Reviewerly/exHarmony huggingface dataset:
Global Convergence and Induced Kernels of Gradient-Based Meta-Learning with Neural Nets.

Gradient-based meta-learning (GBML) with deep neural nets (DNNs) has become a popular approach for few-shot learning. However, due to the non-convexity of DNNs and the complex bi-level optimization in GBML, the theoretical properties of GBML with DNNs remain largely unknown. In this paper, we first develop a novel theoretical analysis to answer the following questions: Does GBML with DNNs have global convergence guarantees? We provide a positive answer to this question by proving that GBML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. The second question we aim to address is: How does GBML achieve fast adaption to new tasks with experience on past similar tasks? To answer it, we prove that GBML is equivalent to a functional gradient descent operation that explicitly propagates experience from the past tasks to new ones. Finally, inspired by our theoretical analysis, we develop a new kernel-based meta-learning approach. We show that the proposed approach outperforms GBML with standard DNNs on the Omniglot dataset when the number of past tasks for meta-training is small. The code is available at this https URL AI-secure/Meta-Neural-Kernel .
A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction

Recently BERT has achieved a state-of-theart performance in temporal relation extraction from clinical Electronic Medical Records text.However, the current approach is inefficient as it requires multiple passes through each input sequence.We extend a recently-proposed one-pass model for relation classification to a one-pass model for relation extraction.We augment this framework by introducing global embeddings to help with long-distance relation inference, and by multi-task learning to increase model performance and generalizability.Our proposed model produces results on par with the state-of-the-art in temporal relation extraction on the THYME corpus and is much "greener" in computational cost.
Cache Coherence Protocols in Distributed Systems

Distributed systems performance is affected significantly by cache coherence protocols due to their role in data consistency maintaining. Also, cache coherent protocols have a great task for keeping the interconnection of caches in a multiprocessor environment. Moreover, the overall performance of distributed shared memory multiprocessor system is influenced by the used cache coherence protocol type. The major challenge of shared memory devices is to maintain the cache coherently. Therefore, in past years many contributions have been presented to address the cache issues and to improve the performance of distributed systems. This paper reviews in a systematic way a number of methods used for the cache-coherent protocols in a distributed system.
Problem-Based Approach to Develop Creative Thinking in Students Majoring in Mathematics at Teacher Training Universities

This paper looks into the issue of implementing problem-based approach to developing creative thinking in students majoring in Mathematics at teacher training universities.The object of the study is the process of developing creative thinking in students, majoring in Mathematics at teacher training Universities, through task-based approach.The methods used in the present study are theoretical analysis of research papers and resources, as well as a survey, conducted among students and lecturers.This survey is aimed at determining distinctive features of the problems in Elementary Mathematics, which can help to develop creative thinking in students.The aim of this study is to introduce the syllabus of the online-course "Creative Thinking through Teaching Elementary Mathematics" and to design a system of Open-ended and Integrative Problems in order to develop the components of creative thinking in students, majoring in Mathematics at teacher training universities.The tasks of the present study: to determine what key components of creative thinking in students majoring in Mathematics can be developed, when teaching Elementary Mathematics to them; to determine distinctive features of the Rich Tasks that will contribute to developing creative thinking in students in the course of teaching Elementary Mathematics to them; to introduce the syllabus of the online-course "Creative Thinking through Teaching Elementary Mathematics"; to design a system of rich tasks for arranging the learning process.
Persistent Intelligence, Surveillance, and Reconnaissance Using Multiple Autonomous Vehicles With Asynchronous Route Updates

Providing persistent intelligence, reconnaissance, and surveillance of targets is a challenging, but important task when time-critical information is required. In this letter, we provide a decentralized routing algorithm for coordinating multiple autonomous vehicles as they visit a discrete set of pre-defined targets with weighted revisit priorities. The algorithm utilizes a block coordinate ascent algorithm combined with a Monte Carlo tree search to tractably decide each vehicle's route. The result is a non-myopic algorithm for multiple vehicles that is decentralized, computationally tractable, and allows for target prioritization. Guarantees are provided that all targets will have finite revisit times and that the block coordinate ascent algorithm will converge to a block optimal solution. Numerical simulations illustrate the utility of this method by showing that the results are comparable to those of a centralized exhaustive search and that they degrade gracefully with limited communication and scale under increasing numbers of targets and vehicles.
Mobile Learning in Higher Education: A Bibliometric Review

