Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the Score of Atomic Candidate OWL Class Axioms

Candidate axiom scoring is the task of assessing the acceptability of a candidate axiom against the evidence provided by known facts or data. The ability to score candidate axioms reliably is required for automated schema or ontology induction, but it can also be valuable for ontology and/or knowledge graph validation. Accurate axiom scoring heuristics are often computationally expensive, which is an issue if you wish to use them in iterative search techniques like level-wise generate-and-test or evolutionary algorithms, which require scoring a large number of candidate axioms. We address the problem of developing a predictive model as a substitute for reasoning that predicts the possibility score of candidate class axioms and is quick enough to be employed in such situations. We use a semantic similarity measure taken from an ontology’s subsumption structure for this purpose. We show that the approach provided in this work can accurately learn the possibility scores of candidate OWL class axioms and that it can do so for a variety of OWL class axioms.
Can a Robot Shoot an Olympic Recurve Bow? A preliminary study

The ﬁeld of robotics, and more especially humanoid robotics, has several established competitions with research oriented goals in mind. Challenging the robots in a handful of tasks, these competitions provide a way to gauge the state of the art in robotic design, as well as an indicator for how far we are from reaching human performance. The most notable competitions are RoboCup, which has the long-term goal of competing against a real human team in 2050, and the FIRA HuroCup league, in which humanoid robots have to perform tasks based on actual Olympic events. Having robots compete against humans under the same rules is a challenging goal, and, we believe that it is in the sport of archery that humanoid robots have the most potential to achieve it in the near future. In this work, we perform a ﬁrst step in this direction. We present a humanoid robot that is capable of gripping, drawing and shooting a recurve bow at a target 10 meters away with considerable accuracy. Additionally, we show that it is also capable of shooting distances of over 50 meters.
Space-Terrestrial Cooperation Over Spatially Correlated Channels Relying on Imperfect Channel Estimates: Uplink Performance Analysis and Optimization

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network’s spectral efficiency. As for our analytical contributions, upon coherently combing the signals received by the central processing unit (CPU) from the users through the space and terrestrial links, we first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques employed at the APs and the satellite under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput, when maximum ratio combining is utilized for detecting the desired signals. As for our resource allocation contributions, we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed allow us to address the congestion issues appearing when at least one user is served at a rate below his/her target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.
A new weighted ensemble model for phishing detection based on feature selection

A phishing attack is a sort of cyber assault in which the attacker sends fake communications to entice a human victim to provide personal information or credentials. Phishing website identification can assist visitors in avoiding becoming victims of these assaults. The phishing problem is increasing day by day, and there is no single solution that can properly mitigate all vulnerabilities, thus many techniques are used. In this paper, We have proposed an ensemble model that combines multiple base models with a voting technique based on the weights. Moreover, we applied feature selection methods and standardization on the dataset effectively and compared the result before and after applying any feature selection.
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation

Inspired by the recent success of transformers for Natural Language Processing and vision transformer for Computer Vision, many researchers in the medical imaging community have flocked to transformer-based networks for various main stream medical tasks such as classification [1] - [3] segmentation [4] - [6] and registration [7], [8]. In this study, we analyze, two recently published transformer-based network architectures for the task of multimodal head-and-tumor segmentation and compare their performance to the de facto standard 3D segmentation network – the nnU-Net. Our results showed that modelling long-range dependencies may be helpful in cases where large structures are present and/or large field of view is needed. However, for small structures such as head-and-neck tumor, the convolution-based U-Net architecture seemed to perform well, especially when training dataset is small and computational resource is limited.
Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning

