OpenRaiser/Intern-Atlas huggingface dataset:
Misplaced trust? The relationship between trust, ability to identify commercially influenced results, and search engine preference
Venue: arXiv cs.IR 2021
Year: 2021

People have a high level of trust in search engines, especially Google, but only limited knowledge of them, as numerous studies have shown. This leads to the question: To what extent is this trust justified considering the lack of familiarity among users with how search engines work and the business models they are founded on? We assume that trust in Google, search engine preferences, and knowledge of result types are interrelated. To examine this assumption, we conducted a representative online survey with n = 2,012 German internet users. We show that users with little search engine knowledge are more likely to trust and use Google than users with more knowledge. A contradiction revealed itself - users strongly trust Google, yet they are unable to adequately evaluate search results. This may be problematic since it can potentially affect knowledge acquisition. Consequently, there is a need to promote user information literacy to create a more solid foundation for user trust in search engines.
Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision
Venue: arXiv cs.CL 2021
Year: 2021

Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle factor changes and interventions. Analyzing electronic health records (EHR) of patients with AD can help us better understand lifestyle's effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to demonstrate the feasibility of natural language processing (NLP) models to classify lifestyle factors (e.g., physical activity and excessive diet) from clinical texts. We automatically generated labels for the training data by using a rule-based NLP algorithm. We conducted weak supervision for pre-trained Bidirectional Encoder Representations from Transformers (BERT) models on the weakly labeled training corpus. These models include the BERT base model, PubMedBERT(abstracts + full text), PubMedBERT(only abstracts), Unified Medical Language System (UMLS) BERT, Bio BERT, and Bio-clinical BERT. We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle factors for AD. These models were compared on the developed Gold Standard Corpus (GSC) on the two case studies. The PubmedBERT(Abs) model achieved the best performance for physical activity, with its precision, recall, and F-1 scores of 0.96, 0.96, and 0.96, respectively. Regarding classifying excessive diet, the Bio BERT model showed the highest performance with perfect precision, recall, and F-1 scores. The proposed approach leveraging weak supervision could significantly increase the sample size, which is required for training the deep learning models. The study also demonstrates the effectiveness of BERT models for extracting lifestyle factors for Alzheimer's disease from clinical notes.
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: 1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and 2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a noise filter for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ~77% w.r.t. learning-based segmentation methods using pixel-wise labels for training.
Theory of Mind for Deep Reinforcement Learning in Hanabi
Venue: arXiv cs.AI 2021
Year: 2021

The partially observable card game Hanabi has recently been proposed as a new AI challenge problem due to its dependence on implicit communication conventions and apparent necessity of theory of mind reasoning for efficient play. In this work, we propose a mechanism for imbuing Reinforcement Learning agents with a theory of mind to discover efficient cooperative strategies in Hanabi. The primary contributions of this work are threefold: First, a formal definition of a computationally tractable mechanism for computing hand probabilities in Hanabi. Second, an extension to conventional Deep Reinforcement Learning that introduces reasoning over finitely nested theory of mind belief hierarchies. Finally, an intrinsic reward mechanism enabled by theory of mind that incentivizes agents to share strategically relevant private knowledge with their teammates. We demonstrate the utility of our algorithm against Rainbow, a state-of-the-art Reinforcement Learning agent.
BB: Booting Booster for Consumer Electronics with Modern OS
Venue: arXiv cs.DC 2021
Year: 2021

Unconventional computing platforms have spread widely and rapidly following smart phones and tablets: consumer electronics such as smart TVs and digital cameras. For such devices, fast booting is a critical requirement; waiting tens of seconds for a TV or a camera to boot up is not acceptable, unlike a PC or smart phone. Moreover, the software platforms of these devices have become as rich as conventional computing devices to provide comparable services. As a result, the booting procedure to start every required OS service, hardware component, and application, the quantity of which is ever increasing, may take unbearable time for most consumers. To accelerate booting, this paper introduces \textit{Booting Booster} (BB), which is used in all 2015 Samsung Smart TV models, and which runs the Linux-based Tizen OS. BB addresses the init scheme of Linux, which launches initial user-space OS services and applications and manages the life cycles of all user processes, by identifying and isolating booting-critical tasks, deferring non-critical tasks, and enabling execution of more tasks in parallel. BB has been successfully deployed in Samsung Smart TV 2015 models achieving a cold boot in 3.5 s (compared to 8.1 s with full commercial-grade optimizations without BB) without the need for suspend-to-RAM or hibernation. After this successful deployment, we have released the source code via http://opensource.samsung.com, and BB will be included in the open-source OS, Tizen (http://tizen.org).
The Role of Edges in Line Drawing Perception
Venue: arXiv cs.CV 2021
Year: 2021

