Currently, over 90% of Ethereum blocks are built using MEV-Boost, an auction that allows validators to sell their block-building power to builders who compete in an open English auction in each slot. A majority of the...
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ISBN:
(纸本)9783031692307;9783031692314
Currently, over 90% of Ethereum blocks are built using MEV-Boost, an auction that allows validators to sell their block-building power to builders who compete in an open English auction in each slot. A majority of these are produced by integrated builders, operated by trading firms, began to overtake many of the neutral builders. Outside of the integrated builder teams, little is known about which advantages integration confers beyond latency and how latency advantages distort on-chain trading. This paper explores these poorly understood advantages. We make two contributions. First, we point out that integrated builders are able to bid truthfully in their own bundle merge and then decide how much profit to take later in the final stages of the PBS auction when more information is available, making the auction for them look closer to a second-price auction while independent searchers are stuck in a first-price auction. Second, we find that latency disadvantages convey a winner's curse on slow bidders when underlying values depend on a stochastic price process that change as bids are submitted.
In the digital age, user reviews are crucial for decision-making and product development. With the growing volume of Amazon product reviews, traditional analysis methods fall short, highlighting the need for smarter, ...
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ISBN:
(纸本)9783031785535;9783031785542
In the digital age, user reviews are crucial for decision-making and product development. With the growing volume of Amazon product reviews, traditional analysis methods fall short, highlighting the need for smarter, quicker, and more scalable solutions. In this paper, we present a sentiment analysis system tailored for Amazon reviews, utilizing n-grams for better context comprehension and enabling exploration of the machine learning model via real-time application. Evaluation results show model accuracy by concentrating on positive and negative reviews and fine-tuning hyperparameters.
In the complex landscape of online social networks, predicting unfollow events is challenging due to data sparsity, class imbalance, and the dynamic nature of user interactions. This paper presents EDGE-UP, an Enhance...
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ISBN:
(纸本)9783031785405;9783031785412
In the complex landscape of online social networks, predicting unfollow events is challenging due to data sparsity, class imbalance, and the dynamic nature of user interactions. This paper presents EDGE-UP, an Enhanced Dynamic Graph Neural Network (GNN) Ensemble model adeptly designed to overcome these challenges in unfollow prediction. EDGE-UP leverages a large-scale, longitudinal Twitter dataset featuring 58 weekly snapshots across 118,890 users to capture the evolving social dynamics. It minimizes the need for extensive feature engineering by utilizing GNNs for spatial encoding and LSTMs for capturing temporal dynamics, addressing data sparsity and class imbalance through ensemble learning and negative sampling strategies. Our experiments demonstrate EDGE-UP's superior performance in accurately predicting unfollow events, setting a new standard in social network analysis, and offering versatile applicability across different platforms. The code and data are available here: https://***/DSAatUSU/edge-up.
This research examines the many ways that newsgames provide interactive news stories through reflection on prior publications on the topic and the content analysis of 101 newsgames archived for such research. The cont...
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ISBN:
(纸本)9783031784521;9783031784538
This research examines the many ways that newsgames provide interactive news stories through reflection on prior publications on the topic and the content analysis of 101 newsgames archived for such research. The content analysis provides perspective on the past two decades of newsgames, observing the evolution of technologies used to implement, the most common topical foci and the categories of newsgames. The analysis finds the most common categories of newsgames are editorial, news literacy and news quizzes. These games most commonly focus on war, politics, and misinformation and disinformation. While early digital newsgames were typically made with Adobe's Flash, the bulk of contemporary newsgames employ the HTML 5, CSS, and JavaScript technologies. It is also observed that unlike games produced by journalist organizations, many game jam produced newsgames were built using the Unity toolset.
We present a mixed initiative method for comics and visual narrative creation which can be used in an artistic practice and beyond. Mixing analogue human hand drawings with a generative AI tool, this method suggests a...
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ISBN:
(纸本)9783031784521;9783031784538
We present a mixed initiative method for comics and visual narrative creation which can be used in an artistic practice and beyond. Mixing analogue human hand drawings with a generative AI tool, this method suggests a way for generative AI to be incorporated in a turn-taking manner into an existing comic making practice by creating a short comic. The method has been developed within the first author's own artistic practice, and is loosely based on the work of Lynda Barry, specifically her ideas of having devices and helpers, such as a comic kit and the question "and then what happened?" to keep the story moving forward. We suggest that generative AI tools can be used in a similar way, helping steer and move the story while creating surprising twists and turns, and posing creative challenges. Moreover, we demonstrate that a single story can consist of both hand drawn and AI generated images made from the hand drawings. In this paper, we offer 1) two mixed initiative comics created with this method 2) an account of the process and our experience and 3) our reflections on the visuals and on using generative AI as a companion within one's comic-making creative practice.
Auctions, a long-standing method of trading goods and services, are a promising use case for decentralized finance. However, due to the inherent transparency property of blockchains, current sealed-bid auction impleme...
