Reviewerly/exHarmony huggingface dataset:
TEXTRON: Weakly Supervised Multilingual Text Detection through Data  Programming

Authors:
raw_author_name: Kudale, Dhruv
raw_author_name: Subramanian, Venkatapathy
raw_author_name: Chaudhuri, Parag
raw_author_name: Ramakrishnan, Ganesh

Abstract: Several recent deep learning (DL) based techniques perform considerably well on image-based multilingual text detection. However, their performance relies heavily on the availability and quality of training data. There are numerous types of page-level document images consisting of information in several modalities, languages, fonts, and layouts. This makes text detection a challenging problem in the field of computer vision (CV), especially for low-resource or handwritten languages. Furthermore, there is a scarcity of word-level labeled data for text detection, especially for multilingual settings and Indian scripts that incorporate both printed and handwritten text. Conventionally, Indian script text detection requires training a DL model on plenty of labeled data, but to the best of our knowledge, no relevant datasets are available. Manual annotation of such data requires a lot of time, effort, and expertise. In order to solve this problem, we propose TEXTRON, a Data Programming-based approach, where users can plug various text detection methods into a weak supervision-based learning framework. One can view this approach to multilingual text detection as an ensemble of different CV-based techniques and DL approaches. TEXTRON can leverage the predictions of DL models pre-trained on a significant amount of language data in conjunction with CV-based methods to improve text detection in other languages. We demonstrate that TEXTRON can improve the detection performance for documents written in Indian languages, despite the absence of corresponding labeled data. Further, through extensive experimentation, we show improvement brought about by our approach over the current State-of-the-art (SOTA) models, especially for handwritten Devanagari text. Code and dataset has been made available at https://github.com/IITB-LEAP-OCR/TEXTRON
Dimension-free Structured Covariance Estimation

Authors:
raw_author_name: Puchkin, Nikita

Abstract: Given a sample of i.i.d. high-dimensional centered random vectors, we consider a problem of estimation of their covariance matrix $\Sigma$ with an additional assumption that $\Sigma$ can be represented as a sum of a few Kronecker products of smaller matrices. Under mild conditions, we derive the first non-asymptotic dimension-free high-probability bound on the Frobenius distance between $\Sigma$ and a widely used penalized permuted least squares estimate. Because of the hidden structure, the established rate of convergence is faster than in the standard covariance estimation problem.
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation

Authors:
raw_author_name: Yuan, Huizhuo
raw_author_name: Chen, Zixiang
raw_author_name: Gu, Quanquan

Abstract: Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tuning Large Language Models (LLMs). While cutting-edge diffusion models such as Stable Diffusion (SD) and SDXL rely on supervised fine-tuning, their performance inevitably plateaus after seeing a certain volume of data. Recently, reinforcement learning (RL) has been employed to fine-tune diffusion models with human preference data, but it requires at least two images ("winner" and "loser" images) for each text prompt. In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion), where the diffusion model engages in competition with its earlier versions, facilitating an iterative self-improvement process. Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment. Our experiments on the Pick-a-Pic dataset reveal that SPIN-Diffusion outperforms the existing supervised fine-tuning method in aspects of human preference alignment and visual appeal right from its first iteration. By the second iteration, it exceeds the performance of RLHF-based methods across all metrics, achieving these results with less data.
$f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive  Learning

