Reviewerly/exHarmony huggingface dataset:
DVF:Multi-agent Q-learning with difference value factorization

In cooperative multi-agent games, agents are required to learn effective cooperative behaviors within complex action spaces. An effective approach is to utilize the Individual-Global-Max (IGM) principle to decompose the global reward signal into individual contributions from each agent. However, existing methods confront two significant challenges: ensuring the accuracy of the value function decomposition process and guaranteeing monotonic improvement in joint policies. These challenges become exacerbated in scenarios involving non-monotonic matrices. To address these challenges, we introduce a novel and flexible value factorization method called Difference Value Factorization (DVF). The key idea of our method is to transform the IGM principle into a new form DVF-IGM, which addresses non-monotonic constraints by ensuring the consistency of the IGM process between the joint difference value and the complex non-linear sum of independent difference values. A centralized evaluator is employed to estimate global Q-values, which not only enhances expressiveness but also constructs difference values for updating individual value functions. We demonstrate that DVF-IGM is an equivalent transformation of IGM and that DVF has the monotonic improvement property. Empirically, our method has been shown to maintain and recover the optimal policy in non-monotonic matrix games and achieve state-of-the-art performance in cooperative tasks within the StarCraft Multi-Agent Challenge (SMAC).
Quartzite complexities: Non-destructive analysis of bifacial points from Västerbotten, Sweden

Northern Fennoscandia is a geologically complex region affected by both glacial and postglacial processes. Quartzite was a key material type utilized by hunter-gatherers in Northern Sweden around the period 4 000 – 2 000 BP, and is thus critical to the understanding of raw material procurement and material flow within the region. However, there is a severe lack of methodological development in the characterization of these materials, and provenance of locally available geological material is complex and fraught with uncertainty. 126 quartz/quartzite points and preforms were sampled from 47 archaeological sites along the upper Ångerman river valley in Västerbotten, Sweden. The material has been analysed non-destructively using three separate portable spectroscopic instrumentations (Near-infrared, Raman, X-Ray Fluorescence). Evaluation of the spectra and exploratory data analysis using Principal Component Analysis demonstrates detectable differences in the material that likely stem from diagenetic/paragenetic origin. The presence of graphite, muscovite and biotite could likewise provide information on the material's metamorphic grade. In addition to reaffirming the potential of field-based screening instrumentation, these results will benefit future surveys of geological sources in the region. They also indicate potential for the construction of a predictive model that could classify the quartzite based on its chemical characteristic. Such a model would prove useful in future spatial analysis and testing of models of raw material management.
Context-based modeling for accurate logo detection in complex environments

Logo detection involves the tasks of locating and classifying logo objects in images and videos, and has been widely applied in the real world. However, most existing approaches rely on general object detection strategies that do not fully utilize the unique characteristics of logos. This can lead to sub-optimal performance in complex environments, especially when logos are small or have varying sizes and shapes. We observe that logos belonging to the same category often share similar context information, such as background dependency of the logo. This motivates us to incorporate contextual information to improve logo detection. Our proposed method, Context-based Modeling Enhancement Network (CME-Net), aims to enhance the distinctive region feature of logos using contextual information. We achieve this by modeling both the logo and its background region to extract their contextual information. This contextual information serves as a guide for enhancing the saliency of the distinctive regions within the logo image. To further improve the accuracy of detection, we have implemented a scale feature balance strategy. This strategy solves the problem of losing scale information caused by enhancement, ensuring that all scales are appropriately considered. Additionally, noise generated during the enhancement process is also effectively suppressed. By effectively leveraging contextual information, our method successfully tackles the challenge of accurately locating logo objects. Our extensive experiments on four public benchmark datasets demonstrate that CME-Net improves the accuracy of logo detection in complex environments.
CKTN: Commonsense knowledge transfer network for human activity understanding

In most human activity understanding research, various deep learning networks are well designed to fit the character distribution of different human activities in images. After being fed with enough training data, the networks can perceive related patterns passively. Such abilities originate from the stimulation of training data, but these networks lack the ability to perform subjective judgment. When lacking sufficient training data, networks have difficulty perceiving valuable information, which leads to poor performance. However, human beings can infer human activities in complex situations through subjective judgment with their knowledge. On this point, we propose the commonsense knowledge transfer network (CKTN), which is empowered with human cognitive mechanisms and is able to understand human activities by using prior commonsense knowledge. More specifically, we study the structural combination rules of human bodies and explore the co-occurrence relationship between different body parts and objects. Then, we construct the part action cooccurrence probability matrix and human skeleton knowledge matrix, which work as prior knowledge, guiding our network to absorb and reason what the human activities in images are. Compared with traditional deep learning methods, our network realizes the unification of both subjective cognition and passive perception abilities. Experiments show the effectiveness of this method.
Images of Gaussian and other stochastic processes under closed, densely-defined, unbounded linear operators

