Reviewerly/exHarmony huggingface dataset:
Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances

This paper proposes methods for unsupervised lexical acquisition for relative spatial concepts using spoken user utterances. A robot with a flexible spoken dialog system must be able to acquire linguistic representation and its meaning specific to an environment through interactions with humans as children do. Specifically, relative spatial concepts (e.g. front and right) are widely used in our daily lives, however, it is not obvious which object is a reference object when a robot learns relative spatial concepts. Therefore, we propose methods by which a robot without prior knowledge of words can learn relative spatial concepts. The methods are formulated using a probabilistic model to estimate the proper reference objects and distributions representing concepts simultaneously. The experimental results show that relative spatial concepts and a phoneme sequence representing each concept can be learned under the condition that the robot does not know which located object is the reference object. Additionally, we show that two processes in the proposed method improve the estimation accuracy of the concepts: generating candidate word sequences by class n-gram and selecting word sequences using location information. Furthermore, we show that clues to reference objects improve accuracy even though the number of candidate reference objects increases.
Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network

External syntactic and semantic information has been largely ignored by existing neural coreference resolution models.In this paper, we present a heterogeneous graph-based model to incorporate syntactic and semantic structures of sentences.The proposed graph contains a syntactic sub-graph where tokens are connected based on a dependency tree, and a semantic sub-graph that contains arguments and predicates as nodes and semantic role labels as edges.By applying a graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism.Experiments on the OntoNotes 5.0 benchmark show the effectiveness of our proposed model. 1
Relationships of Adolescents’ Stress and Psychological State by Gender and Physical Activity Level during the early COVID19 crisis

The purpose of this study was to explore the relationships of adolescents’ stress and psychological state by gender and physical activity level during the early COVID19 crisis. Data were collected from 1977 middle school students through an online survey. The collected data was analyzed. As a result, there was a significant correlation between gender, physical activity level, COVID19 stress, loneliness, anxiety, depression. There was significant difference in COVID19 stress, loneliness, anxiety, depression by gender, physical activity level. The effects of COVID19 stress between loneliness, anxiety and depression was strong in boys and strenuous physical activity level. Therefore, given the gender and level of physical activity of adolescents, there is a need for a multifaceted approach to develop a program that enable adolescents to enjoyably engage in physical activities.
Provable Meta-Learning of Linear Representations

Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learning -- a key tool for performing meta-learning -- learns a data representation that can transfer knowledge across multiple tasks, which is essential in regimes where data is scarce. Despite a recent surge of interest in the practice of meta-learning, the theoretical underpinnings of meta-learning algorithms are lacking, especially in the context of learning transferable representations. In this paper, we focus on the problem of multi-task linear regression -- in which multiple linear regression models share a common, low-dimensional linear representation. Here, we provide provably fast, sample-efficient algorithms to address the dual challenges of (1) learning a common set of features from multiple, related tasks, and (2) transferring this knowledge to new, unseen tasks. Both are central to the general problem of meta-learning. Finally, we complement these results by providing information-theoretic lower bounds on the sample complexity of learning these linear features.
Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble

This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English.We fine tune each model on each of the task's questions and aggregate their prediction scores using a majority voting approach.TOKOFOU obtains an overall F1 score of 89.7%, ranking first.
Explaining Contextualization in Language Models using Visual Analytics

Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn.In this paper, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on linguistically-informed insights.In particular, we utilize scoring and visual analytics techniques: we use an existing similarity-based score to measure contextualization and integrate it into a novel visual analytics technique, presenting the model's layers simultaneously and highlighting intra-layer properties and inter-layer differences.We show that contextualization is neither driven by polysemy nor by pure context variation.We also provide insights on why BERT fails to model words in the middle of the functionality continuum.* Contribution to the visualization part.
An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers

Most studies on word-level Quality Estimation (QE) of machine translation focus on languagespecific models.The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models.To overcome these problems, we explore different approaches to multilingual, word-level QE.We show that multilingual QE models perform on par with the current language-specific models.In the cases of zeroshot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs.Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.
Multi-type Disentanglement without Adversarial Training

