OpenRaiser/Intern-Atlas huggingface dataset:
Spatio-temporal Data Augmentation for Visual Surveillance
Venue: arXiv cs.CV 2021
Year: 2021

Visual surveillance aims to stably detect a foreground object using a continuous image acquired from a fixed camera. Recent deep learning methods based on supervised learning show superior performance compared to classical background subtraction algorithms. However, there is still a room for improvement in static foreground, dynamic background, hard shadow, illumination changes, camouflage, etc. In addition, most of the deep learning-based methods operates well on environments similar to training. If the testing environments are different from training ones, their performance degrades. As a result, additional training on those operating environments is required to ensure a good performance. Our previous work which uses spatio-temporal input data consisted of a number of past images, background images and current image showed promising results in different environments from training, although it uses a simple U-NET structure. In this paper, we propose a data augmentation technique suitable for visual surveillance for additional performance improvement using the same network used in our previous work. In deep learning, most data augmentation techniques deal with spatial-level data augmentation techniques for use in image classification and object detection. In this paper, we propose a new method of data augmentation in the spatio-temporal dimension suitable for our previous work. Two data augmentation methods of adjusting background model images and past images are proposed. Through this, it is shown that performance can be improved in difficult areas such as static foreground and ghost objects, compared to previous studies. Through quantitative and qualitative evaluation using SBI, LASIESTA, and our own dataset, we show that it gives superior performance compared to deep learning-based algorithms and background subtraction algorithms.
A Two-stage Framework for Compound Figure Separation
Venue: arXiv cs.CV 2021
Year: 2021

Scientific literature contains large volumes of complex, unstructured figures that are compound in nature (i.e. composed of multiple images, graphs, and drawings). Separation of these compound figures is critical for information retrieval from these figures. In this paper, we propose a new strategy for compound figure separation, which decomposes the compound figures into constituent subfigures while preserving the association between the subfigures and their respective caption components. We propose a two-stage framework to address the proposed compound figure separation problem. In particular, the subfigure label detection module detects all subfigure labels in the first stage. Then, in the subfigure detection module, the detected subfigure labels help to detect the subfigures by optimizing the feature selection process and providing the global layout information as extra features. Extensive experiments are conducted to validate the effectiveness and superiority of the proposed framework, which improves the detection precision by 9%.
GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories
Venue: arXiv cs.IT 2021
Year: 2021

Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications. They also find application in various data storage systems because of their excellent performance. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a "convolutional" manner. The number of partitioned components determines the "memory" of SC codes. While adopting higher memories results in improved SC code performance, obtaining optimal SC codes with high memory is known to be hard. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. We propose a probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low complexity optimization algorithms over the cycle properties to construct high memory, high performance quasi-cyclic SC codes. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and codes with uniform partitioning.
Joint Design of Hybrid Beamforming and Phase Shifts in RIS-Aided mmWave Communication Systems
Venue: arXiv cs.IT 2021
Year: 2021

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under signal-to-interference-plus-noise ratio (SINR) constraints. The problem is highly challenging due to the non-convex SINR constraints as well as the non-convex unit-modulus constraints for both the phase shifts at the RIS and the analog beamforming at the BS. A penalty-based algorithm in conjunction with the manifold optimization technique is proposed to handle the problem, followed by an individual optimization method with much lower complexity. Simulation results show that the proposed algorithm outperforms the state-of-art algorithm. Results also show that the joint optimization of RIS response matrix and BS hybrid beamforming is much superior to individual optimization.
Adaptive Scheduling for Machine Learning Tasks over Networks
Venue: arXiv cs.LG 2021
Year: 2021

A key functionality of emerging connected autonomous systems such as smart transportation systems, smart cities, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations. This is increasingly attracting attention under the terms of distributed learning and federated learning. However, in this setup data transfer takes place over communication resources that are shared among many users and tasks or subject to capacity constraints. This paper examines algorithms for efficiently allocating resources to linear regression tasks by exploiting the informativeness of the data. The algorithms developed enable adaptive scheduling of learning tasks with reliable performance guarantees.
Correctable Erasure Patterns in Product Topologies
Venue: arXiv cs.IT 2021
Year: 2021

