Reviewerly/exHarmony huggingface dataset:
Multi-Strategy-Driven Salp Swarm Algorithm for Global Optimization

In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed.First, food sources or random leaders were associated with the current bottle sea squirt at the beginning of the iteration, to which Levy flight random walk and crossover operators with small probability were added to improve the global search and ability to jump out of local optimum.Secondly, the position mean of the leader was used to establish a link with the followers, which effectively avoided the blind following of the followers and greatly improved the convergence speed of the algorithm.Finally, Brownian motion stochastic steps were introduced to improve the convergence accuracy of populations near food sources.The improved method switched under changes in the adaptive parameters, balancing the exploration and development of SSA.In the simulation experiments, the performance of the algorithm was examined using SSA and MSD-SSA on the commonly used CEC benchmark test functions and CEC2017-constrained optimization problems, and the effectiveness of MSD-SSA was verified by solving three real engineering problems.The results showed that MSD-SSA improved the convergence speed and convergence accuracy of the algorithm, and achieved good results in practical engineering problems.
Photonic Spatial-Euler Ising Machine for Solving 20000-vertex Max-cut Problem

We propose a novel quadrature photonic spatial-Euler Ising machine to realize flexible spin-interaction configuration and spin-flip in a single SLM. A 20000-vertex max-cut problem is experimentally solved, showing 80% improvement compared to simulation results and 49% over heuristic algorithm.
Machine Learning Security and Trustworthiness

Machine Learning (ML) is the innovation powerhouse for Intelligent Multimodal Security Systems (IMSS). Along with obvious benefits, ML brings unique risks that require thorough assessment. ML system security builds on "traditional" cybersecurity controls and spreads to cover the expanded attack surfaces for the new ML assets. Moreover, the problem of ML trustworthiness stands in the way of taking full advantage of ML advancements. This chapter introduces the challenges and risks of ML, highlights key trends in the global ML policies, and best practices, summarizes key standardization activities, and provides a detailed description of assets and threats. Putting the above into perspective, we present a practical framework for addressing ML-based IMSS security and trustworthiness and discuss relevant implementation options.
All-optical Nonlinear Activator for Optical Neural Network with Low Threshold Power and Ultrafast Response Speed

We proposed a novel all-optical nonlinear activator based on the hybrid integration of graphene and silicon/metallic double slot structure, which exhibits low threshold power and ultrafast response speed.
All-optical object classification through unknown phase diffusers using a single-pixel diffractive machine vision system

We present an all-optical inference method to directly classify unknown objects through random phase diffusers using a single-pixel spectrally-encoded broadband diffractive network.
Optimizing the design of two-section integrated lasers for a larger excitability regime

Using the Yamada model, the influence of saturable absorber length and mirror reflectivities on the excitability window of an integrated laser is investigated. A short absorber and low reflectivity mirrors are beneficial for a large excitability window.
Overcoming the Rate-Loss Limit without Global Phase Tracking

We achieve a finite-size key rate of 42.64bps over 508km fibers, breaking the fundamental rate-loss limit of point-point quantum key distribution (QKD), via implementing an innovative measurement-device-independent QKD through asynchronous coincidence pairing.
Single-particle–single-photon Coupling Using a Circuital Metamaterial Cavity

We propose a surface-plasmon-inspired circuital-metamaterial cavity that achieves record-breaking interaction strength between charged-particles and photons. The cavity drives emission of multiple microwave photons per particle, enabling strong particle-photon entanglement for applications in free-particles quantum optics.
A Single-Photon Dictionary to Support the Emerging Quantum Industry

We present definitions of terms relevant to single-photon sources and detectors designed to foster a common language and promote clarity and understanding, support commercialization and device specifications, and facilitate the development of single-photon quantum technologies.
High Extinction Ratio Integrated Reconfigurable Distributed Bragg-Reflector from Lithium-Niobate-on-Insulator for Precise Pulse Shaping at Strontium Spectral Lines

We design and demonstrate tunable distributed Bragg reflectors on thin film Lithium-Niobate-on-Insulator, at strontium spectral lines. A Mach-Zehnder-Interferometer geometry allows electrically modulated pulse shaping with high extinction ratios of 30 dB.
Monolithic Integration of Lithium Niobate and Chalcogenide Phase-Change Material for Neuromorphic Computing

We show the integration of chalcogenide phase-change material together with electro-optic modulators in the Lithium-Niobate-On-Insulator platform enabling on-chip calculation and non-volatile memory functionality for neuromorphic computing.
Wavelength-Multiplexed Multi-User Quantum Network Based on High-Dimensional Time-bin Encoding

