Reviewerly/exHarmony huggingface dataset:
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it is a challenging problem in terms of environments’ variability, sensory-motor systems, user habits, but also sparsity of signals and redundancy of models. Therefore, end-to-end systems fail at automatically extracting key features, and need to access context and domain knowledge. We propose to tackle feature extraction for activity recognition in smart homes by merging methods of Natural Language Processing (NLP) and Time Series Classification (TSC) domains.We evaluate the performance of our method with two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). We analyze the contributions of the use of embedding based on term frequency encoding, to improve automatic feature extraction. Moreover we compare the classification performance of Fully Convolutional Network (FCN) from TSC, applied for the first time for activity recognition in smart homes, to Long Short Term Memory (LSTM). The method we propose, shows good performance in offline activity classification. Our analysis also shows that FCNs outperforms LSTMs, and that domain knowledge gained by event encoding and embedding improves significantly the performance of classifiers.
Reinforced History Backtracking for Conversational Question Answering

To model the context history in multi-turn conversations has become a critical step towards a better understanding of the user query in question answering systems. To utilize the context history, most existing studies treat the whole context as input, which will inevitably face the following two challenges. First, modeling a long history can be costly as it requires more computation resources. Second, the long context history consists of a lot of irrelevant information that makes it difficult to model appropriate information relevant to the user query. To alleviate these problems, we propose a reinforcement learning based method to capture and backtrack the related conversation history to boost model performance in this paper. Our method seeks to automatically backtrack the history information with the implicit feedback from the model performance. We further consider both immediate and delayed rewards to guide the reinforced backtracking policy. Extensive experiments on a large conversational question answering dataset show that the proposed method can help to alleviate the problems arising from longer context history. Meanwhile, experiments show that the method yields better performance than other strong baselines, and the actions made by the method are insightful.
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach

The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session.In contrast to a single text, a session may consist of an initial post and an associated sequence of comments.Yet, emerging efforts to enhance the performance of session-based cyberbullying detection have largely overlooked unintended social biases in existing cyberbullying datasets.For example, a session containing certain demographicidentity terms (e.g., "gay" or "black") is more likely to be classified as an instance of cyberbullying.In this paper, we first show evidence of such bias in models trained on sessions collected from different social media platforms (e.g., Instagram).We then propose a contextaware and model-agnostic debiasing strategy that leverages a reinforcement learning technique, without requiring any extra resources or annotations apart from a pre-defined set of sensitive triggers commonly used for identifying cyberbullying instances.Empirical evaluations show that the proposed strategy can simultaneously alleviate the impacts of the unintended biases and improve the detection performance.
Developing Speaking with 21st Century Digital Tools in the English as a Foreign Language Classroom:

In a changing, dynamic world increasingly dependent on technologies, it is necessary to educate children so that they can participate fully in today’s and future society. Literacy thus needs much more than the traditional approaches to reading and writing in students’ first language. After analyzing the concept and implications of new literacies in foreign language learning, a quantitative study was carried out to explore Spanish primary education students’ perceptions (n = 82) on the development of new literacies in the English as a foreign language (EFL) lesson after using the online video discussion platform Flipgrid to practice their oral expression and create meanings in EFL during the COVID-19 pandemic. The analysis also aims to analyze whether there are differences based on gender, age and educational stage. The results show that the participants in this study were motivated to use this digital tool that enables them to make comments to their partners’ videos, send messages about the oral texts, and include additional media, among many other options, going then beyond reading texts on a computer screen. This study concludes with a series of considerations for possible future implementations.
Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction

We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (TtT) non-autoregressive sequence prediction to address the deep issues hidden in CGEC.Considering that most tokens are correct and can be conveyed directly from source to target, and the error positions can be estimated and corrected based on the bidirectional context information, thus we employ a BERTinitialized Transformer Encoder as the backbone model to conduct information modeling and conveying.Considering that only relying on the same position substitution cannot handle the variable-length correction cases, various operations such substitution, deletion, insertion, and local paraphrasing are required jointly.Therefore, a Conditional Random Fields (CRF) layer is stacked on the up tail to conduct non-autoregressive sequence prediction by modeling the token dependencies.Since most tokens are correct and easily to be predicted/conveyed to the target, then the models may suffer from a severe class imbalance issue.To alleviate this problem, focal loss penalty strategies are integrated into the loss functions.Moreover, besides the typical fix-length error correction datasets, we also construct a variable-length corpus to conduct experiments.Experimental results on standard datasets, especially on the variable-length datasets, demonstrate the effectiveness of TtT in terms of sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of error Detection and Correction 1 .
One-shot Face Reenactment Using Appearance Adaptive Normalization

