Miracle-dz/UniFAR-FTUs huggingface dataset:
Parking Occupancy Detection using Thermal Camera

Parking a vehicle is a daunting task during peak hours. The search for a parking space leads to congestion and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. This paper aims to identify parking occupancy in an open parking lot which consists of free parking spaces using a thermal camera. A thermal camera is capable of detecting vehicles in any weather and light conditions based on emitted heat and it can also be installed in public places with less restrictions. However, a thermal camera is expensive compared to a colour camera. A thermal camera can detect vehicles based on the emitted heat without any illumination. Vehicles appear bright or dark based on heat emitted by the vehicles. In order to identify vehicles, pre-trained vehicle detection algorithms, Histogram of Oriented Gradient detectors, Faster Regional Convolutional Neural Network (FRCNN) and modified Faster RCNN deep learning networks were implemented in this paper. The detection rates of the detectors reduced with diminishing of heat in the vehicles. Modified Faster RCNN deep learning network produced better detection results compared to other detectors. However, the detection rates can further be improved with larger and diverse training dataset.
Taming MAML: Efficient unbiased meta-reinforcement learning

While meta reinforcement learning (Meta-RL) methods have achieved remarkable success, obtaining correct and low variance estimates for policy gradients remains a significant challenge. In particular, estimating a large Hessian, poor sample efficiency and unstable training continue to make Meta-RL difficult. We propose a surrogate objective function named, Taming MAML (TMAML), that adds control variates into gradient estimation via automatic differentiation. TMAML improves the quality of gradient estimation by reducing variance without introducing bias. We further propose a version of our method that extends the meta-learning framework to learning the control variates themselves, enabling efficient and scalable learning from a distribution of MDPs. We empirically compare our approach with MAML and other variance-bias trade-off methods including DICE, LVC, and action-dependent control variates. Our approach is easy to implement and outperforms existing methods in terms of the variance and accuracy of gradient estimation, ultimately yielding higher performance across a variety of challenging Meta-RL environments.
Evaluation and Comparison of Dynamic Call Graph Generators for JavaScript

JavaScript is the most popular programming language these days and it is also the core language of the node.js environment. Sharing code is a simple task in this environment and the shared code can be easily reused as building blocks to create new applications. This vibrant and ever growing environment is not perfect though. Due to the large amount of reused code, even simple applications can have a lot of indirect dependencies. Developers may not even be aware of the fact that some of these dependencies could contain malware, since harmful code can be hidden relatively easily due to the dynamic nature of JavaScript. Dynamic software analysis is one way of detecting suspicious activities. Call graphs can reveal the internal workings of an application and they have been used successfully for malware detection. In node.js, no tool has been available for directly generating JavaScript call graphs before. In this paper, we are going to introduce three tools that can be used to generate call graphs for further analysis. We show that call graphs contain a significant amount of engine-specific information but filters can be used to reduce such differences.
Driver Cell Phone Usage Violation Detection using License Plate Recognition Camera Images

The increased use of digital video and image processing technology has paved the way for extending the traffic enforcement applications to a wider range of violations as well as making the enforcement process more efficient. Automated traffic enforcement has mainly been applied towards speed and red light violations detection. In recent years, there has been an extension to other violation detection tasks such as seat-belt usage, tailgating and toll payment violations. In the recent years, automated driver cell phone usage violation detection methods have aroused considerable interest since it results in higher mortality rates than the intoxicated driving. In this study, we propose a novel automated technique towards driver’s phone usage violation detection using deep learning algorithms. Using an existing license plate recognition camera system placed on an overhead gantry, installed on a highway, real world images are captured during day and night time. We performed experiments using more than 5900 real world images and achieved an overall accuracy of 90.8 % in the driver cell phone usage violation detection task.
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems

Extreme classification is a classification task on an extremely large number of labels (tags). User generated labels for any type of online data can be sparing per individual user but intractably large among all users. It would be useful to automatically select a smaller, standard set of labels to represent the whole label set. We can then solve efficiently the problem of multi-label learning with an intractably large number of interdependent labels, such as automatic tagging of Wikipedia pages. We propose a submodular maximization framework with linear cost to find informative labels which are most relevant to other labels yet least redundant with each other. A simple prediction model can then be trained on this label subset. Our framework includes both label-label and label-feature dependencies, which aims to find the labels with the most representation and prediction ability. In addition, to avoid information loss, we extract and predict outlier labels with weak dependency on other labels. We apply our model to four standard natural language data sets including Bibsonomy entries with users assigned tags, web pages with user assigned tags, legal texts with EUROVOC descriptors(A topic hierarchy with almost 4000 categories regarding different aspects of European law) and Wikipedia pages with tags from social bookmarking as well as news videos for automated label detection from a lexicon of semantic concepts. Experimental results show that our proposed approach improves label prediction quality, in terms of precision and nDCG, by 3% to 5% in three of the 5 tasks and is competitive in the others, even with a simple linear prediction model. An ablation study shows how different data sets benefit from different aspects of our model, with all aspects contributing substantially to at least one data set.
Scalable Price Targeting 1 Jean-Pierre Dubé , Chicago Booth and NBER

