Reviewerly/exHarmony huggingface dataset:
Multi-Mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.
Joining Forces! Reusing Contracts for Deductive Verifiers Through Automatic Translation

Deductive verifiers can be used to prove the correctness of programs by specifying the program's intended behaviour using annotations such as pre- and postconditions. Unfortunately, most verifiers use their own unique specification language for those contract-based annotations. While many of them have similar concepts and syntax, there are numerous semantic differences and subtleties that make it very difficult to reuse specifications between verifiers. But reusing specifications could help overcome one of the bottlenecks of deductive verification, namely writing specifications. Therefore, we present the Specification Translator, a tool to automatically translate annotations for deductive verifiers. It currently supports Java programs annotated for OpenJML, Krakatoa and VerCors. Using the Specification Translator, we show that we can reuse 81% of the annotations, which would otherwise need to be manually translated. Moreover, it allows to reuse tools such as Daikon that generate annotations only in the syntax of one specific tool.
Reasoning About Exceptional Behavior at the Level of Java Bytecode

A program's exceptional behavior can substantially complicate its control flow, and hence accurately reasoning about the program's correctness. On the other hand, formally verifying realistic programs is likely to involve exceptions—a ubiquitous feature in modern programming languages. In this paper, we present a novel approach to verify the exceptional behavior of Java programs, which extends our previous work on ByteBack. ByteBack works on a program's bytecode, while providing means to specify the intended behavior at the source-code level; this approach sets ByteBack apart from most state-of-the-art verifiers that target source code. To explicitly model a program's exceptional behavior in a way that is amenable to formal reasoning, we introduce Vimp: a high-level bytecode representation that extends the Soot framework's Grimp with verification-oriented features, thus serving as an intermediate layer between bytecode and the Boogie intermediate verification language. Working on bytecode through this intermediate layer brings flexibility and adaptability to new language versions and variants: as our experiments demonstrate, ByteBack can verify programs involving exceptional behavior in all versions of Java, as well as in Scala and Kotlin (two other popular JVM languages).
Using Augmented Small Multimodal Models to Guide Large Language Models for Multimodal Relation Extraction

Multimodal Relation Extraction (MRE) is a core task for constructing Multimodal Knowledge images (MKGs). Most current research is based on fine-tuning small-scale single-modal image and text pre-trained models, but we find that image-text datasets from network media suffer from data scarcity, simple text data, and abstract image information, which requires a lot of external knowledge for supplementation and reasoning. We use Multimodal Relation Data augmentation (MRDA) to address the data scarcity problem in MRE, and propose a Flexible Threshold Loss (FTL) to handle the imbalanced entity pair distribution and long-tailed classes. After obtaining prompt information from the small model as a guide model, we employ a Large Language Model (LLM) as a knowledge engine to acquire common sense and reasoning abilities. Notably, both stages of our framework are flexibly replaceable, with the first stage adapting to multimodal related classification tasks for small models, and the second stage replaceable by more powerful LLMs. Through experiments, our EMRE2llm model framework achieves state-of-the-art performance on the challenging MNRE dataset, reaching an 82.95% F1 score on the test set.
Ensemble Learning for Multi-Label Classification with Unbalanced Classes: A Case Study of a Curing Oven in Glass Wool Production

