OpenRaiser/Intern-Atlas huggingface dataset:
PAWLS: PDF Annotation With Labels and Structure
Venue: arXiv cs.CL 2021
Year: 2021

Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to use the information contained within PDF documents for training models or data analysis, because annotating these documents is difficult. In this paper, we present PDF Annotation with Labels and Structure (PAWLS), a new annotation tool designed specifically for the PDF document format. PAWLS is particularly suited for mixed-mode annotation and scenarios in which annotators require extended context to annotate accurately. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models. A read-only PAWLS server is available at https://pawls.apps.allenai.org/ and the source code is available at https://github.com/allenai/pawls.
The Shifting Sands of Motivation: Revisiting What Drives Contributors in Open Source
Venue: arXiv cs.SE 2021
Year: 2021

Open Source Software (OSS) has changed drastically over the last decade, with OSS projects now producing a large ecosystem of popular products, involving industry participation, and providing professional career opportunities. But our field's understanding of what motivates people to contribute to OSS is still fundamentally grounded in studies from the early 2000s. With the changed landscape of OSS, it is very likely that motivations to join OSS have also evolved. Through a survey of 242 OSS contributors, we investigate shifts in motivation from three perspectives: (1) the impact of the new OSS landscape, (2) the impact of individuals' personal growth as they become part of OSS communities, and (3) the impact of differences in individuals' demographics. Our results show that some motivations related to social aspects and reputation increased in frequency and that some intrinsic and internalized motivations, such as learning and intellectual stimulation, are still highly relevant. We also found that contributing to OSS often transforms extrinsic motivations to intrinsic, and that while experienced contributors often shift toward altruism, novices often shift toward career, fun, kinship, and learning. OSS projects can leverage our results to revisit current strategies to attract and retain contributors, and researchers and tool builders can better support the design of new studies and tools to engage and support OSS development.
DAHash: Distribution Aware Tuning of Password Hashing Costs
Venue: arXiv cs.CR 2021
Year: 2021

An attacker who breaks into an authentication server and steals all of the cryptographic password hashes is able to mount an offline-brute force attack against each user's password. Offline brute-force attacks against passwords are increasingly commonplace and the danger is amplified by the well documented human tendency to select low-entropy password and/or reuse these passwords across multiple accounts. Moderately hard password hashing functions are often deployed to help protect passwords against offline attacks by increasing the attacker's guessing cost. However, there is a limit to how "hard" one can make the password hash function as authentication servers are resource constrained and must avoid introducing substantial authentication delay. Observing that there is a wide gap in the strength of passwords selected by different users we introduce DAHash (Distribution Aware Password Hashing) a novel mechanism which reduces the number of passwords that an attacker will crack. Our key insight ishat a resource-constrained authentication server can dynamically tune the hardness parameters of a password hash function based on the (estimated) strength of the user's password. We introduce a Stackelberg game to model the interaction between a defender (authentication server) and an offline attacker. Our model allows the defender to optimize the parameters of DAHash e.g., specify how much effort is spent to hash weak/moderate/high strength passwords. We use several large scale password frequency datasets to empirically evaluate the effectiveness of our differentiated cost password hashing mechanism. We find that the defender who uses our mechanism can reduce the fraction of passwords that would be cracked by a rational offline attacker by around 15%.
Spectrum Attention Mechanism for Time Series Classification
Venue: arXiv cs.LG 2021
Year: 2021

Time series classification(TSC) has always been an important and challenging research task. With the wide application of deep learning, more and more researchers use deep learning models to solve TSC problems. Since time series always contains a lot of noise, which has a negative impact on network training, people usually filter the original data before training the network. The existing schemes are to treat the filtering and training as two stages, and the design of the filter requires expert experience, which increases the design difficulty of the algorithm and is not universal. We note that the essence of filtering is to filter out the insignificant frequency components and highlight the important ones, which is similar to the attention mechanism. In this paper, we propose an attention mechanism that acts on spectrum (SAM). The network can assign appropriate weights to each frequency component to achieve adaptive filtering. We use L1 regularization to further enhance the frequency screening capability of SAM. We also propose a segmented-SAM (SSAM) to avoid the loss of time domain information caused by using the spectrum of the whole sequence. In which, a tumbling window is introduced to segment the original data. Then SAM is applied to each segment to generate new features. We propose a heuristic strategy to search for the appropriate number of segments. Experimental results show that SSAM can produce better feature representations, make the network converge faster, and improve the robustness and classification accuracy.
Persistent Covering of a Graph under Latency and Energy Constraints
Venue: arXiv cs.RO 2021
Year: 2021

