Reviewerly/exHarmony huggingface dataset:
An Intelligent Time-Adaptive Data-Driven Method for Sensor Fault Diagnosis in Induction Motor Drive System

Three-phase pulsewidth modulation inverter fed induction motor drive system is widely applied in high power drive applications. Sensor faults are very common in the drive system, which, once occur, might result in degraded system performance or even system shutdown. In order to rapidly and accurately diagnose the sensor faults, this paper proposes an intelligent time-adaptive data-driven method to identify the fault location and fault type of sensors in the drive system. An emerging machine learning technology named extreme learning machine (ELM) is applied to learn the sensor fault dataset; an ensemble ELM classifier is then designed to improve diagnostic accuracy, based on which a time-adaptive fault diagnosis process is proposed to achieve a high and balanced diagnostic accuracy and speed. As a data-driven method, the proposed method only employs the phase current, dc-link voltage, and speed signals as the inputs to the ensemble ELM classifiers and requires no additional sensors and other hardware. Simulated and experimental tests show that the proposed method can rapidly and accurately detect the fault sensor location and identify offset fault, stuck fault, and noise faults with an average diagnostic accuracy of 98% and the average decision time of 10 ms after the fault occurs. Moreover, such diagnosis method is robust to the fluctuation of catenary voltage and dc-link voltage, fault severity, and variation of model parameters, speed, and load.
The emergence of number and syntax units in

Yair Lakretz, German Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, Marco Baroni. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped.In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in.As our agent engages in conversation, it also estimates user satisfaction in its responses.When the conversation appears to be going well, the user's responses become new training examples to imitate.When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot's dialogue abilities further.On the PERSONACHAT chitchat dataset with over 131k training examples, we find that learning from dialogue with a selffeeding chatbot significantly improves performance, regardless of the amount of traditional supervision.
Understanding the Blockchain Oracle Problem: A Call for Action

Scarce and niche in the literature just a few years ago, the blockchain topic is now the main subject in conference papers and books. However, the hype generated by the technology and its potential implications for real-world applications is flawed by many misconceptions about how it works and how it is implemented, creating faulty thinking or overly optimistic expectations. Too often, characteristics such as immutability, transparency, and censorship resistance, which mainly belong to the bitcoin blockchain, are sought in regular blockchains, whose potential is barely comparable. Furthermore, critical aspects such as oracles and their role in smart contracts receive few literature contributions, leaving results and theoretical implications highly questionable. This literature review of the latest papers in the field aims to give clarity to the blockchain oracle problem by discussing its effects in some of the most promising real-world applications. The analysis supports the view that the more trusted a system is, the less the oracle problem impacts.
Discriminative Feature Learning for Unsupervised Video Summarization

In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video. Specifically, we tackle two critical issues based on our empirical observations: (i) Ineffective feature learning due to flat distributions of output importance scores for each frame, and (ii) training difficulty when dealing with longlength video inputs. To alleviate the first problem, we propose a simple yet effective regularization loss term called variance loss. The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance. For the second problem, we design a novel two-stream network named Chunk and Stride Network (CSNet) that utilizes local (chunk) and global (stride) temporal view on the video features. Our CSNet gives better summarization results for long-length videos compared to the existing methods. In addition, we introduce an attention mechanism to handle the dynamic information in videos. We demonstrate the effectiveness of the proposed methods by conducting extensive ablation studies and show that our final model achieves new state-of-the-art results on two benchmark datasets.
Design and Characterization of a 28-nm Bulk-CMOS Cryogenic Quantum Controller Dissipating Less Than 2 mW at 3 K

Implementation of an error-corrected quantum computer is believed to require a quantum processor with a million or more physical qubits, and, in order to run such a processor, a quantum control system of similar scale will be required.Such a controller will need to be integrated within the cryogenic system and in close proximity with the quantum processor in order to make such a system practical.Here, we present a prototype cryogenic CMOS quantum controller designed in a 28-nm bulk CMOS process and optimized to implement a 16-word (4-bit) XY gate instruction set for controlling transmon qubits.After introducing the transmon qubit, including a discussion of how it is controlled, design considerations are discussed, with an emphasis on error rates and scalability.The circuit design is then discussed.Cryogenic performance of the underlying technology is presented, and the results of several quantum control experiments carried out using the integrated controller are described.This article ends with a comparison to the state of the art and a discussion of further research to be carried out.It has been shown that the quantum control IC achieves promising performance while dissipating less than 2 mW of total ac and dc power and requiring a digital data stream of less than 500 Mb/s.
Co-Mining: Deep Face Recognition With Noisy Labels

