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Challenge Based learning in the 4IR: Results on the Application of the Tec21 Educational Model in an Energetic Efficiency Improvement to a Rustic Industry 2nd

Challenge Based Learning in the 4IR: Results on the Applicat...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Manuel Reyna-Gonzalez, Juan Ramirez-Medrano, Alicia Membrillo-Hernandez, Jorge Tecnol Monterrey Escuela Ingn & Ciencias Dept Bioingn Campus Ciudad Mexico Mexico City DF Mexico Tecnol Monterrey Writing Lab TecLabs Vicerrectoria Invest & Transferencia Tecnol Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico

ISBN: (纸本)9783030402747;9783030402730

The Fourth Industrial Revolution (4IR) is the result of an integration and compound effects of multiple "exponential technologies", such as artificial intelligence, biotechnology and nanomaterials. The 4IR extends the paradigm of the industrial revolution to a future in which the most familiar exponential technology is the significant increase in the power of the computer and the decrease in the cost of storage. When these exponential digital technologies are combined with other technologies that expand in a similar way (biotechnology, nanotechnology and artificial intelligence), a convergence called "singularity" is created. The 4IR can also allow technological solutions to the environmental threats that arise from the accumulation of CO2 and other greenhouse gases from the massive factories that arise from our first two industrial revolutions. In this article, we report on the results of the application of a challenge based learning teaching technique with students of the Sustainable Development engineering Program of the Tecnologico de Monterrey, Mexico. The challenge consisted on the application of engineering solutions to the energetic improvement of a rural artisan production factory of Mezcal, a very popular beverage in Mexico. In the first instance, the areas of energy opportunity were evaluated. The students determined that the production of mezcal decreases drastically after a few weeks of operation because the water used in the cooling process, which is in a closed circuit, reaches temperatures above 60 degrees C in the tank where it condenses the mezcal The cooling system was modified to increase the mixing and increase the heat transfer coefficient. Additionally a home cooling tower was built in atmospheric natural circulation to control the temperature of the cooling water that is stored in the cistern. With these modifications it was possible to maintain the temperature of the cooling water at 22 degrees C, which increased the production of mezcal, fa

关键词： Tec21 model Educational innovation 4IR Sustainability Higher education CBL

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Basic Study to Reduce the Artifact from Brain Activity data with Auto-regressive Model 22nd

Basic Study to Reduce the Artifact from Brain Activity Data ...

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22nd international conference on Human-Computer Interaction,HCII 2020

作者： Shimizu, Shunji Hori, Masaya Inoue, Hiroaki Kikuchi, Yu Kiryu, Takuya Miwakeichi, Fumikazu Department of Applied Information Engineering Suwa University of Science Chino Japan Graduate School of Engineering and Management Suwa University of Science Chino Japan Shimizu Laboratory Suwa University of Science Chino Japan Department of Statistical Modeling The Institute of Statistical Mathematics Tachikawa Japan

ISBN: (纸本)9783030601270

Near Infra-Red Spectroscopy (NIRS) is possible to measure brain activity signals with low invasive and low physical restraint, so it is also expected as a useful clinical tool in the fields of medicine and education. However, measurement and detection of weak signal of brain activities such as human intention and recall of memory, is very difficult because noise signals such as heartbeat, respiration, and body movement disturb the brain activity signal and data. In this study, we attempt to reduce the effect of the artifacts ingredient by the body movement from the data of NIRS using the Auto- Regressive (AR) model. From the experimental results, we confirmed that the peaks of FFT analysis of brain activity measurement data and ECG data were almost identical. Particularly, characteristic peaks around 0.5 Hz and 1.3 Hz were detected. The peak around 0.5 Hz was effected based on position change of the head inclining. The peak around 1.3 Hz was effected based on the periodical R wave in the ECG data. Also, AR model was estimated from AR coefficients by the above results. So, we could separate the component of artifacts based on heartbeats and separate the component of artifacts based on positioning change of the head inclining, from the brain activity signals. It means that using AR model from the brain activity measurement data by NIRS, we could reduce the effects of artifacts based on heartbeats and reduce the effects artifacts based on position change of the head inclining. © 2020, Springer Nature Switzerland AG.

关键词： Brain

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An Ensemble Classification Approach Using Improvised Attribute Selection

An Ensemble Classification Approach Using Improvised Attribu...

