OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Video Encoding For Different Video Codecs
Venue: arXiv cs.MM 2021
Year: 2021

By 2022, we expect video traffic to reach 82% of the total internet traffic. Undoubtedly, the abundance of video-driven applications will likely lead internet video traffic percentage to a further increase in the near future, enabled by associate advances in video devices' capabilities. In response to this ever-growing demand, the Alliance for Open Media (AOM) and the Joint Video Experts Team (JVET) have demonstrated strong and renewed interest in developing new video codecs. In the fast-changing video codecs' landscape, there is thus, a genuine need to develop adaptive methods that can be universally applied to different codecs. In this study, we formulate video encoding as a multi-objective optimization process where video quality (as a function of VMAF and PSNR), bitrate demands, and encoding rate (in encoded frames per second) are jointly optimized, going beyond the standard video encoding approaches that focus on rate control targeting specific bandwidths. More specifically, we create a dense video encoding space (offline) and then employ regression to generate forward prediction models for each one of the afore-described optimization objectives, using only Pareto-optimal points. We demonstrate our adaptive video encoding approach that leverages the generated forward prediction models that qualify for real-time adaptation using different codecs (e.g., SVT-AV1 and x265) for a variety of video datasets and resolutions. To motivate our approach and establish the promise for future fast VVC encoders, we also perform a comparative performance evaluation using both subjective and objective metrics and report on bitrate savings among all possible pairs between VVC, SVT-AV1, x265, and VP9 codecs.
Visual FUDGE: Form Understanding via Dynamic Graph Editing
Venue: arXiv cs.CV 2021
Year: 2021

We address the problem of form understanding: finding text entities and the relationships/links between them in form images. The proposed FUDGE model formulates this problem on a graph of text elements (the vertices) and uses a Graph Convolutional Network to predict changes to the graph. The initial vertices are detected text lines and do not necessarily correspond to the final text entities, which can span multiple lines. Also, initial edges contain many false-positive relationships. FUDGE edits the graph structure by combining text segments (graph vertices) and pruning edges in an iterative fashion to obtain the final text entities and relationships. While recent work in this area has focused on leveraging large-scale pre-trained Language Models (LM), FUDGE achieves almost the same level of entity linking performance on the FUNSD dataset by learning only visual features from the (small) provided training set. FUDGE can be applied on forms where text recognition is difficult (e.g. degraded or historical forms) and on forms in resource-poor languages where pre-training such LMs is challenging. FUDGE is state-of-the-art on the historical NAF dataset.
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Venue: arXiv cs.CL 2021
Year: 2021

Recent years have witnessed great progress on building emotional chatbots. Tremendous methods have been proposed for chatbots to generate responses with given emotions. However, the emotion changes of the user during the conversation has not been fully explored. In this work, we study the problem of positive emotion elicitation, which aims to generate responses that can elicit positive emotion of the user, in human-machine conversation. We propose a weakly supervised Emotion Eliciting Machine (EEM) to address this problem. Specifically, we first collect weak labels of user emotion status changes in a conversion based on a pre-trained emotion classifier. Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation. An emotion eliciting factor is introduced on top of the dual structure to balance the positive and negative emotional impacts on the generated response during emotion elicitation. The factor also provides a fine-grained controlling manner for emotion elicitation. Experimental results on a large real-world dataset show that EEM outperforms the existing models in generating responses with positive emotion elicitation.
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing
Venue: arXiv cs.DS 2021
Year: 2021

We present the first provable Least-Squares Value Iteration (LSVI) algorithms that have runtime complexity sublinear in the number of actions. We formulate the value function estimation procedure in value iteration as an approximate maximum inner product search problem and propose a locality sensitive hashing (LSH) [Indyk and Motwani STOC'98, Andoni and Razenshteyn STOC'15, Andoni, Laarhoven, Razenshteyn and Waingarten SODA'17] type data structure to solve this problem with sublinear time complexity. Moreover, we build the connections between the theory of approximate maximum inner product search and the regret analysis of reinforcement learning. We prove that, with our choice of approximation factor, our Sublinear LSVI algorithms maintain the same regret as the original LSVI algorithms while reducing the runtime complexity to sublinear in the number of actions. To the best of our knowledge, this is the first work that combines LSH with reinforcement learning resulting in provable improvements. We hope that our novel way of combining data-structures and iterative algorithm will open the door for further study into cost reduction in optimization.
Reachability and Top-k Reachability Queries with Transfer Decay
Venue: arXiv cs.DB 2021
Year: 2021

