Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated Active Learning Approach for Understanding Fatigue Theory

It is well known that more than 90% of metal components in mechanical systems fails due to fatigue. With this in mind, a basic working understanding of fatigue theory is very important to mechanical engineering students. Fatigue theory, however, is only covered with a few lectures in a typical undergraduate mechanical engineering program’s curriculum. Typical treatment could be as few as four lectures during two weeks in our mechanical program at Wentworth Institute of Technology. Because of this, some students were typically confused about fatigue theory and might not have a basic working understanding of fatigue theory. Students will typically develop a better understanding of a topic if the same topic is presented to students in different ways. To facilitate the development of a basic working understanding of fatigue theory, we proposed an integrated active learning approach for teaching fatigue theory. This included four different exposures to discuss and explore fatigue theory. These four different approaches were lecturing & homework assignments, physical fatigue testing, FEA simulation of fatigue life of fatigue specimens, and the theoretical calculation of fatigue life of fatigue specimens. After the proposed approach was successfully implemented in the spring semester of 2017, we interviewed some students and conducted a class survey to obtain feedback regarding the approach presented in this paper. Student feedback indicated that students benefited from this integrated active learning approach for teaching fatigue theory. 100 percent of students agreed that they had a much better basic understanding of fatigue theory through this multi-faceted approach. This paper will present and explain in detail the integrated active learning approach for teaching fatigue theory. The class survey data analysis is also presented and analyzed.
Imbalanced Data Optimization Combining K-Means and SMOTE

With the wide application of imbalanced data processing in various fields, such as credit card fraud identification, network intrusion detection, cancer detection, commodity recommendation, software defect prediction, and customer churn prediction, imbalanced data has become one of the current research hotspots. When classifying imbalanced data sets, aiming at the problems of low classification accuracy of negative class samples in the random forest algorithm and marginalization for selecting new samples in the SMOTE algorithm, a new algorithm, KMS_SMOTE, is proposed to deal with imbalanced data sets. In order to avoid the problem of marginalization of new samples, the K-Means algorithm is used to classify the negative class samples to obtain the centroid of the negative class samples, and then the new data set is obtained by selecting the samples near the centroid. Finally, in order to verify the effect of the KMS_SMOTE algorithm, it is compared with the SMOTE algorithm on the data sets of UCI machine learning. The experimental results show that the KMS_SMOTE algorithm effectively improves the classification performance of the random forest algorithm on the imbalanced data set.
COMPARISON OF NEURAL NETWORK AND MULTIVARIATE DISCRIMINANT ANALYSIS IN SELECTING NEW COWPEA VARIETY

Adewole, Adetunji Philip * Department of Computer Science, University of Agriculture, Abeokuta philipwole@yahoo.com Sofoluwe, A. B. Department of Computer Science, University of Lagos, Akoka Agwuegbo , Samuel Obi-Nnamdi Department of Statistics, University of Agriculture, Abeokuta E-mail: agwuegbo_son@yahoo.com ABSTRACT In this study, neural networks (NN) algorithm and multivariate discriminant (MDA) based model were developed to classify ten (10) varieties of cowpea which were widely planted in Kano. In order to demonstrate the validity of our model, we use the case study to build a neural network model using Multilayer Feedforward Neural Network, and compare its classification performance against the Multivariate discriminant analysis. Two groups of data (Spray and Nospray) were used. Twenty kernels were used as training data set and test data to classify cowpea seed varieties. The neural network classified the new cowpea seed varieties based on the information it is trained with. At the end both methods were compared for their strength and weakness. It is noted that NN performed better than MDA, so that NN could be considered as a support tool in the process of selection of new cowpea varieties.
Fast speaker adaptation using extended diagonal linear transformation for deep neural networks

This paper explores new techniques that are based on a hidden‐layer linear transformation for fast speaker adaptation used in deep neural networks (DNNs). Conventional methods using affine transformations are ineffective because they require a relatively large number of parameters to perform. Meanwhile, methods that employ singular‐value decomposition (SVD) are utilized because they are effective at reducing adaptive parameters. However, a matrix decomposition is computationally expensive when using online services. We propose the use of an extended diagonal linear transformation method to minimize adaptation parameters without SVD to increase the performance level for tasks that require smaller degrees of adaptation. In Korean large vocabulary continuous speech recognition (LVCSR) tasks, the proposed method shows significant improvements with error‐reduction rates of 8.4% and 17.1% in five and 50 conversational sentence adaptations, respectively. Compared with the adaptation methods using SVD, there is an increased recognition performance with fewer parameters.
Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.
REMOTE SENSING SCENE CLASSIFICATION USING MULTIPLE PYRAMID POOLING

