Heterogeneous Information Networks (HINs) provide a powerful framework for modeling multi-typed entities and relations, typically defined under a fixed schema. Yet, most research assumes this structure is given, overlooking the fact that alternative designs can emphasize different aspects of the data and substantially influence downstream performance. As a theoretical foundation for such designs, we introduce the principle of entity-attribute duality: attributes can be atomized as entities with their associated relations, while entities can, in turn, serve as attributes of others. This principle motivates atomic HIN, a canonical representation that makes all modeling choices explicit and achieves maximal expressiveness. Building on this foundation, we propose a systematic framework for task-specific schema refinement. Within this framework, we demonstrate that widely used benchmarks correspond to heuristic refinements of the atomic HIN—often far from optimal. Across eight datasets, refinement alone enables a simplified Relational GCN (sRGCN) to achieve state-of-the-art performance on node- and link-level tasks, with further gains from advanced HGNNs. These results highlight schema design as a key dimension in heterogeneous graph modeling. By releasing the atomic HINs, searched schemas, and refinement framework, we enable principled benchmarking and open the way for future work on schema-aware learning, automated structure discovery, and next-generation HGNNs.
Efficient resource orchestration is essential for ensuring high Quality of Service (QoS) and reliability in Software-defined Networks (SDNs). This paper introduces an optimization-based algorithm that integrates Lagrangian Relaxation (LR) and Queueing Theory to enhance admission control and priority scheduling in SDNs. The proposed approach overcomes the limitations of traditional binary admission control methods by enabling Partial Admission Control (PAC), which allows more flexible resource allocation. The system's performance is significantly improved through the use of non-preemptive and preemptive priority scheduling, while LR techniques effectively manage complex network conditions. \evan{Specifically, the proposed Bisection-Search (B-S) heuristic leverages the Lagrangian multipliers generated during the optimization process to intelligently guide resource allocation, consistently producing high-quality feasible solutions ( ). These solutions are validated against the theoretical bound ( ) provided by the LR method, demonstrating a provably small duality gap.} The proposed algorithm is evaluated through extensive simulations across diverse network scales, traffic loads, and delay constraints, demonstrating substantial improvements in network performance and service differentiation. These results provide a comprehensive analysis of the performance envelope of the proposed framework, highlighting the trade-offs between solution quality, computational complexity, and network scale. The study offers an adaptive and mathematically grounded solution, demonstrating its effectiveness in complex, high-contention networking environments.
Wet electrodes with conductive gel are widely applied as the gold standard for recording EEG signals due to their low impedance between the scalp and the electrode. However, their extensive preparation time before data collection and the required cleaning afterward make them impractical for real-world Brain-Computer Interface (BCI) applications. Recent advancements in semi-dry electrodes, which use a minimal amount of conductive material and achieve a comparable signal-to-noise quality to wet electrodes, present an alternative approach for continuous EEG monitoring when comparing to dry electrodes. Our prior study introduced a potential solution for overcoming challenges related to hair-layer penetration and dose control through 3D-printed, watermill-shaped EEG electrodes. Based on those promising results, this study prototypes three designs of watermill-shaped EEG electrodes and refines the fabrication process to scale production and accommodate diverse hairstyles in real-world scenarios. Eight different wig styles which were made of either human or synthetic hair were tested in offline experiments to evaluate hair-layer penetration performance and gel-applying application efficiency. In the real-world experiment, 15 participants with varying hairstyles were recruited in neurophysiological experiments. Statistical analysis revealed that the watermill electrodes consumed significantly less gel than wet electrodes (p<0.001), with the star electrode requiring the fewest mean rolls to achieve target impedance (1.94 rolls). The results demonstrate that the watermill-shaped electrode effectively works across different hairstyles, ensuring consistent hair-layer penetration and controlled application of conductive material. These findings establish the proposed electrode as a viable semi-dry solution for real-world BCI applications.
