OpenRaiser/Intern-Atlas huggingface dataset:
Finding Effective Geo-Social Group for Impromptu Activity with Multiple Demands
Venue: arXiv cs.DB 2019
Year: 2019

Geo-social group search aims to find a group of people proximate to a location while socially related. One of the driven applications for geo-social group search is organizing an impromptu activity. This is because the social cohesiveness of a found geo-social group ensures a good communication atmosphere and the spatial closeness of the geo-social group reduces the preparation time for the activity. Most existing works treat geo-social group search as a problem that finds a group satisfying a single social constraint while optimizing the spatial proximity. However, when an impromptu activity has additional demands on attendees, e.g., the activity requires that the attendees have certain set of skills, the existing works cannot find an effective geo-social group efficiently. In this paper, we study how to find a group that is most proximate to a query location while satisfying multiple constraints. Specifically, the multiple constraints on which we focus include social constraint, size constraint and keyword constraint. We propose a novel search framework which first effectively narrows down the search space with theoretical guarantees and then efficiently finds the optimum result. Although our model considers multiple constraints, novel techniques devised in this paper ensure that search cost is equivalent to parameterized constant times of one time social constraint checking on a vastly restricted search space. We conduct extensive experiments on both real and semi-synthetic datasets for demonstrating the efficiency of the proposed search algorithm. To evaluate the effectiveness, we conduct two case studies on real datasets, demonstrating the superiority of our proposed model.
Manifesto for Improved Foundations of Relational Model
Venue: arXiv cs.DB 2019
Year: 2019

Normalized relations extended with inherited attributes can be more faithful to reality and support logical navigation free queries, properties available at present only through specific views. Adding inherited attributes can be nonetheless always less procedural than to define any such views. Present schemes should even typically suffice for relations with foreign keys. Implementing extended relations on popular DBSs appears also simple. Relational model should evolve accordingly, for benefit of likely millions of DBAs, clients, developers.
Real-Time Prediction of Delay Distribution in Service Systems using Mixture Density Networks
Venue: arXiv cs.PF 2019
Year: 2019

Motivated by interest in providing more efficient services in customer service systems, we use statistical learning methods and delay history information to predict the conditional distribution of the customers' waiting times in queueing systems. From the predicted distributions, descriptive statistics of the system such as the mean, variance and percentiles of the waiting times can be obtained, which can be used for delay announcements, SLA conformance and better system management. We model the conditional distributions by mixtures of Gaussians, parameters of which can be estimated using Mixture Density Networks. The evaluations show that exploiting more delay history information can result in much more accurate predictions under realistic time-varying arrival assumptions.
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and flexibility. However, they also have such intrinsic limitations as lacking explainability and suffering from data sparsity. In this paper, we propose an end-to-end joint learning framework to get around these limitations without introducing any extra overhead by distilling structured knowledge from a differentiable path-based recommendation model. Through extensive experiments, we show that our proposed framework can achieve state-of-the-art recommendation performance and meanwhile provide interpretable recommendation reasons.
Enjoy the Untrusted Cloud: A Secure, Scalable and Efficient SQL-like Query Framework for Outsourcing Data
Venue: arXiv cs.CR 2019
Year: 2019

While the security of the cloud remains a concern, a common practice is to encrypt data before outsourcing them for utilization. One key challenging issue is how to efficiently perform queries over the ciphertext. Conventional crypto-based solutions, e.g. partially/fully homomorphic encryption and searchable encryption, suffer from low performance, poor expressiveness and weak compatibility. An alternative method that utilizes hardware-assisted trusted execution environment, i.e., Intel SGX, has emerged recently. On one hand, such work lacks of supporting scalable access control over multiple data users. On the other hand, existing solutions are subjected to the key revocation problem and knowledge extractor vulnerability. In this work, we leverage the newly hardware-assisted methodology and propose a secure, scalable and efficient SQL-like query framework named QShield. Building upon Intel SGX, QShield can guarantee the confidentiality and integrity of sensitive data when being processed on an untrusted cloud platform. Moreover, we present a novel lightweight secret sharing method to enable multi-user access control in QShield, while tackling the key revocation problem. Furthermore, with an additional trust proof mechanism, QShield guarantees the correctness of queries and significantly alleviates the possibility to build a knowledge extractor. We implemented a prototype for QShield and show that QShield incurs minimum performance cost.
Distributional Reinforcement Learning for Energy-Based Sequential Models
Venue: arXiv cs.LG 2019
Year: 2019

Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models. In the first phase of training, an Energy-Based model (EBM) over sequences is derived. This EBM has high representational power, but is unnormalized and cannot be directly exploited for sampling. To address this issue [Parshakova et al., CoNLL 2019] proposes a distillation technique, which can only be applied under limited conditions. By relating this problem to Policy Gradient techniques in RL, but in a \emph{distributional} rather than \emph{optimization} perspective, we propose a general approach applicable to any sequential EBM. Its effectiveness is illustrated on GAM-based experiments.
Psychoacoustic Sonification as User Interface for Human-Machine Interaction
Venue: arXiv cs.HC 2019
Year: 2019

When operating a machine, the operator needs to know some spatial relations, like the relative location of the target or the nearest obstacle. Often, sensors are used to derive this spatial information, and visual displays are deployed as interfaces to communicate this information to the operator. In this paper, we present psychoacoustic sonification as an alternative interface for human-machine interaction. Instead of visualizations, an interactive sound guides the operator to the desired target location, or helps her avoid obstacles in space. By considering psychoacoustics --- i.e., the relationship between the physical and the perceptual attributes of sound --- in the audio signal processing, we can communicate precisely and unambiguously interpretable direction and distance cues along three orthogonal axes to a user. We present exemplary use cases from various application areas where users can benefit from psychoacoustic sonification.
Interleaved Composite Quantization for High-Dimensional Similarity Search
Venue: arXiv cs.LG 2019
Year: 2019

Similarity search retrieves the nearest neighbors of a query vector from a dataset of high-dimensional vectors. As the size of the dataset grows, the cost of performing the distance computations needed to implement a query can become prohibitive. A method often used to reduce this computational cost is quantization of the vector space and location-based encoding of the dataset vectors. These encodings can be used during query processing to find approximate nearest neighbors of the query point quickly. Search speed can be improved by using shorter codes, but shorter codes have higher quantization error, leading to degraded precision. In this work, we propose the Interleaved Composite Quantization (ICQ) which achieves fast similarity search without using shorter codes. In ICQ, a small subset of the code is used to approximate the distances, with complete codes being used only when necessary. Our method effectively reduces both code length and quantization error. Furthermore, ICQ is compatible with several recently proposed techniques for reducing quantization error and can be used in conjunction with these other techniques to improve results. We confirm these claims and show strong empirical performance of ICQ using several synthetic and real-word datasets.
RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms
Venue: arXiv cs.LG 2019
Year: 2019

Semi-Supervised Learning (SSL) algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful. However, our experiments illustrate several shortcomings that prior SSL algorithms suffer from. In particular, poor performance when unlabeled and labeled data distributions differ. To address these observations, we develop RealMix, which achieves state-of-the-art results on standard benchmark datasets across different labeled and unlabeled set sizes while overcoming the aforementioned challenges. Notably, RealMix achieves an error rate of 9.79% on CIFAR10 with 250 labels and is the only SSL method tested able to surpass baseline performance when there is significant mismatch in the labeled and unlabeled data distributions. RealMix demonstrates how SSL can be used in real world situations with limited access to both data and compute and guides further research in SSL with practical applicability in mind.
Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks
Venue: arXiv cs.LG 2019
Year: 2019

Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally trained with supervised L1-loss functions do not discriminate between low and high frequencies in the data, the error is not minimized efficiently for higher bands. This directly correlates with the quality of the perceived results, since missing high frequency details are easily noticeable. In this paper, we analyze the reconstruction quality of generative networks and present a frequency-aware loss function that is able to focus on specific bands of the dataset during training time. We show that our approach improves reconstruction quality of fluid simulation data in mid-frequency bands, yielding perceptually better results while requiring comparable training time.
Field Label Prediction for Autofill in Web Browsers
Venue: arXiv cs.HC 2019
Year: 2019

Automatic form fill is an important productivity related feature present in major web browsers, which predicts the field labels of a web form and automatically fills values in a new form based on the values previously filled for the same field in other forms. This feature increases the convenience and efficiency of users who have to fill similar information in fields in multiple forms. In this paper we describe a machine learning solution for predicting the form field labels, implemented as a web service using Azure ML Studio.
Research Frontiers in Transfer Learning -- a systematic and bibliometric review
Venue: arXiv cs.DL 2019
Year: 2019