Most consumer-level low-cost unmanned aerial vehicles (UAVs) have limited battery power and long charging time. Due to these energy constraints, they cannot accomplish many practical tasks, such as monitoring a sport or political event for hours. The problem of providing the service to cover an area for an extended time is known as persistent covering in the literature. In the past, researchers have proposed various hardware platforms, such as battery-swapping mechanisms, to provide persistent covering. However, algorithmic approaches are limited mostly due to the computational complexity and intractability of the problem. Approximation algorithms have been considered to segment a large area into smaller cells that require periodic visits under the latency constraints. However, these methods assume unlimited energy. In this paper, we explore geometric and topological properties that allow us to significantly reduce the size of the optimization problem. Consequently, the proposed method can efficiently determine the minimum number of UAVs needed and schedule their routes to cover an area persistently. We demonstrated experimentally that the proposed algorithm has better performance than the baseline methods.
Environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework
Venue: arXiv cs.NI 2021
Year: 2021

The huge research interest in cellular vehicle-to-everything (C-V2X) communications in recent days is attributed to their ability to schedule multiple access more efficiently as compared to its predecessor technology, i.e., dedicated short-range communications (DSRC). However, one of the foremost issues still remaining is the need for the V2X to operate stably in a highly dynamic environment. This paper proposes a way to exploit the dynamicity. That is, we propose a resource allocation mechanism adaptive to the environment, which can be an efficient solution for air interface congestion that a V2X network often suffers from. Specifically, the proposed mechanism aims at granting a higher chance of transmission to a vehicle with a higher crash risk. As such, the channel access is prioritized to those with urgent needs. The proposed framework is established based on reinforcement learning (RL), which is modeled as a contextual multi-armed bandit (MAB). Importantly, the framework is designed to operate at a vehicle autonomously without any assistance from a central entity, which, henceforth, is expected to make a particular fit to distributed V2X network such as C-V2X mode 4.
Frame based equipment channel access enhancements in NR unlicensed spectrum for the URLLC transmissions
Venue: arXiv cs.NI 2021
Year: 2021

Ultra-reliable low-latency communication (URLLC) in 5G New Radio has been originally defined only for licensed spectrum. However, due to new use cases in the Industry 4.0 scenarios, URLLC operation is currently being extended to unlicensed spectrum in the ongoing Release 17 of the 3rd Generation Partnership Project. Although in such controlled environments we can guarantee the absence of any other technology sharing the channel on a long-term basis, the uncertainty of obtaining channel access through load based equipment (LBE) or frame based equipment (FBE) can impede with the latency requirements of URLLC. In FBE, the transmitters can be prioritized to support data with different requirements and have lower energy consumption and latency compared to LBE with a big contention window size. In this paper we analyze the performance of FBE in an unlicensed controlled environment through a Markov chain. Based on this analysis, we propose two schemes to improve the URLLC performance in FBE: The first scheme allows the transmitters to use multiple fixed frame period (FFP) configurations while the second scheme configures the FFP's starting point of each transmitter based on its priority. The simulations show the benefits of these schemes compared to the URLLC transmission of existing schemes.
Appliance Operation Modes Identification Using Cycles Clustering
Venue: arXiv cs.LG 2021
Year: 2021

The increasing cost, energy demand, and environmental issues has led many researchers to find approaches for energy monitoring, and hence energy conservation. The emerging technologies of Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to efficiently conserve energy and improve the utilization of energy consumption. Smart Home Energy Management Systems (SHEMSs) have the potential to contribute in energy conservation through the application of Demand Response (DR) in the residential sector. In this paper, we propose appliances Operation Modes Identification using Cycles Clustering (OMICC) which is SHEMS fundamental approach that utilizes the sensed residential disaggregated power consumption in supporting DR by providing consumers the opportunity to select lighter appliance operation modes. The cycles of the Single Usage Profile (SUP) of an appliance are extracted and reformed into features in terms of clusters of cycles. These features are then used to identify the operation mode used in every occurrence using K-Nearest Neighbors (KNN). Operation modes identification is considered a basis for many potential smart DR applications within SHEMS towards the consumers or the suppliers
A Compositional Sheaf-Theoretic Framework for Event-Based Systems
Venue: arXiv cs.RO 2021
Year: 2021

