Reviewerly/exHarmony huggingface dataset:
Is magnitude estimation worth the trouble?

This chapter compares three response methods commonly used in sentence acceptability experiments: yes/no, an n-point (Likert) scale, and magnitude estimation. An experiment was conducted in which participants were randomly assigned to one of these three response methods. The experiment examined three syntactic phenomena with different degrees of expected acceptability contrasts: presence or absence of inversion in wh-questions (an extremely clear contrast), the that-trace effect (a relatively clear contrast), and subextraction from embedded subjects and wh-subjects (a relatively subtle contrast). In addition, the inversion cases compared second-person pronoun, third-person pronoun, and lexical DP subjects (an extremely subtle contrast). The experiment shows that the three response methods provide strikingly consistent results across a wide range of contrast types, from very clear to very subtle. Given the well-known disadvantages of magnitude estimation, including difficulties both for experimenters and for participants, the yes/no and n-point response methods appear to be more reasonable choices for most sentence acceptability experiments.
Testing MaxEnt with sound symbolism: A stripy wug-shaped curve in Japanese Pokémon names

An issue that is actively explored in the contemporary linguistics literature is how to account for probabilistic generalizations, for which there are currently various competing theories. To bear on this debate, Hayes (2021) proposes that we examine a grammatical framework in terms of its quantitative signature, typical probabilistic patterns that the framework is predicted to generate. In this research report, I zoom in on the quantitative signature of maximum entropy harmonic grammar (MaxEnt HG), because this framework has proven useful in modeling probabilistic generalizations across different linguistic domains. Given a linear scale of violations of one constraint, MaxEnt yields a sigmoid curve. When another constraint is relevant and can be violated multiple times, this sigmoid curve can be shifted, yielding multiple sigmoid curves, which results in a stripy wug-shaped curve. Expanding upon a previous study (Kawahara 2020b), the current experiment demonstrates that we observe a stripy wug-shaped curve in a particularly clear manner in patterns of sound symbolism, systematic associations between sounds and meanings. Concretely, the experiment with Japanese speakers shows that the judgment of Pokémons' evolution status is affected by the mora counts of nonce names, resulting in a sigmoid curve, and that this sigmoid curve is shifted according to the number of voiced obstruents contained in the names. The overall results suggest that MaxEnt is a useful tool for modeling systematic sound-meaning correspondences.
Blockchain-based Circular-Secure Encryption

Even if graphical authentications are the greatest substitute to subdue the flaws of alphanumeric authentications, nowadays, alphanumeric passwords are widely preferred by most service providers. The encrypted password repository is cracked by the intruder using the rainbow table attack. Rainbow tables stimulate password-cracking considerably faster than former techniques such as brute-force approach and dictionary attacks. Here, a robust encryption technique is utilized before saving the password in the repository. Blockchain-Based Circular Fused Encryption and Wedges (CFW) algorithm is a novel encryption technique that delivers firm resilience versus rainbow table attacks and other prominent attacks. The original password database contains the hash of the particular password. In this work, the hash of the previous password is stored for the current password’s hash value and so on, based on the time of creating the password in the password database table. The last hash value is stored in the table’s first hash of the password and it is circularly fused. Moreover, it provides strong security by widening the intricacy of guessing and cracking attacks.
A tool for merging extensions of abstract argumentation frameworks

We describe a tool that allows the merging of extensions of argumentation frameworks, following the approach defined by (In Proceedings of the Fifteenth International Conference on Principles of Knowledge Representation and Reasoning (KR’16) (2016) 33–42). The tool is implemented in Java, and is highly modular thanks to Object Oriented Programming (OOP) principles. We describe a short experimental study that assesses the scalability of the approach, as well as the impact on runtime of using an integrity constraint.
Internet of Things Security: Attacks, Solutions, Strengths and Limitations

Internet of Things (IoT) has become an intangible part of regular life over the past decade. There are innovations in new wearable smart devices to various domestic appliances on a very regular basis. But IoT devices owing to the intricacy they have with our daily lives also usually store a large amount of sensitive data making IoT devices target large information security attacks. In this article, we have attempted to accumulate various security threats that IoT devices can face and the current landscape of research on countermeasures for the same. Later, we have presented a contrast to analyse the difference between major security frameworks and their efficacy. The research is further concluded on examination of potential research dimensions, scope, and related challenges in multiple research aspects.
Paper: Metadata Quality in Time of Diverse Research Outputs

