The attack also extended to the gathering of its vehicles and forces, located in Khalat Warda. Hezbollah utilized artillery shells and appropriate missiles during the operation, resulting in verified casualties. This incident underscores the ongoing ...

Hezbollah strikes Israel's 'Za'reit' battalion headquarters in Khalat Warda

Ahmed's wife Warda, 25, and their two young boys, Abdel Naser, aged two, and two-month-old Omar, also died. "They found Mohammed's body, and then Ahmed's," Wafaa says, before breaking down into tears. "The rest of the family... my uncle, Asad, Marwan ...

Ten members of one Palestinian family - including two-month-old baby - killed in strike

In Maryam Keshavarz’s Sundance Audience Award winner The Persian Version, the choices, traumas and joys of multiple Iranian and Iranian American women are traced through a single bloodline. For the writer-director,

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Leila is closer with her eight brothers, who tease her lovingly, as well as her grandmother, Mamanjoon (Bella Warda), who offers her guidance. “The Persian Version” chronicles all of these experiences and more — there is a critical subplot involving ...

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Halloween is next week but Mid-Michigan celebrates early with several community events, trunk-or-treats and more! Check by county to see what’s available for you and the family.

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Inspired by the remarkable durability of ancient Roman construction materials in seawater, a University of Texas at Arlington civil engineering researcher is attempting to duplicate Roman concrete by developing 3D-printed materials to restore damaged or dying coral reefs.

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Pratigya Decorated Knight filly out of Sweet Gentle Kiss. Remarkable Flight (IRE) 12-1 (9-2) Tracked leaders, pushed along over 3f out, gradually dropped away, 6th of 8, 9 1/4l behind Warda Jamila (9-2) at Leicester 1m fll stks (4) gs in Oct. Rouge Sellier ...

14:05 Newbury

EXTRA a keda funda 1 di mart 1976, ku su meta prinsipal pa trese informashonnan alkaso, ophetivo, verifiká i sin sensashon negativo. Pa EXTRA edukashon di nos pueblo ta hopi haltu den nos prioridatnan.

Van Huffelen asombrá ku a warda te den pitu pa kaba di atendé refinansiamentu

The Thomas Jefferson Cavaliers' girls tennis team fell to the Grain Valley Eagles 6-0 in the semifinals of the 2023 Class 2 Tennis Championships on Friday at Springfield's Cooper Tennis

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ChatGPT in Classrooms: Opportunities, Challenges, and Ethical Considerations