Abstract. Remote sensing image scene classification has gained remarkable attention, due to its versatile use in different applications like geospatial object detection, ground object information extraction, environment monitoring and etc. The scene not only contains the information of the ground objects, but also includes the spatial relationship between the ground objects and the environment. With rapid growth of the amount of remote sensing image data, the need for automatic annotation methods for image scenes is more urgent. This paper proposes a new framework for high resolution remote sensing images scene classification based on convolutional neural network. To eliminate the requirement of fixed-size input image, multiple pyramid pooling strategy is equipped between convolutional layers and fully connected layers. Then, the fixed-size features generated by multiple pyramid pooling layer was extended to one-dimension fixed-length vector and fed into fully connected layers. Our method could generate a fixed-length representation regardless of image size, at the same time get higher classification accuracy. On UC-Merced and NWPU-RESISC45 datasets, our framework achieved satisfying accuracies, which is 93.24% and 88.62% respectively.
Gearbox housing design – topology optimization through generative design

Detta examensarbete anvander ett systematiskt tillvagagangssatt for att omkonstruera ett vaxelladshus till ett elektriskt fordon med avsikt att forbattra prestanda med avseende pa hallfasthet, livs .
Automated Brain Tumour Segmentation Using Deep Fully Convolutional Residual Networks

Automated brain tumour segmentation has the potential of making a massive improvement in disease diagnosis, surgery, monitoring and surveillance. However, this task is extremely challenging. Here, we describe our automated segmentation method using 2D CNNs that are based on U-Net. To deal with class imbalance effectively, we have used a weighted Dice loss function. We found that increasing the depth of the 'U' shape beyond a certain level results in a decrease in performance, so it is essential to choose an optimum depth. We also found that 3D contextual information cannot be captured by a single 2D network that is trained with patches extracted from multiple views whereas an ensemble of three 2D networks trained in multiple views can effectively capture the information and deliver much better performance. We obtained Dice scores of 0.79 for enhancing tumour, 0.90 for whole tumour, and 0.82 for tumour core on the BraTS 2018 validation set. Our method using 2D network consumes very less time and memory, and is much simpler and easier to implement compared to the state-of-the-art methods that used 3D networks; still, it manages to achieve comparable performance to those methods.
Fake News Identification using Machine Learning

: Fake news is nothing but junk news also we can say hoaxes that consists of disinformation spread via social media. Fake news is written and published usually with the intent to mislead in order to damage an organization or a person. Fake news can be spread via traditional print, broadcast news. Nowadays Controlling of fake news is become important issue. For that various methods are used. It is difficult to minimize 100% hoax from social media, but at least there is a technology which can reduce the hoax in social media. With the help of different classifier one can identify fake news. In this work I used two different classifiers which are SVM classifier and Naive Bayes Classifier With this classifiers I achieved 60.97% accuracy with the SVM classifier and a 59.76% with the naïve bayes classifier. This Paper shows the operational comparison of both the classifiers.
Enhancing Trustability of Android Applications via User-Centric Flexible Permissions

The Android OS market is experiencing a growing share globally. It is becoming the mobile platform of choice for an increasing number of users. People rely on Android mobile devices for surfing the web, purchasing products, or to be part of a social network. The large amount of personal information that is exchanged makes privacy an important concern. As a result, the trustability of mobile apps is a fundamental aspect to be considered, particularly with regard to meeting the expectations of end users. The rigidities of the Android permission model confine end users into a secondary role, offering the only option of choosing between either privacy or functionalities. In this paper, we aim at improving the trustability of Android apps by proposing a user-centric approach to the flexible management of Android permissions. The proposed approach empowers end users to selectively grant permission by specifying (i) the desired level of permissions granularity and (ii) the specific features of the app in which the chosen permission levels are granted. Four experiments have been designed, conducted, and reported for evaluating it. The experiments consider performance, usability, and acceptance from both the end user's and developer's perspective. Results confirm confidence on the approach.
POSDAO: Proof of Stake Decentralized Autonomous Organization

