Reviewerly/exHarmony huggingface dataset:
The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study

Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases. We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.
Multilevel Text Alignment with Cross-Document Attention

Text alignment finds application in tasks such as citation recommendation and plagiarism detection. Existing alignment methods operate at a single, predefined level and cannot learn to align texts at, for example, sentence and document levels. We propose a new learning approach that equips previously established hierarchical attention encoders for representing documents with a cross-document attention component, enabling structural comparisons across different levels (document-to-document and sentence-to-document). Our component is weakly supervised from document pairs and can align at multiple levels. Our evaluation on predicting document-to-document relationships and sentence-to-document relationships on the tasks of citation recommendation and plagiarism detection shows that our approach outperforms previously established hierarchical, attention encoders based on recurrent and transformer contextualization that are unaware of structural correspondence between documents.
Single-shot digital phase-shifting Moiré patterns for 3D topography

A simple and robust technique of Moiré topography with single-image capture and incorporating digital filtering along with a four-step digitally implemented phase-shifting method is introduced for three-dimensional (3D) surface mapping. Feature details in the order of tens to hundreds of microns can be achieved using interferometrically generated structured light to illuminate the object surface. Compared to the traditional optical phase-shifting method, a digital phase-shifting method based on Fourier processing is implemented with computer-generated sinusoidal patterns derived from the recorded deformed fringes. This enables a single capture of the image that can be used to reconstruct the 3D topography of the surface. Single-shot imaging is simple to implement experimentally and avoids errors in introducing the correct phase shifts. The feasibility of this technique is verified experimentally, and applications to metallic surfaces are demonstrated.
An approach for underwater image enhancement based on color correction and dehazing

Due to the absorption and scattering effect on light when traveling in water, underwater images exhibit serious weakening such as color deviation, low contrast, and blurry details. Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation. To address these problems, a new approach for single underwater image enhancement based on fusion technology was proposed in this article. First, the original image is preprocessed by the white balance algorithm and dark channel prior dehazing technologies, respectively; then two input images were obtained by color correction and contrast enhancement; and finally, the enhanced image was obtained by utilizing the multiscale fusion strategy which is based on the weighted maps constructed by combining the features of global contrast, local contrast, saliency, and exposedness. Qualitative results revealed that the proposed approach significantly removed haze, corrected color deviation, and preserved image naturalness. For quantitative results, the test with 400 underwater images showed that the proposed approach produced a lower average value of mean square error and a higher average value of peak signal-to-noise ratio than the compared method. Moreover, the enhanced results obtain the highest average value in terms of underwater image quality measures among the comparable methods, illustrating that our approach achieves superior performance on different levels of distorted and hazy images.
Circuit Amortization Friendly Encodingsand Their Application to Statistically Secure Multiparty Computation

At CRYPTO 2018, Cascudo et al. introduced Reverse Multiplication Friendly Embeddings (RMFEs). These are a mechanism to compute $$\delta $$ parallel evaluations of the same arithmetic circuit over a field $$\mathbb {F}_q$$ at the cost of a single evaluation of that circuit in $$\mathbb {F}_{q^d}$$ , where $$\delta < d$$ . Due to this inequality, RMFEs are a useful tool when protocols require to work over $$\mathbb {F}_{q^d}$$ but one is only interested in computing over $$\mathbb {F}_q$$ . In this work we introduce Circuit Amortization Friendly Encodings (CAFEs), which generalize RMFEs while having concrete efficiency in mind. For a Galois Ring $$R = GR(2^{k},d)$$ , CAFEs allow to compute certain circuits over $$\mathbb {Z}_{2^k}$$ at the cost of a single secure multiplication in R. We present three CAFE instantiations, which we apply to the protocol for MPC over $$\mathbb {Z}_{2^k}$$ via Galois Rings by Abspoel et al. (TCC 2019). Our protocols allow for efficient switching between the different CAFEs, as well as between computation over $$GR(2^{k},d)$$ and $$\mathbb {F}_{2^{d}}$$ in a way that preserves the CAFE in both rings. This adaptability leads to efficiency gains for e.g. Machine Learning applications, which can be represented as highly parallel circuits over $$\mathbb {Z}_{2^k}$$ followed by bit-wise operations. From an implementation of our techniques, we estimate that an SVM can be evaluated on 250 images in parallel up to $$\times 7$$ more efficiently using our techniques, compared to the protocol from Abspoel et al. (TCC 2019).
Taxonomy of secure data dissemination techniques for IoT environment

