Reviewerly/exHarmony huggingface dataset:
Improvement of Library Services through Friends of the Ambassador Mier

The improvement of elementary school library services is very important especially to increase interest in reading, the limited availability of elementary school librarians has an impact on the need for cooperation to improve library services with mier ambassador friends namely library staff partners from the elements of students who have a hobby of reading. The purpose of this collaboration is to optimize the potential of school library services. Forms of cooperation undertaken include to improve the implementation of library activities programs, library promotion, reading habits habituation, reaward reading and fostering collection. This descriptive study uses a qualitative approach. The observation guide was made as a research instrument to collect data relating to the effectiveness of the collaboration of library staff and friends of Ambassador Ambassador to the increased interest in reading readers. The results of this study indicate that the library of SD Muhammadiyah 3 IKROM Wage really needs to collaborate with friends of Ambassador Ambassador to improve school library services
Data Privacy Protection in News Crowdfunding in the Era of Artificial Intelligence

This paper aims to study the protection of data privacy in news crowdfunding in the era of artificial intelligence. This paper respectively quotes the encryption algorithm of artificial intelligence data protection and the BP neural network prediction model to analyze the data privacy protection in news crowdfunding in the artificial intelligence era. Finally, this paper also combines the questionnaire survey method to understand the public’s awareness of privacy. The results of this paper show that artificial intelligence can promote personal data awareness and privacy, improve personal data and privacy measures and methods, and improve the effectiveness and level of privacy and privacy. In the analysis, the survey found that male college students only have 81.1% of the cognition of personal trait information, only 78.5% of network trace information, and only 78.3% of female college students’ cognition of personal credit.
no1s1 - a blockchain-based DAO prototype for autonomous space

We introduce our ongoing research on no1s1 (“no-ones-one”), a meditation pod that aims to be the first autonomous space. To frame our early thinking, we conceptualize what we call Decentralized Autonomous Space (DAS) as a Decentralized Autonomous Organization (DAO) linked to a physical location. DAOs leverage a combination of Decentralized Ledger Technology (DLT) and the Internet of Things (IoT) to create self-governing coordination mechanisms through smart contracts. Therefore, DAS can self-create and self-manage, and ultimately self-own. DAS is presented as a potentially disruptive paradigm of future housing and infrastructure with wide-ranging implications to the built environment.
Theoretical study of reflection spectroscopy for superconducting quantum parametrons

Abstract Superconducting parametrons in the single-photon Kerr regime, also called KPOs, have been attracting increasing attention in terms of their applications to quantum annealing and universal quantum computation. It is of practical importance to obtain information of superconducting parametrons operating under an oscillating pump field. Spectroscopy can provide information of a superconducting parametron under examination, such as energy level structure, and also useful information for calibration of the pump field. We theoretically study the reflection spectroscopy of superconducting parametrons, and develop a method to obtain the reflection coefficient. We present formulae of the reflection coefficient, the nominal external and the internal decay rates, and examine the obtained spectra. It is shown that the difference of the populations of energy levels manifests itself as a dip or peak in the amplitude of the reflection coefficient, and one can directly extract the coupling strength between the energy levels by measuring the nominal decay rates when the pump field is sufficiently large.
Research on Object Detection Method Based on FF-YOLO for Complex Scenes

YOLO v3 has poor accuracy in target location recognition, and the detection effect needs to be improved in complex scenes with dense target distribution and large size differences. To solve this problem, an improved multi-scale target detection algorithm based on feature fusion (FF-YOLO) is proposed in this paper. Firstly, the residual structure in Darknet53 backbone of YOLO v3 is replaced by the optimized dense connection network FCN-DenseNet, and features are extracted effectively through feature reuse, and the problem of vanishing gradient is further alleviated. Secondly, based on the three-scale prediction mechanism of YOLO v3, we added a fourth detection scale to make the network learn more shallower location information. Finally, Spatial Pyramid Pooling (SPP) module is added before each detection layer to make the local feature information deeply fused.It increases the receptive field of the backbone network and significantly isolates the most important contextual feature. Experiments show that FF-YOLO can effectively improve the detection accuracy of multi-scale targets in complex scenes. On Pascal VOC2007 data set, the mAP of FF-YOLO is 5.8% higher than that of YOLO v3. At the same time, the mAP of FF-YOLO for medium and small targets are 1.5% and 2.2% higher than that of YOLO v3 on MS COCO data set.
End-to-End Language Diarization for Bilingual Code-Switching Speech

