Reviewerly/exHarmony huggingface dataset:
Cooperative detection based on the adaptive interacting multiple model-information filtering algorithm

This paper develops a multiaircraft cooperative detection scheme for effectively improving the accuracy when detecting the highly maneuvering target, where the cooperative estimate and observation trajectory planning of multiaircraft are addressed simultaneously. Based on the error compression ratio of the Markov matrix, an adaptive IMM algorithm is proposed for single detection, the convergence characteristic of which is theoretically proven meanwhile. Then, an adaptive IMM-information filtering algorithm is derived by embedding information filtering into the adaptive IMM algorithm for the fusion of the detection information. Next, the coordinated planning of observation trajectories is achieved for multiaircrafts by relying on receding horizon optimization (RHO) and the detection information. The cooperative detection and trajectory planning scheme is illustrated by the simulation results.
Quantum-enhanced least-square support vector machine: Simplified quantum algorithm and sparse solutions

Quantum algorithms can enhance machine learning in different aspects. Here, we study quantum-enhanced least-square support vector machine (LS-SVM). Firstly, a novel quantum algorithm that uses continuous variable to assist matrix inversion is introduced to simplify the algorithm for quantum LS-SVM, while retaining exponential speed-up. Secondly, we propose a hybrid quantum-classical version for sparse solutions of LS-SVM. By encoding a large dataset into a quantum state, a much smaller transformed dataset can be extracted using quantum matrix toolbox, which is further processed in classical SVM. We also incorporate kernel methods into the above quantum algorithms, which uses both exponential growth Hilbert space of qubits and infinite dimensionality of continuous variable for quantum feature maps. The quantum LS-SVM exploits quantum properties to explore important themes for SVM such as sparsity and kernel methods, and stresses its quantum advantages ranging from speed-up to the potential capacity to solve classically difficult machine learning tasks.
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

Document summarisation can be formulated as a sequential decision-making problem, which can be solved by Reinforcement Learning (RL) algorithms. The predominant RL paradigm for summarisation learns a cross-input policy, which requires considerable time, data and parameter tuning due to the huge search spaces and the delayed rewards. Learning input-specific RL policies is a more efficient alternative, but so far depends on handcrafted rewards, which are difficult to design and yield poor performance. We propose RELIS, a novel RL paradigm that learns a reward function with Learning-to-Rank (L2R) algorithms at training time and uses this reward function to train an input-specific RL policy at test time. We prove that RELIS guarantees to generate near-optimal summaries with appropriate L2R and RL algorithms. Empirically, we evaluate our approach on extractive multi-document summarisation. We show that RELIS reduces the training time by two orders of magnitude compared to the state-of-the-art models while performing on par with them.
Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning

We consider the differentially private sparse learning problem, where the goal is to estimate the underlying sparse parameter vector of a statistical model in the high-dimensional regime while preserving the privacy of each training example. We propose a generic differentially private iterative gradient hard threshoding algorithm with a linear convergence rate and strong utility guarantee. We demonstrate the superiority of our algorithm through two specific applications: sparse linear regression and sparse logistic regression. Specifically, for sparse linear regression, our algorithm can achieve the best known utility guarantee without any extra support selection procedure used in previous work \cite{kifer2012private}. For sparse logistic regression, our algorithm can obtain the utility guarantee with a logarithmic dependence on the problem dimension. Experiments on both synthetic data and real world datasets verify the effectiveness of our proposed algorithm.
Building Human-Machine Trust via Interpretability

Developing human-machine trust is a prerequisite for adoption of machine learning systems in decision critical settings (e.g healthcare and governance). Users develop appropriate trust in these systems when they understand how the systems make their decisions. Interpretability not only helps users understand what a system learns but also helps users contest that system to align with their intuition. We propose an algorithm, AVA: Aggregate Valuation of Antecedents, that generates a consensus feature attribution, retrieving local explanations and capturing global patterns learned by a model. Our empirical results show that AVA rivals current benchmarks.
CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network

