Reviewerly/exHarmony huggingface dataset:
Semantic frame induction through the detection of communities of verbs and their arguments

Abstract Resources such as FrameNet, which provide sets of semantic frame definitions and annotated textual data that maps into the evoked frames, are important for several NLP tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame and verb arguments that play the same semantic role. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances or arguments as nodes connected by edges if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of SemEval 2019, we outperformed all of the previous approaches to the task, achieving the current state-of-the-art performance.
Students’ Self-Regulated Learning in Iconic Mobile Learning System in English Cross-Disciplined Program

The purpose of this study was to assess learners' self-regulated learning (SRL) levels and the effect toward an attitude of m-learning adoption in assisting English learning.By using mlearning, students were expected to have a flexible way to learn and gained self-regulated learning since it would help them to learn based on their pace of learning.The respondents in this study were 66 students from two different senior high schools in Jepara regency who enrolled in the English cross-disciplined program.In this ex-post-facto study, the researcher used a phenomenological approach to describe SRL aspects while students were using self-designed mobile software for speaking skills which also contained a national iconic woman hero.From the investigation of aspects of Learning Plan and Strategy, Intrinsic Motivation, Learning Control, Self-evaluation, and Confidence, this study found that the respondents were categorized as moderate regulators.From those five aspects of self-regulated learning investigated, it indicated that students had higher Self-evaluation than other aspects in that condition.Then the attitude to adopt m-learning is influenced by SRL.It can be potential to foster the effectiveness of English learning and further learning process since self-regulated learning can positively support the long-life learning characters.
Image encryption based on elliptic curve cryptosystem

Image encryption based on elliptic curve cryptosystem and reducing its complexity is still being actively researched. Generating matrix for encryption algorithm secret key together with Hilbert matrix will be involved in this study. For a first case we will need not to compute the inverse matrix for the decryption processing cause the matrix that be generated in encryption step was self invertible matrix. While for the second case, computing the inverse matrix will be required. Peak signal to noise ratio (PSNR), and unified average changing intensity (UACI) will be used to assess which case is more efficiency to encryption the grayscale image.
Moodle LMS Integration with Amazon Alexa: A Practical Experience

The frequency of interaction between teachers and students through Learning Management Systems (LMSs) is continuously rising. However, recent studies highlight the challenges presented in current LMSs to meet the specific needs of the student, regarding usability and learnability. With the motivation to support the research of effectiveness when using a Voice User Interface (VUI) for education, this paper presents the work done (RQ1) to build the basic architecture for an Alexa skill for educational purposes, including its integration with Moodle, and (RQ2) to establish whether Moodle currently provides the necessary tools for voice-content creation for develop voice-first applications, aiming to provide new scientific insight to help researchers on future works of similar characteristics. As a result of this work, we provide guidelines for the architecture of an Alexa skill application integrated with Moodle through safe protocols, such as Alexa’s Account Linking Web Service, while our findings ratify the need for additional tooling within Moodle platform for voice-content creation in order to create an appealing voice experience, with the capabilities to process Moodle data structures and produce sensible sentences that can be understood by users when spoken by a voice device.
Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

Abstract Total generalized variation models have recently demonstrated high-quality denoising capacity for single image. In this paper, we present an accurate denoising method for depth map. Our method uses a weighted second-order total generalized variational model for Gaussian noise removal. By fusing an edge indicator function into the regularization term of the second-order total generalized variational model to guide the diffusion of gradients, our method aims to use the first or second derivative to enhance the intensity of the diffusion tensor. We use the first-order primal–dual algorithm to minimize the proposed energy function and achieve high-quality denoising and edge preserving result for depth maps with high -intensity noise. Extensive quantitative and qualitative evaluations in comparison to bench-mark datasets show that the proposed method provides significant higher accuracy and visual improvements than many state-of-the-art denoising algorithms.
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question answering). Our source code and pretrained representations are available at https://github.com/studio-ousia/luke.
A Comprehensive Proposal for Blockchain-Oriented Smart City

