Reviewerly/exHarmony huggingface dataset:
Leveraging Pyramidal Feature Hierarchy for 3D Reconstruction

Most state-of-the-art 3D reconstruction methods with CNNs have focused on completion and generalization of the reconstructed 3D models. Although the reconstructed 3D models may look complete, mostly lose out in detail causing a wider gap between the reconstructed 3D model and the groundtruth. We propose a method that adapts a pyramidal hierarchical-based network. Our strategy is to focus on improving the reconstruction of the detailed parts which comprise of thin and fine parts of the 3D models. Improving the detailed parts of the 3D model helps improve the accuracy and the overall shape of the 3D model resulting in a reconstructed 3D model which looks closer to the groundtruth. The advantage of using a pyramidal hierarchical-based network is that it builds a feature pyramid which considers high-level semantics at different scales. This builds a network that is tailored to focus on the detailed parts of the 3D model while considering the overall shape of the 3D model.
Review on Image Captioning and Speech Synthesis Techniques

Caption generation may be a challenging AI problem to create a text description for a given picture. It needs to know the content of the image i.e., features and to transform these features of the image into words by a language model. Recently, deep learning techniques have accomplished results for this problem. The human-generated speech and the computer-generated speech are different. The quality of generated speech is defined by Intelligibility and naturalness. The quality of the audio generated is the Intelligibility and Naturalness is the quality of the speech generated, to judge the quality of generated speech. Deep Learning models demonstrate remarkably effective at learning the inherent features of information.
Design of robust image watermarking technique based on shearlet transform and QR matrix decomposition

Watermarking is the method by which a watermark image related to the original cover image is embedded into the cover image. Images have become an important part in digital media. Thus it is important to secure the image data and prevent its illegal duplication. Image watermarking aims at serving this purpose. Wavelet technique is commonly used technique to embed watermark on the cover image in transform domain. The watermark data and cover image are not distorted after applying transform; hence this technique is used widely in this field. The upgraded version of wavelet technique is shearlet transform, thus upgraded version improves the presentation of multidimensional data and transform is mainly based on multidirectional and multi-resolution. The shearlet transform overcome the drawback of wavelet’s edge presentation of image which is the main characteristic of shearlet and hence the host image is modelled with different texture. There are two characteristics of watermark, namely, Imperceptibility and robustness. Imperceptibility is achieved using Shearlet transform, whereas robustness and security improvement is achieved using QR decomposition. Analyzing the experiments undergone on the various attack of host image with distinct texture characteristics and thus the result shows that this technique has a good resistance to attacks and provides a efficient transparency and high robustness.
Color Image Quality Assessment Based on Full Reference and Blind Image Quality Measures

The degradation of the image at the acquisition and transmission severely affects the various stages of the image processing and leads to the unexpected results. So, it is necessary to be aware of the source and reason of the various noises that are incorporated with the original image. The image quality measures compute the quality of the corrupted or degraded image with or without reference (input) image. The comparison of the degraded and reference image produces numerical score that decides the quality of the image. The proposed work tests the images incorporated the various noises such as salt and pepper, Poisson, Gaussian and speckle. Blind (no reference) and full reference quality measures investigate the amount of degradation in images. Full reference quality measures are very simple to compute but do not correlate with human perception, whereas blind reference measures prove its supremacy in this aspect.
User-Assisted QoS Control for QoE Enhancement in Audiovisual and Haptic Interactive IP Communications

In this paper, we propose a user-assisted QoS control scheme that utilizes media adaptive buffering to enhance QoE of audiovisual and haptic IP communications. The scheme consists of two modes: a manual mode and an automatic mode. It enables users to switch between these two modes according to their inclinations. We compare four QoS control schemes: the manual mode only, the automatic mode only, the switching scheme starting with the manual mode, and the switching scheme starting with the automatic mode. We assess the effects of the four schemes, user attributes, and tasks on QoE through a subjective experiment which provides information on users' behavior in addition to QoE scores. As a result of the experiment, we show that the user-assisted QoS control scheme can enhance QoE. Furthermore, we notice that the proper QoS control scheme depends on user attributes and tasks.
There and Back Again: Linking Online and Offline Spaces in/of Czech Emo Subculture

This chapter examines the recent popularity of emo subculture in Prague and other cities in the Czech Republic, focusing on the role of online and offline spaces in this latest emo resurgence and the interrelation of these spaces (public and private), as manifested and constructed by subcultural practices. Based on ethnographic research of contemporary Czech emo subculture both online and offline, the conclusion is reached that even if emo is a prime example of a contemporary new-mediated subculture unable to exist without online modes of communication, emo still puts great emphasis on communication offline, giving it at least as much importance as online interaction. It is further argued that even if online and offline are distinguishable analytically, they are quite inseparable in Czech emo practice.
Assessing the instrument reliability and validity of risk mitigation for anti software ageing model during software maintenance