Locality enables storage systems to recover failed nodes from small subsets of surviving nodes. The setting where nodes are partitioned into subsets, each allowing for local recovery, is well understood. In this work we consider a generalization introduced by Gopalan et al., where, viewing the codewords as arrays, constraints are imposed on the columns and rows in addition to some global constraints. Specifically, we present a generic method of adding such global parity-checks and derive new results on the set of correctable erasure patterns. Finally, we relate the set of correctable erasure patterns in the considered topology to those correctable in tensor-product codes.
Computational Workflows for Designing Input Devices
Venue: arXiv cs.HC 2021
Year: 2021

Input devices, such as buttons and sliders, are the foundation of any interface. The typical user-centered design workflow requires the developers and users to go through many iterations of design, implementation, and analysis. The procedure is inefficient, and human decisions highly bias the results. While computational methods are used to assist various design tasks, there has not been any holistic approach to automate the design of input components. My thesis proposed a series of Computational Input Design workflows: I envision a sample-efficient multi-objective optimization algorithm that cleverly selects design instances, which are instantly deployed on physical simulators. A meta-reinforcement learning user model then simulates the user behaviors when using the design instance upon the simulators. The new workflows derive Pareto-optimal designs with high efficiency and automation. I demonstrate designing a push-button via the proposed methods. The resulting designs outperform the known baselines. The Computational Input Design process can be generalized to other devices, such as joystick, touchscreen, mouse, controller, etc.
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series
Venue: arXiv cs.LG 2021
Year: 2021

Forecasting of time series in continuous systems becomes an increasingly relevant task due to recent developments in IoT and 5G. The popular forecasting model ARIMA is applied to a large variety of applications for decades. An online variant of ARIMA applies the Online Newton Step in order to learn the underlying process of the time series. This optimization method has pitfalls concerning the computational complexity and convergence. Thus, this work focuses on the computational less expensive Online Gradient Descent optimization method, which became popular for learning of neural networks in recent years. For the iterative training of such models, we propose a new approach combining different Online Gradient Descent learners (such as Adam, AMSGrad, Adagrad, Nesterov) to achieve fast convergence. The evaluation on synthetic data and experimental datasets show that the proposed approach outperforms the existing methods resulting in an overall lower prediction error.
Cross-lingual Visual Pre-training for Multimodal Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Pre-trained language models have been shown to improve performance in many natural language tasks substantially. Although the early focus of such models was single language pre-training, recent advances have resulted in cross-lingual and visual pre-training methods. In this paper, we combine these two approaches to learn visually-grounded cross-lingual representations. Specifically, we extend the translation language modelling (Lample and Conneau, 2019) with masked region classification and perform pre-training with three-way parallel vision & language corpora. We show that when fine-tuned for multimodal machine translation, these models obtain state-of-the-art performance. We also provide qualitative insights into the usefulness of the learned grounded representations.
Few-Shot Website Fingerprinting Attack
Venue: arXiv cs.CR 2021
Year: 2021

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and Harmonious Data Augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intra-sample and inter-sample data transformations that can be used in harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. {For instance, in the more challenging and realistic evaluation scenario with WTF-PAD based defense, our HDA method surpasses the previous state-of-the-art results by more than 4% in absolute classification accuracy in the 20-shot learning case.
RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
Venue: arXiv cs.CL 2021
Year: 2021

Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities. Although progresses have been achieved, existing methods are heuristically motivated and theoretical understanding of such embeddings is comparatively underdeveloped. This paper extends the random walk model (Arora et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) to derive a scoring function that evaluates the strength of a relation R between two entities h (head) and t (tail). Moreover, we show that marginal loss minimisation, a popular objective used in much prior work in KGE, follows naturally from the log-likelihood ratio maximisation under the probabilities estimated from the KGEs according to our theoretical relationship. We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. Using the derived objective, accurate KGEs are learnt from FB15K237 and WN18RR benchmark datasets, providing empirical evidence in support of the theory.
Joint Active and Passive Beamforming for Intelligent Reflecting Surface Aided Multiuser MIMO Communications
Venue: arXiv cs.IT 2021
Year: 2021

