OpenRaiser/Intern-Atlas huggingface dataset:
An Automatic Schema-Instance Approach for Merging Multidimensional Data Warehouses
Venue: arXiv cs.DB 2021
Year: 2021

Using data warehouses to analyse multidimensional data is a significant task in company decision-making.The data warehouse merging process is composed of two steps: matching multidimensional components and then merging them. Current approaches do not take all the particularities of multidimensional data warehouses into account, e.g., only merging schemata, but not instances; or not exploiting hierarchies nor fact tables. Thus, in this paper, we propose an automatic merging approach for star schema-modeled data warehouses that works at both the schema and instance levels. We also provide algorithms for merging hierarchies, dimensions and facts. Eventually, we implement our merging algorithms and validate them with the use of both synthetic and benchmark datasets.
How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns, which is closely related to real-world datasets; (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior; and (4) attention mechanism may exacerbate the issue of insufficient training data. Based on these findings, we show that a straightforward variant of RE model can achieve significant improvements (6% AUC on average) on two real-world datasets as compared with three state-of-the-art baselines. Our codes and datasets are available at https://github.com/zig-kwin-hu/how-KG-ATT-help.
Filling the Tax Gap via Programmable Money
Venue: arXiv cs.CR 2021
Year: 2021

We discuss the problem of facilitating tax auditing assuming "programmable money", i.e., digital monetary instruments that are managed by an underlying distributed ledger. We explore how a taxation authority can verify the declared returns of its citizens and create a counter-incentive to tax evasion by two distinct mechanisms. First, we describe a design which enables auditing it as a built-in feature with minimal changes on the underlying ledger's consensus protocol. Second, we offer an application-layer extension, which requires no modification in the underlying ledger's design. Both solutions provide a high level of privacy, ensuring that, apart from specific limited data given to the taxation authority, no additional information - beyond the information already published on the underlying ledger - is leaked.
Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption
Venue: arXiv cs.AI 2021
Year: 2021

Probabilistic sentential decision diagrams are a class of structured-decomposable probabilistic circuits especially designed to embed logical constraints. To adapt the classical LearnSPN scheme to learn the structure of these models, we propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit. Sum nodes are thus learned by recursively clustering batches in the initial data base, while the partitioning of the variables obeys a given input vtree. Preliminary experiments show that the proposed approach might properly fit training data, and generalize well to test data, provided that these remain consistent with the underlying logical base, that is a relaxation of the training data base.
Fine-Grained Emotion Prediction by Modeling Emotion Definitions
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we propose a new framework for fine-grained emotion prediction in the text through emotion definition modeling. Our approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. Our models outperform existing state-of-the-art for fine-grained emotion dataset GoEmotions. We further show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the generalization capability of the models.
The Role of Functional Programming in Management and Orchestration of Virtualized Network Resources Part I. System structure for Complex Systems and Design Principles
Venue: arXiv cs.SE 2021
Year: 2021

This is part I of the follow-up lecture notes of the lectures given by the authors at the Three \CO" (Composability, Comprehensibility, Correctness) Winter School held in Košice, Slovakia, in January 2018, and Summer School held in Budapest, Hungary, in June 2019. In this part we explain the role of functional programming paradigm in the management of complex software systems, and how the functional programming concepts play important role in the designing such systems. Key prerequisite for implementing functional programming concepts is properly designed system structure following well defined design principles and rules. That is the main goal of this lecture to introduce students with proper system modeling. Furthermore, we also explain how new emerging technologies are designed in such a way that they enforce the development of systems that comply to the design rules inspired by the functional programming. This is extremely important in view of the current network evolution and virtualization concepts, which will require many functional programming concepts in the network services and functions, as will be discussed in part II of these lecture notes. These notes provide an introduction to the subject, with the goal of explaining the problems and the principles, methods and techniques used for their solution. The worked examples and exercises serve students as the teaching material, from which they can learn how to use design principles to model effective system structures. Here we focus on students understanding of importance of effective system structures for coordination of development and management processes that are driven by business goals and further evolution.
Federated Action Recognition on Heterogeneous Embedded Devices
Venue: arXiv cs.DC 2021
Year: 2021

