Reviewerly/exHarmony huggingface dataset:
Enhanced Prediction Accuracy in Complex Systems: An Approach Integrating Fuzzy K-Clustering and Fuzzy Neural Network

The quest for heightened precision in fuzzy system predictions has culminated in the development of an innovative model that integrates a Fuzzy K-Clustering (FKC) algorithm with a fuzzy neural network (FNN). In this approach, the novel FKC algorithm, herein introduced, undertakes the clustering of sample data. Subsequently, the clustering outcomes inform the configuration of the FNN, specifically guiding the determination of node quantities across its layers and the initial network parameters. A distinctive hybrid learning algorithm, designated as the Conjugate Recursive Least Squares (CRLS), facilitates the optimization of network parameters via distinct methods tailored to parameter types. This model underwent empirical validation using 2-minute interval average wind speed data from surface meteorological stations in China. Analytical comparisons between model predictions and actual wind speed data revealed an average absolute error of 0.2764m/s, an average absolute percentage error of 2.33%, and a maximum error of 0.6035m/s. The findings substantiate the model's superior predictive capability. This study thus presents a significant advancement in fuzzy system prediction methodologies, underscoring the potential of the FKC and FNN in complex data analysis.
Improved YOLOv7-Based Algorithm for Detecting Foreign Objects on the Roof of a Subway Vehicle

Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs.
Comparison of consecutive and restained sections for image registration in histopathology

SignificanceAlthough the registration of restained sections allows nucleus-level alignment that enables a direct analysis of interacting biomarkers, consecutive sections only allow the transfer of region-level annotations. The latter can be achieved at low computational cost using coarser image resolutions.PurposeIn digital histopathology, virtual multistaining is important for diagnosis and biomarker research. Additionally, it provides accurate ground truth for various deep-learning tasks. Virtual multistaining can be obtained using different stains for consecutive sections or by restaining the same section. Both approaches require image registration to compensate for tissue deformations, but little attention has been devoted to comparing their accuracy.ApproachWe compared affine and deformable variational image registration of consecutive and restained sections and analyzed the effect of the image resolution that influences accuracy and required computational resources. The registration was applied to the automatic nonrigid histological image registration (ANHIR) challenge data (230 consecutive slide pairs) and the hyperparameters were determined. Then without changing the parameters, the registration was applied to a newly published hybrid dataset of restained and consecutive sections (HyReCo, 86 slide pairs, 5404 landmarks).ResultsWe obtain a median landmark error after registration of 6.5 μm (HyReCo) and 24.1 μm (ANHIR) between consecutive sections. Between restained sections, the median registration error is 2.2 and 0.9 μm in the two subsets of the HyReCo dataset. We observe that deformable registration leads to lower landmark errors than affine registration in both cases (p < 0.001), though the effect is smaller in restained sections.ConclusionDeformable registration of consecutive and restained sections is a valuable tool for the joint analysis of different stains.
Data Mining for Quality Prediction in Software-as-A-Service Concept: A Case Study in Offset Printing Company

Purpose: This research aims to design a model of quality prediction system using data mining methods in the software-as-a-service (Saas) environment in order to facilitate manufacturers to be able to analyse process and product quality without software investment.&#x0D; Methodology/Approach: To develop the quality prediction model, this study utilized a data mining methodology to extract knowledge from historical data collected from the offset printing industry, focusing on manufacturing parameters. Four classification algorithms (Decision Tree, k-NN, Naive Bayes, Random Tree) were employed and compared to identify the most precise model specifically tailored for offset printing. Subsequently, the prediction model was integrated into a web-based system to enable quality prediction.&#x0D; Findings: This study demonstrates practicality of integrating quality prediction into the SaaS framework, specifically for offset printing. This integration is designed to predict how manufacturing control parameters, such as printing slope angle, engine temperature, paper size, ink density, and roller speed, relate to the occurrence of product defects such as crumpled paper and imprecise printing.&#x0D; Research Limitation/implication: Generalizability is constrained by its focus on the offset printing industry, and prediction accuracy relies on historical data, which can vary across manufacturing sectors, affecting model performance.&#x0D; Originality/Value of paper: This article presents the concept and practical implementation of utilizing data mining in a SaaS environment to enhance the quality of manufacturing, with a particular emphasis on the offset printing sector.
A Virtual Reality 3D Game: A Comparison Between an Immersive Virtual Reality Application and A Desktop Experience

