Reviewerly/exHarmony huggingface dataset:
A Consent-Based Privacy-Compliant Personal Data-Sharing System

Personal data is becoming increasingly valuable in business, as the insights that can be obtained from data processing continue to improve. However, it also can cause adverse effects on individuals. To improve data quality while satisfying privacy compliance, companies now have focused on collecting informed consent from individuals to directly handle personal data without applying any privacy-preserving techniques. Even though the companies obtain consent to use personal data, to improve transparency and accountability to ensure that companies deal with personal data according to consent, it is necessary for a system to comply with privacy requirements. Therefore, this paper proposes a new consent-based privacy-compliant personal data-sharing system that considers personal data-sharing flows and requirements obtained from enterprises and privacy frameworks, respectively. By analyzing a general process and the roles of actors for data sharing in enterprise environments according to standard privacy frameworks, this paper has proposed system requirements, system architecture, and detailed procedure for a consent-based privacy-compliant processing method that considers compliance checking as well as consent checking. To show the feasibility of the proposed system, this paper demonstrates a prototype and the performance analysis in the lab and real-world environments.
Guidance Information Assisted Reconstruction of Masked Faces

The application of deep learning to face inpainting has led to the development of a variety of methods. Guidance information, including edges, landmarks, segmentation maps, and sketches, has been increasingly incorporated alongside input images to achieve more stable structural restoration in face inpainting. Most of the methods utilizing guidance information predict the occluded part’s guidance information and subsequently incorporate it as part of the inputs to the inpainting module. However, these methods can adversely affect the final reconstruction result if the guidance information is incorrectly predicted. Therefore, we propose a face reconstruction method using guidance information outside of the occluded area. Additionally, we employed a conventional edge detection method and downsized the overall model structure due to the substantial computational expenses associated with utilizing deep neural networks for generating guidance information. During experiments conducted on the CelebA-HQ datasets, the proposed method demonstrated superior performance compared to other approaches, as evidenced by higher values in the SSIM, PSNR, and FID metrics.
Randomized Polar Codes for Anytime Distributed Machine Learning

We present a novel distributed computing framework that is robust to slow compute nodes, and is capable of both approximate and exact computation of linear operations. The proposed mechanism integrates the concepts of randomized sketching and polar codes in the context of coded computation. We propose a sequential decoding algorithm designed to handle real valued data while maintaining low computational complexity for recovery. Additionally, we provide an anytime estimator that can generate provably accurate estimates even when the set of available node outputs is not decodable. We demonstrate the potential applications of this framework in various contexts, such as large-scale matrix multiplication and black-box optimization. We present the implementation of these methods on a serverless cloud computing system and provide numerical results to demonstrate their scalability in practice, including ImageNet scale computations.
A Request for Clarity over the End of Sequence Token in the Self-Critical Sequence Training

The Image Captioning research field is currently compromised by the lack of transparency and awareness over the End-of-Sequence token (<Eos>) in the Self-Critical Sequence Training. If the <Eos> token is omitted, a model can boost its performance up to +4.1 CIDEr-D using trivial sentence fragments. While this phenomenon poses an obstacle to a fair evaluation and comparison of established works, people involved in new projects are given the arduous choice between lower scores and unsatisfactory descriptions due to the competitive nature of the research. This work proposes to solve the problem by spreading awareness of the issue itself. In particular, we invite future works to share a simple and informative signature with the help of a library called SacreEOS. Code available at: https://github.com/jchenghu/sacreeos .
SynthCap: Augmenting Transformers with Synthetic Data for Image Captioning

Image captioning is a challenging task that combines Computer Vision and Natural Language Processing to generate descriptive and accurate textual descriptions for input images. Research efforts in this field mainly focus on developing novel architectural components to extend image captioning models and using large-scale image-text datasets crawled from the web to boost final performance. In this work, we explore an alternative to web-crawled data and augment the training dataset with synthetic images generated by a latent diffusion model. In particular, we propose a simple yet effective synthetic data augmentation framework that is capable of significantly improving the quality of captions generated by a standard Transformer-based model, leading to competitive results on the COCO dataset.
Analysis of Research Trends on General Mathematics at Universities : Focusing on Papers Published in Korean Journals Over the Past 10 Years

