Reviewerly/exHarmony huggingface dataset:
SSD with multi-scale feature fusion and attention mechanism

Abstract In the field of the Internet of Things, image acquisition equipment is the very important equipment, which will generate lots of invalid data during real-time monitoring. Analyzing the data collected directly from the terminal by edge calculation, we can remove invalid frames and improve the accuracy of system detection. SSD algorithm has a relatively light and fast detection speed. However, SSD algorithm do not take full advantage of both shallow and deep information of data. So a multiscale feature fusion attention mechanism structure based on SSD algorithm has been proposed in this paper, which combines multiscale feature fusion and attention mechanism. The adjacent feature layers for each detection layer are fused to improve the feature information expression ability. Then, the attention mechanism is added to increase the attention of the feature map channels. The results of the experiments show that the detection accuracy of the optimized model is improved, and the reliability of edge calculation has been improved.
Ethnomathematics: Exploration of Fractal Geometry in Gate Ornaments of the Sumenep Jamik Mosque Using the Lindenmayer System

Jamik Sumenep Mosque, a heritage site of the Sumenep Kingdom, was built to emphasize the acculturation of Chinese, Islamic, and Madurese cultures. This ethnomathematics research aims to reveal and explore the fractal geometry forms in the gateway of Jamik Sumenep Mosque. This study employed qualitative methods with an ethnographic approach. Research data were gathered through observation, field notes, documentation, interviews, and literature studies. Field observations were computationally analyzed using the Lindenmayer system through the L-Studio application to examine fractal shapes. The study found that the length and angle size of the ornamental parts form the basis of fractal geometry in the mosque gateway's ornamentation, thereby confirming the presence of fractal geometry concepts. These findings can be utilized in teaching fractal geometry, applied mathematics, and computational geometry. Further research could explore non-Euclidean geometry methods, such as stochastic L-system methods.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of radio waves such that numerous devices can upload their model updates concurrently for aggregation. However, the uplink channel noise incurs considerable model aggregation distortion, which is critically determined by the device scheduling and compromises the learned model performance. In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PO-FL</i> , to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation. We prove the unbiasedness of this aggregation scheme and demonstrate the convergence of PO-FL on both convex and non-convex loss functions. Our convergence bounds unveil that the device scheduling affects the learning performance through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">communication distortion</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global update variance</i> . Based on the convergence analysis, we further develop a channel and gradient-importance aware algorithm to optimize the device scheduling probabilities in PO-FL. Extensive simulation results show that the proposed PO-FL framework with channel and gradient-importance awareness achieves faster convergence and produces better models than baseline methods.
A Chinese Face Dataset with Dynamic Expressions and Diverse Ages Synthesized by Deep Learning

Abstract Facial stimuli have gained increasing popularity in research. However, the existing Chinese facial datasets primarily consist of static facial expressions and lack variations in terms of facial aging. Additionally, these datasets are limited to stimuli from a small number of individuals, in that it is difficult and time-consuming to recruit a diverse range of volunteers across different age groups to capture their facial expressions. In this paper, a deep-learning based face editing approach, StyleGAN, is used to synthesize a Chinese face dataset, namely SZU-EmoDage, where faces with different expressions and ages are synthesized. Leverage on the interpolations of latent vectors, continuously dynamic expressions with different intensities, are also available. Participants assessed emotional categories and dimensions (valence, arousal and dominance) of the synthesized faces. The results show that the face database has good reliability and validity, and can be used in relevant psychological experiments. The availability of SZU-EmoDage opens up avenues for further research in psychology and related fields, allowing for a deeper understanding of facial perception.
Improving Social Engineering Resilience In Enterprises

&#x0D; Social Engineering (SE) is a significant problem for enterprises. Cybercriminals continue developing new and sophisticated methods to trick individuals into disclosing confidential information or granting unauthorized access to infrastructure systems. These attacks remain a significant threat to enterprise systems despite significant investments in technical architecture and security measures. User awareness training and other behavioral interventions are critical for improving SE resilience. However, their effectiveness still needs to be determined, as personality traits may turn some individuals more susceptible to SE attacks. &#x0D; &#x0D; This paper aims to provide a comprehensive assessment of the state of knowledge in this field, identifying best practices for improving SE resilience in organizations and supporting the development of new research studies to address this issue. Its goal is to help enterprises of any size develop a framework to reduce the risk of successful SE attacks and create a culture of security awareness.
Geographical and practical challenges in the implementation of digital health passports for cross-border COVID-19 pandemic management: a narrative review and framework for solutions