The objective in image co-segmentation is to jointly segment unknown common objects from a given set of images. In this paper, we propose a novel deep convolution neural network based end-to-end co-segmentation model. It is composed of a metric learning and decision network leading to a novel conditional siamese encoder-decoder network for estimating a co-segmentation mask. The role of the metric learning network is to find an optimum latent feature space where objects of the same class are closer and that of different classes are separated by a certain margin. Depending on the extracted features, the decision network decides whether input images have common objects or not and the encoder-decoder network produces a cosegmentation mask accordingly. Key aspects of the architecture are as follows. First, it is completely class agnostic and does not require any semantic information. Second, in addition to producing masks, the decoder network also learns similarity across image pairs that improves co-segmentation significantly. Experimental results reflect an excellent performance of our method compared to state of-the-art methods on challenging co-segmentation datasets.
Multi-Task Networks with Universe, Group, and Task Feature Learning

We present methods for multi-task learning that take advantage of natural groupings of related tasks.Task groups may be defined along known properties of the tasks, such as task domain or language.Such task groups represent supervised information at the inter-task level and can be encoded into the model.We investigate two variants of neural network architectures that accomplish this, learning different feature spaces at the levels of individual tasks, task groups, as well as the universe of all tasks:(1) parallel architectures encode each input simultaneously into feature spaces at different levels; (2) serial architectures encode each input successively into feature spaces at different levels in the task hierarchy.We demonstrate the methods on natural language understanding (NLU) tasks, where a grouping of tasks into different task domains leads to improved performance on ATIS, Snips, and a large inhouse dataset.
Cubature Information Gaussian Mixture Probability Hypothesis Density Approach for Multi Extended Target Tracking

In multi-extended target tracking, each target may generate more than one observation. The traditional probability hypothesis density (PHD)-based methods are no longer effective in such scenarios. Recently, the Gaussian mixture PHD approach for the extended target tracking (ET-GM-PHD) has been presented to solve such a problem. The tracking performance of this approach has been restricted by the following disadvantages. First, it only focuses on the linear models. When targets are moving with nonlinear models, it may lead to great tracking errors. Second, the birth intensities are commonly assumed as a prior. In practice, these intensities are always unknown. In order to improve the tracking performance of the traditional ET-GM-PHD approach, a novel extended target tracking approach, namely the ET-cubature information GM-PHD (ET-CIGM-PHD) approach, has been proposed in this paper. To be more specific, we, first, utilize the cubature information filter (CIF) and gating methods to predict and update the Gaussian mixture components of the ET-GM-PHD approach. In the merit of high estimating accuracy of the CIF method, the tracking accuracy of the traditional ET-GM-PHD approach can be significantly improved. Due to the gating method, only part of cells can be used to construct the observation set in the update stage. Thus, the computational load of our approach can be significantly reduced. Then, we propose an adaptive initiating method for the birth intensity initiating. In our method, we utilize the estimated target set to select the most possible partition. Then, we remove cells associated with the estimated targets from the selected partition. The left cells are used to initiate the birth intensity. With the help of the above implementations, the birth intensity can be adaptively initiated. Using such a method, our approach can solve the cases that the prior information of birth intensity is rather little. The simulation results demonstrate the effectiveness of our approach.
Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space

Machine learning techniques, especially deep neural networks (DNNs), have been widely adopted in various applications. However, DNNs are recently found to be vulnerable against adversarial examples, i.e., maliciously perturbed inputs that can mislead the models to make arbitrary prediction errors. Empirical defenses have been studied, but many of them can be adaptively attacked again. Provable defenses provide provable error bound of DNNs, while such bound so far is far from satisfaction. To address this issue, in this paper, we present our approach named Robustra for effectively improving the provable error bound of DNNs. We leverage the adversarial space of a reference model as the feasible region to solve the min-max game between the attackers and defenders. We solve its dual problem by linearly approximating the attackers' best strategy and utilizing the monotonicity of the slack variables introduced by the reference model. The evaluation results show that our approach can provide significantly better provable adversarial error bounds on MNIST and CIFAR10 datasets, compared to the state-of-the-art results. In particular, bounded by L^infty, with epsilon = 0.1, on MNIST we reduce the error bound from 2.74% to 2.09%; with epsilon = 0.3, we reduce the error bound from 24.19% to 16.91%.
Semantic Characterization of Data Services through Ontologies

We study the problem of associating formal semantic descriptions to data services. We base our proposal on the Ontology-based Data Access paradigm, where a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on the notion of source-to-ontology (s-to-o) rewriting, which comes in three variants, called sound, complete and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an s-to-o rewriting of a given data service), and computation (computing an s-to-o rewriting of a data service).
Mobile Gaming on Personal Computers with Direct Android Emulation