The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance. The core of our network is a novel mechanism called appearance adaptive normalization, which can effectively integrate the appearance information from the input image into our face generator by modulating the feature maps of the generator using the learned adaptive parameters. Furthermore, we specially design a local net to reenact the local facial components (i.e., eyes, nose and mouth) first, which is a much easier task for the network to learn and can in turn provide explicit anchors to guide our face generator to learn the global appearance and pose-and-expression. Extensive quantitative and qualitative experiments demonstrate the significant efficacy of our model compared with prior one-shot methods.
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding

Recently, various neural models for multiparty conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction.However, these existing methods on MPC usually represent interlocutors and utterances individually and ignore the inherent complicated structure in MPC which may provide crucial interlocutor and utterance semantics and would enhance the conversation understanding process.To this end, we present MPC-BERT, a pre-trained model for MPC understanding that considers learning who says what to whom in a unified model with several elaborated self-supervised tasks.Particularly, these tasks can be generally categorized into (1) interlocutor structure modeling including reply-to utterance recognition, identical speaker searching and pointer consistency distinction, and (2) utterance semantics modeling including masked shared utterance restoration and shared node detection.We evaluate MPC-BERT on three downstream tasks including addressee recognition, speaker identification and response selection.Experimental results show that MPC-BERT outperforms previous methods by large margins and achieves new state-of-the-art performance on all three downstream tasks at two benchmarks.
Disentangled Motif-aware Graph Learning for Phrase Grounding

In this paper, we propose a novel graph learning framework for phrase grounding in the image. Developing from the sequential to the dense graph model, existing works capture coarse-grained context but fail to distinguish the diversity of context among phrases and image regions. In contrast, we pay special attention to different motifs implied in the context of the scene graph and devise the disentangled graph network to integrate the motif-aware contextual information into representations. Besides, we adopt interventional strategies at the feature and the structure levels to consolidate and generalize representations. Finally, the cross-modal attention network is utilized to fuse intra-modal features, where each phrase can be computed similarity with regions to select the best-grounded one. We validate the efficiency of disentangled and interventional graph network (DIGN) through a series of ablation studies, and our model achieves state-of-the-art performance on Flickr30K Entities and ReferIt Game benchmarks.
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification

We introduce a new approach for the task of Controllable Text Simplification, where systems rewrite a complex English sentence so that it can be understood by readers at different grade levels in the US K-12 system.It uses a non-autoregressive model to iteratively edit an input sequence and incorporates lexical complexity information seamlessly into the refinement process to generate simplifications that better match the desired output complexity than strong autoregressive baselines.Analysis shows that our model's local edit operations are combined to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting.
EXPLORING STUDENTS’ ATTITUDE TOWARDS ENGLISH ONLINE LEARNING USING MOODLE DURING COVID-19 PANDEMIC AT SMK YADIKA BANDARLAMPUNG

The ministry of education in Indonesia had declared the spread out of Covid-19 pandemic. To overcome this current situation, Learning from Home (LFH) is implemented for all subjects in the educational institution. LFH is conducted to all courses, including English class, through an online learning platform, one of which is Moodle. Moodle can cover up teaching and learning English courses during the spread out of Covid-19. Theoretically, the researcher organized the framework by examining students’ attitudes and analyzing how students overcome difficulties in learning English during online learning through Moodle. The researcher used self-analysis and student opinions to determine the degree of consensus based on how students felt or acted in response to the current situation. While, the favorable or unfavorable (positive or negative) of students’ attitude can be changed due to the characteristics of students’ thoughts (cognitive), feeling (affective), and behavioral (conative). However, to reduce the students’ negative attitude is to change the focus of students and the students’ acceptance. In this research, the researcher used qualitative research that focused on word descriptively. The questionnaire was given to 30 students and 6 students participated in an interview session. The questionnaire statements and questions of interview were adapted from the research of Brown (1994) about components of students’ attitude. The result of this research shows a positive attitude to implement Moodle as a learning platform. They have also demonstrated a positive attitude toward the importance of learning English in vocational high school. However, the students also find some self-problem in learning English during the implementation of Learning from Home (LFH), where students sometimes lack self-management to follow the learning activities.
Visual enhanced gLSTM for image captioning

