OpenRaiser/Intern-Atlas huggingface dataset:
NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance
Venue: arXiv cs.NE 2024
Year: 2024

In this study, we applied the NEAT (NeuroEvolution of Augmenting Topologies) algorithm to stock trading using multiple technical indicators. Our approach focused on maximizing earning, avoiding risk, and outperforming the Buy & Hold strategy. We used progressive training data and a multi-objective fitness function to guide the evolution of the population towards these objectives. The results of our study showed that the NEAT model achieved similar returns to the Buy & Hold strategy, but with lower risk exposure and greater stability. We also identified some challenges in the training process, including the presence of a large number of unused nodes and connections in the model architecture. In future work, it may be worthwhile to explore ways to improve the NEAT algorithm and apply it to shorter interval data in order to assess the potential impact on performance.
Evaluating the effectiveness, reliability and efficiency of a multi-objective sequential optimization approach for building performance design
Venue: arXiv cs.NE 2024
Year: 2024

The complexity of performance-based building design stems from the evaluation of numerous candidate design options, driven by the plethora of variables, objectives, and constraints inherent in multi-disciplinary projects. This necessitates optimization approaches to support the identification of well performing designs while reducing the computational time of performance evaluation. In response, this paper proposes and evaluates a sequential approach for multi-objective design optimization of building geometry, fabric, HVAC system and controls for building performance. This approach involves sequential optimizations with optimal solutions from previous stages passed to the next. The performance of the sequential approach is benchmarked against a full factorial search, assessing its effectiveness in finding global optima, solution quality, reliability to scale and variations of problem formulations, and computational efficiency compared to the NSGA-II algorithm. 24 configurations of the sequential approach are tested on a multi-scale case study, simulating 874 to 4,147,200 design options for an office building, aiming to minimize energy demand while maintaining thermal comfort. A two-stage sequential process-(building geometry + fabric) and (HVAC system + controls) identified the same Pareto-optimal solutions as the full factorial search across all four scales and variations of problem formulations, demonstrating 100% effectiveness and reliability. This approach required 100,700 function evaluations, representing a 91.2% reduction in computational effort compared to the full factorial search. In contrast, NSGA-II achieved only 73.5% of the global optima with the same number of function evaluations. This research indicates that a sequential optimization approach is a highly efficient and robust alternative to the standard NSGA-II algorithm.
Linked Data on Geo-annotated Events and Use Cases for the Resilience of Ukraine
Venue: arXiv cs.CY 2024
Year: 2024

The mission of resilience of Ukrainian cities calls for international collaboration with the scientific community to increase the quality of information by identifying and integrating information from various news and social media sources. Linked Data technology can be used to unify, enrich, and integrate data from multiple sources. In our work, we focus on datasets about damaging events in Ukraine due to Russia's invasion between February 2022 and the end of April 2023. We convert two selected datasets to Linked Data and enrich them with additional geospatial information. Following that, we present an algorithm for the detection of identical events from different datasets. Our pipeline makes it easy to convert and enrich datasets to integrated Linked Data. The resulting dataset consists of 10K reported events covering damage to hospitals, schools, roads, residential buildings, etc. Finally, we demonstrate in use cases how our dataset can be applied to different scenarios for resilience purposes.
Leveraging Social Media Data and Artificial Intelligence for Improving Earthquake Response Efforts
Venue: arXiv cs.CY 2024
Year: 2024

The integration of social media and artificial intelligence (AI) into disaster management, particularly for earthquake response, represents a profound evolution in emergency management practices. In the digital age, real-time information sharing has reached unprecedented levels, with social media platforms emerging as crucial communication channels during crises. This shift has transformed traditional, centralized emergency services into more decentralized, participatory models of disaster situational awareness. Our study includes an experimental analysis of 8,900 social media interactions, including 2,920 posts and 5,980 replies on X (formerly Twitter), following a magnitude 5.1 earthquake in Oklahoma on February 2, 2024. The analysis covers data from the immediate aftermath and extends over the following seven days, illustrating the critical role of digital platforms in modern disaster response. The results demonstrate that social media platforms can be effectively used as real-time situational awareness tools, delivering critical information to society and authorities during emergencies.
Achieving Carbon Neutrality for I/O Devices
Venue: arXiv cs.CY 2024
Year: 2024

Achieving carbon neutrality has become a critical goal in mitigating the environmental impacts of human activities, particularly in the face of global climate challenges. Input/Output (I/O) devices, such as keyboards, mice, displays, and printers, contribute significantly to greenhouse gas emissions through their manufacturing, operation, and disposal processes. In this paper, we explores sustainable strategies for achieving carbon neutrality in I/O devices, emphasizing the importance of environmentally conscious design and development. Through a comprehensive review of existing literature and best approaches, we introduces a framework to outline approaches for reducing the carbon footprint of I/O devices. The result underscore the necessity of integrating sustainability into the lifecycle of I/O devices to support global carbon neutrality goals and promote long-term environmental sustainability.
Results of the 2024 Video Browser Showdown
Venue: arXiv cs.MM 2024
Year: 2024

