Tootfinder

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

Implication Problems over Positive Semirings
Minna Hirvonen
https://arxiv.org/abs/2510.08112 https://arxiv.org/pdf/2510.08112

Implication Problems over Positive Semirings
We study various notions of dependency in semiring team semantics. Semiring teams are essentially database relations, where each tuple is annotated with some element from a positive semiring. We consider semiring generalizations of several dependency notions from database theory and probability theory, including functional and inclusion dependencies, marginal identity, and (probabilistic) independence. We examine axiomatizations of implication problems, which are rule-based characterizations fo…

The CSA releases Matter 1.4.2 to improve reliability, stability, and cross-platform coordination, like Wi-Fi-only commissioning to cut its Bluetooth dependency (Jennifer Pattison Tuohy/The Verge)
https://www.theverge.com/matter/757179/matter-1…

Matter’s latest update doubles down on stability and pushes the platforms to play better together
Updates to how Wi-Fi routers work in Matter, how devices connect to the network, and the addition of Wi-Fi commissioning arrive with the latest Matter spec.

It's a Complete Haystack: Understanding Dependency Management Needs in Computer-Aided Design
Kathy Cheng, Alison Olechowski, Shurui Zhou
https://arxiv.org/abs/2508.05940 htt…

It's a Complete Haystack: Understanding Dependency Management Needs in Computer-Aided Design
In today's landscape, hardware development teams face increasing demands for better quality products, greater innovation, and shorter manufacturing lead times. Despite the need for more efficient and effective processes, hardware designers continue to struggle with a lack of awareness of design changes and other collaborators' actions, a persistent issue in decades of CSCW research. One significant and unaddressed challenge is understanding and managing dependencies between 3D CAD (computer-aid…

Dependency-Aware Execution Mechanism in Hyperledger Fabric Architecture
Sanyam Kaul, Manaswini Piduguralla, Gayathri Shreeya Patnala, Sathya Peri
https://arxiv.org/abs/2509.07425

Dependency-Aware Execution Mechanism in Hyperledger Fabric Architecture
Hyperledger Fabric is a leading permissioned blockchain framework for enterprise use, known for its modular design and privacy features. While it strongly supports configurable consensus and access control, Fabric can face challenges in achieving high transaction throughput and low rejection rates under heavy workloads. These performance limitations are often attributed to endorsement, ordering, and validation bottlenecks. Further, optimistic concurrency control and deferred validation in Fabri…

@… the window from the timeline I measured was ~5 hours but to solve the root issue would require architecture changes to npm publish workflows — but until something changes upstream, yeah — more alerts! more emails! I want to know every time a build dependency in my projects is compromised as soon as possible so that I can make the best choices for my projects
Fro…

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models
Chang Dai, Hongyu Shan, Mingyang Song, Di Liang
https://arxiv.org/abs/2509.05218

HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models
Positional encoding mechanisms enable Transformers to model sequential structure and long-range dependencies in text. While absolute positional encodings struggle with extrapolation to longer sequences due to fixed positional representations, and relative approaches like Alibi exhibit performance degradation on extremely long contexts, the widely-used Rotary Positional Encoding (RoPE) introduces oscillatory attention patterns that hinder stable long-distance dependency modelling. We address the…

just read the first time about paper/paperdesign and their ultra fast zero-dependency shaders, interesting piece of open source code to look into
https://shaders.paper.design/

Paper Shaders – Ultra-fast zero-dependency shaders
Shaders for you to use in your projects, as React components or GLSL.

Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach
Siamak Farshidi, Amir Saberhabibi, Behbod Eskafi, Niloofar Nikfarjam, Sadegh Eskandari, Slinger Jansen, Michel Chaudron, Bedir Tekinerdogan
https://arxiv.org/abs/2508.05693

Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach
Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development workflows, but their suggestions often overlook dependency evaluation, emphasize popularity over suitability, and lack reproducibility. This creates risks for projects that require transparency, long-term reliability, maintainability, and informed architectura…

Beyond Constant Parameters: Hyper Prediction Models and HyperMPC
Jan W\k{e}grzynowski, Piotr Kicki, Grzegorz Czechmanowski, Maciej Krupka, Krzysztof Walas
https://arxiv.org/abs/2508.06181

Beyond Constant Parameters: Hyper Prediction Models and HyperMPC
Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by computational complexity and state representation. To address this limitation, we propose the Hyper Prediction Model (HyperPM) - a novel approach in which we project the unmodeled dynamics onto a time-dependent dynamics model. This time-dependency is captured throu…

Rethinking the long-range dependency in Mamba/SSM and transformer models
Cong Ma, Kayvan Najarian
https://arxiv.org/abs/2509.04226 https://arxiv.org/pdf/25…

Rethinking the long-range dependency in Mamba/SSM and transformer models
Long-range dependency is one of the most desired properties of recent sequence models such as state-space models (particularly Mamba) and transformer models. New model architectures are being actively developed and benchmarked for prediction tasks requiring long-range dependency. However, the capability of modeling long-range dependencies of these models has not been investigated from a theoretical perspective, which hinders a systematic improvement on this aspect. In this work, we mathematical…

