Reviewerly/exHarmony huggingface dataset:
Patch-based adaptive temporal filter and residual evaluation

In coherent imaging systems, speckle is a signal-dependent noise that visually strongly degrades images' appearance. A huge amount of SAR data has been acquired from different sensors with different wavelengths, resolutions, incidences and polarizations. We extend the nonlocal filtering strategy to the temporal domain and propose a patch-based adaptive temporal filter (PATF) to take advantage of well-registered multi-temporal SAR images. A patch-based generalised likelihood ratio test is processed to suppress the changed object effects on the multitemporal denoising results. Then, the similarities are transformed into corresponding weights with an exponential function. The denoised value is calculated with a temporal weighted average. Spatial adaptive denoising methods can improve the patch-based weighted temporal average image when the time series is limited. The spatial adaptive denoising step is optional when the time series is large enough. Without reference image, we propose using a patch-based auto-covariance residual evaluation method to examine the ratio image between the noisy and denoised images and look for possible remaining structural contents. It can process automatically and does not rely on a supervised selection of homogeneous regions. It also provides a global score for the whole image. Numerous results demonstrate the effectiveness of the proposed time series denoising method and the usefulness of the residual evaluation method.
MCMC-driven learning

This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chain Monte Carlo (MCMC) and machine learning$\unicode{x2014}$which includes black-box variational inference, adaptive MCMC, normalizing flow construction and transport-assisted MCMC, surrogate-likelihood MCMC, coreset construction for MCMC with big data, Markov chain gradient descent, Markovian score climbing, and more$\unicode{x2014}$within one common framework. By doing so, the theory and methods developed for each may be translated and generalized.
Hand Shape and Gesture Recognition using Multiscale Template Matching,  Background Subtraction and Binary Image Analysis

This paper presents a hand shape classification approach employing multiscale template matching. The integration of background subtraction is utilized to derive a binary image of the hand object, enabling the extraction of key features such as centroid and bounding box. The methodology, while simple, demonstrates effectiveness in basic hand shape classification tasks, laying the foundation for potential applications in straightforward human-computer interaction scenarios. Experimental results highlight the system's capability in controlled environments.
Improving Non-autoregressive Machine Translation with Error Exposure and  Consistency Regularization

Being one of the IR-NAT (Iterative-refinemennt-based NAT) frameworks, the Conditional Masked Language Model (CMLM) adopts the mask-predict paradigm to re-predict the masked low-confidence tokens. However, CMLM suffers from the data distribution discrepancy between training and inference, where the observed tokens are generated differently in the two cases. In this paper, we address this problem with the training approaches of error exposure and consistency regularization (EECR). We construct the mixed sequences based on model prediction during training, and propose to optimize over the masked tokens under imperfect observation conditions. We also design a consistency learning method to constrain the data distribution for the masked tokens under different observing situations to narrow down the gap between training and inference. The experiments on five translation benchmarks obtains an average improvement of 0.68 and 0.40 BLEU scores compared to the base models, respectively, and our CMLMC-EECR achieves the best performance with a comparable translation quality with the Transformer. The experiments results demonstrate the effectiveness of our method.
GPT-4's assessment of its performance in a USMLE-based case study

This study investigates GPT-4's assessment of its performance in healthcare applications. A simple prompting technique was used to prompt the LLM with questions taken from the United States Medical Licensing Examination (USMLE) questionnaire and it was tasked to evaluate its confidence score before posing the question and after asking the question. The questionnaire was categorized into two groups-questions with feedback (WF) and questions with no feedback(NF) post-question. The model was asked to provide absolute and relative confidence scores before and after each question. The experimental findings were analyzed using statistical tools to study the variability of confidence in WF and NF groups. Additionally, a sequential analysis was conducted to observe the performance variation for the WF and NF groups. Results indicate that feedback influences relative confidence but doesn't consistently increase or decrease it. Understanding the performance of LLM is paramount in exploring its utility in sensitive areas like healthcare. This study contributes to the ongoing discourse on the reliability of AI, particularly of LLMs like GPT-4, within healthcare, offering insights into how feedback mechanisms might be optimized to enhance AI-assisted medical education and decision support.
Distribution-Free Rates in Neyman-Pearson Classification

We consider the problem of Neyman-Pearson classification which models unbalanced classification settings where error w.r.t. a distribution $\mu_1$ is to be minimized subject to low error w.r.t. a different distribution $\mu_0$. Given a fixed VC class $\mathcal{H}$ of classifiers to be minimized over, we provide a full characterization of possible distribution-free rates, i.e., minimax rates over the space of all pairs $(\mu_0, \mu_1)$. The rates involve a dichotomy between hard and easy classes $\mathcal{H}$ as characterized by a simple geometric condition, a three-points-separation condition, loosely related to VC dimension.
Persuading a Learning Agent

