Reviewerly/exHarmony huggingface dataset:
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training

We present the TAPAS contribution to the Shared Task on Statement Verification and Evidence Finding with Tables (SemEval 2021 Task 9, Wang et al. (2021)).SEMTABFACT Task A is a classification task of recognising if a statement is entailed, neutral or refuted by the content of a given table.We adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task.We learn two binary classification models: A first model to predict if a statement is neutral or non-neutral and a second one to predict if it is entailed or refuted.As the shared task training set contains only entailed or refuted examples, we generate artificial neutral examples to train the first model.Both models are pre-trained using a MASKLM objective, intermediate counter-factual and synthetic data (Eisenschlos et al., 2020) and TABFACT (Chen et al., 2020), a large table entailment dataset.We find that the artificial neutral examples are somewhat effective at training the first model, achieving 68.03 test F1 versus the 60.47 of a majority baseline.For the second stage, we find that the pre-training on the intermediate data and TABFACT improves the results over MASKLM pre-training (68.03 vs 57.01).
Industrial Revolution: Blockchain as a Wave for Industry 4.0 and IIoT

In ancient times, humans were much predisposed to utilizing their handcreated tools to finish any kind of work. From the last few years, businesses related to production have been expanding. People began to rely on tools, machines, and smart gadgets to process their work, as these kinds of tools help them to achieve their target in the given time. . Since the 18th century, the world has experienced a lot of revolutions in industry across the globe. This type of growth in technology has made a base for industrial revolutions. New technology was introduced called “blockchain.”Probably, blockchain technology is the most disruptive technology in the modern digital economy. The ability of blockchain technology has been greatly described in many research works, media, and majorly in the fields of finance and payment. One existing research is at the organizational stage, where it implements the architecture for internet safety as well as inconvertibility. “Industry 4.0” and “IIoT (Industrial Internet of Things)” are involved in its rising applications. Thus, in this paper, current applications of blockchain in IIoT as well as Industry 4.0 setups, existing open issues, modern application sectors, challenges related to IIoT, and their solutions are illustrated. The main focus of this paper is to empower and facilitate research in this field, which will help the developers in blockchain acquisition as well as investment in the “Industry 4.0” and “Industrial Internet of Things” space.
Navigation of iliac crest graft harvest using markerless augmented reality and cutting guide technology: A pilot study

Defects of the facial skeleton often require complex reconstruction with vascularized grafts. This trial elucidated the usability, visual perception and accuracy of a markerless augmented reality (AR)-guided navigation for harvesting iliac crest transplants.Random CT scans were used to virtually plan two common transplant configurations on 10 iliac crest models, each printed four times. The transplants were harvested using projected AR and cutting guides. The duration and accuracies of the angulation, distance and volume between the planned and executed osteotomies were measured.AR was characterized by the efficient use of time and accurate rendition of preoperatively planned geometries. However, vertical osteotomies and complex anatomical settings displayed significant inferiority of AR guidance compared to cutting guides.This study demonstrated the usability of a markerless AR setup for harvesting iliac crest transplants. The visual perception and accuracy of the AR-guided osteotomies constituted remaining weaknesses against cutting guide technology.
Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images

Doctors conventionally analyzed echocardiographic images for diagnosing congenital heart diseases (CHDs).However, this process is laborious and depends on the experience of the doctors.This study investigated the use of deep learning algorithms for the image detection of the ventricular septal defect (VSD), the most common type.Color Doppler echocardiographic images containing three types of VSDs were tested with color doppler ultrasound medical images.To the best of our knowledge, this study is the first one to solve this object detection problem by using a modified YOLOv4-DenseNet framework.Because some techniques of YOLOv4 are not suitable for echocardiographic object detection, we revised the algorithm for this problem.The results revealed that the YOLOv4-DenseNet outperformed YOLOv4, YOLOv3, YOLOv3-SPP, and YOLOv3-DenseNet in terms of metric mAP-50.The F1-score of YOLOv4-DenseNet and YOLOv3-DenseNet were better than those of others.Hence, the contribution of this study establishes the feasibility of using deep learning for echocardiographic image detection of VSD investigation and a better YOLOv4-DenseNet framework could be employed for the VSD detection.
DEVELOPING FUN THINKERS BOOK LEARNING MEDIA ON THE TOPIC OF PERISTIWA ALAM YANG TERJADI DI INDONESIA AT GRADE V OF ELEMENTARY SCHOOL