Tool Graph Retriever: Exploring Dependency Graph-based Tool Retrieval for Large Language Models
Linfeng Gao, Yaoxiang Wang, Minlong Peng, Jialong Tang, Yuzhe Shang, Mingming Sun, Jinsong Su
https://arxiv.org/abs/2508.05152

Tool Graph Retriever: Exploring Dependency Graph-based Tool Retrieval for Large Language Models
With the remarkable advancement of AI agents, the number of their equipped tools is increasing rapidly. However, integrating all tool information into the limited model context becomes impractical, highlighting the need for efficient tool retrieval methods. In this regard, dominant methods primarily rely on semantic similarities between tool descriptions and user queries to retrieve relevant tools. However, they often consider each tool independently, overlooking dependencies between tools, whi…

Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature
S. Mobina Hosseini, Bahareh Soleimanpour Salmasi
https://arxiv.org/abs/2508.05842 https:…

Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature
The X-ray photons substantially affect the thermal and ionization states of the intergalactic medium (IGM) during the Epoch of Reionization (EoR), thereby significantly influencing the 21-cm line observables such as its sky-averaged (global) brightness temperature. Nevertheless, the complicated dependency of astrophysical processes on a broad spectrum of parameters, including X-ray efficiency, spectral characteristics, and gas dynamics, makes precisely simulating the effect of X-ray flux challe…

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

On the Interplay of Cube Learning and Dependency Schemes in QCDCL Proof Systems
Abhimanyu Choudhury, Meena Mahajan
https://arxiv.org/abs/2510.05876 https://

On the Interplay of Cube Learning and Dependency Schemes in QCDCL Proof Systems
Quantified Conflict Driven Clause Leaning (QCDCL) is one of the main approaches to solving Quantified Boolean Formulas (QBF). Cube-learning is employed in this approach to ensure that true formulas can be verified. Dependency Schemes help to detect spurious dependencies that are implied by the variable ordering in the quantifier prefix of QBFs but are not essential for constructing (counter)models. This detection can provably shorten refutations in specific proof systems, and is expected to spe…

OTESGN:Optimal Transport Enhanced Syntactic-Semantic Graph Networks for Aspect-Based Sentiment Analysis
Xinfeng Liao, Xuanqi Chen, Lianxi Wang, Jiahuan Yang, Zhuowei Chen, Ziying Rong
https://arxiv.org/abs/2509.08612

OTESGN:Optimal Transport Enhanced Syntactic-Semantic Graph Networks for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to identify aspect terms and determine their sentiment polarity. While dependency trees combined with contextual semantics effectively identify aspect sentiment, existing methods relying on syntax trees and aspect-aware attention struggle to model complex semantic relationships. Their dependence on linear dot-product features fails to capture nonlinear associations, allowing noisy similarity from irrelevant words to obscure key opinion terms. Motivate…

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Wenxin Mao, Zhitao Wang Long Wang, Sirong Chen, Cuiyun Gao, Luyang Cao, Ziming Liu, Qiming Zhang, Jun Zhou, Zhi Jin
https://arxiv.org/abs/2508.03379

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Large language models (LLMs) excel at generating code from natural language (NL) descriptions. However, the plain textual descriptions are inherently ambiguous and often fail to capture complex requirements like intricate system behaviors, conditional logic, and architectural constraints; implicit data dependencies in service-oriented architectures are difficult to infer and handle correctly. To bridge this gap, we propose a novel step-by-step code generation framework named UML2Dep by leveragi…

from my link log —
Basic dependency injection with objects in OCaml.
https://gr-im.github.io/a/dependency-injection.html
saved 2025-08-18

AttestLLM: Efficient Attestation Framework for Billion-scale On-device LLMs
Ruisi Zhang, Yifei Zhao, Neusha Javidnia, Mengxin Zheng, Farinaz Koushanfar
https://arxiv.org/abs/2509.06326

AttestLLM: Efficient Attestation Framework for Billion-scale On-device LLMs
As on-device LLMs(e.g., Apple on-device Intelligence) are widely adopted to reduce network dependency, improve privacy, and enhance responsiveness, verifying the legitimacy of models running on local devices becomes critical. Existing attestation techniques are not suitable for billion-parameter Large Language Models (LLMs), struggling to remain both time- and memory-efficient while addressing emerging threats in the LLM era. In this paper, we present AttestLLM, the first-of-its-kind attestatio…

Utilizing Composer Packages to Accelerate Laravel-Based Project Development Among Students: A Pedagogical and Practical Framework
Rohaizah Abdul Wahid, Muhamad Said Nizamuddin Nadim, Suliana Sulaiman, Syahmi Akmal Shaharudin, Muhammad Danial Jupikil, Iqqwan Jasman Su Azlan Su
https://arxiv.org/abs/2508.05747

Utilizing Composer Packages to Accelerate Laravel-Based Project Development Among Students: A Pedagogical and Practical Framework
Laravel has emerged as a foundational framework in university web development curricula. However, despite its scaffolding capabilities, students often struggle to complete projects within limited academic timelines. This conceptual paper introduces Composer, PHP's standard dependency manager, and categorizes a curated selection of Composer packages that significantly reduce development effort while fostering professional software practices. Grounded in practical and pedagogical considerations, …

software_dependencies: Software dependencies (2010)
Several networks of software dependencies. Nodes represent libraries and a directed edge denotes a library dependency on another.
This network has 550 nodes and 3691 edges.
Tags: Technological, Software, Unweighted
https://networks.skewed.de/n…

🚨🚨🚨 uv has fixed my pet issue https://github.com/astral-sh/uv/issues/6314 and `uv tool install ansible --with-executables-from ansible-core` works now! 🚨🚨🚨
for us, that was the last blocker before going all-in on uv.

