Reviewerly/exHarmony huggingface dataset:
Estimation of Human Age and Gender Based on LBP Features Using Two Level Decision by SVM

Automated face and age recognition is becoming an important technology due to the huge amount of biometric transactions will be carried and processed daily by Authorization agencies. Now a days Estimation of human age is a very significant characteristic in terms of identity authentication. In order to assess one’s individual age, selection of features from the human face is significant and vital with the perspective of experimental accuracy. Some of the Real world Age estimation applications are Biometrics, Security management, 3D face construction and cosmetology. Statistical and regression based are the traditional and earlier techniques commonly used age estimation technique which works on results of interrelationship and correlation among the different values of age for face images. The current work is an enhancement of existing techniques used previously for feature extraction using Local binary patterns with regional features of 2D Wavelet transformation image and age estimation can be accomplished by using SVM Classifier.
Lifting Tabular Data to RDF: A Survey

Tabular data formats (e.g. CSV and spreadsheets) combine ease of use, versatility and compatibility with information management systems. Despite their numerous advantages, these formats typically rely on column headers and out-of-band agreement to convey semantics. There is clearly a large gap with respect to the Semantic Web, which uses RDF as a graph-based data model, while relying on ontologies for well-defined semantics. Several systems have been developed to close this gap, supporting the conversion of tabular data to RDF. This study is a survey of these systems, which have been analyzed and compared. We identified commonalities and differences among them, discussed different approaches and derived useful insights on the task.
Construction of a Dataset for Static and Dynamic Hand Tracking Using a Non-invasive Environment

The research consists of hand gesture recognition using a commercial sensor called Leap Motion Controller.Nogales, Ruben For the study, we determined hand tracking carried out for 5 static and 4 dynamic gestures.Benalcazar, Marco E. For this work, the stages of the gesture recognition study were divided into data acquisition, preprocessing, feature extraction, and classification.Toalumbo, Bryan For the data acquisition stage, we programmed an interface in MATLAB, which generates a structure of output data in images and spatial position forms.Palate, Alexis The preprocessing consists of taking the data acquired by the leap motion controller and transform them into data readable by MATLAB using the MEX compiler, these data are captured during the execution of the gesture.Martinez, Ronnie Besides, the proposed data acquisition interface collect demographic data, hand injuries, and the light intensity of the environment in which the data is taken. These data can be used in hand gesture recognition systems and application fields such as medicine, engineering, and robotics, among others.Vargas, Javier The development of the acquisition system is parameterized with 30 repetitions at 60 frames per second and the sampling time at 5 s. Finally, the system previews a graph of the gesture recognition test structure in specific activity time.
Fully convolutional DenseNet with adversarial training for semantic segmentation of high-resolution remote sensing images

Semantic segmentation is an important and foundational task in the application of high-resolution remote sensing images (HRRSIs). However, HRRSIs feature large differences within categories and minor variances across categories, posing a significant challenge to the high-accuracy semantic segmentation of HRRSIs. To address this issue and obtain powerful feature expressiveness, a deep conditional generative adversarial network (DCGAN), integrating fully convolutional DenseNet (FC-DenseNet) and Pix2pix, is proposed. The DCGAN is composed of a generator–discriminator pair, which is built on a modified downsampling unit of FC-DenseNet. The proposed method possesses strong feature expression ability because of its skip connections, the very deep network structure and multiscale supervision introduced by FC-DenseNet, and the supervision from the discriminator. Experiments on a Deep Globe Land Cover dataset demonstrate the feasibility and effectiveness of this approach for the semantic segmentation of HRRSIs. The results also reveal that our method can mitigate the influence of class imbalance. Our approach for precise semantic segmentation can effectively facilitate the application of HRRSIs.
ASSESSING THE EFFECTIVENESS OF CYBERSECURITY TRAINING AND RAISING AWARENESS WITHIN THE MARITIME DOMAIN

