Reviewerly/exHarmony huggingface dataset:
Adaptive Advantage in Entanglement-Assisted Communications

Entanglement is known to boost the efficiency of classical communication. In distributed computation, for instance, exploiting entanglement can reduce the number of communicated bits or increase the probability to obtain a correct answer. Entanglement-assisted classical communication protocols usually consist of two successive rounds: first, a Bell test round, in which the parties measure their local shares of the entangled state, and then a communication round, where they exchange classical messages. Here, we go beyond this standard approach and investigate adaptive uses of entanglement: we allow the receiver to wait for the arrival of the sender's message before measuring their share of the entangled state. We first show that such adaptive protocols improve the success probability in random access codes. Second, we show that once adaptive measurements are used, an entanglement-assisted bit becomes a strictly stronger resource than a qubit in prepare-and-measure scenarios. We briefly discuss the extension of these ideas to scenarios involving quantum communication and we identify resource inequalities.
A Study on Analyzing Teachers' Perception and Needs of Using Metaverse in Elementary Online Learning Environment

Metaverse is a new type of Internet application and social form that integrates a variety of new technologies.It has the characteristics of multi-technology, sociality, and hyper spatiotemporality.This paper introduces the development status of Metaverse, from the five perspectives of network infrastructure, management technology, basic common technology, virtual reality object connection, and virtual reality convergence, it introduces the technical framework of Metaverse.This paper also introduces the nature of Metaverse's social and hyper spatiotemporality, and discusses the first application areas of Metaverse and some of the problems and challenges it may face.
FLERT: Document-Level Features for Named Entity Recognition

Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries. However, the use of transformer-based models for NER offers natural options for capturing document-level features. In this paper, we perform a comparative evaluation of document-level features in the two standard NER architectures commonly considered in the literature, namely "fine-tuning" and "feature-based LSTM-CRF". We evaluate different hyperparameters for document-level features such as context window size and enforcing document-locality. We present experiments from which we derive recommendations for how to model document context and present new state-of-the-art scores on several CoNLL-03 benchmark datasets. Our approach is integrated into the Flair framework to facilitate reproduction of our experiments.
BACA: Superpixel Segmentation with Boundary Awareness and Content Adaptation

Superpixels could aggregate pixels with similar properties, thus reducing the number of image primitives for subsequent advanced computer vision tasks. Nevertheless, existing algorithms are not effective enough to tackle computing redundancy and inaccurate segmentation. To this end, an optimized superpixel generation framework termed Boundary Awareness and Content Adaptation (BACA) is presented. Firstly, an adaptive seed sampling method based on content complexity is proposed in the initialization stage. Different from the conventional uniform mesh initialization, it takes content differentiation into consideration to incipiently eliminate the redundancy of seed distribution. In addition to the efficient initialization strategy, this work also leverages contour prior information to strengthen the boundary adherence from whole to part. During the similarity calculation of inspecting the unlabeled pixels in the non-iterative clustering framework, a multi-feature associated measurement is put forward to ameliorate the misclassification of boundary pixels. Experimental results indicate that the two optimizations could generate a synergistic effect. The integrated BACA achieves an outstanding under-segmentation error (3.34%) on the BSD dataset over the state-of-the-art performances with a minimum number of superpixels (345). Furthermore, it is not limited to image segmentation and can be facilitated by remote sensing imaging analysis.
Mapping Research Developments on Mathematics Communication: Bibliometric Study by VosViewer

This research aims to determine the map of the development of research on Mathematics Communication, namely a study of bibliometrics by VosViewer. The study was conducted starting in January 2021 by searching the keyword “Mathematics Communication” in the Science Direct database and choosing the open access system. The search result data is then stored in the form of a Research Information System (RIS) and analyzed descriptively using VosViewer to see developments on the topic from year to year, the types of documents that are widely published, the name of the author who has discussed the most about the case, the name of the publishing institution and the topics that appear most frequently from thousands of journals that have been processed using VosViewer. The results showed that the map of the development of research on Mathematics Communication has existed since 1997 and increases rapidly from year to year. The article that publishes the most on this topic is Research Articles with the publisher’s name Procedia Computer Science as many as 2662 documents. Meanwhile, the author who published the most on this topic was Yusof, Yudariah Mohammad, until May 28, 2021. The most dominant issue that appears was “communication”, followed by the topic of “systems” and “students”. Network visualization shows that a line demonstrates the relationship between issues between the descriptors in each field. The more relationships between the lines indicate the closer relationships between documents.ch on that topic.: Indicate the main conclusions or interpretations.
New Methods for Contextual Communication in Modeling Software Platform (MSP)