Humans can learn from very few samples, demonstrating an outstanding generalization ability that learning algorithms are still far from reaching. Currently, the most successful models demand enormous amounts of well-labeled data, which are expensive and difficult to obtain, becoming one of the biggest obstacles to the use of machine learning in practice. This scenario shows the massive potential for Transfer Learning, which aims to harness previously acquired knowledge to the learning of new tasks more effectively and efficiently. In this systematic review, we apply a quantitative method to select the main contributions to the field and make use of bibliographic coupling metrics to identify research frontiers. We further analyze the linguistic variation between the classics of the field and the frontier and map promising research directions.
Lower Dimensional Kernels for Video Discriminators
Venue: arXiv cs.CV 2019
Year: 2019

This work presents an analysis of the discriminators used in Generative Adversarial Networks (GANs) for Video. We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimisation difficult. We also show that this curvature becomes more extreme as the maximal kernel dimension of video discriminators increases. With these observations in hand, we propose a family of efficient Lower-Dimensional Video Discriminators for GANs (LDVD GANs). The proposed family of discriminators improve the performance of video GAN models they are applied to and demonstrate good performance on complex and diverse datasets such as UCF-101. In particular, we show that they can double the performance of Temporal-GANs and provide for state-of-the-art performance on a single GPU.
Adversarial VC-dimension and Sample Complexity of Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial attacks during the testing phase of neural networks pose a challenge for the deployment of neural networks in security critical settings. These attacks can be performed by adding noise that is imperceptible to humans on top of the original data. By doing so, an attacker can create an adversarial sample, which will cause neural networks to misclassify. In this paper, we seek to understand the theoretical limits of what can be learned by neural networks in the presence of an adversary. We first defined the hypothesis space of a neural network, and showed the relationship between the growth number of the entire neural network and the growth number of each neuron. Combine that with the adversarial Vapnik-Chervonenkis(VC)-dimension of halfspace classifiers, we concluded the adversarial VC-dimension of the neural networks with sign activation functions.
Topic subject creation using unsupervised learning for topic modeling
Venue: arXiv cs.LG 2019
Year: 2019

We describe the use of Non-Negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) algorithms to perform topic mining and labelling applied to retail customer communications in attempt to characterize the subject of customers inquiries. In this paper we compare both algorithms in the topic mining performance and propose methods to assign topic subject labels in an automated way.
Enabling Smartphone-based Estimation of Heart Rate
Venue: arXiv cs.LG 2019
Year: 2019

Continuous, ubiquitous monitoring through wearable sensors has the potential to collect useful information about users' context. Heart rate is an important physiologic measure used in a wide variety of applications, such as fitness tracking and health monitoring. However, wearable sensors that monitor heart rate, such as smartwatches and electrocardiogram (ECG) patches, can have gaps in their data streams because of technical issues (e.g., bad wireless channels, battery depletion, etc.) or user-related reasons (e.g. motion artifacts, user compliance, etc.). The ability to use other available sensor data (e.g., smartphone data) to estimate missing heart rate readings is useful to cope with any such gaps, thus improving data quality and continuity. In this paper, we test the feasibility of estimating raw heart rate using smartphone sensor data. Using data generated by 12 participants in a one-week study period, we were able to build both personalized and generalized models using regression, SVM, and random forest algorithms. All three algorithms outperformed the baseline moving-average interpolation method for both personalized and generalized settings. Moreover, our findings suggest that personalized models outperformed the generalized models, which speaks to the importance of considering personal physiology, behavior, and life style in the estimation of heart rate. The promising results provide preliminary evidence of the feasibility of combining smartphone sensor data with wearable sensor data for continuous heart rate monitoring.
Callisto: Entropy based test generation and data quality assessment for Machine Learning Systems
Venue: arXiv cs.LG 2019
Year: 2019

Machine Learning (ML) has seen massive progress in the last decade and as a result, there is a pressing need for validating ML-based systems. To this end, we propose, design and evaluate CALLISTO - a novel test generation and data quality assessment framework. To the best of our knowledge, CALLISTO is the first blackbox framework to leverage the uncertainty in the prediction and systematically generate new test cases for ML classifiers. Our evaluation of CALLISTO on four real world data sets reveals thousands of errors. We also show that leveraging the uncertainty in prediction can increase the number of erroneous test cases up to a factor of 20, as compared to when no such knowledge is used for testing. CALLISTO has the capability to detect low quality data in the datasets that may contain mislabelled data. We conduct and present an extensive user study to validate the results of CALLISTO on identifying low quality data from four state-of-the-art real world datasets.
Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms
Venue: arXiv cs.SI 2019
Year: 2019