In recent years, the use of digital means is rapidly expanding throughout the whole world and the maritime industry is not an exception; dealing effectively with the issue of “Cyber Security” should be a high priority issue for the numerous maritime stakeholders. It is also useful to point out that the maritime sector still maintains its importance for today’s global economy: acting as the backbone of world trade, approximately 80-90% of goods and raw materials are served nowadays by the international shipping industry. Additionally, the so-called "digitalization phenomenon" is rapidly expanding in all sectors of the economy, including the maritime one; both onboard ships and in ports, Information Technology (IT) applications systems are numerous, and very often designed without taking into account the growing cyber risks. The Cyber-MAR Project is focused on hyper-realistic simulation and emulation of maritime systems (e.g. Supply Chain) and aims to create a federated Cyber Range (CR Cyber-MAR) which will include various platforms and interconnected systems onboard a ship or on land, in order to facilitate a real-time simulation of cyber-attacks and relevant malicious attempts. Cutting a long way short, CR Cyber-MAR will be an integral part of an ambitious IT security training offer, aimed at target groups with different levels of experience and technical skills available. The analysis at hand will initially deliver a literature review in relation to education and training on cybersecurity, with a very dedicated focus on the maritime domain. Then, a discussion of the position of the International Maritime Organization on the wider topic of maritime cybersecurity will be taking place. Afterward, the impact of a training session affiliated with the Project under discussion will be provided. A conclusion clearly standing out is that the vast majority of participants confirmed an improvement in their cybersecurity skills, as well as an increased level of awareness towards cyber threats.
Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.
PipeMare: Asynchronous Pipeline Parallel DNN Training

Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware utilization. Unfortunately, to preserve the statistical efficiency of sequential training, existing PP techniques sacrifice hardware efficiency by decreasing pipeline utilization or incurring extra memory costs. In this paper, we investigate to what extent these sacrifices are necessary. We devise PipeMare, a simple yet robust training method that tolerates asynchronous updates during PP execution without sacrificing utilization or memory, which allows efficient use of fine-grained pipeline parallelism. Concretely, when tested on ResNet and Transformer networks, asynchrony enables PipeMare to use up to $2.7\times$ less memory or get $4.3\times$ higher pipeline utilization, with similar model quality, when compared to state-of-the-art synchronous PP training techniques.
Towards a Secured Blockchain-based Smart Grid

The widespread utilization of smart grids is due to their flexibility to support the two-way flow of electricity and data. The critical nature of smart grids evokes traditional network attacks. Due to the advantages of blockchains in terms of ensuring trustworthiness and security, a significant body of literature has been recently developed to secure smart grid operations. We categorize the blockchain applications in smart grid into three categories: energy trading, infrastructure management, and smart-grid operations management. This paper provides an extensive survey of these works and the different ways to utilize blockchains in smart grid in general. We propose an abstract system to overcome a critical cyberattack; namely, the fake data injection, as previous works did not consider such an attack.
Group matrix ring codes and constructions of self-dual codes

Abstract In this work, we study codes generated by elements that come from group matrix rings. We present a matrix construction which we use to generate codes in two different ambient spaces: the matrix ring $$M_k(R)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>M</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>R</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> and the ring R , where R is the commutative Frobenius ring. We show that codes over the ring $$M_k(R)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>M</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>R</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> are one sided ideals in the group matrix ring $$M_k(R)G$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>M</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>R</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>G</mml:mi> </mml:mrow> </mml:math> and the corresponding codes over the ring R are $$G^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>G</mml:mi> <mml:mi>k</mml:mi> </mml:msup> </mml:math> -codes of length kn . Additionally, we give a generator matrix for self-dual codes, which consist of the mentioned above matrix construction. We employ this generator matrix to search for binary self-dual codes with parameters [72, 36, 12] and find new singly-even and doubly-even codes of this type. In particular, we construct 16 new Type I and 4 new Type II binary [72, 36, 12] self-dual codes.
Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.
Path Planning for a Solar-Powered UAV Inspecting Mountain Sites for Safety and Rescue

This paper focuses on the application using a solar-powered unmanned aerial vehicle (UAV) to inspect mountain sites for the purpose of safety and rescue. An inspection path planning problem is formulated, which looks for the path for an UAV to visit a set of sites where people may appear while avoiding collisions with mountains and maintaining positive residual energy. A rapidly exploring random tree (RRT)-based planning method is proposed. This method firstly finds a feasible path that satisfies the residual energy requirement and then shortens the path if there is some abundant residual energy at the end. Computer simulations are conducted to demonstrate the performance of the proposed method.
Empirical Comparison of Search Heuristics for Genetic Improvement of Software

Genetic improvement (GI) uses automated search to improve existing software. It has been successfully used to optimize various program properties, such as runtime or energy consumption, as well as for the purpose of bug fixing. GI typically navigates a space of thousands of patches in search for the program mutation that best improves the desired software property. While genetic programming (GP) has been dominantly used as the search strategy, more recently other search strategies, such as local search, have been tried. It is, however, still unclear which strategy is the most effective and efficient. In this article, we conduct an in-depth empirical comparison of a total of 18 search processes using a set of eight improvement scenarios. Additionally, we also provide new GI benchmarks and we report on new software patches found. Our results show that, overall, local search approaches achieve better effectiveness and efficiency than GP approaches. Moreover, improvements were found in all scenarios (between 15% and 68%). A replication package can be found online: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/bloa/tevc_2020_artefact</uri> .
Smart Decentralization of Personal Health Records with Physician Apps and Helper Agents on Blockchain: Platform Design and Implementation Study