We study the welfare implications of scalable price targeting, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. Targeted prices are computed by solving the firm’s Bayesian Decision-Theoretic pricing problem based on a database with a high-dimensional vector of customer features that are observed prior to the price quote. To identify the causal effect of price on demand, we first run a large, randomized price experiment and use these data to train our demand model. We use l1 regularization (lasso) to select the set of customer features that moderate the heterogeneous treatment effect of price on demand. We use a weighted likelihood Bayesian bootstrap to quantify the firm’s approximate statistical uncertainty in demand and profitability. We then conduct a second experiment that implements our proposed price targeting scheme out of sample. Theoretically, both firm and customer surplus could rise with scalable price targeting. Optimized uniform pricing improves revenues by 64.9% relative to the control pricing, whereas scalable price targeting improves revenues by 81.5%. Firm profits increase by over 10% under targeted pricing relative to optimal uniform pricing. Customer surplus declines by less than 1% with price targeting; although nearly 70% of customers are charged less than the uniform price. Smaller firms with fewer job benefits are more likely to buy in the targeted pricing cell versus the uniform pricing cell. Our weighted likelihood bootstrap estimator also predicts demand and demand uncertainty out of sample better than a post-selection inference approach.
Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards

We study Lipschitz bandits, where a learner repeatedly plays one arm from an infinite arm set and then receives a stochastic reward whose expectation is a Lipschitz function of the chosen arm. Most of existing work assume the reward distributions are bounded or at least sub-Gaussian, and thus do not apply to heavy-tailed rewards arising in many real-world scenarios such as web advertising and financial markets. To address this limitation, in this paper we relax the assumption on rewards to allow arbitrary distributions that have finite (1 + )-th moments for some ∈ (0, 1], and propose algorithms that enjoy a sublinear regret of Õ ( T (dz +1)/(dz + +1) ) where T is the time horizon and dz is the zooming dimension. The key idea is to exploit the Lipschitz property of the expected reward function by adaptively discretizing the arm set, and employ upper confidence bound policies with robust mean estimators designed for heavy-tailed distributions. Furthermore, we provide a lower bound for Lipschitz bandits with heavy-tailed rewards, and show that our algorithms are optimal in terms of T . Finally, we conduct numerical experiments to demonstrate the effectiveness of our algorithms.
Emerging Concepts and Trends in Collaborative Modeling: A Survey

Just as in other engineering disciplines, software engineering is well suited to collaboration; Having different perspectives and diverse experiences strengthens engineering projects. Software modeling is a fundamental aspect of software engineering and is becoming increasingly collaborative. Collaborative modeling approaches are maturing and related research is growing significantly. While surveys exist on collaborative modeling tools and research, they are aimed at academics and can be verbose. In this article, we conduct a research survey intended to provide practitioners and researchers an accessible and abstract at-a-glance perspective of emerging trends and directions in collaborative modeling. We complete a systematic literature review, which we crosscheck with existing surveys. To explicate trends in the last five complete years and overall trends, we perform concept extraction and domain analysis by analyzing abstracts. We visualize these trends in word clouds and trend charts, and provide insights. We hope this article helps spread awareness of collaborative modeling trends and future directions, and educates practitioners and researchers.
COMIC: Multi-view Clustering Without Parameter Selection

