Reviewerly/exHarmony huggingface dataset:
Teaching with children's literature in initial teacher education: Developing equitable literacy pedagogy through talk about books

Teaching about children’s literature in pre-service teacher education is quite rare, even though research shows it is crucial for teachers to be good at teaching reading as well as being committed readers (Commeyras et al., 2003; Cremin et al., 2009). Emphasis on the reading process can sideline the importance of talking about quality literature to engage students in reading (Author, 2016). I have positioned the role of talk about books as a core part of our undergraduate degree. In this way, my pre-service teachers are alerted to the potential of the ‘fiction effect’ to improve equitable engagement with reading (Jerrim &amp; Moss, 2019) for all students.&#x0D; &#x0D; The paper explores how an initial teacher education course in Australia partnered with local schools to create authentic interactions about children’s literature. A dialogic approach to learning (Alexander, 2020) was adopted to teach pre-service teachers to develop equitable literacy pedagogy informed by children's literature. During their education program the pre-service teachers received letters from school children who wrote about their reading preferences. The letters were discussed for evidence of reading habits and new books were sought as recommendations for children to read. Through considering their own reading identities pre-service teachers collectively developed their knowledge about children’s literature as they developed knowledge of literacy pedagogy. The development of habits of noticing (Simpson et al., 2020) through iterative discussion helped the pre-service teachers’ learn about their students, learn from their students, and encouraged them to take a more holistic view of the teaching of reading.
Regularising Knowledge Transfer by Meta Functional Learning

Machine learning classifiers’ capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Moreover, we adopt an Iterative Update strategy on MFL (MFL-IU). This improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Experiments on three Few-Shot Learning (FSL) benchmarks (miniImageNet, CIFAR-FS and CUB) show that meta functional learning for regularisation knowledge transfer can benefit improving FSL classifiers.
VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.
EXPLORATORY RESEARCH ON CULTURAL COMPUTING FOR HUMAN-COMPUTER INTERACTION. THE CASE OF SUSTAINABILITY.

Abstract The digital transition refers to the fact that information technology (IT) tools are used in all our activities on a daily basis. In this article, we will study the use of IT tools in engineering activities. It is possible to say that today IT tools accompany engineers in their professional practices. This presence of computing has also enabled the development and considerable changes in human-technologies interactions. Moreover, the socio-economic context has evolved considerably, and environmental issues have taken on an important role in engineering. We ask whether and to what extent these two contexts (digital and ecological) have changed the expectations of design professionals with regard to IT tools. Should the way of addressing the type of human-machine interaction in engineering tools be modified in depth? The objective of this paper is to understand what types of human-computer interaction would allow a more satisfying user experience for those future engineers who are using new technologies and marked by the ecological urgency. To do so, we will focus on a particular engineering context (design for sustainability) and a particular engineering practice (LCA practice).
The Development of Digital Competences for University Tourism Teachers

The aim of the research was to determine the virtual competences of Ecuadorian tourism teachers during the A-2021 cycle. A mixed research approach was used. A quantitative analysis was applied first, followed by a qualitative analysis. The sample selection was participatory and non-probabilistic. The sample consisted of 1003 active university teachers in Ecuador. A questionnaire comprising 106 questions divided into four variables was designed. A multivariate analysis of variance (MANOVA) and the Kruskal-Wallis test were carried out. The findings indicate the applicability of virtual competences by university teachers. These competences are of a medium level and do not comply with the comprehensive competences of student care. The technological training level of university teaching staff is low. Regarding the use and knowledge of technology, respondents indicated a high level of understanding. Attitudes towards and methodology use in information and communication technologies showed weaknesses in usage and accessibility. In conclusion, a matrix of virtual competences for university tourism teachers is presented. The application of this methodology considers the competences in a comprehensive and problem-oriented manner.
THE ROLE OF SELF- EDUCATION IN THE DEVELOPMENT OF TEACHERS’ PROFESSIONAL COMPETENCE IN MODERN CONDITIONS OVER THE COURSE OF FEDERAL STATE EDUCATIONAL STANDARD OF HIGHER PROFESSIONAL EDUCATION

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Exploring Low-light Object Detection Techniques

