Reviewerly/exHarmony huggingface dataset:
Teaching Artificial Intelligence and Robotics to Undergraduate Systems Engineering Students

The skills of artificial intelligence (AI) and robotics provide a wide window of job opportunities for the following professionals: computer scientists, mechanical engineers, system engineers, computer engineers, biomedical engineers, and electrical engineers.Additionally, other professionals benefiting from AI and robotics' job opportunities are information technologists, informatic engineers, electronic engineers, data scientists, industrial engineers, big data engineers, and related specialists in the dynamic field of engineering robotics.Therefore, the purpose of this research was to study the effort of teaching AI and robotics to undergraduate systems engineering students at the Polytechnic University Institute "Santiago Mariño" in Barinas, Venezuela.Consequently, the methodology used in this academic research was the case study approach, which included three phases, namely Initiation Phase, Fieldwork Phase, and Closing Phase.In that sense, the design of research adopted in this study was based on the development of an exploratory single case study method.As a result, the Theoretical Framework created as a cornerstone of this research highlighted the following three research variables: (1) Robotic Applications, ( 2) Mechanics of Robotic Manipulation and Computer Vision, and (3) Object-oriented Analysis and Design (OOAD) and Object-oriented (OO) High-level Programming Languages.In conclusion, two nondirectional null hypotheses were tested, leading to the positive answers of the following research questions: (1) "Can undergraduate systems engineering students apply OOAD and OO High-level Programming Languages to analyze, design, and develop Robotic Applications?" and (2) "Can undergraduate systems engineering students use Mechanics of Robotic Manipulation and Computer Vision to analyze, design, and develop Robotic Applications?" as stated in this case study.
Non-local NetVLAD Encoding for Video Classification

This paper describes our solution for the 2 $$^\text {nd}$$ YouTube-8M video understanding challenge organized by Google AI. Unlike the video recognition benchmarks, such as Kinetics and Moments, the YouTube-8M challenge provides pre-extracted visual and audio features instead of raw videos. In this challenge, the submitted model is restricted to 1 GB, which encourages participants focus on constructing one powerful single model rather than incorporating of the results from a bunch of models. Our system fuses six different sub-models into one single computational graph, which are categorized into three families. More specifically, the most effective family is the model with non-local operations following the NetVLAD encoding. The other two family models are Soft-BoF and GRU, respectively. In order to further boost single models performance, the model parameters of different checkpoints are averaged. Experimental results demonstrate that our proposed system can effectively perform the video classification task, achieving 0.88763 on the public test set and 0.88704 on the private set in terms of GAP@20, respectively. We finally ranked at the fourth place in the YouTube-8M video understanding challenge.
A Vibrating Mechanism to Prevent Neural Networks from Overfitting

In this paper, we propose a Vibrating Mechanism, which can be understood as a continuous version of Dropout and can achieve a certain L1 regularization effect at the same time. In Dropout, the parameters are discarded with a probability obeying Bernoulli distribution. While in the proposed Vibrating Mechanism, the parameters are sampled from a truncated Gaussian distribution or uniform distribution. The mean of the distribution is the result trained by the previous iteration, and the standard deviation of the distribution is the absolute value of the previous iteration training result multiplied by a proportional coefficient. Besides, the performance improvement is theoretically analyzed from the perspective of hyperplane segmentation. The effectiveness of the proposed Vibrating Mechanism is demonstrated by applying it for a text classification task. We choose AG dataset for test. The result shows that the Vibrating Mechanism can achieve better classification accuracy without using L1 regularization or Dropout, which verifies the performance improvement of the Vibrating Mechanism.
Multi-View Multi-Label Learning With View-Label-Specific Features

In multi-view multi-label learning, each object is represented by multiple data views, and belongs to multiple class labels simultaneously. Generally, all the data views have a contribution to the multi-label learning task, but their contributions are different. Besides, for each data view, each class label is only associated with a subset data features, and different features have different contributions to each class label. In this paper, we propose a novel framework VLSF for multi-view multi-label learning, i.e., multi-view multi-label learning with View-Label-Specific Features. Specifically, we first learn a low dimensional label-specific data representation for each data view and construct a multi-label classification model based on it by exploiting label correlations and view consensus, and learn the contribution weight of each data view to multi-label learning task for all the class labels jointly. Then, the final prediction can be made by combing the prediction results of all the classifiers and the learned contribution weights. The extensive comparison experiments with the state-of-the-art approaches manifest the effectiveness of the proposed method VLSF.
Better, Faster, Stronger Sequence Tagging Constituent Parsers