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Information Technology (ACIT)

作者： Muhammad Qasim Memon Shengquan Qu Yu Lu Aasma Memon Abdul Rehman Memon Advanced Innovation Center for Future Education Faculty of Education Beijing Normal University Beijing China University of Sindh Jamshoro Pakistan Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (纸本)9781665419963

The advancement in data mining allows educational data to find patterns, improving the quality of the educational processes. Assessment of students' performance is of great importance for themselves as well as benefitting the educational institutes. In this regard, main attributes are generally proclaimed in the educational data mining (EDM) settings as a significant concern in learning analytics. In this research, we improved the prediction of students' performance. We evaluated the features containing six attributes in several domains: demographic, personal, academic, parental support, psychometric, and learning logs. The prediction of different classification algorithms using ensemble methods affects the accuracy and comprehensibility of the early prediction. To do so, we improvised attributes selection techniques applied to the data containing 11814 students in the biology course. The validation of the classification algorithms achieves better accuracy with ensemble methods. The result performances show the appropriateness of performing prediction and evaluating both filter and wrapper-based methods for feature selection. Our findings also show the students' performance with the most impactful features using ensemble methods and the feasibility of creating a prediction model with a reasonable accuracy rate.

关键词： Predictive models Filtering algorithms Information filters Feature extraction Biology Classification algorithms data mining

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Cyberbullying Detection in Social Networks Using Deep learning Based Models 22nd

Cyberbullying Detection in Social Networks Using Deep Learni...

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22nd international conference on Big data Analytics and Knowledge Discovery (DaWaK)

作者： Dadvar, Maral Eckert, Kai Stuttgart Media Univ Web Based Informat Syst & Serv Nebelstr 8 D-70569 Stuttgart Germany

ISBN: (纸本)9783030590659;9783030590642

Cyberbullying is a disturbing online misbehaviour with troubling consequences. It appears in different forms, and in most of the social networks, it is in textual format. Automatic detection of such incidents requires intelligent systems. Most of the existing studies have approached this problem with conventional machine learning models and the majority of the developed models in these studies are adaptable to a single social network at a time. Recently deep learning based models have been used for similar objectives, claiming that they can overcome the limitations of the conventional models, and improve the detection performance. In this paper, we investigated the findings of a recent literature in this regard and validated their findings using the same datasets as they did. We further expanded the work by applying the developed methods on a new dataset. We aimed to further investigate the performance of the models in new social media platforms. Our findings show that the deep learning based models outperform the machine learning models previously applied to the same dataset. We believe that the deep learning based models can also benefit from integrating other sources of information and looking into the impact of profile information of the users in social networks.

关键词： Deep learning Online bullying Neural networks Social networks Transfer learning YouTube

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Improving System Utilization on Wireless HPC Systems with Torus Interconnects 22

Improving System Utilization on Wireless HPC Systems with To...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Li, Kangkang Qiu, Yitao Jiang, Congfeng Malawski, MacIej Nabrzyski, Jarek Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Agh University of Science and Technology Department of Computer Science Krakow Poland University of Notre Dame Department of Computer Science and Engineering Notre DameIN United States

ISBN: (纸本)9781728176499

Recent advances in wireless technologies promote the concept of wireless HPC systems, in which the wired cable interconnect in a conventional HPC system is augmented with wireless links to conFigure the system for desired interconnection topology. In this paper, we study the problem of improving system utilization and job scheduling efficiency on HPC systems with torus interconnects. First, we design and analyze a novel wireless HPC system to improve system utilization, avoid inter-job communication interference, and achieve efficient job scheduling. Second, we present a topology-Aware job scheduling algorithm, which is applicable to both of our proposed wireless HPC system and a conventional 3D torus HPC system. Third, the simulation results validate the efficiency of our proposed wireless HPC system in improving system utilization. At last, we apply machine learning to quantify the impact of job arrival rate and the number of backfilling jobs on the system utilization of HPC systems with torus interconnects. © 2020 IEEE.

关键词： Scheduling

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Privacy-Preservation in Distributed Deep Neural Networks via Encryption of Selected Gradients 22

Privacy-Preservation in Distributed Deep Neural Networks via...

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作者： Emmanuel, Antwi-Boasiako Zhou, Shijie Liao, Yongjian Liu, Qihe University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9781728176499

In this paper, privacy preservation in Distributed Deep Neural Networks (DDNN) is re-visited. We focus on the use of Homomorphic Encryption (HE) in the DDNN. Although HE provides some level of effective way to bring privacy to the DDNN, its use is usually associated with high overheads, especially communication cost between the Honest-but-Curious (HbC) cloud server and participants. To this end, we propose an alternative approach for privacy preservation in the DDNN. Ability to select and encrypt selected gradients homomorphically, is the main appeal of this paper and has proven to provide good privacy, reduce communication cost to a considerable level (ie. 0.04MB and 0.13MB when 1% and 10% of total gradients are selected, encrypted and shared respectively) whilst maintaining acceptable level of accuracy. A learning with error (LWE) based additive Homomorphic Encryption is used and comparison is made with closely related works to show how efficient our approach is. Furthermore, we propose the use of non-malleable code to protect the integrity of ciphertexts that are transmitted between participants and the HbC cloud server. This paper is the first to combine partial sharing and Homomorphic Encryption in the DDNN. © 2020 IEEE.