The APE Conferences aim to provide a better understanding of scholarly communication and the role of information in science, education and society. They encourage debate about the future of value-added scientific publishing, information dissemination and access to scientific results and offer an independent forum for open discourse.
Infrared image super-resolution with dual mechanism and residual triplet attention

We propose an infrared image super-resolution (SR) method with a dual mechanism and residual triplet attention block (TRAB). The dual mechanism provides an extra mapping from a high-resolution image to a low-resolution one, which can help the SR process converge faster. Besides, we designed a RTAB that is a lightweight and efficient attention block to integrate both channel and spatial attention. The designed RTAB can help the SR network to extract more noteworthy high-frequency information such as edges and corners, which make the visual effect better. We verify the effectiveness of the proposed method through experiments. Compared with the existing four state-of-the-art methods, the proposed method has better performance in visual effect and evaluation indicators.
Flooding

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 57, Issue 2 p. 22895A-22896C Policy and Practice Flooding First published: 12 April 2020 https://doi.org/10.1111/j.1467-6346.2020.09380.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume57, Issue2April 2020Pages 22895A-22896C RelatedInformation
APHC: Auditable and Privacy Preserving Health QR Code Based on Blockchain

The outbreak of COVID-19 has brought great pain to people around the world. As the epidemic continuing, prevention and control measures become particularly important. Then, the health QR code has been designed to tracing and controlling the epidemic. Through the health code, the confirmed cases and close contacts will be traced quickly. However, the health code records a great deal of residents’ privacy information and if it is leaked, the consequences will be severe. Although some existing health code schemes preserve the privacy, but most of them either do not support fine-grained auditability or are centralized health code storage. Therefore, we propose an auditable and privacy-preserving health QR code scheme based on blockchain.
A Sentiment Analysis Model for COVID-19 News in Chinese Social Networks

The current prevalence of COVID-19 (coronavirus disease 2019) has produced a wide variety of responses in different regions around the world, particularly on social media and microblogs. The increasing volume of user-generated content on the social networking websites has made sentiment analysis a powerful tool for understanding the human emotional state. In this study, we perform an extensive analysis of the sentiments obtained from Chinese microblogs since the earliest reports of COVID-19. We introduce a new model for sentiment classification using Bidirectional Encoder Representations from Transformers (BERT) and obtain accuracy results over 74%. The experimental results show how the public epidemic prevention policy affects the emotions and sentiments of the public as obtained from semantic analysis of microblogs and social media during the COVID-19 pandemic.
Classification of Parkinson’s disease patients based on spectrogram using local binary pattern descriptors

Abstract Extreme learning machine is an algorithm that has shown a good performance facing classification and regression problems. It has gained great acceptance by the scientific community due to the simplicity of the model and its sola great generalization capacity. This work proposes the use of extreme learning machine neural networks to carry out the classification between Parkinson’s disease patients and healthy individuals. The descriptor used corresponds to the feature vector generated applying the local binary Pattern algorithm to the grayscale spectrograms. The spectrograms are obtained from the audio signal samples from the considered repository. Experiments are conducted with single hidden layer and multilayer extreme learning machine networks comparing the results of each structure. Results show that hierarchical extreme learning machine with three hidden layers has a better general performance over multilayer extreme learning machine networks and a single hidden layer extreme learning machine. The rate of success obtained is within the ranges presented in the literature. However, the hierarchical network training time is considerably faster compared to multilayer networks of three or two hidden layers.
On managing security in smart e-health applications

Distributed machine learning can give an adaptable but strong shared condition for the design of trusted AI applications; this is mainly due to lack of privacy of centralised remote learning mechanisms. This notwithstanding, also distributed approaches have been compromised by several attack models (mainly data poisoning): in such a situation, a malicious member of the learning party may inject bad data. As such applications are growing in criticality, learning models must face with security and protection just as with versatility issues. The aim of the paper is to improve these applications by providing extra security features for distributed and federated learning mechanisms: more in the details, the paper examines specific concerns such as the utilisation of blockchain, homomorphic cryptography and meta-modelling techniques to ensure protection as well as other non-functional properties.
Sample Selection