The Industrial Internet of Things (IIoT), which integrates sensors into the manufacturing system, provides new paradigms and technologies to industry. The massive acquisition of data, in an industrial context, brings with it a number of challenges to guarantee its quality and reliability, and to ensure that the results of data analysis and modelling are accurate, reliable, and reflect the real phenomena being studied. Common problems encountered with real industrial databases are missing data, outliers, anomalies, unbalanced classes, and non-exhaustive historical data. Unlike papers present in the literature that respond to those problems in a dissociated way, the work performed in this article aims to address all these problems at once. A comprehensive framework for data flow encompassing data acquisition, preprocessing, and machine class classification is proposed. The challenges of missing data, outliers, and anomalies are addressed with critical and novel class outliers distinguished. The study also tackles unbalanced class classification and evaluates the impact of missing data on classification accuracy. Several machine learning models for the operating state classification are implemented. The study also compares the performance of the proposed framework with two existing methods: the Histogram Gradient Boosting Classifier and the Extreme Gradient Boosting classifier. It is shown that using “hard voting” ensemble learning methods to combine several classifiers makes the final classifier more robust to missing data. An application is carried out on data from a real industrial dataset. This research contributes to narrowing the theory–practice gap in leveraging IIoT technologies, offering practical insights into data analytics implementation in real industrial scenarios.
Impact of the Stimulus Presentation Structure on Subjective Video Quality Assessment

A study of video quality perceived by user (Quality of Experience, QoE) was conducted with an examination of whether and how the order and structure of the video sequence presentation affects subjective assessment. For this purpose, the influence of content variability/repeatability, the quality of the preceding sequence, and the sequence order were analyzed. Observations on the correlation of QoE with the micro-structure of sequence presentation are described, which can be the basis for hypotheses of the dependence of QoE assessment on the abovementioned factors. The observed relationships regarding the influence of the number of video repetitions and the impact of the predecessor’s quality on subjective evaluation are consistent with research work on the influence of the order/arrangement and structure of research stimuli on the results of subjective evaluations. Areas for further research are indicated, including relating obtained results in the area of QoE to other cognitive sciences, such as psychological, medical, and economic sciences.
Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling

Intention detection and slot filling are two major subtasks in building a spoken language understanding (SLU) system. These two tasks are closely related to each other, and information from one will influence the other, establishing a bidirectional contributory relationship. Existing studies have typically modeled the two-way connection between these two tasks simultaneously in a unified framework. However, these studies have merely contributed to the research direction of fully using the correlations between feature information of the two tasks, without sufficient focusing on and utilizing native textual semantics. In this article, we propose a semantic guidance (SG) framework, enabling enhancing the understanding of textual semantics by dynamically gating the information from both tasks to acquire semantic features, ultimately leading to higher joint task accuracy. Experimental results on two widely used public datasets show that our model achieves state-of-the-art performance.
Who's the Best Detective? Large Language Models vs. Traditional Machine Learning in Detecting Incoherent Fourth Grade Math Answers

Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection of incoherent answers. One option is to automate the review with Large Language Models (LLM). They have a powerful discursive ability that can be used to explain decisions. In this paper, we analyze the responses of fourth graders in mathematics using three LLMs: GPT-3, BLOOM, and YOU. We used them with zero, one, two, three and four shots. We compared their performance with the results of various classifiers trained with Machine Learning (ML). We found that LLMs perform worse than MLs in detecting incoherent answers. The difficulty seems to reside in recursive questions that contain both questions and answers, and in responses from students with typical fourth-grader misspellings. Upon closer examination, we have found that the ChatGPT model faces the same challenges.
Enhancing Asian Indigenous Language Processing through Deep Learning-Based Handwriting Recognition and Optimization Techniques

Asian indigenous language or autochthonous language is a language which is native to a region and spoken by indigenous people in Asia. This language is a linguistically different community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages are attained important internet amongst research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is most ancient Indian language that is mostly exploited in Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs capsule network (CapsNet) model for feature vector generation with HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.
Recent Trends in Deep Learning for Conversational AI