&lt;span style="line-height: 107%; font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"&gt;Changing habits leads to changes in ways of learning and teaching. The widespread use of smartphones appears as an opportunity and at the same time as a need for adoption in higher education institutions. The purpose of this paper is to analyze the scientific production on mobile learning in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. The sample was composed by 450 articles in total. The results obtained by bibliometric analysis showed that the publication rates continue to increase, in which journals they are published, which are the organizations and countries that publish the most, how the evolution of perspective has changed since 2010 and which are the most cited articles. We concluded that since mobile learning in higher education is a reality, there still seems to be a possibility for evolution in good quality publications.&lt;/span&gt;
Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems

Hyperparameter selection is a crucial part of building neural machine translation (NMT) systems across both academia and industry. Fine-grained adjustments to a model’s architecture or training recipe can mean the difference between a positive and negative research result or between a state-of-the-art and underperforming system. While recent literature has proposed methods for automatic hyperparameter optimization (HPO), there has been limited work on applying these methods to neural machine translation (NMT), due in part to the high costs associated with experiments that train large numbers of model variants. To facilitate research in this space, we introduce a lookup-based approach that uses a library of pre-trained models for fast, low cost HPO experimentation. Our contributions include (1) the release of a large collection of trained NMT models covering a wide range of hyperparameters, (2) the proposal of targeted metrics for evaluating HPO methods on NMT, and (3) a reproducible benchmark of several HPO methods against our model library, including novel graph-based and multiobjective methods.
The Effects of Voice-based AI Chatbots on Korean EFL Middle School Students’ Speaking Competence and Affective Domains

This study examines the effects of voice-based AI (Artificial Intelligence) chatbots on Korean EFL middle school students' speaking competence and their related affective domains: level of interest, belief, motivation, and perceived anxiety.Participants were 44 freshmen students from one Korean middle school who took part in this study.The students divided into one control group and one experimental group.During the ten-week experimental period, the students engaged in 10 chat sessions with the voice-based AI chatbot 'Echodot.'To take an examination of the effects on the students' speaking competence, they took the NEAT speaking test as pre-and post-tests.As for the affective factors, structured questionnaire surveys were conducted before and after the treatment to find out if there are any changes in their affective domains.Findings reveal that the AI chatbot effectively contributed to an improvement in speaking ability among EFL students.The survey results indicate that the students' affective aspects of AI chatbot-assisted English learning changed positively over time.Accordingly, this study provides valuable insight into the use of AI chatbots in English learning in EFL environments, suggesting that EFL teachers should try to integrate AI chatbots into their classrooms.
Efficient Vulnerability Detection based on abstract syntax tree and Deep Learning

The automatic vulnerability detection on program source code is an important research topic. With the development of artificial intelligence, deep learning has been applied to vulnerability detection. Existing methods do not make full use of the syntax structure of the program source code that only treats the code as plain text, which brings much redundancy. Moreover, to avoid computation overhead caused by redundancy, existing methods often use the truncate method to process variable-length data, which also cause data loss. In this paper, we propose a data processing method based on the abstract syntax tree to extract all syntax features and reduce data redundancy. Besides, we apply the pack-padded method on the Bi-GRU network to train variable-length data without truncation and padding. Compared with the current methods, our framework does not rely on the experts or predefined rules so that it is suitable to process a large number of source code. To evaluate the ability of our framework, we collect the vulnerability dataset that includes more than 260,000 functions in 118 types of CWE, which is larger than the dataset of existing research. Experiments show that our framework has better performance than existing methods.
Assessing and Minimizing the Impact of OCR Quality on Named Entity Recognition

In digital libraries, the accessibility of digitized documents is directly related to the way they are indexed. Named entities are one of the main entry points used to search and retrieve digital documents. However, most digitized documents are indexed through their OCRed version and OCR errors may hinder their accessibility. This paper aims to quantitatively estimate the impact of OCR quality on the performance of named entity recognition (NER). We tested state-of-the-art NER techniques over several evaluation benchmarks, and experimented with various levels and types of synthesised OCR noise so as to estimate the impact of OCR noise on NER performance. We share all corresponding datasets. To the best of our knowledge, no other research work has systematically studied the impact of OCR on named entity recognition over datasets in multiple languages. The final outcome of this study is an evaluation over historical newspaper data of the national library of Finland, resulting in an increase of around 11% points in terms of F1-measure over the best-known results to this day.
Characteristics of ‘Problem-Based Learning’ in Post-COVID-19 Workplace

