Reviewerly/exHarmony huggingface dataset:
Effect of Storage Time on the Physical, Chemical, and Rheological Properties of Blueberry Jam: Experimental Measurements and Artificial Neural Network Simulation

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel's local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.
MSFE-PANet: Improved YOLOv4-Based Small Object Detection Method in Complex Scenes

With the rapid development of computer vision and artificial intelligence technology, visual object detection has made unprecedented progress, and small object detection in complex scenes has attracted more and more attention. To solve the problems of ambiguity, overlap and occlusion in small object detection in complex scenes. In this paper, a multi-scale fusion feature enhanced path aggregation network MSFE-PANet is proposed. By adding attention mechanism and feature fusion, the fusion of strong positioning information of deep feature map and strong semantic information of shallow feature map is enhanced, which helps the network to find interesting areas in complex scenes and improve its sensitivity to small objects. The rejection loss function and network prediction scale are designed to solve the problems of missing detection and false detection of overlapping and blocking small objects in complex backgrounds. The proposed method achieves an accuracy of 40.7% on the VisDrone2021 dataset and 89.7% on the PASCAL VOC dataset. Comparative analysis with mainstream object detection algorithms proves the superiority of this method in detecting small objects in complex scenes.
Understanding service quality concerns from public discourse in Indonesia state electric company

Indonesia State Electricity Company (PLN) has transformed its business process into a more modern practice with the intention of boosting efficiency and enhancing service quality. In the digital economy space, customer expectation is more complex, influenced by the network effect and public opinion. Facing these facts, it is imperative for PLN to be able to listen to its customers' voices using publicly available social media channels. Understanding and acting upon large-scale opinions and conversations is a nontrivial task. To filter large-scale data, we employ Natural Language Processing (NLP) methodology called Latent Dirichlet Allocation (LDA) to find the dominant topics in public discourse. It is followed by UTAUT2 model implementation to further clarify the most critical issues found. Finally, this study modified the e-ServQual and e-RecServQual methodologies to propose the best and most personalized solution for each issue. By using the proposed approach, PLN could accelerate the implementation of data-driven decision-making and induce sustainability.
Solar Zoning Maps of Algeria Based on Sunshine Duration Data and Kriging Method

Algeria is a large and sunny country, offering significant opportunities for solar energy development.To effectively harness solar energy, it is crucial to understand the distribution of solar radiation.In this study, we employ the Kriging method to analyze the data on sunshine duration from 56 meteorological stations across Algeria between 1992 and 2002.By utilizing Kriging, we interpolate the available data to create solar zoning maps, which enable us to categorize Algeria into regions with similar energy qualities.These maps provide valuable insights into the spatial and temporal distribution of solar energy resources throughout the country.The utilization of the Kriging method for interpolation enhances our ability to assess the solar potential of different regions in Algeria, facilitating better planning and utilization of solar energy resources.The results from this study have significant implications for the development and implementation of solar energy projects in Algeria, as they provide crucial information for evaluating the feasibility and viability of such initiatives.
Hybrid Encryption Technique to Enhance Security of Health Data in Cloud Environment

Data security is a primary concern in cloud computing as data is traveling over the internet and is originated from various sources.Encryption techniques are used to protect sensitive information from unauthenticated users, but sometimes brute force methods can identify the hidden data.To improve data privacy and authentication, we proposed a method that combines Advanced Encryption Standard and proxy reencryption algorithm with a Honey encryption algorithm and N-th degree Truncated Polynomial Ring Unit (NTRU) or Number Theory Research Unit).Advanced Encryption Standard (AES) is a popular symmetric encryption method that encrypts and decrypts data using a secret key.Proxy re-encryption is a cryptographic technique that allows a third party to transform cipher texts from one key to another without seeing the plaintext.Honey encryption is a relatively new technique that adds realistic-looking but bogus data to encrypted messages, making it difficult for attackers to determine if the decrypted message is real or fake.NTRU is a cryptosystem that uses the public key on the polynomial ring.By combining these techniques, the proposed method can improve data security for outsourced data in the cloud.Unauthorized users may face challenges accessing messages that appear to be legitimate when Honey encryption is combined with Hybrid cryptography.Overall, the use of these techniques provides enhanced security and protection to the user's data, and ensure that only authorized user can access and manipulate sensitive data, stored in the cloud.
Data privacy for machine learning and statistics