Images acquired by computer vision systems under low light conditions have multiple characteristics like high noise, lousy illumination, reflectance, and bad contrast, which make object detection tasks difficult. Much work has been done to enhance images using various pixel manipulation techniques, as well as deep neural networks - some focused on improving the illumination, while some on reducing the noise. Similarly, considerable research has been done in object detection neural network models. In our work, we break down the problem into two phases: 1)First, we explore which image enhancement algorithm is more suited for object detection tasks, where accurate feature retrieval is more important than good image quality. Specifically, we look at basic histogram equalization techniques and unpaired image translation techniques. 2)In the second phase, we explore different object detection models that can be applied to the enhanced image. We conclude by comparing all results, calculating mean average precisions (mAP), and giving some directions for future work.
Efficient Local Planning with Linear Function Approximation

We study query and computationally efficient planning algorithms with linear function approximation and a simulator. We assume that the agent only has local access to the simulator, meaning that the agent can only query the simulator at states that have been visited before. This setting is more practical than many prior works on reinforcement learning with a generative model. We propose two algorithms, named confident Monte Carlo least square policy iteration (Confident MC-LSPI) and confident Monte Carlo Politex (Confident MC-Politex) for this setting. Under the assumption that the Q-functions of all policies are linear in known features of the state-action pairs, we show that our algorithms have polynomial query and computational costs in the dimension of the features, the effective planning horizon, and the targeted sub-optimality, while these costs are independent of the size of the state space. One technical contribution of our work is the introduction of a novel proof technique that makes use of a virtual policy iteration algorithm. We use this method to leverage existing results on $\ell_\infty$-bounded approximate policy iteration to show that our algorithm can learn the optimal policy for the given initial state even only with local access to the simulator. We believe that this technique can be extended to broader settings beyond this work.
Deep Learning dalam Mengindetifikasi Jenis Bangunan Heritage dengan Algoritma Convolutional Neural Network

A heritage building is a building that has a distinctive style or tradition from a culture whose activities are carried out continuously until now and are used as a characteristic of that culture. The problems that occur in the community are the lack of knowledge to recognize the types of heritage buildings and the lack of digital documentation. Another problem that occurs in identifying heritage buildings is that there are similarities between heritage buildings and new buildings that imitate the architectural style of heritage buildings from ornaments. This can raise doubts in the information related to the original history of heritage buildings for the public or visitors. This study aims to apply the Convolutional Neural Network (CNN) to identify the types of heritage buildings. The benefits of this research can be found in the characteristics of a building based on ornaments so that it can be used to obtain information about the types of heritage buildings in Indonesia. A dataset of 7184 images of ornaments from heritage buildings were used which were taken directly at the Yogyakarta location, namely; Mataram Grand Mosque, Taqwa Wonokromo Mosque, Kalang House, Joglo KH Ahmad Dahlan and Ketandan. It is necessary to identify the heritage building because the object of the building can become extinct at any time, so to maintain it, documentation is needed as an effort to preserve culture and for education. Based on the evaluation of the performance of the tests carried out using the confusion matrix method from 391 ornamental images, the results obtained are 98% accuracy
A Telegram Corpus for Hate Speech, Offensive Language, and Online Harm

We provide a new text corpus from the social medium Telegram, which is rich in indirect forms of divisive speech. We scraped all messages from one channel of Donald Trump supporters, covering a large part of his presidency, from late 2016 until January 2021, including the January 6 Capitol riot. The discussion among the group members, over this long time period, includes the spread of disinformation, disparaging of out-group members, and other forms of harmful speech. To enable research into the role of harmful speech in political discourse, we added two types of annotations to the corpus: (i) automatic annotations of offensive language for all messages, and (ii) our own manual annotations of harmful language for a portion of the posts leading up to the January 2021 Capitol riot and its aftermath.
Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.
Comparative analysis based on clustering algorithms

Abstract This article summarizes and evaluates the clustering effects of commonly used clustering algorithms on data sets with different density distributions. In this paper, circled datasets, different sized datasets, and Gaussian mixture datasets were designed as the typical datasets. Then, the K-means, Gaussian mixture clustering, DBSCAN, and Agglomerative clustering were developed to evaluate the clustering performance on these datasets. The results show that the DBSCAN is more stable when the density distributions of the data sets are not clear. Besides, the Agglomerative clustering that calculates the shortest distance can determine the type of data set. Moreover, it is not appropriate to use only a single clustering algorithm to analyze a Gaussian mixture dataset. It is recommended to use multiple clusters to process the dataset after preprocessing.
Analytical Approach for Security of Sensitive Business Cloud

