OpenRaiser/Intern-Atlas huggingface dataset:
Private Interdependent Valuations
Venue: arXiv cs.GT 2021
Year: 2021

We consider the single-item interdependent value setting, where there is a monopolist, $n$ buyers, and each buyer has a private signal $s_i$ describing a piece of information about the item. Each bidder $i$ also has a valuation function $v_i(s_1,\ldots,s_n)$ mapping the (private) signals of all buyers to a positive real number representing their value for the item. This setting captures scenarios where the item's information is asymmetric or dispersed among agents, such as in competitions for oil drilling rights, or in auctions for art pieces. Due to the increased complexity of this model compared to standard private values, it is generally assumed that each bidder's valuation function $v_i$ is public knowledge. But in many situations, the seller may not know how a bidder aggregates signals into a valuation. In this paper, we design mechanisms that guarantee approximately-optimal social welfare while satisfying ex-post incentive compatibility and individual rationality for the case where the valuation functions are private to the bidders. When the valuations are public, it is possible for optimal social welfare to be attained by a deterministic mechanism under a single-crossing condition. In contrast, when the valuations are the bidders' private information, we show that no finite bound can be achieved by any deterministic mechanism even under single-crossing. Moreover, no randomized mechanism can guarantee better than an $n$-approximation. We thus consider valuation functions that are submodular over signals (SOS), introduced in the context of combinatorial auctions in a recent breakthrough paper by Eden et al. [EC'19]. Our main result is an $O(\log^2 n)$-approximation for buyers with private signals and valuations under the SOS condition. We also give a tight $Θ(k)$-approximation for the case each agent's valuation depends on at most $k$ other signals even for unknown $k$.
Classifying YouTube Comments Based on Sentiment and Type of Sentence
Venue: arXiv cs.IR 2021
Year: 2021

As a YouTube channel grows, each video can potentially collect enormous amounts of comments that provide direct feedback from the viewers. These comments are a major means of understanding viewer expectations and improving channel engagement. However, the comments only represent a general collection of user opinions about the channel and the content. Many comments are poorly constructed, trivial, and have improper spellings and grammatical errors. As a result, it is a tedious job to identify the comments that best interest the content creators. In this paper, we extract and classify the raw comments into different categories based on both sentiment and sentence types that will help YouTubers find relevant comments for growing their viewership. Existing studies have focused either on sentiment analysis (positive and negative) or classification of sub-types within the same sentence types (e.g., types of questions) on a text corpus. These have limited application on non-traditional text corpus like YouTube comments. We address this challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models. We evaluate each combination of statistical measure and the machine learning model using cross validation and $F_1$ scores. The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.
Geometrical holographic display
Venue: arXiv cs.GR 2021
Year: 2021

Is it possible to realize a holographic display with commercial available components and devices? Is it possible to manipulate light to reconstruct light field without using coherent light and complicated optical components? Is it possible to minimize the amount of date involved in building 3D scenes? Is it possible to design a holographic video display that doesn't require huge computational cost? Is it possible to realize a holographic video display with portable form like a flat-panel display commercialized nowadays? This research gives yes answers to all the above questions. A novel geometrical holographic display was proposed, which uses geometrical optical principle to reproduce realistic 3D images with all human visual cues and without visual side effects. In addition, a least necessary light field representation was introduced which can provide guidance for how to minimize the amount of data involved when designing a FP3D or building 3D scenes. Finally, a proof-of-concept prototype is set up which can provide true 3D scenes with depth range larger than 5m.
Deep Keyphrase Completion
Venue: arXiv cs.IR 2021
Year: 2021

Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval. Though previous studies have made substantial efforts for automated keyphrase extraction and generation, surprisingly, few studies have been made for \textit{keyphrase completion} (KPC). KPC aims to generate more keyphrases for document (e.g. scientific publication) taking advantage of document content along with a very limited number of known keyphrases, which can be applied to improve text indexing system, etc. In this paper, we propose a novel KPC method with an encoder-decoder framework. We name it \textit{deep keyphrase completion} (DKPC) since it attempts to capture the deep semantic meaning of the document content together with known keyphrases via a deep learning framework. Specifically, the encoder and the decoder in DKPC play different roles to make full use of the known keyphrases. The former considers the keyphrase-guiding factors, which aggregates information of known keyphrases into context. On the contrary, the latter considers the keyphrase-inhibited factor to inhibit semantically repeated keyphrase generation. Extensive experiments on benchmark datasets demonstrate the efficacy of our proposed model.
Revisiting spatio-temporal layouts for compositional action recognition
Venue: arXiv cs.CV 2021
Year: 2021