Th is research aim ed to develop fun thinkers book learning media on the topic of " Peristiwa alam yang terjadi di Indonesia " at grade V of elementary school and to know the feasibility of the learning media. This research was research and development involving 6 steps: 1) problem analysis, 2) dat a information collect ion , 3) product design, 4) design validation , 5) product revision, and 6) product test ing . The subject on this study were 10 students at grade V. The dat a were collected through interviews, quistionaire, the validity from material expert s , l anguage experts , media expert s , and a quistionaire of students ' respons e . The result of th is research was fun thinkers book learning media which consist ed of two components : a book and the frame. The feasibility of the media was obtained from the result of the assesment from the experts . They were media experts, material expert s , language experts , and quistionnaire of the students response with the average precentage of 90 . 8%, 97.5%, 81.82%, and 82.23% respectively. Based on these result s , it was concluded that fun thinkers book learning media was feasible to be used as a learning media on the topic of " Peristiwa alam yang terjadi di Indonesia ".
A new Multi Sine-Cosine algorithm for unconstrained optimization problems

The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify &amp; intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.
Automatically Extracting Features Using Genetic Programming for Low-Quality Fish Image Classification

Fish image classification is an important task in the protection of precious marine resources. However, this task is difficult due to the low-quality images and the high inter-class variations across images. Most existing methods use high-quality images for classification and need domain knowledge. In this paper, we develop a genetic programming (GP) approach to automatically selecting image operators to deal with the low-quality images and extracting effective features from these images for low-quality fish image classification. To achieve this, a new program structure and a new function set are developed. With these designs, the proposed GP approach can evolve solutions that use effective filtering or restoration operators to deal with the input image, select informative regions from the fish image, and extract effective global and/or local features from the fish images. The results show that the proposed approach achieves significantly better performance than 12 benchmark methods, including a state-of-the-art GP approach, on the well-known fish image classification dataset. Further analysis shows the high interpretability of the evolved GP trees and the effectiveness of the employed image filtering or restoration operators.
Conceptualization of media competence as an "augmented competence"

Introduction: The ever-progressing transformation of the media as a result of digitization trends, the new characteristics of the communication environment and the associated communication practices and forms of user behaviour result in new educational needs and demands. Their reassessment in the context of current scientific discourses takes the form of revision and redefinition of the concept of media competence. Methodology: through a reflexive analysis of the concepts and models, we present the conceptualization of media competence as an extended competence to the area of personal, social, cultural and civic competencies. Results: The result of the work is a reassessment of conceptual positions in the projection of features of media competence in the context of the significant phenomena of the converged digital environment: participatory paradigm, hyperconnectivity, proliferation of disinformation. This approach requires the extension of skills and abilities in accessing, evaluating, analyzing, creating and communicating news and media; skills combining the previous forms of literacy and skills of digital environment (Livingstone, 2004; Pérez Tornero, Celot, Varis, 2007; Hobbs, 2008). Discussion: Our approach is associated with broader educational demands and needs aimed at the development of the individual´s personality in a more holistic way, connected with the more complex needs of a society where media presence is higher. Conclusions: The changes in the subject of ´Learning about the media´ are resassed at the end of the paper.
How Patient Organizations Can Drive FAIR Data Efforts to Facilitate Research and Health Care: A Report of the Virtual Second International Meeting on Duchenne Data Sharing, March 3, 2021

For patients with rare diseases such as Duchenne and Becker muscular dystrophy (DMD/BMD), access to their health data is key to being able to advocate for themselves and be in control of their care. Since 2018, the DMD/BMD patient community has been committed to making DMD/BMD-related data FAIR, i.e., Findable, Accessible, Interoperable, and Reusable. On March 3, 2021, the second international meeting on FAIR data sharing for DMD/BMD was held virtually.The aim of this meeting report is to summarize the presentations and discussions of the meeting.During this meeting, the progress of FAIRification efforts since the first international meeting in 2019, new developments, stakeholder perspectives, and experiences from implementing FAIR data principles in practice were presented and discussed.Over 120 attendees representing various stakeholder groups (ie, patient organizations, clinicians, clinical and academic researchers, pharmaceutical companies, regulators, and EU organizations) from 22 countries participated in the meeting. This meeting report summarizes the presentations and discussions from the meeting, provides an overview of the key lessons learned since the first meeting, and outlines the next steps.Patient organizations are key drivers of the FAIRification process in practice and dialogue with stakeholders is critical to success.
ZeRØ: Zero-Overhead Resilient Operation Under Pointer Integrity Attacks