Authors:
raw_author_name: Sun, Sun
raw_author_name: Yu, Yaoliang

Abstract: In self-supervised contrastive learning, a widely-adopted objective function is InfoNCE, which uses the heuristic cosine similarity for the representation comparison, and is closely related to maximizing the Kullback-Leibler (KL)-based mutual information. In this paper, we aim at answering two intriguing questions: (1) Can we go beyond the KL-based objective? (2) Besides the popular cosine similarity, can we design a better similarity function? We provide answers to both questions by generalizing the KL-based mutual information to the $f$-Mutual Information in Contrastive Learning ($f$-MICL) using the $f$-divergences. To answer the first question, we provide a wide range of $f$-MICL objectives which share the nice properties of InfoNCE (e.g., alignment and uniformity), and meanwhile result in similar or even superior performance. For the second question, assuming that the joint feature distribution is proportional to the Gaussian kernel, we derive an $f$-Gaussian similarity with better interpretability and empirical performance. Finally, we identify close relationships between the $f$-MICL objective and several popular InfoNCE-based objectives. Using benchmark tasks from both vision and natural language, we empirically evaluate $f$-MICL with different $f$-divergences on various architectures (SimCLR, MoCo, and MoCo v3) and datasets. We observe that $f$-MICL generally outperforms the benchmarks and the best-performing $f$-divergence is task and dataset dependent.
Large Scale Constrained Clustering With Reinforcement Learning

Authors:
raw_author_name: Schesch, Benedikt

Abstract: Given a network, allocating resources at clusters level, rather than at each node, enhances efficiency in resource allocation and usage. In this paper, we study the problem of finding fully connected disjoint clusters to minimize the intra-cluster distances and maximize the number of nodes assigned to the clusters, while also ensuring that no two nodes within a cluster exceed a threshold distance. While the problem can easily be formulated using a binary linear model, traditional combinatorial optimization solvers struggle when dealing with large-scale instances. We propose an approach to solve this constrained clustering problem via reinforcement learning. Our method involves training an agent to generate both feasible and (near) optimal solutions. The agent learns problem-specific heuristics, tailored to the instances encountered in this task. In the results section, we show that our algorithm finds near optimal solutions, even for large scale instances.
How Much Does Each Datapoint Leak Your Privacy? Quantifying the  Per-datum Membership Leakage

Authors:
raw_author_name: Azize, Achraf
raw_author_name: Basu, Debabrota

Abstract: We study the per-datum Membership Inference Attacks (MIAs), where an attacker aims to infer whether a fixed target datum has been included in the input dataset of an algorithm and thus, violates privacy. First, we define the membership leakage of a datum as the advantage of the optimal adversary targeting to identify it. Then, we quantify the per-datum membership leakage for the empirical mean, and show that it depends on the Mahalanobis distance between the target datum and the data-generating distribution. We further assess the effect of two privacy defences, i.e. adding Gaussian noise and sub-sampling. We quantify exactly how both of them decrease the per-datum membership leakage. Our analysis builds on a novel proof technique that combines an Edgeworth expansion of the likelihood ratio test and a Lindeberg-Feller central limit theorem. Our analysis connects the existing likelihood ratio and scalar product attacks, and also justifies different canary selection strategies used in the privacy auditing literature. Finally, our experiments demonstrate the impacts of the leakage score, the sub-sampling ratio and the noise scale on the per-datum membership leakage as indicated by the theory.
Feature Accentuation: Revealing 'What' Features Respond to in Natural  Images

Authors:
raw_author_name: Hamblin, Chris
raw_author_name: Fel, Thomas
raw_author_name: Konkle, Talia
raw_author_name: Alvarez, George

Abstract: Efforts to decode neural network vision models necessitate a comprehensive grasp of both the spatial and semantic facets governing feature responses within images. Most research has primarily centered around attribution methods, which provide explanations in the form of heatmaps, showing where the model directs its attention for a given feature. However, grasping 'where' alone falls short, as numerous studies have highlighted the limitations of those methods and the necessity to understand 'what' the model has recognized at the focal point of its attention. In parallel, 'Feature visualization' offers another avenue for interpreting neural network features. This approach synthesizes an optimal image through gradient ascent, providing clearer insights into 'what' features respond to. However, feature visualizations only provide one global explanation per feature; they do not explain why features activate for particular images. In this work, we introduce a new method to the interpretability tool-kit, 'feature accentuation', which is capable of conveying both where and what in arbitrary input images induces a feature's response. At its core, feature accentuation is image-seeded (rather than noise-seeded) feature visualization. We find a particular combination of parameterization, augmentation, and regularization yields naturalistic visualizations that resemble the seed image and target feature simultaneously. Furthermore, we validate these accentuations are processed along a natural circuit by the model. We make our precise implementation of feature accentuation available to the community as the Faccent library, an extension of Lucent.
TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and  Agent Generation