Federated learning allows a large number of devices to jointly learn a model without sharing data. In this work, we enable clients with limited computing power to perform action recognition, a computationally heavy task. We first perform model compression at the central server through knowledge distillation on a large dataset. This allows the model to learn complex features and serves as an initialization for model fine-tuning. The fine-tuning is required because the limited data present in smaller datasets is not adequate for action recognition models to learn complex spatio-temporal features. Because the clients present are often heterogeneous in their computing resources, we use an asynchronous federated optimization and we further show a convergence bound. We compare our approach to two baseline approaches: fine-tuning at the central server (no clients) and fine-tuning using (heterogeneous) clients using synchronous federated averaging. We empirically show on a testbed of heterogeneous embedded devices that we can perform action recognition with comparable accuracy to the two baselines above, while our asynchronous learning strategy reduces the training time by 40%, relative to synchronous learning.
Studying the anonymity trilemma with a discrete-event mix network simulator
Venue: arXiv cs.CR 2021
Year: 2021

In this work, we present a discrete event mix network simulator, which allows analysing how anonymity, latency, and bandwidth overhead are affected by various scenarios of deployment and design choices. These design choices include network topology, mixing technique, volume of traffic, latency requirements, packet size or use of cover traffic. To the best of our knowledge, this is the first such simulator as work on it began in 2017 to analyze the Loopix mix network, and the code of our simulator is available under an open-source license. To demonstrate the capabilities of our simulator, we perform an empirical analysis of the impact of core design choices on anonymity, scalability and latency in Elixxir, HOPR and Nym, currently deployed mix network infrastructures that make a variety of different choices in their design.
Integer-Programming-Based Narrow-Passage Multi-Robot Path Planning with Effective Heuristics
Venue: arXiv cs.RO 2021
Year: 2021

We study optimal Multi-robot Path Planning (MPP) on graphs, in order to improve the efficiency of multi-robot system (MRS) in the warehouse-like environment. We propose a novel algorithm, OMRPP (One-way Multi-robot Path Planning) based on Integer programming (IP) method. We focus on reducing the cost caused by a set of robots moving from their initial configuration to goal configuration in the warehouse-like environment. The novelty of this work includes: (1) proposing a topological map extraction based on the property of warehouse-like environment to reduce the scale of constructed IP model; (2) proposing one-way passage constraint to prevent the robots from having unsolvable collisions in the passage. (3) developing a heuristic architecture that IP model can always have feasible initial solution to ensure its solvability. Numerous simulations demonstrate the efficiency and performance of the proposed algorithm.
Co-Optimization of Design and Fabrication Plans for Carpentry
Venue: arXiv cs.GR 2021
Year: 2021

Past work on optimizing fabrication plans given a carpentry design can provide Pareto-optimal plans trading off between material waste, fabrication time, precision, and other considerations. However, when developing fabrication plans, experts rarely restrict to a single design, instead considering families of design variations, sometimes adjusting designs to simplify fabrication. Jointly exploring the design and fabrication plan spaces for each design is intractable using current techniques. We present a new approach to jointly optimize design and fabrication plans for carpentered objects. To make this bi-level optimization tractable, we adapt recent work from program synthesis based on equality graphs (e-graphs), which encode sets of equivalent programs. Our insight is that subproblems within our bi-level problem share significant substructures. By representing both designs and fabrication plans in a new bag of parts(BOP) e-graph, we amortize the cost of optimizing design components shared among multiple candidates. Even using BOP e-graphs, the optimization space grows quickly in practice. Hence, we also show how a feedback-guided search strategy dubbed Iterative Contraction and Expansion on E-graphs(ICEE) can keep the size of the e-graph manage-able and direct the search toward promising candidates. We illustrate the advantages of our pipeline through examples from the carpentry domain.
Anomaly-based Intrusion Detection System Using Fuzzy Logic
Venue: arXiv cs.CR 2021
Year: 2021

