OpenRaiser/Intern-Atlas huggingface dataset:
Datapath Combinational Equivalence Checking With Hybrid Sweeping Engines and Parallelization
Venue: arXiv cs.DC 2024
Year: 2024

In the application of IC design for microprocessors, there are often demands for optimizing the implementation of datapath circuits, on which various arithmetic operations are performed. Combinational equivalence checking (CEC) plays an essential role in ensuring the correctness of design optimization. The most prevalent CEC algorithms are based on SAT sweeping, which utilizes SAT to prove the equivalence of the internal node pairs in topological order, and the equivalent nodes are merged. Datapath circuits usually contain equivalent pairs for which the transitive fan-in cones are small but have a high XOR chain density, and proving such node pairs is very difficult for SAT solvers. An exact probability-based simulation (EPS) is suitable for verifying such pairs, while this method is not suitable for pairs with many primary inputs due to the memory cost. We first reduce the memory cost of EPS and integrate it to improve the SAT sweeping method. Considering the complementary abilities of SAT and EPS, we design an engine selection heuristic to dynamically choose SAT or EPS in the sweeping process, according to XOR chain density. Our method is further improved by reducing unnecessary engine calls by detecting regularity. Furthermore, we parallelized the SAT and EPS engines of HybridCEC, leading to the parallel CEC prover. Experiments on a benchmark suite from industrial datapath circuits show that our method is much faster than the state-of-the-art CEC tool namely ABC &cec on nearly all instances, and is more than 100x faster on 30% of the instances, 1000x faster on 12% of the instances. In addition, the 64 threads version of our method achieved 77x speedup.
AI Meets Natural Hazard Risk: A Nationwide Vulnerability Assessment of Data Centers to Natural Hazards and Power Outages
Venue: arXiv cs.CY 2024
Year: 2024

Our society is on the verge of a revolution powered by Artificial Intelligence (AI) technologies. With increasing advancements in AI, there is a growing expansion in data centers (DCs) serving as critical infrastructure for this new wave of technologies. This technological wave is also on a collision course with exacerbating climate hazards which raises the need for evaluating the vulnerability of DCs to various hazards. Hence, the objective of this research is to conduct a nationwide vulnerability assessment of (DCs) in the United States of America (USA). DCs provide such support; however, if an unplanned disruption (like a natural hazard or power outage) occurs, the functionality of DCs are in jeopardy. Unplanned downtime in DCs cause severe economic and social repercussions. With the Local Indicator of Spatial Association (LISA) test, the research found that there are a large percentage of DCs that are in non-vulnerable areas of disruption; however, there is still a notable percentage in disruption prone areas. For example, earthquakes, hurricanes, and tornadoes have the most DCs in vulnerable areas. After identifying these vulnerabilities, the research identified areas within the USA that have minimal vulnerabilities to both the aforementioned natural hazards and power outages with the BI-LISA test. After doing a composite vulnerability score on the Cold-Spots from the BILISA analysis, the research found three counties with the low vulnerability scores. These are Koochiching, Minnesota (0.091), Schoolcraft, Michigan (0.095), and Houghton, Michigan (0.096).
Battery-free, stretchable, and autonomous smart packaging
Venue: arXiv cs.CY 2024
Year: 2024

In the food industry, innovative packaging solutions are increasingly important for reducing food waste and for contributing to global sustainability efforts. However, current food packaging is generally passive and unable to adapt to changes in the food environment in real-time. To address this, we have developed a battery-less and autonomous smart packaging system that wirelessly powers closed-loop sensing and release of active compounds. This system integrates a gas sensor for real-time food monitoring, a Near-Field Communication (NFC) antenna, and a controlled release of active compounds to prevent quality deterioration in the complex food environment. We have demonstrated the ability of the developed smart packaging system, to continuously monitor the freshness of fish products and to trigger the release of active compounds when the food starts to spoil. The system was able to extend the shelf-life of the food product up to 14 days, due to the controlled release of antioxidant and antibacterial compounds. Our system could pave the way towards an Internet of Things solution that addresses protection, active prevention of food spoilage and sustainability, facing all the current challenges of the food packaging industry.
Artificial Intelligence for Sustainable Urban Biodiversity: A Framework for Monitoring and Conservation
Venue: arXiv cs.CY 2024
Year: 2024

