OpenRaiser/Intern-Atlas huggingface dataset:
GraVF-M: Graph Processing System Generation for Multi-FPGA Platforms
Venue: arXiv cs.DC 2019
Year: 2019

Due to the irregular nature of connections in most graph datasets, partitioning graph analysis algorithms across multiple computational nodes that do not share a common memory inevitably leads to large amounts of interconnect traffic. Previous research has shown that FPGAs can outcompete software-based graph processing in shared memory contexts, but it remains an open question if this advantage can be maintained in distributed systems. In this work, we present GraVF-M, a framework designed to ease the implementation of FPGA-based graph processing accelerators for multi-FPGA platforms with distributed memory. Based on a lightweight description of the algorithm kernel, the framework automatically generates optimized RTL code for the whole multi-FPGA design. We exploit an aspect of the programming model to present a familiar message-passing paradigm to the user, while under the hood implementing a more efficient architecture that can reduce the necessary inter-FPGA network traffic by a factor equal to the average degree of the input graph. A performance model based on a theoretical analysis of the factors influencing performance serves to evaluate the efficiency of our implementation. With a throughput of up to 5.8 GTEPS (billions of traversed edges per second) on a 4-FPGA system, the designs generated by GraVF-M compare favorably to state-of-the-art frameworks from the literature and reach 94% of the projected performance limit of the system.
Rugby-Bot: Utilizing Multi-Task Learning & Fine-Grained Features for Rugby League Analysis
Venue: arXiv cs.LG 2019
Year: 2019

Sporting events are extremely complex and require a multitude of metrics to accurate describe the event. When making multiple predictions, one should make them from a single source to keep consistency across the predictions. We present a multi-task learning method of generating multiple predictions for analysis via a single prediction source. To enable this approach, we utilize a fine-grain representation using fine-grain spatial data using a wide-and-deep learning approach. Additionally, our approach can predict distributions rather than single point values. We highlighted the utility of our approach on the sport of Rugby League and call our prediction engine "Rugby-Bot".
Gaze Gestures and Their Applications in human-computer interaction with a head-mounted display
Venue: arXiv cs.HC 2019
Year: 2019

A head-mounted display (HMD) is a portable and interactive display device. With the development of 5G technology, it may become a general-purpose computing platform in the future. Human-computer interaction (HCI) technology for HMDs has also been of significant interest in recent years. In addition to tracking gestures and speech, tracking human eyes as a means of interaction is highly effective. In this paper, we propose two UnityEyes-based convolutional neural network models, UEGazeNet and UEGazeNet*, which can be used for input images with low resolution and high resolution, respectively. These models can perform rapid interactions by classifying gaze trajectories (GTs), and a GTgestures dataset containing data for 10,200 "eye-painting gestures" collected from 15 individuals is established with our gaze-tracking method. We evaluated the performance both indoors and outdoors and the UEGazeNet can obtaine results 52\% and 67\% better than those of state-of-the-art networks. The generalizability of our GTgestures dataset using a variety of gaze-tracking models is evaluated, and an average recognition rate of 96.71\% is obtained by our method.
AREA: Adaptive Reference-set Based Evolutionary Algorithm for Multiobjective Optimisation
Venue: arXiv cs.NE 2019
Year: 2019

Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, these algorithms depends largely on problem characteristics, and there is a need to improve their performance for a wider range of problems. References, which are often specified by the decision maker's preference in different forms, are a very effective method to improve the performance of algorithms but have not been fully explored in literature. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state of the arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper.
The Role of Coded Side Information in Single-Server Private Information Retrieval
Venue: arXiv cs.IT 2019
Year: 2019

