Reviewerly/exHarmony huggingface dataset:
Deep Residual Neural Network for Age Classification with Face Image

One of the challenges in computer vision is age classification. There have been many methods used to classify someone age from the image of their faces. Convolutional neural network (CNN) gives a high accuracy but it cannot be used on many layers. Therefore, a residual technique is applied on convolutional neural network then named residual neural network. In this paper, some Residual Networks are applied to develop an age classification with face image using the Adience dataset that has 19,370 face images from 2,284 individuals grouped into eight categories: 0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, and 60-100 years. Three techniques: cyclical learning rate, data augmentation, and transfer learning are observed. Six training scenarios are performed to select the best model. Experimental results show that Resnet34 is the best model with an average F1 score of 0.792 that is achieved by data augmentation, transfer learning, and trained on the image with size 224 x 224 pixels.
Development of a Motion Sensing System Based on Visual Servoing of an Eye-in-Hand Electrohydraulic Parallel Manipulator

The design and implementation of a real-time object motion sensing and trajectory replicating system, dependent on the image-based visual servoing capabilities of an electrohydraulic eye-in-hand parallel manipulator has been described in this paper. The tracking system consists of a monocular vision apparatus affixed to the top platform of a six-cylinder parallel manipulator constructed with industry grade double-acting single rod hydraulic actuators and budget proportional valves. The control algorithm is an amalgamation of three principles: machine vision based object tracking, image based visual servoing and actuator-level feedback control utilizing the inverse kinematics of the parallel manipulator. The real-time experiments are performed on a laboratory scale parallel manipulator setup with the aid of a virtual tracking object rendered on a computer monitor. The results for a few reference trajectories reveal that accurate tracking of target motions can be achieved with visual servoing algorithms designed for parallel manipulators in real-time.
Using Metrics Suites to Improve the Measurement of Privacy in Graphs

Social graphs are widely used in research (e.g., epidemiology) and business (e.g., recommender systems). However, sharing these graphs poses privacy risks because they contain sensitive information about individuals. Graph anonymization techniques aim to protect individual users in a graph, while graph de-anonymization aims to re-identify users. The effectiveness of anonymization and de-anonymization algorithms is usually evaluated with privacy metrics. However, it is unclear how strong existing privacy metrics are when they are used in graph privacy. In this paper, we study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three criteria: monotonicity indicates whether the metric indicates lower privacy for stronger adversaries; for within-scenario comparisons, evenness indicates whether metric values are spread evenly; and for between-scenario comparisons, shared value range indicates whether metrics use a consistent value range across scenarios. Our extensive experiments indicate that no single metric fulfills all three criteria perfectly. We therefore use methods from multi-criteria decision analysis to aggregate multiple metrics in a metrics suite, and we show that these metrics suites improve monotonicity compared to the best individual metric. This important result enables more monotonic, and thus more accurate, evaluations of new graph anonymization and de-anonymization algorithms.
Protecting Assets with Heterogeneous Valuations under Behavioral Probability Weighting

We consider a security setting involving a defender who is required to invest (subject to a budget constraint) in protecting a given set of nodes against attacks. Each node has a certain value to the defender, along with a probability of being successfully compromised, which is a function of the investment in that node by the defender. In this setting, we consider the impacts of behavioral probability weighting (vis-à-vis the probability of successful attack) on the investment strategies; such probability weighting, where humans overweight low probabilities and underweight high probabilities, has been identified by behavioral economists to be a common feature of human decision-making. We show that under appropriate conditions on the probability of successful attack, the defender's optimization problem is convex (even under probability weighting). Furthermore, we show that behavioral probability weighting causes the defender to shift more of her investments to the higher-valued nodes and underinvest in the low-value nodes, compared to the case where the defender perceives the probability of attack correctly. In particular, the number of nodes that have positive investment decreases as the defender becomes more behavioral.
Seismology in the Cloud: A New Streaming Workflow

Abstract Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. In this work, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.
A weighted bounded Hessian variational model for image labeling and segmentation

