Societies can become a conspiratorial society where there is a majority of humans that believe, and therefore spread, conspiracy theories. Artificial intelligence gave rise to social media bots that can spread conspiracies in an automated fashion. Currently, organizations combat the spread of conspiracies through manual fact-checking processes and the dissemination of counter-narratives. However, the effects of harnessing the same automation to create useful bots are not well explored. To address this, we create BotSim, an Agent-Based Model of a society in which useful bots are introduced into a small world network. These useful bots are: Info-Correction Bots, which correct bad information into good, and Good Bots, which put out good messaging. The simulated agents interact through generating, consuming and propagating information. Our results show that, left unchecked, Bad Bots can create a conspiratorial society, and this can be mitigated by either Info-Correction Bots or Good Bots; however, Good Bots are more efficient and sustainable than Info-Correction Bots . Proactive good messaging is more resource-effective than reactive information correction. With our observations, we expand the concept of bots as a malicious social media agent towards automated social media agent that can be used for both good and bad purposes. These results have implications for designing communication strategies to maintain a healthy social cyber ecosystem.
The tendency of generative artificial intelligence (AI) to ‘hallucinate’ false information is well known; AI-generated citations to non-existent sources have penetrated the bibliographies of peer-reviewed publications. Drawing from the Transparency and Openness Promotion guidelines, American judicial contention with generative AI, and the submission of prior art to the US Patent and Trademark Office, the author proposes that journals require authors to submit the full text of each cited source along with their manuscripts, thereby preventing authors from citing material whose full text they cannot produce. This solution requires limited additional work by authors or editors while effectively immunizing journals against hallucinated references.
Academies and learned societies, Bibliography. Library science. Information resources
Andrés Holgado-Sánchez, Holger Billhardt, Sascha Ossowski
et al.
Aligning AI systems with human values and the value-based preferences of various stakeholders (their value systems) is key in ethical AI. In value-aware AI systems, decision-making draws upon explicit computational representations of individual values (groundings) and their aggregation into value systems. As these are notoriously difficult to elicit and calibrate manually, value learning approaches aim to automatically derive computational models of an agent's values and value system from demonstrations of human behaviour. Nonetheless, social science and humanities literature suggest that it is more adequate to conceive the value system of a society as a set of value systems of different groups, rather than as the simple aggregation of individual value systems. Accordingly, here we formalize the problem of learning the value systems of societies and propose a method to address it based on heuristic deep clustering. The method learns socially shared value groundings and a set of diverse value systems representing a given society by observing qualitative value-based preferences from a sample of agents. We evaluate the proposal in a use case with real data about travelling decisions.
Mechanical search (MS) in cluttered environments remains a significant challenge for autonomous manipulators, requiring long-horizon planning and robust state estimation under occlusions and partial observability. In this work, we introduce XPG-RL, a reinforcement learning framework that enables agents to efficiently perform MS tasks through explainable, priority-guided decision-making based on raw sensory inputs. XPG-RL integrates a task-driven action prioritization mechanism with a learned context-aware switching strategy that dynamically selects from a discrete set of action primitives such as target grasping, occlusion removal, and viewpoint adjustment. Within this strategy, a policy is optimized to output adaptive threshold values that govern the discrete selection among action primitives. The perception module fuses RGB-D inputs with semantic and geometric features to produce a structured scene representation for downstream decision-making. Extensive experiments in both simulation and real-world settings demonstrate that XPG-RL consistently outperforms baseline methods in task success rates and motion efficiency, achieving up to 4.5$\times$ higher efficiency in long-horizon tasks. These results underscore the benefits of integrating domain knowledge with learnable decision-making policies for robust and efficient robotic manipulation. The project page for XPG-RL is https://yitingzhang1997.github.io/xpgrl/.
