pwc-archive/papers-with-abstracts huggingface dataset:
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning
Authors: Baturalp Buyukates, Emre Ozfatura, Sennur Ulukus, Deniz Gunduz
Date: 2021-03-01 00:00:00
Tasks: Clustering

Distributed implementations are crucial in speeding up large scale machine learning applications. Distributed gradient descent (GD) is widely employed to parallelize the learning task by distributing the dataset across multiple workers. A significant performance bottleneck for the per-iteration completion time in distributed synchronous GD is $straggling$ workers. Coded distributed computation techniques have been introduced recently to mitigate stragglers and to speed up GD iterations by assigning redundant computations to workers. In this paper, we consider gradient coding (GC), and propose a novel dynamic GC scheme, which assigns redundant data to workers to acquire the flexibility to dynamically choose from among a set of possible codes depending on the past straggling behavior. In particular, we consider GC with clustering, and regulate the number of stragglers in each cluster by dynamically forming the clusters at each iteration; hence, the proposed scheme is called $GC$ $with$ $dynamic$ $clustering$ (GC-DC). Under a time-correlated straggling behavior, GC-DC gains from adapting to the straggling behavior over time such that, at each iteration, GC-DC aims at distributing the stragglers across clusters as uniformly as possible based on the past straggler behavior. For both homogeneous and heterogeneous worker models, we numerically show that GC-DC provides significant improvements in the average per-iteration completion time without an increase in the communication load compared to the original GC scheme.
Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model
Authors: B. D. M. De Zoysa, Y. A. M. M. A. Ali, M. D. I. Maduranga, Indika Perera, Saliya Ekanayake, Anil Vullikanti
Date: 2021-02-27 00:00:00
Tasks: Computer Security, Spam detection

The problem of finding dense components of a graph is a widely explored area in data analysis, with diverse applications in fields and branches of study including community mining, spam detection, computer security and bioinformatics. This research project explores previously available algorithms in order to study them and identify potential modifications that could result in an improved version with considerable performance and efficiency leap. Furthermore, efforts were also steered towards devising a novel algorithm for the problem of densest subgraph discovery. This paper presents an improved implementation of a widely used densest subgraph discovery algorithm and a novel parallel algorithm which produces better results than a 2-approximation.
Automated Creative Optimization for E-Commerce Advertising
Authors: Jin Chen, Ju Xu, Gangwei Jiang, Tiezheng Ge, Zhiqiang Zhang, Defu Lian, Kai Zheng
Date: 2021-02-28 00:00:00
Tasks: AutoML, Click-Through Rate Prediction, Thompson Sampling, Variational Inference

Advertising creatives are ubiquitous in E-commerce advertisements and aesthetic creatives may improve the click-through rate (CTR) of the products. Nowadays smart advertisement platforms provide the function of compositing creatives based on source materials provided by advertisers. Since a great number of creatives can be generated, it is difficult to accurately predict their CTR given a limited amount of feedback. Factorization machine (FM), which models inner product interaction between features, can be applied for the CTR prediction of creatives. However, interactions between creative elements may be more complex than the inner product, and the FM-estimated CTR may be of high variance due to limited feedback. To address these two issues, we propose an Automated Creative Optimization (AutoCO) framework to model complex interaction between creative elements and to balance between exploration and exploitation. Specifically, motivated by AutoML, we propose one-shot search algorithms for searching effective interaction functions between elements. We then develop stochastic variational inference to estimate the posterior distribution of parameters based on the reparameterization trick, and apply Thompson Sampling for efficiently exploring potentially better creatives. We evaluate the proposed method with both a synthetic dataset and two public datasets. The experimental results show our method can outperform competing baselines with respect to cumulative regret. The online A/B test shows our method leads to a 7 increase in CTR compared to the baseline.
An Efficient Indexing and Searching Technique for Information Retrieval for Urdu Language
Authors: Muhammad Mudassar Qureshi, Muhammad Shoaib, Kalsoom
Date: 2021-02-28 00:00:00
Tasks: Information Retrieval, Retrieval

