Reviewerly/exHarmony huggingface dataset:
Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images

The appearance of ships is easily affected by external factors—illumination, weather conditions, and sea state—that make ship classification a challenging task. To facilitate realization of enhanced ship-classification performance, this study proposes a ship classification method based on multi-feature fusion with a convolutional neural network (CNN). First, an improved CNN characterized by shallow layers and few parameters is proposed to learn high-level features and capture structural information. Second, handcrafted features of the histogram of oriented gradients (HOG) and local binary patterns (LBP) are combined with high-level features extracted by the improved CNN in the last fully connected layer to obtain discriminative feature representation. The handcrafted features supplement the edge information and spatial texture information of the ship images. Then, the Softmax function is used to classify different types of ships in the output layer. Effectiveness of the proposed method is evaluated based on its application to two datasets—one self-built and the other publicly available, called visible and infrared spectrums (VAIS). As observed, the proposed method demonstrated attainment of average classification accuracies equal to 97.50% and 93.60%, respectively, when applied to these datasets. Additionally, results obtained in terms of the F1-score and confusion matrix demonstrate the proposed method to be superior to some state-of-the-art methods.
Comparison of a Gamified and Non-Gamified Virtual Reality Training Assembly Task

By using simulations in virtual reality (VR), people have the chance to train without supervision in a safe and controlled environment. VR simulation training allows users to gain new skills and apply them to real-life situations. However, the learning curve of this technology from a novice level could influence the expected learning results of a training session. A training approach based on the combination of VR and gamification could speed up this overall learning process and not just for a novice. In this paper we evaluate how gamification in a VR training session can improve the efficiency of the training and the accuracy of the task execution in a real-world practical test. In the training scenario of this study, 50 randomly assigned participants were divided into two groups. The groups were assigned to a gamified and a non-gamified version of the same VR training and were then guided through a step-by-step tutorial outlining how to solve an assembly task. Performance differences were evaluated based on time taken and specific errors made during the training session. The results of this study show, in general, that beneficial effects can be attributed to the use of gamification in the conducted VR training simulation, particularly for the VR novice participants.
Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

In recent years, background subtraction techniques have been used in vision and image applications for moving target detection. However, most methods cannot provide fine results due to dynamic backgrounds, noise, etc. The Gaussian mixture model (GMM) is a background modeling method commonly used in moving target detection. The traditional GMM method is vulnerable to noise interference, especially from dynamic backgrounds; thus, its detection performance is not good. Because of the influence of background noise and dynamic effects on moving target detection, we propose a method of moving target detection for dynamic backgrounds based on improved GMM background subtraction. This method can be divided into three stages. First, in the background modeling stage, to facilitate calculation and improve modeling speed, the video frame is blocked, and the background model is reconstructed using the image block averaging method. Second, in the moving target detection stage, the method of combining wavelet semi-threshold function denoising with mathematical morphology closed operation is used for denoising, which effectively eliminates the influence of noise and improves the detection effect. Third, in the background updating stage, the adaptive background updating method is used to update the background to improve detection results. The simulation results show that the improved method can reduce noise and dynamic background interference while improving moving target detection, thereby proving the effectiveness and adaptability of the proposed method.
Evaluating the factors that influence cloud technology adoption—comparative case analysis of health and non-health sectors: A systematic review

Cloud technology has brought great benefits to the health industry, including enabling improvement in the quality of services. The objective of this review study is to investigate the reported factors affecting the adoption of cloud in the health sector by comparing studies in the health and non-health sectors. This article is a systematized review of studies conducted in 2018. From 541 articles, 47 final articles were selected and classified into two categories: health and non-health studies; conclusions were drawn from the two sectors by comparing their effective factors. Based on the results of this review, the factors were categorized as technological, organizational, environmental, and individual. The results of this review study could be a beneficial guide to the health empirical research on cloud adoption. Individual domains have not been examined in health sector studies. Since the process of adoption of new technologies in organizations is time-consuming, due to the lack of managerial knowledge about the efficient factors, recognition of these factors by decision-makers while planning for cloud adoption becomes of great importance. The findings of this review study aim to help health decision-makers by increasing their awareness of the cloud and of the factors that impact decisions at both the organizational and individual levels.
Estimating and Synthesizing QoE Based on QoS Measurement for Improving Multimedia Services on Cellular Networks Using ANN Method