A Health Service Area (HSA) is a group of geographic regions served by similar health care facilities. The delineation of HSAs plays a pivotal role in the characterization of health care services available in an area, enabling a better planning and regulation of health care services. Though Dartmouth HSAs have been the standard delineation for decades, previous work has recently shown an improved HSA delineation using a network-based approach, in which HSAs are the communities extracted by the Louvain algorithm in hospital-patient discharge networks. Given the existent heterogeneity of communities extracted by different community detection algorithms, a comparative analysis of community detection algorithms for optimal HSA delineation is lacking. In this work, we compared HSA delineations produced by community detection algorithms using a large-scale dataset containing different types of hospital-patient discharges spanning a 7-year period in US. Our results replicated the heterogeneity among community detection algorithms found in previous works, the improved HSA delineation obtained by a network-based, and suggested that Infomap may be a more suitable community detection for HSA delineation since it finds a high number of HSAs with high localization index and a low network conductance.
PFaRA: a Platoon Forming and Routing Algorithm for Same-Day Deliveries
Venue: arXiv cs.MA 2019
Year: 2019

Platoons, vehicles that travel very close together acting as one, promise to improve road usage on freeways and city roads alike. We study platoon formation in the context of same-day delivery in urban environments. Multiple self-interested logistic service providers (LSP) carry out same-day deliveries by deploying autonomous electric vehicles that are capable of forming and traveling in platoons. The novel aspect that we consider in our research is heterogeneity of platoons in the sense that vehicles are equipped with different capabilities and constraints, and belong to different providers. Our aim is to examine how these platoons can form and their potential properties and benefits. We present a platoon forming and routing algorithm, called PFaRA, that finds longest common routes for multiple vehicles, while also respecting vehicle preferences and constraints. PFaRA consists of two parts, a speed clustering step and a linear optimisation step. To test the approach, a simulation was used, working with realistic urban network data and background traffic models. Our results showed that the performance of our approach is comparable to a simple route-matching one, but it leads to better utility values for vehicles and by extension the LSPs. We show that the grouping provided is viable and provides benefits to all vehicles participating in the platoon.
Impact of Traffic Conditions and Carpool Lane Availability on Peer to Peer Ridesharing Demand
Venue: arXiv cs.SI 2019
Year: 2019

A peer to peer ridesharing system connects drivers who are using their personal vehicles to conduct their daily activities with passengers who are looking for rides. A well-designed and properly implemented ridesharing system can bring about social benefits, such as alleviating congestion and its adverse environmental impacts, as well as personal benefits in terms of shorter travel times and or financial savings for the individuals involved. In this paper, the goal is to study the impact of availability of carpool lanes and traffic conditions on ridesharing demand using an agent based simulation model. Agents will be given the option to use their personal vehicles, or participate in a ridesharing system. An exact many to many ride matching algorithm, where each driver can pick up and drop off multiple passengers and each passenger can complete his or her trip by transferring between multiple vehicle, is used to match drivers with passengers. The proposed approach is implemented in AnyLogic ABS software with a real travel data set of Los Angeles, California. The results of this research will shed light on the types of urban settings that will be more recipient towards ridesharing services.
Collaborative Filtering vs. Content-Based Filtering: differences and similarities
Venue: arXiv cs.IR 2019
Year: 2019

Recommendation Systems (SR) suggest items exploring user preferences, helping them with the information overload problem. Two approaches to SR have received more prominence, Collaborative Filtering, and Content-Based Filtering. Moreover, even though studies are indicating their advantages and disadvantages, few results empirically prove their characteristics, similarities, and differences. In this work, an experimental methodology is proposed to perform comparisons between recommendation algorithms for different approaches going beyond the "precision of the predictions". For the experiments, three algorithms of recommendation were tested: a baseline for Collaborative Filtration and two algorithms for Content-based Filtering that were developed for this evaluation. The experiments demonstrate the behavior of these systems in different data sets, its main characteristics and especially the complementary aspect of the two main approaches.
Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity
Venue: arXiv cs.CL 2019
Year: 2019

Image captioning as a multimodal task has drawn much interest in recent years. However, evaluation for this task remains a challenging problem. Existing evaluation metrics focus on surface similarity between a candidate caption and a set of reference captions, and do not check the actual relation between a caption and the underlying visual content. We introduce a new diagnostic evaluation framework for the task of image captioning, with the goal of directly assessing models for grammaticality, truthfulness and diversity (GTD) of generated captions. We demonstrate the potential of our evaluation framework by evaluating existing image captioning models on a wide ranging set of synthetic datasets that we construct for diagnostic evaluation. We empirically show how the GTD evaluation framework, in combination with diagnostic datasets, can provide insights into model capabilities and limitations to supplement standard evaluations.