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Innovating Academic Writing through Flipped Classroom Instruction

Innovating Academic Writing through Flipped Classroom Instru...

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IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management

作者： Dylyn A. Junio Argel A. Bandala Deren of Aied Linguisics Eecronics nd Counicions Engineering Deren De L Se UniversiyDeren of Aied Linguisics Eecronics nd Counicions Engineering Deren De L Se Universiy

ISBN: (数字)9781728130446

ISBN: (纸本)9781728130453

Flipped Classroom Instruction has become a trend and a popular instructional model of the recent time in which the typical lecture and homework features of a course are inverted. this method transforms classroom pedagogy into dynamic and interactive manner where the teacher guides and facilitates students' learning process. the present study investigates the students' perceptions on flipped classroom instruction in academic writing class in contrast to traditional teaching. To achieve this, an instructional design infused with multimedia tools in writing lessons were provided and implemented in writing classes. the mixed method was used to collect qualitative and quantitative data. the results revealed a positive impact of flipped learning to students which also indicate their improved academic writing skills in certain ways.

关键词： Education Writing Online services Task analysis Collaboration Face Interviews

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Behaviour-aware Malware Classification: Dynamic Feature Selection

Behaviour-aware Malware Classification: Dynamic Feature Sele...

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international conference on Knowledge and Systems engineering

作者： Phai Vu Dinh Qi Shi Nathan Shone Nguyen Viet Hung Phan Huy Dung Tran Nguyen Ngoc Department of Information Security Le Quy Don Techincal University Hanoi VietNam Department of Computer Science Liverpool John Moores University Liverpool UK

Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly difficult. Reliable identification is based upon characteristic features being detectable within an object. However, the limitations and expense of current malware feature extraction methods is significantly hindering this process. In this paper, we present a new method for identifying malware based on behavioural feature extraction. Our proposed method has been evaluated using seven classification methods whilst analysing 2,068 malware samples from eight different families. the results achieved thus far have demonstrated promising improvements over existing approaches.

关键词： Malware Feature extraction Machine learning Dictionaries Support vector machines data mining

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Robust Compression Technique for YOLOv3 on Real-Time Vehicle Detection

Robust Compression Technique for YOLOv3 on Real-Time Vehicle...

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international conference on Information Technology and Electrical engineering (ICITEE)

作者： Nattanon Krittayanawach Peerapon Vateekul Chulalongkorn University Big Data Analytics and IoT Center (CUBIC)

For vehicle detection, YOLOv3 has shown promising accuracy. Since the number of parameters in this network can be more than ten million parameters, it cannot be fit into a commodity camera. In this paper, we propose a compression mechanism designed specifically for YOLOv 3's network by removing unnecessary filters. Since YOLOv3 composes of two network components: backbone and pyramid networks, we propose a robust pruning mechanism to prune filters of each network separately. this can help to avoid over-pruning the network in some part of the model making our model more robust. there are two main pruning criteria investigated: Average Percentage of Zero (APoZ) and Sum Magnitude Weight. the experiment was conducted on UA-DETRAC. the results show that our compression mechanism with APoZ criterion can reduce more than 90% of the network size, while the accuracy is even higher than the full model for about 2%.

关键词： Computational modeling Object detection Machine learning Computational efficiency Feature extraction Cameras Task analysis

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A data-Driven Framework for Survivable Wireless Sensor Networks 11

A Data-Driven Framework for Survivable Wireless Sensor Netwo...

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11th international conference on Contemporary Computing, IC3 2018

作者： Kaur Sandhu, Jasminder Kumar Verma, Anil Singh Rana, Prashant Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9781538668351

the data-driven technique uses real-world readings or simulated dataset to draw inference about the behavior of communication network. the design of the network is further optimized to enhance the performability according to the inference drawn. the performability of the network is dependent on the performance parameters of the network such as packet delivery ratio, packets dropped, delay, throughput, and data rate. the data rate prediction is carried out using different machine learning techniques. Further, the performability of the network is directly associated with its survivability. Better is the network performability, more is the survivability of that particular network. this work proposes a framework for survivable Wireless Sensor Network which predicts the data rate of the network. the past experience serves as an optimized way to traverse data in the network with efficient data rate. A primary dataset designed with the help of simulations is used for this work. Also, the robustness of best predictive model is checked with the help of N-fold cross-validation technique. © 2018 IEEE.