Natural images are usually composed of multiple objects at different scales in flat and slanted regions. Traditional labeling/segmentation approaches based on total variation minimization may produce staircase results with discontinuities and rough boundaries. In this paper, we propose a novel weighted variational model for image labeling/segmentation in the space of functions of bounded Hessian, the weights of which are automatically estimated based on edge information of the observed images. Especially, by minimizing the combined first and second-order regularizer, our model can overcome the shortage of total variation and provide more meaningful results. The efficient alternating direction method of multipliers based algorithm is established, all subproblems of which can be solved by either the fast Fourier transform or closed-form solution. We further introduce the weighted bounded Hessian regularizer into the two-stage segmentation framework for dealing with noisy and blurry image segmentation problems. Numerous experiments are conducted on both two-phase and multi-phase labeling/segmentation problems. By comparing with several state-of-the-art methods both qualitatively and quantitatively, it demonstrates that the proposed models can prominently improve the accuracy of image labeling and segmentation.
Cyclic-routing of Unmanned Aerial Vehicles

Various missions carried out by Unmanned Aerial Vehicles (UAVs) are concerned with permanent monitoring of a predefined set of ground targets under relative deadline constraints, i.e., the targets have to be revisited ‘indefinitely’ and there is an upper bound on the time between two consecutive successful scans of each target. A solution to the problem is a set of routes—one for each UAV—that jointly satisfy these constraints. Our goal is to find a solution with the least number of UAVs. We show that the decision version of the problem (given k , is there a solution with k UAVs?) is PSPACE-complete. On the practical side, we propose a portfolio approach that combines the strengths of constraint solving and model checking. We present an empirical evaluation of the different solution methods on several hundred randomly generated instances.
Proposal Gesture Recognition Algorithm Combining CNN for Health Monitoring

Today, artificial intelligence (AI) has become a trend that is studied by many research. It simulates the process of learning human thinking for computers or supercomputers used to handle certain types of work such as controlling a house, image recognition research, processing data of disease to give treatment regimens, processing data for self-learning, answering questions of patient and customers. Besides, the interaction between humans and computers is more diverse. Using hand gestures is considered an effective method to communicate with each other for health monitoring system. The proposal algorithm consists of three steps, namely capturing frame from videos, extracting their characteristic, and classifying actions. In proposal algorithm, we select several gestures such as baby waking up, walking, and falling for detecting and warning to doctor. The results show that proposal algorithm improves the accuracy up to 90 percent. However, disadvantage of algorithm is to requires long processing time and large memory.
Latitude and binocular perception based blind stereoscopic omnidirectional image quality assessment for VR system

With the rapid development of 5G technologies, it is possible to perform high-speed transmission of virtual reality (VR) visual contents. As one of techniques related to VR visual contents, omnidirectional image/video quality assessment has been becoming one of important issues. In this paper, based on latitude characteristics of omnidirectional image and binocular perception of human visual system, we propose a new blind stereoscopic omnidirectional image quality assessment (BSOIQA) method. Firstly, with the latitude characteristics of omnidirectional image, an improved omnidirectional image saliency model is established to estimate the visual behavior of users. Secondly, to describe the perceptual characteristics of the left and right views of stereoscopic omnidirectional image (SOI), Weber local descriptor is used to obtain differential excitation image and orientation image. The differential excitation image is combined with saliency map to obtain the statistical features of quantized orientation image, and multi-scale statistical features are extracted from the left and right views of SOI. Thirdly, tensor decomposition is used to fuse the left and right views of SOI to extract binocular perception features. Dual-tree dual-density complex wavelet transform, image information entropy and singular value decomposition are used to extract binocular fusion features, and the latitude characteristics of SOI are taken into account in image blocking. The gradient map of the absolute difference image of the left and right views of SOI is calculated, and its histogram statistical features are extracted to represent binocular rivalry. In addition, the statistical characteristics of the binocular product image are used to represent the local structure information of binocular perception. Finally, the feature vector of SOI is created with feature screening and used to predict the objective quality of SOI, and an SOI subjective assessment dataset (NBU-SOID) is also established to evaluate the proposed BSOIQA method. Experimental results on NBU-SOID and other databases show that the proposed BSOIQA method is in better consistence with human visual perception, compared with the other state-of-the-art methods.
Neural Network-based Vehicle Image Classification for IoT Devices

