OpenRaiser/Intern-Atlas huggingface dataset:
Achieving Correlated Equilibrium by Studying Opponent's Behavior Through Policy-Based Deep Reinforcement Learning
Venue: arXiv cs.GT 2020
Year: 2020

Game theory is a very profound study on distributed decision-making behavior and has been extensively developed by many scholars. However, many existing works rely on certain strict assumptions such as knowing the opponent's private behaviors, which might not be practical. In this work, we focused on two Nobel winning concepts, the Nash equilibrium and the correlated equilibrium. Specifically, we successfully reached the correlated equilibrium outside the convex hull of the Nash equilibria with our proposed deep reinforcement learning algorithm. With the correlated equilibrium probability distribution, we also propose a mathematical model to inverse the calculation of the correlated equilibrium probability distribution to estimate the opponent's payoff vector. With those payoffs, deep reinforcement learning learns why and how the rational opponent plays, instead of just learning the regions for corresponding strategies and actions. Through simulations, we showed that our proposed method can achieve the optimal correlated equilibrium and outside the convex hull of the Nash equilibrium with limited interaction among players.
The Potential of Social Media Analytics for Improving Social Media Communication of Emergency Agencies
Venue: arXiv cs.SI 2020
Year: 2020

A growing number of people use social media to seek information or coordinate relief activities in times of crisis. Thus, social media is increasingly deployed by emergency agencies as well to reach more people in crisis situations. However, the large amount of available data on social media could also be used by emergency agencies to understand how they are perceived by the public and to improve their communication. In this study, we examined the Twitter communication about the German emergency agency "Johanniter-Unfall-Hilfe" by conducting a frequency, sentiment, social network and content analysis. The results reveal that a right-wing political cluster politically instrumentalised an incident related to this agency. Furthermore, some individuals used social media to express criticism. It can be concluded that the use of social media analytics in the daily routine of emergency management professionals can be beneficial for improving their social media communication strategy.
CryptoCam: Privacy Conscious Open Circuit Television
Venue: arXiv cs.CR 2020
Year: 2020

The prevalence of Closed Circuit Television (CCTV) in today's society has given rise to an inherent asymmetry of control between the watchers and the watched. A sense of unease relating to the unobservable observer (operator) often leads to a lack of trust in the camera and its purpose, despite security cameras generally being present as a protective device. In this paper, we detail our concept of Open Circuit Television and prototype CryptoCam, a novel system for secure sharing of video footage to individuals and potential subjects nearby. Utilizing point-of-capture encryption and wireless transfer of time-based access keys for footage, we have developed a system to encourage a more open approach to information sharing and consumption. Detailing concerns highlighted in existing literature we formalize our over-arching concept into a framework called Open Circuit Television (OCTV). Through CryptoCam we hope to address this asymmetry of control by providing subjects with data equity, discoverability and oversight.
Accumulator Bet Selection Through Stochastic Diffusion Search
Venue: arXiv cs.AI 2020
Year: 2020

An accumulator is a bet that presents a rather unique payout structure, in that it combines multiple bets into a wager that can generate a total payout given by the multiplication of the individual odds of its parts. These potentially important returns come however at an increased risk of a loss. Indeed, the presence of a single incorrect bet in this selection would make the whole accumulator lose. The complexity of selecting a set of matches to place an accumulator bet on, as well as the number of opportunities to identify winning combinations have both dramatically increased with the easier access to online and offline bookmakers that bettors have nowadays. We address this relatively under-studied combinatorial aspect of sports betting, and propose a binary optimization model for the problem of selecting the most promising combinations of matches, in terms of their total potential payout and probability of a win, to form an accumulator bet. The results of an ongoing computational experiment, in which our model is applied to real data pertaining to the four main football leagues in the world over a complete season, are presented and compared to those of single bet selection methods.
Motion Segmentation using Frequency Domain Transformer Networks
Venue: arXiv cs.CV 2020
Year: 2020

