pwc-archive/papers-with-abstracts huggingface dataset:
Next Check-ins Prediction via History and Friendship on Location-Based Social Networks
Authors: Yijun Su, Xiang Li, Wei Tang, Ji Xiang, Yuanye He
Date: 2018-06-25 00:00:00
Proceeding: IEEE International Conference on Mobile Data Management (MDM) 2018 6
Tasks: Collaborative Filtering, Prediction

With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preferences, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.
CARec: Content-Aware Point-of-Interest Recommendation via Adaptive Bayesian Personalized Ranking
Authors: Baoping Liu, Yijun Su, Daren Zha, Neng Gao, Ji Xiang.
Date: 2019-12-09 00:00:00
Proceeding: International Conference on Neural Information Processing 2019 12
Tasks: Recommendation Systems

Location-based social networks (LBSNs) offer researchers user-generated content data to study users’ intrinsic patterns of preference. One important application of such study is to provide a personalized point-of-interest (POI) recommender system to improve users’ experience in LBSNs. However, most of the existing methods provide limited improvements on POI recommendation because they separately employ textual sentiment or latent topic and ignore the mutual effect between them. In this paper, we propose a novel content-aware POI recommendation framework via an adaptive Bayesian Personalized Ranking. First, we make full use of users’ check-in records and reviews to capture users’ intrinsic preferences (i.e., check-in, sentiment, and topic preferences). Then, by aggregating users’ intrinsic preferences, we devise an adaptive Bayesian Personalized Ranking to generate the personalized ranked list of POIs for users. Finally, extensive experiments on two real-world datasets demonstrate that our framework significantly
outperforms other state-of-the-art POI recommendation models in various metrics.
FGCRec: Fine-Grained Geographical Characteristics Modeling for Point-of-Interest Recommendation
Authors: Yijun Su, Xiang Li, Baoping Liu, Daren Zha, Ji Xiang, Wei Tang and Neng Gao.
Date: 2020-06-07 00:00:00
Proceeding: IEEE International Conference on Communications 2020 6
Tasks: Recommendation Systems

With the popularity of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has become an essential location-based service to help people explore novel locations. Although the massive check-in data bring a good opportunity, there are still many challenges in building personalized POI recommender systems based on geographical information. First, current coarse-grained geographical models provide considerably limited improvements on POI recommendations and fail to capture the overall impact of fine-grained geographical characteristics in LBSNs. Second, previous methods such as matrix factorization always give equal weight to each positive example and may not distinguish between their different contributions in learning the objective function. To cope with these challenges, we develop a fine-grained POI recommendation framework that makes full use of the geographical characteristics from both users' and locations' perspectives. For capturing the fine-grained geographical influence, we present a unified probability distribution model based on four key geographical characteristics. For mining more contribution information from positive examples, we assign a higher weight to highlight the contribution of a higher check-in frequency by employing a logistic matrix factorization. Finally, experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.
FGRec: A Fine-Grained Point-of-Interest Recommendation Framework by Capturing Intrinsic Influences
Authors: Yijun Su, Jia-Dong Zhang, Xiang Li, Daren Zha, Ji Xiang, Wei Tang, and Neng Gao
Date: 2020-07-19 00:00:00
Proceeding: IEEE International Joint Conference on Neural Network 2020 7
Tasks: Recommendation Systems

Point-of-interest (POI) recommendation has become an important service to help users discover attractive locations. A variety of available check-in data make it possible to build a personalized POI recommender system, but the extreme sparsity of check-in data poses a severe challenge for POI recommendation. Recent studies mainly utilize social information, categorical information and/or geographical information to supplement the highly sparse check-in data. However, these studies often apply shallow methods for the extra information and provide considerably limited improvements on POI recommendation. In this paper, we propose a fine-grained POI recommendation framework, called FGRec to capture the intrinsic influences of social, categorical and geographical information on the check-in behaviors of users. First, we study the social influence in depth by exploiting the multi-hop social friends and top-n nearest neighbor friends, not only the direct friends (i.e., 1-hop friends). Second, we investigate the categorical influence by factorizing both user-POI and user-category matrices simultaneously over the same user embedding space, rather than simply using the popularity of POI categories. Third, we explore the geographical influence by integrating two types of distance (i.e., the distance between user homes and POIs and the distance among POIs) into a unified probability distribution over check-in POIs, instead of modeling them separately. Finally, experimental results on two large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.
Neural Demographic Prediction in Social Media with Deep Multi-View Multi-Task Learning
Authors: Yantong Lai, Yijun Su, Cong Xue, Daren Zha
Date: 2021-04-06 00:00:00
Proceeding: International Conference on Database Systems for Advanced Applications（DASFAA） 2021 4
Tasks: Multi-Task Learning

