Hasil untuk "Academies and learned societies"

Renato Kenniti Silvestre Agata

O artigo “As raízes sociais e econômicas dos Principia de Newton”, apresentado pelo físico soviético Boris Hessen (1893-1936) no II Congresso Internacional de História da Ciência e da Tecnologia (1931), em Londres, inaugurou na História da Ciência a linha interpretativa conhecida como externalismo, ao aplicar o materialismo dialético para interpretar as teorias de Isaac Newton (1643-1727). Ao afirmar que a produção intelectual do físico britânico tinha raízes nos desafios técnicos da economia burguesa ascendente, Hessen estava escrevendo em defesa da Teoria da Relatividade e da Mecânica Quântica, ambas sob ataque na União Soviética, consideradas como idealistas e “ciência burguesa”.

Pedro Fernandez de Souza

Neste artigo, aborda-se a passagem da neurologia à psicanálise, na obra de Freud, por uma análise histórico-conceitual do seu vocabulário. Primeiramente, notam-se, em textos neurológicos, palavras essenciais à teoria freudiana, quais seja, Übertragung, Erregung e Reiz. Em seguida, cotejando-se o Projeto de uma psicologia científica (1895) com a Interpretação dos sonhos (1900), observa-se a permanência de termos neurológicos no vocabulário psicanalítico de Freud, mas nisso há uma série de deslocamentos lexicais: na psicanálise, os termos neurofisiológicos mantêm sua acepção funcionalista, e não mais anatômica, o que marca a especificidade epistemológica do empreendimento científico freudiano.

Riky Supratama, Husna Nashihin, Henik Nur Indahsari et al.

This study aims to analyze the student essay assessment system in Islamic Religious Education, focusing on the use of conventional methods and web-based application support. A qualitative approach with a comparative study design was employed. Data were collected through interviews with Islamic Education teachers of Grade XI at SMKN Muhammadiyah 1 Yogyakarta, which utilizes the Broexam Unlock examination application. Teachers play a crucial role in enhancing assessment competencies, including preparing test materials, developing scoring rubrics, and conducting manual evaluations. The results indicate that the conventional essay assessment method presents weaknesses, such as requiring considerable time and effort due to manual corrections. Conversely, the use of web-based applications such as Exambro offers greater efficiency in time and workload, as the system automatically generates results after the exam. These findings highlight that technology utilization through web-based applications can serve as a strategic innovation to enhance the effectiveness of Islamic Religious Education assessment systems in the digital era.

Aniket Sule, Niruj Mohan Ramanujam, Moupiya Maji et al.

Astronomy, of all the sciences, is possibly the one with the most public appeal across all age groups. This is also evidenced by the existence of a large number of planetaria and amateur astronomy societies, which is unique to the field. Astronomy is known as a `gateway science', with an ability to attract students who then proceed to explore their interest in other STEM fields too. Astronomy's link to society is therefore substantive and diverse. In this white paper, six key areas are analysed, namely outreach and communication, astronomy education, history and heritage, astronomy for development, diversity, and hiring practices for outreach personnel. The current status of each of these areas is described, followed by an analysis of what is needed for the future. A set of recommendations for institutions, funding agencies, and individuals are evolved for each specific area. This work charts out the vision for how the astronomy-society connection should take shape in the future, and attempts to provide a road-map for the various stakeholders involved.

Justin B. Bullock, Samuel Hammond, Seb Krier

This paper examines how artificial general intelligence (AGI) could fundamentally reshape the delicate balance between state capacity and individual liberty that sustains free societies. Building on Acemoglu and Robinson's 'narrow corridor' framework, we argue that AGI poses distinct risks of pushing societies toward either a 'despotic Leviathan' through enhanced state surveillance and control, or an 'absent Leviathan' through the erosion of state legitimacy relative to AGI-empowered non-state actors. Drawing on public administration theory and recent advances in AI capabilities, we analyze how these dynamics could unfold through three key channels: the automation of discretionary decision-making within agencies, the evolution of bureaucratic structures toward system-level architectures, and the transformation of democratic feedback mechanisms. Our analysis reveals specific failure modes that could destabilize liberal institutions. Enhanced state capacity through AGI could enable unprecedented surveillance and control, potentially entrenching authoritarian practices. Conversely, rapid diffusion of AGI capabilities to non-state actors could undermine state legitimacy and governability. We examine how these risks manifest differently at the micro level of individual bureaucratic decisions, the meso level of organizational structure, and the macro level of democratic processes. To preserve the narrow corridor of liberty, we propose a governance framework emphasizing robust technical safeguards, hybrid institutional designs that maintain meaningful human oversight, and adaptive regulatory mechanisms.

Tuan Pham, Sidney Redner, Lourens Waldorp et al.

