Reviewerly/exHarmony huggingface dataset:
Context-aggregator: An approach of loss- and class imbalance-aware aggregation in federated learning

Federated Learning (FL) is an emerging distributed learning paradigm which offers data privacy to contributing nodes in the collaborating environment. By exploiting the Individual datasets of different hospitals in FL setting could be used to develop reliable screening, diagnosis, and treatment predictive models to tackle major challenges such as pandemics. FL can enable the development of very diverse medical imaging datasets and thus provide more reliable models for all participating nodes, including those with low quality data. However, the issue with the traditional Federated Learning paradigm is the degradation of generalization power due to poorly trained local models at the client nodes. The generalization power of the FL paradigm can be improved by considering the relative learning contribution of client nodes. Simple aggregation of learning parameters in the standard FL model faces a diversity issue and results in more validation loss during the learning process. This issue can be resolved by considering the relative contribution of each client node participating in the learning process. The class imbalance at each site is another significant challenge that greatly impacts the performance of the aggregated learning model. This work considers Context Aggregator FL based on the context of loss-factor and class-imbalance issues by incorporating the relative contribution of the collaborating nodes in FL by proposing Validation-Loss based Context Aggregator (CAVL) and Class Imbalance based Context Aggregator (CACI). The proposed Context Aggregator is evaluated on several different Covid-19 imaging classification datasets present on participating nodes. The evaluation results show that Context Aggregator performs better than standard Federating average Learning algorithms and FedProx Algorithm for Covid-19 image classification problems.
Knowledge-based BERT word embedding fine-tuning for emotion recognition

Emotion recognition has received considerable attention in recent years, with the popularity of social media. It is noted, however, that the state-of-the-art language models such as Bidirectional Encoder Representations from Transformers (BERT) may not produce the best performance in emotion recognition. We found the main cause of the problem is that the embedding of emotional words from the pre-trained BERT model may not exhibit high between-class difference and within-class similarity. While BERT model fine-tuning is a common practice when it is applied to specific tasks, this may not be practical in emotion recognition because most datasets are small and some texts are short and noisy, without containing much useful contextual information. In this paper, we propose to use the knowledge of emotion vocabulary to fine-tune embedding of emotional words. As a separate module independent of the embedding learning model, the fine-tuning model aims to produce emotional word embedding with improved within-class similarity and between-class difference. By combining the emotionally discriminative fine-tuned embedding with contextual information-rich embedding from pre-trained BERT model, the emotional features underlying the texts could be more effectively captured in the subsequent feature learning module, which in turn leads to improved emotion recognition performance. The knowledge-based word embedding fine-tuning model is tested on five datasets of emotion recognition, and the results and analysis demonstrate the effectiveness of the proposed method.
A Spatiotemporal Analysis of Teacher Practices in Supporting Student Learning and Engagement in an AI-Enabled Classroom

Research indicates that teachers play an active and important role in classrooms with AI tutors. Yet, our scientific understanding of the way teacher practices around AI tutors mediate student learning is far from complete. In this paper, we investigate spatiotemporal factors of student-teacher interactions by analyzing student engagement and learning with an AI tutor ahead of teacher visits (defined as episodes of a teacher being in close physical proximity to a student) and immediately following teacher visits. To conduct such integrated, temporal analysis around the moments when teachers visit students, we collect fine-grained, time-synchronized data on teacher positions in the physical classroom and student interactions with the AI tutor. Our case study in a K12 math classroom with a veteran math teacher provides some indications on factors that might affect a teacher's decision to allocate their limited classroom time to their students and what effects these interactions have on students. For instance, teacher visits were associated more with students' in-the-moment behavioral indicators (e.g., idleness) than a broader, static measure of student needs such as low prior knowledge. While teacher visits were often associated with positive changes in student behavior afterward (e.g., decreased idleness), there could be a potential mismatch between students visited by the teacher and who may have needed it more at that time (e.g., students who were disengaged for much longer). Overall, our findings indicate that teacher visits may yield immediate benefits for students but also that it is challenging for teachers to meet all needs – suggesting the need for better tool support.
Algebra Error Classification with Large Language Models

Automated feedback as students answer open-ended math questions has significant potential in improving learning outcomes at large scale. A key part of automated feedback systems is an error classification component, which identifies student errors and enables appropriate, predefined feedback to be deployed. Most existing approaches to error classification use a rule-based method, which has limited capacity to generalize. Existing data-driven methods avoid these limitations but specifically require mathematical expressions in student responses to be parsed into syntax trees. This requirement is itself a limitation, since student responses are not always syntactically valid and cannot be converted into trees. In this work, we introduce a flexible method for error classification using pre-trained large language models. We demonstrate that our method can outperform existing methods in algebra error classification, and is able to classify a larger set of student responses. Additionally, we analyze common classification errors made by our method and discuss limitations of automated error classification.
Reducing the Cost: Cross-Prompt Pre-finetuning for Short Answer Scoring

