Reviewerly/exHarmony huggingface dataset:
Optimal Path Planning for an Autonomous Mobile Robot Using Dragonfly Algorithm

Navigation, path generation and obstacle avoidance are considered as the key challenges in the area of autonomous mobile robots.In this article, a new meta-heuristic optimization technique called Dragonfly Algorithm (DA) is employed for the navigation of autonomous mobile robot in an unknown cluttered environment filled with several static obstacles.This new meta-heuristic Dragonfly algorithm is inspired from the static and dynamic swarming behaviours of dragonflies in nature.Two objective functions, target seeking and obstacle avoidance are formulated based on the distance between the robot, target and the obstacles and is optimized using the proposed DA for obtaining optimal path.After every iteration, based on the objective function values the robot proceeds towards the globally best agent in the swarm in a sequence of permutation which finally leads to the target.A variety of static environment is modelled and the algorithm is tested both through simulation and experimentally.The proposed algorithm shows that the robot reaches the target without colliding any obstacles while generating a smooth optimal trajectory.
Multi-Task Learning with Language Modeling for Question Generation

This paper explores the task of answer-aware questions generation.Based on the attentionbased pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure.Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions.On both SQuAD and MARCO datasets, our multitask learning model boosts the performance, achieving state-of-the-art results.Moreover, human evaluation further proves the high quality of our generated questions.
Movie Plot Analysis via Turning Point Identification

According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and thematic units (e.g., setup, complications, aftermath).We propose the task of turning point identification in movies as a means of analyzing their narrative structure.We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering.We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays.Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.
Concept Pointer Network for Abstractive Summarization

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details.Inspired by the popular pointer generator sequence-tosequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization.The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts.The model then points to the most appropriate choice using both the concept set and original source text.This joint approach generates abstractive summaries with higher-level semantic concepts.The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set.Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets.A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework.
Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing

Prior work on cross-lingual dependency parsing often focuses on capturing the commonalities between source and target languages and overlooks the potential of leveraging linguistic properties of the languages to facilitate the transfer.In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially.Specifically, we explore several types of corpus linguistic statistics and compile them into corpus-wise constraints to guide the inference process during the test time.We adapt two techniques, Lagrangian relaxation and posterior regularization, to conduct inference with corpus-statistics constraints.Experiments show that the Lagrangian relaxation and posterior regularization inference improve the performances on 15 and 17 out of 19 target languages, respectively.The improvements are especially significant for target languages that have different word order features from the source language.
On the use of distributed semantics of tweet metadata for user age prediction

Social media data represent an important resource for behavioral analysis of the aging population. This paper addresses the problem of age prediction from Twitter dataset, where the prediction issue is viewed as a classification task. For this purpose, an innovative model based on Convolutional Neural Network is devised. To this end, we rely on language-related features and social media specific metadata. More specifically, we introduce two features that have not been previously considered in the literature: the content of URLs and hashtags appearing in tweets. We also employ distributed representations of words and phrases present in tweets, hashtags and URLs, pre-trained on appropriate corpora in order to exploit their semantic information in age prediction. We show that our CNN-based classifier, when compared with baseline models, yields an improvement of up to 12.3% for Dutch dataset, 9.8% for English1 dataset, and 6.6% for English2 dataset in the micro-averaged F1 score.
Artificial Intelligence and Deep Learning in Video Games A Brief Review

Artificial Intelligence has been used in many fields since the term was coined six decades ago. Artificial Intelligence in video games was introduced with Atari 2600's early titles such as `Computer Space' and `Pong' and has come a long way since. However, it has become expected of developers to have flawless AI which is as human-like as possible. Neural Networks are allowing AI systems to become smarter and used in ways such as OpenAI does with their game-playing bots. Many game-playing Neural Networks use reinforcement learning to train as it allows for more efficient training for larger games where the number of possible positions and combinations of game mechanics are extremely large. This paper provides an introductory literature review of these core fields of research as they applied within a video game context.
Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks

Abstract With the popularity of e-commerce, posting online product reviews expressing customer’s sentiment or opinion towards products has grown exponentially. Sentiment analysis is a computational method that plays an essential role in automating the extraction of subjective information i.e. customer’s sentiment or opinion from online product reviews. Two approaches commonly used in Sentiment analysis tasks are supervised approaches and lexicon-based approaches. In supervised approaches, Sentiment analysis is seen as a text classification task. The result depends not only on the robustness of the machine learning algorithm but also on the utilized features. Bag-of-word is a common utilized features. As a statistical feature, bag-of-word does not take into account semantic of words. Previous research has indicated the potential of semantic in supervised SA task. To augment the result of sentiment analysis, this paper proposes a method to extract text features named sentence level features (SLF) and domain sensitive features (DSF) which take into account semantic of words in both sentence level and domain level of product reviews. A word sense disambiguation based method was adapted to extract SLF. For every similarity employed in generating SLF, the SentiCircle-based method was enhanced to generate DSF. Results of the experiments indicated that our proposed semantic features i.e. SLF and SLF + DSF favorably increase the performance of supervised sentiment analysis on product reviews.
Experimental realization of a reference-frame-independent decoy BB84 quantum key distribution based on Sagnac interferometer