Self-supervised prediction is a powerful mechanism to learn representations that capture the underlying structure of the data. Despite recent progress, the self-supervised video prediction task is still challenging. One of the critical factors that make the task hard is motion segmentation, which is segmenting individual objects and the background and estimating their motion separately. In video prediction, the shape, appearance, and transformation of each object should be understood only by predicting the next frame in pixel space. To address this task, we propose a novel end-to-end learnable architecture that predicts the next frame by modeling foreground and background separately while simultaneously estimating and predicting the foreground motion using Frequency Domain Transformer Networks. Experimental evaluations show that this yields interpretable representations and that our approach can outperform some widely used video prediction methods like Video Ladder Network and Predictive Gated Pyramids on synthetic data.
Indoor Millimeter-Wave Systems: Design and Performance Evaluation
Venue: arXiv cs.NI 2020
Year: 2020

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. While we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. The paper introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts, and then moves on to experimentally-validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.
A Large Dataset of Historical Japanese Documents with Complex Layouts
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning-based approaches for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale. One major hurdle is the lack of large datasets for training robust models. In particular, little training data exist for Asian languages. To this end, we present HJDataset, a Large Dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts. A semi-rule based method is developed to extract the layout elements, and the results are checked by human inspectors. The resulting large-scale dataset is used to provide baseline performance analyses for text region detection using state-of-the-art deep learning models. And we demonstrate the usefulness of the dataset on real-world document digitization tasks. The dataset is available at https://dell-research-harvard.github.io/HJDataset/.
Tensor completion using enhanced multiple modes low-rank prior and total variation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An block successive upper-bound minimization algorithm is applied to solve the model. Subsequence convergence of our algorithm can be established, and our algorithm converges to the coordinate-wise minimizers in some mild conditions. Several experiments on three types of public data sets show that our algorithm can recover a variety of low-rank tensors from significantly fewer samples than the other testing tensor completion methods.
Lightweight Mask R-CNN for Long-Range Wireless Power Transfer Systems
Venue: arXiv cs.CV 2020
Year: 2020

Resonant Beam Charging (RBC) is a wireless charging technology which supports multi-watt power transfer over meter-level distance. The features of safety, mobility and simultaneous charging capability enable RBC to charge multiple mobile devices safely at the same time. To detect the devices that need to be charged, a Mask R-CNN based dection model is proposed in previous work. However, considering the constraints of the RBC system, it's not easy to apply Mask R-CNN in lightweight hardware-embedded devices because of its heavy model and huge computation. Thus, we propose a machine learning detection approach which provides a lighter and faster model based on traditional Mask R-CNN. The proposed approach makes the object detection much easier to be transplanted on mobile devices and reduce the burden of hardware computation. By adjusting the structure of the backbone and the head part of Mask R-CNN, we reduce the average detection time from $1.02\mbox{s}$ per image to $0.6132\mbox{s}$, and reduce the model size from $245\mbox{MB}$ to $47.1\mbox{MB}$. The improved model is much more suitable for the application in the RBC system.
RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning
Venue: arXiv cs.CR 2020
Year: 2020

Recent advances in the Internet of Things (IoT) technology have led to a surge on the popularity of sensing applications. As a result, people increasingly rely on information obtained from sensors to make decisions in their daily life. Unfortunately, in most sensing applications, sensors are known to be error-prone and their measurements can become misleading at any unexpected time. Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted. Existing studies often regard sensor reliability monitoring and sensor data cleaning as separate problems. In this work, we propose RelSen, a novel optimization-based framework to address the two problems simultaneously via utilizing the mutual dependence between them. Furthermore, RelSen is not application-specific as its implementation assumes a minimal prior knowledge of the process dynamics under monitoring. This significantly improves its generality and applicability in practice. In our experiments, we apply RelSen on an outdoor air pollution monitoring system and a condition monitoring system for a cement rotary kiln. Experimental results show that our framework can timely identify unreliable sensors and remove sensor measurement errors caused by three types of most commonly observed sensor faults.
Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning
Venue: arXiv cs.CL 2020
Year: 2020

Knowledge graph-based dialogue systems can narrow down knowledge candidates for generating informative and diverse responses with the use of prior information, e.g., triple attributes or graph paths. However, most current knowledge graph (KG) cover incomplete domain-specific knowledge. To overcome this drawback, we propose a knowledge graph based proactive dialogue generation model (KgDg) with three components, improved model-agnostic meta-learning algorithm (MAML), knowledge selection in knowledge triplets embedding, and knowledge aware proactive response generator. For knowledge triplets embedding and selection, we formulate it as a problem of sentence embedding to better capture semantic information. Our improved MAML algorithm is capable of learning general features from a limited number of knowledge graphs, which can also quickly adapt to dialogue generation with unseen knowledge triplets. Extensive experiments are conducted on a knowledge aware dialogue dataset (DuConv). The results show that KgDg adapts both fast and well to knowledge graph-based dialogue generation and outperforms state-of-the-art baseline.
On synchronization of partial automata
Venue: arXiv cs.FL 2020
Year: 2020