We study the role of coded side information in single-server Private Information Retrieval (PIR). An instance of the single-server PIR problem includes a server that stores a database of $K$ independently and uniformly distributed messages, and a user who wants to retrieve one of these messages from the server. We consider settings in which the user initially has access to a coded side information which includes a linear combination of a subset of $M$ messages in the database. We assume that the identities of the $M$ messages that form the support set of the coded side information as well as the coding coefficients are initially unknown to the server. We consider two different models, depending on whether the support set of the coded side information includes the requested message or not. We also consider the following two privacy requirements: (i) the identities of both the demand and the support set of the coded side information need to be protected, or (ii) only the identity of the demand needs to be protected. For each model and for each of the privacy requirements, we consider the problem of designing a protocol for generating the user's query and the server's answer that enables the user to decode the message they need while satisfying the privacy requirement. We characterize the (scalar-linear) capacity of each setting, defined as the ratio of the number of information bits in a message to the minimum number of information bits downloaded from the server over all (scalar-linear) protocols that satisfy the privacy condition. Our converse proofs rely on new information-theoretic arguments---tailored to the setting of single-server PIR and different from the commonly-used techniques in multi-server PIR settings. We also present novel capacity-achieving scalar-linear protocols for each of the settings being considered.
Adaptive Transfer Learning of Multi-View Time Series Classification
Venue: arXiv cs.LG 2019
Year: 2019

Time Series Classification (TSC) has been an important and challenging task in data mining, especially on multivariate time series and multi-view time series data sets. Meanwhile, transfer learning has been widely applied in computer vision and natural language processing applications to improve deep neural network's generalization capabilities. However, very few previous works applied transfer learning framework to time series mining problems. Particularly, the technique of measuring similarities between source domain and target domain based on dynamic representation such as density estimation with importance sampling has never been combined with transfer learning framework. In this paper, we first proposed a general adaptive transfer learning framework for multi-view time series data, which shows strong ability in storing inter-view importance value in the process of knowledge transfer. Next, we represented inter-view importance through some time series similarity measurements and approximated the posterior distribution in latent space for the importance sampling via density estimation techniques. We then computed the matrix norm of sampled importance value, which controls the degree of knowledge transfer in pre-training process. We further evaluated our work, applied it to many other time series classification tasks, and observed that our architecture maintained desirable generalization ability. Finally, we concluded that our framework could be adapted with deep learning techniques to receive significant model performance improvements.
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Venue: arXiv cs.LG 2019
Year: 2019

Large-scale machine learning training suffers from two prior challenges, specifically for nuclear-norm constrained problems with distributed systems: the synchronization slowdown due to the straggling workers, and high communication costs. In this work, we propose an asynchronous Stochastic Frank Wolfe (SFW-asyn) method, which, for the first time, solves the two problems simultaneously, while successfully maintaining the same convergence rate as the vanilla SFW. We implement our algorithm in python (with MPI) to run on Amazon EC2, and demonstrate that SFW-asyn yields speed-ups almost linear to the number of machines compared to the vanilla SFW.
Stochastic Geometry-Based Analysis of Airborne Base Stations with Laser-powered UAVs
Venue: arXiv cs.IT 2019
Year: 2019

One of the most promising solutions to the problem of limited flight time of unmanned aerial vehicles (UAVs), is providing the UAVs with power through laser beams emitted from Laser Beam Directors (LBDs) deployed on the ground. In this letter, we study the performance of a laser-powered UAV-enabled communication system using tools from stochastic geometry. We first derive the energy coverage probability, which is defined as the probability of the UAV receiving enough energy to ensure successful operation (hovering and communication). Our results show that to ensure energy coverage, the distance between the UAV and its dedicated LBD must be below a certain threshold, for which we derive an expression as a function of the system parameters. Considering simultaneous information and power transmission through the laser beam using power splitting technique, we also derive the joint energy and the Signal-to-noise Ratio (SNR) coverage probability. The analytical and simulation results reveal some interesting insights. For instance, our results show that we need at least 6 LBDs/10km^2 to ensure a reliable performance in terms of energy coverage probability.
Go with the Flow: Perception-refined Physics Simulation
Venue: arXiv cs.CV 2019
Year: 2019

For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior. Nevertheless, inferring specifics from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces. This paper addresses the problem of inferring such latent physical properties from observations. Our solution is an iterative refinement procedure with simulation at its core. The algorithm gradually updates the physical model parameters by running a simulation of the observed phenomenon and comparing the current simulation to a real-world observation. The physical similarity is computed using an embedding function that maps physically similar examples to nearby points. As a tangible example, we concentrate on flags curling in the wind -- a seemingly simple phenomenon but physically highly involved. Based on its underlying physical model and visual manifestation, we propose an instantiation of the embedding function. For this mapping, modeled as a deep network, we introduce a spectral decomposition layer that decomposes a video volume into its temporal spectral power and corresponding frequencies. In experiments, we demonstrate our method's ability to recover intrinsic and extrinsic physical parameters from both simulated and real-world video.
Minimum entropy stochastic block models neglect edge distribution heterogeneity
Venue: arXiv cs.SI 2019
Year: 2019

