Weidong Wang, Mian Muhammad Yasir Khalil, Leta Yobsan Bayisa
Abstract Gaussian processes (GPs) are a powerful and popular framework for addressing machine learning problems, particularly for time-dependent data such as that generated by the Internet of Things (IoT). GPs offer a compelling choice for constructing real-valued nonlinear models due to their inherent flexibility and ability to quantify uncertainty. However, traditional GP methods are often hindered by cubic computational complexity, making them impractical for the massive and potentially unbounded datasets commonly encountered in IoT applications. To address this issue, researchers have developed various sparse approximation methods that significantly reduce the computational burden of GPs. Among these, pseudo-point approximations have proven to be highly influential, leveraging a subset of the training data to represent the entire observation space. The variational sparse GP is a state-of-the-art approach that approximates the posterior distribution of GP models, enabling faster and more efficient predictions in real-time data series. However, integrating variational inference into the GP framework for sequentially arriving data remains a significant challenge, particularly when dealing with time series data where the underlying data distribution evolves over time. In this paper, we propose the OnLine Variational Gaussian Process (OLVGP) algorithm, which introduces a novel approach for dynamically managing the number of inducing points based on the concept of eigenfunction inducing features. Unlike traditional methods that rely on a fixed number of inducing points, OLVGP adaptively adjusts the number of inducing points as new data arrives and optimizes them from the model, ensuring that the model remains computationally efficient while maintaining high predictive accuracy. Our method capitalizes on the sophisticated design of the online variational inference framework, ensuring a technically robust derivation and implementation. We validate the effectiveness and efficiency of our approach through both synthetic examples and real-world experiments. The results demonstrate that OLVGP not only substantially reduces computational costs compared to traditional sparse GP methods but also dynamically adapts to the evolving data, delivering improved performance in time series prediction.
Computer engineering. Computer hardware, Information technology
The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time‐series data. The use of interventional data in the causal analysis is crucial for real‐world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well‐known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)
Climate change continues to be a major global challenge, largely due to substantial emissions from fossil fuels. Although countries are pushing for global decarbonization through green hydrogen, the increasing use of crypto operations like Bitcoin mining has worsened the climate crisis. This work examines the potential of combining Bitcoin mining operations with green hydrogen technology to support climate mitigation strategies. Findings suggest that integrating Bitcoin mining with green hydrogen infrastructure can drive the expansion of solar and wind power capacities, thus strengthening conventional mitigation frameworks. Moreover, incentives for green hydrogen power can boost the capacity for negative emission technologies, enabling states to mine Bitcoins with the economic potential to capture at least 7.4 tCO2-eq per Bitcoin. Therefore, the proposed technological frameworks, which merge green hydrogen and Bitcoin mining with suitable policy measures, can significantly enhance clean energy production and carbon capture capabilities, contributing to climate sustainability.
Chemical engineering, Computer engineering. Computer hardware
David Spielmann, Ognjen Glamočanin, Mirjana Stojilović
State-of-the-art sensors for measuring FPGA voltage fluctuations are time-to-digital converters (TDCs). They allow detecting voltage fluctuations in the order of a few nanoseconds. The key building component of a TDC is a delay line, typically implemented as a chain of fast carry propagation multiplexers. In FPGAs, the fast carry chains are constrained to dedicated logic and routing, and need to be routed strictly vertically. In this work, we present an alternative approach to designing on-chip voltage sensors, in which the FPGA routing resources replace the carry logic. We present three variants of what we name a routing delay sensor (RDS): one vertically constrained, one horizontally constrained, and one free of any constraints. We perform a thorough experimental evaluation on both the Sakura-X side-channel evaluation board and the Alveo U200 datacenter card, to evaluate the performance of RDS sensors in the context of a remote power side-channel analysis attack. The results show that our best RDS implementation in most cases outperforms the TDC. On average, for breaking the full 128-bit key of an AES-128 cryptographic core, an adversary requires 35% fewer side-channel traces when using the RDS than when using the TDC. Besides making the attack more effective, given the absence of the placement and routing constraint, the RDS sensor is also easier to deploy.
Computer engineering. Computer hardware, Information technology
Sustainability is a contemporary global challenge that could be resolved only with the active and effective contribution of businesses. Thus, this paper aims to shed light on factors influencing entrepreneurs’ responsible behaviour. The analysis is based on the Hungarian merged dataset of the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) 2021 and 2022 (n=697). The results are based on statistical analyses, namely non-parametric correlation analyses and factor analysis. The findings show that variables concerning entrepreneurs’ responsible attitudes and behaviours significantly correlate with each other – except for two variables concerning directly with the SDGs, namely SDG awareness and considering SDG in KPIs. Using the five correlated variables, two factors can be created, where variables concerning intentions decouple from those concerning taking any steps towards minimising environmental or maximising social impacts. These results implicate that although entrepreneurs tend to consider environmental and/or societal aspects of their business decisions, they come short of taking steps towards them. Thus, responsible actions should be incentivised with education or targeted aids.