Authors:
raw_author_name: Wu, Zhiyong
raw_author_name: Yao, Junfeng
raw_author_name: Su, Jinsong

Abstract: The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks. However, these agents often struggle during task execution due to methodological constraints, such as error propagation and limited adaptability. To address this issue, we propose a multi-agent framework based on dynamic Task Decomposition and Agent Generation (TDAG). This framework dynamically decomposes complex tasks into smaller subtasks and assigns each to a specifically generated subagent, thereby enhancing adaptability in diverse and unpredictable real-world tasks. Simultaneously, existing benchmarks often lack the granularity needed to evaluate incremental progress in complex, multi-step tasks. In response, we introduce ItineraryBench in the context of travel planning, featuring interconnected, progressively complex tasks with a fine-grained evaluation system. ItineraryBench is designed to assess agents' abilities in memory, planning, and tool usage across tasks of varying complexity. Our experimental results reveal that TDAG significantly outperforms established baselines, showcasing its superior adaptability and context awareness in complex task scenarios.
Density of identifying codes of hexagonal grids with finite number of rows

Authors:
raw_author_name: Rudini Sampaio
raw_author_name: Gabriel A. G. Sobral
raw_author_name: Yoshiko Wakabayashi

Abstract: In a graph $G$, a set $C\subseteq V(G)$ is an identifying code if, for all vertices $v$ in $G$, the sets $N[v]\cap C$ are all nonempty and pairwise distinct, where $N[v]$ denotes the closed neighbourhood of $v$. We focus on the minimum density of identifying codes of infinite hexagonal grids $H_k$ with $k$ rows, denoted by $d^*(H_k)$, and present optimal solutions for $k\leq 5$. Using discharging method, we also prove a lower bound in terms of maximum degree for the minimum-density identifying codes of well-behaved infinite graphs. We prove that $d^*(H_2)=9/20$, $d^*(H_3)=6/13\approx 0.4615$, $d^*(H_4)=7/16=0.4375$ and $d^*(H_5)=11/25=0:44$. We also prove that $H_2$ has a unique periodic identifying code with minimum density.
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games

Authors:
raw_author_name: Liu, Liangqi
raw_author_name: Liang, Hao
raw_author_name: Luo, Zhi-Quan

Abstract: Many real-world problems involving multiple decision-makers can be modeled as an unknown game characterized by partial observations. Addressing the challenges posed by partial information and the curse of multi-agency, we developed Thompson sampling-type algorithms, leveraging information about opponent's action and reward structures. Our approach significantly reduces experimental budgets, achieving a more than tenfold reduction compared to baseline algorithms in practical applications like traffic routing and radar sensing. We demonstrate that, under certain assumptions about the reward structure, the regret bound exhibits merely a logarithmic dependence on the total action space size, effectively mitigating the curse of multi-agency. Additionally, this research introduces the Optimism-then-NoRegret framework, a novel contribution that integrates both our proposed methodologies and existing algorithms in the field.
Quantum state discrimination enhanced by path signature

Authors:
raw_author_name: Cao, Shuxiang
raw_author_name: Zheng, Jian-Qing
raw_author_name: Alghadeer, Mohammed
raw_author_name: Fasciati, Simone D
raw_author_name: Bakr, Mustafa
raw_author_name: Lyons, Terry
raw_author_name: Leek, Peter

Abstract: Quantum state discrimination plays an essential role in quantum technology, crucial for quantum error correction, metrology, and sensing. While conventional methods rely on integrating readout signals or classifying raw signals, we developed a method to extract information about state transitions during readout, based on the path signature method, a tool for analyzing stochastic time series. The hardware experiments demonstrate an improvement in transmon qutrit state readout fidelity from 85.9 $\pm$ 1.0% to 91.0 $\pm$ 0.5%, without the need for additional hardware. This method has the potential to become a foundational tool for quantum technology.
A System-Level Dynamic Binary Translator using Automatically-Learned  Translation Rules

