Search Results (145)

Retro-style video games often draw on the appearance, conventions, and cultural memory of earlier games. This creates a copyright question: when does reference to a shared historical style become proximity to protected expression? This article examines how modern retro-style games publicly construct their relationship with earlier copyright-protected works. It combines a comparative copyright-law framework with quantitative content analysis of 740 official Steam descriptions, equally divided between retro and modern samples. Descriptions were manually coded for five positioning strategies: Derivative, Transform, Unique, Rights, and Silence; the article also includes illustrative notes on cosine-based visual-similarity screening and AI-disclosure analysis. The findings show that retro games are especially marked by transformative rhetoric, which appears much more often in the retro sample than in the modern sample. Rights-related language is more closely associated with institutional production, paid distribution, derivative positioning, and earlier release years. The visual check indicates that inspiration-based descriptions may coincide with measurable scene-level proximity, although such scores remain preliminary and cannot establish infringement. The article concludes that retro-style games should be assessed through a graded framework of legal proximity, moving from general reference to historical style and genre vocabulary toward source-specific reconstruction of protected expressive elements. Full article

The booming development of open science and the digital economy has rendered scientific data a core productive factor and a legally protected investment asset under international investment agreements (IIAs). While China has established a nationwide scientific data-sharing system to advance data circulation and technological innovation, the mandatory data disclosure mechanisms, frequent policy adjustments, and imperfect data quality control embedded in this framework generate inherent tensions with IIA investment protection obligations. This paper defines its core research focus on normative conflicts between China’s scientific data-sharing framework and IIAs, relevant investment arbitration risks, and balanced solutions that reconcile international treaty compliance with China’s legitimate data regulatory authority. Drawing on doctrinal legal analysis, normative comparison, and arbitral case review, it systematically examines legal frictions between domestic data-sharing rules and IIA provisions. It first clarifies scientific data’s dual attributes as valuable economic assets and protected investments, then identifies three key dispute risks: data IP infringement and asset depreciation from mandatory sharing, rising compliance costs and frustrated investor expectations due to abrupt regulatory changes, and investment losses caused by uncorrected erroneous shared data. Further, it analyzes legal restrictions imposed by core IIA clauses that substantially curtail China’s domestic data governance autonomy. To balance regulatory sovereignty and international treaty compliance, this study proposes targeted optimization paths: clarifying the boundary of mandatory data sharing to protect data intellectual property rights, standardizing framework adjustment procedures to stabilize investor expectations, and constructing a full-process data quality control system. Full article

The core predicament of energy transition lies not in the availability of clean technologies, but in whether an economy possesses the institutional capacity and social foundation to systematically regulate its carbon-energy metabolic processes. Drawing upon co-evolutionary theory from evolutionary economics, this paper constructs a composite indicator of Low-Carbon Green Energy Eco-Co-evolution (LCEE) encompassing three functional dimensions: efficiency advancement, kinetic energy replacement, and boundary adherence. Concurrently, by integrating innovation ecosystem theory with inclusive development theory, we propose the concept of “Inclusive Potential of Regional Innovation Ecosystems” (IEP), characterizing the systemic potential for transforming innovation outcomes into social welfare across four dimensions: Knowledge Matrix Abundance (KMF), Cultural Capillary Permeation (CCP), Technological Community Succession (TCS), and Social Root Nourishment (SRN). Taking China’s 2016 intellectual property (IP) powerhouse construction pilot as the institutional prototype of Industrial Intellectual Property Upgrading Reform (IPR), we incorporate IPR, IEP, and LCEE into a unified causal analytical framework, proposing a testable transmission logic of ‘institutional supply → ecological development → co-evolutionary synergy. Using panel data from 30 Chinese provincial-level administrative regions over 2010–2022, we employ a Spatial Durbin Difference-in-Differences (SDM-DID) model to identify the direct and spatial spillover effects of IPR on LCEE, and embed a Double Machine Learning (DML) framework to test the mediating mechanism of IEP while controlling for high-dimensional nonlinear interference. The findings reveal that IPR exerts a significant and robust direct promoting effect on LCEE, generating positive spatial spillovers to neighboring regions through the public disclosure of patent information. IEP significantly promotes local LCEE, yet its spatial spillover lacks statistical support due to structural conflicts in inter-dimensional transmission attributes. IEP plays a significant partial mediating role between IPR and LCEE, with the indirect effect accounting for over one-third of the total effect, a finding robust to alternative machine learning algorithms, sample split adjustments, and exclusion of contemporaneous competing policies. Sub-path tests reveal that KMF bears the strongest mediating efficacy, serving as the primary transmission channel, while CCP exhibits full mediation—the institutional effect on LCEE in the cultural dimension depends almost entirely on the mediating transformation through the public cultural service system. Heterogeneity analysis further demonstrates full mediation in the Low-Carbon Green Energy Eco-Kinetic Replacement (KER) dimension, indicating that the institutional catalytic effect on clean energy substitution must be realized through IEP transformation. This paper provides empirical evidence for the proposed causal pathway through which institutional public goods indirectly enhance the synergistic quality of carbon-energy transition via the inclusive potential of innovation ecosystems, providing theoretical foundations and policy implications that, while grounded in China’s institutional context, may offer valuable reference points for emerging market economies facing similar dual pressures of technological constraints and green transition. Full article

