Reviewerly/exHarmony huggingface dataset:
APPLICATION OF DIGITAL IMAGE CORRELATION TO CAPTURE THE CRACK MOUTH OPENING DISPLACEMENT OF THE NOTCHED STEEL FIBER REINFORCED CONCRETE (SFRC) BEAM

The application of digital image correlation (DIC) to capture any point in the experimental test is found to be promising. Using the DIC and appropriate tool can overcome the limitation of the traditional sensors to capture the movement or displacement in the tested specimen. In this study, an open-source DIC called Digital Image Correlation Engine (DICE) is used to capture the crack mouth opening displacement (CMOD) of the notched steel fiber reinforced concrete (SFRC) beam with points tracking method. There are three beam specimens with different sizes and similar aspect ratios reported in this paper. All beams have 0.5% steel fiber volumetric content. The SFRC beams are marked with points and tested under a three-point bending flexural test. Custom firmware for Canon DSLR 650D digital camera called Magic Lantern is used to capture the pictures per one second. DICE software is used to analyze the point movements and dumped the output file. ParaView 5.9.0 is then used to visualize the data. A method to calibrate the point coordinate with actual measurement is proposed. A small script is written in Visual Basic Application (VBA) in Microsoft Excel to correlate the displacement for each point of interest with the recorded applied load. With the developed script, any point of interest tracked with DICE can be related to the recorded data from the data logger. From this study, the obtained CMOD with the corresponding applied load is presented, which can be used to investigate the flexural fracture energy of the SFRC beam.
TBLT in context

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Top-K Pseudo Labeling for Semi-Supervised Image Classification

In this paper, a top-k pseudo labeling method for semi-supervised self-learning is proposed. Pseudo labeling is a key technology in semi-supervised self-learning. Briefly, the quality of the pseudo label generated largely determined the convergence of the neural network and the accuracy obtained. In this paper, the authors use a method called top-k pseudo labeling to generate pseudo label during the training of semi-supervised neural network model. The proposed labeling method helps a lot in learning features from unlabeled data. The proposed method is easy to implement and only relies on the neural network prediction and hyper-parameter k. The experiment results show that the proposed method works well with semi-supervised learning on CIFAR-10 and CIFAR-100 datasets. Also, a variant of top-k labeling for supervised learning named top-k regulation is proposed. The experiment results show that various models can achieve higher accuracy on test set when trained with top-k regulation.
Extreme Learning Machine-Based Operational State Recognition: A Feasibility Study with Mechanical Vibration Data

The benefits of digital twins and accurate near real-time on-site condition monitoring of heavy machinery or load-bearing structures are undeniable. Both demand computationally light and accurate models based on continuously measured data. Extreme Learning Machine (ELM) algorithm provides the means for building accurate and fast predicting classification models. Therefore, the feasibility of the ELM algorithm for building models for near real-time operational state recognition of a rotating machine was studied. Three different models, called one, two, and six cycles, built using the ELM algorithm were compared with corresponding models trained using Support Vector Machine (SVM) and linear regression (LR) algorithms based on their accuracy and prediction times. The comparisons show that the SVM algorithm produces the best accuracy, but with the cost of high prediction times. The LR models have the lowest prediction time. In contrast, the ELM model for the two cycles presents better performance than the corresponding LR and SVM models when the combination of accuracy and the prediction time is considered. The great benefit of the ELM method comes from its mathematical properties: new data can be added to the ELM model without the need to retrain the whole model, and the model is competent to take strong nonlinearities into account. Thus, the possibilities of the ELM algorithm to act as a novelty detector in operational state recognition shall be investigated.
DebtCom: Technical Debt-Aware Service Recomposition in SaaS Cloud