We propose two end-to-end neural configurations for language diarization on bilingual code-switching speech.The first, a BLSTM-E2E architecture, includes a set of stacked bidirectional LSTMs to compute embeddings and incorporates the deep clustering loss to enforce grouping of languages belonging to the same class.The second, an XSA-E2E architecture, is based on an x-vector model followed by a self-attention encoder.The former encodes frame-level features into segmentlevel embeddings while the latter considers all those embeddings to generate a sequence of segment-level language labels.We evaluated the proposed methods on the dataset obtained from the shared task B in WSTCSMC 2020 and our handcrafted simulated data from the SEAME dataset.Experimental results show that our proposed XSA-E2E architecture achieved a relative improvement of 12.1% in equal error rate and a 7.4% relative improvement on accuracy compared with the baseline algorithm in the WSTCSMC 2020 dataset.Our proposed XSA-E2E architecture achieved an accuracy of 89.84% with a baseline of 85.60% on the simulated data derived from the SEAME dataset.
Review and comparison of algorithms and software for mixed-integer derivative-free optimization

Abstract This paper reviews the literature on algorithms for solving bound-constrained mixed-integer derivative-free optimization problems and presents a systematic comparison of available implementations of these algorithms on a large collection of test problems. Thirteen derivative-free optimization solvers are compared using a test set of 267 problems. The testbed includes: (i) pure-integer and mixed-integer problems, and (ii) small, medium, and large problems covering a wide range of characteristics found in applications. We evaluate the solvers according to their ability to find a near-optimal solution, find the best solution among currently available solvers, and improve a given starting point. Computational results show that the ability of all these solvers to obtain good solutions diminishes with increasing problem size, but the solvers evaluated collectively found optimal solutions for 93% of the problems in our test set. The open-source solvers MISO and NOMAD were the best performers among all solvers tested. MISO outperformed all other solvers on large and binary problems, while NOMAD was the best performer on mixed-integer, non-binary discrete, small, and medium-sized problems.
Predicting Entity Relations across Different Security Databases by Using Graph Attention Network

Security databases such as Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) maintain diverse high-quality security concepts, which are treated as security entities. Meanwhile, security entities are documented with many potential relation types that profit for security analysis and comprehension across these three popular databases. To support reasoning security entity relationships, translation-based knowledge graph representation learning treats each triple independently for the entity prediction. However, it neglects the important semantic information about the neighbor entities around the triples. To address it, we propose a text-enhanced graph attention network model (text-enhanced GAT). This model highlights the importance of the knowledge in the 2−hop neighbors surrounding a triple, under the observation of the diversity of each entity. Thus, we can capture more structural and textual information from the knowledge graph about the security databases. Extensive experiments are designed to evaluate the effectiveness of our proposed model on the prediction of security entity relationships. Moreover, the experimental results outperform the state-of-the-art by Mean Reciprocal Rank (MRR) 0.132 for detecting the missing relationships.
Detecting Environmental, Social and Governance (ESG) Topics Using Domain-Specific Language Models and Data Augmentation

Despite recent advances in deep learning-based language modelling, many natural language processing (NLP) tasks in the financial domain remain challenging due to the paucity of appropriately labelled data. Other issues that can limit task performance are differences in word distribution between the general corpora – typically used to pre-train language models – and financial corpora, which often exhibit specialized language and symbology. Here, we investigate two approaches that can help to mitigate these issues. Firstly, we experiment with further language model pre-training using large amounts of in-domain data from business and financial news. We then apply augmentation approaches to increase the size of our data-set for model fine-tuning. We report our findings on an Environmental, Social and Governance (ESG) controversies data-set and demonstrate that both approaches are beneficial to accuracy in classification tasks.
Relational cohesion between users and smart voice assistants

