Reviewerly/exHarmony huggingface dataset:
Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Robotic construction assembly planning aims to find feasible assembly sequences as well as the corresponding robot-paths and can be seen as a special case of task and motion planning (TAMP). As construction assembly can well be parallelized, it is desirable to plan for multiple robots acting concurrently. Solving TAMP instances with many robots and over a long time-horizon is challenging due to coordination constraints, and the difficulty of choosing the right task assignment. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to jointly solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multi-robot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple construction case-studies to show the robustness over long planning horizons and scalability to many objects and agents. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world.
Giving commands to a self-driving car: How to deal with uncertain situations?

Current technology for autonomous cars primarily focuses on getting the passenger from point A to B. Nevertheless, it has been shown that passengers are afraid of taking a ride in self-driving cars. One way to alleviate this problem is by allowing the passenger to give natural language commands to the car. However, the car can misunderstand the issued command or the visual surroundings which could lead to uncertain situations. It is desirable that the self-driving car detects these situations and interacts with the passenger to solve them. This paper proposes a model that detects the uncertain situations when a command is given and finds the visual objects causing it. Optionally, a question generated by the system describing the uncertain objects is included. We argue that if the car could explain the objects in a human-like way, passengers could gain more confidence in the car’s abilities. Thus, we investigate how to (1) detect uncertain situations and their underlying causes, and (2) how to generate clarifying questions for the passenger. When evaluating on the Talk2Car dataset, we show that the proposed model, Uncertainty Resolving System (URS), improves 12.6% in terms of IoU.5 compared to not using URS. Furthermore, we designed a referring expression generator (REG) Attribute-Referring Expression Generator (A-REG) tailored to a self-driving car setting which yields a relative improvement of 6% METEOR and 8% ROUGE-l compared with state-of-the-art REG models, and is three times faster.
Fast Nearest Neighbor Machine Translation

Though nearest neighbor Machine Translation (kNN-MT) (Khandelwal et al., 2020) has proved to introduce significant performance boosts over standard neural MT systems, it is prohibitively slow since it uses the entire reference corpus as the datastore for the nearest neighbor search.This means each step for each beam in the beam search has to search over the entire reference corpus.kNN-MT is thus two-orders slower than vanilla MT models, making it hard to be applied to real-world applications, especially online services.In this work, we propose Fast kNN-MT to address this issue.Fast kNN-MT constructs a significantly smaller datastore for the nearest neighbor search: for each word in a source sentence, Fast kNN-MT first selects its nearest tokenlevel neighbors, which is limited to tokens that are the same as the query token.Then at each decoding step, in contrast to using the entire corpus as the datastore, the search space is limited to target tokens corresponding to the previously selected reference source tokens.This strategy avoids search through the whole datastore for nearest neighbors and drastically improves decoding efficiency.Without loss of performance, Fast kNN-MT is two-orders faster than kNN-MT, and is only two times slower than the standard NMT model.Fast kNN-MT enables the practical use of kNN-MT systems in real-world MT applications. 1
“Call me sexist, but...” : Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples

Research has focused on automated methods to effectively detect sexism online. Although overt sexism seems easy to spot, its subtle forms and manifold expressions are not. In this paper, we outline the different dimensions of sexism by grounding them in their implementation in psychological scales. From the scales, we derive a codebook for sexism in social media, which we use to annotate existing and novel datasets, surfacing their limitations in breadth and validity with respect to the construct of sexism. Next, we leverage the annotated datasets to generate adversarial examples, and test the reliability of sexism detection methods. Results indicate that current machine learning models pick up on a very narrow set of linguistic markers of sexism and do not generalize well to out-of-domain examples. Yet, including diverse data and adversarial examples at training time results in models that generalize better and that are more robust to artifacts of data collection. By providing a scale-based codebook and insights regarding the shortcomings of the state-of-the-art, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.
Efficient Continuous Control with Double Actors and Regularized Critics