Given the changing workloads from the tenants, it is not uncommon for a service composition running in the multi-tenant SaaS cloud to encounter under-utilization and over-utilization on the component services. Both cases are undesirable and it is therefore nature to mitigate them by recomposing the services to a newly optimized composition plan once they have been detected. However, this ignores the fact that under-/over-utilization can be merely caused by temporary effects, and thus the advantages may be short-term, which hinders the long-term benefits that could have been created by the original composition plan, while generating unnecessary overhead and disturbance via recomposition. In this article, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DebtCom</monospace> , a framework that determines whether to trigger recomposition based on the technical debt metaphor and time-series prediction of workload. In particular, we propose a service debt model, which has been explicitly designed for the context of service composition, to quantify the debt. Our core idea is that recomposition can be unnecessary if the under-/over-utilization only cause temporarily negative effects, and the current composition plan, although carries debt, can generate greater benefit in the long-term. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DebtCom</monospace> on a large scale service system with up to 10 abstract services, each of which has 100 component services, under real-world dataset and workload traces. The results confirm that, in contrast to the state-of-the-art, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DebtCom</monospace> achieves better utility while having lower cost and number of recompositions, rendering each composition plan more sustainable.
Jointly Distributed Filtering Based on Generalized Maximum Correntropy Criterion: Memory-Based Event-Triggered Cases

This article addresses jointly distributed entropy filtering issues based on the generalized maximum correntropy criterion (GMCC) for discrete-time stochastic parameter systems with fault and non-Gaussian noise effects. By taking current and historical triggered information, a memory-based event-triggered scheme with a time-varying threshold is put forward to govern the network communication. According to the constructed jointly distributed entropy filter with a two-step form, the upper bounds of the filtering error covariance matrices are derived and an ideal filter gain is obtained to maximize GMCC. Furthermore, an accessible gain is received via fixed-point iterative rules and the corresponding convergence is disclosed in theory. Finally, an application of the proposed distributed filter in ballistic object tracking is provided to show its effectiveness under non-Gaussian environments.
Extended Target Tracking in Human–Robot Coexisting Environments via Multisensor Information Fusion: A Heteroscedastic Gaussian Process Regression-Based Approach

In this article, a new systematic approach to sensor fusion and state estimation is proposed for extended target tracking in human–robot coexisting environments. The developed method, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human feature-based extended target tracking via multisensor information fusion</i> (HFBETT-MSIF), can assimilate information from the onboard camera and sonar sensor of a mobile robot in a unified way, during tracking of a pair of human shoes. A novel generalized measurement model containing the complete information of the human target is formulated for both sensors, thus rendering the tracking system potentially robust to the failure of any one sensor. The study illustrates how <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heteroscedastic Gaussian process</i> (HGP) regression can be used to derive the measurement model. It also develops an advanced HGP model, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bias-minimized most likely HGP</i> , to interpret the real-world shoe-contour data subjected to heteroscedastic noise. Performance evaluations conducted for real-life shoe tracking demonstrate the supremacy of the HFBETT-MSIF.
ContextLoc++: A Unified Context Model for Temporal Action Localization

Effectively tackling the problem of temporal action localization (TAL) necessitates a visual representation that jointly pursues two confounding goals, i.e., fine-grained discrimination for temporal localization and sufficient visual invariance for action classification. We address this challenge by enriching the local, global and multi-scale contexts in the popular two-stage temporal localization framework. Our proposed model, dubbed ContextLoc++, can be divided into three sub-networks: L-Net, G-Net, and M-Net. L-Net enriches the local context via fine-grained modeling of snippet-level features, which is formulated as a query-and-retrieval process. Furthermore, the spatial and temporal snippet-level features, functioning as keys and values, are fused by temporal gating. G-Net enriches the global context via higher-level modeling of the video-level representation. In addition, we introduce a novel context adaptation module to adapt the global context to different proposals. M-Net further fuses the local and global contexts with multi-scale proposal features. Specially, proposal-level features from multi-scale video snippets can focus on different action characteristics. Short-term snippets with fewer frames pay attention to the action details while long-term snippets with more frames focus on the action variations. Experiments on the THUMOS14 and ActivityNet v1.3 datasets validate the efficacy of our method against existing state-of-the-art TAL algorithms.
BPMN2 user story: Web application for generate user story from BPMN

