Reviewerly/exHarmony huggingface dataset:
Single Telecom Photons from a Diamond Silicon Vacancy Center via Quantum Frequency Conversion§

We perform low noise conversion of single photons from a diamond silicon vacancy color center to the telecom O-band. We demonstrate preservation of the photonic quantum properties, enabling their usage in deployed quantum networking applications.
Direct Iterative Photonic Integrated Matrix Inverter

We demonstrate the first chip-based real-valued direct photonic matrix inverter using coherent sources. At a rate of 2×10 5 inversions/s, a 16×16 diagonal matrix and a 2×2 arbitrary matrix are inverted with 99.2% and 93.7% accuracy.
VLSI Quantum Graph Photonics

We report a graph-based very-large-scale-integrated silicon-photonic quantum chip. Having the correspondence between quantum chip and graph theory, we demonstrate the generation of multiphoton multidimensional entanglement and determination of modulussquared Hafnian (Permanent) of graph’s adjacency matrices.
All-Optical Spiking Membrane III-V Laser on Si

We present an all-optical photonic spiking neuron based on a membrane laser on silicon with optical feedback. Ultrafast dynamics with ~200-ps refractory periods using 10-GHz signals are demonstrated, at a laser-energy cost of ~10 pJ/spike.
In situ Training of Silicon Photonic Neural Networks: from Classical to Quantum

Photonic neural networks perform ultrafast inference operations but are trained on slow computers. We highlight on-chip network training enabled by silicon photonics. We introduce quantum photonic neural networks and discuss the role of weak nonlinearities.
Effects of imbalanced modulation in continuous-variable quantum key distribution

We experimentally investigate the impact of non-optimal state preparation on a Gaussian-modulated coherent state CVQKD system. We show that modulation with imbalanced strength in the amplitude and phase quadratures reduces the optimal key rate over certain distance.
Incremental learning of radio modulation classification based on sample recall

Radio modulation classification has always been an important technology in the field of communications. The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks. In this paper, we propose a sample memory and recall framework for incremental learning of radio modulation classification. For data with different signal-to-noise ratios, we use a partial memory strategy by selecting appropriate samples for memorizing. We compare the performance of our proposed method with three baselines through a large number of simulation experiments. Results show that our method achieves far higher classification accuracy than fine-tuning method and feature extraction method. Furthermore, it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.
ANALYZING TEACHING HABITS AND PRACTICES: SCHEMA THEORY IN ESL TEACHERS’ READING INSTRUCTION

&lt;p&gt;When reading, many researchers have theorized that readers construct information from a text by combining written information with their abstract knowledge structure called schema. Therefore, the reader’s knowledge is considered an important component in fully understanding a text’s meaning. Considering this, the paper sought to inquire about the everyday use of schema theory by teachers of English as a second language (ESL) in reading instruction. Specifically, this research study aimed to determine how reading teachers view schema theory, analyze their teaching practices based on ESL teachers’ responses to schema theory, and gather opinions and perceptions about the effects of applying schema theory in lesson preparation and delivery. The objectives were achieved through an appreciative inquiry-based questionnaire that was provided to participants chosen through convenience sampling. Afterward, the data were then analyzed through descriptive statistics and thematic analysis. The results showed that activities that promote independent realization schema are preferred among ESL teachers since they believed students learn in their own ways. The researcher also discovered that the participants had overall positive perceptions regarding activating schema in students, noting increased recollection and relation of ideas. Finally, the participants mentioned that they required more improvement to create and apply better techniques for better reading instruction. The researcher thus recommended enhancing teacher education, strengthening their conceptual knowledge about schemata, and giving them opportunities to immerse in diverse classrooms for a better understanding and application of schema theory to fully use schemata that play a major role in the development of their students.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0101/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
What globally unique, persistent, resolvable identifiers do you use for datasets?

What globally unique, persistent, resolvable identifiers do you use for datasets? I want to know about either (a) a challenge you’re facing, and what you’ve tried; or (2) a choice you recently made, and how it’s going. Context: For each question of the FAIR Implementation Profile (FIP) Ontology, I want to collect and discuss folks’ choices and challenges on my podcast, Machine-Centric Science. Please email me at podcast@polyneme.xyz with: an email subject of either (a)...
Reason for Opposite Movement of GDP Deflator Inflation with CPI Inflation in Korea