The statistical inference of stochastic block models as emerged as a mathematicaly principled method for identifying communities inside networks. Its objective is to find the node partition and the block-to-block adjacency matrix of maximum likelihood i.e. the one which has most probably generated the observed network. In practice, in the so-called microcanonical ensemble, it is frequently assumed that when comparing two models which have the same number and sizes of communities, the best one is the one of minimum entropy i.e. the one which can generate the less different networks. In this paper, we show that there are situations in which the minimum entropy model does not identify the most significant communities in terms of edge distribution, even though it generates the observed graph with a higher probability.
Visual Hide and Seek
Venue: arXiv cs.AI 2019
Year: 2019

We train embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator. We place a variety of obstacles in the environment for the prey to hide behind, and we only give the agents partial observations of their environment using an egocentric perspective. Although we train the model to play this game from scratch, experiments and visualizations suggest that the agent learns to predict its own visibility in the environment. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, effect the learned policy. Our results suggest that, although agent weaknesses make the learning problem more challenging, they also cause more useful features to be learned. Our project website is available at: http://www.cs.columbia.edu/ ~bchen/visualhideseek/.
On the Kernel of $\Z_{2^s}$-Linear Simplex and MacDonald Codes
Venue: arXiv cs.IT 2019
Year: 2019

The $\Z_{2^s}$-additive codes are subgroups of $\Z^n_{2^s}$, and can be seen as a generalization of linear codes over $\Z_2$ and $\Z_4$. A $\Z_{2^s}$-linear code is a binary code which is the Gray map image of a $\Z_{2^s}$-additive code. We consider $\Z_{2^s}$-additive simplex codes of type $α$ and $β$, which are a generalization over $\Z_{2^s}$ of the binary simplex codes. These $\Z_{2^s}$-additive simplex codes are related to the $\Z_{2^s}$-additive Hadamard codes. In this paper, we use this relationship to establish the kernel of their binary images, under the Gray map, the $\Z_{2^s}$-linear simplex codes. Similar results can be obtained for the binary Gray map image of $\Z_{2^s}$-additive MacDonald codes.
A Framework for Secure Digital Administration
Venue: arXiv cs.CR 2019
Year: 2019

The efficiency and service quality in public administration can be improved by using electronic documents (or e-docs) and digital signature to speed up their activity and at the same time to better satisfy customer needs. This paper presents an XML-based document exchange system that integrates a platform for the management (creation, search, storage) of e-docs and a secure trustworthy environment for digitally signing e-docs. The framework provides security services, like privacy, authorization, authentication and non-repudiation. Possibly a mobile terminal equipped with a smartcard reader and integrating WYSIWYS features could be used for viewing and signing e-docs. The proposed system can be easily integrated with the infrastructure (e.g. database system) already in use at each administration site. It is described also the use of the system in a real world service.
PFirewall: Semantics-Aware Customizable Data Flow Control for Home Automation Systems
Venue: arXiv cs.CR 2019
Year: 2019

Emerging Internet of Thing (IoT) platforms provide a convenient solution for integrating heterogeneous IoT devices and deploying home automation applications. However, serious privacy threats arise as device data now flow out to the IoT platforms, which may be subject to various attacks. We observe two privacy-unfriendly practices in emerging home automation systems: first, the majority of data flowed to the platform are superfluous in the sense that they do not trigger any home automation; second, home owners currently have nearly zero control over their data. We present PFirewall, a customizable data-flow control system to enhance user privacy. PFirewall analyzes the automation apps to extract their semantics, which are automatically transformed into data-minimization policies; these policies only send minimized data flows to the platform for app execution, such that the ability of attackers to infer user privacy is significantly impaired. In addition, PFirewall provides capabilities and interfaces for users to define and enforce customizable policies based on individual privacy preferences. PFirewall adopts an elegant man-in-the-middle design, transparently executing data minimization and user-defined policies to process raw data flows and mediating the processed data between IoT devices and the platform (via the hub), without requiring modifications of the platform or IoT devices. We implement PFirewall to work with two popular platforms: SmartThings and openHAB, and set up two real-world testbeds to evaluate its performance. The evaluation results show that PFirewall is very effective: it reduces IoT data sent to the platform by 97% and enforces user defined policies successfully.
Combining Machine Learning Models using combo Library
Venue: arXiv cs.LG 2019
Year: 2019