In the context of an efficient network traffic engineering process where the network continuously measures a new traffic matrix and updates the set of paths in the network, an automated process is required to quickly and efficiently identify when and what set of paths should be used. Unfortunately, the burden of finding the optimal solution for the network updating process in each given time interval is high since the computation complexity of optimization approaches using linear programming increases significantly as the size of the network increases. In this paper, we use deep reinforcement learning to derive a data-driven algorithm that does the path selection in the network considering the overhead of route computation and path updates. Our proposed scheme leverages information about past network behavior to identify a set of robust paths to be used for multiple future time intervals to avoid the overhead of updating the forwarding behavior of routers frequently. We compare the results of our approach to other traffic engineering solutions through extensive simulations across real network topologies. Our results demonstrate that our scheme fares well by a factor of 40% with respect to reducing link utilization compared to traditional TE schemes such as ECMP. Our scheme provides a slightly higher link utilization (around 25%) compared to schemes that only minimize link utilization and do not care about path updating overhead.
Quarter-symmetric metric connection in a P-Sasakian manifold

Abstract In this paper, we consider a quarter-symmetric metric connection in a P-Sasakian manifold. We investigate the curvature tensor and the Ricci tensor of a P-Sasakian manifold with respect to the quarter-symmetric metric connection. We consider semisymmetric P-Sasakian manifold with respect to the quarter- symmetric metric connection. Furthermore, we consider generalized recurrent P-Sasakian manifolds and prove the non-existence of recurrent and pseudosymmetric P-Sasakian manifolds with respect to the quarter-symmetric metric connection. Finally, we construct an example of a 5-dimensional P-Sasakian manifold admitting quarter-symmetric metric connection which verifies Theorem 4.1.
Estimating and Assessing Differential Equation Models with Time-Course Data

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This Article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy, and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI’s efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.
Count-Free Weisfeiler-Leman and Group Isomorphism

We investigate the power of counting in Group Isomorphism. We first leverage the count-free variant of the Weisfeiler--Leman Version I algorithm for groups (Brachter&Schweitzer, LICS 2020) in tandem with limited non-determinism and limited counting to improve the parallel complexity of isomorphism testing for several families of groups. These families include: - Direct products of non-Abelian simple groups. - Coprime extensions, where the normal Hall subgroup is Abelian and the complement is an $O(1)$-generated solvable group with solvability class $\text{poly} \log \log n$. This notably includes instances where the complement is an $O(1)$-generated nilpotent group. This problem was previously known to be in $\textsf{P}$ (Qiao, Sarma,&Tang, STACS 2011), and the complexity was recently improved to $\textsf{L}$ (Grochow&Levet, FCT 2023). - Graphical groups of class $2$ and exponent $p>2$ (Mekler, J. Symb. Log., 1981) arising from the CFI and twisted CFI graphs (Cai, F\"urer,&Immerman, Combinatorica 1992) respectively. In particular, our work improves upon previous results of Brachter&Schweitzer (LICS 2020). We finally show that the $q$-ary count-free pebble game is unable to distinguish even Abelian groups. This extends the result of Grochow&Levet (ibid), who established the result in the case of $q = 1$. The general theme is that some counting appears necessary to place Group Isomorphism into $\textsf{P}$.
Comparison of simultaneous wind measurements using colocated VHF meteor radar and MF spaced antenna radar systems

Comparisons of wind velocities at heights from 80 to 98 km have been made using two different techniques. The first method involves the determination of winds using meteor drifts (e.g., Avery et al., 1990; Stubbs, 1973). This was done by observing meteors using the University of Adelaide VHF radar situated approximately 40 km north of Adelaide, Australia, at Buckland Park. The second method used to determine winds was the spaced antenna technique (e.g., Briggs, 1984) using an MF radar at the same site. The two radar systems are independent, the VHF radar operating at 54.1 MHz and the MF radar at 1.98 MHz. The spatial separation of the two radars is approximately 600 m. Simultaneous data obtained from September 10 to 20, 1993, are presented here. The agreement between the two techniques is good below 90 km, while above 90 km we find that the spaced antenna technique yields smaller wind speeds than the meteor drift technique. Several possible reasons for these discrepancies are discussed.
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning

In this paper, we consider the problem of adjusting the exploration rate when using value-of-information-based exploration. We do this by converting the value-of-information optimization into a problem of finding equilibria of a flow for a changing exploration rate. We then develop an efficient path-following scheme for converging to these equilibria and hence uncovering optimal action-selection policies. Under this scheme, the exploration rate is automatically adapted according to the agent's experiences. Global convergence is theoretically assured. We first evaluate our exploration-rate adaptation on the Nintendo GameBoy games Centipede and Millipede. We demonstrate aspects of the search process, like that it yields a hierarchy of state abstractions. We also show that our approach returns better policies in fewer episodes than conventional search strategies relying on heuristic, annealing-based exploration-rate adjustments. We then illustrate that these trends hold for deep, value-of-information-based agents that learn to play ten simple games and over forty more complicated games for the Nintendo GameBoy system. Performance either near or well above the level of human play is observed.
evoML Yellow Paper: Evolutionary AI and Optimisation Studio

Machine learning model development and optimisation can be a rather cumbersome and resource-intensive process. Custom models are often more difficult to build and deploy, and they require infrastructure and expertise which are often costly to acquire and maintain. Machine learning product development lifecycle must take into account the need to navigate the difficulties of developing and deploying machine learning models. evoML is an AI-powered tool that provides automated functionalities in machine learning model development, optimisation, and model code optimisation. Core functionalities of evoML include data cleaning, exploratory analysis, feature analysis and generation, model optimisation, model evaluation, model code optimisation, and model deployment. Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.
Holistic risk assessment of inference attacks in machine learning

As machine learning’s expanding application, there are more and more unignorable privacy and safety issues. Especially inference attacks against Machine Learning models allow adversaries to infer sensitive information about the target model, such as training data, model parameters, etc. Inference attacks can lead to serious consequences, including violating individuals’ privacy, compromising the intellectual property of the owner of the machine learning model. As far as concerned, researchers have studied and analyzed in depth several types of inference attacks, albeit in isolation, but there’s still a lack of a holistic rick assessment of inference attacks against machine learning models, such as their application in different scenarios, the common factors affecting the performance of these attacks and the relationship among the attacks. As a result, this paper performs a holistic risk assessment of different inference attacks against Machine Learning models. This paper focuses on three kinds of representative attacks: membership inference attack, attribute inference attack and model stealing attack. And a threat model taxonomy is established. A total of 12 target models using three model architectures, including AlexNet, ResNet18 and Simple CNN, are trained on four datasets, namely CelebA, UTKFace, STL10 and FMNIST. Membership inference attack, attribute attack and model stealing attack are executed against these target models under different threat models respectively, and the performance of inference attacks is evaluated with attack accuracy as an indicator, which demonstrates that the complexity of the training dataset and overfitting level of the target models have a significant impact on the performance of inference attacks, while the effectiveness of the model stealing attack is negatively correlated with the membership inference attack.
RECAP: Retrieval Augmented Music Captioner

With the prevalence of stream media platforms serving music search and recommendation, interpreting music by understanding audio and lyrics interactively has become an important and challenging task. However, many previous works focus on reﬁning individual components of encoder-decoder architecture mapping music to caption tokens, ignoring the potential usage of audio and lyrics correspondence. In this paper, we propose to explicitly learn the multi-modal alignment with retrieval augmentation by contrastive learning. By learning audio-lyrics correspondence, the model is guided to learn better cross-modal attention weights, thus generating high-quality caption words. We provide both theoretical and empirical results that demonstrate the advantage of the proposed method
PABAU: Privacy Analysis of Biometric API Usage

Biometric data privacy is becoming a major concern for many organizations in the age of big data, particularly in the ICT sector, because it may be easily exploited in apps. Most apps utilize biometrics by accessing common application programming interfaces (APIs); hence, we aim to categorize their usage. The categorization based on behavior may be closely correlated with the sensitive processing of a user’s biometric data, hence highlighting crucial biometric data privacy assessment concerns. We propose PABAU, Privacy Analysis of Biometric API Usage. PABAU learns semantic features of methods in biometric APIs and uses them to detect and categorize the usage of biometric API implementation in the software according to their privacy-related behaviors. This technique bridges the communication and background knowledge gap between technical and non-technical individuals in organizations by providing an automated method for both parties to acquire a rapid understanding of the essential behaviors of biometric API in apps, as well as future support to data protection officers (DPO) with legal documentation, such as conducting a Data Protection Impact Assessment (DPIA).
The heat equation with the dynamic boundary condition as a singular limit of problems degenerating at the boundary

