OpenRaiser/Intern-Atlas huggingface dataset:
Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics
Venue: arXiv cs.CV 2019
Year: 2019

The shear volumes of data generated from earth observation and remote sensing technologies continue to make major impact; leaping key geospatial applications into the dual data and compute intensive era. As a consequence, this rapid advancement poses new computational and data processing challenges. We implement a novel remote sensing data flow (RESFlow) for advanced machine learning and computing with massive amounts of remotely sensed imagery. The core contribution is partitioning massive amount of data based on the spectral and semantic characteristics for distributed imagery analysis. RESFlow takes advantage of both a unified analytics engine for large-scale data processing and the availability of modern computing hardware to harness the acceleration of deep learning inference on expansive remote sensing imagery. The framework incorporates a strategy to optimize resource utilization across multiple executors assigned to a single worker. We showcase its deployment across computationally and data-intensive on pixel-level labeling workloads. The pipeline invokes deep learning inference at three stages; during deep feature extraction, deep metric mapping, and deep semantic segmentation. The tasks impose compute intensive and GPU resource sharing challenges motivating for a parallelized pipeline for all execution steps. By taking advantage of Apache Spark, Nvidia DGX1, and DGX2 computing platforms, we demonstrate unprecedented compute speed-ups for deep learning inference on pixel labeling workloads; processing 21,028~Terrabytes of imagery data and delivering an output maps at area rate of 5.245sq.km/sec, amounting to 453,168 sq.km/day - reducing a 28 day workload to 21~hours.
OD-GCN: Object Detection Boosted by Knowledge GCN
Venue: arXiv cs.CV 2019
Year: 2019

Classical CNN based object detection methods only extract the objects' image features, but do not consider the high-level relationship among objects in context. In this article, the graph convolutional networks (GCN) is integrated into the object detection framework to exploit the benefit of category relationship among objects, which is able to provide extra confidence for any pre-trained object detection model in our framework. In experiments, we test several popular base detection models on COCO dataset. The results show promising improvement on mAP by 1-5pp. In addition, visualized analysis reveals the benchmark improvement is quite reasonable in human's opinion.
Three Branches: Detecting Actions With Richer Features
Venue: arXiv cs.CV 2019
Year: 2019

We present our three branch solutions for International Challenge on Activity Recognition at CVPR2019. This model seeks to fuse richer information of global video clip, short human attention and long-term human activity into a unified model. We have participated in two tasks: Task A, the Kinetics challenge and Task B, spatio-temporal action localization challenge. For Kinetics, we achieve 21.59% error rate. For the AVA challenge, our final model obtains 32.49% mAP on the test sets, which outperforms all submissions to the AVA challenge at CVPR 2018 for more than 10% mAP. As the future work, we will introduce human activity knowledge, which is a new dataset including key information of human activity.
Null Space Analysis for Class-Specific Discriminant Learning
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we carry out null space analysis for Class-Specific Discriminant Analysis (CSDA) and formulate a number of solutions based on the analysis. We analyze both theoretically and experimentally the significance of each algorithmic step. The innate subspace dimensionality resulting from the proposed solutions is typically quite high and we discuss how the need for further dimensionality reduction changes the situation. Experimental evaluation of the proposed solutions shows that the straightforward extension of null space analysis approaches to the class-specific setting can outperform the standard CSDA method. Furthermore, by exploiting a recently proposed out-of-class scatter definition encoding the multi-modality of the negative class naturally appearing in class-specific problems, null space projections can lead to a performance comparable to or outperforming the most recent CSDA methods.
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
Venue: arXiv cs.CL 2019
Year: 2019

Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the linearization exploration work of Elman [8], we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. As a result, the new model is adapted to different levels of language understanding required by downstream tasks. The StructBERT with structural pre-training gives surprisingly good empirical results on a variety of downstream tasks, including pushing the state-of-the-art on the GLUE benchmark to 89.0 (outperforming all published models), the F1 score on SQuAD v1.1 question answering to 93.0, the accuracy on SNLI to 91.7.
Blockchain Tree for eHealth
Venue: arXiv cs.CR 2019
Year: 2019