A huge amount of data is generated from the interaction of various sensors and Internet of Things (IoT) enabled devices used in various smart industrial applications. This enormous amount of data requires fast processing, huge storage capacity, secure dissemination, and aggregation to make it resistant from the attackers. Secure data dissemination for IoT-based applications has been a prominent issue in consideration with the heterogeneity in generated data. Existing secure data dissemination schemes are inadequate to handle secure data distribution. Research communities across the globe are focused on the delivery of the data among the sensor nodes and overlook the difficulty of its secure streaming. Hence, there is a need to validate the performance of secure data distribution schemes for IoT networks using relevant parameters. Motivated from the aforementioned facts, in this study, we perform a comprehensive review on the state-of-the-art techniques, which can verify and validate the performance of data dissemination schemes for IoT networks. We present a solution taxonomy of various verification and validation methods along with their merits and demerits. Finally, recent issues and future directions on verification and validation methods for the secure data distribution in an IoT network is presented.
Concept Factorization With Local Centroids

Data clustering is a fundamental problem in the field of machine learning. Among the numerous clustering techniques, matrix factorization-based methods have achieved impressive performances because they are able to provide a compact and interpretable representation of the input data. However, most of the existing works assume that each class has a global centroid, which does not hold for data with complicated structures. Besides, they cannot guarantee that the sample is associated with the nearest centroid. In this work, we present a concept factorization with the local centroids (CFLCs) approach for data clustering. The proposed model has the following advantages: 1) the samples from the same class are allowed to connect with multiple local centroids such that the manifold structure is captured; 2) the pairwise relationship between the samples and centroids is modeled to produce a reasonable label assignment; and 3) the clustering problem is formulated as a bipartite graph partitioning task, and an efficient algorithm is designed for optimization. Experiments on several data sets validate the effectiveness of the CFLC model and demonstrate its superior performance over the state of the arts.
Hierarchical Multimodal Attention for Deep Video Summarization

The way people consume sports on TV has drastically evolved in the last years, particularly under the combined effects of the legalization of sport betting and the huge increase of sport analytics. Several companies are nowadays sending observers in the stadiums to collect live data of all the events happening on the field during the match. Those data contain meaningful information providing a very detailed description of all the actions occurring during the match to feed the coaches and staff, the fans, the viewers, and the gamblers. Exploiting all these data, sport broadcasters want to generate extra content such as match highlights, match summaries, players and teams analytics, etc., to appeal subscribers. This paper explores the problem of summarizing professional soccer matches as automatically as possible using both the aforementioned event-stream data collected from the field and the content broadcasted on TV. We have designed an architecture, introducing first (1) a Multiple Instance Learning method that takes into account the sequential dependency among events and then (2) a hierarchical multimodal attention layer that grasps the importance of each event in an action. We evaluate our approach on matches from two professional European soccer leagues, showing its capability to identify the best actions for automatic summarization by comparing with real summaries made by human operators.
Deep-Modal: Real-Time Impact Sound Synthesis for Arbitrary Shapes

Model sound synthesis is a physically-based sound synthesis method used to generate audio content in games and virtual worlds. We present a novel learning-based impact sound synthesis algorithm called Deep-Modal. Our approach can handle sound synthesis for common arbitrary objects, especially dynamic generated objects, in real-time. We present a new compact strategy to represent the mode data, corresponding to frequency and amplitude, as fixed-length vectors. This is combined with a new network architecture that can convert shape features of 3D objects into mode data. Our network is based on an encoder-decoder architecture with the contact positions of objects and external forces embedded. Our method can synthesize interactive sounds related to objects of various shapes at any contact position, as well as objects of different materials and sizes. The synthesis process only takes ~0.01s on a GTX 1080 Ti GPU. We show the effectiveness of Deep-Modal through extensive evaluation using different metrics, including recall and precision of prediction, sound spectrogram, and a user study.
Semantic Segmentation with Peripheral Vision