In this study, in order to provide a developmental discussion on general mathematics in universities and the direction for follow-up research, research trends were examined for papers published in Korean domestic academic journals over the past 10 years. The results of the research are as follows: First, among studies related to mathematics education and general education, research on general mathematics in universities is severely lacking. Considering that nearly all universities offer courses related to general mathematics, professors and researchers responsible for general mathematics should dedicate more attention to research on general college mathematics. Second, concerning the subjects and content areas of mathematics, research on general mathematics shows a bias towards basic general mathematics for science and engineering students. Therefore, there is a need for research on basic general mathematics for students in the humanities and social sciences, as well as research on pure general mathematics applicable to all students. Third, there is a lack of research on evaluation and educational technology in the field of general mathematics. Evaluation plays a crucial role in education, and it is important to conduct research on the evaluation of general mathematics and the utilization of educational technology, especially considering the increasing emphasis on the use of various technological tools in mathematics education. Fourth, in terms of research methods, the majority of studies focus on quantitative research. Therefore, it is necessary to employ diverse research methods that are suitable for the teaching and learning environment of general mathematics. Fifth, concerning the research topic on teaching and learning, while there have been numerous studies targeting science and engineering students, there is a need for a variety of studies on teaching and learning that encompass all students, including those in the humanities and social sciences, as well as the professors themselves.
A Normative Approach to Privacy-Preserving Recommender Systems: Integrating Matrix Factorization and Genetic Algorithms

As recommendation systems heavily depend on user data, these systems are susceptible to potential privacy breaches. To mitigate this issue, differential privacy (DP) protection techniques have been developed to offer robust privacy safeguards. Nevertheless, a majority of the extant DP-based recommendation algorithms tend to introduce excessive noise, consequently impairing the quality of recommendations. In response, this study presents a novel DP-preserving recommendation algorithm that integrates matrix factorization (MF) and a genetic algorithm (GA). Initially, the MF problem is transformed into two interrelated optimization problems, namely, the user-hidden factor and the item-hidden factor. Subsequently, GA is employed to address these optimization issues. An enhancement index mechanism is incorporated into the individual selection of GA, while the variation process of GA is devised based on identifying crucial hidden factors. Utilizing the enhancement index mechanism aids in minimizing the algorithm’s perturbation level, thereby achieving an optimal balance between privacy protection and algorithm utility. Experimental analyses, encompassing recommendation accuracy, efficiency, and parameter variation effects, are conducted on Last.fm and Flixster datasets. The findings corroborate that the proposed system outperforms existing alternatives under stringent privacy constraints, thereby attesting to its efficacy.
Enhancement method for non-uniform scattering images of turbid underwater based on neural network

Due to the influence of light absorption and scattering in the water medium, underwater images often appear with different degrees of blur, color distortion, and so on. The physical model-based method needs to establish image degradation processes and improve image quality by assuming and estimating complex parameters, which limits the generalization ability and practical application effect of these methods. To solve the above problems, this paper proposes an image enhancement method of non-uniform scattering in turbid underwater based on neural network. First, the high-frequency and low-frequency components of underwater images were obtained through the difference of gaussian (DOG) and bilateral filtering (BF), respectively. Then, for the high-frequency component, pulse coupled neural networks (PCNN) to improve the edge details of the image. For low-frequency components, it is carried out in two steps: an adaptive automatic white balance and polynomial regression (AAWBPR) algorithm is proposed, which can effectively eliminate the color bias under different color temperatures and improve the brightness of images; the adaptive color and contrast enhancement (ACCE) method is used to further enhance the color and contrast of images. Finally, Decom-Net and the improved U-Net method are used to reduce the errors caused by the non-uniform scattering effect, weaken the noise interference, and enhance the overall effect of images. Extensive experiments show that the proposed method can effectively improve the quality of turbid underwater non-uniform scattering images, enhance the contrast of images, reduce the complexity of parameters, and has good generalization performance and practical application effects.
Connecting Cloud Computing and Machine Learning Through Functional Situation-Awareness: A User-Centric Smart Monitoring Application

For many parents, the time between their children leaving home and the ensuing arrival at school is a grey area with uncertainty. The need of being aware of the highly critical safetyness situations there- fore arises. Situation-aware computing serves as a promising technique, both theoretically and pragmatically, to bring smart monitoring to the field. It seamlessly integrates the benefits offered by machine learning techniques and cloud computing technologies with an emphasis on user centric situations. In this paper we present a real world smart monitoring application: its underlying model, its human computer interactive design and design principles, its support from the theory and practice of situation-awareness computing, as well as its implementation details and viability test. This application also serves the purpose of elaborating important attributes of safety-aware situation computing, with a strong linkage to safety critical human computer interaction. The goal of this application is to accurately recognize and identify the faces of children, under relevant environmental contexts, when they enter or exit a bus, and then promptly alert their parents. Two main components, one being a distributed bus subsystem and the other being a central cloud-based subsystem, are built into this application highlighting lightweight to effectively serve the users. The novelty of our approach lies in the fact that we deeply embrace functional programming paradigm to tackle the specification of the two subsystems at the design level with rigorous computational semantics, which in turn contributes to the quality implementation of the lower level details.
Generation of Over-8-dB Squeezed Light by a Broadband Waveguide Optical Parametric Amplifier Toward Fault-Tolerant Quantum Computers

