scholarly journals NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone Data

Computers ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Ghassan F. Bati ◽  
Vivek K. Singh
Keyword(s):  
Deep Learning ◽  
Social Relationships ◽  
Interpersonal Trust ◽  
Well Being ◽  
Learning Approach ◽  
Learning Approaches ◽  
Interaction Data ◽  
Machine Learning Methods ◽  
Social Internet Of Things ◽  
One Year

Interpersonal trust mediates multiple socio-technical systems and has implications for personal and societal well-being. Consequently, it is crucial to devise novel machine learning methods to infer interpersonal trust automatically using mobile sensor-based behavioral data. Considering that social relationships are often affected by neighboring relationships within the same network, this work proposes using a novel neighbor-aware deep learning architecture (NADAL) to enhance the inference of interpersonal trust scores. Based on analysis of call, SMS, and Bluetooth interaction data from a one-year field study involving 130 participants, we report that: (1) adding information about neighboring relationships improves trust score prediction in both shallow and deep learning approaches; and (2) a custom-designed neighbor-aware deep learning architecture outperforms a baseline feature concatenation based deep learning approach. The results obtained at interpersonal trust prediction are promising and have multiple implications for trust-aware applications in the emerging social internet of things.

scholarly journals Identifier les attitudes des étudiants en géographie après leurs études secondaires

2009 ◽  
Vol 38 (1) ◽  
Author(s):  
Burckin Dal
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Student Learning ◽  
Education Student ◽  
Learning Approach ◽  
Learning Approaches ◽  
Premier Cycle ◽  
Degree Of Confidence ◽  
One Year

Ce travail expose les résultats d’une étude réalisée dans le but d’analyser la démarche d’apprentissage d’un groupe d’étudiants de premier cycle de géographie. Seront traitées ici les démarches d’apprentissage qu’ils adoptent et la façon dont évolue leur niveau de confiance en soi après un an d’enseignement supérieur. Les étudiants étaient confrontés à un programme visant au développement des capacités dans le cadre de la géographie, qui mettait l’accent sur une démarche d’apprentissage en profondeur. Les résultats montrent que, bien que leur niveau de confiance en leur capacité d’étudier et d’apprendre ait augmenté, leur démarche d’apprentissage est devenue de plus en plus instrumentale. This paper shows the results of a study carried out in order to analyse the learning approach in geography of cohorts of students on entry to a geography degree. After one year of higher education, student learning approaches and their degree of confidence are examined. A program aimed at the development of the learning capacities based on a deep learning approach was proposed to students. The results indicate that although their degrees of confidence in their capacity to study increased, their learning approaches became increasingly instrumental.

scholarly journals DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

2020 ◽  
Vol 34 (01) ◽  
pp. 598-605
Author(s):  
Chaoran Cheng ◽  
Fei Tan ◽  
Zhi Wei
Keyword(s):  
Deep Learning ◽  
Large Data ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Learning Approach ◽  
Learning Approaches ◽  
Genomic Variants ◽  
Low Resource ◽  
End To End ◽  
Genomic Variant

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.

scholarly journals A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 551-604
Author(s):  
Damien Warren Fernando ◽  
Nikos Komninos ◽  
Thomas Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Learning Techniques ◽  
New Directions ◽  
Encryption Schemes ◽  
Malware Evolution ◽  
Number Of Citations ◽  
Research Studies

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.

scholarly journals Emotion analysis of Arabic tweets using deep learning approach

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Massa Baali ◽  
Nada Ghneim
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Media Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Approaches ◽  
Social Media Analysis ◽  
Deep Convolutional Neural Networks ◽  
Emotion Analysis ◽  
Classification Tasks

Abstract Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.

Mixed Approaches to Learning in the Flipped Classroom: How Students Approach the Learning Environment

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Margrét Sigrún Sigurðardóttir ◽  
Thamar Melanie Heijstra
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Learning Environment ◽  
Learning Process ◽  
Flipped Classroom ◽  
Approaches To Learning ◽  
Male Students ◽  
Learning Approach ◽  
Learning Approaches ◽  
Surface Approach

Flipped teaching is a trend within higher education. Through flipped teaching the learning environment can be altered by moving the lecture out of the classroom through online recordings, while in-classroom sessions focus on active learning and engaging students in their own learning process. In this paper, we used focus groups comprised of male students in a qualitative research course with the aim of understanding the ways in which we might improve active student engagement and motivation within the flipped classroom. The findings indicated that, within the flipped classroom, students mix surface and deep-learning approaches. The online recordings, which students interact with through a surface approach, can function as a stepping stone toward a deep-learning approach to in-class activities, but only if students come to class prepared. The findings therefore suggest that students must be made aware of the importance of preparation prior to flipped classroom in-class activities to ensure the active learning process is successful. By not listening to the recordings (e.g., due to technological failure, as was the case in this study), students can result in only employing a surface approach.

scholarly journals A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5160 ◽  
Author(s):  
Seokju Ham ◽  
Seok-Youn Han ◽  
Seokgoo Kim ◽  
Hyung Jun Park ◽  
Kee-Jun Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Traditional Approach ◽  
Fault Classification ◽  
Learning Approach ◽  
Health State ◽  
Features Selection ◽  
Learning Approaches ◽  
Current Signal ◽  
K Nearest Neighbor

