OpenRaiser/Intern-Atlas huggingface dataset:
Localization of Critical Findings in Chest X-Ray without Local Annotations Using Multi-Instance Learning
Venue: arXiv cs.LG 2020
Year: 2020

The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings. While deep learning (DL) models has become a promising predictive technology with near-human accuracy, they commonly suffer from a lack of explainability, which is an important aspect for clinical deployment of DL models in the highly regulated healthcare industry. For example, localizing critical findings in an image is useful for explaining the predictions of DL classification algorithms. While there have been a host of joint classification and localization methods for computer vision, the state-of-the-art DL models require locally annotated training data in the form of pixel level labels or bounding box coordinates. In the medical domain, this requires an expensive amount of manual annotation by medical experts for each critical finding. This requirement becomes a major barrier for training models that can rapidly scale to various findings. In this work, we address these shortcomings with an interpretable DL algorithm based on multi-instance learning that jointly classifies and localizes critical findings in CXR without the need for local annotations. We show competitive classification results on three different critical findings (pneumothorax, pneumonia, and pulmonary edema) from three different CXR datasets.
Autonomous Control of a Line Follower Robot Using a Q-Learning Controller
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, a MIMO simulated annealing SA based Q learning method is proposed to control a line follower robot. The conventional controller for these types of robots is the proportional P controller. Considering the unknown mechanical characteristics of the robot and uncertainties such as friction and slippery surfaces, system modeling and controller designing can be extremely challenging. The mathematical modeling for the robot is presented in this paper, and a simulator is designed based on this model. The basic Q learning methods are based pure exploitation and the epsilon-greedy methods, which help exploration, can harm the controller performance after learning completion by exploring nonoptimal actions. The simulated annealing based Q learning method tackles this drawback by decreasing the exploration rate when the learning increases. The simulation and experimental results are provided to evaluate the effectiveness of the proposed controller.
Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo
Venue: arXiv cs.CL 2020
Year: 2020

This paper studies how the linguistic components of blogposts collected from Sina Weibo, a Chinese microblogging platform, might affect the blogposts' likelihood of being censored. Our results go along with King et al. (2013)'s Collective Action Potential (CAP) theory, which states that a blogpost's potential of causing riot or assembly in real life is the key determinant of it getting censored. Although there is not a definitive measure of this construct, the linguistic features that we identify as discriminatory go along with the CAP theory. We build a classifier that significantly outperforms non-expert humans in predicting whether a blogpost will be censored. The crowdsourcing results suggest that while humans tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterparts, they in general cannot make a better guess than our model when it comes to `reading the mind' of the censors in deciding whether a blogpost should be censored. We do not claim that censorship is only determined by the linguistic features. There are many other factors contributing to censorship decisions. The focus of the present paper is on the linguistic form of blogposts. Our work suggests that it is possible to use linguistic properties of social media posts to automatically predict if they are going to be censored.
Plant Stem Segmentation Using Fast Ground Truth Generation
Venue: arXiv cs.CV 2020
Year: 2020

Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental results show the viability of both our proposed ground truth approach and deep learning based stem segmentation.
Weight distributions and weight hierarchies of two classes of binary linear codes
Venue: arXiv cs.IT 2020
Year: 2020

Linear codes with a few weights can be applied to communication, consumer electronics and data storage system. In addition, the weight hierarchy of linear codes has many applications such as on the type II wire-tap channel, dealing with $t$-resilient functions and trellis or branch complexity of linear codes and so on. In this paper, we first present a formula for computing the weight hierarchies of linear codes constructed by the generalized method of defining sets. Then, we construct two classes of binary linear codes with a few weights and determine their weight distributions and weight hierarchies completely. Some codes of them can be used in secret sharing schemes.
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns. In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework. Our framework adds generative assumption to the VAE by requiring a subset of the latent variables to generate an auxiliary set of observable data. This additional generative assumption primes the latent variables to local information and encourages the other latent variables to represent global information. We examine three different flavours of VAEs with different generative assumptions. We show that the framework can effectively disentangle local and global information within these models leads to improved representation, with better clustering and unsupervised object detection benchmarks. Finally, we establish connections between SPLIT and recent research in cognitive neuroscience regarding the disentanglement in human visual perception. The code for our experiments is at https://github.com/51616/split-vae .
Towards a Framework for the Design, Implementation and Reporting of Methodology Scoping Reviews
Venue: arXiv cs.DL 2020
Year: 2020