Automated Short Answer Scoring (SAS) is the task of automatically scoring a given input to a prompt based on rubrics and reference answers. Although SAS is useful in real-world applications, both rubrics and reference answers differ between prompts, thus requiring a need to acquire new data and train a model for each new prompt. Such requirements are costly, especially for schools and online courses where resources are limited and only a few prompts are used. In this work, we attempt to reduce this cost through a two-phase approach: train a model on existing rubrics and answers with gold score signals and finetune it on a new prompt. Specifically, given that scoring rubrics and reference answers differ for each prompt, we utilize key phrases, or representative expressions that the answer should contain to increase scores, and train a SAS model to learn the relationship between key phrases and answers using already annotated prompts (i.e., cross-prompts). Our experimental results show that finetuning on existing cross-prompt data with key phrases significantly improves scoring accuracy, especially when the training data is limited. Finally, our extensive analysis shows that it is crucial to design the model so that it can learn the task's general property. We publicly release our code and all of the experimental settings for reproducing our results ( https://github.com/hiro819/Reducing-the-cost-cross-prompt-prefinetuning-for-SAS ).
Improving Bug Assignment and Developer Allocation in Software Engineering through Interpretable Machine Learning Models

Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug’s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers’ profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with “black box” AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system’s effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history.
The economic impact of location on a solar farm co-located with energy storage

Deploying energy storage (ES), alongside renewable generation, can help to decarbonise electricity grids. A key aspect of deploying these is choosing a suitable location, which is both geographically feasible and economical. Previous studies identify locations with suitable geographies; here we focus on the economic impact of location. We explore how the maximised profits, determined using a mixed integer linear programming (MILP) optimisation model, of a solar farm co-located with ES vary in different regions around Great Britain (GB) as a case study. We perform a cost–benefit analysis from the point of view of a distribution-connected solar farm owner. Real solar generation data is used, along with a weather model, to accurately represent forecast and actual output. For solar farms without ES profits are higher in locations with greater solar irradiance. However for sites with ES we find greater profit variation, primarily due to different distribution charges. For the majority of GB, ES does not add sufficient value to offset its high upfront costs and is not worth adding to solar sites. Additionally, it is found to be uneconomical to add ES to most existing solar farms, despite many studies highlighting the grid benefits this would bring. We recommend that distribution network operator and market pricing better reflects the value which ES can bring to the electricity system economical to add to solar sites. To encourage increased co-location distribution operators should offer greater a differential between non-intermittent generation and intermittent generation payments, in particular at times of high system demand.
Deteriorated Characters Restoration for Early Japanese Books Using Enhanced CycleGAN

Early Japanese books, classical humanities resources in Japan, have great historical and cultural value. However, Kuzushi-ji, the old character in early Japanese books, is scratched, faded ink, and lost due to weathering and deterioration over the years. The restoration of deteriorated early Japanese books has tremendous significance in cultural revitalization. In this paper, we introduce augmented identity loss and propose enhanced CycleGAN for deteriorated character restoration, which combines domain discriminators and augmented identity loss. This enhanced CycleGAN makes it possible to restore multiple levels of deterioration in the early Japanese books. It obtains the high readability of the actual deteriorated characters, which is proved by higher structural similarity(SSIM) and accuracy of deep learning models than standard CycleGAN and traditional image processing. In particular, SSIM increases by 8.72%, and the accuracy of ResNet50 for damaged characters improves by 1.1% compared with the competitive CycleGAN. Moreover, we realize the automatic restoration of pages of early Japanese books written about 300 years ago.
A blockchain-based cloud forensics architecture for privacy leakage prediction with cloud

Digital forensics has intelligently evolved into cloud forensics, which protects against cybercrime. However, centralized evidence gathering and maintenance reduces the credibility of digital evidence. This article suggests a unique digital forensic architecture for the Infrastructure as a Service (IaaS) cloud combining rapidly expanding Software Defined Networking (SDN) and Blockchain technologies to address this serious issue. Blockchain helps to store the gathered evidence, which means it is shared among many peers in the proposed forensic architecture. The Secure-Ring-Verification-based Authentication (SRVA) technique is recommended to ensure security from suspicious accounts. The Harmony Search Optimization (HSO) technique helps to produce secret keys that strengthen the cloud environment. Depending on their level of sensitivity, all information is encrypted and kept on a cloud server. Sensitive Aware Deep Elliptic Curve Cryptography (SAD-ECC) method was used for encryption. In the cloud, a piece of information is saved, and the SDN manager creates the blocks and stores the history of that piece of data as metadata. Utilizing Secure Hashing Algorithm-3 (SHA-3), a Merkle Hash Tree is constructed for each block. The proposed technology uses Fuzzy-based Smart Contracts (FSCs) to enable clients to trail their data. At last, evidence analysis is made possible by creating a Logical-Graph of Evidence (LGoE) using data collected through Blockchain. The network simulator-3.26 and java (for cloud and Blockchain) environment are used for experiments. A thorough investigation shows that the suggested forensic architecture exhibits favorable response, evidence insertion, and verification times.
Gauging investors' investment decisions in the crypto market through the PRISM of behavioral biases: a fuzzy AHP approach