The design of access control mechanisms for healthcare systems is challenging: it must strike the right balance between permissions and restrictions. In this work, we propose a novel approach that is based on the Blockchain technology for storage patient medical data and create an audit logging system able to protect health data from unauthorized modification and access. The proposed method consists of a tree structure: a main chain linked with the patient's identity and one or several Subchains which are used for storing additional critical data (e.g., medical diagnoses or access logs).
Evaluation of a Recommender System for Assisting Novice Game Designers
Venue: arXiv cs.AI 2019
Year: 2019

Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improve developer workflow. We present a recommender system for assisting humans in game design as well as a rigorous human subjects study to validate it. The AI-driven game design assistance system suggests game mechanics to designers based on characteristics of the game being developed. We believe this method can bring creative insights and increase users' productivity. We conducted quantitative studies that showed the recommender system increases users' levels of accuracy and computational affect, and decreases their levels of workload.
LTL to Smaller Self-Loop Alternating Automata and Back
Venue: arXiv cs.FL 2019
Year: 2019

Self-loop alternating automata (SLAA) with Büchi or co-Büchi acceptance are popular intermediate formalisms in translations of LTL to deterministic or nondeterministic automata. This paper considers SLAA with generic transition-based Emerson-Lei acceptance and presents translations of LTL to these automata and back. Importantly, the translation of LTL to SLAA with generic acceptance produces considerably smaller automata than previous translations of LTL to Büchi or co-Büchi SLAA. Our translation is already implemented in the tool LTL3TELA, where it helps to produce small deterministic or nondeterministic automata for given LTL formulae.
Finding and counting permutations via CSPs
Venue: arXiv cs.DS 2019
Year: 2019

Permutation patterns and pattern avoidance have been intensively studied in combinatorics and computer science, going back at least to the seminal work of Knuth on stack-sorting (1968). Perhaps the most natural algorithmic question in this area is deciding whether a given permutation of length $n$ contains a given pattern of length $k$. In this work we give two new algorithms for this well-studied problem, one whose running time is $n^{k/4 + o(k)}$, and a polynomial-space algorithm whose running time is the better of $O(1.6181^n)$ and $O(n^{k/2 + 1})$. These results improve the earlier best bounds of $n^{0.47k + o(k)}$ and $O(1.79^n)$ due to Ahal and Rabinovich (2000) resp. Bruner and Lackner (2012) and are the fastest algorithms for the problem when $k \in Ω(\log{n})$. We show that both our new algorithms and the previous exponential-time algorithms in the literature can be viewed through the unifying lens of constraint-satisfaction. Our algorithms can also count, within the same running time, the number of occurrences of a pattern. We show that this result is close to optimal: solving the counting problem in time $f(k) \cdot n^{o(k/\log{k})}$ would contradict the exponential-time hypothesis (ETH). For some special classes of patterns we obtain improved running times. We further prove that $3$-increasing and $3$-decreasing permutations can, in some sense, embed arbitrary permutations of almost linear length, which indicates that an algorithm with sub-exponential running time is unlikely, even for patterns from these restricted classes.
Space-Efficient Construction of Compressed Suffix Trees
Venue: arXiv cs.DS 2019
Year: 2019

We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let $n$ be the text length and $σ$ be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree intervals in $O(n\logσ)$ time using just $o(n\logσ)$ bits of working space on top of the input BWT. Using these algorithms as building blocks, for any parameter $0 < ε\leq 1$ we show how to build the PLCP bitvector and the balanced parentheses representation of the suffix tree topology in $O\left(n(\logσ+ ε^{-1}\cdot \log\log n)\right)$ time using at most $n\logσ\cdot(ε+ o(1))$ bits of working space on top of the input BWT and the output. In particular, this implies that we can build a compressed suffix tree from the BWT using just succinct working space (i.e. $o(n\logσ)$ bits) and any time in $Θ(n\logσ) + ω(n\log\log n)$. This improves the previous most space-efficient algorithms, which worked in $O(n)$ bits and $O(n\log n)$ time. We also consider the problem of merging BWTs of string collections, and provide a solution running in $O(n\logσ)$ time and using just $o(n\logσ)$ bits of working space. An efficient implementation of our LCP construction and BWT merge algorithms use (in RAM) as few as $n$ bits on top of a packed representation of the input/output and process data as fast as $2.92$ megabases per second.
Multi-view Clustering with the Cooperation of Visible and Hidden Views
Venue: arXiv cs.LG 2019
Year: 2019

