OpenRaiser/Intern-Atlas huggingface dataset:
PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification
Venue: arXiv cs.HC 2021
Year: 2021

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral compounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.
Approximate Nearest-Neighbor Search for Line Segments
Venue: arXiv cs.CG 2021
Year: 2021

Approximate nearest-neighbor search is a fundamental algorithmic problem that continues to inspire study due its essential role in numerous contexts. In contrast to most prior work, which has focused on point sets, we consider nearest-neighbor queries against a set of line segments in $\mathbb{R}^d$, for constant dimension $d$. Given a set $S$ of $n$ disjoint line segments in $\mathbb{R}^d$ and an error parameter $\varepsilon > 0$, the objective is to build a data structure such that for any query point $q$, it is possible to return a line segment whose Euclidean distance from $q$ is at most $(1+\varepsilon)$ times the distance from $q$ to its nearest line segment. We present a data structure for this problem with storage $O((n^2/\varepsilon^{d}) \log (Δ/\varepsilon))$ and query time $O(\log (\max(n,Δ)/\varepsilon))$, where $Δ$ is the spread of the set of segments $S$. Our approach is based on a covering of space by anisotropic elements, which align themselves according to the orientations of nearby segments.
Reputation and Pricing Dynamics in Online Markets
Venue: arXiv cs.GT 2021
Year: 2021

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers' reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers' reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares; if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longer-term interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers' reputations and prices at the stable state.
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning
Venue: arXiv cs.CL 2021
Year: 2021

This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. Our system uses a large pre-trained language model as the encoder and an additional dual multi-head co-attention layer to strengthen the relationship between passages and question-answer pairs, following the current state-of-the-art model DUMA. The main difference is that we stack the passage-question and question-passage attention modules instead of calculating parallelly to simulate re-considering process. We also add a layer normalization module to improve the performance of our model. Furthermore, to incorporate our known knowledge about abstract concepts, we retrieve the definitions of candidate answers from WordNet and feed them to the model as extra inputs. Our system, called WordNet-enhanced DUal Multi-head Co-Attention (WN-DUMA), achieves 86.67% and 89.99% accuracy on the official blind test set of subtask 1 and subtask 2 respectively.
Expected-Cost Analysis for Probabilistic Programs and Semantics-Level Adaption of Optional Stopping Theorems
Venue: arXiv cs.PL 2021
Year: 2021

In this article, we present a semantics-level adaption of the Optional Stopping Theorem, sketch an expected-cost analysis as its application, and survey different variants of the Optional Stopping Theorem that have been used in static analysis of probabilistic programs.
Connectivity and Collision Constrained Opportunistic Routing for Emergency Communication using UAV
Venue: arXiv cs.NI 2021
Year: 2021

Emergency communication is extremely important to aid rescue and search operation in the aftermath of any disaster. In such scenario, Unmanned Aerial Vehicle (UAV) networks may be used to complement the damaged cellular networks over large areas. However, in such UAV networks, routing is a challenge, owing to high UAV mobility, intermittent link quality between UAVs, dynamic three dimensional (3D) UAV topology and resource constraints. Though several UAV routing approaches have been proposed, none of them so far have addressed inter UAV coverage, collision and routing in an integrated manner. In this paper, we consider a scenario where network of UAVs, operating at different heights from ground, with inter UAV coverage and collision constraints, are sent on a mission to collect disaster surveillance data and route it to Terrestrial Base Station via multi-hop UAV path. Analytical expressions for coverage probability (Pcov) and collision probability (Pcoll) are derived and minimum (Rmin) and maximum (Rmax) distance between UAVs are empirically calculated. We then propose a novel Multi-hop Opportunistic 3D Routing (MO3DR) algorithm with inter UAV coverage and collision constraints such that at every hop expected progress of data packet is maximized. The numerical results obtained from closed form mathematical modelling are validated through extensive simulation and their trade-off with variation in network parameters such as path loss component, trajectory divergence etc. are demonstrated. Finally, we obtain empirical optimality condition for inter UAV distance for the given application requirement.
Enabling Homomorphically Encrypted Inference for Large DNN Models
Venue: arXiv cs.CR 2021
Year: 2021