The central challenge of the energy transition lies in whether an economy possesses the institutional capacity to systematically regulate its own energy inputs and carbon emissions. Drawing upon social metabolism theory, this study constructs an indicator of carbon–energy metabolic control capacity (CMCC). Building on business ecosystem theory, it conceptualizes the intelligent economic ecosystem (IEE) and incorporates industrial chain intellectual property empowerment (IP) into a causal framework of institutional provision → ecosystem development → enhancement of metabolic control capacity. Using panel data from 30 provincial-level administrative regions in China covering the period 2010–2022, this study employs a spatial Durbin difference-in-differences (SDID) model and a double machine learning (DML) framework for empirical analysis. The results indicate that industrial chain intellectual property empowerment significantly enhances carbon–energy metabolic control capacity and generates positive spatial spillover effects on neighboring regions through the public diffusion of patent information. Furthermore, intelligent economic ecological development serves as a significant partial mediator between intellectual property empowerment and carbon–energy metabolic control capacity, with the indirect effect accounting for more than one-third of the total effect. This mediating mechanism remains robust after replacing machine learning algorithms, altering sample-splitting ratios, controlling for concurrent innovation policies, and excluding the impact of the COVID-19 pandemic. Path-specific mediation analysis further reveals that computing power acquisition and value transformation together with digital substrate robustness constitute the dominant transmission channels, whereas innovation metabolic flux contributes a relatively smaller mediating effect due to the long gestation period required for translating fundamental research into practical applications. Heterogeneity analysis further demonstrates that the transmission mechanism exhibits full mediation in the dimension of metabolic structure, indicating that the contribution of industrial chain intellectual property empowerment to the clean substitution of energy structures depends almost entirely on the mediating role of the intelligent economic ecosystem. These findings provide clear actionable guidelines for three specific policy-making domains to advance low-carbon transitions. First, intellectual property authorities should transition from quantity-driven patent creation to establishing cross-regional patent navigation and industrial chain IP pooling. Second, digital economy and industry regulators need to prioritize computing power value conversion (CCV) over raw infrastructure expansion to mitigate energy rebound effects. Third, energy and environmental agencies ought to integrate real-time algorithmic dispatching with green finance incentives. Ultimately, this study demonstrates that achieving deep low-carbon transformation requires leveraging institutional public goods to catalyze digital ecosystems, which in turn enable precise, dynamic carbon–energy metabolic control. Full article

Evaluating whether an intellectual property (IP) prototype faithfully preserves the visual identity and semantic intent of its original concept design is an important yet challenging task in product design and creative prototyping. Existing evaluation practices mainly rely on manual inspection or global image-level similarity comparison, which are subjective, difficult to reproduce, and insufficient for localizing identity-critical deviations. To address this problem, this paper proposes PartSense-IP, a part-aware vision–language sensor fusion framework for visual–semantic consistency evaluation of IP prototypes. The proposed framework takes a 2D concept image, an optional textual design description, and multi-view RGB-D sensor observations of a prototype as inputs. It first constructs a multi-view prototype representation and decomposes both the concept and prototype observations into design-relevant parts. Dense visual features, color and shape descriptors, and vision–language semantic embeddings are then extracted to evaluate part-level consistency. A Part-Aware Visual–Semantic Consistency Fusion (PVCF) algorithm is further developed to integrate shape, color, local visual similarity, semantic alignment, and cross-view stability into a unified IP consistency score. In addition to scalar scoring, PartSense-IP generates localized difference maps, 3D inconsistency visualization, and interpretable design feedback for prototype refinement. Experiments on the proposed IP-ProtoSense evaluation protocol demonstrate that PartSense-IP outperforms representative vision–language, dense-visual, segmentation-based, and 3D multimodal baselines in consistency scoring, inconsistency detection, localization, ablation, and robustness evaluation. Full article

