OpenRaiser/Intern-Atlas huggingface dataset:
Generating Interactive WebSocket Applications in TypeScript
Venue: arXiv cs.PL 2020
Year: 2020

Advancements in mobile device computing power have made interactive web applications possible, allowing the web browser to render contents dynamically and support low-latency communication with the server. This comes at a cost to the developer, who now needs to reason more about correctness of communication patterns in their application as web applications support more complex communication patterns. Multiparty session types (MPST) provide a framework for verifying conformance of implementations to their prescribed communication protocol. Existing proposals for applying the MPST framework in application developments either neglect the event-driven nature of web applications, or lack compatibility with industry tools and practices, which discourages mainstream adoption by web developers. In this paper, we present an implementation of the MPST framework for developing interactive web applications using familiar industry tools using TypeScript and the React.js framework. The developer can use the Scribble protocol language to specify the protocol and use the Scribble toolchain to validate and obtain the local protocol for each role. The local protocol describes the interactions of the global communication protocol observed by the role. We encode the local protocol into TypeScript types, catering for server-side and client-side targets separately. We show that our encoding guarantees that only implementations which conform to the protocol can type-check. We demonstrate the effectiveness of our approach through a web-based implementation of the classic Noughts and Crosses game from an MPST formalism of the game logic.
Unpack Local Model Interpretation for GBDT
Venue: arXiv cs.LG 2020
Year: 2020

A gradient boosting decision tree (GBDT), which aggregates a collection of single weak learners (i.e. decision trees), is widely used for data mining tasks. Because GBDT inherits the good performance from its ensemble essence, much attention has been drawn to the optimization of this model. With its popularization, an increasing need for model interpretation arises. Besides the commonly used feature importance as a global interpretation, feature contribution is a local measure that reveals the relationship between a specific instance and the related output. This work focuses on the local interpretation and proposes an unified computation mechanism to get the instance-level feature contributions for GBDT in any version. Practicality of this mechanism is validated by the listed experiments as well as applications in real industry scenarios.
Binary Golay Spreading Sequences and Reed-Muller Codes for Uplink Grant-Free NOMA
Venue: arXiv cs.IT 2020
Year: 2020

Non-orthogonal multiple access (NOMA) is an emerging technology for massive connectivity in machine-type communications (MTC). In code-domain NOMA, non-orthogonal spreading sequences are uniquely assigned to all devices, where active ones attempt a grant-free access to a system. In this paper, we study a set of user-specific, non-orthogonal, binary spreading sequences for uplink grant-free NOMA. Based on Golay complementary sequences, each spreading sequence provides the peak-to-average power ratio (PAPR) of at most 3 dB for multicarrier transmission. Exploiting the theoretical connection to Reed-Muller codes, we conduct a probabilistic analysis to search for a permutation set for Golay sequences, which presents theoretically bounded low coherence for the spreading matrix. Simulation results confirm that the PAPR of transmitted multicarrier signals via the spreading sequences is significantly lower than those for random bipolar, Gaussian, and Zadoff-Chu (ZC) sequences. Also, thanks to the low coherence, the performance of compressed sensing (CS) based joint channel estimation (CE) and multiuser detection (MUD) using the spreading sequences turns out to be superior or comparable to those for the other ones. Unlike ZC sequences, the binary Golay spreading sequences have only two phases regardless of the sequence length, which can be suitable for low cost MTC devices.
Epitomic Variational Graph Autoencoder
Venue: arXiv cs.LG 2020
Year: 2020

Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of latent variables fail to capture any information about the input data and the corresponding hidden units become inactive. This adversely affects learning diverse and interpretable latent representations. As variational graph autoencoder (VGAE) extends VAE for graph-structured data, it inherits the over-pruning problem. In this paper, we adopt a model based approach and propose epitomic VGAE (EVGAE),a generative variational framework for graph datasets which successfully mitigates the over-pruning problem and also boosts the generative ability of VGAE. We consider EVGAE to consist of multiple sparse VGAE models, called epitomes, that are groups of latent variables sharing the latent space. This approach aids in increasing active units as epitomes compete to learn better representation of the graph data. We verify our claims via experiments on three benchmark datasets. Our experiments show that EVGAE has a better generative ability than VGAE. Moreover, EVGAE outperforms VGAE on link prediction task in citation networks.
Sparse Concept Coded Tetrolet Transform for Unconstrained Odia Character Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Feature representation in the form of spatio-spectral decomposition is one of the robust techniques adopted in automatic handwritten character recognition systems. In this regard, we propose a new image representation approach for unconstrained handwritten alphanumeric characters using sparse concept coded Tetrolets. Tetrolets, which does not use fixed dyadic square blocks for spectral decomposition like conventional wavelets, preserve the localized variations in handwritings by adopting tetrominoes those capture the shape geometry. The sparse concept coding of low entropy Tetrolet representation is found to extract the important hidden information (concept) for superior pattern discrimination. Large scale experimentation using ten databases in six different scripts (Bangla, Devanagari, Odia, English, Arabic and Telugu) has been performed. The proposed feature representation along with standard classifiers such as random forest, support vector machine (SVM), nearest neighbor and modified quadratic discriminant function (MQDF) is found to achieve state-of-the-art recognition performance in all the databases, viz. 99.40% (MNIST); 98.72% and 93.24% (IITBBS); 99.38% and 99.22% (ISI Kolkata). The proposed OCR system is shown to perform better than other sparse based techniques such as PCA, SparsePCA and SparseLDA, as well as better than existing transforms (Wavelet, Slantlet and Stockwell).
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
Venue: arXiv cs.CV 2020
Year: 2020

This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives. Initially, we analyze the limitations of the traditional CBIR systems that rely on the hand-crafted RS image descriptors. Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront. In particular, we present the theoretical properties of the most recent DL based CBIR systems for the characterization of the complex semantic content of RS images. After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. Finally, the most promising research directions in RS CBIR are discussed.
Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler
Venue: arXiv cs.SE 2020
Year: 2020

In this work, we apply anomaly detection to source code and bytecode to facilitate the development of a programming language and its compiler. We define anomaly as a code fragment that is different from typical code written in a particular programming language. Identifying such code fragments is beneficial to both language developers and end users, since anomalies may indicate potential issues with the compiler or with runtime performance. Moreover, anomalies could correspond to problems in language design. For this study, we choose Kotlin as the target programming language. We outline and discuss approaches to obtaining vector representations of source code and bytecode and to the detection of anomalies across vectorized code snippets. The paper presents a method that aims to detect two types of anomalies: syntax tree anomalies and so-called compiler-induced anomalies that arise only in the compiled bytecode. We describe several experiments that employ different combinations of vectorization and anomaly detection techniques and discuss types of detected anomalies and their usefulness for language developers. We demonstrate that the extracted anomalies and the underlying extraction technique provide additional value for language development.
High Bandwidth Memory on FPGAs: A Data Analytics Perspective
Venue: arXiv cs.DC 2020
Year: 2020

FPGA-based data processing in datacenters is increasing in popularity due to the demands of modern workloads and the ensuing necessity for specialization in hardware. Driven by this trend, vendors are rapidly adapting reconfigurable devices to suit data and compute intensive workloads. Inclusion of High Bandwidth Memory (HBM) in FPGA devices is a recent example. HBM promises overcoming the bandwidth bottleneck, faced often by FPGA-based accelerators due to their throughput oriented design. In this paper, we study the usage and benefits of HBM on FPGAs from a data analytics perspective. We consider three workloads that are often performed in analytics oriented databases and implement them on FPGA showing in which cases they benefit from HBM: range selection, hash join, and stochastic gradient descent for linear model training. We integrate our designs into a columnar database (MonetDB) and show the trade-offs arising from the integration related to data movement and partitioning. In certain cases, FPGA+HBM based solutions are able to surpass the highest performance provided by either a 2-socket POWER9 system or a 14-core XeonE5 by up to 1.8x (selection), 12.9x (join), and 3.2x (SGD).
Analyzing autoencoder-based acoustic word embeddings
Venue: arXiv cs.CL 2020
Year: 2020

Recent studies have introduced methods for learning acoustic word embeddings (AWEs)---fixed-size vector representations of words which encode their acoustic features. Despite the widespread use of AWEs in speech processing research, they have only been evaluated quantitatively in their ability to discriminate between whole word tokens. To better understand the applications of AWEs in various downstream tasks and in cognitive modeling, we need to analyze the representation spaces of AWEs. Here we analyze basic properties of AWE spaces learned by a sequence-to-sequence encoder-decoder model in six typologically diverse languages. We first show that these AWEs preserve some information about words' absolute duration and speaker. At the same time, the representation space of these AWEs is organized such that the distance between words' embeddings increases with those words' phonetic dissimilarity. Finally, the AWEs exhibit a word onset bias, similar to patterns reported in various studies on human speech processing and lexical access. We argue this is a promising result and encourage further evaluation of AWEs as a potentially useful tool in cognitive science, which could provide a link between speech processing and lexical memory.
Visualization of Methods Changeability Based on VCS Data
Venue: arXiv cs.SE 2020
Year: 2020

