Reviewerly/exHarmony huggingface dataset:
Blockchain and Cyber-Physical Systems

Cyber-Physical Systems (CPS) engage computational and physical capacities to support modern industrial production. When digital resources are used to support the physical systems, reducing human intervention and increasing automation, it is essential to provide a high level of information security. Information security encompasses data confidentiality, integrity and availability, and any of these three components can be compromised, thus leading to devastating consequences. Blockchain is an emerging technology that could improve the data integrity of Cyber-Physical Systems. However, introducing distributed information fortifications such as blockchain brings out more challenges, which this chapter discusses in detail.
IoT CPS Interpretation for Healthcare Systems

The Internet of Things (IoT) has empowered the integration of many enabling technologies to mark the highest position in the healthcare systems, Smart Grids, and Supply Chain Management. The intelligent healthcare systems support the remote patient monitoring system that avoids the direct appointment to doctors and disease prediction, Tele Healthcare, and surgery assistive system. The recent pandemic Covid-19 shakes all the basements of humans and the wealth and prosperity of the world. In this pandemic situation, IoT supports the globe through online mode healthcare and emergency services for the patients. This article depicts the taxonomy of IoT-based healthcare systems. The Internet of Medical Things (IoMT) deals with all the predictions and services for the healthcare system, and it is a branch of IoT. The IoT support in the healthcare system was firm in a pandemic. IoT-based healthcare systems provide more incredible and unimaginable applications like Telemedicine, IoHT, and more to the world. The Internet of Healthcare Things (IoHT) entirely deals with the healthcare domain. Cyber Physical System supports IoT in all the ways to be firm as to work smartly in all domains like Industries, Healthcare and all applications.
Tail Prediction for Heterogeneous Data Center Clusters

Service providers need to meet their service level objectives (SLOs) to ensure better client experiences. Predicting tail sojourn times of applications is an essential step to combat long tail latency. Therefore, as an attempt to further unravel the power of our prediction model, new study scenarios for heterogeneous environments will be introduced in this research by using either of two methods: white- or black-box solutions. This research presents several techniques for modeling clusters of inhomogeneous nodes. Those techniques are recognized as heterogeneous fork-join queuing networks (HFJQNs). Moreover, included in the research is a nested-event-based simulation model, borrowing help from multi-core technologies. This model adopts the multiprocessing technique to take part in its design to enable different architectural designs for all computing nodes. This novel implementation of the simulation model is believed to be the next logical step for research studies targeting heterogeneous clusters in addition to the several provided scenarios. Experimental results confirm that even with the existence of such heterogeneous conditions, the tail latency can be predicted at high-load regions with an approximated relative error of less than 15%.
Mammogram classification using VGG-16 architecture

The breast image x-ray is done to obtain the digital mammogram images acquired as input for the proposed model. Then the data augmentation technique is applied to a small dataset image to manipulate multiple sets of images. The breast cancer diagnosis includes preprocessing pre-processing the Deep Convolutional Neural Network (DCNN) with Transfer Learning (T.L.) method aiding the healthcare center in automated digital medical imaging technology. Our experiment used a pre-trained VGG-16 model, which is fine-tuned by freezing some of the layers to avoid over-fitting because our dataset is minimal. The network image input shape with its pixel size is 224x224x3. The number of filters we employ doubles roughly at every step or through each stack of convolutional layers. The primary drawback was that the number of parameters to be learned was enormous. Hence, fine-tuning of the VGG-16 Net transfer model reduces the variable parameters in the CONV layers and improves computation time.
An improved YOLOv5 algorithm for obscured target recognition

