Reviewerly/exHarmony huggingface dataset:
Self-Attentive Graph Convolution Network With Latent Group Mining and Collaborative Filtering for Personalized Recommendation

The remarkable progress of machine learning has led to some state-of-the-art algorithms in personalized recommendation. Previous recommendation algorithms generally learn users’ and items’ representations based on a user-item rating matrix. However, these methods only consider a user's own preference, but ignore the influence of the user's social circles. In this paper, we propose a novel recommendation algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Attentive Graph Convolution Network with Latent Group Mining and Collaborative Filtering</i> , which consists of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Latent Group Mining</i> (LGM) module, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collaborative Embedding</i> (CE) module and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Attentive Graph Convolution</i> (SAGC) module. The LGM module analyzes users’ social circles by exploring their latent groups and generates group embedding for users and items. The CE module uses a graph embedding method to provide semantic collaborative embedding for users and items. The SAGC module fuses users’ (items’) collaborative embedding and group embedding by a self-attentive graph convolution network to learn their fine-grained representations for rating prediction. We conduct experiments on different real-world datasets, which validates that our algorithm outperforms the state-of-the-art algorithms.
Multiscale Image Dehazing and Restoration: An Application for Visual Surveillance

The captured outdoor images and videos may appear blurred due to haze, fog, and bad weather conditions. Water droplets or dust particles in the atmosphere cause the light to scatter, resulting in very limited scene discernibility and deterioration in the quality of the image captured. Currently, image dehazing has gained much popularity because of its usability in a wide variety of applications. Various algorithms have been proposed to solve this ill-posed problem. These algorithms provide quite promising results in some cases, but they include undesirable artifacts and noise in haze patches in adverse cases. Some of these techniques take unrealistic processing time for high image resolution. In this paper, to achieve real-time halo-free dehazing, fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches. It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images. A coarse transmission map is estimated by using the minimum of different size patches. Then a cascaded fast guided filter is used to refine the transmission map. We introduce an efficient scaling technique for transmission map estimation, which gives an advantage of very low-performance degradation for a high-resolution image. For performance evaluation, quantitative, qualitative and computational time comparisons have been performed, which provide quiet faithful results in speed, quality, and reliability of handling bright surfaces.
Referee: Towards Reference-Free Cross-Speaker Style Transfer with Low-Quality Data for Expressive Speech Synthesis

Cross-speaker style transfer (CSST) in text-to-speech (TTS) synthesis aims at transferring a speaking style to the synthesised speech in a target speaker&#x2019;s voice. Most previous CSST approaches rely on expensive high-quality data carrying desired speaking style during training and require a reference utterance to obtain speaking style descriptors as conditioning on the generation of a new sentence. This work presents Referee, a robust reference-free CSST approach for expressive TTS, which fully leverages low-quality data to learn speaking styles from text. Referee is built by cascading a text-to-style (T2S) model with a style-to-wave (S2W) model. Phonetic PosteriorGram (PPG), phoneme-level pitch and energy contours are adopted as fine-grained speaking style descriptors, which are predicted from text using the T2S model. A novel pretrain-refinement method is adopted to learn a robust T2S model by only using readily accessible low-quality data. The S2W model is trained with high-quality target data, which is adopted to effectively aggregate style descriptors and generate high-fidelity speech in the target speaker&#x2019;s voice. Experimental results are presented, showing that Referee outperforms a global-style-token (GST)-based baseline approach in CSST.
Spatial assessment of gross vertical reservoir heterogeneity using geostatistics and GIS-based machine-learning classifiers: A case study from the Zubair Formation, Rumaila oil field, southern Iraq

