Reviewerly/exHarmony huggingface dataset:
Diagnosis of Diabetes using an Extreme Learning Machine Algorithm based Model

Diabetes is a disorder caused by an increase in blood glucose levels due to insulin secretion deficiency (type 1 diabetes) or impaired insulin activity (type 2 diabetes). More than 90% of people with this condition are diagnosed with type 2 diabetes. Diabetes is now the leading cause of blindness, end-stage renal failure, non-traumatic limb amputations, heart disease, and stroke. Due to the high prevalence of type 2 diabetes in recent years, the prognosis and early diagnosis of the disease have gained much importance. In this paper a study on the types of diabetes is made and a model is proposed and developed for diagnosis of type 2 diabetes using Extreme Learning Machine (ELM) method.
Non-parametric predictive inference for solving multi-label classification

Decision Trees (DTs) have been adapted to Multi-Label Classification (MLC). These adaptations are known as Multi-Label Decision Trees (ML-DT). In this research, a new ML-DT based on the Nonparametric Predictive Inference Model on Multinomial data (NPI-M) is proposed. The NPI-M is an imprecise probabilities model that provides good results when it is applied to DTs in standard classification. Unlike other models based on imprecise probabilities, the NPI-M is a nonparametric approach and it does not make unjustified assumptions before observing data. It is shown that the new ML-DT based on the NPI-M is more robust to noise than the ML-DT based on precise probabilities. As the intrinsic noise in MLC might be higher than in traditional classification, it is expected that the new ML-DT based on the NPI-M outperforms the already existing ML-DT. This fact is validated with an exhaustive experimentation carried out in this work on different MLC datasets with several levels of added noise. In it, many MLC evaluation metrics are employed in order to measure the performance of the algorithms. The experimental analysis shows that the proposed ML-DT based on NPI-M obtains better results than the ML-DT that uses precise probabilities, especially when we work on data with noise.
An Exploratory Study on Automatic Architectural Change Analysis Using Natural Language Processing Techniques

Continuous architecture is vital for developing large, complex software systems and supporting continuous delivery, integration, and testing practices. Researchers and practitioners investigate models and rules for managing change to support architecture continuity. They employ manual techniques to analyze software change, categorizing the changes as perfective, corrective, adaptive, and preventive. However, a manual approach is impractical for analyzing systems involving thousands of artefacts as it is time-consuming, labor-intensive, and error-prone. In this paper, we investigate whether an automatic technique incorporating free-form natural language text (e.g., developers' communication and commit messages) is an effective solution for architectural change analysis. Our experiments with multiple projects showed encouraging results for detecting architectural messages using our proposed language model. Although architectural change categorization for the preventive class is moderate, the outcome for the random dataset is insignificant in general (around a 45% F1 score). We investigated the causes of the unpromising outcome. Overall, our study reveals that our automated architectural change analysis tool would be fruitful only if the developers provide considerable technical details in the commit messages or other text.
Recognizing Non-Manual Signs in Turkish Sign Language

Recognition of non-manual components in sign language has been a neglected topic, partly due to the absence of annotated non-manual sign datasets. We have collected a dataset of videos with non-manual signs, displaying facial expressions and head movements and prepared frame-level annotations. In this paper, we present the Turkish Sign Language (TSL) non-manual signs dataset and provide a baseline system for non-manual sign recognition. A deep learning based recognition system is proposed, in which the pre-trained ResNet Convolutional Neural Network (CNN) is employed to recognize question, negation side to side and negation up-down, affirmation and pain movements and expressions. Our subject independent method achieves 78.49% overall frame-level accuracy on 483 TSL videos performed by six subjects, who are native TSL signers. Prediction results of consecutive frames are filtered for analyzing the qualitative results.
Survey on Anomaly Detection in Wireless Sensor Networks (WSNs)

The development of wireless sensor networks (WSNs) has given rise to smart systems that can be found in almost every industry, ranging from healthcare to transportation. Smart systems enable many organizations to execute their businesses efficiently and effectively in a timely manner. The high number of smart systems that exist in today's society has been made possible by advancements being made in embedded systems and wireless networking. Since data is constantly being collected and analyzed in a WSN application, it's important to ensure that the data being processed is precise, valid, consistent, and accurate. Therefore, in this survey, we are going to be examining the different types of anomalies that exist in WSNs, specifically focusing on data anomalies. In addition, we will also be discussing the different anomaly detection methods and further explore two specific anomaly detection methods, statistical-based and cluster-based, by examining two different implementations. The two different implementations used the temperature data of Laverton, VIC, Australia [14] and produced the same results, verifying the accuracy of the two anomaly detection methods.
Multi-phase trajectory optimization for an aerial-aquatic vehicle considering the influence of navigation error

