Reviewerly/exHarmony huggingface dataset:
Utilizing Artificial Intelligence-based Paraphrasing Tool in EFL Writing Class: A Focus on Indonesian University Students’ Perceptions

&lt;span&gt;This &lt;/span&gt;&lt;span lang="EN-US"&gt;study aims to investigate students’ perception of &lt;em&gt;QuillBot&lt;/em&gt; utilization in an EFL academic writing&lt;/span&gt;&lt;span lang="EN-US"&gt;course. This phenomenological case study involved &lt;/span&gt;&lt;span&gt;20&lt;/span&gt;&lt;span lang="EN-US"&gt; s&lt;/span&gt;&lt;span&gt;ixth&lt;/span&gt;&lt;span lang="EN-US"&gt;-semester students majoring in English Education as the participants. Data were collected through an online questionnaire distributed to all of the participants and face-to-face interviews with five participants who were purposefully selected based on their responses to the questions in the online questionnaire&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span lang="EN-US"&gt;The results of the data analysis reveal that the students exhibited positive responses toward the utilization of &lt;/span&gt;&lt;em&gt;&lt;span&gt;QuillBot&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; in academic writing.&lt;/span&gt;&lt;span lang="EN-US"&gt; Furthermore, they also find &lt;em&gt;QuillBot&lt;/em&gt; beneficial for three reasons; first, it helps improve the content or argument; second, it minimizes grammatical errors, and third, it enhances the language used in their manuscripts. It could be concluded that integrating artificial intelligence such as online paraphrasing tools helps EFL students overcome some &lt;/span&gt;&lt;span lang="EN-US"&gt;difficulties encountered in writing academic papers, and consequently improve their writing products.&lt;/span&gt;&lt;span lang="EN-US"&gt;This study implies that the use of AI-based technology in language classrooms would be advantageous in a variety of ways; however, further research is required to determine the relative importance of each element regarding students' use of the AI-based paraphrasing tool, particularly &lt;/span&gt;&lt;em&gt;&lt;span&gt;QuillBot.&lt;/span&gt;&lt;/em&gt;&lt;p&gt; &lt;/p&gt;
Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow

The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network’s capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving.
Trend and Knowledge Structure of Cryptocurrency Reserch in the Scopus Database

Purpose: This study aims to utilize the bibliometric method to investigate the most important characteristics and key research topics in the literature on cryptocurrency research. Theoretical framework: This study used a text mining framework based on domain-level and knowledge structure analysis. Design/methodology/approach: Based on domain-level and knowledge structure analysis, this study used data from the Scopus database, which included 1,685 published articles from 2018 to 2023 on cryptocurrency research. Data analytics and visualization may be accomplished with the bibliometrix package in R software. Findings: The result found that, there has been a fifty percent annual growth in cryptocurrency research since 2018. Studying the most frequently used terms and phrases in the research makes it possible to see which research areas have the greatest impact. According to the results, (1) cryptocurrency market, (2) market efficiency, (3) herding behavior, (4) COVID pandemic, (5) safe haven, (6) stock markets, (7) financial markets, and (8) volatility spillovers should be the emphasis of future research. Research, Practical &amp; Social implications: This article will be useful to scholars and practitioners looking for research directions. Based on the trending topics and knowledge structure of cryptocurrency research, this research also suggests potential new study topics for the future. Originality/value: The value of these findings revealed an increase and a new aspect of cryptocurrency research in the business field related to the continued expansion of empirical research documents, researchers/authors, global collaboration, and co-citations.
COVID-19 digital contact tracing worked — heed the lessons for future pandemics

For all the controversy over decentralized contact-tracing apps, data show that these privacy-preserving tools saved thousands of lives during the pandemic. National and international authorities must invest in the technology now. For all the controversy over decentralized contact-tracing apps, data show that these privacy-preserving tools saved thousands of lives during the pandemic. National and international authorities must invest in the technology now.
Alternative Text Pre-Processing using Chat GPT Open AI