Comprehensive Review of AI Chatbots and ChatGPT in Education

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ali Ghanbari, Dept. of Computer Science, Iowa State University; (2) Deepak-George Thomas, Dept. of Computer Science, Iowa State University; (3) Muhammad Arbab Arshad, Dept. of Computer Science, Iowa State University; (4) Hridesh Rajan, Dept. of Computer Science, Iowa State University. Table of Links Abstract & Introduction Background Motivating Example Proposed Approach Supported DNN Bugs Evaluation Threats to Validity Related Work Conclusion, Acknowledgments & References IX. CONCLUSION This paper revisits mutation-based fault localization in the context of DNN and presents a novel DNN fault localization technique, named deepmufl. The technique is based on the idea of mutating a pre-trained DNN model and calculating suspiciousness values according to Metallaxis and MUSE approaches, Ochiai and SBI formulas, and two types of impacts of mutations on the results of test data points. deepmufl is compared to state-of-the-art static and dynamic fault localization systems [11], [8], [34], [12] on a benchmark of 109 model bugs. In this benchmark, while deepmufl is slower than the other tools, it proved to be almost two times more effective than them in terms of the total number of bugs detected and it detects 21 bugs that none of the studied tools were able to detect. We further studied the impact of mutation selection on fault localization time. We observed that we can halve the time taken to perform fault localization by deepmufl, while losing only 7.55% of the previously detected bugs. ACKNOWLEDGMENTS The authors thank Anonymous ASE 2023 Reviewers for their valuable feedback. We also thank Mohammad Wardat for his instructions on querying StackOverflow. This material is based upon work supported by the National Science Foundation (NSF) under the grant #2127309 to the Computing Research Association for the CIFellows Project. This work is also partially supported by the NSF grants #2223812, #2120448, and #1934884. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. REFERENCES [1] IEEE Standard Classification for Software Anomalies, 2010. [2] A. McPeak, “What’s the true cost of a software bug?” https://smartbear. com/blog/software-bug-cost/, 2017, accessed 08/10/23. [3] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. [4] M. J. Islam, G. Nguyen, R. Pan, and H. Rajan, “A comprehensive study on deep learning bug characteristics,” in ESEC/FSE, 2019, pp. 510–520. [5] M. J. Islam, R. Pan, G. Nguyen, and H. Rajan, “Repairing deep neural networks: Fix patterns and challenges,” in ICSE, 2020, pp. 1135–1146. [6] Y. Zhang, Y. Chen, S.-C. Cheung, Y. Xiong, and L. Zhang, “An empirical study on tensorflow program bugs,” in ISSTA, 2018, pp. 129–140. [7] N. Humbatova, G. Jahangirova, G. Bavota, V. Riccio, A. Stocco, and P. Tonella, “Taxonomy of real faults in deep learning systems,” in ICSE, 2020, pp. 1110–1121. [8] M. Wardat, B. D. Cruz, W. Le, and H. Rajan, “Deepdiagnosis: automatically diagnosing faults and recommending actionable fixes in deep learning programs,” in ICSE. IEEE, 2022, pp. 561–572. [9] K. Pei, Y. Cao, J. Yang, and S. Jana, “Deepxplore: Automated whitebox testing of deep learning systems,” in SOSP, 2017, pp. 1–18. [10] J. Kim, R. Feldt, and S. Yoo, “Guiding deep learning system testing using surprise adequacy,” in ICSE, 2019, pp. 1039–1049. [11] M. Wardat, W. Le, and H. Rajan, “Deeplocalize: fault localization for deep neural networks,” in ICSE, 2021, pp. 251–262. [12] A. Nikanjam, H. B. Braiek, M. M. Morovati, and F. Khomh, “Automatic fault detection for deep learning programs using graph transformations,” TOSEM, vol. 31, no. 1, pp. 1–27, 2021. [13] M. Usman, D. Gopinath, Y. Sun, Y. Noller, and C. S. Pas˘ areanu, “Nn ˘ repair: Constraint-based repair of neural network classifiers,” in CAV, 2021, pp. 3–25. [14] X. Zhang, J. Zhai, S. Ma, and C. Shen, “Autotrainer: An automatic dnn training problem detection and repair system,” in ICSE, 2021, pp. 359–371. [15] W. E. Wong, R. Gao, Y. Li, R. Abreu, and F. Wotawa, “A survey on software fault localization,” TSE, vol. 42, no. 8, pp. 707–740, 2016. [16] M. Papadakis and Y. Le Traon, “Using mutants to locate” unknown” faults,” in ICST, 2012, pp. 691–700. [17] S. Moon, Y. Kim, M. Kim, and S. Yoo, “Ask the mutants: Mutating faulty programs for fault localization,” in ICST, 2014, pp. 153–162. [18] R. A. DeMillo, R. J. Lipton, and F. G. Sayward, “Hints on test data selection: Help for the practicing programmer,” IEEE Computer, vol. 11, pp. 34–41, 1978. [19] R. Abreu, P. Zoeteweij, and A. J. Van Gemund, “On the accuracy of spectrum-based fault localization,” in TAICPART-MUTATION, 2007, pp. 89–98. [20] V. Dallmeier, C. Lindig, and A. Zeller, “Lightweight Bug Localization with AMPLE,” in ISAADD, 2005, pp. 99–104. [21] J. A. Jones, M. J. Harrold, and J. Stasko, “Visualization of test information to assist fault localization,” in ICSE, 2002, pp. 467–477. [22] L. Naish, H. J. Lee, and K. Ramamohanarao, “A model for spectra-based software diagnosis,” TOSEM, vol. 20, no. 3, pp. 1–32, 2011. [23] S. Yoo, “Evolving human competitive spectra-based fault localisation techniques,” in SBSE, 2012, pp. 244–258. [24] X. Xie, F.-C. Kuo, T. Y. Chen, S. Yoo, and M. Harman, “Provably optimal and human-competitive results in sbse for spectrum based fault localisation,” in SBSE, 2013, pp. 224–238. [25] S. Ma, Y. Liu, W.