Abstract Microbial technologies constitute a huge and unique potential for confronting major humanitarian and biosphere challenges, especially in the realms of sustainability and providing basic goods and services where they are needed and particularly in low‐resource settings. These technologies are evolving rapidly. Powerful approaches are being developed to create novel products, processes, and circular economies, including new prophylactics and therapies in healthcare, bioelectric systems, and whole‐cell understanding of metabolism that provides novel insights into mechanisms and how they can be utilised for applications. The modulation of microbiomes promises to create important applications and mitigate problems in a number of spheres. Collectively, microbial technologies save millions of lives each year and have the potential, through increased deployment, to save many more. They help restore environmental health, improve soil fertility, enable regenerative agriculture, reduce biodiversity losses, reduce pollution, and mitigate polluted environments. Many microbial technologies may be considered to be ‘healing’ technologies – healing of humans, of other members of the biosphere, and of the environment. This is the Age of Microbial Technology . However, the current exploitation of microbial technologies in the service of humanity and planetary health is woefully inadequate and this failing unnecessarily costs many lives and biosphere deterioration. Microbiologists – the practitioners of these healing technologies – have a special, preordained responsibility to promote and increase their deployment for the good of humanity and the planet. To do this effectively – to actually make a difference – microbiologists will need to partner with key enablers and gatekeepers, players such as other scientists with essential complementary skills like bioengineering and bioinformatics, politicians, financiers, and captains of industry, international organisations, and the general public. Orchestration and coordination of the establishment and functioning of effective partnerships will best be accomplished by learned societies, their academies, and the international umbrella organisations of learned societies. Effective dedication of players to the tasks at hand will require unstinting support from employers, particularly the heads of institutes of higher education and of research establishments. Humanity and the biosphere are currently facing challenges to their survival not experienced for millennia. Effectively confronting these challenges is existential, and microbiologists and their learned societies have pivotal roles to play: they must step up and act now .
Uma das mais bem sucedidas obras do campo da História das Ciências no Brasil, “A Matemática no Brasil” conquistou quatro edições ao longo de 30 anos. Um clássico que permanece atual graças a nova edição da Editora Blücher. A presente resenha destaca os pontos mais significativos da obra e suas características textuais e materiais, enfatizando a importância de reedições de trabalhos como este para a difusão do campo.
Academies and learned societies, Natural history (General)
Journal and book editors in most disciplines are faced with a flood of meta-analyses, which critical reviews have shown are not always of sufficient quality. In the short run, editors could give targeted instructions to authors and make specific recommendations to reviewers to ensure that not only meta-analyses but also research syntheses more broadly, published under their watch, meet acceptable publication standards. In order to achieve satisfactory improvements in the long run, editors should foster fundamental changes in the way the publication of negative and non-significant results is handled.
Academies and learned societies, Bibliography. Library science. Information resources
Uljad Berdica, Matthew Jackson, Niccolò Enrico Veronese
et al.
Soft robotic manipulators offer operational advantage due to their compliant and deformable structures. However, their inherently nonlinear dynamics presents substantial challenges. Traditional analytical methods often depend on simplifying assumptions, while learning-based techniques can be computationally demanding and limit the control policies to existing data. This paper introduces a novel approach to soft robotic control, leveraging state-of-the-art policy gradient methods within parallelizable synthetic environments learned from data. We also propose a safety oriented actuation space exploration protocol via cascaded updates and weighted randomness. Specifically, our recurrent forward dynamics model is learned by generating a training dataset from a physically safe \textit{mean reverting} random walk in actuation space to explore the partially-observed state-space. We demonstrate a reinforcement learning approach towards closed-loop control through state-of-the-art actor-critic methods, which efficiently learn high-performance behaviour over long horizons. This approach removes the need for any knowledge regarding the robot's operation or capabilities and sets the stage for a comprehensive benchmarking tool in soft robotics control.
The topic of pluralism-multiculturalism is increasingly being discussed and socialized considering that Indonesia is a plural and multi-cultural nation, Often this situation makes society often divided. Inevitably often cause riots, hostility and war. One of the initiators of Pluralism in Indonesia is Ulil Abshar Abdalla, he is often mentioned as the successor of Cak Nur (Nurcholis Madjid). Ulil also established a network called the Jaringan Islam Liberal (JIL) to channel and spread his ideas about liberalism. This research is a library research with a character study approach combined with hermeneutic. Liberal Islam Network (JIL) was established in Jakarta on August 21, 2001 using intellectual networks located in Jakarta, Yogyakarta, Makassar, Bandung and several other cities. The vision promoted by JIL is to present a more responsive and progressive theology with the key to freedom and liberation.
