Reviewerly/exHarmony huggingface dataset:
Preserving Meher and Woirata Corpus Languages using Neural Machine Translation

Authors:
raw_author_name: Yulius Prabowo
raw_author_name: None Nazarudin
raw_author_name: Tanwey Ratumanan
raw_author_name: Martinus Maslim

Abstract: Research on languages, particularly regional languages, is extremely challenging to conduct because there is very little or no language corpus available, particularly for Indonesia's regional languages. This project seeks to construct a translation machine for Indonesian in Meher and Woirata languages, and vice versa. However, to be able to achieve this, a corpus of Meher and Woirata languages must first be developed. The production of this corpus was carried out through field studies, the researcher requested various speakers of this language to translate manually and then compared the results from several translators through focus group talks to identify the appropriate use of words. The outcomes of this translation process are then written in the form of a database of Indonesian-Meher and Indonesian-Woirata language pairings which will subsequently be utilized as a learning database for the translation machine that will be created. This research succeeded in collecting 714.000 words in the Meher language and 805.000 words in the Woirata language. These results were then employed as a machine translation learning corpus, the output of the translation carried out by this machine was then validated through direct assessment by speakers of the two languages. The results of this testing indicated an accuracy above 80% for both translation into the Meher language and translation into the Woirata language. From the research carried out, it can be concluded that the construction of the Meher language corpus and the Woirata language corpus which was carried out through field research was successful in gathering and establishing a language corpus for these two languages. Apart from that, the experimental results suggest that the employment of translation algorithms to convert Indonesian into regional languages and vice versa may be carried out and provide translations with acceptable accuracy. The contribution of this research is in the establishment of the Meher and Woirata language corpus so that it can be generally accessed by anyone who requires it.
Nursing Team Training and Standardized Management in Hybrid OR.

Authors:
raw_author_name: Xiaoping Zhu
raw_author_name: Ying Xu
raw_author_name: Haixia Hu

Abstract: Analyzing the impact of nursing workforce development, training and standardization on hybrid operating theatres.Thirty nurses in the mixed operating room of the First Affiliated Hospital of Nanchang University from January 2021 to December 2021 were selected as the control group to receive routine nursing management and training methods and another thirty nurses were selected as the experimental group to receive nursing team construction, training and standardized management based on conventional methods. Nurses' theoretical and operational scores, nurses' satisfaction, surgeon satisfaction with nurses, and nursing service quality scores were compared between two groups at baseline and after intervention.After the intervention, nurses in both groups had a significant improvement in theoretical and operational scores than those at baseline, and nurses in the experimental group had better scores than those in the control group, The difference was statistically significant (P = .002, P = .004). Nursing quality of surgical preparation, environmental management, surgical safety, and instrument management in the intervention group were significantly better than those at baseline and better than those in the control group. The difference was statistically significant (P = .001, P = .001, P = .001, P = .001). Satisfaction of nurses and doctors in the intervention group was significantly better than those at baseline and better than those in the control group. The difference was statistically significant (P = .002, P = .001).The effect of nursing team construction and training and standardized management of hybrid operating Room was ideal, which can improve nurses' theoretical knowledge and practical skills, and enhance the satisfaction of nurses and surgeons, providing patients with higher quality nursing services, which is worth adopting.
FPGA/MATLAB Hardware in the Loop Testbed for Stochastic Artificial Neural Networks

Authors:
raw_author_name: Scott Koziol
raw_affiliation_string: Baylor University
raw_author_name: Eugene Chabot
raw_affiliation_string: University of Rhode Island

Abstract: Abstract Abstract—This paper presents a Hardware in the Loop (HWIL) testbed for evaluating a Stochastic Artificial Neural Network (SANN). The SANN is implemented in a Field Programmable Gate Array (FPGA), and this testbed allows test data to be generated on a computer using MATLAB, and sent to the FPGA SANN over a Universal Asynchronous Receiver/Transmitter (UART) interface. Initial hardware results are presented.
An REU/RET Project: IoT Platform and Network Data Visualization

Authors:
raw_author_name: Otily Toutsop
raw_affiliation_string: Morgan State University
raw_author_name: kevin kornegay
raw_affiliation_string: Morgan State University
raw_author_name: Marcial Tienteu