It has often been conjectured that the effectiveness of line drawings can be explained by the similarity of edge images to line drawings. This paper presents several problems with explaining line drawing perception in terms of edges, and how the recently-proposed Realism Hypothesis of Hertzmann (2020) resolves these problems. There is nonetheless existing evidence that edges are often the best features for predicting where people draw lines; this paper describes how the Realism Hypothesis can explain this evidence.
DBL: Efficient Reachability Queries on Dynamic Graphs (Complete Version)
Venue: arXiv cs.DB 2021
Year: 2021

Reachability query is a fundamental problem on graphs, which has been extensively studied in academia and industry. Since graphs are subject to frequent updates in many applications, it is essential to support efficient graph updates while offering good performance in reachability queries. Existing solutions compress the original graph with the Directed Acyclic Graph (DAG) and propose efficient query processing and index update techniques. However, they focus on optimizing the scenarios where the Strong Connected Components(SCCs) remain unchanged and have overlooked the prohibitively high cost of the DAG maintenance when SCCs are updated. In this paper, we propose DBL, an efficient DAG-free index to support the reachability query on dynamic graphs with insertion-only updates. DBL builds on two complementary indexes: Dynamic Landmark (DL) label and Bidirectional Leaf (BL) label. The former leverages landmark nodes to quickly determine reachable pairs whereas the latter prunes unreachable pairs by indexing the leaf nodes in the graph. We evaluate DBL against the state-of-the-art approaches on dynamic reachability index with extensive experiments on real-world datasets. The results have demonstrated that DBL achieves orders of magnitude speedup in terms of index update, while still producing competitive query efficiency.
Advances and Challenges in Conversational Recommender Systems: A Survey
Venue: arXiv cs.IR 2021
Year: 2021

Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to answer two important questions well due to inherent shortcomings: (a) What exactly does a user like? (b) Why does a user like an item? The shortcomings are due to the way that static models learn user preference, i.e., without explicit instructions and active feedback from users. The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally. In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users. Considerable efforts, spread across disparate settings and applications, have been put into developing CRSs. Existing models, technologies, and evaluation methods for CRSs are far from mature. In this paper, we provide a systematic review of the techniques used in current CRSs. We summarize the key challenges of developing CRSs in five directions: (1) Question-based user preference elicitation. (2) Multi-turn conversational recommendation strategies. (3) Dialogue understanding and generation. (4) Exploitation-exploration trade-offs. (5) Evaluation and user simulation. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI). Based on these research directions, we discuss some future challenges and opportunities. We provide a road map for researchers from multiple communities to get started in this area. We hope this survey can help to identify and address challenges in CRSs and inspire future research.
Trusted Data Notifications from Private Blockchains
Venue: arXiv cs.CR 2021
Year: 2021

Private blockchain networks are used by enterprises to manage decentralized processes without trusted mediators and without exposing their assets publicly on an open network like Ethereum. Yet external parties that cannot join such networks may have a compelling need to be informed about certain data items on their shared ledgers along with certifications of data authenticity; e.g., a mortgage bank may need to know about the sale of a mortgaged property from a network managing property deeds. These parties are willing to compensate the networks in exchange for privately sharing information with proof of authenticity and authorization for external use. We have devised a novel and cryptographically secure protocol to effect a fair exchange between rational network members and information recipients using a public blockchain and atomic swap techniques. Using our protocol, any member of a private blockchain can atomically reveal private blockchain data with proofs in exchange for a monetary reward to an external party if and only if the external party is a valid recipient. The protocol preserves confidentiality of data for the recipient, and in addition, allows it to mount a challenge if the data turns out to be inauthentic. We also formally analyze the security and privacy of this protocol, which can be used in a wide array of practical scenarios
Experiences & Challenges with Server-Side WiFi Indoor Localization Using Existing Infrastructure
Venue: arXiv cs.NI 2021
Year: 2021

Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, %provided by Aruba, Cisco, etc., that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps. In this paper, we present our experiences in deploying a scalable location system for such venues. We show that server-side localization is not trivial and present two unique challenges associated with this approach, namely Cardinality Mismatch and High Client Scan Latency. The "Mismatch" challenge results in a significant mismatch between the set of access points (APs) reporting a client in the offline and online phases, while the "Latency" challenge results in a low number of APs reporting data for any particular client. We collect three weeks of detailed ground truth data (~200 landmarks), from a WiFi setup that has been deployed for more than four years, to provide evidences for the extent and understanding the impact of these problems. Our analysis of real-world client devices reveal that the current trend for the clients is to reduce scans, thereby adversely impacting their localization accuracy. We analyze how localization is impacted when scans are minimal. We propose heuristics to alleviate reduction in the accuracy despite lesser scans. Besides the number of scans, we summarize the other challenges and pitfalls of real deployments which hamper the localization accuracy.
Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery
Venue: arXiv cs.CV 2021
Year: 2021

A key requirement for autonomous on-orbit proximity operations is the estimation of a target spacecraft's relative pose (position and orientation). It is desirable to employ monocular cameras for this problem due to their low cost, weight, and power requirements. This work presents a novel convolutional neural network (CNN)-based monocular pose estimation system that achieves state-of-the-art accuracy with low computational demand. In combination with a Blender-based synthetic data generation scheme, the system demonstrates the ability to generalize from purely synthetic training data to real in-space imagery of the Northrop Grumman Enhanced Cygnus spacecraft. Additionally, the system achieves real-time performance on low-power flight-like hardware.
On the Evolution of Word Order
Venue: arXiv cs.CL 2021
Year: 2021

Most natural languages have a predominant or fixed word order. For example in English the word order is usually Subject-Verb-Object. This work attempts to explain this phenomenon as well as other typological findings regarding word order from a functional perspective. In particular, we examine whether fixed word order provides a functional advantage, explaining why these languages are prevalent. To this end, we consider an evolutionary model of language and demonstrate, both theoretically and using genetic algorithms, that a language with a fixed word order is optimal. We also show that adding information to the sentence, such as case markers and noun-verb distinction, reduces the need for fixed word order, in accordance with the typological findings.
Optimistic and Adaptive Lagrangian Hedging
Venue: arXiv cs.LG 2021
Year: 2021

In online learning an algorithm plays against an environment with losses possibly picked by an adversary at each round. The generality of this framework includes problems that are not adversarial, for example offline optimization, or saddle point problems (i.e. min max optimization). However, online algorithms are typically not designed to leverage additional structure present in non-adversarial problems. Recently, slight modifications to well-known online algorithms such as optimism and adaptive step sizes have been used in several domains to accelerate online learning -- recovering optimal rates in offline smooth optimization, and accelerating convergence to saddle points or social welfare in smooth games. In this work we introduce optimism and adaptive stepsizes to Lagrangian hedging, a class of online algorithms that includes regret-matching, and hedge (i.e. multiplicative weights). Our results include: a general general regret bound; a path length regret bound for a fixed smooth loss, applicable to an optimistic variant of regret-matching and regret-matching+; optimistic regret bounds for $Φ$ regret, a framework that includes external, internal, and swap regret; and optimistic bounds for a family of algorithms that includes regret-matching+ as a special case.
Generative hypergraph clustering: from blockmodels to modularity
Venue: arXiv cs.SI 2021
Year: 2021