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ISBN:
(纸本)9783031692307;9783031692314
Auctions, a long-standing method of trading goods and services, are a promising use case for decentralized finance. However, due to the inherent transparency property of blockchains, current sealed-bid auction implementations on smart contracts requires a bidder to send at least two transactions to the underlying blockchain: a bidder must first commit their bid in the first transaction during the bidding period and reveal their bid in the second transaction once the revealing period starts. In addition, the smart contract often requires a deposit to incentivize bidders to reveal their bids, rendering unnecessary financial burdens and risks to bidders. We address these drawbacks by enforcing delayed execution in the blockchain execution layer to all transactions. In short, the blockchain only accepts encrypted transactions, and when the blockchain has finalized an encrypted transaction, the consensus group decrypts and executes it. This architecture enables ZeroAuction, a sealed-bid auction smart contract with zero deposit requirement. ZeroAuction relies on the blockchain enhanced with delayed execution to hide and bind the bids within the encrypted transactions and, after a delay period, reveals them automatically by decrypting and executing the transactions. Because a bidder only needs to interact with the blockchain once instead of two times to participate in the auction, ZeroAuction significantly reduces the latency overhead along with eliminating the deposit requirement.
We propose a simple model of protesters scattered throughout a city who want to gather into large and mobile groups. This model relies on random walkers on a street network that follow tactics built from a set of basi...
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ISBN:
(纸本)9783031785375;9783031785382
We propose a simple model of protesters scattered throughout a city who want to gather into large and mobile groups. This model relies on random walkers on a street network that follow tactics built from a set of basic rules. Our goal is to identify the most important rules for fast flocking of walkers. We explore a wide set of tactics and show the central importance of a specific rule based on alignment. Other rules alone perform poorly, but our experiments show that combining alignment with them enhances flocking.
Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity providers (LPs) incentivized to invest...
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ISBN:
(纸本)9783031786754;9783031786761
Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity providers (LPs) incentivized to invest their assets into a liquidity pool. However, the prices at which a pooled asset is traded is often more stale than the prices on centralized and more liquid exchanges. This leads to the LPs suffering losses to arbitrage. This problem is addressed by adapting market prices to trader behavior, captured via the classical market microstructure model of Glosten and Milgrom. In this paper, we propose the first optimal Bayesian and the first model-free data-driven algorithm to optimally track the external price of the asset. The notion of optimality that we use enforces a zero-profit condition on the prices of the market maker, hence the name ZeroSwap. This ensures that the market maker balances losses to informed traders with profits from noise traders. The key property of our approach is the ability to estimate the external market price without the need for price oracles or loss oracles. Our theoretical guarantees on the performance of both these algorithms, ensuring the stability and convergence of their price recommendations, are of independent interest in the theory of reinforcement learning. We empirically demonstrate the robustness of our algorithms to changing market conditions.
In the broader machine learning literature, data-generation methods demonstrate promising results by generating additional informative training examples via augmenting sparse labels. Such methods are less studied in g...
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ISBN:
(纸本)9783031785405;9783031785412
In the broader machine learning literature, data-generation methods demonstrate promising results by generating additional informative training examples via augmenting sparse labels. Such methods are less studied in graphs due to the intricate dependencies among nodes in complex topology structures. This paper presents a novel node generation method that infuses a small set of high-quality synthesized nodes into the graph as additional labeled nodes to optimally expand the propagation of labeled information. By simply infusing additional nodes, the framework is orthogonal to the graph learning and downstream classification techniques, and thus is compatible with most popular graph pre-training (self-supervised learning), semi-supervised learning, and meta-learning methods. The contribution lies in designing the generated node set by solving a novel optimization problem. The optimization places the generated nodes in a manner that: (1) minimizes the classification loss to guarantee training accuracy and (2) maximizes label propagation to low-confidence nodes in the downstream task to ensure high-quality propagation. Theoretically, we show that the above dual optimization maximizes the global confidence of node classification. Our Experiments demonstrate statistically significant performance improvements over 14 baselines on 10 publicly available datasets.
Although Virtual Reality technology offers a variety of experiences, Mixed Reality (MR) is a relatively new field, offering the ability to merge real and digital environments in sophisticated ways. Perception is at th...
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ISBN:
(纸本)9783031784491;9783031784507
Although Virtual Reality technology offers a variety of experiences, Mixed Reality (MR) is a relatively new field, offering the ability to merge real and digital environments in sophisticated ways. Perception is at the core of this integration and our experience of them: As humans, we perceive reality with our senses;we feel rain on our skin, we see the drops, we hear the sound, we can taste and smell the freshness of the air in real life. When creating and experiencing digitally enhanced realities, we still rely on the senses, but practically all VR/MR use only the audio-visual source, because other sensory modalities (e.g., tactile and olfactory) are not generally supported by the platforms. In this paper, we are concerned with the following questions: How can we reach other senses through a MR audio-visual environment? Can we create dreamlike experiences and feel music and art as our reality? Music and art therapies are well-established practices, so can MR have the potential to provide mindfulness? The pilot study presented in this paper offers insights into the nature of cross-modal associations and synesthesia interaction of senses theory as a framework for multisensory design. The benefits of multisensory stimulation are not fully explored in the field of neuroscience, but there are statements linking synesthesia (multisensory or cross-modal perception) with creativity and increased mental ability. Our results with a pilot study of our DreaMR series of experiences indicate a much greater ability for multisensory processing with MR in the general public than expected.
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