The proliferation of machine learning services in the last few years has raised data privacy concerns. Homomorphic encryption (HE) enables inference using encrypted data but it incurs 100x-10,000x memory and runtime overheads. Secure deep neural network (DNN) inference using HE is currently limited by computing and memory resources, with frameworks requiring hundreds of gigabytes of DRAM to evaluate small models. To overcome these limitations, in this paper we explore the feasibility of leveraging hybrid memory systems comprised of DRAM and persistent memory. In particular, we explore the recently-released Intel Optane PMem technology and the Intel HE-Transformer nGraph to run large neural networks such as MobileNetV2 (in its largest variant) and ResNet-50 for the first time in the literature. We present an in-depth analysis of the efficiency of the executions with different hardware and software configurations. Our results conclude that DNN inference using HE incurs on friendly access patterns for this memory configuration, yielding efficient executions.
Symbiotic Communications: Where Marconi Meets Darwin
Venue: arXiv cs.IT 2021
Year: 2021

With the proliferation of wireless applications, the electromagnetic (EM) space is becoming more and more crowded and complex. This makes it a challenging task to accommodate the growing number of radio systems with limited radio resources. In this paper, by considering the EM space as a radio ecosystem, and leveraging the analogy to the natural ecosystem in biology, a novel symbiotic communication (SC) paradigm is proposed through which the relevant radio systems, called symbiotic radios (SRs), in a radio ecosystem form a symbiotic relationship (e.g., mutualistic symbiosis) through intelligent resource/service exchange. Radio resources include, e.g., spectrum, energy, and infrastructure, while typical radio services are communicating, relaying, and computing. The symbiotic relationship can be realized via either symbiotic coevolution or symbiotic synthesis. In symbiotic coevolution, each SR is empowered with an evolutionary cycle alongside the multi-agent learning, while in symbiotic synthesis, the SRs ingeniously optimize their operating parameters and transmission protocols by solving a multi-objective optimization problem. Promisingly, the proposed SC paradigm breaks the boundary of radio systems, thus providing us a fresh perspective on radio resource management and new guidelines to design future wireless communication systems.
Contracting and Involutive Negations of Probability Distributions
Venue: arXiv cs.AI 2021
Year: 2021

A dozen papers have considered the concept of negation of probability distributions (pd) introduced by Yager. Usually, such negations are generated point-by-point by functions defined on a set of probability values and called here negators. Recently it was shown that Yager negator plays a crucial role in the definition of pd-independent linear negators: any linear negator is a function of Yager negator. Here, we prove that the sequence of multiple negations of pd generated by a linear negator converges to the uniform distribution with maximal entropy. We show that any pd-independent negator is non-involutive, and any non-trivial linear negator is strictly contracting. Finally, we introduce an involutive negator in the class of pd-dependent negators that generates an involutive negation of probability distributions.
AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation
Venue: arXiv cs.CL 2021
Year: 2021

This paper proposes a generative language model called AfriKI. Our approach is based on an LSTM architecture trained on a small corpus of contemporary fiction. With the aim of promoting human creativity, we use the model as an authoring tool to explore machine-in-the-loop Afrikaans poetry generation. To our knowledge, this is the first study to attempt creative text generation in Afrikaans.
Differentiable Drawing and Sketching
Venue: arXiv cs.CV 2021
Year: 2021

We present a bottom-up differentiable relaxation of the process of drawing points, lines and curves into a pixel raster. Our approach arises from the observation that rasterising a pixel in an image given parameters of a primitive can be reformulated in terms of the primitive's distance transform, and then relaxed to allow the primitive's parameters to be learned. This relaxation allows end-to-end differentiable programs and deep networks to be learned and optimised and provides several building blocks that allow control over how a compositional drawing process is modelled. We emphasise the bottom-up nature of our proposed approach, which allows for drawing operations to be composed in ways that can mimic the physical reality of drawing rather than being tied to, for example, approaches in modern computer graphics. With the proposed approach we demonstrate how sketches can be generated by directly optimising against photographs and how auto-encoders can be built to transform rasterised handwritten digits into vectors without supervision. Extensive experimental results highlight the power of this approach under different modelling assumptions for drawing tasks.
Adaptive surrogates of crashworthiness models for multi-purpose engineering analyses accounting for uncertainty
Venue: arXiv cs.CE 2021
Year: 2021