The astounding technological advancements and massive urbanization calls for the advent of “Smart cities” to ensure the best living standards of its residents. Smart cities use information and communication technologies (ICT) to generate efficiencies, improvise sustainability, encourage economic development, allow business to thrive, boost innovation, assure judicious energy and resource consumption, reduce wastage and enhance quality of life for people. This thus requires perpetual storage of data related to basic components of a smart city and its continual surveillance. The fundamental characteristics of a blockchain make them the most lucrative platform to store valuable data essential for smooth functioning of smart cities. It also ensures the privacy, authenticity and confidentiality of this data. This chapter first identifies the essential elements of a smart city, then provides a detailed literature on the existing techniques to realize a smart city, highlights their shortcomings and explains how blockchain contributes towards their effective implementation.
Sentence-Embedding and Similarity via Hybrid Bidirectional-LSTM and CNN Utilizing Weighted-Pooling Attention

Neural networks have received considerable attention in sentence similarity measuring systems due to their efficiency in dealing with semantic composition. However, existing neural network methods are not sufficiently effective in capturing the most significant semantic information buried in an input. To address this problem, a novel weighted-pooling attention layer is proposed to retain the most remarkable attention vector. It has already been established that long short-term memory and a convolution neural network have a strong ability to accumulate enriched patterns of whole sentence semantic representation. First, a sentence representation is generated by employing a siamese structure based on bidirectional long short-term memory and a convolutional neural network. Subsequently, a weighted-pooling attention layer is applied to obtain an attention vector. Finally, the attention vector pair information is leveraged to calculate the score of sentence similarity. An amalgamation of both, bidirectional long short-term memory and a convolutional neural network has resulted in a model that enhances information extracting and learning capacity. Investigations show that the proposed method outperforms the state-of-the-art approaches to datasets for two tasks, namely semantic relatedness and Microsoft research paraphrase identification. The new model improves the learning capability and also boosts the similarity accuracy as well.
Describing Subjective Experiment Consistency by p-Value P--P Plot

There are phenomena that cannot be measured without subjective testing. However, subjective testing is a complex issue with many influencing factors. These interplay to yield either precise or incorrect results. Researchers require a tool to classify results of subjective experiment as either consistent or inconsistent. This is necessary in order to decide whether to treat the gathered scores as quality ground truth data. Knowing if subjective scores can be trusted is key to drawing valid conclusions and building functional tools based on those scores (e.g., algorithms assessing the perceived quality of multimedia materials). We provide a tool to classify subjective experiment (and all its results) as either consistent or inconsistent. Additionally, the tool identifies stimuli having irregular score distribution. The approach is based on treating subjective scores as a random variable coming from the discrete Generalized Score Distribution (GSD). The GSD, in combination with a bootstrapped G-test of goodness-of-fit, allows to construct p-value P--P plot that visualizes experiment's consistency. The tool safeguards researchers from using inconsistent subjective data. In this way, it makes sure that conclusions they draw and tools they build are more precise and trustworthy. The proposed approach works in line with expectations drawn solely on experiment design descriptions of 21 real-life multimedia quality subjective experiments.
TimeCache: Using Time to Eliminate Cache Side Channels when Sharing Software

