Reviewerly/exHarmony huggingface dataset:
Reciprocal Human Machine Learning (RHML): Human-AI Collaboration based on theories of dyadic learning

In this position paper we advocate a Reciprocal Human Machine Learning paradigm based on two theories of human-human learning behavior. Drawing from Jörg's theory of reciprocal learning in dyads and the Jewish tradition of Havruta - pair-based study, we suggest that human-machine collaboration based on these established human-human collaborative forms can achieve a rich and robust human-in-the-learning-loop (HITLL) framework in which both parties experience learning over time.
The Path to AGI Goes through Embodiment

Recent advances in large language models have raised the question of whether these language models alone could lead to artificial general intelligence (AGI). In this short position essay, we argue that embodiment is not only required for achieving AGI, but also that embodiment is the key to convincingly demonstrate AGI capabilities. There is no single widely-accepted, objective test for AGI, so therefore whether a system has achieved AGI is a subjective judgement. We argue that a language-only system or one that cannot demonstrate success in the real world would not be convincing.
Integrating Curricula with Replays: Its Effects on Continual Learning

Humans engage in learning and reviewing processes with curricula when acquiring new skills or knowledge. This human learning behavior has inspired the integration of curricula with replay methods in continual learning agents. The goal is to emulate the human learning process, thereby improving knowledge retention and facilitating learning transfer. Existing replay methods in continual learning agents involve the random selection and ordering of data from previous tasks, which has shown to be effective. However, limited research has explored the integration of different curricula with replay methods to enhance continual learning. Our study takes initial steps in examining the impact of integrating curricula with replay methods on continual learning in three specific aspects: the interleaved frequency of replayed exemplars with training data, the sequence in which exemplars are replayed, and the strategy for selecting exemplars into the replay buffer. These aspects of curricula design align with cognitive psychology principles and leverage the benefits of interleaved practice during replays, easy-to-hard rehearsal, and exemplar selection strategy involving exemplars from a uniform distribution of difficulties. Based on our results, these three curricula effectively mitigated catastrophic forgetting and enhanced positive knowledge transfer, demonstrating the potential of curricula in advancing continual learning methodologies. Our code and data are available: https://github.com/ZhangLab-DeepNeuroCogLab/Integrating-Curricula-with-Replays
Evaluation Dimensions for Assessing Question Answer Systems for Lay Users: The Case of DiseaseGuru

Question answer (QA) systems can serve as vital tools to address lay users’ information needs in healthcare. While QA systems have the potential to lessen information overload and provide quality answers to users, it is important to holistically evaluate their performance. Here we propose multiple dimensions for this purpose comprising lexical similarity, semantic similarity, absence of contradictions and readability of responses. We then use the dimensions to evaluate DiseaseGuru, a generative large language model-based chronic disease QA system we developed that integrates knowledge graph technology to provide quality responses to lay users. The results are presented comparing it with three benchmark algorithms across the different dimensions. We also propose metrics for lay users and medical professionals for a future field study to evaluate the system.
Ethical Challenges of Using Synthetic Data

There is an outburst of digitized medical data with the growing adoption of Electronic Health Record (EHR) systems but have restricted access due to legal compliances. This lack of data accessibility has piqued the interest in generating and using synthetic data. Synthetic data is programmatically generated using the statistical properties of the real dataset. Although synthetic data tackles the issue of legal compliance, there are some ethical concerns associated with it. In this paper, we discuss three ethical concerns of synthetic medical data such as fairness, privacy and unwarranted use. Further, we identify a need to develop a practical framework for statistical evaluation metrics for synthetically generated data.
Constructing and Interpreting Causal Knowledge Graphs from News

Many financial jobs rely on news to learn about causal events in the past and present, to make informed decisions and predictions about the future. With the ever-increasing amount of news available online, there is a need to automate the extraction of causal events from unstructured texts. In this work, we propose a methodology to construct causal knowledge graphs (KGs) from news using two steps: (1) Extraction of Causal Relations, and (2) Argument Clustering and Representation into KG. We aim to build graphs that emphasize on recall, precision and interpretability. For extraction, although many earlier works already construct causal KGs from text, most adopt rudimentary pattern-based methods. We close this gap by using the latest BERT-based extraction models alongside pattern-based ones. As a result, we achieved a high recall, while still maintaining a high precision. For clustering, we utilized a topic modelling approach to cluster our arguments, so as to increase the connectivity of our graph. As a result, instead of 15,686 disconnected subgraphs, we were able to obtain 1 connected graph that enables users to infer more causal relationships from. Our final KG effectively captures and conveys causal relationships, validated through experiments, multiple use cases and user feedback.
Integrating Medication Recommendation and Lab Test Response Prediction for Enhanced Clinical Decision Support

