Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework

This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs

Quantum Photovoltaic Cells Driven by Photon Pulses

We investigate the quantum thermodynamics of two quantum systems, a two-level system and a four-level quantum photocell, each driven by photon pulses as a quantum heat engine. We set these systems to be in thermal contact only with a cold reservoir while the heat (energy) source, conventionally given from a hot thermal reservoir, is supplied by a sequence of photon pulses. The dynamics of each system is governed by a coherent interaction due to photon pulses in terms of the Jaynes-Cummings Hamiltonian together with the system-bath interaction described by the Lindblad master equation. We calculate the thermodynamic quantities for the two-level system and the quantum photocell including the change in system energy, power delivered by photon pulses, power output to an external load, heat dissipated to a cold bath, and entropy production. We thereby demonstrate how a quantum photocell in the cold bath can operate as a continuum quantum heat engine with the sequence of photon pulses continuously applied. We specifically introduce the power efficiency of the quantum photocell in terms of the ratio of output power delivered to an external load with current and voltage to the input power delivered by the photon pulse. Our study indicates a possibility that a quantum system driven by external fields can act as an efficient quantum heat engine under non-equilibrium thermodynamics.Comment: 10 pages, 8 figures, submitte

Metric and Tool Support for Instant Feedback of Source Code Readability

In the software maintenance phase, comprehending the legacy source code is inevitable, which consumes most of the time of the phase. The better the code is readable, the easier it is for code readers to comprehend the system based on the source code. This paper proposes an enhanced source code readability metric to quantitatively measure the extent of code readability. In addition, we developed a tool support named Instant R. Gauge to update the code on the fly based on the readability feedback of the current code. The tool also provides the history of the readability change so that developers recognize the more readable code and gradually change their coding habit without any annoying advice. The suggested readability metric achieves 75.74% of explanatory power, and our experiment showed that readability of most of the methods authored in our tool is higher than that of the methods without our approach

SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration

The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with this concern while interacting with ill-intentioned users, such as those who explicitly make hate speech or elicit harmful responses. However, discussions on sensitive issues can become toxic even if the users are well-intentioned. For safer models in such scenarios, we present the Sensitive Questions and Acceptable Response (SQuARe) dataset, a large-scale Korean dataset of 49k sensitive questions with 42k acceptable and 46k non-acceptable responses. The dataset was constructed leveraging HyperCLOVA in a human-in-the-loop manner based on real news headlines. Experiments show that acceptable response generation significantly improves for HyperCLOVA and GPT-3, demonstrating the efficacy of this dataset.Comment: 19 pages, 10 figures, ACL 202

Quantum teleportation via a W state

We investigate two schemes of the quantum teleportation with a $W$ state, which belongs to a different class from a Greenberger-Horne-Zeilinger class. In the first scheme, the $W$ state is shared by three parties one of whom, called a sender, performs a Bell measurement. It is shown that quantum information of an unknown state is split between two parties and recovered with a certain probability. In the second scheme, a sender takes two particles of the $W$ state and performs positive operator valued measurements in two ways. For two schemes, we calculate the success probability and the average fidelity. We show that the average fidelity of the second scheme cannot exceed that of the first one.Comment: 7 pages, 1 figur

The In-Depth Investigation of Performance Attribution Effect in Fitness Service: Before, During, and After Service Operation

The overall purpose of my dissertation is to investigate whether, why, and when performance attribution influences consumer behaviors before, during, and after service operation in the fitness service context. The first study identifies the before-service effect of performance attribution by investigating the impact of trainers' attribution of their competent outcomes on service registration intention. The second study pinpoints the during-service effect of performance attribution by delving into the interactive effect of trainers' attribution of competent outcomes and customers' implicit mindset on service compliance. The third study explores the after-service effect of performance attribution by examining the effect of trainers' attribution of adverse outcomes on customer retaliation. These studies adopt and use an experimental design in data collection to examine the causal relationship among focal variables. These studies build upon each other by extending the scopes, outcome contexts, and boundary conditions of performance attribution effect in the fitness service context. These studies enrich both attribution theory and performance attribution literature by establishing various theoretical connections with adjacent theories. These studies also provide diverse practical implications about how to effectively utilize performance attribution to elicit customer behaviors conducive to fitness service organizations

Technological requirements of profile machining

Abstract: The term ‘profile machining ’ is used to refer to the milling of vertical surfaces described by profile curves. Profile machining requires higher precision (1/1000 mm) than regular 3D machining (1/100 mm) with the erosion of sharp vertices should being especially avoided. Although, profile machining is very essential for making trimming and flange dies, it seldom brought into focus. This paper addresses the technological requirements of profile machining including machining width and depth control, minimizing toolware, and protecting sharp vertices. Issues of controller alarms are also addressed

Geometric Zone-Control Algorithm for Collision and Deadlock Avoidance in AGV System

Automated guided vehicle (AGV) system control presents several challenges, among which deadlock situations are particularly problematic, as they can significantly reduce the overall performance of the AGV system. Existing studies are based on the assumption that there is sufficient space between nodes and links in AGV guidepath topology. This study proposes a novel zone-control algorithm for AGV systems designed to prevent collisions and deadlocks. The proposed algorithm involves a zone-partitioning technique that considers both AGV geometry and guidepath topology. This method identifies all collision-prone areas and divides the AGV guidepath into zones. By effectively employing these zones, the zone-control algorithm successfully addresses and resolves deadlock problems in AGV systems. The effectiveness of the proposed algorithm was evaluated against state-of-the-art methods using irregular layouts. Experimental results demonstrated that the proposed method effectively handled delivery tasks, resulting in a 58–85% improved performance, thereby verifying its efficacy. The proposed algorithm offers a practical and effective solution for AGV systems with irregular guidepath topologies at real manufacturing sites