A quarter of the world population uses the smartphones to access the Internet and various types of multimedia services on the cellular networks, leading to the importance of focusing on user-centric approach, based on the Quality of Experience (QoE) metric, to measure the business success for the Mobile Network Operator (MNO). Although, the quality of the network can be improved with the help of Quality of Service (QoS), but it does not indicate the user satisfaction. Therefore, it is of vital importance to use the QoE along with the QoS parameters to evaluate and improve the quality of the network. In this paper, we propose the QoE modelling, based on the QoS parameters, by using the Artificial Neural Network (ANN) method to evaluate and synthesize the QoE in the actual environment, with the help of Drive Tests. The relationship between the QoS parameters and Opinion Score (OS) has been analyzed and investigated, prior to the selection of QoS parameters for the creation of QoE model and the process of parameter synthesis. The datasets of QoS parameters and OS have been collected with the help of end devices and the subjective evaluation method from the group of defined users, for each of the following multimedia services, i.e., YouTube, Facebook, Line, and the Web browser, in the real environment. The human behavior can be efficiently learned by using the properties of ANN from the collected datasets, to generate the QoE model along with the estimation of QoE score, instead of using the real humans. The results obtained from the process of parameter synthesis have been used as the main guiding paradigm for improving the performance of the networks in terms of user-centric approach based on the QoS parameters.
An optimized technique for brain tumor classification and detection with radiation dosage calculation in MR image

The tumor cell leads brain to abnormal growth birth. The Brain Tumors (BT) leads to damage or affected brain if it is not predicted early stage and rectified properly with proper treatment. The proper treatment as advised by the physician need to be followed based on the size of the tumor and its position. The accurate finding of tumor position and size is the difficult task. In recent years to structures of the body in internal position had seen detailed, the Magnetic Resonance (MR) technique if the radiology used in medical imaging. This paper describes a novel approach for BT MLTS-HSO segmentation and extracts the features then classified with different classifiers (KNN, DSVM, NB, and RBFN) for MR BT images. The present approach is SVM, NN, and ANFIS where the seed point is selected on a scale based and detected the tumor region and also compute the performance metrics and radiation dosage. Thus the proposed system is the ability of the calculate size and position of the tumor. It has more accurate prediction of the required surgery and other therapy procedures.
Formation of SQL from Natural Language Query using NLP

Today, everyone has their own personal devices that connects to the internet. Every user tries to get the information that they require through internet. Most of the information is in the form of a database. A user who wants to access a database but having limited or no knowledge of database languages faces a challenging and difficult situation. Hence, there is a need for a system that enables the users to access the information in the database. This paper aims to develop such a system using NLP by giving structured natural language question as input and receiving SQL query as the output, to access the related information from the railways reservation database with ease. The steps involved in this process are tokenization, lemmatization, parts of speech tagging, parsing and mapping. The dataset used for the proposed system has a set of 2880 structured natural language queries on train fare and seats available. We have achieved 98.89 per cent accuracy. The paper would give an overall view of the usage of Natural Language Processing (NLP) and use of regular expressions to map the query in English language to SQL.
Single-camera single-axis vision method applied to measure vibrations

Increased interest has been witnessed for full-field techniques measuring vibrations. 3D vision methods coupled to two high-speed cameras have proven to be a valid solution to measure 3D displacements, notably with the Stereo Digital Image Correlation (SDIC) tools. The now conventional pseudo-stereo system with a single high-speed camera and a four-mirror adapter, generating two virtual cameras, may also be used, even if it is rather complex to operate and remains limited to small objects. In a logic of simplification of the protocol, the authors present here a set-up requiring a single high-speed camera and no mirrors, with the associated full-field single-axis vision method. The latter is logically designed to measure the vibrations of items whose displacements are locally along a single axis (usually normal to the surface). This paper reports firstly the results of the full-field measurement of the vibrations of a plate, validated by a comparison with those obtained with the four-mirror adapter set-up, and secondly the application to the full-field measurement of the vibrations of a tambourine. Thirdly, the conventional pseudo-stereo technique and the proposed vision method are compared and assessed, in order to establish their respective limits and potential complementarity. Finally, once the application of the method to plane objects is validated, measurements have been carried out on a non-planar object: a bent plate; the results obtained highlight the possibility of using the proposed approach for non-planar surfaces.
MBPA: A Medibchain-Based Privacy-Preserving Mutual Authentication in TMIS for Mobile Medical Cloud Architecture

