Reviewerly/exHarmony huggingface dataset:
Real-Time Dense Reconstruction of Tissue Surface From Stereo Optical Video

We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time, the basic idea of which is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. To handle the common low-texture regions on tissue surfaces, we propose effective post-processing steps for the local stereo matching method to enlarge the radius of constraint, which include outliers removal, hole filling, and smoothing. Since the tissue models obtained by stereo matching are limited to the field of view of the imaging modality, we propose a model mosaicking method by using a novel feature-based simultaneously localization and mapping (SLAM) method to align the models. Low-texture regions and the varying illumination condition may lead to a large percentage of feature matching outliers. To solve this problem, we propose several algorithms to improve the robustness of the SLAM, which mainly include 1) a histogram voting-based method to roughly select possible inliers from the feature matching results; 2) a novel 1-point RANSAC-based PnP algorithm called as DynamicR1PPnP to track the camera motion; and 3) a GPU-based iterative closest points (ICP) and bundle adjustment (BA) method to refine the camera motion estimation results. Experimental results on exand in vivo data showed that the reconstructed 3D models have high-resolution texture with an accuracyerror of less than 2 mm. Most algorithms are highly parallelized for GPU computation, and the average runtime for processing one key frame is 76.3 ms on stereo images with 960 × 540 resolution.
Fuzzy kernel feature selection with multi-objective differential evolution algorithm

In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response.
Web Page Classification Using RNN

Web page classification is an information retrieval application that provides useful information that can be a basis for many different application domains. In this study, a deep learning-based system has been developed for the classification of web pages. The meta tag information contained in the web page is used to classify a web page. The meta tags used are title, description and keywords. RNN based deep learning architecture was used during the tests. Transfer learning is the name given to the approach to building a machine learning model with the use of pre-trained parameters to solve a problem. The effect of using transfer learning on the system has also been examined. According to the results obtained, success rate of web page classification system is approximately 85%. It is not observed that transfer learning has significant contribution to the success rates. However, the use of transfer learning has reduced the consumed system resources.
Shapes and Context: In-The-Wild Image Synthesis &amp; Manipulation

We introduce a data-driven model for interactively synthesizing in-the-wild images from semantic label input masks. Our approach is dramatically different from recent work in this space, in that we make use of no learning. Instead, our approach uses simple but classic tools for matching scene context, shapes, and parts to a stored library of exemplars. Though simple, this approach has several notable advantages over recent work: (1) because nothing is learned, it is not limited to specific training data distributions (such as cityscapes, facades, or faces); (2) it can synthesize arbitrarily high-resolution images, limited only by the resolution of the exemplar library; (3) by appropriately composing shapes and parts, it can generate an exponentially large set of viable candidate output images (that can say, be interactively searched by a user). We present results on the diverse COCO dataset, significantly outperforming learning-based approaches on standard image synthesis metrics. Finally, we explore user-interaction and user-controllability, demonstrating that our system can be used as a platform for user-driven content creation.
EOG-Based Eye Movement Classification and Application on HCI Baseball Game

Electrooculography (EOG) is considered as the most stable physiological signal in the development of human-computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.
BlockTrail: A Scalable Multichain Solution for Blockchain-Based Audit Trails

Blockchain-based audit trails provide a consensus-driven and tamper-proof trail of system events that are helpful in creating provenance in enterprise solutions. However, taking into account the transaction bulk generated by these applications and the throughput limitations of existing blockchains, a single ledger for record keeping can be inefficient and costly. To that end, we see an imperative need for a new blockchain design that is capable of addressing current challenges, without compromising security and provenance. Hence, we propose BlockTrail, a scalable and efficient blockchain solution for auditing applications. BlockTrail fragments the legacy blockchain systems into layers of co-dependent hierarchies, thereby reducing the time and space complexity, and increasing the throughput. BlockTrail is prototyped on "Practical Byzantine Fault Tolerance" (PBFT) protocol with a custom-built blockchain. Experiments with BlockTrail show that compared to the conventional schemes, BlockTrail is more efficient, and has less storage footprint.
A Novel Hyperparameter-Free Approach to Decision Tree Construction That Avoids Overfitting by Design

