OpenRaiser/Intern-Atlas huggingface dataset:
Relay-aided Slotted Aloha for Optical Wireless Communications
Venue: arXiv cs.NI 2022
Year: 2022

We consider a relay-aided Slotted ALOHA solution for uplink random access for an Optical Wireless Communications (OWC)-based Internet of Things (IoT). The first phase of uplink, the one between IoT devices and the relays, is realized using indoor OWC, while the second phase, between the relays and a base station, represents the long-range RF transmission based on low-power wide area network such as LoRaWAN and occurs outdoors. The throughput performance dependence on the OWC and RF channel conditions is observed. The behavior of the performance gain due to adding relays is highlighted and investigated under different channel and traffic conditions.
Scope of Pre-trained Language Models for Detecting Conflicting Health Information
Venue: arXiv cs.CL 2022
Year: 2022

An increasing number of people now rely on online platforms to meet their health information needs. Thus identifying inconsistent or conflicting textual health information has become a safety-critical task. Health advice data poses a unique challenge where information that is accurate in the context of one diagnosis can be conflicting in the context of another. For example, people suffering from diabetes and hypertension often receive conflicting health advice on diet. This motivates the need for technologies which can provide contextualized, user-specific health advice. A crucial step towards contextualized advice is the ability to compare health advice statements and detect if and how they are conflicting. This is the task of health conflict detection (HCD). Given two pieces of health advice, the goal of HCD is to detect and categorize the type of conflict. It is a challenging task, as (i) automatically identifying and categorizing conflicts requires a deeper understanding of the semantics of the text, and (ii) the amount of available data is quite limited. In this study, we are the first to explore HCD in the context of pre-trained language models. We find that DeBERTa-v3 performs best with a mean F1 score of 0.68 across all experiments. We additionally investigate the challenges posed by different conflict types and how synthetic data improves a model's understanding of conflict-specific semantics. Finally, we highlight the difficulty in collecting real health conflicts and propose a human-in-the-loop synthetic data augmentation approach to expand existing HCD datasets. Our HCD training dataset is over 2x bigger than the existing HCD dataset and is made publicly available on Github.
Linking Contexts from Distinct Data Sources in Zero Trust Federation
Venue: arXiv cs.CR 2022
Year: 2022

An access control model called Zero Trust Architecture (ZTA) has attracted attention. ZTA uses information of users and devices, called context, for authentication and authorization. Zero Trust Federation (ZTF) has been proposed as a framework for extending an idea of identity federation to support ZTA. ZTF defines CAP as the entity that collects context and provides it to each organization (Relying Party; RP) that needs context for authorization based on ZTA. To improve the quality of authorization, CAPs need to collect context from various data sources. However, ZTF did not provide a method for collecting context from data sources other than RP. In this research, as a general model for collecting context in ZTF, we propose a method of linking identifiers between the data source and CAP. This method provides a way to collect context from some of such data sources in ZTF. Then, we implemented our method using RADIUS and MDM as data sources and confirmed that their contexts could be collected and used.
Active Keyword Selection to Track Evolving Topics on Twitter
Venue: arXiv cs.SI 2022
Year: 2022

How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as economics, political science, and public health have often done this by querying Twitter's public API endpoints with hand-picked topical keywords to search or stream discussions. However, despite the API's accessibility, it remains difficult to select and update keywords to collect high-quality data relevant to topics of interest. In this paper, we propose an active learning method for rapidly refining query keywords to increase both the yielded topic relevance and dataset size. We leverage a large open-source COVID-19 Twitter dataset to illustrate the applicability of our method in tracking Tweets around the key sub-topics of Vaccine, Mask, and Lockdown. Our experiments show that our method achieves an average topic-related keyword recall 2x higher than baselines. We open-source our code along with a web interface for keyword selection to make data collection from Twitter more systematic for researchers.
Traffic Accident Risk Forecasting using Contextual Vision Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Recently, the problem of traffic accident risk forecasting has been getting the attention of the intelligent transportation systems community due to its significant impact on traffic clearance. This problem is commonly tackled in the literature by using data-driven approaches that model the spatial and temporal incident impact, since they were shown to be crucial for the traffic accident risk forecasting problem. To achieve this, most approaches build different architectures to capture the spatio-temporal correlations features, making them inefficient for large traffic accident datasets. Thus, in this work, we are proposing a novel unified framework, namely a contextual vision transformer, that can be trained in an end-to-end approach which can effectively reason about the spatial and temporal aspects of the problem while providing accurate traffic accident risk predictions. We evaluate and compare the performance of our proposed methodology against baseline approaches from the literature across two large-scale traffic accident datasets from two different geographical locations. The results have shown a significant improvement with roughly 2\% in RMSE score in comparison to previous state-of-art works (SoTA) in the literature. Moreover, our proposed approach has outperformed the SoTA technique over the two datasets while only requiring 23x fewer computational requirements.
A Dive into WhatsApp's End-to-End Encryption
Venue: arXiv cs.CR 2022
Year: 2022

