Miracle-dz/UniFAR-FTUs huggingface dataset:
MoCha: Large-Scale Driving Pattern Characterization for Usage-based Insurance

Given widely adopted vehicle tracking technologies, usage-based insurance has been a rising market over the past few years. With potential discounts from insurance companies, customers voluntarily install sensing devices in their vehicles for insurance companies, which are utilized to analyze their historical driving patterns to derive the risks of future driving. However, it is challenging to characterize and predict driving patterns, especially for new users with limited data. To address this issue, we propose and evaluate a system called MoCha to accurately characterize driving patterns for usage-based insurance. The key question we aim to explore with MoCha is whether we can fully explore long-term driving patterns of new users with only limited historical data of themselves by leveraging abundant data of other users and contextual information. To answer this question, we design (i) a multi-level driving pattern modeling component to capture the spatial-temporal dependency on both individual and group level, and (ii) a multi-task learning method to utilize underlying relations of driving metrics and predict multiple driving metrics simultaneously. We implement and evaluate MoCha with real-world on-board diagnostics data from a large insurance company with more than 340,000 vehicles. Further, we validate the usefulness of MoCha by predicting driving risks based on real-world claim data in a Chinese city, Shenzhen.
DHS: Adaptive Memory Layout Organization of Sketch Slots for Fast and Accurate Data Stream Processing

Data stream processing is a crucial computation task in data mining applications. The rigid and fixed data structures in existing solutions limit their accuracy, throughput, and generality in measurement tasks. We propose Dynamic Hierarchical Sketch (DHS), a sketch-based hybrid solution targeting these properties. During the online stream processing, DHS hashes items to buckets and organizes cells in each bucket dynamically; the size of all cells in a bucket is adjusted adaptively to the actual size and distribution of flows. Thus, memory is efficiently used to precisely record elephant flows and cover more mice flows. Implementation and evaluation show that DHS achieves high accuracy, high throughput, and high generality on five measurement tasks: flow size estimation, flow size distribution estimation, heavy hitter detection, heavy changer detection, and entropy estimation.
Meaning Error Rate: ASR domain-specific metric framework

Speech recognition became a popular task during the last decade. Automatic speech recognition (ASR) systems are used in many fields: virtual assistants, call-center automation, device speech interfaces, etc. Each application defines its own measure of quality. Improvement in one domain could lead to loss of the recognition quality in the other domain. For ASR services open to the public, it is essential to provide reasonable quality for all customers in their scenarios. State-of-the-art metrics currently do not fit well for this purpose as they do not adapt to domain specifics. In our work, we build a speech recognition quality evaluation framework that unifies feedback coming from different types of customers into a single metric. For this purpose, we collect feedback from customers, train a new dedicated metric for each customer based on their feedback, and finally aggregate these metrics in a single criterion of quality. The resulting metrics have two significant properties: they compare recognition quality in different domains, and their results are easy to interpret.
Cluster-Reduce: Compressing Sketches for Distributed Data Streams

Sketches, a type of probabilistic algorithms, have been widely accepted as the approximate summary of data streams. Compressing sketches is the best choice in distributed data streams to reduce communication overhead. The ideal compression algorithm should meet the following three requirements: high efficiency of compression procedure, support of direct query without decompression, and high accuracy of compressed sketches. However, no prior work can meet these requirements at the same time. Especially, the accuracy is poor after compression using existing methods. In this paper, we propose Cluster-Reduce, a framework for compressing sketches, which can meet all three requirements. Our key technique nearness clustering rearranges the adjacent counters with similar values in the sketch to significantly improve the accuracy. We use Cluster-Reduce to compress four kinds of sketches in two use-cases: distributed data streams and distributed machine learning. Extensive experimental results show that Cluster-Reduce can achieve up to 60 times smaller error than prior works. The source codes of Cluster-Reduce are available at Github anonymously[1].
Hierarchical Reinforcement Learning for Scarce Medical Resource Allocation with Imperfect Information