Conversational AI has seen unprecedented growth in recent years due to which Chatbots have been made available. Conversational AI primarily focuses on text or speech inputs, identifying the intention behind them, and responding to users with relevant information. Natural Language Processing (NLP), Natural Language Understanding (NLU), Machine Learning (ML), and speech recognition offer a personalized experience that mimics human-like engagement in conversational AI systems. Conversational AI systems like Google Meena, Amazon’s Alexa, Facebook’s BlenderBot, and OpenAI’s GPT-3 are trained using Deep Learning (DL) techniques that mimic a human brain-like structure and are trained on huge amounts of text data to provide open-domain conversations. The aim of this chapter is to highlight Conversational AI and NLP techniques behind it. The chapter focuses on DL architectures useful in building Conversational AI systems. The chapter discusses what are the recent advances in Conversational AI and how they are useful, what are the challenges, and what is the scope and future of conversational AI. This will help researchers to understand state-of-the-art frameworks and how they are useful in building Conversational AI models.
Cyber-physical decentralized planning for communizing

This paper proposes a decentralized planning constructed as a cyber-physical system to jointly manage supply and demand, including aspects related to production and circulation, without the mediation of money. Our aim is to provide a concrete technical solution for a future society based on communizing and commons-based resource allocation as an attempt to move in-against-and-beyond the value-form, which is the social “force-field” that characterizes the capitalist mode of production. This contribution is divided into three articulated parts: (i) a review of the elementary forms that jointly determine the capitalist social organization, (ii) a defense of the proposition that the money-form must be destroyed to enable a new mode of production based on communizing, and (iii) a proposal of a cyber-physical implementation of a jointly decentralized production planning and resource allocation over large infrastructures that enable a multilevel polycentric governance as a variation of the Interactive Economic Planning and Optimized Selections (I-EPOS) algorithm when coordination is needed.
Reviews and Reviewing: Approaches to Research Synthesis. An Annual Review of Information Science and Technology (ARIST) paper

Abstract Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions.
A novel ensemble model of multi-class credit assessment based on multi-source fusion theory

With the development of artificial intelligence technology, the assessment method based on machine learning, especially the ensemble learning method, has attracted more and more attention in the field of credit assessment. However, most of the ensemble assessment models are complex in structure and costly in time for parameter tuning, few of them break through the limitations of lightweight, universal and efficient. This paper present a new ensemble model for personal credit assessment. First, considering the conflicts and differences among multiple sources of information, a new method is proposed to correct the category prior information by using the difference measure. Then, the revised prior information is fused with the current sample information with the help of Bayesian data fusion theory. The model can integrate the advantages of multiple benchmark classifiers to reduce the interference of uncertain information. To verify the effectiveness of the proposed model, several typical ensemble classification models are selected and empirically studied using real customer credit data from a commercial bank in China, and the results show that among various assessment criteria: the proposed model not only effectively improves the multi-class classification performance, but also outperforms other advanced multi-class classification credit assessment models in terms of parameter tuning and generalizability. This paper supports commercial banks and other financial institutions examination and approval work.
Deep learning approach for denoising low-SNR correlation plenoptic images

Correlation Plenoptic Imaging (CPI) is a novel volumetric imaging technique that uses two sensors and the spatio-temporal correlations of light to detect both the spatial distribution and the direction of light. This novel approach to plenoptic imaging enables refocusing and 3D imaging with significant enhancement of both resolution and depth of field. However, CPI is generally slower than conventional approaches due to the need to acquire sufficient statistics for measuring correlations with an acceptable signal-to-noise ratio (SNR). We address this issue by implementing a Deep Learning application to improve image quality with undersampled frame statistics. We employ a set of experimental images reconstructed by a standard CPI architecture, at three different sampling ratios, and use it to feed a CNN model pre-trained through the transfer learning paradigm U-Net architecture with VGG-19 net for the encoding part. We find that our model reaches a Structural Similarity (SSIM) index value close to 1 both for the test sample (SSIM = [Formula: see text]) and in 5-fold cross validation (SSIM = [Formula: see text]); the results are also shown to outperform classic denoising methods, in particular for images with lower SNR. The proposed work represents the first application of Artificial Intelligence in the field of CPI and demonstrates its high potential: speeding-up the acquisition by a factor 20 over the fastest CPI so far demonstrated, enabling recording potentially 200 volumetric images per second. The presented results open the way to scanning-free real-time volumetric imaging at video rate, which is expected to achieve a substantial influence in various applications scenarios, from monitoring neuronal activity to machine vision and security.
Photonic optical accelerators: The future engine for the era of modern AI?

