Complexity of Nonemptiness in Control Argumentation Frameworks

Verification in incomplete argumentation frameworks

We introduce Incomplete Bipolar Argumentation Frameworks (iBAFs), the extension of Dung’s Abstract Argumentation Frameworks (AAFs) allowing the simultaneous presence of supports (borrowed from BAFs – Bipolar AAFs) and of uncertain elements of the argumentation graph (borrowed from iAAFs – incomplete AAFs). We investigate the computational complexity of verification problem (under the possible perspective) and the acceptance problem, by studying its sensitivity to the semantics of supports and the semantics of extensions. On the one hand, we show that adding supports on top of incompleteness does not affect the complexity of the acceptance. On the other hand, surprisingly, we show that the joint use of bipolarity and incompleteness has a deep impact on the complexity of the verification: for the semantics under which the verification over AAFs is polynomial-time solvable, although moving from AAFs to BAFs or to iAAFs does not change the complexity, the complexity of the verification over iBAFs may increase up to NP-complete.

We propose natural generalizations of the credulous and skeptical acceptance problems in abstract argumentation for incomplete argumentation frameworks [3]. This continues earlier work on a similar generalization of the verification problem. We provide a full analysis of the computational complexity of the generalized problems for all original semantics, showing that, in almost all cases, acceptance problems for incomplete argumentation frameworks are significantly harder than the respective problems for argumentation frameworks without uncertainty. All our hardness results for the classes NP, coNP, Πp2, and Σp2

The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.

Among the abundance of generalizations of abstract argumentation frameworks, the formalism of abstract dialectical frameworks (ADFs) proved to be powerful in modelling various argumentation problems. Implementations of reasoning tasks that come within ADFs struggle with their high computational complexity. Thus methods simplifying the evaluation process are required. One such method is splitting, which was shown to be an effective optimization technique in other nonmonotonic formalisms. We apply this approach to ADFs by providing suitable techniques for directional splitting (allowing links only from the first to the second part of the splitting) under all the standard semantics of ADFs as well as preliminary results on general splitting.

A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations.

This paper is an introduction to the computational complexity theory. I believe that the standard courses in complexity theory make some things much more important than they really are, while things which I find extremely interesting are marginalized. Thus, it can be seen as my subjective view on what is important and interesting in computational complexity, and what is not. We state the basic facts from computational complexity theory using the blackboard computations (one-dimensional cellular automata) as the standard computation model; we argue the benefits of such approach. This article is somewhat based on my series of talks from Warsztaty Logiczne 2014 in Białka Tatrzańska, although these talks covered more topics, such as NL=coNL, and links between BPP and the polynomial hierarchy.

In order to improve the security and performance of authentication protocol in manufacturing grid, identity-based authentication protocol under standard model is constructed. Combining with the environment of manufacturing grid, this protocol can work under escrow and non-escrow models and different trust domains model. By introducing pseudo random function set, new authentication protocol realizes explicit double authentication under standard model. Compared to identity-based authentication protocol under other standard models, this protocol has lower computation complexity, so it is fit for application in resources-constrained environment of manufacturing grid. At last, based on hypothesis of difficulty, the safety of new authentication protocol is proved under standard model.

Typical protocols in the multi-party private set operations (MPSO) setting enable m > 2 parties to perform certain secure computation on the intersection or union of their private sets, realizing a very limited range of MPSO functionalities. Most works in this field focus on just one or two specific functionalities, resulting in a large variety of isolated schemes and a lack of a unified framework in MPSO research. In this work, we present an MPSO framework, which allows m parties, each holding a set, to securely compute any set formulas (arbitrary compositions of a finite number of binary set operations, including intersection, union and difference) on their private sets. Our framework is highly versatile and can be instantiated to accommodate a broad spectrum of MPSO functionalities. To the best of our knowledge, this is the first framework to achieve such a level of flexibility and generality in MPSO, without relying on generic secure multi-party computation (MPC) techniques. Our framework exhibits favorable theoretical and practical performance. With computation and communication complexity scaling linearly with the set size n, it achieves optimal complexity that is on par with the naive solution for widely used functionalities, such as multi-party private set intersection (MPSI), MPSI with cardinality output (MPSI-card), and MPSI with cardinality and sum (MPSI-card-sum), for the first time in the standard semi-honest model. Furthermore, the instantiations of our framework, which primarily rely on symmetric-key techniques, provide efficient protocols for MPSI, MPSI-card, MPSI-card-sum, and multi-party private set union (MPSU), with online performance that either surpasses or matches the state of the art in standard semi-honest model. At the technical core of our framework is a newly introduced primitive called predicative zero-sharing. This primitive captures the universality of a number of MPC protocols and is composable. We believe it may be of independent interest.