Text Pre-Processing is the first step in Sentiment Analysis. Categorizing a sentiment in a dataset is part of the Text-Preprocessing stage to get the optimal model accuracy value. Generative Pretrained Transformer, often known as Chat GPT, is a Machine Learning model that can automatically generate realistic and meaningful text. This study aims to examine the capability of GPT Chat as an alternative in the Text-Pre-Processing stage by utilizing GPT Chat 3 from the openai.com website in the Text-Pre-Processing stage of the collected tweet data. The data used in this research is the result of crawling Twitter by inserting the keyword "Chat GPT”. This study method was carried out by measuring performance using the K-Nearest Neighbor and Naïve Bayes Algorithms to find the best performance value and compare it with the Text-Preprocessing generated by Rapidminer. It is shown that the performance accuracy produced using the K-Nearest Neighbor Algorithm is 73.57% using the Linear Sampling method. The comparison result with the Text-Preprocessing method using Rapidminer indeed shows a better accuracy of 75.33%, which means it has a narrow difference of 1.76% with the Chat GPT Text Pre-Processing method. However, both are still in the same category, which is Fair Classification. The results of this research show that Chat GPT can be an alternative in Text-Preprocessing datasets for sentiment analysis.
Development of a compact<i>Ansatz</i>via operator commutativity screening: Digital quantum simulation of molecular systems

Recent advancements in quantum information and quantum technology have stimulated a good deal of interest in the development of quantum algorithms toward the determination of the energetics and properties of many-fermionic systems. While the variational quantum eigensolver is the most optimal algorithm in the noisy intermediate scale quantum era, it is imperative to develop compact Ansätze with low-depth quantum circuits that are physically realizable in quantum devices. Within the unitary coupled cluster framework, we develop a disentangled Ansatz construction protocol that can dynamically tailor an optimal Ansatz using the one- and two-body cluster operators and a selection of rank-two scatterers. The construction of the Ansatz may potentially be performed in parallel over multiple quantum processors through energy sorting and operator commutativity prescreening. With a significant reduction in the circuit depth toward the simulation of molecular strong correlation, our dynamic Ansatz construction protocol is shown to be highly accurate and resilient to the noisy circumstances of the near-term quantum hardware.
POSTER: Leveraging eBPF to enhance sandboxing of WebAssembly runtimes

WebAssembly is a binary instruction format designed as a portable compilation target enabling the deployment of untrusted code in a safe and efficient manner. While it was originally designed to be run inside web browsers, modern runtimes like Wasmtime and WasmEdge can execute WebAssembly directly on various systems. In order to access system resources with a universal hostcall interface, a standardization effort named WebAssembly System Interface (WASI) is currently undergoing. With specific regard to the file system, runtimes must prevent hostcalls to access arbitrary locations, thus they introduce security checks to only permit access to a pre-defined list of directories. This approach not only suffers from poor granularity, it is also error-prone and has led to several security issues. In this work we replace the security checks in hostcall wrappers with eBPF programs, enabling the introduction of fine-grained per-module policies. Preliminary experiments confirm that our approach introduces limited overhead to existing runtimes.
The SAFE Procedure: A Practical Stopping Heuristic for Active Learning-Based Screening in Systematic Reviews and Meta-Analyses

Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of errors made by the current model. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. Our main conclusion of this paper is that relying on a single stopping rule is not sufficient and employing an eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening processThe SAFE procedure combines different heuristics to avoid stopping too early and potentially missing relevant records. The SAFE procedure takes into account the model's accuracy and uncertainty, as well as the cost of continuing to label records. This procedure for using active learning in systematic literature review screening provides a practical and efficient approach that can save significant time and resources while ensuring a conservative approach to determining when to end the active learning process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. The proposed method can assist researchers in identifying relevant records early, which can ultimately lead to improved evidence synthesis and decision-making in many fields.
NFT Marketplace for Blockchain based Digital Assets using ERC-721 Token Standard

