Reviewerly/exHarmony huggingface dataset:
A Formal Representation for Intelligent Decision-Making in Games

The study of intelligent game-playing has gained tremendous attention in the past few decades. The recent development of artificial intelligence (AI) players (e.g., the Go player AlphaGo) has made intelligent game-playing even more prominent in both academia and industry. The performance of state-of-the-art AI players benefits greatly from machine learning techniques, based on which, players can make estimations and decisions even without understanding the games. Although AI machines show great superiority over humans in terms of data processing and complex computation, there remains a vast distance between artificial intelligence and human intelligence with respect to the abilities of context understanding and reasoning. In this paper, we explore the theoretical foundation of intelligent game-playing from a logical perspective. The proposed logic, by considering the computational limits in practical game-playing, drops the ideal assumptions in existing logics for the classical game model. We show that under logical framework, the basis of decision-making for agents in game scenarios can be formally represented and analyzed. Moreover, by characterizing the solutions of games, this logic is able to formalize players’ rational decision-making during practical game-playing.
Quantum-classical hybrid information processing via a single quantum system

Current quantum technologies bring a new integration of quantum data with classical data for hybrid processing. However, the frameworks of these technologies are restricted to a single classical or quantum task, which limits their flexibility in near-term applications. We propose a quantum reservoir processor to harness quantum dynamics in computational tasks requiring both classical and quantum inputs. This analog processor comprises a quantum network in which quantum data are incident to the network and classical data are encoded via a coherent field exciting the network. We perform a multitasking application of quantum tomography and nonlinear equalization of classical channels. Interestingly, the tomography can be performed in a closed-loop manner via the feedback control of classical data. Therefore, if the classical input comes from a dynamical system, embedding this system in a closed loop enables hybrid processing even if access to the external classical input is interrupted. Finally, we demonstrate preparing quantum depolarizing channels as a quantum machine learning technique for quantum data processing.
Delay Optimization for Consensus Communication in Blockchain-Based End-Edge-Cloud Network

With the rapid development of smart IoT technology, various innovative mobile applications improve many aspects of our daily life. End-edge-cloud collaboration provides data transmission in connecting heterogeneous IoT devices and machines with improvements in high quality of service and capacity. However, the end-edge cloud architecture still remains some challenges including the risks of data privacy and tolerance transmission delay. Blockchain is a promising solution to enable data processing in a secure and efficient way. In this paper, blockchain is considered as an infrastructure of the end-edge-cloud network and the time cost of the PBFT consensus is analyzed from the perspective of the leader’s position. Considering the concurrent processing of tasks in cellular networks, multi-intelligent deep reinforcement learning is used to train the assignment strategy of the edge server. The numerical results show that the proposed method can achieve better performance improvement in terms of the time consumption of data processing.
Federated Classification for Multiple Blockchain Systems

As blockchain technology continues to advance, it has become increasingly utilized as a fundamental infrastructure in various industries, such as business, justice, and finance. The widespread adoption of blockchain technology has created a pressing need for effective information exchange among different institutional units within blockchain networks. Fortunately, cross-chain technology has emerged as a promising solution for enhancing information interaction among diverse blockchain units. In this study, we examined several variables and employed multiple methodologies to validate our proposed hypothesis. Using cross-chain technology, we introduce a blockchain cross-chain federated learning framework (BCFL) that facilitates the interaction and mutual verification of data and parameters across different blockchains. This approach enables federated learning without the need to collect or coordinate model weights on a central server, while also enhancing the security of the federated learning process through the consensus algorithm mechanism of blockchains. Finally, we conduct a comparative analysis of the effectiveness of BCFL compared to traditional machine learning and centralized federated learning.
An Automatic Deployment Method for Hybrid Cloud Simulation Platform

The simulation resources are now virtualized and deployed on cloud platform. It can provide on-demand simulation tests, improving the efficiency of simulation systems. Simulation resources are packaged into virtual machines or containers. Complex software are packaged into virtual machines, and simple simulation services are packaged into containers. However, how to deploy simulation systems under resource constraints is a problem worth studying. This paper studies a virtual machine and container based hybrid cloud simulation platform. An automatic deployment method is proposed to reduce labor costs and errors of manual deployment. A simulation case is applied to verify the usefulness and efficiency of our approach.
Towards Privacy-Preserving Decentralized Reputation Management for Vehicular Crowdsensing