Support including dependency executables (e.g., `--include-deps`) in `uv tool install` · Issue #6314 · astral-sh/uv
hi, congrats on 0.3.0! I've noticed one very useful pipx option that uv tool is currently missing: --install-deps. It means that it also adds the CLIs of packages it depends on. This is crucial for...

The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R. #rstats

GitHub - fastverse/fastverse: An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R - fastverse/fastverse

Responding to the growing demand from organisations worldwide to reduce their dependency on US cloud providers, German software giant SAP has announced plans to invest more than €20 billion over the next decade in sovereign cloud infrastructure, research and development and personnel.

SAP promises €20bn+ to expand its sovereign cloud services
Responding to the growing demand from organisations worldwide to reduce their dependency on US cloud providers, German software giant SAP has announced plans to invest ...

Added a graphic showing the dependency tree of the #Linux @kernel-vanilla #Fedora coprs to https://fedoraproject.org/wi…

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
Tuo Wang, Adithya Kulkarni, Tyler Cody, Peter A. Beling, Yujun Yan, Dawei Zhou
https://arxiv.org/abs/2509.07925

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semantic dependencies, relying on token-level probability measures that fail to capture structural relationships within the generated text. We propose GENUINE: Graph ENhanced mUlti-level uncertaINty Estimation for Large Language Models, a structure-aware framework that leverages dependency parse trees and hierarchical graph …

Dependency on Meta AI Chatbot in Messenger Among STEM and Non-STEM Students in Higher Education
Hilene E. Hernandez, Rhiziel P. Manalese, Roque Francis B. Dianelo, Jaymark A. Yambao, Almer B. Gamboa, Lloyd D. Feliciano, Mike Haizon M. David, Freneil R. Pampo, John Paul P. Miranda
https://arxiv.org/abs/2507.21059

Dependency on Meta AI Chatbot in Messenger Among STEM and Non-STEM Students in Higher Education
To understand the potential dependency of tertiary students regarding Meta AI in the academic context. This descriptive cross-sectional study surveyed 872 tertiary students from public and private institutions in Luzon, Philippines. Demographic information and perceptions on Meta AI dependency based on existing literature were collected. Descriptive statistics were used to summarize the data and differences between STEM and non-STEM students were analyzed using the Mann-Whitney U test. The resu…

"Spain has broken the ruinous link between power prices and volatile fossil fuels, something its European neighbours are desperate to do. Spain risks sliding back into costly gas reliance amid post-blackout fears. Boosting grids and batteries will help Spain break free from fossil dependency for good."
It looks like the link is only partially broken, not through planning but due to scale.
Decoupled: how Spain cut the link between gas and power prices using

Decoupled: how Spain cut the link between gas and power prices using renewables | Ember
Spain has some of the lowest wholesale electricity prices in Europe, largely owing to the country’s strong solar and wind growth which reduced the influence of expensive coal and gas power on the electricity market.

Nitrogen-doped carbon quantum dots enhance bacteria growth and biosensing signals
David Rutherford, Marketa \v{S}lapal Ba\v{r}inkov\'a, Jaroslav Kuli\v{c}ek, Jelena Kozic, Jovana Prekodravac Filipovi\'c, Bohuslav Rezek
https://arxiv.org/abs/2510.06454

Nitrogen-doped carbon quantum dots enhance bacteria growth and biosensing signals
Carbon quantum dots (CQDs) are known for their antibacterial properties and ability to inhibit bacteria growth. In the current study, we observed a dopant and concentration-dependency on the antibacterial effect of CQDs. High concentrations of CQDs completely inhibited bacteria growth yet low concentrations enhanced growth. Unlike undoped CQDs, nitrogen-doped CQDs (N-CQDs) enhanced bacteria growth in a concentration-dependant manner and nitrogen/iron co-doped CQDs (Fe/N-CQDs) resulted in growth…

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning
Sumeet Ramesh Motwani, Alesia Ivanova, Ziyang Cai, Philip Torr, Riashat Islam, Shital Shah, Christian Schroeder de Witt, Charles London
https://arxiv.org/abs/2510.07312

h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning
Large language models excel at short-horizon reasoning tasks, but performance drops as reasoning horizon lengths increase. Existing approaches to combat this rely on inference-time scaffolding or costly step-level supervision, neither of which scales easily. In this work, we introduce a scalable method to bootstrap long-horizon reasoning capabilities using only existing, abundant short-horizon data. Our approach synthetically composes simple problems into complex, multi-step dependency chains o…

Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation
Nakyung Lee, Yeongoon Kim, Minhae Oh, Suhwan Kim, Jin Woo Koo, Hyewon Jo, Jungwoo Lee
https://arxiv.org/abs/2509.07324

Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation
Transformer-based self-attention mechanism serves as the core of modern language models, yet it often suffers from localization, where attentions collapse onto a limited subset of tokens and fail to capture long-range dependencies. To address this issue, we propose Self-Attention One-step Belief Propagation (SAOBP), a refinement framework that injects multi-hop relationships through a belief propagation process. To interpret and quantify these interactions, we introduce Global Token Dependency …

Reclaiming Europe’s Digital Sovereignty https://www.noemamag.com/reclaiming-europes-digital-sovereignty

Reclaiming Europe’s Digital Sovereignty
Europe can accept permanent technological dependency, or it can build democratic digital systems rooted in climate commitments, labor protections and social diversity.

