Reviewerly/exHarmony huggingface dataset:
Optimized path planning of an unmanned vehicle in an unknown environment using the PSO algorithm

Abstract Today, the use of drones has expanded, particularly in high-risk and/or inaccessible environments, or situations where the cost of human resource is high. One of the most important uses of these unmanned vehicles is in rescue fields where they carry instruments and resources, or transfer wounded people. One of the most important discussions in this regard is the issue of routing these cars in an unknown environment. The first step for the vehicle to start its mission is to drive around environmental barriers. Environmental barriers can be divided into two categories. The first category comprises barriers that can be located on a map using satellite imagery and known maps. The second category contains obstacles that the car may encounter while navigating the path and but which have not been anticipated. To solve this problem, this research uses the PSO method to optimize offline routing in an environment with a specified map. The vehicle then may encounter new obstacles when moving on the planned path and identify those obstacles using sensors. In this case, using the neural network algorithm, it can obtain an optimal pseudo-path to circumvent the obstacle. In fact, the issue is divided into two sections. The first issue is the optimal routing with the PSO method, and the second section tackles the problem of dealing with unplanned obstacles using the neural network algorithm.
Sensor‐based activity recognition independent of device placement and orientation

Abstract Human activity recognition (HAR) is a prominent subfield of pervasive computing and also provides context of many applications such as healthcare, education, and entertainment. Most wearable HAR studies assume that sensing device placement and orientation are fixed and never change. However, this condition is actually not always guaranteed in the real scenario and recognition result is influenced by the distortion as consequence. To handle this, our work proposes a new model based on convolutional neural network to extract robust features which are invariant of device placement and orientation, to train machine learning classifiers. We first carry out experiments to show negative effects of this problem. Then, we apply the convolutional neural network–based hybrid structure on the HAR. Results show that our method provides 15% to 40% accuracy promotion on public data set and 10% to 20% promotion on our own data set, both with distortion.
“SHOULD I STAY OR SHOULD I GO?”: A MULTIPLE-CRITERIA GROUP DECISION-MAKING APPROACH TO SME INTERNATIONALIZATION

Due to domestic markets’ current economic conditions, companies increasingly feel that they need to become actively involved in international trade. However, small and medium-sized enterprises (SMEs) typically face financial and intellectual constraints during internationalization processes. This means that decision makers must consider a wide range of different variables before deciding to internationalize firms. This study sought to integrate a well-established problem structuring method (i.e., cognitive mapping) and a multiple-criteria decision analysis (MCDA) technique (i.e., Decision EXpert (DEX)) in order to develop a decision model suitable for the identification and assessment of variables influencing SME internationalization capability. Using data collected during face-to-face meetings with a panel of experts (i.e., SME entrepreneurs and chief executive officers (CEOs)), a constructivist method of evaluating SME internationalization capability was created and tested using real data. The results confirm that the dual methodology adopted facilitates the development of a robust evaluation model that can improve decisionmaking processes in the context in question. Specifically, the proposed model identifies product features as the most important factor in SME capability for successful internationalization. In addition, internal factors are significantly more relevant than external factors. The integrated use of cognitive mapping and DEX provides decision makers with a well-informed perspective on SME internationalization capability. We know of no prior research reporting the use of these two methodologies in the SME internationalization context.
Can women break the glass ceiling?

In October 2017, there happened the uprising of an unprecedented online movement on social media by women across the world who started publicly sharing their untold stories of being sexually harassed along with the hashtag #MeToo (or some variants of it). Those stories did not only strike the silence that had long hid the perpetrators, but also allowed women to discharge some of their bottled-up grievances, and revealed many important information surrounding sexual harassment. In this paper, we present our analysis of about one million such tweets collected between October 15 and October 31, 2017 that reveals some interesting patterns and attributes of the people, place, emotions, actions, and reactions related to the tweeted stories. Based on our analysis, we also advance the discussion on the potential role of online social media in breaking the silence of women by factoring in the strengths and limitations of these platforms.
Software Architecture Design of Cloud Platforms in Automotive Domain: An Online Survey