Purpose Research in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context. Design/methodology/approach A fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study. Findings The findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria. Research limitations/implications This survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market. Practical implications The study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large. Originality/value To the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.
Symbolic knowledge injection meets intelligent agents: QoS metrics and experiments

Abstract Bridging intelligent symbolic agents and sub-symbolic predictors is a long-standing research goal in AI. Among the recent integration efforts, symbolic knowledge injection (SKI) proposes algorithms aimed at steering sub-symbolic predictors’ learning towards compliance w.r.t. pre-existing symbolic knowledge bases. However, state-of-the-art contributions about SKI mostly tackle injection from a foundational perspective, often focussing solely on improving the predictive performance of the sub-symbolic predictors undergoing injection. Technical contributions, in turn, are tailored on individual methods/experiments and therefore poorly interoperable with agent technologies as well as among each others. Intelligent agents may exploit SKI to serve many purposes other than predictive performance alone—provided that, of course, adequate technological support exists: for instance, SKI may allow agents to tune computational, energetic, or data requirements of sub-symbolic predictors. Given that different algorithms may exist to serve all those many purposes, some criteria for algorithm selection as well as a suitable technology should be available to let agents dynamically select and exploit the most suitable algorithm for the problem at hand. Along this line, in this work we design a set of quality-of-service (QoS) metrics for SKI, and a general-purpose software API to enable their application to various SKI algorithms—namely, platform for symbolic knowledge injection (PSyKI). We provide an abstract formulation of four QoS metrics for SKI, and describe the design of PSyKI according to a software engineering perspective. Then we discuss how our QoS metrics are supported by PSyKI. Finally, we demonstrate the effectiveness of both our QoS metrics and PSyKI via a number of experiments, where SKI is both applied and assessed via our proposed API. Our empirical analysis demonstrates both the soundness of our proposed metrics and the versatility of PSyKI as the first software tool supporting the application, interchange, and numerical assessment of SKI techniques. To the best of our knowledge, our proposals represent the first attempt to introduce QoS metrics for SKI, and the software tools enabling their practical exploitation for both human and computational agents. In particular, our contributions could be exploited to automate and/or compare the manifold SKI algorithms from the state of the art. Hence moving a concrete step forward the engineering of efficient, robust, and trustworthy software applications that integrate symbolic agents and sub-symbolic predictors.
Exploiting optical flow guidance for parallel structure video inpainting

To solve the problem of video repair, we propose a new optical flow guidance solution that uses parallel structured convolution and attention networks to jointly infer video missing regions. In the network, a parallel structural model based on convolution and attention networks guided by optical flow is used to extract feature information to integrate the spatial and temporal context of video frames. This method integrates information between the target frame and the reference frame. To enhance feature learning capabilities using convolution and attention mechanisms, the feature fusion module fuses local and global features in an interactive manner, maximizing the retention of local and global representations. Our model produces visually satisfactory and time consistent results, while demonstrating on two benchmark datasets that our method outperforms the most advanced methods in terms of quantity and user research.
Multi-objective ω-Regular Reinforcement Learning

The expanding role of reinforcement learning (RL) in safety-critical system design has promoted ω-automata as a way to express learning requirements—often non-Markovian—with greater ease of expression and interpretation than scalar reward signals. However, real-world sequential decision making situations often involve multiple, potentially conflicting, objectives. Two dominant approaches to express relative preferences over multiple objectives are: (1) weighted preference , where the decision maker provides scalar weights for various objectives, and (2) lexicographic preference , where the decision maker provides an order over the objectives such that any amount of satisfaction of a higher-ordered objective is preferable to any amount of a lower-ordered one. In this article, we study and develop RL algorithms to compute optimal strategies in Markov decision processes against multiple ω-regular objectives under weighted and lexicographic preferences. We provide a translation from multiple ω-regular objectives to a scalar reward signal that is both faithful (maximising reward means maximising probability of achieving the objectives under the corresponding preference) and effective (RL quickly converges to optimal strategies). We have implemented the translations in a formal reinforcement learning tool, Mungojerrie , and we present an experimental evaluation of our technique on benchmark learning problems.