-C. Lee, X. Zhang, and A. Grama, “Mode: automated neural network model debugging via state differential analysis and input selection,” in ESEC/FSE, 2018, pp. 175–186. [26] H. F. Eniser, S. Gerasimou, and A. Sen, “Deepfault: Fault localization for deep neural networks,” in FASE, 2019, pp. 171–191. [27] N. Humbatova, G. Jahangirova, and P. Tonella, “Deepcrime: mutation testing of deep learning systems based on real faults,” in ISSTA, 2021, pp. 67–78. [28] Q. Hu, L. Ma, X. Xie, B. Yu, Y. Liu, and J. Zhao, “Deepmutation++: A mutation testing framework for deep learning systems,” in ASE, 2019, pp. 1158–1161. [29] L. Ma, F. Zhang, J. Sun, M. Xue, B. Li, F. Juefei-Xu, C. Xie, L. Li, Y. Liu, J. Zhao et al., “Deepmutation: Mutation testing of deep learning systems,” in ISSRE, 2018, pp. 100–111. [30] M. Papadakis and Y. Le Traon, “Metallaxis-fl: mutation-based fault localization,” STVR, vol. 25, no. 5-7, pp. 605–628, 2015. [31] F. Chollet et al., “Keras,” https://keras.io, 2015. [32] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “Tensorflow: A system for large-scale machine learning,” in OSDI, 2016, p. 265–283. [33] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, highperformance deep learning library,” in NIPS, 2019, pp. 8024–8035. [34] E. Schoop, F. Huang, and B. Hartmann, “Umlaut: Debugging deep learning programs using program structure and model behavior,” in CHI, 2021, pp. 1–16. [35] W. E. Wong and A. P. Mathur, “Reducing the cost of mutation testing: An empirical study,” JSS, pp. 185–196, 1995. [36] A. Ghanbari, D.-G. Thomas, M. A. Arshad, and H. Rajan, “Mutationbased fault localization of deep neural networks,” https://github.com/ ali-ghanbari/deepmufl-ase-2023, 2023. 37] J. H. Andrews, L. C. Briand, and Y. Labiche, “Is mutation an appropriate tool for testing experiments?” in ICSE, 2005, pp. 402–411. [38] M. Papadakis, M. Kintis, J. Zhang, Y. Jia, Y. Le Traon, and M. Harman, “Mutation testing advances: an analysis and survey,” in Advances in Computers, 2019, vol. 112, pp. 275–378. [39] V. Debroy and W. E. Wong, “Using mutation to automatically suggest fixes for faulty programs,” in ICST, 2010, pp. 65–74. [40] A. Ghanbari, S. Benton, and L. Zhang, “Practical program repair via bytecode mutation,” in ISSTA, 2019, pp. 19–30. [41] G. Fraser and A. Arcuri, “Achieving scalable mutation-based generation of whole test suites,” ESE, pp. 783–812, 2015. [42] F. C. M. Souza, M. Papadakis, Y. Le Traon, and M. E. Delamaro, “Strong mutation-based test data generation using hill climbing,” in IWSBST, 2016, pp. 45–54. [43] D. Shin, S. Yoo, M. Papadakis, and D.-H. Bae, “Empirical evaluation of mutation-based test case prioritization techniques,” STVR, p. e1695, 2019. [44] J. P. Galeotti, C. A. Furia, E. May, G. Fraser, and A. Zeller, “Inferring loop invariants by mutation, dynamic analysis, and static checking,” TSE, pp. 1019–1037, 2015. [45] A. Groce, I. Ahmed, C. Jensen, and P. E. McKenney, “How verified is my code? falsification-driven verification (t),” in ASE, 2015, pp. 737– 748. [46] B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. I. Jordan, “Scalable statistical bug isolation,” ACM SIGPLAN Notices, vol. 40, no. 6, pp. 15–26, 2005. [47] R. Abreu, P. Zoeteweij, and A. J. Van Gemund, “An evaluation of similarity coefficients for software fault localization,” in PRDC, 2006, pp. 39–46. [48] Wikipedia contributors, “Hierarchical data format — Wikipedia, the free encyclopedia,” 2022, accessed 08/10/23. [49] H. Coles, T. Laurent, C. Henard, M. Papadakis, and A. Ventresque, “Pit: a practical mutation testing tool for java,” in ISSTA, 2016, pp. 449–452. [50] P. Ammann and J. Offutt, Introduction to software testing. Cambridge University Press, 2016. [51] R. Just, F. Schweiggert, and G. M. Kapfhammer, “Major: An efficient and extensible tool for mutation analysis in a java compiler,” in ASE, 2011, pp. 612–615. [52] Y.-S. Ma, J. Offutt, and Y. R. Kwon, “Mujava: an automated class mutation system,” STVR, pp. 97–133, 2005. [53] D. Schuler and A. Zeller, “Javalanche: Efficient mutation testing for java,” in ESEC/FSE, 2009, pp. 297–298. [54] “Junit,” http://junit.org/, 2019, accessed 08/10/23. [55] “Testng documentation,” https://testng.org/doc/documentation-main. html, 2017, accessed 08/10/23. [56] M. M. Morovati, A. Nikanjam, F. Khomh, Z. Ming et al., “Bugs in machine learning-based systems: A faultload benchmark,” arXiv, 2022. [57] X. Li and L. Zhang, “Transforming programs and tests in tandem for fault localization,” Proceedings of the ACM on Programming Languages, vol. 1, no. OOPSLA, pp. 1–30, 2017. [58] C. Parnin and A. Orso, “Are automated debugging techniques actually helping programmers?” in ISSTA, 2011, pp. 199–209. [59] P. S. Kochhar, X. Xia, D. Lo, and S. Li, “Practitioners’ expectations on automated fault localization,” in ISSTA, 2016, pp. 165–176. [60] D. Shin and D.-H. Bae, “A theoretical framework for understanding mutation-based testing methods,” in ICST, 2016, pp. 299–308. [61] A. V. Pizzoleto, F. C. Ferrari, J. Offutt, L. Fernandes, and M. Ribeiro, “A systematic literature review of techniques and metrics to reduce the cost of mutation testing,” Journal of Systems and Software, vol. 157, p. 110388, 2019. [62] J. Zhang, “Scalability studies on selective mutation testing,” in ICSE, vol. 2, 2015, pp. 851–854. [63] A. J. Offutt, J. Pan, K. Tewary, and T. Zhang, “An experimental evaluation of data flow and mutation testing,” Software: Practice and Experience, vol. 26, no. 2, pp. 165–176, 1996. [64] scikit-learn Contributors, “scikit-learn: Machine learning in python,” 2020, accessed 08/10/23. [Online]. Available: https://scikit-learn.org/stable/ [65] R. Heckel, “Graph transformation in a nutshell,” ENTCS, vol. 148, no. 1, pp. 187–198, 2006. [66] J. Cao, M. Li, X. Chen, M. Wen, Y. Tian, B. Wu, and S.-C. Cheung, “Deepfd: Automated fault diagnosis and localization for deep learning programs,” in ICSE, 2022, p. 573–585. [67] Y. Ishimoto, M. Kondo, N. Ubayashi, and Y. Kamei, “Pafl: Probabilistic automaton-based fault localization for recurrent neural networks,” IST, vol. 155, p. 107117, 2023. [68] Y. Sun, H. Chockler, X. Huang, and D. Kroening, “Explaining image classifiers using statistical fault localization,” in ECCV, 2020, pp. 391–406.