Controlling the style of natural language by disentangling the latent space is an important step towards interpretable machine learning. After the latent space is disentangled, the style of a sentence can be transformed by tuning the style representation without affecting other features of the sentence. Previous works usually use adversarial training to guarantee that disentangled vectors do not affect each other. However, adversarial methods are difficult to train. Especially when there are multiple features (e.g., sentiment, or tense, which we call style types in this paper), each feature requires a separate discriminator for extracting a disentangled style vector corresponding to that feature. In this paper, we propose a unified distribution-controlling method, which provides each specific style value (the value of style types, e.g., positive sentiment, or past tense) with a unique representation. This method contributes a solid theoretical basis to avoid adversarial training in multi-type disentanglement. We also propose multiple loss functions to achieve a style-content disentanglement as well as a disentanglement among multiple style types. In addition, we observe that if two different style types always have some specific style values that occur together in the dataset, they will affect each other when transferring the style values. We call this phenomenon training bias , and we propose a loss function to alleviate such training bias while disentangling multiple types. We conduct experiments on two datasets (Yelp service reviews and Amazon product reviews) to evaluate the style-disentangling effect and the unsupervised style-transfer performance on two style types: sentiment and tense. The experimental results show the effectiveness of our model.
Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. AI is being rapidly applied across industries and is developing as a core technology to enable a society led by knowledge and information. An AI education focused on problem solving and learning is efficient for computer science education. Thus, the time has come to prepare for AI education along with existing software education so that they can adapt to the social and job changes enabled by AI. In this paper, we explain a classification method for AI machine learning models and propose an AI education model using teachable machines. Non-computer majors can understand the importance of data and the AI model concept based on specific cases using AI education tools to understand and experiment with AI even without the knowledge of mathematics, and use languages such as Python, if necessary. Through the application of the machine learning model, AI can be smoothly utilized in their field of interest. If such an AI education model is activated, it will be possible to suggest the direction of AI education for collaboration with AI experts through the application of AI technology.
Adversarial Defence by Diversified Simultaneous Training of Deep Ensembles

Learning-based classifiers are susceptible to adversarial examples. Existing defence methods are mostly devised on individual classifiers. Recent studies showed that it is viable to increase adversarial robustness by promoting diversity over an ensemble of models. In this paper, we propose adversarial defence by encouraging ensemble diversity on learning high-level feature representations and gradient dispersion in simultaneous training of deep ensemble networks. We perform extensive evaluations under white-box and black-box attacks including transferred examples and adaptive attacks. Our approach achieves a significant gain of up to 52% in adversarial robustness, compared with the baseline and the state-of-the-art method on image benchmarks with complex data scenes. The proposed approach complements the defence paradigm of adversarial training, and can further boost the performance. The source code is available at https://github.com/ALIS-Lab/AAAI2021-PDD.
Teacher Guided Neural Architecture Search for Face Recognition

Knowledge distillation is an effective tool to compress large pre-trained convolutional neural networks (CNNs) or their ensembles into models applicable to mobile and embedded devices. However, with expected flops or latency, existing methods are hand-crafted heuristics. They propose to pre-define the target student network for knowledge distillation, which may be sub-optimal because it requires much effort to explore a powerful student from the large design space. In this paper, we develop a novel teacher guided neural architecture search method to directly search for a student network with flexible channel and layer sizes. Specifically, we define the search space as the number of the channels/layers, which is sampled based on the probability distribution and is learned by minimizing the search objective of the student network. The maximum probability for the size in each distribution serves as the final searched width and depth of the target student network. Extensive experiments on a variety of face recognition benchmarks have demonstrated the superiority of our method over the state-of-the-art alternatives.
B-IoT (Blockchain Internet of Things): A Way to Enhance IoT Applications via Blockchain

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Challenges of releasing audio material for spoken data: The case of the London-Lund Corpus 2

This article aims to describe key challenges of preparing and releasing audio material for spoken data and to propose solutions to these challenges. We draw on our experience of compiling the new London-Lund Corpus 2 (LLC-2), where transcripts are released together with the audio files. However, making the audio material publicly available required careful consideration of how to, most effectively, 1) align the transcripts with the audio and 2) anonymise personal information in the recordings. First, audio-to-text alignment was solved through the insertion of timestamps in front of speaker turns in the transcription stage, which, as we show in the article, may later be used as a valuable complement to more robust automatic segmentation. Second, anonymisation was done by means of a Praat script, which replaced all personal information with a sound that made the lexical information incomprehensible but retained the prosodic characteristics. The public release of the LLC-2 audio material is a valuable feature of the corpus that allows users to extend the corpus data relative to their own research interests and, thus, broaden the scope of corpus linguistics. To illustrate this, we present three studies that have successfully used the LLC-2 audio material.
Non-interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning

We propose a novel primitive called \(\textsf {NIVA}\) that allows the distributed aggregation of multiple users’ secret inputs by multiple untrusted servers. The returned aggregation result can be publicly verified in a non-interactive way, i.e. the users are not required to participate in the aggregation except for providing their secret inputs. \(\textsf {NIVA}\) allows the secure computation of the sum of a large amount of users’ data and can be employed, for example, in the federated learning setting in order to aggregate the model updates for a deep neural network. We implement \(\textsf {NIVA}\) and evaluate its communication and execution performance and compare it with the current state-of-the-art, i.e. Segal et al. protocol (CCS 2017) and Xu et al. VerifyNet protocol (IEEE TIFS 2020), resulting in better user’s communicated data and execution time.
Secure Data Transfer over Internet Using Image Steganography: Review

