OpenRaiser/Intern-Atlas huggingface dataset:
Personalized Query Auto-Completion Through a Lightweight Representation of the User Context
Venue: arXiv cs.IR 2019
Year: 2019

Query Auto-Completion (QAC) is a widely used feature in many domains, including web and eCommerce search, suggesting full queries based on a prefix typed by the user. QAC has been extensively studied in the literature in the recent years, and it has been consistently shown that adding personalization features can significantly improve the performance of QAC. In this work we propose a novel method for personalized QAC that uses lightweight embeddings learnt through fastText. We construct an embedding for the user context queries, which are the last few queries issued by the user. We also use the same model to get the embedding for the candidate queries to be ranked. We introduce ranking features that compute the distance between the candidate queries and the context queries in the embedding space. These features are then combined with other commonly used QAC ranking features to learn a ranking model. We apply our method to a large eCommerce search engine (eBay) and show that the ranker with our proposed feature significantly outperforms the baselines on all of the offline metrics measured, which includes Mean Reciprocal Rank (MRR), Success Rate (SR), Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Our baselines include the Most Popular Completion (MPC) model as well as a ranking model without our proposed features. The ranking model with the proposed features results in a $20-30\%$ improvement over the MPC model on all metrics. We obtain up to a $5\%$ improvement over the baseline ranking model for all the sessions, which goes up to about $10\%$ when we restrict to sessions that contain the user context. Moreover, our proposed features also significantly outperform text based personalization features studied in the literature before, and adding text based features on top of our proposed embedding based features results only in minor improvements.
IVO: Inverse Velocity Obstacles for Real Time Navigation
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we present "IVO: Inverse Velocity Obstacles" an ego-centric framework that improves the real time implementation. The proposed method stems from the concept of velocity obstacle and can be applied for both single agent and multi-agent system. It focuses on computing collision free maneuvers without any knowledge or assumption on the pose and the velocity of the robot. This is primarily achieved by reformulating the velocity obstacle to adapt to an ego-centric framework. This is a significant step towards improving real time implementations of collision avoidance in dynamic environments as there is no dependency on state estimation techniques to infer the robot pose and velocity. We evaluate IVO for both single agent and multi-agent in different scenarios and show it's efficacy over the existing formulations. We also show the real time scalability of the proposed methodology.
A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications
Venue: arXiv cs.IT 2019
Year: 2019

In this paper the throughput and energy efficiency of an unlicensed machine type communications network is studied. If an outage event happens in the network, there is a possibility for packet retransmissions in order to obtain a lower error probability. The model consist of a network with two types of users, Licensed and unlicensed users. The licensed users allocated uplink channel is also used by the unlicensed users. However, it is done in a way that no harm is done to the licensed users' transmission from sharing the same channel with the unlicensed users. However, licensed users' transmission causes interference on the unlicensed network. Poisson point process is used here to model the location of the nodes and the effect of interference on the network. We study how different factors such as number of retransmissions, SIR threshold and outage can effect the throughput and energy efficiency of the network. Throughput and energy efficiency are also both studied in constrained optimization problems where the constraints are the SIR threshold and number of retransmission attempts. We also show why it is important to use limited transmissions and what are the benefits.
Learning to Cooperate in D2D Caching Networks
Venue: arXiv cs.NI 2019
Year: 2019

We consider a wireless device-to-device (D2D) cooperative network where memory-endowed nodes store and exchange content. Each node generates random file requests following an unknown and possibly arbitrary spatio-temporal process, and a base station (BS) delivers any file that is not found at its neighbors' cache, at the expense of higher cost. We design an online learning algorithm which minimizes the aggregate delivery cost by assisting each node to decide which files to cache and which files to fetch from the BS and other devices. Our policy relies on the online gradient descent algorithm, is amenable to distributed execution, and achieves asymptotically optimal performance for any request pattern, without prior information.
Hierarchical Policy Learning is Sensitive to Goal Space Design
Venue: arXiv cs.LG 2019
Year: 2019

Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding improved sample efficiency, the ability to deal with long time horizons and transferability of sub-policies to tasks outside the training distribution. It is often implemented as a master policy providing goals to a sub-policy. Ideally, we would like the goal-spaces to be learned, however, properties of optimal goal spaces still remain unknown and consequently there is no method yet to learn optimal goal spaces. Motivated by this, we systematically analyze how various modifications to the ground-truth goal-space affect learning in hierarchical models with the aim of identifying important properties of optimal goal spaces. Our results show that, while rotation of ground-truth goal spaces and noise had no effect, having additional unnecessary factors significantly impaired learning in hierarchical models.
Learning to Control in Metric Space with Optimal Regret
Venue: arXiv cs.LG 2019
Year: 2019

