Reviewerly/exHarmony huggingface dataset:
Multi-lingual and Cross-genre Discourse Unit Segmentation

We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text into minimal discourse units that are involved in s coherence relation. We apply a RandomForest and an LSTM based approach for all data sets, and we improve over a simple baseline assuming simple sentence or clause-like segmentation. Performance however varies a lot depending on language, and more importantly genre, with f-scores ranging from 73.00 to 94.47.
On Consistent Vertex Nomination Schemes

Given a vertex of interest in a network $G_1$, the vertex nomination problem seeks to find the corresponding vertex of interest (if it exists) in a second network $G_2$. A vertex nomination scheme produces a list of the vertices in $G_2$, ranked according to how likely they are judged to be the corresponding vertex of interest in $G_2$. The vertex nomination problem and related information retrieval tasks have attracted much attention in the machine learning literature, with numerous applications to social and biological networks. However, the current framework has often been confined to a comparatively small class of network models, and the concept of statistically consistent vertex nomination schemes has been only shallowly explored. In this paper, we extend the vertex nomination problem to a very general statistical model of graphs. Further, drawing inspiration from the long-established classification framework in the pattern recognition literature, we provide definitions for the key notions of Bayes optimality and consistency in our extended vertex nomination framework, including a derivation of the Bayes optimal vertex nomination scheme. In addition, we prove that no universally consistent vertex nomination schemes exist. Illustrative examples are provided throughout.
Identification of Retinoblastoma Using Backpropagation Neural Network

Abstract Retinoblastoma (eye cancer) is an eye disease that is usually suffered by children that attack the thin nerve tissue behind the eyes (the part which is sensitive to light). Retinoblastoma can attack one or both eyes and it is a type of disease that can be caused by a genetic mutation called Retinoblastoma1 (RB1). On manual physical examination using ophthalmoscopy by a doctor or an expert there is a yellowish white / white cancer on the fundus that is often caused by the vascularization. That is why it needs a method that can be done to identify retinoblastoma disease through retinal fundus images automatically. In this research the method used is Backpropagation Neural Network using input of retinal fundus image. The stages which is done to identify retinoblastoma disease are image processing (resize, grey scaling, morphological close operation, and optic disk elimination), feature extraction using Grey Level Co-occurrence Matrix method and then classification using backpropagation neural network. After testing on the system in this research, it was concluded that the method used is able to identify retinoblastoma disease with accuracy 90%.
Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction

Kazuma Hashimoto, Yoshimasa Tsuruoka. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Maximum likelihood estimation in hidden Markov models with inhomogeneous noise

We consider parameter estimation in finite hidden state space Markov models with time-dependent inhomogeneous noise, where the inhomogeneity vanishes sufficiently fast. Based on the concept of asymptotic mean stationary processes we prove that the maximum likelihood and a quasi-maximum likelihood estimator (QMLE) are strongly consistent. The computation of the QMLE ignores the inhomogeneity, hence, is much simpler and robust. The theory is motivated by an example from biophysics and applied to a Poisson- and linear Gaussian model.
Discovering Options for Exploration by Minimizing Cover Time

One of the main challenges in reinforcement learning is solving tasks with sparse reward. We show that the difficulty of discovering a distant rewarding state in an MDP is bounded by the expected cover time of a random walk over the graph induced by the MDP's transition dynamics. We therefore propose to accelerate exploration by constructing options that minimize cover time. The proposed algorithm finds an option which provably diminishes the expected number of steps to visit every state in the state space by a uniform random walk. We show empirically that the proposed algorithm improves the learning time in several domains with sparse rewards.
Transferring Obligations Through Synchronizations

One common approach for verifying safety properties of multithreaded programs is assigning appropriate permissions, such as ownership of a heap location, and obligations, such as an obligation to send a message on a channel, to each thread and making sure that each thread only performs the actions for which it has permissions and it also fulfills all of its obligations before it terminates. Although permissions can be transferred through synchronizations from a sender thread, where for example a message is sent or a condition variable is notified, to a receiver thread, where that message or that notification is received, in existing approaches obligations can only be transferred when a thread is forked. In this paper we introduce two mechanisms, one for channels and the other for condition variables, that allow obligations, along with permissions, to be transferred from the sender to the receiver, while ensuring that there is no state where the transferred obligations are lost, i.e. where they are discharged from the sender thread but not loaded onto the receiver thread yet. We show how these mechanisms can be used to modularly verify deadlock-freedom of a number of interesting programs, such as some variations of client-server programs, fair readers-writers locks, and dining philosophers, which cannot be modularly verified without such transfer. We also encoded the proposed separation logic-based proof rules in the VeriFast program verifier and succeeded in verifying the mentioned programs.
Development and Bench-marking of Agoraphilic Navigation Algorithm in Dynamic Environment