We derive the dynamic boundary condition for the heat equation as a limit of boundary layer problems. We study convergence of their weak and strong solutions as the width of the layer tends to zero. We also discuss Γ-convergence of the functionals generating these flows. Our analysis of strong solutions depends on a new version of the Reilly identity.
Nucleosynthesis in Outflows from Black Hole–Neutron Star Merger Disks with Full GR(ν)RMHD

Along with binary neutron star mergers, the inspiral and merger of a black hole and a neutron star is a predicted site of r-process nucleosynthesis and associated kilonovae. For the right mass ratio, very large amounts of neutron-rich material (relative to the dynamical ejecta) may become unbound from the post-merger accretion disk. We simulate a suite of four post-merger disks with three-dimensional general-relativistic magnetohydrodynamics with time-dependent Monte Carlo neutrino transport. We find that within 104 GM BH/c 3 (∼200–500 ms), the outflows from these disks are very close to the threshold conditions for robust r-process nucleosynthesis. For these conditions, the detailed properties of the outflow determine whether a full r-process can or cannot occur, implying that a wide range of observable phenomena is possible. We show that on average the disk outflow lanthanide fraction is suppressed relative to the solar isotopic pattern. In combination with the dynamical ejecta, these outflows imply a kilonova with both blue and red components.
NP4G : Network Programming for Generalization

Automatic programming has been actively studied for a long time by various approaches including genetic programming. In recent years, automatic programming using neural networks such as GPT-3 has been actively studied and is attracting a lot of attention. However, these methods are illogical inference based on experience by enormous learning, and their thinking process is unclear. Even using the method by logical inference with a clear thinking process, the system that automatically generates any programs has not yet been realized. Especially, the inductive inference generalized by logical inference from one example is an important issue that the artiﬁcial intelligence can acquire knowledge by itself. In this study, we propose NP4G: Network Programming for Generalization, which can automatically generate programs by inductive inference. Because the proposed method can realize "sequence", "selection", and "iteration" in programming and can satisfy the conditions of the structured program theorem, it is expected that NP4G is a method automatically acquire any programs by inductive inference. As an example, we automatically construct a bitwise NOT operation program from several training data by generalization using NP4G. Although NP4G only randomly selects and connects nodes, by adjusting the number of nodes and the number of phase of "Phased Learning", we show the bitwise NOT operation programs are acquired in a comparatively short time and at a rate of about 7 in 10 running. The source code of NP4G is available on GitHub as a public repository 1 .
Circular flows in mono‐directed signed graphs

In this paper, the concept of circular ‐flow in a mono‐directed signed graph is introduced. That is a pair , where is an orientation on and satisfies that for each positive edge and for each negative edge , and the total in‐flow equals the total out‐flow at each vertex. This is the dual notion of circular colorings of signed graphs and is distinct from the concept of circular flows in bi‐directed graphs associated with signed graphs studied in the literature. We first explore the connection between circular ‐flows and modulo ‐orientations in signed graphs. Then we focus on the upper bounds for the circular flow indices of signed graphs in terms of the edge‐connectivity, where the circular flow index of a signed graph is the minimum value such that it admits a circular ‐flow. We prove that every 3‐edge‐connected signed graph admits a circular 6‐flow and every 4‐edge‐connected signed graph admits a circular 4‐flow. More generally, for , we show that every ‐edge‐connected signed graph admits a circular ‐flow, every ‐edge‐connected signed graph has a circular ‐flow with , and every ‐edge‐connected signed graph admits a circular ‐flow. Moreover, the ‐edge‐connectivity condition is shown to be sufficient for a signed Eulerian graph to admit a circular ‐flow, and applying this result to planar graphs, we conclude that every signed bipartite planar graph of negative girth at least admits a homomorphism to the negative even cycles .