The environment-induced multi-phase trajectory optimization problem is studied in this paper, and the underwater target tracking task is focused on. The task is finished by an aerial-aquatic coaxial eight-rotor vehicle and is divided into two phases, i.e., the diving phase and the underwater navigation phase. The dynamic model and constraints on angular velocity of rotor in each phase are established to understand the motion characteristic. Then the model of navigation information and terrain matching are contained in the trajectory optimization model to reflect the influence of underwater navigation error on the quality of trajectory. Correspondingly, the forms of collision detection and cost function are changed to adapt to the inaccurate navigation information. To obtain the trajectory with the minimum terminal position error, an improved teach & learn-based optimization (ITLBO) algorithm is developed to strengthen the influence of individual historical optimal solution. Besides, Chebyshev collocation points are applied to determine the locations of control variables. Simulation results demonstrate that the established navigation error-based trajectory optimization model can reflect the real situation of multi-phase task. Especially, it is able to calculate the collision probability between the vehicle and the obstacle when GPS is unavailable underwater, thus ensuring the safety of underwater navigation. Compare to other common effective algorithms, the proposed ITLBO algorithm is in general more suitable for solving this problem because it is swarm-based and can obtain good solution without worrying about the inappropriate values of user-defined parameters.
SparkDA: RDD-Based High-Performance Data Anonymization Technique for Spark Platform

Recent proposals in data anonymization have mostly been focused around MapReduce, though the advantages of Spark have been well documented. To address this concern, we propose a new novel data anonymization technique for Apache Spark. SparkDA, our proposal, takes the full advantages of innovative Spark features, such as better partition control, in-memory process, and cache management for iterative operations, while providing high data utility with privacy. These are achieved by proposing data anonymization algorithms through Spark’s Resilient Distributed Dataset (RDD). Our data anonymization algorithms are implemented at two main data processing RDD transformations, FlatMapRDD and ReduceByKeyRDD, respectively. Our experimental results show that our proposed approach provides required data privacy and utility levels while providing scalability with high-performance that are essential to many large datasets.
Stress Estimation Using Digital Image Correlation with Compensation of Camera Motion-Induced Error

Measurement of stress levels from an in-service structure can provide important and useful information regarding the current state of a structure. The stress relaxation method (SRM) is the most conventional and practical method, which has been widely accepted for measuring residual stresses in metallic materials. The SRM showed strong potential for stress estimation of civil engineering structures, when combined with digital image correlation (DIC). However, the SRM/DIC methods studied thus far have practical issues regarding camera vibration during hole drilling. To minimize the error induced by the camera motion, the imaging system is installed at a distance from the specimen resulting in the low pixel density and the large extent of the inflicted damage. This study proposes an SRM/DIC-based stress estimation method that allows the camera to be removed during hole drilling and relocated to take the after-drilling image. Since the imaging system can be placed as close to the specimen as possible, a high pixel density can be achieved such that subtle displacement perturbation introduced by a small damage can be acquired by DIC. This study provides a detailed mathematical formulation for removing the camera relocation-induced false displacement field in the DIC result. The proposed method is validated numerically and experimentally.
Modern Trends of Engineering Pedagogy Development

The article is devoted to modern trends of engineering pedagogy development that are global due to the processes of globalization and internationalization of higher technical education. The tendencies of engineering pedagogy development as a pedagogical science are connected with the key pedagogy concepts of teaching aims, content, forms, technologies, and aids. The trends of development are determined by the necessity to overcome the contradiction between fast changes in the sphere of engineering activities and certain conservatism, lag effect typical for engineering education system. At present, there are several general trends. Fast changes of modern society require regular analysis of social needs, specification and updating of engineering education aims, which takes place in the conditions of changeability and uncertainty and are more and more based on prediction. New forms of engineers’ training appear in order to overcome the gap between the requirements of engineers’ activities and the results of their education, and they are organized by joint efforts of universities, researchers, industry and business. New technologies of education involving the latest achievements in science, techniques and technologies are being developed and introduced in the educational process. Regular changes in educational process make the engineering education system to some extent unstable and unbalanced, and this trend may be expected to intensify. In order to correspond to the situation and to develop new technical teachers’ competences, various specialized diversified courses are offered to meet specific requirements of particular target groups.
Safe Interoperability for Web of Things Devices and Systems