Abstract The rapid global spread of infectious diseases, epitomized by the recent COVID-19 pandemic, has highlighted the critical need for effective cross-border pandemic management strategies. Digital health passports (DHPs), which securely store and facilitate the sharing of critical health information, including vaccination records and test results, have emerged as a promising solution to enable safe travel and access to essential services and economic activities during pandemics. However, the implementation of DHPs faces several significant challenges, both related to geographical disparities and practical considerations, necessitating a comprehensive approach for successful global adoption. In this narrative review article, we identify and elaborate on the critical geographical and practical barriers that hinder global adoption and the effective utilization of DHPs. Geographical barriers are complex, encompassing disparities in vaccine access, regulatory inconsistencies, differences across countries in data security and users' privacy policies, challenges related to interoperability and standardization, and inadequacies in technological infrastructure and limited access to digital technologies. Practical challenges include the possibility of vaccine contraindications and breakthrough infections, uncertainties surrounding natural immunity, and limitations of standard tests in assessing infection risk. To address geographical disparities and enhance the functionality and interoperability of DHPs, we propose a framework that emphasizes international collaboration to achieve equitable access to vaccines and testing resources. Furthermore, we recommend international cooperation to establish unified vaccine regulatory frameworks, adopting globally accepted standards for data privacy and protection, implementing interoperability protocols, and taking steps to bridge the digital divide. Addressing practical challenges requires a meticulous approach to assessing individual risk and augmenting DHP implementation with rigorous health screenings and personal infection prevention measures. Collectively, these initiatives contribute to the development of robust and inclusive cross-border pandemic management strategies, ultimately promoting a safer and more interconnected global community in the face of current and future pandemics.
CoEdIT: Text Editing by Task-Specific Instruction Tuning

We introduce CoEdIT, a state-of-the-art text editing system for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as “Make the sentence simpler” or “Write it in a more neutral style,” and outputs the edited text. We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions). Our model (1) achieves state-of-the-art performance on various text editing benchmarks, (2) is competitive with publicly available largest-sized LLMs trained on instructions while being ~60x smaller, (3) is capable of generalizing to unseen edit instructions, and (4) exhibits abilities to generalize to composite instructions containing different combinations of edit actions. Through extensive qualitative and quantitative analysis, we show that writers prefer the edits suggested by CoEdIT relative to other state-of-the-art text editing models. Our code, data, and models are publicly available at https://github.com/vipulraheja/coedit.
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts

As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks. In this work, we propose XoT, an integrated problem solving framework by prompting LLMs with diverse reasoning thoughts. For each question, XoT always begins with selecting the most suitable method then executes each method iteratively. Within each iteration, XoT actively checks the validity of the generated answer and incorporates the feedback from external executors, allowing it to dynamically switch among different prompting methods. Through extensive experiments on 10 popular math reasoning datasets, we demonstrate the effectiveness of our proposed approach and thoroughly analyze the strengths of each module. Moreover, empirical results suggest that our framework is orthogonal to recent work that makes improvements on single reasoning methods and can further generalise to logical reasoning domain. By allowing method switching, XoT provides a fresh perspective on the collaborative integration of diverse reasoning thoughts in a unified framework.
Ranking LLM-Generated Loop Invariants for Program Verification

Saikat Chakraborty, Shuvendu Lahiri, Sarah Fakhoury, Akash Lal, Madanlal Musuvathi, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, Nikhil Swamy. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.
Economic Analysis of Rooftop Based On-Grid and Off-Grid Photovoltaic Systems in Equatorial Area

Through a thorough analysis using Net Present Cost (NPC) over a 20-year period, this research presents a comprehensive and economically optimized solar panel design methodology. The study examines two different PV system configurations: On-Grid PV and Off-Grid PV, using sophisticated simulation and analytical techniques with the aid of HOMER Pro software. The simulation results offer compelling new information about these systems' economic viability. The simulation results in an NPC value of IDR 31,386,360,- for the Off-Grid PV configuration. The On-Grid PV system, in contrast, exhibits a significantly lower NPC value of IDR 8,903,329,- emphasizing its superior economic performance. This On-Grid PV system boasts a significant energy generation capacity of 5,012 kWh/year in addition to favorable cost efficiency. Notably, this is greater than the National Power Company's 1,186 kWh/year energy output. These results highlight the financial benefits of the On-Grid PV system and demonstrate its capability to provide affordable and sustainable energy solutions over a long period. The thorough analysis carried out in this study aids in the optimization of solar panel designs, offers insightful information for future sustainable energy projects, and emphasizes the crucial part that economic factors play in influencing the adoption of renewable energy technologies.
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?

Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts, including visual question answering. However, it remains unclear whether these models possess the capability to answer questions that are not only querying visual content but knowledge-intensive and information-seeking. In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge. Using InfoSeek, we analyze various pre-trained visual question answering models and gain insights into their characteristics. Our findings reveal that state-of-the-art pre-trained multi-modal models (e.g., PaLI-X, BLIP2, InstructBLIP) face challenges in answering visual information-seeking questions, but fine-tuning on the InfoSeek dataset elicits models to use fine-grained knowledge that was learned during pre-training. Furthermore, we show that accurate visual entity recognition can be used to improve performance on InfoSeek by retrieving relevant documents, showing a significant space for improvement.
Transcending Scaling Laws with 0.1% Extra Compute

Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Prediction of COVID-19 Patients’ Emergency Room Revisit using Multi-Source Transfer Learning

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn’t consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informative for developing a prediction model to identify COVID-19 patients who are very likely to revisit an ER within 7 days after discharge.
Insurance Portfolio Analysis as Containment Testing

Insurance Portfolio Analysis (IPA) is the process of comparing multiple, potentially overlapping insurance portfolios with an eye to detecting and characterizing redundancies and gaps in coverage. Unfortunately, insurees usually do not have the time or patience to compare policies from multiple insurance providers, and they often do not have the legal background needed to understand the complex legal wording of the contracts associated with those policies. Past work has shown that, by encoding policies as logic programs, it is possible to automatically determine compliance of specific claims with a policy’s terms and conditions. In this paper, we show that it is also possible to automatically analyze multiple-policy portfolios for gaps and redundancies by assessing coverage over multiple hypothetical claims. We formalize the process of IPA and show how to use well-studied techniques for logic program containment testing to automate the process.
BUNDESTAG-MINE: Natural Language Processing for Extracting Key Information from Government Documents

As governments worldwide continue to release vast amounts of textual information, the need for efficient and insightful tools to extract, interpret and present this data has become increasingly critical. Towards solving this issue, we present the BUNDESTAG-MINE: an environment that periodically retrieves pertinent data from the German parliament, parses and analyzes it using pipelines for natural language processing, and then displays the results in a web application that is publicly accessible. BUNDESTAG-MINE helps to extract key information from parliamentary documents in a visually appealing matter for many use cases. For instance, the tool can be leveraged by journalists for news detection, lawyers for compliance checking, linguists for discourse analysis, and the broad public to inform themselves about the positions of political party members on a topic.
How understanding large language models can inform the use of ChatGPT in physics education

Abstract The paper aims to fulfil three main functions: (1) to serve as an introduction for the physics education community to the functioning of Large Language Models (LLMs), (2) to present a series of illustrative examples demonstrating how prompt-engineering techniques can impact LLMs performance on conceptual physics tasks and (3) to discuss potential implications of the understanding of LLMs and prompt engineering for physics teaching and learning. We first summarise existing research on the performance of a popular LLM-based chatbot (ChatGPT) on physics tasks. We then give a basic account of how LLMs work, illustrate essential features of their functioning, and discuss their strengths and limitations. Equipped with this knowledge, we discuss some challenges with generating useful output with ChatGPT-4 in the context of introductory physics, paying special attention to conceptual questions and problems. We then provide a condensed overview of relevant literature on prompt engineering and demonstrate through illustrative examples how selected prompt-engineering techniques can be employed to improve ChatGPT-4’s output on conceptual introductory physics problems. Qualitatively studying these examples provides additional insights into ChatGPT’s functioning and its utility in physics problem solving. Finally, we consider how insights from the paper can inform the use of LMMs in the teaching and learning of physics.
Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.