This paper is a contribution to concepts and methodologies of context definition in Engineering Model System (EMS). Change for contextual modeling was important paradigm shift in engineering at beginning of this new century. By now, EMS is a very complex model because it is required to serve all engineering activities during lifecycle of an engineering achievement (EA). In an EMS, the structure of contexts serves integration as glue. An EA is the objective of an engineering program and generally is an industrial or commercial product. Four new concepts and methods were introduced during the work for this paper with the requirement of their integration in EMS namely the Integrated Lifecycle Engineering (ILE), the Integrated Autonomous Model System (IAMS), the Reactive Context Structure (RCS), and the Integrated Research Project (IRP). ILE organizes research intensive engineering activities to support connection between essential engineering and relevant modeling software platform (MPS) activities. ILE includes fundamental and problem solving research in new context. IAMS is based on formerly published EMS concepts and includes new specifics. In the center of IAMS, enhanced structure of context definitions and their organization constitute glue in the demanded complex model. IAMS is characterized as an enhanced media for engineering communication. RCS serves as integrated unit of IAMS to enhance context driven reactive communication. In this way, IAMS includes two ways reactive context driven connection between IAMS and the physically operated Cyber Physical Biological System (CPBS) it represents. IAMS serves as digital twin of CPBS. The above new concepts and methods are suitable to develop as user defined components in EMS using MPS resources. IRP is introduced as a specific integrated application of ILE, IAMS, and RCS, concentrating on PhD student research specifics.
Novel scheme for secure data transmission based on mesoscopic twin beams and photon-number-resolving detectors

Quantum resources can improve the quality and security of data transmission. A novel communication protocol based on the use of mesoscopic twin-beam (TWB) states of light is proposed and discussed. The message sent by Alice to Bob is encoded in binary single-mode thermal states having two possible mean values, both smaller than the mean value of the TWB. Such thermal states are alternately superimposed to the portion of TWB sent to Bob. We demonstrate that in the presence of an eavesdropping attack that intercepts and substitutes part of the signal with a thermal noise, Bob can still successfully decrypt the message by evaluating the noise reduction factor for detected photons. The protocol opens new perspectives in the exploitation of quantum states of light for applications to Quantum Communication.
Efektivitas Pembelajaran Daring Melalui Whatsapp terhadap Minat dan Hasil Belajar Biologi

This study aims to determine the effect of online learning through WhatsApp on students' interest and learning outcomes in Biology at SMA Negeri 1 Bengkulu City. The method used in this research is a pre-experiment with a one-group pre-test-post-test design. The research sample was students of class XI IPA 4 SMA Negeri 1 Bengkulu City, totaling 29 students. The results showed an effect before and after being given treatment using online learning via WhatsApp on the interests and learning outcomes of students at SMA Negeri 1 Bengkulu City. In conclusion, WhatsApp is effectively used as a medium in the online Biology teaching and learning process.&#x0D; Keywords: Learning Outcomes, Interests, Online Learning, WhatsApp
Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis

Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future. A total of 2844 publications from the Web of Science Core Collection were included in the bibliometric analysis. The country/region, institution, author, journal, keyword, and references were analyzed by using VOSviewer and CiteSpace. The results showed that the number of publications has grown rapidly in the last five years. The USA is the most productive and influential country with 937 publications and 23,010 citations, and most of the authors and institutions with higher numbers of publications and citations are from the USA. Keyword analysis showed that breast cancer, prostate cancer, colorectal cancer, and lung cancer are the tumor types of greatest concern. Co-citation analysis showed that classification and nucleus segmentation are the main research directions of AI-based HI studies. Transfer learning and self-supervised learning in HI is on the rise. This study performed the first bibliometric analysis of AI in HI from multiple indicators, providing insights for researchers to identify key cancer types and understand the research trends of AI application in HI.
On the Relation between Sensitivity and Accuracy in In-context Learning