We have developed a multi-user QKD network using wavelength multiplexing and high-dimensional arrival-time encoding. We have demonstrated an order of magnitude key rate improvement against recent implementations with &gt;1 bits/photon efficiency.
Quick Quantum Steering: Overcoming Loss and Noise with Qudits

We formulate and demonstrate a test of quantum steering under extreme conditions of loss and noise, free of the fair-sampling loophole, with a dramatic reduction in measurement time counter-intuitively achieved through the use of qudits.
Fractal superconducting nanowire single-photon detector at 1540 nm with 91% system detection efficiency

We demonstrate a fiber-coupled fractals uperconducting nanowire single-photon detector working in dual bands, with 91% system detection efficiency (SDE) at the wavelength of 1540 nm and with 61% SDE at 520 nm.
Benchmarks for near-term photonic quantum processors

We present a framework to efficiently simulate noisy measurement-based photonic quantum processors and characterize their computing power in terms of well-known metrics such as the quantum volume. We present our results for the GKP cluster states.
Third Pathway for AI Development: Introducing the Model of Even Distribution of Synthetic Intelligence in Society

The rapid development of Artificial Intelligence creates both opportunities and risks. Although AI has the potential to solve many major issues facing humanity, current models of societies organization and functioning increase the spectrum and scale of the threats. This paper presents an alternative approach on how societies can function and develop in the emerging age of AI. The new proposal is based on the concept of even distribution of Synthetic Intelligence in society, the architecture of local computing clusters, and personal computing power (PCP) inalienably assigned to each human being. The new approach is, in fact, the strengthening of the best-proven strategy provided over millennia by evolution: a fairly even distribution of intelligence within the species. The first section of this article focuses on determining key dangers related to the current models. The next sections introduce the conceptual foundations of the new approach, a set of key social and technological changes that should be implemented, scenarios for international deployment, and a discussion of the possible influence of the new model on the de-escalation of AI threats.
Quantum Photonic chip-based Realization of Logic Gates

Quantum logic gates are the backbones of quantum information processing (QIP), wherein the typical three-qubit Fredkin and Toffoli gates are essential in quantum computation and communication. Here we propose a scheme for realizing such kinds of three-qubit based on a programmable nonunitary matrix on a photonic chip.
Continuous-Variable Nonclassicality Detection under Coarse-Grained Measurement

We experimentally detect the nonclassicality of continuous-variable quantum states under coarse-grained measurement. Furthermore, we show that the coarse-grained measurement generally outperforms the ideal fine-grained measurement in nonclassicality detection.
Accuracy Self-Estimation in an 8-Dimensional Quantum State Identification at a Telecom Wavelength

We experimentally obtain single-shot confidences of a state estimation measurement with M =8 arbitrary sets of non-orthogonal states. The 8-dimensional vector of confidences represents the best knowledge of the input state for each individual quantum measurement.
Toward Single Photon Detection using Nanophotonic Parametric Amplifiers

Ultra-intense parametric amplification can be used for amplifying single photons to macroscopic levels. We experimentally detected coherent states with 〈 N 〉=4.86 with 17% efficiency and 2.5% dark count probability.
Image Sensors Based on Nonlinear Optical Neural Networks

We use a multilayer, nonlinear optical neural network as a pre-processor for machine vision. On tasks including identification o f r eal o bjects i lluminated by broad-band incoherent light, we show that nonlinear optical pre-processing outperforms linear pre-processing.
Back-Propagation-Free Photonic Deep Learning with Random Nonlinear Projection

We demonstrate back-propagation-free photonic deep learning by extending a biologically inspired training called direct feedback alignment. The potential for accelerated computation with competitive performance was confirmed through numerical simulation and optoelectric hardware implementation.
Tensorized Optical Multimodal Fusion Network

We propose the first tensorized optical multimodal fusion network architecture with a self-attention mechanism and low-rank tensor fusion. Simulation results show 51.3× less hardware requirement and 3.7 × 10 13 MAC/J energy efficiency.
The Effectiveness of Nutrition Comic, Cards and Video on Nutritional Knowledge of Elementary School Students in Sebatik Island, North Kalimantan

