Reviewerly/exHarmony huggingface dataset:
Angle-resolved spectral reflectometry with a digital light processing projector

We describe a novel approach for angle-resolved spectral reflectometry using a digital light processing (DLP) projector. Here, the DLP generates ring patterned images which are projected on the back focal plane of an objective lens. This way, the proposed method quickly changes the angle of incidence with ease based on the relation between the radius of the back focal plane and the angle of incidence. As a result, a detector captures the intensity of the image plane based on the angular and spectral axis. As the proposed method detects the interesting spot of a sample image, it can easily locate the measurement spot with viewing the full field of view, and the spot size is reduced by adopting the fiber. This method is verified by comparing the measurement output of the thin-film samples with a commercial ellipsometer. The result shows that our the proposed method enables the high accuracy of the thin-film inspection.
Reading Comprehension in Czech via Machine Translation and Cross-Lingual Transfer

Reading comprehension is a well studied task, with huge training datasets in English. This work focuses on building reading comprehension systems for Czech, without requiring any manually annotated Czech training data. First of all, we automatically translated SQuAD 1.1 and SQuAD 2.0 datasets to Czech to create training and development data, which we release at http://hdl.handle.net/11234/1-3249. We then trained and evaluated several BERT and XLM-RoBERTa baseline models. However, our main focus lies in cross-lingual transfer models. We report that a XLM-RoBERTa model trained on English data and evaluated on Czech achieves very competitive performance, only approximately 2% points worse than a model trained on the translated Czech data. This result is extremely good, considering the fact that the model has not seen any Czech data during training. The cross-lingual transfer approach is very flexible and provides a reading comprehension in any language, for which we have enough monolingual raw texts.
Investigating the Impact of Pre-trained Word Embeddings on Memorization in Neural Networks

The sensitive information present in the training data, poses a privacy concern for applications as their unintended memorization during training can make models susceptible to membership inference and attribute inference attacks. In this paper, we investigate this problem in various pre-trained word embeddings (GloVe, ELMo and BERT) with the help of language models built on top of it. In particular, firstly sequences containing sensitive information like a single-word disease and 4-digit PIN are randomly inserted into the training data, then a language model is trained using word vectors as input features, and memorization is measured with a metric termed as exposure. The embedding dimension, the number of training epochs, and the length of the secret information were observed to affect memorization in pre-trained embeddings. Finally, to address the problem, differentially private language models were trained to reduce the exposure of sensitive information.
Inferences and Metainferences in ST

Abstract In a recent paper, Barrio, Tajer and Rosenblatt establish a correspondence between metainferences holding in the strict-tolerant logic of transparent truth S T + and inferences holding in the logic of paradox L P + . They argue that L P + is S T + ’s external logic and they question whether S T + ’s solution to the semantic paradoxes is fundamentally different from L P + ’s. Here we establish that by parity of reasoning, S T + can be related to L P + ’s dual logic K + . We clarify the distinction between internal and external logic and argue that while S T + ’s nonclassicality can be granted, its self-dual character does not tie it to L P + more closely than to K + .
Fully Bayesian Learning of Multivariate Beta Mixture Models

Mixture models have been widely used as statistical learning paradigms in various unsupervised machine learning applications, where labeling a vast amount of data is impractical and costly. They have shown a significant success and convincing performance in many real-world problems such as medical applications, image clustering and anomaly detection. In this paper, we explore a fully Bayesian analysis of multivariate Beta mixture model and propose a solution for the problem of estimating parameters using Markov Chain Monte Carlo technique. We exploit Gibbs sampling within Metropolis-Hastings for Monte Carlo simulation. We also obtained prior distribution which is a conjugate for multivariate Beta. The performance of our proposed method is evaluated and compared with Bayesian Gaussian mixture model via challenging applications, including cell image categorization and network intrusion detection. Experimental results confirm that the proposed technique can provide an effective solution comparing to similar alternatives.
Evaluating a Visual Approach for Understanding JavaScript Source Code

To characterize the building blocks of a legacy software system (e.g., structure, dependencies), programmers usually spend a long time navigating its source code. Yet, modern integrated development environments (IDEs) do not provide appropriate means to efficiently achieve complex software comprehension tasks. To deal with this unfulfilled need, we present Hunter, a tool for the visualization of JavaScript applications. Hunter visualizes source code through a set of coordinated views that include a node-link diagram that depicts the dependencies among the components of a system, and a treemap that helps programmers to orientate when navigating its structure.
Boundary-Aware Network for Kidney Tumor Segmentation