This chapter gives an overview of the field of data privacy for machine learning and statistics. We discuss privacy models as computational definitions of privacy. We show that they are different definitions based on different assumptions on what type of disclosure needs to be avoided. In particular, we present k-anonymity, privacy for re-identification, and differential privacy. These are the most established privacy models. They are useful for publishing a database or a computation from a database (e.g., the output of a function or query on a database). Then, we also present a few methods that are compliant with these privacy models. They are examples of those found in the literature for achieving good privacy levels. We also discuss in this text the need for finding a good trade-off between utility and privacy. We provide examples to illustrate both the definitions and the methods. Examples are for a small database, but the discussion is general and the same privacy models and data privacy methods apply to more complex databases. That is, they can be applied to non-SQL databases including graphs and social networks, text, or images.
Set-based Particle Swarm Optimization for Data Clustering: Comparison and Analysis of Control Parameters

Data clustering is a highly studied field of data science and computational intelligence. Population-based algorithms such as particle swarm optimization (PSO) have shown to be effective at data clustering. Set-based particle swarm optimization (SBPSO) is a generic set-based PSO variant that has shown promise in clustering stationary and non-stationary data. In this paper, SBPSO is used to cluster fifteen datasets with diverse characteristics. The clustering ability of SBPSO is compared in depth to the performance of six other tuned clustering algorithms. A sensitivity analysis of the SBPSO control parameters is performed to determine the effect that variation in these control parameters have on swarm diversity and other measures. SBPSO ranked third from among the algorithms evaluated and proved a viable clustering algorithm. A trade-off between swarm diversity and clustering ability was discovered, and the control parameters that control this trade-off were determined.
A Study on Environmental Trends and Sustainability in the Ocean Economy Using Topic Modeling: South Korean News Articles

The ocean economy plays a critical role in global economic growth, yet it confronts substantial environmental risks. This study employs topic modeling of South Korean news articles to analyze the evolving trends of environmental risks and sustainability in ocean economy. A dataset comprising 50,213 articles from 2008 to 2022 is examined, revealing prevalent environmental concerns that have persisted over the years. The findings demonstrate an increasing emphasis on sustainability and marine environmental issues, as evidenced by prominent keywords related to construction, safety, plastic pollution, and ecosystem conservation. Through Latent Dirichlet Allocation (LDA) in topic modeling, 10 distinct themes are identified, encompassing sustainable fisheries management, accident and disaster response, polar environment, carbon neutrality, microplastic pollution, habitat ecosystems, cruise tourism development, nuclear power plant pollution, and infectious diseases. The outcomes highlight the necessity of collaborative efforts and international partnerships, underpinned by diplomatic cooperation, to effectively address transboundary environmental challenges encountered in the ocean-based industries.
A self-adaptive system of systems architecture to enable its ad-hoc scalability

The concept of System of Systems (SoS) refers to a collection of Constituent Systems (CSs) that interact to deliver an emergent behavior that cannot be achieved by any individual CS on its own. The focus of this research is on the ad-hoc scalability of SoS, meaning that the size of the system can change during operation by adding or removing a CS or changing the size of existing CSs. The Unmanned Vehicle Fleet (UVF) is selected as a practical example to showcase the challenge and solution of ad-hoc scalability. UVF has various applications in fields such as search and rescue, intelligent transportation and mobility, but it operates in a dynamic environment that is prone to uncertainties like changing missions, increasing range and capacity, UV failures, and battery requirements. The proposed solution to overcome these uncertainties is a self-adaptive system that can dynamically change the UVF architecture in real-time, allowing for upscaling or downscaling the size of the UVF. The Mission Control Center (MCC) can control this change either through fully-automatic mode based on performance criteria like battery utilization and communication traffic, or through manual mode where the operator makes the decision. A multi-agent environment and rule management engine are implemented to simulate the UVF behavior and validate the proposed solution in both automatic and manual modes.
Forgetting curve models: A systematic review aimed at consolidating the main models and outlining possibilities for future research in production