Uncertainty Quantification (UQ) is a booming discipline for complex computational models based on the analysis of robustness, reliability and credibility. UQ analysis for nonlinear crash models with high dimensional outputs presents important challenges. In crashworthiness, nonlinear structural behaviours with multiple hidden modes require expensive models (18 hours for a single run). Surrogate models (metamodels) allow substituting the full order model, introducing a response surface for a reduced training set of numerical experiments. Moreover, uncertain input and large number of degrees of freedom result in high dimensional problems, which derives to a bottle neck that blocks the computational efficiency of the metamodels. Kernel Principal Component Analysis (kPCA) is a multidimensionality reduction technique for non-linear problems, with the advantage of capturing the most relevant information from the response and improving the efficiency of the metamodel. Aiming to compute the minimum number of samples with the full order model. The proposed methodology is tested with a practical industrial problem that arises from the automotive industry.
Single Test Image-Based Automated Machine Learning System for Distinguishing between Trait and Diseased Blood Samples
Venue: arXiv cs.CV 2021
Year: 2021

We introduce a machine learning-based method for fully automated diagnosis of sickle cell disease of poor-quality unstained images of a mobile microscope. Our method is capable of distinguishing between diseased, trait (carrier), and normal samples unlike the previous methods that are limited to distinguishing the normal from the abnormal samples only. The novelty of this method comes from distinguishing the trait and the diseased samples from challenging images that have been captured directly in the field. The proposed approach contains two parts, the segmentation part followed by the classification part. We use a random forest algorithm to segment such challenging images acquitted through a mobile phone-based microscope. Then, we train two classifiers based on a random forest (RF) and a support vector machine (SVM) for classification. The results show superior performances of both of the classifiers not only for images which have been captured in the lab, but also for the ones which have been acquired in the field itself.
FastCTF: A Robust Solver for Conduction Transfer Function Coefficients and Thermal Response Factors
Venue: arXiv cs.CE 2021
Year: 2021

Conduction transfer functions (CTF) are commonly used in the building services to quickly estimate hourly conduction heat loads through multilayered walls without resorting to expensive, time-consuming solutions of the heat equation. It is essential for any software developed for this purpose to be able to simulate walls of varying weight with a high degree of accuracy. A robust algorithm for computing CTF coefficients and thermal response factors based on power series expansions of solutions of the governing equations in the complex s-domain is presented and validated. These series expansions are used to to construct Padé approximants of the system's transfer functions, which greatly simplifies the inversion of the solution from the complex domain to the time domain, and allows for an easy recovery of a time series representation via the Z-transform. The algorithm is also implemented in an open-source C++ code. Its performance is validated with respect to exact theoretical frequency characteristics and its results are compared with data generated by previously established methods for computing CTF coefficients / response factors.
Looney Tunes: Exposing the Lack of DRM Protection in Indian Music Streaming Services
Venue: arXiv cs.CR 2021
Year: 2021