Segmentation of the kidney and kidney tumors using computed tomography (CT) is a crucial step in related surgical procedures. Although many deep learning models have been constructed to solve this problem, most of them ignore the boundary information. In this paper, we propose a boundary-aware network (BA-Net) for kidney and kidney tumor segmentation. This model consists of a shared 3D encoder, a 3D boundary decoder, and a 3D segmentation decoder. In contrast to existing boundary-involved methods, we first introduce the skip connections from the boundary decoder to the segmentation decoder, incorporating the boundary prior as the attention that indicates the error-prone regions into the segmentation process, and then define the consistency loss to push both decoders towards producing the same result. Besides, we also use the strategies of multi-scale input and deep supervision to extract hierarchical structural information, which can alleviate the issues caused by variable tumor sizes. We evaluated the proposed BA-Net on the kidney tumor segmentation challenge (KiTS19) dataset. The results suggest that the boundary decoder and consistency loss used in our model are effective and the BA-Net is able to produce relatively accurate segmentation of the kidney and kidney tumors.
Talking Face Generation with Expression-Tailored Generative Adversarial Network

A key of automatically generating vivid talking faces is to synthesize identity-preserving natural facial expressions beyond audio-lip synchronization, which usually need to disentangle the informative features from multiple modals and then fuse them together. In this paper, we propose an end-to-end Expression-Tailored Generative Adversarial Network (ET-GAN) to generate an expression enriched talking face video of arbitrary identity. Different from talking face generation based on identity image and audio, an expressional video of arbitrary identity serves as the expression source in our approach. Expression encoder is proposed to disentangle expression-tailored representation from the guiding expressional video, while audio encoder disentangles audio-lip representation. Instead of using single image as identity input, multi-image identity encoder is proposed by learning different views of faces and merging a unified representation. Multiple discriminators are exploited to keep both image-aware and the video-aware realistic details, including a spatial-temporal discriminator for visual continuity of expression synthesis and facial movements. We conduct extensive experimental evaluations on quantitative metrics, expression retention quality and audio-visual synchronization. The results show the effectiveness of our ET-GAN in generating high quality expressional talking face videos against existing state-of-the-arts.
ReViCe: Reusing Victim Cache to Prevent Speculative Cache Leakage

Spectre and Meltdown attacks reveal the perils of speculative execution, a prevalent technique used in modern processors. This paper proposes ReViCe, a hardware technique to mitigate speculation based attacks. ReViCe allows speculative loads to update caches early but keeps any replaced line in the victim cache. In case of misspeculation, replaced lines from the victim cache are used to restore the caches, thereby preventing any cache-based Spectre and Meltdown attacks. Moreover, ReViCe injects jitter to conceal any timing difference due to speculative lines. Together speculation restoration and jitter injection allow ReViCe to make speculative execution secure. We present the design of ReViCe following a set of security principles and evaluate its security based on shared-core and cross-core attacks exploiting various Spectre variants and cache side channels. Our scheme incurs 2-6% performance overhead. This is less than the state-of-the-art hardware approaches. Moreover, ReViCe achieves these results with minimal area and energy overhead (0.06% and 0.02% respectively).
Demonstrating ADS-B AND CPDLC Attacks with Software-Defined Radio

Several studies have shown insufficient security in air traffic communication. Controller-Pilot Datalink Communications (CPDLC) is used to communicate in text over the VHF data link, and Automatic Dependent Surveillance – Broadcast (ADS-B) determines the position of an aircraft. The vulnerability of air data communication was confirmed by successful experiments using Software-Defined Radio, where both CPDLC and ADS-B messages were transmitted in a safe environment. Neither ADS-B messages nor CPDLC messages are encrypted during transmission. The encoding of FANS-1/A messages was demonstrated, and the experiments showed that it is possible to send such messages with relatively inexpensive technology.
The Case for Usable AI

Artificial intelligence (AI) is a frequently used term - and has seen decades of use - in the video games industry. Yet, while academic AI research recently produced notable advances both in different methods and in real-world applications, the use of modern AI techniques, such as deep learning remains curiously sparse in commercial video games. Related work has shown that there is a notable separation between AI in games and academic AI, down to the level of the definitions of what AI is and means. To address the practical barriers that sustain this gap, we conducted a series of interviews with industry professionals. The outcomes underline requirements that are often overlooked: While academic (games) AI research tends to focus on problem-solving capacity, industry professionals highlight the importance of "usability aspects of AI'': the ability to produce plausible outputs (effectiveness), computational performance (efficiency) and ease of implementation (ease of use).
A design methodology for matching smart health requirements

Summary As now well established, the world population is aging rapidly and, according to World Health Organization (WHO), the amount of people aged 60 years and older is expected to total 2 billion in 2050. For this reason, an emerging important issue is the definition of a new generation of healthcare platforms capable of monitoring people's quality of life. In this article, we propose a new methodology that supports the entire requirement elicitation process starting from the initial phase of gathering the requirements, both clinical, technological, and end‐user, up to the choice of the most suitable solution. Our proposal provides a new new iterative model in the smart healthcare field research area. Furthermore we apply our proposal in a real scenario and we report the end‐to‐end implementation of the proposed methodology.
Photovoltaic power potential in West Africa using long-term satellite data