Timing side channels have been used to extract cryptographic keys and sensitive documents even from trusted enclaves. Specifically, cache side channels created by reuse of shared code or data in the memory hierarchy have been exploited by several known attacks, e.g., evict+reload for recovering an RSA key and Spectre variants for leaking speculatively loaded data.In this paper, we present TimeCache, a cache design that incorporates knowledge of prior cache line access to eliminate cache side channels due to reuse of shared software (code and data). Our goal is to retain the benefits of a shared cache of allowing each process access to the entire cache and of cache occupancy by a single copy of shared software. We achieve our goal by implementing per-process cache line visibility so that the processes do not benefit from cached data brought in by another process until they have incurred a corresponding miss penalty. Our design achieves low overhead by using a novel combination of timestamps and a hardware design to allow efficient parallel comparisons of the timestamps. The solution works at all the cache levels without the need to limit the number of security domains, and defends against an attacker process running on the same core, on a another hyperthread, or on another core.Our implementation in the gem5 simulator demonstrates that the system is able to defend against RSA key extraction. We evaluate performance using SPEC2006 and PARSEC and observe the overhead of TimeCache to be 1.13% on average. Delay due to first access misses adds the majority of the overhead, with the security context bookkeeping incurred at the time of a context switch contributing 0.02% of the 1.13%.
Direct and indirect effects of TQM on the patients’ satisfaction and loyalty in the Jordanian health care sector

The aim of this research is to investigate the gap or SERVQUAL scale amongst the findings of the previous studies for missing investigations and the impression to apply for overall standard quality management of three variables such as independent variable (Service quality), mediator variable (Patients' Satisfaction) and dependent variable (Patients' Loyalty) in Jordanian health care sector.Moreover, the current study aims to clarify the effects of the Total Quality Management (TQM) represented collectively by a specific strategy, leadership management, human resource focus information, client focus, method management, and analysis method management on customer loyalty.In order to test the objectives, a random sample size consisting of 800 national and international patient's expectations from the private and public hospitals in the central region of Jordan was collected.The quantitative analysis was designed to conduct the survey, questionnaires, and gather the primary information to determine the category of data and techniques of data collection.The response data were analyzed using the IBM SPSS 19.00 version and Amos 20.00 version to assess the response value, descriptive statistic, screenings of the data composed of linearity and homoscedasticity, correlations, reliability, and validity.Multiple reliability results indicated that the dimensions of each experimental variables were considered reliable and satisfactory.Based on the outcome, this study verified that customer satisfaction carries a weak mediating effect.Moreover, the results exhibited that TQM had had a significant impact on both customer loyalty and customer satisfaction.
Domain gap in adapting self-supervised depth estimation methods for stereo-endoscopy

Abstract In endoscopy, depth estimation is a task that potentially helps in quantifying visual information for better scene understanding. A plethora of depth estimation algorithms have been proposed in the computer vision community. The endoscopic domain however, differs from the typical depth estimation scenario due to differences in the setup and nature of the scene. Furthermore, it is unfeasible to obtain ground truth depth information owing to an unsuitable detection range of off-the-shelf depth sensors and difficulties in setting up a depth-sensor in a surgical environment. In this paper, an existing self-supervised approach, called Monodepth [1], from the field of autonomous driving is applied to a novel dataset of stereo-endoscopic images from reconstructive mitral valve surgery. While it is already known that endoscopic scenes are more challenging than outdoor driving scenes, the paper performs experiments to quantify the comparison, and describe the domain gap and challenges involved in the transfer of these methods.
A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.
Context-Aware Anomaly Detection for the Distributed Data Validation Network in Industry 4.0 Environments

In the Industry 4.0 context, especially when considering large factories producing costly goods, monitoring sensor values is important to ensure high quality. This reduces large costs for mending faulty products or recall of those. Different approaches are used to ensure efficient monitoring and validation of sensor values. The Distributed Data Validation Network (DDVN) can remove single points of failure. Still, not every anomaly in the validation procedure means that errors or attacks have occurred. Other reasons like maintenance procedures, updates of firmware, or changed materials can lead to False-Positive (FP) or False-Negative (FN) detection of errors. To reduce these, we incorporate context information in the validation procedure. Further, we show how the appropriate context information is selected and used on a real machine data set.
A Survey on Privacy Vulnerabilities in Permissionless Blockchains