The study of oil-field reservoir heterogeneity is an important task in the oil industry as it affects waterflooding, developing injection production systems, and optimizing hydrocarbon production. In this study, vertical reservoir heterogeneity was quantified using the Lorenz statistical index, empirical Bayesian kriging, and seven machine-learning classifiers (Classification and Regression Trees, Boosted Regression Trees, Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighbors, and Support Vector Machine with three different kernels (linear, radial, and polynomial) under the geographic information system platform. The main pay zone of the Zubair Formation in the Rumaila oil field from southern Iraq was used as a case study. The degree of heterogeneity was first quantified using the Lorenz index, and a borehole-heterogeneity inventory location map was prepared according to the determined Lorenz index. Information about five factors influencing the heterogeneity, namely, porosity, permeability, volume of shale, reservoir-unit thickness, and depth to the top of reservoir unit, was collected based on available cores, nuclear magnetic resonance log, gamma-ray logs, and drilling-information logs. Factors from these sources were interpolated to show their spatial distribution using the empirical Bayesian kriging technique. The relationship between the borehole inventory map of vertical heterogeneity and the five factors was examined using the seven machine-learning classifiers. Two statistical-error measures, namely, accuracy and Cohen's kappa, were used to verify the performance of the classifiers in both training and testing stages. Results proved that Random Forest, Support Vector Machine with radial kernel function, and Logistic Regression were the best models. The probabilities of the best performance models were then interpolated and classified into five heterogeneity zones: Very low, low, moderate, high, and very high. The high-very high classes for each of these models approximately occupy 60% of the oil field and are mainly distributed in the middle and north of the field, whereas the other classes encompass about 40% of the field and mostly occur in the south. This distribution of classes is most likely related to the distribution and complexity of former depositional environments.
Defect inspection for underwater structures based on line-structured light and binocular vision

Surface defect inspection for underwater structures is important. However, the inspection technologies based on passive vision cannot meet accuracy requirements. In this paper, we propose a two-stage method based on structured light images for defect detection. In the first stage, light stripes are extracted based on the analysis of hue, saturation, value (HSV) space and gray space. Then a hole-filling method is applied to ensure stripe integrity. In the second stage, depth information for all light stripes is calculated to synthesize a depth map, which is segmented for defect localization and measurement. Experimental results have verified the feasibility and effectiveness of our method.
Progressive compressed records

Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices. A common approach to conserve bandwidth involves resizing or compressing data prior to training. We introduce Progressive Compressed Records (PCRs), a data format that uses compression to reduce the overhead of fetching and transporting data, effectively reducing the training time required to achieve a target accuracy. PCRs deviate from previous storage formats by combining progressive compression with an efficient storage layout to view a single dataset at multiple fidelities---all without adding to the total dataset size. We implement PCRs and evaluate them on a range of datasets, training tasks, and hardware architectures. Our work shows that: (i) the amount of compression a dataset can tolerate exceeds 50% of the original encoding for many DL training tasks; (ii) it is possible to automatically and efficiently select appropriate compression levels for a given task; and (iii) PCRs enable tasks to readily access compressed data at runtime--- utilizing as little as half the training bandwidth and thus potentially doubling training speed.
DPTCN: A novel deep CNN model for short text classification

As an important branch of Nature Language Processing (NLP), how to extract useful text information and effective long-range associations has always been a bottleneck for text classification. With the great effort of deep learning researchers, deep Convolutional Neural Networks (CNNs) have made remarkable achievements in Computer Vision but still controversial in NLP tasks. In this paper, we propose a novel deep CNN named Deep Pyramid Temporal Convolutional Network (DPTCN) for short text classification, which is mainly consisting of concatenated embedding layer, causal convolution, 1/2 max pooling down-sampling and residual blocks. It is worth mentioning that our work was highly inspired by two well-designed models: one is temporal convolutional network for sequential modeling; another is deep pyramid CNN for text categorization; as their applicability and pertinence remind us how to build a model in a special domain. In the experiments, we evaluate the proposed model on 7 datasets with 6 models and analyze the impact of three different embedding methods. The results prove that our work is a good attempt to apply word-level deep convolutional network in short text classification.
Zero-Knowledge Reparation of the Véron and AGS Code-based Identification Schemes