Quantum key distribution (QKD) can generate secure key bits between remote users employing the features of quantum physics. However, a shared reference frame is necessary for QKD systems in most scenarios. A reference-frame-independent (RFI) scheme can tolerate the reference frame drifting between legitimate remote users, which is significant in the operation of relative moving terminals such as satellites and aircraft. We design and experimentally demonstrate an RFI-BB84-QKD system by joint encoding with the polarization and orbital angular momentum states of the photons. We use self-compensating fiber Sagnac interferometers to perform high-speed polarization modulation, and q-plates to passively manipulate the rotation-invariant photon states, which makes the system feasible for high-speed operation using off-the-shelf components.
Semantic Image Segmentation by Scale-Adaptive Networks

Semantic image segmentation is an important yet unsolved problem. One of the major challenges is the large variability of the object scales. To tackle this scale problem, we propose a Scale-Adaptive Network (SAN) which consists of multiple branches with each one taking charge of the segmentation of the objects of a certain range of scales. Given an image, SAN first computes a dense scale map indicating the scale of each pixel which is automatically determined by the size of the enclosing object. Then the features of different branches are fused according to the scale map to generate the final segmentation map. To ensure that each branch indeed learns the features for a certain scale, we propose a scale-induced ground-truth map and enforce a scale-aware segmentation loss for the corresponding branch in addition to the final loss. Extensive experiments over the PASCAL-Person-Part, the PASCAL VOC 2012, and the Look into Person datasets demonstrate that our SAN can handle the large variability of the object scales and outperforms the state-of-the-art semantic segmentation methods.
Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope

The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes.
Testing the Generalization Power of Neural Network Models across NLI Benchmarks

Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks.However, the success of these models turns out to be largely benchmark specific.We show that models trained on a natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmarks is the same or similar.We train six high performing neural network models on different datasets and show that each one of these has problems of generalizing when we replace the original test set with a test set taken from another corpus designed for the same task.In light of these results, we argue that most of the current neural network models are not able to generalize well in the task of natural language inference.We find that using large pre-trained language models helps with transfer learning when the datasets are similar enough.Our results also highlight that the current NLI datasets do not cover the different nuances of inference extensively enough.
Path Planning of UAV for Oilfield Inspection Based on Improved Grey Wolf Optimization Algorithm

It is difficult for the traditional manual inspection method to satisfy the current management requirements. Now, UAV inspection technology has been used by more and more enterprises. In the UAV inspection, path planning is an important work. An improved grey wolf optimization algorithm is proposed for the path planning of UAV in oilfield environment in this paper. Firstly, the model of the oilfield environment is built; secondly, the initial path is produced by the basic grey wolf optimization (GWO) algorithm; and then, the fruit fly optimization (FOA) algorithm is used to continue the local optimization of the optimal solution; finally, the optimal path is generated. Compared with some other methods, the simulation results show that the improved grey wolf optimization algorithm is effective.
Verification of Smart Contract Business Logic

Smart contracts have been argued to be a means of building trust between parties by providing a self-executing equivalent of legal contracts. And yet, code does not always perform what it was originally intended to do, which resulted in losses of millions of dollars. Static verification of smart contracts is thus a pressing need. This paper presents an approach to verifying smart contracts written in Solidity by automatically translating Solidity into Java and using KeY, a deductive Java verification tool. In particular, we solve the problem of rolling back the effects of aborted transactions by exploiting KeY’s native support of JavaCard transactions. We apply our approach to a smart contract which automates a casino system, and discuss how the approach addresses a number of known shortcomings of smart contract development in Solidity.
Multimodal Learning toward Micro-Video Understanding

Micro-videos, a new form of user-generated content, have been spreading widely across various social platforms, such as Vine, Kuaishou, and TikTok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to their brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm. The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for high-order micro-video understanding.
Blockchain-Based Verifiable Multi-Keyword Ranked Search on Encrypted Cloud With Fair Payment

In traditional cloud computing system, searchable encryption is deemed as a core technology to realize data confidentiality protection and information retrieval functions.However, the online payment problem and mutual distrust between cloud platforms and users may hinder the wide adoption of cloud service.In this paper, we construct a blockchain based multi-keyword ranked search with fair payment (BMFP) system, which leverages smart contracts to verify the correctness and completeness of the search result, and automatically execute the fair payment operations.The system realizes public verifiability on a multi-keyword ranked search result.The data owner manages the search authority, and a concrete fair payment smart contract is designed.The BMFP is compatible with Ethereum, and the verification algorithm executed by the smart contract is cost-efficient.
Neural Feature Search: A Neural Architecture for Automated Feature Engineering

