OpenRaiser/Intern-Atlas huggingface dataset:
End to End Video Segmentation for Driving : Lane Detection For Autonomous Car
Venue: arXiv cs.CV 2018
Year: 2018

Safety and decline of road traffic accidents remain important issues of autonomous driving. Statistics show that unintended lane departure is a leading cause of worldwide motor vehicle collisions, making lane detection the most promising and challenge task for self-driving. Today, numerous groups are combining deep learning techniques with computer vision problems to solve self-driving problems. In this paper, a Global Convolution Networks (GCN) model is used to address both classification and localization issues for semantic segmentation of lane. We are using color-based segmentation is presented and the usability of the model is evaluated. A residual-based boundary refinement and Adam optimization is also used to achieve state-of-art performance. As normal cars could not afford GPUs on the car, and training session for a particular road could be shared by several cars. We propose a framework to get it work in real world. We build a real time video transfer system to get video from the car, get the model trained in edge server (which is equipped with GPUs), and send the trained model back to the car.
Visual Social Relationship Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Social relationships form the basis of social structure of humans. Developing computational models to understand social relationships from visual data is essential for building intelligent machines that can better interact with humans in a social environment. In this work, we study the problem of visual social relationship recognition in images. We propose a Dual-Glance model for social relationship recognition, where the first glance fixates at the person of interest and the second glance deploys attention mechanism to exploit contextual cues. To enable this study, we curated a large scale People in Social Context (PISC) dataset, which comprises of 23,311 images and 79,244 person pairs with annotated social relationships. Since visually identifying social relationship bears certain degree of uncertainty, we further propose an Adaptive Focal Loss to leverage the ambiguous annotations for more effective learning. We conduct extensive experiments to quantitatively and qualitatively demonstrate the efficacy of our proposed method, which yields state-of-the-art performance on social relationship recognition.
Parallel Sparse Tensor Decomposition in Chapel
Venue: arXiv cs.DC 2018
Year: 2018

In big-data analytics, using tensor decomposition to extract patterns from large, sparse multivariate data is a popular technique. Many challenges exist for designing parallel, high performance tensor decomposition algorithms due to irregular data accesses and the growing size of tensors that are processed. There have been many efforts at implementing shared-memory algorithms for tensor decomposition, most of which have focused on the traditional C/C++ with OpenMP framework. However, Chapel is becoming an increasingly popular programing language due to its expressiveness and simplicity for writing scalable parallel programs. In this work, we port a state of the art C/OpenMP parallel sparse tensor decomposition tool, SPLATT, to Chapel. We present a performance study that investigates bottlenecks in our Chapel code and discusses approaches for improving its performance. Also, we discuss features in Chapel that would have been beneficial to our porting effort. We demonstrate that our Chapel code is competitive with the C/OpenMP code for both runtime and scalability, achieving 83%-96% performance of the original code and near linear scalability up to 32 cores.
More Effective Ontology Authoring with Test-Driven Development
Venue: arXiv cs.AI 2018
Year: 2018

Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent {\em test-first} approach that aims to reduce authoring time and increase authoring efficiency. Current TDD testing falls short on coverage of OWL features and possible test outcomes, the rigorous foundation thereof, and evaluations to ascertain its effectiveness. We aim to address these issues in one instantiation of TDD for ontology authoring. We first propose a succinct, logic-based model of TDD testing and present novel TDD algorithms so as to cover also any OWL 2 class expression for the TBox and for the principal ABox assertions, and prove their correctness. The algorithms use methods from the OWL API directly such that reclassification is not necessary for test execution, therewith reducing ontology authoring time. The algorithms were implemented in TDDonto2, a Protégé plugin. TDDonto2 was evaluated on editing efficiency and by users. The editing efficiency study demonstrated that it is faster than a typical ontology authoring interface, especially for medium size and large ontologies. The user evaluation demonstrated that modellers make significantly less errors with TDDonto2 compared to the standard Protégé interface and complete their tasks better using less time. Thus, the results indicate that Test-Driven Development is a promising approach in an ontology development methodology.
Trollslayer: Crowdsourcing and Characterization of Abusive Birds in Twitter
Venue: arXiv cs.CY 2018
Year: 2018