The global aging population presents significant challenges for societies worldwide, particularly in an increasingly digitalized era. The Learning Society is crucial in preparing different societies and their people to address these challenges effectively. This paper extends this concept and proposes a new conceptual framework, Learning Societies for Digital Aging, empowering all members across various sectors from different ages to acquire and develop the necessary knowledge, skills, and competencies to navigate and thrive in an increasingly digital world. It presents seven guiding principles for developing this conceptual framework: 1) Centering Humanistic Values, 2) Embracing Digital, 3) Cultivating Learning Societies, 4) Advancing Inclusiveness, 5) Taking Holistic Approaches, 6) Encouraging Global Knowledge Sharing, and 7) Fostering Adaptability. By integrating these guiding principles into the design, implementation, and evaluation of formal, nonformal, and informal learning opportunities for people of all ages, stakeholders can contribute to creating and nurturing learning societies that cater to aging populations in the digital world. This paper aims to provide a foundation for further research and action toward building more inclusive, adaptive, and supportive learning environments that address the challenges of digital aging and foster more empathetic, informed, and prepared societies for the future of aging.
A’am Rifaldi Khunaifi, Arif Supriyadi, Dedy Setyawan
Guru SMAN 1 Pandih Batu belum menggunakan Learning Management System (LMS) secara maksimal sehingga membutuhkan pelatihan dalam menggunakan aplikasi ini. Tujuan kegiatan ini adalah untuk meningkatkan pemahaman guru mengenai LSM khususnya menggunakan Sevima Edlink. Metode yang digunakan dalam pelatihan ini adalah demonstrasi, praktek, dan diskusi. Peserta pada kegiatan ini adalah 30 orang guru di SMAN 1 Pandih Batu. Target pelatihan ini yaitu guru mampu menggunakan Sevima Edlink, pembuatan topik materi, invite siswa, pengunggahan materi pelajaran, dan kuis daring. Hasil dari kegiatan ini menunjukan terjadi peningkatan kemampuan berdasarkan hasil pretest dengan nilai 62,3 meningkat pada hasil postest dengan nilai 80,5. Hasil skor N-Gain menunjukan terjadi peningkatan kemampuan guru sebesar 0,48 dengan kategori sedang. Peserta menilai bahwa pelatihan ini dapat memberikan manfaat berupa peningkatan pengetahuan dan keterampilan diri bagi peserta dengan metode ceramah maupun kegiatan demonstrasi dan peserta juga menilai bahwa pemateri baik ceramah maupun praktek sangat baik dalam penguasaan metode dan materi.
Food processing and manufacture, Academies and learned societies
This paper describes a CNN-based multi-frame post-processing approach based on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN. This method has been integrated with the Versatile Video Coding Test Model (VTM) 15.2 to enhance the visual quality of the final reconstructed content. The evaluation results on the CLIC 2022 validation sequences show consistent coding gains over the original VVC VTM at the same bitrates when assessed by PSNR. The integrated codec has been submitted to the Challenge on Learned Image Compression (CLIC) 2022 (video track), and the team name associated with this submission is BVI_VC.
Atok Miftachul Hudha, Sukarsono Sukarsono, Ning Rahayu Handayani
The application of character values through classroom learning has not been maximal because teachers have difficulty choosing the right learning model. The OIDDE (Orientation, Identify, Discussion, Decision, and Engage in Behavior) learning model is appropriate to implement. This article aims to describe the implementation of teacher assistance activities in developing character values through learning by applying the OIDDE learning model. The subjects of the service were 12 teachers at SD / MI levels, 12 teachers from SMP / MTs, and 15 teachers at SMA / SMK / MA levels from 39 schools in Batu City, East Java. The activity was carried out for 6 months using the Forum Group Discussion (FGD) method, workshops, hands-on practice, outreach, and evaluation. The evaluation of community service is carried out thoroughly, from the process to the output, especially the seriousness of the teacher in participating in the activities to the skills in applying the model. The results of the process evaluation show that, the service activity is carried out well, the average attendance is 85%, and as many as 100% of the teachers become the Master Teacher model. OIDDE learning, and has the right to provide tutors with OIDDE learning models.
