Reviewerly/exHarmony huggingface dataset:
Hierarchical Sequence Labeling Model for Aspect Sentiment Triplet Extraction

Aspect sentiment triplet extraction is an emerging task in aspect-based sentiment analysis, which aims at simultaneously identifying the aspect, the opinion expression, and the sentiment from a given review sentence. Existing studies divide this task into many sub-tasks and process them in a pipeline manner, which ignores the relevance between different sub-tasks and leads to error accumulation. In this paper, we propose a hierarchical sequence labeling model (HSLM) to recognize the sentiment triplets in an end-to-end manner. Concretely, HSLM consists of an aspect-level sequence labeling module, an opinion-level sequence labeling module, and a sentiment-level sequence labeling module. To learn the interactions between the above three modules, we further design three information fusion mechanisms, including aspect feature fusion mechanism, opinion feature fusion mechanism, and global feature fusion mechanism to refine high-level semantic information. To verify the effectiveness of our model, we conduct comprehensive experiments on four benchmark datasets. The experimental results demonstrate that our model achieves state-of-the-art performances.
Flexibly configuring task and motion planning problems for mobile manipulators

Robotic manipulation requires the planning at a symbolic level (task planning) and at a geometric level (motion planning). This paper presents a planning framework for both levels of planning that includes an easy way to configure their interconnection. Motion planning is done using The Kautham Project, which is equipped with the Open Motion Planning Library suite of sampling-based motion planners, and task planning is done using the Fast Forward task planner. Both planning levels can be accessed through Robotic Operating System interfaces using services. A client program then uses these task and motion planning services and an XML configuration file that defines the linkage between symbolic actions and geometric values, to compute the sequence of feasible robot motions that allow to successfully execute a manipulation task. An illustrative example using the TIAGo mobile manipulator in a kitchen environment is presented where the flexibility in configuring different instances of manipulation tasks is shown.
Non-well-founded Proof Theory of Transitive Closure Logic

Supporting inductive reasoning is an essential component is any framework of use in computer science. To do so, the logical framework must extend that of first-order logic. Transitive closure logic is a known extension of first-order logic that is particularly straightforward to automate. While other extensions of first-order logic with inductive definitions are a priori parametrized by a set of inductive definitions, the addition of a single transitive closure operator has the advantage of uniformly capturing all finitary inductive definitions. To further improve the reasoning techniques for transitive closure logic, we here present an infinitary proof system for it, which is an infinite descent –style counterpart to the existing (explicit induction) proof system for the logic. We show that the infinitary system is complete for the standard semantics and subsumes the explicit system. Moreover, the uniformity of the transitive closure operator allows semantically meaningful complete restrictions to be defined using simple syntactic criteria. Consequently, the restriction to regular infinitary (i.e., cyclic ) proofs provides the basis for an effective system for automating inductive reasoning.
Finding Achilles' Heel

Neural network-based models are notoriously known for their adversarial vulnerability. Recent adversarial machine learning mainly focused on images, where a small perturbation can be simply added to fool the learning model. Very recently, this practice has been explored in human action video attacks by adding perturbation to key frames. Unfortunately, frame selection is usually computationally expensive in run-time, and adding noises to all frames is unrealistic, either. In this paper, we present a novel yet efficient approach to address this issue. Multi-modal video data such as RGB, depth and skeleton data have been widely used for human action modeling, and they have been demonstrated with superior performance than a single modality. Interestingly, we observed that the skeleton data is more "vulnerable" under adversarial attack, and we propose to leverage this "Achilles' Heel" to attack multi-modal video data. In particular, first, an adversarial learning paradigm is designed to perturb skeleton data for a specific action under a black box setting, which highlights how body joints and key segments in videos are subject to attack. Second, we propose a graph attention model to explore the semantics between segments from different modalities and within a modality. Third, the attack will be launched in run-time on all modalities through the learned semantics. The proposed method has been extensively evaluated on multi-modal visual action datasets, including PKU-MMD and NTU-RGB+D to validate its effectiveness.
POISE: Efficient Cross-Domain Chinese Named Entity Recognization via Transfer Learning