Samples are small amounts chosen to represent a larger pool; sampling dips into the pool to choose which will be analysed, with conclusions extended to the larger pool. Trade-offs happen between i) having samples of sufficiently large and representative to enable reasonable results; ii) the costs associated with data collection and processing. (1) <bold>Overview</bold>—i) terminology—proper drawing and representation {random, stratified, bias, weight}, counts per subgroup {over-, under- and balanced sample}; repetitive {replacement, bootstrap, k-fold, jack-knife}; artificial increases {bagging, boosting, stacking}; ii) optimal and minimum sample sizes; iii) law of diminishing data returns. (2) <bold>THOR samples</bold>—i) sample types—training, hold-out, out-of-time, recent; ii) sample count guidelines; iii) review of observation windows and assignment; iv) sampling plan and outcome. (3) <bold>Afterthoughts</bold>—i) further review of un- and under-populated characteristics; ii) means of randomly extracting an exact number of cases; iii) housekeeping rules regarding code assignments for each sample.
Training models

The article considers the problem of training organization models. It is noted that the training model reflects the approaches to the organization of the interaction between the subjects of education and training processes. Any model is a kind of ideal system that shows the real object. Any change in the system, as a result of the changes applied, leads to the emergence of a new system, unlike the previous one. For this reason, there can theoretically be a myriad of learning models; some may differ slightly from each other, some may have major differences. The training model is a systematized set of basic laws of student and teacher activity in the implementation of training. Key words: education model, learning model, developmental learning model, computer learning model, problem learning model, game learning model, project-based learning model, distance learning model
Data Variance-Based Distributed Outlier Detection in Wireless Sensor Networks

Outlier detection is a key component for the proper functioning of a wireless sensor network. It helps in identifying malicious events taking place in the field and faulty sensors. This paper presents a distributed outlier detection technique that separates outlying cases from normal data locally at sensors and sends only the summarized information of data to sink nodes to minimize the communication overhead in the resource-constraint wireless sensor network. The proposed solution employs variance of normal data as a measure to identify outliers. The performance of the proposed method has been evaluated on real-world datasets collected by WSN deployment projects. Experimental results prove the effectiveness of the proposed method in terms of higher outlier detection rate, lower false alarm rate and lower communication overhead as compared to the simulated centralized approach.
Research on Robot Dynamic Path Planning Based on Multiple Linear Reinforcement Learning

Abstract Under the background of digital and intelligent transformation, all walks of life are facing the conflict between internal stability and rapidly changing external needs, and the conflict between stable background and dynamic foreground. To solve these conflicts and problems, it needs to take new means to adjust from multiple dimensions. Draw support from the new info and communication operation support system, it could form a digital Internet info support system with the cloud platform, which can effectively establish fast and flexible front-end utilizations and support the rapid development, agile iteration and on-demand adjustment of related businesses. The Internet backbone network needs to make the info open. The new info communication operation support system based on the Internet can enable the info resources to be transmitted as needed. The research and utilization of cloud resource visualization and controllability has gradually become the key to the development of new Internet communication operation system. On the premise of systematically combing the Internet info and communication operation support system, this paper comprehensively studies the utilization of cloud resources, and analyses the utilization scenarios of cloud resources of the new education communication operation system, in order to better ameliorate the more efficient and secure utilization of cloud resources and communication operation system inside and outside the enterprise.
Debating Future Displays (and Interfaces) for Electric Vehicles

Abstract Achieving good legibility and high contrast ratio are continuous challenges for future display and interfaces technology.
The Serials Librarian and Accreditation

All academic libraries are subject to accreditation by one or more accrediting agencies. This workshop provided an overview of the regional accreditation process for higher education, discussed the standards for academic libraries in six regions, and offered suggestions for the role of the serials librarian in the accreditation process. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Breast Cancer Screening: Comparison of Screening Modalities in Taiwan