• A visual enhanced guiding long short-term memory is proposed for image captioning. • Visual features combined with text is used to guide long short-term memory. • Visual information is added to the model for avoiding gradient diminishing. • Region based visual enhancement method by region of interest or salient region is proposed. • Image based visual enhancement method by visual words is proposed. For reducing the negative impact of the gradient diminishing on guiding long-short term memory (gLSTM) model in image captioning, we propose a visual enhanced gLSTM model for image caption generation. In this paper, the visual features of image’s region of interest (RoI) are extracted and used as guiding information in gLSTM, in which visual information of RoI is added to gLSTM for generating more accurate image captions. Two visual enhanced methods based on region and entire image are proposed respectively. Among them the visual features from the important semantic region by CNN and the full image visual features by visual words are extracted to guide the LSTM for generating the most important semantic words. Then the visual features and text features of similar images are respectively projected to the common semantic space to obtain visual enhancement guiding information by canonical correlation analysis, and added to each memory cell of gLSTM for generating caption words. Compared with the original gLSTM method, visual enhanced gLSTM model focuses on important semantic region, which is more in line with human perception of images. Experiments on Flickr8k dataset illustrate that the proposed method can achieve more accurate image captions, and outperform the baseline gLSTM algorithm and other popular image captioning methods.
Understanding shared links and their intentions to meet information needs in modern code review:

Abstract Code reviews serve as a quality assurance activity for software teams. Especially for Modern Code Review, sharing a link during a review discussion serves as an effective awareness mechanism where “Code reviews are good FYIs [for your information].”. Although prior work has explored link sharing and the information needs of a code review, the extent to which links are used to properly conduct a review is unknown. In this study, we performed a mixed-method approach to investigate the practice of link sharing and their intentions. First, through a quantitative study of the OpenStack and Qt projects, we identify 19,268 reviews that have 39,686 links to explore the extent to which the links are shared, and analyze a correlation between link sharing and review time. Then in a qualitative study, we manually analyze 1,378 links to understand the role and usefulness of link sharing. Results indicate that internal links are more widely referred to (93% and 80% for the two projects). Importantly, although the majority of the internal links are referencing to reviews, bug reports and source code are also shared in review discussions. The statistical models show that the number of internal links as an explanatory factor does have an increasing relationship with the review time. Finally, we present seven intentions of link sharing, with providing context being the most common intention for sharing links. Based on the findings and a developer survey, we encourage the patch author to provide clear context and explore both internal and external resources, while the review team should continue link sharing activities. Future research directions include the investigation of causality between sharing links and the review process, as well as the potential for tool support.
TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment

This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robustness, driving comfort and efficiency. Starting from the hybrid optimization-based framework, we introduce two warm start methods, temporal and dual variable warm starts, to improve the efficiency. We also reformulates the problem to improve the robustness and efficiency. We name this new algorithm TDR-OBCA. With these changes, compared with original hybrid optimization we achieve a 96.67% failure rate decrease with respect to initial conditions, 13.53% increase in driving comforts and 3.33% to 44.82% increase in planner efficiency as obstacles number scales. We validate our results in hundreds of simulation scenarios and hundreds of hours of public road tests in both U.S. and China. Our source code is available at https://github.com/ApolloAuto/apollo.
ZNN With Fuzzy Adaptive Activation Functions and Its Application to Time-Varying Linear Matrix Equation

