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Time series forecasting is a crucial yet challenging task in machine learning, requiring domain-specific knowledge due to its wide-ranging applications. While recent Transformer models have improved forecasting capabilities, they come with high computational costs. Linear-based models have shown better accuracy than Transformers but still fall short of ideal performance. To address these challenges, we introduce the Decomposition State-Space Recurrent Neural Network (DSSRNN), a novel framework designed for both long-term and short-term time series forecasting. DSSRNN uniquely combines decomposition analysis to capture seasonal and trend components with state-space models and physics-based equations. We evaluate DSSRNN's performance on indoor air quality datasets, focusing on CO2 concentration prediction across various forecasting horizons. Results demonstrate that DSSRNN consistently outperforms state-of-the-art models, including transformer-based architectures, in terms of both Mean Squared Error (MSE) and Mean Absolute Error (MAE). For example, at the shortest horizon (T=96) in Office 1, DSSRNN achieved an MSE of 0.378 and an MAE of 0.401, significantly lower than competing models. Additionally, DSSRNN exhibits superior computational efficiency compared to more complex models. While not as lightweight as the DLinear model, DSSRNN achieves a balance between performance and efficiency, with only 0.11G MACs and 437MiB memory usage, and an inference time of 0.58ms for long-term forecasting. This work not only showcases DSSRNN's success but also establishes a new benchmark for physics-informed machine learning in environmental forecasting and potentially other domains.

Document Visual Question Answering (VQA) requires models to interpret textual information within complex visual layouts and comprehend spatial relationships to answer questions based on document images. Existing approaches often lack interpretability and fail to precisely localize answers within the document, hindering users' ability to verify responses and understand the reasoning process. Moreover, standard metrics like Average Normalized Levenshtein Similarity (ANLS) focus on text accuracy but overlook spatial correctness. We introduce DLaVA, a novel method that enhances Multimodal Large Language Models (MLLMs) with answer localization capabilities for Document VQA. Our approach integrates image annotation directly into the MLLM pipeline, improving interpretability by enabling users to trace the model's reasoning. We present both OCR-dependent and OCR-free architectures, with the OCR-free approach eliminating the need for separate text recognition components, thus reducing complexity. To the best of our knowledge, DLaVA is the first approach to introduce answer localization within multimodal QA, marking a significant step forward in enhancing user trust and reducing the risk of AI hallucinations. Our contributions include enhancing interpretability and reliability by grounding responses in spatially annotated visual content, introducing answer localization in MLLMs, proposing a streamlined pipeline that combines an MLLM with a text detection module, and conducting comprehensive evaluations using both textual and spatial accuracy metrics, including Intersection over Union (IoU). Experimental results on standard datasets demonstrate that DLaVA achieves SOTA performance, significantly enhancing model transparency and reliability. Our approach sets a new benchmark for Document VQA, highlighting the critical importance of precise answer localization and model interpretability.

We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased contrastive learning techniques to 1) work with attributed graphs, 2) enabling a mini-batch based approach, and 3) achieving scalability. Based on a multi-class tuplet loss function, we present two algorithms -- DMT for semi-supervised learning and DMAT-i for the unsupervised case. Analyzing our methods, we provide a generalization bound for the downstream node classification task and for the first time relate tuplet loss to contrastive learning. Through extensive experiments, we show high scalability of representation construction, and in applying the method for three downstream tasks (node clustering, node classification, and link prediction) better consistency over any single existing method., Comment: This is the complete version of a published paper with appendix including detailed proofs

Graph-level representations (and clustering/classification based on these representations) are required in a variety of applications. Examples include identifying malicious network traffic, prediction of protein properties, and many others. Often, data has to stay in isolated local systems (i.e., cannot be centrally shared for analysis) due to a variety of considerations like privacy concerns, lack of trust between the parties, regulations, or simply because the data is too large to be shared sufficiently quickly. This points to the need for federated learning for graph-level representations, a topic that has not been explored much, especially in an unsupervised setting. Addressing this problem, this paper presents a new framework we refer to as Federated Contrastive Learning of Graph-level Representations (FCLG). As the name suggests, our approach builds on contrastive learning. However, what is unique is that we apply contrastive learning at two levels. The first application is for local unsupervised learning of graph representations. The second level is to address the challenge associated with data distribution variation (i.e. the ``Non-IID issue") when combining local models. Through extensive experiments on the downstream task of graph-level clustering, we demonstrate FCLG outperforms baselines (which apply existing federated methods on existing graph-level clustering methods) with significant margins., Comment: Accepted in BigData 2024. This is a preprint

