Reviewerly/exHarmony huggingface dataset:
Artificial intelligence accelerator using photonic computing

Artificial intelligence (AI) and its applications require an increasing demand for computing resources that is far beyond the developing speed of electronic computing processors. Photonic computing, processing the information using photons instead of electrons, has been considered the next generation of computing modality to achieve disruptive improvements in computing speed and efficiency. This chapter systematically introduces ways to build the photonic hardware accelerator to implement a type of AI model, i.e., artificial neural network, based on photonic computing. The introduced photonic neural network architectures are built on the most fundamental building blocks of optical weighted interconnections and optical nonlinear neuron functionalities. As the photonic AI accelerator requires the cooperative work of various high-performance photonic devices, the researches of the on-chip photonic input-output interface, photon memory, and photon modulator are also summarized. Furthermore, we present detailed overviews of in-situ optical backpropagation training methods for photonic AI accelerators to achieve large-scale photonic intelligence computing with high accuracy.
Artificial neural networks

At the end of this chapter, you will be able to: Present the estimations of artificial neural networks of the multilayer feedforward networks type. Understand the need for the correct use of activation functions. Demonstrate the calculations of weights and bias values and the calculation of model errors for each iterative procedure. Estimate artificial neural network models in R-based language, and learn to interpret their results.
Enhanced and interactive graphs

At the end of this chapter, you will be able to: Understand the basic syntax of the package plotly Build bidimensional and tridimensional graphics in R-based language with the package plotly
Dense Depth-Guided Generalizable NeRF

Neural rendering approaches enable photo-realistic rendering on novel view synthesis tasks while their per-scene optimization remains an issue for scalability. Recent methods introduce novel neural radiance field (NeRF) frameworks that generalize to unseen scenes on-the-fly by combining multi-view stereo with differentiable volume rendering. These generalizable NeRF methods synthesize the colors of 3D ray points by learning the consistency of image features projected from given nearby views. Since the consistency is computed on the 2D projected image space, it is vulnerable to occlusion and local shape variation by viewing direction. To solve this problem, we present dense depth-guided generalizable NeRF that leverages the depth as the signed distance between the ray point and the object surface of the scene. We first generate the dense depth maps from sparse 3D points of structure from motion (SfM) which is an inevitable step to obtain camera poses. Next, the dense depth maps are exploited as complementary features invariant to the sparsity of nearby views and mask for occlusion handling. Experiments demonstrate that our approach outperforms existing generalizable NeRF methods for widely used real and synthetic datasets.
Introduction to R-based language

At the end of this chapter, you will be able to: Demonstrate the installation of R-based language and its integrated development environment (IDE), RStudio software. Introduce the grammar and some basic syntax of R-based language. Learn to work with objects, understand the need for functions and their arguments, and install and load packages. Introduce how to load, manipulate, and save datasets.
Data visualization and multivariate graphs

At the end of this chapter, you will be able to: Understand the basic syntax of the package ggplot2 Learn to blend graphics layers with the package ggplot2 Build graphics in R-based language with the package ggplot2
Understanding and Mitigating Twin Function Misuses in Operating System Kernel

Major operating system kernels expose twin functions, which are groups of internal primitives that have mostly common but slightly diverging semantics, to kernel modules and subsystems. They are created to make the basic primitives work well in various scenarios. Unfortunately, though being expected as solutions, twin functions may turn to problem-makers in practice. As we have observed from over 500 patches applied to upstream Linux and FreeBSD, developers choose an improper one from the twins, leaving the kernel with stability and security bugs as well as error-prone code. In this paper, we aim to understand and mitigate the twin function misuse problem. First, we provide an informative discussion on the misuse-fix patches. We find that violating the constraints from calling context, missing the primitives with better performance, lacking the necessary security enhancements, and breaking the kernel coding style are the four major factors that lead to misuse. We then identify the programming rules from the patches and apply them with a static program analysis tool extended from Coccinelle, including callgraph tainting and type-based function pointer resolving. We have 136 patches accepted by the Linux community and fix 320 new misuses in the upstream Linux kernel.
Time-Optimal Spiral Trajectories With Closed-Form Solutions