The reputation of a vehicle is a critical role in most vehicular crowdsensing applications, which incentivizes vehicles to perform crowdsensing tasks by submitting high-quality data and getting remunerated accordingly. Unfortunately, existing centralized reputation systems are vulnerable to collusion attacks, and decentralized approaches are susceptible to Sybil attacks. What’s worse, both of them have privacy leakage and fairness problems. To address these issues, we take advantage of various cryptographic primitives and the blockchain technology to present a privacy-preserving decentralized reputation management system. Specifically, a compact traceable ring signature is proposed to provide identity privacy protection and resist Sybil attacks. To ensure fairness, the quantification of data quality is fulfilled by combining the rating feedback mechanism with comprehensive updating factors. Additionally, our system allows the reputation update automatically through smart contracts deployed on the consortium blockchain. The authenticity of the reputation can be verified by a zero-knowledge proof when a vehicle shows its reputation. Finally, a proof-of-concept prototype system by Parity Ethereum is presented. Extensive security analysis and implementations demonstrate the feasibility and efficiency of the proposed system.
Design and Development of Alcohol Level Detector for Liquid Herbal using Fuzzy C-Means Method

Consuming alcoholic beverages improperly or excessively has a very bad impact on the health of the human body, it can even cause accidents, fights, theft, murder and the risk of death. This research produces a tool that can detect alcohol content in liquid herbal using the MQ-3 sensor, DHT22 sensor, data logger to store test results data and the test results are grouped by means of manual calculations with Microsoft Excel and Matlab software simulations. The type of liquid herbal used is liquid herbal for rheumatic pain, gout, dizziness, colds and coughs. In each liquid herbal test, it takes 10 minutes/liquid herbal medicine and is repeated 6 times for each liquid herbal test. Based on the test that the higher the temperature affects the increase in the proportion of the alcohol content of the liquid herbal and the minimum temperature obtained is 30.8°C to 32.9°C. Microsoft excel calculations and matlab simulation software, clusters are divided into 3 according to the type of alcohol content group and the iteration stops at the 12th iteration with the objective function value = 0.00074 &lt; error = 0.001. From the results using Matlab Software, 8 data were obtained in group A with an average alcohol content 9,9%, 9 data in group B with an average alcohol content of 9,05% and 13 data in the content group C with an average alcohol content of 10,73%.
Smoothing level selection for density estimators based on the moments

This paper introduces an approach to select the bandwidth or smoothing parameter in multiresolution (MR) density estimation and nonparametric density estimation. It is based on the evolution of the second, third and fourth central moments and the shape of the estimated densities for different bandwidths and resolution levels. The proposed method has been applied to density estimation by means of multiresolution densities as well as kernel density estimation (MRDE and KDE respectively). The results of the simulations and the empirical application demonstrate that the level of resolution resulting from the moments method performs better with multimodal densities than the Bayesian Information Criterion (BIC) for multiresolution densities estimation and the plug-in for kernel densities estimation.
Steering witnesses for unknown Gaussian quantum states

Abstract We define and fully characterize the witnesses based on second moments detecting steering in Gaussian states by means of Gaussian measurements. All such tests, which arise from linear combination of variances or second moments of canonical operators, are easily implemented in experiments. We propose also a set of linear constraints fully characterizing steering witnesses when the steered party has one bosonic mode, while in the general case the constraints restrict the set of tests detecting steering. Given an unknown quantum state we implement a semidefinite program providing the appropriate steering test with respect to the number of random measurements performed. Thus, it is a ‘repeat-until-success’ method allowing for steering detection with less measurements than in full tomography. We study the efficiency of steering detection for two-mode squeezed vacuum states, for two-mode general unknown states, and for three-mode continuous variable GHZ states. In addition, we discuss the robustness of this method to statistical errors.
Tractable skew-normal approximations via matching