Longer software lifecycle is important to ensure its ability to provide on-going function and services. However, software relevancy and importance may reduce once its performance and quality degrades through inevitable ageing progress. Even though software ageing is inevitable, the progress of ageing in software could be slowed down and delayed. To achieve this, we may need to determine and understand the causes contributing to this phenomenon. Little attention had been paid to address software ageing from software engineering perspective, ageing caused by failures to modify the software or from the results of software changes. Hence, this motivate the study to determine the risks of performing software changes that influence software ageing occurrences and propose a method to tackle software ageing from software engineering perspective to reduce risks impact before it become apparent through risk mitigation. This paper discusses on the reliability and validity of instrument designed to conduct a study among software practitioners in order to gain insight on the risks of performing changes to software influencing software ageing and determine risk mitigation effect on the relationship between software ageing and anti software ageing. Preliminary study was conducted through content validity interview and pilot study. The results from pilot study conducted shows the items in the questionnaire has a good level of reliability to measure each variable in the model, hence the instrument can be used for further studies.
FPGA Based Real-Time Face Authorization System for Electronic Voting System

In this paper, we have developed a standalone authentication application by using a face recognition technique with NI MyRIO and LabVIEW, along with the integration of the OpenCV library and NI VISION tool kit. The designed system can recognize and allow the authenticated person(s) keeping in view of existing systems limitation in a real-time environment, specifically caused by delay. The proposed solution is designed followed by a performance perspective and comparative analysis through Matlab, Raspberry pie, LabVIEW, and FPGA. The overall system is a combination of hardware and software, employing parallel processing techniques and modules such as NI MyRIO FPGA. The proposed designed system is tested in real-time scenarios and can be tailored to the electronic voting system via NADRA database.
PRRAT_AM—An advanced ant-miner to extract accurate and comprehensible classification rules

Ant-Miner, a rule-based classification algorithm, has been successfully applied for classification tasks but it has some limitations such as getting stuck in local optima, high selective pressure, fixed exploration and exploitation rate, and premature convergence. In this paper, we have proposed a novel Ant-Miner based technique based on new Pheromone update method, Rule Rejection threshold, Adaptive gamma, and altered Tournament selection (PRRAT_AM) that caters to these limitations. The proposed algorithm introduced an adaptive gamma parameter to avoid fixed exploration and exploitation rate. To decrease the selective pressure, pheromone is updated by weighted average of rule length, rule quality and heuristic of the path. Ants are selected using improved tournament selection strategy to update the pheromone. Rules that covered less than one percent of the training examples are rejected to generate generic rules. These improvements aid PRRAT_AM in avoiding premature convergence and high selective pressure. We have tested the proposed approach on eight publicly available data-sets on standard benchmark performance measures that include accuracy and F1-score. The proposed approach has been compared with state of the art versions of Ant-Miner and with various data mining algorithms. The experimental results showed that the proposed approach achieved better results when compared with other techniques in terms of standard performance measures and convergence speed.
A Speed Up Method for Guetzli Encoder

JPEG [1] is a widely used image compression format. It has a great economic value for making the image file smaller but guarantee the image visual quality. Guetzli [2] is applied to compress and obtain images conforming to the JPEG standard. It evaluates the quality of the image through Butterugli [3], compresses the JPEG image by replacing the quantization table and filtering the DCT coefficients. The advantage of Guetzli is that it uses Butterugli to restrict and supervise the image optimization process, so that the file is reduced while the image quality is strictly guaranteed. However, the disadvantage of Guetzli is the time cost of its iterative process and the considerable space complexity during the calculation. To solve this problem, two methods are proposed to speed up the Guetzli calculation process. First, tighten the preliminary replacement process of DCT coefficients. Second, speed up the backfill process of the DCT coefficients. And the combination of these two methods can be used to speed up the compression process. Our method can effectively speed up the Guetzli compression process without lead into extra distortion of images. As a result, on the premise of ensuring quality, 73% of the iteration time savings was realized at the cost of 3.8% file size increase.
Image Colorization Using GANs and Perceptual Loss

