Reviewerly/exHarmony huggingface dataset:
Superbizarre Is Not Superb: Derivational Morphology Improves BERT’s Interpretation of Complex Words

How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words?We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives.We show that PLMs can be interpreted as serial dual-route models, i.e., the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words.This hypothesis is confirmed by a series of semantic probing tasks on which Del-BERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation.Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.
Perception-Based Temporal Logic Planning in Uncertain Semantic Maps

In this article, we address a multi-robot planning problem in environments with partially unknown semantics. The environment is assumed to have a known geometric structure (e.g., walls) and to be occupied by static labeled landmarks with uncertain positions and classes. This modeling approach gives rise to an uncertain semantic map generated by semantic simultaneous localization and mapping algorithms. Our goal is to design control policies for robots equipped with noisy perception systems so that they can accomplish collaborative tasks captured by global temporal logic specifications. To specify missions that account for environmental and perceptual uncertainty, we employ a fragment of linear temporal logic (LTL), called co-safe LTL, defined over perception-based atomic predicates modeling probabilistic satisfaction requirements. The perception-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that are updated online to adapt to a continuously learned semantic map. We provide extensive experiments to demonstrate the efficiency of the proposed planning architecture.
D-LEMA: Deep Learning Ensembles from Multiple Annotations - Application to Skin Lesion Segmentation

Medical image segmentation annotations suffer from inter- and intra-observer variations even among experts due to intrinsic differences in human annotators and ambiguous boundaries. Leveraging a collection of annotators’ opinions for an image is an interesting way of estimating a gold standard. Although training deep models in a supervised setting with a single annotation per image has been extensively studied, generalizing their training to work with datasets containing multiple annotations per image remains a fairly unexplored problem. In this paper, we propose an approach to handle annotators’ disagreements when training a deep model. To this end, we propose an ensemble of Bayesian fully convolutional networks (FCNs) for the segmentation task by considering two major factors in the aggregation of multiple ground truth annotations: (1) handling contradictory annotations in the training data originating from inter-annotator disagreements and (2) improving confidence calibration through the fusion of base models’ predictions. We demonstrate the superior performance of our approach on the ISIC Archive and explore the generalization performance of our proposed method by cross-dataset evaluation on the PH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and DermoFit datasets.
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks

Quantization plays an important role in deep neural network (DNN) hardware. In particular, logarithmic quantization has multiple advantages for DNN hardware implementations, and its weakness in terms of lower performance at high precision compared with linear quantization has been recently remedied by what we call selective two-word logarithmic quantization (STLQ). However, there is a lack of training methods designed for STLQ or even logarithmic quantization in general. In this paper we propose a novel STLQ-aware training method, which significantly out-performs the previous state-of-the-art training method for STLQ. Moreover, our training results demonstrate that with our new training method, STLQ applied to weight parameters of ResNet-18 can achieve the same level of performance as state-of-the-art quantization method, APoT, at 3-bit precision. We also apply our method to various DNNs in image enhancement and semantic segmentation, showing competitive results.
SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans

We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion. Our self-supervised approach learns to jointly inpaint geometry and color by correlating an incomplete RGB-D scan with a more complete version of that scan. Notably, rather than relying on 3D reconstruction losses to inform our 3D geometry and color reconstruction, we propose adversarial and perceptual losses operating on 2D renderings in order to achieve high-resolution, high-quality colored reconstructions of scenes. This exploits the high-resolution, self-consistent signal from individual raw RGB-D frames, in contrast to fused 3D reconstructions of the frames which exhibit inconsistencies from view-dependent effects, such as color balancing or pose inconsistencies. Thus, by informing our 3D scene generation directly through 2D signal, we produce high-quality colored reconstructions of 3D scenes, outperforming state of the art on both synthetic and real data.
Multilingual Healthcare Chatbot Using Machine Learning