Many approximate Bayesian inference methods assume a particular parametric form for approximating the posterior distribution. A Gaussian distribution provides a convenient density for such approaches; examples include the Laplace, penalized quasi-likelihood, Gaussian variational, and expectation propagation methods. Unfortunately, these all ignore potential posterior skewness. The recent work of Durante et al. [Skewed Bernstein-von Mises theorem and skew-modal approximations; 2023. ArXiv preprint arXiv:2301.03038.] addresses this using skew-modal (SM) approximations, and is theoretically justified by a skewed Bernstein-von Mises theorem. However, the SM approximation can be impractical to work with in terms of tractability and storage costs, and uses only local posterior information. We introduce a variety of matching-based approximation schemes using the standard skew-normal distribution to resolve these issues. Experiments were conducted to compare the performance of this skew-normal matching method (both as a standalone approximation and as a post-hoc skewness adjustment) with the SM and existing Gaussian approximations. We show that for small and moderate dimensions, skew-normal matching can be much more accurate than these other approaches. For post-hoc skewness adjustments, this comes at very little cost in additional computational time.
Boundary refinement network with semantic embedding connections for UAV aerial image semantic segmentation

Deep-learning-based semantic segmentation is the research focus for unmanned aerospace vehicle (UAV) aerial images analysis. However, there are problems in segmenting small and narrow objects and boundary regions, due to the large size differences between objects and the unbalanced class data in aerial images. A network named SEC-BRNet is proposed for the boundary refinement problem. First, the semantic embedding connections and progressive upsampling decoder are used to obtain spatial details for generating fused feature maps, which are then concatenated in decoding process level by level for recovering the boundary details. Second, a multiloss training strategy is developed for data imbalance and boundary roughness problems, including cross-entropy loss, Dice loss, and active boundary loss. After extensive experiments, our network could achieve 84.8% mIoU and 89.04% Boundary IoU on the AeroScapes dataset and achieve 62.81% mIoU and 90.78% Boundary IoU on the Semantic Drone Dataset. The experimental results indicate that the proposed SEC-BRNet performs well in semantic segmentation task for UAV aerial images.
Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation

Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor influencing user purchase decisions. Unfortunately, existing methods for session-based recommendation only aim at capturing user interest preference, while ignoring user price preference. Actually, there are primarily two challenges preventing us from accessing price preference. First, the price preference is highly associated to various item features (i.e., category and brand), which asks us to mine price preference from heterogeneous information. Second, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling. To handle above challenges, we propose a novel approach Bi-Preference Learning Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation. Specifically, the customized heterogeneous hypergraph networks with a triple-level convolution are devised to capture user price and interest preference from heterogeneous features of items. Besides, we develop a Bi-Preference Learning schema to explore mutual relations between price and interest preference and collectively learn these two preferences under the multi-task learning architecture. Extensive experiments on multiple public datasets confirm the superiority of BiPNet over competitive baselines. Additional research also supports the notion that the price is crucial for the task.
HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks

Binary neural network (BNN), where both the weight and the activation values are represented with one bit, provides an attractive alternative to deploy highly efficient deep learning inference on resource-constrained edge devices. However, our investigation reveals that, to achieve satisfactory accuracy gains, state-of-the-art (SOTA) BNNs, such as FracBNN and ReActNet, usually have to incorporate various auxiliary floating-point components and increase the model size, which in turn degrades the hardware performance efficiency. In this paper, we aim to quantify such hardware inefficiency in SOTA BNNs and further mitigate it with negligible accuracy loss. First, we observe that the auxiliary floating-point (AFP) components consume an average of 93% DSPs, 46% LUTs, and 62% FFs, among the entire BNN accelerator resource utilization. To mitigate such overhead, we propose a novel algorithm-hardware co-design, called FuseBNN , to fuse those AFP operators without hurting the accuracy. On average, FuseBNN reduces AFP resource utilization to 59% DSPs, 13% LUTs, and 16% FFs. Second, SOTA BNNs often use the compact MobileNetV1 as the backbone network but have to replace the lightweight 3 × 3 depth-wise convolution (DWC) with the 3 × 3 standard convolution (SC, e.g., in ReActNet and our ReActNet-adapted BaseBNN) or even more complex fractional 3 × 3 SC (e.g., in FracBNN) to bridge the accuracy gap. As a result, the model parameter size is significantly increased and becomes 2.25 × larger than that of the 4-bit direct quantization with the original DWC (4-Bit-Net); the number of multiply-accumulate operations is also significantly increased so that the overall LUT resource usage of BaseBNN is almost the same as that of 4-Bit-Net. To address this issue, we propose HyBNN , where we binarize depth-wise separation convolution (DSC) blocks for the first time to decrease the model size and incorporate 4-bit DSC blocks to compensate for the accuracy loss. For the ship detection task in synthetic aperture radar imagery on the AMD-Xilinx ZCU102 FPGA, HyBNN achieves a detection accuracy of 94.8% and a detection speed of 615 frames per second (FPS), which is 6.8 × faster than FuseBNN+ (94.9% accuracy) and 2.7 × faster than 4-Bit-Net (95.9% accuracy). For image classification on the CIFAR-10 dataset on the AMD-Xilinx Ultra96-V2 FPGA, HyBNN achieves 1.5 × speedup and 0.7% better accuracy over SOTA FracBNN.
Principles of Open Scholarly Infrastructure (POSI) version 1.1: Reflections from adopters