Background: In view of the growth of published papers, there is an increasing need for studies that summarise scientific research. An increasingly common review is a 'Methodology scoping review', which provides a summary of existing analytical methods, techniques and software, proposed or applied in research articles, which address an analytical problem or further an analytical approach. However, guidelines for their design, implementation and reporting are limited. Methods: Drawing on the experiences of the authors, which were consolidated through a series of face-to-face workshops, we summarise the challenges inherent in conducting a methodology scoping review and offer suggestions of best practice to promote future guideline development. Results: We identified three challenges of conducting a methodology scoping review. First, identification of search terms; one cannot usually define the search terms a priori and the language used for a particular method can vary across the literature. Second, the scope of the review requires careful consideration since new methodology is often not described (in full) within abstracts. Third, many new methods are motivated by a specific clinical question, where the methodology may only be documented in supplementary materials. We formulated several recommendations that build upon existing review guidelines. These recommendations ranged from an iterative approach to defining search terms through to screening and data extraction processes. Conclusion: Although methodology scoping reviews are an important aspect of research, there is currently a lack of guidelines to standardise their design, implementation and reporting. We recommend a wider discussion on this topic.
Theoretically Expressive and Edge-aware Graph Learning
Venue: arXiv cs.LG 2020
Year: 2020

We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged.
Bounds for the capacity error function for unidirectional channels with noiseless feedback
Venue: arXiv cs.IT 2020
Year: 2020

In digital systems such as fiber optical communications, the ratio between probability of errors of type $1\to 0$ and $0 \to 1$ can be large. Practically, one can assume that only one type of error can occur. These errors arecalled asymmetric. Unidirectional errors differ from asymmetric type of errors; here both $1 \to 0$ and $0 \to 1$ type of errors are possible, but in any submittedcodeword all the errors are of the same type. This can be generalized for the $q$-ary case. We consider $q$-ary unidirectional channels with feedback and give bounds for the capacity error function. It turns out that the bounds depend on the parity of the alphabet $q$. Furthermore, we show that for feedback, the capacity error function for the binary asymmetric channel is different from the symmetric channel. This is in contrast to the behavior of the function without feedback.
Limits on Gradient Compression for Stochastic Optimization
Venue: arXiv cs.IT 2020
Year: 2020

We consider stochastic optimization over $\ell_p$ spaces using access to a first-order oracle. We ask: {What is the minimum precision required for oracle outputs to retain the unrestricted convergence rates?} We characterize this precision for every $p\geq 1$ by deriving information theoretic lower bounds and by providing quantizers that (almost) achieve these lower bounds. Our quantizers are new and easy to implement. In particular, our results are exact for $p=2$ and $p=\infty$, showing the minimum precision needed in these settings are $Θ(d)$ and $Θ(\log d)$, respectively. The latter result is surprising since recovering the gradient vector will require $Ω(d)$ bits.
Towards Graph Representation Learning in Emergent Communication
Venue: arXiv cs.LG 2020
Year: 2020

Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we flatten the complex representation of entities and their attributes into a single word or a sentence. In this paper we use graph convolutional networks to support the evolution of language and cooperation in multi-agent systems. Motivated by an image-based referential game, we propose a graph referential game with varying degrees of complexity, and we provide strong baseline models that exhibit desirable properties in terms of language emergence and cooperation. We show that the emerged communication protocol is robust, that the agents uncover the true factors of variation in the game, and that they learn to generalize beyond the samples encountered during training.
What went wrong?: Identification of Everyday Object Manipulation Anomalies
Venue: arXiv cs.RO 2020
Year: 2020

Extending the abilities of service robots is important for expanding what they can achieve in everyday manipulation tasks. On the other hand, it is also essential to ensure them to determine what they can not achieve in certain cases due to either anomalies or permanent failures during task execution. Robots need to identify these situations, and reveal the reasons behind these cases to overcome and recover from them. In this paper, we propose and analyze a Long Short-Term Memories-based (LSTM-based) awareness approach to reveal the reasons behind an anomaly case that occurs during a manipulation episode in an unstructured environment. The proposed method takes into account the real-time observations of the robot by fusing visual, auditory and proprioceptive sensory modalities to achieve this task. We also provide a comparative analysis of our method with Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs). The symptoms of anomalies are first learned from a given training set, then they can be classified in real-time based on the learned models. The approaches are evaluated on a Baxter robot executing object manipulation scenarios. The results indicate that the LSTM-based method outperforms the other methods with a 0.94 classification rate in revealing causes of anomalies in case of an unexpected deviation.
All that Glitters is not Bitcoin -- Unveiling the Centralized Nature of the BTC (IP) Network
Venue: arXiv cs.NI 2020
Year: 2020

