Reviewerly/exHarmony huggingface dataset:
Learning from Noisy Labels with Complementary Loss Functions

Recent researches reveal that deep neural networks are sensitive to label noises hence leading to poor generalization performance in some tasks. Although different robust loss functions have been proposed to remedy this issue, they suffer from an underfitting problem, thus are not sufficient to learn accurate models. On the other hand, the commonly used Cross Entropy (CE) loss, which shows high performance in standard supervised learning (with clean supervision), is non-robust to label noise. In this paper, we propose a general framework to learn robust deep neural networks with complementary loss functions. In our framework, CE and robust loss play complementary roles in a joint learning objective as per their learning sufficiency and robustness properties respectively. Specifically, we find that by exploiting the memorization effect of neural networks, we can easily filter out a proportion of hard samples and generate reliable pseudo labels for easy samples, and thus reduce the label noise to a quite low level. Then, we simply learn with CE on pseudo supervision and robust loss on original noisy supervision. In this procedure, CE can guarantee the sufficiency of optimization while the robust loss can be regarded as the supplement. Experimental results on benchmark classification datasets indicate that the proposed method helps achieve robust and sufficient deep neural network training simultaneously.
Every Bite Is an Experience: Key Point Analysis of Business Reviews

Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary.These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views.Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data.We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata.We show empirically that these novel extensions of KPA substantially improve its performance.We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.
The Role of Using Cloud Computing in Improving the Quality of Accounting Education in Palestinian Universities in Light of the Covid-19 Pandemic

The study mainly aimed at identifying the role of using cloud computing in improving the quality of accounting education in Palestinian universities in light of the Covid-19 pandemic, and to answer research questions and test the study hypotheses, the researchers adopted the descriptive and analytical approach, and the questionnaire was used as a study tool, and the study community is formed of the (63) members of the teaching staff working in the accounting departments in the faculties of commerce in the Palestinian universities in Gaza Strip, and due to the small size of the community, the method of comprehensive community enumeration was used. The results of the study also found that the use of cloud computing in accounting education helps faculty members and students to access and retrieve their files and applications at any time and from anywhere in light of the Covid-19 pandemic and it also helps develop students' technological skills to suit the requirements of the labor market. The study also recommended the necessity of using cloud computing in accounting education in Palestinian universities for its role in developing the professional and technological skills of students, and improving their educational attainment by obtaining the largest amount of useful information, especially in light of the Covid-19 pandemic.
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations

Open-domain dialog systems have a usercentric goal: to provide humans with an engaging conversation experience.User engagement is one of the most important metrics for evaluating open-domain dialog systems, and could also be used as real-time feedback to benefit dialog policy learning.Existing work on detecting user disengagement typically requires hand-labeling many dialog samples.We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem.Specifically, instead of manually labeling training samples, we first use a set of labeling heuristics to label training samples automatically.We then denoise the weakly labeled data using the Shapley algorithm.Finally, we use the denoised data to train a user engagement detector.Our experiments show that HERALD improves annotation efficiency significantly and achieves 86% user disengagement detection accuracy in two dialog corpora.Our implementation is available at https:
Double Oracle Algorithm for Computing Equilibria in Continuous Games

Many efficient algorithms have been designed to recover Nash equilibria of various classes of finite games. Special classes of continuous games with infinite strategy spaces, such as polynomial games, can be solved by semidefinite programming. In general, however, continuous games are not directly amenable to computational procedures. In this contribution, we develop an iterative strategy generation technique for finding a Nash equilibrium in a whole class of continuous two-person zero-sum games with compact strategy sets. The procedure, which is called the double oracle algorithm, has been successfully applied to large finite games in the past. We prove the convergence of the double oracle algorithm to a Nash equilibrium. Moreover, the algorithm is guaranteed to recover an approximate equilibrium in finitely-many steps. Our numerical experiments show that it outperforms fictitious play on several examples of games appearing in the literature. In particular, we provide a detailed analysis of experiments with a version of the continuous Colonel Blotto game.
Deep Learning for Detecting Human Activities From Piezoelectric-Based Kinetic Energy Signals