Measurement-based optical quantum computers (MBOQCs) using continuous-variables (CVs) of traveling wave of light have a potential for ultra-fast fault-tolerant quantum information processing [1]. In CV MBOQCs, broadband high-level squeezed light is the most essential quantum resource. Broad bandwidth not only enables high-clock frequency computation, but also contributes to the realization of large-scale quantum computers using time-domain-multiplexed quantum entanglement. This is because squeezed light with a broader bandwidth enables us to define many more quantum wave packets in a narrow time range. On the other hand, high-level squeezing contributes to high-fidelity quantum operation and suppression of quantum errors. For example, over-8.3-dB squeezing enables us to achieve a fault-tolerant CV MBOQC with Gottesman-Kitaev-Preskill (GKP) coding [2].
Photonic Machines for Large-Scale Applications and Fundamental Physics

Which is the simplest way to process information with light? If we use integrated optics, we need highly optimized fabrication steps for devices with many channels. If we use wavelength multiplexing, we need sophisticated modulators and expensive filters. But optics deals with beams of light, and there is no potential limit to the information density we can store in a beam of light. So, we can use holograms to exploit the three-dimensional information and demonstrate demanding computational tasks as natural language processing [1].
How Much Noise ChatGPT is Making: A Sentiment Analysis Approach

Present study analyses the social mentions with regard to ChatGPT on different websites. For this purpose, the analysis has been performed with the help of Brand24®, which is a social media analysis tool. The results of Sentiment analysis have been performed through Netnographic analysis and presented in the form of volume of social mentions, types of sources, contexts of discussion, most active sites, most influential sites, trending hashtags and lastly the numerical summary of social sentiments have been presented. In addition, Details like gender distribution, age distribution top countries using ChatGPT, and interest of audience has also been presented with the help of SimilarWeb. SimilarWeb is an online tool which provides performance of website. The current investigation holds numerous implications for companies like Openai, marketers, and society at large. Uniqueness of the study lies in the selection of topic for sentiment analysis. As far as the knowledge of researcher is concerned, none of the study have explored and explained the sentiment analysis of AI based website, which is the motivation behind selection of this topic for performing Netnographic analysis of social sentiments.
Time-Stretched Imaging Flow Cytometry and Photonic Neuromorphic Processing for Particle Classification

In this work, we investigate the compression capabilities of a photonic neuromorphic accelerator relying on an optical spectrum slicing technique [1], following a high-flow 1D imaging cytometry setup, able to image 62000 particles/sec. The utilized image scheme relies on a technique, where spatial information is transfered to the spectrum and subsequently through dispersion to the temporal domain. Therefore, it can replace high speed spectrometers with a single photodetector [2]. On the other hand, the elevated image acquisition rate, trigger the generation of a staggering amount of data, which can cause a processing backlog at the digital backend. In this context, we explore, for the first time, the use of a photonic neuromorphic pre-processing unit, following the cytometer that can reduce the number of trainable parameters at a lightweight digital backend by a factor of 2.5, while at the same time, it can project incoming data to a high dimensional space boosting accuracy.
Quantum Randomness Certification Through Three-state Discrimination

Consider a device which, on each round, prepares one pure quantum state out of three possible ones. The overlaps between the possible states are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert \langle\psi_{0}\vert \psi_{1}\rangle\vert \geq\delta, \vert \langle\psi_{0}\vert \psi_{2}\rangle\vert \geq\Delta_{02}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert \langle\psi_{1}\vert \psi_{2}\rangle\vert \geq\Delta_{12}$</tex> . A measurement will be performed on each round. Our goal is to certify the amount of randomness that can be extracted from the measurement.
Photonic Delay-Based Reservoir Computers as Deep Neural Network Preprocessors

