and their applications to EAQECCs
Venue: arXiv cs.IT 2021
Year: 2021

The Euclidean hull of a linear code $C$ is the intersection of $C$ with its Euclidean dual $C^\perp$. The hull with low dimensions gets much interest due to its crucial role in determining the complexity of algorithms for computing the automorphism group of a linear code and checking permutation equivalence of two linear codes. The Euclidean hull of a linear code has been applied to the so-called entanglement-assisted quantum error-correcting codes (EAQECCs) via classical error-correcting codes. In this paper, we consider linear codes with one-dimensional Euclidean hull from algebraic geometry codes. Several classes of optimal linear codes with one-dimensional Euclidean hull are constructed. Some new EAQECCs are presented.
Galleon: Reshaping the Square Peg of NFV
Venue: arXiv cs.DC 2021
Year: 2021

Software is often used for Network Functions (NFs) -- such as firewalls, NAT, deep packet inspection, and encryption -- that are applied to traffic in the network. The community has hoped that NFV would enable rapid development of new NFs and leverage commodity computing infrastructure. However, the challenge for researchers and operators has been to align the square peg of high-speed packet processing with the round hole of cloud computing infrastructures and abstractions, all while delivering performance, scalability, and isolation. Past work has led to the belief that NFV is different enough that it requires novel, custom approaches that deviate from today's norms. To the contrary, we show that we can achieve performance, scalability, and isolation in NFV judiciously using mechanisms and abstractions of FaaS, the Linux kernel, NIC hardware, and OpenFlow switches. As such, with our system Galleon, NFV can be practically-deployable today in conventional cloud environments while delivering up to double the performance per core compared to the state of the art.
Wearable Sensors for Spatio-Temporal Grip Force Profiling
Venue: arXiv cs.RO 2021
Year: 2021

Wearable biosensor technology enables real-time, convenient, and continuous monitoring of users behavioral signals. Such include signals relative to body motion, body temperature, biological or biochemical markers, and individual grip forces, which are studied in this paper. A four step pick and drop image guided and robot assisted precision task has been designed for exploiting a wearable wireless sensor glove system. Individual spatio temporal grip forces are analyzed on the basis of thousands of individual sensor data, collected from different locations on the dominant and non-dominant hands of each of three users in ten successive task sessions. Statistical comparisons reveal specific differences between grip force profiles of the individual users as a function of task skill level (expertise) and time.