pwc-archive/papers-with-abstracts huggingface dataset:
Automated Architecture Design for Deep Neural Networks
Authors: Steven Abreu
Date: 2019-08-22 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, GPU

Machine learning has made tremendous progress in recent years and received large amounts of public attention. Though we are still far from designing a full artificially intelligent agent, machine learning has brought us many applications in which computers solve human learning tasks remarkably well. Much of this progress comes from a recent trend within machine learning, called deep learning. Deep learning models are responsible for many state-of-the-art applications of machine learning. Despite their success, deep learning models are hard to train, very difficult to understand, and often times so complex that training is only possible on very large GPU clusters. Lots of work has been done on enabling neural networks to learn efficiently. However, the design and architecture of such neural networks is often done manually through trial and error and expert knowledge. This thesis inspects different approaches, existing and novel, to automate the design of deep feedforward neural networks in an attempt to create less complex models with good performance that take away the burden of deciding on an architecture and make it more efficient to design and train such deep networks.
Testing Neural Program Analyzers
Authors: Md. Rafiqul Islam Rabin, Ke Wang, Mohammad Amin Alipour
Date: 2019-08-25 00:00:00
Tasks: Method name prediction

Deep neural networks have been increasingly used in software engineering and program analysis tasks. They usually take a program and make some predictions about it, e.g., bug prediction. We call these models neural program analyzers. The reliability of neural programs can impact the reliability of the encompassing analyses. In this paper, we describe our ongoing efforts to develop effective techniques for testing neural programs. We discuss the challenges involved in developing such tools and our future plans. In our preliminary experiment on a neural model recently proposed in the literature, we found that the model is very brittle, and simple perturbations in the input can cause the model to make mistakes in its prediction.
Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households
Authors: Jordan Holweger, Marina Dorokhova, Lionel Bloch, Christophe Ballif, Nicolas Wyrsch
Date: 2019-08-20 00:00:00
Tasks: Management, Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power consumption of an entire house. This information allows households to receive customized advice on how to better manage their electrical consumption. In this paper, we present an alternative NILM method that breaks down the aggregated power signal into categories of appliances. The ultimate goal is to use this approach for demand-side management to estimate potential flexibility within the electricity consumption of households. Our method is implemented as an algorithm combining NILM and load profile simulation. This algorithm, based on a Markov model, allocates an activity chain to each inhabitant of the household, deduces from the whole-house power measurement and statistical data the appliance usage, generate the power profile accordingly and finally returns the share of energy consumed by each appliance category over time. To analyze its performance, the algorithm was benchmarked against several state-of-the-art NILM algorithms and tested on three public datasets. The proposed algorithm is unsupervised; hence it does not require any labeled data, which are expensive to acquire. Although better performance is shown for the supervised algorithms, our proposed unsupervised algorithm achieves a similar range of uncertainty while saving on the cost of acquiring labeled data. Additionally, our method requires lower computational power compared to most of the tested NILM algorithms. It was designed for low-sampling-rate power measurement (every 15 min), which corresponds to the frequency range of most common smart meters.
Spam Review Detection with Graph Convolutional Networks
Authors: Ao Li, Zhou Qin, Runshi Liu, Yiqun Yang, Dong Li
Date: 2019-08-22 00:00:00

Customers make a lot of reviews on online shopping websites every day, e.g., Amazon and Taobao. Reviews affect the buying decisions of customers, meanwhile, attract lots of spammers aiming at misleading buyers. Xianyu, the largest second-hand goods app in China, suffering from spam reviews. The anti-spam system of Xianyu faces two major challenges: scalability of the data and adversarial actions taken by spammers. In this paper, we present our technical solutions to address these challenges. We propose a large-scale anti-spam method based on graph convolutional networks (GCN) for detecting spam advertisements at Xianyu, named GCN-based Anti-Spam (GAS) model. In this model, a heterogeneous graph and a homogeneous graph are integrated to capture the local context and global context of a comment. Offline experiments show that the proposed method is superior to our baseline model in which the information of reviews, features of users and items being reviewed are utilized. Furthermore, we deploy our system to process million-scale data daily at Xianyu. The online performance also demonstrates the effectiveness of the proposed method.
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
Authors: Tong Geng, Ang Li, Runbin Shi, Chunshu Wu, Tianqi Wang, Yanfei Li, Pouya Haghi, Antonino Tumeo, Shuai Che, Steve Reinhardt, Martin Herbordt
Date: 2019-08-23 00:00:00
Tasks: 4k

Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio. In many applications, however, information and their relationships are better expressed with graphs. Graph Convolutional Networks (GCNs) appear to be a promising approach to efficiently learn from graph data structures, having shown advantages in many critical applications. As with other deep learning modalities, hardware acceleration is critical. The challenge is that real-world graphs are often extremely large and unbalanced; this poses significant performance demands and design challenges. In this paper, we propose Autotuning-Workload-Balancing GCN (AWB-GCN) to accelerate GCN inference. To address the issue of workload imbalance in processing real-world graphs, three hardware-based autotuning techniques are proposed: dynamic distribution smoothing, remote switching, and row remapping. In particular, AWB-GCN continuously monitors the sparse graph pattern, dynamically adjusts the workload distribution among a large number of processing elements (up to 4K PEs), and, after converging, reuses the ideal configuration. Evaluation is performed using an Intel D5005 FPGA with five commonly-used datasets. Results show that 4K-PE AWB-GCN can significantly elevate PE utilization by 7.7x on average and demonstrate considerable performance speedups over CPUs (3255x), GPUs (80.3x), and a prior GCN accelerator (5.1x).
Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval
Authors: ErkunYang, 1 ChengDeng, 1 WeiLiu, 2 XianglongLiu, 3 DachengTao, 4 XinboGao1
Date: 2017-02-12 00:00:00
Proceeding: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence 2017 2
Tasks: Cross-Modal Retrieval, Deep Hashing, Retrieval

With beneﬁts of low storage cost and fast query speed, crossmodal hashing has received considerable attention recently. However,almostallexistingmethodsoncross-modalhashing cannot obtain powerful hash codes due to directly utilizing hand-crafted features or ignoring heterogeneous correlations acrossdifferentmodalities,whichwillgreatlydegradetheretrieval performance. In this paper, we propose a novel deep cross-modal hashing method to generate compact hash codes through an end-to-end deep learning architecture, which can effectively capture the intrinsic relationships between various modalities. Our architecture integrates different types of pairwise constraints to encourage the similarities of the hash codes from an intra-modal view and an inter-modal view, respectively. Moreover, additional decorrelation constraints are introduced to this architecture, thus enhancing the discriminative ability of each hash bit. Extensive experiments show that our proposed method yields state-of-the-art results on two cross-modal retrieval datasets.
Joint multi-person detection and tracking from overlapping cameras
Authors: Martijn C. Liem, Dariu M. Gavrila
Date: 2013-06-23 00:00:00
Proceeding: Computer Vision and Image Understanding 2013 6
Tasks: Benchmarking, Human Detection, Position, Re-Ranking

We present a system to track the positions of multiple persons in a scene from overlapping cameras. The
distinguishing aspect of our method is a novel, two-step approach that jointly estimates person position
and track assignment. The proposed approach keeps solving the assignment problem tractable, while taking into account how different assignments influence feature measurement. In a hypothesis generation
stage, the similarity between a person at a particular position and an active track is based on a subset
of cues (appearance, motion) that are guaranteed observable in the camera views. This allows for efficient
computation of the K-best joint estimates for person position and track assignment under an approximation of the likelihood function. In a subsequent hypothesis verification stage, the known person positions
associated with these K-best solutions are used to define a larger set of actually visible cues, which
enables a re-ranking of the found assignments using the full likelihood function.
We demonstrate that our system outperforms the state-of-the-art on four challenging multi-person
datasets (indoor and outdoor), involving 3–5 overlapping cameras and up to 23 persons simultaneously.
Two of these datasets are novel: we make the associated images and annotations public to facilitate
benchmarking.
International Relations Perspectives on Technology
Authors: Hassan Imam Essa Hassan
Date: 2019-07-30 00:00:00
Proceeding: International Journal of Business and Applied Social Science 2019 7

This paper addresses the literature of International Relations approaches to technology as a form of power in international politics in two stages: First, the current IR approaches to technological power in international politics which includes instrumentalism, essentialism and Social Construction of Technology (SCOT). Second, the historical materialist approaches to technological power in IR which includes instrumentalism, essentialism and critical theory of technology.
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
Authors: Dogancan Temel, Min-Hung Chen, Ghassan AlRegib
Date: 2019-08-29 00:00:00
Tasks: Traffic Sign Detection, Traffic Sign Recognition

Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. Existing traffic sign datasets are limited in terms of type and severity of challenging conditions. Metadata corresponding to these conditions are unavailable and it is not possible to investigate the effect of a single factor because of simultaneous changes in numerous conditions. To overcome the shortcomings in existing datasets, we introduced the CURE-TSD-Real dataset, which is based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems. We test the performance of two benchmark algorithms and show that severe conditions can result in an average performance degradation of 29% in precision and 68% in recall. We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics. Moreover, we show that mean magnitude spectrum of changes in video sequences under challenging conditions can be an indicator of detection performance. CURE-TSD-Real dataset is available online at https://github.com/olivesgatech/CURE-TSD.
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs
Authors: Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang
Date: 2017-10-23 00:00:00
Proceeding: Mountain View, CA, USA 2017 10

Microblogs have become popular media for news propagation
in recent years. Meanwhile, numerous rumors and fake news
also bloom and spread wildly on the open social media plat-
forms. Without verication, they could seriously jeopardize
the credibility of microblogs. We observe that an increasing
number of users are using images and videos to post news
in addition to texts. Tweets or microblogs are commonly
composed of text, image and social context. In this paper,
we propose a novel Recurrent Neural Network with an at-
tention mechanism (att-RNN) to fuse multimodal features
for eective rumor detection. In this end-to-end network,
image features are incorporated into the joint features of
text and social context, which are obtained with an LSTM
(Long-Short Term Memory) network, to produce a reliable
fused classication. The neural attention from the outputs
of the LSTM is utilized when fusing with the visual features.
Extensive experiments are conducted on two multimedia ru-
mor datasets collected from Weibo and Twitter. The results
demonstrate the eectiveness of the proposed end-to-end
att-RNN in detecting rumors with multimodal contents.
Scientific Statement Classification over arXiv.org
Authors: Deyan Ginev, Bruce R. Miller
Date: 2019-08-29 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Decoder, General Classification

We introduce a new classification task for scientific statements and release a large-scale dataset for supervised learning. Our resource is derived from a machine-readable representation of the arXiv.org collection of preprint articles. We explore fifty author-annotated categories and empirically motivate a task design of grouping 10.5 million annotated paragraphs into thirteen classes. We demonstrate that the task setup aligns with known success rates from the state of the art, peaking at a 0.91 F1-score via a BiLSTM encoder-decoder model. Additionally, we introduce a lexeme serialization for mathematical formulas, and observe that context-aware models could improve when also trained on the symbolic modality. Finally, we discuss the limitations of both data and task design, and outline potential directions towards increasingly complex models of scientific discourse, beyond isolated statements.
Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention
Authors: Zhiliang Zeng, Xianzhi Li, Ying Kin Yu, Chi-Wing Fu
Date: 2019-08-29 00:00:00
Proceeding: ICCV 2019 10

This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and different types of rooms, in the floor layouts. To this end, we model a hierarchy of floor plan elements and design a deep multi-task neural network with two tasks: one to learn to predict room-boundary elements, and the other to predict rooms with types. More importantly, we formulate the room-boundary-guided attention mechanism in our spatial contextual module to carefully take room-boundary features into account to enhance the room-type predictions. Furthermore, we design a cross-and-within-task weighted loss to balance the multi-label tasks and prepare two new datasets for floor plan recognition. Experimental results demonstrate the superiority and effectiveness of our network over the state-of-the-art methods.
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting
Authors: Vishwanath A. Sindagi, Vishal M. Patel
Date: 2019-08-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Crowd Counting

Crowd counting presents enormous challenges in the form of large variation in scales within images and across the dataset. These issues are further exacerbated in highly congested scenes. Approaches based on straightforward fusion of multi-scale features from a deep network seem to be obvious solutions to this problem. However, these fusion approaches do not yield significant improvements in the case of crowd counting in congested scenes. This is usually due to their limited abilities in effectively combining the multi-scale features for problems like crowd counting. To overcome this, we focus on how to efficiently leverage information present in different layers of the network. Specifically, we present a network that involves: (i) a multi-level bottom-top and top-bottom fusion (MBTTBF) method to combine information from shallower to deeper layers and vice versa at multiple levels, (ii) scale complementary feature extraction blocks (SCFB) involving cross-scale residual functions to explicitly enable flow of complementary features from adjacent conv layers along the fusion paths. Furthermore, in order to increase the effectiveness of the multi-scale fusion, we employ a principled way of generating scale-aware ground-truth density maps for training. Experiments conducted on three datasets that contain highly congested scenes (ShanghaiTech, UCF_CC_50, and UCF-QNRF) demonstrate that the proposed method is able to outperform several recent methods in all the datasets.
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention
Authors: Biao Zhang, Ivan Titov, Rico Sennrich
Date: 2019-08-29 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder, Machine Translation, Translation