&lt;em&gt; The Upstream platform was created in order to discuss all things open. Open scholarly infrastructure and the need for the community to assess such infrastructures are surely at the heart of enabling the open research movement. &lt;/em&gt;
Peculiarities of profile distribution of metals in soils of geochemically conjugated landscapes of Chelyabinsk city

The article discusses the results of soil-geochemical studies carried out on the territory of the Chelyabinsk urban district. The features of the profile distribution of pollutants within the landscape catena are established. The maximum concentrations of heavy metals are confined to the upper soil horizons of the eluvial position under forest vegetation. The soils of the superaquatic position under moist meadow and swamp vegetation are less polluted. The most polluted soil layer is located at a depth of 4-10 cm from the surface. This may be due to a decrease in heterogeneous emissions from the city’s metallurgical enterprises over the past 30 years.
Technology of identification of natural resource potential in geodynamic ecological and economic assessment of the territory of the developed hydrocarbon deposit

The article investigates the use of aggregated models for geodynamic assessment of the ecological and economic assessment of the territory of the hydrocarbon field under development, including the values of reservoir pressure drops from the initial level. The significant potential of the developed identification technology and the proposed state models suggests the need for further research and enrichment of the created scientific direction. The results of the study allow us to build a more efficient hydrocarbon production program.
Dissemination effect of data papers on scientific datasets

Abstract Open data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research.
Higher Education's Position in Shaping the Workforce of the Future and the Importance of Adapting to the Digital Age

Changes in teaching and administration at universities are dramatic. These dramatic shifts are being prompted by a number of factors. Micro and Nano degrees, as well as digital transformation, online education, tech-savvy students, operating expenses, and other factors, are all instances of such influences. Students in higher education make use of a variety of digital technology to supplement traditional lecture-based classroom instruction. Technology from the Fourth Industrial Revolution has made its way into HEIs, requiring them to adapt to the digital revolution (DT) in all its forms. Recently, there has been a lot of interest in applying digital transformation methodologies to the education sector, as these methods allow us to characterise the numerous interrelationships among stakeholder in a digitally enabled environment of teaching and learning. The purpose of this research is to highlight the unique aspects of the digital shift implementation process in academic settings. In addition, we will discuss how digital learning fits within the larger context of contemporary universities. More research is needed to understand how universities can adapt to the fourth industrial age and digital transformation.
Attribute-based anonymous credential: Delegation, traceability, and revocation

Attribute-based anonymous credential schemes have been envisioned with the motivation to allow users to prove the possession of their attributes interactively with service providers anonymously. So far, three major properties: delegation, traceability, and revocation, have been considered in attribute-based anonymous credential schemes. Delegation gives credential owners the ability to issue more restricted credentials to other users. Traceability helps the organization to find out invalid users or expired credentials. Revocation allows the organization to revoke someone’s right of use. Although these properties are practical in reality, no existing schemes provide all of them. In this paper, we propose a delegatable, traceable, and revocable attribute-based anonymous credential scheme, with the security analysis of anonymity, unforgeability and traceability. We also find out that partial attributes of the credential can be updated by the issuer interactively with the credential holder, which is more efficient than regenerating a new one. Finally, we implement our scheme and the result of the experiment shows that it is practical and efficient.
Eager Term Rewriting For The Fracterm Calculus Of Common Meadows

Abstract Eager equality is a novel semantics for equality in the presence of partial operations. We consider term rewriting for eager equality for arithmetic in which division is a partial operator. We use common meadows which are essentially fields that contain an absorptive element $\bot $. The idea is that term rewriting is supposed to be semantics preserving for non-$\bot $ terms only. We show soundness and adequacy results for eager term rewriting w.r.t. the class of all common meadows. However, we show that an eager term rewrite system which is complete for common meadows of rational numbers is not easy to obtain, if it exists at all.