Software engineers have a wide variety of tools and techniques that can help them improve the quality of their code, but still, a lot of bugs remain undetected. In this paper we build on the idea that if a particular fragment of code is changed too often, it could be caused by some technical or architectural issues, therefore, this fragment requires additional attention from developers. Most teams nowadays use version control systems to track changes in their code and organize cooperation between developers. We propose to use data from version control systems to track the number of changes for each method in a project for a selected time period and display this information within the IDE's code editor. The paper describes such a tool called Topias built as a plugin for IntelliJ IDEA. Its source code is available at https://github.com/JetBrains-Research/topias. A demonstration video can be found at https://www.youtube.com/watch?v=xsqc4gCTxfA.
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Venue: arXiv cs.LG 2020
Year: 2020

Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data. The learning objective of GANs usually minimizes some measure discrepancy, \textit{e.g.}, $f$-divergence~($f$-GANs) or Integral Probability Metric~(Wasserstein GANs). With $f$-divergence as the objective function, the discriminator essentially estimates the density ratio, and the estimated ratio proves useful in further improving the sample quality of the generator. However, how to leverage the information contained in the discriminator of Wasserstein GANs (WGAN) is less explored. In this paper, we introduce the Discriminator Contrastive Divergence, which is well motivated by the property of WGAN's discriminator and the relationship between WGAN and energy-based model. Compared to standard GANs, where the generator is directly utilized to obtain new samples, our method proposes a semi-amortized generation procedure where the samples are produced with the generator's output as an initial state. Then several steps of Langevin dynamics are conducted using the gradient of the discriminator. We demonstrate the benefits of significant improved generation on both synthetic data and several real-world image generation benchmarks.
Open access institutional and news media tweet dataset for COVID-19 social science research
Venue: arXiv cs.SI 2020
Year: 2020

As COVID-19 quickly became one of the most concerned global crisis, the demand for data in academic research is also increasing. Currently, there are several open access Twitter datasets, but none of them is dedicated to the institutional and news media Twitter data collection, to fill this blank, we retrieved data from 69 institutional/news media Twitter accounts, 17 of them were related to government and international organizations, 52 of them were news media across North America, Europe and Asia. We believe our open access data can provide researchers more availability to conduct social science research.
TRRespass: Exploiting the Many Sides of Target Row Refresh
Venue: arXiv cs.CR 2020
Year: 2020

After a plethora of high-profile RowHammer attacks, CPU and DRAM vendors scrambled to deliver what was meant to be the definitive hardware solution against the RowHammer problem: Target Row Refresh (TRR). A common belief among practitioners is that, for the latest generation of DDR4 systems that are protected by TRR, RowHammer is no longer an issue in practice. However, in reality, very little is known about TRR. In this paper, we demystify the inner workings of TRR and debunk its security guarantees. We show that what is advertised as a single mitigation mechanism is actually a series of different solutions coalesced under the umbrella term TRR. We inspect and disclose, via a deep analysis, different existing TRR solutions and demonstrate that modern implementations operate entirely inside DRAM chips. Despite the difficulties of analyzing in-DRAM mitigations, we describe novel techniques for gaining insights into the operation of these mitigation mechanisms. These insights allow us to build TRRespass, a scalable black-box RowHammer fuzzer. TRRespass shows that even the latest generation DDR4 chips with in-DRAM TRR, immune to all known RowHammer attacks, are often still vulnerable to new TRR-aware variants of RowHammer that we develop. In particular, TRRespass finds that, on modern DDR4 modules, RowHammer is still possible when many aggressor rows are used (as many as 19 in some cases), with a method we generally refer to as Many-sided RowHammer. Overall, our analysis shows that 13 out of the 42 modules from all three major DRAM vendors are vulnerable to our TRR-aware RowHammer access patterns, and thus one can still mount existing state-of-the-art RowHammer attacks. In addition to DDR4, we also experiment with LPDDR4 chips and show that they are susceptible to RowHammer bit flips too. Our results provide concrete evidence that the pursuit of better RowHammer mitigations must continue.
TimeGate: Conditional Gating of Segments in Long-range Activities
Venue: arXiv cs.CV 2020
Year: 2020