We study a repeated Bayesian persuasion problem (and more generally, any generalized principal-agent problem with complete information) where the principal does not have commitment power and the agent uses algorithms to learn to respond to the principal's signals. We reduce this problem to a one-shot generalized principal-agent problem with an approximately-best-responding agent. This reduction allows us to show that: if the agent uses contextual no-regret learning algorithms, then the principal can guarantee a utility that is arbitrarily close to the principal's optimal utility in the classic non-learning model with commitment; if the agent uses contextual no-swap-regret learning algorithms, then the principal cannot obtain any utility significantly more than the optimal utility in the non-learning model with commitment. The difference between the principal's obtainable utility in the learning model and the non-learning model is bounded by the agent's regret (swap-regret). If the agent uses mean-based learning algorithms (which can be no-regret but not no-swap-regret), then the principal can do significantly better than the non-learning model. These conclusions hold not only for Bayesian persuasion, but also for any generalized principal-agent problem with complete information, including Stackelberg games and contract design.
Region Feature Descriptor Adapted to High Affine Transformations

To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially categorizes images with different affine degrees to simulate affine transformations and generate a new set of images. Subsequently, it calculates neighborhood information for feature points on this new image set. Finally, the descriptor is generated by combining the grayscale histogram of the maximum stable extremal region to which the feature point belongs and the normalized position relative to the grayscale centroid of the feature point's region. Experimental results, comparing feature matching metrics under affine transformation scenarios, demonstrate that the proposed descriptor exhibits higher precision and robustness compared to existing classical descriptors. Additionally, it shows robustness when integrated with other descriptors.
Efficient $\Phi$-Regret Minimization with Low-Degree Swap Deviations in  Extensive-Form Games

Recent breakthrough results by Dagan, Daskalakis, Fishelson and Golowich [2023] and Peng and Rubinstein [2023] established an efficient algorithm attaining at most $\epsilon$ swap regret over extensive-form strategy spaces of dimension $N$ in $N^{\tilde O(1/\epsilon)}$ rounds. On the other extreme, Farina and Pipis [2023] developed an efficient algorithm for minimizing the weaker notion of linear-swap regret in $\mathsf{poly}(N)/\epsilon^2$ rounds. In this paper, we take a step toward bridging the gap between those two results. We introduce the set of $k$-mediator deviations, which generalize the untimed communication deviations recently introduced by Zhang, Farina and Sandholm [2024] to the case of having multiple mediators. We develop parameterized algorithms for minimizing the regret with respect to this set of deviations in $N^{O(k)}/\epsilon^2$ rounds. This closes the gap in the sense that $k=1$ recovers linear swap regret, while $k=N$ recovers swap regret. Moreover, by relating $k$-mediator deviations to low-degree polynomials, we show that regret minimization against degree-$k$ polynomial swap deviations is achievable in $N^{O(kd)^3}/\epsilon^2$ rounds, where $d$ is the depth of the game, assuming constant branching factor. For a fixed degree $k$, this is polynomial for Bayesian games and quasipolynomial more broadly when $d = \mathsf{polylog} N$ -- the usual balancedness assumption on the game tree.
Studying links via plats: split and composite links

Our main results concern changing an arbitrary plat presentation of a split or composite link to one which is obviously recognizable as being split or composite. Pocket moves, first described in \cite{unlinkviaplats}, are utilized -- a pocket move alters a plat presentation without changing its link type, its bridge index or the double coset. A plat presentation of a split link is split if the planar projection of the plat presentation is not connected. We prove that pocket moves are the only obstruction to representing split links by split plat presentations. Since any pocket move corresponds to a sequence of double coset moves, we have the corollary that the double coset of every plat presentation of a split link has a split plat presentation. We obtain an analogous result for composite links by utilizing flip moves, which were also first described in the second author's work, arXiv:2308.00732 [math.GT].
Baking the First Business Computer

Abstract LEO 1 was a pioneering computer that powered a catering empire. The team preserving its memory has won an award for its innovative work. Martin Cooper MBCS reports.
Implementation of Talking Stick Learning Model to Improve English Learning Outcomes in Islamic Elementary School