Deep convolutional neural networks exhibit exceptional performance on many computer vision tasks, including image semantic segmentation. Pre-trained networks trained on a relevant and large benchmark have a notable impact on these successful achievements. However, confronting a domain shift, usage of pre-trained deep encoders cannot boost the performance of those models. In general, transfer learning is not a general solution for various computer vision applications with small accessible image databases. An alternative approach is to develop stronger deep network models applicable to any problem rather than encouraging scientists to explore available pre-trained encoders for their computer vision tasks. To deviate the direction of the research trend in image semantic segmentation toward more effective models, we proposed an innovative convolutional module simulating the peripheral ability of the human eyes. By utilizing our module in an encoder-decoder configuration, after extensive experiments, we achieved acceptable outcomes on several challenging benchmarks, including PASCAL VOC2012 and CamVid.
Deep Learning Approach to Cell Classificatio in Human Peripheral Blood

In the health-care sector, things are distinct, unlike other industrial platforms. The priority of the sector is very high and both the players of it and patients need the highest level of services in terms of medical care and diagnostic tools. Almost always, the diagnosis of the medical data is being processed by healthcare experts. When it comes to image analysis, it is thoroughly a challenging process because of the subjective points of view, and possible complexities in the image. Deep learning promises good performance for medical image processing with its success on other real field applications. In this study, a deep learning-based method is proposed for the classification of human peripheral blood cells (PBC). Most of the hematological diseases can be diagnosed with the help of analysis on PBC whilst the morphological interpretation of the abnormalities in PBC is still a complicated process. Hence, this study presents an automated system for the classification of eight types of PBC with a deep learning approach. The proposed model uses a ShuffleNet structure for automatic feature extraction and decision-making with a tremendous dataset. The performance values of the proposed model are high and also the model outperforms the main study that provides the dataset.
Quantifying the spatial scale mismatch between satellite-derived solar irradiance and <i>in situ</i> measurements: A case study using CERES synoptic surface shortwave flux and the Oklahoma Mesonet

The spatial scale mismatch between gridded irradiance products and in situ measurements is perhaps the least understood topic in solar resource assessment. However, it has a profound impact on virtually all solar applications that involve satellite-derived or reanalysis irradiance data. This paper investigates spatial scale mismatch through a kriging-based upscaling method. Point-location measurements from a monitoring network are upscaled to the size of a satellite-derived irradiance footprint. Subsequently, satellite-derived irradiance is validated against both the nearest point-location measurements and the upscaled areal averages, and the error reduction can, thus, be used to quantify the amount of spatial scale mismatch. In that, a new measure is proposed. The empirical part of the paper considers a synoptic scale satellite-derived irradiance product, namely, National Aeronautics and Space Administration's Clouds and the Earth's Radiant Energy System synoptic surface shortwave flux, and a mesoscale monitoring network, namely, the Oklahoma Mesonet. Based on two years of hourly data and the proposed measure, the spatial scale mismatch is found to be 45% for the U.S. state of Oklahoma.
Experimental demonstration of the near-quantum optimal receiver

We implement the cyclic quantum receiver based on the theoretical proposal of Roy Bondurant and demonstrate experimentally below the shot-noise limit (SNL) discrimination of quadrature phase-shift keying signals (PSK). We also experimentally test the receiver generalized for longer communication alphabet lengths and coherent frequency shift keying (CFSK) encoding. Using off-the-shelf components, we obtain state discrimination error rates that are 3 dB and 4.6 dB below the SNLs of ideal classical receivers for quadrature PSK and CFSK encodings, respectively. The receiver unconditionally surpasses the SNL for M=8 PSK and CFSK. This receiver can be used for the simple and robust practical implementation of quantum-enhanced optical communication.
ICECAP: Information Concentrated Entity-aware Image Captioning

Most current image captioning systems focus on describing general image content, and lack background knowledge to deeply understand the image, such as exact named entities or concrete events. In this work, we focus on the entity-aware news image captioning task which aims to generate informative captions by leveraging the associated news articles to provide background knowledge about the target image. However, due to the length of news articles, previous works only employ news articles at the coarse article or sentence level, which are not fine-grained enough to refine relevant events and choose named entities accurately. To overcome these limitations, we propose an Information Concentrated Entity-aware news image CAPtioning (ICECAP) model, which progressively concentrates on relevant textual information within the corresponding news article from the sentence level to the word level. Our model first creates coarse concentration on relevant sentences using a cross-modality retrieval model and then generates captions by further concentrating on relevant words within the sentences. Extensive experiments on both BreakingNews and GoodNews datasets demonstrate the effectiveness of our proposed method, which outperforms other state-of-the-arts.
Learning Optimization-based Adversarial Perturbations for Attacking Sequential Recognition Models