Purpose This study aims to examine users’ affective relationships with smart voice assistants (SVAs) and aims to analyze how these relationships explain user engagement behaviors toward the brands of SVAs. Drawing on relational cohesion theory, it proposes that cohesion between users and SVAs influences brand engagement behaviors, that is, continuing purchasing other products of the brand, providing knowledge to the brand and referring the brand. Design/methodology/approach Data from a survey of 717 US regular SVA users confirm the validity of the measurement scales and provide the input for the covariance-based structural equation modeling. Findings The results demonstrate that frequent user-SVA interactions evoke positive emotions, which encourage cohesive relationships. Pleasured-satisfaction and interest emerge as strong emotions. Moreover, relational cohesion between users and SVAs promotes engagement with the brand of the assistant. Originality/value This paper applies an interpersonal approach in a context that, to date, has been examined from a predominantly technological perspective. It shows that users develop positive emotions toward smart technologies through their interactions, and establishes the importance of building affective relationships. To the best of the authors’ knowledge, this is the first study to analyze cohesion between users and smart technologies and to examine the effect of this cohesion on user engagement with the brand.
Patterns for Hybrid Quantum Algorithms

Quantum computers have the potential to solve certain problems faster than classical computers. However, the computations that can be executed on current quantum devices are still limited. Hybrid algorithms split the computational tasks between classical and quantum computers circumventing some of these limitations. Therefore, they are regarded as promising candidates for useful applications in the near future. But especially for novices in quantum computing, it is hard to identify why a particular splitting strategy is proposed by an algorithm. In this work, we describe the best practices for splitting strategies as patterns to foster a common understanding of hybrid algorithms.
Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks

Abstract Researchers address the generalization problem of deep image processing networks mainly through extensive use of data augmentation techniques such as random flips, rotations, and deformations. A data augmentation technique called mixup, which constructs virtual training samples from convex combinations of inputs, was recently proposed for deep classification networks. The algorithm contributed to increased performance on classification in a variety of datasets, but so far has not been evaluated for image segmentation tasks. In this paper, we tested whether the mixup algorithm can improve the generalization performance of deep segmentation networks for medical image data. We trained a standard U-net architecture to segment the prostate in 100 T2-weighted 3D magnetic resonance images from prostate cancer patients, and compared the results with and without mixup in terms of Dice similarity coefficient and mean surface distance from a reference segmentation made by an experienced radiologist. Our results suggest that mixup offers a statistically significant boost in performance compared to non-mixup training, leading to up to 1.9% increase in Dice and a 10.9% decrease in surface distance. The mixup algorithm may thus offer an important aid for medical image segmentation applications, which are typically limited by severe data scarcity.
The Merchant: Avoiding Payment Channel Depletion through Incentives

Payment channels networks drastically increase the throughput and hence scalability of blockchains by performing transactions off-chain. Hence, they are a promising substrate for all decentralized applications that require instant payments. In an off-chain payment, parties deposit coins in a channel and then perform transactions without invoking the global consensus mechanism of the blockchain. However, the transaction value is limited by the capacity of the channel, i.e., the amount of funds available on a channel. These funds decrease when a transaction is sent and increase when a transaction is received on the channel. Recent research indicates that there is an imbalance between sending and receiving transactions, which leads to channel depletion in the sense that one of these operations becomes impossible over time due to the lack of available funds. We incentivize the balanced use of payment channels through fees. Whereas the current fee model depends solely on the transaction value, our fee policies encourage transactions that have a positive effect on the balance in a channel and discourage those that have a negative effect. This paper first defines necessary properties of fee strategies. Then, it introduces two novel fees strategies that provably satisfy all necessary properties. Our extensive simulation study reveals that these incentives increase the effectiveness of payments by 8% to 19%.
Rich specifications for Ethereum smart contract verification