Designing code-based signatures is both an important and challenging problem. A standard way to tackle it consists to use the Fiat-Shamir heuristic along with an identification scheme that is required to be zero-knowledge. The authors of [1] have highlighted an issue within the zero-knowledge proof of the Veron identification scheme [2]. It turns out that the zero-knowledge proof of the AGS protocol [3] is impacted in a similar way. In this paper, we present a masking technique that solves the aforementioned issue without inducing any performance penalty. We introduce the Masked Veron and Masked AGS protocols that both leverage this masking technique and provide their zero-knowledge proofs. In addition, we present a new technique improving the performances of signatures built from code-based identification schemes subject to the attack described in [4]. The Masked Veron and Masked AGS protocols feature all the existing performance improvements from the literature.
SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence

In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese, from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset, as a new benchmark dataset built based on a strict annotation scheme for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese aspect-based sentiment task. Our experiments show that our approach achieves the best performances (in F1-score) of 84.48% for the aspect task and 63.06% for the sentiment task, which performs several conventional machine learning and deep learning systems. Lastly, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in the future.
AI Content Moderation, Racism and (de)Coloniality

Abstract The article develops a critical approach to AI in content moderation adopting a decolonial perspective. In particular, the article asks: to what extent does the current AI moderation system of platforms address racist hate speech and discrimination? Based on a critical reading of publicly available materials and publications on AI in content moderation, we argue that racialised people have no significant input in the definitions and decision making processes on racist hate speech and are also exploited as their unpaid labour is used to clean up platforms and to train AI systems. The disregard of the knowledge and experiences of racialised people and the expropriation of their labour with no compensation reproduce rather than eradicate racism. In theoretically making sense of this, we draw influences from Anibal Quijano’s theory of the coloniality of power and the centrality of race, concluding that in its current iteration, AI in content moderation is a technology in the service of coloniality. Finally, the article develops a sketch for a decolonial approach to AI in content moderation, which aims to centre the voices of racialised communities and to reorient content moderation towards repairing, educating and sustaining communities.
Can automated machine translation evaluation metrics be used to assess students’ interpretation in the language learning classroom?

The use of translation and interpreting (T&I) in the language learning classroom is commonplace, serving various pedagogical and assessment purposes. Previous utilization of T&I exercises is driven largely by their potential to enhance language learning, whereas the latest trend has begun to underscore T&I as a crucial skill to be acquired as part of transcultural competence for language learners and future language users. Despite their growing popularity and utility in the language learning classroom, assessing T&I is time-consuming, labor-intensive and cognitively taxing for human raters (e.g., language teachers), primarily because T&I assessment entails meticulous evaluation of informational equivalence between the source-language message and target-language renditions. One possible solution is to rely on automated quality metrics that are originally developed to evaluate machine translation (MT). In the current study, we investigated the viability of using four automated MT evaluation metrics, BLEU, NIST, METEOR and TER, to assess human interpretation. Essentially, we correlated the automated metric scores with the human-assigned scores (i.e., the criterion measure) from multiple assessment scenarios to examine the degree of machine-human parity. Overall, we observed fairly strong metric-human correlations for BLEU (Pearson’s r = 0.670), NIST (r = 0.673) and METEOR (r = 0.882), especially when the metric computation was conducted on the sentence level rather than the text level. We discussed these emerging findings and others in relation to the feasibility of operationalizing MT metrics to evaluate students’ interpretation in the language learning classroom.Supplemental data for this article is available online at https://doi.org/10.1080/09588221.2021.1968915 .
FinBERT—A Deep Learning Approach to Extracting Textual Information

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Is Heuristic Sampling Necessary in Training Deep Object Detectors?