Explanations of polarization often rely on one of the three mechanisms: homophily, bounded confidence, and community-based interactions. Models based on these mechanisms consider the lack of interactions as the main cause of polarization. Given the increasing connectivity in modern society, this explanation of polarization may be insufficient. We aim to show that in involvement-based models, society becomes more polarized as its connectedness increases. To this end, we propose a minimal voter-type model (called I-voter) that incorporates involvement as a key mechanism in opinion formation and study its dependence on network connectivity. We describe the steady-state behaviour of the model analytically, at the mean-field and the moment-hierarchy levels and stress the generality of our findings by considering various extensions and different network topologies.

Haoxiang Guan, Jiyan He, Liyang Fan et al.

Understanding how complex societal behaviors emerge from individual cognition and interactions requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (ABMs) have been employed to study these dynamics for decades, but are constrained by simplified agent behaviors that fail to capture human complexity. Recent advances in large language models (LLMs) offer new opportunities by enabling agents to exhibit sophisticated social behaviors that go beyond rule-based logic, yet face significant scaling challenges. Here we present Light Society, an agent-based simulation framework that advances both fronts, efficiently modeling human-like societies at planetary scale powered by LLMs. Light Society formalizes social processes as structured transitions of agent and environment states, governed by a set of LLM-powered simulation operations, and executed through an event queue. This modular design supports both independent and joint component optimization, supporting efficient simulation of societies with over one billion agents. Large-scale simulations of trust games and opinion propagation--spanning up to one billion agents--demonstrate Light Society's high fidelity and efficiency in modeling social trust and information diffusion, while revealing scaling laws whereby larger simulations yield more stable and realistic emergent behaviors.

Kevin Riehl, Michail Makridis, Anastasios Kouvelas

Advancements in computer science, artificial intelligence, and control systems of the recent have catalyzed the emergence of cybernetic societies, where algorithms play a significant role in decision-making processes affecting the daily life of humans in almost every aspect. Algorithmic decision-making expands into almost every industry, government processes critical infrastructure, and shapes the life-reality of people and the very fabric of social interactions and communication. Besides the great potentials to improve efficiency and reduce corruption, missspecified cybernetic systems harbor the threat to create societal inequities, systematic discrimination, and dystopic, totalitarian societies. Fairness is a crucial component in the design of cybernetic systems, to promote cooperation between selfish individuals, to achieve better outcomes at the system level, to confront public resistance, to gain trust and acceptance for rules and institutions, to perforate self-reinforcing cycles of poverty through social mobility, to incentivize motivation, contribution and satisfaction of people through inclusion, to increase social-cohesion in groups, and ultimately to improve life quality. Quantitative descriptions of fairness are crucial to reflect equity into algorithms, but only few works in the fairness literature offer such measures; the existing quantitative measures in the literature are either too application-specific, suffer from undesirable characteristics, or are not ideology-agnostic. Therefore, this work proposes a quantitative, transactional, distributive fairness framework, which enables systematic design of socially feasible decision-making systems. Moreover, it emphasizes the importance of fairness and transparency when designing algorithms for equitable, cybernetic societies.

Md Kamrul Hossain Siam, Manidipa Bhattacharjee, Shakik Mahmud et al.

The Machine learning (ML) is a rapidly evolving field of technology that has the potential to greatly impact society in a variety of ways. However, there are also concerns about the potential negative effects of ML on society, such as job displacement and privacy issues. This research aimed to conduct a comprehensive analysis of the current and future impact of ML on society. The research included a thorough literature review, case studies, and surveys to gather data on the economic impact of ML, ethical and privacy implications, and public perceptions of the technology. The survey was conducted on 150 respondents from different areas. The case studies conducted were on the impact of ML on healthcare, finance, transportation, and manufacturing. The findings of this research revealed that the majority of respondents have a moderate level of familiarity with the concept of ML, believe that it has the potential to benefit society, and think that society should prioritize the development and use of ML. Based on these findings, it was recommended that more research is conducted on the impact of ML on society, stronger regulations and laws to protect the privacy and rights of individuals when it comes to ML should be developed, transparency and accountability in ML decision-making processes should be increased, and public education and awareness about ML should be enhanced.