The management of intellectual property (IP) agreements in universities continues to rely on static legal documents that are signed, archived, and consulted when necessary, but whose content is rarely formalized to facilitate their operation and verification. Consequently, obligations, permissions, restrictions, deadlines, scopes, and exceptions often remain scattered across clauses drafted in natural language, annexes, emails, and different document versions, which hinders their monitoring and makes compliance review dependent on intensive legal and administrative work. In response to this limitation, this article proposes an ontology to formalize non-disclosure agreements (NDAs) at the University of Caldas, Colombia, understood as a specific case within the broader management of IP agreements. The proposal adopts a modular Semantic Web architecture composed of a reusable ontological core and a specialized profile for NDAs. Its construction followed the METHONTOLOGY methodology, and its specification was supported by Competency Questions (CQs), which were subsequently translated into SHACL constraints and SPARQL queries. In addition, a SKOS vocabulary is incorporated to normalize synonyms and terminological variants typical of legal drafting in Spanish, together with a lightweight weak supervision layer based on regular expressions, SKOS, and structural signals to support clause labeling and the batch generation of RDF instances. Thus, the proposal enables querying, traceability, and verification over NDA content, while offering a formal basis for progressing toward automatable controls and their eventual articulation with smart contracts. Full article

The transition to a globalized, fabless semiconductor manufacturing model has integrated third-party foundries and external intellectual property (IP) vendors into the integrated circuit (IC) design cycle. While this collaborative system promotes innovation, it also exposes layouts to security threats. Protecting these designs is paramount; however, applying traditional encryption methodologies fundamentally alters the syntactic hierarchy of the industry-standard GDSII stream format, causing electronic design automation (EDA) tools to crash. Furthermore, a full encryption hinders modern system-on-chip (SoC) development, where different teams require access to specific modules of the design, without exposing the entire IP. To resolve this issue between collaborative layout sharing and zero-trust security, this paper presents a software implementing an encryption engine. By applying the NIST-standardized FF1 Format-Preserving Encryption (FPE) algorithm directly to the geometric data, the proposed software obfuscates sensitive spatial coordinates and structural nomenclature while maintaining the native GDSII format. The engine embeds multi-tiered cryptographic access control directly into the layout, utilizing native metadata properties. This framework allows proprietary logic to be securely compartmentalized, ensuring that interacting parties only view the specific structures they possess the clearance to decrypt. Full article

The fundamental dilemma of energy transition lies in whether an economy can guide its energy system to break free from deep dependence on fossil fuels in a sustained and orderly manner. This requires not only institutional incentives for innovation but also, more critically, a social-level shift in focus from “physical expansion” to “human development.” This paper incorporates these two conditions into a unified causal framework. Taking the pilot program for the construction of IP-strong provinces in China launched in 2016 as a quasi-natural experiment, and using panel data from 30 provincial-level administrative regions in China over the period 2010–2022, this study employs the Spatial Durbin Difference-in-Differences (SDM-DID) model and the Double Machine Learning (DML) method to examine the joint impacts and transmission mechanisms of the regional IP strong chain and the deep synergy between investment in physical capital and investment in human capital on energy green controllability. The findings are as follows. First, both the IP strong chain and deep synergy significantly improve energy green controllability. The local effect of deep synergy is far greater than the direct effect of the IP system itself, making it the core structural force driving the green transition. Second, the institutional dividend of the IP strong chain generates positive spatial spillovers to neighboring regions through the patent information disclosure channel. In contrast, the spatial spillovers of deep synergy are obstructed by administrative barriers and fiscal boundaries. Third, deep synergy plays a significant partial mediating role in the process through which the IP strong chain affects energy green controllability, with more than one-third of the total policy effect being released through this channel. Fourth, a path-wise test reveals a notable structural difference: the human capital investment path significantly outperforms the physical capital investment path in terms of transmission efficiency and robustness. This indicates that, at the current stage, the institutional effectiveness of the IP system in driving the green transition is largely achieved by improving the quality, capacity, and security level of human capital, rather than by restructuring the physical capital stock. The above conclusions remain robust after replacing the machine learning algorithm, adjusting the sample split ratio, and excluding the interference of concurrent competitive policies. This paper reveals the complete causal chain through which institutional public goods are transmitted to system governance capacity via the factor allocation structure, providing new empirical evidence for understanding the deep-seated relationship between intellectual property governance and the energy transition. Full article

