Demand Response is utilized around the globe to alleviate the peak demand economically and to manage reliability-compromising emergencies in power systems. Sri Lanka requires an effective Demand Response system to cat...
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ISBN:
(纸本)9781665437530
Demand Response is utilized around the globe to alleviate the peak demand economically and to manage reliability-compromising emergencies in power systems. Sri Lanka requires an effective Demand Response system to cater the peak demand more economically than dispatching expensive thermal power plants, while minimizing sub-optimal consumption patterns exhibited by consumers during peak demand periods. therefore, this paper is focused on the development of analgorithm for an automated Demand Response system for large facilities, which is customized to suit the requirements of the Sri Lankan power system. Under this system, boththe utility organization and the consumers are expected to be mutually benefited. this algorithm consists of three levels: deciding on whether or not to execute an automated Demand Response event for a particular time interval, determining the optimum facility-level demand reductions, and determining the optimum appliance- level demand reductions. Mixed integer nonlinear programming and a heuristic method are used to solve the optimization problems in this algorithm. Results of this algorithm are analysed using a miniature model of the automated Demand Response system, consisting of fifteen power plants and five industrial and general-purpose facilities.
Data integrity is critical to the secure operation of a computer system. Applications need to know that the data that they access is trustworthy. Many current production-level integrity models are tightly coupled to a...
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ISBN:
(纸本)9789897584916
Data integrity is critical to the secure operation of a computer system. Applications need to know that the data that they access is trustworthy. Many current production-level integrity models are tightly coupled to a specific domain, (e.g., databases), or only apply after the fact (e.g., backups). In this paper we propose a recommendation-based trust model, called Admonita, for data integrity that is applicable to any structured data in a system and provides a measure of trust to applications on-the-fly. the proposed model is based on the Biba integrity model and utilizes the concept of an Integrity Verification Procedure (IVP) proposed by Clark-Wilson. Admonita incorporates subjective logic to maintain the trustworthiness of data and applications in a system. To prevent critical applications from losing trust, Admonita also incorporates the principle of weak tranquility to ensure that highly trusted applications can maintain their trust levels. We develop a simple algebra around these elements and describe how it can be used to calculate the trustworthiness of system entities. By applying subjective logic, we build a powerful, artificial and reasoning trust model for implementing data integrity.
Linear logic is an important logic for modelling resources and decomposing computational interpretations of proofs. Decision problems for fragments of linear logic exhibiting "infinitary" behaviour (such as ...
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Secure multi-party computation (MPC) is a promising technique for privacy-persevering applications. A number of MPC frameworks have been proposed to reduce the burden of designing customized protocols, allowing non-ex...
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ISBN:
(纸本)9783031131851;9783031131844
Secure multi-party computation (MPC) is a promising technique for privacy-persevering applications. A number of MPC frameworks have been proposed to reduce the burden of designing customized protocols, allowing non-experts to quickly develop and deploy MPC applications. To improve performance, recent MPC frameworks allow users to declare variables secret only for these which are to be protected. However, in practice, it is usually highly non-trivial for non-experts to specify secret variables: declaring too many degrades the performance while declaring too less compromises privacy. To address this problem, in this work we propose an automated security policy synthesis approach to declare as few secret variables as possible but without compromising security. Our approach is a synergistic integration of type inference and symbolic reasoning. the former is able to quickly infer a sound-but sometimes conservative-security policy, whereas the latter allows to identify secret variables in a security policy that can be declassified in a precise manner. Moreover, the results from symbolic reasoning are fed back to type inference to refine the security types even further. We implement our approach in a new tool PoS4MPC. Experimental results on five typical MPC applications confirm the efficacy of our approach.
the equational unification problem, where the underlying equational theory may be given as the union of component equational theories, appears often in practice in many fields such as automatedreasoning, logic progra...
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ISBN:
(数字)9783030684464
ISBN:
(纸本)9783030684457;9783030684464
the equational unification problem, where the underlying equational theory may be given as the union of component equational theories, appears often in practice in many fields such as automatedreasoning, logicprogramming, declarative programming, and the formal analysis of security protocols. In this paper, we investigate the unification problem in the non-disjoint union of equational theories via the combination of hierarchical unification procedures. In this context, a unification algorithm known for a base theory is extended with some additional inference rules to take into account the rest of the theory. We present a simple form of hierarchical unification procedure. the approach is particularly well-suited for any theory where a unification procedure can be obtained in a syntactic way using transformation rules to process the axioms of the theory. Hierarchical unification procedures are exemplified with various theories used in protocol analysis. Next, we look at modularity methods for combining theories already using a hierarchical approach. In addition, we consider a new complexity measure that allows us to obtain terminating (combined) hierarchical unification procedures.
Symbolic execution is a way of modelling the program state without executing it. It can be used to reason about the behaviour of the program statically. One of the use cases for using symbolic execution is finding sof...
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ISBN:
(数字)9798350386592
ISBN:
(纸本)9798350386608
Symbolic execution is a way of modelling the program state without executing it. It can be used to reason about the behaviour of the program statically. One of the use cases for using symbolic execution is finding software bugs. Clang Static Analyzer can perform static analysis using symbolic execution, and the specific problems to detect are described in imperative $\mathrm{C}++$ code. One of the alternative ways of describing software bugs is linear temporal logic. this approach leads to a declarative description of the problem and allows for a higher level of abstraction. We present a mapping between the declarative temporal logic approach and the imperative Clang Static Analyzer implementation, highlighting the semantic equivalences of temporal logical formulas and $\mathrm{C}++$ programs explicitly focusing on finding bugs statically. We briefly show the implementation details of our extension and present how to use temporal logic for symbolic execution in a powerful manner.
this paper investigates the application of Vision Transformers (ViTs), specifically DETR (DEtection TRans-former), for the detection and classification of digital logic gates in hand-sketched digital logic circuits (D...