We live in a generation where the world around us is witnessing technological revolutions every single day, and as a result of this, everything around us is getting digitized with the touch of technology. In order to keep up the pace of this technological revolution and help reaching this progress its zenith, one of the most important aspects that needs to be taken care of is security. One of the biggest boons of technology in the recent times has been the invention of smartphones. As smartphones started becoming more popular, affordable and easily accessible, hundreds of free messaging applications were launched, but WhatsApp emerged as the ultimate winner in the race. This paper describes one of the most important and popular features of WhatsApp, the End-to-End (E2E) encryption system, which sets it apart from most other messaging applications and is one of the reasons which helped it become so popular.
UniColor: A Unified Framework for Multi-Modal Colorization with Transformer
Venue: arXiv cs.CV 2022
Year: 2022

We propose the first unified framework UniColor to support colorization in multiple modalities, including both unconditional and conditional ones, such as stroke, exemplar, text, and even a mix of them. Rather than learning a separate model for each type of condition, we introduce a two-stage colorization framework for incorporating various conditions into a single model. In the first stage, multi-modal conditions are converted into a common representation of hint points. Particularly, we propose a novel CLIP-based method to convert the text to hint points. In the second stage, we propose a Transformer-based network composed of Chroma-VQGAN and Hybrid-Transformer to generate diverse and high-quality colorization results conditioned on hint points. Both qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art methods in every control modality and further enables multi-modal colorization that was not feasible before. Moreover, we design an interactive interface showing the effectiveness of our unified framework in practical usage, including automatic colorization, hybrid-control colorization, local recolorization, and iterative color editing. Our code and models are available at https://luckyhzt.github.io/unicolor.
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages
Venue: arXiv cs.CL 2022
Year: 2022

Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XALIGNV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area.
Piercing Diametral Disks Induced by Edges of Maximum Spanning Tree
Venue: arXiv cs.CG 2022
Year: 2022

Let $P$ be a set of points in the plane and let $T$ be a maximum-weight spanning tree of $P$. For an edge $(p,q)$, let $D_{pq}$ be the diametral disk induced by $(p,q)$, i.e., the disk having the segment $\overline{pq}$ as its diameter. Let $\cal{D_T}$ be the set of the diametral disks induced by the edges of $T$. In this paper, we show that one point is sufficient to pierce all the disks in $\cal{D_T}$, thus, the set $\cal{D_T}$ is Helly. Actually, we show that the center of the smallest enclosing circle of $P$ is contained in all the disks of $\cal{D_T}$, and thus the piercing point can be computed in linear time.
Leveraging GPU Tensor Cores for Double Precision Euclidean Distance Calculations
Venue: arXiv cs.DC 2022
Year: 2022

Tensor cores (TCs) are a type of Application-Specific Integrated Circuit (ASIC) and are a recent addition to Graphics Processing Unit (GPU) architectures. As such, TCs are purposefully designed to greatly improve the performance of Matrix Multiply-Accumulate (MMA) operations. While TCs are heavily studied for machine learning and closely related fields, where their high efficiency is undeniable, MMA operations are not unique to these fields. More generally, any computation that can be expressed as MMA operations can leverage TCs, and potentially benefit from their higher computational throughput compared to other general-purpose cores, such as CUDA cores on Nvidia GPUs. In this paper, we propose the first double precision (FP64) Euclidean distance calculation algorithm, which is expressed as MMA operations to leverage TCs on Nvidia GPUs, rather than the more commonly used CUDA cores. To show that the Euclidean distance can be accelerated in a real-world application, we evaluate our proposed TC algorithm on the distance similarity self-join problem, as the most computationally intensive part of the algorithm consists of computing distances in a multi-dimensional space. We find that the performance gain from using the tensor core algorithm over the CUDA core algorithm depends weakly on the dataset size and distribution, but is strongly dependent on data dimensionality. Overall, TCs are a compelling alternative to CUDA cores, particularly when the data dimensionality is low ($\leq{4}$), as we achieve an average speedup of $1.28\times$ and up to $2.23\times$ against a state-of-the-art GPU distance similarity self-join algorithm. Furthermore, because this paper is among the first to explore the use of TCs for FP64 general-purpose computation, future research is promising.
Sensing Aided OTFS Channel Estimation for Massive MIMO Systems
Venue: arXiv cs.IT 2022
Year: 2022