To improve the performance of deep learning methods in case of a lack of labeled data for entity annotation in entity recognition tasks, this study proposes transfer learning schemes that combine the character to be the word to convert low-resource data symmetry into high-resource data. We combine character embedding, word embedding, and the embedding of the label features using high- and low-resource data based on the BiLSTM-CRF model, and perform the feature-transfer and parameter-sharing tasks in two domains of the BiLSTM network to annotate with zero resources. Before transfer learning, we must first calculate the label similarity between two different domains and select the label features with large similarity for feature transfer mapping. All training parameters of the source domain in the model are shared during the BiLSTM network processing and CRF layer. In addition, we also use the method of combining characters and words to reduce the problem of word segmentation across domains and reduce the error rate in label mapping. The results of experiments show that in terms of the overall F1 score, the proposed model without supervision was superior by 9.76 percentage points to the general parametric shared transfer learning method, and by 9.08 and 12.38 percentage points, respectively, to two recent high–low resource learning methods. The proposed scheme improves performance in terms of transfer learning between the high- and low-resource data and can identify the predicted data in the target domain.
Revisited: Comparison of Empirical Methods to Evaluate Visualizations Supporting Crafting and Assembly Purposes

Ubiquitous, situated, and physical visualizations create entirely new possibilities for tasks contextualized in the real world, such as doctors inserting needles. During the development of situated visualizations, evaluating visualizations is a core requirement. However, performing such evaluations is intrinsically hard as the real scenarios are safety-critical or expensive to test. To overcome these issues, researchers and practitioners adapt classical approaches from ubiquitous computing and use surrogate empirical methods such as Augmented Reality (AR), Virtual Reality (VR) prototypes, or merely online demonstrations. This approach's primary assumption is that meaningful insights can also be gained from different, usually cheaper and less cumbersome empirical methods. Nevertheless, recent efforts in the Human-Computer Interaction (HCI) community have found evidence against this assumption, which would impede the use of surrogate empirical methods. Currently, these insights rely on a single investigation of four interactive objects. The goal of this work is to investigate if these prior findings also hold for situated visualizations. Therefore, we first created a scenario where situated visualizations support users in do-it-yourself (DIY) tasks such as crafting and assembly. We then set up five empirical study methods to evaluate the four tasks using an online survey, as well as VR, AR, laboratory, and in-situ studies. Using this study design, we conducted a new study with 60 participants. Our results show that the situated visualizations we investigated in this study are not prone to the same dependency on the empirical method, as found in previous work. Our study provides the first evidence that analyzing situated visualizations through different empirical (surrogate) methods might lead to comparable results.
Remarks on black hole complexity puzzle

A bstract Recently a certain conceptual puzzle in the AdS/CFT correspondence, concerning the growth of quantum circuit complexity and the wormhole volume, has been identified by Bouland-Fefferman-Vazirani and Susskind. In this note, we propose a resolution of the puzzle and save the quantum Extended Church-Turing thesis by arguing that there is no computational shortcut in measuring the volume due to gravitational backreaction from bulk observers. A certain strengthening of the firewall puzzle from the computational complexity perspective, as well as its potential resolution, is also presented.
Characteristics and Challenges of Agile Software Development Adoption in Brazilian Government

Governments worldwide have been working to provide better digital services to citizens. In Brazil, this initiative is ongoing since the 2000âs with the aim create better digital solutions that provide access to government information, improvements in public services, and increase social participation. One of the strategies for developing digital solutions - i.e. software solutions - is the adoption of agile software development (ASD) methods, which are forms of software processes that enable delivering working software in a timely manner to respond to customer needs. While industry surveys are performed annually to understand ASD adoption in companies, little is known about the adoption of ASD in Brazilian government organizations and which are the challenges faced by these organizations. The goal of this study is thus to describe agile software development adoption in the Brazilian public sector, by showing the characteristics for adoption and challenges. We conducted a survey with practitioners of government-based organizations in Brazil and statistically analyzed data. Out of the 167 responses, we learned that ASD projects are mostly successful and, on their majority, they are conducted combined with other software development approaches. Also, accelerating product delivery and increasing productivity are ranked as the main reasons for agile adoption, followed by cultural change and resistance to change as the main challenges still faced by Brazilian government IT organizations in the use of ASD.
Geometric Stability Classification: Datasets, Metamodels, and Adversarial Attacks

