Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights

The autoencoder is an unsupervised learning paradigm that aims to create a compact latent representation of data by minimizing the reconstruction loss. However, it tends to overlook the fact that most data (images) are embedded in a lower-dimensional space, which is crucial for effective data representation. To address this limitation, we propose a novel approach called Low-Rank Autoencoder (LoRAE). In LoRAE, we incorporated a low-rank regularizer to adaptively reconstruct a low-dimensional latent space while preserving the basic objective of an autoencoder. This helps embed the data in a lower-dimensional space while preserving important information. It is a simple autoencoder extension that learns low-rank latent space. Theoretically, we establish a tighter error bound for our model. Empirically, our model's superiority shines through various tasks such as image generation and downstream classification. Both theoretical and practical outcomes highlight the importance of acquiring low-dimensional embeddings.Comment: Accepted @ IEEE/CVF WACV 202

Legally Enforceable Smart-Contract Languages:A Systematic Literature Review

Smart contracts are a key component of today’s blockchains. They are critical in controlling decentralized autonomous organizations (DAO). However, smart contracts are not yet legally binding nor enforceable; this makes it difficult for businesses to adopt the DAO paradigm. Therefore, this study reviews existing Smart Contract Languages (SCL) and identifies properties that are critical to any future SCL for drafting legally binding contracts. This is achieved by conducting a Systematic Literature Review (SLR) of white- and grey literature published between 2015 and 2019. Using the SLR methodology, 45 Selected and 28 Supporting Studies detailing 45 state-of-the-art SCLs are selected. Finally, 10 SCL properties that enable legally compliant DAOs are discovered, and specifications for developing SCLs are explored

Building Global Societies on Collective Intelligence: Challenges and Opportunities

Digital disruptions caused by use of technologies like social media arguably present a formidable challenge to democratic values and in-turn to Collective Intelligence (CI or “wisdom-of-crowd”), which the former is an emblem of. These challenges such as misinformation, partisan bias, polarization, and rising mistrust in institutions (incl. mainstream media), present a new threat to collectives both online and offline—amplifying the risk of turning “wise” crowds “mad”, and rendering their actions counterproductive. Considering the increasingly important role crowds play in solving today’s socio-political, technological, and economical issues, and in shaping our future, we identify time-critical challenges and potential solutions that require urgent attention, if future CI systems are to sustain their indispensable role as global deliberation instruments

Building Global Societies on Collective Intelligence: Challenges and Opportunities

Digital disruptions caused by the use of technologies like social media arguably present a formidable challenge to democratic values and in turn to Collective Intelligence. Challenges such as misinformation, partisan bias, polarization, and rising mistrust in institutions (including mainstream media), present a new constant threat to collectives both online and offline—amplifying the risk of turning ‘wise’ crowds ‘mad’, and rendering their actions counterproductive. Considering the increasingly important role crowds play in solving today’s socio-political, technological, and economical issues, and in shaping our future, it is vital to protect crowd-oriented systems against such disruptions. In this commentary, we identify time-critical challenges and potential solutions from emerging work on diversity, transparency, collective dynamics, and machine behavior, that require urgent attention, if future CI systems are to sustain their indispensable role as global deliberation instruments