Reviewerly/exHarmony huggingface dataset:
Smart Contract Invocation Protocol (SCIP): A Protocol for the Uniform Integration of Heterogeneous Blockchain Smart Contracts

Blockchains are distributed ledgers that enable the disintermediation of collaborative processes and, at the same time, foster trust among partners. Modern blockchains support smart contracts, i.e., software deployed on the blockchain, and guarantee their repeatable, deterministic execution. Alas, blockchains and smart contracts lack standardization. Therefore, smart contracts come with heterogeneous properties, APIs and data formats. This hinders the integration of smart contracts running in different blockchains, e.g., into enterprise business processes. This paper introduces the Smart Contract Invocation Protocol (SCIP), which unifies interacting with smart contracts of different blockchains. The protocol supports invoking smart contract functions, monitoring function executions, emitted events, and transaction finality, as well as querying a blockchain. The protocol is accompanied by a prototypical implementation of a SCIP endpoint in the form of a gateway.
Is it an institution, digital or social inclusion that matters for inclusive growth? A panel data analysis

This study empirically examines the impact of institutional quality, social inclusion and digital inclusion on inclusive growth across different economies characterized by different income groups. Particularly, the study examines the impact of institutions on inclusive growth by using the panel data for 83 countries over the period 2010-2017. For empirical specification, we used two-steps system-GMM estimation technique to tackle endogeneity and min-max normalized indexing technique to construct the indices for inclusive growth, social inclusion, digital inclusion and institutional quality. The results of this study show that there is a direct link between institutional quality and inclusive growth for a higher-income group of countries but not in the rest of the income groups. Contribution of social and digital inclusivity is significant in all three income groups, except for social inclusion in middle-income countries. From the policy point of view, these findings suggest that establishing and strengthening the institutional structure in low- and middle-income countries can contribute towards better and higher inclusive growth.
Constraints and benefits of the blockchain use for real estate and property rights

Purpose Many recent social media posts and news may create a perception of big success in the use of blockchain for the real estate industry, land registration and protection of titles and property rights. A sobering outlook is crucial because misleading concepts may bury the whole idea of blockchain use. This paper aims to research the possibilities of blockchain and other distributed ledger technologies (DLT) and applicability of these technologies for different purposes in real estate, property rights and public registries. Design/methodology/approach This research is framed with policy studies and focuses on property rights, land registration regulatory framework and information and communication technologies innovations. The context of this paper is decentralization which has been developed in political science studies and the role of blockchain and DLT in it. Therefore, the provided analysis of blockchain and DLT is interdisciplinary research to interpret the facets of DLT technologies in the context of real estate and land title registration. Findings Permissioned and private DLT systems cannot be considered a significant evolutionary step in government systems. Blockchain, which is distinguished from permissioned systems as the technology of the immutable ledger that does not require authorities, is a new word in governance. However, this technology has some principal features that can restrain its implementation at the state level and thus require further research and development. The application of blockchain requires a proper architecture of overlaid technologies to support changes of outdated and mistaken data, address issues of digital identity and privacy, legal compliance and enforceability of smart contracts and scalability of the ledger. Originality/value This paper shows the constraints of the technology’s properties which were not explained before in the context of title rights and land registration even though technological limits are known in more specific technical sources. Along with the known benefits this meant to help to avoid misinterpretation of some DLT features by non-technical people. A multidisciplinary approach in analyzing the technology and laws helped to better understand what can and cannot be beneficial for public registries and the protection of property rights. The presented outcomes can be laid down as requirements for the technical protocols aimed at addressing the issues of DLT and public policies to put blockchain at the service of society.
Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning

Safety in Human-Robot Collaboration (HRC) is a bottleneck to HRC-productivity in industry. With robots being the main source of hazards, safety engineers use over-emphasized safety measures, and carry out lengthy and expensive risk assessment processes on each HRC-layout reconfiguration. Recent advances in deep Reinforcement Learning (RL) offer solutions to add intelligence and comprehensibility of the environment to robots. In this paper, we propose a framework that uses deep RL as an enabling technology to enhance intelligence and safety of the robots in HRC scenarios and, thus, reduce hazards incurred by the robots. The framework offers a systematic methodology to encode the task and safety requirements and context of applicability into RL settings. The framework also considers core components, such as behavior explainer and verifier, which aim for transferring learned behaviors from research labs to industry. In the evaluations, the proposed framework shows the capability of deep RL agents learning collision-free point-to-point motion on different robots inside simulation, as shown in the supplementary video.
NOH-NMS

Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives. This problem is more severe in pedestrian detection because the instance density varies more intensively. However, previous works on NMS don't consider or vaguely consider the factor of the existent of nearby pedestrians. Thus, we propose \heatmapname (\heatmapnameshort ), which pinpoints the objects nearby each proposal with a Gaussian distribution, together with \nmsname, which dynamically eases the suppression for the space that might contain other objects with a high likelihood. Compared to Greedy-NMS, our method, as the state-of-the-art, improves by $3.9%$ AP, $5.1%$ Recall, and $0.8%$ MR\textsuperscript-2 on CrowdHuman to $89.0%$ AP and $92.9%$ Recall, and $43.9%$ MR\textsuperscript-2 respectively.
Computer-assisted EFL writing and evaluations based on artificial intelligence: a case from a college reading and writing course

Purpose The aim of this study is to explore students' expectations and perceived effectiveness of computer-assisted review tools, and the differences in reliability and validity between human evaluation and automatic evaluation, to find a way to improve students' English writing ability. Design/methodology/approach Based on the expectancy disconfirmation theory (EDT) and Intelligent Computer-Assisted Language Learning (ICALL) theory, an experiment is conducted through the observation method, semistructured interview method and questionnaire survey method. In the experiment, respondents were asked to write and submit four essays on three online automated essay evaluation (AEE) systems in total, one essay every two weeks. Also, two teacher raters were invited to score the first and last papers of each student. The respondents' feedbacks were investigated to confirm the effectiveness of the AEE system; the evaluation results of the AEE systems and teachers were compared; descriptive statistics was used to analyze the experimental data. Findings The experiment revealed that the respondents held high expectations for the computer-assisted evaluation tools, and the effectiveness of computer scoring feedback on students was higher than that of teacher scoring feedback. Moreover, at the end of the writing project, the students' independent learning ability and English writing ability were significantly improved. Besides, there was a positive correlation between students' initial expectations of computer-assisted learning tools and the final evaluation of learning results. Originality/value The innovation lies in the use of observation methods, questionnaire survey methods, data analysis, and other methods for the experiment, and the combination of deep learning theory, EDT and descriptive statistics, which has particular reference value for future works.
Video Deblurring via Spatiotemporal Pyramid Network and Adversarial Gradient Prior

Video deblurring is to restore sharp frames from a blurry sequence. It is a challenging low-level vision task because the blur caused by camera shake, object motions and depth variations is heterogeneous in both spatial and temporal dimensions. Traditional methods usually work on a fixed spatiotemporal scale. However, the spatiotemporal scale of blurs in the video can vastly vary in the real-world situation. To address this challenge, we propose a Spatiotemporal Pyramid Network (SPN) to dynamically learn different spatiotemporal cues for video deblurring. Specifically, inside SPN, a spatiotemporal pyramid module is employed to effectively capture both spatial information and temporal information from the blurry sequence in a pyramid mode. An image reconstruction module constructs the sharp center frame through the obtained spatiotemporal information. Additionally, inspired by the statistical image prior and adversarial learning, we extend SPN and propose a Spatiotemporal Pyramid Generative Adversarial Network (SPGAN), which conducts adversarial discrimination in the gradient space. It helps the network produce more realistic sharp video frames. Experiments conducted on benchmarks demonstrate that the proposed methods achieve state-of-the-art results in terms of PSNR, SSIM and visual quality.
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning

In the past few years, we have witnessed the great progress of image super-resolution (SR) thanks to the power of deep learning. However, a major limitation of the current image SR approaches is that they assume a pre-determined degradation model or kernel, e.g. bicubic, controls the image degradation process. This makes them easily fail to generalize in a real-world or non-ideal environment since the degradation model of an unseen image may not obey the pre-determined kernel used when training the SR model. In this work, we introduce a simple yet effective zero-shot image super-resolution model. Our zero-shot SR model learns an image-specific super-resolution network (SRN) from a low-resolution input image alone, without relying on external training sets. To circumvent the difficulty caused by the unknown internal degradation model of an image, we propose to learn an image-specific degradation simulation network (DSN) together with our image-specific SRN. Specifically, we exploit the depth information, naturally indicating the scales of local image patches, of an image to extract the unpaired high/low-resolution patch collection to train our networks. According to the benchmark test on four datasets with depth labels or estimated depth maps, our proposed depth guided degradation model learning-based image super-resolution (DGDML-SR) achieves visually pleasing results and can outperform the state-of-the-arts in perceptual metrics.
GitLab: work where you want, when you want

Abstract GitLab is a software company that works “all remote” at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office and its employees can work from anywhere they choose. Any step of the organizational life of a GitLab employee (e.g., hiring, onboarding and firing) is performed remotely, except for a yearly companywide gathering. GitLab strongly relies on asynchronous coordination, allowing employees to work anytime they want. After highlighting some of the main practices implemented by GitLab to effectively work all remotely and asynchronously, I asked renowned organizational scientists their thoughts on this interesting case and to question the generalizability of the all remote asynchronous model. Understanding whether and under what conditions this model can succeed can be of guidance for organizational designers that are now considering different remote models in response of the COVID-19 shock and its aftermath.
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill.Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging.In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention.Unlike softmax classifiers, we leverage BERTstyle pairwise encoding to train a binary classifier that estimates the best matched training example for a user input.We propose to boost the discriminative ability by transferring a natural language inference (NLI) model.Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embeddingbased nearest neighbor approaches.More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.
BoMaNet

Recent work on stealing machine learning (ML) models from inference engines with physical side-channel attacks warrant an urgent need for effective side-channel defenses. This work proposes the first fully-masked neural network inference engine design.
Generation of generalized hybrid entanglement in cavity electro–optic systems

Abstract Schrödinger’s cat paradox has revealed the entanglement between microscopic and macroscopic objects. Recently, several approaches have been proposed to generate such hybrid entangled state. In this paper, we demonstrate the generation of generalized hybrid entangled state in a cavity electro–optic system. The hybrid entangled state between the optical cavity and the microwave field is generated by a one-step evolution. Numerical simulations show that the high-fidelity entangled state is obtained even if the decay of the system is considered. Also, this proposal can be applied to generate macroscopic quantum superposition states of the microwave field.
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction

Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings.However, people can grasp new knowledge by learning a few instances.To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood.Unlike previous single-task few-shot problems, relational triple extraction is more challenging as the entities and relations have implicit correlations.In this paper, We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples, namely, entity pairs and corresponding relations.To be specific, we design a hybrid prototypical learning mechanism that bridges text and knowledge concerning both entities and relations.Thus, implicit correlations between entities and relations are injected.Additionally, we propose a prototype-aware regularization to learn more representative prototypes.Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.
A Method for Detection of Small Moving Objects in UAV Videos

Detection of small moving objects is an important research area with applications including monitoring of flying insects, studying their foraging behavior, using insect pollinators to monitor flowering and pollination of crops, surveillance of honeybee colonies, and tracking movement of honeybees. However, due to the lack of distinctive shape and textural details on small objects, direct application of modern object detection methods based on convolutional neural networks (CNNs) shows considerably lower performance. In this paper we propose a method for the detection of small moving objects in videos recorded using unmanned aerial vehicles equipped with standard video cameras. The main steps of the proposed method are video stabilization, background estimation and subtraction, frame segmentation using a CNN, and thresholding the segmented frame. However, for training a CNN it is required that a large labeled dataset is available. Manual labelling of small moving objects in videos is very difficult and time consuming, and such labeled datasets do not exist at the moment. To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds. The experimental results on detection of flying honeybees show that by using a combination of classical computer vision techniques and CNNs, as well as synthetic training sets, the proposed approach overcomes the problems associated with direct application of CNNs to the given problem and achieves an average F1-score of 0.86 in tests on real-world videos.
Best Practices for Implementing FAIR Vocabularies and Ontologies on the Web