A goal of this paper is to introduce the new construction of an automaton with shortest synchronizing word of length $O(d^{\frac{n}{d}})$, where $d \in \mathbb{N}$ and $n$ is the number of states for that automaton. Additionally we introduce new transformation from any synchronizable DFA or carefully synchronizable PFA of $n$ states to carefully synchronizable PFA of $d \cdot n$ states with shortest synchronizing word of length $Ω(d^{\frac{n}{d}})$.
Fast GPU 3D Diffeomorphic Image Registration
Venue: arXiv cs.DC 2020
Year: 2020

3D image registration is one of the most fundamental and computationally expensive operations in medical image analysis. Here, we present a mixed-precision, Gauss--Newton--Krylov solver for diffeomorphic registration of two images. Our work extends the publicly available CLAIRE library to GPU architectures. Despite the importance of image registration, only a few implementations of large deformation diffeomorphic registration packages support GPUs. Our contributions are new algorithms to significantly reduce the run time of the two main computational kernels in CLAIRE: calculation of derivatives and scattered-data interpolation. We deploy (i) highly-optimized, mixed-precision GPU-kernels for the evaluation of scattered-data interpolation, (ii) replace Fast-Fourier-Transform (FFT)-based first-order derivatives with optimized 8th-order finite differences, and (iii) compare with state-of-the-art CPU and GPU implementations. As a highlight, we demonstrate that we can register $256^3$ clinical images in less than 6 seconds on a single NVIDIA Tesla V100. This amounts to over 20$\times$ speed-up over the current version of CLAIRE and over 30$\times$ speed-up over existing GPU implementations.
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures
Venue: arXiv cs.LG 2020
Year: 2020

Convolutional Neural Networks (CNNs) have become common in many fields including computer vision, speech recognition, and natural language processing. Although CNN hardware accelerators are already included as part of many SoC architectures, the task of achieving high accuracy on resource-restricted devices is still considered challenging, mainly due to the vast number of design parameters that need to be balanced to achieve an efficient solution. Quantization techniques, when applied to the network parameters, lead to a reduction of power and area and may also change the ratio between communication and computation. As a result, some algorithmic solutions may suffer from lack of memory bandwidth or computational resources and fail to achieve the expected performance due to hardware constraints. Thus, the system designer and the micro-architect need to understand at early development stages the impact of their high-level decisions (e.g., the architecture of the CNN and the amount of bits used to represent its parameters) on the final product (e.g., the expected power saving, area, and accuracy). Unfortunately, existing tools fall short of supporting such decisions. This paper introduces a hardware-aware complexity metric that aims to assist the system designer of the neural network architectures, through the entire project lifetime (especially at its early stages) by predicting the impact of architectural and micro-architectural decisions on the final product. We demonstrate how the proposed metric can help evaluate different design alternatives of neural network models on resource-restricted devices such as real-time embedded systems, and to avoid making design mistakes at early stages.
Quantum algorithms for computational geometry problems
Venue: arXiv cs.CG 2020
Year: 2020

We study quantum algorithms for problems in computational geometry, such as POINT-ON-3-LINES problem. In this problem, we are given a set of lines and we are asked to find a point that lies on at least $3$ of these lines. POINT-ON-3-LINES and many other computational geometry problems are known to be 3SUM-HARD. That is, solving them classically requires time $Ω(n^{2-o(1)})$, unless there is faster algorithm for the well known 3SUM problem (in which we are given a set $S$ of $n$ integers and have to determine if there are $a, b, c \in S$ such that $a + b + c = 0$). Quantumly, 3SUM can be solved in time $O(n \log n)$ using Grover's quantum search algorithm. This leads to a question: can we solve POINT-ON-3-LINES and other 3SUM-HARD problems in $O(n^c)$ time quantumly, for $c<2$? We answer this question affirmatively, by constructing a quantum algorithm that solves POINT-ON-3-LINES in time $O(n^{1 + o(1)})$. The algorithm combines recursive use of amplitude amplification with geometrical ideas. We show that the same ideas give $O(n^{1 + o(1)})$ time algorithm for many 3SUM-HARD geometrical problems.
Knowledge Management Systems Requirements Specifications
Venue: arXiv cs.OH 2020
Year: 2020