In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios. We study the sensitivity of ICL with respect to multiple perturbation types. First, we find that label bias obscures the true sensitivity, and therefore prior work may have significantly underestimated ICL sensitivity. Second, we observe a strong negative correlation between ICL sensitivity and accuracy: predictions sensitive to perturbations are less likely to be correct. Motivated by these findings, we propose \textsc{SenSel}, a few-shot selective prediction method that abstains from sensitive predictions. Experiments on ten classification datasets show that \textsc{SenSel} consistently outperforms two commonly used confidence-based and entropy-based baselines on abstention decisions.
Disease prediction based on multi-type data fusion from Chinese electronic health record

Disease prediction by using a variety of healthcare data to assist doctors in disease diagnosis is becoming a more and more important research topic recently. This paper proposes a disease prediction model that fuses multiple types of encoded representations of Chinese electronic health records (EHRs). The model framework utilizes a multi-head self-attention mechanism, which combines textual and numerical features to enhance text representations. The BiLSTM-CRF and TextCNN models are used, respectively, to extract entities and then obtain the embedding representations of them. The representations of text and entities in it are combined together for formulating representations of EHRs. The experimental results on EHRs data collected from a Three Grade Class B Hospital General in Gansu Province, China, show that our model achieved an F1 score of 91.92%, which outperforms the previous baseline methods.
A Modified Equilibrium Optimizer Using Opposition-Based Learning and Teaching-Learning Strategy

Equilibrium Optimizer (EO) is a newly developed intelligent optimization algorithm inspired by control volume mass balance models. EO has been proven to have an excellent solution effect on some optimization problems, with the advantages of ease of implementation and strong adaptability. However, the original EO has some disadvantages when solving complex multimodal problems, including an immature balance between exploration and exploitation, the high probability of falling into local optima entrapment, and the slow rate of convergence. In order to address these shortcomings, a modified equilibrium optimizer (OTLEO) is proposed using teaching-learning-based optimization (TLBO) and opposition-based learning (OBL). These modifications aim to maintain the diversity of the solutions, expand the algorithm’s search range, improve exploration and exploitation capabilities, and avoid local optima. To verify and analyze the modified equilibrium optimizer algorithm’s performance, the OTLEO was tested for 32 classical benchmark functions. All algorithms are independently run 30 times in the same environment. Thereafter, the comparative evaluation against the OTLEO and other six representative algorithms is conducted. Four real-world engineering application problems, including multiple sequence alignment and so on, are used for additional validation. The experimental results, statistical tests, qualitative analysis, and stability analysis demonstrate that the proposed OTLEO outperforms the original EO and other algorithms.
An Analysis of Internet of Things Computer Network Security and Remote Control Technology

In order to solve the problem of the specific application of Internet of Things computer network security monitoring technology in infrastructure construction, a method of remote network monitoring system design is proposed, taking tunnel security construction as an example. According to the characteristics of construction tunnel safety monitoring, this method provides theoretical guidance from several aspects, such as computer remote monitoring, data decision analysis, safety control, personnel positioning, and data management. Through experiments, it is found that in the safety detection experiment, when the data is extracted within 2 minutes for detection, the detection accuracy is higher than 80%, and if it is 4 minutes, the detection accuracy is higher than 90%; in the response time test, the local control response time is generally less than 1 s. In the remote control response time test, the response time can generally be within 3 s, and it may be greater than 5 s. The designed construction tunnel monitoring system transmits the collected data to the monitoring terminal remotely. Then, the sensor data is analyzed for timing decision-making. This method can effectively calculate the safety status of each monitoring unit of the current tunnel according to a certain algorithm and formulate corresponding emergency measures according to the safety status.
Quantum noise ciphered optical stealth communication based on equivalent spectral encoding

To provide secure and covert transmission for optical communication system at the same time, a quantum-noise stream ciphered optical stealth communication approach is proposed for the first time. In the proposed system, the optical pulses are time spread by a chromatic dispersion device and then phase modulated with optical codes at chip rate to realize an equivalent spectral encoding. Binary optical codes are converted to multiple level phase-shift keying signal according to Y-00 protocol. The encoded optical signals are attenuated to mesoscopic coherent states and sent to a public channel. The ciphered signals are stealthy transmitted under public channel noise. The availability and covertness of the optical stealth channel is verified by system simulations.
Neurosymbolic AI