When recognizing a long-range activity, exploring the entire video is exhaustive and computationally expensive, as it can span up to a few minutes. Thus, it is of great importance to sample only the salient parts of the video. We propose TimeGate, along with a novel conditional gating module, for sampling the most representative segments from the long-range activity. TimeGate has two novelties that address the shortcomings of previous sampling methods, as SCSampler. First, it enables a differentiable sampling of segments. Thus, TimeGate can be fitted with modern CNNs and trained end-to-end as a single and unified model.Second, the sampling is conditioned on both the segments and their context. Consequently, TimeGate is better suited for long-range activities, where the importance of a segment heavily depends on the video context.TimeGate reduces the computation of existing CNNs on three benchmarks for long-range activities: Charades, Breakfast and MultiThumos. In particular, TimeGate reduces the computation of I3D by 50% while maintaining the classification accuracy.
Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification
Venue: arXiv cs.SI 2020
Year: 2020

Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. We model this behavior using social network and language-based features, which improve classifier performance. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.
Analysis of Diffusion Based Molecular Communication with Multiple Transmitters having Individual Random Information Bits
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we present an analytical framework to derive the performance of a molecular communication system where a transmitter bio-nano-machine (TBN) is communicating with a fully-absorbing spherical receiver bio-nano-machine (RBN) in a diffusive propagation medium in the presence of other TBNs. We assume that transmit bits at each TBN is random and different than transmit bits at other TBNs. We model the TBNs using a marked Poisson point process (PPP) with their locations as points of PPP and transmit symbols as marks. We consider both inter-symbol interference (ISI) and co-channel interference (CCI). ISI is caused by molecules transmitted in the previous slots while CCI is due to the molecules emitted from other TBNs. We derive the bit error probability of this system by averaging over the distribution of the transmit bits as opposed to the past approaches consisting of conditioning on previous transmit bits and/or assuming the transmit bits of every TBN are the same. Using numerical results, we validate our analysis and provide various design insights about the system, for example, the impact of detection threshold on the system performance. We also show the importance of accurately incorporating the randomness of transmit bits in the analysis.
BlackBox Toolkit: Intelligent Assistance to UI Design
Venue: arXiv cs.HC 2020
Year: 2020

User Interface (UI) design is an creative process that involves considerable reiteration and rework. Designers go through multiple iterations of different prototyping fidelities to create a UI design. In this research, we propose to modify the UI design process by assisting it with artificial intelligence (AI). We propose to enable AI to perform repetitive tasks for the designer while allowing the designer to take command of the creative process. This approach makes the machine act as a black box that intelligently assists the designers in creating UI design. We believe this approach would greatly benefit designers in co-creating design solutions with AI.
Energy-aware Allocation of Graph Jobs in Vehicular Cloud Computing-enabled Software-defined IoV
Venue: arXiv cs.DC 2020
Year: 2020

Software-defined internet of vehicles (SDIoV) has emerged as a promising paradigm to realize flexible and comprehensive resource management, for next generation automobile transportation systems. In this paper, a vehicular cloud computing-based SDIoV framework is studied wherein the joint allocation of transmission power and graph job is formulated as a nonlinear integer programming problem. To effectively address the problem, a structure-preservation-based two-stage allocation scheme is proposed that decouples template searching from power allocation. Specifically, a hierarchical tree-based random subgraph isomorphism mechanism is applied in the first stage by identifying potential mappings (templates) between the components of graph jobs and service providers. A structure-preserving simulated annealing-based power allocation algorithm is adopted in the second stage to achieve the trade-off between the job completion time and energy consumption. Extensive simulations are conducted to verify the performance of the proposed algorithms.
Software Radios for Unmanned Aerial Systems
Venue: arXiv cs.NI 2020
Year: 2020

As new use cases are emerging for unmanned aerial systems (UAS), advanced wireless communications technologies and systems need to be implemented and widely tested. This requires a flexible platform for development, deployment, testing and demonstration of wireless systems with ground and aerial nodes, enabling effective 3D mobile communications and networking. In this paper, we provide a comparative overview of software-defined radios (SDRs), with a specific focus on SDR hardware and software that can be used for aerial wireless experimentation and research. We discuss SDR hardware requirements, features of available SDR hardware that can be suitable for small UAS, and power measurements carried out with a subset of these SDR hardware. We also present SDR software requirements, available open-source SDR software, and calibration/benchmarking of SDR software. As a case study, we present AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless, and discuss various different experiments that can be supported in that platform using SDRs, for verification/testing of future wireless innovations, protocols, and technologies.