Telecare Medical Information System (TMIS) integrates various types of mobile devices and communication technologies to upgrade the traditional face-to-face medical treatment model to intelligent one, which can provide the flexible and convenient e-health care.Due to the complexity and openness of Internet, e-health care data is grabbing the interest of cyber attackers.Hence, security and privacy are still our dominant concerns.Fortunately, blockchain technology leverages decentralized or distributed process to ensure data security.A MediBchain-based privacy-preserving mutual authentication for mobile medical cloud architecture (abbreviated to MBPA) is proposed in this paper.MBPA scheme not only mitigates the weaknesses of existing ones, but has other advantages.First, MBPA scheme supports patients anonymity and traceability since the patient's identity is hidden in two dynamic anonyms and a static anonym and only the trusted center can recover his/her real identity.Second, each MediBchain node shares a secret value, which realizes authentication with extremely low computional cost between terminals and MediBchain nodes.Finally, MBPA scheme is proven safely against passive and active attacks under elliptic curve computational Diffie-Hellman problem (ECDHP) assumption in random oracle model.Hence, these features make MBPA scheme very suitable for computation-limited mobile devices compared with other related existing schemes.
Personalized image quality assessment with Social-Sensed aesthetic preference

Personalization is emerging as a key research objective for image aesthetics assessment, and how to incorporate personal preferences into aesthetics models is a crucial issue to be solved. Prior studies usually require users to explicitly express their aesthetic preferences in certain ways, which are time-consuming and labor-intensive. In this paper, inspired by the observation that human cognition and behavior influence each other, we propose to sense user aesthetic preferences from their favoring behavior on social media platforms. In this manner, personalized image aesthetics assessment can be realized without adding any extra burden to users. Towards this goal, we gather user favoring behavior over professional social photos and consider both user personal preference and common aesthetic standard to deal with the unreliability of user favoring behavior. Besides, we follow the idea of collaborative filtering and optimize the pairwise ranking between images to alleviate the data sparsity problem. Finally, a deep neural network architecture is developed for personalized aesthetics modeling. A simulated evaluation is carried out on two benchmark aesthetics datasets, even though users’ true preferences cannot be directly observed. The results demonstrate the potential of our approach for personalized image aesthetics assessment.
A combined entropy-based approach for a proactive credit scoring

Lenders, such as credit card companies and banks, use credit scores to evaluate the potential risk posed by lending money to consumers and, therefore, mitigating losses due to bad debt. Within the financial technology domain, an ideal approach should be able to operate proactively, without the need of knowing the behavior of non-reliable users. Actually, this does not happen because the most used techniques need to train their models with both reliable and non-reliable data in order to classify new samples. Such a scenario might be affected by the cold-start problem in datasets, where there is a scarcity or total absence of non-reliable examples, which is further worsened by the potential unbalanced distribution of the data that reduces the classification performances. In this paper, we overcome the aforementioned issues by proposing a proactive approach, composed of a combined entropy-based method that is trained considering only reliable cases and the sample under investigation. Experiments done in different real-world datasets show competitive performances with several state-of-art approaches that use the entire dataset of reliable and unreliable cases.
Intuitionistic fuzzy control of twin rotor multiple input multiple output systems

This paper proposes an intuitionistic fuzzy control method for twin rotor multi-input and multi-output (twin rotor MIMO) systems. Twin rotor MIMO systems are often used to measure the performance of control systems as they are extremely sensitive to environmental factors. The use of the intuitionis tic fuzzy control method for modeling these uncertainties offers an effective way to increase the robustness of the control system to uncertainties in the structure of twin rotor MIMO systems. In this study, two intuitionistic fuzzy controllers are designed, namely for the main and tail rotors separately and then combine the outputs of these rotors. Also, this method is compared with the classical optimal PID method in terms of stability and performance by various simulations and experiments.
Bodystorming Human-Robot Interactions

Designing and implementing human-robot interactions requires numerous skills, from having a rich understanding of social interactions and the capacity to articulate their subtle requirements, to the ability to then program a social robot with the many facets of such a complex interaction. Although designers are best suited to develop and implement these interactions due to their inherent understanding of the context and its requirements, these skills are a barrier to enabling designers to rapidly explore and prototype ideas: it is impractical for designers to also be experts on social interaction behaviors, and the technical challenges associated with programming a social robot are prohibitive. In this work, we introduce Synthé, which allows designers to act out, or bodystorm, multiple demonstrations of an interaction. These demonstrations are automatically captured and translated into prototypes for the design team using program synthesis. We evaluate Synthé in multiple design sessions involving pairs of designers bodystorming interactions and observing the resulting models on a robot. We build on the findings from these sessions to improve the capabilities of Synthé and demonstrate the use of these capabilities in a second design session.
Turn-by-Wire: Computationally Mediated Physical Fabrication

