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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
We propose Conditional Adapter (CoDA), a parameter-efficient transfer
learning method that also improves inference efficiency. CoDA generalizes
beyond standard adapter approaches to enable a new way of balancing speed and
accuracy using conditional computation. Starting with an existing dense
pretrained model, CoDA adds sparse activation together with a small number of
new parameters and a light-weight training phase. Our experiments demonstrate
that the CoDA approach provides an unexpectedly efficient way to transfer
knowledge. Across a variety of language, vision, and speech tasks, CoDA
achieves a 2x to 8x inference speed-up compared to the state-of-the-art Adapter
approach with moderate to no accuracy loss and the same parameter efficiency
BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Twitter bot detection has become a crucial task in efforts to combat online
misinformation, mitigate election interference, and curb malicious propaganda.
However, advanced Twitter bots often attempt to mimic the characteristics of
genuine users through feature manipulation and disguise themselves to fit in
diverse user communities, posing challenges for existing Twitter bot detection
models. To this end, we propose BotMoE, a Twitter bot detection framework that
jointly utilizes multiple user information modalities (metadata, textual
content, network structure) to improve the detection of deceptive bots.
Furthermore, BotMoE incorporates a community-aware Mixture-of-Experts (MoE)
layer to improve domain generalization and adapt to different Twitter
communities. Specifically, BotMoE constructs modal-specific encoders for
metadata features, textual content, and graphical structure, which jointly
model Twitter users from three modal-specific perspectives. We then employ a
community-aware MoE layer to automatically assign users to different
communities and leverage the corresponding expert networks. Finally, user
representations from metadata, text, and graph perspectives are fused with an
expert fusion layer, combining all three modalities while measuring the
consistency of user information. Extensive experiments demonstrate that BotMoE
significantly advances the state-of-the-art on three Twitter bot detection
benchmarks. Studies also confirm that BotMoE captures advanced and evasive
bots, alleviates the reliance on training data, and better generalizes to new
and previously unseen user communities.Comment: Accepted at SIGIR 202
Composing games into complex institutions
Game theory is used by all behavioral sciences, but its development has long
centered around tools for relatively simple games and toy systems, such as the
economic interpretation of equilibrium outcomes. Our contribution,
compositional game theory, permits another approach of equally general appeal:
the high-level design of large games for expressing complex architectures and
representing real-world institutions faithfully. Compositional game theory,
grounded in the mathematics underlying programming languages, and introduced
here as a general computational framework, increases the parsimony of game
representations with abstraction and modularity, accelerates search and design,
and helps theorists across disciplines express real-world institutional
complexity in well-defined ways. Relative to existing approaches in game
theory, compositional game theory is especially promising for solving game
systems with long-range dependencies, for comparing large numbers of
structurally related games, and for nesting games into the larger logical or
strategic flows typical of real world policy or institutional systems.Comment: ~4000 words, 6 figure
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
PERANCANGAN GAME FPS (FIRST PERSON SHOOTER) BREAK ON CAMPUS DENGAN MENGGUNAKAN UNREAL ENGINE
MUHAMMAD ALIF ZUHARLAN (2023) : PERANCANGAN GAME FPS (FIRST PERSON SHOOTER)
BREAK ON CAMPUS DENGAN MENGGUNAKAN UNREAL
ENGINE
Dalam keseharian manusia saat ini pembelajaran sangat diutamakan, baik itu dari sekolah, maupun
universitas. Hal ini dilakukan selain menambah pengetahuan manusia, dapat juga mengubah perilaku
manusia agar berpikir kritis dalam suatu permasalahan. Proses pembelajaran ini dilakukan ketika kita masi
kecil, dengan metode belajar dari orang tua kita seperti bagaimana kita berbicara sopan santun, makan,
berjalan dan sebagainya. Metode ini berfungsi agar kedepannya dapat melakukan secara otodidak hingga ke
tahap pembelajaran berikutnya. Namun dengan hadirnya game atau bisa disebut juga dengan video game
mulai beradaptasi kedalam keseharian manusia di era ini. Sehingga pembelajaran yang selama ini sama sekali
tidak pernah diperhatikan kembali. Permasalahan itu dirasakan hingga kini, dan sangat sulit untuk di
hilangkan terutama dikalangan pelajar. Untuk itu perlu adannya game yang dapat membuat pelajar bisa
sekaligus melakukan pembelajaran didalam game tersebut dengan membuat game edukasi. Game edukasi
saat ini sudah banyak diterapkan, namun masih banyak perlu pengembangan. Penelitian dan Pengembangan
game FPS (First Person Shooter) ini merupakan jajaran game yang sangat diminati dikarenakan banyaknya
fitur menarik yang menuai para pelajar ingin terus memainkannya. Namun, game yang seperti ini jarang
sekali menyuguhkan fitur pembelajaran, namun justru diperbanyak fitur kekerasan dan sejenisnya. Game
yang akan dibuat ini berfungsi sebagai media perantara pembelajaran dan juga dapat digunakan dari kalangan
pelajar yang setiap harinya berkaitan dengan game. Perancangan game ini dibantu dengan aplikasi UE
(Unreal Engine) yang tidak banyak memasukkan bahasa pemrograman atau c++. Game ini akan membuat
para pelajar akan merasakan sensasi bermain game sekaligus mendapatkan pembelajaran. Yang berupakan
video pembelajaran yang sangat di perlukan untuk dapat bersaing di universitas atau sebagainya
Berdasarakan uji kelayakan menggunakan metode UAT, game ini menghasilkan angka 90% dan dapat
disimpulkan pengujian game ini sangat baik dan dapat dikembangkan lebih lagi untuk kedepannya.
Kata Kunci : Game edukasi, Unreal engine, FPS
GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering
In this work, we present an end-to-end Knowledge Graph Question Answering
(KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text
pre-trained language model. The model takes a question in natural language as
input and produces a simpler form of the intended SPARQL query. In the simpler
form, the model does not directly produce entity and relation IDs. Instead, it
produces corresponding entity and relation labels. The labels are grounded to
KG entity and relation IDs in a subsequent step. To further improve the
results, we instruct the model to produce a truncated version of the KG
embedding for each entity. The truncated KG embedding enables a finer search
for disambiguation purposes. We find that T5 is able to learn the truncated KG
embeddings without any change of loss function, improving KGQA performance. As
a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata
datasets on end-to-end KGQA over Wikidata.Comment: 16 pages single column format accepted at ESWC 2023 research trac
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