Reviewerly/exHarmony huggingface dataset:
Mean-field Analysis of Generalization Errors

We propose a novel framework for exploring weak and $L_2$ generalization errors of algorithms through the lens of differential calculus on the space of probability measures. Specifically, we consider the KL-regularized empirical risk minimization problem and establish generic conditions under which the generalization error convergence rate, when training on a sample of size $n$, is $\mathcal{O}(1/n)$. In the context of supervised learning with a one-hidden layer neural network in the mean-field regime, these conditions are reflected in suitable integrability and regularity assumptions on the loss and activation functions.
FedNoisy: Federated Noisy Label Learning Benchmark

Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data isolation may be complicated by data quality, making it more vulnerable to noisy labels. Many efforts exist to defend against the negative impacts of noisy labels in centralized or federated settings. However, there is a lack of a benchmark that comprehensively considers the impact of noisy labels in a wide variety of typical FL settings. In this work, we serve the first standardized benchmark that can help researchers fully explore potential federated noisy settings. Also, we conduct comprehensive experiments to explore the characteristics of these data settings and unravel challenging scenarios on the federated noisy label learning, which may guide method development in the future. We highlight the 20 basic settings for more than 5 datasets proposed in our benchmark and standardized simulation pipeline for federated noisy label learning. We hope this benchmark can facilitate idea verification in federated learning with noisy labels. \texttt{FedNoisy} is available at \codeword{https://github.com/SMILELab-FL/FedNoisy}.
SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT

In recent years, deep learning-based solar forecasting using all-sky images has emerged as a promising approach for alleviating uncertainty in PV power generation. However, the stochastic nature of cloud movement remains a major challenge for accurate and reliable solar forecasting. With the recent advances in generative artificial intelligence, the synthesis of visually plausible yet diversified sky videos has potential for aiding in forecasts. In this study, we introduce \emph{SkyGPT}, a physics-informed stochastic video prediction model that is able to generate multiple possible future images of the sky with diverse cloud motion patterns, by using past sky image sequences as input. Extensive experiments and comparison with benchmark video prediction models demonstrate the effectiveness of the proposed model in capturing cloud dynamics and generating future sky images with high realism and diversity. Furthermore, we feed the generated future sky images from the video prediction models for 15-minute-ahead probabilistic solar forecasting for a 30-kW roof-top PV system, and compare it with an end-to-end deep learning baseline model SUNSET and a smart persistence model. Better PV output prediction reliability and sharpness is observed by using the predicted sky images generated with SkyGPT compared with other benchmark models, achieving a continuous ranked probability score (CRPS) of 2.81 (13\% better than SUNSET and 23\% better than smart persistence) and a Winkler score of 26.70 for the test set. Although an arbitrary number of futures can be generated from a historical sky image sequence, the results suggest that 10 future scenarios is a good choice that balances probabilistic solar forecasting performance and computational cost.
GIO: Gradient Information Optimization for Training Dataset Selection

It is often advantageous to train models on a subset of the available train examples, because the examples are of variable quality or because one would like to train with fewer examples, without sacrificing performance. We present Gradient Information Optimization (GIO), a scalable, task-agnostic approach to this data selection problem that requires only a small set of (unlabeled) examples representing a target distribution. GIO begins from a natural, information-theoretic objective that is intractable in practice. Our contribution is in showing that it can be made highly scalable through a simple relaxation of the objective and a highly efficient implementation. In experiments with machine translation, spelling correction, and image recognition, we show that GIO delivers outstanding results with very small train sets. These findings are robust to different representation models and hyperparameters for GIO itself. GIO is task- and domain-agnostic and can be applied out-of-the-box to new datasets and domains.
EM-Network: Oracle Guided Self-distillation for Sequence Learning

We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning. In contrast to conventional methods, it is trained with oracle guidance, which is derived from the target sequence. Since the oracle guidance compactly represents the target-side context that can assist the sequence model in solving the task, the EM-Network achieves a better prediction compared to using only the source input. To allow the sequence model to inherit the promising capability of the EM-Network, we propose a new self-distillation strategy, where the original sequence model can benefit from the knowledge of the EM-Network in a one-stage manner. We conduct comprehensive experiments on two types of seq2seq models: connectionist temporal classification (CTC) for speech recognition and attention-based encoder-decoder (AED) for machine translation. Experimental results demonstrate that the EM-Network significantly advances the current state-of-the-art approaches, improving over the best prior work on speech recognition and establishing state-of-the-art performance on WMT'14 and IWSLT'14.
Taming Diffusion Models for Music-driven Conducting Motion Generation

Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion. Prior works have applied Generative Adversarial Networks (GAN) to this task, but the promising diffusion model, which recently showed its advantages in terms of both training stability and output quality, has not been exploited in this context. This paper presents Diffusion-Conductor, a novel DDIM-based approach for music-driven conducting motion generation, which integrates the diffusion model to a two-stage learning framework. We further propose a random masking strategy to improve the feature robustness, and use a pair of geometric loss functions to impose additional regularizations and increase motion diversity. We also design several novel metrics, including Frechet Gesture Distance (FGD) and Beat Consistency Score (BC) for a more comprehensive evaluation of the generated motion. Experimental results demonstrate the advantages of our model.
Memory-Constrained Algorithms for Convex Optimization via Recursive Cutting-Planes

We propose a family of recursive cutting-plane algorithms to solve feasibility problems with constrained memory, which can also be used for first-order convex optimization. Precisely, in order to find a point within a ball of radius $\epsilon$ with a separation oracle in dimension $d$ -- or to minimize $1$-Lipschitz convex functions to accuracy $\epsilon$ over the unit ball -- our algorithms use $\mathcal O(\frac{d^2}{p}\ln \frac{1}{\epsilon})$ bits of memory, and make $\mathcal O((C\frac{d}{p}\ln \frac{1}{\epsilon})^p)$ oracle calls, for some universal constant $C \geq 1$. The family is parametrized by $p\in[d]$ and provides an oracle-complexity/memory trade-off in the sub-polynomial regime $\ln\frac{1}{\epsilon}\gg\ln d$. While several works gave lower-bound trade-offs (impossibility results) -- we explicit here their dependence with $\ln\frac{1}{\epsilon}$, showing that these also hold in any sub-polynomial regime -- to the best of our knowledge this is the first class of algorithms that provides a positive trade-off between gradient descent and cutting-plane methods in any regime with $\epsilon\leq 1/\sqrt d$. The algorithms divide the $d$ variables into $p$ blocks and optimize over blocks sequentially, with approximate separation vectors constructed using a variant of Vaidya's method. In the regime $\epsilon \leq d^{-\Omega(d)}$, our algorithm with $p=d$ achieves the information-theoretic optimal memory usage and improves the oracle-complexity of gradient descent.
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training

Due to the semantic complexity of the Relation extraction (RE) task, obtaining high-quality human labelled data is an expensive and noisy process. To improve the sample efficiency of the models, semi-supervised learning (SSL) methods aim to leverage unlabelled data in addition to learning from limited labelled data points. Recently, strong data augmentation combined with consistency-based semi-supervised learning methods have advanced the state of the art in several SSL tasks. However, adapting these methods to the RE task has been challenging due to the difficulty of data augmentation for RE. In this work, we leverage the recent advances in controlled text generation to perform high quality data augmentation for the RE task. We further introduce small but significant changes to model architecture that allows for generation of more training data by interpolating different data points in their latent space. These data augmentations along with consistency training result in very competitive results for semi-supervised relation extraction on four benchmark datasets.
Ore- and Pósa-Type Conditions for Partitioning 2-Edge-Coloured Graphs into Monochromatic Cycles

In 2019, Letzter confirmed a conjecture of Balogh, Bar\'at, Gerbner, Gy\'arf\'as and S\'ark\"ozy, proving that every large $2$-edge-coloured graph $G$ on $n$ vertices with minimum degree at least $3n/4$ can be partitioned into two monochromatic cycles of different colours. Here, we propose a weaker condition on the degree sequence of $G$ to also guarantee such a partition and prove an approximate version. This resembles a similar generalisation to an Ore-type condition achieved by Bar\'at and S\'ark\"ozy. Continuing work by Allen, B\"ottcher, Lang, Skokan and Stein, we also show that if $\operatorname{deg}(u) + \operatorname{deg}(v) \geq 4n/3 + o(n)$ holds for all non-adjacent vertices $u,v \in V(G)$, then all but $o(n)$ vertices can be partitioned into three monochromatic cycles.
Experimental comparison of PageRank vector calculation algorithms