Gaussian Processes (GPs) are widely-used tools in spatial statistics and machine learning and the formulae for the mean function and covariance kernel of a GP [Formula: see text] that is the image of another GP [Formula: see text] under a linear transformation [Formula: see text] acting on the sample paths of [Formula: see text] are well known, almost to the point of being folklore. However, these formulae are often used without rigorous attention to technical details, particularly when [Formula: see text] is an unbounded operator such as a differential operator, which is common in many modern applications. This note provides a self-contained proof of the claimed formulae for the case of a closed, densely-defined operator [Formula: see text] acting on the sample paths of a square-integrable (not necessarily Gaussian) stochastic process. Our proof technique relies upon Hille’s theorem for the Bochner integral of a Banach-valued random variable.
Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability

The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper presents a novel approach designed to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.
Hidden Markov model for spatial analysis of three-dimensional mineralization distribution: Insights into magma flow and mineral exploration targets in the Jinchuan Ni-Cu-(PGE) sulfide deposit, China

The spatial distribution of mineralization includes significant clues about the magma/fluid flow process for ore formation. However, given the complex nature of the ore-forming process and its weak signature in the mineralization data, revealing the magma/fluid flow process in their mineralization footprint presents a considerable challenge. In this paper, we present a hidden Markov model (HMM) for the analysis of three-dimensional (3D) spatial distribution of mineralization. The HMM assumes the magma flow is a Markov process that is "hidden" in the "observable" mineralization features. Under this setting, the HMM quantifies the states of magma flow and links them as a whole in a probabilistic manner. This allows for the comprehensive combination of mineralization information, facilitating holistic inference of magma flow hidden in the mineralization signal. Based on the maximum likelihood principle, the magma trajectories and the associated injection points are inferred from the HMM. The proposed model is applied to spatial analysis of the mineralization in the world-class Jinchuan Ni-Cu-(PGE) magmatic sulfide deposit in NW China. Based on the 3D mineralization data interpolated from drill assays, the HMM infers magma trajectories and the associated injection points at Jinchuan, which indicate the Jinchuan deposit was formed by a multiple magma conduit system that comprises four independent injection points of magma ingress. According to the identified magma trajectories and injection points, four sites beneath the injection points and along the extension of magma trajectories are considered promising for future mineral exploration. With the insights gained from this case study, the proposed HMM can serve as an effective tool in the exploration information system to unveil magma/fluid flow history and guide exploration efforts toward concealed, deep-seated mineralization.
Machine learning approach to optimal task scheduling in cloud communication

Cloud communication is a combination of distributed computing and parallel computing. One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial completeness (NP) of cloud systems. To solve this problem, various approximation techniques based on swarm intelligence have been developed. This study proposes a dual machine learning strategy using kmeans to optimize performance and aid in selecting cloud scheduling technologies. The first technique is called Efficient Kmeans (Ekmeans) and the second technique is called Kmeans HEFT (KmeanH), where HEFT stands for Heterogeneous Earliest End Time. Our main contribution is to reduce processing time and increase speed and efficiency for a given set of tasks. We evaluate the impact of both algorithms on different virtual machines (ranging from 2 to 32) and task sizes (ranging from 50 to 1000).
Transformation of a class of pure multipartite entangled states

Entanglement transformation can give insight into quantum entanglement and provide desired resources for quantum information processing. However, the complexity for analyzing entanglement transformation exponentially grows with the increased particle numbers. We find that a sort of multipartite entangled states can be described by the Valence-bond state (VBS) picture and can be decomposed into a series of cells. Thus the study of multipartite entanglement can be greatly simplified. We propose an inverse entanglement resource searching method by which one can find states that can be transformed into desired entangled resource states via local operations and classical communication (LOCC) and stochastic LOCC (SLOCC). In the case of bipartite entanglement, our method includes Nielsen’s majorization theorem as a special case. We apply the method to measurement based quantum computation (MBQC) and find some resource states for MBQC that can be transformed into Cluster states via LOCC or SLOCC. We also construct the Hamiltonians of two-body nearest neighbor interactions with the found resource states as their eigenstates.
Scale perception data compression method with blockchain technology for UAV communication

The demand for high data throughput and security in Unmanned Aerial Vehicle (UAV) communication is rising. Efficient data transmission remains challenging due to limited wireless resources. This paper introduces a Scale Perception Compression Method that leverages compression and blockchain technology to optimize UAV communication. The method involves normalizing data into matrices, generating compression and decompression matrix codebook pairs, and selecting suitable pairs for small or large-scale matrices. Simulation results show that the error rate is associated with the distributions of the data and compression matrix. It remains relatively low when the data and compression matrix subjected to Uniform distribution compared to Gaussian distribution, regardless of the values of the number of rows m and columns r in compression matrix. Bubble method is more suitable to find out the proper matrix pair to compress and decompress transmitted data for small-scale codebook because it is stable, fast and results in lower difference between original and recovered data matrices. The same applies to Merge method for the large-scale codebook. The ER is always lower based on a small-scale codebook searching by Bubble method than that of on a large-scale codebook searching by Merge method, which indicates the former is sufficient for compression.
High-precision and high-speed surface topography measurement method of microstructures based on laser scanning transverse differential confocal

