Reviewerly/exHarmony huggingface dataset:
Research of Privacy-Preserving in Federated Learning

Federated learning is a mechanism for coordinating multiple participants to train models in response to the development of big data and artificial intelligence technologies. Federated learning not only introduces a large number of parameter exchange processes, but is also subject to attacks involving untrustworthy devices, so stronger privacy means are urgently needed to protect the data held by the parties. This paper briefly introduces the architecture and types of federated learning, analyzes the privacy risks and attack strategies, and summarizes the privacy protection technologies. By applying these technologies, this paper proposes a privacy protection scheme from three levels: center, local, local &amp; central.
FreeGaze: A Framework for 3D Gaze Estimation Using Appearance Cues from a Facial Video

Gaze is a significant behavioral characteristic that can be used to reflect a person’s attention. In recent years, there has been a growing interest in estimating gaze from facial videos. However, gaze estimation remains a challenging problem due to variations in appearance and head poses. To address this, a framework for 3D gaze estimation using appearance cues is developed in this study. The framework begins with an end-to-end approach to detect facial landmarks. Subsequently, we employ a normalization method and improve the normalization method using orthogonal matrices and conduct comparative experiments to prove that the improved normalization method has a higher accuracy and a lower computational time in gaze estimation. Finally, we introduce a dual-branch convolutional neural network, named FG-Net, which processes the normalized images and extracts eye and face features through two branches. The extracted multi-features are then integrated and input into a fully connected layer to estimate the 3D gaze vectors. To evaluate the performance of our approach, we conduct ten-fold cross-validation experiments on two public datasets, namely MPIIGaze and EyeDiap, achieving remarkable accuracies of 3.11° and 2.75°, respectively. The results demonstrate the high effectiveness of our proposed framework, showcasing its state-of-the-art performance in 3D gaze estimation.
Optimizing Cloud Computing Applications with a Data Center Load Balancing Algorithm

Delivering scalable and on-demand computing resources to users through the usage of the cloud has become a common paradigm. The issues of effective resource utilisation and application performance optimisation, however, become more pressing as the demand for cloud services rises. In order to ensure efficient resource allocation and improve application performance, load balancing techniques are essential in dispersing incoming network traffic over several servers. The workload balancing in the context of cloud computing, particularly in the Infrastructure as a Service (IaaS) model, continues to be difficult. Due to available virtual machines and the limited resources, efficient job allocation is essential. To prevent prolonged execution delays or machine breakdowns, cloud service providers must maintain excellent performance and avoid overloading or underloading hosts. The importance of task scheduling in load balancing necessitates compliance with Service Level Agreement (SLA) standards established by cloud developers for consumers. The suggested technique takes into account Quality of Service (QoS) job parameters, VM priorities, and resource allocation in order to maximise resource utilisation and improve load balancing. The proposed load balancing method is in line with the results in the body of existing literature by resolving these problems and the current research gap. According to experimental findings, the Dynamic LBA algorithm currently in use is outperformed by an average resource utilisation of 78%. The suggested algorithm also exhibits excellent performance in terms of accelerated Makespan and decreased execution time.
Focus on Physics: Quickly Teaching Speed, Velocity, and Acceleration–Part 1

"Focus on Physics: Quickly Teaching Speed, Velocity, and Acceleration–Part 1." The Science Teacher, 86(6), pp. 10–11 Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, 12th edition; Conceptual Physical Science, 6th edition, coauthored with daughter Leslie Hewitt and nephew John Suchocki; and Conceptual Integrated Science, new 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
Validation of Novel Semantic Word-Retrieval Tasks as Sensitive to Word-Finding Difficulty in Multiple Sclerosis (P10-4.005)

Tuesday, April 5May 3, 2022Free AccessValidation of Novel Semantic Word-Retrieval Tasks as Sensitive to Word-Finding Difficulty in Multiple Sclerosis (P10-4.005)Emily Dvorak and James SumowskiAuthors Info & AffiliationsMay 3, 2022 issue98 (18_supplement)https://doi.org/10.1212/WNL.98.18_supplement.1245 Letters to the Editor
AEVAE: Adaptive Evolutionary Autoencoder for Anomaly Detection in Time Series