In English subjects, students tend to be embarrassed because they feel inadequate. With a talking stick, students must actively do something that the teacher asks them to do when they get a stick. The objective study of this research is how the implementation of talking stick in English subject, and how the learning outcomes of English can increase. The subject of this research is student of MI An-Nuriyah, class 4. Through Classroom Action Research methods, by qualitative analyze method, and collecting data by interview, observation, and documentation conducted at MI An-Nuriyah Bangkalan, there were significant results, including In cycle I, the percentage of learning outcomes obtained by students was 58.33% and experienced a significant increase in cycle II, classically the percentage of student learning outcomes reached 83.33%, this shows that the application of the Talking Stick Learning Model was successfully tested in subjects English. In cycle II, student learning outcomes exceeded the expected success target, namely ?80%.
Tinted de Bruijn Graphs for efficient read extraction from sequencing datasets

The study of biological sequences often relies on using reference genomes, yet achieving accurate assemblies remains challenging. Consequently, de novo analysis directly from raw reads, without pre-processing, is frequently more practical. We identify a very commonly shared need across various applications: identifying reads containing a specific kmer in a dataset. This kmer-to-reads association would be pivotal in multiple contexts, including genotyping, bacterial strain resolution, profiling, data compression, error correction or assembly. While this challenge appears similar to the extensively researched colored de Bruijn graph problem, resolving it at the read level would be prohibitively resource-intensive in practical applications. In this work, we demonstrate its tractable resolution by leveraging certain assumptions for sequencing dataset indexing. To tackle this challenge, we introduce the Tinted de Bruijn Graph concept, an altered version of the colored de Bruijn graph where each read within a sequencing dataset represents a unique source. We developed K2R, a highly scalable index that implement such search efficiently within this framework. K2Rs performance, in terms of index size, memory footprint, throughput, and construction time, is benchmarked against leading methods, including hashing techniques (e.g., Short Read Connector) and full-text indexing (e.g., Spumoni and Movi), across various datasets. K2R consistently outperforms contemporary solutions in most metrics and is the only tool capable of scaling to larger datasets. To prove K2R scalability we indexed two human datasets of the T2T consortium: the 126X coverage ONT dataset was indexed in 18hours using 19GB of RAM for a final index of 9.5GB and the 56X coverage HiFi dataset was constructed in 90 minutes using 5Gb of RAM for a final index of 207Mb. The K2R index, developed in C++, is open source and available on Github github.com/LeaVandamme/K2R.
Review of the OSI Model and TCP/IP Protocol Suite on Modern Network Communication

Layered approaches to networking are possible. Network architects categorize protocols in order to simplify their designs. Each layer has its own protocol for talking to the outside world. Every component of the network implements some of the nth layer. Messages are sent back and forth between these parts. Layer n protocol data units [n-PDU] are the official name for these transmissions. All the steps necessary for efficient conversation are covered and these steps are organized into layers for easy comprehension. Layered architecture describes this kind of planning for a communications system. The OSI model is a framework for developing and implementing network-based software applications. It also serves as a basis for the development of new networking protocol hardware, and architectures. This document compares and contrasts the OSI Reference Model seven layers with those of the TCP/IP Model four. There are distinct roles at each successive level. All Internet communication duties may be traced back to the TCP/IP reference model.
Ensemble feature selection using weighted concatenated voting for text classification

Following the increasing number of high dimensional data, selecting relevant features has always been better handled by filter feature selection techniques due to its improved generalization, faster training time, dimensionality reduction, less prone to overfitting, and improved model performance. However, the most used feature selection methods are unstable; a feature selection method chooses different subsets of characteristics that produce different classification accuracy. Selecting an appropriate hybrid harnesses the local feature relevant to the discriminative power of filter methods for improved text classification, which is lacking in past literature. In this paper, we proposed a novel multi-univariate hybrid feature selection method (MUNIFES) for enhanced discriminative power between the features and the target class. The proposed method utilizes multi-iterative processes to select the best feature sets from each univariate feature selection method. MUNIFES has employed the ensemble of multi-filter discriminative strength of Chi-Square (Chi2), Analysis of Variance (ANOVA), and Infogain methods to select optimal feature subsets. To evaluate the success of the proposed method, several experiments were performed on the 20newsgroup dataset and its variant (17newsgroup) with 10 classifiers (including ensemble, classification and optimization algorithms, and Artificial Neural Network (ANN)), compared with the state-of-the-art feature selection methods. The MUNIFES results indicated a better accuracy classification performance.
PENINGKATAN PROMOSI DAN PENGARSIPAN DIGITAL SANGGAR SENI TARI DAN BUDAYA SAWO KECIK MELALUI PEMBUATAN WEBSITE AI