Abstract This research surveys current knowledge about forgetting curves and their application in production, aiming to identify the main characteristics and tendencies and research gaps on this topic. Faced with the need to improve tools that allow production planners to predict programmed batches with greater precision, it was found that there are still gaps to be filled that allow the application of learning and forgetting techniques in the production process. To compose the scope of this research, a systematization of the existing literature was carried out, using the keywords ‘forgetting curves’, ‘total forgetting’, ‘learning and forgetting curve’ and ‘forgetting effects’, in the databases of Science Direct, Scielo, Scopus, Web of Science and Google Academics, which allowed classifying and organizing the developed models into 3 groups: Deterministic models, Statistical models and Functional models. This systematic process consisted of selecting databases, filtering publications, reviewing information, and analysing models, providing a detailed analysis on a topic that, despite being promising, is poorly explored in the industry, demonstrating and indicating gaps in research and application. To be filled.
A model of pre-adoptive appraisal toward metaverse banking: Moving from industry 4.0 to industry 5.0

The goal of the current study is to delve into the key drivers of commercial banks’ switching intentions to embrace metaverse banking (SITA). The use of structural equation modeling allowed the hypothesized model, which was supported by statistical data obtained from surveys given to a convenience and snowball sample of employees in banking industry, to be empirically validated. These surveys were distributed during the Industry 4.0 stage and the emerging Industry 5.0 stage, respectively. The results analysis verified relationships between important predictors and SITA in terms of significance and effect magnitude.
Homography Estimation for Camera Calibration in Complex Topological Scenes

Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most analysis applications. However, security cameras are susceptible to environmental conditions and small camera movements, resulting in a need for an automated re-calibration method that can account for these varying conditions. In this paper, we present an automated camera-calibration process leveraging a dictionary-based approach that does not require prior knowledge on any camera settings. The method consists of a custom implementation of a Spatial Transformer Network (STN) and a novel topological loss function. Experiments reveal that the proposed method improves the IoU metric by up to 12% w.r.t. a state-of-the-art model across five synthetic datasets and the World Cup 2014 dataset.
Enhancing Healthcare Management during COVID-19: A Patient-Centric Architectural Framework Enabled by Hyperledger Fabric Blockchain

The highly transmissible COVID-19 virus has wreaked havoc on the global economy, health, and lives. The abrupt burst and exponential spread of this pandemic has shown the inadequacies of existing healthcare institutions in handling a public health emergency. As governments around the world strive to re-establish their economies, open workplaces, ensure safe journeys, and return to regular life, they require solutions to reduce losses. The proposed framework provides virtual assistance from various medical practitioners and physicians. Furthermore, it promotes the accuracy of information gathered from COVID-19 patients, which can aid in the launch of a variety of government decisions and public guidelines aimed at combating health exigencies. The authors present a revolutionary blockchain-based solution that builds trust between the medical professionals and patients while preventing accidental coronavisrus transmission. This solution also keeps track of COVID-19 patients and improves EHR management, which can be a viable solution for common EHR challenges such as lowering the risk of patient data loss, maintaining privacy and security, and obtaining immutable consensus on the maintenance of health records, gaps in hospital communication, and inefficient clinical data retrieval methods. This research work describes a COVID-19 patient-centric blockchain-based EHR employing JavaScript-based smart contracts for a decentralized healthcare management system. The proposed Hyperledger fabric and a Composer-based working prototype ensure the model’s security and the authenticity of the health records. The authors used the Hyperledger Caliper benchmarking tool, which measures latency, throughput, resource utilization, etc., under different conditions and control parameters. The findings highlight the importance of the proposed blockchain-enabled architecture in revolutionizing healthcare administration during and after the COVID-19 pandemic, promoting enhanced clinical outcomes and supporting patient-centered care.
The Importance of Expressing Politeness: English Education Student's Perspectives

This study focuses on how important it is to apply politeness in speaking with someone from the perspective of English Education students. To do this research, researchers used qualitative methods. One of the techniques that researchers use in this study is the non-interactive technique that is the questionnaire. This questionnaire was chosen to explore deeper information about the opinions of English Education Students at State Islamic University of North Sumatera Medan starting from the 2nd â€“ 8th Semester (participants or respondents) on the importance of expressing politeness. Researchers chose the questionnaire technique in order to shorten the time and got more participants rather than had to conduct interviews. This study reports the choice of being polite for people is influenced by differences in power relations, such as notion of status, age, gender, and social distance. This study emphasizes that politeness is needed to be implemented since rudeness creates conflict between one to another person.
A guided light system for agile individual addressing of Ba<sup>+</sup> qubits with 10<sup>−4</sup> level intensity crosstalk