The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management. This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation. Key findings show that: (a) AI enhances species detection and monitoring, achieving over 90% accuracy in urban wildlife tracking and invasive species management; (b) integrating data from remote sensing, acoustic monitoring, and citizen science enables large-scale ecosystem analysis; and (c) AI decision tools improve conservation planning and resource allocation, increasing prediction accuracy by up to 18.5% compared to traditional methods. The research presents an AI-Driven Framework for Urban Biodiversity Management, highlighting AI's impact on monitoring, conservation strategies, and ecological outcomes. Implementation strategies include: (a) standardizing data collection and model validation, (b) ensuring equitable AI access across urban contexts, and (c) developing ethical guidelines for biodiversity monitoring. The study concludes that integrating AI in urban biodiversity conservation requires balancing innovation with ecological wisdom and addressing data quality, socioeconomic disparities, and ethical concerns.
A survey on pioneering metaheuristic algorithms between 2019 and 2024
Venue: arXiv cs.NE 2024
Year: 2024

This review examines over 150 new metaheuristics of the last six years (between 2019 and 2024), underscoring their profound influence and performance. Over the past three decades, more than 500 new metaheuristic algorithms have been proposed, with no slowdown in sight. An overwhelming abundance that complicates the process of selecting and assessing the most effective solutions for complex optimization challenges. Our evaluation centers on pivotal criteria, including annual citation metrics, the breadth of the addressed problem types, source code availability, user friendly parameter configurations, innovative mechanisms and operators, and approaches designed to mitigate traditional metaheuristic issues such as stagnation and premature convergence. We further explore recent high impact applications of the past six years' most influential 23 metahueristic algorithms, shedding light on their advantages and limitations, while identifying challenges and potential avenues for future research.
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions
Venue: arXiv cs.LG 2024
Year: 2024

Large models (LMs) have immense potential in Internet of Things (IoT) systems, enabling applications such as intelligent voice assistants, predictive maintenance, and healthcare monitoring. However, training LMs on edge servers raises data privacy concerns, while deploying them directly on IoT devices is constrained by limited computational and memory resources. We analyze the key challenges of training LMs in IoT systems, including energy constraints, latency requirements, and device heterogeneity, and propose potential solutions such as dynamic resource management, adaptive model partitioning, and clustered collaborative training. Furthermore, we propose a split knowledge distillation framework to efficiently distill LMs into smaller, deployable versions for IoT devices while ensuring raw data remains local. This framework integrates knowledge distillation and split learning to minimize energy consumption and meet low model training delay requirements. A case study is presented to evaluate the feasibility and performance of the proposed framework.
NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal Emotion-Cause Analysis in Conversations
Venue: arXiv cs.CL 2024
Year: 2024

This paper describes the architecture of our system developed for Task 3 of SemEval-2024: Multimodal Emotion-Cause Analysis in Conversations. Our project targets the challenges of subtask 2, dedicated to Multimodal Emotion-Cause Pair Extraction with Emotion Category (MECPE-Cat), and constructs a dual-component system tailored to the unique challenges of this task. We divide the task into two subtasks: emotion recognition in conversation (ERC) and emotion-cause pair extraction (ECPE). To address these subtasks, we capitalize on the abilities of Large Language Models (LLMs), which have consistently demonstrated state-of-the-art performance across various natural language processing tasks and domains. Most importantly, we design an approach of emotion-cause-aware instruction-tuning for LLMs, to enhance the perception of the emotions with their corresponding causal rationales. Our method enables us to adeptly navigate the complexities of MECPE-Cat, achieving a weighted average 34.71% F1 score of the task, and securing the 2nd rank on the leaderboard. The code and metadata to reproduce our experiments are all made publicly available.
The Synergy of Automated Pipelines with Prompt Engineering and Generative AI in Web Crawling
Venue: arXiv cs.IR 2024
Year: 2024