With the adoption of Semantic Web technologies, an increasing number of vocabularies and ontologies have been developed in different domains, ranging from Biology to Agronomy or Geosciences. However, many of these ontologies are still difficult to find, access and understand by researchers due to a lack of documentation, URI resolving issues, versioning problems, etc. In this chapter we describe guidelines and best practices for creating accessible, understandable and reusable ontologies on the Web, using standard practices and pointing to existing tools and frameworks developed by the Semantic Web community. We illustrate our guidelines with concrete examples, in order to help researchers implement these practices in their future vocabularies.
Exploiting spatio-temporal representation for 3D human action recognition from depth map sequences

Human action recognition based on 3D data is attracting increasing attention because it could provide more abundant spatial and temporal information compared with RGB videos. The challenge of the depth map based method is to capture the cues between spatial appearances and temporal motions. In this paper, we propose a straightforward and efficient framework for modeling the human action based on depth map sequences, considering the short-term and long-term dependencies. A frame-level feature, termed depth-oriented gradient vector (DOGV), is developed to capture the appearance and motion in a short-term duration. For a long-term dependence, we construct convolutional neural networks (CNNs) based backbone to aggregate frame-level features in the space and time sequence. The proposed method is comprehensively evaluated on four public benchmark datasets, including NTU RGB+D, NTU RGB+D 120, PKU-MMD and UOW LSC. The experimental results demonstrate that the proposed approach can solve the problem of 3D human action recognition in an efficient way and achieve the state-of-the-art performance.
Progressive Generation of Long Text with Pretrained Language Models

Large-scale language models (LMs) pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators.However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e.g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus.Previous planning-then-generation methods also fall short of producing such long text in various domains.To overcome the limitations, we propose a simple but effective method of generating text in a progressive manner, inspired by generating images from low to high resolution.Our method first produces domain-specific content keywords and then progressively refines them into complete passages in multiple stages.The simple design allows our approach to take advantage of pretrained LMs at each stage and effectively adapt to any target domain given only a small set of examples.We conduct a comprehensive empirical study with a broad set of evaluation metrics, and show that our approach significantly improves upon the fine-tuned large LMs and various planning-then-generation methods in terms of quality and sample efficiency.Human evaluation also validates that our model generations are more coherent. 1
QBugs: A Collection of Reproducible Bugs in Quantum Algorithms and a Supporting Infrastructure to Enable Controlled Quantum Software Testing and Debugging Experiments

Reproducibility and comparability of empirical results are at the core tenet of the scientific method in any scientific field. To ease reproducibility of empirical studies, several benchmarks in software engineering research, such as Defects4J, have been developed and widely used. For quantum software engineering research, however, no benchmark has been established yet. In this position paper, we propose a new benchmark-named QBugs-which will provide experimental subjects and an experimental infrastructure to ease the evaluation of new research and the reproducibility of previously published results on quantum software engineering.
Low-Light Image Enhancement via Poisson Noise Aware Retinex Model

Limited by the number of available photons collected by a pixel and the stability of the imaging system, images captured under poor illumination conditions are often degraded by heavy shot noise and low contrast. In this letter, we propose a novel low-light image enhancement algorithm using the Poisson noise-aware Retinex model, in which the Poisson distribution is considered to formulate the fidelity term in the Retinex model for the first time. Furthermore, the space-variant weight maps for total variation regularization terms are calculated according to the piecewise smooth prior of illumination component and the Poisson noise distribution prior, which contributes to noise suppression in different noise intensity while preserving image details and structures. We get the optimal solution of the Poisson noise-aware Retinex model by an iterative optimization algorithm. Finally, the enhanced image is obtained by gamma correcting the estimated illumination map. Experimental results demonstrate that the proposed model performs better than state-of-the-art methods in low-light image enhancement and noise suppression.