Chemical engineering, Computer engineering. Computer hardware
We propose NTT implementations with each supporting at least one parameter of NTRU and one parameter of NTRU Prime. Our implementations are based on size-1440, size-1536, and size-1728 convolutions without algebraic assumptions on the target polynomial rings. We also propose several improvements for the NTT computation. Firstly, we introduce dedicated radix-(2, 3) butterflies combining Good–Thomas FFT and vector-radix FFT. In general, there are six dedicated radix-(2, 3) butterflies and they together support implicit permutations. Secondly, for odd prime radices, we show that the multiplications for one output can be replaced with additions/subtractions. We demonstrate the idea for radix-3 and show how to extend it to any odd prime. Our improvement also applies to radix-(2, 3) butterflies. Thirdly, we implement an incomplete version of Good–Thomas FFT for addressing potential code size issues. For NTRU, our polynomial multiplications outperform the state-of-the-art by 2.8%−10.3%. For NTRU Prime, our polynomial multiplications are slower than the state-of-the-art. However, the SotA exploits the specific structure of coefficient rings or polynomial moduli, while our NTT-based multiplications exploit neither and apply across different schemes. This reduces the engineering effort, including testing and verification.
Computer engineering. Computer hardware, Information technology
Renewable energy and carbon dioxide capture and storage can cut carbon dioxide emissions, and negative emissions technologies are effective methods of carbon dioxide removal. The integration of the three is an important approach to mitigate surface temperature rise and achieve the climate change vision. This paper presents an improved algebraic targeting approach for multi-period energy planning integrating fossil energy, renewable energy, carbon capture and storage, and negative emission technologies. In this work, the risk hedging effect of negative emission technologies and the operational lifetime of carbon capture and storage are considered to reduce the amount of carbon capture and storage deployed. The approach can reduce the economic costs, environmental costs, and likelihood of stranded assets in low-carbon energy planning. This multi-period algebraic targeting approach is demonstrated through a case study. The results show that multi-period low-carbon energy planning can achieve better deployment of resources and technologies and reduce the pressure to reduce carbon emissions in the early stages of planning.
Chemical engineering, Computer engineering. Computer hardware
Chemical looping hydrogen production (CLH2) is a new technology used to produce H2 from fuel while separating CO2 simultaneously. Rice husk is considered as a promising energy source in the future because of its high energy potential and carbon neutral characteristics, although currently it is not being used effectively. This research aims to build an integrated conversion system of rice husks to H2 with high energy efficiency. The developed system mainly consists of superheated steam drying, steam gasification, syngas chemical looping, and Haber-Bosch process. To enhance the efficiency, exergy recovery and process integration technologies are adopted. Fe2O3 and Al2O3 are used as oxygen carrier and heat carrier for CLH2. The energy efficiency is evaluated using Aspen Plus. The effect of chemical looping temperature, and the effect of recycle to feed stream ratio for NH3 synthesis were selected for the parameters and evaluated. The highest NH3 efficiency achieved through the simulation was 5.77 % and the power efficiency was 1.96 %.
Chemical engineering, Computer engineering. Computer hardware
The scientific workflow deployment in the cloud environment is different from the traditional independent task scheduling,and the scheduling time and cost should be considered simultaneously.To address the problem,a scientific workflow scheduling method based on budget allocation is proposed.The mapping between workflow tasks and virtual machine resources is divided into two stages:budget allocation,and resource provision and scheduling.In order to optimize budget usage,a budget allocation algorithm based on fast-priority,called FFTD,and budget allocation algorithm based on slow-priority,called SFTD,are designed to achieve sub-allocation of budget among tasks.The task selection is performed based on the descending order of the earliest completion time of the task,and the resources are allocated according to the sub-budget of the single task when the virtual machine is reusable,thereby ensuring smooth scheduling of the workflow task.Five kinds of conventional types of scientific workflows are introduced to test the performance of the algorithm under different types of workflow structures and different budget constraints.The results show that the FFTD algorithm has shorter scheduling time and higher virtual machine resource utilization and satisfaction rate of budget constraints than the BDT-AI algorithm in 72%,88%and 84% experimental scenarios,and the overall performance is better.
Jason Maximino C. Ongpeng, Kathleen B. Aviso, Dominic C. Y. Foo
et al.
