Reviewerly/exHarmony huggingface dataset:
NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets

Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of the generators. It can enumerate generators as well by similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs.
MeshSOS: An IoT Based Emergency Response System

Due to the limited mobility and technical knowledge, senior and disabled citizens of the society face difficulties during emergencies.Most hardware/software emergency assistance/response systems available in the market have a complex user interface even for the general public.Requesting help using these systems requires sharing information such as type and location of the event, which wastes precious time to respond to the event.Often, citizens end up handling the event themselves instead of waiting for someone to arrive at the event location.Hence, it is necessary to design a simple but incredibly robust system which bypasses the challenges of traditional emergency response systems.In this paper, we propose an Internet of Things (IoT) based mesh-enabled emergency response system called MeshSOS, which enables senior and disabled citizens to get assistance by simply pressing a button.Use of mesh networking along with WiFi made our system robust to network failures.We have also developed a central monitoring application for healthcare and security agencies to handle emergency events proactively.Initial field experiments and simulations show that our system has the potential to improve the robustness and response time in an energy and cost-efficient manner.
Corrigendum: Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis

In the original article, we neglected to include the funder Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), CR1920ED617RBCX008562. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.We would like to add the above grant information to the Acknowledgement section as follows: "This work was funded by the Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), under project number CR1920ED617RBCX008562 (Interpretability for Deep Learning Models in Healthcare)."
Research on Intelligent Chat Robot Based on Big Data Analysis

With the rapid development of Internet technologies such as big data and artificial intelligence, chat robots have begun to enter people's daily life, providing a lot of convenience for people's life and work. At present, many research institutions focus on the research and development of chat robots, but this field is still in its infancy, and chat robots in the market are more or less deficient. Based on the deep learning technology in big data, this paper constructs and trains the model of attention mechanism, and realizes the generative dialogue chat robot in the open field, which can intelligently generate smooth responses according to the wide input of users; The model is implemented by deep learning TensorFlow coding library, and the results show that the model proposed in this paper is better than the traditional language model in continuous and meaningful dialogue.
Towards Counteracting Adversarial Perturbations to Resist Adversarial Examples

Studies show that neural networks are susceptible to adversarial attacks. This exposes a potential threat to neural network-based artificial intelligence systems. We observe that the probability of the correct result outputted by the network increases by applying small perturbations generated for class labels other than the original predicted one to adversarial examples. Based on this observation, we propose a method of counteracting adversarial perturbations to resist adversarial examples. In our method, we randomly select a number of class labels and generate small perturbations for these selected labels. The generated perturbations are added together and then clamped to a specified space. The obtained perturbation is finally added to the adversarial example to counteract the adversarial perturbation contained in the example. The proposed method is applied at inference time and does not require retraining or finetuning the model. We validate the proposed method on CIFAR-10 and CIFAR-100. The experimental results demonstrate that our method effectively improves the defense performance of the baseline methods, especially against strong adversarial examples generated using more iterations.
ChemistryQA: A Complex Question Answering Dataset from Chemistry

Many Question Answering (QA) tasks have been studied in NLP and employed to evaluate the progress of machine intelligence. One kind of QA tasks, such as Machine Reading Comprehension QA, is well solved by end-to-end neural networks; another kind of QA tasks, such as Knowledge Base QA, needs to be translated to a formatted representations and then solved by a well-designed solver. We notice that some real-world QA tasks are more complex, which cannot be solved by end-to-end neural networks or translated to any kind of formal representations. To further stimulate the research of QA and development of QA techniques, in this work, we create a new and complex QA dataset, ChemistryQA, based on real-world chemical calculation questions. To answer chemical questions, machines need to understand questions, apply chemistry and Math knowledge, and do calculation and reasoning. To help researchers ramp up, we build two baselines: the first one is BERT-based sequence to sequence model, and the second one is an extraction system plus a graph search based solver. These two methods achieved 0.164 and 0.169 accuracy on the development set, respectively, which clearly demonstrate that new techniques are needed for complex QA tasks. ChemistryQA dataset will be available for public download once the paper is published.
ARIADNE D3.1 Initial report on policies and strategies