Korea's CPI inflation reached 6.3% in Jul. 2022, the fastest increase since Nov. 1998. However, the quarterly GDP deflator inflation for the third quarter of 2022 was only 0.2% over the same quarter of 2021. The differences between Korea's CPI and GDP deflator are the way of measuring price and the set of goods and services they cover. The goods and services in the investment, government expenditures, export, and import are not in the consumer's basket but in GDP. Moreover, imported goods and services contribute to the GDP negatively. If the import price increases when other things stand still, the GDP deflator decreases. In contrast, the cost of consumer baskets generally increases. Therefore, the import price changes only can induce the opposite moves of the CPI and the GDP deflator inflations.
Rancang Bangun Alat Pengamatan Gas Radon Menggunakan Rn53 Stk

Radon (Rn222) is a radioactive element derived from Uranium (U238) which emits alpha particles in its decay process. Detection of radon gas is important because it is not harmful to health. Besides being harmful to health, radon detection is also carried out to determine changes in radon gas related to seismic activity. The Meteorology, Climatology, and Geophysics Agency (BMKG) conducts observations of radon gas concentrations to determine changes in seismic activity. The design of the radon gas measurement instrument using the RN53 sensor is to monitor radon gas concentrations at the location and can be sent via the GSM network to the Thingspeak platform. The design of this instrument using the RN53 sensor has a correction of -92.90 Bq/m3, so it is necessary to make adjustments as well as calibration and comparisons again. The observations from the system are displayed on the LCD, stored on the microSD, and sent to Thingspeak.
Dynamic Vehicle Detection in Satellite Video With Multiframe Brightness Gradient

Satellite video with high temporal resolution can provide a broad view for surface dynamic monitoring, especially traffic monitoring. However, the limited vehicle features and complex background information make it difficult to detect vehicles simply from video frame features such as spectrum, shape, and texture. In this letter, a new satellite video dynamic vehicle detection method for traffic monitoring is proposed, which considers the relation between vehicles and the background in a time series. Road buffers are obtained according to the various road levels in order to focus on dynamic vehicles, and then, a multiframe brightness gradient (MBG) is proposed to detect potential dynamic vehicles on the buffers, and the detection results are optimized by the cumulative trajectory of motion. This approach can effectively suppress false detection caused by parallax motion and illumination variation in satellite videos. The experimental results of three satellite videos show that the proposed method can effectively achieve dynamic vehicle detection with a high positive detection rate and low false alarm rate.
5G-SRNG: 5G Spectrogram-based Random Number Generation for Devices with Low Entropy Sources

Random number generation (RNG) is a crucial element in security protocols, and its performance and reliability are critical for the safety and integrity of digital systems. This is especially true in 5G networks with many devices with low entropy sources. This paper proposes 5G-SRNG, an end-to-end random number generation solution for devices with low entropy sources in 5G networks. Compared to traditional RNG methods, the 5G-SRNG relies on hardware or software random number generators, using 5G spectral information, such as from spectrum-sensing or a spectrum-aware feedback mechanism, as a source of entropy. The proposed algorithm is experimentally verified, and its performance is analysed by simulating a realistic 5G network environment. Results show that 5G-SRNG outperforms existing RNG in all aspects, including randomness, partial correlation and power, making it suitable for 5G network deployments.
The remaining forces of Donghak and the trend of righteous army in Honam since 1894: Focusing on the 『Hongjae diary』 of Ki Haeng-hyun, a Buan Confucian scholar

『Hongjae Diary(1866-1911)』 is a diary written by Ki Haeng-hyeon(1843-?) who lived in Buan-gun, Jeolla-do. Ki haeng-hyeon recorded events, news, and rumors in Gobu, Taein, Namwon, and Jeonju, where the Donghak Peasant Revolution took place. He also recorded the trends of Yeonghakdang and Hwajeok, which were called the remaining forces of Donghak. The central government recognized it as a group like Donghak and the righteous army and suppressed the righteous army commander. In this way, Donghak-gun which was disbanded after the Donghak Peasant Revolution, continued its momentum as the remaining forces of Donghak, such as Yeonghakdang, Hwajeok, and Sujeok. Amid the continued suppression of the Donghak, the Iljinhoe was organized, and a person working as a Iljinhoe appeared in Buan. Iljinhoe intervened in local government authority and was also involved in the administration of village. This power of Iljinhoe was further strengthened when the Russo-Japanese War ended with Japan's victory and Japan-Korea Treaty of 1905 was signed. Since the signing of the Japan-Korea Treaty of 1905, righteous army has occurred in all parts of the country. The activities of the righteous army became stronger with the abdication of Gojong and the dissolution of the Korean army in 1907. Ki Haeng-hyeon recorded the size of the righteous army identified through newspapers, the murder or expulsion of local officials. After the Donghak Peasant Revolution, Donghak-gun in Honam returned to Yeonghakdang and continued its momentum as the remaining forces of Donghak including Hwajeok, and Sujeok. In an era when the sovereignty of the country was lost, Donghak's remaining forces were converted to righteous army by fighting against the Japanese army. Donghak-gun, suppressed by the Japanese military, rejoined the ranks of resistance against Japanese invasion as a righteous army.
Sistem Informasi Manajemen pada PT. Buyung Putra Pangan Berbasis Web