Convolutional Neural Networks (CNNs) have previously provided unforeseen results in automatic image analysis and interpretation, an area which has numerous applications in both consumer electronics and industry. However, the signal processing related to CNNs is computationally very demanding, which has prohibited their use in the smallest embedded computing platforms, to which many Internet of Things (IoT) devices belong. Fortunately, in the recent years researchers have developed many approaches for optimizing the performance and for shrinking the memory footprint of CNNs. This paper presents a neural-network-based image classifier that has been trained to classify vehicle images into four different classes. The neural network is optimized by a technique called binarization, and the resulting binarized network is placed to an IoT-class processor core for execution. Binarization reduces the memory footprint of the CNN by around 95% and increases performance by more than $6 \times $. Furthermore, we show that by utilizing a custom instruction ’popcount’ of the processor, the performance of the binarized vehicle classifier can still be increased by more than $2 \times $, making the CNN-based image classifier suitable for the smallest embedded processors.
A retrospective on Telos as a metamodeling language for requirements engineering

Telos is a conceptual modeling language intended to capture software knowledge, such as software system requirements, domain knowledge, architectures, design decisions and more. To accomplish this, Telos was designed to be extensible in the sense that the concepts used to capture software knowledge can be defined in the language itself, instead of being built-in. This extensibility is accomplished through powerful metamodeling features, which proved very useful for interrelating heterogeneous models from requirements, model-driven software engineering, data integration, ontology engineering, cultural informatics and education. We trace the evolution of ideas and research results in the Telos project from its origins in the late eighties. Our account looks at the semantics of Telos, its various implementations and its applications. We also recount related research by other groups and the cross-influences of ideas thereof. We conclude with lessons learnt.
Blockchain-based P2P multimedia content distribution using collusion-resistant fingerprinting

Due to the popularization of low-cost broadband Internet access, the amount of the digital data that is illegally redistributed is growing, making content creators and owners lose their income. Fingerprinting, a watermarking-based technology for embedding buyer identifications in legally distributed contents, has emerged as a promising approach to fight illegal redistribution. On the other hand, the blockchain technology is also shaping up to handle the challenge of digital copyright protection. With blockchain, media producers can authorize and manage their copyrights on a public ledger. In this paper, we present a blockchain-based distribution system which blends different technologies (collusion-resistant fingerprinting, perceptual hash functions, and a peer-to-peer file distribution network) to provide copyright protection, collusion resistance, atomic payment, piracy tracing, transparency, proof-of-delivery, revocable privacy (to a buyer), and dispute resolution. The paper also analyzes several security and privacy compromising attacks and countermeasures.
Use of Homomorphic Encryption with GPS in Location Privacy

Homomorphic Encryption is not a widely used encryption technique because it has a lot of drawbacks. It is very slow in performance and lacks efficiency. This makes its application difficult and inefficient. Ways to improve the performance of homomorphic encryption by realizing fully homomorphic systems has been in vain. Furthermore, additions to the encryption through different cryptosystems have done little to increase its efficiency. This paper talks about a method of applying homomorphic encryption in its existing state of characteristics to provide location privacy at run time. This is done in companionship of Global Positioning System which uses satellites to work out the coordinates of the user's location.
Optimal Multi-Agent Persistent Monitoring of the Uncertain State of a Finite Set of Targets