Whether it's for work or personal well-being, keeping secrets or private information has become part of our everyday existence. Therefore, several researchers acquire an entire focus on secure transmitting secret information. Confidential information is collectively referred to as Steganography for inconspicuous digital media such as video, audio, and images. In disguising information, Steganography plays a significant role. Traditional Steganography faces a further concern of discovery as steganalysis develops. The safety of present steganographic technologies thus has to be improved. In this research, some of the techniques that have been used to hide information inside images have been reviewed. According to the hiding domain, these techniques can be divided into two main parts: The spatial Domain and Transform Domain. In this paper, three methods for each Domain have been chosen to be studied and evaluated. These are; Least Significant Bit (LSB), Pixel Value Difference (PVD), Exploiting Modification Direction (EMD), contourlet transform, Discrete Wavelet Transformation (DWT), and, Discrete Cosine Transformation (DCT). Finally, the best results that have been obtained in terms of higher PSNR, Capacity, and more robustness and security are discussed.
OpenEDS2020 Challenge on Gaze Tracking for VR: Dataset and Results

This paper summarizes the OpenEDS 2020 Challenge dataset, the proposed baselines, and results obtained by the top three winners of each competition: (1) Gaze prediction Challenge, with the goal of predicting the gaze vector 1 to 5 frames into the future based on a sequence of previous eye images, and (2) Sparse Temporal Semantic Segmentation Challenge, with the goal of using temporal information to propagate semantic eye labels to contiguous eye image frames. Both competitions were based on the OpenEDS2020 dataset, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display with two synchronized eye-facing cameras. The dataset, which we make publicly available for the research community, consists of 87 subjects performing several gaze-elicited tasks, and is divided into 2 subsets, one for each competition task. The proposed baselines, based on deep learning approaches, obtained an average angular error of 5.37 degrees for gaze prediction, and a mean intersection over union score (mIoU) of 84.1% for semantic segmentation. The winning solutions were able to outperform the baselines, obtaining up to 3.17 degrees for the former task and 95.2% mIoU for the latter.
Remote preparation of a general single-photon hybrid state

We present a technique for remotely preparing an arbitrary single-photon hybrid state between two distant nodes. This remote state preparation (RSP) is accomplished with a hyper-entangled state in polarization and time-bin degrees of freedom (DoFs) using some experimentally available optical elements and devices. In our RSP, the sender performs unitary operations on her hyper-entangled photon in accordance with her knowledge of the desired state to be transmitted. By performing a single-photon projective measurement and classical communication, the target state will be reconstructed at the remote receiver’s quantum system. Furthermore, we discuss the RSP via a partially hyper-entangled state. We expect our study can pave the way towards the investigation of long-distance quantum communication scenarios.
Machine Learning-Based Pre-impact Fall Detection and Injury Prevention for the Elderly with Wearable Inertial Sensors

Falls are the leading cause of death and non-fatal injuries among older people, thus pre-impact fall detection that detects a fall before body-ground impact is of crucial significance. 32 young subjects performed different types of falls and daily activities, and their motion data was recorded by a wearable inertial sensor to establish a large-scale motion dataset. Five commonly used machine learning algorithms were applied and compared thoroughly in terms of accuracy and practicality for pre-impact fall detection. Results showed that in terms of sensitivity, specificity and lead time, both support vector machine (SVM: 99.77%, 93.10%, 362 ± 150 ms) and random forest (RF: 100%, 92.90%, 357 ± 145 ms) achieved better results than other 3 models. SVM showed a much shorter latency (66 ms) than RF (1047 ms) running in a microcontroller. Those findings suggest that SVM has the highest potential to be embedded into a wearable sensor based system to provide real-time fall protection for the elderly.
The Use of Smartphone English Language Learning Apps in the Process of Learning English: Slovak EFL Students’ Perspectives

In recent years, an accelerating trend in an undergoing shift from the use of traditional desktop computers towards the utilisation of smartphones for language learning purposes has been detected. In line with these trends, this study aims to investigate Slovak EFL learners’ attitudes and perceptions of English language learning apps (ELLA) regarding practicing and learning English. Furthermore, the differences in the perception of ELLA between the male and female research participants are also analysed. The target population totalled 158 Slovak university EFL learners, 48 males and 110 females, who were required to display their level of agreement to statements by responding to a 5-point Likert scale questionnaire. The instrument consisted of 30 statements altogether, involving items on apps’ usage and apps’ perception. The calculated means indicate that the participants’ attitudes and perceptions of ELLA range between being neutral and positive, and that EFL learners tend to practice language systems and skills to varying degrees. Moreover, 30 independent-sample t-tests, which were run in order to determine the differences between the male and female attitudes and perceptions, reveal that 50% of statements on apps’ usage and apps’ perception differ significantly, demonstrating considerable differences between the two sexes. The achieved results seem to contribute to the existing research on the use of smartphones and ELLA in the context of EFL learning by casting more light on mobile language learning, an area that merits further scientific exploration.
LocalGLMnet: interpretable deep learning for tabular data

Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models. The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models, but provides superior predictive power benefiting from the art of representation learning. This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition in the spirit of Shapley values and integrated gradients.
Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.