Reviewerly/exHarmony huggingface dataset:
Cryptography‐based deep artificial structure for secure communication using IoT‐enabled cyber‐physical system

Internet of things (IoTs) enabled cyber-physical systems is a system that provides communication between physical devices and cyber environment. They run independently without any user interaction. Because the IoT devices are vulnerable to a variety of attacks, security is a noteworthy factor in the development process during communication. To improve secure communication with minimum time consumption, a novel technique called jackknife regressive Schmidt Samoa cryptography-based deep artificial structure learning (JRSSC-DASL) is introduced. Initially, the data is monitored by IoT devices and is collected from the dataset. The proposed deep artificial structure learning technique trains the gathered data with multiple layers. Then, the collected data is analysed in the first hidden layer with the help of the jackknife regression function by learning the feature and it classifies the data with higher accuracy. The classified data is sent to the next hidden layer where encryption is performed using Schmidt Samoa (SS) encryption algorithm. Then, the encrypted data is sent to the cloud server where the decryption is performed using the SS decryption algorithm. The cloud server obtains the original data and it is stored in their database for further processing. This process enhances the security of data communication and achieves high data confidentiality with less processing time. Experimental estimation is performed on the factors such as classification accuracy, confidentiality rate, processing time and memory usage to the number of data sensed from IoT device. Conferred results reveal that the proposed JRSSC-DASL technique has high confidentiality rate and minimum processing time as well as memory usage when compared to state-of-the-art methods.
Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads

European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.
A novel intuitionistic fuzzy soft set based colonogram enhancement for polyps localization

Abstract This article represents a colonogram enhancement approach using intuitionistic fuzzy set (IFS) and fuzzy soft set (FSS) to improve the visual quality and highlight the local details in enhanced images without artifacts. The proposed method has the advantages of IFSs and fuzzy soft sets (FSSs) which can deal with gray‐level ambiguity in colonoscopy images. The combination of IFS and FSS works on intricate color variations with an adaptive parametric maps. First, the S‐membership function has been applied on the images to map into intuitionistic fuzzy space. We obtain intuitionistic fuzzy image from source image followed by decomposition of several blocks. Thereafter, the FSS is employed to get subsequent intuitionistic fuzzy soft matrix from individual image block. The FSS utilizes a class of parametric coefficients to obtain the hesitant score of individual pixels in each block through the soft‐score measure (SSM). Finally, the proposed method intensifies each membership degree in all blocks in the fuzzy plane using SSM and achieves an enhanced colonogram via defuzzification. Extensive experiment involving large data sets shows that the designed method exhibits better performance in contrast enhancement and visual quality improvement of colonograms for malformation detection (such as a polyps, malignant precancerous cells) in comparison with the state‐of‐art methods.
Relationship between the Metadata and Relevance Criteria of Scientific Data

The purpose of this study is to explore the information retrieval process in scientific data and to better understand the concepts and internal relationships of metadata and relevance criteria. Qualitative and quantitative analyses were performed using interview and eye movement data from 36 subjects. The results show that users paid attention to 45 types of metadata and used nine relevance criteria to judge the relevance of scientific data. There was a complex relationship between the metadata and criteria, mainly manifesting as one stimulus–multiple responses and multiple stimuli–one response. Metadata associated with the relevance criterion of topicality is the most complex, which includes common metadata and subject-related metadata. Metadata associated with the other relevance criteria (such as quality and authority) has no obvious professional characteristics. What's more, because of the essential difference between scientific data and documents, users use different criteria. When retrieving data, users pay more attention to the availability of data and whether they can be further analyzed and processed. This study clarifies the concepts of metadata and relevance criteria as well as their roles in relevance judgments. In addition, this study deepens the understanding of the scientific data relevance judgments and their cognitive process and provides a theoretical basis for improving scientific data-sharing platforms.
Rowing Simulation using Rower Machine in Virtual Reality