Recognizing human actions is fundamentally a spatio-temporal reasoning problem, and should be, at least to some extent, invariant to the appearance of the human and the objects involved. Motivated by this hypothesis, in this work, we take an object-centric approach to action recognition. Multiple works have studied this setting before, yet it remains unclear (i) how well a carefully crafted, spatio-temporal layout-based method can recognize human actions, and (ii) how, and when, to fuse the information from layout and appearance-based models. The main focus of this paper is compositional/few-shot action recognition, where we advocate the usage of multi-head attention (proven to be effective for spatial reasoning) over spatio-temporal layouts, i.e., configurations of object bounding boxes. We evaluate different schemes to inject video appearance information to the system, and benchmark our approach on background cluttered action recognition. On the Something-Else and Action Genome datasets, we demonstrate (i) how to extend multi-head attention for spatio-temporal layout-based action recognition, (ii) how to improve the performance of appearance-based models by fusion with layout-based models, (iii) that even on non-compositional background-cluttered video datasets, a fusion between layout- and appearance-based models improves the performance.
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach
Venue: arXiv cs.LG 2021
Year: 2021

Bus system is a critical component of sustainable urban transportation. However, the operation of a bus fleet is unstable in nature, and bus bunching has become a common phenomenon that undermines the efficiency and reliability of bus systems. Recently research has demonstrated the promising application of multi-agent reinforcement learning (MARL) to achieve efficient vehicle holding control to avoid bus bunching. However, existing studies essentially overlook the robustness issue resulting from various events, perturbations and anomalies in a transit system, which is of utmost importance when transferring the models for real-world deployment/application. In this study, we integrate implicit quantile network and meta-learning to develop a distributional MARL framework -- IQNC-M -- to learn continuous control. The proposed IQNC-M framework achieves efficient and reliable control decisions through better handling various uncertainties/events in real-time transit operations. Specifically, we introduce an interpretable meta-learning module to incorporate global information into the distributional MARL framework, which is an effective solution to circumvent the credit assignment issue in the transit system. In addition, we design a specific learning procedure to train each agent within the framework to pursue a robust control policy. We develop simulation environments based on real-world bus services and passenger demand data and evaluate the proposed framework against both traditional holding control models and state-of-the-art MARL models. Our results show that the proposed IQNC-M framework can effectively handle the various extreme events, such as traffic state perturbations, service interruptions, and demand surges, thus improving both efficiency and reliability of the system.
Machine-Learning Identification of Hemodynamics in Coronary Arteries in the Presence of Stenosis
Venue: arXiv cs.LG 2021
Year: 2021

Prediction of the blood flow characteristics is of utmost importance for understanding the behavior of the blood arterial network, especially in the presence of vascular diseases such as stenosis. Computational fluid dynamics (CFD) has provided a powerful and efficient tool to determine these characteristics including the pressure and velocity fields within the network. Despite numerous studies in the field, the extremely high computational cost of CFD has led the researchers to develop new platforms including Machine Learning approaches that instead provide faster analyses at a much lower cost. In this study, we put forth a Deep Neural Network framework to predict flow behavior in a coronary arterial network with different properties in the presence of any abnormality like stenosis. To this end, an artificial neural network (ANN) model is trained using synthetic data so that it can predict the pressure and velocity within the arterial network. The data required to train the neural network were obtained from the CFD analysis of several geometries of arteries with specific features in ABAQUS software. Blood pressure drop caused by stenosis, which is one of the most important factors in the diagnosis of heart diseases, can be predicted using our proposed model knowing the geometrical and flow boundary conditions of any section of the coronary arteries. The efficiency of the model was verified using three real geometries of LAD's vessels. The proposed approach precisely predicts the hemodynamic behavior of the blood flow. The average accuracy of the pressure prediction was 98.7% and the average velocity magnitude accuracy was 93.2%. According to the results of testing the model on three patient-specific geometries, model can be considered as an alternative to finite element methods as well as other hard-to-implement and time-consuming numerical simulations.
A Strongly-Labelled Polyphonic Dataset of Urban Sounds with Spatiotemporal Context
Venue: arXiv cs.SD 2021
Year: 2021

