Reviewerly/exHarmony huggingface dataset:
Learning data-driven decision-making policies in multi-agent environments for autonomous systems

Autonomous systems such as Connected Autonomous Vehicles (CAVs), assistive robots are set improve the way we live. Autonomous systems need to be equipped with capabilities to Reinforcement Learning (RL) is a type of machine learning where an agent learns by interacting with its environment through trial and error, which has gained significant interest from research community for its promise to efficiently learn decision making through abstraction of experiences. However, most of the control algorithms used today in current autonomous systems such as driverless vehicle prototypes or mobile robots are controlled through supervised learning methods or manually designed rule-based policies. Additionally, many emerging autonomous systems such as driverless cars, are set in a multi-agent environment, often with partial observability. Learning decision making policies in multi-agent environments is a challenging problem, because the environment is not stationary from the perspective of a learning agent, and hence the Markov properties assumed in single agent RL does not hold. This paper focuses on learning decision-making policies in multi-agent environments, both in cooperative settings with full observability and dynamic environments with partial observability. We present experiments in simple, yet effective, new multi-agent environments to simulate policy learning in scenarios that could be encountered by an autonomous navigating agent such as a CAV. The results illustrate how agents learn to cooperate in order to achieve their objectives successfully. Also, it was shown that in a partially observable setting, an agent was capable of learning to roam around its environment without colliding in the presence of obstacles and other moving agents. Finally, the paper discusses how data-driven multi-agent policy learning can be extended to real-world environments by augmenting the intelligence of autonomous vehicles.
A memory of motion for visual predictive control tasks

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.
TopFilter

Background: In the context of software development, GitHub has been at the forefront of platforms to store, analyze and maintain a large number of software repositories. Topics have been introduced by GitHub as an effective method to annotate stored repositories. However, labeling GitHub repositories should be carefully conducted to avoid adverse effects on project popularity and reachability. Aims: We present TopFilter, a novel approach to assist open source software developers in selecting suitable topics for GitHub repositories being created. Method: We built a project-topic matrix and applied a syntactic-based similarity function to recommend missing topics by representing repositories and related topics in a graph. The ten-fold cross-validation methodology has been used to assess the performance of TopFilter by considering different metrics, i.e., success rate, precision, recall, and catalog coverage. Result: The results show that TopFilter recommends good topics depending on different factors, i.e., collaborative filtering settings, considered datasets, and pre-processing activities. Moreover, TopFilter can be combined with a state-of-the-art topic recommender system (i.e., MNB network) to improve the overall prediction performance. Conclusion: Our results confirm that collaborative filtering techniques can successfully be used to provide relevant topics for GitHub repositories. Moreover, TopFilter can gain a significant boost in prediction performances by employing the outcomes obtained by the MNB network as its initial set of topics.
Recommending tags for pull requests in GitHub

In GitHub, contributors make code changes, then create and submit pull requests to projects. Tags are a simple and effective way to attach additional information to pull requests and facilitate their organization. However, little effort has been devoted to study pull requests' tags in GitHub. Our objective in this paper is to propose an approach which automatically recommends tags for pull requests in GitHub. We make a survey on the usage of tags in pull requests. Survey results show that tags are useful for developers to track, search or classify pull requests. But some respondents think that it is difficult to choose right tags and keep consistency of tags. 60.61% of respondents think that a tag recommendation tool is useful. In order to help developers choose tags, we propose a method FNNRec which uses feed-forward neural network to analyze titles, description, file paths and contributors. We evaluate the effectiveness of FNNRec on 10 projects containing 68,497 tagged pull requests. The experimental results show that on average, FNNRec outperforms approach TagDeepRec and TagMulRec by 62.985% and 24.953% in terms of F1−score@3, respectively. FNNRec is useful to find appropriate tags and improve tag setting process in GitHub.
TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation

Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Network-Tree (TNT) learning framework for explainable decision-making, where the knowledge is alternately transferred between the tree model and DNNs. Specifically, the proposed TNT learning framework exerts the advantages of different models at different stages: (1) a novel James–Stein Decision Tree (JSDT) is proposed to generate better knowledge representations for DNNs, especially when the input data are in low-frequency or low-quality; (2) the DNNs output high-performing prediction result from the knowledge embedding inputs and behave as a teacher model for the following tree model; and (3) a novel distillable Gradient Boosted Decision Tree (dGBDT) is proposed to learn interpretable trees from the soft labels and make a comparable prediction as DNNs do. Extensive experiments on various machine learning tasks demonstrated the effectiveness of the proposed method.
Computer Vision-based Methodology to Improve Interaction for People with Motor and Speech Impairment

Augmentative and Alternative Communication (AAC) aims to complement or replace spoken language to compensate for expression difficulties faced by people with speech impairments. Computing systems have been developed to support AAC; however, partially due to technical problems, poor interface, and limited interaction functions, AAC systems are not widespread, adopted, and used, therefore reaching a limited audience. This article proposes a methodology to support AAC for people with motor impairments, using computer vision and machine learning techniques to allow for personalized gestural interaction. The methodology was applied in a pilot system used by both volunteers without disabilities, and by volunteers with motor and speech impairments, to create datasets with personalized gestures. The created datasets and a public dataset were used to evaluate the technologies employed for gesture recognition, namely the Support Vector Machine (SVM) and Convolutional Neural Network (using Transfer Learning), and for motion representation, namely the conventional Motion History Image and Optical Flow-Motion History Image (OF-MHI). Results obtained from the estimation of prediction error using K-fold cross-validation suggest SVM associated with OF-MHI presents slightly better results for gesture recognition. Results indicate the technical feasibility of the proposed methodology, which uses a low-cost approach, and reveals the challenges and specific needs observed during the experiment with the target audience.
Towards Dynamic Dependable Systems Through Evidence-Based Continuous Certification

Future cyber-physical systems are expected to be dynamic, evolving while already being deployed. Frequent updates of software components are likely to become the norm even for safety-critical systems. In this setting, a full re-certification before each software update might delay important updates that fix previous bugs, or security or safety issues. Here we propose a vision addressing this challenge, namely through the evidence-based continuous supervision and certification of software variants in the field. The idea is to run both old and new variants of component software inside the same system, together with a supervising instance that monitors their behavior. Updated variants are phased into operation after sufficient evidence for correct behavior has been collected. The variants are required to explicate their decisions in a logical language, enabling the supervisor to reason about these decisions and to identify inconsistencies. To resolve contradictory information, the supervisor can run a component analysis to identify potentially faulty components on the basis of previously observed behavior, and can trigger micro-experiments which plan and execute system behavior specifically aimed at reducing uncertainty. We spell out our overall vision, and provide a first formalization of the different components and their interplay. In order to provide efficient supervisor reasoning as well as automatic verification of supervisor properties we introduce SupERLog, a logic specifically designed to this end.
A New Effective and Efficient Measure for Outlying Aspect Mining

Outlying Aspect Mining (OAM) aims to find the subspaces (a.k.a. aspects) in which a given query is an outlier with respect to a given data set. Existing OAM algorithms use traditional distance/density-based outlier scores to rank subspaces. Because these distance/density-based scores depend on the dimensionality of subspaces, they cannot be compared directly between subspaces of different dimensionality. Z-score normalisation has been used to make them comparable. It requires to compute outlier scores of all instances in each subspace. This adds significant computational overhead on top of already expensive density estimation—making OAM algorithms infeasible to run in large and/or high-dimensional datasets. We also discover that Z-score normalisation is inappropriate for OAM in some cases. In this paper, we introduce a new score called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which is independent of the dimensionality of subspaces. This enables the scores in subspaces with different dimensionalities to be compared directly without any additional normalisation. Our experimental results revealed that SiNNE produces better or at least the same results as existing scores; and it significantly improves the runtime of an existing OAM algorithm based on beam search.
The Use of 360° Video by International Humanitarian Aid Organizations to Spread Social Messages and Increase Engagement