Abstract: Abstract Internet of Things (IoT) is a connected network of devices that exchange data using different protocols. The application of IoT ranges from intelligent TVs and intelligent Refrigerators to smart Transportation. This research aims to provide students with hands-on training on how to develop an IoT platform that supports device management, connectivity, and data management. People tend to build interconnected devices without having a basic understanding of how the IoT platform backend function. Studying the Arm Pelion will help to understand how IoT devices operate under the hood. This past summer, Morgan State University has hosted undergraduate engineering students and high school STEM teachers to conduct IoT security research in the Cybersecurity Assurance & Policy (CAP) Center. The research project involved integrating various hardware sensor devices and real-time data monitoring using the Arm Pelion IoT development platform. Some of the student/teacher outcomes from the project include: 1) Learning about IoT Technology and security; 2) Programming an embedded system using Arm Mbed development board and IDE; 3 3) Developing a network of connected IoT devices using different protocols such as LWM2M, MQTT, CoAP; 4) Investigating the cybersecurity risks associated with the platform; and 5) Using data analysis and visualization to understand the network data and packet flow. First, the student/teacher must consider the IoT framework to understand how to address the security. The IoT framework describes the essential functions of an IoT network, breaking it down into separate layers. These layers include an application layer, middleware layer, and connectivity layer. The application layer allows the users to access the platform via a smartphone or any other dashboard. The Middleware layer represents the backend system that provides edge devices with data management, messaging, application services, and authentication. Finally, the connectivity layer includes devices that connect the user to the network, including Bluetooth or WiFi. The platform consists of several commercial IoT devices such as a smart camera, baby monitor, smart light, and other devices. We then create algorithms to classify the network data flow; to visualize the packets flow in the network and the structure of the packets data frame over time.
A rule-based machine learning model for financial fraud detection

Authors:
raw_author_name: Saiful Islam
raw_affiliation_string: Chittagong University of Engineering and Technology
raw_author_name: Md. Mokammel Haque
raw_affiliation_string: Chittagong University of Engineering and Technology
raw_author_name: Abu Naser Mohammad Rezaul Karim
raw_affiliation_string: International Islamic University Chittagong

Abstract: Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
Enhancing healthcare services through cloud service: a systematic review

Authors:
raw_author_name: Bo Guo
raw_affiliation_string: Universiti Selangor
raw_author_name: Nur Syufiza Ahmad Shukor
raw_affiliation_string: Universiti Selangor

Abstract: Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare.
Enhanced transformer long short-term memory framework for datastream prediction

Authors:
raw_author_name: Mofreh Mohamed Salem
raw_affiliation_string: Mansoura University
raw_author_name: Asmaa Hamdy Rabie
raw_affiliation_string: Mansoura University
raw_author_name: Ali Ibrahim El-Desouky
raw_affiliation_string: Mansoura University

Abstract: In machine learning, datastream prediction is a challenging issue, particularly when dealing with enormous amounts of continuous data. The dynamic nature of data makes it difficult for traditional models to handle and sustain real-time prediction accuracy. This research uses a multi-processor long short-term memory (MPLSTM) architecture to present a unique framework for datastream regression. By employing several central processing units (CPUs) to divide the datastream into multiple parallel chunks, the MPLSTM framework illustrates the intrinsic parallelism of long short-term memory (LSTM) networks. The MPLSTM framework ensures accurate predictions by skillfully learning and adapting to changing data distributions. Extensive experimental assessments on real-world datasets have demonstrated the clear superiority of the MPLSTM architecture over previous methods. This study uses the transformer, the most recent deep learning breakthrough technology, to demonstrate how well it can handle challenging tasks and emphasizes its critical role as a cutting-edge approach to raising the bar for machine learning.
Unconventional photon blockade in the two-photon Jaynes–Cummings model with two-frequency cavity drivings and atom driving

Authors:
raw_author_name: Xin Liu
raw_affiliation_string: School of Physics and Technology, University of Jinan, Jinan, 250022, CHINA
raw_author_name: Xin-He Zhang
raw_affiliation_string: School of Physics and Technology, University of Jinan, Jinan, 250022, CHINA