Authors:
raw_author_name: Yang, Zhaohui
raw_author_name: Wang, Wenwen
raw_author_name: Yew, Pen-Chung
raw_author_name: Zhang, Weihua

Abstract: System-level emulators have been used extensively for system design, debugging and evaluation. They work by providing a system-level virtual machine to support a guest operating system (OS) running on a platform with the same or different native OS that uses the same or different instruction-set architecture. For such system-level emulation, dynamic binary translation (DBT) is one of the core technologies. A recently proposed learning-based DBT approach has shown a significantly improved performance with a higher quality of translated code using automatically learned translation rules. However, it has only been applied to user-level emulation, and not yet to system-level emulation. In this paper, we explore the feasibility of applying this approach to improve system-level emulation, and use QEMU to build a prototype. ... To achieve better performance, we leverage several optimizations that include coordination overhead reduction to reduce the overhead of each coordination, and coordination elimination and code scheduling to reduce the coordination frequency. Experimental results show that it can achieve an average of 1.36X speedup over QEMU 6.1 with negligible coordination overhead in the system emulation mode using SPEC CINT2006 as application benchmarks and 1.15X on real-world applications.
A Morpho-syntactic Analysis of Human-moderated Hate Speech Samples from Wykop.pl Web Service

Authors:
raw_author_name: Inez Okulska
raw_affiliation_string: NASK National Research Institute, Warszawa, Poland
raw_author_name: Anna Kołos
raw_affiliation_string: NASK National Research Institute, Warszawa, Poland

Abstract: The dynamic increase in user-generated content on the web presents significant challenges in protecting Internet users from exposure to offensive material, such as cyberbullying and hate speech, while also minimizing the spread of wrongful conduct. However, designing automated detection models for such offensive content remains complex, particularly in languages with limited publicly available data. To address this issue, our research collaborates with the Wykop.pl web service to fine-tune a model using genuine content that has been banned by professional moderators. In this paper, we focus on the Polish language and discuss the notion of datasets and annotation frameworks, presenting our stylometric analysis of Wykop.pl content to identify morpho-syntactic structures that are commonly applied in cyberbullying and hate speech. By doing so, we contribute to the ongoing discussion on offensive language and hate speech in sociolinguistic studies, emphasizing the need to consider user-generated online content.
Introduction to 3D Deep Learning

Authors:
raw_author_name: Xulei Yang
raw_author_name: Xiao Li
raw_author_name: Hao Su

Abstract: World Scientific Annual Review of Artificial IntelligenceAccepted Papers No AccessIntroduction to 3D Deep LearningXulei Yang, Xiao Li, and Hao SuXulei Yang, Xiao Li Search for more papers by this author , and Hao Su Search for more papers by this author https://doi.org/10.1142/S2811032324300019Cited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 8 November 2023 Accepted 13 February 2024 PDF download
The Effects of Artificial Intelligence (AI) on Marketing

Authors:
raw_author_name: Ana Filipa Vieira Lopes Joaquim
raw_affiliation_string: ISLA Santarém, Portugal

Abstract: This chapter explores how AI is transforming marketing strategies, customer segmentation, personalized advertising, and customer relationship management (CRM). Additionally, it investigates the ethical considerations and challenges associated with AI implementation in marketing.
The Role of Digital Transformation and Its Effects on the Education Sector

Authors:
raw_author_name: Amrik Singh
raw_affiliation_string: Lovely Professional University, India