Recently, the Distributed Denial of Service (DDOS) attacks has been used for different aspects to denial the number of services for the end-users. Therefore, there is an urgent need to design an effective detection method against this type of attack. A fuzzy inference system offers the results in a more readable and understandable form. This paper introduces an anomaly-based Intrusion Detection (IDS) system using fuzzy logic. The fuzzy logic inference system implemented as a detection method for Distributed Denial of Service (DDOS) attacks. The suggested method was applied to an open-source DDOS dataset. Experimental results show that the anomaly-based Intrusion Detection system using fuzzy logic obtained the best result by utilizing the InfoGain features selection method besides the fuzzy inference system, the results were 91.1% for the true-positive rate and 0.006% for the false-positive rate.
Revisiting Catastrophic Forgetting in Class Incremental Learning
Venue: arXiv cs.CV 2021
Year: 2021

Although the concept of catastrophic forgetting is straightforward, there is a lack of study on its causes. In this paper, we systematically explore and reveal three causes for catastrophic forgetting in Class Incremental Learning(CIL). From the perspective of representation learning,(i) intra-phase forgetting happens when the learner fails to correctly align the same-phase data as training proceeds and (ii) inter-phase confusion happens when the learner confuses the current-phase data with the previous-phase. From the task-specific point of view, the CIL model suffers from the problem of (iii) classifier deviation. After investigating existing strategies, we observe that there is a lack of study on how to prevent the inter-phase confusion. To initiate the research on this specific issue, we propose a simple yet effective framework, Contrastive Class Concentration for CIL (C4IL). Our framework leverages the class concentration effect of contrastive learning, yielding a representation distribution with better intra-class compactibility and inter-class separability. Empirically, we observe that C4IL significantly lowers the probability of inter-phase confusion and as a result improves the performance on multiple CIL settings of multiple datasets.
'No, auntie, that's false': Female baby boomers develop critical skills to confront fake news with guidance from relatives
Venue: arXiv cs.CY 2021
Year: 2021

The spread of fake news has been increasing, which gives rise to a special interest in the development of identification and coping skills among news consumers so that they can filter out misleading information. Studies suggest that older people share more fake news from social media. There is scarce literature that analyse how baby boomers behave in the face of fake news. The purpose of this study is to examine how female baby boomers deal with fake news on Facebook and their available resources to learn how to identify and handle dubious information. A qualitative study and thematic analysis were conducted using information obtained from interviewing female baby boomers. Four themes emerge from the analysis, revealing that participants recognise that they can identify fake news but may not always be able to do so due to limitations in their understanding of an issue or uncertainty about its source. Participants show participants empirically develop critical identification and filtering skills with the assistance from close family members.
MLDev: Data Science Experiment Automation and Reproducibility Software
Venue: arXiv cs.LG 2021
Year: 2021

In this paper we explore the challenges of automating experiments in data science. We propose an extensible experiment model as a foundation for integration of different open source tools for running research experiments. We implement our approach in a prototype open source MLDev software package and evaluate it in a series of experiments yielding promising results. Comparison with other state-of-the-art tools signifies novelty of our approach.
IRLCov19: A Large COVID-19 Multilingual Twitter Dataset of Indian Regional Languages
Venue: arXiv cs.SI 2021
Year: 2021

Emerged in Wuhan city of China in December 2019, COVID-19 continues to spread rapidly across the world despite authorities having made available a number of vaccines. While the coronavirus has been around for a significant period of time, people and authorities still feel the need for awareness due to the mutating nature of the virus and therefore varying symptoms and prevention strategies. People and authorities resort to social media platforms the most to share awareness information and voice out their opinions due to their massive outreach in spreading the word in practically no time. People use a number of languages to communicate over social media platforms based on their familiarity, language outreach, and availability on social media platforms. The entire world has been hit by the coronavirus and India is the second worst-hit country in terms of the number of active coronavirus cases. India, being a multilingual country, offers a great opportunity to study the outreach of various languages that have been actively used across social media platforms. In this study, we aim to study the dataset related to COVID-19 collected in the period between February 2020 to July 2020 specifically for regional languages in India. This could be helpful for the Government of India, various state governments, NGOs, researchers, and policymakers in studying different issues related to the pandemic. We found that English has been the mode of communication in over 64% of tweets while as many as twelve regional languages in India account for approximately 4.77% of tweets.
Approximating Sumset Size
Venue: arXiv cs.DS 2021
Year: 2021