In this paper, we study two challenges in clustering analysis, namely, how to cluster multi-view data and how to perform clustering without parameter selection on cluster size. To this end, we propose a novel objective function to project raw data into one space in which the projection embraces the geometric consistency (GC) and the cluster assignment consistency (CAC). To be specific, the GC aims to learn a connection graph from a projection space wherein the data points are connected if and only if they belong to the same cluster. The CAC aims to minimize the discrepancy of pairwise connection graphs induced from different views based on the view-consensus assumption, i.e., different views could produce the same cluster assignment structure as they are different portraits of the same object. Thanks to the view-consensus derived from the connection graph, our method could achieve promising performance in learning view-specific representation and eliminating the heterogeneous gaps across different views. Furthermore, with the proposed objective, it could learn almost all parameters including the cluster number from data without labor-intensive parameter selection. Extensive experimental results show the promising performance achieved by our method on five datasets comparing with nine state-of-the-art multi-view clustering approaches.
Improving Knowledge Base Construction from Robust Infobox Extraction

A capable, automatic Question Answering (QA) system can provide more complete and accurate answers using a comprehensive knowledge base (KB). One important approach to constructing a comprehensive knowledge base is to extract information from Wikipedia infobox tables to populate an existing KB. Despite previous successes in the Infobox Extraction (IBE) problem (e.g., DBpedia), three major challenges remain: 1) Deterministic extraction patterns used in DBpedia are vulnerable to template changes; 2) Over-trusting Wikipedia anchor links can lead to entity disambiguation errors; 3) Heuristic-based extraction of unlinkable entities yields low precision, hurting both accuracy and completeness of the final KB. This paper presents a robust approach that tackles all three challenges. We build probabilistic models to predict relations between entity mentions directly from the infobox tables in HTML. The entity mentions are linked to identifiers in an existing KB if possible. The unlinkable ones are also parsed and preserved in the final output. Training data for both the relation extraction and the entity linking models are automatically generated using distant supervision. We demonstrate the empirical effectiveness of the proposed method in both precision and recall compared to a strong IBE baseline, DBpedia, with an absolute improvement of 41.3% in average F1. We also show that our extraction makes the final KB significantly more complete, improving the completeness score of list-value relation types by 61.4%.
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction

We present POLITEX (POLicy ITeration with EXpert advice), a variant of policy iteration where each policy is a Boltzmann distribution over the sum of action-value function estimates of the previous policies, and analyze its regret in continuing RL problems. We assume that the value function error after running a policy for τ time steps scales as ε(τ) = ε0 + Õ( √ d/τ), where ε0 is the worst-case approximation error and d is the number of features in a compressed representation of the state-action space. We establish that this condition is satisfied by the LSPE algorithm under certain assumptions on the MDP and policies. Under the error assumption, we show that the regret of POLITEX in uniformly mixing MDPs scales as Õ(dT 3/4 + ε0T ), where Õ(·) hides logarithmic terms and problem-dependent constants. Thus, we provide the first regret bound for a fully practical model-free method which only scales in the number of features, and not in the size of the underlying MDP. Experiments on a queuing problem confirm that POLITEX is competitive with some of its alternatives, while preliminary results on Ms Pacman (one of the standard Atari benchmark problems) confirm the viability of POLITEX beyond linear function approximation.
Sparse Extreme Multi-label Learning with Oracle Property

The pioneering work of sparse local embeddings for extreme classification (SLEEC) (Bhatia et al., 2015) has shown great promise in multi-label learning. Unfortunately, the statistical rate of convergence and oracle property of SLEEC are still not well understood. To fill this gap, we present a unified framework for SLEEC with nonconvex penalty. Theoretically, we rigorously prove that our proposed estimator enjoys oracle property (i.e., performs as well as if the underlying model were known beforehand), and obtains a desirable statistical convergence rate. Moreover, we show that under a mild condition on the magnitude of the entries in the underlying model, we are able to obtain an improved convergence rate. Extensive numerical experiments verify our theoretical findings and the superiority of our proposed estimator.
Enabling Real-time Neural IME with Incremental Vocabulary Selection

Input method editor (IME) converts sequential alphabet key inputs to words in a target language. It is an indispensable service for billions of Asian users. Although the neural-based language model is extensively studied and shows promising results in sequence-to-sequence tasks, applying a neural-based language model to IME was not considered feasible due to high latency when converting words on user devices. In this work, we articulate the bottleneck of neural IME decoding to be the heavy softmax computation over a large vocabulary. We propose an approach that incrementally builds a subset vocabulary from the word lattice. Our approach always computes the probability with a selected subset vocabulary. When the selected vocabulary is updated, the stale probabilities in previous steps are fixed by recomputing the missing logits. The experiments on Japanese IME benchmark shows an over 50x speedup for the softmax computations comparing to the baseline, reaching real-time speed even on commodity CPU without losing conversion accuracy. The approach is potentially applicable to other incremental sequence-to-sequence decoding tasks such as real-time continuous speech recognition.
Linear-Complexity Data-Parallel Earth Mover's Distance Approximations