Abstract: Digitization can be termed as an improvement in education as it refers to the modernization of business models or different activities related to education. The word “digitalization” refers to the transformation of information or other information of the joint into a highly organized plan as a blueprint. The COVID-19 outbreak also affected the world economy and put pressure on education. With the plague spreading so quickly, there are few plans to switch to online learning or face-to-face classes, and no one seems to anticipate the dangers and opportunities that come with it. There is a sudden change in business, too. The consequences are huge, as teachers now hope to help eliminate learning and education. However, PostCorona 19 online learning and education is probably not what the Ministry of Education promotes. Few schools were aware of the new system in the past, but its widespread adoption has led most schools, colleges, and universities to become familiar with the importance of high rebellion in their learning environments.
A StrongREJECT for Empty Jailbreaks

Authors:
raw_author_name: Abbeel, Pieter
raw_author_name: Svegliato, Justin
raw_author_name: Emmons, Scott
raw_author_name: Watkins, Olivia
raw_author_name: Toyer, Sam

Abstract: The rise of large language models (LLMs) has drawn attention to the existence of "jailbreaks" that allow the models to be used maliciously. However, there is no standard benchmark for measuring the severity of a jailbreak, leaving authors of jailbreak papers to create their own. We show that these benchmarks often include vague or unanswerable questions and use grading criteria that are biased towards overestimating the misuse potential of low-quality model responses. Some jailbreak techniques make the problem worse by decreasing the quality of model responses even on benign questions: we show that several jailbreaking techniques substantially reduce the zero-shot performance of GPT-4 on MMLU. Jailbreaks can also make it harder to elicit harmful responses from an "uncensored" open-source model. We present a new benchmark, StrongREJECT, which better discriminates between effective and ineffective jailbreaks by using a higher-quality question set and a more accurate response grading algorithm. We show that our new grading scheme better accords with human judgment of response quality and overall jailbreak effectiveness, especially on the sort of low-quality responses that contribute the most to over-estimation of jailbreak performance on existing benchmarks. We release our code and data at https://github.com/alexandrasouly/strongreject.
LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs

Authors:
raw_author_name: Yamanaka, Yuuki

Abstract: System logs are some of the most important information for the maintenance of software systems, which have become larger and more complex in recent years. The goal of log-based anomaly detection is to automatically detect system anomalies by analyzing the large number of logs generated in a short period of time, which is a critical challenge in the real world. Previous studies have used a log parser to extract templates from unstructured log data and detect anomalies on the basis of patterns of the template occurrences. These methods have limitations for logs with unknown templates. Furthermore, since most log anomalies are known to be point anomalies rather than contextual anomalies, detection methods based on occurrence patterns can cause unnecessary delays in detection. In this paper, we propose LogELECTRA, a new log anomaly detection model that analyzes a single line of log messages more deeply on the basis of self-supervised anomaly detection. LogELECTRA specializes in detecting log anomalies as point anomalies by applying ELECTRA, a natural language processing model, to analyze the semantics of a single line of log messages. LogELECTRA outperformed existing state-of-the-art methods in experiments on the public benchmark log datasets BGL, Sprit, and Thunderbird.
Symbolic Autoencoding for Self-Supervised Sequence Learning

Authors:
raw_author_name: Amani, Mohammad Hossein
raw_author_name: Josifoski, Martin
raw_author_name: Peyrard, Maxime
raw_author_name: West, Robert

Abstract: Traditional language models, adept at next-token prediction in text sequences, often struggle with transduction tasks between distinct symbolic systems, particularly when parallel data is scarce. Addressing this issue, we introduce \textit{symbolic autoencoding} ($\Sigma$AE), a self-supervised framework that harnesses the power of abundant unparallel data alongside limited parallel data. $\Sigma$AE connects two generative models via a discrete bottleneck layer and is optimized end-to-end by minimizing reconstruction loss (simultaneously with supervised loss for the parallel data), such that the sequence generated by the discrete bottleneck can be read out as the transduced input sequence. We also develop gradient-based methods allowing for efficient self-supervised sequence learning despite the discreteness of the bottleneck. Our results demonstrate that $\Sigma$AE significantly enhances performance on transduction tasks, even with minimal parallel data, offering a promising solution for weakly supervised learning scenarios.