The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architecture for machine translation results in poor convergence and high computational overhead. Our empirical analysis suggests that convergence is poor due to gradient vanishing caused by the interaction between residual connections and layer normalization. We propose depth-scaled initialization (DS-Init), which decreases parameter variance at the initialization stage, and reduces output variance of residual connections so as to ease gradient back-propagation through normalization layers. To address computational cost, we propose a merged attention sublayer (MAtt) which combines a simplified averagebased self-attention sublayer and the encoderdecoder attention sublayer on the decoder side. Results on WMT and IWSLT translation tasks with five translation directions show that deep Transformers with DS-Init and MAtt can substantially outperform their base counterpart in terms of BLEU (+1.1 BLEU on average for 12-layer models), while matching the decoding speed of the baseline model thanks to the efficiency improvements of MAtt.
Automating Agential Reasoning: Proof-Calculi and Syntactic Decidability for STIT Logics
Authors: Tim Lyon, Kees van Berkel
Date: 2019-08-29 00:00:00
Tasks: Model extraction

This work provides proof-search algorithms and automated counter-model extraction for a class of STIT logics. With this, we answer an open problem concerning syntactic decision procedures and cut-free calculi for STIT logics. A new class of cut-free complete labelled sequent calculi G3LdmL^m_n, for multi-agent STIT with at most n-many choices, is introduced. We refine the calculi G3LdmL^m_n through the use of propagation rules and demonstrate the admissibility of their structural rules, resulting in auxiliary calculi Ldm^m_nL. In the single-agent case, we show that the refined calculi Ldm^m_nL derive theorems within a restricted class of (forestlike) sequents, allowing us to provide proof-search algorithms that decide single-agent STIT logics. We prove that the proof-search algorithms are correct and terminate.
On the Power of Multiple Anonymous Messages
Authors: Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker
Date: 2019-08-29 00:00:00

An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much smaller than what is possible in the local model, while relying on weaker trust assumptions than in the central model. In this paper, we study basic counting problems in the shuffled model and establish separations between the error that can be achieved in the single-message shuffled model and in the shuffled model with multiple messages per user. For the problem of frequency estimation for $n$ users and a domain of size $B$, we obtain: - A nearly tight lower bound of $\tilde{\Omega}( \min(\sqrt[4]{n}, \sqrt{B}))$ on the error in the single-message shuffled model. This implies that the protocols obtained from the amplification via shuffling work of Erlingsson et al. (SODA 2019) and Balle et al. (Crypto 2019) are essentially optimal for single-message protocols. A key ingredient in the proof is a lower bound on the error of locally-private frequency estimation in the low-privacy (aka high $\epsilon$) regime. - Protocols in the multi-message shuffled model with $poly(\log{B}, \log{n})$ bits of communication per user and $poly\log{B}$ error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms. For the related selection problem on a domain of size $B$, we prove: - A nearly tight lower bound of $\Omega(B)$ on the number of users in the single-message shuffled model. This significantly improves on the $\Omega(B^{1/17})$ lower bound obtained by Cheu et al. (Eurocrypt 2019), and when combined with their $\tilde{O}(\sqrt{B})$-error multi-message protocol, implies the first separation between single-message and multi-message protocols for this problem.
Human-grounded Evaluations of Explanation Methods for Text Classification
Authors: Piyawat Lertvittayakumjorn, Francesca Toni
Date: 2019-08-29 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, General Classification, text-classification, Text Classification

Due to the black-box nature of deep learning models, methods for explaining the models' results are crucial to gain trust from humans and support collaboration between AIs and humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2) justifying model predictions, and (3) helping humans investigate uncertain predictions. The results highlight dissimilar qualities of the various explanation methods we consider and show the degree to which these methods could serve for each purpose.