Utilizing the demographic information of social media users is very essential for personalized online services. However, it is difficult to collect such information in most realistic scenarios. Luckily, the reviews posted by users can provide rich clues for inferring their demographics, since users with different demographics such as gender and age usually have differences in their contents and expressing styles. In this paper, we propose a neural approach for demographic prediction based on user reviews. The core of our approach is a deep multi-view multi-task learning model. Our model first learns context representations from reviews using a context encoder, which takes semantics and syntactics into consideration. Meanwhile, we learn sentiment and topic representations from selected sentiment and topic words using a word encoder separately, which consists of a convolutional neural network to capture the local contexts of reviews in word-level. Then, we learn a unified user representation from context, sentiment and topic representations and apply multi-task learning for inferring user’s gender and age simultaneously. Experimental results on three real-world datasets validate the effectiveness of our approach. To facilitate future research, we release the codes and datasets at https://github.com/icmpnorequest/DASFAA2021_DMVMT.
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count Estimation
Authors: Fabian-Robert Stöter, Soumitro Chakrabarty, Bernd Edler, Emanuël Habets
Date: 2018-10-28 00:00:00
Proceeding: IEEE/ACM Transactions on Audio, Speech, and Language Processing 2018 10
Tasks: blind source separation, speaker-diarization, Speaker Diarization

Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance. We propose a unifying probabilistic paradigm, where deep neural network architectures are used to infer output posterior distributions. These probabilities are in turn processed to yield discrete point estimates. Designing such architectures often involves two important and complementary aspects that we investigate and discuss. First, we study how recent advances in deep architectures may be exploited for the task of speaker count estimation. In particular, we show that convolutional recurrent neural networks outperform recurrent networks used in a previous study when adequate input features are used. Even for short segments of speech mixtures, we can estimate up to five speakers, with a significantly lower error than other methods. Second, through comprehensive evaluation, we compare the best-performing method to several baselines, as well as the influence of gain variations, different data sets, and reverberation. The output of our proposed method is compared to human performance. Finally, we give insights into the strategy used by our proposed method.
MC-Calib: A generic and robust calibration toolbox for multi-camera systems
Authors: Francois Rameau, Jinsun Park, Oleksandr Bailo, In So Kweon
Date: 2022-01-12 00:00:00
Proceeding: Computer Vision and Image Understanding 2022 1
Tasks: Camera Calibration

In this paper, we present MC-Calib, a novel and robust toolbox dedicated to the calibration of complex synchronized multi-camera systems using an arbitrary number of fiducial marker-based patterns. Calibration results are obtained via successive stages of refinement to reliably estimate both the poses of the calibration boards and cameras in the system. Our method is not constrained by the number of cameras, their overlapping field-of-view (FoV), or the number of calibration patterns used. Moreover, neither prior information about the camera system nor the positions of the checkerboards are required. As a result, minimal user interaction is needed to achieve an accurate and robust calibration which makes this toolbox accessible even with limited computer vision expertise. In this work, we put a strong emphasis on the versatility and the robustness of our technique. Specifically, the hierarchical nature of our strategy allows to reliably calibrate complex vision systems even under the presence of noisy measurements. Additionally, we propose a new strategy for best-suited image selection and initial parameters estimation dedicated to non-overlapping FoV cameras. Finally, our calibration toolbox is compatible with both, perspective and fisheye cameras. Our solution has been validated on a large number of real and synthetic sequences including monocular, stereo, multiple overlapping cameras, non-overlapping cameras, and converging camera systems. Project page: https://github.com/rameau-fr/MC-Calib
To SMOTE, or not to SMOTE?
Authors: Yotam Elor, Hadar Averbuch-Elor
Date: 2022-01-21 00:00:00
Tasks: Binary Classification

Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data. Balancing is commonly achieved by duplication of minority samples or by generation of synthetic minority samples. While it is well known that balancing affects each classifier differently, most prior empirical studies did not include strong state-of-the-art (SOTA) classifiers as baselines. In this work, we are interested in understanding whether balancing is beneficial, particularly in the context of SOTA classifiers. Thus, we conduct extensive experiments considering three SOTA classifiers along the weaker learners used in previous investigations. Additionally, we carefully discern proper metrics, consistent and non-consistent algorithms and hyper-parameter selection methods and show that these have a significant impact on prediction quality and on the effectiveness of balancing. Our results support the known utility of balancing for weak classifiers. However, we find that balancing does not improve prediction performance for the strong ones. We further identify several other scenarios for which balancing is effective and observe that prior studies demonstrated the utility of balancing by focusing on these settings.
Under-Approximating Expected Total Rewards in POMDPs
Authors: Alexander Bork, Joost-Pieter Katoen, Tim Quatmann
Date: 2022-01-21 00:00:00

We consider the problem: is the optimal expected total reward to reach a goal state in a partially observable Markov decision process (POMDP) below a given threshold? We tackle this -- generally undecidable -- problem by computing under-approximations on these total expected rewards. This is done by abstracting finite unfoldings of the infinite belief MDP of the POMDP. The key issue is to find a suitable under-approximation of the value function. We provide two techniques: a simple (cut-off) technique that uses a good policy on the POMDP, and a more advanced technique (belief clipping) that uses minimal shifts of probabilities between beliefs. We use mixed-integer linear programming (MILP) to find such minimal probability shifts and experimentally show that our techniques scale quite well while providing tight lower bounds on the expected total reward.
Object Detection in Aerial Images: What Improves the Accuracy?
Authors: Hashmat Shadab Malik, Ikboljon Sobirov, Abdelrahman Mohamed
Date: 2022-01-21 00:00:00
Tasks: Object, object-detection, Object Detection, Object Detection In Aerial Images

Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in a diverse set of object categories. Recently, deep learning-based object detection approaches have been actively explored for the problem of object detection in aerial images. In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images. We conduct extensive experiments on the challenging iSAID dataset. The resulting adapted Faster R-CNN obtains a significant mAP gain of 4.96% over its vanilla baseline counterpart on the iSAID validation set, demonstrating the impact of different strategies investigated in this work.
Inferring Brain Dynamics via Multimodal Joint Graph Representation EEG-fMRI
Authors: Jalal Mirakhorli
Date: 2022-01-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Recent studies have shown that multi-modeling methods can provide new insights into the analysis of brain components that are not possible when each modality is acquired separately. The joint representations of different modalities is a robust model to analyze simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI). Advances in precision instruments have given us the ability to observe the spatiotemporal neural dynamics of the human brain through non-invasive neuroimaging techniques such as EEG & fMRI. Nonlinear fusion methods of streams can extract effective brain components in different dimensions of temporal and spatial. Graph-based analyzes, which have many similarities to brain structure, can overcome the complexities of brain mapping analysis. Throughout, we outline the correlations of several different media in time shifts from one source with graph-based and deep learning methods. Determining overlaps can provide a new perspective for diagnosing functional changes in neuroplasticity studies.
Towards Building Economic Models of Conversational Search
Authors: Leif Azzopardi, Mohammad Aliannejadi, Evangelos Kanoulas
Date: 2022-01-21 00:00:00
Tasks: Conversational Search, Descriptive

Various conceptual and descriptive models of conversational search have been proposed in the literature -- while useful, they do not provide insights into how interaction between the agent and user would change in response to the costs and benefits of the different interactions. In this paper, we develop two economic models of conversational search based on patterns previously observed during conversational search sessions, which we refer to as: Feedback First where the agent asks clarifying questions then presents results, and Feedback After where the agent presents results, and then asks follow up questions. Our models show that the amount of feedback given/requested depends on its efficiency at improving the initial or subsequent query and the relative cost of providing said feedback. This theoretical framework for conversational search provides a number of insights that can be used to guide and inform the development of conversational search agents. However, empirical work is needed to estimate the parameters in order to make predictions specific to a given conversational search setting.
Meta Learning MDPs with Linear Transition Models
Authors: Robert Müller, Aldo Pacchiano
Date: 2022-01-21 00:00:00
Tasks: Meta-Learning