On a business trip at PT. Buyung Putra Pangan is still constrained by recording data on rice sales and rice production results every month which is done manually using a book, so it is difficult for the manager or secretary to see the recording of rice sales data and production results in the book directly if outside the office. So that there are no errors in recording data on the results of production, purchase, production and sales of rice. The system can make it easier for managers to monitor performance in doing business at PT. Buyung Putra Pangan. The method used is the RUP (Rational Unified Process) method, which is a software development method that is carried out iteratively, focuses on architecture (architecture-centric), is more directed based on use cases (use case driven). The design is built to display a management information system that can be used by users by using Use Case Diagrams to build a system for recording data on sales, purchases and rice production data. This application can help the Admin to manage the data contained in the website application, the Manager can see the recording of rice being moved from one place to another and the Manager can see the results of reports from rice sales and production in detail because they can find out the total sales per day and per month.
Analisis Struktur dan Nilai Moral dalam Novel Si Bedog Panjang Karya Ki Umbara

The background of this research is that the researcher is interested in discussing and analyzing this novel, because no one has analyzed it structurally and morally. In addition, the novel Si Bedog Panjang written by Ki Umbara describes the daily life of a schoolboy which is suitable as a novel reading material. The purpose of this study is to find out the structure of the novel Si Bedog Panjang as well as the values contained in the novel Si Bedog Panjang by Ki Umbara. This research certainly uses descriptive methods. With literature study techniques as well as instruments in the form of data cards. The results of this study found that there are 3 moral values in the Novel Si Bedog Panjang, seen from the relationship between humans and themselves, there are 3 moral values in relationships with other humans, and also moral values towards the Creator there are 5.
Is this news article still relevant? Ranking by contemporary relevance in archival search

Abstract Our civilization creates enormous volumes of digital data, a substantial fraction of which is preserved and made publicly available for present and future usage. Additionally, historical born-analog records are progressively being digitized and incorporated into digital document repositories. While professionals often have a clear idea of what they are looking for in document archives, average users are likely to have no precise search needs when accessing available archives (e.g., through their online interfaces). Thus, if the results are to be relevant and appealing to average people, they should include engaging and recognizable material. However, state-of-the-art document archival retrieval systems essentially use the same approaches as search engines for synchronic document collections. In this article, we develop unique ranking criteria for assessing the usefulness of archived contents based on their estimated relationship with current times, which we call contemporary relevance . Contemporary relevance may be utilized to enhance access to archival document collections, increasing the likelihood that users will discover interesting or valuable material. We next present an effective strategy for estimating contemporary relevance degrees of news articles by utilizing learning to rank approach based on a variety of diverse features, and we then successfully test it on the New York Times news collection. The incorporation of the contemporary relevance computation into archival retrieval systems should enable a new search style in which search results are meant to relate to the context of searchers’ times, and by this have the potential to engage the archive users. As a proof of concept, we develop and demonstrate a working prototype of a simplified ranking model that operates on the top of the Portuguese Web Archive portal (arquivo.pt).
UAV swarm control algorithm based on Kalman filter and particle swarm optimization

This paper studies the passive localization and control of two-dimensional planar UAV swarm under velocity information and azimuth information. We propose a UAV swarm control algorithm based on azimuth information and velocity information. This paper establishes a positioning model through azimuth information. On this basis, the IMU information is fused for attitude calculation. Moreover, the inertial navigation positioning is established by using the attitude angle and velocity information, so as to explore and use the optimal estimation theory. Through Kalman filter technology, the Gaussian noise interference in UAV positioning is greatly eliminated, and the positioning algorithm of UAV swarm is established. In addition, using the azimuth information, the particle swarm optimization is used for path planning, and the target path planning results with higher accuracy are obtained. Through computer simulation analysis, the algorithm has the characteristics of high positioning accuracy and fast iteration speed. The algorithm can better meet the practical engineering application requirements.
GPS may one day predict quakes

An analysis of GPS readings taken prior to earthquakes has revealed a potential warning sign
Statistical analysis of the beta distributed random light field

