Reviewerly/exHarmony huggingface dataset:
Egocentric-View Fingertip Detection for Air Writing Based on Convolutional Neural Networks

This research investigated real-time fingertip detection in frames captured from the increasingly popular wearable device, smart glasses. The egocentric-view fingertip detection and character recognition can be used to create a novel way of inputting texts. We first employed Unity3D to build a synthetic dataset with pointing gestures from the first-person perspective. The obvious benefits of using synthetic data are that they eliminate the need for time-consuming and error-prone manual labeling and they provide a large and high-quality dataset for a wide range of purposes. Following that, a modified Mask Regional Convolutional Neural Network (Mask R-CNN) is proposed, consisting of a region-based CNN for finger detection and a three-layer CNN for fingertip location. The process can be completed in 25 ms per frame for 640×480 RGB images, with an average error of 8.3 pixels. The speed is high enough to enable real-time “air-writing”, where users are able to write characters in the air to input texts or commands while wearing smart glasses. The characters can be recognized by a ResNet-based CNN from the fingertip trajectories. Experimental results demonstrate the feasibility of this novel methodology.
Learning to Generate Questions by Learning to Recover Answer-containing Sentences

To train a question answering model based on machine reading comprehension (MRC), significant effort is required to prepare annotated training data composed of questions and their answers from contexts.Recent research has focused on synthetically generating a question from a given context and an annotated (or generated) answer by training an additional generative model to augment the training data.In light of this research direction, we propose a novel pre-training approach that learns to generate contextually rich questions, by recovering answer-containing sentences.We evaluate our method against existing ones in terms of the quality of generated questions, and fine-tuned MRC model accuracy after training on the data synthetically generated by our method.We consistently improve the question generation capability of existing models such as T5 and UniLM, and achieve state-ofthe-art results on MS MARCO and NewsQA, and comparable results to the state-of-the-art on SQuAD.Additionally, the data synthetically generated by our approach is beneficial for boosting up the downstream MRC accuracy across a wide range of datasets, such as SQuAD-v1.1,v2.0, KorQuAD and BioASQ, without any modification to the existing MRC models.Furthermore, our method shines especially when a limited amount of pre-training or downstream MRC data is given.
Comparative Evaluation for Effectiveness Analysis of Policy Based Deep Reinforcement Learning Approaches

Deep Reinforcement Learning (DRL) has proven to be a very strong technique with results in various applications in recent years. Especially the achievements in the studies in the field of robotics show that much more progress will be made in this field. Undoubtedly, policy choices and parameter settings play an active role in the success of DRL. In this study, an analysis has been made on the policies used by examining the DRL studies conducted in recent years. Policies used in the literature are grouped under three different headings: value-based, policy-based and actor-critic. However, the problem of moving a common target using Newton's law of motion of collaborative agents is presented. Trainings are carried out in a frictionless environment with two agents and one object using four different policies. Agents try to force an object in the environment by colliding it and try to move it out of the area it is in. Two-dimensional surface is used during the training phase. As a result of the training, each policy is reported separately and its success is observed. Test results are discussed in section 5. Thus, policies are tested together with an application by providing information about the policies used in deep reinforcement learning approaches.
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples

Previous work on adversarial attacks on dependency parsers has mostly focused on attack methods, as opposed to the quality of adversarial examples, which in previous work has been relatively low.To address this gap, we propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters, and then verify their quality using automatic and human evaluations.We perform analysis with different parsing models and observe that: (i) injecting words not used in the training stage is an effective attack strategy; (ii) adversarial examples generated against a parser strongly depend on the parser model, the token embeddings, and even the specific instantiation of the model (i.e., a random seed).We use these insights to improve the robustness of English parsing models, relying on adversarial training and model ensembling.
Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation

Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source domain so that a model can be trained with annotations from the latter. However, such methods highly rely on an image translator or feature extractor trained in an elaborated mechanism including adversarial training, which brings in extra complexity and instability in the adaptation process. Furthermore, these methods mainly focus on taking advantage of the labeled source dataset, leaving the unlabeled target dataset not fully utilized. In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. BiSIDA aligns domains by not only transferring source images into the style of target images but also transferring target images into the style of source images to perform high-dimensional perturbation on the unlabeled target images, which is crucial to the success in applying consistency regularization in segmentation tasks. Extensive experiments show that our BiSIDA achieves new state-of-the-art on two commonly-used synthetic-to-real domain adaptation benchmarks: GTA5-to-CityScapes and SYNTHIA-to-CityScapes. Code and pretrained style transfer model are available at: https://github.com/wangkaihong/BiSIDA.
Dealing With Context Awareness for Service-Oriented Systems

In recent years, rapid advances in the enabling technologies for mobile and ubiquitous computing, software paradigms, embedded sensor technologies, and wide range of wired and wireless protocols have been witnessed. Specifically, context-aware services-oriented applications are emerging as the next computing paradigm in which infrastructure and services are seamlessly available. Context-awareness, being an important ingredient, plays a vital role in enabling such interactive smart environments. More recently, the increasing popularity of ontologies has led to new ontology-based models of context because of their potential to support sophisticated ontology-based reasoning methods. This paper presents an architecture for the development of context-aware services based on ontologies. The authors highlight the context metamodel and discuss about reasoning process. This research also presents the semantic approach for service adaptation in context aware environment.
Effective Attention Sheds Light On Interpretability

An attention matrix of a transformer selfattention sublayer can provably be decomposed into two components and only one of them (effective attention) contributes to the model output.This leads us to ask whether visualizing effective attention gives different conclusions than interpretation of standard attention.Using a subset of the GLUE tasks and BERT, we carry out an analysis to compare the two attention matrices, and show that their interpretations differ.Effective attention is less associated with the features related to the language modeling pretraining such as the separator token, and it has more potential to illustrate linguistic features captured by the model for solving the end-task.Given the found differences, we recommend using effective attention for studying a transformer's behavior since it is more pertinent to the model output by design.
Towards Enabling Learnware to Handle Unseen Jobs

The learnware paradigm attempts to change the current style of machine learning deployment, i.e., user builds her own machine learning application almost from scratch, to a style where the previous efforts of other users can be reused, given a publicly available pool of machine learning models constructed by previous users for various tasks. Each learnware is a high-quality pre-trained model associated with its specification. Although there are many models in the learnware market, only a few, even none, may be potentially helpful for the current job. Therefore, how to identify and deploy useful models becomes one of the main concerns, which particularly matters when the user’s job involves certain unseen parts not covered by the current learnware market. It becomes more challenging because, due to the privacy consideration, the raw data used for training models in the learnware market are inaccessible. In this paper, we develop a novel scheme that works can effectively reuse the learnwares even when the user’s job involves unseen parts. Despite the raw training data are inaccessible, our approach can provably identify samples from the unseen parts while assigning the rest to proper models in the market for predicting under a certain condition. Empirical studies also validate the efficacy of our approach.
Illumination-based adaptive saliency detection network through fusion of multi-source features

Salient object detection (SOD) tasks aim to outline the most concerned part of human vision, which is widely used in computer vision fields. Due to possibility of the insufficient illumination in the application environment (such as night or dim indoor environment), RGB images from visible channels usually lose most of their performance, while thermal images can improve the detection performance. Therefore, it is in urgent need of a robust saliency detection method, which can handle complex illumination conditions and take use of features from multiple sources intelligently. Accordingly, we propose our ‘illumination based multi-source fused salient object detection network’ (IAN-MF-SOD network). Taking the illumination condition as a quantitative reference, we guide features from two sources to fuse adaptively and intelligently, so that our method can enhance both of their advantages. For different illumination conditions, we distribute different fusion weights for each RGB–thermal image pair. Well fused images are generated as inputs to a trained SOD network to obtain saliency maps. Due to the analysis of our proposed IAN-score, our method performs favorably against traditional RGB-based SOD networks.
Joint Multi-Decoder Framework with Hierarchical Pointer Network for Frame Semantic Parsing

