Reviewerly/exHarmony huggingface dataset:
Research on Classification of Kazakh Questions Integrate with Multi-feature Embedding

Kazakh is an agglutinative language, and this feature results in data sparseness to some extent. In addition, the Kazakh question sentences lack strict grammatical rules, and the word forms are changeable and irregular. Due to this, this article proposes a CNN+BiGRU question classification model and attention mechanism that integrate multi-features based on the Kazakh language characteristics. Kazakh words and language features are used as input for the neural network. The CNN network generates high-dimensional semantic features and transmits the output to the BiGRU network. The BiGRU layer models the context information, then the Attention layer concentrates on the input features, filters out the unnecessary information, and completes the classification with SoftMax. The research in this paper shows that our model effectively integrates language features, avoids data sparsity, improves the model's performance during training, and has higher performance on the classification of Kazakh questions.
Speech Emotion Recognition Using a New Hybrid Quaternion-Based Echo State Network-Bilinear Filter

Echo state network (ESN) is one of the efficient tools for displaying dynamic data. However, there are limitations to model high-dimensional data by ESNs. The most important limitation is the high amount of memory consumed due to their echo state and the linear output of the ESN network, which prevents the increase of reservoir units and the effective use of higher-order statistics of the features provided by its reservoir units. In this research, a new structure based on ESN is presented, in which quaternion algebra is used to compress the network data with the simple split function, and the output linear combiner is replaced by a multidimensional bilinear filter. This filter will be used for nonlinear calculations of the output layer of the ESN. In addition, the two-dimensional principal component analysis (2dPCA) technique is used to reduce the number of data transferred to the bilinear filter. In this study, the coefficients and the weights of the quaternion nonlinear ESN (QNESN) are optimized using genetic algorithm (GA). In order to prove the effectiveness of the proposed model compared to the previous methods, experiments for speech emotion recognition (SER) have been performed on EMODB dataset. Comparisons show that the proposed QNESN network performs better than the simple ESN and most currently SER systems.
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification

To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal classification is one of the most important tasks to achieve effective and fair review assignments. Proposal classification aims to classify a proposal into a length-variant sequence of labels. In this paper, we formulate the proposal classification problem into a hierarchical multi-label classification task. Although there are certain prior studies, proposal classification exhibit unique features: 1) the classification result of a proposal is in a hierarchical discipline structure with different levels of granularity; 2) proposals contain multiple types of documents; 3) domain experts can empirically provide partial labels that can be leveraged to improve task performances. In this paper, we focus on developing a new deep proposal classification framework to jointly model the three features. In particular, to sequentially generate labels, we leverage previously-generated labels to predict the label of next level; to integrate partial labels from experts, we use the embedding of these empirical partial labels to initialize the state of neural networks. Our model can automatically identify the best length of label sequence to stop next label prediction. Finally, we present extensive results to demonstrate that our method can jointly model partial labels, textual information, and semantic dependencies in label sequences, and, thus, achieve advanced performances.
TPL: Trajectory Planner for Target Tracking in Low-Light Environments

Unmanned Aerial Vehicles (UAVs) have drawn attention in recent years due to the wide spectrum of utilization. Out of those areas, autonomous aerial tracking is one of the fields that could be applied widely. The main purpose of this paper is to develop a novel trajectory planner for autonomous aerial tracking that is feasible even in areas where light is scarce. The poor lighting condition is handled by equipping and utilizing a thermal camera and a Time-of-Flight (ToF) camera on the UAV. In addition, minimum snap trajectory is adopted to track the target and kinodynamics of the UAV is considered at the same time, which is optimized by solving a quadratic programming with corridor constraints considering the noises of the cameras. Moreover, based on the former information, the proposed system predicts the future motion of the target considering dynamic constraints. The performance of the proposed approach is validated by intensive simulations and real-world experiments.
eCPDP: Early Cross-Project Defect Prediction