A compositional sheaf-theoretic framework for the modeling of complex event-based systems is presented. We show that event-based systems are machines, with inputs and outputs, and that they can be composed with machines of different types, all within a unified, sheaf-theoretic formalism. We take robotic systems as an exemplar of complex systems and rigorously describe actuators, sensors, and algorithms using this framework.
Proof Theory of Partially Normal Skew Monoidal Categories
Venue: arXiv cs.LO 2021
Year: 2021

The skew monoidal categories of Szlachányi are a weakening of monoidal categories where the three structural laws of left and right unitality and associativity are not required to be isomorphisms but merely transformations in a particular direction. In previous work, we showed that the free skew monoidal category on a set of generating objects can be concretely presented as a sequent calculus. This calculus enjoys cut elimination and admits focusing, i.e. a subsystem of canonical derivations, which solves the coherence problem for skew monoidal categories. In this paper, we develop sequent calculi for partially normal skew monoidal categories, which are skew monoidal categories with one or more structural laws invertible. Each normality condition leads to additional inference rules and equations on them. We prove cut elimination and we show that the calculi admit focusing. The result is a family of sequent calculi between those of skew monoidal categories and (fully normal) monoidal categories. On the level of derivability, these define 8 weakenings of the (unit,tensor) fragment of intuitionistic non-commutative linear logic.
A Digital Corpus of St. Lawrence Island Yupik
Venue: arXiv cs.CL 2021
Year: 2021

St. Lawrence Island Yupik (ISO 639-3: ess) is an endangered polysynthetic language in the Inuit-Yupik language family indigenous to Alaska and Chukotka. This work presents a step-by-step pipeline for the digitization of written texts, and the first publicly available digital corpus for St. Lawrence Island Yupik, created using that pipeline. This corpus has great potential for future linguistic inquiry and research in NLP. It was also developed for use in Yupik language education and revitalization, with a primary goal of enabling easy access to Yupik texts by educators and by members of the Yupik community. A secondary goal is to support development of language technology such as spell-checkers, text-completion systems, interactive e-books, and language learning apps for use by the Yupik community.
ADMM-based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks
Venue: arXiv cs.RO 2021
Year: 2021

This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positions, which enables the sampling optimization problem to be formulated by the use of the log determinant of a predicted covariance matrix at next sampling locations. The control, movement and nonholonomic dynamics constraints of the mobile sensors are also considered in the adaptive sampling optimization problem. In order to tackle the nonlinearity and nonconvexity of the objective function in the optimization problem we first exploit the linearized alternating direction method of multipliers (L-ADMM) method that can effectively simplify the objective function, though it is computationally expensive since a nonconvex problem needs to be solved exactly in each iteration. We then propose a novel approach called the successive convexified ADMM (SC-ADMM) that sequentially convexify the nonlinear dynamic constraints so that the original optimization problem can be split into convex subproblems. It is noted that both the L-ADMM algorithm and our SC-ADMM approach can solve the sampling optimization problem in either a centralized or a distributed manner. We validated the proposed approaches in 1000 experiments in a synthetic environment with a real-world dataset, where the obtained results suggest that both the L-ADMM and SC- ADMM techniques can provide good accuracy for the monitoring purpose. However, our proposed SC-ADMM approach computationally outperforms the L-ADMM counterpart, demonstrating its better practicality.
PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection
Venue: arXiv cs.CR 2021
Year: 2021

Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT platforms. Most of the data flowing out to a platform actually do not trigger automation actions, while homeowners currently have no control once devices are bound to the platform. We present PFirewall, a customizable data-flow control system to enhance the privacy of IoT platform users. PFirewall automatically generates data-minimization policies, which only disclose minimum amount of data to fulfill automation. In addition, PFirewall provides interfaces for homeowners to customize individual privacy preferences by defining user-specified policies. To enforce these policies, PFirewall transparently intervenes and mediates the communication between IoT devices and the platform, without modifying the platform, IoT devices, or hub. Evaluation results on four real-world testbeds show that PFirewall reduces IoT data sent to the platform by 97% without impairing home automation, and effectively mitigates user-activity inference/tracking attacks and other privacy risks.
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
Venue: arXiv cs.CV 2021
Year: 2021

Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Five benchmark Hyperspectral datasets (i.e., SalinasA, Salinas, Indian Pines, Pavia University, Pavia Center, and Botswana) are used for experimental evaluation. The experimental results show that the proposed pipeline outperformed in terms of generalization performance, statistical significance, and computational complexity, as compared to the state-of-the-art 2D/3D CNN models except commonly used computationally expensive design choices.
Application of Lexical Features Towards Improvement of Filipino Readability Identification of Children's Literature
Venue: arXiv cs.CL 2021
Year: 2021

Proper identification of grade levels of children's reading materials is an important step towards effective learning. Recent studies in readability assessment for the English domain applied modern approaches in natural language processing (NLP) such as machine learning (ML) techniques to automate the process. There is also a need to extract the correct linguistic features when modeling readability formulas. In the context of the Filipino language, limited work has been done [1, 2], especially in considering the language's lexical complexity as main features. In this paper, we explore the use of lexical features towards improving the development of readability identification of children's books written in Filipino. Results show that combining lexical features (LEX) consisting of type-token ratio, lexical density, lexical variation, foreign word count with traditional features (TRAD) used by previous works such as sentence length, average syllable length, polysyllabic words, word, sentence, and phrase counts increased the performance of readability models by almost a 5% margin (from 42% to 47.2%). Further analysis and ranking of the most important features were shown to identify which features contribute the most in terms of reading complexity.
On $-1$-differential uniformity of ternary APN power functions
Venue: arXiv cs.IT 2021
Year: 2021

Very recently, a new concept called multiplicative differential and the corresponding $c$-differential uniformity were introduced by Ellingsen et al. A function $F(x)$ over finite field $\mathrm{GF}(p^n)$ to itself is called $c$-differential uniformity $δ$, or equivalent, $F(x)$ is differentially $(c,δ)$ uniform, when the maximum number of solutions $x\in\mathrm{GF}(p^n)$ of $F(x+a)-F(cx)=b$, $a,b,c\in\mathrm{GF}(p^n)$, $c\neq1$ if $a=0$, is equal to $δ$. The objective of this paper is to study the $-1$-differential uniformity of ternary APN power functions $F(x)=x^d$ over $\mathrm{GF}(3^n)$. We obtain ternary power functions with low $-1$-differential uniformity, and some of them are almost perfect $-1$-nonlinear.
$α$-Information-theoretic Privacy Watchdog and Optimal Privatization Scheme
Venue: arXiv cs.IT 2021
Year: 2021

This paper proposes an $α$-lift measure for data privacy and determines the optimal privatization scheme that minimizes the $α$-lift in the watchdog method. To release data $X$ that is correlated with sensitive information $S$, the ratio $l(s,x) = \frac{p(s|x)}{p(s)} $ denotes the `lift' of the posterior belief on $S$ and quantifies data privacy. The $α$-lift is proposed as the $L_α$-norm of the lift: $\ell_α(x) = \| (\cdot,x) \|_α = (E[l(S,x)^α])^{1/α}$. This is a tunable measure: When $α< \infty$, each lift is weighted by its likelihood of appearing in the dataset (w.r.t. the marginal probability $p(s)$); For $α= \infty$, $α$-lift reduces to the existing maximum lift. To generate the sanitized data $Y$, we adopt the privacy watchdog method using $α$-lift: Obtain $\mathcal{X}_ε$ containing all $x$'s such that $\ell_α(x) > e^ε$; Apply the randomization $r(y|x)$ to all $x \in \mathcal{X}_ε$, while all other $x \in \mathcal{X} \setminus \mathcal{X}_ε$ are published directly. For the resulting $α$-lift $\ell_α(y)$, it is shown that the Sibson mutual information $I_α^{S}(S;Y)$ is proportional to $E[ \ell_α(y)]$. We further define a stronger measure $\bar{I}_α^{S}(S;Y)$ using the worst-case $α$-lift: $\max_{y} \ell_α(y)$. We prove that the optimal randomization $r^*(y|x)$ that minimizes both $I_α^{S}(S;Y)$ and $\bar{I}_α^{S}(S;Y)$ is $X$-invariant, i.e., $r^*(y|x) = R(y), \forall x\in \mathcal{X}_ε$ for any probability distribution $R$ over $y \in \mathcal{X}_ε$. Numerical experiments show that $α$-lift can provide flexibility in the privacy-utility tradeoff.
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain
Venue: arXiv cs.LG 2021
Year: 2021

Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice. In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution. Our framework protects all parties' privacy in the model building phase and transparently evaluates contributions based on the model updates. The experiment with the handwritten digits dataset demonstrates that the proposed method can effectively evaluate the contributions.