The Archimedean spiral is space-filling plane curve that is found in applications ranging from coverage path planning for robot exploration to scan pattern generation for medical imaging. The constant linear velocity (CLV) parameterization of this spiral is of particular interest due to its fixed path velocity and isotropic sampling capability, but the high accelerations near its origin singularity yield poor trajectory tracking that limit its utility. Here, I derive a closed-form time-optimal time scaling for CLV spirals with large path velocities that mitigates the singularity by inspecting the CLV spiral's acceleration envelope. When applied to two degree-of-freedom Cartesian scanner, I demonstrate that this approach reduces trajectory tracking error by up to 97.1% as compared to naïve CLV spirals with low computational overhead. I further show that this time scaling eliminates the central image distortion near the origin for scanning applications that rely on CLV spirals.
Cross Spectral Disparity Estimation From VIS and NIR Paired Images Using Disentangled Representation and Reversible Neural Networks

There exists spectral gap between visible (VIS) and near infrared (NIR) images causing dissimilar intensity according to reflection property of objects and materials. Therefore, it has a limit of applying traditional stereo matching to cross spectral disparity estimation. In this paper, we propose cross spectral disparity estimation from VIS and NIR paired images using disentangled representation and reversible neural networks. We build a supervised learning framework based on reversible blocks to extract scene features robust against the spectral gap. Reversible blocks decompose features into scene and style components to bridge the spectral gap between VIS and NIR images. We perform stereo matching on the scene component to get an initial disparity map by a 3D convolutional neural network. To generate clear edges in the disparity map, we use a semantic segmentation network as auxiliary information to refine the initial disparity map. Besides, to consider the lack of the ground truth, we synthesize reference disparity maps with guided image filtering. Experimental results demonstrate that the proposed method achieves accurate edges in disparity along object boundaries and outperforms the state-of-the-art methods in both visual comparison and quantitative measurements.
A proposal to improve on existing Grapheme-to-Phoneme conversion models informed by linguistics

Grapheme-To-Phoneme (G2P) converts an input letter sequence into its corresponding pronunciation. Several rule-based G2P models are available for Korean, but their performance in view of individual phonological rules in Korean is not well-investigated. We establish phonological rules to be reflected in a G2P model for Korean, and test the model performance of two most popular G2P models of Korean, i.e., g2pk and KoG2P, regarding their performance on each of these rules. We created a golden corpus to evaluate the performance of the current G2P models based on manual phonetic transcription. We then measured the performance of two G2P models, and identified the phonological rules in which the models show relative success or failure in deriving the correct output. We implemented additional phonological rules such as h-deletion, ui-variation, consonant place assimilation, and restructured the ordering of rules such as h/th-neutralization in the model. We show that our revised model makes a substantial improvement on model performance. Further, we argue that the major limitation of the current rule-based approaches to G2P is in its binary approach to phonological rules and lack of information about prosodic boundaries. We propose that a rule-based G2P system should reflect the stochastic nature of phonological processes informed by existing research on the gradient nature of phonological rule application as a function of factors such as lexical frequency and speech register.
The Impact of Augmented Reality on the Learning Abilities of Primary and Secondary Students at the Cognitive and Affective levels: A Meta-analysis

Objective - The present study investigates the impact of augmented reality (AR) on the learning abilities of primary and secondary students at the cognitive and affective levels. Methodology/Technique - The data of 59 relevant domestic and international studies between 2010 and 2021, including 83 studies and 4123 samples, were analyzed through CMA for meta-analysis. Finding - The overall effect size of AR technology on the teaching effectiveness of primary and secondary school students was 0.598, which had a positive contribution. The impact of AR technology on primary and secondary school students was stronger at the affective levels than at the cognitive levels. Novelty - In the process of learning with AR-assisted resources for primary and secondary school students, the school levels, teaching methods, and resource types did not differ significantly, and the differences in teaching outcomes were manifested in the different subject content. Type of Paper - Empirical Keywords: Augmented reality (AR); Learning effects; Meta-analysis; Primary and Secondary students; Cognitive; Affective. JEL Classification: I21, I26. Reference to this paper should be referred to as follows: Shen, Q; Tsai, P. (2022). The Impact of Augmented Reality on the Learning Abilities of Primary and Secondary Students at the Cognitive and Affective levels: A Meta-analysis, GATR-Global J. Bus. Soc. Sci. Review, 10(4), 220–228. https://doi.org/10.35609/gjbssr.2022.10.4(1)
Exploring Autonomous Learning of English Language through Digital Literacy: A Study in Pakistani Context