Virtual Reality (VR) is a computer technology that simulates the 3D environment and commonly used to experience an immersive environment. Simulation is a recreation of the real-world used in VR to allow users to interact and feel the presence. The simulation is also recently popular among sports athletes for training and healthcare. However, some simulation does not give immersive experience, drawbacks in concentration, and user encouragement. Therefore, this project aims to develop a rowing simulation game using VR. The motivation is to prepare a platform for athletes training themselves without feeling the need to travel from one place to another. This project also for non-athlete to experience and enjoy rower machines while playing game simulation in VR. Three phases have been carried out in which the first phase is to analyze the VR and simulation of rower sports. Followed by the second phase, the interface, game element, and collaboration of VR for the rower sport. Finally, the third phase is integrating the rower machine with the tracker to sync the movement and actions. The evaluation performed is based on usability and user acceptance. Based on the results, this project has successfully produced the rowing game simulation using VR, and it has provided realism to the users.
Time-Multiplexed Spiking Convolutional Neural Network Based on VCSELs for Unsupervised Image Classification

In this work, we present numerical results concerning a multilayer “deep” photonic spiking convolutional neural network, arranged so as to tackle a 2D image classification task. The spiking neurons used are typical two-section quantum-well vertical-cavity surface-emitting lasers that exhibit isomorphic behavior to biological neurons, such as integrate-and-fire excitability and timing encoding. The isomorphism of the proposed scheme to biological networks is extended by replicating the retina ganglion cell for contrast detection in the photonic domain and by utilizing unsupervised spike dependent plasticity as the main training technique. Finally, in this work we also investigate the possibility of exploiting the fast carrier dynamics of lasers so as to time-multiplex spatial information and reduce the number of physical neurons used in the convolutional layers by orders of magnitude. This last feature unlocks new possibilities, where neuron count and processing speed can be interchanged so as to meet the constraints of different applications.
Experimental comparison of various excitation and acquisition techniques for modal analysis of violins

For decades, modal analysis has been a tool to gain insights in the vibrational and acoustical behavior of music instruments. This study provides a critical comparison of various experimental modal analysis approaches with the violin as a case study. Both contact and non-contact excitation and different acquisition approaches are considered. The influence of different boundary conditions (clamped, supported, free/free) on the vibrational response of the violin is investigated. The response is analyzed to extract relevant modal parameters (frequency, damping and mode shape) within the frequency range of 0–1000 Hz. The performance of the different approaches is evaluated in terms of (i) signature vibration modes, (ii) experimental reproducibility, (iii) contact requirement and (iv) cost of required equipment. Two optimal approaches are proposed for performing modal analysis on violins: a contact method for use in the violinmaker's practice, and a non-contact method for use with fragile instruments.
Multiperspective Progressive Structure Adaptation for JPEG Steganography Detection Across Domains

The aim of steganography detection is to identify whether the multimedia data contain hidden information. Although many detection algorithms have been presented to solve tasks with inconsistent distributions between the source and target domains, effectively exploiting transferable correlation information across domains remains challenging. As a solution, we present a novel multiperspective progressive structure adaptation (MPSA) scheme based on active progressive learning (APL) for JPEG steganography detection across domains. First, the source and target data originating from unprocessed steganalysis features are clustered together to explore the structures in different domains, where the intradomain and interdomain structures can be captured to provide adequate information for cross-domain steganography detection. Second, the structure vectors containing the global and local modalities are exploited to reduce nonlinear distribution discrepancy based on APL in the latent representation space. In this way, the signal-to-noise ratio (SNR) of a weak stego signal can be improved by selecting suitable objects and adjusting the learning sequence. Third, the structure adaptation across multiple domains is achieved by the constraints for iterative optimization to promote the discrimination and transferability of structure knowledge. In addition, a unified framework for single-source domain adaptation (SSDA) and multiple-source domain adaptation (MSDA) in mismatched steganalysis can enhance the model's capability to avoid a potential negative transfer. Extensive experiments on various benchmark cross-domain steganography detection tasks show the superiority of the proposed approach over the state-of-the-art methods.
An augmented reality based Virtual dressing room using Haarcascades Classifier

Virtual dressing rooms are modern way of shopping where customers experience realistic dress fitting and order online. As trying different dresses at shopping centers is very time consuming and customers are not sure how the dress looks on them. Therefore, there is a need to identify efficient methods to improve virtual dressing rooms and increase e-commerce businesses. All the existing virtual dressing room methods required Kinect sensors for virtual dress fitting and Kinect sensors are expensive. Our motivation is to create such virtual dressing room that overcome the constraint of Kinect sensor input. We present a dynamic virtual dressing room based on web cam to acquire input video and allow user to experience virtual dress fitting. Our proposed model comprised of following steps including face, skin, lower body detection and dynamic distance estimation for precise virtual reality. We have evaluated our algorithm on 50 subjects using 10 dresses.
Dissimilarity Random Forest Clustering

