Reviewerly/exHarmony huggingface dataset:
Health Index for Satellite System Based on Characteristics of Telemetry Data

Since satellites are non-repairable systems, it is important to detect anomalies early to prevent failures. Data-driven approaches to anomaly detection, which have been activelystudied in recent years, have problems such as low explainability and insufficient training data in initial operation. Thus, we propose a health index that is commonly available for all satellites. It is possible to share training data and examples of anomalies by monitoringhealth status with the same index across different satellites. In this study, we extended our health index defined for the satellite power system to the entire satellite system. Then,we applied the health index to the operational data of the Suzaku satellite and confirmed that the index is useful for anomaly detection.
Anomaly detection for yield improvement in glass production

Predictive maintenance using manufacturing sensor data has attracted attention for reducing defects and selecting appropriate actions. This paper proposes an anomaly detection method using lasso regression and group-wise variable selection based on FTA (Fault Tree Analysis) domain knowledge. We evaluated our approach using real factory data and found that its precision and false positive rate are 66% and 30%, respectively. Moreover, we validate that the visualization of the contribution rate for anomaly detection is helpful for factory maintenance.
Study on the Intent-based System Design Automation Method for Fault Detection and Fault Tolerance

ICT systems are becoming an important infrastructure indispensable for business operations in various industries. While the requirements are becoming more diverse and complex, stable service provision is required. As a result, the burden on operations is increasing, and there are growing expectations for automation technology to solve this problem. This study investigates techniques to significantly reduce the burden on operators by automating the construction and maintenance of systems that satisfy Intent, a concise description of users' requirements. However, even using the results of previous research, it has been difficult to automate operations with consideration for the stability of service provision. Therefore, this paper describes an automated method based on intent for detecting failures that occur in the target system and an automated design method for fault-tolerant systems. First, we describe the basic concept, present the results of thought experiments, and discuss its effectiveness.
Unsupervised Retrieval Based Multivariate Time Series Anomaly Detection and Diagnosis with Deep Binary Coding Models

Retrieval based multivariate time series anomaly detection and diagnosis refer to identifying abnormal status in certain time steps and pinpointing the root cause input variables, i.e., sensors, by comparing a current time series segment and its relevant ones that are retrieved from huge amount of historical data. Binary coding with a deep neural network can be applied to reduce the computational cost of the retrieval tasks. However, it is hard to pinpoint the root cause sensors that are responsible for the anomaly, once multivariate time series segments are transformed into binary codes. In this paper, we present an unsupervised retrieval based multivariate time series anomaly detection and diagnosis method with deep binary coding model, to secure both efficiency and explainability. Specifically, we first transform input multivariate time series segments into low dimensional features with a temporal encoder. Subsequently, two hash functions predict two binary codes with different lengths from each feature. The binary codes with two different lengths can contribute to accelerate both anomaly detection and anomaly diagnosis. Experiments performed on datasets from various domains including real optical network, demonstrate the effectiveness and efficiency of the proposed method.
The Current Research Trend about Machine Learning as a New Way of Fostering Islamic Education in School

This study has the purpose of observing the usage of machine learning as a new way of teaching Islamic education according to the Qur'an and hadith to the young generation. The method used for data collection is bibliometrics, using secondary data in the form of metadata on the Scopus website. The sample is divided into two charts: the Qur'an and the hadith. Around 49 samples were found for the Qur'an category and 16 for the Hadith category. The data was analyzed using bibliometric methods with two software. The result of this study was that the most used words in the author's keywords for Qur'an subjects were machine learning SVM, KNN, and ANN. This means that SVM, KNN, and ANN were used for certain purposes and situations in analyzing the interpretation result inside the text of the Qur'an done by Ulamas. The practical implication of this study is to be used as a reference for Islamic education in Indonesia to use machine learning features.
The Efforts of The Youth of The Bahung Mosque, Sibatu Batu Village, Sei Dadap Sub-District in Improving the Ability to Write Sermon Texts

