Reviewerly/exHarmony huggingface dataset:
Non-classical correlations between a quantum probe and complex quantum  systems in presence of noise

Authors:
raw_author_name: Mathew, Bijoy John
raw_author_name: Srivastava, Sanchit
raw_author_name: Shaji, Anil

Abstract: Non-classical correlations generated within a quantum probe system when it interacts with a large, macroscopic system can signal the presence of quantum features in the latter. Theoretical models have considered how entanglement generated in photosynthetic bacteria can be probed using light that interacts with them. More recently, a tardigrade was entangled to a transmon qubit. We consider a detailed model including noise for such systems wherein a small quantum probe interacts with a large system in order to delineate the regimes with respect to couping strengths and noise levels in which such signatures of quantumness in macroscopic systems can realistically be detected.
On the Potential of Network-Based Features for Fraud Detection

Authors:
raw_author_name: van Zeelt, Mickey

Abstract: Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace with evolving fraud tactics, leading to high false positive rates and missed detections. Machine learning techniques offer a promising solution by leveraging historical data to identify fraudulent patterns. This article explores using the personalised PageRank (PPR) algorithm to capture the social dynamics of fraud by analysing relationships between financial accounts. The primary objective is to compare the performance of traditional features with the addition of PPR in fraud detection models. Results indicate that integrating PPR enhances the model's predictive power, surpassing the baseline model. Additionally, the PPR feature provides unique and valuable information, evidenced by its high feature importance score. Feature stability analysis confirms consistent feature distributions across training and test datasets.
On the need for effective tools for debugging quantum programs

Authors:
raw_author_name: Di Matteo, Olivia

Abstract: The ability to incorporate quantum phenomena in computing unlocks a host of new ways to make mistakes. This work surveys existing studies and approaches to debugging quantum programs. It then presents a set of examples that stem from first-hand experience, intended to motivate future research on the subject and the development of novel tools and techniques.
Cryptomite: A versatile and user-friendly library of randomness  extractors

Authors:
raw_author_name: Foreman, Cameron
raw_author_name: Yeung, Richie
raw_author_name: Edgington, Alec
raw_author_name: Curchod, Florian J.

Abstract: We present Cryptomite, a Python library of randomness extractor implementations. The library offers a range of two-source, seeded and deterministic randomness extractors, together with parameter calculation modules, making it easy to use and suitable for a variety of applications. We also present theoretical results, including new extractor constructions and improvements to existing extractor parameters. The extractor implementations are efficient in practice and tolerate input sizes of up to $2^{40} > 10^{12}$ bits. They are also numerically precise (implementing convolutions using the Number Theoretic Transform to avoid floating point arithmetic), making them well suited to cryptography. The algorithms and parameter calculation are described in detail, including illustrative code examples and performance benchmarking.
Biased Estimator Channels for Classical Shadows

Authors:
raw_author_name: Cai, Zhenyu
raw_author_name: Jnane, Hamza
raw_author_name: Koczor, Bálint

Abstract: Extracting classical information from quantum systems is of fundamental importance, and classical shadows allow us to extract a large amount of information using relatively few measurements. Conventional shadow estimators are unbiased and thus approach the true mean in the infinite-sample limit. In this work, we consider a biased scheme, intentionally introducing a bias by rescaling the conventional classical shadows estimators can reduce the error in the finite-sample regime. The approach is straightforward to implement and requires no quantum resources. We analytically prove average case as well as worst- and best-case scenarios, and rigorously prove that it is, in principle, always worth biasing the estimators. We illustrate our approach in a quantum simulation task of a $12$-qubit spin-ring problem and demonstrate how estimating expected values of non-local perturbations can be significantly more efficient using our biased scheme.
PANORAMIA: Privacy Auditing of Machine Learning Models without  Retraining

Authors:
raw_author_name: Lautraite, Hadrien
raw_author_name: Gambs, Sébastien
raw_author_name: Lécuyer, Mathias

Abstract: We introduce a privacy auditing scheme for ML models that relies on membership inference attacks using generated data as "non-members". This scheme, which we call PANORAMIA, quantifies the privacy leakage for large-scale ML models without control of the training process or model re-training and only requires access to a subset of the training data. To demonstrate its applicability, we evaluate our auditing scheme across multiple ML domains, ranging from image and tabular data classification to large-scale language models.
The Manifold Density Function: An Intrinsic Method for the Validation of  Manifold Learning