Software architectures play an important role in the success of cloud platforms in automotive domain. To review the architecture designs in this context, it is necessary to explore different design decisions such as design styles, quality attributes (QAs), and evaluation methodologies. To this end, we aimed to investigate (i) architectural design styles, (ii) major QAs, and (iii) architecture evaluation methods that are applied in the cloud platforms in the automotive domain. We conducted an online survey to collect data from participants in industry and academia. Methodologies, such as descriptive statistics and grounded theory, were used to analyse the data. We collected 42 valid responses from participants with different roles, backgrounds, and years of experience. Considering the survey objectives, (i) event-driven and service-oriented architecture (SOA) were the most applied design styles to fulfil QAs. (ii) Availability, reliability, and security were the major QAs among other attributes. Finally, (iii) active reviews from intermediate design (ARID) and the scenario-based architecture analysis method (SAAM) were mostly applied when evaluating the architecture of cloud platforms in the automotive domain. The results of our survey show a spectrum of different applicable design styles, QAs, and evaluation methods. For selecting the set of architectural design decisions, one should consider the business scenarios and relevant quality requirements that should be supported by the cloud platforms in the automotive domain.
Discriminant Analysis for Radar Signal Classification

Discriminant analysis is a technique used in statistics and machine learning to separate two or more classes of objects or events. We introduce linear, quadratic, and mixture discriminant analysis methods into radar signal classification. However, the selection of an appropriate discriminant analysis method can be difficult and no comparison study of these discriminant analyses for radar signal classification can be found in the open literature. This article presents a theoretical analysis and practical comparison of these three discriminant analysis methods for radar signal classification. The theoretical analysis is derived from Bayes' theorem. The practical comparison study is performed on a dataset consisting of five radars emissions. The advantages and drawbacks of each discriminant analysis method are highlighted. This study demonstrates that quadratic discriminant analysis (QDA) is predominantly a better method for radar signal classification using our radar dataset. On average, it demonstrates 95%, 93%, and 86.6% classification accuracy for three, four, and five radar emitters in our radar dataset, respectively. Linear discriminant analysis (LDA) achieves on average 88.7%, 84.9%, and 79.2% classification accuracy for three, four, and five radar emitters, respectively. Mixture discriminant analysis (MDA) also achieves the same classification performance as QDA. Theoretical analysis shows that both LDA and QDA are a special case of MDA and that MDA can set up more decision boundaries than LDA and QDA if the feature distribution in the dataset is an ensemble of Gaussian distributions. Therefore, MDA is recommended for radar signal classification.
Online Signature Verification Using a Single-template Strategy with Mean Templates and Local Stability-weighted Dynamic Time Warping

This study proposes a novel single-template strategy that uses mean templates and local stability-weighted dynamic time warping (LS-DTW) as a means of improving the speed and accuracy of online signature verification. Specifically, we adopt a recent time-series averaging method, Euclidean barycenter-based DTW barycenter averaging, to obtain effective mean templates while preserving intra-user variability among reference samples. Then, we estimate the local stability of the mean template set using multiple matching points that detect significant distorted trajectories in the warping paths of DTW. Subsequently, to boost discriminative power in the verification phase, we use the LS-DTW distances that incorporate the local stability sequence as the weights for the cost function of DTW warping between the set of mean templates and a test sample. Experimental results confirm the effectiveness of the proposed method using a common SVC2004 Task2 dataset.
Fall Detection in EHR using Word Embeddings and Deep Learning

Electronic health records (EHR) are an important source of information to detect adverse events in patients. In-hospital fall incidents represent the largest category of adverse event reports. The detection of such incidents leads to better understanding of the event and improves the quality of patient health care. In this work, we evaluate several language models with state-of-the art recurrent neural networks (RNN) to detect fall incidents in progress notes. Our experiments show that the deep-learning approach outperforms previous works in the task of detecting fall events. Vector representation of words in the biomedical domain was able to detect falls with an F-Measure of 90%. Additionally, we made available an annotated dataset with 1,078 de-identified progress notes for replication purposes.
TiDEC: A Two-Layered Integrated Decision Cycle for Population Evolution

Agent-based simulation is a useful approach for the analysis of dynamic population evolution. In this field, the existing models mostly treat the migration behavior as a result of utility maximization, which partially ignores the endogenous mechanisms of human decision making. To simulate such a process, this article proposes a new cognitive architecture called the two-layered integrated decision cycle (TiDEC) which characterizes the individual's decision-making process. Different from the previous ones, the new hybrid architecture incorporates deep neural networks for its perception and implicit knowledge learning. The proposed model is applied in China and U.S. population evolution. To the best of our knowledge, this is the first time that the cognitive computation is used in such a field. Computational experiments using the actual census data indicate that the cognitive model, compared with the traditional utility maximization methods, cannot only reconstruct the historical demographic features but also achieve better prediction of future evolutionary dynamics.
Fractional Tikhonov regularization to improve the performance of extreme learning machines

