The development of AI systems and benchmarks has been rapidly increasing, yet there has been a disproportionately small amount of examination into the domains used to evaluate these systems. Most benchmarks introduce bias by focusing on a particular type of domain or combine different domains without consideration of their relative complexity or similarity. We propose the Artificial Intelligence Quotient (AIQ) framework as a means for measuring the similarity and complexity of domains in order to remove these biases and assess the scope of intelligent capabilities evaluated by a benchmark composed of multiple domains. These measures are evaluated with several intuitive experiments using simple domains with known complexities and similarities. We construct test suites using the AIQ framework and evaluate them using known AI systems to validate that AIQ-based benchmarks capture an agent’s intelligence.

Cross-site Scripting (XSS) vulnerabilities continue to compromise web application security due to delayed detection by periodic scans. This paper proposes a novel real-time, traffic-based detection system that inspects HTTP request-response flows to verify exploitability dynamically. Unlike existing solutions that rely on static rules or post-analysis, the introduced proxy-based framework passively tracks and correlates incoming requests with their reflections in outgoing responses, specifically examining executable contexts. Evaluation using established testing suites demonstrates that the system accurately identifies 66% of exploitable XSS vulnerabilities confirmed by dynamic scanners, with no false positives. The results highlight that real-time traffic analysis effectively complements existing tools, providing immediate and actionable vulnerability insights, significantly narrowing the window for attackers and accelerating the defensive response.

Detecting faults vs. Exposing failures: Orthogonal measures of test suite effectiveness

The Bat Algorithm (BA), inspired by the echolocation behavior of bats, has gained prominence as a promising search-based technique for global optimization. This paper explores its application in automatic test suite generation (TSG) through a creative methodology that inserts mutants in the original data and verifies correctness using mathematical constraints. It further provides a comparative analysis against three established search-based algorithms (SBAs): Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithm. The analysis focuses on test suite size as a primary metric of the algorithm's effectiveness. Through rigorous experimentation, the performance of the Bat Algorithm has been validated in producing better results compared to the baseline algorithms. It outperformed the performance in terms of reduction in test suite size, achieving a 71.8%, 55.82%, and 18.99% reduction compared to GA, PSO, and ABC, respectively. These results highlight its potential for enhancing TSG in various domains of computer science.

As Li-ion batteries age, degradation mechanisms within the cell can lead to release of transition metals from the cathode lattice. The dissolution of transition metals from the cathode and deposition on the anode have been linked to increased capacity fade and poor coulombic efficiency, attributed to constant degradation of electrolyte at the anode interface, disruption of SEI layer formation, and lithium inventory loss.1,2This mechanism occurs in multiple commercial cathode materials including both layered oxide cathodes (such as lithium nickel manganese cobalt oxide - NMC) and olivine structured cathodes (such as lithium iron phosphate – LFP).In this study, Orbia Fluor & Energy Materials seeks to identify commercially relevant conditions that lead to transition metal deposition, measure the concomitant performance degradation and develop electrolytes to mitigate this impact based on proprietary fluorinated materials. To accomplish this goal, a comprehensive suite of cell tests, including RT/HT cycling and HT storage, were conducted in multiple cell chemistries (Gr/LFP, Gr/NMC811, LTO/LFP, and Gr/Li-rich Mn-rich) to understand the hallmarks in each system for cell degradation. Investigations include exploring correlations between transition metal deposition (measured by ICP-OES) and electrochemical performance metrics, including capacity fade, cell impedance, and gas generation. The effect of individual electrolyte components, such as solvents, salts, and additives, were evaluated to identify electrolyte formulations that improve performance through reduced transition metal deposition. The impact of non-traditional electrolyte designs, such as localized high concentration electrolyte (LHCE) formulations is also studied.The results of this study provide practical insights into how the development of fluorinated electrolyte solutions based in solid mechanistic understanding can enable next-generation Li-ion technologies.1.Jung, R.; Linsenmann, F.; Thomas, R.; Wandt, J.; Solchenbach, S.; Maglia, F.; Stinner, C.; Tromp, M.; Gasteiger, H.A. Nickel, Manganese, and Cobalt Dissolution from Ni-Rich NMC and Their Effects on NMC622-Graphite Cells. Journal of the Electrochemical Society 2019, 166(2), A378.2.Joshi, T; Eom, K.; Yushin, G.; Fuller, T. Effects of Dissolved Transition Metals on the Electrochemical Performance and SEI in Lithium-Ion Batteries. Journal of the Electrochemical Society 2014. 161 (12), A1915-A1921.

Animal-assisted interventions (AAIs) or Services (AAS) may cause stress in participating dogs, making the selection of suitable individuals essential to prevent strain. Different non-standardized approaches currently exist to assess dogs’ suitability for AAIs. This preliminary study aimed at evaluating two combined tools, a behavioural aptitude test (SuiTe) and an ad hoc revised questionnaire incorporating C-BARQ, for pre-screening dog suitability for AAIs, also in relation to salivary cortisol measured by enzyme immunoassay in N = 38 dogs. Dogs’ behavioural responses to environmental and social stimuli were scored on an X-Y scale and classified by two independent evaluators as suitable (S), pending suitability (P), or unsuitable (U). Non-parametric tests were performed (p < 0.05). Results indicated significant differences between dogs classified as S or P versus U, both in SuiTe valence scores (higher in S and P) and in separation, attachment, and fear/anxiety behaviours assessed by the questionnaire (higher in U). However, suitability in the SuiTe was lower than that assessed by caregivers through an open question. Our study highlights the complexity of this assessment and the limited awareness of caregivers regarding the situations their dogs face every day. Future analyses will refine this multiparametric approach within a One Welfare perspective, ensuring the welfare of both animals and humans involved in AAIs.