The Internet of Things (IoT) enables connectivity between devices, thereby allowing them to interact with each other. A recurring problem is the emergence of siloed IoT platforms due to proprietary standards. Recently, the World Wide Web Consortium (W3C) proposed a human-readable and machine-understandable format called Thing Description (TD). It allows to uniformly describe device and service interfaces of different IoT standards with syntactic and semantic information, and hence enables semantic interoperability. However, describing the sequential behavior of devices, which is essential for many cyber-physical systems, is not covered. In this paper, we extend our initial contribution of describing such sequential behavior as an extension within TDs, thereby increasing their semantic expressiveness through possible, valid state transitions. This enables safe and desired operation of devices as well as scalability by modeling systems as sequential compositions of Things. We show in a case study that previously unmodelable behavior can now be expressed and the overall manual intervention requirements of the state-of-the-art implementations can be significantly reduced.
Rethinking the Value of Asynchronous Solvers for Distributed Deep Learning

In recent years, the field of machine learning has seen significant advances as data becomes more abundant and deep learning models become larger and more complex. However, these improvements in accuracy [2] have come at the cost of longer training time. As a result, state-of-the-art models like OpenAI's GPT-2 [18] or AlphaZero [20] require the use of distributed systems or clusters in order to speed up training. Currently, there exist both asynchronous and synchronous solvers for distributed training. In this paper, we implement state-of-the-art asynchronous and synchronous solvers, then conduct a comparison between them to help readers pick the most appropriate solver for their own applications. We address three main challenges: (1) implementing asynchronous solvers that can outperform six common algorithm variants, (2) achieving state-of-the-art distributed performance for various applications with different computational patterns, and (3) maintaining accuracy for large-batch asynchronous training. For asynchronous algorithms, we implement an algorithm called EA-wild, which combines the idea of non-locking wild updates from Hogwild! [19] with EASGD. Our implementation is able to scale to 217,600 cores and finish 90 epochs of training the ResNet-50 model on ImageNet in 15 minutes (the baseline takes 29 hours on eight NVIDIA P100 GPUs). We conclude that more complex models (e.g., ResNet-50) favor synchronous methods, while our asynchronous solver outperforms the synchronous solver for models with a low computation-communication ratio. The results are documented in this paper; for more results, readers can refer to our supplemental website 1.
A Comprehensive Study on Deep Image Classification with Small Datasets

Convolutional Neural Networks (CNNs) showed state-of-the-art accuracy in image classification on large-scale image datasets. However, CNNs show considerable poor performance in classifying tiny data since their large number of parameters over-fit the training data. We investigate the classification characteristics of CNNs on tiny data, which are important for many practical applications. This study analyzes the performance of CNNs for direct and transfer learning based training approaches. Evaluation is performed on two publicly available benchmark datasets. Our study shows the accuracy change when altering the DCNN depth in direct training to indicate the optimal depth for direct training. Further, fine-tuning source and target network with lower learning rate gives higher accuracy for tiny image classification.
Color image segmentation based on hybridization between Canny and k-means

Image segmentation is an important step in any image analysis process. In the literature, there are two dual approaches. The contour segmentation approach consists in locating the object boundaries and the segmentation approach by region consists in partitioning the image into a set of regions. The best results of segmentation are obtained by cooperating these two approaches. They are more effective because the disadvantages of one method can be overcome by the advantages of another method. In an image processing system, the most important operation is image segmentation. To date, there is no universal method of image segmentation. Any technique is only effective for a given type of image, for a given type of application, and in a given computer context. Because of these constraints, the various image processing strategies that have been proposed have asserted their inadequacies and limitations. It is therefore perfectly normal to explore new horizons and find new methods that are more flexible and more effective. In this paper, we will propose and discuss a cooperative approach between a k-means unsupervised classification method and Canny's contour detection method to improve color image segmentation results. First, we will apply the contour segmentation process by Canny's algorithm, and then we integrate the results obtained by another unsupervised classification segmentation process, which is k-means. Experimental results show the strengths of our approach in terms of accuracy, simplicity and efficiency.
Ensemble Tracking Based on Diverse Collaborative Framework With Multi-Cue Dynamic Fusion