Dependency Chain Analysis of ROS 2 DDS QoS Policies: From Lifecycle Tutorial to Static Verification
Sanghoon Lee, Junha Kang, Kyung-Joon Park
https://arxiv.org/abs/2509.03381 ht…

Dependency Chain Analysis of ROS 2 DDS QoS Policies: From Lifecycle Tutorial to Static Verification
Robot Operating System 2 (ROS 2) relies on the Data Distribution Service (DDS), which offers more than 20 Quality of Service (QoS) policies governing availability, reliability, and resource usage. Yet ROS 2 users lack clear guidance on safe policy combinations and validation processes prior to deployment, which often leads to trial-and-error tuning and unexpected runtime failures. To address these challenges, we analyze DDS Publisher-Subscriber communication over a life cycle divided into Disco…

Deep dependency on a technology lack of agency disagreement with leadership creates anger.
Great observation by @…
This explains so well why after being a macOS developer for 20 years, I can't stand Apple right now.

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

Trump’s 50 percent tariffs landed like a declaration of economic war on India,
undercutting enormous investments made by American companies to hedge their dependency on China.
India’s hard work to present itself to the world as the best alternative to Chinese factories
— what business executives and big money financiers have embraced as part of the China Plus One strategy
— has been left in tatters.
Now, less than a week since the tariffs took full effect, offic…

India Was the Economic Alternative to China. Trump Ended That.
A lurch in policy has shaken the India-U.S. economic alliance against China, leaving India little choice but to consider reversing its own strategy.

Time-dependency in hyperbolic Anderson model: Stratonovich regime
Xia Chen
https://arxiv.org/abs/2510.01412 https://arxiv.org/pdf/2510.01412

Time-dependency in hyperbolic Anderson model: Stratonovich regime
In this paper, the hyperbolic Anderson equation generated by a time-dependent Gaussian noise is under investigation in two fronts: The solvability and large-$t$ asymptotics. The investigation leads to a necessary and sufficient condition for existence and a precise large-$t$ limit form for the expectation of the solution. Three major developments are made for achieving these goals: A universal bound for Stratonovich moment that guarantees the Stratonovich integrability and ${\cal L}^2$-converge…

Comparing transitive dependency version resolution in #Rust and #Java
https://blog.frankel.ch/dependency-ver

There are light years between (1) “I thought this code was useful, so I shared it online and slapped an OSS license on it” and (2) “We set up a foundation to fund ongoing maintenance of this critical OSS project while guaranteeing its independence.”
The “dependency, fork, or rewrite?” question looks very, very different for (1) vs (2).
Yet how many of us think about where any given library dependency sits on that continuum when we add a dependency? Even if we try, how can we ••tell••?
5/

MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition
Feng-Qi Cui, Zhen Lin, Xinlong Rao, Anyang Tong, Shiyao Li, Fei Wang, Changlin Chen, Bin Liu
https://arxiv.org/abs/2509.04344

MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) faces significant challenges due to long-tailed category distributions and complexity of spatio-temporal feature modeling. While existing deep learning-based methods have improved DFER performance, they often fail to address these issues, resulting in severe model induction bias. To overcome these limitations, we propose a novel multi-instance learning framework called MICACL, which integrates spatio-temporal dependency modeling and long-tailed contr…

Released a new version of ha-mqtt-discoverable
#iot @… #homeassistant

@… I think it’s largely inertia. We built performance optimisations for computers in the 1960s, put them into the instruction sets, and never looked back.
Part of me wonders what would have happened if we had Lisp machines, fixed point, dependency-based evaluation, hardware actors…
Part of me wonders what would happen if we started making them now,…

Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects
Jerin Yasmin, Wenxin Jiang, James C. Davis, Yuan Tian
https://arxiv.org/abs/2509.06085

Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects
Pre-trained models (PTMs) are machine learning models that have been trained in advance, often on large-scale data, and can be reused for new tasks, thereby reducing the need for costly training from scratch. Their widespread adoption introduces a new class of software dependency, which we term Software Dependencies 2.0, extending beyond conventional libraries to learned behaviors embodied in trained models and their associated artifacts. The integration of PTMs as software dependencies in real…

Universal Beta Splatting
Rong Liu, Zhongpai Gao, Benjamin Planche, Meida Chen, Van Nguyen Nguyen, Meng Zheng, Anwesa Choudhuri, Terrence Chen, Yue Wang, Andrew Feng, Ziyan Wu
https://arxiv.org/abs/2510.03312

Universal Beta Splatting
We introduce Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering. Unlike fixed Gaussian primitives, Beta kernels enable controllable dependency modeling across spatial, angular, and temporal dimensions within a single representation. Our unified approach captures complex light transport effects, handles anisotropic view-dependent appearance, and models scene dynamics without re…