Mutation-based Fault Localization of Deep Neural Networks: Conclusion, Acknowledgments & References

DevOps for Scientific Computing is the alchemical fusion of containers, Git Repositories, and CI/CD Engines, birthing a revolution in scientific inquiry, where reproducibility and transparency meet the boundless frontiers of rigorous research. 🚀 Amid the dynamic realm of scientific inquiry, where curiosity fuels innovation and exploration knows no bounds, a groundbreaking transformation is underway. It's a transformation that converges the tenets of DevOps with the intricate world of scientific computing, ushering in a new era of rigor, reproducibility, and transparency. This blog post charts an uncharted course into the profound influence of DevOps on the scientific domain, highlighting the interplay between three pivotal pillars: Containers, Git Repositories, and CI/CD Engines. 🚀 Unveiling the Three Pillars 1. Containers: The Scientific Enigma Unraveled 🧩 Scientific computing has always been a puzzle of dependencies and configurations, where the need to replicate the perfect computational environment can be a Sisyphean task. Here, the enigma is containers, the all-encompassing key to unlock scientific progress. Containers encapsulate complete, reproducible computational environments that transcend physical limitations. This portability allows researchers to traverse platforms and infrastructures effortlessly, from local workstations to colossal computing clusters, even extending their reach to the celestial cloud, typified by Kubernetes. Containers, in their singularity, eliminate the complexities of setup, enabling a fluid exchange of knowledge and ideas among scientists. 📦 2. Git Repositories: The Alchemist's Chronicle 🔮 At the heart of scientific inquiry lies code – the recipe to unlock the secrets of data. This code represents the philosopher's stone, the core of every scientific investigation. It's imperative to preserve an unadulterated historical record of its transformation, a role diligently performed by Git repositories. Git, the venerable alchemist, offers distributed version control, proven invaluable in the realm of DevOps, and effortlessly adapted to scientific needs. These repositories, eternal chronicles of code evolution, foster transparency, collaboration, and dispute resolution. They are fortified with cryptographic timestamps, ensuring the immutability and legal sanctity of the code's history – a shield of authenticity in the uncertain world of scientific exploration. 🔗 3. CI/CD Engines: Forging a Seamless Frontier 🛠️ The heart of DevOps, the forge where software is crafted, is Continuous Integration (CI) and Continuous Delivery (CD). These engines optimize software development, yet their potential extends beyond the IT industry. CI involves the constant amalgamation of code changes, while CD automates deployment. These engines, like GitLab CI/CD, offer the architectural blueprint for scientific workflows. By orchestrating pipelines, CI/CD engines enable researchers to abstract away the intricacies of their environments. The orchestration and automation of these pipelines elevate scientific computing, making it a seamless venture. Researchers traverse the unknown frontier with ease, enabled by their CI/CD compass. 🚧 Integration Examples Three tales of integration illustrate the harmonious marriage of these three pillars in redefining scientific computing. 1. Centralized Pipelines: Navigating an Ocean of Consistency 🏢 Centralized pipelines, the cornerstone of computational science, unfold in the comfort of homogeneous environments – think high-performance computing clusters or cloud platforms. These pristine, managed sanctuaries harbor the magnificence of containerized DevOps workflows. Researchers, charting courses through these environments, find consistency, scalability, and elegance. The translation of scientific work across these platforms is no longer a Herculean task; it is a voyage of discovery in an ocean of computational consistency. 🌐 2. Decentralized Pipelines: Bridging Heterogeneous Horizons 🌍 For those who tread the uncharted path of scaling out an analysis, the DevOps torch illuminates their way. It's the bridge spanning the chasm between local exploration and the computational cluster. The faithful Docker container, partner-in-crime of scientists, carries the burden. It hosts their code, their dreams, and their ambitions, in an environment harmonized for both solitary exploration and grandiose cluster performances. DevOps equips researchers to quell scaling fears, test their might against localized adversaries, and seamlessly cross into the realm of computational giants. ⚖️ 3. Minimizing Scale Out Friction: A Journey of Fluid Transition 🧘 The journey from local testing grounds to the battlefield of scalability can be tumultuous. This transition is where shortcuts can lead to disaster, and where DevOps emerges as the guiding sage. The constant companion of researchers is the versatile Docker container, accommodating local forays, and large-scale skirmishes with equal ease. It allows the validation of pipelines in a controlled environment, a simulator for the grand clash on the computational cluster. DevOps ensures a transition devoid of strife, a harmonious crescendo to the symphony of scientific discovery. 📈 Beneficial Side Effects Beyond the immediate effects, the infusion of DevOps into scientific computing ripples with wider benefits. 1. Superior Rigor, Reproducibility, and Transparency 📚 DevOps tools elevate the standards of scientific rigor, reproducibility, and transparency. They provide a robust foundation for research, rendering scalability and parallelism seamless. The shared environments and Git repositories eliminate barriers to replicating experiments. This potent combination of DevOps tools and principles opens new horizons, ensuring that research is conducted with the utmost rigor and transparency. 📖 2. FAIR Principles and Interoperable APIs: A Symphony of Data Reuse 🔄 DevOps frameworks echo the harmonious notes of FAIR principles, heralding the era of interoperable research data. Through structured pipelines with rich metadata, datasets are primed for sharing and indexing, adhering to the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data. DevOps also paves the way for the creation of common, interoperable APIs across diverse scientific domains, accelerating collaboration and propelling scientific exploration. 🔄 3. Compliance With Open Data Mandates: A New Horizon 🌐 In an age where open data mandates are on the rise, DevOps tools ensure that researchers are well-prepared to meet these demands. The tools guide researchers on data release, simplifying the path to compliance. By seamlessly adopting DevOps practices, researchers align with open data mandates, enriching the scientific ecosystem with accessibility and transparency. 📊 Conclusion The confluence of DevOps and scientific computing is nothing short of a revolution. It reshapes the frontier of scientific inquiry, redefining how researchers navigate the intricacies of computational analysis. With DevOps for Scientific Computing, researchers can: Elevate scientific rigor, reproducibility, and transparency. Champion the adoption of FAIR principles and interoperable APIs. Embrace open data mandates with grace and ease. This revolution not only optimizes research workflows but also stands as a symbol of trust and integrity in a world where science's role in society is more crucial than ever. 🌍 In summary, is the transformative force that accelerates scientific progress, enhances the quality of research, and reinforces the integrity of scientific endeavors. DevOps for Scientific Computing It's the symphony where technology and knowledge harmoniously converge, ensuring the pursuit of scientific knowledge remains as captivating and enlightening as ever. 🌟 References Wolfgang Alschner. "The computational analysis of international law". Research Methods in International Law. Edward Elgar Publishing, 2021. Sumon Biswas, Mohammad Wardat, and Hridesh Rajan. "The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large". Proceedings of the 44th International Conference on Software Engineering, 2022. Jennifer Bryan. "Excuse me, do you have a moment to talk about version control?" The American Statistician, 2018. Yolanda Gil et al. "Examining the Challenges of Scientific Workflows". Computer, 2007. Gregory M Kurtzer, Vanessa Sochat, and Michael W Bauer. "Singularity: Scientific containers for mobility of compute". PLoS One, 2017. Christine Laine et al. "Reproducible research: moving toward research the public can really trust". Ann Intern Med, Mar. 2007. Karthik Ram. "Git can facilitate greater reproducibility and increased transparency in science". Source Code Biol Med, 2013. Roger D Peng. "Reproducible research in computational science". Science, Dec. 2011. Russell A Poldrack et al. "Scanning the horizon: towards transparent and reproducible neuroimaging research". Nat Rev Neurosci, Feb. 2017. Mark D Wilkinson et al. "The FAIR Guiding Principles for scientific data management and stewardship". Sci Data, Mar. 2016.