The rich data available in Electronic Health Records has led to the development of numerous systems for disease inference, mortality prediction, and personalized medication recommendations. However, integrating these diverse systems to enhance clinical decision-making in disease management remains an under-explored territory. This work describes a unified system that melds two distinct clinical tasks, namely medication recommendation and lab test response prediction, to bolster clinical decision support. This system could assist clinicians in personalizing dosage titrations and add-on medications. We present empirical studies from real-world datasets to demonstrate the potential of such a system.
Correction for Nano et al., Cell survival following direct executioner-caspase activation

The noninteractive zero-knowledge proof (NIZKP) is underlying a broad spectrum of applications. The major categories of NIZKP are instantiated with deterministic hash functions in the real world. However, the outputs of deterministic hash ...Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, noninteractive zero-knowledge proof (...
Tool or Actor? Expert Improvisers' Evaluation of a Musical AI “Toddler”

Abstract In this article we introduce the coadaptive audiovisual instrument CAVI. This instrument uses deep learning to generate control signals based on muscle and motion data of a performer's actions. The generated signals control time-based live sound-processing modules. How does a performer perceive such an instrument? Does it feel like a machine learning–based musical tool? Or is it an actor with the potential to become a musical partner? We report on an evaluation of CAVI after it had been used in two public performances. The evaluation is based on interviews with the performers, audience questionnaires, and the creator's self-analysis. Our findings suggest that the perception of CAVI as a tool or actor correlates with the performer's sense of agency. The perceived agency changes throughout a performance based on several factors, including perceived musical coordination, the balance between surprise and familiarity, a “common sense,” and the physical characteristics of the performance setting.
Pengaruh media pembelajaran digital terhadap hasil belajar matematika: meta analisis

Therefore, this study aims to determine the influence of digital-based learning media on mathematics learning outcomes, which is examined based on the aspects of educational levels, digital-medias used, and learning materials. The research method is a systematic literature review with meta-analysis. The instrument used is form of article coding sheet containing data for meta-analysis in the form is sample size, mean, and standard deviation of control groups and experiments from articles that have been selected based on the results in the Publish or Perish application and effect size calculations assisted by the OpenMEE application. The result of this study indicates that digital-based learning media has positive influence of 1.115 on mathematics learning outcomes from the 21 articles that were meta-analyzed. Firstly, in terms of education level, digital-based learning media is more effectively used at the elementary level by 1.415. Secondly, in terms of the media used, digital-based learning media is more effective using Macromedia Flash of 1.028. Lastly, in terms of the learning material, digital-based learning media is more effective in Dimensi Tiga by 1.812.
A STUDY ON THE INFLUENCING MECHANISM OF SHARED LEADERSHIP STYLE ON TEAM INNOVATION PERFORMANCE: MEDIATED BY KNOWLEDGE SHARING AND ORGANIZATIONAL INNOVATION CLIMATE

This study mainly used a combination of qualitative and quantitative mixed method to investigate the mechanism of shared leadership style's influence on team innovation performance. Through the survey, descriptive statistical analysis, CITC test, factor analysis and testing the reliability and validity of the questionnaire were performed for each variable. Correlation analysis and control variables ANOVA were used, and the model was validated. &#x0D; The study found that shared leadership styles positively contribute to (shared transformational leadership, shared transactional leadership, and shared empowerment leadership) team innovation performance, and organizational innovation climate and knowledge sharing have a mediating role in the relationship between shared transformational leadership and shared transactional leadership, i.e., shared transformational leadership and shared transactional leadership styles contribute to team innovation performance through the mediating role of organizational innovation climate and knowledge sharing. Discussion and recommendations around each finding were also provided by the researcher by the end of the study.
Identification of hybrids between the Japanese giant salamander (<i>Andrias japonicus</i>) and Chinese giant salamander (<i>Andrias</i> cf. <i>davidianus</i>) using deep learning and smartphone images