This report presents the results of the 13th Video Browser Showdown, held at the 2024 International Conference on Multimedia Modeling on the 29th of January 2024 in Amsterdam, the Netherlands.
An Agent Framework for Real-Time Financial Information Searching with Large Language Models
Venue: arXiv cs.IR 2024
Year: 2024

Financial decision-making requires processing vast amounts of real-time information while understanding their complex temporal relationships. While traditional search engines excel at providing real-time information access, they often struggle to comprehend sophisticated user intentions and contextual nuances. Conversely, Large Language Models (LLMs) demonstrate reasoning and interaction capabilities but may generate unreliable outputs without access to current data. While recent attempts have been made to combine LLMs with search capabilities, they suffer from (1) restricted access to specialized financial data, (2) static query structures that cannot adapt to dynamic market conditions, and (3) insufficient temporal awareness in result generation. To address these challenges, we present FinSearch, a novel agent-based search framework specifically designed for financial applications that interface with diverse financial data sources including market, stock, and news data. Innovatively, FinSearch comprises four components: (1) an LLM-based multi-step search pre-planner that decomposes user queries into structured sub-queries mapped to specific data sources through a graph representation; (2) a search executor with an LLM-based adaptive query rewriter that executes the searching of each sub-query while dynamically refining the sub-queries in its subsequent node based on intermediate search results; (3) a temporal weighting mechanism that prioritizes information relevance based on the deduced time context from the user's query; (4) an LLM-based response generator that synthesizes results into coherent, contextually appropriate outputs. To evaluate FinSearch, we construct FinSearchBench-24, a benchmark of 1,500 four-choice questions across the stock market, rate changes, monetary policy, and industry developments spanning from June to October 2024.
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillation (KD)-based methods can alleviate these issues by transferring the knowledge to a small student, which trains a student based on the predictions of a cumbersome teacher. However, these methods encounter difficulties for \textit{LLM-based KD in SBR}. 1) It is expensive to make LLMs predict for all instances in KD. 2) LLMs may make ineffective predictions for some instances in KD, e.g., incorrect predictions for hard instances or similar predictions as existing recommenders for easy instances. In this paper, we propose an active LLM-based KD method in SBR, contributing to sustainable AI. To efficiently distill knowledge from LLMs with limited cost, we propose to extract a small proportion of instances predicted by LLMs. Meanwhile, for a more effective distillation, we propose an active learning strategy to extract instances that are as effective as possible for KD from a theoretical view. Specifically, we first formulate gains based on potential effects (e.g., effective, similar, and incorrect predictions by LLMs) and difficulties (e.g., easy or hard to fit) of instances for KD. Then, we propose to maximize the minimal gains of distillation to find the optimal selection policy for active learning, which can largely avoid extracting ineffective instances in KD. Experiments on real-world datasets show that our method significantly outperforms state-of-the-art methods for SBR.
V-SQL: A View-based Two-stage Text-to-SQL Framework
Venue: arXiv cs.DB 2024
Year: 2024

The text-to-SQL task aims to convert natural language into Structured Query Language (SQL) without bias. Recently, text-to-SQL methods based on large language models (LLMs) have garnered significant attention. The core of mainstream text-to-SQL frameworks is schema linking, which aligns user queries with relevant tables and columns in the database. Previous methods focused on schema linking while neglecting to enhance LLMs' understanding of database schema. The complex coupling relationships between tables in the database constrain the SQL generation capabilities of LLMs. To tackle this issue, this paper proposes a simple yet effective strategy called view-based schema. This strategy aids LLMs in understanding the database schema by decoupling tightly coupled tables into low-coupling views. We then introduce V-SQL, a view-based two-stage text-to-SQL framework. V-SQL involves the view-based schema strategy to enhance LLMs' understanding of database schema. Results on the authoritative datasets Bird indicate that V-SQL achieves competitive performance compared to existing state-of-the-art methods.
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction
Venue: arXiv cs.IR 2024
Year: 2024