This letter investigates the joint active and passive beamforming optimization for intelligent reflecting surface (IRS) aided multiuser multiple-input multiple-output systems with the objective of maximizing the weighted sum-rate. We show that this problem can be solved via a matrix weighted mean square error minimization equivalence. In particular, for the optimization of the passive IRS beamforming, we first propose an iterative algorithm with excellent performance based on the manifold optimization. By using the matrix fractional programming technique to obtain a more tractable object function, we then propose a low complexity algorithm based on the majorization-minimization method. Numerical results verify the convergence of our proposed algorithms and the significant performance improvement over the communication scenario without IRS assistance.
JTrack: A Digital Biomarker Platform for Remote Monitoring in Neurological and Psychiatric Diseases
Venue: arXiv cs.CY 2021
Year: 2021

Objective: Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital phenotyping. Method: JTrack consists of an Android-based smartphone application and a web-based project management dashboard. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes. The dashboard also provides management tools to monitor and manage data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. JTrack was developed to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. Results: JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. The main components of the JTrack platform and examples of data being collected using JTrack are presented here. Conclusion: Smartphone-based Digital Biomarker data may provide valuable insight into daily life behaviour in health and disease. JTrack provides an easy and reliable open-source solution for collection of such data.
An open-source tool to assess the carbon footprint of research
Venue: arXiv cs.CY 2021
Year: 2021

Research institutions are bound to contribute to greenhouse gas emission (GHG) reduction efforts for several reasons. First, part of the scientific community's research deals with climate change issues. Second, scientists contribute to students' education: they must be consistent and role models. Third the literature on the carbon footprint of researchers points to the high level of some individual footprints. In a quest for consistency and role models, scientists, teams of scientists or universities have started to quantify their carbon footprints and debate on reduction options. Indeed, measuring the carbon footprint of research activities requires tools designed to tackle its specific features. In this paper, we present an open-source web application, GES 1point5, developed by an interdisciplinary team of scientists from several research labs in France. GES 1point5 is specifically designed to estimate the carbon footprint of research activities in France. It operates at the scale of research labs, i.e. laboratoires, which are the social structures around which research is organized in France and the smallest decision making entities in the French research system. The application allows French research labs to compute their own carbon footprint along a standardized, open protocol. The data collected in a rapidly growing network of labs will be used as part of the Labos 1point5 project to estimate France's research carbon footprint. At the time of submitting this manuscript, 89 research labs had engaged with GES 1point5 to estimate their greenhouse gas emissions. We expect that an international adoption of GES 1point5 (adapted to fit domestic specifics) could contribute to establishing a global understanding of the drivers of the research carbon footprint worldwide and the levers to decrease it.
Performance Evaluation of Convolutional Neural Networks for Gait Recognition
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, a performance evaluation of well-known deep learning models in gait recognition is presented. For this purpose, the transfer learning scheme is adopted to pre-trained models in order to fit the models to the CASIA-B dataset for solving a gait recognition task. In this context, 18 popular Convolutional Neural Networks (CNNs), were re-trained using Gait Energy Images (GEIs) of CASIA-B containing almost 14000 images of 124 classes under various conditions, and their performance was studied in terms of accuracy. Moreover, the performance of the studied models is managed to be explained by examining the parts of the images being considered by the models towards providing their decisions. The experimental results are very promising since almost all the models achieved a high accuracy of over 90%, which is robust to the increasing number of classes. Furthermore, an important outcome of this study is the fact that a recognition problem can be effectively solved by using CNNs pre-trained to different problems, thus eliminating the need for customized model design.
Codes approaching the Shannon limit with polynomial complexity per information bit
Venue: arXiv cs.IT 2021
Year: 2021