The Earth Mover’s Distance (EMD) is a stateof-the art metric for comparing discrete probability distributions, but its high distinguishability comes at a high cost in computational complexity. Even though linear-complexity approximation algorithms have been proposed to improve its scalability, these algorithms are either limited to vector spaces with only a few dimensions or they become ineffective when the degree of overlap between the probability distributions is high. We propose novel approximation algorithms that overcome both of these limitations, yet still achieve linear time complexity. All our algorithms are data parallel, and thus, we take advantage of massively parallel computing engines, such as Graphics Processing Units (GPUs). On the popular text-based 20 Newsgroups dataset, the new algorithms are four orders of magnitude faster than a multi-threaded CPU implementation of Word Mover’s Distance and match its nearest-neighbors-search accuracy. On MNIST images, the new algorithms are four orders of magnitude faster than a GPU implementation of the Sinkhorn’s algorithm while offering a slightly higher nearest-neighbors-search accuracy.
Hierarchical User and Item Representation with Three-Tier Attention for Recommendation

Utilizing reviews to learn user and item representations is useful for recommender systems. Existing methods usually merge all reviews from the same user or for the same item into a long document. However, different reviews, sentences and even words usually have different informativeness for modeling users and items. In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation. Our model contains three major components, i.e., a sentence encoder to learn sentence representations from words, a review encoder to learn review representations from sentences, and a user/item encoder to learn user/item representations from reviews. In addition, we incorporate a three-tier attention network in our model to select important words, sentences and reviews. Besides, we combine the user and item representations learned from the reviews with user and item embeddings based on IDs as the final representations to capture the latent factors of individual users and items. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation

Tractable probabilistic models obviate the need for unreliable approximate inference approaches and as a result often yield accurate query answers in practice. However, most tractable models that achieve state-of-the-art generalization performance (measured using test set likelihood score) use latent variables. Such models admit poly-time marginal (MAR) inference but do not admit poly-time (full) maximum-a-posteriori (MAP) inference. To address this problem, in this paper, we propose a novel approach for inducing cutset networks, a well-known tractable, highly inter-pretable representation that does not use latent variables and admits linear time MAR as well as MAP inference. Our approach addresses a major limitation of existing techniques that learn cut-set networks from data in that their accuracy is quite low as compared to latent variable models such as ensembles of cutset networks and sum-product networks. The key idea in our approach is to construct deep cutset networks by not only learning them from data but also compiling them from a more accurate latent tractable model. We show experimentally that our new approach yields more accurate MAP estimates as compared with existing approaches and signiﬁcantly improves the test set log-likelihood score of cutset networks bringing them closer in terms of generalization performance to latent variable models.
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network

In this paper, we propose an effective deep learning framework for inducing courteous behavior in customer care responses. The interaction between a customer and the customer care representative contributes substantially to the overall customer experience. Thus it is imperative for customer care agents and chatbots engaging with humans to be personal, cordial and emphatic to ensure customer satisfaction and retention. Our system aims at automatically transforming neutral customer care responses into courteous replies. Along with stylistic transfer (of courtesy), our system ensures that responses are coherent with the conversation history, and generates courteous expressions consistent with the emotional state of the customer. Our technique is based on a reinforced pointer-generator model for the sequence to sequence task. The model is also conditioned on a hierarchically encoded and emotionally aware conversational context. We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: ‘generic’ and ‘courteous’. We perform quantitative and qualitative analyses on established and task-specific metrics, both automatic and human evaluation based. Our evaluation shows that the proposed models can generate emotionally-appropriate courteous expressions while preserving the content. Experimental results also prove that our proposed approach performs better than the baseline models.
Event Detection without Triggers

The goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined as the word or phrase that most clearly expresses an event occurrence. As a consequence, existing approaches required both annotated triggers and event types in training data. However, triggers are nonessential to event detection, and it is time-consuming for annotators to pick out the “most clearly” word from a given sentence, especially from a long sentence. The expensive annotation of training corpus limits the application of existing approaches. To reduce manual effort, we explore detecting events without triggers. In this work, we propose a novel framework dubbed as Type-aware Bias Neural Network with Attention Mechanisms (TBNNAM), which encodes the representation of a sentence based on target event types. Experimental results demonstrate the effectiveness. Remarkably, the proposed approach even achieves competitive performances compared with state-of-the-arts that used annotated triggers.