Web crawling is a critical technique for extracting online data, yet it poses challenges due to webpage diversity and anti-scraping mechanisms. This study investigates the integration of generative AI tools Claude AI (Sonnet 3.5) and ChatGPT4.0 with prompt engineering to automate web scraping. Using two prompts, PROMPT I (general inference, tested on Yahoo News) and PROMPT II (element-specific, tested on Coupons.com), we evaluate the code quality and performance of AI-generated scripts. Claude AI consistently outperformed ChatGPT-4.0 in script quality and adaptability, as confirmed by predefined evaluation metrics, including functionality, readability, modularity, and robustness. Performance data were collected through manual testing and structured scoring by three evaluators. Visualizations further illustrate Claude AI's superiority. Anti-scraping solutions, including undetected_chromedriver, Selenium, and fake_useragent, were incorporated to enhance performance. This paper demonstrates how generative AI combined with prompt engineering can simplify and improve web scraping workflows.
ACL-rlg: A Dataset for Reading List Generation
Venue: arXiv cs.DL 2024
Year: 2024

Familiarizing oneself with a new scientific field and its existing literature can be daunting due to the large amount of available articles. Curated lists of academic references, or reading lists, compiled by experts, offer a structured way to gain a comprehensive overview of a domain or a specific scientific challenge. In this work, we introduce ACL-rlg, the largest open expert-annotated reading list dataset. We also provide multiple baselines for evaluating reading list generation and formally define it as a retrieval task. Our qualitative study highlights the fact that traditional scholarly search engines and indexing methods perform poorly on this task, and GPT-4o, despite showing better results, exhibits signs of potential data contamination.
Recent Developments in Deep Learning-based Author Name Disambiguation
Venue: arXiv cs.DL 2024
Year: 2024

Author Name Disambiguation (AND) is a critical task for digital libraries aiming to link existing authors with their respective publications. Due to the lack of persistent identifiers used by researchers and the presence of intrinsic linguistic challenges, such as homonymy, the development of Deep Learning algorithms to address this issue has become widespread. Many AND deep learning methods have been developed, and surveys exist comparing the approaches in terms of techniques, complexity, performance. However, none explicitly addresses AND methods in the context of deep learning in the latest years (i.e. timeframe 2016-2024). In this paper, we provide a systematic review of state-of-the-art AND techniques based on deep learning, highlighting recent improvements, challenges, and open issues in the field. We find that DL methods have significantly impacted AND by enabling the integration of structured and unstructured data, and hybrid approaches effectively balance supervised and unsupervised learning.
2024 Google Scholar Research Interest Ranking for Top 3260 Computer Science Authors
Venue: arXiv cs.DL 2024
Year: 2024

Computer science research spans a diverse array of topics, with scholars exploring numerous subfields. This paper examines the self-reported research interests of the top 3,260 most cited computer science authors on Google Scholar. Using the scholarly Python library, we systematically retrieved and classified their interests into predefined categories based on the Computer Science Ontology (CSO). The analysis highlights a hierarchy of primary research areas, including Artificial Intelligence, Software Engineering, Data Mining, and Computer Systems. Additionally, it investigates the distribution of these interests, identifying emerging trends, established fields, and areas with relatively less attention. These findings provide a current snapshot of research priorities and serve as a foundation for guiding future studies in computer science.
Design of Pulse Shapes Based on Sampling with Gaussian Prefilter
Venue: arXiv cs.IT 2025
Year: 2025

Two new pulse shapes for communications are presented. The first pulse shape generates a set of pulses without intersymbol interference (ISI) or ISI-free for short. In the neighborhood of the origin it is similar in shape to the classical cardinal sine function but is of exponential decay at infinity. This pulse shape is identical to the interpolating function of a generalized sampling theorem with Gaussian prefilter. The second pulse shape is obtained from the first pulse shape by spectral factorization. Besides being also of exponential decay at infinity, it has a causal appearance since it is of superexponential decay for negative times. It is closely related to the orthonormal generating function considered earlier by Unser in the context of shift-invariant spaces. This pulse shape is not ISI-free but it generates a set of orthonormal pulses. The second pulse shape may also be used to define a receive matched filter so that at the filter output the ISI-free pulses of the first kind are recovered.
A Simple Proof of the Existence of a Planar Separator
Venue: arXiv cs.CG 2025
Year: 2025