Quantitatively, the number of preachers who preach through writing is much less than preachers who use spoken language. Therefore, this paper focuses on efforts to improve sermon text-writing skills. This paper uses action research, which is one type of qualitative research. This action research examines things that happen in the community or target group, where the results can be directly worn or felt by the community or target group. The result of this research is the achievement of increased skills, as evidenced by the results of the pretest and post-test. Besides that, there are several inhibiting and supporting factors in the efforts of mosque youth to improve sermon writing skills.
Prediction Retweet Using User-Based and Content-Based with Artificial Neural Network-Harmony Search

Online social networking services allow users to post content in the form of text, images or videos. Twitter is a microblogging social networking service that enables its users to send and read text-based messages of up to 140 characters. Retweet is one of the features in Twitter that is important in disseminating information, popular tweets reflect the latest trends on Twitter, the main mechanism that encourages information dissemination is the possibility for users to re-share content posted by their social connections, then it can flow throughout the system. Retweets happen when someone republishes or forwards a post to their homepage and personal profile. Most retweets are credited to the original author of the original post. The retweet prediction system uses an Artificial neural network optimized for Harmony search with tweets about the Jakarta-Bandung Fast Train, which shows the best results when the oversampling method has been carried out with an f1 score of 96.8%.
Video Steganography

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into the digital video. It is art of hiding secret data in a non-secret digital carrier called cover media. Data exchange occurs with a variety of purpose, ranging from sharing information or just basic communication between people. Data exchanges that occurs every day make data traffic busier. Data theft is made even easier by utilizing the large number of message sender and the busy data traffic. One popular method used to secure data is steganography method. In this research, End of File steganography method is successfully implemented in the various types of multimedia, namely image, audio, and video. this method provides the effect of increasing the size then it is advisable to use video because generally, video size is always larger than image size because it contains image and audio in one file at a time. Keywords- Data Exchange; Steganography; Multimedia; Security
Implementation and empirical evaluation of a quantum machine learning pipeline for local classification

In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. A well-known locality technique is the k -nearest neighbors ( k -NN) algorithm, of which several quantum variants have been proposed; nevertheless, they have not been employed yet as a preliminary step of other QML models. Instead, for the classical counterpart, a performance enhancement with respect to the base models has already been proven. In this paper, we propose and evaluate the idea of exploiting a quantum locality technique to reduce the size and improve the performance of QML models. In detail, we provide (i) an implementation in Python of a QML pipeline for local classification and (ii) its extensive empirical evaluation. Regarding the quantum pipeline, it has been developed using Qiskit, and it consists of a quantum k -NN and a quantum binary classifier, both already available in the literature. The results have shown the quantum pipeline’s equivalence (in terms of accuracy) to its classical counterpart in the ideal case, the validity of locality’s application to the QML realm, but also the strong sensitivity of the chosen quantum k -NN to probability fluctuations and the better performance of classical baseline methods like the random forest.
Memorability-based multimedia analytics for robotic interestingness prediction system using trimmed Q-learning algorithm

Abstract Mobile robots are increasingly employed in today’s environment. Perceiving the environment to perform a task plays a major role in the robots. The service robots are wisely employed in the fully (or) partially known user’s environment. The exploration and exploitation of the unknown environment is a tedious task. This paper introduces a novel Trimmed Q-learning algorithm to predict interesting scenes via efficient memorability-oriented robotic behavioral scene activity training. The training process involves three stages: online learning and short-term and long-term learning modules. It is helpful for autonomous exploration and making wiser decisions about the environment. A simplified three-stage learning framework is introduced to train and predict interesting scenes using memorability. A proficient visual memory schema (VMS) is designed to tune the learning parameters. A role-based profile arrangement is made to explore the unknown environment for a long-term learning process. The online and short-term learning frameworks are designed using a novel Trimmed Q-learning algorithm. The underestimated bias in robotic actions must be minimized by introducing a refined set of practical candidate actions. Finally, the recalling ability of each learning module is estimated to predict the interesting scenes. Experiments conducted on public datasets, SubT, and SUN databases demonstrate the proposed technique’s efficacy. The proposed framework has yielded better memorability scores in short-term and online learning at 72.84% and in long-term learning at 68.63%.
Color image guided depth image reconstruction based on a total variation network