Authors:
raw_author_name: Schupbach, Jordan
raw_author_name: Fasy, Brittany Terese
raw_author_name: Rieck, Bastian

Abstract: We introduce the manifold density function, which is an intrinsic method to validate manifold learning techniques. Our approach adapts and extends Ripley's $K$-function, and categorizes in an unsupervised setting the extent to which an output of a manifold learning algorithm captures the structure of a latent manifold. Our manifold density function generalizes to broad classes of Riemannian manifolds. In particular, we extend the manifold density function to general two-manifolds using the Gauss-Bonnet theorem, and demonstrate that the manifold density function for hypersurfaces is well approximated using the first Laplacian eigenvalue. We prove desirable convergence and robustness properties.
Reward Poisoning Attack Against Offline Reinforcement Learning

Authors:
raw_author_name: Singh, Gagandeep

Abstract: We study the problem of reward poisoning attacks against general offline reinforcement learning with deep neural networks for function approximation. We consider a black-box threat model where the attacker is completely oblivious to the learning algorithm and its budget is limited by constraining both the amount of corruption at each data point, and the total perturbation. We propose an attack strategy called `policy contrast attack'. The high-level idea is to make some low-performing policies appear as high-performing while making high-performing policies appear as low-performing. To the best of our knowledge, we propose the first black-box reward poisoning attack in the general offline RL setting. We provide theoretical insights on the attack design and empirically show that our attack is efficient against current state-of-the-art offline RL algorithms in different kinds of learning datasets.
Patch-based adaptive temporal filter and residual evaluation

Authors:
raw_author_name: Deledalle, Charles-Alban
raw_author_name: Nicolas, Jean-Marie
raw_author_name: Tupin, Florence

Abstract: 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

Authors:
raw_author_name: Bouchard-Côté, Alexandre
raw_author_name: Campbell, Trevor
raw_author_name: Pleiss, Geoff
raw_author_name: Surjanovic, Nikola

Abstract: 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.
Improving Non-autoregressive Machine Translation with Error Exposure and  Consistency Regularization

Authors:
raw_author_name: Chen, Xinran
raw_author_name: Duan, Sufeng
raw_author_name: Liu, Gongshen

Abstract: 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

Authors:
raw_author_name: Lamichhane, Bishal

Abstract: 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

Authors:
raw_author_name: Kalan, Mohammadreza M.
raw_author_name: Kpotufe, Samory

Abstract: 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.
Region Feature Descriptor Adapted to High Affine Transformations

Authors:
raw_author_name: Zhang, Shaojie
raw_author_name: Wang, Yinghui
raw_author_name: Liu, Peixuan
raw_author_name: Yang, Jinlong
raw_author_name: Yan, Tao
raw_author_name: Huang, Liangyi
raw_author_name: Wang, Mingfeng

Abstract: 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

Authors:
raw_author_name: Anagnostides, Ioannis
raw_author_name: Farina, Gabriele
raw_author_name: Sandholm, Tuomas

Abstract: 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.
Implementation of Talking Stick Learning Model to Improve English Learning Outcomes in Islamic Elementary School

Authors:
raw_author_name: Nurul Makrifah
raw_affiliation_string: STIT Al-Ibrohimy Bangkalan, Indonesia
raw_author_name: Nur Fauzi
raw_affiliation_string: STAI Syaichona Moh. Cholil Bangkalan, Indonesia

Abstract: 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

Authors:
raw_author_name: Bastien Cazaux
raw_affiliation_string: UMR9189 CRIStAL, Univ Lille, CNRS, Centrale, F-59000, Lille, France
raw_author_name: Antoine Limasset
raw_affiliation_string: CNRS

Abstract: 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.
Ensemble feature selection using weighted concatenated voting for text classification

Authors:
raw_author_name: Keng Hoon Gan
raw_affiliation_string: School of Computer Sciences, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang, Malaysia

Abstract: 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

Authors:
raw_author_name: Henny Suharyati
raw_affiliation_string: Universitas Pakuan

Abstract: 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

Authors:
raw_author_name: Sergey B. Yurchenko

Abstract: 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.