Facing the outbreak of COVID-19, shortage in medical resources becomes increasingly outstanding. Therefore, efficient strategies for medical resource allocation are urgently called for. Reinforcement learning (RL) is powerful for decision making, but three key challenges exist in solving this problem via RL: (1) complex situation and countless choices for decision making in the real world; (2) only imperfect information are available due to the latency of pandemic spreading; (3) limitations on conducting experiments in real world since we cannot set pandemic outbreaks arbitrarily. In this paper, we propose a hierarchical reinforcement learning method with a corresponding training algorithm. We design a decomposed action space to deal with the countless choices to ensure efficient and real time strategies. We also design a recurrent neural network based framework to utilize the imperfect information obtained from the environment. We build a pandemic spreading simulator based on real world data, serving as the experimental platform. We conduct extensive experiments and the results show that our method outperforms all the baselines, which reduces infections and deaths by 14.25% on average.
Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning

Fast and efficient access to environmental and life data is key to the successful disaster response. Vehicular crowdsourcing (VC) by a group of unmanned vehicles (UVs) like drones and unmanned ground vehicles to collect these data from Point-of-Interests (PoIs) e.g., possible survivor spots and fire site, provides an efficient way to assist disaster rescue. In this paper, we explicitly consider to navigate a group of UVs in a 3-dimensional (3D) disaster workzone to maximize the amount of collected data, geographical fairness, energy efficiency, while minimizing data dropout due to limited transmission rate. We propose DRL-DisasterVC(3D), a distributed deep reinforcement learning framework, with a repetitive experience replay (RER) to improve learning efficiency, and a clipped target network to increase learning stability. We also use a 3D convolutional neural network (3D CNN) with multi-head-relational attention (MHRA) for spatial modeling, and add auxiliary pixel control (PC) for spatial exploration. We designed a novel disaster response simulator, called "DisasterSim", and conduct extensive experiments to show that DRL-DisasterVC(3D) outperforms all five baselines in terms of energy efficiency when varying the numbers of UVs, PoIs and SNR threshold.
TopNet: Learning from Neural Topic Model to Generate Long Stories

Long story generation (LSG) is one of the coveted goals in natural language processing. Different from most text generation tasks, LSG requires to output a long story of rich content based on a much shorter text input, and often suffers from information sparsity. In this paper, we propose TopNet to alleviate this problem, by leveraging the recent advances in neural topic modeling to obtain high-quality skeleton words to complement the short input. In particular, instead of directly generating a story, we first learn to map the short text input to a low-dimensional topic distribution (which is pre-assigned by a topic model). Based on this latent topic distribution, we can use the reconstruction decoder of the topic model to sample a sequence of inter-related words as a skeleton for the story. Experiments on two benchmark datasets show that our proposed framework is highly effective in skeleton word selection and significantly outperforms the state-of-the-art models in both automatic evaluation and human evaluation.
Towards the D-Optimal Online Experiment Design for Recommender Selection

Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluations are prone to the bias of history data. Finding the optimal online experiment is nontrivial since both the users and displayed recommendations carry contextual features that are informative to the reward. While the problem can be formalized via the lens of multi-armed bandits, the existing solutions are found less satisfactorily because the general methodologies do not account for the case-specific structures, particularly for the e-commerce recommendation we study. To fill in the gap, we leverage the D-optimal design from the classical statistics literature to achieve the maximum information gain during exploration, and reveal how it fits seamlessly with the modern infrastructure of online inference. To demonstrate the effectiveness of the optimal designs, we provide semi-synthetic simulation studies with published code and data for reproducibility purposes. We then use our deployment example on Walmart.com to fully illustrate the practical insights and effectiveness of the proposed methods.
Toward Explainable Deep Anomaly Detection