In order to improve the effect of the Exp-Sign activation function (ESAF) and the Sinh-Sign activation function (SSAF) on the convergence and robustness of the zeroing neural network (ZNN) model, two fuzzy adaptive activation functions, named FAESAF and FASSAF, are constructed by using a Mamdani fuzzy logic controller (MFLC) in this article. Thus, a novel ZNN with the FAESAF and the FASSAF is proposed to solve the time-varying linear matrix equation. Different from the ESAF and the SSAF, whose parameters are fixed, the newly constructed FAESAF and FASSAF have an adaptive property, which comes from the fact that their parameters are intelligently generated by the MFLC according to the error norm of the ZNN model. In order to highlight the superior predefined time convergence and robustness of the corresponding ZNN model with the FAESAF and the FASSAF, several theorems are provided, and the corresponding proof is given in detail. Furthermore, the ESAF and the SSAF with different values of parameters are used as a comparison in numerical experiments to verify the superior performance of the FAESAF and the FASSAF. From theoretical analysis and numerical results, we can conclude that the ZNN model with the FAESAF and the FASSAF has better predefined time convergence and robustness compared to the ZNN model with the ESAF and the SSAF under the same conditions.
Experimental entanglement generation for quantum key distribution beyond 1 Gbit/s

Top-performance sources of photonic entanglement are an indispensable resource for many applications in quantum communication, most notably quantum key distribution. However, up to now, no source has been shown to simultaneously exhibit the high pair-creation rate, broad bandwidth, excellent state fidelity, and low intrinsic loss necessary for gigabit secure key rates. In this work, we present for the first time a source of polarization-entangled photon pairs at telecommunication wavelengths that covers all these needs of real-world quantum-cryptographic applications, thus enabling unprecedented quantum-secure key rates of more than 1 Gbit/s. Our source is designed to optimally exploit state-of-the-art telecommunication equipment and detection systems. Any technological improvement of the latter would result in an even higher rate without modification of the source. We discuss the used wavelength-multiplexing approach, including its potential for multi-user quantum networks and its fundamental limitations. Our source paves the way for high-speed quantum encryption approaching present-day internet bandwidth.
Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation

Unsupervised domain adaptation (UDA) has been received increasing attention since it does not require labels in target domain. Most existing UDA methods learn domain-invariant features by minimizing discrepancy distance computed by a certain metric between domains. However, these discrepancy-based methods cannot be robustly applied to unsupervised time series domain adaptation (UTSDA). That is because discrepancy metrics in these methods contain only low-order and local statistics, which have limited expression for time series distributions and therefore result in failure of domain matching. Actually, the real-world time series are always non-local distributions, i.e., with non-stationary and non-monotonic statistics. In this paper, we propose an Adversarial Spectral Kernel Matching (AdvSKM) method, where a hybrid spectral kernel network is specifically designed as inner kernel to reform the Maximum Mean Discrepancy (MMD) metric for UTSDA. The hybrid spectral kernel network can precisely characterize non-stationary and non-monotonic statistics in time series distributions. Embedding hybrid spectral kernel network to MMD not only guarantees precise discrepancy metric but also benefits domain matching. Besides, the differentiable architecture of the spectral kernel network enables adversarial kernel learning, which brings more discriminatory expression for discrepancy matching. The results of extensive experiments on several real-world UTSDA tasks verify the effectiveness of our proposed method.
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss

A number of studies point out that current Visual Question Answering (VQA) models are severely affected by the language prior problem, which refers to blindly making predictions based on the language shortcut. Some efforts have been devoted to overcoming this issue with delicate models. However, there is no research to address it from the view of the answer feature space learning, despite the fact that existing VQA methods all cast VQA as a classification task. Inspired by this, in this work, we attempt to tackle the language prior problem from the viewpoint of the feature space learning. An adapted margin cosine loss is designed to discriminate the frequent and the sparse answer feature space under each question type properly. In this way, the limited patterns within the language modality can be largely reduced to eliminate the language priors. We apply this loss function to several baseline models and evaluate its effectiveness on two VQA-CP benchmarks. Experimental results demonstrate that our proposed adapted margin cosine loss can enhance the baseline models with an absolute performance gain of 15\% on average, strongly verifying the potential of tackling the language prior problem in VQA from the angle of the answer feature space learning.