Target detection is an important problem in computer vision and has important research value in the fields of pedestrian tracking, license plate recognition, and unmanned vehicles [1].The Viola-Jones algorithm is used to detect frontal face images, which improves the speed of face detection by tens or hundreds of times while obtaining the same or even better accuracy, but for special and HOG captures local shape information better and has good invariance to both geometric and optical changes, SVM solves machine learning in small sample cases, but its feature descriptor acquisition process is complex and has high dimensionality, leading to poor real-time performance, and the support vector machine algorithm is difficult to implement for large-scale training samples, while the deep learning-based YOLOv5 target detection algorithm combines the advantages of Viola-Jones algorithm and HOG+SVM algorithm to make up for the shortcomings of the above two algorithms, which is not only very stable for occlusion and complex case processing, but also can be implemented for large regular training samples, but the accuracy of YOLOv5 for target detection is not ideal, and this paper adds SENet mechanism in YOLOv5, which can make the network better to learn the locations in the images that need attention. Therefore, this paper first introduces the traditional target detection algorithm, then introduces and analyzes the Yolov5 algorithm, and improves and optimizes it, and compares it with the traditional target detection algorithm, and the results show that the improved Yolov5 algorithm has better results for target detection.
The Fundamental Rights of Online Platforms: A Digital 'Hercules'?

The text addresses the rights and duties of online platforms from a constitutional law perspective. It explores the situation where an online platform removes content on grounds of violation of its community standards, even though said content does not represent illegal content under the new Digital Services Regulation. Indeed, it argues that regulations forcing digital platforms to restore contents that violate their community standards amount to an interference in the peaceful enjoyment of a possession under article 1 of Protocol no. 1 to the ECHR. Provided, of course, that decisions of content moderation are reasonable, non-arbitrary and fully in line with the digital platform model of business and purpose as enshrined in its community standards. We sustain that current regulations place digital platforms in a difficult position, where they cannot prohibit more than what would be prohibited if the dispute reached the courts, while at the same time they have to remove everything that the courts would prohibit, if they want to avoid liability.
Spoken Digit Classification using Deep Learning Algorithms

The deep learning technique uses speech recognition in many different applications, including voice assistants, voice authentication, audio transcriptions, etc. Children who are dyslexic, blind persons and those with impairments can all benefit from spoken digit recognition. The goal of this paper is to create spoken digit recognition for the categorization of digits from 0 to 9 utilizing Convolution Neural Networks (CNN) and Long Short -Term Memory neural networks. With the addition of autoencoders, the performance of the CNN model is assessed. Finally, a comparative analysis is performed on the performances of the models based on the performance metrics.
Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images

Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites’ stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.
Internet of Things Ultraviolet Sterilizer Receiver Box: How to Design and Construct?

COVID-19 is a virus that can spread through objects and depending on environmental conditions. It can last for several hours without destroying structures or disintegrating. Due to the COVID-19 pandemic, people were restricted from going outside to buy essential goods. Based on these conditions, it is necessary to design an IoT-based virus-safe package receipt box that is intuitive and comfortable for users to use, whether sending or receiving packages. People can control the system via remote control. Sterilization is carried out intelligently by considering the packet received. Design and construction of Internet of Things Ultraviolet Sterilizer Receiver Box use the Design Science Research Method. The methods consist of six stages: identify problem motivate, define the objective of a solution, design development, demonstration, evaluation, and communication. There are three parts of the system: sterilizer receiver box, mobile application, and Internet of Things. The system successfully passes the five testing scenarios: package received detection, package retrieved detection, ultraviolet irradiation, data service, and locking. The IoT-based virus-safe package receipt box can help maintain public health by preventing the COVID-19 virus with ultraviolet sterilization and supporting a modern lifestyle where people who work when the package arrives do not have to worry about the packages arriving.
Voicing in Ngamambo: A Descriptive perspective

This paper describes voicing in Ngamambo, a semi Grassfields Bantu language in the North West Region of Cameroon. The language is classified under the Momo sub-language family (Eberhard, David M., Gray F. Simons and Charles D. Fenning, 2020). Ngamambo is unwritten, and research on the language is scanty. The only available literature on the language is by Asongwed &amp; Hyman (1976)), Achiri-Taboh (2014) and Lem Atanga (2020) However, there has been some recent attempt by the Mbu Language Committee (MLC) to study the language. Interest in the study of Ngamambo stems from the imperative of undertaking a comprehensive description of the language. Preliminary research has revealed the existence of voicing in the language. Voicing is a process whereby the pronunciation of a word is influenced by one of the sounds. Data was obtained from Ngamambo native speakers (informants) over six months. The originality of this study resides in the fact that very little research has been carried out on the language. The authors of this paper discuss one aspect of the language and hope that subsequent studies will determine if voicing is also present in other Grassfields languages, especially the Momo sub-language family. The phonological process of voicing in Ngamambo has been observed when a voiceless sound becomes voiced depending on the environment. It is hoped that understanding this phenomenon would lead to a better understanding of voicing related to language learning.
NRTIRL Based NN-RRT* Path Planner in Human-Robot Interaction Environment