The prevalence of location tracking systems has resulted in large volumes of spatiotemporal data generated every day. Addressing reachability queries on such datasets is important for a wide range of applications (surveillance, public health, social networks, etc.) A spatiotemporal reachability query identifies whether a physical item (or information etc.) could have been transferred from the source object $O_S$ to the target object $O_T$ during a time interval $I$ (either directly, or through a chain of intermediate transfers). In previous research on spatiotemporal reachability queries, the number of such transfers is not limited, and the weight of a piece of transferred information remains the same. This paper introduces novel reachability queries, which assume a scenario of information decay. Such queries arise when the value of information that travels through the chain of intermediate objects decreases with each transfer. To address such queries efficiently over large spatiotemporal datasets, we introduce the RICCdecay algorithm. Further, the decay scenario leads to an important extension: if there are many different sources of information, the aggregate value of information an object can obtain varies. As a result, we introduce a top-k reachability problem, identifying the k objects with the highest accumulated information. We also present the RICCtopK algorithm that can efficiently compute top-k reachability with transfer decay queries. An experimental evaluation shows the efficiency of the proposed algorithms over previous approaches.
Can Self Reported Symptoms Predict Daily COVID-19 Cases?
Venue: arXiv cs.LG 2021
Year: 2021

The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths. While vaccination is an important intervention, its roll-out is slow and unequal across the globe. Therefore, extensive testing still remains one of the key methods to monitor and contain the virus. Testing on a large scale is expensive and arduous. Hence, we need alternate methods to estimate the number of cases. Online surveys have been shown to be an effective method for data collection amidst the pandemic. In this work, we develop machine learning models to estimate the prevalence of COVID-19 using self-reported symptoms. Our best model predicts the daily cases with a mean absolute error (MAE) of 226.30 (normalized MAE of 27.09%) per state, which demonstrates the possibility of predicting the actual number of confirmed cases by utilizing self-reported symptoms. The models are developed at two levels of data granularity - local models, which are trained at the state level, and a single global model which is trained on the combined data aggregated across all states. Our results indicate a lower error on the local models as opposed to the global model. In addition, we also show that the most important symptoms (features) vary considerably from state to state. This work demonstrates that the models developed on crowd-sourced data, curated via online platforms, can complement the existing epidemiological surveillance infrastructure in a cost-effective manner. The code is publicly available at https://github.com/parthpatwa/Can-Self-Reported-Symptoms-Predict-Daily-COVID-19-Cases.
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Venue: arXiv cs.LG 2021
Year: 2021

Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks. In the literature, however, most tricks or techniques are either briefly mentioned as implementation details or only visible in source code. In this paper, we first summarize some existing effective tricks used in GCNs mini-batch training. Based on this, two novel tricks named GCN_res Framework and Embedding Usage are proposed by leveraging residual network and pre-trained embedding to improve baseline's test accuracy in different datasets. Experiments on Open Graph Benchmark (OGB) show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%~2.84%. We open source our implementation at https://github.com/ytchx1999/PyG-OGB-Tricks.
Online Multimodal Transportation Planning using Deep Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

In this paper we propose a Deep Reinforcement Learning approach to solve a multimodal transportation planning problem, in which containers must be assigned to a truck or to trains that will transport them to their destination. While traditional planning methods work "offline" (i.e., they take decisions for a batch of containers before the transportation starts), the proposed approach is "online", in that it can take decisions for individual containers, while transportation is being executed. Planning transportation online helps to effectively respond to unforeseen events that may affect the original transportation plan, thus supporting companies in lowering transportation costs. We implemented different container selection heuristics within the proposed Deep Reinforcement Learning algorithm and we evaluated its performance for each heuristic using data that simulate a realistic scenario, designed on the basis of a real case study at a logistics company. The experimental results revealed that the proposed method was able to learn effective patterns of container assignment. It outperformed tested competitors in terms of total transportation costs and utilization of train capacity by 20.48% to 55.32% for the cost and by 7.51% to 20.54% for the capacity. Furthermore, it obtained results within 2.7% for the cost and 0.72% for the capacity of the optimal solution generated by an Integer Linear Programming solver in an offline setting.
Enabling self-verifiable mutable content items in IPFS using Decentralized Identifiers
Venue: arXiv cs.CR 2021
Year: 2021