The work aims to design and implement a 3D interactive and addictive object avoidance game using the Unity platform. The implementation of the immersive virtual reality application uses any smart mobile device as an input and output device, utilizing its accelerometer and compass to record the orientation and rotation data of the device in 3D space and capture the digital environment stereoscopically on the device screen. A comparative study between a virtual reality and a desktop real-time 3D game is performed to analyze the various attributes of the game and determine which medium is most effective.
Implementation of Cybersecurity Situation Awareness Model in Saudi SMEs

Saudi Small and Medium-sized Enterprises (SMEs) are witnessing rapid growth in technology and innovation. However, this growth is accompanied by increased cybersecurity threats, which pose significant challenges for SMEs. Cyber threats are becoming more complex and sophisticated, with SMEs becoming prime targets due to their weaker cybersecurity defenses. Hence, there exists a rich literature on critical challenges facing SMEs. Existing literature on these challenges addresses many research issues (e.g., finance, technology adoption, and management) associated with SMEs. However, one critical issue that has so far received no rigorous attention is cybersecurity situation awareness for research in the SME context. Thus, this study used a quantitative approach aiming to empirically test a model of cybersecurity situational awareness that can support SMEs in Saudi Arabia to implement cybersecurity measures and precautions with efficacy. An online survey of 350 participants was conducted to collect the research data. The study identified a significant positive relationship between Cyber Situational Awareness (Csa) and Implementation of Cybersecurity Controls (Icsc), suggesting that enhancing awareness can contribute to better control implementation. The study identified a significant positive relationship between Cyber Situational Awareness (Csa) and Implementation of Cybersecurity Controls (Icsc), suggesting that enhancing awareness can contribute to better control implementation. Finally, the paper provides several interesting findings and outlines future research directions.
Revisiting Nearest-Neighbor-Based Information Set Decoding

The syndrome decoding problem lies at the heart of code-based cryptographic constructions. Information Set Decoding (ISD) algorithms are commonly used to assess the security of these systems. The most efficient ISD algorithms rely heavily on nearest neighbor search techniques. However, the runtime result of the fastest known ISD algorithm by Both-May (PQCrypto ’18) was recently challenged by Carrier et al. (Asiacrypt ’22), which introduce themselves a new technique called RLPN decoding which yields improvements over ISD for codes with small rates $$\frac{k}{n}\le 0.3$$ . In this work we first revisit the Both-May algorithm, by giving a clean exposition and a corrected analysis. In this context we confirm the result by Carrier et al. that the initial analysis is flawed and conclude with the same runtime exponent. Our work aims at fully substantiating the corrected runtime exponent by a detailed analysis. Furthermore, we show that the Both-May algorithm significantly improves on memory complexity over previous algorithms. Our main contribution is therefore to give the correct perspective on the significance of the Both-May algorithm and to clarify any remaining doubts on the corrected baseline. Further, we outline a potential strategy for enhancing the Both-May algorithm by merging two of its subroutines, by introducing a fixed-weight nearest neighbor variant. Although we do not obtain immediate improvements, the nearest neighbor variant has already found novel applications in recent research. This initiated study of algorithms to solve the fixed-weight variant could potentially lead to future improvements through our construction.
The Hidden Shortcomings of (D)AOs – An Empirical Study of On-Chain Governance

Decentralized autonomous organizations (DAOs) are a recent innovation in organizational structures, which are already widely used in the blockchain ecosystem. We empirically study the on-chain governance systems of 21 DAOs and open source the live dataset. The DAOs we study are of various size and activity, and govern a wide range of protocols and services, such as decentralized exchanges, lending protocols, infrastructure projects and common goods funding. Our analysis unveils a high concentration of voting rights, a significant hidden monetary costs of on-chain governance systems, as well as a remarkably high amount of pointless governance activity.
SISTEM CERDAS MONITORING KANDANG KENARI BERBASIS IoT DENGAN ALGORITMA C.45 THINGSPEAK