Explicit modelling of subject dependency in BCI decoding
Michele Romani, Francesco Paissan, Andrea Foss\`a, Elisabetta Farella
https://arxiv.org/abs/2509.23247 https://

Explicit modelling of subject dependency in BCI decoding
Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data, often requiring lengthy calibration phases. In this work, we present an end-to-end approach that explicitly models the subject dependency using lightweight convolutional neural networks (CNNs) conditioned on the subject's identity. Our method integrates hyperparameter optimization strategies that prioritize class imbalance and evaluates two conditioning mechanisms to adapt pre-trained models to…

Construction of $(n,n)$-functions with low differential-linear uniformity
Xi Xie, Nian Li, Qiang Wang, Xiangyong Zeng, Yinglong Du
https://arxiv.org/abs/2508.01190 https://

Construction of $(n,n)$-functions with low differential-linear uniformity
The differential-linear connectivity table (DLCT), introduced by Bar-On et al. at EUROCRYPT'19, is a novel tool that captures the dependency between the two subciphers involved in differential-linear attacks. This paper is devoted to exploring the differential-linear properties of $(n,n)$-functions. First, by refining specific exponential sums, we propose two classes of power functions over $\mathbb{F}_{2^n}$ with low differential-linear uniformity (DLU). Next, we further investigate the differ…

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

I have a habbit of making (too) many (small) packages for functionality that might be reused in different context. {box} might be an alternative by making scripts into modlues that can be loaded: #RStats

‘box’: Write Reusable, Composable and Modular R Code
A modern module system for R. Organise code into hierarchical, composable, reusable modules, and use it effortlessly across projects via a flexible, declarative dependency loading syntax.

Question for #dotnet and #developer folks. How long does it typically take your team to evaluate, proof of concept, implement, and deploy a medium-to-large dependency in your solutions?
🙏Boosts are appreciated

Partial Functional Dynamic Backdoor Diffusion-based Causal Model
Xinwen Liu, Lei Qian, Song Xi Chen, Niansheng Tang
https://arxiv.org/abs/2509.00472 https://

Partial Functional Dynamic Backdoor Diffusion-based Causal Model
We introduce a Partial Functional Dynamic Backdoor Diffusion-based Causal Model (PFD-BDCM), specifically designed for causal inference in the presence of unmeasured confounders with spatial heterogeneity and temporal dependency. The proposed PFD-BDCM framework addresses the restrictions of the existing approaches by uniquely integrating models for complex spatio-temporal dynamics with the analysis of multi-resolution variables. Specifically, the framework systematically mitigates confounding bi…

Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints
Zihan Lin, Haojie Liu, Randall R. Rojas
https://arxiv.org/abs/2507.20039 https://

Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints
This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error Variance Decomposition (FEVD), transforming influence relationships into a cost-based network. Specifically, FEVD breaks down the VAR's forecast error variance to quantify how much each stock's shock…

No comment.

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted

RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats
Arjun Juneja
https://arxiv.org/abs/2508.03879 https://arxiv.org/pdf/2508.…

RX-INT: A Kernel Engine for Real-Time Detection and Analysis of In-Memory Threats
Malware and cheat developers use fileless execution techniques to evade traditional, signature-based security products. These methods include various types of manual mapping, module stomping, and threadless injection which work entirely within the address space of a legitimate process, presenting a challenge for detection due to ambiguity between what is legitimate and what isn't. Existing tools often have weaknesses, such as a dependency on Portable Executable (PE) structures or a vulnerabilit…

A Markov switching discrete-time Hawkes process: application to the monitoring of bats behavior
Anna Bonnet, St\'ephane Robin
https://arxiv.org/abs/2507.20153 https://

A Markov switching discrete-time Hawkes process: application to the monitoring of bats behavior
Over the past few decades, the Hawkes process has become a popular framework for modeling temporal events thanks to its flexibility to capture different dependency structures. The objective of this work is to model call sequences emitted by bats for echolocation, whose patterns are known to change depending on the animal's activity. The novelty of the model lies in the combination of a Hawkes-type dependency from past events, as well as a latent variable that encodes changes in bat behavior. Mo…

PyTrim: A Practical Tool for Reducing Python Dependency Bloat
Konstantinos Karakatsanis, Georgios Alexopoulos, Ioannis Karyotakis, Foivos Timotheos Proestakis, Evangelos Talos, Panos Louridas, Dimitris Mitropoulos
https://arxiv.org/abs/2510.00674

PyTrim: A Practical Tool for Reducing Python Dependency Bloat
Dependency bloat is a persistent challenge in Python projects, which increases maintenance costs and security risks. While numerous tools exist for detecting unused dependencies in Python, removing these dependencies across the source code and configuration files of a project requires manual effort and expertise. To tackle this challenge we introduce PYTRIM, an end-to-end system to automate this process. PYTRIM eliminates unused imports and package declarations across a variety of file types,…

@… hmm! we want ranges so every dependency doesn’t have their own ultra-specific version of chalk (or whatever commonly installed dep) installed, no?