Combining neural networks with symbolic representations might make them more versatile and dependable.
A deep learning scheme for efficient multimedia IoT data compression

Multimedia Internet of Things (MIoT) devices and networks will face many power and communication overhead constraints given the volume of multimedia sensed data. One classic approach to overcoming the difficulty of large-scale data is to use lossy compression. However, current lossy compression algorithms require a limited compression rate to maintain acceptable perceived image quality. This is commonly referred to as the image quality-compression ratio trade-off. Motivated by current breakthroughs in computer vision, this article proposes recovering high-quality decompressed images at the application server level using a deep learning-based super-resolution model. As a result, this paper proposes ignoring the trade-off between image quality and size and increasing the reduction size further by using a lossy compressor with downscaling to conserve energy. The experimental study demonstrates that the proposed technique effectively improves the visual quality of compressed and downscaled images. The proposed solution was evaluated on resource-constrained microcontrollers. The obtained results show that the transmission latency and energy consumption can be decreased by up to 10% compared to conventional lossy compression techniques.
Phase estimation of an SU(1,1) interferometer with a coherent superposition squeezed vacuum in a realistic case

The phase sensitivity of SU(1,1) interferometer is investigated using a coherent state and an m -coherent superposition squeezed vacuum states as inputs and the intensity detection. Photon-subtraction, photon-addition and photon superposition are three special cases. Both ideal and realistic cases are considered. It is shown that the coefficient s of coherent superposition can modulate the performance of phase sensitivity, especially in a small squeezing region. Even in the presence of photon losses, the three-kind of non-Gaussian operations can achieve the improvement of measure precision, and the photon addition presents the best robustness compared to the photon subtraction and coherent superposition. For small squeezing, the first-order non-Gaussian operation may be the most preferred in improving phase sensitivity if considering the limitations of experimental conditions. Our results may be helpful for the practical application of quantum information.
Deep Learning for Forgery Face Detection Using Fuzzy Fisher Capsule Dual Graph

In digital manipulation, creating fake images/videos or swapping face images/videos with another person is done by using a deep learning algorithm is termed deep fake. Fake pornography is a harmful one because of the inclusion of fake content in the hoaxes, fake news, and fraud things in the financial. The Deep Learning technique is an effective tool in the detection of deep fake images or videos. With the advancement of Generative adversarial networks (GAN) in the deep learning techniques, deep fake has become an essential one in the social media platform. This may threaten the public, therefore detection of deep fake images/videos is needed. For detecting the forged images/videos, many research works have been done and those methods are inefficient in the detection of new threats or newly created forgery images or videos, and also consumption time is high. Therefore, this paper focused on the detection of different types of fake images or videos using Fuzzy Fisher face with Capsule dual graph (FFF-CDG). The data set used in this work is FFHQ, 100K-Faces DFFD, VGG-Face2, and Wild Deep fake. The accuracy for FFHQ datasets, the existing and proposed systems obtained the accuracy of 81.5%, 89.32%, 91.35%, and 95.82% respectively.
Central Bank Digital Currency: Financial Inclusion vs Disintermediation

An overlapping-generations model with income heterogeneity is developed to analyze the impact of introducing a Central Bank Digital Currency (CBDC) on financial inclusion, and its potential adverse effect on bank funding.We highlight the role of two design parameters: the fixed cost of CBDC usage and the interest rate it pays, and derive principles for maximum inclusion and for mitigating the inclusion-intermediation trade-off.Agents' choice of money instrument is endogenously driven by income heterogeneity.Pre-CBDC, wealthier agents adopt deposits, while poorer agents adopt cash and remain unbanked.CBDCs with low fixed costs (and low interest rates) are adopted by cash holders and directly increase inclusion.CBDCs with high fixed costs (and high interest rates) are adopted by deposit holders and increase inclusion by raising deposit rates.The former allows for more favorable inclusion-intermediation trade-offs.We calibrate the model to match the US income distribution and aggregate share of unbanked households.A CBDC 50% cheaper (30% more expensive) than bank deposits decreases financial exclusion by 93% (71%) without impacting intermediation.In comparison, making the deposit market perfectly competitive would only decrease exclusion by 45%.
Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PDF