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 Kinara Al Ghiffari, Hisyam Fariqi, M. Dani Rahmatullah, M. Reza Zulfikarsyah, M. Roziq Syarwan Evendi, Tanvi Azmia Fathoni, Nesa Giarfina, Badrus Zaman, Indra Kharisma Raharjana; BPMN2 user story: Web application for generate user story from BPMN. AIP Conference Proceedings 25 January 2023; 2554 (1): 040003. https://doi.org/10.1063/5.0103685 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
Notes on Image Watermarking and Steganography

Digital information can be reproduced, duplicated, and distributed easily which makes information hiding like image watermarking and steganography as powerful security techniques. Image watermarking and steganography techniques make the embedding hidden data unnoticeable where the sender’s identity is hidden by image watermarking while the existence message is concealed by image steganography. This paper gives a basic framework for image watermarking and steganography. Then it figures out some basic differences based on the existing research works on image watermarking and steganography. After then, it highlights state-of-the-art methods based on basic design requirements. Some challenges along with suggestions are given, and a future research direction is provided.
Implementation of Twisted Edwards Curve-Based Unified Group Operation for ECC Processor

Exchanging information throughout the online or virtual media is one of the rapidly growing issues. Importantly, there must have security concerns for exchanging the information and thanks to cryptography to handle the concerned points. The group operation along with the elliptic curve cryptography (ECC) processor ensures the desired security. This work focuses on the newly invented twisted Edwards curve for executing group operations to enable enhanced security. Two novel architectures, referred to as first and second architectures of unified group operation, are designed as 256-bit over the prime field. On the Virtex-7 field-programmable gate array, the proposed architectures are realized. Interestingly, the second design reduces 128 clock cycles than the first one and also compared to other available designs in the literature. From the best of the author’s knowledge, the designed architectures provide better performance with respect to throughput and slice area than the other related work, and thus, it is realizable as an effective design for the ECC processor on FPGA.
A Multigene Genetic Programming Approach for Soil Classification and Crop Recommendation

The economy of Bangladesh depends to a large extent on agriculture. Besides, a large number of the total population are employed in this sector. In Bangladesh, the population is fast expanding while the overall amount of arable land is constantly diminishing. Because various crops require different soil types, identifying and selecting the proper kind of soil is critical to ensuring optimal crop yield while working with limited land resources. In this study, we present a soil classification method using symbolic regression of multigene genetic programming. Dataset for this work is collected from Soil Resource Development Institute, Government of the people’s republic of Bangladesh. GPTIPS toolbox is used to select the appropriate features for training and developing a mathematical model. In a short period of time, the model generates correct results for both the training and testing datasets. Besides, the error rate for soil type classification is extremely low. Finally, suitable crops are recommended based on the accurate classification. According to the results, the proposed multigene genetic programming (MGGP)-based approach performs the best in terms of accuracy, with an accuracy of 98.04 percent. Moreover, our proposed soil classification method outperforms many existing soil classification methods.
Controlling Information Risk in E-commerce

"The wise hear in silence, the wise see in the invisible", which illustrates the importance of e-commerce enterprises to prevent information security risks. This chapter presents the information security risk management process, participants, and their responsibilities, and explains what is information security risk assessment. This paper introduces the form of information security risk assessment, the process of information security risk assessment, as well as the process and method of information security risk disposal, and analyzes the information security risk control measures, mainly including technical measures and normative measures. Among them, technical measures include cryptography, key management technology, network encryption and information protection technology, emerging encryption technology, digital signature technology, authentication technology, digital certificates, and public key infrastructure, while normative measures include information security management measures, information security management organizations, information security standards, information security laws and regulations, etc.
Prediction of positive Covid-19 confirmation cases in Indonesia with parametric and nonparametric approaches

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 Ahmad Yusuf Albadri, Idrus Syahzaqi, M. Fariz Fadillah Mardianto; Prediction of positive Covid-19 confirmation cases in Indonesia with parametric and nonparametric approaches. AIP Conference Proceedings 25 January 2023; 2554 (1): 030014. https://doi.org/10.1063/5.0108510 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
Lumen and nuclei detection in histopathology of prostate cancer based on morphological feature extraction