In recommender systems, post-click conversion rate (CVR) estimation is an essential task to model user preferences for items and estimate the value of recommendations. Sample selection bias (SSB) and data sparsity (DS) are two persistent challenges for post-click conversion rate (CVR) estimation. Currently, entire-space approaches that exploit unclicked samples through knowledge distillation are promising to mitigate SSB and DS simultaneously. Existing methods use non-conversion, conversion, or adaptive conversion predictors to generate pseudo labels for unclicked samples. However, they fail to consider the unbiasedness and information limitations of these pseudo labels. Motivated by such analysis, we propose an entire-space variational information exploitation framework (EVI) for CVR prediction. First, EVI uses a conditional entire-space CVR teacher to generate unbiased pseudo labels. Then, it applies variational information exploitation and logit distillation to transfer non-click space information to the target CVR estimator. We conduct extensive offline experiments on six large-scale datasets. EVI demonstrated a 2.25\% average improvement compared to the state-of-the-art baselines.
XPath Agent: An Efficient XPath Programming Agent Based on LLM for Web Crawler
Venue: arXiv cs.IR 2024
Year: 2024

We present XPath Agent, a production-ready XPath programming agent specifically designed for web crawling and web GUI testing. A key feature of XPath Agent is its ability to automatically generate XPath queries from a set of sampled web pages using a single natural language query. To demonstrate its effectiveness, we benchmark XPath Agent against a state-of-the-art XPath programming agent across a range of web crawling tasks. Our results show that XPath Agent achieves comparable performance metrics while significantly reducing token usage and improving clock-time efficiency. The well-designed two-stage pipeline allows for seamless integration into existing web crawling or web GUI testing workflows, thereby saving time and effort in manual XPath query development. The source code for XPath Agent is available at https://github.com/eavae/feilian.
Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projects
Venue: arXiv cs.SE 2024
Year: 2024

Large Language Models (LLMs) have helped programmers increase efficiency through code generation, comprehension, and repair. However, their application to large-scale projects remains challenging due to complex interdependencies and the extensive size of modern codebases. Although Knuth's concept of Literate Programming (LP) combines code and natural language to convey logic and intent, its potential for enhancing relationships in large projects has not been fully explored. In this study, we introduce the idea of Interoperable LP (ILP), which leverages literate programming principles to enhance the development of both small-scale documents and large-scale projects with LLMs. We investigate how LLMs perform under ILP-style instructions for both document-oriented tasks and entire projects. Recognizing that many researchers rely on well-structured templates to guide LLMs, we propose a concise prompt engineering method to write LP documents so LLMs can better be involved in code generation. We also examine the capacity of various LLMs to generate Scheme and Python code on the RepoBench benchmark, illustrating the advantages of our approach. Our findings indicate that ILP with LLMs can enhance LLM-based code generation in large-scale project development.
Set-Rationalizable Choice and Self-Stability
Venue: arXiv cs.MA 2025
Year: 2025

A common assumption in modern microeconomic theory is that choice should be rationalizable via a binary preference relation, which \citeauthor{Sen71a} showed to be equivalent to two consistency conditions, namely $α$ (contraction) and $γ$ (expansion). Within the context of \emph{social} choice, however, rationalizability and similar notions of consistency have proved to be highly problematic, as witnessed by a range of impossibility results, among which Arrow's is the most prominent. Since choice functions select \emph{sets} of alternatives rather than single alternatives, we propose to rationalize choice functions by preference relations over sets (set-rationalizability). We also introduce two consistency conditions, $\hatα$ and $\hatγ$, which are defined in analogy to $α$ and $γ$, and find that a choice function is set-rationalizable if and only if it satisfies $\hatα$. Moreover, a choice function satisfies $\hatα$ and $\hatγ$ if and only if it is \emph{self-stable}, a new concept based on earlier work by \citeauthor{Dutt88a}. The class of self-stable social choice functions contains a number of appealing Condorcet extensions such as the minimal covering set and the essential set.
Probabilistic Methods on Erdos Problems
Venue: arXiv cs.DM 2025
Year: 2025

The ideas here are a continuation of a previous article. Some of the applications of the main ideas in the previous article are explained, along with some limitations of the general ideas. There are situations where additional hypotheses allow the use of the main ideas in problems that are not immediately accessible.
Probability Bracket Notation: Multivariable Systems and Static Bayesian Networks
Venue: arXiv cs.AI 2025
Year: 2025

We expand the Probability Bracket Notation (PBN), a symbolic framework inspired by the Dirac notation in quantum mechanics, to multivariable probability systems and static Bayesian networks (BNs). By defining joint, marginal, and conditional probability distributions (PDs), as well as marginal and conditional expectations, we demonstrate how to express dependencies among multiple random variables and manipulate them algebraically in PBN. Using the well-known Student BN as an example of probabilistic graphical models (PGMs), we illustrate how to apply PBN to analyze predictions, inferences (using both bottom-up and top-down approaches), and expectations. We then extend PBN to BNs with continuous variables. After reviewing linear Gaussian networks, we introduce a customized Healthcare BN that includes both continuous and discrete random variables, utilizes user-specific data, and provides tailored predictions via discrete-display (DD) nodes that proxy for their continuous-variable parents. Compared to traditional probability notation, PBN offers an operator-driven framework that unifies and simplifies the analysis of probabilistic models, with potential as both an educational tool and a practical platform for causal reasoning, inference, expectation, data analytics, machine learning, and artificial intelligence.
Compression with wildcards: All models of a Boolean 2-CNF
Venue: arXiv cs.CC 2025
Year: 2025