Kenari birds are one of the singing birds that have a very beautiful voice, the melody and variety of their crooks are very numerous, from there the opportunity arises to try breeding kenari birds because their price also has a high economic value and can be developed into a business that continues to grow. In the making of cages, the temperature of the environment (temperature of the animal cage environment) must also be studied. In addition, the safety of kenari from insect and rat disturbances must also be considered. The room temperature conducive for kenari livestock is between 23 and 27 C°.&#x0D; This study aims to maintain the quality of the temperature of the kenari bird cage to remain well preserved, thereby maintaining the health of the livestock birds. The data is calculated and processed using the DHT11 microcontroller sensor to maintain the temperature and humidity of the cage, the distance sensor for bird feed automatically by looking at the distance of the feeding place, the data is sent to thingspeak and then processed using the C4.5 algorithm and produces an accuracy level of 86%.
Solution Transfer in Evolutionary Optimization: An Empirical Study on Sequential Transfer

Knowledge transfer from optimized problems has emerged as a promising technique for enhancing evolutionary search. However, most studies in this domain primarily concentrate on devising knowledge transfer mechanisms for specific problem domains, often lacking the examination of the fundamental aspects of knowledge transfer, i.e., what, when and how to transfer across diverse scenarios. This not only restricts the generality of these algorithms but also hinders their practical applicability. In light of this, this paper: 1) reviews a vast array of techniques associated with the crucial aspects of solution transfer and 2) conducts a series of experiments to explore the underlying transfer mechanisms that enhance the evolutionary search. In particular, we first define solution transferability in the context of evolutionary search, which provides a new perspective in understanding what, when and how to transfer in enhancing evolutionary search. Next, through comprehensive experiments, we find that the approximation and evaluation of solution transferability is of great importance in designing what, when and how to transfer towards enhanced evolutionary search. Furthermore, our empirical study also discusses the counterintuitive performance improvements unrelated to the search experience of source tasks. The source code for reproducing our experiments is available at https://github.com/XmingHsueh/STO-EC.
Enhancing Logistics with The Internet of Things: A Secured and Efficient Distribution and Storage Model Utilizing Blockchain Innovations and Interplanetary File System

Logistics is defined as the process of transporting, storing, and delivering goods from the producer to the final user. In today’s globalized world, logistics have become increasingly complex, making it imperative to address challenges related to data integrity, transparency, and secure storage. The incorporation of IoT devices in logistics allows for real-time monitoring of goods, vehicles, and environmental conditions. However, this generates vast amounts of data, which necessitates a reliable and secure data storage and management system. These aforementioned issues can be addressed by deploying a blockchain-based solution. Blockchain is an innovative technology that operates on a decentralized database system and it has different applications which include finance, healthcare, and so on. This research proposed a blockchain-IoT-based model for enhancing the logistics process. The proposed model utilized the Interplanetary file system for the secured and efficient storage of logistics data on a distributed and decentralized network and the SHA-256 hashing algorithm to ensure the anonymity of users’ private information. The model also establishes rules by using smart contracts, which increases efficiency. The performance evaluation of the proposed model was done based on the security, latency, cost, and throughput of the transaction. The experimental results and performance evaluation show that the proposed model is more efficient and secure than the existing blockchain-based systems. Additionally, the proposed model offers the real-time monitoring of goods while in transit. The proposed model offers the solution to the security, storage, and interoperability challenges in the IoT logistics system. It also provides recommendations to logistics stakeholders to adopt blockchain technology. Despite the implications, the limitation of this study is that it was tested in a controlled environment.
Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood model

Purpose This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context. Design/methodology/approach Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users. Findings The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively. Originality/value This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation. Research highlights The study investigates customers' adoption of AI chatbots' recommendation. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths. The central and peripheral cues are generalized according to cooperative principle theory. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice. Central and peripheral cues affect customers' adoption to recommendation through trust in AI. Customers' mind perception positively moderates the central and peripheral paths.
Review of Recent Trends in the Detection of Hate Speech and Offensive Language on Social Media