This paper introduces SINGA:PURA, a strongly labelled polyphonic urban sound dataset with spatiotemporal context. The data were collected via several recording units deployed across Singapore as a part of a wireless acoustic sensor network. These recordings were made as part of a project to identify and mitigate noise sources in Singapore, but also possess a wider applicability to sound event detection, classification, and localization. This paper introduces an accompanying hierarchical label taxonomy, which has been designed to be compatible with other existing datasets for urban sound tagging while also able to capture sound events unique to the Singaporean context. This paper details the data collection, annotation, and processing methodologies for the creation of the dataset. We further perform exploratory data analysis and include the performance of a baseline model on the dataset as a benchmark.
Differential Privacy in Cognitive Radio Networks: A Comprehensive Survey
Venue: arXiv cs.CR 2021
Year: 2021

Background/Introduction: Integrating cognitive radio (CR) with traditional wireless networks is helping solve the problem of spectrum scarcity in an efficient manner. The opportunistic and dynamic spectrum access features of CR provide the functionality to its unlicensed users to utilize the underutilized spectrum at the time of need because CR nodes can sense vacant bands of spectrum and can also access them to carry out communication. Various capabilities of CR nodes depend upon efficient and continuous reporting of data with each other and centralized base stations, which in turn can cause leakage in privacy. Experimental studies have shown that the privacy of CR users can be compromised easily during the cognition cycle, because they are knowingly or unknowingly sharing various personally identifiable information (PII), such as location, device ID, signal status, etc. In order to preserve this privacy leakage, various privacy preserving strategies have been developed by researchers, and according to us differential privacy is the most significant among them.
Neural network is heterogeneous: Phase matters more
Venue: arXiv cs.LG 2021
Year: 2021

We find a heterogeneity in both complex and real valued neural networks with the insight from wave optics, claiming a much more important role of phase than its amplitude counterpart in the weight matrix. In complex-valued neural networks, we show that among different types of pruning, the weight matrix with only phase information preserved achieves the best accuracy, which holds robustly under various settings of depth and width. The conclusion can be generalized to real-valued neural networks, where signs take the place of phases. These inspiring findings enrich the techniques of network pruning and binary computation.
W[1]-hardness of Outer Connected Dominating set in d-degenerate Graphs
Venue: arXiv cs.CC 2021
Year: 2021

A set $D \subseteq V$ of a graph $G = (V,E)$ is called an outer-connected dominating set of $G$ if every vertex $v$ not in $D$ is adjacent to at least one vertex in $D$, and the induced subgraph of $G$ on $V \setminus D$ is connected. The Minimum Outer-connected Domination problem is to find an outer-connected dominating set of minimum cardinality for the input graph $G$. Given a positive integer $k$ and a graph $G = (V, E)$, the Outer-connected Domination Decision problem is to decide whether $G$ has an outer-connected dominating set of cardinality at most $k$. The Outer-connected Domination Decision problem is known to be NP-complete, even for bipartite graphs. We study the problem of outer-connected domination on sparse graphs from the perspective of parameterized complexity and show that it is W[1]-hard on d-degenerate graphs, while the original connected dominating set has FTP algorithm on d-degenerate graphs.
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting
Venue: arXiv cs.AI 2021
Year: 2021

Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy - which serve as critical infrastructures for most societal operations. However, due to the requirements of reliability, accountability, and explainability, it is risky to directly apply AI-based methods to power systems because society cannot afford cascading failures and large-scale blackouts, which easily cost billions of dollars. To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model. The goal is to provide a high level of confidence to energy utility users. For illustration purposes, the paper uses power system event forecasting (PEF) as an example, which carefully analyzes synchrophasor patterns measured by the Phasor Measurement Units (PMUs). Such a physical understanding leads to a data-driven framework that reduces the dimensionality with physics and forecasts the event with high credibility. Specifically, for dimensionality reduction, machine learning arranges physical information from different dimensions, resulting inefficient information extraction. For event forecasting, the supervised learning model fuses the results of different models to increase the confidence. Finally, comprehensive experiments demonstrate the high accuracy, efficiency, and reliability as compared to other state-of-the-art machine learning methods.
GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Venue: arXiv cs.IR 2021
Year: 2021

Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item nodes function as the main element of GCNs to perform information propagation and generate informative representations. Nevertheless, an underlying challenge lies in the quality of interaction graph, since observed interactions with less-interested items occur in implicit feedback (say, a user views micro-videos accidentally). This means that the neighborhoods involved with such false-positive edges will be influenced negatively and the signal on user preference can be severely contaminated. However, existing GCN-based recommender models leave such challenge under-explored, resulting in suboptimal representations and performance. In this work, we focus on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges. Towards this end, we devise a new GCN-based recommender model, \emph{Graph-Refined Convolutional Network} (GRCN), which adjusts the structure of interaction graph adaptively based on status of model training, instead of remaining the fixed structure. In particular, a graph refining layer is designed to identify the noisy edges with the high confidence of being false-positive interactions, and consequently prune them in a soft manner. We then apply a graph convolutional layer on the refined graph to distill informative signals on user preference. Through extensive experiments on three datasets for micro-video recommendation, we validate the rationality and effectiveness of our GRCN. Further in-depth analysis presents how the refined graph benefits the GCN-based recommender model.
Categorical Difference and Related Brain Regions of the Attentional Blink Effect
Venue: arXiv cs.AI 2021
Year: 2021

Attentional blink (AB) is a biological effect, showing that for 200 to 500ms after paying attention to one visual target, it is difficult to notice another target that appears next, and attentional blink magnitude (ABM) is a indicating parameter to measure the degree of this effect. Researchers have shown that different categories of images can access the consciousness of human mind differently, and produce different ranges of ABM values. So in this paper, we compare two different types of images, categorized as animal and object, by predicting ABM values directly from image features extracted from convolutional neural network (CNN), and indirectly from functional magnetic resonance imaging (fMRI) data. First, for two sets of images, we separately extract their average features from layers of Alexnet, a classic model of CNN, then input the features into a trained linear regression model to predict ABM values, and we find higher-level instead of lower-level image features determine the categorical difference in AB effect, and mid-level image features predict ABM values more correctly than low-level and high-level image features. Then we employ fMRI data from different brain regions collected when the subjects viewed 50 test images to predict ABM values, and conclude that brain regions covering relatively broader areas, like LVC, HVC and VC, perform better than other smaller brain regions, which means AB effect is more related to synthetic impact of several visual brain regions than only one particular visual regions.
Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task
Venue: arXiv cs.CL 2021
Year: 2021

This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. We participated in all three evaluation tracks including Large Track and two Small Tracks where the former one is unconstrained and the latter two are fully constrained. Our model submissions to the shared task were initialized with DeltaLM\footnote{\url{https://aka.ms/deltalm}}, a generic pre-trained multilingual encoder-decoder model, and fine-tuned correspondingly with the vast collected parallel data and allowed data sources according to track settings, together with applying progressive learning and iterative back-translation approaches to further improve the performance. Our final submissions ranked first on three tracks in terms of the automatic evaluation metric.
Automatic Evaluation and Moderation of Open-domain Dialogue Systems
Venue: arXiv cs.CL 2021
Year: 2021

The development of Open-Domain Dialogue Systems (ODS)is a trending topic due to the large number of research challenges, large societal and business impact, and advances in the underlying technology. However, the development of these kinds of systems requires two important characteristics:1) automatic evaluation mechanisms that show high correlations with human judgements across multiple dialogue evaluation aspects (with explainable features for providing constructive and explicit feedback on the quality of generative models' responses for quick development and deployment)and 2) mechanisms that can help to control chatbot responses,while avoiding toxicity and employing intelligent ways to handle toxic user comments and keeping interaction flow and engagement. This track at the 10th Dialogue System Technology Challenge (DSTC10) is part of the ongoing effort to promote scalable and toxic-free ODS. This paper describes the datasets and baselines provided to participants, as well as submission evaluation results for each of the two proposed subtasks.