Blockchain technology has drawn increasing interest in recent years as a technology that will dominate a wide range of industries in the future The non-fungible token is a brand-new kind of Ethereum - based token that is unique and indivisible (NFT). Recently, investors have shown a lot of interest in a specific category of blockchain-based virtual assets called NFTs. Since the beginning of 2021, the phenomenon and its marketplaces have expanded considerably. The Ethereum network is used in this project to demonstrate and test the concept of digital ownership transfers and control. This project offers the ability to create, purchase, sell, and trade NFTs. This experiment shows how the reader can generate an NFT token on their own using the Ethereum network’s ERC721 token standard. Moreover, it addresses distribution and setup. Yet it is the another case for the blockchain which is the possibility of making money with it. This application’s users will be able to buy and sell NFTs using the virtual currency, Ether.
Blockchain for Data Originality in Pharma Manufacturing

Abstract Purpose This paper analyses the feasibility of tracking data originality for pharmaceutical manufacturing in a tamper-proof manner using a geographically distributed system. The main research question is whether it is possible to ensure the traceability of drug manufacturing through the use of smart contracts and a private blockchain network. Methods This work employs a private Ethereum network with a proof-of-authority consensus algorithm to allow participating nodes to commit the medicament manufacturing originality as transactions in blocks. We use smart contracts to assess the “Original” principle of the ALCOA+ data integrity principles for full sensor-enabled production lines within pharmaceutical manufacturing plants. We have evaluated our data originality assessment approach employing a temporal series of 1300 reports generated based on real datasets from pharma production lines. Out of these reports, 300 reports have been randomly tampered with to make them “unoriginal” (i.e., falsified). Results Evaluation consistently shows that the proposed approach systematically detects all the manufacturing records whether original or not, together with any source of falsification. By randomly injecting four common data falsification types, their approach effectively detects tampering and ensures the authenticity of the data originality acquired by sensors within manufacturing lines. Conclusion The approach of using a private blockchain network with a proof-of-authority consensus algorithm and smart contracts is a feasible method to track data originality for pharmaceutical manufacturing in a tamper-proof manner. In addition, this approach effectively detects tampering and ensures the authenticity of the data originality acquired by sensors within manufacturing lines.
Nontrivial one-loop recursive reduction relation

In arXiv:2204.03190, we proposed a universal method to reduce one-loop integrals with both tensor structure and higher-power propagators. But the method is quite redundant as it does not utilize the results of lower rank cases when addressing certain tensor integrals. Recently, we found a remarkable recursion relation arXiv:2203.16881,2205.03000, where a tensor integral is reduced to lower-rank integrals and \textit{lower terms} corresponding to integrals with one or more propagators being canceled. However, the expression of the lower terms is unknown. In this paper, we derive this non-trivial recursion relation for non-degenerate and degenerate cases and provides an explicit expression for the lower terms, thus simplifying and speeding up the reduction process.
Economic freedom, inclusive growth, and financial development: A heterogeneous panel analysis of developing countries

The effective and efficient management of financial systems and resources fosters a socioeconomic climate conducive to technological and innovative advancement, thereby fostering long-term economic growth. The study used panel data from 72 countries classified as less financially developed between 2009 and 2017 to examine the role of economic freedom and inclusive growth in financial development. For the long-run estimations, we utilised the linear dynamic panel GMM-IV estimator, panel corrected standard errors (PCSE) linear regression method, and contemporaneous correlation estimator, a generalised least squares method. Our analyses indicate that economic liberty, inclusive growth, and capital stock significantly contribute to financial development in a positive manner. Moreover, inclusive growth contributes positively to overall financial development by enhancing economic freedom. Regardless of exogenous and endogenous shocks, we found that the tax burden and investment freedom are negative drivers of financial development as measured by the overall financial development index. In contrast, protection of property rights, government spending, monetary freedom, and financial freedom are positive and significant drivers of economic growth.
Enhancing Grey Wolf Optimizer With Levy Flight for Engineering Applications