Let $W$ be a finite set which simultaneously serves as the universe of any poset $(W,\preceq)$ and as the vertex set of any graph $G$. Our algorithm, abbreviated A-I-I, enumerates (in a compressed format using don't-care symbols) all $G$-independent order ideals of $(W,\preceq)$. For many instances the high-end Mathematica implementation of A-I-I compares favorably to the hardwired Mathematica commands {\tt BooleanConvert} and {\tt SatisfiabilityCount}. The A-I-I can be parallelized and adapts to a polynomial total time algorithm that enumerates the modelset of any Boolean 2-CNF.
Model-Checking PCTL Properties of Stateless Probabilistic Pushdown Systems
Venue: arXiv cs.LO 2025
Year: 2025

In this communication, we resolve a longstanding open question in the probabilistic verification of infinite-state systems. We show that model checking {\it stateless probabilistic pushdown systems (pBPA)} against {\it probabilistic computational tree logic (PCTL)} is generally undecidable.
Verifying Procedural Programs via Constrained Rewriting Induction
Venue: arXiv cs.LO 2025
Year: 2025

This paper aims to develop a verification method for procedural programs via a transformation into Logically Constrained Term Rewriting Systems (LCTRSs). To this end, we extend transformation methods based on integer TRSs to handle arbitrary data types, global variables, function calls and arrays, as well as encode safety checks. Then we adapt existing rewriting induction methods to LCTRSs and propose a simple yet effective method to generalize equations. We show that we can automatically verify memory safety and prove correctness of realistic functions. Our approach proves equivalence between two implementations, so in contrast to other works, we do not require an explicit specification in a separate specification language.
Achieving Dalenius' Goal of Data Privacy with Practical Assumptions
Venue: arXiv cs.CR 2025
Year: 2025

Current differential privacy frameworks face significant challenges: vulnerability to correlated data attacks and suboptimal utility-privacy tradeoffs. To address these limitations, we establish a novel information-theoretic foundation for Dalenius' privacy vision using Shannon's perfect secrecy framework. By leveraging the fundamental distinction between cryptographic systems (small secret keys) and privacy mechanisms (massive datasets), we replace differential privacy's restrictive independence assumption with practical partial knowledge constraints ($H(X) \geq b$). We propose an information privacy framework achieving Dalenius security with quantifiable utility-privacy tradeoffs. Crucially, we prove that foundational mechanisms -- random response, exponential, and Gaussian channels -- satisfy Dalenius' requirements while preserving group privacy and composition properties. Our channel capacity analysis reduces infinite-dimensional evaluations to finite convex optimizations, enabling direct application of information-theoretic tools. Empirical evaluation demonstrates that individual channel capacity (maximal information leakage of each individual) decreases with increasing entropy constraint $b$, and our framework achieves superior utility-privacy tradeoffs compared to classical differential privacy mechanisms under equivalent privacy guarantees. The framework is extended to computationally bounded adversaries via Yao's theory, unifying cryptographic and statistical privacy paradigms. Collectively, these contributions provide a theoretically grounded path toward practical, composable privacy -- subject to future resolution of the tradeoff characterization -- with enhanced resilience to correlation attacks.
Online Dynamic Programming
Venue: arXiv cs.LG 2025
Year: 2025

We propose a general method for combinatorial online learning problems whose offline optimization problem can be solved efficiently via a dynamic programming algorithm defined by an arbitrary min-sum recurrence. Examples include online learning of Binary Search Trees, Matrix-Chain Multiplications, $k$-sets, Knapsacks, Rod Cuttings, and Weighted Interval Schedulings. For each of these problems we use the underlying graph of subproblems (called a multi-DAG) for defining a representation of the solutions of the dynamic programming problem by encoding them as a generalized version of paths (called multipaths). These multipaths encode each solution as a series of successive decisions or components over which the loss is linear. We then show that the dynamic programming algorithm for each problem leads to online algorithms for learning multipaths in the underlying multi-DAG. The algorithms maintain a distribution over the multipaths in a concise form as their hypothesis. More specifically we generalize the existing Expanded Hedge and Component Hedge algorithms for the online shortest path problem to learning multipaths. Additionally, we introduce a new and faster prediction technique for Component Hedge which in our case directly samples from a distribution over multipaths, bypassing the need to decompose the distribution over multipaths into a mixture with small support.