Feature engineering is a crucial step for developing effective machine learning models. Traditionally, feature engineering is performed manually, which requires much domain knowledge and is time-consuming. In recent years, many automated feature engineering methods have been proposed. These methods improve the accuracy of a machine learning model by automatically transforming the original features into a set of new features. However, existing methods either lack ability to perform high-order transformations or suffer from the feature space explosion problem. In this paper, we present Neural Feature Search (NFS), a novel neural architecture for automated feature engineering. We utilize a recurrent neural network based controller to transform each raw feature through a series of transformation functions. The controller is trained through reinforcement learning to maximize the expected performance of the machine learning algorithm. Extensive experiments on public datasets illustrate that our neural architecture is effective and outperforms the existing state-of-the-art automated feature engineering methods. Our architecture can efficiently capture potentially valuable high-order transformations and mitigate the feature explosion problem.
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems

With the advent of conversational assistants like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial settings.These systems typically include a Spoken Language understanding component which consists of two tasks: Intent Classification (IC) and Slot Labeling (SL).Generally, these two tasks are modeled together jointly to achieve best performance.However, this joint modeling adds to model obfuscation.In this work, we first design framework for a modularization of joint IC-SL task to enhance architecture transparency.Then, we explore a number of selfattention, convolutional, and recurrent models, contributing a large-scale analysis of modeling paradigms for IC+SL across two datasets.Finally, using this framework, we propose a class of 'label-recurrent' models that are nonrecurrent apart from a 10-dimensional representation of the label history, and show that our proposed systems are highly accurate (achieving over 30% error reduction in SL over the state-of-the-art on the Snips dataset), as well as fast, at 2x the inference and 2/3 to 1/2 the training time of comparable recurrent models, thus giving an edge in critical real-world systems.
Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

In the area of health care, fall is a dangerous problem for aged persons.Sometimes, they are a serious cause of death.In addition to that, the number of aged persons will increase in the future.Therefore, it is necessary to develop an accurate system to detect fall.In this paper, we present spatiotemporal method to detect fall form videos filmed by surveillance cameras.Firstly, we computed key points of human skeleton.We calculated distances and angles between key points of each two pair sequences frames.After that, we applied Principal Component Analysis (PCA) to unify the dimension of features.Finally, we utilized Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbors (KNN) to classify features.We found that SVM is the best classifier to our method.The results of our algorithm are as follow: accuracy is 98.5%, sensitivity is 97% and the specificity is 100%.
Terminal crossover and steering-based particle swarm optimization algorithm with disturbance

Particle swarm optimization (PSO) is an efficient and simple evolutionary algorithm, which has been successfully applied to solve optimization problems in many real-world fields. Nevertheless, the disadvantage of easy loss of population diversity makes it difficult for particles to jump out of local optimum. The deterioration of population diversity often occurs in the terminal iteration stage. Therefore, to overcome this drawback, terminal crossover and steering-based PSO with distribution(TCSPSO) is proposed in this paper. Firstly, to enhance the diversity of population, a new crossover mechanism is constructed. Meanwhile, in order to make the particle easily jump out of the local optimum, a global disturbance is utilized and the direction of motion of the particle is changed at the later stage. Finally, a nonlinear inertia weight and elastic mechanism are introduced to balance exploration and exploitation better. 34 benchmark functions and two engineering problems are utilized to verify the promising performance of TCSPSO, experimental results and statistical analysis indicate that TCSPSO has competitive performance compared with 15 state-of-the-art algorithms.
Experimental Quantification of Coherence of a Tunable Quantum Detector

Quantum coherence is a fundamental resource that quantum technologies exploit to achieve performance beyond that of classical devices. A necessary prerequisite to achieve this advantage is the ability of measurement devices to detect coherence from the measurement statistics. Based on a recently developed resource theory of quantum operations, here we quantify experimentally the ability of a typical quantum-optical detector, the weak-field homodyne detector, to detect coherence. We derive an improved algorithm for quantum detector tomography and apply it to reconstruct the positive-operator-valued measures of the detector in different configurations. The reconstructed positive-operator-valued measures are then employed to evaluate how well the detector can detect coherence using two computable measures. As the first experimental investigation of quantum measurements from a resource theoretical perspective, our work sheds new light on the rigorous evaluation of the performance of a quantum measurement apparatus.
Monocular Visual Object 3D Localization in Road Scenes

3D localization of objects in road scenes is important for autonomous driving and advanced driver-assistance systems (ADAS). However, with common monocular camera setups, 3D information is difficult to obtain. In this paper, we propose a novel and robust method for 3D localization of monocular visual objects in road scenes by joint integration of depth estimation, ground plane estimation, and multi-object tracking techniques. Firstly, an object depth estimation method with depth confidence is proposed by utilizing the monocular depthmap from a CNN. Secondly, an adaptive ground plane estimation using both dense and sparse features is proposed to localize the objects when their depth estimation is not reliable. Thirdly, temporal information is taken into consideration by a new object tracklet smoothing method. Unlike most existing methods which only consider vehicle localization, our method is applicable for common moving objects in the road scenes, including pedestrians, vehicles, cyclists, etc. Moreover, the input depthmap can be replaced by some equivalent depth information from other sensors, like LiDAR, depth camera and Radar, which makes our system much more competitive compared with other object localization methods. As evaluated on KITTI dataset, our method achieves favorable performance on 3D localization of both pedestrians and vehicles when compared with the state-of-the-art vehicle localization methods, though no published performance on pedestrian 3D localization can be compared with, from the best of our knowledge.