Cross-project Defect Prediction (CPDP) aims to build a defect prediction model to recognize target project's defective modules by utilizing other source project's historical data. In addition, Transfer Learning (TL) has been widely applied at CPDP to improve prediction performance by alleviating the data distribution discrepancy between the source and the target project. However, existing TL-based CPDP techniques are not applicable at the unit testing phase since they require the entire historical target project data for TL. As a result, they lose a chance of increasing the product's reliability in the unit testing phase by applying the prediction results to identify defects. Thus, the objective of this paper is to apply prediction results at the unit testing phase. To this end, we propose an early CPDP model (eCPDP) which is TL-based CPDP technique using Singular Value Decomposition applicable at the unit testing phase. We compare the performance of eCPDP with state-of-the-art TL-based CPDP techniques on effort-unaware and effort-aware performance metrics over 17 project datasets. Experimental result demonstrates that eCPDP executed during the unit testing stage is one of the best techniques compared to baselines executed after the unit testing stage on both types of metrics. Thus, we show that eCPDP is an applicable CPDP model at the unit testing phase, and it can help practitioners find and fix defects in an earlier phase than other TL-based CPDP techniques.
Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets

Semisupervised deep learning (SSDL) is a popular strategy to leverage unlabeled data for machine learning when labeled data is not readily available. In real-world scenarios, different unlabeled data sources are usually available, with varying degrees of distribution mismatch regarding the labeled datasets. It begs the question, which unlabeled dataset to choose for good SSDL outcomes. Oftentimes, semantic heuristics are used to match unlabeled data with labeled data. However, a quantitative and systematic approach to this selection problem would be preferable. In this work, we first test the SSDL MixMatch algorithm under various distribution mismatch configurations to study the impact on SSDL accuracy. Then, we propose a quantitative unlabeled dataset selection heuristic based on dataset dissimilarity measures. These are designed to systematically assess how distribution mismatch between the labeled and unlabeled datasets affects MixMatch performance. We refer to our proposed method as deep dataset dissimilarity measures (DeDiMs), designed to compare labeled and unlabeled datasets. They use the feature space of a generic Wide-ResNet, which can be applied prior to learning, are quick to evaluate, and model agnostic. The strong correlation in our tests between MixMatch accuracy and the proposed DeDiMs suggests that this approach can be a good fit for quantitatively ranking different unlabeled datasets prior to SSDL training.
Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path

This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second-best path recognition method by explicitly excluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a different potential function for recognition at each level. In addition, we demonstrate that recognizing innermost entities first results in better performance than the conventional outermost entities first scheme. We provide extensive experimental results on ACE2004, ACE2005, GENIA, and NNE datasets to show the effectiveness and efficiency of our proposed method.
Distributed ledger technologies in vehicular mobile edge computing: a survey

Abstract Blockchain-based systems, coined by distributed ledger technologies (DLTs), have rapidly received tremendous interest from academia, industries, and governments. Recent literature has revealed many research and developments on applying DLTs to the Internet of things (IoT), cloud-edge computing. In this survey, we conduct a comprehensive survey of the newly appeared concepts, theories, platforms, and DLTs-facilitated applications of vehicular networks and mobile edge computing (MEC). We also review the selections of the available DLTs related platforms and tools. Future research directions and issues are discussed, including security, privacy, scalability issues, and multiple applications in various domains.
Comprehensive Evaluation Method of Teaching Effect Based on Particle Swarm Optimization Neural Network Model

The important role of teaching evaluation system is embodied in: starting from the teaching goal and the vocational education teaching activities. This paper studies the optimization algorithm and optimization system, it not only makes the algorithm involve basic mathematical operations, and the computer support required for the data processing process is not high, but it also improves the evaluation of the degree of optimization. In view of these characteristics, this paper has conducted in-depth research to fully prove the feasibility and superiority of the content of this article. The specific summary is as follows: (1) Introduced the design concept of particle swarm optimization teaching evaluation system. (2) The use of object-oriented programming algorithms makes it easier for the algorithm to find an entry point, solve practical problems, and optimize the reusability of the algorithm method. (3) Particle swarm optimization based on quantum behavior, adjusting parameter values, the highest and the lowest, greatly reduces the difficulty of program parameter adjustment. (4) In terms of operation, it can quickly and efficiently complete the maintenance of teacher teaching information, evaluation relationship management of teacher teaching quality evaluation, evaluation content management, student evaluation, supervision evaluation, college leadership evaluation, evaluation performance management, and other operations. The interface is extremely humane. It adopts a web-style tour method. There are many types of functions, and the system includes common functions required for general teacher teaching information management and quality evaluation, and while providing various functions, it closely integrates the various actual needs of the college. The security performance is good. The system provides user name and password verification, which improves the security of the system. Database management is convenient and fast, providing a database-friendly management interface, and timely and accurate query. (5) This system adopts an object-oriented development method. During the development process, full consideration of the user’s needs enabled the system to have powerful functions and streamlined procedures. In the end, this application software basically completed the goals required by the requirements analysis.
Classical and neural network approaches to object detection in underwater robotics competitions