The COVID-19 pandemic forced many workplaces to adapt to drastic changes in the work environment around the world. Despite the changes in the previous years in relevance to new technologies and digitisation, the abrupt changes of the pandemic require steep-learning. As the world emerges from the lockdown, more collaborative approaches to solving complex problems are needed. Therefore, this paper investigates the requirements of Problem-based Learning (PBL) in the new normal; an era expected in the post-COVID-19 pandemic. This research uses the dimensions of both the new normal demands and the competency to be learned through the analysis of a questionnaire that investigates the need for PBL. The authors illustrate how the utilisation of PBL helps in the selective approach of the types of possible problems to be solved. The implication of this paper is that it shed light into the rising need for PBL as an attitude after COVID-19 and establish early tools to encourage further more in-depth research on the subject.
Smart Policing: A Critical Review of the Literature

Smart policing refers to the application of data-driven approaches by police authorities. Although the concept is not new, the vast availability of potentially relevant data sources in policing calls for a critical and consolidated review of the literature. To synthesise the evidence on smart policing, we present a systematic review of 112 articles across all relevant disciplines. Earlier work has concentrated on social media communications or predictive policing while this review identifies several new applications associated with new forms of data and their corresponding roles to policing. We develop a framework to show the connections between smart use of data and police approaches and strategies. We discuss how smart policing can be an area of increased interest in digital government and public management research.
The Defense of Adversarial Example with Conditional Generative Adversarial Networks

Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause of the adversarial examples is unclear. Therefore, it is challenging to build a defense mechanism. In this paper, we propose an image-to-image translation model to defend against adversarial examples. The proposed model is based on a conditional generative adversarial network, which consists of a generator and a discriminator. The generator is used to eliminate adversarial perturbations in the input. The discriminator is used to distinguish generated data from original clean data to improve the training process. In other words, our approach can map the adversarial images to the clean images, which are then fed to the target deep learning model. The defense mechanism is independent of the target model, and the structure of the framework is universal. A series of experiments conducted on MNIST and CIFAR10 show that the proposed method can defend against multiple types of attacks while maintaining good performance.
A Multi-Objective Genetic Programming Hyper-Heuristic Approach to Uncertain Capacitated Arc Routing Problems

The Uncertain Capacitated Arc Routing Problem (UCARP) is a very important problem which has many real world applications. Genetic Programming Hyper-heuristic (GPHH), which can automatically evolve effective routing policies, is considered as a promising technique that can handle UCARP effectively. However, GP-evolved routing policies are often very complex and hard to be understood and trusted by human users. In this paper, we aim to improve the interpretability of the GP-evolved routing policies by reducing the size of the GP-evolved routing policies since smaller routing policies tend to be easier to understand. We propose a new Multi-Objective GP (MOGP) to optimise the performance (total cost) and size simultaneously. One main challenge is that the size is much easier to be optimised than the performance. Thus, the population tends to be biased to the small but poor routing policies and quickly lose the ability of exploration. To address this issue, we propose a MOGP approach with α dominance strategy (α-MOGP) which can balance the tradeoff between performance and individual size. The experimental results showed that α-MOGP could obtain much smaller routing policies than the state-of-the-art single-objective GPHH, without deteriorating the performance. Compared with traditional MOGP, α-MOGP can obtain a much better and more widespread Pareto front.
Digital Spatial Models in Technology as a Development Tool of the Intellectual Creative Aspect within Education

This is a conceptual approach to knowledge acquisition at technical universities. The emphasis is on the consistent possession of knowledge: the acquisition of new and preservation of the existing ones. At the same time, the grapho-geometric component in technical education is one of the most important elements of fundamental (general engineering) education. It is carried out at the initial stage in the process of studying technical disciplines included in the relevant curricula. Applied geometry plays an important role in this process, and it includes such directions as descriptive geometry, analytic geometry, engineering graphics, and computer graphics that provide spatial interpretation of geometric objects and display them on a plane. This combination of subjects studied significantly enriches the geometric development within student's intellectual world.
Accelerated training of bootstrap aggregation-based deep information extraction systems from cancer pathology reports