Abstract Human‐mediated hybridization between native and non‐native species is causing biodiversity loss worldwide. Hybridization has contributed to the extinction of many species through direct and indirect processes such as loss of reproductive opportunity and genetic introgression. Therefore, it is essential to manage hybrids to conserve biodiversity. However, specialized knowledge is required to identify the target species based on visual characteristics when two species have similar features. Although image recognition technology can be a powerful tool for identifying hybrids, studies have yet to utilize deep learning approaches. Hence, this study aimed to identify hybrids between the native Japanese giant salamander ( Andrias japonicus ) and the non‐native Chinese giant salamander ( Andrias cf. davidianus ) using EfficientNetV2 and smartphone images. We used smartphone images of 11 individuals of native A. japonicus (five training and six test images) and 20 individuals of hybrids between A. japonicus and A. cf. davidianus (five training and 15 test images). In our experimental environment, an AI model constructed with EfficientNetV2 exhibited 100% accuracy in identifying hybrids. In addition, gradient‐weighted class activation mapping revealed that the AI model was able to classify A. japonicus and hybrids between A. japonicus and A. cf. davidianus on the basis of the dorsal head spot patterning. Our approach thus enables the identification of hybrids against A. japonicus , which was previously considered difficult by non‐experts. Furthermore, since this study achieved reliable identification using smartphone images, it is expected to be applied to a wide range of citizen science projects.
Exploring the Emerging Financial Development Model of "Blockchain + Credit Guarantee" Based on Information Asymmetry Theory

Small and medium-sized enterprises (SMEs) are an important part of relieving the pressure of social employment and increasing the development and vitality of the world economy. However, the disadvantages of SMEs in terms of their scale and normality have led to problems such as difficulties in financing during their development, which have increased the difficulty of their development and growth. To address this social reality, it is urgent to establish a perfect credit guarantee system for SMEs. With the continuous development of China's information technology, financial institutions are also transforming, providing new ideas for the improvement of the credit guarantee system for SMEs. Over the years, the implementation of "blockchain + credit guarantee" has brought unprecedented revolutionary impact to the financial industry. It has not only greatly expanded the application scope of blockchain technology, but also greatly improved the credit guarantee mechanism for SMEs, greatly enhanced their financing efficiency, and effectively reduced their risks, laying a solid foundation for their long-term stable development. To better support the development of SMEs, we should accelerate the digital transformation to realize the effective allocation of financial resources and enhance the market competitiveness of SMEs, to better meet their economic and social development needs.
The application of task-based teaching method in elementary school mathematics education

In contemporary mathematics education, the task-based teaching method has garnered significant attention and exploration as an efficient instructional approach. This article integrates multiple instructional cases to elucidate the concept and characteristics of the task-based teaching method. It delves into the role of task-based teaching method and explores key considerations and issues that should be paid attention to when employing this approach in elementary school mathematics education.
Instance Selection Via Voronoi Neighbors for Binary Classification Tasks

Large datasets available in many applications have enabled the training of binary classifiers to match or even outperform humans. However, the large volume of data introduces computational burden during the training and calibration of model parameters. Since the optimal decision surface (ODS) of a classification task is often determined by a few nearby instances, a novel PDOC-V method is proposed to identify them. A Bayesian probability model is adopted to describe the ODS. An instance is close to the ODS if its probability of belonging to the positive and negative classes is similar. The probabilities of an instance are estimated by partitioning the input space into cells containing a single instance via the Voronoi diagram and inspecting its Voronoi neighbors. A randomized ray shooting algorithm is adopted to accelerate our algorithm. In many natural datasets, the spatial distribution of instances is often uneven. For such datasets, our method is more robust than existing distance-based instance selection methods. Comprehensive experiments suggest that common classifiers trained on instances selected by PDOC-V can accurately recover the ODS. Moreover, for many natural datasets, common classifiers trained on 10% - 20% of instances can achieve more than 98% of the full set performance.
HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing

Minimizing privacy leakage while ensuring data utility is a critical problem in a privacy-preserving data publishing task, from which data holders can boost platform engagements or enlarge data values. Most prior research concerned only with either privacy-insensitive or exact private data and resorts to a single obscuring method to achieve a privacy-utility tradeoff, which is inadequate for real-life hybrid data especially when facing machine learning-based inference attacks. This work takes a pilot study on privacy-preserving data publishing when both widely adopted generalization and obfuscation operations are employed for privacy-heterogeneous data protection. Specifically, we first propose novel measures for privacy and utility values quantification and formulate the hybrid privacy-preserving data obscuring problem to account for the joint effect of generalization and obfuscation. We then design a novel protection mechanism called HyObscure, which decomposes the original problem into three sub-problems to cross-iteratively optimize the hybrid operations for maximum privacy protection under a certain data utility guarantee. The convergence of the iterative process and the privacy leakage bound of HyObscure are also provided in theory. Extensive experiments demonstrate that HyObscure significantly outperforms a variety of state-of-the-art baseline methods when facing various inference attacks in different scenarios.
The types of research data receiving scholarly credit within and across the science, engineering, and mathematics fields

This study examined the types of data that receive formal scholarly credit within and across the science, engineering, and mathematics (SEM) fields. The topics of whether data types are used in a way that encourages data reuse has not been actively studied. This study applied an exploratory method because formal data citation is a relatively new area. The Data Citation Index (DCI) of the Web of Science (WoS) was selected because the DCI provides a single access point to 400 data repositories worldwide across multiple disciplines. Nearly all citations were of quantitative data. The types that received the most credit were, in descending order, ribonucleic acid (RNA), crystal structure, protein sequence data, crystallographic data, Sequence Read Archive (SRA), genomic, images, nucleotide sequencing information, molecular structure, and crystallographic information, though citation was diverse across the various disciplines within these fields. In particular, qualitative data received no scholarly credit. This study contributes to better understanding of data types for data reuse.
Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence (CI) is a group of nature-simulated computational models and processes for addressing difficult real-life problems. The CI is useful in the UAV domain as it produces efficient, precise, and rapid solutions. Besides, unmanned aerial vehicles (UAV) developed a hot research topic in the smart city environment. Despite the benefits of UAVs, security remains a major challenging issue. In addition, deep learning (DL) enabled image classification is useful for several applications such as land cover classification, smart buildings, etc. This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification (MDLS-UAVIC) model in a smart city environment. The major purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels. The proposed MDLS-UAVIC model follows a two-stage process: encryption and image classification. The encryption technique for image encryption effectively encrypts the UAV images. Next, the image classification process involves an Xception-based deep convolutional neural network for the feature extraction process. Finally, shuffled shepherd optimization (SSO) with a recurrent neural network (RNN) model is applied for UAV image classification, showing the novelty of the work. The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset, and the outcomes are examined in various measures. It achieved a high accuracy of 98%.
USING AND UNDERSTANDING OF SEMIOTIC ELEMENTS IN ONLINE MEDIA

Symbolic activity widely disseminated through online communication has become a media communication trend in recently. The signs that are used to replace language are adapted in such a way that they serve as models of meaning, which symbolically link perception to cognition. Through the use of these signs in communication, we manage to distribute our knowledge in different meaningful situations, which have an effect on the common use of language.This paper aims to describe and analyze the development of symbolic meanings (combination of written text with different visual forms) in media communication, through the expression and content they convey in communication. Understanding text and language richness in visual meaning systems (emoji and language) will be evidenced from social networks (i.e: Facebook) and online media, using observational and descriptive methods. The collected data show that the images in the text play the role of a meaning system interacting with the language, manifesting thoughts and perceptions through the emotional attitude of the speaker. At the end of this paper, it will be shown that images play a special role in media communication, because they convey connotative meanings as forms of expressions and emotions, which are combined with the common use of language
Pengembangan Media Papan Hitung pada Materi Konsep Operasi Hitung Bagi Siswa Sekolah Dasar

This research aims to determine the analysis of the needs for calculating board media, to describe the steps for developing calculating board media, material for the calculation operations of addition, subtraction, multiplication and division and to determine the validity of the calculating board media developed in class 3 of SDN 305 Langkidi. This research uses a type of development research using the ADDIE model. The steps taken in this research are needs analysis, design, product development and evaluation. The subjects of this research were 27 students and a class teacher. Data analysis uses qualitative and quantitative. The results of the calculation board media feasibility test from design experts obtained a percentage score of 71.87% in the valid category, while material experts got 84.37% which was also in the very valid category so that it could be used in learning.