Photonic reservoir computing (RC) has been effectively used for solving various complex problems. RC systems are straightforward to train when compared to deep neural networks (DNNs), and doing RC in the photonics domain offers the advantage of high-speed performance, low-energy consumption and the possibility of high inherent parallelism [1], [2]. We propose and investigate to use the output of such a reservoir to preprocess the input data before this data is send though a DNN. The main idea here is to use such a photonic reservoir to transform the input data into a higher dimensional state-space, which could allow the DNN to process the data with increased performance. Based on numerical simulations of delay-based reservoirs using a single-mode semiconductor laser [3]–[6], we show that using such a preprocessing reservoir results in an improved performance of DNNs, and that we do not need to carefully fine-tune the parameters of the preprocessing reservoir.
Feature Chirality in Deep Learning Models

As deep learning applications extensively increase by leaps and bounds, their interpretability has become increasingly prominent. As a universal property, chirality exists widely in nature, and applying it to the explanatory research of deep learning may be helpful to some extent. Inspired by a recent study that used CNN (convolutional neural network), which applied visual chirality, to distinguish whether an image is flipped or not. In this paper, we study feature chirality innovatively, which shows how the statistics of deep learning models’ feature data are changed by training. We rethink the feature-level chirality property, propose the feature chirality, and give the measure. Our analysis of feature chirality on AlexNet, VGG, and ResNet reveals similar but surprising results, including the prevalence of feature chirality in these models, the initialization methods of the models do not affect feature chirality. Our work shows that feature chirality implies model evaluation, interpretability of the model, and model parameters optimization.
Resilience of Quantum Key Distribution Source Against Laser-Damage Attack by a Variety of Lasers

Quantum key distribution (QKD) systems provide quantum-safe key exchange. Therefore, complete security analysis of implementations of QKD protocols is in the focus of interest of a worldwide information-security community. For today, a number of QKD loopholes are closed by countermeasures, which are also considered in emerging QKD security evaluation and certification [1]–[3]. However, new threats to practical QKD implementations are still found, such as the laser-damage attack, which is a powerful hacking strategy. In investigations, CW laser radiation is most often used, but in contrast to it, the interaction of pulsed laser radiation with optical materials may lead to a wide range of effects, like nonlinear effects, dielectric breakdown, etc.
Impact of Photonic Integration Platforms on the Performance of Neuromorphic Accelerators

Artificial Intelligence (AI) has recently proven to be a powerful and versatile tool, able to achieve super-human capabilities in an ever increasing number of fields such as image recognition, game playing, and text generation [1]. Fig. 1A depicts a typical Deep Neural Network (DNN), the underpin of modern AI: a layered structure built upon a simple computing primitive. The deployment of DNNs represents a major challenge where cumbersome and energy-intensive CPUs/GPUs cannot be exploited, such as in edge computing [2]. In this scenario, analog photonics is promising for realizing AI accelerators meeting the bandwidth and power consumption requirements. Several photonic neuromorphic processors have been recently demonstrated, proving advantages in respect to electronic solutions in terms of bandwidth, latency, and power consumption [2].
Ensemble Based Classification for Classification of Ductal Carcinoma using Reduced Feature Set

Ductal Carcinoma is one of the main causes of death in women. Ductal Carcinoma's affected population has been increasing annually. All living creatures are composed of unique and identifiable cells, which define their physical appearance and purpose. Cells are the basic building block in the human body that construct a particular human organ. In the human body, cells usually replicate themselves in a particular sequence. Bacterial infection in the human body might affect the growth sequence of cells, which enables them to replicate their selves in an uncontrolled sequence. Ductal Carcinoma is a bacterial infection that is caused due to presence of abnormal cells in the duct. It usually occurs due to the outgrowth of cells lining the milk ducts. The chances of ductal carcinoma in humans can be reduced by adopting an early prognosis. The effective prognosis of ductal carcinoma can be done by using learning algorithms such as K-Mean Clustering, K-Nearest Neighbor, Naive Bayes, Neural Network, RBF network, and Support Vector Machine. A number of studies have reported the use and effect of these algorithms However, most of the studies are suffering in terms of accuracy because of insignificant attributes. This study aims to address this concern of accuracy. To execute the intended work, the authors have selected a renowned dataset entitled “Wisconsin Breast Cancer Dataset” which contains all the vital attributes for the effective prognosis of ductal carcinoma such as Bland chromatin, Cell shape/size uniformity, Clump Thickness, Marginal Adhesion, Normal Nuclei, Single Epithelial Cell Size, Bare Nuclei, and Mitosis. The machine learning approach is known to be beneficial in efficiently predicting ductal carcinoma. In this study, we implemented a machine learning approach using the Bagging approach with Naive Bayes. The methodology used has achieved higher accuracy of 97.56 with a lower root mean square error of 0.1612 as compared to the existing study.