David H. Wolpert, Kyle Harper

Social evolutionary theory seeks to explain increases in the scale and complexity of human societies, from origins to present. Over the course of the twentieth century, social evolutionary theory largely fell out of favor as a way of investigating human history, just when advances in complex systems science and computer science saw the emergence of powerful new conceptions of complex systems, and in particular new methods of measuring complexity. We propose that these advances in our understanding of complex systems and computer science should be brought to bear on our investigations into human history. To that end, we present a new framework for modeling how human societies co-evolve with their biotic environments, recognizing that both a society and its environment are computers. This leads us to model the dynamics of each of those two systems using the same, new kind of computational machine, which we define here. For simplicity, we construe a society as a set of interacting occupations and technologies. Similarly, under such a model, a biotic environment is a set of interacting distinct ecological and environmental processes. This provides novel ways to characterize social complexity, which we hope will cast new light on the archaeological and historical records. Our framework also provides a natural way to formalize both the energetic (thermodynamic) costs required by a society as it runs, and the ways it can extract thermodynamic resources from the environment in order to pay for those costs -- and perhaps to grow with any left-over resources.

Margaret Beazley

The 14th Biennial Meeting of the International Human Rights Network of Academies and Scholarly Societies (IHRN) began with an Early Career Scholars Panel, during which up-and-coming scholars deliberated on present-day global human rights issues. The moderator of the panel was Prof Catherine Burns (Associate Professor of Medical History, University of Witwatersrand) and the panel members were Lt Col Dr Esewu Mxolisi Mathebula (South African Association of PhDs), Mr Michael Martin (New Voices in Sciences, Engineering and Medicine, U.S. National Academies), Prof Mzukisi Njotini (Dean of the Faculty of Law, University of Fort Hare, South African Young Academy of Science) and Prof Martha Bradley (Associate Professor in the Department of Public Law, University of Johannesburg, Future Professors Programme). In this session, the Early Career Scholars gave their perspectives on topics related to the theme of the IHRN meeting, ‘The Role of National Academies and Universities in Promoting Human Rights and Enhancing Equality.’ The session had posed a greater number of questions than it had provided answers for. However, some questions stood out, namely how scholars communicate their scientific knowledge in ways that are respectful and dignified, but still critical and engaged across class, gender, hierarchy and region. ASSAf was acknowledged for bringing scholars and colleagues from learned societies together at this event and addressing current and controversial issues. Science can correct itself only through processes such as this.

Margaret Beazley

Salman Faris Insani, Ariyani Wahyu Wijayanti, Gustita Arnawati Putri

Pandemi COVID-19 mempengaruhi dunia bisnis melalui penurunan perekonomian serta aktivitas lintas sektor dan wilayah di Indonesia. Kondisi ini merugikan bagi para pemilik UMKM di berbagai bidang. Menyikapi kondisi tersebut, tim kegiatan pengabdian masyarakat mengusulkan untuk melakukan pelatihan pelaksanaan angket kepuasan konsumen secara online, dengan menggunakan software google formulir. Usulan ini disetujui oleh 10 pemilik UMKM di berbagai bidang di kawasan solo raya sebagai mitra pengabdian. Untuk menjawab permasalahan, tim PKM menyelenggarakan kegiatan pengabdian melalui 3 tahapan yaitu sosialisasi, pelaksanaan, dan evaluasi. Tahapan pelaksanaan terbagi menjadi lima sub kegiatan yang terdiri dari mendesain angket dan format, mendesain teknik pengumpulan sampel dengan google formulir, mengelola data dengan google formulir, menginterpretasikan hasil, serta pengarsipan dengan aplikasi google drive. Hasil evaluasi membuktikan bahwa di setiap tahapan kegiatan nilai tes akhir menunjukkan peningkatan sebesar 25% dibandingkan dengan nilai tes awal. Temuan ini mengindikasi bahwa mitra UMKM memperoleh peningkatan pengetahuan dan kecakapan sesuai harapan. Dapat disimpulkan bahwa kegiatan pelatihan ini dinyatakan berhasil.

Shanti Veronica br Siahaan, Friska Debi, Helminus Mardi et al.

Pelatihan peningkatan kemampuan membuat laporan keuangan dengan menggunakan aplikasi berbasis android ini bertujuan untuk membantu pengurus BUM Desa Panyanggar dan pengelola unit wisata BUM Desa yaitu Pokdarwis Sepadang Hill dan Pokdarwis Riam Palayo di Desa Cipta Karya, Bengkayang dalam pembuatan laporan keuangan agar lebih cepat dan lebih mudah di akses oleh berbagai pihak yang berkepentingan. Metode pelatihan yang digunakan adalah metode ceramah, diskusi dan simulasi yang bertujuan untuk mengetahui dan memberi solusi terhadap permasalahan yang dihadapi peserta dalam kegiatan pembuatan laporan keuangan menggunakan aplikasi. Hasil dari kegiatan pengabdian masyarakat yang diikuti oleh enam orang ini menunjukkan peserta dapat menggunakan aplikasi Akuntansi UKM dan aplikasi Catatan Keuangan dengan baik sehingga dapat menghilangkan perulangan pencatatan dan perhitungan yang dilakukan secara manual. pada periode harian, mingguan dan bulanan laporan keuangan yang selama ini dilakukan menjadi hanya satu kali input dan dapat diunduh serta dibagikan ke pengguna lainnya berdasarkan periode yang diinginkan serta penghematan waktu pengerjaan. Agar dapat mengikuti program pelatihan penggunaan SIA BUM Desa selanjutnya, diharapkan BUM Desa memiliki perangkat computer atau laptop yang sesuai dengan spesifikasi yang diperlukan disertai dengan kemauan untuk berdisiplin dalam melakukan penginputan data laporan keuangan disertai bukti yang benar dan dapat dipertanggung jawabkan.