Recent advances in diffusion models have significantly improved text-to-image (T2I) generation, but they often struggle to balance fine-grained precision with high-level control. Methods like ControlNet and T2I-Adapter excel at following sketches by seasoned artists but tend to be overly rigid, replicating unintentional flaws in sketches from novice users. Meanwhile, coarse-grained methods, such as sketch-based abstraction frameworks, offer more accessible input handling but lack the precise control needed for detailed, professional use. To address these limitations, we propose KnobGen, a dual-pathway framework that democratizes sketch-based image generation by seamlessly adapting to varying levels of sketch complexity and user skill. KnobGen uses a Coarse-Grained Controller (CGC) module for high-level semantics and a Fine-Grained Controller (FGC) module for detailed refinement. The relative strength of these two modules can be adjusted through our knob inference mechanism to align with the user's specific needs. These mechanisms ensure that KnobGen can flexibly generate images from both novice sketches and those drawn by seasoned artists. This maintains control over the final output while preserving the natural appearance of the image, as evidenced on the MultiGen-20M dataset and a newly collected sketch dataset.

We present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre-defined frequencies in the input image and employs a reconstruction loss to pre-train the model. While achieving promising results, such an implementation has two fundamental limitations as identified in our paper. First, using pre-defined frequencies overlooks the variability of image frequency responses. Second, pre-trained with frequency-filtered images, the resulting model needs relatively more data to adapt to naturally looking images during fine-tuning. To address these drawbacks, we propose FOurier transform compression with seLf-Knowledge distillation (FOLK), integrating two dedicated ideas. First, inspired by image compression, we adaptively select the masked-out frequencies based on image frequency responses, creating more suitable SSL tasks for pre-training. Second, we employ a two-branch framework empowered by knowledge distillation, enabling the model to take both the filtered and original images as input, largely reducing the burden of downstream tasks. Our experimental results demonstrate the effectiveness of FOLK in achieving competitive performance to many state-of-the-art SSL methods across various downstream tasks, including image classification, few-shot learning, and semantic segmentation.

In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of contrastive learning-based vision-language models, particularly CLIP, in handling detail-oriented and fine-grained tasks like segmentation. While CLIP and its variants excel in the global alignment of image and text representations, they often struggle to capture the fine-grained details necessary for precise segmentation. To overcome these challenges, we propose a novel framework that employs patch-level comparison of self-distillation and pixel-level reconstruction losses, enhanced with an attention-based token removal mechanism. This approach selectively retains semantically relevant tokens, enabling the model to focus on the image's critical regions aligned with the specific functions of our model, including textual information processing, patch comparison, and image reconstruction, ensuring that the model learns high-level semantics and detailed visual features. Our experiments demonstrate that DetailCLIP surpasses existing CLIP-based and traditional self-supervised learning (SSL) models in segmentation accuracy and exhibits superior generalization across diverse datasets. DetailCLIP represents a significant advancement in vision-language modeling, offering a robust solution for tasks that demand high-level semantic understanding and detailed feature extraction. https://github.com/KishoreP1/DetailCLIP.

Automating the annotation of scanned documents is challenging, requiring a balance between computational efficiency and accuracy. DocParseNet addresses this by combining deep learning and multi-modal learning to process both text and visual data. This model goes beyond traditional OCR and semantic segmentation, capturing the interplay between text and images to preserve contextual nuances in complex document structures. Our evaluations show that DocParseNet significantly outperforms conventional models, achieving mIoU scores of 49.12 on validation and 49.78 on the test set. This reflects a 58% accuracy improvement over state-of-the-art baseline models and an 18% gain compared to the UNext baseline. Remarkably, DocParseNet achieves these results with only 2.8 million parameters, reducing the model size by approximately 25 times and speeding up training by 5 times compared to other models. These metrics, coupled with a computational efficiency of 0.039 TFLOPs (BS=1), highlight DocParseNet's high performance in document annotation. The model's adaptability and scalability make it well-suited for real-world corporate document processing applications. The code is available at https://github.com/ahmad-shirazi/DocParseNet

Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper addresses the critical need for advanced predictive methods in road safety by conducting a comprehensive review of recent advancements in applying machine learning (ML) techniques to traffic accident analysis and prediction. It examines 191 studies from the last five years, focusing on predicting accident risk, frequency, severity, duration, as well as general statistical analysis of accident data. To our knowledge, this study is the first to provide such a comprehensive review, covering the state-of-the-art across a wide range of domains related to accident analysis and prediction. The review highlights the effectiveness of integrating diverse data sources and advanced ML techniques to improve prediction accuracy and handle the complexities of traffic data. By mapping the current landscape and identifying gaps in the literature, this study aims to guide future research towards significantly reducing traffic-related deaths and injuries by 2030, aligning with the World Health Organization (WHO) targets., Comment: A review paper, 26 pages

Expansion-enhanced sparse lexical representation improves information retrieval (IR) by minimizing vocabulary mismatch problems during lexical matching. In this paper, we explore the potential of jointly learning dense semantic representation and combining it with the lexical one for ranking candidate information. We present a hybrid information retrieval mechanism that maximizes lexical and semantic matching while minimizing their shortcomings. Our architecture consists of dual hybrid encoders that independently encode queries and information elements. Each encoder jointly learns a dense semantic representation and a sparse lexical representation augmented by a learnable term expansion of the corresponding text through contrastive learning. We demonstrate the efficacy of our model in single-stage ranking of a benchmark product question-answering dataset containing the typical heterogeneous information available on online product pages. Our evaluation demonstrates that our hybrid approach outperforms independently trained retrievers by 10.95% (sparse) and 2.7% (dense) in MRR@5 score. Moreover, our model offers better interpretability and performs comparably to state-of-the-art cross encoders while reducing response time by 30% (latency) and cutting computational load by approximately 38% (FLOPs)., Comment: 10 pages, 5 figures, ECNLP 7 @ LREC-COLING 2024

Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled training data available. Additionally, upon deployment, these methods are usually used to process a large volume of data on a daily basis, imposing a high maintenance cost on medical facilities. In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges. LoGoNet integrates a novel feature extractor within a U-shaped architecture, leveraging Large Kernel Attention (LKA) and a dual encoding strategy to capture both long-range and short-range feature dependencies adeptly. This is in contrast to existing methods that rely on increasing network capacity to enhance feature extraction. This combination of novel techniques in our model is especially beneficial in medical image segmentation, given the difficulty of learning intricate and often irregular body organ shapes, such as the spleen. Complementary, we propose a novel SSL method tailored for 3D images to compensate for the lack of large labeled datasets. The method combines masking and contrastive learning techniques within a multi-task learning framework and is compatible with both Vision Transformer (ViT) and CNN-based models. We demonstrate the efficacy of our methods in numerous tasks across two standard datasets (i.e., BTCV and MSD). Benchmark comparisons with eight state-of-the-art models highlight LoGoNet's superior performance in both inference time and accuracy.

Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash occurs, and informing safety policies, regulations, and targeted interventions. Despite numerous studies on accident prediction over the past decades, many have limitations in terms of generalizability, reproducibility, or feasibility for practical use due to input data or problem formulation. To address existing shortcomings, we propose CrashFormer, a multi-modal architecture that utilizes comprehensive (but relatively easy to obtain) inputs such as the history of accidents, weather information, map images, and demographic information. The model predicts the future risk of accidents on a reasonably acceptable cadence (i.e., every six hours) for a geographical location of 5.161 square kilometers. CrashFormer is composed of five components: a sequential encoder to utilize historical accidents and weather data, an image encoder to use map imagery data, a raw data encoder to utilize demographic information, a feature fusion module for aggregating the encoded features, and a classifier that accepts the aggregated data and makes predictions accordingly. Results from extensive real-world experiments in 10 major US cities show that CrashFormer outperforms state-of-the-art sequential and non-sequential models by 1.8% in F1-score on average when using ``sparse'' input data., Comment: The paper is accepted In 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI (UrbanAI 23), November 13, 2023, Hamburg, Germany

Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quality "ahead-of-time" approximations. Undoubtedly, accurate indoor air quality approximation significantly helps provide a healthy indoor environment, optimize associated energy consumption, and offer human comfort. However, it is crucial to design an ML architecture to capture the domain knowledge, so-called problem physics. In this study, we propose six novel physics-based ML models for accurate indoor pollutant concentration approximations. The proposed models include an adroit combination of state-space concepts in physics, Gated Recurrent Units, and Decomposition techniques. The proposed models were illustrated using data collected from five offices in a commercial building in California. The proposed models are shown to be less complex, computationally more efficient, and more accurate than similar state-of-the-art transformer-based models. The superiority of the proposed models is due to their relatively light architecture (computational efficiency) and, more importantly, their ability to capture the underlying highly nonlinear patterns embedded in the often contaminated sensor-collected indoor air quality temporal data.

Driving risk prediction has been a topic of much research over the past few decades to minimize driving risk and increase safety. The use of demographic information in risk prediction is a traditional solution with applications in insurance planning, however, it is difficult to capture true driving behavior via such coarse-grained factors. Therefor, the use of telematics data has gained a widespread popularity over the past decade. While most of the existing studies leverage demographic information in addition to telematics data, our objective is to maximize the use of telematics as well as contextual information (e.g., road-type) to build a risk prediction framework with real-world applications. We contextualize telematics data in a variety of forms, and then use it to develop a risk classifier, assuming that there are some weak risk labels available (e.g., past traffic citation records). Before building a risk classifier though, we employ a novel data-driven process to augment weak risk labels. Extensive analysis and results based on real-world data from multiple major cities in the United States demonstrate usefulness of the proposed framework., Comment: Preprint submitted for peer-review

Road construction projects maintain transportation infrastructures. These projects range from the short-term (e.g., resurfacing or fixing potholes) to the long-term (e.g., adding a shoulder or building a bridge). Deciding what the next construction project is and when it is to be scheduled is traditionally done through inspection by humans using special equipment. This approach is costly and difficult to scale. An alternative is the use of computational approaches that integrate and analyze multiple types of past and present spatiotemporal data to predict location and time of future road constructions. This paper reports on such an approach, one that uses a deep-neural-network-based model to predict future constructions. Our model applies both convolutional and recurrent components on a heterogeneous dataset consisting of construction, weather, map and road-network data. We also report on how we addressed the lack of adequate publicly available data - by building a large scale dataset named "US-Constructions", that includes 6.2 million cases of road constructions augmented by a variety of spatiotemporal attributes and road-network features, collected in the contiguous United States (US) between 2016 and 2021. Using extensive experiments on several major cities in the US, we show the applicability of our work in accurately predicting future constructions - an average f1-score of 0.85 and accuracy 82.2% - that outperform baselines. Additionally, we show how our training pipeline addresses spatial sparsity of data., Comment: In Proceedings of the 30th ACM SIGSPATIAL, International Conference on Advances in Geographic Information Systems (2022) [accepted as a short paper]

Web-based interactions can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits on accuracy as GCNs have been shown to suffer from over-smoothing issues. Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability. This paper addresses this open problem by relating the Laplacian smoothing to the Generalized PageRank and applying a random-walk based algorithm as a scalable graph filter. This forms the basis for our scalable deep clustering algorithm, RwSL, where through a self-supervised mini-batch training mechanism, we simultaneously optimize a deep neural network for sample-cluster assignment distribution and an autoencoder for a clustering-oriented embedding. Using 6 real-world datasets and 6 clustering metrics, we show that RwSL achieved improved results over several recent baselines. Most notably, we show that RwSL, unlike all other deep clustering frameworks, can continue to scale beyond graphs with more than one million nodes, i.e., handle web-scale. We also demonstrate how RwSL could perform node clustering on a graph with 1.8 billion edges using only a single GPU.