Playing Android games on Windows x86 PCs has gained enormous popularity in recent years, and the de facto solution is to use mobile emulators built with the AOVB (Android-x86 On VirtualBox) architecture. When playing heavy 3D Android games with AOVB, however, users often suffer unsatisfactory smoothness due to the considerable overhead of full virtualization. This paper presents DAOW, a game-oriented Android emulator implementing the idea of direct Android emulation, which eliminates the overhead of full virtualization by directly executing Android app binaries on top of x86-based Windows. Based on pragmatic, efficient instruction rewriting and syscall emulation, DAOW offers foreign Android binaries direct access to the domestic PC hardware through Windows kernel interfaces, achieving nearly native hardware performance. Moreover, it leverages graphics and security techniques to enhance user experiences and prevent cheating in gaming. As of late 2018, DAOW has been adopted by over 50 million PC users to run thousands of heavy 3D Android games. Compared with AOVB, DAOW improves the smoothness by 21% on average, decreases the game startup time by 48%, and reduces the memory usage by 22%.
Transitioning to a Hyperledger Fabric Quantum-Resistant Classical Hybrid Public Key Infrastructure

Hyperledger Fabric (HLF) is a permissioned, blockchain designed by IBM and uses Public Key Infrastructure (PKI), for digital signatures, and digital identities (X.509 certificates), which are critical to the operational security of its network.On 24 January 2019, Aetna, Anthem, Health Care Service Corporation, PNC Bank, and IBM announced a collaboration to establish a blockchain-based ecosystem for the healthcare industry [1].Quantum computing poses a devastating impact on PKI and estimates of its large-scale commercial arrival should not be underestimated and cannot be predicted.The HIPAA (Health Insurance Portability and Accountability Act) and General Data Protection Regulation (GDPR), requires "reasonable" measures to be taken to protect Protected Health Information (PHI), and Personally Identifiable Information (PII).However, HLF's ecosystem is not post-quantum resistant, and all data that is transmitted over its network is vulnerable to immediate or later decryption by large scale quantum computers.This research presents independent evaluation and testing of the National Institute of Standards and Technology (NIST), based Second Round Candidate Post-Quantum Cryptography (PQC), lattice-based digital signature scheme qTESLA.The second-round submission is much improved, however; its algorithm characteristics and parameters are such that it is unlikely to be a quantum-resistant "as is," pure "plug-and-play" function and replacement for HLF's PKI.This work also proposes that qTESLA's public keys be used to create a quantum-resistant-classical hybrid PKI near-term replacement.
Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.
Optimization of sand casting performance parameters and missing data prediction

Due to a wide range of applications, sand casting occupies an important position in modern casting practice. The main purpose of this study was to optimize the performance parameters of sand casting based on grey relational analysis and predict the missing data using back propagation (BP) neural network. First, the influence of human factors was eliminated by adopting the objective entropy weight method, which also saved manpower. The larger variation degree in the evaluation indicators, indicating that the evaluated projects had good discrimination in this regard, the larger weight should be given to these evaluation indicators. Second, the performance parameters of sand casting were optimized based on grey relational analysis, providing a reference for sand milling. The larger the grey relational degree, the closer the evaluated project was to the ideal project. Third, this paper provided a new method for determining the number of hidden neurons in a network according to the mean square error of training samples, and venting quality was predicted based on BP neural network. The relevant theory was deduced before predicting missing data, such that there will be a general understanding regarding the prediction principle of BP neural network. Fourth, to demonstrate the validity of BP neural network adopted in the process of missing data prediction, grey system theory was applied to compare the result of missing data prediction.
Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming

Existing reinforcement learning (RL)-based adaptive bitrate (ABR) approaches outperform the previous fixed control rules based methods by improving the Quality of Experience (QoE) score, as the QoE metric can hardly provide clear guidance for optimization, finally resulting in the unexpected strategies. In this paper, we propose Tiyuntsong, a self-play reinforcement learning approach with generative adversarial network (GAN)-based method for ABR video streaming. Tiyuntsong learns strategies automatically by training two agents who are competing against each other. Note that the competition results are determined by a set of rules rather than a numerical QoE score that allows clearer optimization objectives. Meanwhile, we propose GAN Enhancement Module to extract hidden features from the past status for preserving the information without the limitations of sequence lengths. Using testbed experiments, we show that the utilization of GAN significantly improves the Tiyuntsong's performance. By comparing the performance of ABRs, we observe that Tiyuntsong also betters existing ABR algorithms in the underlying metrics.
An Online Annotation Assistant for Argument Schemes