How to obtain good value estimation is a critical problem in Reinforcement Learning (RL). Current value estimation methods in continuous control, such as DDPG and TD3, suffer from unnecessary over- or under- estimation. In this paper, we explore the potential of double actors, which has been neglected for a long time, for better value estimation in the continuous setting. First, we interestingly find that double actors improve the exploration ability of the agent. Next, we uncover the bias alleviation property of double actors in handling overestimation with single critic, and underestimation with double critics respectively. Finally, to mitigate the potentially pessimistic value estimate in double critics, we propose to regularize the critics under double actors architecture. Together, we present Double Actors Regularized Critics (DARC) algorithm. Extensive experiments on challenging continuous control benchmarks, MuJoCo and PyBullet, show that DARC significantly outperforms current baselines with higher average return and better sample efficiency.
A Provably Secure and Efficient Range Query Scheme for Outsourced Encrypted Uncertain Data From Cloud-Based Internet of Things Systems

The outsourcing of data is becoming increasingly commonplace as data is constantly been synchronized between user systems (e.g., personal computers and sensor devices) and cloud computing servers. However, to ensure data privacy, it is necessary to encrypt sensitive data prior to outsourcing. Limitations such as measurement, network delays, and data obfuscation may, however, result in uncertain data. Compared with searching over encrypted certain data, processing queries for encrypted uncertain data is more challenging. In this article, we propose a secure and efficient range query scheme over outsourced encrypted uncertain data, for example, from Internet of Things (IoT) systems. Specifically, we use pivot mapping to map data to a low-dimensional space to facilitate calculation and processing while preserving some of the original relevance among the data. Additionally, we encode data and then map codes into multiple Bloom filters which are organized by a binary tree-based index. Our scheme achieves data privacy, hides the relevance among data, and also supports efficient queries. We analyze the security and evaluate the performance of our approach using experiments on Microsoft Azure. The analysis and experimental results demonstrate that our proposed approach is secure and efficient.
Towards Accurate and Compact Architectures via Neural Architecture Transformer

Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even a well-designed/searched architecture may still contain many nonsignificant or redundant modules/operations (e.g., some intermediate convolution or pooling layers). Such redundancy may not only incur substantial memory consumption and computational cost but also deteriorate the performance. Thus, it is necessary to optimize the operations inside an architecture to improve the performance without introducing extra computational cost. To this end, we have proposed a Neural Architecture Transformer (NAT) method which casts the optimization problem into a Markov Decision Process (MDP) and seeks to replace the redundant operations with more efficient operations, such as skip or null connection. Note that NAT only considers a small number of possible replacements/transitions and thus comes with a limited search space. As a result, such a small search space may hamper the performance of architecture optimization. To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization. Specifically, we present a two-level transition rule to obtain valid transitions, i.e., allowing operations to have more efficient types (e.g., convolution <inline-formula><tex-math notation="LaTeX">${\to }$</tex-math></inline-formula> separable convolution) or smaller kernel sizes (e.g., <inline-formula><tex-math notation="LaTeX">$5{\times }5 {\to } 3{\times }3$</tex-math></inline-formula> ). Note that different operations may have different valid transitions. We further propose a Binary-Masked Softmax (BMSoftmax) layer to omit the possible invalid transitions. Last, based on the MDP formulation, we apply policy gradient to learn an optimal policy, which will be used to infer the optimized architectures. Extensive experiments show that the transformed architectures significantly outperform both their original counterparts and the architectures optimized by existing methods.
Quantum Architecture Search with Meta‐Learning

Abstract Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling and quantum machine learning models. The performances of VQAs largely depend on the architecture of parameterized quantum circuits (PQCs). Quantum architecture search (QAS) aims to automate the design of PQCs in different VQAs with classical optimization algorithms. However, current QAS algorithms do not use prior experiences and search the quantum architecture from scratch for each new task, which is inefficient and time consuming. In this paper, a meta quantum architecture search (MetaQAS) algorithm is proposed, which learns good initialization heuristics of the architecture (i.e., meta‐architecture), along with the meta‐parameters of quantum gates from a number of training tasks such that they can adapt to new tasks with fewer gradient updates, which leads to fast learning on new tasks. The proposed MetaQAS can be used with arbitrary gradient‐based QAS algorithms. Simulation results on variational quantum compiling (VQC) and quantum approximate optimization algorithm (QAOA) show that the architectures optimized by MetaQAS converge faster than a state‐of‐the‐art gradient‐based QAS algorithm, namely DQAS. MetaQAS also achieves a better solution than DQAS after fine‐tuning of gate parameters.
Ab Antiquo: Neural Proto-language Reconstruction