A large class of today’s systems require high levels of availability and security. Unfortunately, state-of-the-art security solutions tend to induce crashes and raise exceptions when under attack, trading off availability for security. In this work, we propose ZeRØ, a pointer integrity mechanism that can continue program execution even when under attack. ZeRØ proposes unique memory instructions and a novel metadata encoding scheme to protect code and data pointers. The combination of instructions and metadata allows ZeRØ to avoid explicitly tagging every word in memory, eliminating performance overheads. Moreover, ZeRØ is a deterministic security primitive that requires minor microarchitectural changes. We show that ZeRØ is better than commercially available state-of-the-art hardware primitives, e.g., ARM’s Pointer Authentication (PAC), by a significant margin. ZeRØ incurs zero performance overheads on the SPEC CPU2017 benchmarks, and our VLSI measurements show low power and area overheads.
Optical three-dimensional displacement measurement based on moiré methodology and its application to space structures

Optical methods providing full-field deformation information is of interest to mechanical engineers. In this study, an accurate optical three-dimensional displacement measurement method using a single camera attached to a precision 3-axis moving stage was developed. This paper presents the fundamental measurement principle and experiments for validating the measurement accuracy. The in-plane and out-of-plane displacements are simultaneously determined by calculating the phase difference and the phase gradient difference of the moiré fringe before and after deformation. The developed method was applied to displacement measurement in a loading test of a carbon fiber reinforced plastics (CFRP) cylindrical payload 2.5 m in diameter and 2.0 m long used in a solid rocket. The three-dimensional displacement results obtained from the developed method showed a good agreement with those measured with the extensometers conventionally employed in the displacement measurement. The developed method is useful for inspection in the space field and to secure the health monitoring of large-scale structures.
Comparative Analysis of the Use of Kanban and Scrum Methodologies in IT Projects

Modern businesses throughout the world operate in a market characterized by constant changes.These changes are becoming more dynamic from year to year.It is the project-based approach that will allow one to solve problems and ensure high efficiency of project-oriented operations.The purpose of the study is a comparative analysis of the impact of the use of IT project management methodologies of Kanban and Scrum on a company's financial performance.The authors identify the essence of the agile methodology, its conceptual foundations, and explore the key aspects of its efficient implementation.The popularity of the practical application of the agile approach in various business areas is analyzed.The authors also choose the best methodology for a particular IT project for the optimization of such economic and financial indicators as project cost, profit from project implementation, and implementation time.The key differences between the most popular agile technologies are identified.The requirements for forming an agile team are examined considering the main issues during the implementation of agile management technologies.The primary stages in the implementation of an IT project using the Scrum methodologies are defined.
Signature-Based Abduction with Fresh Individuals and Complex Concepts for Description Logics

Given a knowledge base and an observation as a set of facts, ABox abduction aims at computing a hypothesis that, when added to the knowledge base, is sufficient to entail the observation. In signature-based ABox abduction, the hypothesis is further required to use only names from a given set. This form of abduction has applications such as diagnosis, KB repair, or explaning missing entailments. It is possible that hypotheses for a given observation only exist if we admit the use of fresh individuals and/or complex concepts built from the given signature, something most approaches for ABox abduction so far do not allow or only allow with restrictions. In this paper, we investigate the computational complexity of this form of abduction---allowing either fresh individuals, complex concepts, or both---for various description logics, and give size bounds on the hypotheses if they exist.
On the Application of ϵ-Lexicase Selection in the Generation of Dispatching Rules