A representative method to improve a depth image is to use an aligned high-quality color image to guide the depth image by migrating the color details to the depth image. In the process of color-guided depth reconstruction, there often is a misalignment of the edge of the color image used to guide the depth image reconstruction and the depth discontinuity of the depth image. This makes the results suffer from texture copy artifacts and blurring depth discontinuities. In this paper, we use a total variation deep network founded on deep learning and high-resolution color images. The experimental result indicates that under the guidance of high-resolution colors, the depth image recovered is closest to the ground truth in the edge contour, the PSNR and FSIM index are suboptimal for 64×, and the contour and position information recovered from the reconstructed depth image can be retained in the very low-resolution depth image.
Design and Development of Morphological Analyzer for Tigrigna Verbs using Hybrid Approach

Morphological analyzer is the base for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs.
Dehazing in hyperspectral images: the GRANHHADA database

Abstract In this study, we present an analysis of dehazing techniques for hyperspectral images in outdoor scenes. The aim of our research is to compare different dehazing approaches for hyperspectral images and introduce a new hyperspectral image database called GRANHHADA (GRANada Hyperspectral HAzy Database) containing 35 scenes with various haze conditions. We conducted three experiments to assess dehazing strategies, using the Multi-Scale Convolutional Neural Network (MS-CNN) algorithm. In the first experiment, we searched for optimal triplets of spectral bands to use as input for dehazing algorithms. The results revealed that certain bands in the near-infrared range showed promise for dehazing. The second experiment involved sRGB dehazing, where we generated sRGB images from hyperspectral data and applied dehazing techniques. While this approach showed improvements in some cases, it did not consistently outperform the spectral band-based approach. In the third experiment, we proposed a novel method that involved dehazing each spectral band individually and then generating an sRGB image. This approach yielded promising results, particularly for images with a high level of atmospheric dust particles. We evaluated the quality of dehazed images using a combination of image quality metrics including reference and non-reference quality scores. Using a reduced set of bands instead of the full spectral image capture can contribute to lower processing time and yields better quality results than sRGB dehazing. If the full spectral data are available, then band-per-band dehazing is a better option than sRGB dehazing. Our findings provide insights into the effectiveness of different dehazing strategies for hyperspectral images, with implications for various applications in remote sensing and image processing.
Axial shift mapping: a self-referencing test for measuring the axial figure of near-cylindrical surfaces

Lateral shearing self-referencing interferometry methods shift the surface under test between measurements to separate its topography from that of the reference surface. However, rigid body errors occur during shifting, creating an ambiguity in the quadratic term of the extracted surfaces. We present axial shift mapping, a lateral shearing self-referencing interferometry method for cylinders, in which the quadratic ambiguity is resolved by measuring the rigid body errors using known artifact mirrors residing in the interferometer’s field of view. First, one-dimensional lines of a flat mirror are measured with 2.8 nm RMS difference compared to a three flat test. Then, axial shift mapping is extended to cylindrical surfaces using a computer generated hologram. We find that axial shift mapping results in full surface extraction of cylindrical optics, along the axial direction, with a repeatability of 4.4 nm RMS. We also find that the reference surface extracted through axial shift mapping is within 4.5 nm RMS of the transmitted wavefront error of the computer generated hologram substrate, which was expected to be the largest contribution of reference wavefront error.
Impact of Innovation on Adoption of Block Chain Technology with the Moderating Role of Digital Marketing

Purpose: This research aims to examine the impact of innovation on blockchain technology adoption within the context of digital marketing.&#x0D; Methodology: A combined qualitative and quantitative approach is employed, with a focus on case studies that use blockchain technology as a lens to assess the influence of innovation on digital marketing.&#x0D; Findings: Our linear regression analysis reveals that while digital marketing contributes to innovation and blockchain adoption, the observed impact is not statistically significant.&#x0D; Unique Contribution to Theory, Practice, and Policy: Given the increasing significance of data security and compliance with regulations like the General Data Protection Regulation (GDPR), it is imperative for digital marketing agencies to prioritize these aspects. By emphasizing these elements, businesses can harness the full potential of blockchain in their advertising strategies.
Solving Fuzzy Convex Programming Problems via a Projection Neural Network Framework