Background:The double burden of malnutrition still occurs in Indonesia, especially in school-age children.The prevalence of underweight and obese children aged 5-12 years in Nunukan Regency, North Kalimantan was 4.2% and 20.2%, respectively.One of the causes of nutritional problems in school-age children is an inappropriate food consumption pattern.This can be influenced by the lack of nutritional knowledge.This study aims to determine the effectiveness of nutrition cards, comics and videos on nutrition knowledge of elementary school students.Methods: This was a Quasi Experimental Design with a Nonequivalent Control Group research design.The subjects were 83 elementary school students aged 10-13 years on Sebatik Island, North Kalimantan.Students were selected using purposive sampling method.Data of nutritional knowledge were obtained using the pretest-posttest form, that taken before and after the intervention using cards, comics and nutrition videos.Data were analyzed using paired T-test and oneway ANOVA with p < 0.05.Results: The results showed that there were significant differences in the mean pretest and posttest scores before and after education in each treatment group (p < 0.05).There was a significant difference in the changes in the pretest and posttest scores between the treatment groups (p < 0.05).In addition, based on Bonferroni's post hoc test, comics were more effective than controls group and statistically significant (p < 0.05).Conclusion: Comic was more effective to increase nutrition knowledge than the control group.Educational media should be made as attractive as possible so that the message can be delivered optimally to students.
The Level of Elderly Knowledge of PHBS in Facing the New Normal Era with the Virtual Learning Method

New normal is a term issued by the government in order to loosen social restrictions.In this new normal era elderly are one part of the population who have a high vulnerability to the Covid-19 Virus.Therefore, in the new normal era, is necessary for the elderly to carry out an activity that can keep them from being exposed to the Covid-19 virus.One way is to maintain a Clean and Healthy Lifestyle (CHL).The purpose of this study was to determine the relationship between the elderly and PHBS in facing the New Normal Era with the virtual learning.The method used is this research is a type of non-experimental research, the research design used in this study is analytic observation, namely studying the relationship between two or more variables to find a relationship if there is, how closely related or no relationship.A sample of 60 showed that the subjects were male 13 (21.6%)and female 47 (78.3%).The results of hypothesis testing using the Spearman correlation test show that there is a relationship between the elderly and PHBS in facing the New Normal Era with the virtual learning method.The conclusion obtained is that there is a strong relationship between self-cleaning, clean environment, eating patterns, and activities in the elderly in facing the New Normal Era.
The Effect of Management Information System Implementation in Drug Management on the Performance of Hospital Pharmacy Installation Employees

The implementation of the Hospital Management Information System (SIMRS) in drug management must be able to provide access to accurate and timely information.The problems with implementing SIMRS in pharmacy installations are invalid drug stock data, the unavailable feature of the report on the expenditure of goods per unit, and the human resource factors that have been accustomed to the old application.These problems can result in employee activities in the process of planning and reporting drugs that are less accurate and not timely so decision-making will lead to financial losses for the hospital.This study aims to determine the effect of SIMRS implementation in drug management on the performance of Pharmacy Installation employees at RSPAU dr. S. Hardjolukito.This type of research is an analytic survey with a cross-sectional design.The number of samples was obtained by 46 people using the purposive sampling technique.The results showed that there was a positive influence between the implementation of SIMRS in drug management on employee performance when viewed from the t-test with t count > t table (19.951 > 2.015368).The coefficient of determination of the R-value is 0.949, close to 1, so the relationship between SIMRS implementation in drug management and employee performance is very close.SIMRS implementation in drug management is in the high category (56.52%).Employee performance is in the high category (69.57%).The better the implementation of SIMRS in drug management at the pharmacy installation of RSPAU dr. S. Hardjolukito then the level of employee performance will be better.
A Comparative Study to Detect Cervical Dysplasia by Using Pap Smear Images

Neoplasia is a process that produces abnormal as well as excessive growth of tissue. It persists in this manner after caseation of the stimuli which evoked the change. Cervical cancer or neoplasm (squamous cell carcinoma) is the fourth most common cause among the different types of cancers present worldwide. However, cervical cancer can be effectively prevented by routine screening by taking cervical smears and staining by Papanicolaou stain and reporting them according to the Bethesda system 2001 of cervicovaginal cytology. Due to the ever-increasing awareness, increasing workload and paucity of trained pathologists, AI and machine learning are increasingly used for reporting of Pap smear. Despite years of research in this field, precise segmentation of cervical image cells remains a challenge since there can be presence of cluster of cells. Here, we are going to discuss the comparative study of the traditional classification methods which are mainly dependent on main features like morphology, texture, and size of nucleus in the cell. This analysis is quite helpful for developing an automated classification of cells which helps in initial cervical screening.
Efficient and Novel Architecture of Golay Encoder and Decoder for McEliece Cryptosystem

As technology advances, it is projected that the development of a quantum computer may jeopardize various existing public key cryptosystems. The integrity and confidentiality of communications will be negotiated. Code-based cryptosystems are compatible with existing networks and communication protocols as well as secure against any attacks. Security and effectiveness are traded off in the use of code-based cryptography. In the first part of this work, a McEliece cryptosystem is successfully designed employing an extended Golay code. In the second part of this work, efficient encoder and decoder architecture for extended binary Golay code have been developed and employed after validating the proposed algorithm for McEliece cryptosystem. The synthesis results show that the proposed hardware structures for Golay code are superior to those already in use considering various performance metrics. High throughput, minimal latency and low area are achieved for both hardware modules. For secure high-speed communications, the proposed Golay encoder and decoder is a promising option in the McEliece cryptosystem.
Digital Image Tampering Detection Using Deep Learning: A Survey