As a follow up on the initial ARIADNE project, the overall objective of ARIADNEplus is to serve archaeological researchers and data management communities by proceeding to improve data sharing and the (re)use of data resources, which are dispersed through Europe and often difficult to discover and access as different silos (institutional, national or disciplinary) still exist. The objective of this report is to: • Support the creation of FAIR data in the archaeological sector • Define and spread guidelines to good practices in archaeological data management • Adapt standard quality criteria for datasets and data to the archaeological case, and support their implementation among users.
Business Process Management and Service Oriented Architecture Integration for Transactional Banking Application

To implement Service Oriented Architecture (SOA) in a banking company for transactional banking activity, this paper aims to overcome the problem of several related surrounding systems which has the same function in the different system by using Domain Driven Design. The paper proposes an approach to SOA using Web Services for service delivery in the area of transactional banking activities. Several systems will be connected to a BUS that will wait on and manage any request coming from other applications. To design the system then in this paper also will identify which application will consume the same function and analyze which function belongs to a certain domain. The paper finds that the implementation of an SOA system for transactional banking activities can be achieved by rapidly changing the habit of scaling up the system and application. This study shows that banking activity has a huge number of systems that should sync one and another to maintain the data integrity, availability, interoperability, and composability of the system.
On Feature Selection Using Anisotropic General Regression Neural Network.

The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Therefore, the development of feature selection methods to recognize irrelevant features is a crucial topic in machine learning. Here we show how the General Regression Neural Network used with an anisotropic Gaussian Kernel can be used to perform feature selection. A number of numerical experiments are conducted using simulated data to study the robustness of the proposed methodology and its sensitivity to sample size. Finally, a comparison with four other feature selection methods is performed on several real world datasets.
Accelerating energy digitisation policies and plans in the context of the COVID-19 pandemic

This year the evolution of the strategy and plans implementation in order to combat climate change through energy efficiency measures has been confronted with a new challenge, namely the impact of the COVID-19 pandemic in all areas of life At a time when the expansion of IoT use through platforms to monitor, scale and optimise processes specific to urban agglomerations and industrial centres, the digitisation of the energy sector has acquired a new dimension and a central place in the sphere of priorities for the coagulation of the financial efforts The article presents the status of the implementation of this objective prior to the pandemic period, the new conditions and priorities imposed on the basis of changes in electricity consumption and the opportunities for the development of IoT-based systems for the energy sector At present, some of the major challenges to be addressed to smart technologies are: accelerating the pace of deployment, achieving the mix of energy from traditional and renewable sources, securing energy systems and networks in order to resist in the situation of potential variable energy flows or cyber attacks
PENERAPAN METODE ELIMINASI DAN SUBSTITUSI PADA RANCANGAN APLIKASI PENYELESAIAN SISTEM PERSAMAAN LINIER

Information technology is a study of design, implementation, development, support or management of computer-based on information system, particularly in the application hardware and software. The developments in information technology today has grown rapidly so as to encourage the development of technology in other fields, one in the field is mathematics education. Mathematics is the study of quantity, structure, geometry, and changes at a number. One of the commonly studied mathematics is a linear equations system.  Linear equations system is an algebraic equation, which each tribe contains a constant, or constant multiplication with a single variabel. The purpose of this research was to produce a settlement application linear equations system, and improve data accuracy and faster calculation process to facilitate the learning process for beginners. In this research, the authors use descriptive research design, the authors describe an application made in accordance facts by collecting data and then the data were used to test the question or hypothesis to the event experienced at this time. In this research there is the attempt to describe, record and interpret the condition now then do an evalution.The author uses Unified Modeling Language (UML) to model the application as an analytical technique. In designing the application linear equations system, authors using Visual Basic.Net. The application resulted from this research can do the calculation of linear equations system that can be useful to solve the problems faced by people who want to learn the process of calculating linear equations system that fairly complex when completed manually as well as minimize the errors that occurred in the process of calcutation in linear equations system.From the whole process of this research it can be concluded that application of the settlement linear equations system can be used to help the system work fast and precise on inputing data and doing calculation in linear equations system. The author hopes readers and application developers in the future can be able to enhance and improve the shortcomings and limitations contained in this application. Keywords : Aplication, Mathematics, Linear Equations, Elimination, Substitution.
Impact of Web Content on Profitability and Market Share of Selected Newspapers: A Case Study of United Arab Emirates (UAE)