The healthcare sector is one of the largest focus areas in the world today. Health problems are becoming increasingly common. India faces a huge challenge in terms of managing rural healthcare. Early diagnosis and treatment of diseases can play an instrumental role. Physical consultation, particularly in the rural areas, is costly and time consuming. The solution is adopting healthcare chatbots. The proposed solution describes a multilingual healthcare chatbot application that can perform disease diagnosis based on user symptoms. It also responds to user queries by calculating sentence similarity by using TF-IDF and Cosine Similarity techniques and choosing the most appropriate response from its knowledge database. The multilingual capabilities of the chatbot system make it highly suitable for use in rural India. The chatbot application currently supports three languages namely English, Hindi and Gujarati. The chatbot application converses with the user using concepts of Natural Language Processing and also supports speech to text and text to speech conversion so that the user can also communicate using voice. Five different Machine Learning algorithms have been analyzed for disease prediction. The Random Forest Classifier produces the best results and gives an accuracy of 98.43%. Thus, it is used as the system's core classifier.
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks

Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing framework that expresses multiple parameters using a single code, and has recently achieved state-of-the-art network compression on a range of core vision and natural language processing tasks. Key to the success of vector quantization is deciding which parameter groups should be compressed together. Previous work has relied on heuristics that group the spatial dimension of individual convolutional filters, but a general solution remains unaddressed. This is desirable for pointwise convolutions (which dominate modern architectures), linear layers (which have no notion of spatial dimension), and convolutions (when more than one filter is compressed to the same codeword). In this paper we make the observation that the weights of two adjacent layers can be permuted while expressing the same function. We then establish a connection to rate-distortion theory and search for permutations that result in networks that are easier to compress. Finally, we rely on an annealed quantization algorithm to better compress the network and achieve higher final accuracy. We show results on image classification, object detection, and segmentation, reducing the gap with the uncompressed model by 40 to 70% w.r.t. the current state of the art. All our experiments can be reproduced using the code at https://github.com/uber-research/permute-quantize-finetune.
“Alexa, Can I Program You?”: Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa

Growing up in an artificial intelligence-filled world, with Siri and Amazon Alexa often within arm's—or speech's—reach, could have significant impact on children. Conversational agents could influence how students anthropomorphize computer systems or develop a theory of mind. Previous research has explored how conversational agents are used and perceived by children within and outside of learning contexts. This study investigates how middle and high school students' perceptions of Alexa change through programming their own conversational agents in week-long AI education workshops. Specifically, we investigate the workshops' influence on student perceptions of Alexa's intelligence, friendliness, aliveness, safeness, trustworthiness, human-likeness, and feelings of closeness. We found that students felt Alexa was more intelligent and felt closer to Alexa after the workshops. We also found strong correlations between students' perceptions of Alexa's friendliness and trustworthiness, and safeness and trustworthiness. We recommend designers carefully consider personification, transparency, playfulness and utility when designing conversational agents for learning contexts.
OIS-RF: A novel overlap and imbalance sensitive random forest

Classifier learning with imbalanced data is one of the main challenges in the data mining community. An ensemble of classifiers is a popular solution to this problem, and it has acquired significant attention owing to its better performance as compared to individual classifiers. In this paper, we propose an imbalanced classification ensemble method, which is hereafter referred to as overlap and imbalanced sensitive random forest (OIS-RF). We consider the existence of overlap in imbalanced data and create a new coefficient called Hard To Learn (HTL) which aims to measure the degree of importance for each training instance. In this regard, OIS-RF focuses more on learning the instances with high importance in each sub-dataset. Furthermore, to encourage the diversity of the ensemble, a weighted bootstrap method is proposed to generate sub-datasets containing diverse local information. The proposed method is evaluated on imbalanced datasets and is supported by statistical analyses. The results show that our method outperforms 9 state-of-the-art ensemble algorithms.
Consistency-based Active Learning for Object Detection