Blockchains are typically managed by peer-to-peer (P2P) networks providing the support and substrate to the so-called distributed ledger (DLT), a replicated, shared, and synchronized data structure, geographically spread across multiple nodes. The Bitcoin (BTC) blockchain is by far the most well known DLT, used to record transactions among peers, based on the BTC digital currency. In this paper, we focus on the network side of the BTC P2P network, analyzing its nodes from a purely network measurements-based approach. We present a BTC crawler able to discover and track the BTC P2P network through active measurements, and use it to analyze its main properties. Through the combined analysis of multiple snapshots of the BTC network as well as by using other publicly available data sources on the BTC network and DLT, we unveil the BTC P2P network, locate its active nodes, study their performance, and track the evolution of the network over the past two years. Among other relevant findings, we show that (i) the size of the BTC network has remained almost constant during the last 12 months - since the major BTC price drop in early 2018, (ii) most of the BTC P2P network resides in US and EU countries, and (iii) despite this western network locality, most of the mining activity and corresponding revenue is controlled by major mining pools located in China. By additionally analyzing the distribution of BTC coins among independent BTC entities (i.e., single BTC addresses or groups of BTC addresses controlled by the same actor), we also conclude that (iv) BTC is very far from being the decentralized and uncontrolled system it is so much advertised to be, with only 4.5% of all the BTC entities holding about 85% of all circulating BTC coins.
Software-Defined Location Privacy Protection for Vehicular Networks
Venue: arXiv cs.CR 2020
Year: 2020

While the adoption of connected vehicles is growing, security and privacy concerns are still the key barriers raised by society. These concerns mandate automakers and standardization groups to propose convenient solutions for privacy preservation. One of the main proposed solutions is the use of Pseudonym-Changing Strategies (PCSs). However, ETSI has recently published a technical report which highlights the absence of standardized and efficient PCSs [1]. This alarming situation mandates an innovative shift in the way that the privacy of end-users is protected during their journey. Software-Defined Networking (SDN) is emerging as a key 5G enabler to manage the network in a dynamic manner. SDN-enabled wireless networks are opening up new programmable and highly-flexible privacy-aware solutions. We exploit this paradigm to propose an innovative software-defined location privacy architecture for vehicular networks. The proposed architecture is context-aware, programmable, extensible, and able to encompass all existing and future pseudonym-changing strategies. To demonstrate the merit of our architecture, we consider a case study that involves four pseudonym-changing strategies, which we deploy over our architecture and compare with their static implementations. We also detail how the SDN controller dynamically switches between the strategies according to the context.
Comparison of Syntactic and Semantic Representations of Programs in Neural Embeddings
Venue: arXiv cs.SE 2020
Year: 2020

Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new tool. This work aims to be the first empirical study comparing the effectiveness of natural language models and static analysis graph based models in representing programs in deep learning systems. It compares graph convolutional networks using different graph representations in the task of program embedding. It shows that the sparsity of control flow graphs and the implicit aggregation of graph convolutional networks cause these models to perform worse than naive models. Therefore it concludes that simply augmenting purely linguistic or statistical models with formal information does not perform well due to the nuanced nature of formal properties introducing more noise than structure for graph convolutional networks.
PCGRL: Procedural Content Generation via Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
Venue: arXiv cs.LG 2020
Year: 2020

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocation in the large-scale mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) based solution is proposed, which includes the following components. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.
CounterExample Guided Neural Synthesis
Venue: arXiv cs.LO 2020
Year: 2020

Program synthesis is the generation of a program from a specification. Correct synthesis is difficult, and methods that provide formal guarantees suffer from scalability issues. On the other hand, neural networks are able to generate programs from examples quickly but are unable to guarantee that the program they output actually meets the logical specification. In this work we combine neural networks with formal reasoning: using the latter to convert a logical specification into a sequence of examples that guides the neural network towards a correct solution, and to guarantee that any solution returned satisfies the formal specification. We apply our technique to synthesising loop invariants and compare the performance to existing solvers that use SMT and existing techniques that use neural networks. Our results show that the formal reasoning based guidance improves the performance of the neural network substantially, nearly doubling the number of benchmarks it can solve.
Cognitive Radio Networks: An Information Theoretic Perspective
Venue: arXiv cs.IT 2020
Year: 2020

Information-theoretic limits of {\it cognitive radio} networks have been under exploration for more than a decade. Although such limits are unknown for many networks, including the simplest case with two pairs of transmitter-receiver, there are several cases for which the capacity limits are obtained either exactly or up to a constant gap. The goal of this chapter is to provide insights into the nature of transmission techniques associated with optimal communication when cognitive radio technology is used. %, with an emphasis on 5G networks. Outlining the state of the art in the information-theoretic analysis of different cognitive systems, we highlight the salient features/points of the capacity-achieving or capacity-approaching strategies that should be considered in wireless network design paradigms based on this technology. In particular, we emphasize the interaction of cognitive radio with emerging technologies for 5G networks.
Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video. Specifically, given an untrimmed video and a query sentence, our goal is to localize a temporal segment in the video that semantically corresponds to the query sentence, with no reliance on any temporal annotation during training. We propose a two-stage model to tackle this problem in a coarse-to-fine manner. In the coarse stage, we first generate a set of fixed-length temporal proposals using multi-scale sliding windows, and match their visual features against the sentence features to identify the best-matched proposal as a coarse grounding result. In the fine stage, we perform a fine-grained matching between the visual features of the frames in the best-matched proposal and the sentence features to locate the precise frame boundary of the fine grounding result. Comprehensive experiments on the ActivityNet Captions dataset and the Charades-STA dataset demonstrate that our two-stage model achieves compelling performance.