As the world is getting further digitized it has become crucial to be up to date with current trends to ensure better survival. In fact, in order for organizations to survive among competitors, updating has become necessary. With this change, an impact has been created upon almost every industry including the e-publishing industry, which found itself forced to adapt to the changes over the time period. The introduction of several new news apps and websites has even made the standardized newspapers rethink their publishing methods, and ultimately adopt e-publishing. This research paper analyzes the impact of web content usage among newspaper publications and also its impact on their business. Hence, this study is carried out among selective newspaper publications in UAE and also among selective readers group.
Comparing Writing vs. Smartphone Tapping Speed

As COVID-19 pushes the world into emergency remote teaching mode, many teachers struggle with designing online or computer-mediated language learning activities due to having little to no prior experience. It is imperative to understand how the use of technology affects not only the processes of learning, but also the outcomes. Particularly in Japan, where smartphone ownership among adults aged 18-34 topped 96% in 2018 (Pew Research Center, 2019), many students use their phones to access their schools’ learning management systems (LMS) and complete assignments (i.e., mobile learning). The current study therefore sought to elucidate how different writing media can affect the execution of a simple writing task by examining the differences in transcription speeds between handwriting and ‘tapping’ on a smartphone. A total of 176 participants were divided into 3 groups (L1-English, writing in English; L1-Japanese, writing in Japanese; and L1-Japanese, writing in English), and their times-on-task were recorded. While no difference was found for the L1-English group, the L1-Japanese groups were found to be significantly faster at one task than the other (tapping in Japanese and writing in English). Pedagogical implications suggest the need for instructors to be aware of the extreme difficulty language learners may have when using mobile devices for writing tasks.
Non-iterative Parallel Text Generation via Glancing Transformer

Although non-autoregressive models with one-iteration generation achieve remarkable inference speed-up, they still fall behind their autoregressive counterparts in prediction accuracy. The non-autoregressive models with the best accuracy currently rely on multiple decoding iterations, which largely sacrifice the inference speed of non-autoregressive models. Inspired by the way of learning word dependencies in autoregressive and iterative-decoding models, we propose Glancing Transformer (GLAT) with a glancing language model (GLM), which learns to capture the word dependency gradually. Experiments on three benchmarks demonstrate that our approach can significantly improve the accuracy of non-autoregressive models without multiple decoding iterations. In particular, GLAT achieves state-of-the-art results among non-iterative models and even outperforms top iterative counterparts in some specific benchmarks.
Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning

Semi-Supervised Few-shot Learning (SS-FSL) investigates the benefit of incorporating unlabelled data in few-shot settings. Recent work has relied on the popular Semi-Supervised Learning (SSL) concept of iterative pseudo-labelling, yet often yield models that are susceptible to error propagation and are sensitive to initialisation. Alternative work utilises the concept of consistency regularisation (CR), a popular SSL state of the art technique where a student model is trained to consistently agree with teacher predictions under different input perturbations, without pseudo-label requirements. However, applications of CR to the SS-FSL set-up struggle to outperform pseudo-labelling approaches; limited available training data yields unreliable early stage predictions and requires fast convergence that is not amenable for, typically slower to converge, CR approaches. In this paper, we introduce a prototype-based approach for SS-FSL that exploits model consistency in a robust manner. Our Dynamic Prototype Refinement (DPR) approach is a novel training paradigm for few-shot model adaptation to new unseen classes, combining concepts from metric and meta-gradient based FSL methods. New class prototypes are alternatively refined 1) explicitly, using labelled and unlabelled data with high confidence class predictions and 2) implicitly, by model fine-tuning using a data selective CR loss. DPR affords CR convergence, with the explicit refinement providing an increasingly stronger initialisation. We demonstrate method efficacy and report extensive experiments on two competitive benchmarks; miniImageNet and tieredImageNet. The ability to effectively utilise and combine information from both labelled base-class and auxiliary unlabelled novel-class data results in significant accuracy improvements.
PENGEMBANGAN MEDIA BULETIN MATEMATIKA BERBASIS PENDEKATAN REALISTIK PADA SISWA SEKOLAH DASAR

