Reviewerly/exHarmony huggingface dataset:
$f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive  Learning

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

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

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

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

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.
A critical review of emerging cybersecurity threats in financial technologies

The rapid evolution of financial technologies (FinTech) has revolutionized the financial landscape, providing unprecedented convenience and efficiency. However, this technological advancement has also exposed the financial sector to an escalating array of cybersecurity threats. This paper presents a critical review of emerging cybersecurity threats in FinTech, analyzing the challenges and vulnerabilities faced by financial institutions in an era of increasing digitalization. The study delves into the complex landscape of cyber threats, exploring the spectrum from traditional threats, such as phishing and malware attacks, to sophisticated and evolving threats like ransomware, supply chain attacks, and artificial intelligence-driven cyber threats. The analysis highlights the interconnectedness of FinTech platforms, making them susceptible to systemic risks and cascading failures. Furthermore, the paper evaluates the impact of regulatory frameworks and compliance measures on mitigating cybersecurity risks in the FinTech domain. It assesses the effectiveness of current strategies and suggests potential enhancements to address the dynamic nature of cyber threats. A significant portion of the review focuses on the role of human factors in cybersecurity, emphasizing the need for robust training and awareness programs to empower financial professionals and users alike. Social engineering attacks, insider threats, and the exploitation of human vulnerabilities are discussed in detail, underscoring the importance of a holistic approach to cybersecurity. This critical review aims to provide a comprehensive understanding of the emerging cybersecurity landscape in FinTech. By shedding light on the evolving threats and vulnerabilities, the paper contributes to the ongoing discourse on safeguarding the integrity and security of financial technologies. As the financial industry continues to embrace technological innovations, a proactive and adaptive cybersecurity strategy is imperative to ensure the resilience of the financial ecosystem in the face of evolving cyber threats.
Density of identifying codes of hexagonal grids with finite number of rows

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.
A Glimpse into Japanese Vocabulary Expressions and Imagery Related to the COVID-19 Pandemic as Reflected in Modern Japanese Popular Tanka -Focused on Collocation-

This study aimed to investigate one aspect of the changes brought about by the COVID-19 pandemic in the field of language as evidenced in contemporary Japanese popular songs tanka, which encapsulate the Japanese people’s genuine emotions. Currently, tanka enjoy popularity particularly among the younger generation in Japan, with a wealth of works accessible primarily via social media. These compositions are viewed as significant cultural and linguistic resources, providing insights into modern Japanese individuals’ psyches and linguistic expressions.BR Consequently, this research involved collecting tanka compositions employing terms such as “corona” and “corona pandemic.” The study aimed to emphasize how individuals have encapsulated their experiences of the COVID-19 pandemic using specific vocabulary and imagery, with a particular focus on the collocations associated with these terms.BR To achieve the research objectives, the author first identified frequently used vocabulary and analyzed the imagery associated with each term in various works.BR The analytical results enabled an exploration of the imagery linked to various terms that are difficult to discern through mere dictionary interpretation, as well as the evolving patterns on the linguistic landscape brought about by COVID-19.BR Next, the works’ contents were analyzed, with a focus on frequently occurring words. It was found that the images evoked by words such as “wife” were as they had always been; however, words such as “mask” and “COVID-19 pandemic” are difficult to grasp based solely on dictionary definitions due to the evocation of additional or expanded images.
A study of honorifics and honorific expressions in the Korean secondary school teacher recruitment examination

This paper is concerned with honorifics and honorific expressions in the Japanese language teacher employment examination in the period 2002–2023. The research utilized quantitative and qualitative perspectives and, through analysis and discussion, found that Sonkeigo, Kenjogo I, and Kenjogo II are often used as subjects in honorifics. Moreover, to pass the test, an understanding of the nature of the honorifics Sonkeigo, Kenjogo I, and Kenjogo II is required. Exam questions also addressed Kenjogo I, which uses double honorifics and giving and receiving verbs. Regarding honorific expressions, the exams included many items related to their use in interpersonal relationships, with a focus on the understanding that Uchi-soto’s use of honorific expressions differs from Korean honorific expressions. Polite language behavior features asking for permission, complimenting, and inviting. We found the issue of patronage to be interesting, and this paper discusses differences in the nature of honorifics, their classification, aspects of their conversion into honorifics, and the advantages and disadvantages of recognizing honorific expressions.
A Study on the Variation Patterns of the Japanese Tale “The Monkey’s Liver”