The floral export industry, particularly the export of Phalaenopsis orchids, plays a pivotal role in the agricultural economy. Double-spike orchids hold significantly higher commercial value compared to single-spike varieties. Traditionally, small-scale farms face challenges due to limited data volume and availability, making the application of Machine Learning (ML) or Deep Learning (DL) techniques for improving the accuracy of double-spike orchid predictions highly demanding. However, this study presents an innovative approach to predicting and enhancing the double-spike rates of Phalaenopsis orchids, addressing critical challenges in the floriculture industry. By leveraging advanced ML, DL, and Federated Learning (FL) frameworks, the research integrates horticultural trait extraction with predictive modeling to optimize orchid cultivation practices. The methodology includes a multi-stage process utilizing You Only Look Once version 8 (YOLOv8) for extracting key features from orchid images, such as leaf dimensions and count, combined with historical spike data to train models including Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), TabNet, and Gated Adaptive Network for Deep Automated Learning of Features (GANDALF). The results demonstrate that FL effectively resolves issues of limited data availability and privacy concerns for small-scale farms, enabling secure data collaboration and improving model performance. Continual learning further enhances predictive accuracy by dynamically incorporating new data, ensuring sustained adaptability and relevance. Application of the proposed framework significantly improves the ability of orchid growers to identify and prioritize double-spike orchids, a highly valuable trait in export markets, increasing their competitiveness and profitability.
Quantum teleportation enables high-security communications through end-to-end quantum entangled pairs. End-to-end entangled pairs are created by using swapping processes to consume short entangled pairs and generate long pairs. However, due to environmental interference, entangled pairs decohere over time, resulting in low fidelity. Thus, generating entangled pairs at the right time is crucial. Moreover, the swapping process also causes additional fidelity loss. To this end, this paper presents a short time slot protocol, where a time slot can only accommodate a process. It has a more flexible arrangement of entangling and swapping processes than the traditional long time slot protocol. It raises a new optimization problem TETRIS for finding strategies of entangling and swapping for each request to maximize the fidelity sum of all accepted requests. To solve the TETRIS, we design two novel algorithms with different optimization techniques. Finally, the simulation results manifest that our algorithms can outperform the existing methods by up to 60∼78% in general, and by 20∼75% even under low entangling probabilities.
Graph pooling has gained significant progress in recent years as an effective solution for graph-level property classification tasks. With the emergence of research on Heterogeneous Information Networks (HINs), this paper argues that graph-level datasets for graph classification should be treated as HINs rather than homogeneous graphs to enhance information aggregation. We propose HINPool, a novel and general graph pooling framework for graph-level property classification with HINs. First, we devise a systematic HIN construction procedure from the original data to capture complex interactions. Next, we introduce a type-aware heterogeneous graph pooling method featuring a Type-Aware Selector (TAS) to select essential nodes and a Readout Aggregator (RA) to fuse critical information into a graph-level representation. Finally, a cross-layer fusion function is applied to combine the output embeddings from each graph pooling layer, creating a final graph representation for downstream classification tasks. Our approach achieves near state-of-the-art performance on widely used graph classification benchmark datasets, demonstrating significant improvements in four out of five datasets. This work redefines the strategy for graph-level property classification with HGNNs and heterogeneous graph pooling to model intricate relationships, enhancing performance without requiring extensive domain-specific knowledge.
Biometric recognition plays an increasingly pivotal role in cybersecurity, where the CIA triad, Confidentiality, Integrity, and Availability, forms the cornerstone of information security, with authentication as a critical yet challenging component. This paper presents the Biometric Multi-modal Authentication System using Geometric Programming (BMMA-GPT), tailored for deployment in Fast IDentity Online (FIDO/FIDO2)-enabled environments and Zero Trust Architectures (ZTA). The system employs a dual-threshold mechanism integrated with Defense-in-Depth (DiD) strategies to simultaneously enhance accuracy, efficiency, and security. The underlying optimization problem is formulated as a mathematical programming task and reformulated into a Geometric Programming (GP) model to efficiently compute optimal biometric permutations and verification thresholds under constrained estimation errors. BMMA-GPT enables the flexible integration of multiple biometric modalities, allowing dynamic adjustments to meet both individual user profiles and organizational security requirements. It achieves a high Area Under Curve (AUC) of approximately 0.99 while maintaining authentication latency under 1.5 seconds. This design supports Chief Information Security Officers (CISOs) in configuring tailored authentication processes with minimal computational cost, enhancing resilience against spoofing attacks and ensuring seamless user experience. By aligning biometric verification with DiD principles and GP-based optimization, the proposed framework offers a scalable and robust solution for identity authentication in complex digital ecosystems.