Digital images can be considered as a source of information in various cases. But digital images have been tampered using various image editing software like Adobe Photoshop, GNU Image Manipulation Program (GIMP), etc. Many techniques are employed to tamper an image like copy-move, splicing, removal, etc. Traditional methods that use handcrafted features and laid the foundation of tampering detection are not that efficient. Deep learning proves to be an effective method to detect changes in an image. These methods have greater accuracy than traditional methods because they can extract complex features from an image at a faster rate. They are also automatic which saves time and effort. In this paper, we present a detailed survey of deep learning-based techniques for image tampering detection.
Prediction of Client Term Deposit Subscription Using Machine Learning

The majority of the banking industry’s income is often derived from long-term deposits which are subscribed by the clients. Understanding clients’ behaviour and attributes is crucial for banks for their growth in sales. To help with this, marketing tactics are used to target new clients producing a lot of information about client traits and other important features. In recent years, it has been found that a variety of data analysis techniques can be used to analyse the customer’s traits along with the factors that have a big impact on consumers’ decisions to subscribe to term deposits. Bank deposits are among the important challenges faced by a financial institution. It can be challenging to anticipate a customer's likelihood of becoming a depositor by analysing associated data. According to current estimates, the finance and business sector has suffered as a result of the crisis economic and the economy's ongoing collapse. As a result, banks are struggling to attract consumers because of the economic crisis. Marketing is regarded as a practical tool. The banking sector is trying to get customers to consider term deposits. Recognizing the objective of the company. The main aim is to predict a term deposit subscription by a customer according to the history data analysis of the client’s behaviour. The data has been explored thoroughly and then a model in been built using ML Algorithms to predict the desired subscription. The algorithms are being compared, and the best accuracy given by the algorithm has been proposed as the best for prediction.
Classification via Two-Way Comparisons (Extended Abstract)

Given a weighted, ordered query set Q and a partition of Q into classes, we study the problem of computing a minimum-cost decision tree that, given any query $$q\in Q$$ , uses equality tests and less-than comparisons to determine the class to which q belongs. Such a tree can be much smaller than a lookup table, and much faster and smaller than a conventional search tree. We give the first polynomial-time algorithm for the problem. The algorithm extends naturally to the setting where each query has multiple allowed classes.
Literacy-based tex books to improve the character and language competence of elementary students

The purpose of this study was to produce and determine the feasibility, practicality, and legibility of reading literacy-based textbooks using the type of research and development of the 4D model. The research instruments were arranged based on the level of research needs, namely material expert validation, product validation, and linguist validation. The results of the product feasibility assessment by validation experts obtained 95.8% media validation, 95% material validation, and 91.96% language validation. The product readability rating obtained 89.83%, supported by 90.75% language literacy story questions and 89% character-based language story test results. The findings of this study indicate that reading literacy-based textbooks can attract students' attention and are interactive, the presentation of the content supported by pictures and various color compositions can attract reading interest and stimulate critical thinking skills. So as to help students have the literacy skills needed at this time.
Investigating the Effect of Students’ Knowledge, Beliefs, and Digital Citizenship Skills on the Prevention of Cybercrime

The growing prevalence of cybercrime, particularly among young adults, necessitates the promotion of digital citizenship to educate students about responsible online behavior and to equip them with the skills to mitigate cyber risks. The specific objective of this study was to investigate the effect of digital citizenship skills on the prevention of cybercrime among higher education students. A mixed-method approach, including surveys and interviews, was employed to collect data from 652 students in Saudi Arabia. This study found that digital citizenship generally has a significant impact on students’ awareness and prevention of cybercrime through the development of responsible online behavior. Knowledge of digital law came first, followed by beliefs about digital manners. Digital communication skills came third, followed by digital rights, knowledge, and duties in fourth place. Then, digital commerce skills and digital health beliefs came fifth and sixth, respectively. This was followed by digital access skills, then digital security, and finally digital culture. The results also revealed a negative statistical relationship between digital citizenship and cybercrimes’ various forms including national, financial, banking, social, immoral, insulting, slanderous, defaming, threatening, and harassment in virtual learning environments. These findings have significant implications for the understanding of how higher education institutions can promote digital citizenship and prevent cybercrime by integrating digital citizenship education into their curriculum, providing training for educators, and establishing clear policies and guidelines for responsible online behavior.