Decision trees are an extremely popular machine learning technique. Unfortunately, overfitting in decision trees still remains an open issue that sometimes prevents achieving good performance. In this paper, we present a novel approach for the construction of decision trees that avoids the overfitting by design, without losing accuracy. A distinctive feature of our algorithm is that it requires neither the optimization of any hyperparameters, nor the use of regularization techniques, thus significantly reducing the decision tree training time. Moreover, our algorithm produces much smaller and shallower trees than traditional algorithms, facilitating the interpretability of the resulting models. For reproducibility, we provide an open source version of the algorithm.
Visual Tracking via Dynamic Memory Networks

Template-matching methods for visual tracking have gained popularity recently due to their good performance and fast speed. However, they lack effective ways to adapt to changes in the target object's appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking. The reading and writing process of the external memory is controlled by an LSTM network with the search feature map as input. A spatial attention mechanism is applied to concentrate the LSTM input on the potential target as the location of the target is at first unknown. To prevent aggressive model adaptivity, we apply gated residual template learning to control the amount of retrieved memory that is used to combine with the initial template. In order to alleviate the drift problem, we also design a "negative" memory unit that stores templates for distractors, which are used to cancel out wrong responses from the object template. To further boost the tracking performance, an auxiliary classification loss is added after the feature extractor part. Unlike tracking-by-detection methods where the object's information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target's appearance changes by updating the external memory. Moreover, the capacity of our model is not determined by the network size as with other trackers - the capacity can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information. Extensive experiments on the OTB and VOT datasets demonstrate that our trackers perform favorably against state-of-the-art tracking methods while retaining real-time speed.
A Multi-Task Hierarchical Approach for Intent Detection and Slot Filling

Spoken language understanding (SLU) plays an integral part in every dialogue system. To understand the intention of the user and extract the necessary information to help the user achieve desired goals is a challenging task. In this work, we propose an end-to-end hierarchical multi-task model that can jointly perform both intent detection and slot filling tasks for the datasets of varying domains. The primary aim is to capture context information in a dialogue to help the SLU module in a dialogue system to correctly understand the user and assist the user in achieving the desired goals. It is vital for the SLU module to capture the past information along with the present utterance said by the user to retrieve correct information. The dependency and correlation between the two tasks, i.e. intent detection and slot filling makes the multi-task learning framework effective in capturing the desired information provided by the user. We use Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to capture contextual information for the utterances. We employ Conditional Random Field (CRF) to model label dependency. Both character and word level embeddings are provided as input to the models. We create a benchmark corpus for the SLU tasks, on TRAINS and FRAMES dataset for capturing more realistic and natural utterances spoken by the speakers in a human/machine dialogue system. Experimental results on multiple datasets of various domains (ATIS, SNIP, TRAINS and FRAMES) show that our proposed approach is effective compared to the individual models and the state-of-the-art methods.
Thermal entanglement and thermal discord in two-qubit Heisenberg XYZ chain with Dzyaloshinskii–Moriya interactions