关键词： Machine learning

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Feature extraction and selection for EEG and motion data in tasks of the mental status assessing pilot study using emotiv EPOC+ headset signals 11

Feature extraction and selection for EEG and motion data in ...

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11th international conference on Biomedical Electronics and Devices, BIODEVICES 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Syskov, Alexey Borisov, Vasilii Tetervak, Vsevolod Kublanov, Vladimir Ural Federal University named after the first President of Russia B.N. Yeltsin 19 Mira str. Yekaterinburg620002 Russia

ISBN: (纸本)9789897582776

In the paper the results of extracting and selection the features of EEG data and accelerometer for mental status evaluation are shown. We have used 14 channel wireless EEG-system Emotiv EPOC+ with accelerometer (motional data - MD) for short-term recording under several functional states for 10 healthy subjects: Functional rest (rest state), TOVA-test (mental load), Hyperventilation (physical load) and Aftereffect (after test state). We then extracted core features from EEG-only and MD-only data using principal component analysis. After that, supervised learning methods were used for mental state classification: EEG-only core features for AF3, T7, O1, T8, AF4 channels, MD-only core features and EEG- MD integrated core features. Experimental results showed that integrated core features for mental status evaluation have higher prediction accuracy 92,0% for decision tree method. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Accelerometers

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Benefits of Using Remote Labs in intelligent Control Teaching 1

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20th international conference on Interactive Collaborative learning (ICL) - Teaching and learning in a Digital World

作者： Cardoso, Alberto Gil, Paulo Urbano, Diana Palma, Luis Brito Univ Coimbra CISUC Dept Informat Engn Coimbra Portugal NOVA Univ Lisbon FCT Dept Elect Engn Lisbon Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (数字)9783319732046

ISBN: (纸本)9783319732046;9783319732039

the use of remote and virtual labs in teaching and learning activities can be very useful in different high education courses, especially in engineering courses. this paper presents the use of a remote experiment in a laboratory work of intelligent Control subject of an engineering course. A remote lab system can represent an important tool to be used in practical classes, complementing the experiments in the lab and contributing to the enhancement of the students' experimental skills. For this purposes, a remote setup was considered to provide the interaction with the remote lab through the Internet, where students can visualize the lab using a Web camera and observe data in real time from the remote system and download it. Different type of experiments can be accomplished using this setup. this work includes some preliminary results about the assessment of the use of the remote lab by students of an engineering subject. the results are encouraging to continue to use this kind of online experimentation resource for teaching activities in different areas of engineering courses.

关键词： Remote laboratories intelligent control Teaching Online experimentation Assessment

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intelligent Decision-Making for 3-Dimensional Dynamic Obstacle Avoidance of UAV Based on Deep Reinforcement learning

Intelligent Decision-Making for 3-Dimensional Dynamic Obstac...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Xiao Han Jing Wang Jiayin Xue Qinyu Zhang College of Electronic and Communication Engineering Harbin Institute of Technology (Shenzhen) Shenzhen China

With the growing utilization of UAV in reconnaissance, agriculture, logistics and entertainment, Autonomous collision avoidance during flight has become a necessary capability for modern UAV to detect the surrounding environment and guarantee their own safety. Autonomous obstacle avoidance is a typical agent decision-making problem. Unfortunately, existing traditional decision-making methods perform poorly in this specific realm, In particular, it is unable to meet the requirements of three-dimensional obstacle avoidance of UAV, so we introduce the deep reinforcement learning (DRL) technique into autonomous obstacle avoidance. We model the obstacle avoidance process as a Markov Decision Process and introduce a structure composed of double joint neural network estimators as the decision-maker, whose input is omnidirectional sonar readings and whose output is a value function estimating future rewards. Also, we propose an adaption in the procedure of memory replay to optimize the sampling, where we assign weights to the transitions and sample them accordingly. Our method is applied in a 3-dimensional physic environment, which contains both random dynamic obstacles and floating bouncing obstacles. the goal of the drone is to reach the terminal point without crash. Double Q learning method with priority sampling, by comparison, achieves the most excellent performance in our simulation. Compared with the traditional algorithms, the proposed algorithm not only ensures the quality of decision making, enabling the agent to learn the optimal strategy, but also effectively improves the performance of the task and the efficiency of decision making. Simulation results demonstrate its effectiveness.

