scholarly journals Embodied Multimodal Multitask Learning

2020 ◽  
Author(s):  
Devendra Singh Chaplot ◽  
Lisa Lee ◽  
Ruslan Salakhutdinov ◽  
Devi Parikh ◽  
Dhruv Batra
Keyword(s):  
Knowledge Transfer ◽  
Language Learning ◽  
Learning Models ◽  
Task Knowledge ◽  
New Words ◽  
Embodied Language ◽  
New Concepts ◽  
Proposed Model ◽  
3D Environments ◽  
Answering Questions

Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the inability to transfer to new words and concepts not seen during training using only a few examples. We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations. We create scenarios and datasets to quantify cross-task knowledge transfer and show that the proposed model outperforms a range of baselines in simulated 3D environments. We also show that this disentanglement of representations makes our model modular and interpretable which allows for transfer to instructions containing new concepts.

Higher-Order Thinking Skills (HOTS)-based Formative Assessment: A Proposed Model for Language Learning Assessment

2020 ◽  
Vol 9 (2) ◽  
pp. 115
Author(s):  
Wiyaka Wiyaka ◽  
Entika Fani Prastikawati ◽  
AB Prabowo Kusumo Adi
Keyword(s):  
Formative Assessment ◽  
Language Learning ◽  
Higher Order Thinking ◽  
Thinking Skills ◽  
Higher Order ◽  
Formative Assessments ◽  
Assessment Model ◽  
Higher Order Thinking Skills ◽  
Learning Assessments ◽  
Proposed Model

<div><p class="StyleABSTRAKenCambria">The integration of higher-order thinking skills (HOTS) in language learning assessments has become a crucial issue in 21st-century learning. However, not many teachers are aware of the need to incorporate HOTS in assessments due to their insufficient knowledge and the absence of good examples. Further, there is not much research and literature on HOTS-based formative assessment that can be used as references. This research aims to fill the existing gap by providing a model of higher-order thinking skills (HOTS)-based formative assessments for English learning, especially in junior high schools. By employing research and development design, this research describes the validation of the assessment model. The proposed model of assessment may be used as a prototype for assessing language learning.</p></div><p> </p>

scholarly journals An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

A framework of decision making based on bipolar fuzzy competition hypergraphs

2021 ◽  
pp. 1-21
Author(s):  
Sundas Shahzadi ◽  
Areen Rasool ◽  
Musavarah Sarwar ◽  
Muhammad Akram
Keyword(s):  
Biological Sciences ◽  
Decision Making ◽  
Comparative Analysis ◽  
Social Networking ◽  
Real World ◽  
Fuzzy Models ◽  
New Concepts ◽  
Proposed Model ◽  
Business Market ◽  
Strength Of Competition

Bipolarity plays a key role in different domains such as technology, social networking and biological sciences for illustrating real-world phenomenon using bipolar fuzzy models. In this article, novel concepts of bipolar fuzzy competition hypergraphs are introduced and discuss the application of the proposed model. The main contribution is to illustrate different methods for the construction of bipolar fuzzy competition hypergraphs and their variants. Authors study various new concepts including bipolar fuzzy row hypergraphs, bipolar fuzzy column hypergraphs, bipolar fuzzy k-competition hypergraphs, bipolar fuzzy neighborhood hypergraphs and strong hyperedges. Besides, we develop some relations between bipolar fuzzy k-competition hypergraphs and bipolar fuzzy neighborhood hypergraphs. Moreover, authors design an algorithm to compute the strength of competition among companies in business market. A comparative analysis of the proposed model is discuss with the existing models such bipolar fuzzy competition graphs and fuzzy competition hypergraphs.

Examining the Use of Vocabulary Learning Strategies among EFL Learners in Baghlan University of Afghanistan

2020 ◽  
pp. 1-10
Keyword(s):  
Language Learning ◽  
Learning Strategies ◽  
Quantitative Research ◽  
English Language ◽  
Vocabulary Learning ◽  
Vocabulary Knowledge ◽  
Social Strategies ◽  
Efl Learners ◽  
New Words ◽  
Vocabulary Learning Strategies

Vocabulary learning is one of the problems in language learning skills. Tackling such problems is to provide useful and effective strategies for enhancing students’ VLSs. Therefore, this study aims to survey vocabulary learning strategies (VLSs) utilized among English as a Foreign Language learners (EFL) in Baghlan University of Afghanistan, and to study the high and least frequently used VLSs that contributes to the learners’ vocabulary knowledge. This study utilizes a descriptive quantitative research method with 67 EFL learners who participated in the survey questionnaire adopted from Oxford (1990) taxonomy of VLS from different faculties of Baghlan University. The findings indicated that EFL learners preferably utilize VLSs at a medium level, and the highly used vocabulary learning strategies are the social strategies through which they ask the native speakers, teachers, and classmates for the meanings of new words in English language conversation. Determination, cognitive, and memory strategies are respectively followed by the learners. Whereas, metacognitive strategies are the least used strategies among EFL learners, the reason is that they only focus on the materials related to examination; explore anything about the new words for learning, and rarely think of their improvement in vocabulary learning.