Finding PageRank vector is of great scientific and practical interest due to its applicability to modern search engines.Despite the fact that this problem is reduced to finding the eigenvector of the stochastic matrix P, the need for new algorithms is justified by a large size of the input data.To achieve no more than linear execution time, various randomized methods have been proposed, returning the expected result only with some probability close enough to one.We will consider two of them by reducing the problem of calculating the PageRank vector to the problem of finding equilibrium in an antagonistic matrix game, which is then solved using the Grigoriadis -Khachiyan algorithm.This implementation works effectively under the assumption of sparsity of the input matrix.As far as we know, there are no successful implementations of neither the Grigoriadis -Khachiyan algorithm nor its application to the task of calculating the PageRank vector.The purpose of this paper is to fill this gap.The article describes an algorithm giving pseudocode and some details of the implementation.In addition, it discusses another randomized method of calculating the PageRank vector, namely, Markov chain Monte Carlo (MCMC), in order to compare the results of these algorithms on matrices with different values of the spectral gap.The latter is of particular interest, since the magnitude of the spectral gap strongly affects the convergence rate of MCMC and does not affect the other two approaches at all.The comparison was carried out on two types of generated graphs: chains and d-dimensional cubes.The experiments, as predicted by the theory, demonstrated the effectiveness of the Grigoriadis -Khachiyan algorithm in comparison with MCMC for sparse graphs with a small spectral gap value.The written code is publicly available, so everyone can reproduce the results themselves or use this implementation for their own needs.The work has a purely practical orientation, no theoretical results were obtained.
Academic Information Service Chatbot Using HMM and AIML

UIN Suska Riau campus led to an escalation amount of data and information that must be maintained, such as academic information. UIN Suska Riau is responsible for managing and providing academic information to students and other academic communities. We can ask the Customer Care Center (C3) in Academic System or come directly to the PTIPD UIN Suska Riau office for academic questions. There still has limitations to serving existing questions submitted through C3 because officers can only serve during working hours both online and offline. Chatbots can be used to support the work of C3 officers in serving the questions asked. This system is built based on Named Entity Recognition (NER) using Artificial Intelligence Markup Language (AIML). We perform NER analysis using HMM. This study uses the contents of the academic manual as a base knowledge with 150 categories of questions and 30 answers that produce an accuracy of 55%.
Classification of the Projective Line over Galois Field of Order 31

Our research is related to the projective line over the finite field, in this paper, the main purpose is to classify the sets of size K on the projective line PG (1,31), where K = 3,…,7 the number of inequivalent K-set with stabilizer group by using the GAP Program is computed.
Kesulitan Belajar Akuntansi Keuangan (Studi Kasus Pada Materi Merchandise Inventory Management)

This study aims to identify and obtain an overview, data and information that are considered important about learning difficulties in financial accounting subjects (intermediate) on merchandise inventory management materials. In accordance with the methodology this research is a qualitative research, the research uses the condition of the natural object of the researcher as a key instrument in triangulating data collection in induced data analysis so that it places more emphasis on generalized meanings. The factors that cause learning difficulties in various ways are as follows: 1) Lack of student understanding in studying financial accounting subjects (intermediate) on merchandise inventory management materials, 2) Materials in identifying and recording inventory cards using two recording systems merchandise, 3) In recording the valuation of merchandise inventory to find the total cost of goods sold and the total value of the ending inventory using the five methods and 4) At the end of the accounting period, make a report on the inventory of merchandise.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
WhatsApp’s SMS Application: A Tool for Vocabulary Learning

Since this world is progressing and the new generation is increasingly adopting mobile phones in their everyday lives, thus, WhatsApp media and other mobile applications are increasingly being used at educational institutions all around the world. Therefore, the current study investigated the WhatsApp SMS application’s use in the vocabulary learning in the engineering university, Sindh, Pakistan. The study used a mixed-mode quasi-experimental pre-test post-test research design, in which students' vocabulary ability was assessed before they participated in WhatsApp classes and subsequently through post-test after the experiment. Moreover, students' experiences were also documented through the analysis of SMS messages they posted on a WhatsApp group (during experiment). The quantitative data was analysed on SPSS and the findings of the study revealed that WhatsApp’s SMS application usage posited a significant difference in the enhancement of vocabulary of the experimental group of students. Moreover, students’ postings (SMSs) on WhatsApp group were analysed qualitatively following the thematic analysis. The qualitative analysis also revealed positive experiences of students concerning WhatsApp’s SMS application in learning vocabulary. The current study reveals the benefits of WhatsApp’s SMS application on tertiary students worldwide, as well as instructors, syllabus designers who develop courses for tertiary students, and policymakers who make educational policies, by instilling technology-enabled social media tools (specifically WhatsApp) in the ESL classroom context both nationally and internationally.
Academic Performance and Phil-IRI Result: Basis for Intervention Plan