In this paper we introduce POSDAO, a Proof of Stake (POS) algorithm implemented as a decentralized autonomous organization (DAO). It is designed to provide a decentralized, fair, and energy efficient consensus for public blockchains. The algorithm works as a set of smart contracts written in Solidity. POSDAO is implemented with a general purpose BFT consensus protocol such as Authority Round (AuRa) with a proposer node and probabilistic finality, or Honey Badger BFT (HBBFT), leaderless and with instant finality. Validators are incentivized to behave in the best interests of a network through a configurable reward structure. The algorithm provides a Sybil control mechanism for managing a set of validators, distributing rewards, and reporting and penalizing malicious validators. The authors provide a reference POSDAO implementation, xDai DPOS, which uses xDai as a stable transactional coin and a representative ERC20 token (DPOS) as a staking token. The reference implementation functions on an Ethereum 1.0 sidechain and utilizes the AuRa consensus protocol. Assets are bridged between the Ethereum mainnet and the xDai DPOS network using several instances of the POA TokenBridge.
Prediction of Multilateral Inflow Control Valve Flow Performance Using Machine Learning

Intelligent multilateral well completions provide downhole flow rate, pressure, and temperature measurements at multiple well segments, which allows for a continuous spatiotemporal data stream. Such an extensive data input poses a challenging task to decide on the optimal strategy of manipulating the inflow control valve (ICV) settings over time for best performance. In this study, we investigate using machine learning to analyze and predict well performance given different ICV settings to ultimately maximize the well output. A commercial reservoir simulator was used to generate two synthetic reservoir models: homogeneous (Case A) and heterogeneous (Case B). These synthetic data were used to train, validate, and test machine learning models. The reservoir cases were generated on the basis of a segmented, trilateral producer completed with three ICV devices installed at tie-in segments. The data used were measurements of wellhead and downhole flow rates across ICV segments over a period of 4,000 days. A total of 1,330 experiments were conducted with an 8-day timestep, generating a total of 667,660 sample data points for each of Case A and Case B. Fully connected neural networks were used to fit the data, while model generalizability was enhanced using regularization techniques, namely L2 regularization and early stopping. Both random sampling and Latin hypercube sampling (LHS) methods were evaluated in constructing the training, validation, and testing splits. Trained with different sample sizes drawn from the 1,330 simulated data histories for the two reservoir models, the proposed neural network showed excellent results. Given only 10 simulated choices of ICV settings for training, the network proved capable of predicting oil/water production profiles at surface for both homogeneous and heterogeneous reservoir models with greater than 0.95 coefficient of determination (R2) when evaluated at unseen, test ICV settings. Extending the problem to downhole flow performance prediction, approximately 40 training simulated settings were necessary to achieve 0.95 R2. We observed that LHS was superior to random sampling in both R2 average and confidence interval. We also found that increasing the training and validation sample sizes increased the test R2 when testing against unseen cases. Study results suggest the applicability of machine reinforcement learning to estimate the well output at different ICV settings, where the neural network model depends fully on the real-time well feedback and production measurements. By using a machine learning approach during the operation of a well with multiple ICV settings, it would be feasible to estimate the lateral-by-lateral output at unseen scenarios. Hence, it becomes possible to maximize the well output by using an optimization algorithm to determine the optimal ICV settings.
Dewey Decimal Classification Trending Downward in U.S. Academic Libraries, but Unlikely to Disappear Completely