This article examines the variation patterns of the Japanese tale “The Monkey’s Liver.” The research covers 24 episodes that have been handed down in different regions of Japan and examines various aspects of the characters and motifs.BR Versions of “The Monkey’s Liver” can be divided into three groups based on content. The stories in the first group end with failure to acquire a living liver due to the “confession” of an aquatic animal other than a jellyfish. Versions in the second group feature an origin story in which a jellyfish makes a “confession,” fails to acquire a living liver, and ultimately loses its bones. Stories in the third group tell of a turtle’s failure due to a jellyfish’s taunts. These stories also feature an origin story explaining how the jellyfish came to have no bones.BR Considering that the second group of stories is the largest and that the jellyfish character has the most appearances overall, it is quite understandable why this tale is also called “The Boneless Jellyfish” or “The Errand Jellyfish.” However, the appearance of this third character and the addition of related motifs can be considered a differentiating feature from the Chinese and Korean tales, which only contain land and aquatic animals.
The Impact of COVID-19 on the Transition to Emergency Remote Teaching

Purpose: The main objective of this study was to investigate the impact of COVID-19 on the transition to emergency remote teaching.&#x0D; Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings revealed that there exists a contextual and methodological gap relating to the impact of COVID-19 on the transition to emergency remote teaching. Preliminary Empirical review revealed that the pandemic underscored the resilience of educators and students in adapting to remote teaching. It emphasized the crucial role of digital technology in education while highlighting disparities in access and the importance of professional development for educators. The study also recognized the significance of addressing the social and emotional well-being of both educators and students during times of crisis. Overall, it offers important lessons for policymakers and educators to ensure the adaptability and effectiveness of educational systems in the face of unforeseen challenges.&#x0D; Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Social Learning Theory and the Technological Acceptance Model (TAM) may be used to anchor future studies on the transition to emergency remote teaching. The study recommends enhancing remote teaching practices by providing comprehensive teacher training in digital pedagogy, ensuring equitable access to technology for educators and students, and fostering a sense of community and collaboration in virtual classrooms. This includes ongoing professional development, addressing the digital divide, creating clear technology guidelines, and promoting interactive online teaching strategies. Additionally, the study underscores the importance of student feedback and the provision of support services to enhance the overall remote learning experience during emergencies.
“QUICK” - Designing and Developing a Blog Application Using The MERN Stack

Abstract: In today's digital age, blogging has emerged as a prevalent means of sharing ideas, experiences, and knowledge across the internet. I made “QUICK” using MERN (MongoDB, Express, React JS and Node JS. My Application not only provides the creation, editing, and deletion of blogs by authors but also build up user experience through advanced features such as categorization, filtering, and search functions. The main focus of this research is to explore how the MERN stack give to the seamless integration of front-end and back-end components. By leveraging React.js for the front end and Express.js and Node.js for the server-side logic, QUICK ensures a responsive and interactive user interface. Moreover, the implementation of features such as category-based organization, filtering options, and robust search, allowing readers to easily discover relevant content based on their preferences. Furthermore, it empowers authors with exclusive rights to edit and delete their own blogs.
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games

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

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

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

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.
What is LLMLingua?

&lt;strong&gt; The AI Helper Turning Mountains of Data into Bite-Sized Instructions &lt;/strong&gt; Author: Aland Astudillo https://orcid.org/0009-0008-8672-3168 LLMs have been changing the way the entire world deals with problems and day-by-day tasks. To make them better for specific applications, they need huge amounts of data and complex and expensive approaches to training them.
Introduction to 3D Deep Learning

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

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.