A fault diagnosis of a train door system is carried out using the motor current signal that operates the door. A test rig is prepared, in which various fault modes are examined by applying extreme conditions, as well as the natural and artificial wears of critical components. Two approaches are undertaken toward the fault classification for comparative purposes: one is the traditional feature-based method that requires several steps for the processing features such as signal segmentation, the extraction of time-domain features, selection by Fisher’s discrimination, and K-nearest neighbor. The other is the deep learning approach by employing the convolutional neural network (CNN) to skip the hand-crafted features extraction process. In the traditional approach, good accuracy is found only after the current signal is segmented into the three velocity regimes, which enhances the discrimination capability. In the CNN, superior accuracy is obtained even by the original raw signal, which is more convenient in terms of implementation. However, in view of practical applications, the traditional approach is more useful in that the features processing can be easily applied to assess the health state of each fault and monitor the progression over time in the real operation, which is not enabled by the deep learning approach.

scholarly journals A Survey on Deep Learning for Human Mobility

2023 ◽  
Vol 55 (1) ◽  
pp. 1-44
Author(s):  
Massimiliano Luca ◽  
Gianni Barlacchi ◽  
Bruno Lepri ◽  
Luca Pappalardo
Keyword(s):  
Deep Learning ◽  
Predictive Power ◽  
Human Mobility ◽  
Well Being ◽  
Learning Approaches ◽  
Comprehensive Description ◽  
Mobility Data ◽  
Flow Generation ◽  
Disease Spreading ◽  
Gps Traces

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

scholarly journals A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease

2021 ◽  
Vol 309 ◽  
pp. 01008
Author(s):  
P. Mounika ◽  
S. Govinda Rao
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Signs And Symptoms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Learning Techniques

Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.

scholarly journals Revisiting the relationship between constructive alignment and learning approaches: A perceived alignment perspective

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253949
Author(s):  
Christian Stamov Roßnagel ◽  
Katrin Lo Baido ◽  
Noleine Fitzallen
Keyword(s):  
Deep Learning ◽  
Mental Workload ◽  
Learning Motivation ◽  
University Teaching ◽  
Learning Goals ◽  
Learning Approach ◽  
Learning Approaches ◽  
Deep Approach ◽  
Course Performance ◽  
Constructive Alignment

The constructive alignment (CA) of university teaching is designed to encourage students to adopt a deep learning approach, which supports meaningful learning. The evidence is mixed, however, with some studies showing that students may adopt a surface approach even when teaching promotes deep learning. To add to the understanding of the relationships between CA and learning approaches, we explored with quantitative measures two potential implications from prior qualitative research. First, we assessed with a novel questionnaire if students’ CA perceptions predicted adaptation towards a deep learning approach. Second, we explored relationships between deep approach adaptation and learning motivation, as well as perceived mental workload. 56 students from two second-year courses in different study programmes completed a learning approach questionnaire in the second (T1), seventh (T2), and the final fourteenth (T3) course week. At T2 and T3, participants also rated the constructive alignment of the course, their learning motivation, and the mental workload. Regression analyses showed that ILO Clarity (i.e. being clear about the intended learning outcomes of the course) and receiving effective feedback were associated with a significant increase in deep approach scores from T2 to T3. That deep approach adaptation was in turn positively related to learning motivation in terms of higher ratings of one’s competence, the importance of high course performance, and course usefulness. Moreover, deep approach adaptation went with higher satisfaction of having accomplished one’s learning goals, but also with stronger feelings of insecurity and stress. Our findings suggest that students’ CA perceptions are meaningful predictors of learning approach adaptation that might eventually be developed into indicators of the effectiveness of CA implementation at the course level.

scholarly journals Students’ Personality Contributes More to Academic Performance than Well-Being and Learning Approach—Implications for Sustainable Development and Education

2020 ◽  
Vol 10 (4) ◽  
pp. 1132-1149
Author(s):  
Paulo Moreira ◽  
Susana Pedras ◽  
Paula Pombo
Keyword(s):  
Sustainable Development ◽  
Academic Performance ◽  
Personality Development ◽  
Well Being ◽  
Cross Sectional Study ◽  
Learning Approach ◽  
Learning Approaches ◽  
Cross Sectional ◽  
Deep Approach To Learning

The present study aimed to describe the predictive role of personality dimensions, learning approaches, and well-being in the academic performance of students. In total, 602 students participated in this cross-sectional study and completed a set of questionnaires assessing personality, learning approach, and well-being. Two indexes were calculated to assess affective and non-affective well-being. The results partially support the hypotheses formulated. Results revealed that personality temperament and character dimensions, deep learning approach, and affective well-being were significant predictors of academic performance. A deep approach to learning was a full and partial mediator of the relationship between personality and academic performance. The results improve the understanding of the differential contribution of personality, type of learning approach, and type of well-being to academic performance. Comprehending that personality is the strongest predictor of academic performance, after controlling the type of learning approach and the type of well-being, informs school policies and decision-makers that it is essential to encourage personality development in adolescents to improve academic performance. These results also have implications for educational policies and practices at various levels, including an emphasis on the role of well-being as an educational asset. Understanding the links between personality, well-being, and education is essential to conceptualize education as a vital societal resource for facing current and future challenges, such as sustainable development.

Sign in / Sign up

Export Citation Format

Share Document