Abstract: In a two-frequency cavity driving and atom driving atom-cavity system, we find the photon blockade effect. In a truncated eigenstates space, we calculate the zero-delay second-order correlation function of the cavity mode analytically and obtain an optimal condition for the photon blockade. By including three transition pathways, we find that higher excitations of the cavity mode can be further suppressed and the zero-delay second-order correlation function can be reduced additionally. Based on the master equation, we simulate the system evolution and find that the analytical solutions match well with the numerical results. Our scheme is robust with small fluctuations of parameters and may be used as a new type of single photon source.
Trading Aggregate Statistics Over Private Internet of Things Data

Authors:
raw_author_name: Zaobo He
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China
raw_author_name: Zhipeng Cai
raw_affiliation_string: Department of Computer Science, Georgia State University, Atlanta, GA, USA

Abstract: The exponential growth of Internet of Things (IoT) data has pushed the boundaries of data analysis, but it has also raised concerns about the increasing commoditization of personal privacy. The intriguing problem of trading private IoT data is the focal point of this paper. We delve into three fundamental questions: Firstly, we determine the minimum privacy that a broker must purchase from data owners to achieve aggregate statistics with a fixed accuracy goal. Secondly, we propose an innovative arbitrage-free pricing framework that empowers brokers to set prices for aggregate statistics, maximizing utility while ensuring fairness. Lastly, we address the challenge of fairly compensating data owners for their privacy losses while providing economic guarantees. To achieve these objectives, we propose a data trading framework in this paper. Our framework considers ubiquitous data correlations and the potential involvement of untrusted brokers, ensuring that the total compensations remain within the brokers budget; each owner is guaranteed to receive non-negative revenues, and cunning data owners cannot exploit misreported privacy demands to gain extra compensations. Experiments on the MovieLens dataset demonstrated the robustness of our framework.
Smart hospital definition: Academic and industrial perspective

Authors:
raw_author_name: Mohammad Sadegh Rezaei
raw_affiliation_string: Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran

Abstract: Healthcare is a social and economic challenge in many countries, exacerbated by today's increasing demand. Many studies demonstrate that hospitals that move towards smartness, and some of their processes are smart, can provide more appropriate treatments and deal with problems more flexibly. It is axiomatic that implementing smart hospitals and healthcare tools requires a clear objective. However, the concept of a smart hospital lacks a comprehensive and broadly accepted definition, leading to varied interpretations and misconceptions. Many developments touted as 'smart' merely digitize existing hospital environments without truly embracing the full potential of smart technology. Furthermore, research studies have neglected to consider industrial perspectives, which will soon cause a gap between industry and academics in this concept. This research aims to explore the attributes of a smart hospital and use them to propose a definition for it, considering both scholarly and industrial viewpoints. The PRISMA method is employed to select academic and practical papers providing definitions and insights into smart hospitals or healthcare. 17 studies are analyzed, and a total, 83 characteristics are identified to describe the smart hospital. These features are categorized into three primary categories: "technologies", "services", and "goals". The most important features are determined by analyzing the frequencies of these characteristics across all the studies. In the results section, these data are presented in graphical form, highlighting both academic and industrial perspectives separately, as well as a combined analysis. Furthermore, an attempt is made to uncover trends in smart hospitals from 2015 to 2023. A comprehensive definition of the smart hospital, encompassing both academic and industrial perspectives, is proposed using the investigated characteristics. This study also presents research opportunities and discusses the existing gap between academia and industry concerning smart hospitals.
Forecasting Models

Authors:
raw_author_name: Sunil Choenni
raw_author_name: Mortaza S Bargh

Abstract: The Elgar Encyclopedia of Crime and Criminal Justice stands apart as the most comprehensive global reference title in its field. New entries will be added every month and PDF downloads will be available once the Encyclopedia is complete.
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems

Authors:
raw_author_name: Zhengbang Zhu
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Rongjun Qin
raw_affiliation_string: National Key Laboratory for Novel Software Technology, Nanjing University, China and Polixir Technologies, China
raw_author_name: Junjie Huang
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Xinyi Dai
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Yang Yu†
raw_affiliation_string: National Key Laboratory for Novel Software Technology, Nanjing University, China and Polixir Technologies, China
raw_author_name: Yong Yu
raw_affiliation_string: Shanghai Jiao Tong University, China
raw_author_name: Weinan Zhang†
raw_affiliation_string: Shanghai Jiao Tong University, China