Abstract. This paper addresses long-term historical changes in solar irradiance in West Africa (3 to 20∘ N and 20∘ W to 16∘ E) and the implications for photovoltaic systems. Here, we use satellite irradiance (Surface Solar Radiation Data Set – Heliosat, Edition 2.1 – SARAH-2.1) and temperature data from a reanalysis (ERA5) to derive photovoltaic yields. Based on 35 years of data (1983–2017), the temporal and regional variability as well as long-term trends in global and direct horizontal irradiance are analyzed. Furthermore, a detailed time series analysis is undertaken at four locations. According to the high spatial resolution SARAH-2.1 data record (0.05∘×0.05∘), solar irradiance is largest (up to a 300 W m−2 daily average) in the Sahara and the Sahel zone with a positive trend (up to 5 W m−2 per decade) and a lower temporal variability (&lt;75 W m−2 between 1983 and 2017 for daily averages). In contrast, the solar irradiance is lower in southern West Africa (between 200 W m−2 and 250 W m−2) with a negative trend (up to −5 W m−2 per decade) and a higher temporal variability (up to 150 W m−2). The positive trend in the north is mostly connected to the dry season, whereas the negative trend in the south occurs during the wet season. Both trends show 95 % significance. Photovoltaic (PV) yields show a strong meridional gradient with the lowest values of around 4 kWh kWp−1 in southern West Africa and values of more than 5.5 kWh kWp−1 in the Sahara and Sahel zone.
Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline

The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.It contains three shared tasks and we participate in the LongSumm shared task.In this paper, we describe our text summarization system, SciSummPip, inspired by SummPip (Zhao et al., 2020) that is an unsupervised text summarization system for multi-document in news domain.Our SciSummPip includes a transformer-based language model SciBERT (Beltagy et al., 2019) for contextual sentence representation, content selection with PageRank (Page et al., 1999), sentence graph construction with both deep and linguistic information, sentence graph clustering and withingraph summary generation.Our work differs from previous method in that content selection and a summary length constraint is applied to adapt to the scientific domain.The experiment results on both training dataset and blind test dataset show the effectiveness of our method, and we empirically verify the robustness of modules used in SciSummPip with BERTScore (Zhang et al., 2019a).
Erdős–Szekeres theorem for cyclic permutations

We provide a cyclic permutation analogue of the Erd\H os-Szekeres theorem. In particular, we show that every cyclic permutation of length $(k-1)(\ell-1)+2$ has either an increasing cyclic sub-permutation of length $k+1$ or a decreasing cyclic sub-permutation of length $\ell+1$, and show that the result is tight. We also characterize all maximum-length cyclic permutations that do not have an increasing cyclic sub-permutation of length $k+1$ or a decreasing cyclic sub-permutation of length $\ell+1$.
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space

Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data.We argue that the typical uncertainty sampling method for active learning is time-consuming and can hardly work in real-time, which may lead to ineffective sample selection.We propose adversarial uncertainty sampling in discrete space (AUSDS) to retrieve informative unlabeled samples more efficiently.AUSDS maps sentences into latent space generated by the popular pre-trained language models, and discover informative unlabeled text samples for annotation via adversarial attack.The proposed approach is extremely efficient compared with traditional uncertainty sampling with more than 10x speedup.Experimental results on five datasets show that AUSDS outperforms strong baselines on effectiveness.
Experiments in Language Variety Geolocation and Dialect Identification.

In this paper we describe the systems we used when participating in the VarDial Evaluation Campaign organized as part of the 7th workshop on NLP for similar languages, varieties and dialects. The shared tasks we participated in were the second edition of the Romanian Dialect Identification (RDI) and the first edition of the Social Media Variety Geolocation (SMG). The submissions of our SUKI team used generative language models based on Naive Bayes and character n-grams.
Unsupervised Learning of Dense Visual Representations

Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, these methods learn global (image-level) representations that are invariant to different views (i.e., compositions of data augmentation) of the same image. However, many visual understanding tasks require dense (pixel-level) representations. In this paper, we propose View-Agnostic Dense Representation (VADeR) for unsupervised learning of dense representations. VADeR learns pixelwise representations by forcing local features to remain constant over different viewing conditions. Specifically, this is achieved through pixel-level contrastive learning: matching features (that is, features that describes the same location of the scene on different views) should be close in an embedding space, while non-matching features should be apart. VADeR provides a natural representation for dense prediction tasks and transfers well to downstream tasks. Our method outperforms ImageNet supervised pretraining (and strong unsupervised baselines) in multiple dense prediction tasks.
Memory Selection Network for Video Propagation