Sequence tagging models for constituent parsing are faster, but less accurate than other types of parsers.In this work, we address the following weaknesses of such constituent parsers: (a) high error rates around closing brackets of long constituents, (b) large label sets, leading to sparsity, and (c) error propagation arising from greedy decoding.To effectively close brackets, we train a model that learns to switch between tagging schemes.To reduce sparsity, we decompose the label set and use multi-task learning to jointly learn to predict sublabels.Finally, we mitigate issues from greedy decoding through auxiliary losses and sentence-level fine-tuning with policy gradient.Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further.On the SPMRL datasets, we observe even greater improvements across the board, including a new state of the art on Basque, Hebrew, Polish and Swedish. 1
Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection

We present LBW-Net, an efficient optimization based method for quantization and training of the low bit-width convolutional neural networks (CNNs). Specifically, we quantize the weights to zero or powers of two by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation. We characterize the combinatorial nature of the low bit-width quantization problem. For 2-bit (ternary) CNNs, the quantization of $N$ weights can be done by an exact formula in $O(N\log N)$ complexity. When the bit-width is three and above, we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive. The free parameter is further determined by network retraining and object detection tests. LBW-Net has several desirable advantages over full-precision CNNs, including considerable memory savings, energy efficiency, and faster deployment. Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart, the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks, and can even do better in some real world visual scenes, while empirically enjoying more than 4$\times$ faster deployment.
Neural Program Induction for KBQA Without Gold Programs or Query Annotations

Neural Program Induction (NPI) is a paradigm for decomposing high-level tasks such as complex question-answering over knowledge bases (KBQA) into executable programs by employing neural models. Typically, this involves two key phases: i) inferring input program variables from the high-level task description, and ii) generating the correct program sequence involving these variables. Here we focus on NPI for Complex KBQA with only the final answer as supervision, and not gold programs. This raises major challenges; namely, i) noisy query annotation in the absence of any supervision can lead to catastrophic forgetting while learning, ii) reward becomes extremely sparse owing to the noise. To deal with these, we propose a noise-resilient NPI model, Stable Sparse Reward based Programmer (SSRP) that evades noise-induced instability through continual retrospection and its comparison with current learning behavior. On complex KBQA datasets, SSRP performs at par with hand-crafted rule-based models when provided with gold program input, and in the noisy settings outperforms state-of-the-art models by a significant margin even with a noisier query annotator.
Jointly Extracting Multiple Triplets with Multilayer Translation Constraints

Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus. Conventional extraction models use a pipeline of named entity recognition and relation classification to extract entities and relations, respectively, which ignore the connection between the two tasks. Recently, several neural network-based models were proposed to tackle the problem, and achieved state-of-the-art performance. However, most of them are unable to extract multiple triplets from a single sentence, which are yet commonly seen in real-life scenarios. To close the gap, we propose in this paper a joint neural extraction model for multitriplets, namely, TME, which is capable of adaptively discovering multiple triplets simultaneously in a sentence via ranking with translation mechanism. In experiment, TME exhibits superior performance and achieves an improvement of 37.6% on F1 score over state-of-the-art competitors.
Automatic Generation of Chinese Short Product Titles for Mobile Display

This paper studies the problem of automatically extracting a short title from a manually written longer description of Ecommerce products for display on mobile devices. It is a new extractive summarization problem on short text inputs, for which we propose a feature-enriched network model, combining three different categories of features in parallel. Experimental results show that our framework significantly outperforms several baselines by a substantial gain of 4.5%. Moreover, we produce an extractive summarization dataset for Ecommerce short texts and will release it to the research community.
Modeling Source Syntax and Semantics for Neural AMR Parsing