We approach the problem of persistent monitoring of a finite set of fixed targets located in a one-dimensional environment with internal, linear, stochastic dynamics. Monitoring is performed by a set of agents with limited sensing range and range-dependent sensing quality. The optimal estimator of the target dynamics from the agent measurements is the Kalman-Bucy Filter. We formulate an optimal control problem to minimize the estimation error across all the targets as a function of the trajectories of the agents. Using Hamiltonian analysis, the structure of the optimal controller is defined and, given this structure, we reformulate the problem as a hybrid systems optimization problem. Using Infinitesimal Perturbation Analysis (IPA), stochastic gradient estimates of the hybrid system are computed and gradient descent is used in order to achieve a locally optimal solution.
A visual-inertial system to determine accurate solar insolation and optimal PV panel orientation at a point and over an area

To provide the best return on investments it is often required to assess the suitability of a site for installation of solar photovoltaic panel and quantify shading and atmospheric losses. The shading analysis is generally done using light detection and ranging or 3D geographic information system-based approaches which are cost-effective only for large-scale analysis. In several cases, particularly in developing countries, LiDAR data or 3D GIS are not available. In this study, a terrestrial image-based system is developed to accurately estimate solar insolation at a place. The positions of obstructions obtained using captured images are integrated with sun position model to provide solar insolation and optimal solar panel orientation. To further refine the result, the effect of sky conditions on the obtained solar insolation is also considered at monthly and yearly scale. About 40% reduction in solar insolation is observed due to shading which rose to 51% when the atmospheric conditions were included in the analysis of the selected sites. Further, an approach to estimate solar insolation over an area using some discrete point location is also presented and demonstrated. Results from 30 sites show that the obtained error in insolation estimate over an area is within 4%.
Secured Blockchain Based Decentralised Internet

Throughout this paper, we try to describe with blockchain technology the decentralization of the internet. A decentralized network that encourages the internet to operate from the smartphone or tablet of anybody instead of centralized servers. A decentralized implementation would be based on a peer-to-peer network that is dependent on a user community. Their machines connected to the internet will host the network, not a community of more powerful servers. Each site would be distributed across thousands of nodes on various devices. The data is therefore not contained, owned by private storage facilities. There is therefore no central point to hack, and no way for an oligarchy of entities to take control of it. A proposed alternative was formed based on a systematic literature review that demonstrates that Internet decentralization is what this modern technology needs in order to address not only the weaknesses of current servers including server down issue, hacking and data manipulation or single point of failure, but also to prevent companies from monetizing the data of citizens through their server and to market them to the advertisers.
Diverse Datasets and a Customizable Benchmarking Framework for Phishing

Phishing is a challenging problem that has been addressed by many researchers in several papers using many different datatsets and techniques~\citedas2019sok. Researchers usually test their proposed methods with limited metrics, datasets, and parameters when presenting new features or approach(es). Hence, the need arises for a benchmarking framework and dataset to evaluate such systems as comprehensively as possible. In this paper, we discuss: (i) our efforts on the creation and dissemination of diverse and representative datasets for phishing email, website and URL detection, and (ii) PhishBench, our framework for benchmarking phishing detection systems. PhishBench allows researchers to evaluate and compare features and classification approaches easily and efficiently on the provided data.
VEKG: Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern Matching

Complex Event Processing (CEP) is a paradigm to detect event patterns over streaming data in a timely manner. Presently, CEP systems have inherent limitations to detect event patterns over video streams due to their data complexity and lack of structured data model. Modelling complex events in unstructured data like videos not only requires detecting objects but also the spatiotemporal relationships among objects. This work introduces a novel video representation technique where an input video stream is converted to a stream of graphs. We propose the Video Event Knowledge Graph (VEKG), a knowledge graph driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. To optimize the run-time system performance, we introduce a graph aggregation method VEKG-TAG, which provides an aggregated view of VEKG for a given time length. We defined a set of operators using event rules which can be used as a query and applied over VEKG graphs to discover complex video patterns. The system achieves an F-Score accuracy ranging between 0.75 to 0.86 for different patterns when queried over VEKG. In given experiments, pattern search time over VEKG-TAG was 2.3X faster as compared to the baseline.