Extreme learning machine (ELM) is a single-hidden-layer feed-forward neural network in which the input weights linking the input layer to the hidden layer are randomly chosen. The output weights which link the hidden layer to the output layer are analytically determined by solving a linear system of equations and hence is one of the fastest learning algorithms. The Moore–Penrose (MP) generalized inverse is normally employed to obtain the output weights of the neural network. Although the random weight parameters between input and hidden layers are need not be tuned, ELM provides good generalization performance with fast learning speed. In general, the data sets from real-world problems tend to make the linear system of ELM ill-conditioned due to the presence of inconsistent noise levels in the input data which leads to unreliable solutions and over-fitting problems. The regularization techniques are developed to address such issues in ELM and it involves estimation of additional variables termed as a regularization parameter. In this context, the proper selection of the regularization parameter is a crucial task as it is going to decide the quality of the solution obtained from the linear system. Further, the popular choice is the Tikhonov regularization technique which penalizes the ℓ2-norm of the model parameters. In ELM, such inclusion results are giving equal weight to singular values of the matrix irrespective of the noise level present in the data. In the presented work, a fractional framework is introduced in the Tikhonov regularized ELM to weigh the singular values with respect to a fractional parameter to reduce the effect of different noise levels. Moreover, an automated golden-section method is applied to choose the optimal fractional parameter. Finally, the generalized cross-validation method is applied for obtaining the suitable value of the regularization parameter. The proposed strategy of applying fractional Tikhonov regularization to ELM results in improvement of performance when compared with the conventional methods with respect to the performance measures. Finally, the results obtained from the proposed fractional regularization is also shown to be statistically significant.
YewPar

Combinatorial search is central to many applications, yet the huge irregular search trees and the need to respect search heuristics make it hard to parallelise. We aim to improve the reuse of intricate parallel search implementations by providing the first general purpose scalable parallel framework for exact combinatorial search, YewPar.
Moving Target Detection Using Inter-Frame Difference Methods Combined with Texture Features and Lab Color Space

Moving target detection from a video has been considered as one of the most crucial tasks in computer vision. It is originated from the object recognition of still objects. Moving target detection is still far from being completely solved due to complex conditions, such as transformed background and changing light. In this research, some classical moving target detection methods have been summarized, and a new inter-frame difference algorithm for moving target detection is proposed. The proposed method is based on a two-frame-difference method using texture features and color features. The method can be used to detect moving targets under a background with a little perturbation. The experimental results show that combining the texture features and color features can improve the performance in the moving target detection.
Text classification using Fuzzy TF-IDF and Machine Learning Models

The representation of the information has an important impact on the text classification task. Several weighting methods were proposed in the literature, and the term frequency-inverse term frequency (TFIDF), the most know on the text treatment field. The FTF-IDF is a vector representation where the components of the TFIDF are presented as inputs to the Fuzzy Inference System (FIS). In this work, we compare several Machin Learning algorithms such as Naïve Bayes and its derivatives, SVM and Random forest classifiers, using the FTF-IDF representation. To improve the quality of the used classifiers, we call sum attribute selection methods. The recognition rate, for the tested systems, is satisfied, where the system based on naïve Bayes classifier, the FTF-IDF weighting terms, and the info gain select attributes method gives 98.7% as accuracy.
Secure Firmware Update over the Air using TrustZone

In this paper, we propose to use Secure Firmware Update over the air of embedded devices, which use TrustZone technology to securely update new firmware. TrustZone will provide authenticity and security of the firmware to be updated in the device. By using trusted application along with host application, it checks integrity, authenticity and security of firmware. We use a digital signature algorithm to verify the authenticity of the firmware with signature verification, which will be carried out securely in TrustZone environment.
Using Neuroevolved Binary Neural Networks to solve reinforcement learning environments

With the explosive interest in the utilization of Neural Networks, several approaches have taken place to make them faster, more accurate or power efficient; one technique used to simplify inference models is the utilization of binary representations for weights, activations, inputs and outputs. This paper presents a novel approach to train from scratch Binary Neural Networks using neuroevolution as its base technique (gradient descent free), to then apply such results to standard Reinforcement Learning environments tested in the OpenAI Gym. The results and code can be found in https://github.com/rval735/BiSUNA.
A Survey on Video Summarization Techniques