In order to explore the effect of external temperature T in quantum correlation, we compute thermal entanglement and thermal discord analytically in the Heisenberg X Y Z model with Dzyaloshinskii–Moriya interaction term $${\varvec{D}} \cdot \left( {\varvec{\sigma }}_1 \times {\varvec{\sigma }}_2 \right) $$ . For the case of thermal entanglement, it is shown that quantum phase transition occurs at $$T = T_\mathrm{c}$$ due to sudden death phenomenon. For antiferromagnetic case, the critical temperature $$T_\mathrm{c}$$ increases with increasing $$|{\varvec{D}}|$$ . For ferromagnetic case, however, $$T_\mathrm{c}$$ exhibits different behavior in the regions $$|{\varvec{D}}| \ge |{\varvec{D}_*}|$$ and $$|{\varvec{D}}| < |{\varvec{D}_*}|$$ , where $${\varvec{D}_*}$$ is particular value of $${\varvec{D}}$$ . It is shown that $$T_\mathrm{c}$$ becomes zero at $$|{\varvec{D}}| = |{\varvec{D}_*}|$$ . We explore the behavior of thermal discord in detail at $$T \approx T_\mathrm{c}$$ . For antiferromagnetic case, the external temperature makes the thermal discord exhibit exponential damping behavior, but it never reaches exact zero. For ferromagnetic case, the thermal entanglement and thermal discord are shown to be zero simultaneously at $$T_\mathrm{c} = 0$$ and $$|{\varvec{D}}| = |{\varvec{D}_*}|$$ . This is unique condition for simultaneous disappearance of thermal entanglement and thermal discord in this model.
Improving Tree-LSTM with Tree Attention

In Natural Language Processing (NLP), we often need to extract information from tree topology. Sentence structure can be represented via a dependency tree or a constituency tree structure. For this reason, a variant of LSTMs, named Tree-LSTM, was proposed to work on tree topology. In this paper, we design a generalized attention framework for both dependency and constituency trees by encoding variants of decomposable attention inside a Tree-LSTM cell. We evaluated our models on a semantic relatedness task and achieved notable results compared to Tree-Lstmbased methods with no attention as well as other neural and non-neural methods and good results compared to Tree-Lstmbased methods with attention.
DCACI: A Decentralized Lightweight Capability Based Access Control Framework using IOTA for Internet of Things

Security and Privacy are some of the important aspects to be considered in the large-scale deployment of Internet of Things (IoT) systems. Due to the large number of IoT devices and the different administrative domains in which they operate, traditional approaches involving a Centralized server for managing Authorizations will not be scalable or efficient. In this paper, we propose a Decentralized Capability-Based Access Control framework using IOTA (DCACI); IOTA is an open-source distributed ledger that enables fee-less micro transactions for the IoT. The DCACI framework enables complete privacy and integrity of the Capability tokens using IOTA's Masked Authenticated Messaging (MAM) technology. It enables device owners and users to Grant, Update, Delegate and Revoke the capability tokens. The proposed DCACI framework has been implemented as a proof-of-concept on a resource constrained machine; the results indicate that it is capable of scaling up to large-scale infrastructure such as a Smart City, having millions of IoT devices.
Learning to Film From Professional Human Motion Videos

We investigate the problem of 6 degrees of freedom (DOF) camera planning for filming professional human motion videos using a camera drone. Existing methods either plan motions for only a pan-tilt-zoom (PTZ) camera, or adopt ad-hoc solutions without carefully considering the impact of video contents and previous camera motions on the future camera motions. As a result, they can hardly achieve satisfactory results in our drone cinematography task. In this study, we propose a learning-based framework which incorporates the video contents and previous camera motions to predict the future camera motions that enable the capture of professional videos. Specifically, the inputs of our framework are video contents which are represented using subject-related feature based on 2D skeleton and scene-related features extracted from background RGB images, and camera motions which are represented using optical flows. The correlation between the inputs and output future camera motions are learned via a sequence-to-sequence convolutional long short-term memory (Seq2Seq ConvLSTM) network from a large set of video clips. We deploy our approach to a real drone cinematography system by first predicting the future camera motions, and then converting them to the drone's control commands via an odometer. Our experimental results on extensive datasets and showcases exhibit significant improvements in our approach over conventional baselines and our approach can successfully mimic the footage of a professional cameraman.
Security Problems of Chaotic Image Encryption Algorithms Based on Cryptanalysis Driven Design Technique