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 S. T. Yukiko Irliyani, Riries Rulaningtyas, S. Anny Rahaju, I. Made Mas Dwiyana Prasetya Wibawa; Lumen and nuclei detection in histopathology of prostate cancer based on morphological feature extraction. AIP Conference Proceedings 25 January 2023; 2554 (1): 060001. https://doi.org/10.1063/5.0106231 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
Sentiment Analysis and Emotion Detection from Khasi Text Data—A Survey

The state of Meghalaya, which is one of India’s seven north-eastern states and shares an international border with Bangladesh, boasts 1.6 million native speakers of the Khasi language (according to the 2001 census). The main objective of this paper is to provide an overview of the most recent techniques and models for sentiment analysis, with special attention to low-resource languages like the Khasi language. The survey summarises the popular data sets and models applied to them, the accuracy obtained from them, and also a comparison of these models. This paper also highlighted the scope and challenges of sentiment analysis and emotion detection on Khasi text data. Finally, a future directions are provided for research work with sentiment analysis with Khasi text data.
Efficient Malaria Cell Image Classification Using Deep Convolutional Neural Network

Malaria is a life-threating disease that affects millions of people, each year. The way of diagnosing malaria is visually examining blood smears under the microscope by skilled technicians for parasite-infected red blood cells. An automatic malaria classification based on machine learning can boost up the diagnosing process more effectively and efficiently to detect malaria in the earlier stage. In this research, an efficient and accurate convolutional neural network based model is proposed to classify malaria parasitic blood cell from microscope slides as whether infected or uninfected. The customized model trained on the NIH dataset consists of 27,578 RBC images, the accuracy of this model is 99.35% with 99.70% sensitivity and 99.00% specificity. The proposed model shows outform compare to others in terms of the performance indicators such as sensitivity, specificity, precision, F1 score, and Matthews’s correlation coefficient.
Interval estimation for nonparametric regression using Fourier series estimator in longitudinal data

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 M. Fariz Fadillah Mardianto, Gunardi, Herni Utami; Interval estimation for nonparametric regression using Fourier series estimator in longitudinal data. AIP Conference Proceedings 25 January 2023; 2554 (1): 030010. https://doi.org/10.1063/5.0103799 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
Emotional artificial neural network: A new ANN model in hydroinformatics

Modeling the hydroenvironmental processes is an important task for water resource management. Over the years, the artificial intelligence (AI)-based models such as the artificial neural networks (ANN) have replaced the empirical models. The ability to ANN models relies on accurately modeling stochastic and complex processes according to observations. The seasonal nature of most of the hydroenvironmental processes such as runoff-rainfall processes, makes the AI models vulnerable to challenges like overfitting problems, hence requiring some data pre/postprocessing processes for obtaining the desired result. In this chapter, an emotional artificial neural network (EANN) which is a recent version of neural networks incorporating artificial emotions into the conventional ANN for modeling the hydroenvironmental processes has been discussed. Early applications of the EANN models for the prediction of hydroenvironmental processes proved to provide higher prediction accuracy than the classic ANN. The improved learning ability of the EANN was due to the presence of the hormonal parameters coupled with the feedback loop joining the neural and the hormonal systems which helps the network to distinguish different conditions and seasonality pattern in the data.
Collaborative design principles for smart clothing

Cross-disciplinary collaboration for the design development of smart clothes with wearable technologies covers a breath of disciplines with their disparate cultures. This hybrid area demands a range of knowledge and expertise that includes technical textiles, clothing and footwear design, human physiology, electrical engineering, and computer science. For products to be fit for purpose, from both aesthetic and functional perspectives, the collaborative team must also address the sociocultural demands of the identified target end user. Decisions made by product design teams have become increasingly complex as fibres and textiles are merged with electronic functionality and componentry both by traditional and state-of-the art technical garment production techniques. This hybrid garment sector demands ‘shared language’ to inform collaboration between key contributors across the cross-disciplinary mix, with their differing terminologies, timelines, standards, and routes to market. The author promotes collaborative design process, in bringing all stakeholders together, from both academia and industry, to engage with end users to help identify and address real user needs. A product development team, working within this cross-disciplinary sector, must also interrogate and address sustainable and ethical considerations throughout the collaborative design research and development process from materials and componentry sourcing and garment production to product end-of-life.