Abstract Trapped ions are one of the leading platforms for quantum information processing, exhibiting the highest gate and measurement fidelities of all contending hardware. In order to realize a universal quantum computer with trapped ions, independent and parallel control over the state of each qubit is necessary. The manipulation of individual qubit states in an ion chain via stimulated Raman transitions generally requires light focused on individual ions. In this manuscript, we present a novel, guided-light individual addressing system for hyperfine Ba + qubits. The system takes advantage of laser-written waveguide technology, enabled by the atomic structure of Ba + , allowing the use of visible light to drive Raman transitions. Such waveguides define the spatial mode of light, suppressing aberrations that would have otherwise accumulated in a free-space optics set up. As a result, we demonstrate a nearest neighbor relative intensity crosstalk on the order of 10 −4 , without any active aberration compensation. This is comparable to or better than other previous demonstrations of individual addressing. At the same time, our modular approach provides independent and agile control over the amplitude, frequency, and phase of each channel; combining the strengths of previous implementations.
A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices

This research presents a novel binary Edwards curve (BEC) accelerator designed specifically for resource-constrained embedded systems. The proposed accelerator incorporates the fixed window algorithm, a two-stage pipelined architecture, and the Montgomery radix-4 multiplier. As a result, it achieves remarkable performance improvements in throughput and resource utilization. Experimental results, conducted on various Xilinx Field Programmable Gate Arrays (FPGAs), demonstrate impressive throughput/area ratios observed for GF(2233). The achieved ratios for Virtex-4, Virtex-5, Virtex-6, and Virtex-7 are 12.2, 19.07, 36.01, and 38.39, respectively. Furthermore, the processing time for one-point multiplication on a Virtex-7 platform is 15.87 µs. These findings highlight the effectiveness of the proposed accelerator for improved throughput and optimal resource utilization.
DZ-SMS: An Authentic Corpus of Algerian SMS

In this article, a complete methodology of a corpus realization of authentic Short Message Service (SMS) from Algerian dialect and which are transcribed in Latin characters or symbols is presented. A linguistic material constituted by 6,000 SMS coming from the different geographical regions of Algeria (Middle, East, and West) corresponding to 42 administrative and geographical departments, have been collected. The coexistence of several dialects through these three regions simultaneously has obliged us to consider and operate a classification of the data for each dialect. This data classification has yielded three extracted regional dialectic corpora, each of them covering a specific number of administrative departments. These treatments are based on the so-called Data-n-gram tokenization targeting the suppression of the stop words, the stemming and the imbalance of the classes linked to the nature of the SMS. Consequently, three text classifiers based on three linear classifiers, namely, Stochastic Gradient Descent (SGD), The Ridge Regression (RDG), and Linear Support Vector Machines, to find out the number of significant corpora to extract from the collected data. A deep analysis of the results has shown that the 5-grams data representation is more representative whereas the stop-words removal and stemming process has generated an information loss that has subsequently inferred an alteration of the recognition rate of about 2%. The emerging problem of classes imbalance has been treated by using three techniques: Random Oversampling, Synthetic Minorities Oversampling Technique (SMOTE), and Adaptive Synthetic (ADASYN). This treatment produced interesting results and enhancements; particularly, the classification by region with the oversampling process SMOTE by using the RDG technique has reached a better percentage of 55.93% whereas the classification by department with the oversampling process ADASYN associated with the SGD has only yielded a maximum score of about 17.11%. The results, which undoubtedly are in favor of the classification by region, have compelled us to create three Subdialectal regional corpora, each, covering a certain number of Algerian departments.
Single-photon path entanglement based on integrated silicon photonics

A silicon photonic platform with high-speed single-photon entanglement generation and detection is demonstrated using decoy state source and homodyne detection. A CHSH Bell violation test is performed on-chip, showing a violation lower bound of 2.59.