To train accurate deep object detectors under the extreme foreground-background imbalance, heuristic sampling methods are always necessary, which either re-sample a subset of all training samples (hard sampling methods, e.g. biased sampling, OHEM), or use all training samples but re-weight them discriminatively (soft sampling methods, e.g. Focal Loss, GHM). In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors. While previous studies have shown that training detectors without heuristic sampling methods would significantly degrade accuracy, we reveal that this degradation comes from an unreasonable classification gradient magnitude caused by the imbalance, rather than a lack of re-sampling/re-weighting. Motivated by our discovery, we propose a simple yet effective Sampling-Free mechanism to achieve a reasonable classification gradient magnitude by initialization and loss scaling. Unlike heuristic sampling methods with multiple hyperparameters, our Sampling-Free mechanism is fully data diagnostic, without laborious hyperparameters searching. We verify the effectiveness of our method in training anchor-based and anchor-free object detectors, where our method always achieves higher detection accuracy than heuristic sampling methods on COCO and PASCAL VOC datasets. Our Sampling-Free mechanism provides a new perspective to address the foreground-background imbalance. Our code is released at https://github.com/ChenJoya/sampling-free.
No-reference video quality assessment based on modeling temporal-memory effects

This study presents a hybrid network for no-reference (NR) video quality assessment (VQA). Besides spatial cues, the network concerns temporal motion effect and temporal hysteresis effect on the visual quality estimation, and two modules are embedded. One module is dedicated to incorporate short-term spatio-temporal features based on spatial quality maps and temporal quality maps, and the follow-up module explores graph convolutional network to quantify the relationship between image frames in a sequence. The proposed network and several popular models are evaluated on three video quality databases (CSIQ, LIVE, and KoNViD-1K). Experimental results indicate that the network outperforms other involved NR models, and its competitive performance is close to that of state-of-the-art full-reference VQA models. Conclusively, short-term spatio-temporal feature fusion benefits the modeling of interaction between spatial and temporal cues in VQA tasks, long-term sequence fusion further improves the performance, and a strong correlation with human subjective judgment is achieved.
Clustering-Based 3-D-MAP Despeckling of SAR Images Using Sparse Wavelet Representation

Image denoising is considered an effective initial processing step in different imaging applications. Over the years, numerous studies have been performed in filtering for different kinds of noises. The block matching with 3-D group filtering has added a new dimension and better results for denoising techniques. This work aims to establish a novel denoising method for multiplicative (speckle) noise employing 3-D arrays resulted from gathering similar patches in clustered areas of an image through the sparse representation based on discrete wavelet transform (DWT) and maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) estimator technique. Experimental results justified a good quality of the filtered image by the novel framework, which appears to demonstrate better denoising performance against state-of-the-art algorithms according to the objective criteria [peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and edge preservation index (EPI)] values and subjective visual perception.
An Analysis of Super-Net Heuristics in Weight-Sharing NAS

Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design and train the shared-weight backbone network, a.k.a. the super-net. Since heuristics substantially vary across different methods and have not been carefully studied, it is unclear to which extent they impact super-net training and hence the weight-sharing NAS algorithms. In this paper, we disentangle super-net training from the search algorithm, isolate 14 frequently-used training heuristics, and evaluate them over three benchmark search spaces. Our analysis uncovers that several commonly-used heuristics negatively impact the correlation between super-net and stand-alone performance, whereas simple, but often overlooked factors, such as proper hyper-parameter settings, are key to achieve strong performance. Equipped with this knowledge, we show that simple random search achieves competitive performance to complex state-of-the-art NAS algorithms when the super-net is properly trained.
Image Splicing Detection Based on Texture Features with Fractal Entropy

Over the past years, image manipulation tools have become widely accessible and easier to use, which made the issue of image tampering far more severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is transposed onto another image. The proposed study consists of four features extraction stages used to extract the important features from suspicious images, namely, Fractal Entropy (FrEp), local binary patterns (LBP), Skewness, and Kurtosis. The main advantage of FrEp is the ability to extract the texture information contained in the input image. Finally, the “support vector machine” (SVM) classification is used to classify images into either spliced or authentic. Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods. Overall, the proposed algorithm achieves an ideal balance between performance, accuracy, and efficacy, which makes it suitable for real-world applications.