This study examines how the resource configurations of early-stage startups are associated with intellectual property (IP) management capability. To achieve this objective, a dual analytical framework integrating hierarchical regression analysis (OLS) with machine learning techniques (XGBoost and SHAP) is employed. Because conventional linear models may not capture complex associations, the analysis also explores potential nonlinear patterns among key variables, which are interpreted as exploratory, model-based tendencies rather than as causal or temporal effects. The empirical findings reveal several important insights. First, the results of the linear regression analysis indicate that the main effects of simple quantitative indicators—such as firm age and organizational size—are not statistically significant. The interaction between the startup period and pre-startup education (H1) is the only relationship to approach statistical significance, although it is borderline and not robust to alternative variable coding. This pattern suggests that IP management capability is associated not with the quantity of inputs but with the preparedness of the entrepreneur’s knowledge resources. Second, the explainable artificial intelligence (XAI)-based analysis surfaces nonlinear patterns that are not captured by conventional linear models. Specifically, the model-estimated contribution of entrepreneurial education is comparatively small among firms in their first two years and larger among firms around the third year, and the model-estimated contribution of organizational size diminishes once the firm reaches roughly thirty employees. These inflections are model-based tendencies observed in SHAP dependence plots and are corroborated by formal segmented (breakpoint) regressions (spline terms p = 0.010 and p = 0.002). Methodologically, the study shows how integrating hierarchical regression with explainable machine learning (XGBoost and SHAP) can reveal nonlinear and threshold patterns that conventional linear models overlook. Building on this, it proposes resource latency as an interpretive lens, rather than an established construct, for age-related patterns in startup resource utilization, to be examined in future longitudinal research. From a practical perspective, the findings suggest the value of sustained support during the early scale-up period and of more systematic management structures as firms grow, while recognizing that these patterns are cross-sectional associations. Full article

Detecting rotated parabolic shapes in digital images remains a significant challenge in computer vision, especially in embedded environments constrained by computational and memory resources. This study introduces a novel field-programmable gate array (FPGA)-based genetic algorithm (GA) architecture specifically tailored for rotated parabola detection, implemented as an intellectual property (IP) core on a PYNQ-Z1 system-on-chip (SoC) platform. The architecture encodes four parabola parameters into fixed-length chromosomes, assesses their geometric consistency with a 640 × 480 binary edge image using a hardware fitness function, and executes the entire evolutionary process in programmable logic. Image pre-processing is executed on an external CPU, using Canny edge detection for synthetic images and Holistically Nested Edge Detection (HED). For real images, post-processing and result visualization are conducted on the ARM processor using the PYNQ framework. Experimental results on synthetic images demonstrate mean accuracies of 98.47% and 95.23%, with detection success rates of up to 96%. For real images, since manually annotated ground truth is not available, results are presented as qualitative observations of convergence consistency across 100 independent runs. These findings demonstrate the feasibility of detecting rotated parabolas on resource-constrained embedded platforms and indicate promising applications in domains where parabolic patterns are prevalent, such as structural inspection, biomedical imaging, and perception modules for autonomous vehicles and driver-assistance systems. Full article

Intellectual property reasoning depends on the interaction of factual context, doctrinal tests, exceptions, evidentiary uncertainty, and regulatory objectives. These features make patent, copyright, and trademark analysis difficult to support through text-level processing or isolated rule encoding. This article proposes a bounded DIKWP-guided semantic modeling framework for representing selected intellectual property reasoning patterns as queryable semantic structures. The framework is conceptual and design-oriented; it is specified at the design level through a formal graph characterization of DIKWP, a modular ontology fragment, rule schemas, SPARQL-style queries, and worked examples from patent, copyright, and trademark reasoning. Methodologically, the study uses a qualitative legal-informatics design approach. The three IP domains are selected because they represent complementary reasoning patterns: claim-element correspondence and equivalence screening in patent law, expression and exception analysis in copyright law, and factor-based confusion assessment in trademark law. The examples are used to derive semantic entities, relations, rule-linked structures, uncertainty annotations, explanation paths, and human-review triggers. DIKWP is treated not as a complete legal ontology or autonomous adjudicator, but as a network-structured meta-architecture for coordinating data, information, knowledge, wisdom, and purpose in reviewable legal decision support. The article illustrates how selected IP reasoning patterns can be represented in forms that remain traceable to legal sources and open to human review. It does not claim empirical validation, jurisdiction-specific doctrinal completeness, or autonomous legal decision-making. Its contribution is to specify how semantic legal representation can be made more operational, auditable, and institutionally constrained in the intellectual property domain. Full article