Current researches on frame semantic parsing include three subtasks, namely frame identification, argument identification and role classification.Most of previous systems process these subtasks independently and ignore their interactions.We introduce a novel architecture based on multi-decoder strategy to handle these subtasks together.The multi-decoder strategy strengthens the interactions.Moreover, we design a hierarchical pointer network for argument identification which reduces the computational complexity.To our best knowledge, it's the first practice to introduce the pointer network into frame semantic parsing.The experiments show improvement over state of the art models on FrameNet dataset.
On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction

Neural network algorithms such as those based on transformers and attention models have excelled on Automatic Text Classification (ATC) tasks.However, such enhanced performance comes at high computational costs.Ensembles of simpler classifiers (i.e., stacking) that exploit algorithmic and representational complementarities have also been shown to produce top-notch performance in ATC, enjoying high effectiveness and potentially lower computational costs.In this context, we present the first and largest comparative study to exploit the cost-effectiveness of stacking of ATC classifiers, consisting of transformers and non-neural algorithms.In particular, we are interested in answering research questions such as: Is it possible to obtain an effective ensemble with significantly less computational cost than the best learning model for a given dataset?Disregarding the computational cost, can an ensemble improve the effectiveness of the best learning model?Besides answering such questions, another main contribution of this paper is the proposal of a low-cost oracle-based method that can predict the best ensemble in each scenario (with and without computational cost limitations) using only a fraction of the available training data.
Fog computing-based logistic supply chain management and organizational agility: The mediating role of user satisfaction

Although fog computing-based logistic supply chain management (Fog computing-based LSCM) is an emerging technology that proved a high impact on services and products, little research has focused on fog computing-based LSCM. Drawing on the Kano model and organization's theory this paper investigates the effect of fog computing-based LSCM on organizational agility. And the role of user satisfaction as mediator between fog computing-based LSCM and organizational agility. A quantitative approach was used, a questionnaire was designed for data collection, Cronbach's Alpha test was performed on a pilot study to examine the internal consistency of questionnaire items. Fog computing-based LSCM was studied based on Supply chain awareness, Connectivity and Logistics, Integration Process, Seamless Supply Chain, Integration of Processes. Data was collected from a random sample of 550 employees of Al-Hassan industrial city‎ in Jordan. Building on the proposed model, Researchers show that fog computing-based LSCM has a positive impact on organizational agility, fog computing-based LSCM has a positive impact on user satisfaction and finally user satisfaction mediates the relationship between fog computing-based LSCM and organizational agility. Implications for the model are discussed.
Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts.Existing approaches generally predict the dialogue state at every turn from scratch.However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn.Therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation.To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history.The Dual Slot Selector determines each slot whether to update slot value or to inherit the slot value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a slot value with high reliability can be obtained for it through the current turn dialogue.The slots selected to be updated are permitted to enter the Slot Value Generator to update values by a hybrid method, while the other slots directly inherit the values from the previous turn.Empirical results show that our method achieves 56.93%, 60.73%, and 58.04% joint accuracy on MultiWOZ 2.0, MultiWOZ 2.1, and Multi-WOZ 2.2 datasets respectively and achieves a new state-of-the-art performance with significant improvements.
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks

Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks. However, traditional defense mechanisms assume a uniform attack over the examples according to the underlying data distribution, which is apparently unrealistic as the attacker could choose to focus on more vulnerable examples. We present a weighted minimax risk optimization that defends against non-uniform attacks, achieving robustness against adversarial examples under perturbed test data distributions. Our modified risk considers importance weights of different adversarial examples and focuses adaptively on harder examples that are wrongly classified or at higher risk of being classified incorrectly. The designed risk allows the training process to learn a strong defense through optimizing the importance weights. The experiments show that our model significantly improves state-of-the-art adversarial accuracy under non-uniform attacks without a significant drop under uniform attacks.
Deep Wasserstein Graph Discriminant Learning for Graph Classification