Many recent advances in machine learning have been motivated by classification problems. For example, classification methods are used to differentiate between “spam” and “non-spam” emails, identify hand written digits, and recognize the content of photos. For each application, a different model and model architecture will often perform best. Therefore, machine learning research has been enabled by readily available benchmark datasets. In particular, benchmark datasets have been used by researchers to demonstrate that novel methods can achieve high accuracy, and to demonstrate common vulnerabilities of classification methods to adversarial attacks. In the recent mechanics literature, there has been substantial interest in machine learning driven metamodels. Metamodels, or models of models, are appealing because once trained, they typically require orders of magnitude less compute time than full fidelity simulations. However, a better understanding of which machine learning methods and model architectures will perform best on mechanical data has been limited. Here we introduce an open source dataset “BIC” (Buckling Instability Classification) where a heterogeneous column is subject to a fixed level of applied displacement and is classified as either “Stable” or “Unstable.” In addition to introducing this benchmark dataset, we show baseline metamodel performance, and show two different types of adversarial attack. We anticipate that the open source BIC dataset will enable the future development of improved methods for classification problems in mechanics.
Generalized Zero-Shot Video Classification via Generative Adversarial Networks

Zero-shot learning (ZSL) is to classify images according to detailed attribute annotations into new categories that are unseen during the training stage. Generalized zero-shot learning (GZSL) adds seen categories to the test samples. Since the learned classifier has inherent bias against seen categories, GZSL is more challenging than traditional ZSL. However, at present, there is no detailed attribute description dataset for video classification. Therefore, the current zero-shot video classification problem is based on the synthesis of generative adversarial networks trained on seen-class features into unseen-class features for ZSL classification. In order to solve this problem, we propose a description text dataset based on the UCF101 action recognition dataset. To the best of our knowledge, this is the first work to add description of the classes to zero-shot video classification. We propose a new loss function that combines visual features with textual features. We extract text features from the proposed text data set, and constrain the process of generating synthetic features based on the principle that videos with similar text types should be similar. Our method reapplies the traditional zero-shot learning idea to video classification. From the experimental point of view, our proposed dataset and method have a positive impact on the generalized zero-shot video classification.
An Improved Local Search Genetic Algorithm with a New Mapped Adaptive Operator Applied to Pseudo-Coloring Problem

In many situations, an expert must visually analyze an image arranged in grey levels. However, the human eye has strong difficulty in detecting details in this type of image, making it necessary to use artificial coloring techniques. The pseudo-coloring problem (PsCP) consists of assigning to a grey-level image, pre-segmented in K sub-regions, a set of K colors that are as dissimilar as possible. This problem is part of the well-known class of NP-Hard problems and, therefore, does not present an exact solution for all instances. Thus, meta-heuristics has been widely used to overcome this problem. In particular, genetic algorithm (GA) is one of those techniques that stands out in the literature and has already been used in PsCP. In this work, we present a new method that consists of an improvement of the GA specialized in solving the PsCP. In addition, we propose the addition of local search operators and rules for adapting parameters based on symmetric mapping functions to avoid common problems in this type of technique such as premature convergence and inadequate exploration in the search space. Our method is evaluated in three different case studies: the first consisting of the pseudo-colorization of real-world images on the RGB color space; the second consisting of the pseudo-colorization in RGB color space considering synthetic and abstract images in which its sub-regions are fully-connected; and the third consisting of the pseudo-colorization in the Munsell atlas color set. In all scenarios, our method is compared with other state-of-the-art techniques and presents superior results. Specifically, the use of mapped automatic adjustment operators proved to be powerful in boosting the proposed meta-heuristic to obtain more robust results in all evaluated instances of PsCP in all the considered case studies.
AISE: Attending to Intent and Slots Explicitly for better spoken language understanding

Spoken language understanding (SLU) plays a central role in dialog systems and typically involves two tasks: intent detection and slot filling. Existing joint models improve the performance by introducing richer words, intents and slots semantic features. However, methods that model the explicit interactions between these features have not been further explored. In this paper, we propose a novel joint model based on the position-aware multi-head masked attention mechanism, which explicitly models the interaction between the word encoding feature and the intent–slot features, thereby generating the context features that contribute to slot filling. In addition, we adopt the multi-head attention mechanism to summarize the utterance-level semantic knowledge for intent detection. Experiments show that our model achieves state-of-the-art results and improves the sentence-level semantic frame accuracy, with 2.30% and 0.69% improvement relative to the previous best model on the SNIPS and ATIS datasets, respectively.
Pairwise Similarity Regularization for Adversarial Domain Adaptation