A large number of recent studies on adversarial attack have verified that a Deep Neural Network (DNN) model designed for non-sequential recognition (NSR) tasks (e.g., classification, detection and segmentation) can be easily fooled by adversarial examples. However, only a few researches pay attention to the adversarial attack on sequential recognition (SR). They either apply the attack methods proposed for NSR to SR by neglecting the sequential dependencies, or focus on attacking specific SR models without considering the generality. In this paper, we study the adversarial attack on the general and popular DNN structure of CNN+RNN, i.e., the combination of convolutional neural network (CNN) and recurrent neural network (RNN), which has been widely used in various SR tasks. We take the scene text recognition (STR) and image captioning (IC) as case study, and derive the objective function for attacking the CNN+RNN based models with targeted and untargeted attack modes, and then developed an optimization-based algorithm to learn adversarial perturbations from the derived gradients of each character (or word) in sequence by incorporating the sequential dependencies. Extensive experiments show that our proposed method can effective fool several state-of-the-arts including four STR models and two IC models with higher successful rate and less time consumption, comparing to three latest attack methods.
Design and Analysis of Binary Scalar Quantizer of Laplacian Source with Applications

A compression method based on non-uniform binary scalar quantization, designed for the memoryless Laplacian source with zero-mean and unit variance, is analyzed in this paper. Two quantizer design approaches are presented that investigate the effect of clipping with the aim of reducing the quantization noise, where the minimal mean-squared error distortion is used to determine the optimal clipping factor. A detailed comparison of both models is provided, and the performance evaluation in a wide dynamic range of input data variances is also performed. The observed binary scalar quantization models are applied in standard signal processing tasks, such as speech and image quantization, but also to quantization of neural network parameters. The motivation behind the binary quantization of neural network weights is the model compression by a factor of 32, which is crucial for implementation in mobile or embedded devices with limited memory and processing power. The experimental results follow well the theoretical models, confirming their applicability in real-world applications.
How did Korea use technologies to manage the COVID-19 crisis? A country report

This paper discusses how Korea has used technologies to respond to COVID-19. It introduces major ICT-enabled efforts for managing this infectious disease-driven crisis in four stages: screening and diagnosis (smart quarantine information system, international traveler information system, and self-health check app for entrants under special entry procedures), epidemiological investigation (epidemiological investigation system and global epidemic prevention platform for digital tracing), patient and contact management (self-quarantine safety protection app, patient management information system, and AI-based automatic counseling), and prevention (micropage, Coronamap, Coronaita, and chatbot). The Korean government's open government principle and proactive transition toward digital transformation has led to successful control of the COVID-19 crisis.
Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors

One of the most powerful features of contextualized models is their dynamic embeddings for words in context, leading to state-of-the-art representations for context-aware lexical semantics. In this paper, we present a post-processing technique that enhances these representations by learning a transformation through static anchors. Our method requires only another pre-trained model and no labeled data is needed. We show consistent improvement in a range of benchmark tasks that test contextual variations of meaning both across different usages of a word and across different words as they are used in context. We demonstrate that while the original contextual representations can be improved by another embedding space from both contextualized and static models, the static embeddings, which have lower computational requirements, provide the most gains.
Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts

Bidirectional Encoder Representations from Transformers (BERT) models achieve state-of-the-art performance on a number of Natural Language Processing tasks. However, their model size on disk often exceeds 1 GB and the process of fine-tuning them and using them to run inference consumes significant hardware resources and runtime. This makes them hard to deploy to production environments. This paper fine-tunes DistilBERT, a lightweight deep learning model, on medical text for the named entity recognition task of Protected Health Information (PHI) and medical concepts. This work provides a full assessment of the performance of DistilBERT in comparison with BERT models that were pre-trained on medical text. For Named Entity Recognition task of PHI, DistilBERT achieved almost the same results as medical versions of BERT in terms of F1 score at almost half the runtime and consuming approximately half the disk space. On the other hand, for the detection of medical concepts, DistilBERT’s F1 score was lower by 4 points on average than medical BERT variants.