This paper presents a summary of research which was conducted in developing a new human-like navigation methodology based on the Agoraphilic algorithm. This new methodology is capable of maneuvering robots in both static and dynamically clutter unknown environments.The Agoraphilic algorithm is an "optimistic" navigation algorithm. The algorithm is based on free space attraction rather than repulsion of obstacles for navigation. Therefore, this algorithm directs robots to follow the free space leading to the goal instead of avoiding obstacles. This approach has eliminated many draw backs of the traditional APF algorithm. However, the major limitation of the previously developed Agoraphilic algorithm could only deal with static environment. The new proposed algorithm has successfully extended the capacity of Agoraphilic algorithm to deal with environment cluttered with dynamic obstacles.The new Agoraphilic algorithm uses a tracking and prediction methodology to estimate the path of unknown moving objects. The estimated locations of the moving objects are combined with static object locations in the robot's visible region to generate time-varying free space attractive forces. These time varying forces maneuver the robot to the goal in dynamically cluttered unknown environment without collusions. To demonstrate the algorithm's ability, several simulations were performed. Furthermore, the new algorithm was tested and bench-marked against other APF published work under similar environment and conditions. The comparative results are discussed and showed the effectiveness of the new Algoraphilic navigation algorithm.
Variational Bayes on Manifolds

Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB methods. This paper extends the scope of VB to the case where the variational parameter space is a Riemannian manifold. We develop an efficient manifold-based VB algorithm that exploits both the geometric structure of the constraint parameter space and the information geometry of the manifold of VB approximating probability distributions. Our algorithm is provably convergent and achieves a convergence rate of order $\mathcal O(1/\sqrt{T})$ and $\mathcal O(1/T^{2-2\epsilon})$ for a non-convex evidence lower bound function and a strongly retraction-convex evidence lower bound function, respectively. We develop in particular two manifold VB algorithms, Manifold Gaussian VB and Manifold Neural Net VB, and demonstrate through numerical experiments that the proposed algorithms are stable, less sensitive to initialization and compares favourably to existing VB methods.
Live Demonstration: Unsupervised Event-Based Learning of Optical Flow, Depth and Egomotion

We propose a demo of our work, Unsupervised Event-based Learning of Optical Flow, Depth and Egomotion, which will also appear at CVPR 2019. Our demo consists of a CNN which takes as input events from a DAVIS-346b event camera, represented as a discretized event volume, and predicts optical flow for each pixel in the image. Due to the generalization abilities of our network, we are able to predict accurate optical flow for a very wide range of scenes, including for very fast motions and challenging lighting.
Deep Multi-Scale Residual Learning-based Blocking Artifacts Reduction for Compressed Images

Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a general problem in block-based image/video compression systems. Various post-processing techniques have been proposed to reduce blocking artifacts, but most of them usually introduce excessive blurring or ringing effects. This paper presents a deep learning-based compression artifacts reduction (or deblocking) framework relying on multi-scale residual learning. Recent popular approaches usually train deep models using a per-pixel loss function with explicit image priors for directly producing deblocked images. Instead, we formulate the problem as learning the residuals (or the artifacts) between original and the corresponding compressed images. In our deep model, each input image is down-scaled first with blocking artifacts naturally reduced. Then, the learned SR (super-resolution) convolutional neural network (CNN) will be used to up-sample the down-scaled version. Finally, the up-scaled version (with less artifacts) and the original input are fed into the learned artifact prediction CNN to obtain the estimated blocking artifacts. As a result, the blocking artifacts can be successfully removed by subtracting the predicted artifacts from the input image while preserving most original visual details.
Why employees matter in the fight against ransomware

Ransomware attacks keep coming and the stakes get higher every day. Each employee in a company, from the newest hire to the CEO, can either strengthen or weaken an organisation's security posture. For this reason, companies should help employees take precautions against the latest ransomware scams. Ransomware attacks keep coming and each employee, from the newest hire to the CEO, can either strengthen or weaken an organisation's security posture. Many malicious attacks can be stopped by endpoint security products and advanced threat protection solutions, but creative scammers keep finding ways to get past these defences. If a company's security programme does not empower employees to defend themselves, they will be more likely to engage in actions that threaten the security of the network, says Marcus Chung of BoldCloud.
Semi-cascade network for driver’s distraction recognition