In human-robot interaction environment, it is of great significance for mobile robots to have the awareness of social rules, to realize the socialization and anthropomorphism of robot navigation behavior, and enhance the scene adaptation ability of socialized navigation. Learning from Demonstration (LfD) can obtain an optimized robot trajectory by learning the expert path. Inspired by the LfD method, we propose a new navigation method to learn navigation behaviors form demonstration paths of experts by Neural Network Rapidly-exploring Random Trees (NN-RRT*) planner in the human-robot interaction environment. First, we propose a new NN-RRT* planner to generate paths. Next, the features of demonstration paths and planned paths are extracted for Inverse Reinforcement Learning (IRL) process. The cost function of the path planner is updated. Finally, a new NN-RRT* that can adapt to the complex human-robot interaction environment is obtained. The experimental results show that comparing with the state-of-the-art methods, the path generated by the new navigation method has a higher degree of anthropomorphism and is suitable for navigation in a complex human-robot interaction environment.
Online Hybrid Kernel Learning Machine with Dynamic Forgetting Mechanism

This paper, for the purpose of meeting challenges of fewer resources of storage and calculation in the detection of ICS intrusion as well as real-time requirements, has particularly designed an online hybrid kernel learning machine with dynamic forgetting mechanism. First, on the basis of online kernel limit learning machine, a dynamic forgetting mechanism is designed to dynamically adjust the amount of forgetting data according to the current block error, which reduces the system burden and improves the detection accuracy. Then, it replaces the former single kernel function with a hybrid kernel function, which successfully advances the accuracy rate and generalized performance. Finally, a hybrid noise-reducing autoencoder is created to perform dimensional reduction of industrial data with huge dimensions, resulting in the improvement of algorithm and efficiency. The validity and superiority of the proposed online hybrid kernel learning machine with dynamic forgetting mechanism are verified through simulation experiments.
NTGAT

Graph Attention Network (GAT) has demonstrated better performance in many graph tasks than previous Graph Neural Networks (GNN). However, it involves graph attention operations with extra computing complexity. While a large amount of existing literature has researched GNN acceleration, few have focused on the attention mechanism in GAT. The graph attention mechanism makes the computation flow different. Therefore, previous GNN accelerators can not support GAT well. Besides, GAT distinguishes the importance of neighbors and makes it possible to reduce the workload through runtime tailoring. We present NTGAT, a software-hardware co-design approach to accelerate GAT with runtime node tailoring. Our work comprises both a runtime node tailoring algorithm and an accelerator design. We propose a pipeline sorting method and a hardware unit to support node tailoring during inference. The experiments show that our algorithm can reduce up to 86% of aggregation workload while incurring slight accuracy loss (<0.4%). And the FPGA based accelerator can achieve up to 3.8× speedup and 4.98× energy efficiency comparing to the GPU baseline.
Advanced Intelligent Security Camera System for the Prediction of Snatching using Machine Learning

In this paper, we propose predictive advanced intelligent security camera system that can predict a snatching incident before its occurrence on streets. In our past studies, although we have been engaged automated detection of snatching incidents using machine learning techniques and we have been proposed many systems, they are not enough to prevent snatching incidents. Therefore, we are now engaging new studies on prediction of snatching using motion prediction techniques. We are developing PASCAS that can predict the occurrence of snatching from sequential videos observed in criminal scenes. It can predict the situations into criminal or non-criminal scenes. The experimental results show that PASCAS can effectively predict snatching incidents with an accuracy of 94.25%。
Two-dimensional displacement estimation of one-dimensional laser speckle images for detection of acoustic vibration