Robust protocols are needed to create a secure computing system. Protocols designed without considering various design criteria result in many vulnerabilities. Security has many aspects. The slightest defect in the design processes affects the whole system security. Chaos based image encryption algorithms have become increasingly popular in the recent period. However, most of these algorithms are often based on cryptanalysis driven design technique. Therefore, the security of these algorithms is doubtful as long as the threat of attack continues. In this paper, attention has been drawn to this problem. First, the security problems of two different chaos based image encryption algorithms have been analyzed. In the second analyzed study, the security problem of the first analyzed study has been examined and a new improved version has been published. The common point of these two analyzed studies is that both the original and the improved algorithm are designed on the basis of the cryptanalysis has driven design approach. In other words, the security of these two algorithms is still doubtful. In this study, security problems have been shown for both algorithms and the encryption algorithms have been broken. In the second part of the study, a road map based on a provable secure driven design approach is presented for future studies to avoid such problems.
Better late than never: a fully-abstract semantics for classical processes

We present Hypersequent Classical Processes (HCP), a revised interpretation of the “Proofs as Processes” correspondence between linear logic and the π-calculus initially proposed by Abramsky [1994], and later developed by Bellin and Scott [1994], Caires and Pfenning [2010], and Wadler [2014], among others. HCP mends the discrepancies between linear logic and the syntax and observable semantics of parallel composition in the π-calculus, by conservatively extending linear logic to hyperenvironments (collections of environments, inspired by the hypersequents by Avron [1991]). Separation of environments in hyperenvironments is internalised by ⊗ and corresponds to parallel process behaviour. Thanks to this property, for the first time we are able to extract a labelled transition system (lts) semantics from proof rewritings. Leveraging the information on parallelism at the level of types, we obtain a logical reconstruction of the delayed actions that Merro and Sangiorgi [2004] formulated to model non-blocking I/O in the π-calculus. We define a denotational semantics for processes based on Brzozowski derivatives, and uncover that non-interference in HCP corresponds to Fubini’s theorem of double antiderivation. Having an lts allows us to validate HCP using the standard toolbox of behavioural theory. We instantiate bisimilarity and barbed congruence for HCP, and obtain a full abstraction result: bisimilarity, denotational equivalence, and barbed congruence coincide.
Integrated commodities in the digital economy

At the heart of the digital economy is the willful imposition of a powerful combination of hardware and software, time and data, surveillance, prediction and behavourial control. This article argues that a central ambition of digital society has been the pursuit of an integrated commodity form. The novelty of this integrated commodity stems not only from the convergence of production and consumption but also from the subsumption of sociability itself. As a consequence, we are required to consider all social actions within digital society as being transactions enshrined within an economy of integrated markets. Our acceptance of these new commodity relations is paving the way for a ‘great integration’ of simultaneous transactions across different social domains. Borrowing from Karl Polanyi’s account of an earlier transformation, this article will propose that the prerequisites of digital society and the integrated commodity form have been established through the constitution of a series of ‘fictitious commodities’.
Constructive Neural Network Learning

In this paper, we aim at developing scalable neural network-type learning systems. Motivated by the idea of constructive neural networks in approximation theory, we focus on constructing rather than training feed-forward neural networks (FNNs) for learning, and propose a novel FNNs learning system called the constructive FNN (CFN). Theoretically, we prove that the proposed method not only overcomes the classical saturation problem for constructive FNN approximation, but also reaches the optimal learning rate when the regression function is smooth, while the state-of-the-art learning rates established for traditional FNNs are only near optimal (up to a logarithmic factor). A series of numerical simulations are provided to show the efficiency and feasibility of CFN.
Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions

Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one parameter controlling the proportion of outliers and another specifying the degree of contamination. Crucially, these parameters do not have to be specified a priori, adding a flexibility to our approach that is absent from other approaches such as trimming. Moreover, each observation is given an a posteriori probability of belonging to a particular cluster, and of being an outlier or not; advantageously, this allows for the automatic detection of outliers. An expectation–conditional maximization algorithm is outlined for parameter estimation and various implementation issues are discussed. The behavior of the proposed model is investigated, and compared with well-established finite mixture approaches, on artificial and real data.
Juniper: A Tree+Table Approach to Multivariate Graph Visualization

Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.