Transforming the Scientific Frontier: The DevOps Revolution

Fallout 4 has a big wasteland with plenty of things to kill. Between the Gunners and the Deathclaws, one can find plenty of targets to shoot at with bullets and lasers. The only problem is that there are only so many guns in the Commonwealth to shoot before you’ve shot them all. Fortunately, the Fallout 4 Nexus has several thousand mods to choose from. Of those several thousand, we have found nine Fallout 4 Weapons mods including some very new mods to give you a few new ways to kill your way to your son faster. Also, some of these weapon mods don’t really fit Fallout 4’s art style which won’t be a problem if you look at our list of . There’s something there to fit these new toys. armor mods Fallout 4 Weapon Mods List Classic Holstered Weapons System (CHW) Cazador Missile Launcher Glock 19x QMW (Quick Modification Weapon)(Only mod weapon) Cowboy Repeater AKA Winchester Model 1892 FACTOR - Modular Rifle DefenseGun Misriah Armory M1928A1 Thompson - Day of Infamy Classic Holstered Weapons System (CHW) by shavkacagarikia shavkacagarikia/Fallout 4 Nexus It’s a little odd to start off a weapons list without using one of the Fallout 4 Weapon Mods but this makes it all better. One of Fallout 4’s more disappointing aspects is the fact that weapons turn invisible when holstered, making it difficult to admire them when going from one part of the wasteland to another. In the early days before this mod, the solution was to equip the weapons as apparel, allowing them to be visible in a janky, unimmersive way. Shavkacagarikia has gotten around this through some form of black magic, introducing a system that properly allows weapons to appear when holstered. It even works on modded weapons! If the way the weapon is holstered looks too janky, there is an in-game tool to tweak its positioning to make sure that it looks just right. Download link: Fallout 4 Nexus Cazador Missile Launcher by TheBigLidowski Utherien/Fallout 4 Nexus The Cazador, modeled after the real-world MATADOR recoilless rifle, is essentially a new kind of missile launcher. The weapon brings some modern flavour to Fallout with its own animations, sounds and integration with the game’s spawns. The Cazador is highly customisable with 13 different ammunition types that can be swapped in the field, giving the weapon plenty of versatility especially considering the unique damage types. It also comes with four scopes with their own functions and eight cosmetic skins. Download Link: Fallout 4 Nexus Glock 19x by Ardent Ardent117/Fallout 4 Nexus You might have a fondness for Wasteland’s selection of American pistols but I can recommend Ardent117’s Austrian varieties. The Glock 19X’s recency easily makes it one of the more modern Fallout 4 Weapon Mods. The weapon benefits from all of the modern world’s weapon customisation. Just about any part of the weapon can be customised with meaningful gameplay changes. The custom sounds and animations really sell the weapon and integrates it perfectly with the game. Q.M.W (Quick Modification Weapon)(Only mod weapon) by Tooun Tooun/Fallout 4 Nexus This mod doesn’t add any weapons but it lets you modify your weapons on the go, if the weapon is compatible with it. The compatible weapons tend to be modern, high quality mods that can be modified to suit many situations. Using this mod makes it possible to take full advantage of the modifications, letting the weapons get more versatile and adds some variety to gameplay. In practice, it is quite similar to weapon customisation in Crysis 3 and Metro Exodus. The menu’s interface doesn’t look remotely lore friendly but it more than makes up for it in functionality. Download Link: Fallout 4 Nexus Cowboy Repeater AKA Winchester Model 1892 by lucasncolombo lucasncolombo/Fallout 4 Nexus Lever actions are rare in the world of Fallout 4 Weapon Mods which makes this mod a treat. It features fantastic textures that bring out a rugged look that is fit for the wasteland. It’s got the custom animations to match its style. The weapon can’t be acquired easily. Instead, it has to be found in its ruined state and repaired after finding a crafting book. Download Link: Fallout 4 Nexus FACTOR - Modular Rifle by VigVam33 VigVam33/Fallout 4 Nexus The Modular Rifle truly lives up to its name. This weapon can be modified to the extent that it can be changed from a laser weapon to a ballistic weapon. It’s got variable zoom sights, multiple barrel lengths with 200 weapon mods. This mod pretty much lets you make a weapon perfect for your needs. DefenseGun by Yona YonaTaku/Fallout 4 Nexus DefenceGun is a fairly old mod that still stands up to its newer counterparts. It adds a weapon platform that resembles the old British Sten from the Second World War. The side-loaded magazine adds a unique touch to the weapon. It’s also available from lower levels, letting it give some variety and a break from the pipe weapons. Misriah Armory NovaFinch/Nexus Misriah Armory is one of the best Fallout 4 Weapon Mods that draw on fictional weapons. The quality of its textures and customisablity are some of the best on the Nexus. We also took a much deeper look at this mod before. Download Link: Fallout 4 Nexus M1928A1 Thompson - Day of Infamy by NWI - Noniac - Wardaddy - Ricoginger Noniac/Fallout 4 Nexus Fallout 4 technically has this weapon in the form of the Submachine Gun. Unfortunately, it doesn’t look half as good and doesn’t have the same feel of this classic weapon. The textures themselves are taken from Day of Infamy which look amazing. You can find it on Triggermen from level 12 and Bosses from Level 20 Final thoughts on these Best Fallout 4 Weapon Mods These nine Fallout 4 Weapon Mods are some of the best mods to come out, especially considering the amazing selection from the last year. These mods will spice up your game with some new toys that will give some life to the game. Also, be sure to check out: Warhammer Age of Sigmar: Storm Ground Arrives May 27, New Trailer Out The 25 Funniest Roblox Memes in 2021 Final Fantasy XIV Reshade Guide