The article considers two approaches to detecting underwater objects in the image, i.e. classical approach and neural network approach. Main advantages and disadvantages of each approach are presented. Various approaches to operation quality were analyzed, including assessment of speed and accuracy, as well as identification of preconditions required to achieve the maximum quality. The article also considers preliminary use of image dehazing methods to improve visibility and contrast. Objects for recognition considered in the article are elements of missions in the Singapore Autonomous Underwater Vehicle Challenge (SAUVC) competitions in Singapore. Nvidia Jetson TX2 single-board computer is the target platform for the proposed methods, analysis of the method speed was carried out both using the graphics processing unit (GPU) for neural network, and without using it in classical and neural network methods in order to obtain potential speed estimate on simpler platforms without the GPU.
Optimising Communication Overhead in Federated Learning Using NSGA-II

Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication’s load, which jeopardises the functioning efficiency. The difficulty of reducing this overhead stands in achieving this without decreasing the model’s efficiency (contradictory relation). To do so, many works investigated the compression of the pre/mid/post-trained models and the communication rounds, separately, although they jointly contribute to the communication overload. Our work aims at optimising communication overhead in federated learning by (I) modelling it as a multi-objective problem and (II) applying a multi-objective optimization algorithm (NSGA-II) to solve it. To the best of the author’s knowledge, this is the first work that (I) explores the add-in that evolutionary computation could bring for solving such a problem, and (II) considers both the neuron and devices features together. We perform the experimentation by simulating a server/client architecture with 4 slaves. We investigate both convolutional and fully-connected neural networks with 12 and 3 layers, 887,530 and 33,400 weights, respectively. We conducted the validation on the MNIST dataset containing 70,000 images. The experiments have shown that our proposal could reduce the communication by 99% and maintain an accuracy equal to the one obtained by the FedAvg Algorithm that uses 100% of communications.
Talking Faces: Audio-to-Video Face Generation

Abstract Talking face generation aims at synthesizing coherent and realistic face sequences given an input speech. The task enjoys a wide spectrum of downstream applications, such as teleconferencing, movie dubbing, and virtual assistant. The emergence of deep learning and cross-modality research has led to many interesting works that address talking face generation. Despite great research efforts in talking face generation, the problem remains challenging due to the need for fine-grained control of face components and the generalization to arbitrary sentences. In this chapter, we first discuss the definition and underlying challenges of the problem. Then, we present an overview of recent progress in talking face generation. In addition, we introduce some widely used datasets and performance metrics. Finally, we discuss open questions, potential future directions, and ethical considerations in this task.
Visual Event-Based Egocentric Human Action Recognition

This paper lies at the intersection of three research areas: human action recognition, egocentric vision, and visual event-based sensors. The main goal is the comparison of egocentric action recognition performance under either of two visual sources: conventional images, or event-based visual data. In this work, the events, as triggered by asynchronous event sensors or their simulation, are spatio-temporally aggregated into event frames (a grid-like representation). This allows to use exactly the same neural model for both visual sources, thus easing a fair comparison. Specifically, a hybrid neural architecture combining a convolutional neural network and a recurrent network is used. It is empirically found that this general architecture works for both, conventional gray-level frames, and event frames. This finding is relevant because it reveals that no modification or adaptation is strictly required to deal with event data for egocentric action classification. Interestingly, action recognition is found to perform better with event frames, suggesting that these data provide discriminative information that aids the neural model to learn good features.
Variable binding and substitution for (nameless) dummies

