Reviewerly/exHarmony huggingface dataset:
Patents and intellectual property assets as non-fungible tokens; key technologies and challenges

With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT). Here non-fungible tokens refer to those with unique and non-substitutable properties. NFT has widely attracted attention, and its protocols, standards, and applications are developing exponentially. It has been successfully applied to digital fantasy artwork, games, collectibles, etc. However, there is a lack of research in utilizing NFT in issues such as Intellectual Property. Applying for a patent and trademark is not only a time-consuming and lengthy process but also costly. NFT has considerable potential in the intellectual property domain. It can promote transparency and liquidity and open the market to innovators who aim to commercialize their inventions efficiently. The main objective of this paper is to examine the requirements of presenting intellectual property assets, specifically patents, as NFTs. Hence, we offer a layered conceptual NFT-based patent framework. Furthermore, a series of open challenges about NFT-based patents and the possible future directions are highlighted. The proposed framework provides fundamental elements and guidance for businesses in taking advantage of NFTs in real-world problems such as grant patents, funding, biotechnology, and so forth.
Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy

Soil contamination by heavy metals has become a serious environmental issue worldwide. Rapidly and reliably obtaining heavy metal concentrations in soil is vital for soil monitoring and remediation. Visible and near-infrared reflectance (VNIR) spectroscopy provides a promising method for the estimation of heavy metal concentrations over large areas. Ninety-three soil samples were collected from a suburb of Wuhan City, Hubei Province, China, and their reflectance spectra were measured in the laboratory. This study aimed to (i) examine the feasibility of using soil reflectance spectra to estimate the concentrations of Cd, Pb, As, Cr, Cu and Zn in suburban soils; (ii) compare the performances of different spectral pretreatments and (iii) explore the mechanism underlying the estimation of heavy metal concentration from VNIR spectra. In particular, we proposed a strategy for the mechanism investigation that combined PCA biplot analysis, correlation and partial correlation analyses. Partial least-square regression was adopted to calibrate the VNIR model. Results showed that the VNIR model provided acceptable estimation accuracies for Cr, As and Cd concentrations with the ratio of the performance to deviation (RPD) values of 2.70, 1.81 and 1.63, respectively, but unsatisfactory estimation accuracies for Pb, Cu and Zn concentrations with the RPD values of 0.70–1.03. Savitzky–Golay smoothing outperformed other spectral pretreatments. The mechanisms underlying the estimation of the six studied heavy metals varied on a case-to-case basis. Specifically, the spectral estimation of Cd (Group I) concentration was attributed to its close correlations with soil organic matter (SOM). Cr and As (Group II) concentrations could be estimated by the VNIR model on the basis of their close correlations with Fe. Pb, Cu and Zn (Group III) concentrations, however, had weak correlations with neither SOM nor Fe, resulting in poor estimations. The proposed strategy on mechanism investigation for heavy metals could be transferred to other study areas. In summary, VNIR spectroscopy combined with the PLSR model is an alternative method for the rapid monitoring of some heavy metal pollution in suburban soils.
AIPNet: Image-to-Image Single Image Dehazing With Atmospheric Illumination Prior

The atmospheric scattering and absorption gives rise to the natural phenomenon of haze, which severely affects the visibility of scenery. Thus, the image taken by the camera can easily lead to over brightness and ambiguity. To resolve an illposed and intractable problem of single image dehazing, we propose a straightforward but remarkable prior-atmospheric illumination prior in this paper. The extensive statistical experiments for different colorspaces and theoretical analyses indicate that the atmospheric illumination in hazy weather mainly has a great influence on the luminance channel in YCrCb colorspace, and has less impact on the chrominance channels. According to this prior, we try to maintain the intrinsic color of hazy scene and enhance its visual contrast. To this end, we apply the multiscale convolutional networks that can automatically identify hazy regions and restore deficient texture information. Compared with previous methods, the deep CNNs not only achieve an end-to-end trainable model, but also accomplish an easy imageto-image system architecture. The extensive comparisons and analyses with existing approaches demonstrate that the proposed approach achieves the state-of-the-art performance on several dehazing effects.
Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis

As a fundamental task of sentiment analysis, aspect-level sentiment analysis aims to identify the sentiment polarity of a specific aspect in the context. Previous work on aspect-level sentiment analysis is text-based. With the prevalence of multimodal user-generated content (e.g. text and image) on the Internet, multimodal sentiment analysis has attracted increasing research attention in recent years. In the context of aspect-level sentiment analysis, multimodal data are often more important than text-only data, and have various correlations including impacts that aspect brings to text and image as well as the interactions associated with text and image. However, there has not been any related work carried out so far at the intersection of aspect-level and multimodal sentiment analysis. To fill this gap, we are among the first to put forward the new task, aspect based multimodal sentiment analysis, and propose a novel Multi-Interactive Memory Network (MIMN) model for this task. Our model includes two interactive memory networks to supervise the textual and visual information with the given aspect, and learns not only the interactive influences between cross-modality data but also the self influences in single-modality data. We provide a new publicly available multimodal aspect-level sentiment dataset to evaluate our model, and the experimental results demonstrate the effectiveness of our proposed model for this new task.
Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis

This study evaluates the performance of 75 clear-sky global irradiance models against 75 ground stations worldwide covering five major Köppen-Geiger climate classifications and overall global performance. After quality control, clear-sky detection and data-availability criteria, there are 4.36 million 1-min valid global horizontal irradiance (GHI) data points for evaluation. This study represents the most encompassing evaluation of its kind in terms of number of models assessed, number of ground stations used, most consistent selection of input variables in terms of temporal and spatial resolution, also using the most rigorous and fair performance assessment criteria and suitable ranking system. A statistically rigorous Principal Component Analysis (PCA) ranking procedure is proposed to replace the conventional ordering method based on single and simple statistics. In particular, it is demonstrated that all 13 error metrics contribute to the variance of GHI estimation and thus must all be accounted for in the ranking procedure. The best performing models in each climate zone of equatorial, arid, temperate, cold and polar are REST2v9.1, REST2v9.1, MAC2, REST2v5 and CLS, respectively. Globally, the top three performing models are MAC2, REST2v5, and REST2v9.1. Many models appear to suffer from over-fitting empirical relationships to training data, resulting in inconsistent worldwide performance. Furthermore, six different formulations of the Linke Turbidity factor (TL) are evaluated through its application to five clear-sky models that require it as an input. The best globally performing TL formulations are either by Ineichen or Gueymard when combined with four of the five Linke-dependent clear-sky models, whilst the Grenier formulation performs best with the Ineichen & Perez model. Significant performance variation is observed depending on the TL formulation selected, hence, careful selection is required for an optimal application. The model codes are available in R [1].
Visualizing and Understanding the Effectiveness of BERT

Language model pre-training, such as BERT, has achieved remarkable results in many NLP tasks.However, it is unclear why the pretraining-then-fine-tuning paradigm can improve performance and generalization capability across different tasks.In this paper, we propose to visualize loss landscapes and optimization trajectories of fine-tuning BERT on specific datasets.First, we find that pre-training reaches a good initial point across downstream tasks, which leads to wider optima and easier optimization compared with training from scratch.We also demonstrate that the finetuning procedure is robust to overfitting, even though BERT is highly over-parameterized for downstream tasks.Second, the visualization results indicate that fine-tuning BERT tends to generalize better because of the flat and wide optima, and the consistency between the training loss surface and the generalization error surface.Third, the lower layers of BERT are more invariant during fine-tuning, which suggests that the layers that are close to input learn more transferable representations of language.
Technological Competence Is a Pre-condition for Effective Implementation of Virtual Reality Head Mounted Displays in Human Neuroscience: A Technological Review and Meta-Analysis

Immersive virtual reality (VR) emerges as a promising research and clinical tool. However, several studies suggest that VR induced adverse symptoms and effects (VRISE) may undermine the health and safety standards, and the reliability of the scientific results. In the current literature review, the technical reasons for the adverse symptomatology are investigated to provide suggestions and technological knowledge for the implementation of VR head-mounted display (HMD) systems in cognitive neuroscience. The technological systematic literature indicated features pertinent to display, sound, motion tracking, navigation, ergonomic interactions, user experience, and computer’s hardware that should be considered by the researchers. Subsequently, a meta-analysis of 44 neuroscientific or neuropsychological studies involving VR HMD systems was performed. The meta-analysis of the VR studies demonstrated that the new generation HMDs induced significantly fewer dropouts and VRISE. Importantly, the commercial versions of the new generation HMDs with ergonomic interactions had zero incidents of adverse symptomatology and dropouts. Solely, HMDs equivalent to or greater than the commercial versions of contemporary HMDs accompanied with ergonomic interactions are suitable for implementation in cognitive neuroscience. In conclusion, researchers’ technological competency, along with meticulous methods and reports pertinent to software, hardware, and VRISE, are paramount to ensure the health and safety standards and the reliability of neuroscientific results.
QASC: A Dataset for Question Answering via Sentence Composition

Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.
An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation

With the expansion of grid-connected solar power generation, the variability of photovoltaic power generation has become increasingly pronounced. Accurate photovoltaic output prediction is necessary to ensure power system stability. In this work, an inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. The former balances the search and mining capabilities at the population location search equation, and the latter helps to increase the diversity of the masses and to void avoid entrapment into local optima. Various meteorological conditions affecting the photovoltaic power generation are discussed and the experimental input data is optimized by grey relational analysis. The results using multiple test functions and the real data of photovoltaic power station in Australia have verified that the proposed model has better optimization performance compared with other models. The proposed method contributes to improve photovoltaic energy prediction, reduces the impact of photovoltaic power penetration into the grid, and maintains the system reliability.
Retrospective Reader for Machine Reading Comprehension

Machine reading comprehension (MRC) is an AI challenge that requires machines to determine the correct answers to questions based on a given passage. MRC systems must not only answer questions when necessary but also tactfully abstain from answering when no answer is available according to the given passage. When unanswerable questions are involved in the MRC task, an essential verification module called verifier is especially required in addition to the encoder, though the latest practice on MRC modeling still mostly benefits from adopting well pre-trained language models as the encoder block by only focusing on the "reading". This paper devotes itself to exploring better verifier design for the MRC task with unanswerable questions. Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yields an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction. The proposed reader is evaluated on two benchmark MRC challenge datasets SQuAD2.0 and NewsQA, achieving new state-of-the-art results. Significance tests show that our model is significantly better than strong baselines.
Long-Distance Free-Space Measurement-Device-Independent Quantum Key Distribution

Measurement-device-independent quantum key distribution (MDI-QKD), based on two-photon interference, is immune to all attacks against the detection system and allows a QKD network with untrusted relays. Since the MDI-QKD protocol was proposed, fiber-based implementations aimed at longer distance, higher key rates, and network verification have been rapidly developed. However, owing to the effect of atmospheric turbulence, MDI-QKD over a free-space channel remains experimentally challenging. Herein, by developing a robust adaptive optics system, high-precision time synchronization and frequency locking between independent photon sources located far apart, we realized the first free-space MDI-QKD over a 19.2-km urban atmospheric channel, which well exceeds the effective atmospheric thickness. Our experiment takes the first step toward satellite-based MDI-QKD. Moreover, the technology developed herein opens the way to quantum experiments in free space involving long-distance interference of independent single photons.Received 7 June 2020Accepted 11 November 2020DOI:https://doi.org/10.1103/PhysRevLett.125.260503© 2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical quantum information processingQuantum communicationQuantum cryptographyQuantum opticsQuantum InformationAtomic, Molecular & Optical
Lightweight image super-resolution with enhanced CNN

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESRCNN is accessible on https://github.com/hellloxiaotian/LESRCNN.
Rank consistent ordinal regression for neural networks with application to age estimation

In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into account. Neural networks were equipped with ordinal regression capabilities by transforming ordinal targets into binary classification subtasks. However, this method suffers from inconsistencies among the different binary classifiers. To resolve these inconsistencies, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.
Syntactic Structure from Deep Learning

Modern deep neural networks achieve impressive performance in engineering applications that require extensive linguistic skills, such as machine translation. This success has sparked interest in probing whether these models are inducing human-like grammatical knowledge from the raw data they are exposed to, and, consequently, whether they can shed new light on long-standing debates concerning the innate structure necessary for language acquisition. In this article, we survey representative studies of the syntactic abilities of deep networks, and discuss the broader implications that this work has for theoretical linguistics.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation.This DL-ACO consists of two ant colony algorithms which run independently and successively.First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety.Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot.Our method is tested by simulations and compared with other path planning algorithms.The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm.Furthermore, its performance is validated by experiments in indoor and outdoor environments.
Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.