Domain adaptation aims at learning a predictive model that can generalize to a new target domain different from the source (training) domain. To mitigate the domain gap, adversarial training has been developed to learn domain invariant representations. State-of-the-art methods further make use of pseudo labels generated by the source domain classifier to match conditional feature distributions between the source and target domains. However, if the target domain is more complex than the source domain, the pseudo labels are unreliable to characterize the class-conditional structure of the target domain data, undermining prediction performance. To resolve this issue, we propose a Pairwise Similarity Regularization (PSR) approach that exploits cluster structures of the target domain data and minimizes the divergence between the pairwise similarity of clustering partition and that of pseudo predictions. Therefore, PSR guarantees that two target instances in the same cluster have the same class prediction and thus eliminate the negative effect of unreliable pseudo labels. Extensive experimental results show that our PSR method significantly boosts the current adversarial domain adaptation methods by a large margin on four visual benchmarks. In particular, PSR achieves a remarkable improvement of more than 5% over the state-of-the-art on several hard-to-transfer tasks.
Image Sentiment Transfer

In this work, we introduce an important but still unexplored research task -- image sentiment transfer. Compared with other related tasks that have been well-studied, such as image-to-image translation and image style transfer, transferring the sentiment of an image is more challenging. Given an input image, the rule to transfer the sentiment of each contained object can be completely different, making existing approaches that perform global image transfer by a single reference image inadequate to achieve satisfactory performance. In this paper, we propose an effective and flexible framework that performs image sentiment transfer at the object level. It first detects the objects and extracts their pixel-level masks, and then performs object-level sentiment transfer guided by multiple reference images for the corresponding objects. For the core object-level sentiment transfer, we propose a novel Sentiment-aware GAN (SentiGAN). Both global image-level and local object-level supervisions are imposed to train SentiGAN. More importantly, an effective content disentanglement loss cooperating with a content alignment step is applied to better disentangle the residual sentiment-related information of the input image. Extensive quantitative and qualitative experiments are performed on the object-oriented VSO dataset we create, demonstrating the effectiveness of the proposed framework.
Neutral Face Game Character Auto-Creation via PokerFace-GAN

Game character customization is one of the core features of many recent Role-Playing Games (RPGs), where players can edit the appearance of their in-game characters with their preferences. This paper studies the problem of automatically creating in-game characters with a single photo. In recent literature on this topic, neural networks are introduced to make game engine differentiable and the self-supervised learning is used to predict facial customization parameters. However, in previous methods, the expression parameters and facial identity parameters are highly coupled with each other, making it difficult to model the intrinsic facial features of the character. Besides, the neural network based renderer used in previous methods is also difficult to be extended to multi-view rendering cases. In this paper, considering the above problems, we propose a novel method named "PokerFace-GAN" for neutral face game character auto-creation. We first build a differentiable character renderer which is more flexible than the previous methods in multi-view rendering cases. We then take advantage of the adversarial training to effectively disentangle the expression parameters from the identity parameters and thus generate player-preferred neutral face (expression-less) characters. Since all components of our method are differentiable, our method can be easily trained under a multi-task self-supervised learning paradigm. Experiment results show that our method can generate vivid neutral face game characters that are highly similar to the input photos. The effectiveness of our method is verified by comparison results and ablation studies.
SketchMan

Human free-hand sketches have been studied in various fields including sketch recognition, synthesis and sketch-based image retrieval. We propose a new challenging task sketch enhancement (SE) defined in an ill-posed space, i.e. enhancing a non-professional sketch (NPS) to a professional sketch (PS), which is a creative generation task different from sketch abstraction, sketch completion and sketch variation. For the first time we release a database of NPS with PS for anime characters. We cast sketch enhancement as an image-to-image translation problem by exploiting the relationship to corresponding intensive or sparse pixel domains for sketch domain. Specifically, we explore three different routines based on conditional generative adversarial network (cGAN), i.e. Sketch-Sketch (SS), Sketch-Colorization-Sketch (SCS) and Sketch-Abstraction-Sketch (SAS). SS is a one-stage model that directly maps NPS to PS, while SCS and SAS are two-stage models where auxiliary inputs, grayscale parsing and shape parsing, are involved. Multiple metrics are used to evaluate the performance of the models in both the sketch domain and other low-level feature domains. With quantitative and qualitative analysis of the experiments, we have established solid baselines, which, we hope, could encourage more research conducted on this task. Our dataset is publicly available via https://github.com/LCXCUC/SketchMan2020.
WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.