As of today, abuse is a pressing issue to participants and administrators of Online Social Networks (OSN). Abuse in Twitter can spawn from arguments generated for influencing outcomes of a political election, the use of bots to automatically spread misinformation, and generally speaking, activities that deny, disrupt, degrade or deceive other participants and, or the network. Given the difficulty in finding and accessing a large enough sample of abuse ground truth from the Twitter platform, we built and deployed a custom crawler that we use to judiciously collect a new dataset from the Twitter platform with the aim of characterizing the nature of abusive users, a.k.a abusive birds, in the wild. We provide a comprehensive set of features based on users' attributes, as well as social-graph metadata. The former includes metadata about the account itself, while the latter is computed from the social graph among the sender and the receiver of each message. Attribute-based features are useful to characterize user's accounts in OSN, while graph-based features can reveal the dynamics of information dissemination across the network. In particular, we derive the Jaccard index as a key feature to reveal the benign or malicious nature of directed messages in Twitter. To the best of our knowledge, we are the first to propose such a similarity metric to characterize abuse in Twitter.
Truly Visual Polymorphic Algebraic Data Structures through Maramafication
Venue: arXiv cs.PL 2018
Year: 2018

This paper presents a so-called maramafication of an essential part of functional programming languages such as Haskell or Clean: the construction of fully polymorphic well-typed algebraic data structures based on type definitions with at most one type parameter. As such, this work extends our previous work, in which only a very limited form of polymorphism was present. Maramafication means the design of visual 'twins' of existing programming constructs using spatial metaphors rooted in common sense or inborn spatial intuition, to achieve self-explanatoriness. This is, among others, useful to considerably reduce the gap between programmers and non-programmers in the creation of programs, for educational purposes, for inclusion of non-typical programmers and for invoking enthusiasm among non-programmers.
TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition
Venue: arXiv cs.CV 2018
Year: 2018

We present Temporal Aggregation Network (TAN) which decomposes 3D convolutions into spatial and temporal aggregation blocks. By stacking spatial and temporal convolutions repeatedly, TAN forms a deep hierarchical representation for capturing spatio-temporal information in videos. Since we do not apply 3D convolutions in each layer but only apply temporal aggregation blocks once after each spatial downsampling layer in the network, we significantly reduce the model complexity. The use of dilated convolutions at different resolutions of the network helps in aggregating multi-scale spatio-temporal information efficiently. Experiments show that our model is well suited for dense multi-label action recognition, which is a challenging sub-topic of action recognition that requires predicting multiple action labels in each frame. We outperform state-of-the-art methods by 5% and 3% on the Charades and Multi-THUMOS dataset respectively.
Balanced Linear Contextual Bandits
Venue: arXiv cs.LG 2018
Year: 2018

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual bandits with linear payoffs that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias. We provide the first regret bound analyses for linear contextual bandits with balancing and show that our algorithms match the state of the art theoretical guarantees. We demonstrate the strong practical advantage of balanced contextual bandits on a large number of supervised learning datasets and on a synthetic example that simulates model misspecification and prejudice in the initial training data.
PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features
Venue: arXiv cs.CV 2018
Year: 2018

Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.
Analysis of the $(μ/μ_I,λ)$-$σ$-Self-Adaptation Evolution Strategy with Repair by Projection Applied to a Conically Constrained Problem
Venue: arXiv cs.NE 2018
Year: 2018