The Beta distribution can effectively describe the non-Gaussian statistical characteristics of the electromagnetic scattering. Conventional random electromagnetic fields always implicitly assume that the number of steps is infinite, but in practice, it is finite. In this paper, we investigate the statistical properties of the Beta distributed random light field, and analyze the new statistical theory by combining the resulting probability density function distribution of the Stokes parameters.
ANAD: Arabic news article dataset

In this paper, we present a modern standard Arabic dataset based on Arabic news articles collected over a one-year period from 01/01/2021 to 12/31/2021. In total, from 12 Arabic news websites, over 500,000 articles were collected, the selection of which was driven by a variety of topics, including sports, economies, local news, politics, tech, tourism, entertainment, cars, health, and art. The development of this dataset will enable data scientists to explore and experiment effectively in the field of natural language processing, and the dataset can also be used to develop machine learning and deep learning models to classify articles according to topic. The dataset is available for download at https://github.com/alaybaa/ArabicArticlesDataset/tree/main.
Online Multikernel Learning Method via Online Biconvex Optimization

Random feature-based online multikernel learning (RF-OMKL) is a promising low-complexity framework for machine learning optimization from continuous streaming data. Nonetheless, it is still an open problem to find an efficient algorithm with an analytical performance guarantee due to the challenge of an underlying online biconvex optimization (OBO). The state-of-the-art method named expert-based online multikernel learning (EoKle) tackled this problem approximately with the lens of expert-based online learning, in which multiple kernels (or experts) optimize their own kernel functions separately and the best sole one is determined via Hedge algorithm. It is asymptotically optimal as to the best sole kernel function in hindsight. We propose collaborative expert-based online multikernel learning (CoKle) by devising a collaborative Hedge (CoHedge) algorithm, in which kernel functions separately optimized as in EoKle are combined in an asymptotically optimal way. It is proved that CoKle is asymptotically optimal as to the best combination of each optimal kernel function in hindsight. Remarkably, this is the first method with a theoretical performance guarantee for expert-based RF-OMKL. Despite its effectiveness, CoKle is inherently suboptimal due to the individual optimization of kernel functions. We address this by presenting an OBO-based method (named BoKle) and partially prove its asymptotic optimality for RF-OMKL. Thus, BoKle can outperform the suboptimal expert-based methods such as CoKle and EoKle. Finally, we demonstrate the superiority of BoKle via experiments with real datasets.
Improving Detection of Unprecedented Anti-forensics Attacks on Sensor Pattern Noises Through Generative Adversarial Networks

Sensor Pattern Noise (SPN) based methods have been widely applied in multimedia forensics. With SPN being a noise-like signal in an image, anti-forensic attacks can be performed by applying different manipulations to attenuate or remove the SPN and mislead the investigators. Thus, forensic investigators should identify the images subject to such manipulations before applying SPN-based methods. Despite neural network-based classifiers having shown their strength in detecting specific anti-forensics attacks, such a classifier may not generalise well for other manipulations. Given that various manipulations can attenuate or remove SPNs and a classifier’s training set may only include certain types of manipulations, a classifier may encounter images subject to attacks unprecedented to it and unable to distinguish them from the pristine ones. To address this problem, we propose a training strategy using Generative Adversarial Networks (GAN). This strategy shifts the classifier’s excessive emphasis on the manipulation-specific features with the resultant classifier generalising better for unprecedented anti-forensics attacks.
An Effective Training Strategy for Enhanced Source Camera Device Identification

Source camera identification is a forensic problem of linking an image in question to the camera used to capture it. This could be a useful tool in forensic applications to identify potential suspects of cybercrime. Over the last decade, several successful attempts have been made to identify the source camera using deep learning. However, existing techniques that provide effective solutions for camera model identification fail to distinguish between different devices of the same model. This is because cameras of different brands and models were used to train the data-driven system when dealing with exact device identification. We show that training the data-driven system on different camera models opens side-channel information on model-specific features, which acts as interference for identifying individual devices of the same model. Thus, we provide an effective training strategy that involves a way to construct the dataset for enhanced source camera device identification. The experimental results suggest that involving only cameras of the same model for training improves the discriminative capability of the data-driven system by eliminating the threat of interfering model-specific features.
DIGITAL RETENTION ACTIVITY PROVIDE

Our Generation and acquisition of business prospective can we redefine with the help of the system where multiple opportunity references and Precedence acquisitions is provided.The system provides elaborated mechanism where Global leads can be organized and all types of interactive references can also be achieved in multidirectional formats.Global opportunity references and various types of business prospects can be organized and can be referenced with the help of different types of optimal methodologies which are promoted now for the usage of different types of methodological references each methodology has to be designed and provided in a amalgamated format for the usage.