#Figma's $300,000 Daily #AWS Bill Highlights Cloud Dependency Risks
https://www.infoq.com/news/2025/07/figma-a…

HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning
Kanglin Qu, Pan Gao, Qun Dai, Yuanhao Sun
https://arxiv.org/abs/2507.19778 https://

HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning
The attention mechanism has become a dominant operator in point cloud learning, but its quadratic complexity leads to limited inter-point interactions, hindering long-range dependency modeling between objects. Due to excellent long-range modeling capability with linear complexity, the selective state space model (S6), as the core of Mamba, has been exploited in point cloud learning for long-range dependency interactions over the entire point cloud. Despite some significant progress, related wor…

INGRID: Intelligent Generative Robotic Design Using Large Language Models
Guanglu Jia, Ceng Zhang, Gregory S. Chirikjian
https://arxiv.org/abs/2509.03842 https://

INGRID: Intelligent Generative Robotic Design Using Large Language Models
The integration of large language models (LLMs) into robotic systems has accelerated progress in embodied artificial intelligence, yet current approaches remain constrained by existing robotic architectures, particularly serial mechanisms. This hardware dependency fundamentally limits the scope of robotic intelligence. Here, we present INGRID (Intelligent Generative Robotic Design), a framework that enables the automated design of parallel robotic mechanisms through deep integration with recipr…

DySTop
Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao, Hongli Xu
https://arxiv.org/abs/2508.01996 https://arxiv.org/pdf/2508.01996

DySTop
Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under hetero…

Are you interested in how dependency-heavy your (or another) package is and why? #rstats

GitHub - jokergoo/pkgndep: Analyzing Dependency Heaviness of R Packages
Analyzing Dependency Heaviness of R Packages. Contribute to jokergoo/pkgndep development by creating an account on GitHub.

LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
Bo Hou, Xin Tan, Kai Zheng, Fang Liu, Yinghao Zhu, Li Zhang
https://arxiv.org/abs/2507.16395

LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
Atomic commits, each of which addresses a single development concern, are a best practice in software development. However, developers frequently produce tangled commits that mix unrelated changes due to practical constraints or unclear boundaries, negatively impacting code review and maintenance. Although prior commit untangling approaches: rule-based, feature-based, or graph-based, have made progress, they often rely on shallow signals and fail to distinguish between explicit dependencies (e.…

PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python
Jacob Mahon, Chenxi Hou, Zhihao Yao
https://arxiv.org/abs/2507.18075 https://

PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python
Python software development heavily relies on third-party packages. Direct and transitive dependencies create a labyrinth of software supply chains. While it is convenient to reuse code, vulnerabilities within these dependency chains can propagate through dependencies, potentially affecting down-stream packages and applications. PyPI, the official Python package repository, hosts many packages and lacks a comprehensive analysis of the prevalence of vulnerable dependencies. This paper introduces…

Measuring Large Language Models Dependency: Validating the Arabic Version of the LLM-D12 Scale
Sameha AlShakhsi, Ala Yankouskaya, Magnus Liebherr, Raian Ali
https://arxiv.org/abs/2508.17063

Measuring Large Language Models Dependency: Validating the Arabic Version of the LLM-D12 Scale
There is an urgent need for reliable, culturally validated instruments to assess psychological responses to AI in general and large language models (LLMs). This need is global issue, but it is especially urgent among Arabic-speaking populations, where AI and LLMs adoption is accelerating, yet psychometric tools remain limited. This study presents the first validation of the LLM-D12, a dual-dimensional scale assessing Instrumental and Relationship Dependency on LLMs, in an Arab sample. A total o…

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted

It’s kind of wild that every JavaScript dependency now requires some kind of build tool or package management.

Dependency Pairs for Expected Innermost Runtime Complexity and Strong Almost-Sure Termination of Probabilistic Term Rewriting
Jan-Christoph Kassing, Leon Spitzer, J\"urgen Giesl
https://arxiv.org/abs/2507.12918

Dependency Pairs for Expected Innermost Runtime Complexity and Strong Almost-Sure Termination of Probabilistic Term Rewriting
The dependency pair (DP) framework is one of the most powerful techniques for automatic termination and complexity analysis of term rewrite systems. While DPs were extended to prove almost-sure termination of probabilistic term rewrite systems (PTRSs), automatic complexity analysis for PTRSs is largely unexplored. We introduce the first DP framework for analyzing expected complexity and for proving positive or strong almost-sure termination (SAST) of innermost rewriting with PTRSs, i.e., finite…

JS-TOD: Detecting Order-Dependent Flaky Tests in Jest
Negar Hashemi, Amjed Tahir, Shawn Rasheed, August Shi, Rachel Blagojevic
https://arxiv.org/abs/2509.00466 https://

JS-TOD: Detecting Order-Dependent Flaky Tests in Jest
We present JS-TOD (JavaScript Test Order-dependency Detector), a tool that can extract, reorder, and rerun Jest tests to reveal possible order-dependent test flakiness. Test order dependency is one of the leading causes of test flakiness. Ideally, each test should operate in isolation and yield consistent results no matter the sequence in which tests are run. However, in practice, test outcomes can vary depending on their execution order. JS-TOD employed a systematic approach to randomising tes…

oh, hello: https://github.com/e18e/action-dependency-diff (@…)

An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
Shuhan Liu, Xing Hu, Xin Xia, David Lo, Xiaohu Yang
https://arxiv.org/abs/2508.21417 https://