We provide a simple proof of the existence of a planar separator by showing that it is an easy consequence of the circle packing theorem. We also reprove other results on separators, including: (A) There is a simple cycle separator if the planar graph is triangulated. Furthermore, if each face has at most $d$ edges on its boundary, then there is a cycle separator of size O(sqrt{d n}). (B) For a set of n balls in R^d, that are k-ply, there is a separator, in the intersection graph of the balls, of size O(k^{1/d}n^{1-1/d}). (C) The k nearest neighbor graph of a set of n points in R^d contains a separator of size O(k^{1/d} n^{1-1/d}). The new proofs are (arguably) significantly simpler than previous proofs.
The Cyan Language
Venue: arXiv cs.PL 2025
Year: 2025

This is the manual of Cyan, a prototype-based object-oriented language. Cyan supports gradual typing (both static and dynamic typing), single inheritance, anonymous functions, generic prototypes with concepts, non-nullable types, partially safe object initialization, an object-oriented exception handling system, and a powerful Metaobject Protocol for metaprogramming at compile-time (this is described elsewhere).
A note reviewing Turing's 1936
Venue: arXiv cs.CC 2025
Year: 2025

By closely rereading the original Turing's 1936 article, we can gain insight about that it is based on the claim to have defined a number which is not computable, arguing that there can be no machine computing the diagonal on the enumeration of the computable sequences. This article provides a careful analysis of Turing's original argument, demonstrating that it cannot be regarded as a conclusive proof. Furthermore, it shows that there is no evidence supporting the existence of a defined number that is not computable.
Can Almost Everybody be Almost Happy? PCP for PPAD and the Inapproximability of Nash
Venue: arXiv cs.CC 2025
Year: 2025

We conjecture that PPAD has a PCP-like complete problem, seeking a near equilibrium in which all but very few players have very little incentive to deviate. We show that, if one assumes that this problem requires exponential time, several open problems in this area are settled. The most important implication, proved via a "birthday repetition" reduction, is that the n^O(log n) approximation scheme of [LMM03] for the Nash equilibrium of two-player games is essentially optimum. Two other open problems in the area are resolved once one assumes this conjecture, establishing that certain approximate equilibria are PPAD-complete: Finding a relative approximation of two-player Nash equilibria (without the well-supported restriction of [Das13]), and an approximate competitive equilibrium with equal incomes [Bud11] with small clearing error and near-optimal Gini coefficient.
Fortuitous sequences of flips of the top of a stack of n burnt pancakes for all n>24
Venue: arXiv cs.DM 2025
Year: 2025

Burnt pancakes problem was defined by Gates and Papadimitriou in 1979. A stack $S$ of pancakes with a burnt side must be sorted by size, the smallest on top, and each pancake with burnt side down. The only operation allowed is to split stack in two parts and flip upper part. $g(S)$ is the minimal number of flips needed to sort stack $S$. Stack $S$ may be $-I_n$ when pancakes are in right order but upside down or $-f_n$ when all pancakes are right side up but sorted in reverse order. Gates et al. proved that $g(-f_n)\ge 3n/2-1$. In 1995 Cohen and Blum proved that $g(-I_n)=g(-f_n)+1\ge 3n/2$. In 1997 Heydari and Sudborough proved that $g(-I_n)\le 3(n+1)/2$ whenever some fortuitous sequence of flips exists. They gave fortuitous sequences for $n$=3, 15, 27 and 31. They showed that two fortuitous sequences $S_n$ and $S_{n'}$ may combine into another fortuitous sequence $S_{n''}$ with $n''=n+n'-3$. So a fortuitous sequence $S_n$ gives a fortuitous sequence $S_{n+12}$. This proves that $g(-I_n)\le 3(n+1)/2$ if $n$ is congruent to 3 modulo 4 and $n\ge 23$. In 2011 Josef Cibulka enhanced Gates and Papadimitriou's lower bound thanks to a potential function. He got so $g(-I_n)\ge3n/2+1$ if $n > 1$ proving thereby, that $g(-I_n)=3(n+1)/2$ if $n$ is congruent to 3 modulo 4 and $n\ge 23$. This paper explains how to build generalized fortuitous sequences for $n=15, 19, 23$ and every $n\ge 25$, odd or even, proving thereby that $g(-I_n)=\lceil 3n/2\rceil+1$ for these $n$. It gives $g(-I_n)$ for all $n$.
Sublinear Random Access Generators for Preferential Attachment Graphs
Venue: arXiv cs.DS 2025
Year: 2025