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.
MV-PBT: Multi-Version Index for Large Datasets and HTAP Workloads
Venue: arXiv cs.DB 2019
Year: 2019

Modern mixed (HTAP) workloads execute fast update-transactions and long-running analytical queries on the same dataset and system. In multi-version (MVCC) systems, such workloads result in many short-lived versions and long version-chains as well as in increased and frequent maintenance overhead. Consequently, the index pressure increases significantly. Firstly, the frequent modifications cause frequent creation of new versions, yielding a surge in index maintenance overhead. Secondly and more importantly, index-scans incur extra I/O overhead to determine, which of the resulting tuple-versions are visible to the executing transaction (visibility-check) as current designs only store version/timestamp information in the base table -- not in the index. Such index-only visibility-check is critical for HTAP workloads on large datasets. In this paper we propose the Multi-Version Partitioned B-Tree (MV-PBT) as a version-aware index structure, supporting index-only visibility checks and flash-friendly I/O patterns. The experimental evaluation indicates a 2x improvement for analytical queries and 15% higher transactional throughput under HTAP workloads (CH-Benchmark). MV-PBT offers 40% higher transactional throughput compared to WiredTiger's LSM-Tree implementation under YCSB.
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks. Despite its success as a defense mechanism, adversarial training fails to generalize well to unperturbed test set. We hypothesize that this poor generalization is a consequence of adversarial training with uniform perturbation radius around every training sample. Samples close to decision boundary can be morphed into a different class under a small perturbation budget, and enforcing large margins around these samples produce poor decision boundaries that generalize poorly. Motivated by this hypothesis, we propose instance adaptive adversarial training -- a technique that enforces sample-specific perturbation margins around every training sample. We show that using our approach, test accuracy on unperturbed samples improve with a marginal drop in robustness. Extensive experiments on CIFAR-10, CIFAR-100 and Imagenet datasets demonstrate the effectiveness of our proposed approach.
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
Venue: arXiv cs.LG 2019
Year: 2019

Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes the graph generation process into a series of sequential steps. While effective for modest sizes, it loses its permutation invariance for larger graphs. Instead, we present a permutation invariant latent-variable generative model relying on graph embeddings to encode structure. Using tools from the random graph literature, our model is highly scalable to large graphs with likelihood evaluation and generation in $O(|V | + |E|)$.
Meta-learning for fast classifier adaptation to new users of Signature Verification systems
Venue: arXiv cs.CV 2019
Year: 2019

Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifiers have access to a few genuine signatures for training, during generalization they also need to discriminate forgeries. This is particularly challenging for skilled forgeries, where a forger practices imitating the user's signature, and often is able to create forgeries visually close to the original signatures. Most work in the literature address this issue by training for a surrogate objective: discriminating genuine signatures of a user and random forgeries (signatures from other users). In this work, we propose a solution for this problem based on meta-learning, where there are two levels of learning: a task-level (where a task is to learn a classifier for a given user) and a meta-level (learning across tasks). In particular, the meta-learner guides the adaptation (learning) of a classifier for each user, which is a lightweight operation that only requires genuine signatures. The meta-learning procedure learns what is common for the classification across different users. In a scenario where skilled forgeries from a subset of users are available, the meta-learner can guide classifiers to be discriminative of skilled forgeries even if the classifiers themselves do not use skilled forgeries for learning. Experiments conducted on the GPDS-960 dataset show improved performance compared to Writer-Independent systems, and achieve results comparable to state-of-the-art Writer-Dependent systems in the regime of few samples per user (5 reference signatures).