The objectives of this study was to develop a mathematics bulletin media based on a realistic approach to fourth-grade elementary school students and its feasibility for use. The development model used in this research is the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model. The data collection technique in this study was conducted by interviewing questioner, test, and documentation. The instruments used in this research was media expert validation sheets, material experts, and teacher practitioners, practicality sheets for students and learning mastery test questions. The results of the research on the development of mathematical newsletter media based on a realistic approach are: 1) this research produces products in the form of mathematical newsletter media based on a realistic approach for grade IV elementary school students, 2) the results of the feasibility analysis of newsletter media based on a realistic approach are obtained from validation by two expert lecturers and classroom teachers. IV elementary schools obtained a mean score of 3.58 with the valid category, the results of the practicality of the product were fulfilled from the student response questionnaire got a score of 98% with very positive criteria, and the product effectiveness test was fulfilled from the learning completeness test with learning completeness reaching 100%.
Construction of classical structures on N qubits by composing diagrammatic eigenbases of Pauli operators

Within the framework of Categorical Quantum Mechanics, it was found that orthogonal bases are subsumed by the so called classical structures. So, there must be classical structures which have the eigenbases of the Pauli X,Y and Z operators as their underlying bases. In this article, we propose a procedure for composing these classical structures on a single qubit which produces classical structures on multiple qubits.As a result, we can present orthogonal bases within a multiple qubit system as diagrams which makes explicit the entanglement structure of those bases.
Analysis on the Countermeasures of Computer Teaching in Higher Vocational Education under the Background of Employment Orientation

Computer Technology is a nascent technological product emerged with the third industrial revolution. It contains a powerful collection of microelectronic technology and semiconductor technology, which brings great convenience to people’s lives and work. Online shopping, online payment, mobile payment, online chat and other endless high-quality technologies allow us to be closer to the world in the era of globalization, and we can embrace the world at home, and the convenience brought by the Internet is inseparable from computer teaching. The epidemic in 2020 is sweeping the world. The pressure of employment directly falls on the shoulders of contemporary vocational students. Computer talents have great development prospects in the market, but the competition is also fierce. regarding the market competition, this problem is now more and more valued by various vocational colleges to adjust the school’s computer teaching system. At present, all higher vocational colleges are guided by employment and carry out computer teaching, which is conducive to cultivating more comprehensive talents for the society.
Business Simulators in the Educational Process: Comparative Evaluation of the Effectiveness of the Use

There is considerable mobility in the educational services market: stakeholders’ requirements, the forms of organizing the educational process, and the training tools used are changing. Testing of changes requires monitoring and evaluating the effectiveness of improvements and the impact on changes in content parameters, the quality of learning materials and the demand for formed managerial competencies. There is a surge in researchers ‘interest in developing methods for evaluating and justifying the increase in improvements from introducing changes to the educational process. This work reflects the results of an experiment conducted by a research group to expand the tools for teaching students of management training programs. The hypothesis is put about a higher rate of information assimilation and the formation of managerial competencies in groups of students in which classes are organized using business simulators, in contrast to those groups of students who master the course using traditional means - problem solving and case presentation. The authors believe that simulators based on the principles of gamification, to a greater extent satisfy the Generation Z students’ needs for mobility, rapid feedback, change of impressions and locations. The proof of the hypothesis is based on the differentiation of the student group into two subgroups, with a preliminary assessment of students ‘ academic performance in economic and management disciplines and input testing of knowledge in Unit Economics.
Tendências contemporâneas da Ciência da Informação

Studies in the area of Information Science are guided over time under bias that cross different fields and theoretical currents from different concepts. The aim of this paper is to analyze the thematic relationship of the doctoral theses of the Information Science Graduate Program at the Federal University of Paraiba (2015 to 2019) with contemporary theories and trends in Information Science. Based on a case study, it conducts a documentary, exploratory and descriptive research with a qualitative and quantitative approach. The results were analyzed from the perspective of content analysis using the thematic analysis technique. It points out that theories or trends of research in Memory, Information Regimes, Organizational Culture, Domain Analysis and Approaches to Archivology, Librarianship and Museology predominate in the theses that make up the documentary corpus. It concludes that the doctoral works of the Graduate Program in Information Science analyzed were developed with characteristics according to the contemporary theories or trends in Information Science identified by Carlos Alberto Avila de Araujo.