Orthogonal time frequency space (OTFS) modulation has the potential to enable robust communications in highly-mobile scenarios. Estimating the channels for OTFS systems, however, is associated with high pilot signaling overhead that scales with the maximum delay and Doppler spreads. This becomes particularly challenging for massive MIMO systems where the overhead also scales with the number of antennas. An important observation however is that the delay, Doppler, and angle of departure/arrival information are directly related to the distance, velocity, and direction information of the mobile user and the various scatterers in the environment. With this motivation, we propose to leverage radar sensing to obtain this information about the mobile users and scatterers in the environment and leverage it to aid the OTFS channel estimation in massive MIMO systems. As one approach to realize our vision, this paper formulates the OTFS channel estimation problem in massive MIMO systems as a sparse recovery problem and utilizes the radar sensing information to determine the support (locations of the non-zero delay-Doppler taps). The proposed radar sensing aided sparse recovery algorithm is evaluated based on an accurate 3D ray-tracing framework with co-existing radar and communication data. The results show that the developed sensing-aided solution consistently outperforms the standard sparse recovery algorithms (that do not leverage radar sensing data) and leads to a significant reduction in the pilot overhead, which highlights a promising direction for OTFS based massive MIMO systems.
Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras
Venue: arXiv cs.CV 2022
Year: 2022

In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved by detecting people using compact embedded devices that are installed on the room's ceiling, and that integrate low-resolution infrared camera, which conceals each person's identity. However, for accurate detection, state-of-the-art deep learning models still require supervised training using a large annotated dataset of images. In this paper, we investigate cost-effective methods that are suitable for person detection based on low-resolution infrared images. Results indicate that for such images, we can reduce the amount of supervision and computation, while still achieving a high level of detection accuracy. Going from single-shot detectors that require bounding box annotations of each person in an image, to auto-encoders that only rely on unlabelled images that do not contain people, allows for considerable savings in terms of annotation costs, and for models with lower computational costs. We validate these experimental findings on two challenging top-view datasets with low-resolution infrared images.
Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis
Venue: arXiv cs.LG 2022
Year: 2022

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM- MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.
Extending Word-Level Quality Estimation for Post-Editing Assistance
Venue: arXiv cs.CL 2022
Year: 2022

We define a novel concept called extended word alignment in order to improve post-editing assistance efficiency. Based on extended word alignment, we further propose a novel task called refined word-level QE that outputs refined tags and word-level correspondences. Compared to original word-level QE, the new task is able to directly point out editing operations, thus improves efficiency. To extract extended word alignment, we adopt a supervised method based on mBERT. To solve refined word-level QE, we firstly predict original QE tags by training a regression model for sequence tagging based on mBERT and XLM-R. Then, we refine original word tags with extended word alignment. In addition, we extract source-gap correspondences, meanwhile, obtaining gap tags. Experiments on two language pairs show the feasibility of our method and give us inspirations for further improvement.
Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge
Venue: arXiv cs.CL 2022
Year: 2022

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model. Experiments on both datasets show that our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains. Additional analyses on the recommendation and generation tasks demonstrate the effectiveness of our model in different scenarios.
Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts
Venue: arXiv cs.CL 2022
Year: 2022

Domain adaptation is an important challenge for neural machine translation. However, the traditional fine-tuning solution requires multiple extra training and yields a high cost. In this paper, we propose a non-tuning paradigm, resolving domain adaptation with a prompt-based method. Specifically, we construct a bilingual phrase-level database and retrieve relevant pairs from it as a prompt for the input sentences. By utilizing Retrieved Phrase-level Prompts (RePP), we effectively boost the translation quality. Experiments show that our method improves domain-specific machine translation for 6.2 BLEU scores and improves translation constraints for 11.5% accuracy without additional training.
Automatic Sign Reading and Localization for Semantic Mapping with an Office Robot
Venue: arXiv cs.RO 2022
Year: 2022

Semantic mapping is the task of providing a robot with a map of its environment beyond the open, navigable space of traditional Simultaneous Localization and Mapping (SLAM) algorithms by attaching semantics to locations. The system presented in this work reads door placards to annotate the locations of offices. Whereas prior work on this system developed hand-crafted detectors, this system leverages YOLOv5 for sign detection and EAST for text recognition. Placards are localized by computing their pose from a point cloud in a RGB-D camera frame localized by a modified ORB-SLAM. Semantic mapping is accomplished in a post-processing step after robot exploration from video recording. System performance is reported in terms of the number of placards identified, the accuracy of their placement onto a SLAM map, the accuracy of the map built, and the correctness transcribed placard text.
Motion Guided Deep Dynamic 3D Garments
Venue: arXiv cs.CV 2022
Year: 2022

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative rollout prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.
TeST: Test-time Self-Training under Distribution Shift
Venue: arXiv cs.CV 2022
Year: 2022

Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time Self-Training (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline test-time adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms, while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks: object detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new state-of-the art for test-time domain adaptation algorithms.