Advancements in technology have reduced information acquisition costs, creating an improved information environment for retail investors. Specifically, new technologies, such as application programming interface (API)...
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Advancements in technology have reduced information acquisition costs, creating an improved information environment for retail investors. Specifically, new technologies, such as application programming interface (API), deliver high -volume, institutionallike raw data directly to Main Street investors. Although greater availability of information can be beneficial, it may also exacerbate retail investors' existing trading deficiencies. Exploiting the sudden shutdown of Yahoo! Finance API, the largest free API for retail investors, this study examines how access to tech -enabled raw financial data affects retail investment. We find that retail trading volumes in stocks favored by active retail investors dropped by 8.6%-10.5% within one month of the API shutdown. The remaining retail trades collectively became more predictive of future returns, suggesting less gambling -like behavior after the API shutdown. Moreover, our randomized controlled experiment affirms the underlying mechanism: tech -enabled access to high -volume historical price data increases individuals' overconfidence, which further leads them to engage in excessive trading. The study reveals an unintended consequence of technology -led, wider data access for retail investors.
Traditional asset management strategy has emphasized building barriers to entry or closely guarding unique assets to maintain a firm's comparative advantage. A new "inverted firm" paradigm, however, has ...
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Traditional asset management strategy has emphasized building barriers to entry or closely guarding unique assets to maintain a firm's comparative advantage. A new "inverted firm" paradigm, however, has emerged. Under this strategy, firms share data seeking to become platforms by opening digital services to third parties and capturing part of their external surplus. This contrasts with a "pipeline" strategy where the firm itself creates value. This paper quantitatively estimates the effect of adopting an inverted firm strategy through the lens of application programming interfaces (APIs), a key enabling technology. Using both public data and those of a private API development firm, we document rapid growth of the API network and connecting apps since 2005. We then perform difference-in-difference and synthetic control analyses and find that public firms adopting public APIs grew an additional 38.7% over 16 years relative to similar nonadopters. We find no significant effect from the use of APIs purely for internal productivity: the pipeline strategy. Within the subset of firms that adopt public APIs, those that attract more third party complementors and those that become more central to the network see faster growth. Using variation in network centrality caused by API degradation, an instrumental variables analysis confirms a causal role for APIs in firm market value. Finally, we document an important downside of public APIs: increased risk of data breach. Overall, these facts lead us to conclude that APIs have a large and positive impact on economic growth and do so primarily by enabling an inverted firm strategy.
Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service...
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Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.
This article explores the transformative potential of generative artificial intelligence (AI) in democratizing filmmaking, focusing on Saga, an AI-powered platform designed to revolutionize every stage of the creative...
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This article explores the transformative potential of generative artificial intelligence (AI) in democratizing filmmaking, focusing on Saga, an AI-powered platform designed to revolutionize every stage of the creative process, from scriptwriting to visual storyboarding and animation.
Software modeling and digital twins are transforming the way software engineers design, operate, and maintain complex systems. In this column, we highlight cutting-edge research presented at the ACM/IEEE 27th Internat...
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Software modeling and digital twins are transforming the way software engineers design, operate, and maintain complex systems. In this column, we highlight cutting-edge research presented at the ACM/IEEE 27th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2024) and the 1st International Conference on Engineering Digital Twins (EDTconf 2024). The selected papers tackle critical challenges in improving system understanding, enhancing stakeholder communication, streamlining design and development processes, optimizing lifecycles, and enabling seamless integration of complex systems.
The advancement and increasing availability of novel technologies are widely recognized as crucial in building both a more resilient society and a sustainable economy. These technology factors are paving the way to a ...
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The advancement and increasing availability of novel technologies are widely recognized as crucial in building both a more resilient society and a sustainable economy. These technology factors are paving the way to a Society 5.0, where all domains, spanning from economic to technical areas, are encouraged to collaborate in the creation of a more dynamic, reliable, and valuable novel ecosystem. However, the enormous fragmentation in standards and protocols, even worsened by the geographical dispersion of these sources, hinders the integration of typically heterogeneous information data and applications produced by different partners. To solve this problem, new cloud computing models have emerged by proposing abstract interfaces and rapid prototyping of services, to specifically address the intrinsic dynamicity of these environments. In this work, we propose leveraging and evolving serverless cloud computing as support for Society 5.0 service integrations by providing a fast development environment that hides most of the complexities of infrastructure management.
In recent years, convolutional neural network (CNN) has achieved great success in the field of network security protection. With the popularization of smart terminals and the gradual increase of power grid informatiza...