Advances in digital fabrication have simultaneously created new capabilities while reinforcing outdated workflows that constrain how, and by whom, these fabrication tools are used. In this paper, we investigate how a new class of hybrid-controlled machines can collaborate with novice and expert users alike to yield a more lucid making experience. We demonstrate these ideas through our system, Turn-by-Wire. By combining the capabilities of a traditional lathe with haptic input controllers that modulate both position and force, we detail a series of novel interaction metaphors that invite a more fluid making process spanning digital, model-centric, computer control, and embodied, adaptive, human control. We evaluate our system through a user study and discuss how these concepts generalize to other fabrication tools.
Ultrasonic Diagnosis of Breast Nodules Using Modified Faster R-CNN

Breast cancer has become the biggest threat to female health. Ultrasonic diagnosis of breast cancer based on artificial intelligence is basically a classification of benign and malignant tumors, which does not meet clinical demand. Besides, the current target detection method performs poorly in detecting small lesions, while it is clinically required to detect nodules below 2 mm. The objective of this study is to (a) propose a diagnostic method based on Breast Imaging Reporting and Data System (BI-RADS) and (b) increase its detectability of small lesions. We modified the framework of Faster R-CNN (Faster Region-based Convolutional Neural Network) by introducing multi-scale feature extraction and multi-resolution candidate bound extraction into the network. Then, it was trained using 852 images of BI-RADS C2, 739 images of C3, and 1662 images of malignancy (BI-RADS 4a/4b/4c/5/6). We compared our model with unmodified Faster R-CNN and YOLO v3 (You Only Look Once v3). The mean average precision (mAP) is significantly increased to 0.913, while its average detection speed is slightly declined to 4.11 FPS (frames per second). Meanwhile, its detectivity of small lesions is effectively improved. Moreover, we also tentatively applied our model on video sequences and got satisfactory results. We modified Faster R-CNN and trained it partly based on BI-RADS. Its detectability of lesions, as well as small nodules, was significantly improved. In view of wide coverage of dataset and satisfactory test results, our method can basically meet clinical needs.
Application of discrete transforms with selective coefficients for blind image watermarking

Abstract Watermarking scheme is not helpful in conveying the original image continually so as identifying the owner's mark from the watermarked image. In the current situation, our chosen topic is significant in real‐time applications like fraud recognition, emergency clinic, and government divisions. In this study, an optimal multiblind watermarking model is proposed for the watermark detection process. Our proposed model is a combination of intelligent domain transforms like Discrete Shearlet Transform and Discrete Curvelet Transform (DCurT). The imperceptibility necessity of the plan is accomplished utilizing optimal coefficients that are performed by applying in DCurT with metaheuristic optimization model that is Grasshopper Algorithm. It is played by a chasing behavior of gathering of grasshoppers and cooperation process, here, Random Grasshopper Optimization is utilized for watermarking. The secret image is embedded with a lower band of optimal coefficients with DCurT, and here, the band is DCur (1, 5). The secret data is embedded in the host images to make it secure, and then, the extraction of the embedding process takes place inversely. For experimentation analysis, 20 digital images are considered, and different attacks are applied in the proposed watermarking model. Thus, the watermarked image looks lossless compared with the host image. Moreover, recent literary works and domain transform are also utilized for strength investigation of the proposed watermarking model.
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?

The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning strategies performed over distinct discrete architecture choices. In the latter case, the optimization is often non-differentiable and also not very amenable to derivative-free optimization methods. Most methods in use today require sizable computational resources. And if we want networks that additionally satisfy resource constraints, the above challenges are exacerbated because the search must now balance accuracy with certain budget constraints on resources. We formulate this problem as the optimization of a set function -- we find that the empirical behavior of this set function often (but not always) satisfies marginal gain and monotonicity principles -- properties central to the idea of submodularity. Based on this observation, we adapt algorithms within discrete optimization to obtain heuristic schemes for neural network architecture search, where we have resource constraints on the architecture. This simple scheme when applied on CIFAR-100 and ImageNet, identifies resource-constrained architectures with quantifiably better performance than current state-of-the-art models designed for mobile devices. Specifically, we find high-performing architectures with fewer parameters and computations by a search method that is much faster.