With the standardization of the Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) in NIST FIPS 203 (2024), efficient hardware support for polynomial ring operations has become critical for practical post-quantum cryptography deployment. The dominant computational workload of ML-KEM arises from matrix–vector multiplications over polynomial rings, which involve repeated Number Theoretic Transform (NTT), pointwise multiplication, and modular addition operations. This work proposes an ML-KEM polynomial ring accelerator leveraging Open Intellectual Property (Open IP) and integrates it into an open hardware Chipyard RISC-V System on Chip (SoC) via a Memory-Mapped I/O (MMIO) interface. The design incorporates an NTT-based datapath with multiplier and adder arrays, and employs a scratchpad memory to enable intermediate data reuse and reduce memory access overhead. The proposed architecture is implemented on a Genesys 2 FPGA development board featuring a Kintex-7 XC7K325T Field Programmable Gate Array (FPGA) (Digilent Inc., Pullman, WA, USA) and evaluated at both kernel and system levels. Experimental results show that the accelerator reduces matrix–vector multiplication latency to 7372 cycles, achieving up to 40× speedup over a software baseline. At the SoC level, the complete ML-KEM implementation achieves performance improvements of 1.6× to 2.1× across different parameter sets. These results demonstrate that integrating Open IP within an open hardware SoC provides an effective and reproducible approach for accelerating ML-KEM. Full article

The emerging Kolmogorov–Arnold networks (KANs) have set a new standard in machine learning (ML) tasks by prevailing over traditionally deployed multilayer perceptrons (MLPs) thanks to their enhanced interpretability through activation function learning, while they require increased computational complexity and memory footprint. Radial-basis function (RBF)-based KAN models maintain high performance over other variants of KANs with considerable size reduction and consequently more efficient execution. Aiming at effectively supporting the inference of RBF-KANs on Internet-of-Things (IoT) devices, this paper focuses on edge-oriented computing and introduces a soft intellectual property (IP) core, written in hardware description language (HDL), targeting the execution of such networks on all-programmable systems-on-chip (APSoC). The proposed design is fully pipelined and runtime configurable, allowing for real-time inference and latency-sensitive neural network deployment on-the-fly. A testbench reveals up to $43.6\times$ speedup when compared with a commercial edge central processing unit (CPU) and consumes considerably less power. The core’s adaptable design enables efficient allocation of resources and meets diverse throughput demands, making it well-suited for a broad range of IoT applications. Full article

Seed governance has become increasingly important in agricultural sustainability, food security, and innovation policy. Many countries have shifted toward stronger intellectual property (IP) protection in plant breeding; however, the institutional consequences of these reforms on seed governance structures remain insufficiently examined. In this review, I analyze the recent transformation of seed governance in Japan in the context of two major legal reforms enacted in 2018 and 2020. Herein, I examine how these reforms have reshaped the institutional architecture of seed governance, based on a comparative institutional review and empirical evidence from nationwide surveys of prefectural governments conducted in 2022 and 2024. The results indicate that Japan’s seed governance system is transitioning from a publicly coordinated seed supply model to a multi-level governance structure that integrates IP protection, regional branding strategies, and strategic management of plant variety circulation. These findings suggest that recent reforms represent a diversification of seed systems, governance functions have been reconfigured across different levels of government, and national IP regimes interact with prefectural agricultural policies and regional economic strategies. Therefore, this review provides important insights into how contemporary seed governance evolves through interactions among IP systems, agricultural innovation policies, and regional development strategies. Full article

Many System-on-Chip (SoC) studies rely solely on simulation and tool-based results, encountering unexpected failures during post-silicon validation. In particular, silicon-level demonstrations of Hardware/Software (HW/SW) functional equivalence, which confirms that an FPGA-validated design operates identically on an ASIC with the same firmware, remain extremely rare. This work proposes a reproducible FPGA-to-silicon verification methodology that establishes HW/SW functional equivalence at the silicon level by applying an identical firmware source code, device driver, and memory map to both platforms. The methodology is validated on an Arm Cortex-M0-based SoC platform fabricated in Samsung 28 nm Low Power Plus (LPP) CMOS technology with a dual Inter-Integrated Circuit (I2C) interface. The fabricated chip integrates two 64KB on-chip memories within a core area of 653 $\mathsf{\mu }$m × 769 $\mathsf{\mu }$m, operates at 125 MHz, and consumes 17.5 mW at the optimal operating point of 1.0 V. The primary contributions are: (1) a reproducible FPGA-to-silicon HW/SW functional equivalence verification methodology based on shared firmware source code, device driver, and memory map across both platforms, (2) silicon-measurement-based performance characterization with verified experimental data, (3) a reproducible design methodology documenting the complete flow from FPGA verification through ASIC fabrication, including static timing closure, place-and-route, and physical verification, and (4) an extensible SoC platform architecture enabling researchers to integrate and validate their own Intellectual Property (IP) via Advanced High-performance Bus (AHB) and I2C interfaces. Full article