Understanding the inferential principles underpinning an argument is essential to the proper interpretation and evaluation of persuasive discourse.Argument schemes capture the conventional patterns of reasoning appealed to in persuasion.The empirical study of these patterns relies on the availability of data about the actual use of argumentation in communicative practice.Annotated corpora of argument schemes, however, are scarce, small, and unrepresentative.Aiming to address this issue, we present one step in the development of improved datasets by integrating the Argument Scheme Key -a novel annotation method based on one of the most popular typologies of argument schemes -into the widely used OVA software for argument analysis.
A Turkish Dataset for Gender Identification of Twitter Users

Author profiling is the identification of an author’s gender, age, and language from his/her texts. With the increasing trend of using Twitter as a means to express thought, profiling the gender of an author from his/her tweets has become a challenge. Although several datasets in different languages have been released on this problem, there is still a need for multilingualism. In this work, we propose a dataset of tweets of Turkish Twitter users which are labeled with their gender information. The dataset has 3368 users in training set and 1924 users in test set where each user has 100 tweets. The dataset is publicly available.
A Multiscale Image Denoising Algorithm Based on Dilated Residual Convolution Network

Image denoising is a classical problem in low-level computer vision. Model-based optimization methods and deep learning approaches are the two main strategies for solving the problem. Model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming. In contrast, deep learning methods have fast speed but the performance of these convolutional neural networks (CNNs) is still inferior. To address this issue, here we propose a novel deep learning model that combines the dilated residual convolution and multi-scale convolution groups. Due to the complex patterns and structures inside an image, the design of hybrid dilated convolution is utilized to learn those patterns. Specifically, the skipped connection is utilized to speed up the training process while maintaining the denoising performance. In order to show the capacity of the proposed work, we do an ablation study to validate the effect of dilated convolution and multi-scale convolution group. Experimental results have demonstrated that our enhanced denoiser can not only achieve promising denoising results, but also become a strong competitor in practical application.
Improving Deep Reinforcement Learning in Minecraft with Action Advice

Training deep reinforcement learning agents complex behaviors in 3D virtual environments requires significant computational resources. This is especially true in environments with high degrees of aliasing, where many states share nearly identical visual features. Minecraft is an exemplar of such an environment. We hypothesize that interactive machine learning (IML), wherein human teachers play a direct role in training through demonstrations, critique, or action advice, may alleviate agent susceptibility to aliasing. However, interactive machine learning is only practical when the number of human interactions is limited, requiring a balance between human teacher effort and agent performance. We conduct experiments with two reinforcement learning algorithms which enable human teachers to give action advice—Feedback Arbitration, and Newtonian Action Advice—under visual aliasing conditions. To assess potential cognitive load per advice type, we vary the accuracy and frequency of various human action advice techniques. The training efficiency, robustness against infrequent and inaccurate advisor input, and sensitivity to aliasing are examined.
A Method for Detecting and Analyzing the Sentiment of Tweets Containing Fuzzy Sentiment Phrases

Owing to the development and dissemination of Twitter, an increasing number of users' opinions about various topics are being published on Twitter and have become a significant data source for numerous applications; one of the most popular is tweet sentiment analysis. Many researchers have tried to solve this problem with different methods. However, previous studies have only focused on sentiment analysis of general tweets without considering a divide-and-conquer strategy. Meanwhile, a large number of tweets contains fuzzy sentiment phrases. Thus, effectively solving fuzzy sentiment phrases may help to significantly improve the performance of sentiment analysis methods. In this study, we concentrate only on the detection and sentiment analysis problem of a specific tweet type that contains fuzzy sentiment phrases. The results show that the proposed method performs relatively well in both tasks.
Bus-Invert Coding as a Low-Power Countermeasure Against Correlation Power Analysis Attack

Bus-invert coding technique is utilized to increase the security of cryptographic circuits (CC) against correlation power analysis attacks. The bus-invert coding technique is suitable to demolish the bijective relationship between the key and leakage information, making it difficult for a side-channel attacker to use correlation power analysis to obtain a secret key. The evaluations show that the number of measurements required to disclose the correct key (MTD) on the CC with bus-invert coding increases by more than 571X as compared to a naive CC, while the power consumption of the CC is reduced by 0.91 per cent.