In this paper we present DisRFC (Dissimilarity Random Forest Clustering), a novel Random Forest Clustering approach which, contrarily to current methods which require in input a vectorial representation, works only with dissimilarities, thus being applicable also to all those problems where a vectorial representation is not available but a descriptive dissimilarity measure can be computed. In the DisRFC approach objects to be clustered are first modelled with a novel RF variant called Unsupervised Dissimilarity Random Forest (UD-RF), which functioning mechanisms are both unsupervised and based on dissimilarities. The trained UD-RF is then used to project objects in a binary vectorial space, where effective K-means procedures can be used to obtain the final clustering. In the paper we present different variants of DisRFC, thoroughly and positively evaluated using 10 different problems.
Improved bag-of-features using grey relational analysis for classification of histology images

Abstract An efficient classification method to categorize histopathological images is a challenging research problem. In this paper, an improved bag-of-features approach is presented as an efficient image classification method. In bag-of-features, a large number of keypoints are extracted from histopathological images that increases the computational cost of the codebook construction step. Therefore, to select the a relevant subset of keypoints, a new keypoints selection method is introduced in the bag-of-features method. To validate the performance of the proposed method, an extensive experimental analysis is conducted on two standard histopathological image datasets, namely ADL and Blue histology datasets. The proposed keypoint selection method reduces the extracted high dimensional features by 95% and 68% from the ADL and Blue histology datasets respectively with less computational time. Moreover, the enhanced bag-of-features method increases classification accuracy by from other considered classification methods.
Deep learning methodology proposal for the classification of erythrocytes and leukocytes

Improving diagnostics has been one of the most important challenges facing health systems in recent years, due to innovation and scientific research, image classification techniques, medical information management systems brought about by health technology, and developments never seen before that enable major breakthroughs in the areas of prevention, early disease detection, and disease control. Blood is investigated through blood count, which is subordinate to manual and/or automated procedures. This reliance on medical fields for new innovations directs this chapter to the development of a deep learning framework for classifying white cell subtypes in digital images that fits the criteria for reliability and efficiency of blood cell detection, making the methodology more accessible to diverse populations. Because laboratory medical examinations are often costly and inaccessible to populations of underdeveloped and developing countries, it is of great importance to create tools that facilitate the obtaining of medical reports at low cost and with high reliability. For this, Python using a Jupyter notebook was developed and experiments were conducted using a dataset containing 12,500 digital images of human blood smear fields comprising nonpathological leukocytes. As a result, the accuracy of the approach was 86.17%, demonstrating the high reliability of the methodology. Therefore the algorithm is considered a reliable, accurate, and inexpensive method and can be employed as the third most practicable procedure for blood counts for often underprivileged people of developing and underdeveloped countries.
Fine-grained correlation analysis for medical image retrieval

Feature fusion in medical image retrieval remains a challenging task because of the high-dimensional data and massive amount of irrelevant information in images. To solve these issues, we propose a novel feature fusion method, called fine-grained correlation analysis (FGCA), for medical image retrieval. First, we analyze the problem that there are many irrelevant local regions in a category. To solve this problem, an image is partitioned into some fine-grained samples. Then, the fine-grained samples with similar characteristics are tagged with the same label by the k-means clustering algorithm. Finally, we investigate how the correlation relationship extracted from the fine-grained samples helps fuse different features and obtain the more discriminative and less redundant information for medical image retrieval. Experiments on three medical image datasets show that our proposed FGCA approach works better than the conventional methods.
<b>CSim</b> <sup> <i>2</i> </sup>