In IPFS content identifiers are constructed based on the item's data therefore the binding between an item's identifier and its data can be deterministically verified. Nevertheless, once an item is modified, its identifier also changes. Therefore when it comes to mutable content there is a need for keeping track of the "latest" IPFS identifier. This is achieved using naming protocols on top of IPFS, such as IPNS and DNSlink, that map a constant name to an IPFS identifier, allowing at the same time content owners to update these mappings. Nevertheless, IPNS relies on a cryptographic key pair that cannot be rotated, and DNSlink does not provide content authenticity protection. In this paper, we propose a naming protocol that combines DNSlink and decentralized identifiers to enable self-verifiable content items. Our protocol provides content authenticity without imposing any security requirement to DNSlink. Furthermore, our protocol prevent fake content even if attackers have access to the DNS server of the content owner or have access to the content owner secret keys. Our proof of concept implementation shows that our protocol is feasible and can be used with existing IPFS tools.
Reconfiguring Hybrid Systems Using SAT
Venue: arXiv cs.AI 2021
Year: 2021

Reconfiguration aims at recovering a system from a fault by automatically adapting the system configuration, such that the system goal can be reached again. Classical approaches typically use a set of pre-defined faults for which corresponding recovery actions are defined manually. This is not possible for modern hybrid systems which are characterized by frequent changes. Instead, AI-based approaches are needed which leverage on a model of the non-faulty system and which search for a set of reconfiguration operations which will establish a valid behavior again. This work presents a novel algorithm which solves three main challenges: (i) Only a model of the non-faulty system is needed, i.e. the faulty behavior does not need to be modeled. (ii) It discretizes and reduces the search space which originally is too large -- mainly due to the high number of continuous system variables and control signals. (iii) It uses a SAT solver for propositional logic for two purposes: First, it defines the binary concept of validity. Second, it implements the search itself -- sacrificing the optimal solution for a quick identification of an arbitrary solution. It is shown that the approach is able to reconfigure faults on simulated process engineering systems.
Strategic alignment between IT flexibility and dynamic capabilities: an empirical investigation
Venue: arXiv cs.CY 2021
Year: 2021

Dynamic capabilities theory emerged as a leading framework in the process of value creation for firms. Its core notion complements the premise of the resource-based view of the firm and is considered an important theoretical and management framework in modern information systems research. However, despite DCTs significant contributions, its strength and core focus are essentially in its use for historical firm performance explanation. Furthermore, valuable contributions have been made by several researchers in order to extend the DCT to fit the constantly changing IT environments and other imperative drivers for competitive performance. However, no DCT extension has been developed which allows firms to integrally assess their current state of maturity in order to derive imperative steps for further performance enhancements. In light of empirical advancement, this paper aims to develop a strategic alignment model for IT flexibility and dynamic capabilities and empirically validates proposed hypotheses using correlation and regression analyses on a large data sample of 322 international firms. We conjecture that the combined synergetic effect of the underlying dimensions of a firms IT flexibility architecture and dynamic capabilities enables organizations to cope with changing environmental conditions and drive competitive firm performance. Findings of this study suggest that there is a significant positive relationship between the firms degree of strategic alignment defined as the degree of balance between all dimensions and competitive firm performance. Strategic alignment can, therefore, be seen as an important condition that significantly influences a firms competitive advantage in constantly changing environments. The proposed framework helps firms assess and improve their maturity and alignment of IT flexibility and dynamic capabilities.
AI Based Landscape Sensing Using Radio Signals
Venue: arXiv cs.IT 2021
Year: 2021

In many sensing applications, typically radio signals are emitted by a radar and from the bounced reflections of the obstacles, inference about the environment is made. Even though radars can be used to sense the landscapes around the user-equipment (UE) such as whether UE is in the forested region, inside buildings, etc., it is not suitable in many wireless applications as many UEs does not have radars in them. Using radar will also increase the cost and power requirements on the UEs in applications requiring sensing of the landscapes. In this paper, we provide a mechanism where basestation (BS) is able to sense the UE's landscape without the use of a radar. We propose an artificial intelligence (AI) based approach with suitable choice of the features derived from the wireless channel to infer the landscape of the UEs. Results for the proposed methods when applied to practical environments such as London city scenario yields a precision score of more than 95 percent.
Deep Active Contours Using Locally Controlled Distance Vector Flow
Venue: arXiv cs.CV 2021
Year: 2021