9 Best Fallout 4 Weapons Mods in 2021

The story begins on a cold winter morning in New York where a small group of people are meeting in a windowless room in an unremarkable corner of UN headquarters. It is the job of these people to communicate humanitarian issues, and in a world on fire, they have their work cut out for them. They have gathered to watch a film called about a Syrian refugee camp. It is February 2014. Virtual Reality Clouds Over Sidra Like everyone else in the room I have heard a lot about Virtual Reality (VR), but this is the first time I have watched a VR film. I am a journalist turned filmmaker and have been telling humanitarian stories in words, pictures and film for 20 years. I have worked and filmed in the Za’atari refugee camp before and have made, or helped to make, several films about the plight of Syrian refugees. I know what to expect. Or so I think. Six minutes later I lay the headset back on the table and take a deep breath. I’m stunned by the way Gabo Arora’s film has dragged me into the heart of his story and I’m daring to think that this could be the storytelling that I’ve been waiting for. It feels like both the end and beginning of a journey. It feels like a glimpse of the future. tool Fast forward 18 months and I’m sitting in an airless refugee tent in Lebanon’s Bekaa Valley. On one side of me sits Nokia’s beautiful Ozo VR camera and my long term cameraman and friend Thomas, and on the other side, former Iraqi refugee-turned-aid-worker Warda and half a dozen young Syrian refugee girls. We are passing round a Samsung Gear headset loaded with a short animated film about dinosaurs to try and explain VR to them. Click play to see what happened when they watched VR for the first time … A long time before I’d ever heard of VR I read somewhere that “the evolution of storytelling is inseparable from the evolution of technology.” I made a note of it at the time and promptly forgot about it, but it was a thought that kept coming back to me during the shoot of our VR film, . Life in the Time of Refuge I had been warned by friends who had been in the VR game for a while already that shooting in VR meant throwing out pretty much everything you think you know about filmmaking and starting over. “ You have to hide to be out of shot … You can’t follow the action … You can’t frame the shot because everything is in shot … And whatever you do, be very careful of movement, because even the slightest movement can make the viewer feel sea sick …” So when Thomas suggested one morning while we were filming by a lake in Finland that we set up the camera in a boat and “get a shot in the middle of the lake,” that sounded like trouble to me. Click play to see what happened when we tried … For sure, it’s way more difficult and time consuming to shoot documentary stories in VR, and the run ’n’ gun style of observational documentary is nigh on impossible (at least for now). Not to mention the challenges of shooting in areas where insecurity is high and people are suspicious of strange people, doing strange things with strange looking bits of kit. Click play below to see what it was like shooting a VR scene close to the Lebanon/Syria border … A s it turns out — and, as I had been warned — much of shooting VR is about jettisoning the things you love about filmmaking and embracing the things you thought you didn’t like. Things like voice over. Remember Robert McKee’s words of warning to a paranoid Nicholas Cage in Charlie Kaufmann’s ?: Adaptation “God help you if you use voice-over in your work, my friends. God help you! That’s flaccid, sloppy writing. Any idiot can write voice-over narration to explain the thoughts of the character.” I thought that too many of the humanitarian VR films that I had seen to date were too reliant on voice over, and that the story needed a chance to develop alongside its characters. With my snooty filmmaker hat on, I saw voice over as the last refuge of the scoundrel so when we started the project I was adamant that we should avoid it. But as we found out, the bottom line is that in VR so much of the director’s ability to let the narrative take its own course is taken out of the equation by the fact that the director and cinematographer are hiding behind a grave at the crucial moment. So in the end we bit the bullet and embraced some ‘flaccid, sloppy writing.’ But you know what, we think it worked out pretty well … Click play to watch the 360 version of our film on Facebook … ends

Shooting VR or: How I learned to stop worrying and love the voice over

A Legion of Sway: How Influencer Marketing Boosted Warhammer 40,000: Space Marine 2

