pwc-archive/papers-with-abstracts huggingface dataset:
Physical Derivatives: Computing policy gradients by physical forward-propagation
Authors: Arash Mehrjou, Ashkan Soleymani, Stefan Bauer, Bernhard Schölkopf
Date: 2022-01-15 00:00:00
Tasks: Model-based Reinforcement Learning

Model-free and model-based reinforcement learning are two ends of a spectrum. Learning a good policy without a dynamic model can be prohibitively expensive. Learning the dynamic model of a system can reduce the cost of learning the policy, but it can also introduce bias if it is not accurate. We propose a middle ground where instead of the transition model, the sensitivity of the trajectories with respect to the perturbation of the parameters is learned. This allows us to predict the local behavior of the physical system around a set of nominal policies without knowing the actual model. We assay our method on a custom-built physical robot in extensive experiments and show the feasibility of the approach in practice. We investigate potential challenges when applying our method to physical systems and propose solutions to each of them.
PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
Authors: Fernando Moreno-Pino, Emese Sükei, Pablo M. Olmos, Antonio Artés-Rodríguez
Date: 2022-01-12 00:00:00

We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core functionalities, such as different initialization algorithms and classical observations models, i.e., continuous and multinoulli, PyHHMM distinctively emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training. These characteristics result in a feature-rich implementation for researchers working with sequential data. PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License. PyHHMM's source code is publicly available on Github (https://github.com/fmorenopino/HeterogeneousHMM) to facilitate adoptions and future contributions. A detailed documentation (https://pyhhmm.readthedocs.io/en/latest), which covers examples of use and models' theoretical explanation, is available. The package can be installed through the Python Package Index (PyPI), via 'pip install pyhhmm'.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Authors: Peng Wu, Haoxuan Li, yuhao deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou
Date: 2022-01-18 00:00:00
Tasks: Causal Inference, Prediction, Recommendation Systems

Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks. Nevertheless, a unified causal analysis framework has not been established yet. Many causal-based prediction and debiasing studies rarely discuss the causal interpretation of various biases and the rationality of the corresponding causal assumptions. In this paper, we first provide a formal causal analysis framework to survey and unify the existing causal-inspired recommendation methods, which can accommodate different scenarios in RS. Then we propose a new taxonomy and give formal causal definitions of various biases in RS from the perspective of violating the assumptions adopted in causal analysis. Finally, we formalize many debiasing and prediction tasks in RS, and summarize the statistical and machine learning-based causal estimation methods, expecting to provide new research opportunities and perspectives to the causal RS community.
Proactive Query Expansion for Streaming Data Using External Source
Authors: Farah Alshanik, Amy Apon, Yuheng Du, Alexander Herzog, Ilya Safro
Date: 2022-01-17 00:00:00
Tasks: Information Retrieval, Retrieval, Topic Models

Query expansion is the process of reformulating the original query by adding relevant words. Choosing which terms to add in order to improve the performance of the query expansion methods or to enhance the quality of the retrieved results is an important aspect of any information retrieval system. Adding words that can positively impact the quality of the search query or are informative enough play an important role in returning or gathering relevant documents that cover a certain topic can result in improving the efficiency of the information retrieval system. Typically, query expansion techniques are used to add or substitute words to a given search query to collect relevant data. In this paper, we design and implement a pipeline of automated query expansion. We outline several tools using different methods to expand the query. Our methods depend on targeting emergent events in streaming data over time and finding the hidden topics from targeted documents using probabilistic topic models. We employ Dynamic Eigenvector Centrality to trigger the emergent events, and the Latent Dirichlet Allocation to discover the topics. Also, we use an external data source as a secondary stream to supplement the primary stream with relevant words and expand the query using the words from both primary and secondary streams. An experimental study is performed on Twitter data (primary stream) related to the events that happened during protests in Baltimore in 2015. The quality of the retrieved results was measured using a quality indicator of the streaming data: tweets count, hashtag count, and hashtag clustering.
Debiased Recommendation with User Feature Balancing
Authors: Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen
Date: 2022-01-16 00:00:00
Tasks: Causal Inference, Recommendation Systems

Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS), which can be hard to estimate and may suffer from the high variance issue. To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing. The general idea is to introduce a projection function to adjust user feature distributions, such that the ideal unbiased learning objective can be upper bounded by a solvable objective purely based on the offline dataset. In the upper bound, the projected user distributions are expected to be equal given different items. From the causal inference perspective, this requirement aims to remove the causal relation from the user to the item, which enables us to achieve unbiased recommendation, bypassing the computation of IPS. In order to efficiently balance the user distributions upon each item pair, we propose three strategies, including clipping, sampling and adversarial learning to improve the training process. For more robust optimization, we deploy an explicit model to capture the potential latent confounders in recommendation systems. To the best of our knowledge, this paper is the first work on debiased recommendation based on confounder balancing. In the experiments, we compare our framework with many state-of-the-art methods based on synthetic, semi-synthetic and real-world datasets. Extensive experiments demonstrate that our model is effective in promoting the recommendation performance.
Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
Authors: Xiaoyun Zhao, Ning Yang, Philip S. Yu
Date: 2022-01-16 00:00:00
Tasks: Domain Adaptation, Recommendation Systems

Cross-domain recommendation (CDR) has been attracting increasing attention of researchers for its ability to alleviate the data sparsity problem in recommender systems. However, the existing single-target or dual-target CDR methods often suffer from two drawbacks, the assumption of at least one rich domain and the heavy dependence on domain-invariant preference, which are impractical in real world where sparsity is ubiquitous and might degrade the user preference learning. To overcome these issues, we propose a Multi-Sparse-Domain Collaborative Recommendation (MSDCR) model for multi-target cross-domain recommendation. Unlike traditional CDR methods, MSDCR treats the multiple relevant domains as all sparse and can simultaneously improve the recommendation performance in each domain. We propose a Multi-Domain Separation Network (MDSN) and a Gated Aspect Preference Enhancement (GAPE) module for MSDCR to enhance a user's domain-specific aspect preferences in a domain by transferring the complementary aspect preferences in other domains, during which the uniqueness of the domain-specific preference can be preserved through the adversarial training offered by MDSN and the complementarity can be adaptively determined by GAPE. Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference. With the integration of the enhanced domain-specific aspect preference and the domain-invariant aspect preference, MSDCR can reach a comprehensive understanding of a user's preference in each sparse domain. At last, the extensive experiments conducted on real datasets demonstrate the remarkable superiority of MSDCR over the state-of-the-art single-domain recommendation models and CDR models.
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning
Authors: Ziwen Du, Ning Yang, Zhonghua Yu, Philip S. Yu
Date: 2022-01-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Traditional robust recommendation methods view atypical user-item interactions as noise and aim to reduce their impact with some kind of noise filtering technique, which often suffers from two challenges. First, in real world, atypical interactions may signal users' temporary interest different from their general preference. Therefore, simply filtering out the atypical interactions as noise may be inappropriate and degrade the personalization of recommendations. Second, it is hard to acquire the temporary interest since there are no explicit supervision signals to indicate whether an interaction is atypical or not. To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users. Particularly, we propose a reinforcement learning framework containing a recommender agent and an auxiliary classifier agent, which are jointly trained with the objective of maximizing the cumulative return of the recommendations made by the recommender agent. During the joint training process, the classifier agent can judge whether the interaction with an item recommended by the recommender agent is atypical, and the knowledge about learning temporary interest from atypical interactions can be transferred to the recommender agent, which makes the recommender agent able to alone make recommendations that balance the general preference and temporary interest of users. At last, the experiments conducted on real world datasets verify the effectiveness of TIARec.
Explaining Cognitive Computing Through the Information Systems Lens
Authors: Samaa Elnagar, Manoj Thomas
Date: 2022-01-16 00:00:00

Cognitive computing (COC) aims to embed human cognition into computerized models. However, there is no scientific classification that delineates the nature of Cognitive Computing. Unlike the medical and computer science fields, Information Systems (IS) has conducted very little research on COC. Although the potential to make important research contributions in this area is great, we argue that the lack of a cohesive interpretation of what constitutes COC has led to inferior COC research in IS. Therefore, we need first to clearly identify COC as a phenomenon to be able to identify and guide prospective research areas in IS. In this research, a phenomenological approach is adopted using thematic analysis to the published literature in COC research. Then, we discuss how IS may contribute to the development of design science artifacts under the COC umbrella. In addition, the paper raises important questions for future research by highlighting how IS researchers could make meaningful contributions to this emerging topic.
A Synthetic Prediction Market for Estimating Confidence in Published Work
Authors: Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmaan Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael McLaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, C. Lee Giles
Date: 2021-12-23 00:00:00

Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress. We develop a synthetic prediction market to assess the credibility of published claims in the social and behavioral sciences literature. We demonstrate our system and detail our findings using a collection of known replication projects. We suggest that this work lays the foundation for a research agenda that creatively uses AI for peer review.
Speech Reconstruction with Reminiscent Sound via Visual Voice Memory
Authors: Joanna Hong, Minsu Kim, Se Jin Park, Yong Man Ro
Date: 2021-11-17 00:00:00
Proceeding: IEEE/ACM Transactions on Audio, Speech, and Language Processing 2021 11
Tasks: Speaker-Specific Lip to Speech Synthesis

The goal of this work is to reconstruct speech from silent video, in both speaker dependent and independent ways. Unlike previous works that have been mostly restricted to a speaker dependent setting, we propose Visual Voice memory to restore essential auditory information to generate proper speech from different speakers and even unseen speakers. The proposed memory takes additional auditory information that corresponds to the input face movements and stores the auditory contexts that can be recalled by the given input visual features. Specifically, the Visual Voice memory contains value and key memory slots, where value memory slots are for saving the audio features, and key memory slots are for storing the visual features in the same location of the saved audio features. Guiding each memory to properly save each feature, the model can adequately produce the speech. Hence, our method employs both video and audio information during training time but does not require any additional auditory input during inference. Our key contributions are: (1) proposing the Visual Voice memory that brings rich information of audio that complements the visual features, thus producing high-quality speech from silent video, and (2) enabling multi-speaker and unseen speaker training by memorizing auditory features and the corresponding visual features. We validate the proposed framework on GRID and Lip2Wav datasets and show that our method surpasses the performance of previous works on both multi-speaker and speaker independent settings. We also demonstrate that the Visual Voice memory contains meaningful information to reconstruct speech.
Multipath-assisted Radio Sensing and Occupancy Detection for Smart In-house Parking in ITS
Authors: Jonas Ninnemann, Paul Schwarzbach, Oliver Michler
Date: 2022-01-16 00:00:00

Joint, radio-based communication, localization and sensing is a rapidly emerging research field with various application potentials. Greatly benefiting from these capabilities, smart city, mobility, and logistic concepts are key components for maximizing the efficiency of modern transportation systems. In urban environments, both the search for parking space and freight transport are time- and space-consuming and present the bottlenecks for these transportation chains. Providing location information for these heterogeneous requirement profiles (both active and passive localization of objects), can be realized by using retrofittable wireless sensor networks, which are typically only deployed for active localization. An additional passive detection of objects can be achieved by assessing signal reflections and multipath properties of the transmission channel stored within the Channel Impulse Response (CIR). In this work, a proof-of-concept realization and preliminary experimental results of a CIR-based occupancy detection for parking lots are presented. As the time resolution is dependent on available bandwidth, the CIR of Ultra-wideband transceivers are used. For this, the CIR is smoothed and time-variant changes within it are detected by performing a background subtraction. Finally, the reflecting objects are mapped to individual parking lots. The developed method is tested in an in-house parking garage. The work provided is a foundation for passive occupancy detection, whose capabilities can prospectively be enhanced by exploiting additional physical layers, such as 5G or even 6G.
Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets
Authors: Mengying Sun, Fei Wang, Olivier Elemento, Jiayu Zhou
Date: 2022-01-20 00:00:00
Proceeding: AAAI 2022 1

In this work, we proposed a DDI detection method based on molecular structures using graph convolutional networks and deep sets. We proposed a more discriminative convolutional layer compared to conventional GCN and achieved permutation invariant prediction without losing the capability of capturing complicated interactions.
Bi-FPNFAS: Bi-Directional Feature Pyramid Network for Pixel-Wise Face Anti-Spoofing by Leveraging Fourier Spectra
Authors: Koushik Roy, Md. Hasan, Labiba Rupty, Md. Sourave Hossain, Shirshajit Sengupta, Shehzad Noor Taus, Nabeel Mohammed
Date: 2021-04-15 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition

The emergence of biometric-based authentication using modern sensors on electronic devices has led to an escalated use of face recognition technologies. While these technologies may seem intriguing, they are accompanied by numerous implicit drawbacks. In this paper, we look into the problem of face anti-spoofing (FAS) on a frame level in an attempt to ameliorate the risks of face-spoofed attacks in biometric authentication processes. We employed a bi-directional feature pyramid network (BiFPN) that is used for convolutional multi-scaled feature extraction on the EfficientDet detection architecture, which is novel to the task of FAS. We further use these convolutional multi-scaled features in order to perform deep pixel-wise supervision. For all of our experiments, we performed evaluations across all major datasets and attained competitive results for the majority of the cases. Additionally, we showed that introducing an auxiliary self-supervision branch tasked with reconstructing the inputs in the frequency domain demonstrates an average classification error rate (ACER) of 2.92% on Protocol IV of the OULU-NPU dataset, which is significantly better than the currently available published works on pixel-wise face anti-spoofing. Moreover, following the procedures of prior works, we performed inter-dataset testing, which further consolidated the generalizability of the proposed models, as they showed optimum results across various sensors without any fine-tuning procedures.
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing
Authors: Md. Sourave Hossain, Labiba Rupty, Koushik Roy, Md. Hasan, Shirshajit Sengupta, Nabeel Mohammed
Date: 2021-03-01 00:00:00
Tasks: Binary Classification, Face Anti-Spoofing, Face Recognition, Neural Architecture Search

Face Anti Spoofing (FAS) systems are used to identify malicious spoofing attempts targeting face recognition systems using mediums such as video replay or printed papers. With increasing adoption of face recognition technology as a biometric authentication method, FAS techniques are gaining in importance. From a learning perspective, such systems pose a binary classification task. When implemented with Neural Network based solutions, it is common to use the binary cross entropy (BCE) function as the loss to optimize. In this study, we propose a variant of BCE that enforces a margin in angular space and incorporate it in training the DeepPixBis model [1]. In addition, we also present a method to incorporate such a loss for attentive pixel wise supervision applicable in a fully convolutional setting. Our proposed approach achieves competitive scores in both intra and inter-dataset testing on multiple benchmark datasets, consistently outperforming vanilla DeepPixBis. Interestingly, in the case of Protocol 4 of OULU-NPU, considered to be the hardest protocol, our proposed method achieves 5.22% ACER, which is only 0.22% higher than the current State of the Art without requiring any expensive Neural Architecture Search.
Wavelet‐attention‐based traffic prediction for smart cities
Authors: Aram Nasser, Vilmos Simon
Date: 2021-11-25 00:00:00
Proceeding: IET Smart Cities Journal 2021 11
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Forecasting, Traffic Prediction

Traffic congestion is a problem facing today's world, especially in smart cities where the economy is booming. Solving this issue by upgrading the traffic infrastructure of the city might be very cost-inefficient as well as time-consuming. With the help of recent technologies, traffic can be predicted to give the authorities the time to react before congestion evolves. As traffic is affected by several external factors, such as weather and anomalies (accidents, not expected road closures etc.), understanding the relationship between traffic and these factors can improve the prediction even further. In this study, a new method, the weather-based traffic analysis (hereafter WBTA), is utilised to investigate the temporal correlations between the traffic flow and the exogenous weather factors at different frequencies and time intervals. In addition, a novel method, the wavelet-attention-based calculation (hereafter WABC) is introduced to help to understand the importance of each external factor, compared with the others. Five weather factors (temperature, wind speed, rain, visibility, and humidity) are analysed, weighted, and merged with each other as one auxiliary input to improve traffic prediction accuracy. Based on that, the wavelet-attention-based prediction model is introduced, where the mean squared error is reduced by 32.3% and 24.52% for one future time step prediction, and 14.9% and 18.22% for five, compared with using the traffic time series alone, and with external factors without weights, respectively.
A Novel Method for Analyzing Weather Effect on Smart City Traffic
Authors: Aram Nasser, Vilmos Simon
Date: 2021-07-05 00:00:00
Proceeding: 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) 2021 7
Tasks: Traffic Prediction

Traffic congestion has become more concerning as the number of cars has rapidly increased in urban environments, resulting in dangerous pollution levels amid global warming. The effect of weather factors on traffic flow is evident thus studying this relationship can improve traffic prediction, which can help us to prevent or dissolve traffic congestion in smart cities where intelligent transportation systems have been deployed. In this paper, a new method, the Weather Based Traffic Analysis (hereafter WBTA) has been proposed to investigate the temporal correlations between traffic flow and exogenous weather factors, at different frequencies and time intervals. This can help in the proper selection of the most important weather factors which influence traffic flow behavior, in addition to the duration of that effect. This can help in choosing the input parameters of a multi-input traffic prediction model. Several weather factors are analyzed, however, for the sake of this paper, the precipitation factor is discussed.