Graph topological structures are crucial to distinguish different-class graphs. In this work, we propose a deep Wasserstein graph discriminant learning (WGDL) framework to learn discriminative embeddings of graphs in Wasserstein-metric (W-metric) matching space. In order to bypass the calculation of W-metric class centers in discriminant analysis, as well as better support batch process learning, we introduce a reference set of graphs (aka graph dictionary) to express those representative graph samples (aka dictionary keys). On the bridge of graph dictionary, every input graph can be projected into the latent dictionary space through our proposed Wasserstein graph transformation (WGT). In WGT, we formulate inter-graph distance in W-metric space by virtue of the optimal transport (OT) principle, which effectively expresses the correlations of cross-graph structures. To make WGDL better representation ability, we dynamically update graph dictionary during training by maximizing the ratio of inter-class versus intra-class Wasserstein distance. To evaluate our WGDL method, comprehensive experiments are conducted on six graph classification datasets. Experimental results demonstrate the effectiveness of our WGDL, and state-of-the-art performance.
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference

Multilingual transformers (XLM, mT5) have been shown to have remarkable transfer skills in zero-shot settings.Most transfer studies, however, rely on automatically translated resources (XNLI, XQuAD), making it hard to discern the particular linguistic knowledge that is being transferred, and the role of expert annotated monolingual datasets when developing task-specific models.We investigate the cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference (NLI), with a focus on the recent largescale Chinese dataset OCNLI.To better understand linguistic transfer, we created 4 categories of challenge and adversarial tasks (totaling 17 new datasets 1 ) for Chinese that build on several well-known resources for English (e.g., HANS, NLI stress-tests).We find that cross-lingual models trained on English NLI do transfer well across our Chinese tasks (e.g., in 3/4 of our challenge categories, they perform as well/better than the best monolingual models, even on 3/5 uniquely Chinese linguistic phenomena such as idioms, pro drop).These results, however, come with important caveats: cross-lingual models often perform best when trained on a mixture of English and high-quality monolingual NLI data (OCNLI), and are often hindered by automatically translated resources (XNLI-zh).For many phenomena, all models continue to struggle, highlighting the need for our new diagnostics to help benchmark Chinese and cross-lingual models.category n
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech

In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech.We show that DPR and CDP are closely related, and a joint model benefits both tasks.We refer to our model as DiscProReco, and it first encodes the tokens in each utterance in a conversation with a directed Graph Convolutional Network (GCN).The token states for an utterance are then aggregated to produce a single state for each utterance.The utterance states are then fed into a biaffine classifier to construct a conversational discourse graph.A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer.The joint model is trained and evaluated on a new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) dataset that we annotated with both two types of information.Experimental results on the SPDPR dataset and other benchmarks show that DiscProReco significantly outperforms the state-of-the-art baselines of both tasks.
Factoring Statutory Reasoning as Language Understanding Challenges

Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case.Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance.To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs.Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them.Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines.Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.
A Conditional Splitting Framework for Efficient Constituency Parsing

We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions.Our parsing model estimates the conditional probability distribution of possible splitting points in a given text span and supports efficient topdown decoding, which is linear in number of nodes.The conditional splitting formulation together with efficient beam search inference facilitate structural consistency without relying on expensive structured inference.Crucially, for discourse analysis we show that in our formulation, discourse segmentation can be framed as a special case of parsing which allows us to perform discourse parsing without requiring segmentation as a pre-requisite.Experiments show that our model achieves good results on the standard syntactic parsing tasks under settings with/without pre-trained representations and rivals state-of-the-art (SoTA) methods that are more computationally expensive than ours.In discourse parsing, our method outperforms SoTA by a good margin.