关键词： Deep neural networks

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Efficient Binary Static Code data Flow Analysis Using Unsupervised learning 4

Efficient Binary Static Code Data Flow Analysis Using Unsupe...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Obert, James Loffredo, Tim Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9781665434102

The ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. © 2021 IEEE

关键词： data flow analysis

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On Artificial Intelligence for Simulation and Design Space Exploration in Gas Turbine Design 22

On Artificial Intelligence for Simulation and Design Space E...

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ACM/IEEE 22nd international conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Pilarski, Sebastian Staniszewski, Martin Villeneuve, Frederic Varro, Daniel McGill Univ Elect & Comp Engn Montreal PQ Canada Siemens Canada Ltd Montreal PQ Canada Siemens Energy Inc Orlando FL USA

ISBN: (纸本)9781728151250

Gas turbine design is a process that requires designing many interrelated subsystems, e.g., performance, secondary air system, air compression, or combustion. Subsystem models are created by various engineering design tools. During the design process there exists an extraordinary amount of generated data resulting from created models, simulation, and engine field tests. This data can be leveraged by artificial intelligence techniques such as machine learning to help accelerate the exploration of the large design spaces existing in the complex system of a gas engine. This paper presents a vision and road map of integrating such AIs and preliminary ideas on relevant AI models for such use cases. We explore increasing the realistic nature of existing simulations, approximating simulations to avoid excess computation, and cumulative effect modeling.

关键词： Artificial intelligence computer aided engineering computational intelligence intelligent systems internal combustion engines knowledge engineering learning systems machine learning prediction methods

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Multi-Layer Perceptron-based Beamformer Design for Next-Generation Full-Duplex Cellular Systems

Multi-Layer Perceptron-based Beamformer Design for Next-Gene...

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ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Sudip Biswas Umesh Singh Kaustuv Nag Indian Institute of Information Technology Guwahati Guwahati India School of Computational and Integrative Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9781665447324

An in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) radio’s self-interference (SI) and co-channel interference (CCI) cancellation strengths usually determine its performance gains over conventional half-duplex ones. Accordingly, this paper explores an alternative to traditional optimization driven design (ODD) techniques available in the literature for beamformer design in IBFD radios. In particular, to mitigate the residual SI and CCI, we propose a run-time data-driven prediction approach to predict the beamforming matrices at the uplink users and the base station. First, we formulate an ODD-based beamforming design problem, which we structurally optimize through sum-rate maximization, and cast it as a second-order cone programming problem. Then, we repeatedly solve this problem to generate a dataset forming a multiple multivariate regression problem. We use the dataset to train a multi-layer perceptron (MLP) employing a supervised learning scheme to solve the associated regression problem. Experimental results demonstrate that the MLP based beamformer design achieves a near-optimal performance at a remarkably high speed for reasonable residual SI and CCI cancellation without the need for explicit channel estimation.

关键词： Array signal processing Supervised learning Full-duplex system Programming Performance gain Multivariate regression Uplink

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Implementation of EMG Signal Measurement for the Control of the 3D Hand Prototype

Implementation of EMG Signal Measurement for the Control of ...

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international Carpathian Control conference (ICCC)

作者： Agata Nawrocka Marcin Nawrocki Andrzej Kot Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland

The presented work focuses on the development of a measuring stand for the electromyographic signal and the hand clamp force signal. Two approaches to the implementation of the topic have been described - the first one uses a specially prepared prototype board and the second one uses the Arduino Uno module for active synchronization of measurements. The methodology of work on both versions was presented along with the measurement procedure used. The idea was to make a special EMG measurement system with parallel transmission of the clamp force value from the dynamometer. The data obtained in this way are to be used to train a machine learning model determining the value of the clamping force of the hand prosthesis. The paper deals with the issues of prosthesis construction, measurement and data storage methods, as well as the course of further planned works on the project. The presented solution has a chance to introduce methods that realistically reduce the development costs of a hand prosthesis, while improving its functioning. To achieve this, the following assumptions were adopted: the acquisition of the EMG signal using a single MyoWare Muscle Sensor (Sparkfun®) sensor on the superficial flexor of the fingers (Latin Musculus flexor digitorum superficialis), a constant ratio of the number of force samples to the number of electromyogram samples, the use of commonly available electronic components and limitations construction costs.

关键词： Electrodes Three-dimensional displays Force Prototypes Electromyography Time measurement Delays

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