Image colorization is of great use for several applications, such as the restoration of old images, as well as enabling the storage of grayscale images, which take up less space, which can later be colorized. But this problem is hard since there exist many possible color combinations for a particular grayscale image. Recent developments have aimed to solve this problem using deep learning. But, for achieving good performance, they require highly processed inputs, along with additional elements, such as semantic maps. In this paper, an attempt has been made for generalizing the procedure of colorization using a conditional Deep Convolutional Generative Adversarial Network (DCGAN) by adding "Perceptual Loss". The network is trained over the CIFAR-100 dataset. The results of the proposed generative model with perceptual loss are compared with the existing state-of-the-art systems normal GAN model and U-Net Convolutional model.
Tangible Tools in Mathematics for Engineering Students: Experimental Activity at Politecnico di Torino

In the article, we present the tasks of the Maths lab at Politecnico di Torino that we tested for the first time in the academic year 2018–19, which were proposed for undergraduate Engineering students in their first year with the aim to integrate active learning into our traditional courses. The student activity in response to the tasks took place in classes of at most 25 individuals. The work was organized in small groups, mainly following a problem-solving approach and a central role was given to artefacts and technological applications. Here, we discuss the main motivations of our lab project, two specific tasks and some data collected by means of a questionnaire, in order to examine the interplay between tangible and virtual tools.
Age and Gender Prediction Using Convolutional Neural Network

Automatic age and gender classification has been widely used in a large amount of applications, particularly in human–computer interaction, biometrics, visual surveillance, electronic customer and commercial applications. Predicting age and gender of humans is very difficult and complicated. Since the increase in use of social media, autonomous prediction of age and gender has become extremely important. Nevertheless, performance of the existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this research paper, we have observed an increase in the performance on these tasks by using convolutional neural networks (CNN). We collected a dataset from ‘Google’ and ‘IMFDB.’ This dataset consisted of about 100,000 images of male and female of different age-groups (5–60). We created a CNN network adjoining the fully connected network using ‘Keras’ library. Following that, three convoluted layers were inserted; the first layer consisting of 64 neurons and window of 7 × 7, followed by a ReLU activation layer and a max-pooling layer of 2 × 2. Two more CNN layers of 100 and 64 neurons were inserted, followed by windows of 5 × 5 and 3 × 3, respectively, with activation function as ReLU. An output was received in matrix form which was flattened using a flatten layer. The layer was then fed to fully connected layers with 64 and 1 neurons each. The final output layer consisted of a single neuron with sigmoid activation function.
A Track-Before-Detect Method for Rotating Radars with Revisit Interval Uncertainty

In traditional track-before-detect (TBD) algorithms, intervals between frames are considered to be uniform. The uncertainty of revisit intervals is not taken into account. However, revisit intervals of the target varies with its states in rotating radars. This may lead to degradation in energy integration due to the use of inaccurate revisit intervals in conventional methods. In this paper, a velocity-filtering-based TBD method with revisit interval uncertainty (RIU-VF-TBD) is proposed to address the above problem. The revisit interval is solved precisely using the space-time joint solution. Using the angle relation between target movement and the motion of antenna, the revisit interval is solved iteratively until accuracy requirement is reached. Each measurement cell in polar coordinates is converted to Cartesian coordinates and predicted according to an assumed velocity and the precisely solved revisit interval. Then, every predicted Cartesian position is converted back to polar coordinates for energy accumulation. Procedures of the revisit interval calculation and energy integration are derived in detail. Simulation results demonstrate the superiority of the proposed algorithm compared with conventional VF-TBD methods.
Modeling the Mechanical Performance of Bendable Display under Cyclic Loading

Bendable displays used in foldable phones have a stack-up of multiple substrate layers bonded by a soft adhesive. The material property of the adhesive layer has a significant impact on the bending and shear deformation behaviour. In this paper we evaluate the impact of adhesive material on the cyclic fold-unfold behaviour of a simplified display assembly using finite element simulations. We use a viscoelastic material model for the adhesive to represent its time dependent behaviour and assess the residual stress over repeated load cycles. A comparison of the system's response to two material models is done to evaluate the factor that drives the residual stresses in the display.
Solar Project Financing, Bankability, and Resource Assessment

This chapter deals with issues involved during solar project financingFinancing and resource assessment. In the first half of the chapter, an overview of financingFinancing and bankabilityBankability of utility-scale photovoltaicPhotovoltaic (PV) (PV) plants is provided, with a slight touch on microgrid PV financingFinancing. The discussion revolves around risk management, which requires rigorous assessment of the financial viability. Since a robust solar radiation dataset is essential for securing competitive financingFinancing for solar-power projects, the second half of the chapter discusses solar resource assessmentSolar resource assessment—a data-oriented exercise. The best practices on solar resource calculation are exemplified through various case studies using data collected at a tropical site: Singapore. Subsequently, 5 latest satellite-derived irradiance products and 2 latest global reanalysis products are reviewed. Detailed instruction for data downloading is also provided to facilitate the worldwide uptake of these high-quality data. Latest scientific methods on site adaptation—a class of procedures to reduce the uncertainties embedded in gridded products—are presented with a Brazilian case study.
A Model for Detecting Conflicts and Dependencies in Non-Functional Requirements Using Scenarios and Use Cases