Tracking with deep neural networks has been verified to arrive at a new level accuracy in many challenging scenarios, but the tracking robustness has been still challenged by model singularity and self-learning loop mechanism. As a promising solution for the limitations, to ensemble diverse tracking strategies into a highly-interactive framework has shown a potential effectiveness in recent studies. In this work, a collaborative tracking framework is proposed by exploiting both discriminative correlation filters and deep classifiers into an ensembling framework. With a multi-cue dynamic fusion scheme performed on all the ensembled members’ outputs, a robust long-term tracking can be achieved by calculating the optimal robustness scores based on a dynamic weighted sum of multi-cue metrics. Meanwhile, the obtained reliable and diverse training samples are also utilized to adaptively update the tracker in each branch with heuristic frequency, which is able to alleviate the training samples’ contamination and model corruption. Experiments on the OTB-2015, Temple color 128, UAV123, VOT2016, and VOT2018 benchmark datasets have shown superior performance in comparison to other state-of-the-art tracking approaches.
Internet of Things and Blockchain Integration: Use Cases and Implementation Challenges

Research on blockchain (BC) and Internet of things (IoT) shows that they can be more powerful when combined or integrated together. However, the technologies are still emerging and face a lot of challenges. The paper focuses on Internet of things integration with the blockchain technology. We reviewed these technologies and identified some use cases of their combination and key issues hindering their integration. These issues are scalability, interoperability, inefficiencies, security, governance and regulation. While these issues are inherent in the current generations of blockchain such as Bitcoin and Ethereum respectively, with a well-designed architecture, the majority of these issues can be solved in the future generation. This work is inspired by the rapid growth in the number of connected devices and the volume of data produced by these devices and the need for security, efficient storage and processing.
Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Summary With the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.
Effect of Residual Stress on Thermal Deformation Behavior

This paper discusses a non-destructive measurement technique of residual stress through optical visualization. The least amount of deformation possible is applied to steel plates by heating the specimens +10 °C from room temperature for initial calibration, and the thermal expansion behavior is visualized with an electronic speckle pattern interferometer sensitive to two dimensional in-plane displacement. Displacement distribution with the thermal deformation and coefficient of thermal expansion are obtained through interferometric fringe analysis. The results suggest the change in the thermal deformation behavior is affected by the external stress initially applied to the steel specimen. Additionally, dissimilar joints of steel and cemented carbide plates are prepared by butt-brazing. The residual stress is estimated based on the stress dependence of thermal expansion coefficient.
Forward-backward visual saliency propagation in Deep NNs vs internal attentional mechanisms

Attention models in deep learning algorithms gained popularity in recent years. In this work, we propose an attention mechanism on the basis of visual saliency maps injected into the Deep Neural Network (DNN) to enhance regions in feature maps during forward-backward propagation in training, and only forward propagation in testing. The key idea is to spatially capture features associated to prominent regions in images and propagate them to deeper layers. During training, first, we take as backbone the well-known AlexNet architecture and then the ResNet architecture to solve the task of building identification of Mexican architecture. Our model equipped with the "external" visual saliency-based attention mechanism outperforms models armed with squeeze-and-excitation units and double-attention blocks.
A Multi-Trans Matrix Factorization Model With Improved Time Weight in Temporal Recommender Systems

In real-world recommender systems, users' interest and products' characteristics tend to go through a distinct series of changes over time. Thus, designing a recommender system that can simultaneously track the temporal dynamics of both drifts becomes a significant task. However, most of the existing temporal recommender systems only focus on users' dynamics, ignoring changes in products' characteristics. In this study, we propose a Multi-Trans matrix factorization (MTMF) model with improved time weight to capture temporal dynamics. Firstly, we introduce a personalized time weight that combines the forgetting curve and item similarity to reduce the impact of outdated information and retain the influence of users' stable preferences. Then, we model user and item dynamics by learning the multiple transitions at the user-factor and factor-item latent space between the ongoing time period and all past time periods. Accordingly, we formulate a joint objective function and take a gradient-based alternating optimization algorithm to solve this joint problem. Experimental results on historical datasets MovieLens show that the recommendation accuracy of MTMF with improved time weight is superior to the existing temporal recommendation methods.