Dynamic online scheduling is a difficult problem which commonly appears in the real world. This is because the decisions have to be performed in a small amount of time using only currently available incomplete information. In such cases dispatching rules (DRs) are the most commonly used methods. Since designing them manually is a difficult task, this process has been successfully automatised by using genetic programming (GP). The quality of the evolved rules depends on the problem instances that are used during the training process. Previous studies demonstrated that careful selection of problem instances on which the solutions should be evaluated during evolution improves the performance of the generated rules. This paper examines the application of the ε-lexicase selection to the design of DRs for the unrelated machines scheduling. This selection offers a better solution diversity since the individuals are selected based on a smaller subset of instances, which leads to the creation of DRs that perform well on the selected instances. The experiments demonstrate that this type of selection can significantly improve the results for the Roulette Wheel and Elimination GP variants, while achieving the same performance as the Steady State Tournament GP. Furthermore, the ε-lexicase based algorithms have a better convergence rate, which means that the increased diversity in the population has a positive effect on the evolution process.
Pair comparison based progressive subjective quality ranking for underwater images

Underwater images contain an interacting mixture of distortions due to the physicochemical properties of the water, suspended organic matter and floating particles in water. Unlike images in traditional natural image quality databases, underwater images are often difficult to acquire with reference images and sets of images with gradient distortion. Therefore, it is even more difficult for the viewers to assign an absolute psychophysical scale to the quality of underwater images. In this paper, we propose a pairwise subjective comparison procedure for underwater images quality ranking inspired by the intuitive suppression and competence mechanisms in visual perception. In the proposed method, we construct a preselection based initial image quality dataset by full pairwise comparison, which also enables online adaptive new image updating. The proposed method is not constrained by the lack of reference images, and is reliable and sensitive to images with discriminable distortion level and various image contents. The proposed pairwise comparison further allows an uncertain choice, which does not require a reinforce human opinion. To the best of our knowledge, this is the first implementation for underwater image subjective quality ranking, and a new approach to the image quality ranking for different image contents with unknown distortion level. We demonstrate that the obtained subjective image ranking correlates well with the human perception of quality difference among the underwater images than that of the single stimuli image quality assessment with finite labor burden. Moreover, our proposed method accurately characterize the gradual degradation in the underwater image sequence taken in controlled conditions. The proposed progressive learning ranking is also an alternative way to realize adaptive extension of the existing image quality databases.
Genetic Programming Approach to Detect Hate Speech in Social Media

Social media sites, which became central to our everyday lives, enable users to freely express their opinions, feelings, and ideas due to a certain level of depersonalization and anonymity they provide.If there is no control, these platforms may be used to propagate hate speech.In fact, in recent years, hate speech has increased on social media.Therefore, there is a need to monitor and prevent hate speech on these platforms.However, manual control is not feasible due to the high traffic of content production on social media sites.Moreover, the language used and the length of the messages provide a challenge when using classical machine learning approaches as prediction methods.This paper presents a genetic programming (GP) model for detecting hate speech where each chromosome represents a classifier employing a universal sentence encoder as a feature.A novel mutation technique that affects only the feature values in combination with the standard one-point mutation technique improves the performance of the GP model by enriching the offspring pool with alternative solutions.The proposed GP model outperforms all state-of-the-art systems for the four publicly available hate speech datasets.
Memory properties of electromigrated Au nanogaps to realize reservoir computing

The reservoir computing (RC) scheme, which employs the inherent computational capabilities of dynamical systems, is a key technology to implement artificial intelligence systems physically. Ensuring the nonlinear expansion of input data through the dynamics of physical systems is a necessary aspect of RC. Previously, we developed artificial synapses of Au nanogaps by using the “activation” technique, which allowed the implementation of synaptic functions such as short-term plasticity, long-term plasticity, and spike-timing-dependent plasticity. The activation technique is an electromigration-based method to control the tunnel resistance of nanogaps. In this study, the memory property of the Au nanogap, using activation for RC, was evaluated via short-term memory (STM) and parity check (PC) tasks. More specifically, memory capacity was introduced to evaluate the performance of the Au nanogap, defined as the sum of squares of the correlation between the outputs of RC and the teacher for delay D = 1 to 6. By utilizing the simple dynamics of short-term plasticity, the memory capacities of the STM and PC tasks were found to be 1.07 and 0.90, respectively, when 10 virtual nodes were used. This demonstrates that the dynamic process of the activation technique enables the Au nanogap-based reservoir to process information directly in the temporal domain. The experimental results can facilitate the development of compact devices to realize physical RC.
Improved ensemble of differential evolution variants

In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators—ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE—one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.