Face recognition has achieved significant progress with the growing scale of collected datasets, which empowers us to train strong convolutional neural networks (CNNs). While a variety of CNN architectures and loss functions have been devised recently, we still have a limited understanding of how to train the CNN models with the label noise inherent in existing face recognition datasets. To address this issue, this paper develops a novel co-mining strategy to effectively train on the datasets with noisy labels. Specifically, we simultaneously use the loss values as the cue to detect noisy labels, exchange the high-confidence clean faces to alleviate the errors accumulated issue caused by the sample-selection bias, and re-weight the predicted clean faces to make them dominate the discriminative model training in a mini-batch fashion. Extensive experiments by training on three popular datasets (\textit{i.e.}, CASIA-WebFace, MS-Celeb-1M and VggFace2) and testing on several benchmarks, including LFW, AgeDB, CFP, CALFW, CPLFW, RFW, and MegaFace, have demonstrated the effectiveness of our new approach over the state-of-the-art alternatives.
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the super-resolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.
Closing the Gap Between Software Engineering Education and Industrial Needs

Many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learned in their university education with what is needed in industry. In this article, we report a literature review of the studies that have been done to make improvements on this issue.
CFTI5Med, the new release of the catalogue of strong earthquakes in Italy and in the Mediterranean area

Abstract A key element for assessing seismic hazard and risk is the availability of a comprehensive dataset on past earthquakes. Here we present the rationale, structure and contents of CFTI5Med ( https://doi.org/10.6092/ingv.it-cfti5 ), the 2018 version of the Catalogue of Strong Earthquakes in Italy: a large multidisciplinary effort including historians, seismologists and geologists. It was conceived in 1989, following the inception of GIS technology, and first published in 1995 to offer a full account of Italy’s strongest earthquakes, of their territorial impact and associated social and economic upheaval. Subsequent versions (1997, 2000, 2007) entailed a fine tuning of research methodologies, included additional research on Italian earthquakes, and were extended to large earthquakes of the Mediterranean area. CFTI5Med comprised an opportunity to streamline the structure of the Catalogue database and propose a renovated user interface. The new front-end (1) grants an easier, intuitive access to the data, including earthquake effects on the environment, and (2) allows all data to be displayed jointly with relevant topographic, geological and seismological overlays published as web services.
Use of Smartphone Applications in English Language Learning—A Challenge for Foreign Language Education

At present, hardly any younger person can imagine life without mobile technologies. They use them on a daily basis, including in language learning. Such learning supported with mobile devices is called mobile learning, which seems beneficial especially thanks to the unique features of mobile applications (e.g., interactivity, ubiquity, and portability) and teachers’ encouragement and feedback. The purpose of this review study is to explore original, peer-reviewed English studies from 2015 to April 2019 and to determine whether mobile applications used in the learning of English as a foreign language are beneficial and/or effective. The methods are based on a literature review of available sources found on the research topic in two acknowledged databases: Web of Science and Scopus. Altogether, 16 original journal studies on the research topic were detected. The results reveal that mobile learning is becoming a salient feature of education as it is a great opportunity for foreign language learning. Its key benefits are as follows: the enhancement of the learner’s cognitive capacity, the learner’s motivation to study in both formal and informal settings, the learner’s autonomy and confidence, as well as the promotion of personalized learning, helping low-achieving students to reach their study goals. Although mobile learning seems to be effective overall, it is desirable to design, plan and implement it with caution, according to students’ needs, and to deliver multiple language skills in authentic learning environments.
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications.It is challenging due to the heterogeneity of KGs and limited seed alignments.In this paper, we propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG).It can make better use of seed alignments to propagate over the entire graphs with KG-based constraints.Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities.As for the crossgraph model, we extend Graph Attention Network (GAT) with projection constraint to robustly encode graphs, and two KGs share the same GAT to transfer structural knowledge as well as to ignore unimportant neighbors for alignment via attention mechanism.Results on publicly available datasets as well as further analysis demonstrate the effectiveness of KECG.
A Benchmark Dataset for Learning to Intervene in Online Hate Speech

Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques.Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future.In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context.In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech.As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets 1 collected from Gab 2 and Reddit 3 .These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk 4 Workers.In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.
Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems

Locating more peaks and refining the solution accuracy on the found peaks are two challenging issues in solving multimodal optimization problems (MMOPs). To deal with these two challenges, a distributed individuals differential evolution (DIDE) algorithm is proposed in this article based on a distributed individuals for multiple peaks (DIMP) framework and two novel mechanisms. First, the DIMP framework provides sufficient diversity by letting each individual act as a distributed unit to track a peak. Based on the DIMP framework, each individual uses a virtual population controlled by an adaptive range adjustment strategy to explore the search space sufficiently for locating a peak and then gradually approach it. Second, the two novel mechanisms named lifetime mechanism and elite learning mechanism (ELM) cooperate with the DIMP framework. The lifetime mechanism is inspired by the natural phenomenon that every organism will gradually age and has a limited lifespan. When an individual runs out of its lifetime and also has good fitness, it is regarded as an elite solution and will be added to an archive. Then the individual restarts a new lifetime, so as to bring further diversity to locate more peaks. The ELM is proposed to refine the accuracy of those elite solutions in the archive, being efficient in dealing with the solution accuracy issue on the found peaks. The experimental results on 20 multimodal benchmark test functions show that the proposed DIDE algorithm has generally better or competitive performance compared with the state-of-the-art multimodal optimization algorithms.
Mis-Classified Vector Guided Softmax Loss for Face Recognition

