Reviewerly/exHarmony huggingface dataset:
Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss

Sequence-to-Sequence (Seq2Seq) models have achieved encouraging performance on the dialogue response generation task. However, existing Seq2Seq-based response generation methods suffer from a low-diversity problem: they frequently generate generic responses, which make the conversation less interesting. In this paper, we address the low-diversity problem by investigating its connection with model over-confidence reflected in predicted distributions. Specifically, we first analyze the influence of the commonly used Cross-Entropy (CE) loss function, and find that the CE loss function prefers high-frequency tokens, which results in low-diversity responses. We then propose a Frequency-Aware Cross-Entropy (FACE) loss function that improves over the CE loss function by incorporating a weighting mechanism conditioned on token frequency. Extensive experiments on benchmark datasets show that the FACE loss function is able to substantially improve the diversity of existing state-of-the-art Seq2Seq response generation methods, in terms of both automatic and human evaluations.
A UAV Video Dataset for Mosaicking and Change Detection From Low-Altitude Flights

In recent years, the technological improvements of low-cost small-scale Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in different tasks.In particular, the use of small-scale UAVs is useful in all these low-altitude tasks in which common UAVs cannot be adopted, such as recurrent comprehensive view of wide environments, frequent monitoring of military areas, real-time classification of static and moving entities (e.g., people, cars, etc.).These tasks can be supported by mosaicking and change detection algorithms achieved at low-altitude.Currently, public datasets for testing these algorithms are not available.This paper presents the UMCD dataset, the first collection of geo-referenced video sequences acquired at low-altitude for mosaicking and change detection purposes.Five reference scenarios are also reported.
DDSketch

Summary statistics such as the mean and variance are easily maintained for large, distributed data streams, but order statistics (i.e., sample quantiles) can only be approximately summarized. There is extensive literature on maintaining quantile sketches where the emphasis has been on bounding the rank error of the sketch while using little memory. Unfortunately, rank error guarantees do not preclude arbitrarily large relative errors, and this often occurs in practice when the data is heavily skewed. Given the distributed nature of contemporary large-scale systems, another crucial property for quantile sketches is mergeablility, i.e., several combined sketches must be as accurate as a single sketch of the same data. We present the first fully-mergeable, relative-error quantile sketching algorithm with formal guarantees. The sketch is extremely fast and accurate, and is currently being used by Datadog at a wide-scale.
Finding the ground state of the Hubbard model by variational methods on a quantum computer with gate errors

Abstract A key goal of digital quantum computing is the simulation of fermionic systems such as molecules or the Hubbard model. Unfortunately, for present and near-future quantum computers the use of quantum error correction schemes is still out of reach. Hence, the finite error rate limits the use of quantum computers to algorithms with a low number of gates. The variational Hamiltonian ansatz (VHA) has been shown to produce the ground state in good approximation in a manageable number of steps. Here we study explicitly the effect of gate errors on its performance. The VHA is inspired by the adiabatic quantum evolution under the influence of a time-dependent Hamiltonian, where the—ideally short—fixed Trotter time steps are replaced by variational parameters. The method profits substantially from quantum variational error suppression, e.g. unitary quasi-static errors are mitigated within the algorithm. We test the performance of the VHA when applied to the Hubbard model in the presence of unitary control errors on quantum computers with realistic gate fidelities.
Terahertz Quantum Cryptography

A well-known empirical rule for the demand of wireless communication systems is that of Edholm's law of bandwidth. It states that the demand for bandwidth in wireless short-range communications doubles every 18 months. With the growing demand for bandwidth and the decreasing cell size of wireless systems, terahertz (THz) communication systems are expected to become increasingly important in modern day applications. With this expectation comes the need for protecting users' privacy and security in the best way possible. With that in mind, we show that quantum key distribution can operate in the THz regime and we derive the relevant secret key rates against realistic collective attacks. In the extended THz range (from 0.1 to 50 THz), we find that below 1 THz, the main detrimental factor is thermal noise, while at higher frequencies it is atmospheric absorption. Our results show that high-rate THz quantum cryptography is possible over distances varying from a few meters using direct reconciliation, to about 220m via reverse reconciliation. We also give a specific example of the physical hardware and architecture that could be used to realize our THz quantum key distribution scheme.
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems

Automatically evaluating dialogue coherence is a challenging but high-demand ability for developing high-quality open-domain dialogue systems.However, current evaluation metrics consider only surface features or utterancelevel semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows.Here, we first consider that the graph structure constituted with topics in a dialogue can accurately depict the underlying communication logic, which is a more natural way to produce persuasive metrics.Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation.Specifically, GRADE incorporates both coarsegrained utterance-level contextualized representations and fine-grained topic-level graph representations to evaluate dialogue coherence.The graph representations are obtained by reasoning over topic-level dialogue graphs enhanced with the evidence from a commonsense graph, including k-hop neighboring representations and hop-attention weights.Experimental results show that our GRADE significantly outperforms other state-of-the-art metrics on measuring diverse dialogue models in terms of the Pearson and Spearman correlations with human judgements.Besides, we release a new large-scale human evaluation benchmark to facilitate future research on automatic metrics.
Journey Towards Tiny Perceptual Super-Resolution

Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks. However, these convolutional models are excessively large and expensive, hindering their effective deployment to end devices. In this work, we propose a neural architecture search (NAS) approach that integrates NAS and generative adversarial networks (GANs) with recent advances in perceptual SR and pushes the efficiency of small perceptual SR models to facilitate on-device execution. Specifically, we search over the architectures of both the generator and the discriminator sequentially, highlighting the unique challenges and key observations of searching for an SR-optimized discriminator and comparing them with existing discriminator architectures in the literature. Our tiny perceptual SR (TPSR) models outperform SRGAN and EnhanceNet on both full-reference perceptual metric (LPIPS) and distortion metric (PSNR) while being up to 26.4 $$\times $$ more memory efficient and 33.6 $$\times $$ more compute efficient respectively.
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation. The overview characterizes adaptation algorithms as based on embeddings, model parameter adaptation, or data augmentation. We present a meta-analysis of the performance of speech recognition adaptation algorithms, based on relative error rate reductions as reported in the literature.
General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification

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Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident.We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19.Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient.To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.
TABE-DAC: Efficient Traceable Attribute-Based Encryption Scheme With Dynamic Access Control Based on Blockchain

The features of decentralization and tamper-proof enable blockchain to be an emerging technology for integrity protection of important data stored on it. Blockchains are also used to combine with cloud storage for access control and sharing of private data. To protect the confidentiality of the private data, attribute-based encryption (ABE) schemes that can provide one-to-many encryption are commonly used as the solutions. However, there are problems, such as inefficiency, key abuse, and inflexibility of access control policy, when adopting ABE solutions. This paper proposes an efficient traceable attribute-based encryption with dynamic access control (TABE-DAC) scheme based on blockchain for fine-grained sharing of encrypted private data on cloud. The proposed TABE-DAC scheme supports traceability for the accountability of malicious users who leak the private key. The proposed solution also realizes dynamic access control where data owners have the flexibility to update access control policy. We also prove the security of the proposed TABE-DAC scheme. Finally, through theoretical comparison and experimental analysis, we verify the efficiency of the proposed solution.
Traditional and modern strategies for optical flow: an investigation

Abstract Optical Flow Estimation is an essential component for many image processing techniques. This field of research in computer vision has seen an amazing development in recent years. In particular, the introduction of Convolutional Neural Networks for optical flow estimation has shifted the paradigm of research from the classical traditional approach to deep learning side. At present, state of the art techniques for optical flow are based on convolutional neural networks and almost all top performing methods incorporate deep learning architectures in their schemes. This paper presents a brief analysis of optical flow estimation techniques and highlights most recent developments in this field. A comparison of the majority of pertinent traditional and deep learning methodologies has been undertaken resulting the detailed establishment of the respective advantages and disadvantages of the traditional and deep learning categories. An insight is provided into the significant factors that affect the success or failure of the two classes of optical flow estimation. In establishing the foremost existing and inherent challenges with traditional and deep learning schemes, probable solutions have been proposed indeed.
A Systematic Review of Blockchain for Consent Management

Blockchain technology was introduced through Bitcoin in a 2008 whitepaper by the mysterious Satoshi Nakamoto. Since its inception, it has gathered great attention because of its unique properties—immutability and decentralized authority. This technology is now being implemented in various fields such as healthcare, IoT, data management, etc., apart from cryptocurrencies. As it is a newly emerging technology, researchers and organizations face many challenges in integrating this technology into other fields. Consent management is one of the essential processes in an organization because of the ever-evolving privacy laws, which are introduced to provide more control to users over their data. This paper is a systematic review of Blockchain’s application in the field of consent and privacy data management. The review discusses the adaptation of Blockchain in healthcare, IoT, identity management, and data storage. This analysis is formed on the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and a process of systematic mapping review. We provide analysis of the development, challenges, and limitations of blockchain technology for consent management.
An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers

In this article, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate nominal process and measurement noise covariances (PMNC) in the presence of outliers is proposed. The probability density functions of state transition and measurement likelihood are modeled as Gaussian–Gamma mixture distributions. The VB inference is used to perform the state and PMNC simultaneously. Simulations show that the effectiveness of the proposed method with inaccurate noise covariances in the presence of outliers environments.
Complement Lexical Retrieval Model with Semantic Residual Embeddings

This paper presents clear, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching signals from a neural embedding matching model.clear explicitly trains the neural embedding to encode language structures and semantics that lexical retrieval fails to capture with a novel residual-based embedding learning method. Empirical evaluations demonstrate the advantages of clear over state-of-the-art retrieval models, and that it can substantially improve the end-to-end accuracy and efficiency of reranking pipelines.
Entity-level Factual Consistency of Abstractive Text Summarization

A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.For example, state-ofthe-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document.We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data.In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.
Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny

In the practical application scenarios of safety helmet detection, the lightweight algorithm You Only Look Once (YOLO) v3-tiny is easy to be deployed in embedded devices because its number of parameters is small. However, its detection accuracy is relatively low, which is why it is not suitable for detecting multi-scale safety helmets. The safety helmet detection algorithm (named SAS-YOLOv3-tiny) is proposed in this paper to balance detection accuracy and model complexity. A light Sandglass-Residual (SR) module based on depthwise separable convolution and channel attention mechanism is constructed to replace the original convolution layer, and the convolution layer of stride two is used to replace the max-pooling layer for obtaining more informative features and promoting detection performance while reducing the number of parameters and computation. Instead of two-scale feature prediction, three-scale feature prediction is used here to improve the detection effect about small objects further. In addition, an improved spatial pyramid pooling (SPP) module is added to the feature extraction network to extract local and global features with rich semantic information. Complete-Intersection over Union (CIoU) loss is also introduced in this paper to improve the loss function for promoting positioning accuracy. The results on the self-built helmet dataset show that the improved algorithm is superior to the original algorithm. Compared with the original YOLOv3-tiny, the SAS-YOLOv3-tiny has significantly improved all metrics (including Precision (P), Recall (R), Mean Average Precision (mAP), F1) at the expense of only a minor speed while keeping fewer parameters and amounts of calculation. Meanwhile, the SAS-YOLOv3-tiny algorithm shows advantages in accuracy compared with lightweight object detection algorithms, and its speed is faster than the heavyweight model.
Matching on Sets: Conquer Occluded Person Re-identification Without Alignment

Occluded person re-identification (re-ID) is a challenging task as different human parts may become invisible in cluttered scenes, making it hard to match person images of different identities. Most existing methods address this challenge by aligning spatial features of body parts according to semantic information (e.g. human poses) or feature similarities but this approach is complicated and sensitive to noises. This paper presents Matching on Sets (MoS), a novel method that positions occluded person re-ID as a set matching task without requiring spatial alignment. MoS encodes a person image by a pattern set as represented by a `global vector’ with each element capturing one specific visual pattern, and it introduces Jaccard distance as a metric to compute the distance between pattern sets and measure image similarity. To enable Jaccard distance over continuous real numbers, we employ minimization and maximization to approximate the operations of intersection and union, respectively. In addition, we design a Jaccard triplet loss that enhances the pattern discrimination and allows to embed set matching into deep neural networks for end-to-end training. In the inference stage, we introduce a conflict penalty mechanism that detects mutually exclusive patterns in the pattern union of image pairs and decreases their similarities accordingly. Extensive experiments over three widely used datasets (Market1501, DukeMTMC and Occluded-DukeMTMC) show that MoS achieves superior re-ID performance. Additionally, it is tolerant of occlusions and outperforms the state-of-the-art by large margins for Occluded-DukeMTMC.
A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

The world is going through one of the most dangerous pandemics of all time with the rapid spread of the novel coronavirus (COVID-19). According to the World Health Organization, the most effective way to thwart the transmission of coronavirus is to wear medical face masks. Monitoring the use of face masks in public places has been a challenge because manual monitoring could be unsafe. This paper proposes an architecture for detecting medical face masks for deployment on resource-constrained endpoints having extremely low memory footprints. A small development board with an ARM Cortex-M7 microcontroller clocked at 480 MHz and having just 496 KB of framebuffer RAM has been used for the deployment of the model. Using the TensorFlow-Lite framework, the model is quantized to further reduce its size. The proposed model is 138 KB post-quantization and runs at the inference speed of 30 FPS.