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In recent years, convolutional neural network (CNN) has achieved great success in the field of network security protection. With the popularization of smart terminals and the gradual increase of power grid informatization and digitization, the protection of power monitoring systems from various cybersecurity threads is a current scientific problem that needs to be solved urgently. To this end, this paper proposes a malware detection method based on genetic algorithm optimization of the CNN-SENet network, which firstly introduces the SENet attention mechanism into the convolutional neural network to enhance the spatial feature extraction capability of the model;then, the application programming interface (API) sequences corresponding to different software behaviors are processed by segmentation and de-duplication, which in turn leads to the sequence feature extraction through the CNN-SENet model;finally, genetic algorithm is used to optimize the hyperparameters of CNN-SENet network to reduce the computational overhead of CNN and to achieve the recognition and classification of different malware at the output layer. The examples under the public dataset containing 8 kinds of malware show that the proposed method is better than the traditional algorithmic model, and can accurately and efficiently achieve malware detection with strong generalization ability.
Customer relationship management (CRM) and enterprise resource planning (ERP) havebeen extensively discussed in the research literature respectively. However, existingstudies do not reach a complete agreement on the C...
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Customer relationship management (CRM) and enterprise resource planning (ERP) havebeen extensively discussed in the research literature respectively. However, existingstudies do not reach a complete agreement on the CRM/ERP integration method, especiallyfor small- and medium-sized enterprises (SMEs). Generally, this work proposed a novelmethod for CRM/ERP integration approach via associative feature technology. A newfeature type, Real-time Data Link Board (RDLB), is developed and detailed as a genericinformation carrier solution for multisystem integration between CRM and ERP *** data elements are mapped into such data boards dynamically and synchronizedacross different data sources, structures, and databases. The contribution of this work isto present a well-defined and generically reusable data carrier definition, alongsiderelated methods for ensuring consistent data modeling during the system integrationphase across various digitalization engineering implementation projects. With thisapproach, the consistent management of data integration across intricate systems is achiev-able throughout application lifecycles, supported by the object-oriented software engineer-ing foundation.
Extraterrestrial habitats involve a tightly coupled combination of hardware, software, and humans while operating in an unforgiving environment that poses many risks, both anticipated and unanticipated. Traditional ap...
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Extraterrestrial habitats involve a tightly coupled combination of hardware, software, and humans while operating in an unforgiving environment that poses many risks, both anticipated and unanticipated. Traditional approaches with such systems of systems focus on reliability, robustness, and redundancy. These approaches seek to avoid failure rather than reduce overall risk. However, faults are inevitable, and understanding and managing the complex and emergent behavior and cascading events of such a complex system is critical. This study describes the development of HabSim, a computational simulation environment intended to support research to establish the know-how to design and operate resilient and autonomous SmartHabs. HabSim is a modular virtual testbed composed of many of the coupled dynamic systems expected in a typical SmartHab. A heterogeneous set of interconnected physics-based and phenomenological models is used to represent the essential functions of a SmartHab. HabSim further considers disruptions and models damage and repair of certain components. This paper discusses a) system and subsystem requirements of the deep space habitat included in the HabSim platform;b) architectural choices made in response to the requirements;c) technical considerations for developing, verifying, configuring, and executing HabSim;and d) illustrative sample results from a simulation of a representative disruption scenario.
As power distribution systems evolve in complexity and scale, the coordination and control of distributed energy resources (DERs), intelligent devices, and agents become increasingly challenging. Distribution utilitie...
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As power distribution systems evolve in complexity and scale, the coordination and control of distributed energy resources (DERs), intelligent devices, and agents become increasingly challenging. Distribution utilities invest in advanced distribution management systems (ADMSs) and distributed energy resource management systems (DERMSs) to enhance distribution systems' reliability, resiliency, and efficiency. Standardizing the application programming interface (API) for ADMS applications is crucial to accelerate the integration of advanced distribution technologies. This paper introduces a distributed application architecture within the GridAPPS-D platform, aiming to address the limitations of centralized architectures in terms of scalability, maintainability, and flexibility. The proposed architecture draws inspiration from the Laminar Coordination Framework and is validated through extensive stakeholder engagement. Emphasizing extensibility, boundary deference, structural scalability, and securability, the layered framework is well-suited for large-scale distribution networks with diverse grid-edge devices and ownership structures. The contributions of this paper include a distributed layered architecture with defined distributed areas, a Common Information Model (CIM)-based standardized API for developing and deploying distributed applications (Distributed App API), and the design process and reference implementation of distributed services and applications. Based on laminar coordination, the software architecture combines centralized, distributed, and edge-control paradigms for effective distributed operations. The paper concludes with extending the centralized API in GridAPPS-D to distributed APIs for standards-based message exchange, emphasizing the need for scalable communication to coordinate diverse distributed agents. This work provides a foundation for advancing the field of distributed control in power distribution systems, supporting both centralized and
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