Indexing techniques are used to improve retrieval of data in response to certain search condition. Inverted files are mostly used for creating indexes. This paper proposes indexing technique for Urdu language. Language processing step in Index creation is different for a particular language. We discuss index creation steps specifically for Urdu language. We explore morphological rules for Urdu language and implement these rules to create Urdu stemmer. We implement our proposed technique with different implementations and compare results. We suggest that indexes should be created without stop words and also index file should be an order index file.
An open-source framework for ExpFinder integrating $N$-gram Vector Space Model and $μ$CO-HITS
Authors: Hung Du, Yong-Bin Kang
Date: 2021-03-01 00:00:00

Finding experts drives successful collaborations and high-quality product development in academic and research domains. To contribute to the expert finding research community, we have developed ExpFinder which is a novel ensemble model for expert finding by integrating an $N$-gram vector space model ($n$VSM) and a graph-based model ($\mu$CO-HITS). This paper provides descriptions of ExpFinder's architecture, key components, functionalities, and illustrative examples. ExpFinder is an effective and competitive model for expert finding, significantly outperforming a number of expert finding models.
A Linguistic Study on Relevance Modeling in Information Retrieval
Authors: Yixing Fan, Jiafeng Guo, Xinyu Ma, Ruqing Zhang, Yanyan Lan, Xueqi Cheng
Date: 2021-03-01 00:00:00
Tasks: Information Retrieval, Natural Language Understanding, Question Answering, Retrieval

Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has always been critical challenges in both information science and computer science research areas. Along with the debate and exploration on relevance, IR has already become a core task in many real-world applications, such as Web search engines, question answering systems, conversational bots, and so on. While relevance acts as a unified concept in all these retrieval tasks, the inherent definitions are quite different due to the heterogeneity of these tasks. This raises a question to us: Do these different forms of relevance really lead to different modeling focuses? To answer this question, in this work, we conduct an empirical study on relevance modeling in three representative IR tasks, i.e., document retrieval, answer retrieval, and response retrieval. Specifically, we attempt to study the following two questions: 1) Does relevance modeling in these tasks really show differences in terms of natural language understanding (NLU)? We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question. 2) If there do exist differences, how can we leverage the findings to enhance the relevance modeling? We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. We believe the way we study the problem as well as our findings would be beneficial to the IR community.
Automated Generation of Interorganizational Disaster Response Networks through Information Extraction
Authors: Yitong Li, Duoduo Liao, Jundong Li, Wenying Ji
Date: 2021-02-27 00:00:00
Tasks: Disaster Response, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating Stakeholder Collaboration Networks (SCNs), this paper proposes a systematic approach to reliable information extraction for stakeholder collaboration and automated network generation. Specifically, stakeholders and their interactions are extracted from texts through Named Entity Recognition (NER), one of the techniques in natural language processing. Once extracted, the collaboration information is transformed into structured datasets to generate the SCNs automatically. A case study of stakeholder collaboration during Hurricane Harvey was investigated and it had demonstrated the feasibility and applicability of the proposed method. Hence, the proposed approach was proved to significantly reduce practitioners' interpretation and data collection workloads. In the end, discussions and future work are provided.
Welfare v. Consent: On the Optimal Penalty for Harassment
Authors: Ratul Das Chaudhury, Birendra Rai, Liang Choon Wang, Dyuti Banerjee
Date: 2021-03-01 00:00:00

The economic approach to determine optimal legal policies involves maximizing a social welfare function. We propose an alternative: a consent-approach that seeks to promote consensual interactions and deter non-consensual interactions. The consent-approach does not rest upon inter-personal utility comparisons or value judgments about preferences. It does not require any additional information relative to the welfare-approach. We highlight the contrast between the welfare-approach and the consent-approach using a stylized model inspired by seminal cases of harassment and the #MeToo movement. The social welfare maximizing penalty for harassment in our model can be zero under the welfare-approach but not under the consent-approach.
In-Car driver response classification using Deep Learning (CNN) based Computer Vision
Authors: Japesh Methuku
Date: 2020-11-16 00:00:00
Tasks: General Classification, Transfer Learning

The number of vehicle accidents increase every year globally. Considering the advancements in technology, it is possible to assess the causes of these accidents and mitigate the fatalities. Driver’s behavior and responses during the travel are crucial for analysis and in this paper, I have discussed how a deep learning system can be used to classify the responses of in-car driver and create an alert system. This paper is a brief description of the architecture of Convolutional Neural Network (CNN) used and the analysis of results obtained. The deep learning system was designed using transfer learning with ResNet50 and the model was able to achieve an accuracy of 89.71%. The model was evaluated on a video of real-world driving environment and key conclusions were drawn to support the research. The scope of the research beyond the academics and the importance in real-world application has been discussed in this paper.
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Authors: 外卖江湖盟主
Date: 2021-03-01 00:00:00