With the quick development of digital video technology, it is possible to upload large videos to YouTube or any other websites, record huge amount of data as news videos, sports videos, and lecture videos and surveillance videos etc. Storage, transfer and processing of video take considerably large amount of time. The user might not have adequate time to watch enter video before downloading or the user needs the search result of video to be quick and precise. In such cases the highlight or summary of the video makes search and indexing operations fast and user can view the highlight or summary of the video before downloading the video.Video summarization is short summary or highlights of the long video. This work is a detailed study on various video summarization techniques.
Ordering-Based Kalman Filter Selective Ensemble for Classification

This paper investigates Kalman Filter-based Heuristic Ensemble (KFHE), which is a new perspective on multi-class ensemble classification with performance significantly better or at least as good as the state-of-the-art algorithms. We prove that the sample weight tuning method used in KFHE is a version of adaptive boosting, and the weight distribution does not change anymore and leads to redundant classifiers when the algorithm iterates enough times. This motivates us to select a sub-ensemble to alleviate the redundancy and improve the performance of the ensemble. An Ordering-based Kalman Filter Selective Ensemble (OKFSE) is proposed in this paper to select a sub-ensemble using the margin distance minimization approach. We demonstrate the effectiveness and robustness of OKFSE through extensive experiments on 20 real-world UCI datasets, and the statistical test shows that OKFSE significantly outperforms the state-of-the-art KFHE and clustering-based pruning methods on these datasets with 5% and 10% class label noise.
Development Virtual Reality IPA (VR-IPA) learning media for science learning

Abstract The science laboratory facilities in schools are inadequate. Therefore, the researcher developed a science learning media by utilizing virtual reality technology in the form of application that runs on smartphone. The objectives of this study are: (1) to determine the validity of VR-IPA media; (2) to determine the practicality of VR-IPA media from the science teachers; and (3) to determine students’ responses to VR-IPA media. This media development used the ADDIE development model. The instruments used were media validation sheets, science teachers’ assessment sheets, and student response sheets. The result shows that: (1) the validity of VR-IPA media is in very good category; (2) the practicality of VR-IPA media is in very good category; and (3) the students’ responses to VR-IPA media are very good. In brief, it can be concluded that VR-IPA media is suitable for use in science learning.
Breast Cancer Detection Using Image Processing Techniques

Cancer is the uncontrolled multiplication of group of cells in a particular location of the body and is the second largest disease leading to the death of women in the world. The disease can be cured if it is detected in early stages. A lot of research has been done to find out the tumor correctly but a 100% accurate method has not been found. Research on breast cancer detection using digital image processing is not new but many new approaches in this field is being considered to accurately predict the tumor region. The present approach is to detect the tumor region visually as well as to figure out in which region the tumor is mostly concentrated. This work majorly focuses on finding out the best algorithm/s to detect the tumor present in the breast. In the proposed work, a variety of algorithms has been applied but the best one suited for cancer detection is the combination of K Means, Closing, Dilation and Canny Edge Detection algorithm.
Adaptive Variational Model for Contrast Enhancement of Low-Light Images

Contrast enhancement plays an important role in image/video processing and computer vision applications. Its main purpose is to adjust the image intensity to enhance the quality and features of the image. In this paper, we propose a simple and efficient adaptive variational model for contrast enhancement for partially shaded low-light images. The key idea of this adaptive approach is to employ the maximum image of the RGB color channels as a classifier to divide the image domain into the relatively bright and dim parts, and then use different fitting terms for each part such that the bright pixels are preserved as close as possible to the original ones while the dim pixels are boosted with brightness and contrast-level parameters to adjust the degree of the strength. With this adaptivity, one can find that the proposed model considerably improves upon the existing variational models in the literature. In this paper, the existence and uniqueness of the minimizer for the variational minimization problem is established. The split Bregman method is used to accomplish an efficient numerical implementation of the adaptive variational model. Moreover, a number of numerical experiments and comparisons with other popular enhancement methods are conducted to demonstrate the high performance of the newly proposed method.
Dual-mode dynamic window approach to robot navigation with convergence guarantees

In this paper, a novel, dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive control framework. The planned path adds information on the connectivity of the free space to the obstacle avoidance capabilities of the dynamic window approach. This allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds. The framework is applied in a combined simulation/hardware implementation to demonstrate the computational feasibility and the ability to cope with the constraints of a dynamic system.