Sequence-to-sequence (seq2seq) approaches formalize Abstract Meaning Representation (AMR) parsing as a translation task from a source sentence to a target AMR graph. However, previous studies generally model a source sentence as a word sequence but ignore the inherent syntactic and semantic information in the sentence. In this paper, we propose two effective approaches to explicitly modeling source syntax and semantics into neural seq2seq AMR parsing. The first approach linearizes source syntactic and semantic structure into a mixed sequence of words, syntactic labels, and semantic labels, while in the second approach we propose a syntactic and semantic structure-aware encoding scheme through a self-attentive model to explicitly capture syntactic and semantic relations between words. Experimental results on an English benchmark dataset show that our two approaches achieve significant improvement of 3.1% and 3.4% F1 scores over a strong seq2seq baseline.
Concept Extraction and Prerequisite Relation Learning from Educational Data

Prerequisite relations among concepts are crucial for educational applications. However, it is difficult to automatically extract domain-specific concepts and learn the prerequisite relations among them without labeled data.In this paper, we first extract high-quality phrases from a set of educational data, and identify the domain-specific concepts by a graph based ranking method. Then, we propose an iterative prerequisite relation learning framework, called iPRL, which combines a learning based model and recovery based model to leverage both concept pair features and dependencies among learning materials. In experiments, we evaluated our approach on two real-world datasets Textbook Dataset and MOOC Dataset, and validated that our approach can achieve better performance than existing methods. Finally, we also illustrate some examples of our approach.
Mapping Consumer Sentiment Toward Wireless Services Using Geospatial Twitter Data

Hyper-dense wireless network deployment is one of the popular solutions to meeting high capacity requirement for 5G delivery. However, current operator understanding of consumer satisfaction comes from call centers and base station quality-of-service (QoS) reports with poor geographic accuracy. The dramatic increase in geo-tagged social media posts adds a new potential to understand consumer satisfaction towards target-specific quality-of-experience (QoE) topics. In our paper, we focus on evaluating users' opinion on wireless service-related topics by applying natural language processing (NLP) to geo-tagged Twitter data. Current generalized sentiment detection methods with generalized NLP corpora are not topic specific. Here, we develop a novel wireless service topic-specific sentiment framework, yielding higher targeting accuracy than generalized NLP frameworks. To do so, we first annotate a new sentiment corpus called SignalSentiWord (SSW) and compare its performance with two other popular corpus libraries, AFINN and SentiWordNet. We then apply three established machine learning methods, namely: Naïve Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network (RNN) to build our topic-specific sentiment classifier. Furthermore, we discuss the capability of SSW to filter noisy and high-frequency irrelevant words to improve the performance of machine learning algorithms. Finally, the real-world testing results show that our proposed SSW improves the performance of NLP significantly.
Improving Human Pose Estimation With Self-Attention Generative Adversarial Networks

Human pose estimation in images is challenging and important for many computer vision applications.Large improvements in human pose estimation have been achieved with the development of convolutional neural networks.Even though, when encountered some difficult cases even the state-ofthe-art models may fail to predict all the body joints correctly.Some recent works try to refine the pose estimator.GAN (Generative Adversarial Networks) has been proved to be efficient to improve human pose estimation.However, GAN can only learn local body joints structural constrains.In this paper, we propose to apply Self-Attention GAN to further improve the performance of human pose estimation.With attention mechanism in the framework of GAN, we can learn long-range body joints dependencies, therefore enforce the entire body joints structural constrains to make all the body joints to be consistent.Our method outperforms other state-of-the-art methods on two standard benchmark datasets MPII and LSP for human pose estimation.Our code is available at: https://github.com/idotc/Hg-SAGAN.
Dynamical Analysis of a Novel Complex Chaotic System and Application in Image Diffusion

In this paper, a novel complex chaotic system is constructed. The characteristics of new complex chaotic system are analyzed by symmetric, dissipative and stability, then dynamical performances studied using bifurcation diagram, Lyapunov exponent spectrum and complexity. On the basis of this, an image diffusion algorithm is proposed based on the model of law of gravity. Security performances of the proposed algorithm are researched through the key space, statistics, information entropy, noise attack. The experimental results illustrates that the novel complex chaotic system has abundant dynamical behaviors and large parameters range, the security analysis shows that the proposed image encryption algorithm possess higher security features. Therefore, the research will provide theoretical guidance and experimental basis for chaotic secure communication and information security.
Handling Syntactic Divergence in Low-resource Machine Translation

Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs.Data augmentation methods such as back-translation make it possible to use monolingual data to help alleviate these issues, but back-translation itself fails in extreme low-resource scenarios, especially for syntactically divergent languages.In this paper, we propose a simple yet effective solution, whereby target-language sentences are re-ordered to match the order of the source and used as an additional source of trainingtime supervision.Experiments with simulated low-resource Japanese-to-English, and real low-resource Uyghur-to-English scenarios find significant improvements over other semi-supervised alternatives 1
The Mathematics of Changing One’s Mind, via Jeffrey’s or via Pearl’s Update Rule

&#x0D; &#x0D; &#x0D; Evidence in probabilistic reasoning may be ‘hard’ or ‘soft’, that is, it may be of yes/no form, or it may involve a strength of belief, in the unit interval [0, 1]. Reasoning with soft, [0, 1]-valued evidence is important in many situations but may lead to different, confusing interpretations. This paper intends to bring more mathematical and conceptual clarity to the field by shifting the existing focus from specification of soft evidence to accomodation of soft evidence. There are two main approaches, known as Jeffrey’s rule and Pearl’s method; they give different outcomes on soft evidence. This paper argues that they can be understood as correction and as improvement. It describes these two approaches as different ways of updating with soft evidence, highlighting their differences, similarities and applications. This account is based on a novel channel-based approach to Bayesian probability. Proper understanding of these two update mechanisms is highly relevant for inference, decision tools and probabilistic programming languages.&#x0D; &#x0D; &#x0D;
Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring

This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.We first propose and formalize two novel analytical assessment tasks: analytic score prediction and justification identification, and then provide the first dataset created for analytic short answer scoring research.Subsequently, we present a neural baseline model and report our extensive empirical results to demonstrate how our dataset can be used to explore new and intriguing technical challenges in short answer scoring.The dataset is publicly available for research purposes.
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network

User and item representation learning is critical for recommendation.Many of existing recommendation methods learn representations of users and items based on their ratings and reviews.However, the user-user and item-item relatedness are usually not considered in these methods, which may be insufficient.In this paper, we propose a neural recommendation approach which can utilize useful information from both review content and user-item graphs.Since reviews and graphs have different characteristics, we propose to use a multi-view learning framework to incorporate them as different views.In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews.In addition, we propose to incorporate a threelevel attention network into this view to select important words, sentences and reviews for learning informative user and item representations.In the graph-view, we propose a hierarchical graph neural network to jointly model the user-item, user-user and item-item relatedness by capturing the first-and secondorder interactions between users and items in the user-item graph.In addition, we apply attention mechanism to model the importance of these interactions to learn informative user and item representations.Extensive experiments on four benchmark datasets validate the effectiveness of our approach.
Dynamic Motion Planning for Autonomous Assistive Surgical Robots

The paper addresses the problem of the generation of collision-free trajectories for a robotic manipulator, operating in a scenario in which obstacles may be moving at non-negligible velocities. In particular, the paper aims to present a trajectory generation solution that is fully executable in real-time and that can reactively adapt to both dynamic changes of the environment and fast reconfiguration of the robotic task. The proposed motion planner extends the method based on a dynamical system to cope with the peculiar kinematics of surgical robots for laparoscopic operations, the mechanical constraint being enforced by the fixed point of insertion into the abdomen of the patient the most challenging aspect. The paper includes a validation of the trajectory generator in both simulated and experimental scenarios.
SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud

High-precision real-time 3D object detection based on the LiDAR point cloud is an important task for autonomous driving.Most existing methods utilize grid-based convolutional networks to handle sparse and cluttered point clouds.However, the performance of object detection is limited by the coarse grid quantization and expensive computational cost.In this paper, we propose a more efficient representation of 3D point clouds and propose SCNet, a single-stage, end-to-end 3D subdivision coding network that learns finer feature representations for vertical grids.SCNet divides each grid into smaller sub-grids to preserve more point cloud information and converts points in the grid to a uniform feature representation through 2D convolutional neural networks.The 3D point cloud is encoded as the fine 2D sub-grid representation, which helps to reduce the computational cost.We validate our SCNet on the KITTI object benchmark in which we show that the proposed object detector produces state-of-the-art results with more than 20 FPS.
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning

Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC).However, these approaches often offer little transparency w.r.t.their inner working mechanisms and lack interpretability.In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC.This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC.First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses.Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses.Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection.Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.