We consider codes for channels with extreme noise that emerge in various low-power applications. Simple LDPC-type codes with parity checks of weight 3 are first studied for any dimension $m\rightarrow\infty.$ These codes form modulation schemes: they improve the original channel output for any $SNR>$ $-6$ dB (per information bit) and gain $3$ dB over uncoded modulation as $SNR$ grows. However, they also have a floor on the output bit error rate (BER) irrespective of their length. Tight lower and upper bounds, which are virtually identical to simulation results, are then obtained for BER at any SNR. We also study a combined scheme that splits $m$ information bits into $b$ blocks and protects each with some polar code. Decoding moves back and forth between polar and LDPC codes, every time using a polar code of a higher rate. For a sufficiently large constant $b$ and $m\rightarrow\infty$, this design yields a vanishing BER at any SNR that is arbitrarily close to the Shannon limit of -1.59 dB. Unlike other existing designs, this scheme has polynomial complexity of order $m\ln m$ per information bit.
A Machine-checked proof of Birkhoff's Variety Theorem in Martin-Löf Type Theory
Venue: arXiv cs.LO 2021
Year: 2021

The Agda Universal Algebra Library (agda-algebras) is a library of types and programs (theorems and proofs) we developed to formalize the foundations of universal algebra in dependent type theory using the Agda programming language and proof assistant. In this paper we draw on and explain many components of the agda-algebras library, which we extract into a single Agda module in order to present a self-contained formal and constructive proof of Birkhoff's HSP theorem in Martin-Löf dependent type theory. In the course of our presentation, we highlight some of the more challenging aspects of formalizing the basic definitions and theorems of universal algebra in type theory. Nonetheless, we hope this paper and the agda-algebras library serve as further evidence in support of the claim that dependent type theory and the Agda language, despite the technical demands they place on the user, are accessible to working mathematicians (such as ourselves) who possess sufficient patience and resolve to formally verify their results with a proof assistant. Indeed, the agda-algebras library now includes a substantial collection of definitions, theorems, and proofs from universal algebra, illustrating the expressive power of inductive and dependent types for representing and reasoning about general algebraic and relational structures.
Writers Gonna Wait: The Effectiveness of Notifications to Initiate Aversive Action in Writing Procrastination
Venue: arXiv cs.HC 2021
Year: 2021

This paper evaluates the use of notifications to reduce aversive-task-procrastination by helping initiate action. Specifically, we focus on aversion to graded writing tasks. We evaluate software designs commonly used by behavior change applications, such as goal setting and action support systems. We conduct a two-phase control trial experiment with 21 college students tasked to write two 3000-word writing assignments (14 students fully completed the experiment). Participants use a customized text editor designed to continuously collect writing behavior. The results from the study reveal that notifications have minimal effect in encouraging users to get started. They can also increase negative effects on participants. Other techniques, such as eliminating distraction and showing simple writing statistics, yield higher satisfaction among participants as they complete the writing task. Furthermore, the incorporation of text mining decreases aversion to the task and helps participants overcome writer's block. Finally, we discuss lessons learned from our evaluation that help quantify the difficulty of behavior change for writing procrastination, with emphasis on goals for the HCI community.
Emergent Communication under Competition
Venue: arXiv cs.LG 2021
Year: 2021

The literature in modern machine learning has only negative results for learning to communicate between competitive agents using standard RL. We introduce a modified sender-receiver game to study the spectrum of partially-competitive scenarios and show communication can indeed emerge in a competitive setting. We empirically demonstrate three key takeaways for future research. First, we show that communication is proportional to cooperation, and it can occur for partially competitive scenarios using standard learning algorithms. Second, we highlight the difference between communication and manipulation and extend previous metrics of communication to the competitive case. Third, we investigate the negotiation game where previous work failed to learn communication between independent agents (Cao et al., 2018). We show that, in this setting, both agents must benefit from communication for it to emerge; and, with a slight modification to the game, we demonstrate successful communication between competitive agents. We hope this work overturns misconceptions and inspires more research in competitive emergent communication.