Blockchain decentralization not only ensures transparency of transactions to eliminate need of trusting third party, but also makes the transactions of the network to be publicly accessible to all the participating peers in the network.As a result, data anonymity and confidentiality are compromised making several business enterprises and industrialists hesitant to adopt the technology.Although research community has proposed various privacy-preserving solutions for blockchain, however, they still lack in efficiency resulting in distrust of industries in opting for the technology.This study is conducted for contributing to the existing body of knowledge corresponding to privacy in blockchains.The fundamental goal of this study is to delve into privacy vulnerabilities of the blockchain network in a permissionless setting by identifying non-trivial roots of factors causing privacy breach in blockchain and presenting limitation of existing privacy preserving mechanisms.Studies with superficial comparison of privacy preserving techniques are available in literature but a detailed and in-depth analysis of their limitations and causes of privacy breach in blockchain is yet not done.Therefore, in this paper we first present comprehensive analysis of various privacy breaching factors of the blockchain networks.Next, we discuss existing cryptographic and noncryptographic solutions in literature.We found out that these existing privacy preserving mechanisms have their own set of limitations and hence are inefficient at current point of time.The existing privacy preserving mechanisms need further consideration of the research community before they're widely adopted and benchmarked.Therefore, in the end, we identified some future directions that need to be addressed to model an efficient privacy preserving mechanism for wider adoption of the blockchain technology.
Microscopic Structure from Motion (SfM) for Microscale 3D Surface Reconstruction

In microscale photogrammetry, the confocal microscopic imaging technique has been the dominant trend. Unlike the confocal imaging mostly for transparent objects, we propose a novel method to construct a 3D shape in microscale for various micro-sized solid objects in a broad spectrµm of applications. Recently, the structure from motion (SfM) demonstrated reliable 3D reconstruction capability for macroscale objects. In this paper, we discuss the results of a novel micro-surface reconstruction method using the Structure from Motion in microscale. The proposed micro SfM technique utilizes the photometric stereovision via microscopic photogrammetry. The main challenges lie in the scanning methodology, ambient light control, and light conditioning for microscale object photography. Experimental results of the microscale SfM, as well as the modeling accuracy analysis of a reconstructed micro-object, are shared in the paper.
Nesterov's method with decreasing learning rate leads to accelerated stochastic gradient descent

We present a coupled system of ODEs which, when discretized with a constant time step/learning rate, recovers Nesterov's accelerated gradient descent algorithm. The same ODEs, when discretized with a decreasing learning rate, leads to novel stochastic gradient descent (SGD) algorithms, one in the convex and a second in the strongly convex case. In the strongly convex case, we obtain an algorithm superficially similar to momentum SGD, but with additional terms. In the convex case, we obtain an algorithm with a novel order $k^{3/4}$ learning rate. We prove, extending the Lyapunov function approach from the full gradient case to the stochastic case, that the algorithms converge at the optimal rate for the last iterate of SGD, with rate constants which are better than previously available.
Long time data series and data stewardship reference model

The need for accessing historical Earth Observation (EO) data series strongly increased in the last ten years, particularly for long-term science and environmental monitoring applications. This trend is likely to increase even more in the future, in particular regarding the growing interest on global change monitoring which is driving users to request time-series of data spanning 20 years and more, and also due to the need to support the United Nations Framework Convention on Climate Change (UNFCCC). While much of the satellite observations are accessible from different data centers, the solution for analyzing measurements collected from various instruments for time series analysis is both difficult and critical. Climate research is a big data problem that involves high data volume of measurements, methods for on-the-fly extraction and reduction to keep up with the speed and data volume, and the ability to address uncertainties from data collections, processing, and analysis. The content of EO data archives is extending from a few years to decades and therefore, their value as a scientific time-series is continuously increasing. Hence there is a strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable. The preservation of EO space data can also be considered as responsibility of the Space Agencies or data owners as they constitute a humankind asset. This publication aims at describing the activities supported by the European Space Agency relating to the Long Time Series generation with all relevant best practices and models needed to organise and measure the preservation and stewardship processes. The Data Stewardship Reference Model has been defined to give an overview and a way to help the data owners and space agencies in order to preserve and curate the space datasets to be ready for long time data series composition and analysis.