Active contours Model (ACM) has been extensively used in computer vision and image processing. In recent studies, Convolutional Neural Networks (CNNs) have been combined with active contours replacing the user in the process of contour evolution and image segmentation to eliminate limitations associated with ACM's dependence on parameters of the energy functional and initialization. However, prior works did not aim for automatic initialization which is addressed here. In addition to manual initialization, current methods are highly sensitive to initial location and fail to delineate borders accurately. We propose a fully automatic image segmentation method to address problems of manual initialization, insufficient capture range, and poor convergence to boundaries, in addition to the problem of assignment of energy functional parameters. We train two CNNs, which predict active contour weighting parameters and generate a ground truth mask to extract Distance Transform (DT) and an initialization circle. Distance transform is used to form a vector field pointing from each pixel of the image towards the closest point on the boundary, the size of which is equal to the Euclidean distance map. We evaluate our method on four publicly available datasets including two building instance segmentation datasets, Vaihingen and Bing huts, and two mammography image datasets, INBreast and DDSM-BCRP. Our approach outperforms latest research by 0.59 ans 2.39 percent in mean Intersection-over-Union (mIoU), 7.38 and 8.62 percent in Boundary F-score (BoundF) for Vaihingen and Bing huts datasets, respectively. Dice similarity coefficient for the INBreast and DDSM-BCRP datasets is 94.23% and 90.89%, respectively indicating our method is comparable to state-of-the-art frameworks.
Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme
Venue: arXiv cs.LG 2021
Year: 2021

Stochastic differential equations (SDEs) are one of the most important representations of dynamical systems. They are notable for the ability to include a deterministic component of the system and a stochastic one to represent random unknown factors. However, this makes learning SDEs much more challenging than ordinary differential equations (ODEs). In this paper, we propose a data driven approach where parameters of the SDE are represented by a neural network with a built-in SDE integration scheme. The loss function is based on a maximum likelihood criterion, under order one Markov Gaussian assumptions. The algorithm is applied to the geometric brownian motion and a stochastic version of the Lorenz-63 model. The latter is particularly hard to handle due to the presence of a stochastic component that depends on the state. The algorithm performance is attested using different simulations results. Besides, comparisons are performed with the reference gradient matching method used for non linear drift estimation, and a neural networks-based method, that does not consider the stochastic term.
Unsupervised Compound Domain Adaptation for Face Anti-Spoofing
Venue: arXiv cs.CV 2021
Year: 2021

We address the problem of face anti-spoofing which aims to make the face verification systems robust in the real world settings. The context of detecting live vs. spoofed face images may differ significantly in the target domain, when compared to that of labeled source domain where the model is trained. Such difference may be caused due to new and unknown spoof types, illumination conditions, scene backgrounds, among many others. These varieties of differences make the target a compound domain, thus calling for the problem of the unsupervised compound domain adaptation. We demonstrate the effectiveness of the compound domain assumption for the task of face anti-spoofing, for the first time in this work. To this end, we propose a memory augmentation method for adapting the source model to the target domain in a domain aware manner. The adaptation process is further improved by using the curriculum learning and the domain agnostic source network training approaches. The proposed method successfully adapts to the compound target domain consisting multiple new spoof types. Our experiments on multiple benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art.
A Clustering Framework for Residential Electric Demand Profiles
Venue: arXiv cs.LG 2021
Year: 2021

The availability of residential electric demand profiles data, enabled by the large-scale deployment of smart metering infrastructure, has made it possible to perform more accurate analysis of electricity consumption patterns. This paper analyses the electric demand profiles of individual households located in the city Amsterdam, the Netherlands. A comprehensive clustering framework is defined to classify households based on their electricity consumption pattern. This framework consists of two main steps, namely a dimensionality reduction step of input electricity consumption data, followed by an unsupervised clustering algorithm of the reduced subspace. While any algorithm, which has been used in the literature for the aforementioned clustering task, can be used for the corresponding step, the more important question is to deduce which particular combination of algorithms is the best for a given dataset and a clustering task. This question is addressed in this paper by proposing a novel objective validation strategy, whose recommendations are then cross-verified by performing subjective validation.