A collective organizational learning model for organizational development

2019 ◽  
Vol 40 (1) ◽  
pp. 107-123 ◽  
Author(s):  
Kung Wong Lau ◽  
Pui Yuen Lee ◽  
Yan Yi Chung
Keyword(s):  
Organizational Learning ◽  
Knowledge Transfer ◽  
Organizational Development ◽  
Learning Model ◽  
Learning Needs ◽  
Learning Models ◽  
Organizational Growth ◽  
Content Type ◽  
New Model ◽  
Business Environments

Purpose Organizational learning is traditionally structured with conventional in-house learning models aiming to equip employees with practical skills for operational needs. In contrast, contemporary goals emphasize unstructured organizational learning provided with learning environments to facilitate employees’ formal and informal knowledge creation. Therefore, the conventional organizational learning models are facing tremendous challenges, and it is crucial to change the traditional modes of practice into a new approach of collective learning and knowledge transfer. As well, the emergence of innovative business environments and tacit knowledge-based society urges a new form of organizational learning model to cope with employees’ learning, knowledge transfer and even knowledge management. The paper aims to discuss these issues. Design/methodology/approach In this study, the authors’ team applied a typological review for systematically analyzing current organizational learning models aiming to modify and create a new collective model. Findings The new model covers the strengths of existing approaches from which the fundamental 3Ps (i.e. principles, purposes and processes of organizational learning) concept is derived from incorporating a development perspective of organizational trajectories and technological innovations. Originality/value The authors envisage that the new model can facilitate organizations to assess and adapt their organizational learning needs and orientations by applying this organic and dynamic model which emphasizes assessment in relation to the competitive environment, technological trends and organizational growth.

Proteome and proteomics: New technologies, new concepts, and new words

1998 ◽  
Vol 19 (11) ◽  
pp. 1853-1861 ◽  
Author(s):  
N. Leigh Anderson ◽  
Norman G. Anderson
Keyword(s):  
New Technologies ◽  
New Words ◽  
New Concepts

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

scholarly journals INVESTIGATING EXPERIENCED AND NOVICE ENGLISH LANGUAGE LEARNERS` PERCEPTIONS of MOST USEFUL VOCABULARY LEARNING STRATEGIES (VLS)

2020 ◽  
Vol 8 (2) ◽  
pp. 288-295
Author(s):  
Elnaz Zariholhosseini ◽  
Ehsan Namaziandost ◽  
Mehdi Nasri
Keyword(s):  
English Language Learners ◽  
Language Learning ◽  
Learning Strategies ◽  
Language Learners ◽  
English Language ◽  
Vocabulary Learning ◽  
New Words ◽  
Novice Learners ◽  
Vocabulary Learning Strategies ◽  
Personal Use

Purpose of the study: This article report’s findings from a study on the differences and similarities between experienced and novice English language learners with regards to their personal use of VLS. Methodology: Closed questionnaire and semi-structure interviews were applied to collect the data. The questionnaire was distributed among 60 (30 experienced learners and 30 novice learners). In addition, 20 learners (10 experienced learners and 10 novice learners) were asked to answer the questions in the interview. Therefore, descriptive statistics, U Mann Whitney test, and independent-sample t-test were run to compare and analyzed the data. Main Findings: The finding showed that there were significant differences between experienced and novice learners’ thoughts towards vocabulary learning strategies and experienced learners used vocabulary learning strategies while learning new words in English language learning. Applications of this study: If the learners are taught how to use each strategy correctly, their understanding of the language can naturally be improved. Moreover, VLS is beneficial throughout the process of vocabulary learning which makes learners more independent and allows teachers to focus on other things as well. Novelty/Originality of this study: To the best of researchers’ knowledge, no study has been done on investigating Iranian experienced and novice English language learners` perceptions towards most useful vocabulary learning strategies (VLS).

scholarly journals On the benefits of multimodal annotations for vocabulary uptake from reading

2021 ◽  
Author(s):  
F Boers ◽  
Paul Warren ◽  
Georgina Grimshaw ◽  
Anna Siyanova
Keyword(s):  
Language Learning ◽  
Coding Theory ◽  
Word Meaning ◽  
Single Mode ◽  
Dual Coding ◽  
Computer Assisted ◽  
Computer Assisted Language Learning ◽  
Dual Coding Theory ◽  
New Words

© 2017 Informa UK Limited, trading as Taylor & Francis Group. Several research articles published in the realm of Computer Assisted Language Learning (CALL) have reported evidence of the benefits of multimodal annotations, i.e. the provision of pictorial as well as verbal clarifications, for vocabulary uptake from reading. Almost invariably, these publications account for the observed benefits with reference to Paivio's Dual Coding Theory, suggesting it is the visual illustration of word meaning that enhances the quality of processing and hence makes new words more memorable. In this discussion article, we explore the possibility that it is not necessarily the multimodality per se that accounts for the reported benefits. Instead, we argue that the provision of multimodal annotations is one of several possible means of inviting more and/or longer attention to the annotations–with amounts of attention given to words being a significant predictor of their retention in memory. After reviewing the available research on the subject and questioning whether invoking Paivio's Dual Coding Theory is an optimal account for reported findings, we report an eye-tracking study the results of which are consistent with the alternative thesis that the advantage of multimodal glosses for word learning lies with the greater quantity of attention these glosses attract in comparison with single-mode glosses. We conclude with a call for further research on combinations and sequences of annotation types, regardless of multimodality, as ways of promoting vocabulary uptake from reading.

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