pwc-archive/papers-with-abstracts huggingface dataset:
On Interpretation and Measurement of Soft Attributes for Recommendation
Authors: Krisztian Balog, Filip Radlinski, Alexandros Karatzoglou
Date: 2021-05-19 00:00:00
Tasks: Attribute, Recommendation Systems

We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques.
An Overview of Computer Supported Query Formulation
Authors: H. A. Proper
Date: 2021-05-19 00:00:00

Most present day organisations make use of some automated information system. This usually means that a large body of vital corporate information is stored in these information systems. As a result, an essential function of information systems should be the support of disclosure of this information. We purposely use the term {\em information disclosure} in this context. When using the term information disclosure we envision a computer supported mechanism that allows for an easy and intuitive formulation of queries in a language that is as close to the user's perception of the universe of discourse as possible. From this point of view, it is only obvious that we do not consider a simple query mechanism where users have to enter complex queries manually and look up what information is stored in a set of relational tables. Without a set of adequate information disclosure avenues an information system becomes worthless since there is no use in storing information that will never be retrieved.
Chameleon 2: An Improved Graph-Based Clustering Algorithm
Authors: Tomas Barton, Tomas Bruna, Pavel Kordik
Date: 2019-01-01 00:00:00
Tasks: Clustering

Traditional clustering algorithms fail to produce human-like results when confronted with data of variable density, complex distributions, or in the presence of noise. We propose an improved graph-based clustering algorithm called Chameleon 2, which overcomes several drawbacks of state-of-the-art clustering approaches. We modified the internal cluster quality measure and added an extra step to ensure algorithm robustness. Our results reveal a significant positive impact on the clustering quality measured by Normalized Mutual Information on 32 artificial datasets used in the clustering literature. This significant improvement is also confirmed on real-world datasets.

The performance of clustering algorithms such as DBSCAN is extremely parameter sensitive, and exhaustive manual parameter tuning is necessary to obtain a meaningful result. All hierarchical clustering methods are very sensitive to cutoff selection, and a human expert is often required to find the true cutoff for each clustering result. We present an automated cutoff selection method that enables the Chameleon 2 algorithm to generate high-quality clustering in autonomous mode.
Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
Authors: Jiansheng Fang, Huazhu Fu, Dan Zeng, Xiao Yan, Yuguang Yan, Jiang Liu
Date: 2021-05-19 00:00:00
Tasks: Diagnostic, Retrieval, Specificity

When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database. The similarity between the query case and retrieved similar cases is determined by visual features extracted from pathologically abnormal regions. However, the manifestation of these regions often lacks specificity, i.e., different diseases can have the same manifestation, and different manifestations may occur at different stages of the same disease. To combat the manifestation ambiguity in medical instance retrieval, we propose a novel deep framework called Y-Net, encoding images into compact hash-codes generated from convolutional features by feature aggregation. Y-Net can learn highly discriminative convolutional features by unifying the pixel-wise segmentation loss and classification loss. The segmentation loss allows exploring subtle spatial differences for good spatial-discriminability while the classification loss utilizes class-aware semantic information for good semantic-separability. As a result, Y-Net can enhance the visual features in pathologically abnormal regions and suppress the disturbing of the background during model training, which could effectively embed discriminative features into the hash-codes in the retrieval stage. Extensive experiments on two medical image datasets demonstrate that Y-Net can alleviate the ambiguity of pathologically abnormal regions and its retrieval performance outperforms the state-of-the-art method by an average of 9.27\% on the returned list of 10.
Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application
Authors: Rongyu Cao, Yixuan Cao, Ganbin Zhou, Ping Luo
Date: 2021-05-14 00:00:00
Tasks: Binary Classification, Passage Retrieval, Retrieval

In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications. However, long documents, containing hundreds or even thousands of pages and variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper on of the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. The empirical experiments based on thousands of long documents from Chinese, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.9726, 0.7291 and 0.9578 in Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications.
Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation at Twitter
Authors: Alim Virani, Jay Baxter, Dan Shiebler, Philip Gautier, Shivam Verma, Yan Xia, Apoorv Sharma, Sumit Binnani, LinLin Chen, Chenguang Yu
Date: 2021-05-13 00:00:00
Tasks: Recommendation Systems

Traditionally, heuristic methods are used to generate candidates for large scale recommender systems. Model-based candidate generation promises multiple potential advantages, primarily that we can explicitly optimize the same objective as the downstream ranking model. However, large scale model-based candidate generation approaches suffer from dataset bias problems caused by the infeasibility of obtaining representative data on very irrelevant candidates. Popular techniques to correct dataset bias, such as inverse propensity scoring, do not work well in the context of candidate generation. We first explore the dynamics of the dataset bias problem and then demonstrate how to use random sampling techniques to mitigate it. Finally, in a novel application of fine-tuning, we show performance gains when applying our candidate generation system to Twitter's home timeline.
Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kid's Products in Search and Recommendations
Authors: Amifa Raj, Ashlee Milton, Michael D. Ekstrand
Date: 2021-05-13 00:00:00
Tasks: Position