Abstract By abstracting over well-known properties of De Bruijn’s representation with nameless dummies, we design a new theory of syntax with variable binding and capture-avoiding substitution. We propose it as a simpler alternative to Fiore, Plotkin, and Turi’s approach, with which we establish a strong formal link. We also show that our theory easily incorporates simple types and equations between terms.
Syntax Customized Video Captioning by Imitating Exemplar Sentences

Enhancing the diversity of sentences to describe video contents is an important problem arising in recent video captioning research. In this paper, we explore this problem from a novel perspective of customizing video captions by imitating exemplar sentence syntaxes. Specifically, given a video and any syntax-valid exemplar sentence, we introduce a new task of Syntax Customized Video Captioning (SCVC) aiming to generate one caption which not only semantically describes the video contents but also syntactically imitates the given exemplar sentence. To tackle the SCVC task, we propose a novel video captioning model, where a hierarchical sentence syntax encoder is first designed to extract the syntactic structure of the exemplar sentence, then a syntax conditioned caption decoder is devised to generate the syntactically structured caption expressing video semantics. As there is no available syntax customized groundtruth video captions, we tackle such a challenge by proposing a new training strategy, which leverages the traditional pairwise video captioning data and our collected exemplar sentences to accomplish the model learning. Extensive experiments, in terms of semantic, syntactic, fluency, and diversity evaluations, clearly demonstrate our model capability to generate syntax-varied and semantics-coherent video captions that well imitate different exemplar sentences with enriched diversities. Code is available at https://github.com/yytzsy/Syntax-Customized-Video-Captioning.
SReachTools Kernel Module: Data-Driven Stochastic Reachability Using Hilbert Space Embeddings of Distributions

We present algorithms for performing data-driven stochastic reachability as an addition to SReachTools, an open-source stochastic reachability toolbox. Our method leverages a class of machine learning techniques known as kernel embeddings of distributions to approximate the safety probabilities for a wide variety of stochastic reachability problems. By representing the probability distributions of the system state as elements in a reproducing kernel Hilbert space, we can learn the "best fit" distribution via a simple regularized least-squares problem, and then compute the stochastic reachability safety probabilities as simple linear operations. This technique admits finite sample bounds and has known convergence in probability. We implement these methods as part of SReachTools, and demonstrate their use on a double integrator system, on a million-dimensional repeated planar quadrotor system, and a cart-pole system with a black-box neural network controller.
11 Artificial Intelligence, Migration and Mobility: Implications for Policy and Practice

World Migration ReportVolume 2022, Issue 1 e00032 Original Article 11 Artificial Intelligence, Migration and Mobility: Implications for Policy and Practice Ana Beduschi, Ana Beduschi Associate Professor of Law, University of ExeterSearch for more papers by this authorMarie McAuliffe, Marie McAuliffe Head, Migration Research and Publications Division, IOMSearch for more papers by this author Ana Beduschi, Ana Beduschi Associate Professor of Law, University of ExeterSearch for more papers by this authorMarie McAuliffe, Marie McAuliffe Head, Migration Research and Publications Division, IOMSearch for more papers by this author First published: 18 April 2022 https://doi.org/10.1002/wom3.32AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume2022, Issue1April 2022e00032 RelatedInformation
Aspect-based Sentiment Classification via Reinforcement Learning

Aspect-based sentiment classification aims to predict sentimental polarities of one or multiple aspects in texts. As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step. State-of-the-art approaches are mainly based on word-level attention learned from recurrent neural network variants (e.g., LSTM) or graph neural networks. From another view, these methods essentially weight and aggregate all possible alignments. However, this mechanism heavily relies on large-scale supervision training: without enough labels, it could easily overfit with difficulty in generalization. To address this challenge, we propose SentRL, a reinforcement learning-based framework for aspect-based sentiment classification. In this framework, input texts are transformed into their dependency graphs. Then, an agent is deployed to walk on the graphs, explores paths from target aspect nodes to their potential sentimental regions, and differentiates the effectiveness of different paths. By limiting the agent’s exploration budget, our method encourages the agent to skip task-irrelevant information and focus on the most effective paths for alignment purpose. Our method considerably reduces the impact of task-irrelevant words and improves generalization performance. Compared with competitive baseline methods, our approach achieves the highest performance on public benchmark datasets with up to 3.7% improvement.