An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
Large language models (LLMs) have developed rapidly in recent years, revolutionizing various fields. Despite their widespread success, LLMs heavily rely on external code dependencies from package management systems, creating a complex and interconnected LLM dependency supply chain. Vulnerabilities in dependencies can expose LLMs to security risks. While existing research predominantly focuses on model-level security threats, vulnerabilities within the LLM dependency supply chain have been overl…

"It Felt Real" Victim Perspectives on Platform Design and Longer-Running Scams
Jingjia Xiao, Qing Xiao, Hong Shen
https://arxiv.org/abs/2510.02680 https://

"It Felt Real" Victim Perspectives on Platform Design and Longer-Running Scams
Longer-running scams, such as romance fraud and "pig-butchering" scams, exploit not only victims' emotions but also the design of digital platforms. Scammers commonly leverage features such as professional-looking profile verification, algorithmic recommendations that reinforce contact, integrated payment systems, and private chat affordances to gradually establish trust and dependency with victims. Prior work in HCI and criminology has examined online scams through the lenses of detection mech…

python_dependency: Python Dependency Network
Python's package dependency networks. Nodes in the network are Python's packages registered to PyPI and edges are dependencies among packages.
This network has 58743 nodes and 108399 edges.
Tags: Technological, Software, Unweighted
https://networks.sk…

EntropyLong: Effective Long-Context Training via Predictive Uncertainty
Junlong Jia, Ziyang Chen, Xing Wu, Chaochen Gao, Zijia Lin, Debing Zhang, Songlin Hu, Binghui Guo
https://arxiv.org/abs/2510.02330

EntropyLong: Effective Long-Context Training via Predictive Uncertainty
Training long-context language models to capture long-range dependencies requires specialized data construction. Current approaches, such as generic text concatenation or heuristic-based variants, frequently fail to guarantee genuine long-range dependencies. We propose EntropyLong, a novel data construction method that leverages predictive uncertainty to verify dependency quality. Our approach identifies high-entropy positions in documents, retrieves semantically relevant contexts from large co…

LLM as an Execution Estimator: Recovering Missing Dependency for Practical Time-travelling Debugging
Yunrui Pei, Hongshu Wang, Wenjie Zhang, Yun Lin, Weiyu Kong, Jin song Dong
https://arxiv.org/abs/2508.18721

LLM as an Execution Estimator: Recovering Missing Dependency for Practical Time-travelling Debugging
Dynamic data dependency, answering "why a variable has this value?", is critical for debugging. Given a program step `s` reading a variable `v`, finding the dynamic definition of `v` is challenging. Traditional methods require either (1) exhaustive instrumentation of all possible definitions of `v` in one run or (2) replicating the run to re-examine reads/writes - both costly. If `v` is defined in a library, instrumentation becomes expensive; for non-deterministic programs, replication is infea…

software_dependencies: Software dependencies (2010)
Several networks of software dependencies. Nodes represent libraries and a directed edge denotes a library dependency on another.
This network has 181 nodes and 470 edges.
Tags: Technological, Software, Unweighted
https://networks.skewed.de/n…

The Path of Self-Evolving Large Language Models: Achieving Data-Efficient Learning via Intrinsic Feedback
Hangfan Zhang, Siyuan Xu, Zhimeng Guo, Huaisheng Zhu, Shicheng Liu, Xinrun Wang, Qiaosheng Zhang, Yang Chen, Peng Ye, Lei Bai, Shuyue Hu
https://arxiv.org/abs/2510.02752

The Path of Self-Evolving Large Language Models: Achieving Data-Efficient Learning via Intrinsic Feedback
Reinforcement learning (RL) has demonstrated potential in enhancing the reasoning capabilities of large language models (LLMs), but such training typically demands substantial efforts in creating and annotating data. In this work, we explore improving LLMs through RL with minimal data. Our approach alternates between the LLM proposing a task and then attempting to solve it. To minimize data dependency, we introduce two novel mechanisms grounded in self-awareness: (1) self-aware difficulty predi…

Visualize dependencies between functions: #rstats

is it too much to ask that a software dependency be:
1. fast
2. permissive open-source licensed
3. lightweight in bundle cost
4. lightweight in installation size
5. well-maintained
6. accurate
7. able to run in all of the marginally different JavaScript runtimes
8. human sourced to avoid ethical and future copyright issues with AI
9. use modern APIs and without vestigial polyfill bloat
10. use best progressive enhancement and accessibility p…

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents
Hengyu An, Jinghuai Zhang, Tianyu Du, Chunyi Zhou, Qingming Li, Tao Lin, Shouling Ji
https://arxiv.org/abs/2508.15310

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents
Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks. However, when interacting with untrusted data sources (e.g., fetching information from public websites), tool responses may contain injected instructions that covertly influence agent behaviors and lead to malicious outcomes, a threat referred to as Indirect Prompt Injection (IPI). Existing defenses typically rely on advanced pro…

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
https://arxiv.org/abs/2507.18504

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many feature interactions are structurally insignificant. This creates a fundamental mismatch as LLMs' self-attention mechanism inevitably distributes focus across all pairs, diluting attention on critical relationships, particularly in datasets with complex dependencies or semanticall…

VDGraph: A Graph-Theoretic Approach to Unlock Insights from SBOM and SCA Data
Howell Xia, Jonah Gluck, Sevval Simsek, David Sastre Medina, David Starobinski
https://arxiv.org/abs/2507.20502