Abstract The article analyses 360° video production in international humanitarian aid nonprofit organizations from 2015 to 2019 as 360° video storytelling is one of the latest innovations in organizational digital communication. Through a content analysis and interviews, a specific use of the 360° video format for particular issues or campaigns in order to bring a distant reality to the organization’s audience has been detected. Thus, putting the users in the shoes of “the other” seems to be the objective pursued. NGOs may soon begin to understand long-term interactivity and engagement not just as action and reaction between organization and receiver (almost non-existent to date), but above all as the receiver’s behaviour, which they may strive to orient towards one of the organization’s end goals, depending on the communication strategy set by the organization’s director. With this objective, common to entities from other sectors, they could be moving towards an innovative conceptualization of engagement.
An Empirical Study on Vocabulary Recall and Learner Autonomy through Mobile?Assisted Language Learning in Blended Learning Settings

This study aims to examine the efficacy of Mobile-Assisted Language Learning (MALL) of English as a foreign or second language (L2) through two perspectives: learning gain and learner autonomy. Previous studies have shown that L2 learning combined with media could activate the learning processes, resulting in an easier recall of the target vocabulary required in L2. In addition, mobileassisted L2 learning could also enhance autonomous learning inasmuch as successful MALL would have to rely mainly on the autonomous learner even in learning contexts where the goal and task are already fixed. Based on this standpoint, the study hypothesizes that the engagement in L2 learning with mobile devices along with a classroom-based writing course could make L2 learners not only achieve the target L2 lexis effectively, leading to better L2 writing performance, but also help them to be more autonomous even in a setting when the task and goal are fixed. To test this hypothesis, both empirical and questionnaire studies were conducted for Japanese undergraduates (n=94). Based on the results of three weeks of L2 academic writing practice between groups learning with and without mobile devices, the findings of our t-test analyses of learners’ vocabulary recall and a questionnaire survey about learner autonomy suggested that MALL significantly contributed not only to L2 vocabulary recall in comprehensive and productive tests, but also to enhancing positive attitudes towards autonomous learning.
Highly Language-Independent Word Lemmatization Using a Machine-Learning Classifier

Lemmatization is a process of finding the base morphological form (lemma) of a word. It is an important step in many natural language processing, information retrieval, and information extraction tasks, among others. We present an open-source language-independent lemmatizer based on the Random Forest classification model. This model is a supervised machine-learning algorithm with decision trees that are constructed corresponding to the grammatical features of the language. This lemmatizer does not require any manual work for hard-coding of the rules, and at the same time it is simple and interpretable. We compare the performance of our lemmatizer with that of the UDPipe lemmatizer on twenty-two out of twenty-five languages we work on for which UDPipe has models. Our lemmatization method shows good performance on different languages from various language groups, and it is easily extensible to other languages.
Discourse structure interacts with reference but not syntax in neural language models

Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability of LMs to learn interactions between different linguistic representations. In particular, we utilized stimuli from psycholinguistic studies showing that humans can condition reference (i.e. coreference resolution) and syntactic processing on the same discourse structure (implicit causality). We compared both transformer and long short-term memory LMs to find that, contrary to humans, implicit causality only influences LM behavior for reference, not syntax, despite model representations that encode the necessary discourse information. Our results further suggest that LM behavior can contradict not only learned representations of discourse but also syntactic agreement, pointing to shortcomings of standard language modeling.
Solar Flare Forecasting Using Time Series and Extreme Gradient Boosting Ensembles

Space weather events may cause damage to several fields, including aviation, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are one of the most significant events, and refer to sudden radiation releases that can affect the Earth's atmosphere within a few hours or minutes. Therefore, it is worth designing high-performance systems for forecasting such events. Although in the literature there are many approaches for flare forecasting, there is still a lack of consensus concerning the techniques used for designing these systems. Seeking to establish some standardization while designing flare predictors, in this study we propose a novel methodology for designing such predictors, further validated with extreme gradient boosting tree classifiers and time series. This methodology relies on the following well-defined machine learning based pipeline: (i) univariate feature selection; (ii) randomized hyper-parameter optimization; (iii) imbalanced data treatment; (iv) adjustment of cut-off point of classifiers; and (v) evaluation under operational settings. To verify our methodology effectiveness, we designed and evaluated three proof-of-concept models for forecasting $\geq C$ class flares up to 72 hours ahead. Compared to baseline models, those models were able to significantly increase their scores of true skill statistics (TSS) under operational forecasting scenarios by 0.37 (predicting flares in the next 24 hours), 0.13 (predicting flares within 24-48 hours), and 0.36 (predicting flares within 48-72 hours). Besides increasing TSS, the methodology also led to significant increases in the area under the ROC curve, corroborating that we improved the positive and negative recalls of classifiers while decreasing the number of false alarms.
Image speckle noise denoising by a multi-layer fusion enhancement method based on block matching and 3D filtering