In recent years, Knowledge Management Systems (KMS) have drawn remarkable attention. However, there is no common understanding of how a knowledge management system should look like or where the corresponding research should be directed at. Based on a number of essential requirements that a KMS should satisfy, this report introduces some possible requirements for the commonwealth's KMS components forming the KMS architecture. Also, these requirements will be analysed through evaluating and measuring there functionality to produce a tangible outcome.
How Reliable are University Rankings?
Venue: arXiv cs.DL 2020
Year: 2020

University or college rankings have almost become an industry of their own, published by US News \& World Report (USNWR) and similar organizations. Most of the rankings use a similar scheme: Rank universities in decreasing score order, where each score is computed using a set of attributes and their weights; the attributes can be objective or subjective while the weights are always subjective. This scheme is general enough to be applied to ranking objects other than universities. As shown in the related work, these rankings have important implications and also many issues. In this paper, we take a fresh look at this ranking scheme using the public College dataset; we both formally and experimentally show in multiple ways that this ranking scheme is not reliable and cannot be trusted as authoritative because it is too sensitive to weight changes and can easily be gamed. For example, we show how to derive reasonable weights programmatically to move multiple universities in our dataset to the top rank; moreover, this task takes a few seconds for over 600 universities on a personal laptop. Our mathematical formulation, methods, and results are applicable to ranking objects other than universities too. We conclude by making the case that all the data and methods used for rankings should be made open for validation and repeatability.
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents
Venue: arXiv cs.LG 2020
Year: 2020

A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to challenges with sparse rewards. Moreover, most traditional reinforcement learning approaches explicitly separate learning and decision making in a way that does not correspond to biological learning. We implement an architecture founded in principles of experimental neuroscience, by combining computationally efficient abstractions of biological algorithms. Our approach is inspired by research on spike-timing dependent plasticity, the transition between short and long term memory, and the role of various neurotransmitters in rewarding curiosity. The Neurons-in-a-Box architecture can learn in a wholly generalizable manner, and demonstrates an efficient way to build and apply representations without explicitly optimizing over a set of criteria or actions. We find it performs well in many environments including OpenAI Gym's Mountain Car, which has no reward besides touching a hard-to-reach flag on a hill, Inverted Pendulum, where it learns simple strategies to improve the time it holds a pendulum up, a video stream, where it spontaneously learns to distinguish an open and closed hand, as well as other environments like Google Chrome's Dinosaur Game.
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
Venue: arXiv cs.CV 2020
Year: 2020

Airborne light detection and ranging (LiDAR) plays an increasingly significant role in urban planning, topographic mapping, environmental monitoring, power line detection and other fields thanks to its capability to quickly acquire large-scale and high-precision ground information. To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures. And some recent works proposed graph convolution neural network based on the inherent topology of point clouds. However, the above point cloud deep learning models only pay attention to exploring local geometric structures, yet ignore global contextual relationships among all points. In this paper, we present a graph attention convolution neural network (GACNN) that can be directly applied to the classification of unstructured 3D point clouds obtained by airborne LiDAR. Specifically, we first introduce a graph attention convolution module that incorporates global contextual information and local structural features. Based on the proposed graph attention convolution module, we further design an end-to-end encoder-decoder network, named GACNN, to capture multiscale features of the point clouds and therefore enable more accurate airborne point cloud classification. Experiments on the ISPRS 3D labeling dataset show that the proposed model achieves a new state-of-the-art performance in terms of average F1 score (71.5\%) and a satisfying overall accuracy (83.2\%). Additionally, experiments further conducted on the 2019 Data Fusion Contest Dataset by comparing with other prevalent point cloud deep learning models demonstrate the favorable generalization capability of the proposed model.
Dynamic Matching Algorithms in Practice
Venue: arXiv cs.DS 2020
Year: 2020

In recent years, significant advances have been made in the design and analysis of fully dynamic maximal matching algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. In this paper, we attempt to bridge the gap between theory and practice that is currently observed for the fully dynamic maximal matching problem. We engineer several algorithms and empirically study those algorithms on an extensive set of dynamic instances.