Active learning aims to improve the performance of the task model by selecting the most informative samples with a limited budget. Unlike most recent works that focus on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between the original and augmented data. CALD has three appealing benefits. (i) CALD is systematically designed by investigating the weaknesses of existing active learning methods, which do not take the unique challenges of object detection into account. (ii) CALD unifies box regression and classification with a single metric, which is not concerned with active learning methods for classification. CALD also focuses on the most informative local region rather than the whole image, which is beneficial for object detection. (iii) CALD not only gauges individual information for sample selection but also leverages mutual information to encourage a balanced data distribution. Extensive experiments show that CALD significantly outperforms existing state-of-the-art task-agnostic and detection-specific active learning methods on general object detection datasets. Based on the Faster R-CNN detector, CALD consistently surpasses the baseline method (random selection) by 2.9/2.8/0.8 mAP on average on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO. Code is available at https://github.com/we1pingyu/CALD
Divergence Optimization for Noisy Universal Domain Adaptation

Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label sets. In practice, however, it is difficult to obtain a large amount of perfectly clean labeled data in a source domain with limited resources. Existing UniDA methods rely on source samples with correct annotations, which greatly limits their application in the real world. Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain. This paper introduces a two-head convolutional neural network framework to solve all problems simultaneously. Our network consists of one common feature generator and two classifiers with different decision boundaries. By optimizing the divergence between the two classifiers’ outputs, we can detect noisy source samples, find "unknown" classes in the target domain, and align the distribution of the source and target domains. In an extensive evaluation of different domain adaptation settings, the proposed method outperformed existing methods by a large margin in most settings.
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models

This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG).Considering the few-shot setting, we leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation.We make three major technical contributions, namely representation alignment for bridging the semantic gap between KG encodings and PLMs, relation-biased KG linearization for deriving better input representations, and multi-task learning for learning the correspondence between KG and text.Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our model on KG-to-text generation task.In particular, our model outperforms all comparison methods on both fully-supervised and fewshot settings.Our code and datasets are available at https:
Multi-Scale Structure-Aware Network for Weakly Supervised Temporal Action Detection

Weakly supervised temporal action detection has better scalability and practicability than fully supervised action detection in reality deployment. However, it is difficult to learn a robust model without temporal action boundary annotations. In this paper, we propose an en-to-end Multi-Scale Structure-Aware Network (MSA-Net) for weakly supervised temporal action detection by exploring both the global structure information of a video and the local structure information of actions. The proposed SA-Net enjoys several merits. First, to localize actions with different durations, each video is encoded into feature representations with different temporal scales. Second, based on the multi-scale feature representation, the proposed model has designed two effective structure modeling mechanisms including global structure modeling and local structure modeling, which can effectively learn discriminative structure aware representations for robust and complete action detection. To the best of our knowledge, this is the first work to fully explore the global and local structure information in a unified deep model for weakly supervised action detection. And extensive experimental results on two benchmark datasets demonstrate that the proposed MSA-Net performs favorably against state-of-the-art methods.
Perceptions of digital technology in nursing education: A qualitative study

The aim of this study was to explore how digital technologies can facilitate interactive learning in a 15-credit theory module on basic nursing. Digital platforms are in widespread use in nursing education; however, the rapid technological advancement provides new opportunities to support teaching and learning. This indicates the need to explore how educational digital technologies can be used as an integral part of learning activities to promote quality and relevance in nursing education. This study was carried out at the Department of Nursing Science at a university in Norway. The university has been using a digital platform for many years and has recently adopted a new platform named Canvas in 2018. An explorative qualitative research design using focus group interviews. Three focus group interviews were conducted with students and teachers involved in a 15-credit module in basic nursing as part of the first year of the bachelor program. A total of 10 participants were recruited for this study and data were analysed using systematic text condensation. The analysis resulted in three themes related to experiences with existing learning activities and general perceptions of digital technologies: ‘pedagogical methodology affects learning and social fellowship’; ‘need for varied, high-quality forms of learning’ and ‘need for structure and predictability’. This study revealed several educational needs and emphasised the importance of digital competence involving professional knowledge and skills to facilitate the educational use of digital technologies.
Customer sentiment analysis with more sensibility