A novel method for the eight most common driver’s distraction actions recognition is presented in this paper. To this end, a semi-cascade network (SCN) with very lightweight architecture is designed. The approach recognizes the morphology of the human face and hands to make judgments about the driver’s actions rather than just judging facial information. In order to subdivide similar actions, a SCN structure which effectively reduces the network’s scale is employed. A joint training approach is proposed for training the network and achieving 95.61% accuracy. In addition, to verify the validity of the method, a dataset containing 100,000 samples is created. Finally, a warning strategy is provided for our system and 93.9% warning rate for the driver’s distraction behavior is achieved.
No Strings Attached: Force and Vibrotactile Feedback in a Virtual Guitar Simulation

The poster describes a multisensory simulation of plucking guitar strings in virtual reality and a user study evaluating the simulation. Auditory feedback is generated by a physics-based simulation of guitar strings, and haptic feedback is provided by a combination of high fidelity vibrotactile actuators and a Phantom Omni. The study compared four conditions: no haptic feedback, vibrotactile feedback, force feedback, and a combination of force and vibrotactile feedback. The results indicate that the combination of vibrotactile and force feedback elicits the most realistic experience, and during this condition, participants were less likely to inadvertently hit strings. Notably, no significant differences were found between the conditions involving either vibrotactile or force feedback.
Learning ad-hoc Compact Representations from Salient Landmarks for Visual Place Recognition in Underwater Environments

In this paper, we propose an approach to learn compact representations from salient landmarks detected by a visual attention algorithm to recognize previously visited places in underwater environments. Instead of using hand-crafted local descriptors as it has been typically done in visual place recognition, we use a convolutional autoencoder to obtain an ad hoc descriptor generator from salient landmarks. The main advantage of using an autoencoder is that it can learn in an unsupervised manner directly from the salient landmarks. In addition, we show that it is possible to do the training with less than 100,000 examples instead of several hundreds of thousands or even millions of labeled examples as in other convolutional architectures. The trained convolutional autoencoder is used to obtain descriptors for salient landmarks that are later utilized in a voting scheme to calculate similarity between images with the objective of finding if a place has already been visited. The proposed method has obtained good results compared to SeqSLAM and FAB-MAP in different datasets obtained from robotic explorations of coral reefs in real life conditions. Moreover, when the visual attention algorithm is used, fewer features are required to get a good performance in terms of precision and recall compared when using the SURF method to extract visual features.
Pseudo-3D Attention Transfer Network with Content-aware Strategy for Image Captioning

In this article, we propose a novel Pseudo-3D Attention Transfer network with Content-aware Strategy (P3DAT-CAS) for the image captioning task. Our model is composed of three parts: the Pseudo-3D Attention (P3DA) network, the P3DA-based Transfer (P3DAT) network, and the Content-aware Strategy (CAS). First, we propose P3DA to take full advantage of three-dimensional (3D) information in convolutional feature maps and capture more details. Most existing attention-based models only extract the 2D spatial representation from convolutional feature maps to decide which area should be paid more attention to. However, convolutional feature maps are 3D and different channel features can detect diverse semantic attributes associated with images. P3DA is proposed to combine 2D spatial maps with 1D semantic-channel attributes and generate more informative captions. Second, we design the transfer network to maintain and transfer the key previous attention information. The traditional attention-based approaches only utilize the current attention information to predict words directly, whereas transfer network is able to learn long-term attention dependencies and explore global modeling pattern. Finally, we present CAS to provide a more relevant and distinct caption for each image. The captioning model trained by maximum likelihood estimation may generate the captions that have a weak correlation with image contents, resulting in the cross-modal gap between vision and linguistics. However, CAS is helpful to convey the meaningful visual contents accurately. P3DAT-CAS is evaluated on Flickr30k and MSCOCO, and it achieves very competitive performance among the state-of-the-art models.
Avenue for apertometer using spectral plane and Fourier correlation analysis