High-performance and high-precision flow monitoring is a crucial function for network management, network bandwidth usage accounting and billing, network security, network forensics, and other important tasks. Nowadays, many commercial switches/routers provide either sFlow, NetFlow, or IPFIX scheme for monitoring the flows traversing a network. sFlow is a scheme widely supported by many switches/routers due to its using a sampling-based method, which greatly reduces the CPU processing load on a switch/router and the network bandwidth required to transmit flow data to a remote collector. However, many small flows may go undetected and the estimated flow data (e.g., the packet count and byte count) for detected flows can significantly deviate from their ground truth. NetFlow, which is Cisco Systems’ proprietary technology, does not use a sampling-based method by default. Instead, it tries to collect complete and correct flow data for every flow. However, as the link speed and the flow arrival rate continue to increase, NetFlow also provides a sampling-based option to reduce the CPU utilization of the switch/router. Because NetFlow is proprietary, an Internet Engineering Task Force (IETF) working group has defined IPFIX as an open flow information export protocol based on NetFlow Version 9. The requirements for IPFIX are defined in the RFC 3917 standards. Basically, IPFIX is the same as NetFlow Version 9. Due to its high demand on the CPU of the switch/router, currently NetFlow is supported only on very high-end switches/routers and its design and implementation on these commercial switches/routers are not published in the literature. In this paper, we design and implement a high-performance and high-precision NetFlow/IPFIX system on a Programming Protocol-independent Packet Processors (P4) hardware switch. Based on a 20 Gbps playback of a packet trace gathered on an Internet backbone link, experimental results show that our novel method significantly outperforms the typical design and implementation method of NetFlow/IPFIX on a P4 hardware switch. For example, for the number of detected flows during the trace period, our method outperforms the typical method by a factor of 5.72. As for the number of flows whose packet and byte counts are correctly counted, our method outperforms the typical method by a factor of 8.57.
A multitude of interconnected risk events---ranging from regulatory changes to geopolitical tensions---can trigger ripple effects across firms. Identifying inter-firm risk relations is thus crucial for applications like portfolio management and investment strategy. Traditionally, such assessments rely on expert judgment and manual analysis, which are, however, subjective, labor-intensive, and difficult to scale. To address this, we propose a systematic method for extracting inter-firm risk relations using Form 10-K filings---authoritative, standardized financial documents---as our data source. Leveraging recent advances in natural language processing, our approach captures implicit and abstract risk connections through unsupervised fine-tuning based on chronological and lexical patterns in the filings. This enables the development of a domain-specific financial encoder with a deeper contextual understanding and introduces a quantitative risk relation score for transparency, interpretable analysis. Extensive experiments demonstrate that our method outperforms strong baselines across multiple evaluation settings.
The fusion of tiny energy harvesting devices with deep neural networks (DNN) optimized for intermittent execution is vital for sustainable intelligent applications at the edge. However, current intermittent-aware neural architecture search (NAS) frameworks overlook the inherent intermittency management overhead (IMO) of DNNs, leading to under-performance upon deployment. Moreover, we observe that straightforward IMO minimization within NAS may degrade solution accuracy. This work explores the relationship between DNN architectural characteristics, IMO, and accuracy, uncovering the varying sensitivity toward IMO across different DNN characteristics. Inspired by our insights, we present two guidelines for leveraging IMO sensitivity in NAS. First, the overall architecture search space can be reduced to exclude parameters with low IMO sensitivity, and second, network blocks with high IMO sensitivity can be primarily focused during the search, facilitating the discovery of highly accurate networks with low IMO. We incorporate these guidelines into TiNAS, which integrates cutting-edge tiny NAS and intermittent-aware NAS frameworks. Evaluations are conducted across various datasets and latency requirements, as well as deployment experiments on a Texas Instruments device under different intermittent power profiles. Compared to two variants, one minimizing IMO and the other disregarding IMO, TiNAS, respectively, achieves up to 38% higher accuracy and 33% lower IMO, with greater improvements for larger datasets. Its deployed solutions also achieve up to a 1.33 times inference speedup, especially under fluctuating power conditions.