Pinch Analysis (PA) methodology was originally developed for determining feasible Heat Integration targets in process plants, and for designing optimal Heat Exchanger Networks to achieve the previously determined energy budget. This powerful methodology has since been extended to a wide range of applications, such as Mass Integration, Carbon Management and Financial PA. All these extensions have the common feature of utilizing a stream quality index that defines direction of flow, in the same way that temperature differences determine heat transfer. In this paper, a graphical PA approach to cash flow analysis and management in engineering projects is proposed in ensuring project sustainability. This method considers the positive and negative cash flows that occur over time in an engineering project. A case study is used to illustrate how this approach can provide insights for managers to synchronize the operations of a single project to stay within the firm’s cash flow limits. Such strategies can potentially affect the sustainability of construction projects. There were three solutions considered in the case study. Based on the results, it was found that from the hypothetical example, the ideal solution is to allot 50 % or 75 % of the total inflow assigned to labour outflow with a pinch period of 1 month. It resulted in a minimum loan value 61.2 %-months for materials outflow.
Chemical engineering, Computer engineering. Computer hardware
Using Ricci curvature flow,the original surface panel measure is pushed forward to the target parameter domain to form the initial panel measure.After transforming the initial panel measure or the target panel measure,a series of continuous transformation panel measure sequences are constructed.Then the parameterized sequence clusters of continuous transformation is constructed by calculating the optimal transmission mapping between the panel measures.The transformation experiments are carried out by using Moebius transformation,curvature reinforcement and importance drive,and results show that compared with the Quasi-area method,this method can construct a variety of different parameterized sequence clusters and achieve better special parameterized effect.
In order to improve the security of Android application software and increase the difficulty of the attacker without affecting the running efficiency of APP as the target,an APP security reinforcement system is designed on the basis of the automatic identification of APP documents.The system identifies the key words by automatic identification of the APP documents.In addition,it determines the security requirements of APP according to the key words,and then gives the corresponding security combination and reinforcement scheme by the security demands to realize the concrete safety reinforcement.The analysis results show that the system can increase the security of APP with the appropriate reinforcement scheme,and protects the legitimate interests of users and developers effectively.
Apply digital image technology in material mechanics property test and research and analyze the application process and structure. At material tensile stage, improve previous calibration and matching method based on traditional stereoscopic measurement, improve the accuracy and timeliness of several calibration points, change the factor and accelerate the matching speed combining small included angle to timely and accurately rebuild three-dimensional topography of deformed surface. Beside, measure the displacement field and strain field by using the unilateral camera. Finally obtain the curves of maximum tensile strain and tensile strain rate of strain point VS time. Application of digital image technology in material mechanics property test is key technology to guarantee the accuracy of test result, so it has certain promotion and application value.
Chemical engineering, Computer engineering. Computer hardware
We study the security of symmetric primitives under the incorrect usage of keys. Roughly speaking, a key-robust scheme does not output ciphertexts/tags that are valid with respect to distinct keys. Key-robustness is a notion that is often tacitly expected/assumed in protocol design — as is the case with anonymous auction, oblivious transfer, or public-key encryption. We formalize simple, yet strong definitions of key robustness for authenticated-encryption, message-authentication codes and PRFs. We show standard notions (such as AE or PRF security) guarantee a basic level of key-robustness under honestly generated keys, but fail to imply keyrobustness under adversarially generated (or known) keys. We show robust encryption and MACs compose well through generic composition, and identify robust PRFs as the main primitive used in building robust schemes. Standard hash functions are expected to satisfy key-robustness and PRF security, and hence suffice for practical instantiations. We however provide further theoretical justifications (in the standardmodel) by constructing robust PRFs from (left-and-right) collision-resistant PRGs.
This paper proposes a new vehicle-to-grid (V2G) control strategy for improving the load factor in the power network.
To operate the proposed strategy, the available storage capacity of the PEVs’ batteries is considered as a battery
energy storage system (BESS) for charging and discharging an amount of power corresponding to the V2G power command.
Due to the remarkable advantages of the technique so-called simple moving average, it is selected for applying in the
proposed V2G control strategy. In this research, for investigating the load factor improvement, the essential data
including the daily-load profiles with 7-day and 14-day periods are used for the 3 studied cases. These 3 studied
cases present the power network with variation of the PEVs locations for describing the PEVs usage and charging or
discharging behavior. The performance of the proposed strategy is simulated and verified by the MATPOWER software.
The simulation results show that the load factors of the 3 studied cases are improved. Moreover, the encouragement
of energy arbitrage for the PEVs owners is also discussed in this paper.