关键词： Collision avoidance Unmanned aerial vehicles Neural networks Decision making Machine learning Heuristic algorithms Training

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Developing a sensor based homecare system the role of bluetooth low-energy in activity monitoring 11

Developing a sensor based homecare system the role of blueto...

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11th international conference on Health Informatics, HEALthINF 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Power, Luke Jackson, Lisa Dunnett, Sarah Department of Aeronautical and Automotive Engineering Loughborough University Epinal Way Loughborough United Kingdom

ISBN: (纸本)9789897582813

Home healthcare systems have become a focus of research due to the shifting care requirements of the elderly. Malnourishment, independence and activity are becoming vital metrics when monitoring patient illness. Monitoring devices described in research however express issues in the consistent remote capture of these metrics. this work presents the role of Bluetooth Low-Energy Beacons (BLE) in community based healthcare by examining how passive activity monitoring can assist patients coping with independence and disease management within their homes as an indoor Proximity System (IPS). BLE sensors will be placed on the patient, in their home and on objects of interest (OOI) such as water bottles, kettles and microwaves. Research described in this paper will focus on accuracy of BLE beacon as an IPS for lifestyle monitoring and its application to intelligent healthcare. this is achieved by creating a model of patient care requirements structured using activities of daily living (ADL) which is evaluated using patient activity pattern recognition in captured sensor data. Pattern analysis uses the changing distance values between BLE sensors to determine movement motion and location which contribute to the activity, sensor based care model. Results support efficacy when using BLE beacons as an IPS with patient activity patterns becoming observable through monitoring with a consistent ability to distinguish interactions in activity patterns capture. Future experiments will focus on analysis captured sensor metrics to determine care outcomes. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved efficacy in delivering remote care. this paper will describe the reasoning for exploring BLE (Bluetooth Low-Energy) for IPS (Indoor Proximity System) as an application of intelligent Healthcare.

关键词： Bluetooth

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Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition 11

Linguistically-driven Framework for Computationally Efficien...

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11th international conference on Language Resources and Evaluation (LREC)

作者： Metaxas, Dimitris Dilsizian, Mark Neidle, Carol Rutgers State Univ Dept Comp Sci CBIM 617 Bowser Rd Piscataway NJ 08854 USA Boston Univ Boston Univ Linguist 621 Commonwealth Ave Boston MA 02215 USA

ISBN: (纸本)9791095546009

We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. the novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. this parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL).

关键词： Sign Recognition Model-based Machine learning Computer Vision American Sign Language (ASL)

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data++: An automated Tool for intelligent data Augmentation Using Wikidata

DATA++: An Automated Tool for Intelligent Data Augmentation ...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Waran Taveekarn Chatchanin Yimudom Supisara Sukkanta Steven Lynden Wudhichart Sawangphol Suppawong Tuarob Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand National Institute of Advanced Industrial Science and Technology Tokyo Japan

In the present, technology has become a big influence that impacts the lives of many humans, with artificial intelligence being one of the most influential elements. Creative feature engineering is an important part of machine learning methodology that supports and manipulates existing data to make it work more efficiently by modifying dimensions of data. Pulling useful information from external sources and combining them, however, are cumbersome since data engineers need to manually find external data sources and process them. therefore, the ability to modify and enrich existing data automatically, using external open data sources could prove crucial to data engineers and scientists looking to enrich their datasets. In this paper, we propose a method that automatically augments a given structured dataset, by inferencing relevant dimension from an external data source with respect to the target attribute. Specifically, our proposed algorithm first creates bloom filters for every instance of data items. Such filters are then used to retrieve relevant information from the linked open data source, which is later processed into additional columns in the target dataset. A case study of three real-world datasets using Wikidata as the external data source is used to empirically validate our proposed method on both regression and classification tasks. the experimental results show that the datasets augmented by our proposed algorithm yield correlation improvement of 23.11 % on average for the regression task, and ROC improvement of 86.50% for the classification task.

关键词： Big data Task analysis Machine learning Predictive models Tools Prediction algorithms

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