The purpose of this mixed-method design research is to (1) investigate the factors that affect students to regulate their autonomous learning of language through digital literacy. (2) find out how digital literacy can be used to create an equal opportunity for all students to be successful autonomous language learners, and (3) analyze to what extent digital literacy helps students to improve their academic achievement. The quantitative data were analyzed by using descriptive statistics, whereas the qualitative data was processed employing thematic analysis. The outcomes indicated that the students participated in many out-of-class English learnin practices. The results of interviews reflected the results of the questionnaires, with learners reporting both external and internal reasons that affected their decision to switch to autonomous learning of English.
Blockchain-based dynamic trust access control game mechanism

The blockchain-based access control mechanism (BACM) is gradually becoming an essential paradigm for solving dynamic and trusted access control problems in the open network environment. However, since the current open network environment has such features as dynamic variability and the uncertainty of user identity, most of the existing BACM cannot solve the access control problems in the current open network environment in a dynamic, flexible, proactive, efficient, and fine-grained approach. In this paper, we propose a novel BACM scheme to address such problems. Specifically, we first design a new, proactive, and fine-grained access control model, by utilizing the dynamicity and fine-grain of the attribute-based access control model, flexibility shown by the trust evaluation mechanism in evaluating the trust level of users, and proactivity shown by the game evaluation mechanism in curbing malicious users who suddenly launch malicious access requests. Second, based on the above access control model, we propose a dynamic, flexible, and proactive BACM for the current open network environment, exploiting the trustworthiness and transparency that the smart contract and the transaction mechanism in blockchain technology show during program execution. Further, a double sliding storage window is built, guaranteeing accurate data acquisition by BACM while efficiently allowing it to acquire time-sensitive data during the permission management process. Meanwhile, a pre-authorization concept is introduced to improve the efficiency and flexibility of BACM in processing access control problems. Security analysis demonstrates that our proposed BACM scheme satisfies the simple security issue and the simple availability issue. Experiments on a real user trust record dataset demonstrate the high effectiveness of the proposed BACM scheme in evaluating and deciding on access requests and the superiorities over most existing schemes in dynamicity, fine granularity, flexibility, and proactivity.
Need analysis of integrated natural science e-module development using Kvisoft Flipbook Maker

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Annisa Rokhim, Sulistyo Saputro, Sentot Budi Rahardjo; Need analysis of integrated natural science e-module development using Kvisoft Flipbook Maker. AIP Conference Proceedings 27 January 2023; 2540 (1): 110020. https://doi.org/10.1063/5.0106198 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Very Low Illumination Image Enhancement via Lightness Mapping

Image taken in low light suffers from poor quality, low contrast, and color distortion, so this image with too low light is difficult to improve due to lack of color information and lack of lightness. Therefore, enhancing images is an important topic because it is included in many important applications such as surveillance, tracking, and medical images. In this paper, we introduce a new method for enhancing very low illumination images. This method consists of two steps, first step includes color restoration by using adding a median filter and min filter, the color restoration applies on each color component R, G, B then converted to HSV color model to extract lightness component (v) then apply the second step on component (v) which include illumination enhancement using sigmoid function and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then recombine with chromatic components (H, S). The HSV model is converted to the RGB model to get the final enhanced image. The proposed method is applied on eight images taken from the DICM database, and the proposed method is compared with other methods such as Parallel Nonlinear Adaptive Enhancement (PNAE), New Nonlinear Adaptive Enhancement (NNAE), Naturalness (NAT) preserved Fusion-based-enhancing (FU), new contrast enhancement (CN), and image enhancement by fuzzy (EF) using image quality metrics as entropy and naturalness image quality assessment evaluator (NIQE). Experimental results show that the proposed method has the highest value than other methods, where the proposed method achieves average quality value (6.65) and (3.80) for eight images of NIQE and entropy metrics.
AI-Based Automated Approach for Trend Data Generation and Competitor Benchmark to Enhance Voice AI Services

The worldwide growth of voice-based NLP system is immense and specifically toward the smartphone platform which interacts with other connected devices. The growth of smartphone and NLP raises user expectation toward specific solutions with high quality End-to-End experience, adaptation of live trending data and better experience than competitor devices to lead in market. The proposed AI-based method to generate market trending structured utterances and method to validate NLP (ASR, NLG, E2E) system across competitor devices. The proposed framework has four novel aspects: (1) generation of market data with structured text utterances, (2) auto script generation for native domain with key E2E validation parameters, (3) identify ASR issues and segregate specific word and generate audio input to system to define language model or accent model improvement areas and (4) semantic approach to validate End-to-End system using dialog responses for all voice-enabled devices. The proposed framework has been used in Samsung voice platform project named as Bixby, which helped to reduce lot of manual effort and enhance NLP in market trending data in real time and user experience to lead in market.
Image Colorization: A Convolutional Network Approach