ABSTRACT This community service activity aimed to develop and launch an artificial intelligence (AI)-based website as a promotional media and digital archive for the Cirebon Sawo Kecik Dance and Culture Art Studio. This is due to the need for this dance studio to increase its exposure in the digital world and archive works of art. In creating this website, a needs analysis was carried out, website development with AI integration, as well as training for studio members. From the results of implementing this activity, it was found that an interactive website can accommodate event promotion, adjust content to visitor preferences, and collect digital archive data of works of art. This community service activity also helped dance studios increasing their visibility online. Therefore, the application of AI technology in the form of a website has helped the Sawo Kecik Dance and Cultural Arts Studio achieve the studio's digital promotion and archiving goals well.
Integrated Information Theory 4.0 is both weakly panpsychist and strongly dualist, but many theories of consciousness are also prone to it

The Integrated Information Theory (IIT) is one of the dominant scientiﬁc theories of consciousness. However, its foundations, mathematical formalism, and predictions on the neural correlates of consciousness are widely criticized in the last few years. A letter, signed by 124 neuroscientists, psychologists, and philosophers, argues that the theory is speculative and untestable to the degree that makes it pseudoscience. Their main concern is its panpsychist flavor. I argue that IIT 4.0’s account of free will is dualist rather than panpsychist. These features are not, however, specific for IIT exclusively. While being metaphysically hostile, panpsychism and dualism are inherent to many theories of consciousness (ToCs) because the former seems to be the most elegant answer to how to seamlessly integrate consciousness into the fabric of physical reality, whereas the latter proposes a straightforward solution to the free will problem. I also argue that a complete theory of consciousness must be inspired by the evolutionary theory of life, yet not being prone to conflate biological free will with the psychological sense of agency.
Dialogue Generation Model with Hierarchical Encoding and Semantic Segmentation of Dialogue Context

International Journal of Software Engineering and Knowledge EngineeringAccepted Papers No AccessDialogue Generation Model with Hierarchical Encoding and Semantic Segmentation of Dialogue ContextXiao Wei, Yidian Lin, and Qitao HuXiao Wei, Yidian Lin Search for more papers by this author , and Qitao Huhttps://orcid.org/0009-0009-8190-9796 Search for more papers by this author https://doi.org/10.1142/S0218194024400011Cited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles in C++ Programming! FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 30 July 2023 Accepted 5 February 2024 PDF download
Enhancing Multi-Layer Perceptron Performance with K-Means Clustering

Machine learning plays a crucial role in identifying patterns within data, with classification being a prominent application. This study investigates the use of Multilayer Perceptron (MLP) classification models and explores preprocessing techniques, particularly K-Means clustering, to enhance model performance. Overfitting, a common challenge in MLP models, is addressed through the application of K-Means clustering to streamline data preparation and improve classification accuracy. The study begins with an overview of overfitting in MLP models, highlighting the significance of mitigating this issue. Various techniques for addressing overfitting are reviewed, including regularization, dropout, early stopping, data augmentation, and ensemble methods. Additionally, the complementary role of K-Means clustering in enhancing model performance is emphasized. Preprocessing using K-Means clustering aims to reduce data complexity and prevent overfitting in MLP models. Three datasets - Iris, Wine, and Breast Cancer Wisconsin - are employed to evaluate the performance of K-Means as a preprocessing technique. Results from cross-validation demonstrate significant improvements in accuracy, precision, recall, and F1 scores when employing K-Means clustering compared to models without preprocessing. The findings highlight the efficacy of K-Means clustering in enhancing the discriminative power of MLP classification models by organizing data into clusters based on similarity. These results have practical implications, underlining the importance of appropriate preprocessing techniques in improving classification performance. Future research could explore additional preprocessing methods and their impact on classification accuracy across diverse datasets, advancing the field of machine learning and its applications
Communities of Quantum Technologies: Stakeholder Identification, Legitimation, and Interaction

International Journal of Quantum InformationAccepted Papers No AccessCommunities of Quantum Technologies: Stakeholder Identification, Legitimation, and InteractionSteven Umbrello, Zeki C. Seskir, and Pieter E. VermaasSteven Umbrello, Zeki C. Seskir Search for more papers by this author , and Pieter E. Vermaashttps://orcid.org/0000-0002-8175-9179 Search for more papers by this author https://doi.org/10.1142/S0219749924500126Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out Annual Physics Catalogue 2019 and recommend us to your library! FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 25 October 2023 Accepted 8 February 2024 PDF download
Research on Cloud Computing

Abstract: Cloud computing represents a pivotal advancement in the IT sector, offering efficient management and distribution of vast amounts of data and resources across the internet. Essentially, it enables accessing IT infrastructure over a network without local installations. Businesses benefit from the flexibility to adjust resource levels according to operational demands, leading to reduced infrastructure costs. Additionally, cloud computing facilitates faster application testing, streamlined management, and adaptable resource allocation. Its widespread application across various contexts underscores its significance in daily life. This paper comprehensively examines cloud computing, encompassing its architecture, characteristics, varieties, service models, advantages, and challenges.