Abstract: Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have achieved better performance in terms of user engagement metrics such as clicks and browsing time. The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption. A natural following question is whether current recommendation algorithms are manipulating user preferences. If so, can we measure the manipulation level? In this article, we present a general framework for benchmarking the degree of manipulations of recommendation algorithms, in both slate recommendation and sequential recommendation scenarios. The framework consists of four stages, initial preference calculation, training data collection, algorithm training and interaction, and metrics calculation that involves two proposed metrics, Manipulation Score and Preference Shift. We benchmark some representative recommendation algorithms in both synthetic and real-world datasets under the proposed framework. We have observed that a high online click-through rate does not necessarily mean a better understanding of user initial preference, but ends in prompting users to choose more documents they initially did not favor. Moreover, we find that the training data have notable impacts on the manipulation degrees, and algorithms with more powerful modeling abilities are more sensitive to such impacts. The experiments also verified the usefulness of the proposed metrics for measuring the degree of manipulations. We advocate that future recommendation algorithm studies should be treated as an optimization problem with constrained user preference manipulations.
Robust partial face recognition using multi-label attributes

Authors:
raw_author_name: Gaoli Sang
raw_affiliation_string: College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China
raw_author_name: Dan Zeng
raw_affiliation_string: Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, Guangdong, China
raw_author_name: Chao Yan
raw_affiliation_string: Artificial Intelligence Laboratory, Sichuan Eface Technology Co., LTD., Chengdu, Sichuan, China; College of Computer Science, Sichuan University, Chengdu, Sichuan, China
raw_author_name: Raymond Veldhuis
raw_affiliation_string: Chair of Data Management and Biometrics (DMB), Faculty of Electrical Engineering, Computer Science and Mathematics (EEMCS), University of Twente, Enschede, Netherlands; Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Gjϕvik, Norway
raw_author_name: Luuk Spreeuwers
raw_affiliation_string: Chair of Data Management and Biometrics (DMB), Faculty of Electrical Engineering, Computer Science and Mathematics (EEMCS), University of Twente, Enschede, Netherlands

Abstract: Partial face recognition (PFR) is challenging as the appearance of the face changes significantly with occlusion. In particular, these occlusions can be due to any item and may appear in any position that seriously hinders the extraction of discriminative features. Existing methods deal with PFR either by training a deep model with existing face databases containing limited occlusion types or by extracting un-occluded features directly from face regions without occlusions. Limited training data (i.e., occlusion type and diversity) can not cover the real-occlusion situations, and thus training-based methods can not learn occlusion robust discriminative features. The performance of occlusion region-based method is bounded by occlusion detection. Different from limited training data and occlusion region-based methods, we propose to use multi-label attributes for Partial Face Recognition (Attr4PFR). A novel data augmentation is proposed to solve limited training data and generate occlusion attributes. Apart from occlusion attributes, we also include soft biometric attributes and semantic attributes to explore more rich attributes to combat the loss caused by occlusions. To train our Attr4PFR, we propose an implicit attributes loss combined with a softmax loss to enforce Attr4PFR to learn discriminative features. As multi-label attributes are our auxiliary signal in the training phase, we do not need them in the inference. Extensive experiments on public benchmark AR and IJB-C databases show our method is 3% and 2.3% improvement compared to the state-of-the-art.
Video reversible data hiding: A systematic review

Authors:
raw_author_name: Jiaqi Wang
raw_affiliation_string: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
raw_author_name: Bo Ou
raw_affiliation_string: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China

Abstract: Reversible data hiding (RDH), also known as lossless data hiding, ensures the losslessly recovery of both the cover medium and the secret message at the decoder. For the video medium, RDH can also provide the effective protection and has attracted a lot of attention due to its unique reversibility. This paper aims to present a systematic review about the existing video RDH methods and the corresponding practical applications. We summarize the basic frameworks and the current optimization aspects in video RDH methods, including (1) the determination for the suitable embedding position, (2) the drift distortion minimization, (3) the extension for security improvement. Then, the fundamental trade-off for the evaluation metrics is quantified, and the performance comparison of the classical methods is given. Finally, the future research directions are discussed to exploit the further idea for this area.