Sensitive cloud storage security is an important part to secure confidential data of the business. Sensitive data security in merged cloud database environment is increased in recent few years. Data centers are located throughout the world. Cloud computing provides virtual resources using the internet facilitates to its consumers. It is an exciting technology and it can reduce the application cost. This chapter throughs light on security of sensitive cloud storage and its solutions. It, in turn, help the users to know all things that are required for security when they are planning to use the cloud storage. Sometimes the performance is being compromised of the business and it requires the highest security level. It is very helpful for the user to choose an appropriate security mechanism for the sensitive cloud database according to the business requirements. This chapter provides the main concept of security in cloud computing. It also throws light on security to sensitive data, all the four-level authentication authorization, data security, network security, cloud security. Increasing threat of security in the growing demand of clouds is becoming a main issue. This chapter also defines a path in which security threats can be very dangerous to cloud computing and how it can be restricted.
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach

One of the challenges for multi-agent reinforcement learning (MARL) is designing efficient learning algorithms for a large system in which each agent has only limited or partial information of the entire system. While exciting progress has been made to analyze decentralized MARL with the network of agents for social networks and team video games, little is known theoretically for decentralized MARL with the network of states for modeling self-driving vehicles, ride-sharing, and data and traffic routing. This paper proposes a framework of localized training and decentralized execution to study MARL with network of states. Localized training means that agents only need to collect local information in their neighboring states during the training phase; decentralized execution implies that agents can execute afterwards the learned decentralized policies, which depend only on agents' current states. The theoretical analysis consists of three key components: the first is the reformulation of the MARL system as a networked Markov decision process with teams of agents, enabling updating the associated team Q-function in a localized fashion; the second is the Bellman equation for the value function and the appropriate Q-function on the probability measure space; and the third is the exponential decay property of the team Q-function, facilitating its approximation with efficient sample efficiency and controllable error. The theoretical analysis paves the way for a new algorithm LTDE-Neural-AC, where the actor-critic approach with over-parameterized neural networks is proposed. The convergence and sample complexity is established and shown to be scalable with respect to the sizes of both agents and states. To the best of our knowledge, this is the first neural network based MARL algorithm with network structure and provably convergence guarantee.
Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals' privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding.We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data.We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio).The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal.The proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.
An Evaluation of the Accuracy of the Machine Translation Systems of Social Media Language

In this age of information technology, it has become possible for people all over the world to communicate in different languages through social media platforms with the help of machine translation (MT) systems.As far as the Arabic-English language pair is concerned, most studies have been conducted on evaluating the MT output for the standard varieties of Arabic, with fewer studies focusing on the vernacular or colloquial varieties.This study attempts to address this gap through presenting an evaluation of the performance of MT output for vernacular or colloquial Arabic in the social media domain.As it is currently the most widely used MT system, Google Translate (GT) has been chosen for evaluating the reliability of its output in the context of translating the Arabic colloquial language (i.e., Egyptian/Cairene Arabic variety) used in social media into English.With this goal in mind, a corpus consisting of Egyptian dialectal Arabic sentences were collected from social media networks, i.e., Facebook and Twitter, and then fed into GT system.The GT output was then evaluated by three human translators to assess their accuracy of translation in terms of adequacy and fluency.The results of the study show that several translation problems have been spotted for GT output.These problems are mainly concerned with wrong equivalents, inappropriate additions and deletions, and transliteration for out-of-vocabulary (OOV) words, which are mostly due to the literal translation of the Arabic vernacular sentences into English.This can be due to the fact that Arabic vernacular varieties are different from the standard language for which MT systems have been basically developed.This, consequently, necessitates the need to upgrade such MT systems to deal with the vernacular varieties.
Offline handwritten signature verification using various Machine Learning Algorithms

In today’s world it is necessary to protect one’s authenticity in order to ensure the protection of personal information that only the authenticate credentials of a person can have access to. Nowadays there is an increase in number of malpractices like signature forgery to access the important information of a person. To encounter signature verification problem, there have been a number of advances in verifying the authenticity of signature using various techniques including Machine Learning and Deep Learning. This paper introduces a novel approach to verify the signatures using difference of gaussian filtering technique, gray level co-occurrence matrix feature extraction technique, principle component analysis and kernel principal component analysis associated with various machine learning algorithms. The publicly available Kaggle offline handwritten signature dataset is used for training. This article compares the accuracy of the dataset on various machine learning algorithms. After training datasets the lowest accuracy achieved is 56.66% for Naive Bayes algorithm. The highest accuracy achieved is 82% for K-Nearest Neighbour (KNN) and 81.66% for Random Forest using principle components and kernel principle components of the dataset.
Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search

There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.