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Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?
Authors: Ian M. Pendleton, Mary K. Caucci, Michael Tynes, Aaron Dharna, Mansoor Ani Najeeb Nellikkal, Zhi Li, Emory M. Chan, Alexander J. Norquist, and Joshua Schrier
Date: 2020-05-26 00:00:00
Tasks: Benchmarking

Discovery of new perovskite materials is motivated by a broad range of materials applications and accelerated by recent advances in machine learning (ML). We herein report dataset augmentation, benchmarking, and interrogation for an ongoing experimental campaign consisting of 9483 halide perovskite synthesis experiments. To address limitations in previous work, we developed an improved description of the reactant concentrations in the experiments (validated against experimental observations) and performed experiments quantifying the excess volume of mixing of γ-butyrolactone/formic acid mixtures used in the perovskite syntheses. Combining this improved description of reactant concentration with other physicochemical features of the reactants, we constructed 1108 ML models to elucidate the roles of the algorithm (k-nearest neighbors, linear support-vector machine, and gradient boosted tree), feature set (12 in total), preprocessing regime (e.g., standardization), and training data holdout scheme on ML predictive ability. ML comparisons illustrated that the chemical accuracy of less sophisticated physical models in a dataset do not hinder interpolative model performance. Analysis of feature contributions showed how ML models “learn” competitive representations for concentration using raw experimental descriptions. Interrogation of the most performant models indicated that the numerical values of physicochemical features were not important, rather these features were being used to identify and interpolate within a particular reactant set. ML models were shown to be capable of making rudimentary extrapolations to untrained chemical systems when compared against basic benchmarks, and models which included the newly developed chemical features were shown to be more reliable than models trained without. These results illustrate how a stepwise comparative approach to machine learning can provide insight into what and how much models are “learning” for a given prediction task.
Federated Intelligence for Active Queue Management in Inter-Domain Congestion
Authors: Cesar A. Gomez, Xianbin Wang, Abdallah Shami
Date: 2021-01-08 00:00:00
Tasks: Federated Learning, Management

Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues. However, finding the right parameters for an AQM scheme is very challenging due to the dynamics of the IP networks. In addition, this problem becomes even more complex in inter-domain scenarios where several organizations interconnect each other with the limitation of not sharing raw and private data. As a result, existing AQM schemes have not been widely employed despite their advantages. Therefore, we present a solution that tackles the challenges of tuning the AQM parameters for inter-domain congestion control scenarios where the network management goes beyond an organization's domain. We then introduce the Federated Intelligence for AQM (FIAQM) architecture, which enhances the existing AQM schemes by leveraging the Federated Learning approach. The proposed FIAQM framework is capable of dynamically adjusting the AQM parameters in a multi-domain setting, which is hard to achieve with the conventional AQM solutions working alone. To this end, FIAQM uses an artificial neural network, trained in a federated manner, to predict beyond-own-domain congestion and an intelligent AQM parameter tuner. The evaluation results show that FIAQM can effectively improve the performance of the inter-domain connections by reducing the congestion on their links while preserving the network data private within each participating domain.
A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
Authors: Rafael Padilla, Wesley L. Passos, Thadeu L. B. Dias, Sergio L. Netto, Eduardo A. B. da Silva
Date: 2021-01-25 00:00:00
Tasks: Object, object-detection, Object Detection

Recent outstanding results of supervised object detection in competitions and challenges are often associated with specific metrics and datasets. The evaluation of such methods applied in different contexts have increased the demand for annotated datasets. Annotation tools represent the location and size of objects in distinct formats, leading to a lack of consensus on the representation. Such a scenario often complicates the comparison of object detection methods. This work alleviates this problem along the following lines: (i) It provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages; (ii) it examines the most used annotation formats, showing how different implementations may influence the assessment results; and (iii) it provides a novel open-source toolkit supporting different annotation formats and 15 performance metrics, making it easy for researchers to evaluate the performance of their detection algorithms in most known datasets. In addition, this work proposes a new metric, also included in the toolkit, for evaluating object detection in videos that is based on the spatio-temporal overlap between the ground-truth and detected bounding boxes.
Auto-Exposure Fusion for Single-Image Shadow Removal
Authors: Lan Fu, Changqing Zhou, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng, Yang Liu, Song Wang
Date: 2021-03-01 00:00:00
Proceeding: CVPR 2021 1
Tasks: Image Shadow Removal, Shadow Removal

Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful state-of-the-art deep neural networks could hardly recover traceless shadow-removed background. This paper proposes a new solution for this task by formulating it as an exposure fusion problem to address the challenges. Intuitively, we can first estimate multiple over-exposure images w.r.t. the input image to let the shadow regions in these images have the same color with shadow-free areas in the input image. Then, we fuse the original input with the over-exposure images to generate the final shadow-free counterpart. Nevertheless, the spatial-variant property of the shadow requires the fusion to be sufficiently `smart', that is, it should automatically select proper over-exposure pixels from different images to make the final output natural. To address this challenge, we propose the shadow-aware FusionNet that takes the shadow image as input to generate fusion weight maps across all the over-exposure images. Moreover, we propose the boundary-aware RefineNet to eliminate the remaining shadow trace further. We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods. We release the model and code in https://github.com/tsingqguo/exposure-fusion-shadow-removal.
Multi-label Classification via Adaptive Resonance Theory-based Clustering
Authors: Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi
Date: 2021-03-02 00:00:00
Tasks: Classification, Clustering, Continual Learning, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.
Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation
Authors: Yukun Su, Ruizhou Sun, Guosheng Lin, Qingyao Wu
Date: 2021-03-02 00:00:00
Proceeding: ICCV 2021 10
Tasks: Data Augmentation, Object, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Data augmentation is vital for deep learning neural networks. By providing massive training samples, it helps to improve the generalization ability of the model. Weakly supervised semantic segmentation (WSSS) is a challenging problem that has been deeply studied in recent years, conventional data augmentation approaches for WSSS usually employ geometrical transformations, random cropping and color jittering. However, merely increasing the same contextual semantic data does not bring much gain to the networks to distinguish the objects, e.g., the correct image-level classification of "aeroplane" may be not only due to the recognition of the object itself, but also its co-occurrence context like "sky", which will cause the model to focus less on the object features. To this end, we present a Context Decoupling Augmentation (CDA) method, to change the inherent context in which the objects appear and thus drive the network to remove the dependence between object instances and contextual information. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.
Image-to-image Translation via Hierarchical Style Disentanglement
Authors: Xinyang Li, Shengchuan Zhang, Jie Hu, Liujuan Cao, Xiaopeng Hong, Xudong Mao, Feiyue Huang, Yongjian Wu, Rongrong Ji
Date: 2021-03-02 00:00:00
Proceeding: CVPR 2021 1
Tasks: Disentanglement, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Translation

Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by involving uncontrolled manipulations to the translation results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to address this issue. Specifically, we organize the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. Correspondingly, a new translation process is designed to adapt the above structure, in which the styles are identified for controllable translations. Both qualitative and quantitative results on the CelebA-HQ dataset verify the ability of the proposed HiSD. We hope our method will serve as a solid baseline and provide fresh insights with the hierarchically organized annotations for future research in image-to-image translation. The code has been released at https://github.com/imlixinyang/HiSD.
A Provably Secure and Efficient Identity-Based Anonymous Authentication Scheme for Mobile Edge Computing
Authors: Xiaoying Jia，Debiao He, Neeraj Kumar, and Kim-Kwang Raymond Choo, Senior Member, IEEE
Date: 2019-02-22 00:00:00
Tasks: Cloud Computing, Edge-computing

Mobile edge computing (MEC) allows one to overcome a number of limitations inherent in cloud computing,although achieving the broad range of security requirements in MEC settings remains challenging. In this paper, we focus on achieving mutual authentication with anonymity and untraceability, as this is crucial in ensuring data security and user privacy. Specifically, we design an identity-based anonymous authenticated key agreement protocol for the MEC environment. The proposed protocol achieves mutual authentication in only a single message exchange round, as well as assures both user anonymity and un-traceability.We then evaluate the security and performance of the protocol, and demonstrate that it achieves the required security properties and outperforms prior approaches in terms of communicational and computational costs.