In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources and training time. The research question that we aimed to answer in this research is whether we could achieve higher task performance scores and accelerate the training by dividing a problem into sub-problems.The data used in this study consist of free text from electronic cancer pathology reports. We applied bagging and partitioned data training using Multi-Task Convolutional Neural Network (MT-CNN) and Multi-Task Hierarchical Convolutional Attention Network (MT-HCAN) classifiers. We split a big problem into 20 sub-problems, resampled the training cases 2,000 times, and trained the deep learning model for each bootstrap sample and each sub-problem-thus, generating up to 40,000 models. We performed the training of many models concurrently in a high-performance computing environment at Oak Ridge National Laboratory (ORNL).We demonstrated that aggregation of the models improves task performance compared with the single-model approach, which is consistent with other research studies; and we demonstrated that the two proposed partitioned bagging methods achieved higher classification accuracy scores on four tasks. Notably, the improvements were significant for the extraction of cancer histology data, which had more than 500 class labels in the task; these results show that data partition may alleviate the complexity of the task. On the contrary, the methods did not achieve superior scores for the tasks of site and subsite classification. Intrinsically, since data partitioning was based on the primary cancer site, the accuracy depended on the determination of the partitions, which needs further investigation and improvement.Results in this research demonstrate that 1. The data partitioning and bagging strategy achieved higher performance scores. 2. We achieved faster training leveraged by the high-performance Summit supercomputer at ORNL.
A Narrative Review: Classification of Pap Smear Cell Image for Cervical Cancer Diagnosis

Cervical cancer develops as cells transformation in the cervix of a female that connects the uterus to the vagina. This cancer may impact the columnal epithelial cells of the cervix and therefore can be expanded to the ... | Find, read and cite all the research you need on Tech Science Press
An Efficient Approach for Measuring Semantic Similarity Combining WordNet and Wikipedia

The measurement of semantic similarity between concepts is an important research topic in natural language processing.In the past, several approaches for measuring the semantic similarity between concepts have been proposed based on WordNet or Wikipedia.However, improvements in the measurement accuracy of most methods have led to a dramatic increase in time complexity, and the existing methods do not effectively integrate WordNet and Wikipedia.In this paper, we focus on designing an efficient semantic similarity method based on WordNet and Wikipedia.To improve the accuracy of WordNet edge-based measures, we propose an edge weight model for combining edge and density information, which assigns a weight to each edge adaptively based on the number of direct hyponyms of the subsumer.Second, to improve the computational efficiencies of the existing Wikipedia link vector-based measures, we propose a new Wikipedia link feature-based semantic similarity method that converts Wikipedia links into semantic knowledge and replaces the TF-IDF statistical weight model in the existing measures.In addition, we propose two new word disambiguation strategies to further improve the accuracy of Wikipedia link-based measures.Finally, to fully exploit the advantages of WordNet and Wikipedia, we propose two new aggregation schemas for combining WordNet ''is-a'' semantics and Wikipedia link semantics to replace the current aggregation schemas that combine WordNet ''is-a'' semantics with category semantics in Wikipedia.The experimental results show that our aggregation models are outstanding in terms of accuracy, efficiency and word coverage compared to state-of-the-art similarity measures.
Attribute-Based Encryption Approach for Storage, Sharing and Retrieval of Encrypted Data in the Cloud

One of the most cost-effective services in cloud computing is storage, used by businesses and individuals to outsource their massive data to untrusted servers. Efforts have studied problems around this application scenario in different fronts: efficiency, flexibility, reliability, and security. In this paper we address the security concerns of cloud storage under the scenario where users encrypt-then-outsource data, share their outsourced data with other users, and the service provider can be queried for searching and retrieval of encrypted data. As main distinctive, we propose a security approach for storage, sharing and retrieval of encrypted data in the cloud fully constructed on the basis of attribute-based encryption (ABE) thus enabling access control mechanisms over both the encrypted data and also for the information retrieval task through search access control. Compared to related works, our approach considers efficient encryption at three different levels: i) bulk encryption of data outsourced to the cloud, ii) keys management for access control over encrypted data by means of digital envelopes from attribute based encryption, and iii) novel construction for attribute based searchable encryption (ABSE). Our underlying ABE algorithms are carefully selected from the body of knowledge and novel constructions for ABSE are provided over the asymmetric setting (Type-III pairings) to support security levels of 128-bits or greater. Experimental results on benchmark data sets demonstrate the viability of our approach for practical realizations using Barreto-Naehrig curves.