Kinetic energy harvesting technologies have been progressively used to power wearable devices and to sense the context through energy generation patterns. However, detecting human activities with signals from kinetic harvesters still needs improvement due to the use of approaches based on handcrafted features and the overfitting to device location or subjects. Hence, in this article, we present a deep learning architecture that leverages the feature extraction capability of the convolutional neural networks and the construction of the temporal sequences of recurrent neural networks to improve existing classification results. To provide sufficient data for the deep learning classifier, we propose three data augmentation methods to increase intraclass variance simulating new users performing the same activities. The proposed architecture outperforms existing approaches of kinetic harvesting-based human activity recognition by 13% of accuracy when the training data are augmented with the proposed methods. Finally, given the dependency of kinetic harvesting signals on device location and subjects, we employ transfer learning to improve the classification performance when the system is exposed to new subjects and locations. Transfer learning helps to increase classification performance by 30% when the device location is changed and 35% when the data come from a new subject.
Neural Stylistic Response Generation with Disentangled Latent Variables

Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style.Meanwhile, using monolingual stylistic data to increase style intensity often leads to the expense of decreasing content relevance.In this paper, we propose to disentangle the content and style in latent space by diluting sentence-level information in style representations.Combining the desired style representation and a response content representation will then obtain a stylistic response.Our approach achieves a higher BERT-based style intensity score and comparable BLEU scores, compared with baselines.Human evaluation results show that our approach significantly improves style intensity and maintains content relevance.
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining

Slot-filling is an essential component for building task-oriented dialog systems.In this work, we focus on the zero-shot slot-filling problem, where the model needs to predict slots and their values, given utterances from new domains without training on the target domain.Prior methods directly encode slot descriptions to generalize to unseen slot types.However, raw slot descriptions are often ambiguous and do not encode enough semantic information, limiting the models' zero-shot capability.To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model.We use a linguistically motivated questioning strategy to turn descriptions into questions, allowing the model to generalize to unseen slot types.Moreover, our QASF model can benefit from weak supervision signals from QA pairs synthetically generated from unlabeled conversations.Our full system substantially outperforms baselines by over 5% on the SNIPS benchmark.
Word-level Sign Language Recognition Using Linguistic Adaptation of 77 GHz FMCW Radar Data

Over the years, there has been much research in both wearable and video-based American Sign Language (ASL) recognition systems. However, the restrictive and invasive nature of these sensing modalities remains a significant disadvantage in the context of Deaf-centric smart environments or devices that are responsive to ASL. This paper investigates the efficacy of RF sensors for word-level ASL recognition in support of human-computer interfaces designed for deaf or hard-of-hearing individuals. A principal challenge is the training of deep neural networks given the difficulty in acquiring native ASL signing data. In this paper, adversarial domain adaptation is exploited to bridge the physical/kinematic differences between the copysigning of hearing individuals (repetition of sign motion after viewing a video), and native signing of Deaf individuals who are fluent in sign language. Domain adaptation results are compared with those attained by directly synthesizing ASL signs using generative adversarial networks (GANs). Kinematic improvements to the GAN architecture, such as the insertion of micro-Doppler signature envelopes in a secondary branch of the GAN, are utilized to boost performance. Word-level classification accuracy of 91.3% is achieved for 20 ASL words.
Correction to: Abusive language detection from social media comments using conventional machine learning and deep learning approaches

A correction to this paper has been published: https://doi.org/10.1007/s00530-021-00819-0
GPU-Accelerated Graph Label Propagation for Real-Time Fraud Detection