Smart contracts are programs that execute in blockchains such as Ethereum to manipulate digital assets. Since bugs in smart contracts may lead to substantial financial losses, there is considerable interest in formally proving their correctness. However, the specification and verification of smart contracts faces challenges that rarely arise in other application domains. Smart contracts frequently interact with unverified, potentially adversarial outside code, which substantially weakens the assumptions that formal analyses can (soundly) make. Moreover, the core functionality of smart contracts is to manipulate and transfer resources; describing this functionality concisely requires dedicated specification support. Current reasoning techniques do not fully address these challenges, being restricted in their scope or expressiveness (in particular, in the presence of re-entrant calls), and offering limited means of expressing the resource transfers a contract performs. In this paper, we present a novel specification methodology tailored to the domain of smart contracts. Our specifications and associated reasoning technique are the first to enable: (1) sound and precise reasoning in the presence of unverified code and arbitrary re-entrancy, (2) modular reasoning about collaborating smart contracts, and (3) domain-specific specifications for resources and resource transfers, expressing a contract's behaviour in intuitive and concise ways and excluding typical errors by default. We have implemented our approach in 2vyper, an SMT-based automated verification tool for Ethereum smart contracts written in Vyper, and demonstrated its effectiveness for verifying strong correctness guarantees for real-world contracts.
Treewidth-Aware Cycle Breaking for Algebraic Answer Set Counting

Probabilistic reasoning, parameter learning, and most probable explanation inference for answer set programming have recently received growing attention. They are only some of the problems that can be formulated as Algebraic Answer Set Counting (AASC) problems. The latter are however hard to solve, and efficient evaluation techniques are needed. Inspired by Vlasser et al.'s Tp-compilation (JAR, 2016), we introduce Tp-unfolding, which employs forward reasoning to break the cycles in the positive dependency graph of a program by unfolding them. Tp-unfolding is defined for any normal answer set program and unfolds programs with respect to unfolding sequences, which are akin to elimination orders in SAT-solving. Using "good" unfolding sequences, we can ensure that the increase of the treewidth of the unfolded program is small. Treewidth is a measure adhering to a program's tree-likeness, which gives performance guarantees for AASC. We give sufficient conditions for the existence of good unfolding sequences based on the novel notion of component-boosted backdoor size, which measures the cyclicity of the positive dependencies in a program. The experimental evaluation of a prototype implementation, the AASC solver aspmc, shows promising results.
DialogueBERT

With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service timely. To satisfy the user, the conversational bots need to understand the user's intention, detect the user's emotion, and extract the key entities from the conversational utterances. However, understanding dialogues is regarded as a very challenging task. Different from common language understanding, utterances in dialogues appear alternately from different roles and are usually organized as hierarchical structures. To facilitate the understanding of dialogues, in this paper, we propose a novel contextual dialogue encoder (i.e. DialogueBERT) based on the popular pre-trained language model BERT. Five self-supervised learning pre-training tasks are devised for learning the particularity of dialouge utterances. Four different input embeddings are integrated to catch the relationship between utterances, including turn embedding, role embedding, token embedding and position embedding. DialogueBERT was pre-trained with 70 million dialogues in real scenario, and then fine-tuned in three different downstream dialogue understanding tasks. Experimental results show that DialogueBERT achieves exciting results with 88.63% accuracy for intent recognition, 94.25% accuracy for emotion recognition and 97.04% F1 score for named entity recognition, which outperforms several strong baselines by a large margin.
Estimating redundancy in clinical text

The current mode of use of Electronic Health Records (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly. Data duplication can lead to propagation of errors, inconsistencies and misreporting of care. Therefore, measures to quantify information redundancy play an essential role in evaluating innovations that operate on clinical narratives. This work is a quantitative examination of information redundancy in EHR notes. We present and evaluate two methods to measure redundancy: an information-theoretic approach and a lexicosyntactic and semantic model. Our first measure trains large Transformer-based language models using clinical text from a large openly available US-based ICU dataset and a large multi-site UK based Hospital. By comparing the information-theoretic efficient encoding of clinical text against open-domain corpora, we find that clinical text is ∼1.5× to ∼3× less efficient than open-domain corpora at conveying information. Our second measure, evaluates automated summarisation metrics Rouge and BERTScore to evaluate successive note pairs demonstrating lexicosyntactic and semantic redundancy, with averages from ∼43 to ∼65%.
Using a Blur Metric to Estimate Linear Motion Blur Parameters

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.
Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming

Abstract Automated commonsense reasoning (CR) is essential for building human-like AI systems featuring, for example, explainable AI. Event calculus (EC) is a family of formalisms that model CR with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g. time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.