We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and actions. We provide a surprisingly simple upper-confidence reinforcement learning algorithm that uses a function approximation oracle to estimate optimistic Q functions from experiences. We show that the regret of the algorithm after $K$ episodes is $O(HL(KH)^{\frac{d-1}{d}}) $ where $L$ is a smoothness parameter, and $d$ is the doubling dimension of the state-action space with respect to the given metric. We also establish a near-matching regret lower bound. The proposed method can be adapted to work for more structured transition systems, including the finite-state case and the case where value functions are linear combinations of features, where the method also achieve the optimal regret.
Accurate Face Detection for High Performance
Venue: arXiv cs.CV 2019
Year: 2019

Face detection has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs). Its central issue in recent years is how to improve the detection performance of tiny faces. To this end, many recent works propose some specific strategies, redesign the architecture and introduce new loss functions for tiny object detection. In this report, we start from the popular one-stage RetinaNet approach and apply some recent tricks to obtain a high performance face detector. Specifically, we apply the Intersection over Union (IoU) loss function for regression, employ the two-step classification and regression for detection, revisit the data augmentation based on data-anchor-sampling for training, utilize the max-out operation for classification and use the multi-scale testing strategy for inference. As a consequence, the proposed face detection method achieves state-of-the-art performance on the most popular and challenging face detection benchmark WIDER FACE dataset.
Performance Evaluation of the NDN Data Plane Using Statistical Model Checking
Venue: arXiv cs.NI 2019
Year: 2019

Named Data Networking (NDN) is an emerging technology for a future internet architecture that addresses weaknesses of the Internet Protocol (IP). Since Internet users and applications have demonstrated an ever-increasing need for high speed packet forwarding, research groups have investigated different designs and implementation for fast NDN data plane forwarders and claimed they were capable of achieving high throughput rates. However, the correctness of these statements is not supported by any verification technique or formal proof. In this paper, we propose using a formal model-based approach to overcome this issue. We consider the NDN-DPDK prototype implementation of a forwarder realized at NIST, which leverages concurrency to enhance overall quality of service. We use our approach to improve its design and to formally show that it can achieve high throughput rates.
A Methodological Review of Visual Road Recognition Procedures for Autonomous Driving Applications
Venue: arXiv cs.CV 2019
Year: 2019

The current research interest in autonomous driving is growing at a rapid pace, attracting great investments from both the academic and corporate sectors. In order for vehicles to be fully autonomous, it is imperative that the driver assistance system is adapt in road and lane keeping. In this paper, we present a methodological review of techniques with a focus on visual road detection and recognition. We adopt a pragmatic outlook in presenting this review, whereby the procedures of road recognition is emphasised with respect to its practical implementations. The contribution of this review hence covers the topic in two parts -- the first part describes the methodological approach to conventional road detection, which covers the algorithms and approaches involved to classify and segregate roads from non-road regions; and the other part focuses on recent state-of-the-art machine learning techniques that are applied to visual road recognition, with an emphasis on methods that incorporate convolutional neural networks and semantic segmentation. A subsequent overview of recent implementations in the commercial sector is also presented, along with some recent research works pertaining to road detections.
Towards Big data processing in IoT: network management for online edge data processing
Venue: arXiv cs.NI 2019
Year: 2019

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, the huge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed. The raw data generated by distributed network terminals are collected and processed by edge servers. The edge servers split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of limited edge computation ability, part of the raw data in huge data sources may be directly transmitted to the cloud. To manage limited resources online, we propose an algorithm based on Lyapunov optimization to jointly optimize the policy of edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay and saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT.
Game-theoretic Analysis to Content-adaptive Reversible Watermarking
Venue: arXiv cs.MM 2019
Year: 2019