Fraud detection is a pressing challenge for most financial and commercial platforms. In this paper, we study the processing pipeline of fraud detection in a large e-commerce platform of TaoBao. Graph label propagation (LP) is a core component in this pipeline to detect suspicious clusters from the user-interaction graph.Furthermore, the run-time of the LP component occupies 75% overhead of TaoBao's automated detection pipeline. To enable real-time fraud detection, we propose a GPU-based framework, called GLP, to support large-scale LP workloads in enterprises.We have identified two key challenges when integrating GPU acceleration into TaoBao's data processing pipeline: (1)programmability for evolving fraud detection logics; (2)demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. We propose novel GPU-centric optimizations by leveraging the community as well as power-law properties of large graphs. Extensive experiments have confirmed the effectiveness of our proposed optimizations. With a single GPU, GLP supports a real billion-scale graph workload from the fraud detection pipeline of TaoBao and achieves 8.2x speedup to the current in-house distributed solution running on high-end multicore machines.
Improving Zero-Shot Translation by Disentangling Positional Information

Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation.Despite being conceptually attractive, it often suffers from low output quality.The difficulty of generalizing to new translation directions suggests the model representations are highly specific to those language pairs seen in training.We demonstrate that a main factor causing the language-specific representations is the positional correspondence to input tokens.We show that this can be easily alleviated by removing residual connections in an encoder layer.With this modification, we gain up to 18.5 BLEU points on zero-shot translation while retaining quality on supervised directions.The improvements are particularly prominent between related languages, where our proposed model outperforms pivot-based translation.Moreover, our approach allows easy integration of new languages, which substantially expands translation coverage.By thorough inspections of the hidden layer outputs, we show that our approach indeed leads to more languageindependent representations. 1
Careful with That! Observation of Human Movements to Estimate Objects Properties

Humans are very effective at interpreting subtle properties of the partner's movement and use this skill to promote smooth interactions. Therefore, robotic platforms that support human partners in daily activities should acquire similar abilities. In this work we focused on the features of human motor actions that communicate insights on the weight of an object and the carefulness required in its manipulation. Our final goal is to enable a robot to autonomously infer the degree of care required in object handling and to discriminate whether the item is light or heavy, just by observing a human manipulation. This preliminary study represents a promising step towards the implementation of those abilities on a robot observing the scene with its camera. Indeed, we succeeded in demonstrating that it is possible to reliably deduct if the human operator is careful when handling an object, through machine learning algorithms relying on the stream of visual acquisition from either a robot camera or from a motion capture system. On the other hand, we observed that the same approach is inadequate to discriminate between light and heavy objects.
Unsupervised Disentanglement of Linear-Encoded Facial Semantics

We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized 3D deformable facial reconstruction method to decompose single-view GAN generations into multiple semantics. Latent representations are then extracted to capture interpretable facial semantics. In this work, we make it possible to get rid of labels for disentangling meaningful facial semantics. Also, we demonstrate that the guided extrapolation along the disentangled representations can help with data augmentation, which sheds light on handling unbalanced data. Finally, we provide an analysis of our learned localized facial representations and illustrate that the semantic information is encoded, which surprisingly complies with human intuition. The overall unsupervised design brings more flexibility to representation learning in the wild.
On the Language Coverage Bias for Neural Machine Translation

Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice.By carefully designing experiments, we provide comprehensive analyses of the language coverage bias in the training data, and find that using only the source-original data achieves comparable performance with using full training data.Based on these observations, we further propose two simple and effective approaches to alleviate the language coverage bias problem through explicitly distinguishing between the source-and target-original training data, which consistently improve the performance over strong baselines on six WMT20 translation tasks.Complementary to the translationese effect, language coverage bias provides another explanation for the performance drop caused by back-translation (Marie et al., 2020).We also apply our approach to both back-and forward-translation and find that mitigating the language coverage bias can improve the performance of both the two representative data augmentation methods and their tagged variants (Caswell et al., 2019).
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities

We have entered the era of big data, and it is considered to be the ”fuel” for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals’ privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performance models shared among multiple parties while still complying with user privacy and data confidentiality requirements. Although FL has been intensively studied and used in real applications, there is still limited research related to its prospects and applications as a FLaaS (Federated Learning as a Service) to interested 3rd parties. In this paper, we present a FLaaS system: DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process. We describe particularly the scenario where our DID-eFed enables the FLaaS among hospitals and research institutions.
Context-Aware Software Vulnerability Classification Using Machine Learning

Managing the vulnerabilities reported by a number of security scanning software is a tedious and time-consuming task, especially in large-scale, modern communication networks. Particular software vulnerabilities can have a range of impacts on an IT system depending on the context in which they were detected. Moreover, scanning software can report thousands of issues, which makes performing operations, such as analysis and prioritization, very costly from an organizational point of view. In this paper, we propose a context-aware software vulnerability classification system, Mixeway, that relies on machine learning to automatize the whole process. By training a model using known and analyzed vulnerabilities together with Natural Language Processing techniques to properly manage the information that the vulnerability description contains, we show that it is possible to predict the class that defines how severe the detected vulnerability is. The experimental results obtained on a real-life dataset collected by Mixeway for about 12 months from the infrastructure of one of the major mobile network operators in Poland prove that the proposed solution is useful and effective.
Evaluating morphological typology in zero-shot cross-lingual transfer

Cross-lingual transfer has improved greatly through multi-lingual language model pretraining, reducing the need for parallel data and increasing absolute performance.However, this progress has also brought to light the differences in performance across languages.Specifically, certain language families and typologies seem to consistently perform worse in these models.In this paper, we address what effects morphological typology has on zero-shot cross-lingual transfer for two tasks: Part-of-speech tagging and sentiment analysis.We perform experiments on 19 languages from four language typologies (fusional, isolating, agglutinative, and introflexive) and find that transfer to another morphological type generally implies a higher loss than transfer to another language with the same morphological typology.Furthermore, POS tagging is more sensitive to morphological typology than sentiment analysis and, on this task, models perform much better on fusional languages than on the other typologies.tools via robust projection across aligned corpora.In
Visibility Detection Algorithm of Single Fog Image Based on the Ratio of Wavelength Residual Energy

Different visibilities and different wavelength attenuations can cause color deviation problem in some dehazing algorithms. A visibility detection algorithm based on a single fog image is proposed. First, the visibility range of the image is preliminarily determined according to the transmissivity; then, the normalized differences between the residual energy ratios of different wavelengths of RGB channels are calculated, and the pixels with large gray deviation of a single channel are filtered to improve the calculation accuracy; finally, the image visibility detection value is calculated. The experimental results show that the proposed algorithm not only effectively reflects the fog image visibility but is also well suited for evaluating the effectiveness of the image defogging algorithms and the restoration degree of the defogging color difference.
Double level set segmentation model based on mutual exclusion of adjacent regions with application to brain MR images

Accurately segmenting adjacent regions in images is one of the greatest challenges in image processing. In this paper, we introduce a double level set segmentation model based on mutual exclusion, which can accurately and independently segment adjacent regions. In order to avoid the creation of intersection regions and ensure the independence of neighboring regions, the model uses two level set functions, and defines the mutual exclusion term of neighboring regions according to the area of the joint region segmented by the two level set functions. Furthermore, we manually set the approximate contours of the target region as a prior information, defining prior constraints in the energy functional to ensure that the model only segments the organization we wanted. Combining the data term, the mutual exclusion term, the prior constraint term, the length term and the regularization term, we give the level set formulation of this double level sets energy functional and apply the gradient descent algorithm to solve its energy minimization problem. Applying the double level sets model to brain MR images, the experimental results show that the model can accurately and independently segment adjacent tissues in brain. The comparison experimental results with other models show that the double level sets model has a higher accuracy than the classical models in segmenting adjacent tissues. In addition, experiments on synthetic images show that the double level sets model can accurately segment images with intensity inhomogeneity and is robust to noise. Code is avaliable at https://github.com/ruicx/Mutual-Exclusion-Level-Set.