Photonic accelerators for Artificial Intelligence (AI) are rapidly advancing, promising to provide revolutionary computational speed for modern AI architectures. By leveraging photons with a bandwidth higher than 100 THz, photonic accelerators tackle the computational demands of AI tasks that GHz electronics alone cannot meet. Photonics accelerators integrate circuitry for matrix–vector operators and ultra-fast feature extractors, enabling energy-efficient and parallel computations that prove crucial for the training and inference of AI models in various applications, including classification, segmentation, and feature extraction. This Perspective discusses modern challenges and opportunities that optical computations open in AI for research and industry.
Critical thinking as a factor in the formation of spiritual and moral values in the changing information world

The article reveals the essence of critical thinking and its influence on the formation of spiritual and moral values in the modern information environment. A description of the techniques for developing critical thinking is presented, with special attention to their practical application.
Data Curation Education: Cross-Disciplinary Analysis of Master's Programs

With growing emphasis on data curation practice in both science and industry, there has been a call for information professionals to take on a substantial role in data curation. Library and information science (LIS) education has been responding to this call by offering various training opportunities from Master's education to professional development. The most recent effort to systematically review a data curation curriculum offered by ALA-accredited LIS schools was in 2012, so it is time to revisit the progress and evolution of data curation education. The main goal of this study is to analyze the course content from the syllabi of various programs to understand what is being taught in LIS schools throughout graduate-level education. Further, because the need for data curation is apparent across different disciplines, and thus not only LIS but also other disciplines have been offering data curation courses, this study also analyzed syllabi from other disciplines. A total of 80 syllabi were analyzed in this study: 15 syllabi from 9 ALA-accredited institutions and 65 syllabi from 53 institutions of Carnegie Classification (CC). Our findings suggest a notable growth in LIS education in data curation since 2012, but LIS education still provides less training in technical skills. There was also a distinctive difference in educational approach to teach data curation between LIS (user- and service-oriented) and other disciplines (technical skills−focused), which brought different strengths and weaknesses in curriculum.
Virtual Reality Training and Library Science Education: Examining the Possibilities

The use of virtual reality (VR) in social work (SW) and pre-service teacher education has become a promising tool for skills development for students. A review of existing scholarship was conducted to provide an overview of the current landscape of VR in SW and pre-service teacher education. This analysis focused on the use of VR in fields that involved working with people, particularly people in crisis. After an initial review of 115 articles, 60 were identified as relevant to this study. Our analysis of recent trends in the literature emphasizes the potential of VR for library and information science (LIS) education. This literature review provides a basis for a study that examines how VR training approaches may be implemented to support LIS graduate students to build communication and de-escalation skills and confidence in interacting with patrons in crisis. Therefore, this literature review demonstrates the value of using VR and informs our proposed future project examining the viability and usability of VR training for this skill development using the social VR platform Mozilla Hubs.
Two notions of medium and their implications for intelligence

Abstract In biology, the term medium is defined as a substance that a biological system exists or grows in or that it travels through. In physics, the term medium is defined as a substance that propagates and transmits the energy from one location to another. These two notions of medium in biology and physics are distinct, yet in reality, their referents frequently coincide in the same material systems in the environment. The air, a medium for terrestrial animals in the sense defined in biology, is a medium that transmits light, mechanical waves, and diffuses molecules from the source in the sense defined in physics. A medium that surrounds each cell in the living body in the sense of biology, at the same time, is an excitable medium that propagates electrical events, mechanical stresses, and the variety of chemical molecules in the sense of physics. In this article, I discuss the implications of this coincidence of the two notions of medium in the real world for the evolution and development of intelligent systems.