Social Media and the Battlefield: Human Error in Modern Warfare

Analyzing Audience Responses to Ukrainian Memetic Warfare Tactics

Authors:
(1) Yelena Mejova, ISI Foundation, Italy;
(2) Arthur Capozzi, Università degli Studi di Torino, Italy;
(3) Corrado Monti, CENTAI, Italy;
(4) Gianmarco De Francisci Morales, CENTAI, Italy. Table of Links Introduction Background and Related Work Methods Results Discussion and References 3 METHODS 3.1 Data collection To examine the use of memetic engagement by Ukraine during the first 8 months of the 2022 conflict, we begin with a large collection of tweets matching war-related keywords (see Appendix A) spanning from 27 February 2022 to 12 October 2022. By examining this collection, we identify three prominent accounts: (1) @Ukraine: self-described as “the official Twitter account of Ukraine”, this verified account is labeled as an “Ukraine government organization” by Twitter. It posts war-related and political updates about Ukraine. (2) @uamemesforces: self-described as the “Source of the best Ukrainian memes”, this unverified account states as its goal ”to convey the truth about what is happening now in Ukraine with the help of memes”.[8] (3) @DefenceU: or “Defense of Ukraine”, is the “Official page of the Ministry of Defense of Ukraine”. Like @Ukraine, this verified account is described by Twitter as a “Ukraine government organization”. Its posts concern battlefield updates and news. We select the accounts @Ukraine and @DefenceU because of their large following (at the end of the collection period), because they are official accounts not associated with individuals or specific media outlets, and because of the focus on the conflict (many popular Ukrainian accounts are by non-relevant celebrities). Additionally, we select @uamemesforces, the most popular Ukrainian unofficial account that posts memes about the conflict. Note that we are limited in the number of accounts we can study due to the labor-intensive labeling process. We retrieve all of the posts for these accounts via Twitter Timeline API. We treat each post with media content as a potential meme, no matter the format. To ensure preserving content posted by these accounts, we also download the images associated with the posts and the users who retweeted or liked the post (see Table 1 for summary statistics). We associate each user with a country in the GeoNames database via their location field (a free-text field optionally filled by the user). Considering only tweets with media, out of 586 042 users, 44% are successfully mapped to a country. We retain only countries with at least 1000 geolocated retweeting users for geographic analysis. In order to explore the relationship between Ukrainian memes and each country’s actions towards the conflict, we use the Ukraine Support Tracker data from the Kiel Institute for the World Economy (downloaded on 20 June 2023), which “quantifies military, financial, and humanitarian aid transferred by governments to Ukraine since the end of diplomatic relations between Russia and Ukraine on January 24, 2022”.[9] The dataset provides a quantitative measure of a country’s aid to Ukraine. Finally, to examine the attitude of each country’s population towards Ukraine, we use the Eurobarometer STD98 survey which took place in winter 2022-2023 in 30 European countries [22]. Specifically, we use use QE2, answer 6: “To what extent do you agree or disagree with: providing financial support to Ukraine.” 3.2 Annotation To understand the visual and narrative attributes of the captured data, all authors perform a manual annotation of a random selection of the tweets. We use all of @Ukraine tweets and a random sample of roughly 40% of those from the other two accounts are selected for analysis, for a total of 1063 tweets (last column of Table 1). We remove self-replies (an account replying to its own post) which have no retweets of their own. Guided by previous literature, we compose a codebook to capture visual and narrative aspects of the content. First, we adopt the significant features of the content characterization framework proposed by Ling et al. [41], which is used to model the virality of political memes. These attributes include: (1) Number of panels: multiple or single; (2) Type of image: photo, screenshot, or illustration; (3) Scale: close-up, medium shot, long shot, other; (4) Type of subject: object, character, scene, creature, text, other; (5) Main attribute of the subject (if character): facial expression, posture, poster (6) Character emotion (if character): positive, negative, or neutral; (7) Contains words: whether image itself (not tweet caption) contains words. Second, we employ the narrative framework by Karpman [35] later extended by de Saint Laurent et al. [18]. It defines two key dimensions to characterize a narrative: moral quality (benevolent or malevolent) and power (strong or weak), thus resulting in four character archetypes: heroes (benevolent, strong), victims (benevolent, weak), villains (malevolent, strong), and fools (malevolent, weak). We also label the actors mentioned in these narratives (as an open category, guided by an initial set of main actors in the conflict, such as Zelensky and Putin). We further contextualize the narratives by their emotional appeal [8], in particular humor, pride, fear, outrage, and compassion (the label was originally open, but the labelers coalesced on this set). Finally, we record whether there is a specific intent in terms of a call to action or information sharing. Note that, as much as possible, we try to label the narratives as intended from the point of view of the posting account (although some biases may creep in, as we discuss in Section 5). Appendix B reports the full codebook. Using this literature-driven codebook, all authors of this manuscript perform a deductive coding of the selected posts (for more on such process see Linneberg and Korsgaard [42]). We rely on Google Translate to translate non-English language content. All coders first annotated a selection of 50 posts, discussed insights and disagreements, and came up with standard labels for emotional appeal, intent, and actor (though the label remained open). Subsequently, the rest of the posts are annotated by one author each, with uncertain cases discussed collectively. Appendix C reports the inter-annotator agreement on a selection of 48 posts (sampled from each account). On average, the agreement is 0.750 for @Ukraine, 0.604 for @uamemesforces, and 0.667 for @DefenceU (with a Krippendorff’s alpha of around 0.65 for all accounts and tasks), which is high considering the subjective nature of the tasks, and that some features were open and could have multiple labels. The annotated dataset is available at https://github.com/corradomonti/ukranian-tweets. 3.3 Popularity model Using the data collected and annotated as per the previous sections, we construct models to find whether there are trends in (𝑖) what kind of content becomes more popular, and (𝑖𝑖) who it reaches. Specifically, we use linear regression to model the log-transformed number of retweets with the content variables outlined above as independent variables. As the three accounts have posted a vastly different number of tweets, we weigh the data points (tweets) inverseproportionally to their representation in the dataset, so that each account weighs equally on the outcomes. We build several versions of the model to ascertain the benefit of each class of variables: visual [41], actor, emotion, intent, and narrative. A baseline model simply uses the identity of the author (i.e., group fixed effects), the number of followers of that account (log transformed) as a proxy for the potential audience, and the number of days from the invasion to account for changing interest in the topic over time. We then choose the best model by the Bayesian Information Criterion (BIC)—a model selection metric that captures both unexplained variation in the dependent variable and the number of explanatory variables. The coefficients of this model reveal the associations between these variables and the virality of the content. Next, we group retweets by the country of the user (those who can be geolocated) to understand the preferences of users who spread these messages. We enrich our characterization of the relationships between each country and Ukraine by considering the amount of financial assistance the country provides to Ukraine (normalized by the country’s GDP) or by their opinion on this topic (via the Eurobarometer survey). As mentioned above, we consider these measures as different proxies of support for Ukraine’s military efforts. First, we examine the retweeting rate (normalized by population). Then, we compare the narrative preferences to the amount of support for Ukraine: are there narratives associated with increased support for Ukraine? We operationalize this preference as the log odds of users from a country retweeting content having a specific narrative. Finally, we examine the geographical distribution of the retweeting rate and narrative preferences across Europe by plotting these on choropleth maps. [8] https://www.patreon.com/uamemesforces [9] https://www.ifw-kiel.de/topics/war-against-ukraine/ukraine-support-tracker This paper is available on arxiv under CC 4.0 license. Authors: (1) Yelena Mejova, ISI Foundation, Italy; (2) Arthur Capozzi, Università degli Studi di Torino, Italy; (3) Corrado Monti, CENTAI, Italy; (4) Gianmarco De Francisci Morales, CENTAI, Italy. Authors: (1) Yelena Mejova, ISI Foundation, Italy; (2) Arthur Capozzi, Università degli Studi di Torino, Italy; (3) Corrado Monti, CENTAI, Italy; (4) Gianmarco De Francisci Morales, CENTAI, Italy. Table of Links Introduction Introduction Background and Related Work Background and Related Work Methods Methods Results Results Discussion and References Discussion and References 3 METHODS 3.1 Data collection To examine the use of memetic engagement by Ukraine during the first 8 months of the 2022 conflict, we begin with a large collection of tweets matching war-related keywords (see Appendix A) spanning from 27 February 2022 to 12 October 2022. By examining this collection, we identify three prominent accounts: (1) @Ukraine: self-described as “the official Twitter account of Ukraine”, this verified account is labeled as an “Ukraine government organization” by Twitter. It posts war-related and political updates about Ukraine. @Ukraine: (2) @uamemesforces: self-described as the “Source of the best Ukrainian memes”, this unverified account states as its goal ”to convey the truth about what is happening now in Ukraine with the help of memes”.