The present research work proposes a model of association and identification of conflicts and dependencies in non-functional requirements through scenarios and use cases. The idea of achieving these identifications will make possible to minimize errors in the management of non-functional requirements. The way in which these conflicts and dependencies are found is through of 3 components: Elastic Search which is a search engine, Kibana, a plugin that works with Elastic Search for the visualization of data and MAICD, which is a web application we developed in.NET that uses NEST (an Elastic Search API). First, we specify all possible scenarios and non-functional requirements of a software project. Then, we perform text analysis on this data with the help of Elastic Search. Finally, results are shown in the web application MAICD with the corresponding visualizations in Kibana.
Biometric Authentication Schemes and Methods on Mobile Devices

Due to the sensitivity and amount of information stored on mobile devices, the need to protect these devices from unauthorized access has become imperative. Among the various mechanisms to manage access on mobile devices, this chapter focused on identifying research trends on biometric authentication schemes. The systematic literature review approach was adopted to guide future researches in the subject area. Consequently, seventeen selected articles from journals in three databases (IEEE, ACM digital library, and SpringerLink) were reviewed. Findings from the reviewed articles indicated that touch gestures are the predominant authentication technique used in mobile devices, particularly in android devices. Furthermore, mimic attacks were identified as the commonest attacks on biometric authentic schemes. While, robust authentication techniques such as dental occlusion, ECG (electrocardiogram), palmprints and knuckles were identified as newly implemented authentication techniques in mobile devices.
Explaining Traffic Situations – Architecture of a Virtual Driving Instructor

Intelligent tutoring systems become more and more common in assisting human learners. Distinct advantages of intelligent tutoring systems are personalized teaching tailored to each student, on-demand availability not depending on working hour regulations and standardized evaluation not subjective to the experience and biases of human individuals. A virtual driving instructor that supports driver training in a virtual world could conduct on-demand personalized teaching and standardized evaluation. We propose an architectural design of a virtual driving instructor system that can comprehend and explain complex traffic situations. The architecture is based on a multi-agent system capable of reasoning about traffic situations and explaining them at an arbitrary level of detail in real-time. The agents process real-time data to produce instances of concepts and relations in an ever-evolving knowledge graph. The concepts and relations are defined in a traffic situation ontology. Finally, we demonstrate the process of reasoning and generating explanations on an overtake scenario.
A Study and Application Development on Monitoring Cardio-Vascular Attack using Internet of Thing (IoT)

A healthy human life is the top priority of every humanbeing in the present era. In a world full of high-tech markets, where everyday a new technology is introduced to solve prevailing world problems in a more efficient way. We must find out some course of actions to save as many human lives as possible. Therefore, this research is related to Internet of Things (IoT). A sensors based mobile application that has the capability to detect heart rate; where the detected pulse rate are imputed into the system that performs some computations to decide whether the human is in a normal state or needs certain medical assistance. The primary motivation behind this research is to comparatively analyze different existing technologies and to develop an application that can build a strong research background to aid researchers in the establishment of an early heart attack detection and prevention system.
Classification of Threat Level in Typing Activity Through Keystroke Dynamics

System intrusion is a major issue in today's data-driven world. Discovering adversarial activities before or as they happen, through any modality, makes a system more secure. A straight forward approach to determine threat level from typing data would be to analyze the text directly. However, natural language processing is hard to implement on complex real life data, therefore we explore how far keystroke dynamics can go in terms of classifying threat levels correctly. We show that keystroke dynamics (KD) on a desktop can be used to classify the type of activity, either benign or adversarial, that a text sample originates from. We show the inefficiencies of popular temporal features for this task. With our proposed set of 14 features we achieve high accuracies (93% to 97%) and low Type 1 and Type 2 errors (3% to 8%) in classifying text samples of different sizes. We further reduce our feature set to eight using correlation analysis with marginal trade-offs in accuracy and error rates. We collected data from 102 users for benign activities and 103 users for adversarial activities recording over 1.9 million keystroke events in total. Our experiments show that a user's typing behavior can reveal the nature of the typing activity, thereby providing vital cues of a system's threat level.