Anomaly detection (AD) has witnessed substantial advancements in recent years due to the increasing need for identifying outliers in various engineering applications that undergo environmental adaptations. Consequently, researchers have focused on developing robust AD methods to enhance system performance. The primary challenge faced by AD algorithms lies in effectively detecting unlabeled abnormalities. This study introduces an adaptive evolutionary autoencoder (AEVAE) approach for AD in time-series data. The proposed methodology leverages the integration of unsupervised machine learning techniques with evolutionary intelligence to classify unlabeled data. The unsupervised learning model employed in this approach is the AE network. A systematic programming framework has been devised to transform AEVAE into a practical and applicable model. The primary objective of AEVAE is to detect and predict outliers in time-series data from unlabeled data sources. The effectiveness, speed, and functionality enhancements of the proposed method are demonstrated through its implementation. Furthermore, a comprehensive statistical analysis based on performance metrics is conducted to validate the advantages of AEVAE in terms of unsupervised AD.
Supply Chain: Optimize the Production Cost Using Machine Learning

Purpose: The objective of this study is to know how we use can machine learning by applying different algorithms on the supply chain to produce in the cheapest site in the world considering different parameters. Theoretical framework: the study has highlighted the iterative queries of digital revolution in the supply chain. The literary view in this article has illustrated the significant role of machine learning and dark data in reducing the production costs, enhancing delivery performance. Design/Methodology/Approach: The company concerned by the case study is a multinational company specializing in flooring and sports surfaces. It operates in 33 production sites, with 520 sites in more than 100 countries. One of the important factors underlying this complexity is the customer base that expects the product at the same cost all over the world, which forces the system that is currently not centralized to produce at a high cost in some countries. Findings: In this article, we use machine learning by applying different algorithms to unstructured data stored in company servers, where the feedback loop is implemented. The expected result is produced in the cheapest site in the world considering delivery costs. Research, Practical &amp; Social implications: We suggest a future research to use all the remaining dark data saved during ordering on the supply chain and to reduce more the costs in the world . Originality/Value: This article provides insights into how dark data analytics can be used to reduce supply chain costs and offers recommendations for organizations looking to leverage dark data in their supply chain operations.
VRL-IQA: Visual Representation Learning for Image Quality Assessment

The growing adoption of digital multimedia devices and the greater reliance on compression and wireless channels for data transmission has brought renewed focus to the traditional challenge of evaluating image quality. Image Quality Assessment (IQA) is needed to optimize bit rate, compression, or processing and communication strategies for these multimedia technologies. Visual representation learning enables the model to undertake upstream training on large-scale data and then fine-tune the model on downstream data using fewer training samples. Data annotation for IQA is expensive due to the difficulty of grading a picture’s quality, the need to gather quality labels from numerous observers, and the diversity of perceptual quality and content of the images. This challenge has limited the amount of the labeled training dataset for IQA to a few thousand. In this study, a deep Convolutional Neural Network is trained on a large-scale image dataset produced by simulating 165 distortion scenarios on 150,000 images resulting in 24.75 million distorted images. These images are labeled via an ensemble of full-reference quality assessment models which assign the quality rating to each distorted image by using its reference image. This trained model is fine-tuned on two datasets TID2013 and Kadid-10K datasets containing simulated distortions and two datasets KonIQ-10K and BIQ2021 containing authentic distortions. The fine-tuning performance has resulted in state-of-the-art IQA performance and yielded a Spearman’s correlation coefficient of 0.921, 0.893, 0.884, and 0.793, respectively. Moreover, comparison with the ImageNet pre-trained model revealed that the proposed VRL-IQA model provides higher performance in terms of Pearson and Spearman’s correlations and achieves the validation criteria with fewer epochs than the ImageNet pre-trained model. These findings contribute to the advancement of IQA, offering a promising approach for robust and accurate quality prediction in various applications.
HISTORICAL AND PEDAGOGICAL ANALYSIS OF V.A. SUKHOMLINSKY’S HOLISTIC PEDAGOGICAL SYSTEM IN THE CONTEXT OF MODERNITY

This article was prepared on the eve of the 105th anniversary of the birth of V.A. Sukhomlinsky. To analyze his holistic pedagogical system, we used the main scientific works of the teacher-researcher, substantiated and proven by many years of practice on the issues of education of a comprehensively developed personality, the organization of educational work with students, cooperation of schools, families and the public in the upbringing of children. The problems of the content, methods and forms of teaching, pedagogical creativity and mastery of the teacher, works of art for children are analyzed. His pedagogical heritage is considered as the author’s system of humanistic views based on the understanding of the philosophical truth that education, upbringing and development of a growing personality is, first of all, the affirmation of harmony of mind and heart, where, according to V.A. Sukhomlinsky, a gentle and subtle melody belongs to the heart, which naturally claims to be modern and timely comprehension. The purpose of the article is to show the significance of V.A. Sukhomlinsky’s views in the context of modern and promising development of education and pedagogical science.
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation

Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However, the current state of conversational recommendation faces numerous challenges due to its relative novelty and limited existing contributions. In this study, we delve into benchmark datasets for developing CRS models and address potential biases arising from the feedback loop inherent in multi-turn interactions, including selection bias and multiple popularity bias variants. Drawing inspiration from the success of generative data via using language models and data augmentation techniques, we present two novel strategies, ‘Once-Aug’ and ‘PopNudge’, to enhance model performance while mitigating biases. Through extensive experiments on ReDial and TG-ReDial benchmark datasets, we show a consistent improvement of CRS techniques with our data augmentation approaches and offer additional insights on addressing multiple newly formulated biases.
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis

Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained Sentiment Analysis task, which has attracted growing research interests recently. Existing work mainly utilizes image information to improve the performance of MABSA task. However, most of the studies overestimate the importance of images since there are many noise images unrelated to the text in the dataset, which will have a negative impact on model learning. Although some work attempts to filter low-quality noise images by setting thresholds, relying on thresholds will inevitably filter out a lot of useful image information. Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data. To achieve this goal, we borrow the idea of Curriculum Learning and propose a Multi-grained Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by adjusting the order of training data. Extensive experimental results show that our framework consistently outperforms state-of-the-art work on three sub-tasks of MABSA.
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks

Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape, and it is becoming clear that the quality of automatic evaluation metrics is not keeping up with the pace of development of generative models. We aim to improve the understanding of current models’ performance by providing a preliminary and hybrid evaluation on a range of open and closed-source generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction (GEC), using both automatic and human evaluation. We also explore the potential of the recently released GPT-4 to act as an evaluator. We find that ChatGPT consistently outperforms many other popular models according to human reviewers on the majority of metrics, while scoring much more poorly when using classic automatic evaluation metrics. We also find that human reviewers rate the gold reference as much worse than the best models’ outputs, indicating the poor quality of many popular benchmarks. Finally, we find that GPT-4 is capable of ranking models’ outputs in a way which aligns reasonably closely to human judgement despite task-specific variations, with a lower alignment in the GEC task.
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning

Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts. In this paper, we develop an approach to use in-context learning (ICL) with large language models (LLMs) for TKG forecasting. Our extensive evaluation compares diverse baselines, including both simple heuristics and state-of-the-art (SOTA) supervised models, against pre-trained LLMs across several popular benchmarks and experimental settings. We observe that naive LLMs perform on par with SOTA models, which employ carefully designed architectures and supervised training for the forecasting task, falling within the (-3.6%, +1.5%) Hits@1 margin relative to the median performance. To better understand the strengths of LLMs for forecasting, we explore different approaches for selecting historical facts, constructing prompts, controlling information propagation, and parsing outputs into a probability distribution. A surprising finding from our experiments is that LLM performance endures (±0.4% Hit@1) even when semantic information is removed by mapping entities/relations to arbitrary numbers, suggesting that prior semantic knowledge is unnecessary; rather, LLMs can leverage the symbolic patterns in the context to achieve such a strong performance. Our analysis also reveals that ICL enables LLMs to learn irregular patterns from the historical context, going beyond frequency and recency biases
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA

Large language models (e.g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding of systems’ specific strengths and weaknesses. To address this limitation, we introduce SALSA, an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. We develop twenty one linguistically grounded edit types, covering the full spectrum of success and failure across dimensions of conceptual, syntactic and lexical simplicity. Using SALSA, we collect 19K edit annotations on 840 simplifications, revealing discrepancies in the distribution of simplification strategies performed by fine-tuned models, prompted LLMs and humans, and find GPT-3.5 performs more quality edits than humans, but still exhibits frequent errors. Using our fine-grained annotations, we develop LENS-SALSA, a reference-free automatic simplification metric, trained to predict sentence- and word-level quality simultaneously. Additionally, we introduce word-level quality estimation for simplification and report promising baseline results. Our data, new metric, and annotation toolkit are available at https://salsa-eval.com.
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification

Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. However, existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation. In this paper, we propose JointMatch, a holistic approach for SSTC that addresses these challenges by unifying ideas from recent semi-supervised learning and the task of learning with noise. JointMatch adaptively adjusts classwise thresholds based on the learning status of different classes to mitigate model bias towards current easy classes. Additionally, JointMatch alleviates error accumulation by utilizing two differently initialized networks to teach each other in a cross-labeling manner. To maintain divergence between the two networks for mutual learning, we introduce a strategy that weighs more disagreement data while also allowing the utilization of high-quality agreement data for training. Experimental results on benchmark datasets demonstrate the superior performance of JointMatch, achieving a significant 5.13% improvement on average. Notably, JointMatch delivers impressive results even in the extremely-scarce-label setting, obtaining 86% accuracy on AG News with only 5 labels per class. We make our code available at https://github.com/HenryPengZou/JointMatch.
Language Model is Suitable for Correction of Handwritten Mathematical Expressions Recognition

Handwritten mathematical expression recognition (HMER) is a multidisciplinary task that generates LaTeX sequences from images. Existing approaches, employing tree decoders within attention-based encoder-decoder architectures, aim to capture the hierarchical tree structure, but are limited by CFGs and pre-generated triplet data, hindering expandability and neglecting visual ambiguity challenges. This article investigates the distinctive language characteristics of LaTeX mathematical expressions, revealing two key observations: 1) the presence of explicit structural symbols, and 2) the treatment of symbols, particularly letters, as minimal units with context-dependent semantics, representing variables or constants. Rooted in these properties, we propose that language models have the potential to synchronously and complementarily provide both structural and semantic information, making them suitable for correction of HMER. To validate our proposition, we propose an architecture called Recognize and Language Fusion Network (RLFN), which integrates recognition and language features to output corrected sequences while jointly optimizing with a string decoder recognition model. Experiments show that RLFN outperforms existing state-of-the-art methods on the CROHME 2014/2016/2019 datasets.
The Benefits of Label-Description Training for Zero-Shot Text Classification

Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data in order to classify among specific label sets in downstream tasks. We propose a simple way to further improve zero-shot accuracies with minimal effort. We curate small finetuning datasets intended to describe the labels for a task. Unlike typical finetuning data, which has texts annotated with labels, our data simply describes the labels in language, e.g., using a few related terms, dictionary/encyclopedia entries, and short templates. Across a range of topic and sentiment datasets, our method is more accurate than zero-shot by 17-19% absolute. It is also more robust to choices required for zero-shot classification, such as patterns for prompting the model to classify and mappings from labels to tokens in the model’s vocabulary. Furthermore, since our data merely describes the labels but does not use input texts, finetuning on it yields a model that performs strongly on multiple text domains for a given label set, even improving over few-shot out-of-domain classification in multiple settings.
Dynamic updating of clinical survival prediction models in a changing environment

Abstract Background Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. In this study, we investigate dynamic model updating of clinical survival prediction models. In contrast to discrete or one-time updating, dynamic updating refers to a repeated process for updating a prediction model with new data. We aim to extend previous research which focused largely on binary outcome prediction models by concentrating on time-to-event outcomes. We were motivated by the rapidly changing environment seen during the COVID-19 pandemic where mortality rates changed over time and new treatments and vaccines were introduced. Methods We illustrate three methods for dynamic model updating: Bayesian dynamic updating, recalibration, and full refitting. We use a simulation study to compare performance in a range of scenarios including changing mortality rates, predictors with low prevalence and the introduction of a new treatment. Next, the updating strategies were applied to a model for predicting 70-day COVID-19-related mortality using patient data from QResearch, an electronic health records database from general practices in the UK. Results In simulated scenarios with mortality rates changing over time, all updating methods resulted in better calibration than not updating. Moreover, dynamic updating outperformed ad hoc updating. In the simulation scenario with a new predictor and a small updating dataset, Bayesian updating improved the C-index over not updating and refitting. In the motivating example with a rare outcome, no single updating method offered the best performance. Conclusions We found that a dynamic updating process outperformed one-time discrete updating in the simulations. Bayesian updating offered good performance overall, even in scenarios with new predictors and few events. Intercept recalibration was effective in scenarios with smaller sample size and changing baseline hazard. Refitting performance depended on sample size and produced abrupt changes in hazard ratio estimates between periods.