In this paper, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is given using a projection recurrent neural network (RNN) scheme. Since there is a few research for resolving of FNLOP by RNNs, we describe a new framework to solve the problem. By reformulating the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are obtained. Moreover, we utilize the KKT conditions into a RNN as a capable tool to solve the problem. Besides, the global convergence and the Lyapunov stability of the neuro-dynamic model are established. In the final step, some simulation examples are stated to validate the obtained results. Reported results are compared with some other previous neural networks.
Multi-Modal Multi-Channel American Sign Language Recognition

In this paper, we propose a machine learning-based multi-stream framework to recognize American Sign Language (ASL) manual signs and nonmanual gestures (face and head movements) in real time from RGB-D videos. Our approach is based on 3D Convolutional Neural Networks (3D CNNs) by fusing the multi-modal features including hand gestures, facial expressions, and body poses from multiple channels (RGB, Depth, Motion, and Skeleton joints). To learn the overall temporal dynamics in a video, a proxy video is generated by selecting a subset of frames for each video which are then used to train the proposed 3D CNN model. We collected a new ASL dataset, ASL-100-RGBD, which contains 42 RGB-D videos captured by a Microsoft Kinect V2 camera. Each video consists of 100 ASL manual signs, along with RGB channel, Depth maps, Skeleton joints, Face features, and HD face. The dataset is fully annotated for each semantic region (i.e. the time duration of each sign that the human signer performs). Our proposed method achieves 92.88% accuracy for recognizing 100 ASL sign glosses in our newly collected ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand gestures from RGB-D videos is further demonstrated on a large-scale dataset, ChaLearn IsoGD, achieving the state-of-the-art results.
High dynamic range 3D measurement based on polarization and multispectrum co-modulation

Three-dimensional (3D) shape measurement serves an important role in many areas, and fringe projection profilometry (FPP) is a widely used 3D measurement technique due to its non-physical contact and high speed. The real measurement scenarios are often mixtures of specular and diffuse reflections, causing overexposed and underexposed areas to co-exist. Currently, utilizing FPP to simultaneously measure overexposed and underexposed areas remains a challenge. To solve this problem, we propose a mixed reflection model and what we believe to be a novel high dynamic range (HDR) 3D measurement method based on polarization and multispectrum co-modulation. In mixed reflection, the fringe images captured by the polarized color camera can be modulated to different intensities between different channels due to the co-modulation effect. By synthesizing all sub-images, high-modulation fringe images are formed and simultaneous reconstruction of overexposed and underexposed surfaces is finally achieved. Compared to conventional methods, the proposed method is more effective for measuring complex reflection situations, especially when objects with specular and diffuse surfaces simultaneously exist in the scene. And what we believe to be a novel no-registration-error calibration framework for multi-channel cameras has also been proposed, which both acquires a significant amount of information in the region with HDR problems and avoids the registration error due to the physical distances between different channels. Experiments were conducted to verify the effectiveness of the proposed method.
Structure optimization of heterogeneous compound eye camera for improving the detection performance

To achieve fast location, precise tracking and accurate identification over a large field of view (FOV), we have proposed a heterogeneous compound eye camera (HeCECam), which consists of a heterogeneous compound eye array, an optical relay system and a CMOS detector. However, the current HeCECam can hardly acquire high-precision 3D information of the targets to realize these applications. To solve this challenge, we propose a scheme on optimizing the structure of the HeCECam to improving the detection performance, including the optimization of the distribution uniformity of the sub-eyes with the proposed “Three-direction center-of-gravity subdivision (TGS)” and the enhancement of the compatibility between heterogeneous compound eyes and the optical relay system with the proposed compensation method for tilt. The TGS significantly reduces the distribution unevenness of sub-eyes down to 117% from the previous 152%, and provides symmetry to the heterogeneous compound eye array. The tilt compensation effectively addresses previous imaging defects, such as distortion of sub-images, increased stray light, and support structures being imaged, and it improves the imaging clarity of the system, especially in external FOV. Based on two proposed methods, we re-design and fabricate the heterogeneous compound eye array to obtain a high-performance prototype. To verify the imaging capacities of the optimized HeCECam, a series of comparison experiments are performed, including blank scene imaging, FOV tests, resolution verification and real-world scene imaging. The results show that the previous imaging defects have been well eliminated, and the optimized prototype has stronger resolving power and wider FOV. This allow the HeCECam to perform better in subsequent practical applications, such as wide-area surveillance, forewarning, and navigation.