A theoretical performance analysis of the $(μ/μ_I,λ)$-$σ$-Self-Adaptation Evolution Strategy ($σ$SA-ES) is presented considering a conically constrained problem. Infeasible offspring are repaired using projection onto the boundary of the feasibility region. Closed-form approximations are used for the one-generation progress of the evolution strategy. Approximate deterministic evolution equations are formulated for analyzing the strategy's dynamics. By iterating the evolution equations with the approximate one-generation expressions, the evolution strategy's dynamics can be predicted. The derived theoretical results are compared to experiments for assessing the approximation quality. It is shown that in the steady state the $(μ/μ_I,λ)$-$σ$SA-ES exhibits a performance as if the ES were optimizing a sphere model. Unlike the non-recombinative $(1,λ)$-ES, the parental steady state behavior does not evolve on the cone boundary but stays away from the boundary to a certain extent.
An incomplete and biased survey on selected resource management for distributed applications as basis for interoperability of medical devices
Venue: arXiv cs.CY 2018
Year: 2018

The popularity and wide spread of IoT technology has brought about a rich hardware infrastructure over which it is possible to run powerful applications that were not previously imagined. Among this infrastructure, there are the medical hardware that is progressively advancing but at a slower pace. Nevertheless, medical devices are more powerful now to run more sophisticated functions and applications and exchange big data with external systems in a secure and safe fashion. Towards the design of an architecture for interoperability of medical devices, this paper initially focuses on the background work that is taken by the author for this objective. The paper briefly describes the role of the software in the advances of medical systems and their possibilities for interoperability. It focuses attention on the distribution software layer that is responsible for connectivity, efficiency, and time-sensitivity in the basic operation of medical systems such as exchange of information and commands across devices and systems. The paper analyses a number of previous work on middleware (mostly performed at his research group and also in a broader research community), and pay especial attention to the middleware for web-based systems and how they relate to the development of distributed medical systems.
Learning Incremental Triplet Margin for Person Re-identification
Venue: arXiv cs.CV 2018
Year: 2018

Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.
Deep Heterogeneous Autoencoders for Collaborative Filtering
Venue: arXiv cs.LG 2018
Year: 2018

This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions. Our model consists of autoencoders, not only for numerical and categorical data, but also for sequential data, which enables capturing user tastes, item characteristics and the recent dynamics of user preference. We learn the autoencoder architecture for each data source independently in order to better model their statistical properties. Our evaluation on two MovieLens datasets and an e-commerce dataset shows that mean average precision and recall improve over state-of-the-art methods.
Demo: Linux Goes Apple Picking: Cross-Platform Ad hoc Communication with Apple Wireless Direct Link
Venue: arXiv cs.NI 2018
Year: 2018

Apple Wireless Direct Link (AWDL) is a proprietary and undocumented wireless ad hoc protocol that Apple introduced around 2014 and which is the base for applications such as AirDrop and AirPlay. We have reverse engineered the protocol and explain its frame format and operation in our MobiCom '18 paper "One Billion Apples' Secret Sauce: Recipe of the Apple Wireless Direct Link Ad hoc Protocol." AWDL builds on the IEEE 802.11 standard and implements election, synchronization, and channel hopping mechanisms on top of it. Furthermore, AWDL features an IPv6-based data path which enables direct communication. To validate our own work, we implement a working prototype of AWDL on Linux-based systems. Our implementation is written in C, runs in userspace, and makes use of Linux's Netlink API for interactions with the system's networking stack and the pcap library for frame injection and reception. In our demonstrator, we show how our Linux system synchronizes to an existing AWDL cluster or takes over the master role itself. Furthermore, it can receive data frames from and send them to a MacBook or iPhone via AWDL. We demonstrate the data exchange via ICMPv6 echo request and replies as well as sending and receiving data over a TCP connection.
Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective
Venue: arXiv cs.NI 2018
Year: 2018

Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attention from both the academia and industry in recent years. As an integration of the embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates the cyber processes into the physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing the coupling effects among the three interacted components in this CPS system, i.e., communication, computation and control, is envisioned as the key to properly utilize all the available resources and hence improve the performance of the UAV networks. In this paper, we present a comprehensive survey on the UAV networks from a CPS perspective. Firstly, we respectively research the basics and advances with respect to the three CPS components in the UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying the UAV networks into three hierarchies, i.e., the cell level, the system level, and the system of system level. Further, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we try to provide a novel insight into the state-of-the-art in the UAV networks.
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
Venue: arXiv cs.LG 2018
Year: 2018

Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels. To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space. In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads.
Coflow Scheduling in Data Centers: Routing and Bandwidth Allocation
Venue: arXiv cs.NI 2018
Year: 2018

In distributed computing frameworks like MapReduce, Spark, and Dyrad, a coflow is a set of flows transferring data between two stages of a job. The job cannot start its next stage unless all flows in the coflow finish. To improve the execution performance of such a job, it is crucial to reduce the completion time of a coflow which can contribute more than 50% of the job completion time. While several schedulers have been proposed, we observe that routing, as a factor greatly impacting the Coflow Completion Time (CCT), has not been well considered. In this paper, we focus on the coflow scheduling problem and jointly consider routing and bandwidth allocation. We first provide an analytical solution to the problem of optimal bandwidth allocation with pre-determined routes. We then formulate the coflow scheduling problem as a Mixed Integer Non-linear Programming problem and present its relaxed convex optimization problem. We further propose two algorithms, CoRBA and its simplified version: CoRBA-fast, that jointly perform routing and bandwidth allocation for a given coflow while minimizes the CCT. Through both offline and online simulations, we demonstrate that CoRBA reduces the CCT by 40%-500% compared to the state-of-the-art algorithms. Simulation results also show that CoRBA-fast can be tens of times faster than all other algorithms with around 10% performance degradation compared to CoRBA, which makes the use of CoRBA-fast very applicable in practice.
Analogy Search Engine: Finding Analogies in Cross-Domain Research Papers
Venue: arXiv cs.IR 2018
Year: 2018

In recent years, with the rapid proliferation of research publications in the field of Artificial Intelligence, it is becoming increasingly difficult for researchers to effectively keep up with all the latest research in one's own domains. However, history has shown that scientific breakthroughs often come from collaborations of researchers from different domains. Traditional search algorithms like Lexical search, which look for literal matches or synonyms and variants of the query words, are not effective for discovering cross-domain research papers and meeting the needs of researchers in this age of information overflow. In this paper, we developed and tested an innovative semantic search engine, Analogy Search Engine (ASE), for 2000 AI research paper abstracts across domains like Language Technologies, Robotics, Machine Learning, Computational Biology, Human Computer Interactions, etc. ASE combines recent theories and methods from Computational Analogy and Natural Language Processing to go beyond keyword-based lexical search and discover the deeper analogical relationships among research paper abstracts. We experimentally show that ASE is capable of finding more interesting and useful research papers than baseline elasticsearch. Furthermore, we believe that the methods used in ASE go beyond academic paper and will benefit many other document search tasks.
From FiLM to Video: Multi-turn Question Answering with Multi-modal Context
Venue: arXiv cs.CL 2018
Year: 2018

Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems. The Audio Visual Scene-aware Dialog (AVSD) challenge, organized as a track of the Dialog System Technology Challenge 7 (DSTC7), proposes a combined task, where a system has to answer questions pertaining to a video given a dialogue with previous question-answer pairs and the video itself. We propose for this task a hierarchical encoder-decoder model which computes a multi-modal embedding of the dialogue context. It first embeds the dialogue history using two LSTMs. We extract video and audio frames at regular intervals and compute semantic features using pre-trained I3D and VGGish models, respectively. Before summarizing both modalities into fixed-length vectors using LSTMs, we use FiLM blocks to condition them on the embeddings of the current question, which allows us to reduce the dimensionality considerably. Finally, we use an LSTM decoder that we train with scheduled sampling and evaluate using beam search. Compared to the modality-fusing baseline model released by the AVSD challenge organizers, our model achieves a relative improvements of more than 16%, scoring 0.36 BLEU-4 and more than 33%, scoring 0.997 CIDEr.