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination. To this end, several margin-based (e.g., angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from three issues: 1) Obviously, they ignore the importance of informative features mining for discriminative learning; 2) They encourage the feature margin only from the ground truth class, without realizing the discriminability from other non-ground truth classes; 3) The feature margin between different classes is set to be same and fixed, which may not adapt the situations very well. To cope with these issues, this paper develops a novel loss function, which adaptively emphasizes the mis-classified feature vectors to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative face features. To the best of our knowledge, this is the first attempt to inherit the advantages of feature margin and feature mining into a unified loss function. Experimental results on several benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives. Our code is available at http://www.cbsr.ia.ac.cn/users/xiaobowang/.
Deep multimodal fusion for semantic image segmentation: A survey

Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance improvement. These fusion approaches take the benefits of multiple information sources and generate an optimal joint prediction automatically. This paper describes the essential background concepts of deep multimodal fusion and the relevant applications in computer vision. In particular, we provide a systematic survey of multimodal fusion methodologies, multimodal segmentation datasets, and quantitative evaluations on the benchmark datasets. Existing fusion methods are summarized according to a common taxonomy: early fusion, late fusion, and hybrid fusion. Based on their performance, we analyze the strengths and weaknesses of different fusion strategies. Current challenges and design choices are discussed, aiming to provide the reader with a comprehensive and heuristic view of deep multimodal image segmentation.
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

When primed with only a handful of training samples, very large, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, finetuned, large, pretrained language models.We demonstrate that the order in which the samples are provided can make the difference between near state-of-the-art and random guess performance: essentially some permutations are "fantastic" and some not.We analyse this phenomenon in detail, establishing that: it is present across model sizes (even for the largest current models), it is not related to a specific subset of samples, and that a given good permutation for one model is not transferable to another.While one could use a development set to determine which permutations are performant, this would deviate from the true fewshot setting as it requires additional annotated data.Instead, we use the generative nature of language models to construct an artificial development set and based on entropy statistics of the candidate permutations on this set, we identify performant prompts.Our method yields a 13% relative improvement for GPTfamily models across eleven different established text classification tasks.
Explainable Artificial Intelligence in education

There are emerging concerns about the Fairness, Accountability, Transparency, and Ethics (FATE) of educational interventions supported by the use of Artificial Intelligence (AI) algorithms. One of the emerging methods for increasing trust in AI systems is to use eXplainable AI (XAI), which promotes the use of methods that produce transparent explanations and reasons for decisions AI systems make. Considering the existing literature on XAI, this paper argues that XAI in education has commonalities with the broader use of AI but also has distinctive needs. Accordingly, we first present a framework, referred to as XAI-ED, that considers six key aspects in relation to explainability for studying, designing and developing educational AI tools. These key aspects focus on the stakeholders, benefits, approaches for presenting explanations, widely used classes of AI models, human-centred designs of the AI interfaces and potential pitfalls of providing explanations within education. We then present four comprehensive case studies that illustrate the application of XAI-ED in four different educational AI tools. The paper concludes by discussing opportunities, challenges and future research needs for the effective incorporation of XAI in education.
Synchronization of chaotic systems and their machine-learning models

Recent advances have demonstrated the effectiveness of a machine-learning approach known as ``reservoir computing'' for model-free prediction of chaotic systems. We find that a well-trained reservoir computer can synchronize with its learned chaotic systems by linking them with a common signal. A necessary condition for achieving this synchronization is the negative values of the sub-Lyapunov exponents. Remarkably, we show that by sending just a scalar signal, one can achieve synchronism in trained reservoir computers and a cascading synchronization among chaotic systems and their fitted reservoir computers. Moreover, we demonstrate that this synchronization is maintained even in the presence of a parameter mismatch. Our findings possibly provide a path for accurate production of all expected signals in unknown chaotic systems using just one observational measure.
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation

Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings.In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks.Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to explicit knowledge of sense distributions or task-specific modelling.As a result, a simple Nearest Neighbors (k-NN) method using our representations is able to consistently surpass the performance of previous systems using powerful neural sequencing models.We also analyse the robustness of our approach when ignoring part-of-speech and lemma features, requiring disambiguation against the full sense inventory, and revealing shortcomings to be improved.Finally, we explore applications of our sense embeddings for concept-level analyses of contextual embeddings and their respective NLMs.