In this position paper, we argue for the need to investigate if and how gender stereotypes manifest in search and recommender systems.As a starting point, we particularly focus on how these systems may propagate and reinforce gender stereotypes through their results in learning environments, a context where teachers and children in their formative stage regularly interact with these systems. We provide motivating examples supporting our concerns and outline an agenda to support future research addressing the phenomena.
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Authors: Hongxiang Fan, Martin Ferianc, Miguel Rodrigues, HongYu Zhou, Xinyu Niu, Wayne Luk
Date: 2021-05-12 00:00:00
Tasks: Autonomous Vehicles, Bayesian Inference, Decision Making, Recommendation Systems, Vocal Bursts Intensity Prediction

Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs are unable to capture their model uncertainty which is crucial for many safety-critical applications including healthcare and autonomous vehicles. In comparison, Bayesian neural networks (BNNs) are able to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their expensive computational cost and limited hardware performance. This work proposes a novel FPGA-based hardware architecture to accelerate BNNs inferred through Monte Carlo Dropout. Compared with other state-of-the-art BNN accelerators, the proposed accelerator can achieve up to 4 times higher energy efficiency and 9 times better compute efficiency. Considering partial Bayesian inference, an automatic framework is proposed, which explores the trade-off between hardware and algorithmic performance. Extensive experiments are conducted to demonstrate that our proposed framework can effectively find the optimal points in the design space.
Foundations of MIMO Radar Detection Aided by Reconfigurable Intelligent Surfaces
Authors: Stefano Buzzi, Emanuele Grossi, Marco Lops, Luca Venturino
Date: 2021-05-19 00:00:00

A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the fundamental problem of target detection in a RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general signal model is introduced, which includes the possibility of using up to two RISs (one close to the radar transmitter and one close to the radar receiver) and subsumes both a monostatic and a bistatic radar configuration with or without a line-of-sight view of the prospective target. Upon resorting to a generalized likelihood ratio test (GLRT), the design of the phase shifts introduced by the RIS elements is formulated as the maximization of the probability of detection in the location under inspection for a fixed probability of false alarm, and suitable optimization algorithms are proposed. The performance analysis shows the benefits granted by the presence of the RISs and shed light on the interplay among the key system parameters, such as the radar-RIS distance, the RIS size, and location of the prospective target. A major finding is that the RISs should be better deployed in the near-field of the radar arrays at both the transmit and the receive side. The paper is concluded by discussing some open problems and foreseen applications.
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images
Authors: Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino
Date: 2021-04-25 00:00:00
Proceeding: SEMEVAL 2021

We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) detecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.
A Decade of Research for Image Compression In Multimedia Laboratory
Authors: Shahrokh Paravarzar, Javaneh Alavi
Date: 2021-04-06 00:00:00
Tasks: Data Compression, Image Compression

With the advancement of technology, we have supercomputers with high processing power and affordable prices. In addition, using multimedia expanded all around the world. This caused a vast use of images and videos in different fields. As this kind of data consists of a large amount of information, there is a need to use compression methods to store, manage or transfer them better and faster. One effective technique, which was introduced is variable resolution. This technique stimulates human vision and divides regions in pictures into two different parts, including the area of interest that needs more detail and periphery parts with less detail. This results in better compression. The variable resolution was used for image, video, and 3D motion data compression. This paper investigates the mentioned technique and some other research in this regard.
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech
Authors: Keisuke Kinoshita, Marc Delcroix, Naohiro Tawara
Date: 2021-05-19 00:00:00
Tasks: Clustering, Constrained Clustering, Decoder, speaker-diarization, Speaker Diarization

Recently, we proposed a novel speaker diarization method called End-to-End-Neural-Diarization-vector clustering (EEND-vector clustering) that integrates clustering-based and end-to-end neural network-based diarization approaches into one framework. The proposed method combines advantages of both frameworks, i.e. high diarization performance and handling of overlapped speech based on EEND, and robust handling of long recordings with an arbitrary number of speakers based on clustering-based approaches. However, the method was only evaluated so far on simulated 2-speaker meeting-like data. This paper is to (1) report recent advances we made to this framework, including newly introduced robust constrained clustering algorithms, and (2) experimentally show that the method can now significantly outperform competitive diarization methods such as Encoder-Decoder Attractor (EDA)-EEND, on CALLHOME data which comprises real conversational speech data including overlapped speech and an arbitrary number of speakers. By further analyzing the experimental results, this paper also discusses pros and cons of the proposed method and reveals potential for further improvement. A set of the code to reproduce the results is available at https://github.com/nttcslab-sp/EEND-vector-clustering.
A Guide to Reducing Carbon Emissions through Data Center Geographical Load Shifting
Authors: Julia Lindberg, Yasmine Abdennadher, Jiaqi Chen, Bernard C. Lesieutre, Line Roald
Date: 2021-05-19 00:00:00