Anomaly explanation, also known as anomaly localization, is as important as, if not more than, anomaly detection in many real-world applications. However, it is challenging to build explainable detection models due to the lack of anomaly-supervisory information and the unbounded nature of anomaly; most existing studies exclusively focus on the detection task only, including the recently emerging deep learning-based anomaly detection that leverages neural networks to learn expressive low-dimensional representations or anomaly scores for the detection task. Deep learning models, including deep anomaly detection models, are often constructed as black boxes, which have been criticized for the lack of explainability of their prediction results. To tackle this explainability issue, there have been numerous techniques introduced over the years, many of which can be utilized or adapted to offer highly explainable detection results. This tutorial aims to present a comprehensive review of the advances in deep learning-based anomaly detection and explanation. We first review popular state-of-the-art deep anomaly detection methods from different categories of approaches, followed by the introduction of a number of principled approaches used to provide anomaly explanation for deep detection models. Through this tutorial, we aim to promote the development in algorithms, theories and evaluation of explainable deep anomaly detection in the machine learning and data mining community. The slides and other materials of the tutorial are made publicly available at https://tinyurl.com/explainableDeepAD.
Practical Fully-Decentralized Secure Aggregation for Personal Data Management Systems

Personal Data Management Systems (PDMS) are flourishing, boosted by legal and technical means like smart disclosure, data portability and data altruism. A PDMS allows its owner to easily collect, store and manage data, directly generated by her devices, or resulting from her interactions with companies or administrations. PDMSs unlock innovative usages by crossing multiple data sources from one or many users, thus requiring aggregation primitives. Indeed, aggregation primitives are essential to compute statistics on user data, but are also a fundamental building block for machine learning algorithms. This paper proposes a protocol allowing for secure aggregation in a massively distributed PDMS environment, which adapts to selective participation and PDMSs characteristics, and is reliable with respect to failures, with no compromise on accuracy. Preliminary experiments show the effectiveness of our protocol which can adapt to several contexts with varying PDMSs characteristics in terms of communication speed or CPU resources and can adjust the aggregation strategy to the estimated selective participation.
A Deep Learning Method for Route and Time Prediction in Food Delivery Service

Online food ordering and delivery service has widely served people's daily demands worldwide, e.g., it has reached a number of 34.9 million online orders per day in Q3 of 2020 in Meituan food delivery platform. For the food delivery service, accurate estimation of the driver's delivery route and time, defined as the FD-RTP task, is very significant to customer satisfaction and driver experience. In the paper, we apply deep learning to the FD-RTP task for the first time, and propose a deep network named FDNET. Different from traditional heuristic search algorithms, we predict the probability of each feasible location the driver will visit next, through mining a large amount of food delivery data. Guided by the probabilities, FDNET greatly reduces the search space in delivery route generation, and the calculation times of time prediction. As a result, various kinds of information can be fully utilized in FDNET within the limited computation time. Careful consideration of the factors having effect on the driver's behaviors and introduction of more abundant spatiotemporal information both contribute to the improvements. Offline experiments over the large-scale real-world dataset, and online A/B test demonstrate the effectiveness of our proposed FDNET.
Norm Adjusted Proximity Graph for Fast Inner Product Retrieval

Efficient inner product search on embedding vectors is often the vital stage for online ranking services, such as recommendation and information retrieval. Recommendation algorithms, e.g., matrix factorization, typically produce latent vectors to represent users or items. The recommendation services are conducted by retrieving the most relevant item vectors given the user vector, where the relevance is often defined by inner product. Therefore, developing efficient recommender systems often requires solving the so-called maximum inner product search (MIPS) problem. In the past decade, there have been many studies on efficient MIPS algorithms. This task is challenging in part because the inner product does not follow the triangle inequality of metric space. Compared with hash-based or quantization-based MIPS solutions, in recent years graph-based MIPS algorithms have demonstrated their strong empirical advantages in many real-world MIPS tasks. In this paper, we propose a new index graph construction method named norm adjusted proximity graph (NAPG), for efficient MIPS. With adjusting factors estimated on sampled data, NAPG is able to select more meaningful data points to connect with when constructing graph-based index for inner product search. Our extensive experiments on a variety of datasets verify that the improved graph-based index strategy provides another strong addition to the pool of efficient MIPS algorithms.
A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks

Trajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches.
AutoSmart: An Efficient and Automatic Machine Learning Framework for Temporal Relational Data

Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giving precise model predictions. An automatic machine learning framework is needed to ease the manual efforts in fine-tuning the models so that the experts can focus more on other problems that really need humans' engagement such as problem definition, deployment, and business services. However, there are three main challenges for building automatic solutions for temporal relational data: 1) how to effectively and automatically mining useful information from the multiple tables and the relations from them? 2) how to be self-adjustable to control the time and memory consumption within a certain budget? and 3) how to give generic solutions to a wide range of tasks? In this work, we propose our solution that successfully addresses the above issues in an end-to-end automatic way. The proposed framework, AutoSmart, is the winning solution to the KDD Cup 2019 of the AutoML Track, which is one of the largest AutoML competition to date (860 teams with around 4,955 submissions). The framework includes automatic data processing, table merging, feature engineering, and model tuning, with a time and memory controller for efficiently and automatically formulating the models. The proposed framework outperforms the baseline solution significantly on several datasets in various domains.
HInT: Hybrid and Incremental Type Discovery for Large RDF Data Sources

The rapid explosion of linked data has resulted into many weakly structured and incomplete data sources, where typing information might be missing. On the other hand, type information is essential for a number of tasks such as query answering, integration, summarization and partitioning. Existing approaches for type discovery, either completely ignore type declarations available in the dataset (implicit type discovery approaches), or rely only on existing types, in order to complement them (explicit type enrichment approaches). Implicit type discovery approaches are based on instance grouping, which requires an exhaustive comparison between the instances. This process is expensive and not incremental. Explicit type enrichment approaches on the other hand, are not able to identify new types and they can not process data sources that have little or no schema information. In this paper, we present HInT, the first incremental and hybrid type discovery system for RDF datasets, enabling type discovery in datasets where type declarations are missing. To achieve this goal, we incrementally identify the patterns of the various instances, we index and then group them to identify the types. During the processing of an instance, our approach exploits its type information, if available, to improve the quality of the discovered types by guiding the classification of the new instance in the correct group and by refining the groups already built. We analytically and experimentally show that our approach dominates in terms of efficiency, competitors from both worlds, implicit type discovery and explicit type enrichment while outperforming them in most of the cases in terms of quality.
Comparison of outcomes of the Epley and Semont maneuvers in posterior canal BPPV: A randomized controlled trial

This study aims to compare the efficacy of the Epley and Semont maneuvers in relieving posterior canal benign paroxysmal positional vertigo (BPPV) arising in the in patients at the Outpatient Department of the Department of Otolaryngology, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand.
Maleic acid hydrotropic fractionation of wheat straw to facilitate value‐added multi‐product biorefinery at atmospheric pressure

Wheat straw was rapidly fractionated using maleic acid (MA) as an acid hydrotrope at atmospheric pressure under a range of conditions. MA hydrotropic fractionation (MAHF) was very selective in dissolving lignin and hemicelluloses resulting in a cellulose‐rich water insoluble solids (WIS), which was evaluated for producing fibers through bleaching and cellulosic nanofibrils by fibrillation, as well as glucose through enzymatic saccharification. The residual lignin (RL) within the WIS has a low degree of condensation, which eased bleaching. The RL was also carboxylated through MAHF, which facilitated nanofibrillation to produce lignin‐containing cellulose nanofibrils. The carboxylation also aided enzymatic saccharification of WIS by decreasing nonproductive cellulase binding to RL through electrostatic repulsion between carboxylated (charged) RL and the cellulase at elevated pH (>cellulase isoelectric point). The dissolved lignin (DL) from MAHF also has a low degree of condensation, making it favorable for further catalytic conversion. Carboxylation improved DL antioxidant capability. MA is an FDA‐approved indirect food additive (21CFR175‐177) and is much less corrosive than other acids typically used for cellulosic fractionation. It is also less soluble which is a distinct and very promising advantage for acid recovery. Therefore, MAHF has notable advantages and significant potential for sustainable biorefinery.