[8] @uamemesforces: (3) @DefenceU: or “Defense of Ukraine”, is the “Official page of the Ministry of Defense of Ukraine”. Like @Ukraine, this verified account is described by Twitter as a “Ukraine government organization”. Its posts concern battlefield updates and news. @DefenceU: We select the accounts @Ukraine and @DefenceU because of their large following (at the end of the collection period), because they are official accounts not associated with individuals or specific media outlets, and because of the focus on the conflict (many popular Ukrainian accounts are by non-relevant celebrities). Additionally, we select @uamemesforces, the most popular Ukrainian unofficial account that posts memes about the conflict. Note that we are limited in the number of accounts we can study due to the labor-intensive labeling process. We retrieve all of the posts for these accounts via Twitter Timeline API. We treat each post with media content as a potential meme, no matter the format. To ensure preserving content posted by these accounts, we also download the images associated with the posts and the users who retweeted or liked the post (see Table 1 for summary statistics). We associate each user with a country in the GeoNames database via their location field (a free-text field optionally filled by the user). Considering only tweets with media, out of 586 042 users, 44% are successfully mapped to a country. We retain only countries with at least 1000 geolocated retweeting users for geographic analysis. In order to explore the relationship between Ukrainian memes and each country’s actions towards the conflict, we use the Ukraine Support Tracker data from the Kiel Institute for the World Economy (downloaded on 20 June 2023), which “quantifies military, financial, and humanitarian aid transferred by governments to Ukraine since the end of diplomatic relations between Russia and Ukraine on January 24, 2022”.[9] The dataset provides a quantitative measure of a country’s aid to Ukraine. Finally, to examine the attitude of each country’s population towards Ukraine, we use the Eurobarometer STD98 survey which took place in winter 2022-2023 in 30 European countries [22]. Specifically, we use use QE2, answer 6: “To what extent do you agree or disagree with: providing financial support to Ukraine.” 3.2 Annotation To understand the visual and narrative attributes of the captured data, all authors perform a manual annotation of a random selection of the tweets. We use all of @Ukraine tweets and a random sample of roughly 40% of those from the other two accounts are selected for analysis, for a total of 1063 tweets (last column of Table 1). We remove self-replies (an account replying to its own post) which have no retweets of their own. Guided by previous literature, we compose a codebook to capture visual and narrative aspects of the content. First, we adopt the significant features of the content characterization framework proposed by Ling et al. [41], which is used to model the virality of political memes. These attributes include: (1) Number of panels: multiple or single; (2) Type of image: photo, screenshot, or illustration; (3) Scale: close-up, medium shot, long shot, other; (4) Type of subject: object, character, scene, creature, text, other; (5) Main attribute of the subject (if character): facial expression, posture, poster (6) Character emotion (if character): positive, negative, or neutral; (7) Contains words: whether image itself (not tweet caption) contains words. Second, we employ the narrative framework by Karpman [35] later extended by de Saint Laurent et al. [18]. It defines two key dimensions to characterize a narrative: moral quality (benevolent or malevolent) and power (strong or weak), thus resulting in four character archetypes: heroes (benevolent, strong), victims (benevolent, weak), villains (malevolent, strong), and fools (malevolent, weak). We also label the actors mentioned in these narratives (as an open category, guided by an initial set of main actors in the conflict, such as Zelensky and Putin). We further contextualize the narratives by their emotional appeal [8], in particular humor, pride, fear, outrage, and compassion (the label was originally open, but the labelers coalesced on this set). Finally, we record whether there is a specific intent in terms of a call to action or information sharing. Note that, as much as possible, we try to label the narratives as intended from the point of view of the posting account (although some biases may creep in, as we discuss in Section 5). Appendix B reports the full codebook. Using this literature-driven codebook, all authors of this manuscript perform a deductive coding of the selected posts (for more on such process see Linneberg and Korsgaard [42]). We rely on Google Translate to translate non-English language content. All coders first annotated a selection of 50 posts, discussed insights and disagreements, and came up with standard labels for emotional appeal, intent, and actor (though the label remained open). Subsequently, the rest of the posts are annotated by one author each, with uncertain cases discussed collectively. Appendix C reports the inter-annotator agreement on a selection of 48 posts (sampled from each account). On average, the agreement is 0.750 for @Ukraine, 0.604 for @uamemesforces, and 0.667 for @DefenceU (with a Krippendorff’s alpha of around 0.65 for all accounts and tasks), which is high considering the subjective nature of the tasks, and that some features were open and could have multiple labels. The annotated dataset is available at https://github.com/corradomonti/ukranian-tweets. 3.3 Popularity model Using the data collected and annotated as per the previous sections, we construct models to find whether there are trends in (𝑖) what kind of content becomes more popular, and (𝑖𝑖) who it reaches. Specifically, we use linear regression to model the log-transformed number of retweets with the content variables outlined above as independent variables. As the three accounts have posted a vastly different number of tweets, we weigh the data points (tweets) inverseproportionally to their representation in the dataset, so that each account weighs equally on the outcomes. We build several versions of the model to ascertain the benefit of each class of variables: visual [41], actor, emotion, intent, and narrative. A baseline model simply uses the identity of the author (i.e., group fixed effects), the number of followers of that account (log transformed) as a proxy for the potential audience, and the number of days from the invasion to account for changing interest in the topic over time. We then choose the best model by the Bayesian Information Criterion (BIC)—a model selection metric that captures both unexplained variation in the dependent variable and the number of explanatory variables. The coefficients of this model reveal the associations between these variables and the virality of the content. Next, we group retweets by the country of the user (those who can be geolocated) to understand the preferences of users who spread these messages. We enrich our characterization of the relationships between each country and Ukraine by considering the amount of financial assistance the country provides to Ukraine (normalized by the country’s GDP) or by their opinion on this topic (via the Eurobarometer survey). As mentioned above, we consider these measures as different proxies of support for Ukraine’s military efforts. First, we examine the retweeting rate (normalized by population). Then, we compare the narrative preferences to the amount of support for Ukraine: are there narratives associated with increased support for Ukraine? We operationalize this preference as the log odds of users from a country retweeting content having a specific narrative. Finally, we examine the geographical distribution of the retweeting rate and narrative preferences across Europe by plotting these on choropleth maps. [8] https://www.patreon.com/uamemesforces [9] https://www.ifw-kiel.de/topics/war-against-ukraine/ukraine-support-tracker This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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