Since Grey Wolf Optimizer (GWO) first introduction, it continues to be used extensively today, owing to its simplicity, easy handling, and applicability to a wide range of problems.Although there are many different GWO variants in the literature, the problem that the GWO produces early convergence and inefficient results have still continued to emerge in their variants.In order to overcome the drawbacks of the GWO, the GWO integrated together with Levy Flight (LFGWO) is proposed.In order to demonstrate the overall performance of the LFGWO, experiments are conducted using the 23 standard benchmark functions and 10 composition functions of CEC 2019 compared with the other eight state-of-art algorithms.The 28 out of 33 average and 27 out of 33 standard deviation values obtained by LFGWO are all less than those obtained by the other eight optimization algorithms, which verified and demonstrated the performance, stability, and robustness of the LFGWO.The extensibility test with different scales of dimensions 50, 100, 300, and 500, is undertaken by comparing LFGWO with GWO and IGWO to assess the dimensional influence on problem consistency and optimization quality.Moreover, the performance of the LFGWO has also been tested on five real-world problems and infinite impulse response (IIR) challenging model identification, experimental results and statistical tests demonstrate that the performance of LFGWO is significantly better than the other compared algorithms, and the LFGWO is capable of solving real-world problems.
Quantum Illumination with a Hetero-Homodyne Receiver and Sequential Detection

We propose a hetero-homodyne receiver for quantum illumination (QI) target detection. Unlike prior QI receivers, it uses a cascaded positive operator-valued measurement (POVM) that does not require a quantum interaction between QI's returned radiation and its stored idler. When used without sequential detection its performance matches the 3 dB quantum advantage over optimum classical illumination (CI) that Guha and Erkmen's [Phys. Rev. A 80, 052310 (2009)] phase-conjugate and parametric amplifier receivers enjoy. When used in a sequential detection QI protocol, the hetero-homodyne receiver offers a 9 dB quantum advantage over a conventional CI radar, and a 3 dB advantage over a CI radar with sequential detection. Our work is a significant step forward toward a practical quantum radar for the microwave region, and, more generally, emphasizes the potential offered by cascaded POVMs for quantum radar.
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations

Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derived from local historical news. However, this approach lacks a global perspective, failing to account for users' hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY (Global-LOcal news Recommendation sYstem), which combines global representations learned from other users with local representations to enhance personalized recommendation systems. We accomplish this by constructing a Global-aware Historical News Encoder, which includes a global news graph and employs gated graph neural networks to enrich news representations, thereby fusing historical news representations by a historical news aggregator. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations.
Breast Cancer Prognosis Based on Transfer Learning Techniques in Deep Neural Networks

Breast cancer is a major cause of death among women in both developed and underdeveloped countries. Early detection and diagnosis of breast cancer are crucial for patients to receive proper treatment and increase their chances of survival. To improve the automatic detection and diagnosis of breast cancer, a new deep learning model called “Breast Cancer Prognosis Based Transfer Learning (BCP-TL)” has been developed. This model uses transfer learning, which applies the knowledge gained from solving one problem to another relevant problem. The model is based on a pre-trained convolutional neural network (CNN) that extracts features from the mammographic image analysis society (MIAS) dataset. Four different CNN architectures were used in thismodel: AlexNet, Xception, ResNeXt, and Channel Boosted CNN. The performance of the model was evaluated using six metrics, including accuracy, sensitivity, specificity, precision, F1-score, and the area under the ROC curve (AUC). The combination of Xception and Channel Boosted CNN showed excellent performance. By combining essential features from multiple iterations, the Channel Boosted CNN can achieve higher accuracy in breast cancer diagnosis, with an overall accuracy of 98.96%. This highlights the potential of the BCP-TL model in effectively detecting and diagnosing breast cancer.