In recent days, due to some application of image colorization as automatic coloring of old pictures, correctly identification of thief from sketch image, etc., image colorization has become hottest area in research domain. Machine learning plays an essential role in this concern. This paper illustrates deep convolutional neural network approach with VGG16 pre-trained classifier that takes grayscale image as input image and gives its equivalent-colored image as output image. In this research, deep convolutional neural network is categorized into encoder, fusion, and decoder part, and VGG16 pre-trained model is used for extracting high-level features from an image. The proposed network is trained till 2000 epochs. The final-colored image is compared with ground truth image.
An Efficient Group Signature Scheme Based on ECDLP

In today’s digital world, group signature plays an important role for different security aspects in institutions, government organizations, etc. Group signature is basically applicable if there is a need of signing a digital document by an authorized group member anonymously on behalf of whole group. We have proposed a group signature scheme in the identity-based setting. Its security relies on elliptic curve discrete logarithm problem (ECDLP). Our proposal is the first ever scheme in ECDLP setting and much more efficient in terms of computational complexity. It is applicable in all those environments where less bandwidth is required as blockchain architectures, blockchain-based mobile-edge computing (BMEC), etc.
A cross-spectral target tracking method based on a hidden Markov model

Abstract Aiming at the problem of target tracking stability in the case of low signal-to-noise ratio and multi-target cross-motion, this paper combines the hidden Markov model and complementary tracking and proposes a new automatic tracking method. The stability of the new algorithm is proved by theoretical analysis and computer simulation experiments. In the case of low signal-of-noise ratio and multi-target cross-motion, the new algorithm has good performance.
K-Means Algorithm to Form Dynamic Cluster Formation to Counter the Static Property of K-Means

Clustering algorithms are quite popular with the big data analysis and essential tools for segregating data into small sections. Analyzing big data is a tough task but can be done easily if the data is segregated into sections on the basis of some parameters or on the basis of some similarity. The aim of clustering in addition cataloging algorithms is to make logic and abstract values from big sets of organized and unorganized data. The developed algorithm is inspired from the elbow methodology to identify the optimal cluster count value for both the cases of minima and maxima. K-means bunching are the kind of solo appreciating that is applied in the cases where we have non-labeled information (i.e., information lacking of characterization classes or gatherings). The main goal of the calculation is to efficiently determine the groups in the statistics and the number of assemblies addressed by the K variable. The calculation performs iteratively to allocate each statistics highpoint from one of the K gatherings reliant on the highpoints which should be specified and statistic attentions are grouped reliant on component’s nearness also. The significances of the K-implies bunching calculation are: the centroids of the K groups that can be applied to name new statistics Labels for the grounding statistics (where each statistics point is assigned to the solitary bunch) and can strongly opposes/counters the static behavior of K-means by refining results of elbow method by using multiple iteration and by using min. of results of well-known distance formulas. Finally, we will have new efficient variant of K-means.
An Improved IMM-GPDA Tracking Algorithm

Abstract To improve the accuracy and efficiency of multi-maneuvering target tracking in dense clutter environments, an improved interactive multi-model generalized probabilistic data association (IMM-GPDA) algorithm is proposed in this paper. The algorithm introduces Doppler information into the measurement data. Firstly, the measurement data is pre-processed by double-threshold regional density clutter elimination, and then the pre-processed data is put into the improved IMM-GPDA algorithm with square-root cubature Kalman filter (SRCKF) and model transition probability adaptation for interaction, association, and filtering. Simulation results show that the algorithm has advanced accuracy, instantaneity, and robustness in tracking environments with multi-maneuvering targets and dense clutters.
Quantum Computation with Continuous-Variable Systems

In this chapter, the Gottesman-Kitaev-Preskill (GKP) code, a quantum error-correcting code for continuous-variable quantum information processing, is studied in the context of optical quantum computation. Especially, the equivalence of its approximations is shown, and the cost-efficient implementation of the universal optical quantum computation using the “GKP magic state” is developed.