VDGraph: A Graph-Theoretic Approach to Unlock Insights from SBOM and SCA Data
The high complexity of modern software supply chains necessitates tools such as Software Bill of Materials (SBOMs) to manage component dependencies, and Software Composition Analysis (SCA) tools to identify vulnerabilities. While there exists limited integration between SBOMs and SCA tools, a unified view of complex dependency-vulnerability relationships remains elusive. In this paper, we introduce VDGraph, a novel knowledge graph-based methodology for integrating vulnerability and dependency d…

jdk: Java SE Dev Kit dependencies (1.6.0.7)
A network of class dependencies within the JDK (Java SE Development Kit) 1.6.0.7 framework. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6434 nodes and 150985 edges.
Tags: Technological, Software, Unweighted, Multigraph
https://

Structural and Connectivity Patterns in the Maven Central Software Dependency Network
Daniel Ogenrwot, John Businge, Shaikh Arifuzzaman
https://arxiv.org/abs/2508.13819 https://…

Structural and Connectivity Patterns in the Maven Central Software Dependency Network
Understanding the structural characteristics and connectivity patterns of large-scale software ecosystems is critical for enhancing software reuse, improving ecosystem resilience, and mitigating security risks. In this paper, we investigate the Maven Central ecosystem, one of the largest repositories of Java libraries, by applying network science techniques to its dependency graph. Leveraging the Goblin framework, we extracted a sample consisting of the top 5,000 highly connected artifacts base…

jdk: Java SE Dev Kit dependencies (1.6.0.7)
A network of class dependencies within the JDK (Java SE Development Kit) 1.6.0.7 framework. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6434 nodes and 150985 edges.
Tags: Technological, Software, Unweighted, Multigraph
https://

Hierarchical Bracketing Encodings Work for Dependency Graphs
Ana Ezquerro, Carlos G\'omez-Rodr\'iguez, David Vilares
https://arxiv.org/abs/2509.09388 https://

Hierarchical Bracketing Encodings Work for Dependency Graphs
We revisit hierarchical bracketing encodings from a practical perspective in the context of dependency graph parsing. The approach encodes graphs as sequences, enabling linear-time parsing with $n$ tagging actions, and still representing reentrancies, cycles, and empty nodes. Compared to existing graph linearizations, this representation substantially reduces the label space while preserving structural information. We evaluate it on a multilingual and multi-formalism benchmark, showing competit…

Tool-Assisted Conformance Checking to Reference Process Models
Bernhard Rumpe, Max Stachon, Sebastian St\"uber, Valdes Voufo
https://arxiv.org/abs/2508.00738 https://

Tool-Assisted Conformance Checking to Reference Process Models
Reference models convey best practices and standards. The reference frameworks necessitate conformance checks to ensure adherence to established guidelines and principles, which is crucial for maintaining quality and consistency in various processes. This paper explores automated conformance checks for concrete process models against reference models using causal dependency analysis of tasks and events. Existing notions of conformance checking for process models focus on verifying process execu…

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted

DEPTH: Hallucination-Free Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement
Yupei Yang, Fan Feng, Lin Yang, Wanxi Deng, Lin Qu, Biwei Huang, Shikui Tu, Lei Xu
https://arxiv.org/abs/2508.14391

DEPTH: Hallucination-Free Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement
Relation extraction enables the construction of structured knowledge for many downstream applications. While large language models (LLMs) have shown great promise in this domain, most existing methods concentrate on relation classification, which predicts the semantic relation type between a related entity pair. However, we observe that LLMs often struggle to reliably determine whether a relation exists, especially in cases involving complex sentence structures or intricate semantics, which lea…

software_dependencies: Software dependencies (2010)
Several networks of software dependencies. Nodes represent libraries and a directed edge denotes a library dependency on another.
This network has 550 nodes and 3691 edges.
Tags: Technological, Software, Unweighted
https://networks.skewed.de/…

TAIBOM: Bringing Trustworthiness to AI-Enabled Systems
Vadim Safronov, Anthony McCaigue, Nicholas Allott, Andrew Martin
https://arxiv.org/abs/2510.02169 https://

TAIBOM: Bringing Trustworthiness to AI-Enabled Systems
The growing integration of open-source software and AI-driven technologies has introduced new layers of complexity into the software supply chain, challenging existing methods for dependency management and system assurance. While Software Bills of Materials (SBOMs) have become critical for enhancing transparency and traceability, current frameworks fall short in capturing the unique characteristics of AI systems -- namely, their dynamic, data-driven nature and the loosely coupled dependencies a…

software_dependencies: Software dependencies (2010)
Several networks of software dependencies. Nodes represent libraries and a directed edge denotes a library dependency on another.
This network has 2124 nodes and 10000 edges.
Tags: Technological, Software, Unweighted
https://networks.skewed.de…

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted

jung: JUNG and javax class dependencies (2012)
A network of software class dependency within the JUNG 2.0.1 and javax 1.6.0.7 library namespaces edu.uci.ics.jung and java/javax. Nodes represent classes and a directed edge indicates a dependency of one class on another.
This network has 6120 nodes and 138706 edges.
Tags: Technological, Software, Unweighted