Inspection systems play a critical role in ensuring product quality and maintaining a company’s reputation. However, current industrial systems are heavily dependent on human labor, which can lead to errors and inefficiencies. Although deep learning architectures have the potential to effectively automate inspection systems, their complexity often introduces computational challenges that can impede system speed. To address this issue, we propose a hybrid deep learning approach called Defect-Lite, which adapts the YOLOv5 object detection algorithm. YOLOv5 is renowned for its robustness in real-time defect identification in industrial environments. Our innovative method integrates the inverted residual layers of MobileNetV2 with the PANet architecture of YOLOv5, improving the speed of the algorithm and reducing its computational complexity. This improvement makes it feasible for real-time applications on lower-power machines. We evaluated the proposed model using custom real-time datasets designed to simulate actual industrial settings, running the model on various systems to demonstrate its adaptability and robustness. Experimental results indicate a precision of 0.743, recall of 0.66, mean average precision (MAP) of 0.739, and an F1 score of approximately 0.69 at 240 frames per second (FPS), based on our custom dataset containing 783 images across five classes of defects. These results significantly surpass the performance of the standard YOLOv5 model and other state-of-the-art approaches. Additionally, we deployed our model on a low-computation Raspberry Pi 4B microprocessor to assess its performance in a real-time scenario, where it successfully provided accurate bounding boxes for defects in live footage. Hence, the proposed defect inspection model is proven to be effective in real-world industrial settings.

A novel finite conductivity fracture model for full-field reservoir simulation is presented. The water, oil and gas mixture flow in the fracture is considered as the steady-state single-phase flow. This technique provides a balance between computational complexity and physical processes detailing in the fracture. The model is based on Embedded Discrete Fracture concept and can be fully implicitly integrated into standard or multisegment well model to represent hydraulic fractures. Results from a simple numerical experiment is demonstrated.

Mathematical modelling of mobility at the human ankle joint is essential for prosthetics and orthotic design. The scope of this study is to show that the ankle joint passive motion can be represented by a one-degree-of-freedom spherical motion. Moreover, this motion is modelled by a one-degree-of-freedom spherical parallel mechanism model, and the optimal pivot-point position is determined. Passive motion and anatomical data were taken from in vitro experiments in nine lower limb specimens. For each of these, a spherical mechanism, including the tibiofibular and talocalcaneal segments connected by a spherical pair and by the calcaneofibular and tibiocalcaneal ligament links, was defined from the corresponding experimental kinematics and geometry. An iterative procedure was used to optimize the geometry of the model, able to predict original experimental motion. The results of the simulations showed a good replication of the original natural motion, despite the numerous model assumptions and simplifications, with mean differences between experiments and predictions smaller than 1.3mm (average 0.33mm) for the three joint position components and smaller than 0.7° (average 0.32°) for the two out-of-sagittal plane rotations, once plotted versus the full flexion arc. The relevant pivot-point position after model optimization was found within the tibial mortise, but not exactly in a central location. The present combined experimental and modelling analysis of passive motion at the human ankle joint shows that a one degree-of-freedom spherical mechanism predicts well what is observed in real joints, although its computational complexity is comparable to the standard hinge joint model.

We define new algorithmic problems and discuss their properties (in particular, we present a careful study of their computational complexity). We apply the new problems to design public key encryption protocols with semantic security relative to their decisional variants. We then show how to provide efficient schemes that are semantically secure under adaptive chosen ciphertext attacks in the random oracle model. Finally, we show that the ideas developed in this extended abstract can be used to design the most efficient known cryptosystem with semantic security under non-adaptive chosen ciphertext attacks in the standard security model.

The process of whitening or decorrelating is used extensively in detection and estimation problems. Far situations where the signal characteristics are time varying, adaptive processing that will track are necessary. The standard block data autoregressive model are computationally inefficient when applied to sliding data frames with high percentage overlaps. Recursive techniques, either of the gradient or exact solution type using exponential weighting of post data, have been shown to track time varying spectral characteristics quite accurately. However when the data sampling rate is very high, the computational complexity is too much for real time implementation or requires several expensive multiplier chips. When the model coefficients are required to have only limited accuracy to meet transmission bandwidth or storage memory requirements, the technique presented here can be used to reduce the hardware implementation requirements. The fast approximate whitening ladder filter uses the ladder structure and reflection coefficients of the fast tracking recursive algorithms above. As in the recursive ladder algorithms, it performs an approximate stage by stage orthogonalization of the input signal, in a computationally and implementationally simple fashion. The accuracy of the reflection coefficient has been compromised for this simplicity. but similar accuracy as that used in speech model can be obtained. At each stage in the ladder, to estimate the reflection coefficient polarity coincidence counting and a table lookup is all that is required. Using a sliding memory where only the sign of the data is stored, this estimation technique can used to adapt to changing signal characteristics. The virtue of the fast approximate whitening ladder filter is its ability to adaptively decorrelate (whiten) a process sampled at rates faster than that possible using computations requiring multiplier chips.

A bstract Due to the success of the Standard Model (SM), it is reasonable to anticipate that the signal of new physics (NP) beyond the SM is small. Consequently, future searches for NP and precision tests of the SM will require high luminosity collider experiments. Moreover, as precision tests advance, rare processes with many final-state particles require consideration which demands the analysis of a vast number of observables. The high luminosity produces a large amount of experimental data spanning a large observable space, posing a significant data-processing challenge. In recent years, quantum machine learning has emerged as a promising approach for processing large amounts of complex data on a quantum computer. In this study, we propose quantum searching neighbor (QSN) and variational QSN (VQSN) algorithms to search for NP. The QSN is a classification algorithm. The VQSN introduces variation to the QSN to process classical data. As applications, we apply the (V) QSN in the phenomenological study of the NP at the Large Hadron Collider and muon colliders. Examples are implemented on a real quantum hardware, which confirms reliable performance under noisy conditions. The results indicate that the VQSN demonstrates superior efficiency in the sense of computational complexity to a classical counterpart k-nearest neighbor algorithm, even when dealing with classical data.