Historical linguists have identified regularities in the process of historic sound change.The comparative method utilizes those regularities to reconstruct proto-words based on observed forms in daughter languages.Can this process be efficiently automated?We address the task of proto-word reconstruction, in which the model is exposed to cognates in contemporary daughter languages, and has to predict the proto word in the ancestor language.We provide a novel dataset for this task, encompassing over 8,000 comparative entries, and show that neural sequence models outperform conventional methods applied to this task so far.Error analysis reveals a variability in the ability of neural model to capture different phonological changes, correlating with the complexity of the changes.Analysis of learned embeddings reveals the models learn phonologically meaningful generalizations, corresponding to well-attested phonological shifts documented by historical linguistics.
Edge: Enriching Knowledge Graph Embeddings with External Text

Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods.While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature.Previous work has partially addressed this issue by enriching knowledge graph entities based on "hard" co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve "soft" augmentation by proposing a knowledge graph enrichment and embedding framework named EDGE.Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level.To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original and augmented graph.To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling.Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of EDGE in link prediction and node classification.
Noise Stability Regularization for Improving BERT Fine-tuning

Fine-tuning pre-trained language models such as BERT has become a common practice dominating leaderboards across various NLP tasks.Despite its recent success and wide adoption, this process is unstable when there are only a small number of training samples available.The brittleness of this process is often reflected by the sensitivity to random seeds.In this paper, we propose to tackle this problem based on the noise stability property of deep nets, which is investigated in recent literature (Arora et al., 2018;Sanyal et al., 2020).Specifically, we introduce a novel and effective regularization method to improve fine-tuning on NLP tasks, referred to as Layer-wise Noise Stability Regularization (LNSR).We extend the theories about adding noise to the input and prove that our method gives a stabler regularization effect.We provide supportive evidence by experimentally confirming that well-performing models show a low sensitivity to noise and fine-tuning with LNSR exhibits clearly higher generalizability and stability.Furthermore, our method also demonstrates advantages over other state-of-the-art algorithms including L 2 -
Official Truths in a War on Fake News: Governmental Fact-Checking in Malaysia, Singapore, and Thailand

This article analyses the practice of state-operated fact-checking websites in Malaysia, Singapore, and Thailand. It is the first empirical study of governmental news corrections in Southeast Asia and covers more than 2,700 official posts published by Malaysia’s Sebenarnya.my, Singapore’s Factually, and Thailand’s Anti-Fake News Center. It finds that correction practices across the sites mainly function to sustain the salience of a supposedly constant and omnipresent fake news threat. Assuming an important role in strategic political communication, official fact checks accompany domestic fake news discourses that prepare the ground for restrictive legislation. At the same time, the analysis did not reveal any propagandistic abuse as the sites refrained from excessively defending governments and accusing political opponents. This finding is qualified regarding Singapore’s Factually that recently changed its approach towards targeting government critics personally.
Probabilistic and Hierarchical Quantum Information Splitting Based on the Non-Maximally Entangled Cluster State

With the emergence of classical communication security problems, quantum communication has been studied more extensively.In this paper, a novel probabilistic hierarchical quantum information splitting protocol is designed by using a non-maximally entangled four-qubit cluster state.Firstly, the sender Alice splits and teleports an arbitrary one-qubit secret state invisibly to three remote agents Bob, Charlie, and David.One agent David is in high grade, the other two agents Bob and Charlie are in low grade.Secondly, the receiver in high grade needs the assistance of one agent in low grade, while the receiver in low grade needs the aid of all agents.While introducing an ancillary qubit, the receiver's state can be inferred from the POVM measurement result of the ancillary qubit.Finally, with the help of other agents, the receiver can recover the secret state probabilistically by performing certain unitary operation on his own qubit.In addition, the security of the protocol under eavesdropping attacks is analyzed.In this proposed protocol, the agents need only single-qubit measurements to achieve probabilistic hierarchical quantum information splitting, which has appealing advantages in actual experiments.Such a probabilistic hierarchical quantum information splitting protocol hierarchical is expected to be more practical in multipartite quantum cryptography.
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA

Recent works have shown that supervised models often exploit data artifacts to achieve good test scores while their performance severely degrades on samples outside their training distribution.Contrast sets (Gardner et al., 2020) quantify this phenomenon by perturbing test samples in a minimal way such that the output label is modified.While most contrast sets were created manually, requiring intensive annotation effort, we present a novel method which leverages rich semantic input representation to automatically generate contrast sets for the visual question answering task.Our method computes the answer of perturbed questions, thus vastly reducing annotation cost and enabling thorough evaluation of models' performance on various semantic aspects (e.g., spatial or relational reasoning).We demonstrate the effectiveness of our approach on the popular GQA dataset (Hudson and Manning, 2019) and its semantic scene graph image representation.We find that, despite GQA's compositionality and carefully balanced label distribution, two strong models drop 13-17% in accuracy on our automatically-constructed contrast set compared to the original validation set.Finally, we show that our method can be applied to the training set to mitigate the degradation in performance, opening the door to more robust models.
Learners’ Perceptions on WhatsApp Integration as a Learning Tool to Develop EFL Spoken Vocabulary

Technology integration has a great impact on learners’ learning styles and everyday life, where various encouraging responses come across in language learning institutions through the use of mobile technology. WhatsApp application instantly transfers information in different modes. At the present time it can be effectively used as a platform to engage learners with conventional classroom setting. The present study is an attempt to investigate learners’ attitudes towards WhatsApp application and their intention for future use. 32 EFL learners were exposed to learn vocabulary by the use of WhatsApp application for a period of 6 weeks. Data were collected by using questionnaire and semi-structured interviews. The result exhibited higher levels (M= 4.4 and Mode 4.37) of the mean scores of the learners’ perception and (M= 4.3) on WhatsApp Usage attitudes. The finding of the study indicated that learners expressed positive attitudes in using WhatsApp applications for vocabulary development. The present study also suggests that the integration of WhatsApp should be fortified to learners, instructors and institutions in developing other language learning process.
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models.To obtain high efficiency, the basic structure of these models is Bi-encoder in most cases.However, this simple structure may cause serious information loss during the encoding of documents since the queries are agnostic.To address this problem, we design a method to mimic the queries on each of the documents by an iterative clustering process and represent the documents by multiple pseudo queries (i.e., the cluster centroids).To boost the retrieval process using approximate nearest neighbor search library, we also optimize the matching function with a two-step score calculation procedure.Experimental results on several popular ranking and QA datasets show that our model can achieve state-of-the-art results.
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding

The generation of stylish Chinese fonts is an important problem involved in many applications. Most of existing generation methods are based on the deep generative models, particularly, the generative adversarial networks (GAN) based models. However, these deep generative models may suffer from the mode collapse issue, which significantly degrades the diversity and quality of generated results. In this paper, we introduce a one-bit stroke encoding to capture the key mode information of Chinese characters and then incorporate it into CycleGAN, a popular deep generative model for Chinese font generation. As a result we propose an efficient method called StrokeGAN, mainly motivated by the observation that the stroke encoding contains amount of mode information of Chinese characters. In order to reconstruct the one-bit stroke encoding of the associated generated characters, we introduce a stroke-encoding reconstruction loss imposed on the discriminator. Equipped with such one-bit stroke encoding and stroke-encoding reconstruction loss, the mode collapse issue of CycleGAN can be significantly alleviated, with an improved preservation of strokes and diversity of generated characters. The effectiveness of StrokeGAN is demonstrated by a series of generation tasks over nine datasets with different fonts. The numerical results demonstrate that StrokeGAN generally outperforms the state-of-the-art methods in terms of content and recognition accuracies, as well as certain stroke error, and also generates more realistic characters.