Identifying driving styles is the task of analyzing the behavior of drivers in order to capture variations that will serve to discriminate different drivers from each other. This task has become a prerequisite for a variety of applications, including usage-based insurance, driver coaching, driver action prediction, and even in designing autonomous vehicles; because driving style encodes essential information needed by these applications. In this paper, we present a deep-neural-network architecture, we term D-CRNN, for building high-fidelity representations for driving style, that combine the power of convolutional neural networks (CNN) and recurrent neural networks (RNN). Using CNN, we capture semantic patterns of driver behavior from trajectories (such as a turn or a braking event). We then find temporal dependencies between these semantic patterns using RNN to encode driving style. We demonstrate the effectiveness of these techniques for driver identification by learning driving style through extensive experiments conducted on several large, real-world datasets, and comparing the results with the state-of-the-art deep-learning and non-deep-learning solutions. These experiments also demonstrate a useful example of bias removal, by presenting how we preprocess the input data by sampling dissimilar trajectories for each driver to prevent spatial memorization. Finally, this paper presents an analysis of the contribution of different attributes for driver identification; we find that engine RPM, Speed, and Acceleration are the best combination of features., Comment: 12 pages, research on driving style representation

Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any fact- or rule-based knowledge source such as an ontology like the UMLS. Moreover, hidden associations between candidate concepts meaningful in the current context, may not exist within a single document, but within the collection, via alternate lexical forms. Therefore, inspired by the recent success of sequence-to-sequence neural models in delivering the state-of-the-art in a wide range of NLP tasks, we develop a novel sequence-to-set framework with neural attention for learning document representations that can effect term transfer within the corpus, for semantically tagging a large collection of documents. We demonstrate that our proposed method can be effective in both a supervised multi-label classification setup for text categorization, as well as in a unique unsupervised setting with no human-annotated document labels that uses no external knowledge resources and only corpus-derived term statistics to drive the training. Further, we show that semi-supervised training using our architecture on large amounts of unlabeled data can augment performance on the text categorization task when limited labeled data is available. Our approach to generate document encodings employing our sequence-to-set models for inference of semantic tags, gives to the best of our knowledge, the state-of-the-art for both, the unsupervised query expansion task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25--based document retrieval system; and also over the MLTM baseline (Soleimani et al, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del.icio.us and Ohsumed datasets. We will make our code and data publicly available., Comment: 8 pages, 4 figures, 1 table

Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For businesses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.

Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-review samples, would perform better than our proposed baselines, at answering specific, subjective product-related queries using reviews. We validate this hypothesis using a small gold standard dataset of question-review pairs evaluated by human experts, significantly surpassing our chosen baselines. Moreover, our approach, using no labeled question-review sentence pair data for training, gives performance at par with another method utilizing labeled question-review samples for the same task., Comment: 8 pages, 1 figure, 6 tables, added additional references to end of section 2.1, removed graphics from referenced works, added to argument in section 2.3 corrected typos, results unchanged

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction., Comment: In Proceedings of the 27th ACM SIGSPATIAL, International Conference on Advances in Geographic Information Systems (2019). arXiv admin note: substantial text overlap with arXiv:1906.05409

This paper presents techniques to detect the "offline" activity a person is engaged in when she is tweeting (such as dining, shopping or entertainment), in order to create a dynamic profile of the user, for uses such as better targeting of advertisements. To this end, we propose a hybrid LSTM model for rich contextual learning, along with studies on the effects of applying and combining multiple LSTM based methods with different contextual features. The hybrid model is shown to outperform a set of baselines and state-of-the-art methods. Finally, this paper presents an orthogonal validation with a real-case application. Our model generates an offline activity analysis for the followers of several well-known accounts, which is quite representative of the expected characteristics of these accounts.

Reducing traffic accidents is an important public safety challenge. However, the majority of studies on traffic accident analysis and prediction have used small-scale datasets with limited coverage, which limits their impact and applicability; and existing large-scale datasets are either private, old, or do not include important contextual information such as environmental stimuli (weather, points-of-interest, etc.). In order to help the research community address these shortcomings we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. US-Accidents currently contains data about $2.25$ million instances of traffic accidents that took place within the contiguous United States, and over the last three years. Each accident record consists of a variety of intrinsic and contextual attributes such as location, time, natural language description, weather, period-of-day, and points-of-interest. We present this dataset in this paper, along with a wide range of insights gleaned from this dataset with respect to the spatiotemporal characteristics of accidents. The dataset is publicly available at https://smoosavi.org/datasets/us_accidents., Comment: New preprint, 6 pages