We study meta-learning in Markov Decision Processes (MDP) with linear transition models in the undiscounted episodic setting. Under a task sharedness metric based on model proximity we study task families characterized by a distribution over models specified by a bias term and a variance component. We then propose BUC-MatrixRL, a version of the UC-Matrix RL algorithm, and show it can meaningfully leverage a set of sampled training tasks to quickly solve a test task sampled from the same task distribution by learning an estimator of the bias parameter of the task distribution. The analysis leverages and extends results in the learning to learn linear regression and linear bandit setting to the more general case of MDP's with linear transition models. We prove that compared to learning the tasks in isolation, BUC-Matrix RL provides significant improvements in the transfer regret for high bias low variance task distributions.
Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?
Authors: Gabriella Kazai, Bhaskar Mitra, Anlei Dong, Nick Craswell, Linjun Yang
Date: 2022-01-21 00:00:00
Tasks: Information Retrieval, Retrieval

Traditional information retrieval (IR) ranking models process the full text of documents. Newer models based on Transformers, however, would incur a high computational cost when processing long texts, so typically use only snippets from the document instead. The model's input based on a document's URL, title, and snippet (UTS) is akin to the summaries that appear on a search engine results page (SERP) to help searchers decide which result to click. This raises questions about when such summaries are sufficient for relevance estimation by the ranking model or the human assessor, and whether humans and machines benefit from the document's full text in similar ways. To answer these questions, we study human and neural model based relevance assessments on 12k query-documents sampled from Bing's search logs. We compare changes in the relevance assessments when only the document summaries and when the full text is also exposed to assessors, studying a range of query and document properties, e.g., query type, snippet length. Our findings show that the full text is beneficial for humans and a BERT model for similar query and document types, e.g., tail, long queries. A closer look, however, reveals that humans and machines respond to the additional input in very different ways. Adding the full text can also hurt the ranker's performance, e.g., for navigational queries.
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Authors: Qianben Chen, Richong Zhang, Yaowei Zheng, Yongyi Mao
Date: 2022-01-21 00:00:00
Tasks: Classification, Contrastive Learning, Data Augmentation, Representation Learning, Sentiment Analysis, Subjectivity Analysis, text-classification, Text Classification

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.
Scales and Hedges in a Logic with Analogous Semantics
Authors: Hedda R. Schmidtke, Sara Coelho
Date: 2022-01-21 00:00:00
Tasks: Decision Making, Logical Reasoning

Logics with analogous semantics, such as Fuzzy Logic, have a number of explanatory and application advantages, the most well-known being the ability to help experts develop control systems. From a cognitive systems perspective, such languages also have the advantage of being grounded in perception. For social decision making in humans, it is vital that logical conclusions about others (cognitive empathy) are grounded in empathic emotion (affective empathy). Classical Fuzzy Logic, however, has several disadvantages: it is not obvious how complex formulae, e.g., the description of events in a text, can be (a) formed, (b) grounded, and (c) used in logical reasoning. The two-layered Context Logic (CL) was designed to address these issue. Formally based on a lattice semantics, like classical Fuzzy Logic, CL also features an analogous semantics for complex fomulae. With the Activation Bit Vector Machine (ABVM), it has a simple and classical logical reasoning mechanism with an inherent imagery process based on the Vector Symbolic Architecture (VSA) model of distributed neuronal processing. This paper adds to the existing theory how scales, as necessary for adjective and verb semantics can be handled by the system.
Gender Bias in Text: Labeled Datasets and Lexicons
Authors: Jad Doughman, Wael Khreich
Date: 2022-01-21 00:00:00

Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of this work is to publicly provide labeled datasets and exhaustive lexicons by collecting, annotating, and augmenting relevant sentences to facilitate the detection of gender bias in English text. Towards this end, we present an updated version of our previously proposed taxonomy by re-formalizing its structure, adding a new bias type, and mapping each bias subtype to an appropriate detection methodology. The released datasets and lexicons span multiple bias subtypes including: Generic He, Generic She, Explicit Marking of Sex, and Gendered Neologisms. We leveraged the use of word embedding models to further augment the collected lexicons.