Abstract In the article, we describe recent trends in the detection of hate speech and offensive language on social media. We accord from the latest studies and scientific contributions. The article describes current trends and the most used methods in connection with the detection of hate speech and offensive language. At the same time, we focus on the importance of emoticons, hashtags, and swearing in the field of social networks. We point out the topicality of the selected topic, describe the next direction of our work, and suggest possible solutions to current problems in this field of research.
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. However, three pivotal questions remain unanswered: (1) How effective are current LLMs in utilizing tools? (2) How can we enhance LLMs’ ability to utilize tools? (3) What obstacles need to be overcome to leverage tools? To address these questions, we introduce API-Bank, a groundbreaking benchmark, specifically designed for tool-augmented LLMs. For the first question, we develop a runnable evaluation system consisting of 73 API tools. We annotate 314 tool-use dialogues with 753 API calls to assess the existing LLMs’ capabilities in planning, retrieving, and calling APIs. For the second question, we construct a comprehensive training set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000 distinct domains. Using this dataset, we train Lynx, a tool-augmented LLM initialized from Alpaca. Experimental results demonstrate that GPT-3.5 exhibits improved tool utilization compared to GPT-3, while GPT-4 excels in planning. However, there is still significant potential for further improvement. Moreover, Lynx surpasses Alpaca’s tool utilization performance by more than 26 pts and approaches the effectiveness of GPT-3.5. Through error analysis, we highlight the key challenges for future research in this field to answer the third question.
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, Christopher Manning. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Automatic Evaluation of Attribution by Large Language Models

A recent focus of large language model (LLM) development, as exemplified by generative search engines, is to incorporate external references to generate and support its claims. However, evaluating the attribution, i.e., verifying whether the generated statement is fully supported by the cited reference, remains an open problem. Although human evaluation is common practice, it is costly and time-consuming. In this paper, we investigate automatic evaluation of attribution given by LLMs. We begin by defining different types of attribution errors, and then explore two approaches for automatic evaluation: prompting LLMs and fine-tuning smaller LMs. The fine-tuning data is repurposed from related tasks such as question answering, fact-checking, natural language inference, and summarization. We manually curate a set of test examples covering 12 domains from a generative search engine, New Bing. Our results on this curated test set and simulated examples from existing benchmarks highlight both promising signals and challenges. We hope our problem formulation, testbeds, and findings will help lay the foundation for future studies on this important problem.
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding

We introduce ZeroSCROLLS, a zero-shot benchmark for natural language understanding over long texts, which contains only test and small validation sets, without training data. We adapt six tasks from the SCROLLS benchmark, and add four new datasets, including two novel information fusing tasks, such as aggregating the percentage of positive reviews. Using ZeroSCROLLS, we conduct a comprehensive evaluation of both open-source and closed large language models, finding that Claude outperforms ChatGPT, and that GPT-4 achieves the highest average score. However, there is still room for improvement on multiple open challenges in ZeroSCROLLS, such as aggregation tasks, where models struggle to pass the naive baseline. As the state of the art is a moving target, we invite researchers to evaluate their ideas on the live ZeroSCROLLS leaderboard.
Inverse Scaling Can Become U-Shaped

Scaling up language models has been empirically shown to improve performance on a wide range of downstream tasks. However, if we were to observe worse performance as a function of scale (inverse scaling) on certain tasks, this would indicate that scaling can also encourage behaviors that are misaligned with human preferences. The Inverse Scaling Prize (McKenzie et al. 2023) identified eleven such inverse scaling tasks, evaluated on models of up to 280B parameters and up to 500 zettaFLOPs of training compute. This paper takes a closer look at these inverse scaling tasks. In this paper, we evaluate models of up to 540B parameters, trained on five times more compute than those evaluated in the Inverse Scaling Prize. With this increased range of model sizes and compute, only four out of the eleven tasks remain inverse scaling. Six tasks exhibit U-shaped scaling, where performance decreases up to a certain size, and then increases again up to the largest model evaluated (the one remaining task displays positive scaling). In addition, 1-shot examples and chain-of-thought can help mitigate undesirable scaling patterns even further. U-shaped scaling suggests that the inverse scaling trend observed in McKenzie et al. (2023) may not continue to hold for larger models, which we attribute to the presence of distractor tasks that only sufficiently large models can avoid.