Multi-view data are becoming common in real-world modeling tasks and many multi-view data clustering algorithms have thus been proposed. The existing algorithms usually focus on the cooperation of different views in the original space but neglect the influence of the hidden information among these different visible views, or they only consider the hidden information between the views. The algorithms are therefore not efficient since the available information is not fully excavated, particularly the otherness information in different views and the consistency information between them. In practice, the otherness and consistency information in multi-view data are both very useful for effective clustering analyses. In this study, a Multi-View clustering algorithm developed with the Cooperation of Visible and Hidden views, i.e., MV-Co-VH, is proposed. The MV-Co-VH algorithm first projects the multiple views from different visible spaces to the common hidden space by using the non-negative matrix factorization (NMF) strategy to obtain the common hidden view data. Collaborative learning is then implemented in the clustering procedure based on the visible views and the shared hidden view. The results of extensive experiments on UCI multi-view datasets and real-world image multi-view datasets show that the clustering performance of the proposed algorithm is competitive with or even better than that of the existing algorithms.
Multi-View Fuzzy Clustering with The Alternative Learning between Shared Hidden Space and Partition
Venue: arXiv cs.LG 2019
Year: 2019

As the multi-view data grows in the real world, multi-view clus-tering has become a prominent technique in data mining, pattern recognition, and machine learning. How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge. Aiming at this, a hidden space sharing multi-view fuzzy clustering (HSS-MVFC) method is proposed in the present study. This method is based on the classical fuzzy c-means clustering model, and obtains associ-ated information between different views by introducing shared hidden space. Especially, the shared hidden space and the fuzzy partition can be learned alternatively and contribute to each other. Meanwhile, the proposed method uses maximum entropy strategy to control the weights of different views while learning the shared hidden space. The experimental result shows that the proposed multi-view clustering method has better performance than many related clustering methods.
Meeting QoS of Users in a Edge to Cloud Platform via Optimally Placing Services and Scheduling Tasks
Venue: arXiv cs.NI 2019
Year: 2019

This paper considers the problem of service placement and task scheduling on a three-tiered edge-to-cloud platform when user requests must be met by a certain deadline. Time-sensitive applications (e.g., augmented reality, gaming, real-time video analysis) have tight constraints that must be met. With multiple possible computation centers, the "where" and "when" of solving these requests becomes paramount when meeting their deadlines. We formulate the problem of meeting users' deadlines while minimizing the total cost of the edge-to-cloud service provider as an Integer Linear Programming (ILP) problem. We show the NP-hardness of this problem, and propose two heuristics based on making decisions on a local vs global scale. We vary the number of users, the QoS constraint, and the cost difference between remote cloud and cloudlets(edge clouds), and run multiple Monte-Carlo runs for each case. Our simulation results show that the proposed heuristics are performing close to optimal while reducing the complexity.
Joint Precoding and Power Control in Small-Cell Networks With Proportional-Rate MISO-BC Backhaul
Venue: arXiv cs.IT 2019
Year: 2019

In the small-cell networks with multiple-input-single-output broadcasting (MISO-BC) backhauls, the joint dirty-paper coding and power control are investigated for the \mbox{MISO-BC} backhauls and access links in order to minimize the system transmit power. Considering the proportional rates of MISO-BC backhauls and flow-conservation constraints, the formulated optimization problem is \mbox{non-convex}. Moreover, the formulated problem couples the precoding vectors with the power-control variables. In order to handle the \mbox{non-convex} optimization problem and decouple the backhaul and access links, the structure of the formulated problem is investigated such that the optimal precoding vectors and optimal power-control variables are independently obtained. Moreover, the optimal precoding vectors are obtained in closed-form expressions. Simulation results are used to show the performance improvement over the benchmark scheme.
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
Venue: arXiv cs.CL 2019
Year: 2019

Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in data. In this work, we examine the geometrical space's contribution to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging, and propose a model, dubbed HyperKG, which exploits the hyperbolic space in order to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model can capture and we show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models.
Serverless Supercomputing: High Performance Function as a Service for Science
Venue: arXiv cs.DC 2019
Year: 2019

Growing data volumes and velocities are driving exciting new methods across the sciences in which data analytics and machine learning are increasingly intertwined with research. These new methods require new approaches for scientific computing in which computation is mobile, so that, for example, it can occur near data, be triggered by events (e.g., arrival of new data), or be offloaded to specialized accelerators. They also require new design approaches in which monolithic applications can be decomposed into smaller components, that may in turn be executed separately and on the most efficient resources. To address these needs we propose funcX---a high-performance function-as-a-service (FaaS) platform that enables intuitive, flexible, efficient, scalable, and performant remote function execution on existing infrastructure including clouds, clusters, and supercomputers. It allows users to register and then execute Python functions without regard for the physical resource location, scheduler architecture, or virtualization technology on which the function is executed---an approach we refer to as "serverless supercomputing." We motivate the need for funcX in science, describe our prototype implementation, and demonstrate, via experiments on two supercomputers, that funcX can process millions of functions across more than 65000 concurrent workers. We also outline five scientific scenarios in which funcX has been deployed and highlight the benefits of funcX in these scenarios.
Type-two Iteration with Bounded Query Revision
Venue: arXiv cs.CC 2019
Year: 2019

Motivated by recent results of Kapron and Steinberg (LICS 2018) we introduce new forms of iteration on length in the setting of applied lambda-calculi for higher-type poly-time computability. In particular, in a type-two setting, we consider functionals which capture iteration on input length which bound interaction with the type-one input parameter, by restricting to a constant either the number of times the function parameter may return a value of increasing size, or the number of times the function parameter may be applied to an argument of increasing size. We prove that for any constant bound, the iterators obtained are equivalent, with respect to lambda-definability over type-one poly-time functions, to the recursor of Cook and Urquhart which captures Cobham's notion of limited recursion on notation in this setting.
Reasoning-Driven Question-Answering for Natural Language Understanding
Venue: arXiv cs.CL 2019
Year: 2019

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.
Taking a Lesson from Quantum Particles for Statistical Data Privacy
Venue: arXiv cs.CR 2019
Year: 2019

Privacy is under threat from artificial intelligence revolution fueled by unprecedented abundance of data. Differential privacy, an established candidate for privacy protection, is susceptible to adversarial attacks, acts conservatively, and leads to miss-implementations because of lacking systematic methods for setting its parameters (known as the privacy budget). An alternative is information-theoretic privacy using entropy with the drawback of requiring prior distribution of the private data. Here, by using the Fisher information, information-theoretic privacy framework is extended to avoid unnecessary assumptions on the private data. The optimal privacy-preserving additive noise, extracted by minimizing the Fisher information, must follow the time-independent Schrodinger's equation. A fundamental trade-off between privacy and utility is also proved, reminiscent of the Heisenberg uncertainty principle.
Interpretable Encrypted Searchable Neural Networks
Venue: arXiv cs.CR 2019
Year: 2019

In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update. The clever combination of machine learning (ML) and SE may be a new way to solve this problem. This paper proposes interpretable encrypted searchable neural networks (IESNN) to explore probabilistic query, balanced index tree construction and automatic weight update in an encrypted cloud environment. In IESNN, probabilistic learning is used to obtain search ranking for searchable index, and probabilistic query is performed based on ciphertext index, which reduces the computational complexity of query significantly. Compared to traditional SE, it is proposed that adversarial learning and automatic weight update in response to user's timely query of the latest data set without expensive communication overhead. The proposed IESNN performs better than the previous works, bringing the query complexity closer to $O(\log N)$ and introducing low overhead on computation and communication.
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
Venue: arXiv cs.CL 2019
Year: 2019

Although the vast majority of knowledge bases KBs are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.