Christopher Reale, Rebecca Russell

Hidden parameters are latent variables in reinforcement learning (RL) environments that are constant over the course of a trajectory. Understanding what, if any, hidden parameters affect a particular environment can aid both the development and appropriate usage of RL systems. We present an unsupervised method to map RL trajectories into a feature space where distance represents the relative difference in system behavior due to hidden parameters. Our approach disentangles the effects of hidden parameters by leveraging a recurrent neural network (RNN) world model as used in model-based RL. First, we alter the standard world model training algorithm to isolate the hidden parameter information in the world model memory. Then, we use a metric learning approach to map the RNN memory into a space with a distance metric approximating a bisimulation metric with respect to the hidden parameters. The resulting disentangled feature space can be used to meaningfully relate trajectories to each other and analyze the hidden parameter. We demonstrate our approach on four hidden parameters across three RL environments. Finally we present two methods to help identify and understand the effects of hidden parameters on systems.

Aulia Rahmawati, Ririn Puspita Tutiasri

Based on recent researches, there is an alarming rate of children addiction towards online games, from the mild ones to the heavy ones. These community empowerment activities focus upon the educational purposes for elementary school children in developing healthy games-online habits. Games online were chosen because they posed more risks in comparison to offline games since the anonymity in the virtual world. Initial interviews, FGDs and training were methods chosen and were continued with educational activities and advocacy. From the initial reviews, it was found that many school-aged children have their own gadgets and have their own social media accounts. It was also found that students spent 2 to 4 hours per day using their gadgets, mostly playing online games without parental supervision. This community empowerment act produces several recommendation and suggestions responding to these problems. Firstly, we recommend children to be open to their parents in regards of their online activities (including games they participate daily). Secondly, we recommend children not to engage in private conversation with strangers in virtual worlds. Thirdly, we also advocate children to produce creative contents instead of being passive consumers. Further advocacies and trainings are needed and will be organise after the school is safe and open after Covid-19 pandemic.

Syed Muhammad Raza, Syed Muhammad Ghazi Hassan, Syed Ali Hassan et al.

To protect the environment from trash pollution, especially in societies, and to take strict action against the red-handed people who throws the trash. As modern societies are developing and these societies need a modern solution to make the environment clean. Artificial intelligence (AI) evolution, especially in Deep Learning, gives an excellent opportunity to develop real-time trash detection using CCTV cameras. The inclusion of this project is real-time trash detection using a deep model of Convolutional Neural Network (CNN). It is used to obtain eight classes mask, tissue papers, shoppers, boxes, automobile parts, pampers, bottles, and juices boxes. After detecting the trash, the camera records the video of that person for ten seconds who throw trash in society. The challenging part of this paper is preparing a complex custom dataset that took too much time. The dataset consists of more than 2100 images. The CNN model was created, labeled, and trained. The detection time accuracy and average mean precision (mAP) benchmark both models' performance. In experimental phase the mAP performance and accuracy of the improved CNN model was superior in all aspects. The model is used on a CCTV camera to detect trash in real-time.

Honglin Bao, Wolfgang Banzhaf

The complexity of cooperative behavior is a crucial issue in multiagent-based social simulation. In this paper, an agent-based model is proposed to study the evolution of cooperative hunting behaviors in an artificial society. In this model, the standard hunting game of stag is modified into a new situation with social hierarchy and penalty. The agent society is divided into multiple layers with supervisors and subordinates. In each layer, the society is divided into multiple clusters. A supervisor controls all subordinates in a cluster locally. Subordinates interact with rivals through reinforcement learning, and report learning information to their corresponding supervisor. Supervisors process the reported information through repeated affiliation-based aggregation and by information exchange with other supervisors, then pass down the reprocessed information to subordinates as guidance. Subordinates, in turn, update learning information according to guidance, following the "win stay, lose shift" strategy. Experiments are carried out to test the evolution of cooperation in this closed-loop semi-supervised emergent system with different parameters. We also study the variations and phase transitions in this game setting.

Tara van Viegen, Athena Akrami, Kate Bonnen et al.

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.