Customers’ evaluations on products can be derived by analyzing online reviews using machine learning. Positive or negative responses can be sensed by words they write in reviews, and topics they compliment or complain about can be grasped by clustering reviews. Combination of those results is regarded as the customers’ sentiment analysis. When reviews are given as free-form text without scores, general-purpose dictionaries are used to recognize sentiment words. However, customers do not only use standard words to express their emotions, but they also use non-grammatical language such as internet jargon. Unfortunately, existing methods cannot capture those sentiment words. Moreover, combination of sentiment words with customer topics simply represents frequencies and does not indicate detailed evaluation patterns. In this study, we propose a customer sentiment analysis method consisting of sentiment propagation and customer review analysis. It works more sensibly by expanding sentiment words from dictionary to those varying words as mentioned above. To implement this, semi-supervised learning is employed to a word graph that is constructed by a word embedding algorithm. Using this more sensible word graph, customer review analysis is conducted. Reviews are grouped into major complaint topics. Meanwhile, an index for customer dissatisfaction is designed by composition of ‘controversy’ and ‘complaint’. The former stands for ‘coverage of dissatisfaction’ while the latter indicates ‘degree of dissatisfaction’. The proposed method was applied to 3,11,550 reviews across five automobiles from ten internet communities. Case study illustrates which parts of automobiles lead to customer dissatisfaction, and therefore where investment and examination are required.
SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Features Recombining

The channel redundancy in feature maps of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources. In this work, we design a novel Slim Convolution (SlimConv) module to boost the performance of CNNs by reducing channel redundancies. Our SlimConv consists of three main steps: Reconstruct, Transform and Fuse, through which the features are splitted and reorganized in a more efficient way, such that the learned weights can be compressed effectively. In particular, the core of our model is a weight flipping operation which can largely improve the feature diversities, contributing to the performance crucially. Our SlimConv is a plug-and-play architectural unit which can be used to replace convolutional layers in CNNs directly. We validate the effectiveness of SlimConv by conducting comprehensive experiments on ImageNet, MS COCO2014, Pascal VOC2012 segmentation, and Pascal VOC2007 detection datasets. The experiments show that SlimConv-equipped models can achieve better performances consistently, less consumption of memory and computation resources than non-equipped conterparts. For example, the ResNet-101 fitted with SlimConv achieves 77.84% top-1 classification accuracy with 4.87 GFLOPs and 27.96M parameters on ImageNet, which shows almost 0.5% better performance with about 3 GFLOPs and 38% parameters reduced.
Multi-Class Confusion Matrix Reduction method and its application on Net Promoter Score classification problem

The paper presents a novel method for reducing a multi-class Confusion Matrix into a 2 × 2 version enabling the use of the relevant performance metrics and methods like the Receiver Operator Characteristic and the Area Under the Curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a specific Confusion Matrix type. The developed method is then exploited for the assessment of several state-of-the-art machine learning algorithms applied on a customer experience metric.
The Logic of Graph Neural Networks

Graph neural networks (GNNs) are deep learning architectures for machine learning problems on graphs. It has recently been shown that the expressiveness of GNNs can be characterised precisely by the combinatorial Weisfeiler-Leman algorithms and by finite variable counting logics. The correspondence has even led to new, higher-order GNNs corresponding to the WL algorithm in higher dimensions.The purpose of this paper is to explain these descriptive characterisations of GNNs.
A knowledge-guide hierarchical learning method for long-tailed image classification

Deep visual recognition methods have achieved excellent performance on artificially constructed image datasets where the data distribution is balanced. However, in real-world scenarios, data distribution is usually extremely imbalanced and exhibit a long-tailed distribution where data in each head class is more than the class in the tail. Many efficient deep learning methods fail to work normally, i.e., they perform well in the head class while poor in the tail class. In this paper, we propose a two-layer Hierarchical-Learning Long-Tailed Recognition (HL-LTR) algorithm which transforms the long-tailed problem into a hierarchical classification problem by constructing a hierarchical superclass tree in which each layer corresponds to a recognition task. In the first layer of the tree, the degree of data imbalance is largely decreased. The recognition task of the second layer is the original long-tailed recognition problem. The training of HL-LTR is top-down. The knowledge learned by the first layer transfers to classes of the second layer and guides the feature learning of the second layer by using attention mechanism module and knowledge distillation method. Compared with directly solving the most difficult long-tailed recognition task, HL-LTR achieves better performance due to its progressive learning method from easy to difficult and effective knowledge transfer strategy.