We consider the problem of sampling from a distribution on graphs, specifically when the distribution is defined by an evolving graph model, and consider the time, space and randomness complexities of such samplers. In the standard approach, the whole graph is chosen randomly according to the randomized evolving process, stored in full, and then queries on the sampled graph are answered by simply accessing the stored graph. This may require prohibitive amounts of time, space and random bits, especially when only a small number of queries are actually issued. Instead, we propose to generate the graph on-the-fly, in response to queries, and therefore to require amounts of time, space, and random bits which are a function of the actual number of queries. We focus on two random graph models: the Barab{á}si-Albert Preferential Attachment model (BA-graphs) and the random recursive tree model. We give on-the-fly generation algorithms for both models. With probability $1-1/\mbox{poly}(n)$, each and every query is answered in $\mbox{polylog}(n)$ time, and the increase in space and the number of random bits consumed by any single query are both $\mbox{polylog}(n)$, where $n$ denotes the number of vertices in the graph. Our results show that, although the BA random graph model is defined by a sequential process, efficient random access to the graph's nodes is possible. In addition to the conceptual contribution, efficient on-the-fly generation of random graphs can serve as a tool for the efficient simulation of sublinear algorithms over large BA-graphs, and the efficient estimation of their performance on such graphs.
Meta-Evaluation of Translation Evaluation Methods: a systematic up-to-date overview
Venue: arXiv cs.CL 2025
Year: 2025

Starting from the 1950s, Machine Translation (MT) was challenged by different scientific solutions, which included rule-based methods, example-based and statistical models (SMT), to hybrid models, and very recent years the neural models (NMT). While NMT has achieved a huge quality improvement in comparison to conventional methodologies, by taking advantage of a huge amount of parallel corpora available from the internet and the recently developed super computational power support with an acceptable cost, it struggles to achieve real human parity in many domains and most language pairs, if not all of them. Alongside the long road of MT research and development, quality evaluation metrics played very important roles in MT advancement and evolution. In this tutorial, we overview the traditional human judgement criteria, automatic evaluation metrics, unsupervised quality estimation models, as well as the meta-evaluation of the evaluation methods. Among these, we will also cover the very recent work in the MT evaluation (MTE) fields, taking advantage of the large size of pre-trained language models for automatic metric customisation towards exactly deployed language pairs and domains. In addition, we also introduce the statistical confidence estimation regarding the sample size needed for human evaluation in real practice simulation. Full tutorial material is \textbf{available} to download at https://github.com/poethan/LREC22_MetaEval_Tutorial.
A $2\ell k$ Kernel for $\ell$-Component Order Connectivity
Venue: arXiv cs.DS 2025
Year: 2025

In the $\ell$-Component Order Connectivity problem ($\ell \in \mathbb{N}$), we are given a graph $G$ on $n$ vertices, $m$ edges and a non-negative integer $k$ and asks whether there exists a set of vertices $S\subseteq V(G)$ such that $|S|\leq k$ and the size of the largest connected component in $G-S$ is at most $\ell$. In this paper, we give a linear programming based kernel for $\ell$-Component Order Connectivity with at most $2\ell k$ vertices that takes $n^{\mathcal{O}(\ell)}$ time for every constant $\ell$. Thereafter, we provide a separation oracle for the LP of $\ell$-COC implying that the kernel only takes $(3e)^{\ell}\cdot n^{O(1)}$ time. On the way to obtaining our kernel, we prove a generalization of the $q$-Expansion Lemma to weighted graphs. This generalization may be of independent interest.