Food processing and manufacture, Academies and learned societies
Family Planning Village (KB) was one of the flagship programs of the Indonesian National Population and Family Planning Agency (BKKBN). IT aimed to synergize the Concept of Family Empowerment Development, Population and Family Planning (Bangga Kencana) in the village sphere. That could improve the utilization of Family Planning services can be accessed by people living in remote villages from family planning service centres and as an effort by BKKBN to create a prosperous and quality family. One of Bangga Kencana mandatory programs in Kb Village was the Toddler Family Development Program (BKB). This community empowerment activity was carried out to activate the BKB group by conducting counselling, coaching, monitoring, and field assistance. From the assessment results, after the training was carried out. It appeared that the cadres of BKB Group did coach public dissemination better. They were proven from the effects of Pre-Test, and Post-Trial Cadre Supervisor BKB where the initial pre-test score averaged 65.5%, after conducting evaluation and education then done Post Test, where the average weight increased to 84.76%. Then there was the recognition of the community based on the results of in-depth interviews that there is an increase in public awareness to participate in the active group of BKB and increase the participation of citizens in every BKB Group coaching activity in Lhambuk Village.
Food processing and manufacture, Academies and learned societies
We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis and show that it improves the sample efficiency of both state-based and image-based RL. We perform infinite-width analysis of our architecture using the Neural Tangent Kernel and theoretically show that tuning the initial variance of the Fourier basis is equivalent to functional regularization of the learned deep network. That is, these learned Fourier features allow for adjusting the degree to which networks underfit or overfit different frequencies in the training data, and hence provide a controlled mechanism to improve the stability and performance of RL optimization. Empirically, this allows us to prioritize learning low-frequency functions and speed up learning by reducing networks' susceptibility to noise in the optimization process, such as during Bellman updates. Experiments on standard state-based and image-based RL benchmarks show clear benefits of our architecture over the baselines. Website at https://alexanderli.com/learned-fourier-features
Este texto faz uma breve leitura crítica do artigo Progresso técnico e mundo da vida social (1965) de Jürgen Habermas, buscando explicitar as ideias do autor acerca dos limites existentes entre os conceitos “ciência” e “literatura”, dentro do processo de construção de sua tese sobre a racionalidade comunicativa [kommunikative Vernunft], com o objetivo de fazer refletir algumas de suas ideias de modo a colaborar com as discussões dos problemas caros à historiografia da ciência e, mais amplamente, aos estudos sociais de ciência e tecnologia na contemporaneidade. O texto examina, também de forma breve, as principais referências utilizadas por Habermas em seu ensaio para tratar da cisão das chamadas ciências empírico-analíticas e das ciências histórico-hermenêuticas, principalmente ao longo da segunda metade do século XX.
Palavras-chave: História da Ciência, Progresso técnico e científico, Jürgen Habermas, Literatura, Modernidade.
Academies and learned societies, Natural history (General)
Kristen Mazur, Mutiara Sondjaja, Matthew Wright
et al.
In the system of approval voting, individuals vote for all candidates they find acceptable. Many approval voting situations can be modeled geometrically, and thus geometric concepts such as the piercing number have a natural interpretation. In this paper, we explore piercing numbers in the setting where voter preferences can be modeled by congruent arcs on a circle -- i.e., in fixed-length circular societies. Given a number of voters and the length of the voter preference arcs, we give bounds on the possible piercing number of the society. Further, we explore which piercing numbers are more likely. Specifically, under the assumption of uniformly distributed voter preference arcs, we determine the probability distribution of the piercing number of societies in which the length of the arcs is sufficiently small. We end with simulations that give estimated probabilities of piercing number for societies with larger voter preference arcs.
Zhengxue Cheng, Heming Sun, Masaru Takeuchi
et al.
In this paper, we provide a detailed description on our approach designed for CVPR 2019 Workshop and Challenge on Learned Image Compression (CLIC). Our approach mainly consists of two proposals, i.e. deep residual learning for image compression and sub-pixel convolution as up-sampling operations. Experimental results have indicated that our approaches, Kattolab, Kattolabv2 and KattolabSSIM, achieve 0.972 in MS-SSIM at the rate constraint of 0.15bpp with moderate complexity during the validation phase.