Pattern discovery in geo-spatiotemporal data (such as traffic and weather data) is about finding patterns of collocation, co-occurrence, cascading, or cause and effect between geospatial entities. Using simplistic definitions of spatiotemporal neighborhood (a common characteristic of the existing general-purpose frameworks) is not semantically representative of geo-spatiotemporal data. We therefore introduce a new geo-spatiotemporal pattern discovery framework which defines a semantically correct definition of neighborhood; and then provides two capabilities, one to explore propagation patterns and the other to explore influential patterns. Propagation patterns reveal common cascading forms of geospatial entities in a region. Influential patterns demonstrate the impact of temporally long-term geospatial entities on their neighborhood. We apply this framework on a large dataset of traffic and weather data at countrywide scale, collected for the contiguous United States over two years. Our important findings include the identification of 90 common propagation patterns of traffic and weather entities (e.g., rain --> accident --> congestion), which results in identification of four categories of states within the US; and interesting influential patterns with respect to the "location", "duration", and "type" of long-term entities (e.g., a major construction --> more traffic incidents). These patterns and the categorization of the states provide useful insights on the driving habits and infrastructure characteristics of different regions in the US, and could be of significant value for applications such as urban planning and personalized insurance., Comment: In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Telematics data is becoming increasingly available due to the ubiquity of devices that collect data during drives, for different purposes, such as usage based insurance (UBI), fleet management, navigation of connected vehicles, etc. Consequently, a variety of data-analytic applications have become feasible that extract valuable insights from the data. In this paper, we address the especially challenging problem of discovering behavior-based driving patterns from only externally observable phenomena (e.g. vehicle's speed). We present a trajectory segmentation approach capable of discovering driving patterns as separate segments, based on the behavior of drivers. This segmentation approach includes a novel transformation of trajectories along with a dynamic programming approach for segmentation. We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio. Analysis and preliminary results show the applicability of approach for finding significant driving patterns., Comment: Accepted in the 3rd PhD workshop, ACM SIGSPATIAL 2016

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of "social agony" to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models. The QDEE framework also allows us to characterize user expertise in novel ways by identifying interesting patterns and roles played by different users in such CQAs., Comment: Accepted in the Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, USA

Because of the increasing availability of spatiotemporal data, a variety of data-analytic applications have become possible. Characterizing driving context, where context may be thought of as a combination of location and time, is a new challenging application. An example of such a characterization is finding the correlation between driving behavior and traffic conditions. This contextual information enables analysts to validate observation-based hypotheses about the driving of an individual. In this paper, we present DriveContext, a novel framework to find the characteristics of a context, by extracting significant driving patterns (e.g., a slow-down), and then identifying the set of potential causes behind patterns (e.g., traffic congestion). Our experimental results confirm the feasibility of the framework in identifying meaningful driving patterns, with improvements in comparison with the state-of-the-art. We also demonstrate how the framework derives interesting characteristics for different contexts, through real-world examples., Comment: Accepted to be published at The 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2017)

Nowadays, the ubiquity of various sensors enables the collection of voluminous datasets of car trajectories. Such datasets enable analysts to make sense of driving patterns and behaviors: in order to understand the behavior of drivers, one approach is to break a trajectory into its underlying patterns and then analyze that trajectory in terms of derived patterns. The process of trajectory segmentation is a function of various resources including a set of ground truth trajectories with their driving patterns. To the best of our knowledge, no such ground-truth dataset exists in the literature. In this paper, we describe a trajectory annotation framework and report our results to annotate a dataset of personal car trajectories. Our annotation methodology consists of a crowd-sourcing task followed by a precise process of aggregation. Our annotation process consists of two granularity levels, one to specify the annotation (segment border) and the other one to describe the type of the segment (e.g. speed-up, turn, merge, etc.). The output of our project, Dataset of Annotated Car Trajectories (DACT), is available online at https://figshare.com/articles/dact_dataset_of_annotated_car_trajectories/5005289 ., Comment: A 10 pages technical report which described the process of preparing a ground-truth dataset

This paper is a contribution to the Computational Science & Engineering Software Sustainability and Productivity Challenges (CSESSP Challenges) Workshop (https://www.nitrd.gov/csessp/), sponsored by the Networking and Information Technology Research and Development (NITRD) Software Design and Productivity (SDP) Coordinating Group, held October 15th-16th 2015 in Washington DC, USA. It introduces the role of software at the National Science Foundation (NSF) and the NSF Software Infrastructure for Sustained Innovation (SI2) program, then describes challenges that the SI2 program has identified, including funding models, career paths, incentives, training, interdisciplinary work, portability, and dissemination, as well as lesson that have been learned.