Hypergraphs are a natural modeling paradigm for a wide range of complex relational systems. A standard analysis task is to identify clusters of closely related or densely interconnected nodes. Many graph algorithms for this task are based on variants of the stochastic blockmodel, a random graph with flexible cluster structure. However, there are few models and algorithms for hypergraph clustering. Here, we propose a Poisson degree-corrected hypergraph stochastic blockmodel (DCHSBM), a generative model of clustered hypergraphs with heterogeneous node degrees and edge sizes. Approximate maximum-likelihood inference in the DCHSBM naturally leads to a clustering objective that generalizes the popular modularity objective for graphs. We derive a general Louvain-type algorithm for this objective, as well as a a faster, specialized "All-Or-Nothing" (AON) variant in which edges are expected to lie fully within clusters. This special case encompasses a recent proposal for modularity in hypergraphs, while also incorporating flexible resolution and edge-size parameters. We show that AON hypergraph Louvain is highly scalable, including as an example an experiment on a synthetic hypergraph of one million nodes. We also demonstrate through synthetic experiments that the detectability regimes for hypergraph community detection differ from methods based on dyadic graph projections. We use our generative model to analyze different patterns of higher-order structure in school contact networks, U.S. congressional bill cosponsorship, U.S. congressional committees, product categories in co-purchasing behavior, and hotel locations from web browsing sessions, finding interpretable higher-order structure. We then study the behavior of our AON hypergraph Louvain algorithm, finding that it is able to recover ground truth clusters in empirical data sets exhibiting the corresponding higher-order structure.
VIO-Aided Structure from Motion Under Challenging Environments
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we present a robust and efficient Structure from Motion pipeline for accurate 3D reconstruction under challenging environments by leveraging the camera pose information from a visual-inertial odometry. Specifically, we propose a geometric verification method to filter out mismatches by considering the prior geometric configuration of candidate image pairs. Furthermore, we introduce an efficient and scalable reconstruction approach that relies on batched image registration and robust bundle adjustment, both leveraging the reliable local odometry estimation. Extensive experimental results show that our pipeline performs better than the state-of-the-art SfM approaches in terms of reconstruction accuracy and robustness for challenging sequential image collections.
Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data
Venue: arXiv cs.CV 2021
Year: 2021

Automatic detection of brain neoplasm in Magnetic Resonance Imaging (MRI) is gaining importance in many medical diagnostic applications. This report presents two improvements for brain neoplasm detection in MRI data: an advanced preprocessing technique is proposed to improve the area of interest in MRI data and a hybrid technique using Convolutional Neural Network (CNN) for feature extraction followed by Support Vector Machine (SVM) for classification. The learning algorithm for SVM is modified with the addition of cost function to minimize false positive prediction addressing the errors in MRI data diagnosis. The proposed approach can effectively detect the presence of neoplasm and also predict whether it is cancerous (malignant) or non-cancerous (benign). To check the effectiveness of the proposed preprocessing technique, it is inspected visually and evaluated using training performance metrics. A comparison study between the proposed classification technique and the existing techniques was performed. The result showed that the proposed approach outperformed in terms of accuracy and can handle errors in classification better than the existing approaches.
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh
Venue: arXiv cs.CY 2021
Year: 2021

Newspapers are trustworthy media where people get the most reliable and credible information compared with other sources. On the other hand, social media often spread rumors and misleading news to get more traffic and attention. Careful characterization, evaluation, and interpretation of newspaper data can provide insight into intrigue and passionate social issues to monitor any big social incidence. This study analyzed a large set of spatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic. The methodology included volume analysis, topic analysis, automated classification, and sentiment analysis of news articles to get insight into the COVID-19 pandemic in different sectors and regions in Bangladesh over a period of time. This analysis will help the government and other organizations to figure out the challenges that have arisen in society due to this pandemic, what steps should be taken immediately and in the post-pandemic period, how the government and its allies can come together to address the crisis in the future, keeping these problems in mind.
Incentive Mechanism Design for Federated Learning: Hedonic Game Approach
Venue: arXiv cs.GT 2021
Year: 2021

Incentive mechanism design is crucial for enabling federated learning. We deal with clustering problem of agents contributing to federated learning setting. Assuming agents behave selfishly, we model their interaction as a stable coalition partition problem using hedonic games where agents and clusters are the players and coalitions, respectively. We address the following question: is there a family of hedonic games ensuring a Nash-stable coalition partition? We propose the Nash-stable set which determines the family of hedonic games possessing at least one Nash-stable partition, and analyze the conditions of non-emptiness of the Nash-stable set. Besides, we deal with the decentralized clustering. We formulate the problem as a non-cooperative game and prove the existence of a potential game.