This study aimed to determine the level of Academic Performance and Phil-IRI result of the Grade IV pupils from the four select schools in Villanueva South District and find the significant relationship of the Academic Performance and Phil-IRI Result and the findings was the basis for an Intervention Plan to be made.This study used the descriptive-correlational design presenting systematic organization of data.There were 173 Grade IV pupils as the respondents of the study during the School Year 2022 -2023.Results revealed that 68 or 39.31 percent are in the Satisfactory level in their Academic Performance which grades ranges between 80 -84.Moreover, 86 or 49.71 percent of them are in the Instructional Level in their Phil-IRI result.Thus, it showed that there is a significant relationship between the Academic Performance and PHIL-IRI result of the pupils.With this, an Intervention Plan was exhibited in this study to improve the Academic Performance and Reading Literacy of the pupils.The Intervention Plan were presented during the In-Service Training to the four schools included in this study.It should be emphasized that the Phil-IRI test result has something to do with the academic performance of the pupils.
Extraction of Malay Root Word that Starts with Letter P in Malay e-Khutbah using Rule Based

Stemming is an important process in text processing especially in Natural Language Processing (NLP). It could extract root word from the affix words in the text. In addition, it helps in extracting useful information that contributes to many area of research study such as Information Retrieval. Several stemming algorithms have been discussed in previous studies. However, there are limited studies on Malay stemming process and the number of experimental data used. In this study, we focus on stemming process of Malay stemming algorithm by using rule-based algorithm for a larger dataset of Malay language text. The syntactic linguistic rule-based method was used in the stemming process involves of removing prefixes, suffixes and, prefixes and suffixes. Training dataset was used in this study which consisted of 3233 sentences from e-khutbah text. The result of the experimental evaluation was done by measuring the precision, recall and f-measure. It was found that the algorithm used in this study showed a promising result based on total of dataset used for each test. The value of precision, recall and F-measure increase to 95%, 97% and 97% respectively. The enhancement of the stemming process has shown a significant impact on Malay text processing which in general improved the performance of NLP applications.
The Rise of the Chatbots

How do we keep track of the truth when bots are becoming increasingly skilled liars?
Fitness test of vehicles using CNN.

&lt;p&gt;Fitness test of vehicles using CNN.&lt;/p&gt;
Characteristics and Operations of Complex Fuzzy Graphs

In this paper, Complex Fuzzy Graph (CFG) analyzed and introduced new concepts in CFG such as Spanning CFG, Complete CFG, path, arc, length, connected, strongest path and weakest path of CFG. We derived some properties of self complementary CFG and defined the operations on direct product, Semi strong product and strong product of CFG. Derived Isomorphic CFG with example is given in this paper. Moreover we introduced density of the graph and balanced complex fuzzy graph.
MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

Consistent hashing is used in distributed systems and networking applications to spread data evenly and efficiently across a cluster of nodes.In this paper, we present MementoHash, a novel consistent hashing algorithm that eliminates known limitations of state-of-the-art algorithms while keeping optimal performance and minimal memory usage.We describe the algorithm in detail, provide a pseudo-code implementation, and formally establish its solid theoretical guarantees.To measure the efficacy of MementoHash, we compare its performance, in terms of memory usage and lookup time, to that of state-of-theart algorithms, namely, AnchorHash, DxHash, and JumpHash.Unlike JumpHash, MementoHash can handle random failures.Moreover, MementoHash does not require fixing the overall capacity of the cluster (as AnchorHash and DxHash do), allowing it to scale indefinitely.The number of removed nodes affects the performance of all the considered algorithms.Therefore, we conduct experiments considering three different scenarios: stable (no removed nodes), one-shot removals (90% of the nodes removed at once), and incremental removals.We report experimental results that averaged a varying number of nodes from ten to one million.Results indicate that our algorithm shows optimal lookup performance and minimal memory usage in its best-case scenario.It behaves better than AnchorHash and DxHash in its average-case scenario and at least as well as those two algorithms in its worst-case scenario.However, the worstcase scenario for MementoHash occurs when more than 70% of the nodes fail, which describes a unlikely scenario.Therefore, MementoHash shows the best performance during the regular life cycle of a cluster.
Navigating Infection Control Processes in a COVID-19 Only Safety-Net Hospital at the Height of the Pandemic

Hospitals faced extraordinary challenges during the pandemic. Some of these were directly related to patient care—expanding capacities, adjusting services, and using new knowledge to save lives in a dynamically changing situation. Other challenges were regulatory. The COVID-19 pandemic significantly disrupted routine hospital infection control practices. We report the results of an interview study with 13 individuals associated with infection control in a small independent hospital. We employed the Systems Engineering Initiative for Patient Safety (SEIPS) model as a theoretical framework and as a basis to analyze data. The findings revealed how routine practices and protocols were displaced in notable ways. Due to COVID-19, clinical activities were modified, and the increased demands of regulatory reporting became laborious, and punitive if reports were late. Strategies are needed to mitigate increases in healthcare-associated infections. Our examination of the information flows, transformation, and needs shows areas in which digital tool creation and the use of a trained informatics workforce could ameliorate and automate many processes.