Detection and recovery of audio signals using optical methods is an appealing topic. Observing the movement of secondary speckle patterns is a convenient method for such a purpose. In order to have less computational cost and faster processing, one-dimensional laser speckle images are captured by an imaging device, while it sacrifices the ability to detect speckle movement along one axis. This paper proposes a laser microphone system to estimate the two-dimensional displacement from one-dimensional laser speckle images. Hence, we can regenerate audio signals in real time even as the sound source is rotating. Experimental results show that our system is capable of reconstructing audio signals under complex conditions.
Student Religious Literacy: A Study of Student Religious Book Preferences State Universities in Indonesia

This study aims to describe and analyze: (1) preference for reading religious books; (2) religious literacy, factors that influence the preference for reading religious books, and (3) formulate recommendations for the development of religious books in State Public Universities. This research used the survey method and literature review. The population is students of State Public Universities in Java, who are Muslims, including: DKI Jakarta, Banten, West Java, Central Java, Yogyakarta Special Region, and East Java. Each province has two State Public Universities, except for Banten, with a sample of 560 respondents, with a distribution of 53-60 respondents. Data collection techniques using literature review. The results of the study: First, the preference for reading religious books is at a „medium“ level, due to a number of factors including: (1) time-consuming college assignments, (2) low motivation due to perceptions of religious issues as „personal rights“, (3) an Islamic Religious Education environment that is less „motivating“, „reinforcing“, (4) the availability of religious books is not suitable for students' needs, and (5) the academic climate is less conducive which limits freedom, the breadth of reading religious books. Second, religious literacy, the micro dimension, is in a moderate position, namely the preference for reading religious books „sufficiently“has a significant effect on the development of students' abilities. Research contributions: (1) provide a factual map of preference for religious/religious books in the millennial generation at public universities in the 2018-2022 range. (2) drafting policy, providing religious books for public higher education.
The Impact of Sample Size on Three-Level Quadratic Cross-Classified Multiple Membership Growth Curve Modeling Estimation

The Impact of Sample Size on Three-Level Quadratic Cross-Classified Multiple Membership Growth Curve Modeling Estimation 의 이용 수, 등재여부, 발행기관, 저자, 초록, 목차, 참고문헌 등 논문에 관한 다양한 정보 및 관련논문 목록과 논문의 분야별 BEST, NEW 논문 목록을 확인 하실 수 있습니다.
Statistical Learning with Sublinear Regret of Propagator Models

We consider a class of learning problems in which an agent liquidates a risky asset while creating both transient price impact driven by an unknown convolution propagator and linear temporary price impact with an unknown parameter. We characterize the trader's performance as maximization of a revenue-risk functional, where the trader also exploits available information on a price predicting signal. We present a trading algorithm that alternates between exploration and exploitation phases and achieves sublinear regrets with high probability. For the exploration phase we propose a novel approach for non-parametric estimation of the price impact kernel by observing only the visible price process and derive sharp bounds on the convergence rate, which are characterised by the singularity of the propagator. These kernel estimation methods extend existing methods from the area of Tikhonov regularisation for inverse problems and are of independent interest. The bound on the regret in the exploitation phase is obtained by deriving stability results for the optimizer and value function of the associated class of infinite-dimensional stochastic control problems. As a complementary result we propose a regression-based algorithm to estimate the conditional expectation of non-Markovian signals and derive its convergence rate.
What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

We apply the statistical sparse jump model, a recently developed, interpretable and robust regime switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new.In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.
Web-based STMIK AKBA Student Attendance Information System by Making QR Codes an Auxiliary Medium

The attendance or attendance system is a recording or provision of information on a person's presence which indicates that the person has provided information about his presence and absence, then it is stated in the form of an attendance report. Along with the development of technology, an attendance recording has also developed, where we used to use paper attendance to fingerprint attendance. In educational institutions such as campuses, attendance has an important role in the process of developing a student. Until now, there are still many campuses that still use conventional attendance to record the presence of students in class, which allows for human error or fraud from both lecturers and students. Therefore, a solution is possible to suppress errors that may occur, namely by creating a website-based attendance or attendance system where the QR Code is used as an auxiliary medium in recording the presence or absence of a student at the STMIK AKBA Makassar Campus.