This Letter proposes a simple and practical approach for numerical aperture measurement of immersion objectives. The basic concept is to generate a reference on the spectral plane and then apply the Abbe sine condition to evaluate the numerical aperture. An extremely cheap, simple but effective and practical example is to locate a coverslip on the focal plane and then record the resulting back focal plane, on which the total internal reflection ring is applied as the reference. To achieve higher repeatability and impose fewer requirements on both the operation procedure and experiment conditions, we propose an algorithm mainly consisting of two-dimensional Fourier correlation analysis and grayscale statistics to automatically identify the radii of total internal reflection and clear aperture. The proposed approach is further applied in the measurement of an objective lens with adjustable pupils when surface plasmon is employed as reference. Experimental verification is provided.
Banking in a Digital Fiat Currency Regime

This chapter explores the future of banking in a digital fiat currency (‘DFC’) regime, defined as a monetary regime in which retail and wholesale consumers have direct access to public digital checking and payments services, independent of the existing bank-centric depository system. I argue that in such a regime, existing banks will continue to perform the valuable social function of underwriting loans and evaluating collateral, even as their checking and payments processing functions will be rendered obsolete. Such a system would improve payments system resiliency, while addressing the safe asset shortage issues associated with insurance caps on bank deposit accounts. At the same time, a DFC regime would necessarily clarify the public nature of the existing credit system, wherein commercial banks are (inherently) entrusted to underwrite loans and subjectively evaluate collateral. Thus, digital fiat currency technology represents more than a mere improvement in payment system efficiency—it has the potential to transform the banking industry, simplify financial regulation, and recast our collective understanding of how money and banking work in the modern economy.
The Blockchain Paradox

Abstract This contribution deals with the problem of interoperability of blockchain technologies. Building on the framework offered by Lawrence Lessig, it will be argued that interoperability cannot be viewed as a simple matter of technological design. Blockchain technologies, in fact, give rise to complex ecosystems, which are shaped by both the architecture and social and market forces. The literature has mainly focused on the effects that the blockchain code has determined on the law of contracts. However, the action of the other modalities, in particular those of market and social forces and their interaction with the code has not been deeply investigated. By isolating the reciprocal effects of the different modalities of regulation—in particular, blockchain code and the law, blockchain code and the market, and blockchain code and social norms—this paper intends to fill this gap and sheds some more light on the internal dynamics of public blockchain. Finally, building on the insight so gained, we will explore the problem of interoperability between ledgers by analysing the pros and cons of the solution proposed so far.
One-shot gesture recognition with attention-based DTW for human-robot collaboration

Purpose This paper aims to present a one-shot gesture recognition approach which can be a high-efficient communication channel in human–robot collaboration systems. Design/methodology/approach This paper applies dynamic time warping (DTW) to align two gesture sequences in temporal domain with a novel frame-wise distance measure which matches local features in spatial domain. Furthermore, a novel and robust bidirectional attention region extraction method is proposed to retain information in both movement and hold phase of a gesture. Findings The proposed approach is capable of providing efficient one-shot gesture recognition without elaborately designed features. The experiments on a social robot (JiaJia) demonstrate that the proposed approach can be used in a human–robot collaboration system flexibly. Originality/value According to previous literature, there are no similar solutions that can achieve an efficient gesture recognition with simple local feature descriptor and combine the advantages of local features with DTW.
Using Statistics to Automate Stochastic Optimization

Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance in machine learning. Rather than changing the learning rate at each iteration, we propose an approach that automates the most common hand-tuning heuristic: use a constant learning rate until progress stops, then drop. We design an explicit statistical test that determines when the dynamics of stochastic gradient descent reach a stationary distribution. This test can be performed easily during training, and when it fires, we decrease the learning rate by a constant multiplicative factor. Our experiments on several deep learning tasks demonstrate that this statistical adaptive stochastic approximation (SASA) method can automatically find good learning rate schedules and match the performance of hand-tuned methods using default settings of its parameters. The statistical testing helps to control the variance of this procedure and improves its robustness.
Modeling Conceptual Understanding in Image Reference Games

An agent who interacts with a wide population of other agents needs to be aware that there may be variations in their understanding of the world. Furthermore, the machinery which they use to perceive may be inherently different, as is the case between humans and machines. In this work, we present both an image reference game between a speaker and a population of listeners where reasoning about the concepts other agents can comprehend is necessary and a model formulation with this capability. We focus on reasoning about the conceptual understanding of others, as well as adapting to novel gameplay partners and dealing with differences in perceptual machinery. Our experiments on three benchmark image/attribute datasets suggest that our learner indeed encodes information directly pertaining to the understanding of other agents, and that leveraging this information is crucial for maximizing gameplay performance.