In order to improve speckle noise denoising of block matching and 3D filtering (BM3D) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3D) based on nonsubsampled contourlet transform (NSCT) has been proposed. The method designs an NSCT hard threshold denoising enhancement to preprocess the image, then uses fusion enhancement in NSCT domain to fuse the preliminary estimation results of images before and after the NSCT hard threshold denoising, finally, BM3D denoising is carried out with the fused image to obtain the final denoising result. Experiments on natural images and medical ultrasound images show that MLFE-BM3D method can achieve better visual effects than BM3D method, the peak signal to noise ratio (PSNR) of the denoised image is increased by 0.5 dB. The MLFE-BM3D method can improve the denoising effect of speckle noise in the texture region, and still maintain a good denoising effect in the smooth region of the image.
Semantic Label Smoothing for Sequence to Sequence Problems

Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label denoising.However, extending such methods directly to seq2seq settings, such as Machine Translation, is challenging: the large target output space of such problems makes it intractable to apply label smoothing over all possible outputs.Most existing approaches for seq2seq settings either do token level smoothing, or smooth over sequences generated by randomly substituting tokens in the target sequence.Unlike these works, in this paper, we propose a technique that smooths over well formed relevant sequences that not only have sufficient n-gram overlap with the target sequence, but are also semantically similar.Our method shows a consistent and significant improvement over the state-of-the-art techniques on different datasets.
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training

The challenge of both achieving task completion by querying the knowledge base and generating human-like responses for task-oriented dialogue systems is attracting increasing research attention.In this paper, we propose a "Two-Teacher One-Student" learning framework (TTOS) for task-oriented dialogue, with the goal of retrieving accurate KB entities and generating human-like responses simultaneously.TTOS amalgamates knowledge from two teacher networks that together provide comprehensive guidance to build a highquality task-oriented dialogue system (student network).Each teacher network is trained via reinforcement learning with a goal-specific reward, which can be viewed as an expert towards the goal and transfers the professional characteristic to the student network.Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student.The usage of discriminators relaxes the rigid coupling between the student and teachers.Extensive experiments on two benchmark datasets (i.e., CamRest and In-Car Assistant) demonstrate that TTOS significantly outperforms baseline methods.For reproducibility, we release the code and data at https://github.com/siat-nlp/TTOS.
FPGA-Based Lightweight Hardware Architecture of the PHOTON Hash Function for IoT Edge Devices

The design of cryptographic engines for the Internet of Things (IoT) edge devices and other ultralightweight devices is a crucial challenge. The emergence of such resource-constrained devices raises significant challenges to current cryptographic algorithms. PHOTON is an ultra-lightweight cryptographic hash function targeting low-resource devices. The currently implemented hardware architectures of PHOTON hash function utilize a large amount of resources and have low operating frequencies with a low rate of throughputs. Maximum operating frequency and throughput of PHOTON architecture can be improved but at the cost of larger area utilization. Therefore, to improve the area-performance trade-offs of PHOTON hash function, an iterative architecture is implemented in this work. The concern is with the most lightweight version of PHOTON hash function with the hash size of 80 bits. It is implemented and verified on several Xilinx and Altera Field Programmable Gate Array (FPGA) devices using their synthesis and simulation tools. Low-cost and high-processing FPGA devices were both considered. The design is optimized for performance, whereas the area utilization is also taken into consideration. The overall performance and logic utilization are benchmarked with the existing implementations. The results show an improvement rate of 10.26% to 51.04% in the speed performance and a reduction rate of 7.55% to 60.64% in area utilization compared to existing implementations of PHOTON hash functions.