Background: Screening for breast cancer is an important method for reducing the death rate in the Westernworld over the past 20-30 years. On this basis, since 2004 , the National Health Bureau of Taiwan has alsobegun providing free screening service. The purpose of the current research is to compare the efficacy of thethree-mammography modalities for breast cancer identification.Methods: Between July 2004 and December 2010, a total of 23,513 eligible women underwent screeningmammography. 6804 of these received screen-film mammography (SFM), 1610 received computedradiography (CR) mammography, and 15,099 received digital mammography (DM). The detection rate ofcarcinoma-in-situ, early detection rate of cancer, and the overall cancer detection rate of each modality wascalculated. To determine the statistical significance of performance differences between these modalitiesChi-square test was used.Results: SFM callback rate was 12.23%, cancer detection rate was 5.29%, Ductal carcinoma in situ (DCIS)detection rate was 1.76%. Computed radiography’s callback rate was 12.67%, with a cancer detection rateof 6.21% and DCIS detection rate of 1.24%. DM has the highest callback rate of 16.2%, but also the highestrate of cancer detection and DCIS detection rate of 9.21% and 3.58% respectively. DM was found to havebetter cancer detection rate and DCIS detection rate as compared to SFM and CR, it is only statisticallysignificant when compared to SFM. Although DM has better cancer and DCIS detection rates, it is alsoassociated with higher callback rate.Conclusions: In terms of image quality and greater contrast, optical mammography has many advantageswhen opposed to screen-film and computed radiography mammography. In the Asian population, wherewomen have higher breast densities, this is particularly vital. The incidence of Taiwanese breast cancer issteadily growing
Implicit Hough Transform Neural Networks for Subspace Clustering

Subspace clustering constitutes a fundamental task in data mining and unsupervised machine learning with myriad applications. We present a novel approach to subspace clustering that detects affine hyperplanes in a given arbitrary-dimensional dataset by explicitly parametrizing them and optimizing their parameters using gradient updates w.r.t. a differentiable loss function. The explicit parametrization allows our model to avoid the exponential search space incurred by models relying on an explicit Hough transform to detect subspaces by searching for high-density points in parameter space. Compared to other existing approaches, our method is highly scalable, can be trained very efficiently on a GPU, is applicable to out-of-sample data, and is amenable to anytime scenarios since training can be stopped at any time and convergence is usually fast. The model can further be viewed as a linear neural network layer and trained end-to-end with an autoencoder to detect arbitrary non-linear correlations. We provide empirical results on a wide array of synthetic datasets with different characteristics following a rigorous evaluation protocol. Our results demonstrate the advantageous properties of our model and additionally reveal that it is particularly robust to jitter and noise present in the data.
Intelligent Image Enhancement System based on Similarity Pixels

The main goal of image enhancement is to enhance the fine details present in the images having low luminance for better image quality. In the digital image processing field, the enhancement and removing the noise from the image is a critical issue; image noise removal is the manipulation of the image data to produce a visually high-quality image. The important details and useful information on image decreasing by the noise where the noise treated as information. The filters are used to remove unwanted information. The filters’ objectives are to improve the image quality. This paper proposes an enhancement image system, which chooses the appropriate filter and value of center pixel depends on the number of similarities adjusted neighbors pixels to the center pixel. The performance of this system is evaluated by using different quality metrics, such as Mean square error (MSE), Peak Signal Noise to Ratio (PSNR), Absolute Mean Brightness Error (AMBE), Measure of Enhancement (EME), and Measure of Enhancement by Entropy (EMEE), Entropy, Second-Order Entropy (SOE), and Image Enhancement Metric (IEM). The proposed enhancement system is efficient in removing noises and enhancing the image quality. Experiments are applied to a set of images, such as Lena, butterfly, etc. with different image sizes. The results show that the enhancement quality was performed well in the proposed system with minimal unexpected artifacts as compared to the other techniques, where the results of the proposed system for MSE, PSNR, AMBE, Entropy, SOE, EME, EMEE, and IEM for baboon image with the size 255x 255 are 2.906, 8.875, 3.92, 5.154, 2.692, 3.915, 0.442 and 3.674 in sequence.
Coverage Analysis of Blockchain-enabled Wireless IoMT Networks

The blockchain-enabled wireless Internet of medical things (IoMT) system has recently drawn extensive attention due to the provision of highly-secured healthcare services. However, multiple simultaneous transmissions in blockchain-enabled IoMT (BC-IoMT) networks can cause interference, consequently degrading the overall performance of the system. It is necessary to investigate the performance of BC-IoMT networks. In this paper, we propose a novel analytical framework for wireless BCIoMT networks with consideration of both the spatial model on geographical random distribution of IoMT users and the temporal model on stochastic nature of data block transmission. With this framework, we derive a closed-form expression of the coverage probability in the wireless BC-IoMT network. Our extensive simulation results verify the accuracy of our theoretical analysis. From the analytical framework, we find that the path loss effect, the number of interfering medical sensor devices, and the threshold of successful transmission have a significant impact on the coverage performance of wireless BC-IoMT networks.