Video propagation is a fundamental problem in video processing where guidance frame predictions are propagated to guide predictions of the target frame. Previous research mainly treats the previous adjacent frame as guidance, which, however, could make the propagation vulnerable to occlusion, large motion and inaccurate information in the previous adjacent frame. To tackle this challenge, we propose a memory selection network, which learns to select suitable guidance from all previous frames for effective and robust propagation. Experimental results on video object segmentation and video colorization tasks show that our method consistently improves performance and can robustly handle challenging scenarios in video propagation.
Inference guided feature generation for generalized zero-shot learning

Generalized zero-shot learning suffers from an extreme data imbalance problem, that is, the training data only come from seen classes while no unseen class data are available. Recently, a number of feature generation methods based on generative adversarial networks (GAN) have been proposed to address this problem. Existing feature generation methods, however, have never considered the under-constrained problem, and thus could generate an unrestricted visual feature corresponding to no meaningful object class. In this paper, we propose to equip the feature generation framework with a parallel inference network that projects visual feature to the semantic descriptor space, constraining to avoid the generation of unrestricted visual features. The two-parallel-stream framework (1) enables our method, termed inference guided feature generation (Inf-FG), to mitigate the under-constrained problem and (2) makes our Inf-FG applicable to transductive ZSL. Our Inf-FG learns the feature generator and the inference network simultaneously by aligning the joint distribution of visual features and semantic descriptors from the feature generator and the joint distribution from the inference network. We evaluate our approach on four benchmark ZSL datasets, including AWA, CUB, SUN, and FLO, on which our method improves our baselines on generalized zero-shot learning.
Modalities, cohesion, and information flow

It is informally understood that the purpose of modal type constructors in programming calculi is to control the flow of information between types. In order to lend rigorous support to this idea, we study the category of classified sets, a variant of a denotational semantics for information flow proposed by Abadi et al. We use classified sets to prove multiple noninterference theorems for modalities of a monadic and comonadic flavour. The common machinery behind our theorems stems from the the fact that classified sets are a (weak) model of Lawvere's theory of axiomatic cohesion. In the process, we show how cohesion can be used for reasoning about multi-modal settings. This leads to the conclusion that cohesion is a particularly useful setting for the study of both information flow, but also modalities in type theory and programming languages at large.
A Mechanised Semantics for HOL with Ad-hoc Overloading

Isabelle/HOL augments classical higher-order logic with ad-hoc overloading of constant definitions— that is, one constant may have several definitions for non-overlapping types. In this paper, we present a mechanised proof that HOL with ad-hoc overloading is consistent. All our results have been formalised in the HOL4 theorem prover.
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

We propose average Localisation-Recall-Precision (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection. aLRP extends the Localisation-Recall-Precision (LRP) performance metric (Oksuz et al., 2018) inspired from how Average Precision (AP) Loss extends precision to a ranking-based loss function for classification (Chen et al., 2020). aLRP has the following distinct advantages: (i) aLRP is the first ranking-based loss function for both classification and localisation tasks. (ii) Thanks to using ranking for both tasks, aLRP naturally enforces high-quality localisation for high-precision classification. (iii) aLRP provides provable balance between positives and negatives. (iv) Compared to on average $\sim$6 hyperparameters in the loss functions of state-of-the-art detectors, aLRP Loss has only one hyperparameter, which we did not tune in practice. On the COCO dataset, aLRP Loss improves its ranking-based predecessor, AP Loss, up to around $5$ AP points, achieves $48.9$ AP without test time augmentation and outperforms all one-stage detectors. Code available at: this https URL .
SpotFast Networks with Memory Augmented Lateral Transformers for Lipreading

This paper presents a novel deep learning architecture for word-level lipreading. Previous works suggest a potential for incorporating a pretrained deep 3D Convolutional Neural Networks as a front-end feature extractor. We introduce SpotFast networks, a variant of the state-of-the-art SlowFast networks for action recognition, which utilizes a temporal window as a spot pathway and all frames as a fast pathway. The spot pathway uses word boundaries information while the fast pathway implicitly models other contexts. Both pathways are fused with dual temporal convolutions, which speed up training. We further incorporate memory augmented lateral transformers to learn sequential features for classification. We evaluate the proposed model on the LRW dataset. The experiments show that our proposed model outperforms various state-of-the-art models, and incorporating the memory augmented lateral transformers makes a $$3.7\%$$ improvement to the SpotFast networks and $$16.1\%$$ compared to finetuning the original SlowFast networks. The temporal window utilizing word boundaries helps improve the performance up to $$12.1\%$$ by eliminating visual silences from coarticulations.