A Review of: Lund, B. , & Agbaji, D. (2018). Use of Dewey Decimal Classification by academic libraries in the United States. Cataloging and Classification Quarterly, 56(7), 653-661. https://doi.org/10.1080/01639374.2018. 1517851 Abstract Objective – To determine the current use of Dewey Decimal Classification in academic libraries in the United States of America (U.S.). Design – Cross-sectional survey using a systematic sampling method. Setting – Online academic library catalogues in the U.S. Subjects – 3,973 academic library catalogues. Methods – The researchers identified 3,973 academic libraries affiliated with degree-granting post-secondary institutions in the U.S. The researchers searched each library’s online catalogue for 10 terms from a predetermined list. From the results of each search, the researchers selected at least five titles, noted the classification scheme used to classify each title, and coded the library as using Dewey Decimal Classification (DDC), Library of Congress Classification (LCC), both DDC and LCC, or other classification schemes. Based on the results of their data collection, the researchers calculated totals. The totals of this current study’s data collection were compared to statistics on DDC usage from two previous reports, one published in 1975 and one in 1996. The researchers performed statistical analyses to determine if there were any discernible trends from the earliest reported statistics through to the current study. Main Results – Collections classified using DDC were present in 717 libraries (18.9%). Adjusting for the increase in the number of academic libraries in the U.S. between 1975 and 2017, DDC usage in academic libraries has declined by 56% in that time frame. The number of libraries with only DDC in evidence is unreported. Conclusion – The previous four decades have seen a significant decrease in the use of DDC in U.S. academic libraries in favour of LCC; however, the rate at which DDC has disappeared from academic libraries has slowed dramatically since the 1960s. There is no clear indication that DDC will disappear from academic libraries completely.
Modeling of Multiple Energy Sources for Hybrid Energy Harvesting IoT Systems

In this article, we present probabilistic energy models for hybrid energy harvesting (HEH) Internet of Things (IoT) nodes. We aim to model the energy obtained from multiple energy sources in a single form with mixture models and to obtain energy characteristics at every stage of HEH. Multiple energy sources are classified into energy source clusters (ESCs), and harvested energies at the output of energy harvesting modules are modeled by using Gaussian mixture models (GMMs). The joint distributions of harvested energies with GMMs are expressed as the sum of the joint densities produced by the possible combinations of component densities. A generalized expression is presented for both independent and correlated ESCs. The distributions of energy levels are obtained based on the joint densities at the output of the energy combiner unit as well as at the energy storage unit. This approach provides a single form and an accurate probabilistic model for each cluster including multiple sources, taking the randomness of ambient energy into account in HEH. Depending on the energy requirements, the characteristics of an energy storage unit can be determined for IoT applications.
SoftSwitch: a centralized honeypot-based security approach usingsoftware-defined switching for secure management of VLAN networks

: Honeypot systems are traps for intruders which simulate real systems such as web, application, and database servers used in information systems. Using these systems, unauthorized and malicious access can be efficiently detected. Honeypot is an entity which acts as a source of valued information and its behavior can be monitored. The inability or difficulty of intrusion detection is a serious security problem in networks including virtual local area network (VLAN). According to the literature, the use of honeypots for intrusion detection and prevention in networks including VLAN is strongly recommended. In this paper, in order to provide security and to detect unauthorized and malicious access to the VLAN, a centralized honeypot-based approach with a software-defined switching is proposed. With the developed and proposed honeypot-based intrusion detection and prevention approach, reduction in false alarm, network traffic, and cybersecurity cost, as well as centralized control, was provided. The proposed system has been run in GNS3 simulation software and successful results have been obtained by reducing false alarm level, network traffic, and cybersecurity cost. The numerical results of the attacks that were detected based on the port and protocol using SoftSwitch are detailed in the performance evaluation subsection.
The use of CIPP model for evaluation of computational algorithm learning program

The objective of this research was to evaluate the learning program of computational algorithm subject in two private universities in Surabaya. The evaluation was performed by using the theoretical framework of CIPP model. There were two research questions presented in this study: what is the research design to evaluate the learning program and what is the profile of the learning style-based computational algorithm learning program according to CIPP model. The results showed that the research design consists of 5 steps, which are: to determine the focus of the research, to collect the data, to test the instruments’ validity and reliability, to analyse the data, and to draw a conclusion. Every research step is formulated based on 4 components, which are: context, input, process, and product. The learning program has several profiles. Firstly, the pre-required environment does not correspond with the characteristics of the population. Secondly, students’ mathematical ability does not meet the qualification set in the lesson plan. Thirdly, the supporting learning facility and instruments are not sufficient. Next, the implementation of the lesson plan is adjusted according to students’ condition. Lastly, the average of students’ computational algorithm scores is 49. It is recommended to conduct matriculation program before the actual learning program for students that do not meet the pre-requirements stated in the lesson plan. The matriculation module consists of the basics of mathematical reasoning, which includes: arithmetic logics, algebraic logics, and mathematical logics.