To make feasible and scalable the verification of large and complex concurrent systems, it is necessary the use of compositional techniques even at the highest abstraction layers. When focusing on the lowest software abstraction layers, such as the implementation or the machine code, the high level of detail of those layers makes the direct verification of properties very difficult and expensive. It is therefore essential to use techniques allowing to simplify the verification on these layers. One technique to tackle this challenge is top-down verification where by means of simulation properties verified on top layers (representing abstract specifications of a system) are propagated down to the lowest layers (that are an implementation of the top layers). There is no need to say that simulation of concurrent systems implies a greater level of complexity, and having compositional techniques to check simulation between layers is also desirable when seeking for both feasibility and scalability of the refinement verification. In this article, we present CSim 2 a (compositional) rely-guarantee-based framework for the top-down verification of complex concurrent systems in the Isabelle/HOL theorem prover. CSim 2 uses CSimpl, a language with a high degree of expressiveness designed for the specification of concurrent programs. Thanks to its expressibility, CSimpl is able to model many of the features found in real world programming languages like exceptions, assertions, and procedures. CSim 2 provides a framework for the verification of rely-guarantee properties to compositionally reason on CSimpl specifications. Focusing on top-down verification, CSim 2 provides a simulation-based framework for the preservation of CSimpl rely-guarantee properties from specifications to implementations. By using the simulation framework, properties proven on the top layers (abstract specifications) are compositionally propagated down to the lowest layers (source or machine code) in each concurrent component of the system. Finally, we show the usability of CSim 2 by running a case study over two CSimpl specifications of an Arinc-653 communication service. In this case study, we prove a complex property on a specification, and we use CSim 2 to preserve the property on lower abstraction layers.
A design of fuzzy inference systems to predict tensile properties of as-cast alloy

Abstract In this study, a design of Mamdani type fuzzy inference systems is presented to predict tensile properties of as-cast alloy. To improve manufacturing of light weight cast components, understanding of mechanical properties of cast components under load is important. The ability of deterministic models to predict the performance of a cast component is limited due to the uncertainty and imprecision in casting data. Mamdani type fuzzy inference systems are introduced as a promising solution. Compared to other artificial intelligence approaches, Mandani type fuzzy models allow for a better result interpretation. The fuzzy inference systems were designed from data and experts’ knowledge and optimized using a genetic algorithm. The experts’ knowledge was used to set up the values for the inference engine and initial values for the database parameters. The rule base was automatically generated from the data which were collected from casting and tensile testing experiments. A genetic algorithm with real-valued coding was used to optimize the database parameters. The quality of the constructed systems was evaluated by comparing predicted and actual tensile properties, including yield strength, Y.modulus, and ultimate tensile strength, of as-case alloy from two series of casting and tensile testing experimental data. The obtained results showed that the quality of the systems has satisfactory accuracy and is similar to or better than several machine learning methods. The evaluation results also demonstrated good reliability and stability of the approach.
Real-time video super-resolution using lightweight depthwise separable group convolutions with channel shuffling

In recent years, convolutional neural networks (CNNs) have accelerated the developments of video super resolution (SR) for achieving higher image quality. However, the computational cost of existing CNN-based video super-resolution is too heavy for real-time applications. In this paper, we propose a new video super-resolution framework using lightweight frame alignment module and well-designed up-sampling module for real-time processing. Specifically, our framework, which is called as Lightweight Shuffle Video Super-Resolution Network (LSVSR), combines channel shuffling, depthwise convolution and pointwise group convolution to significantly reduce the computational burden during frame alignment and high-resolution frame reconstruction. On the public benchmark datasets, our proposed network outperforms the state-of-the-art lightweight video SR networks in terms of objective (PSNR and SSIM) and subjective evaluations, number of network parameters and floating-point operations. Our network can achieve real-time 540P to 2160P 4× super-resolution for more than 60fps using desktop GPUs or mobile phones with neural processing unit.
Combining Natural Language Processing and Blockchain for Smart Contract Generation in the Accounting and Legal Field

The growth of legislation and the demand for systems for automation of tasks has increased the demand for software development, so that we have greater assertiveness and speed in the reading and interpretation of laws in legal activities. Currently, written legislation must be interpreted by an analyst to be encoded in computer programs later, which is often error-prone. In this context, with the popularization of cryptocurrencies, interest was aroused for the use of Blockchain in the legal area. In particular, the use of smart contracts can house business rules in legislation and automate block control. Still, fast, quality code writing can benefit from the use of natural language processing (NLP) to help developers. After revisiting the state-of-the-art, it is perceived that there are no works that unite intelligent contracts and natural language processing in the context of the analysis of legislation. This work presents a computational model to generate intelligent codes from the analysis of legislation, using NLP and Blockchain for such a procedure. The practical and scientific contribution is pertinent for the law to be interpreted in the correct way and in accordance with the latest updates. Also, a prototype and initial tests are presented, which are encouraging and show the relevance of the research theme.