While many games were designed for steganography and robust watermarking, few focused on reversible watermarking. We present a two-encoder game related to the rate-distortion optimization of content-adaptive reversible watermarking. In the game, Alice first hides a payload into a cover. Then, Bob hides another payload into the modified cover. The embedding strategy of Alice affects the embedding capacity of Bob. The embedding strategy of Bob may produce data-extraction errors to Alice. Both want to embed as many pure secret bits as possible, subjected to an upper-bounded distortion. We investigate non-cooperative game and cooperative game between Alice and Bob. When they cooperate with each other, one may consider them as a whole, i.e., an encoder uses a cover for data embedding with two times. When they do not cooperate with each other, the game corresponds to a separable system, i.e., both want to independently hide a payload within the cover, but recovering the cover may need cooperation. We find equilibrium strategies for both players under constraints.
Path Planning for Autonomous Bus Driving in Urban Environments
Venue: arXiv cs.RO 2019
Year: 2019

Driving in urban environments often presents difficult situations that require expert maneuvering of a vehicle. These situations become even more challenging when considering large vehicles, such as buses. We present a path planning framework that addresses the demanding driving task of buses in urban areas. The approach is formulated as an optimization problem using the road-aligned vehicle model. The road-aligned frame introduces a distortion on the vehicle body and obstacles, motivating the development of novel approximations that capture this distortion. These approximations allow for the formulation of safe and non-conservative collision avoidance constraints. Unlike other path planning approaches, our method exploits curbs and other sweepable regions, which a bus must often sweep over in order to manage certain maneuvers. Furthermore, it takes full advantage of the particular characteristics of buses, namely the overhangs, an elevated part of the vehicle chassis, that can sweep over curbs. Simulations are presented, showing the applicability and benefits of the proposed method.
A Parallel Corpus of Theses and Dissertations Abstracts
Venue: arXiv cs.CL 2019
Year: 2019

In Brazil, the governmental body responsible for overseeing and coordinating post-graduate programs, CAPES, keeps records of all theses and dissertations presented in the country. Information regarding such documents can be accessed online in the Theses and Dissertations Catalog (TDC), which contains abstracts in Portuguese and English, and additional metadata. Thus, this database can be a potential source of parallel corpora for the Portuguese and English languages. In this article, we present the development of a parallel corpus from TDC, which is made available by CAPES under the open data initiative. Approximately 240,000 documents were collected and aligned using the Hunalign tool. We demonstrate the capability of our developed corpus by training Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) models for both language directions, followed by a comparison with Google Translate (GT). Both translation models presented better BLEU scores than GT, with NMT system being the most accurate one. Sentence alignment was also manually evaluated, presenting an average of 82.30% correctly aligned sentences. Our parallel corpus is freely available in TMX format, with complementary information regarding document metadata
Curious Meta-Controller: Adaptive Alternation between Model-Based and Model-Free Control in Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representational limitations in making assumptions about the world dynamics and model errors inevitable in complex domains. However, they require a lot of experiences compared to model-based approaches that are typically more sample-efficient. We propose to combine the benefits of the two approaches by presenting an integrated approach called Curious Meta-Controller. Our approach alternates adaptively between model-based and model-free control using a curiosity feedback based on the learning progress of a neural model of the dynamics in a learned latent space. We demonstrate that our approach can significantly improve the sample efficiency and achieve near-optimal performance on learning robotic reaching and grasping tasks from raw-pixel input in both dense and sparse reward settings.
Trajectory Optimization for Rotary-Wing UAVs in Wireless Networks with Random Requests
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies the trajectory optimization problem in a scenario where a single rotary-wing UAV acts as a relay of data payloads for downlink transmission requests generated randomly by two ground nodes (GNs) in a wireless network. The goal is to optimize the UAV trajectory in order to minimize the expected average communication delay to serve these random requests. It is shown that the problem can be cast as a semi-Markov decision process (SMDP), and the resulting minimization problem is solved via multi-chain policy iteration. The optimality of a two-scale optimization approach is proved: the optimal trajectory in the communication phase greedily minimizes the communication delay of the current request while moving between the current start position and a target end position (inner optimization); the end positions are selected to minimize the expected average long-term delay in the SMDP (outer optimization). Numerical simulations show that the expected average delay is minimized when the UAV moves towards the geometric center of the GNs during phases in which it is not actively servicing transmission requests, and demonstrate significant improvements over sensible heuristics. Finally, it is revealed that the optimal end positions of communication phases become increasingly independent of the data payload, for large data payload values.
Investigating the Successes and Failures of BERT for Passage Re-Ranking
Venue: arXiv cs.IR 2019
Year: 2019

The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the reasons behind such substantial improvements. To this aim, we focus on the MS MARCO passage re-ranking dataset and provide potential reasons for the successes and failures of BERT for retrieval. In more detail, we empirically study a set of hypotheses and provide additional analysis to explain the successful performance of BERT.
Same Influenza, Different Responses: Social Media Can Sense a Regional Spectrum of Symptoms
Venue: arXiv cs.SI 2019
Year: 2019