Given a subset $A$ of the $n$-dimensional Boolean hypercube $\mathbb{F}_2^n$, the sumset $A+A$ is the set $\{a+a': a, a' \in A\}$ where addition is in $\mathbb{F}_2^n$. Sumsets play an important role in additive combinatorics, where they feature in many central results of the field. The main result of this paper is a sublinear-time algorithm for the problem of sumset size estimation. In more detail, our algorithm is given oracle access to (the indicator function of) an arbitrary $A \subseteq \mathbb{F}_2^n$ and an accuracy parameter $ε> 0$, and with high probability it outputs a value $0 \leq v \leq 1$ that is $\pm ε$-close to $\mathrm{Vol}(A' + A')$ for some perturbation $A' \subseteq A$ of $A$ satisfying $\mathrm{Vol}(A \setminus A') \leq ε.$ It is easy to see that without the relaxation of dealing with $A'$ rather than $A$, any algorithm for estimating $\mathrm{Vol}(A+A)$ to any nontrivial accuracy must make $2^{Ω(n)}$ queries. In contrast, we give an algorithm whose query complexity depends only on $ε$ and is completely independent of the ambient dimension $n$.
Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition
Venue: arXiv cs.LG 2021
Year: 2021

We offer a new formalism for global explanations of pairwise feature dependencies and interactions in supervised models. Building upon SHAP values and SHAP interaction values, our approach decomposes feature contributions into synergistic, redundant and independent components (S-R-I decomposition of SHAP vectors). We propose a geometric interpretation of the components and formally prove its basic properties. Finally, we demonstrate the utility of synergy, redundancy and independence by applying them to a constructed data set and model.
Generating Lode Runner Levels by Learning Player Paths with LSTMs
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning has been a popular tool in many different fields, including procedural content generation. However, procedural content generation via machine learning (PCGML) approaches can struggle with controllability and coherence. In this paper, we attempt to address these problems by learning to generate human-like paths, and then generating levels based on these paths. We extract player path data from gameplay video, train an LSTM to generate new paths based on this data, and then generate game levels based on this path data. We demonstrate that our approach leads to more coherent levels for the game Lode Runner in comparison to an existing PCGML approach.
Toward Co-creative Dungeon Generation via Transfer Learning
Venue: arXiv cs.LG 2021
Year: 2021

Co-creative Procedural Content Generation via Machine Learning (PCGML) refers to systems where a PCGML agent and a human work together to produce output content. One of the limitations of co-creative PCGML is that it requires co-creative training data for a PCGML agent to learn to interact with humans. However, acquiring this data is a difficult and time-consuming process. In this work, we propose approximating human-AI interaction data and employing transfer learning to adapt learned co-creative knowledge from one game to a different game. We explore this approach for co-creative Zelda dungeon room generation.
Thunder CTF: Learning Cloud Security on a Dime
Venue: arXiv cs.CR 2021
Year: 2021

Organizations have rapidly shifted infrastructure and applications over to public cloud computing services such as AWS (Amazon Web Services), Google Cloud Platform, and Azure. Unfortunately, such services have security models that are substantially different and more complex than traditional enterprise security models. As a result, misconfiguration errors in cloud deployments have led to dozens of well-publicized breaches. This paper describes Thunder CTF, a scaffolded, scenario-based CTF (Capture-the-Flag) for helping students learn about and practice cloud security skills. Thunder CTF is easily deployed at minimal cost and is highly extensible to allow for crowd-sourced development of new levels as security issues evolve in the cloud.
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a discriminatively pretrained encoder followed by a multi-scale generative decoders where the latter explicitly estimate likelihood of the encoded features. Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting. Our experiments on the MVTec dataset show that CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by 1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source our code with fully reproducible experiments.