Recent computing needs have lead technology companies to develop large scale, highly optimized data centers. These data centers represent large loads on electric power networks which have the unique flexibility to shift load both geographically and temporally. This paper focuses on how data centers can use their geographic load flexibility to reduce carbon emissions through clever interactions with electricity markets. Because electricity market clearing accounts for congestion and power flow physics in the electric grid, the carbon emissions associated with electricity use varies between (potentially geographically close) locations. Using our knowledge about this process, we propose a new and improved metric to guide geographic load shifting, which we refer to as the locational marginal carbon emission $\lambda_{\text{CO}_2}$. We compare this and three other shifting metrics on their ability to reduce carbon emissions and generation costs throughout the course of a year. Our analysis demonstrates that $\lambda_{\text{CO}_2}$ is more effective in reducing carbon emissions than more commonly proposed metrics that do not account for the specifics of the power grid.
Dynamic Trajectory and Offloading Control of UAV-enabled MEC under User Mobility
Authors: Zheyuan Yang, Suzhi Bi, Ying-Jun Angela Zhang
Date: 2021-05-19 00:00:00
Tasks: Stochastic Optimization

In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. To control the system operation in sequential time slots, we formulate the problem as a multi-stage stochastic optimization, and propose an online algorithm that optimizes the resource allocation and the UAV trajectory in each stage. We adopt Lyapunov optimization to convert the multi-stage stochastic problem into per-slot deterministic problems with much less optimizing variables. To tackle the non-convex per-slot problem, we use the successive convex approximation (SCA) technique to jointly optimize the resource allocation and the UAV movement. Simulation results show that the proposed online algorithm can satisfy the average UAV energy and queue stability constraints, and significantly outperform the other considered benchmark methods in reducing the energy consumption of ground users.
Optimal Science-time Reorientation Policy for the Comet Interceptor Flyby via Sequential Convex Programming
Authors: Valentin Preda, Andrew Hyslop, Samir Bennani
Date: 2021-05-19 00:00:00

This paper introduces an algorithm to perform optimal reorientation of a spacecraft during a high speed flyby mission that maximizes the time a certain target is kept within the field of view of scientific instruments. The method directly handles the nonlinear dynamics of the spacecraft, sun exclusion constraint, torque and momentum limits on the reaction wheels as well as potential faults in these actuators. A sequential convex programming approach was used to reformulate non-convex pointing objectives and other constraints in terms of a series of novel convex cardinality minimization problems. These subproblems were then efficiently solved even on limited hardware resources using convex programming solvers implementing second-order conic constraints. The proposed method was applied to a scenario that involved maximizing the science time for the upcoming Comet Interceptor flyby mission developed by the European Space Agency. Extensive simulation results demonstrate the capability of the approach to generate viable trajectories even in the presence of reaction wheel failures or prior dust particle impacts.
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations
Authors: Lei Guo, Han Zhao
Date: 2021-05-19 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

In this paper, we present a novel algorithm named synchronous integral Q-learning, which is based on synchronous policy iteration, to solve the continuous-time infinite horizon optimal control problems of input-affine system dynamics. The integral reinforcement is measured as an excitation signal in this method to estimate the solution to the Hamilton-Jacobi-Bellman equation. Moreover, the proposed method is completely model-free, i.e. no a priori knowledge of the system is required. Using policy iteration, the actor and critic neural networks can simultaneously approximate the optimal value function and policy. The persistence of excitation condition is required to guarantee the convergence of the two networks. Unlike in traditional policy iteration algorithms, the restriction of the initial admissible policy is relaxed in this method. The effectiveness of the proposed algorithm is verified through numerical simulations.
Cross-Platform Simulation Architecture with application to truck platooning impact assessment
Authors: Andres Ladino, Lin Xiao, Kingsley Adjenugwhure, Nicolás Deschle, Gerdien Klunder
Date: 2021-05-19 00:00:00

Simulation-based traffic impact assessment studies of advanced technologies such as truck platooning need to be carried out to ascertain their benefits for traffic efficiency, safety and environment. To reduce uncertainty in the results of such simulation-based studies, the same simulation studies can be performed in different simulation software. Many traffic simulation software packages (Aimsun, SymuVia, Vissim, SUMO) are currently available for traffic impact assessment of new technologies such as truckplatooning. However, to fully model and simulate the functionalities of such advanced technologies in different simulation environments, several extensions need to be made to the simulation platforms. In most cases, these extensions have to be programmed in different programming languages (C++, Python) and each simulator has its own simulator specific API. This makes it difficult to reuse software written for a specific functionality in one simulation platform in a different simulation platform. To overcome this issue, this paper presents a novel architecture for cross-platform simulation. The architecture is designed such that a specific functionality such as truck-platooning or any other functionality is made platform independent. We designed a cross-platform architecture for simulating a truck-platooning functionality using Vissim and SymuVia simulation software to determine the traffic flow effects of multi-brand truck platooning in the context of the EU project ENSEMBLE. In this draft paper, we present the structure of the framework as well as some preliminary results from a simple simulation performed with the cross-platform simulator.