Influenza is an acute respiratory infection caused by a virus. It is highly contagious and rapidly mutative. However, its epidemiological characteristics are conventionally collected in terms of outpatient records. In fact, the subjective bias of the doctor emphasizes exterior signs, and the necessity of face-to-face inquiry results in an inaccurate and time-consuming manner of data collection and aggregation. Accordingly, the inferred spectrum of syndromes can be incomplete and lagged. With a massive number of users being sensors, online social media can indeed provide an alternative approach. Voluntary reports in Twitter and its variants can deliver not only exterior signs but also interior feelings such as emotions. These sophisticated signals can further be efficiently collected and aggregated in a real-time manner, and a comprehensive spectrum of syndromes could thus be inferred. Taking Weibo as an example, it is confirmed that a regional spectrum of symptoms can be credibly sensed. Aside from the differences in symptoms and treatment incentives between northern and southern China, it is also surprising that patients in the south are more optimistic, while those in the north demonstrate more intense emotions. The differences sensed from Weibo can even help improve the performance of regressions in monitoring influenza. Our results suggest that self-reports from social media can be profound supplements to the existing clinic-based systems for influenza surveillance.
Differentiable Architecture Search with Ensemble Gumbel-Softmax
Venue: arXiv cs.LG 2019
Year: 2019

For network architecture search (NAS), it is crucial but challenging to simultaneously guarantee both effectiveness and efficiency. Towards achieving this goal, we develop a differentiable NAS solution, where the search space includes arbitrary feed-forward network consisting of the predefined number of connections. Benefiting from a proposed ensemble Gumbel-Softmax estimator, our method optimizes both the architecture of a deep network and its parameters in the same round of backward propagation, yielding an end-to-end mechanism of searching network architectures. Extensive experiments on a variety of popular datasets strongly evidence that our method is capable of discovering high-performance architectures, while guaranteeing the requisite efficiency during searching.
A multimodal lossless coding method for skeletons in videos
Venue: arXiv cs.MM 2019
Year: 2019

Nowadays, skeleton information in videos plays an important role in human-centric video analysis but effective coding such massive skeleton information has never been addressed in previous work. In this paper, we make the first attempt to solve this problem by proposing a multimodal skeleton coding tool containing three different coding schemes, namely, spatial differential-coding scheme, motionvector-based differential-coding scheme and inter prediction scheme, thus utilizing both spatial and temporal redundancy to losslessly compress skeleton data. More importantly, these schemes are switched properly for different types of skeletons in video frames, hence achieving further improvement of compression rate. Experimental results show that our approach leads to 74.4% and 54.7% size reduction on our surveillance sequences and overall test sequences respectively, which demonstrates the effectiveness of our skeleton coding tool.
RSL19BD at DBDC4: Ensemble of Decision Tree-based and LSTM-based Models
Venue: arXiv cs.CL 2019
Year: 2019

RSL19BD (Waseda University Sakai Laboratory) participated in the Fourth Dialogue Breakdown Detection Challenge (DBDC4) and submitted five runs to both English and Japanese subtasks. In these runs, we utilise the Decision Tree-based model and the Long Short-Term Memory-based (LSTM-based) model following the approaches of RSL17BD and KTH in the Third Dialogue Breakdown Detection Challenge (DBDC3) respectively. The Decision Tree-based model follows the approach of RSL17BD but utilises RandomForestRegressor instead of ExtraTreesRegressor. In addition, instead of predicting the mean and the variance of the probability distribution of the three breakdown labels, it predicts the probability of each label directly. The LSTM-based model follows the approach of KTH with some changes in the architecture and utilises Convolutional Neural Network (CNN) to perform text feature extraction. In addition, instead of targeting the single breakdown label and minimising the categorical cross entropy loss, it targets the probability distribution of the three breakdown labels and minimises the mean squared error. Run 1 utilises a Decision Tree-based model; Run 2 utilises an LSTM-based model; Run 3 performs an ensemble of 5 LSTM-based models; Run 4 performs an ensemble of Run 1 and Run 2; Run 5 performs an ensemble of Run 1 and Run 3. Run 5 statistically significantly outperformed all other runs in terms of MSE (NB, PB, B) for the English data and all other runs except Run 4 in terms of MSE (NB, PB, B) for the Japanese data (alpha level = 0.05).