Numerous studies have shown that streaming is now the most preferred way of consuming multimedia content and this is evidenced by the proliferation in the number of streaming service providers as well as the exponential growth in their subscriber base. Riding on the advancements in low cost electronics, high speed communication and extremely cheap data, Over-The-Top (OTT) music streaming is now the norm in the music industry and is worth millions of dollars. This is especially true in India where major players offer the so called freemium models which have active monthly user bases running in to the millions. These services namely, Gaana, Airtel Wynk and JioSaavn attract a significantly bigger audience than their 100% subscription based peers like Amazon Prime Music, Apple Music etc. Given their ubiquity and market dominance, it is pertinent to do a systematic analysis of these platforms so as to ascertain their potential as hotbeds of piracy. This work investigates the resilience of the content protection systems of the four biggest music streaming services (by subscriber base) from India, namely Airtel Wynk, Ganna, JioSaavn and Hungama. By considering the Digital Rights Management (DRM) system employed by Spotify as a benchmark, we analyse the security of these platforms by attempting to steal the streamed content efficiently. Finally, we present a holistic overview of the flaws in their security mechanisms and discuss possible mitigation strategies. To the best of our knowledge, this work constitutes the first attempt to analyze security of OTT music services from India. Our results further confirm the time tested belief that security through obscurity is not a long term solution and leaves such platforms open to piracy and a subsequent loss of revenue for all the stakeholders.
Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation
Venue: arXiv cs.RO 2021
Year: 2021

Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes traditional stability analysis difficult for DRL are the uninterpretable nature of the neural network policies and unknown system dynamics. In this work, stability is obtained by deriving an interpretable deep policy structure based on the $\textit{energy shaping}$ control of Lagrangian systems. Then, stability during physical interaction with an unknown environment is established based on $\textit{passivity}$. The result is a stability guaranteeing DRL in a model-free framework that is general enough for contact-rich manipulation tasks. With an experiment on a peg-in-hole task, we demonstrate, to the best of our knowledge, the first DRL with stability guarantee on a real robotic manipulator.
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Venue: arXiv cs.CV 2021
Year: 2021

Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. In medical image analysis, a well-designed augmentation policy usually requires much expert knowledge and is difficult to generalize to multiple tasks due to the vast discrepancies among pixel intensities, image appearances, and object shapes in different medical tasks. To automate medical data augmentation, we propose a regularized adversarial training framework via two min-max objectives and three differentiable augmentation models covering affine transformation, deformation, and appearance changes. Our method is more automatic and efficient than previous automatic augmentation methods, which still rely on pre-defined operations with human-specified ranges and costly bi-level optimization. Extensive experiments demonstrated that our approach, with less training overhead, achieves superior performance over state-of-the-art auto-augmentation methods on both tasks of 2D skin cancer classification and 3D organs-at-risk segmentation.
Data-Driven Vibrotactile Rendering of Digital Buttons on Touchscreens
Venue: arXiv cs.HC 2021
Year: 2021

Although mobile phones incorporate low-cost vibration motors to enhance touch-based interactions, it is not possible to generate complex tactile effects on their touchscreens. It is also difficult to relate the limited vibrotactile feedback generated by these motors to different types of physical buttons. In this study, we focus on creating vibrotactile feedback on a touchscreen that simulates the feeling of physical buttons using piezo actuators attached to it. We first recorded and analyzed the force, acceleration, and voltage data from twelve participants interacting with three different physical buttons: latch, toggle, and push buttons. Then, a button-specific vibrotactile stimulus was generated for each button based on the recorded data. Finally, we conducted a threealternative forced choice (3AFC) experiment with twenty participants to explore whether the resultant stimulus is distinct and realistic. In our experiment, participants were able to match the three digital buttons with their physical counterparts with a success rate of 83%. In addition, we harvested seven adjective pairs from the participants expressing their perceptual feeling of pressing the physical buttons. All twenty participants rated the degree of their subjective feelings associated with each adjective for all the physical and digital buttons investigated in this study. Our statistical analysis showed that there exist at least three adjective pairs for which participants have rated two out of three digital buttons similar to their physical counterparts.
Integração e Entrega Contínua para aplicações móveis desenvolvidas em React Native
Venue: arXiv cs.SE 2021
Year: 2021

Continuous integration and continuous delivery are not new for developers who create web applications, however in the development of mobile applications this practice is still not very common mainly because of the challenges during the process of distributing the application. In the face of the growing number of applications, a greater requirement for quality and ever-shorter delivery times, delivering a healthy code is often extremely important to keep up with the competition. The purpose of this work is to implement an integration and continuous delivery pipeline for mobile applications developed in React Native. It intends to automate the process of build and delivery of applications developed with this technology.