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ISBN:
(数字)9798331531836
ISBN:
(纸本)9798331531843
this paper investigates the application of Vision Transformers (ViTs), specifically DETR (DEtection TRans-former), for the detection and classification of digital logic gates in hand-sketched digital logic circuits (DLC). A novel dataset of 295 hand-sketched DLC images was developed, capturing all seven fundamental gate types: AND, OR, NOT, NAND, NOR, XOR, and XNOR. the dataset introduces real-world complexities, such as variations in sketching styles, pen types, and paper textures, therefore addressing gaps in existing datasets. the performance of DETR is compared withthe state-of-the-art YOLOv8 model under various preprocessing configurations, including grayscale conversion and image resizing. the results show that YOLO achieves higher overall detection accuracy, while DETR demon-strates strengths in capturing spatial relationships and complex patterns in abstract sketches. Additionally, the study analyses the effects of training duration and dataset structure on performance, highlighting the sensitivity of ViTs to preprocessing and spatial feature preservation. this work advances the understanding of transformer-based models for DLC recognition, advancing the way for more effective automated tools in engineering and education.
the proceedings contain 19 papers. the special focus in this conference is on Rules and reasoning. the topics include: Learning Decision Rules or Learning Decision Models?;correctness of Automatically Generated Choreo...
ISBN:
(纸本)9783030911669
the proceedings contain 19 papers. the special focus in this conference is on Rules and reasoning. the topics include: Learning Decision Rules or Learning Decision Models?;correctness of Automatically Generated Choreography Specifications;conflict-Free Access Rules for Sharing Smart Patient Health Records;structuring Rule Sets Using Binary Decision Diagrams;link Traversal with Distributed Subweb Specifications;event-Based Microcontroller programming in Datalog;Combining Deep Learning and ASP-Based Models for the Semantic Segmentation of Medical Images;A Two-Phase ASP Encoding for Solving Rehabilitation Scheduling;an Answer Set programming Based Framework for High-Utility Pattern Mining Extended with Facets and Advanced Utility Functions;preface;policy-Based automated Compliance Checking;Automatic Generation of Intelligent Chatbots from DMN Decision Models;deep Learning for the Identification of Decision Modelling Components from Text;combining Sub-symbolic and Symbolic Methods for Explainability;practical Rule-Based Qualitative Temporal reasoning for the Semantic Web;logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification;An Evaluation of Meta-reasoning over OWL 2 QL.
automated Production Systems are highly complex mechatronic systems. the increasing complexity of automation software is a main challenge for companies to remain competitive. Added functionality entails software chang...
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ISBN:
(数字)9798350363012
ISBN:
(纸本)9798350363029
automated Production Systems are highly complex mechatronic systems. the increasing complexity of automation software is a main challenge for companies to remain competitive. Added functionality entails software changes, which may cause side effects. Static code analysis and change impact analysis are key methods in computer science for assessing software regarding side effects. However, the requirements of real-time capability, reliability, and mixing different Programmable logic Controller programming languages in the automation software lead to new requirements for code analysis and change management. this paper introduces a set of change categories designed to provide understandable and comprehensive classifications of changes in automation software. Mutable (i.e., changed or modified between versions) software artifacts serve to differentiate change categories. Aiming for unambiguousness and completeness, this set of categories supports the further work of the change analysis, such as automatic change detection and visualization. the change categories are evaluated through an industrial software project programmed in Siemens TIA Portal comprising 262 program organization units in the health sector.
the proceedings contain 29 papers. the special focus in this conference is on Belief Functions. the topics include: On Improving a Group of Evidential Sources with Different Contextual Corrections;measu...
ISBN:
(纸本)9783031178009
the proceedings contain 29 papers. the special focus in this conference is on Belief Functions. the topics include: On Improving a Group of Evidential Sources with Different Contextual Corrections;measure of Information Content of Basic Belief Assignments;belief Functions on Ordered Frames of Discernment;on Modelling and Solving the Shortest Path Problem with Evidential Weights;heterogeneous Image Fusion for Target Recognition Based on Evidence reasoning;cluster Decomposition of the Body of Evidence;evidential Trustworthiness Estimation for Cooperative Perception;an Intelligent System for Managing Uncertain Temporal Flood Events;a Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference;causal Transfer Evidential Clustering;on Conditional Belief Functions in the Dempster-Shafer theory;valid Inferential Models Offer Performance and Probativeness Assurances;a Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases;the Extension of Dempster’s Combination Rule Based on Generalized Credal Sets;a Correspondence Between Credal Partitions and Fuzzy Orthopartitions;toward Updating Belief Functions over Belnap-Dunn logic;real Bird Dataset with Imprecise and Uncertain Values;addressing Ambiguity in Randomized Reinsurance Contracts Using Belief Functions;evidential Filtering and Spatio-Temporal Gradient for Micro-movements Analysis in the Context of Bedsores Prevention;hybrid Artificial Immune Recognition System with Improved Belief Classification Process;a Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels;evidential Clustering by Competitive Agglomeration;imperfect Labels with Belief Functions for Active Learning;an Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers;ordinal Classification Using Single-Model Evidential Extreme Learning Machine;reliability-Based Imbalanced Data Classification with Dempster-Shafer theory;evidential Regress
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