Background The Health Avatar Platform provides a mobile health environment with interconnected patient Avatars, physician apps, and intelligent agents (termed IoA3) for data privacy and participatory medicine; however, its fully decentralized architecture has come at the expense of decentralized data management and data provenance. Objective The introduction of blockchain and smart contract technologies to the legacy Health Avatar Platform with a clinical metadata registry remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data. Methods The Health Avatar Platform decentralizes patient data in appropriate locations (ie, on patients’ smartphones and on physicians’ smart devices). We implemented an Ethereum-based hash chain for all transactions and smart contract–based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into 3 smart contracts, in this case, a health data transaction manager, a transaction status manager, and an application programming interface transaction manager. The actual decentralized health data are managed in an off-chain manner on appropriate smart devices and authenticated by hashed metadata on-chain. Results Metadata of each data transaction are captured in a Health Avatar Platform blockchain node by the smart contracts. We provide workflow diagrams each of the 3 use cases of data push (from a physician app or an intelligent agents to a patient Avatar), data pull (request to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash-chained metadata support data element–level verification of data integrity in subsequent transactions. Smart contracts can incentivize transactions for data sharing and intelligent digital health care services. Conclusions Health Avatar Platform and interconnected patient Avatars, physician apps, and intelligent agents provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions with smart contracts.
Jointly human semantic parsing and attribute recognition with feature pyramid structure in EfficientNets

Pedestrian attributes recognition is an important issue in computer vision and has a special role in the field of video surveillance. The previous methods presented to solve this issue are mainly based on multi-label end-to-end deep neural networks. These methods neglect to apply attributes for defining local feature areas and they suffer from the problems of the bounding box presence. A new framework for jointly human semantic parsing and pedestrian attribute recognition to achieve effective attribute recognition is proposed. By extracting human parts via semantic parsing, both semantic and spatial information can be explored with eliminating of background. The framework also uses multi-scale features to employ rich details and contextual information through proposed attribute recognition-bidirectional feature pyramid network. For baseline network that has a significant impact on the performance, EfficientNet-B3 is selected as a baseline network from The EfficientNet family which provides an appropriate trade-off between the three factors of CNNs scaling (depth/width/resolution). Finally, the proposed framework is tested on datasets PETA, RAP and PA-100k. Experimental results show that our method has superior performance in both mean accuracy and instance-based metrics compared to state-of-the-art results.
Dynamic distributed trust management scheme for the Internet of Things

The Internet of Things (IoT) comprises of a diverse network of homogeneous and heterogeneous nodes that can be accessed through network ubiquitously.In unattended environments, the IoT devices are prone to various attacks including ballot-stuffing, bad-mouthing, self-promotion, on-off, opportunistic behavior attacks, etc.The on-off attack is difficult to detect as nodes switch their behavior from normal to malicious alternatively.A trust management model is a tool to defend the IoT system against malicious activities and provide reliable data exchange.The majority of existing IoT trust management techniques are based on static reward and punishment values in pursuit of trust computation thereby allowing the misbehaving nodes to deliberately perform on-off attacks.Due to the static nature of awarding scores, these schemes fail to identify malicious nodes in certain cases.In this paper, a dynamic and distributed trust management scheme (DDTMS) is proposed where nodes can autonomously evaluate peer nodes' behavior and dynamically grant reward and penalty score.The proposed scheme successfully detects the on-off attack and isolates the misbehaving nodes thereby classifying them into three distinct categories based on their severity levels i.e. low, mild, and severe.Simulation-based performance evaluation shows improved performance of the proposed DDTMS against other state-of-the-art schemes thereby requiring less time and fewer interactions for successfully identifying malevolent behavior of compromised nodes.
EIAS: An Efficient Identity-Based Aggregate Signature Scheme for WSNs Against Coalition Attack

Wireless sensor networks (WSNs) are the major contributors to big data acquisition. The authenticity and integrity of the data are two most important basic requirements for various services based on big data. Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs. However, the process of data acquisitions in WSNs are in open environments, data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence, such as coalition attack. Aimed to provide data authenticity and integrity protection for WSNs, an efficient and secure identity-based aggregate signature scheme (EIAS) is proposed in this paper. Rigorous security proof shows that our proposed scheme can be secure against all kinds of attacks. The performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.
Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.