The manufacturing of microstructured devices requires higher performance regarding the increasing development of finer structures and more complex structural shapes, which poses a challenge for existing measurement techniques in terms of anti-scattering, high-precision and high-speed. Accordingly, this study proposes a new technology for surface topography measurement of microstructures based on laser scanning transverse differential confocal (LSTDCM), which inserts a D-shaped aperture into the existing confocal system, and performs split-focal spot detection on the focal plane. This method exhibits improved axial resolution by performing differential phase subtraction of the front- and back-focal signals to obtain a highly sensitive axial response signal. Further normalization of the transverse differential confocal signal eliminates the multiplicative noise of the system, avoids the impact of changes in roughness on the measurement results, and achieves an anti-scattering measurement. The linear region of the axial response signal around the zero-crossing point were used to achieve a "non-axial scanning" height measurement, which was combined with high-speed beam lateral scanning to achieve a high-speed areal topography measurement. Theoretical analysis and experimental results indicate that, for samples with height changes within the linear region of axial response, the method can complete areal topography measurement in 1.7 s with the axial resolution of 1 nm and the measurement range of 128 × 128 μm. A microstructured device processed using a nanoform single-point diamond machine tool and a semiconductor wafer were employed in the application validation experiment. And achieve a wide range stitching measurement of 1 mm × 1 mm under a 100 × objective along with the transverse scanning stage. The measurement efficiency is about three times higher than that of Olympus OLS4100 confocal microscope. The method provides a new and effective technical approach that is not limited by the surface roughness and complex structure of the tested devices, and can achieve high-precision as well as high-speed optical nondestructive measurement without moving the tested device. Thus, this work is expected to has important practical applications in the field of ultra-precision measurement.
The completeness of reasoning algorithms for clause sets in description logic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg" display="inline" id="d1e76"><mml:mi mathvariant="script">ALC</mml:mi></mml:math>

Metadata and ontologies are used to enable computers to read data on the Semantic Web. Description logics provide formal languages and reasoning algorithms for such metadata and ontologies. Tableau-based algorithms have been developed for deciding concept satisfiability in description logics but they lead to inefficient reasoning for complex logical expressions. The resolution principle and the Davis–Putnam–Logemann–Loveland (DPLL) solve the weakness by treating simplified clausal forms in description logics. However, resolution-based reasoning for description logics causes to compute various combinations of clause pairs to branch on. On the other hand, DPLL-based approaches have not presented a complete and decidable reasoning algorithm for directly deciding the satisfiability of clauses in description logics. In this paper, we propose a decidable and complete reasoning algorithm for clauses in description logic ALC. Using DPLL techniques, this algorithm can reduce reasoning steps for conjunctive normal form (CNF) concepts in ALC. We prove the termination, soundness, and completeness of the reasoning algorithm for the satisfiability of CNF concepts by introducing a restricted tableau for CNF concepts. We show some examples of reasoning steps for applying clauses in ALC to the algorithm.
Attack graph-based stochastic modeling approach for enabling cybersecure semiconductor wafer fabrication

The increasing interconnectivity between information technology (IT) and operational technology (OT) has brought numerous benefits to advanced manufacturing, including the semiconductor industry, by enhancing the quality and by reducing the costs of wafer and integrated circuits (IC) fabrication. However, the transition of the semiconductor industry to secure digital manufacturing has also increased the importance of safeguarding networked assets from cybercriminals. To this end, a flexible scheme is developed in this paper to model wafer fabrication as a directed attack graph. This graph representation is then used as the basis of a two-stage stochastic programming model that considers uncertainty in attack probabilities while minimizing expected cyber risk by employing optimal, budget-constrained countermeasures (i.e., arc interdiction strategies). A sample average approximation coupled with a progressive hedging algorithm or Bender's decomposition are proposed. Numerical experiments are performed to assess the viability of the proposed solution algorithms, the optimal arc interdiction strategies under various budget levels, and the effect of attack probability mappings. The results indicate that computing terminals nearest the manufacturing process have the highest defense priority and that the sample average approximation coupled with a progressive hedging algorithm is a suitable hybrid solution procedure for large instances when compared with sample average approximation coupled with Benders.
Image paragraph captioning with topic clustering and topic shift prediction

Image paragraph captioning involves generating a semantically coherent paragraph describing an image's visual content. The selection and shifting of sentence topics are critical when a human describes an image. However, previous hierarchical image paragraph captioning methods have not fully explored or utilized sentence topics. In particular, the continuous and implicit modeling of topics in these methods makes it difficult to supervise the topic prediction process explicitly. We propose a new method called topic clustering and topic shift prediction (TCTSP) to solve this problem. Topic clustering (TC) in the sentence embedding space generates semantically explicit and discrete topic labels that can be directly used to supervise topic prediction. By introducing a topic shift probability matrix that characterizes human topic shift patterns, topic shift prediction (TSP) predicts subsequent topics that are both logical and consistent with human habits based on visual features and language context. TCTSP can be combined with various image paragraph captioning model structures to improve performance. Extensive experiments were conducted on the Stanford image paragraph dataset, and superior results were reported compared with previous state-of-the-art approaches. In particular, TCTSP improved the consensus-based image description evaluation (CIDEr) performance of image paragraph captioning to 41.67%. The codes are available at https://github.com/tt0059/TCTSP.
Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system's accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.