The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population diversity, and a tendency to be trapped in local optima. To eliminate these drawbacks, this paper proposes an SBOA variant (MESBOA) that integrates a multi-population management strategy with an experience-trend guidance strategy. The proposed method is compared with eight advanced basic/enhanced algorithms of different categories on both the CEC2017 and CEC2022 test suites. Experimental results demonstrate that MESBOA delivers faster convergence, more stable robustness and higher accuracy, achieving mean rankings of 2.500 (CEC2022 10-D), 2.333 (CEC2022 20-D), 1.828 (CEC2017 50-D) and 1.931 (CEC2017 100-D). Moreover, engineering constrained optimization problems further verify its applicability to real-world optimization tasks.

Mandala Chain is the first layer-1 blockchain developed in Indonesia, focusing on interoperability, data security, and ease of adoption. As a blockchain-based platform, Mandala Chain includes key features such as dashboard, connect wallet, project, develop, and mandala academy, which must be verified for proper functionality. This study aims to test these features automatically using Katalon Studio as an automation testing tool. The research adopts the Software Testing Life Cycle (STLC) approach, consisting of Requirement Analysis, Test Planning, Test Case Development, Test Environment Setup, Test Execution, and Test Cycle Closure. This structured method ensures that the testing process is systematic, measurable, and well-documented. A total of 23 test cases were created and organized into seven test suites covering all major features of the Mandala Chain website. The results show that all test cases passed successfully, indicating that the tested features function as specified. The implementation of automated testing using Katalon Studio effectively improves efficiency, ensures result consistency, and minimizes human error risks in verifying the functionality of blockchain-based systems such as Mandala Chain.

Solving dynamic traveling salesman problems (DTSPs) is a challenging task due to the constantly changing freight volumes and demands between different city nodes. To alleviate this issue, a deep reinforcement learning-assisted large neighborhood dynamic robust algorithm (LNDRA-DRL) is proposed in the present study. In the LNDRA-DRL, an end-to-end method is used to produce a high-quality initial robust individual, which is then refined using a large neighborhood dynamic robust algorithm to find final robust solutions. To demonstrate the performance of the proposed LNDRA-DRL, five novel dynamic robust traveling salesman problems (DRTSPs) are constructed based on the TSPLIB benchmark test suite, and three competitive algorithms are used for comparison in experiments. Experimental results demonstrate that the proposed LNDRADRL can find a set of satisfactory robust solutions that are nearly optimal in different dynamic environments. Furthermore, it can reduce the switching times of solutions within an acceptable threshold when environmental conditions change.

Test flakiness refers to the unpredictable behavior of software tests, where a test may pass or fail even when neither the test code nor the system under test has changed. This results in reduced developer productivity, costly and repeated test reruns, hidden or overlooked defects, and diminished confidence in test suites. While the problem has received increasing attention from both practitioners and researchers in recent years, opportunities for focused discussion remain limited. The 2nd International Flaky TestWorkshop (FTW 2025) was held on April 27th, 2025, in conjunction with the 47th International Conference on Software Engineering (ICSE 2025). The program emphasized two main themes, flakiness in specific and previously neglected domains and improving flakiness mitigation techniques, and also included demonstrations of new datasets and tools to support research and practice.

Electromagnetism plays an important role in a variety of applications in gravity that we wish to investigate. To that end, in this work, we present an implementation of the Maxwell equations within the adaptive-mesh pseudospectral numerical relativity code BAMPS. We perform a thorough analysis of the evolution equations as a first order symmetric hyperbolic system of PDEs. This includes both the construction of the characteristic variables for use in our penalty boundary communication scheme, as well as radiation controlling, constraint preserving outer boundary conditions which, for the first time in a numerical context, are shown to be boundary-stable. After choosing a formulation of the Maxwell constraints that we may solve for initial data, we move on to show a suite of numerical tests. Our simulations, both within the Cowling approximation, and in full non-linear evolution, demonstrate rapid convergence of error with resolution, as well as consistency with known quasinormal decay rates on the Kerr background. Finally we evolve the electrovacuum equations of motion with strong data, a good representation of typical critical collapse runs.

Mobile cognitive testing, particularly within an ecological momentary assessment paradigm, is increasingly used for cognitive assessments outside laboratory settings. However, the relationship between mobile cognitive tests and standardized lab-based neuropsychological testing among Spanish speakers remains understudied. This study investigated associations between performance on the National Institutes of Health Toolbox Cognition Battery and a suite of NeuroUX mobile tests among adolescents in rural Ecuador. The Study of Secondary Exposures to Pesticides among Children, Adolescents, and Adults participants completed Spanish versions of the National Institutes of Health Toolbox Cognition Battery and NeuroUX tests as a single session in the summer of 2022 (n = 488). NeuroUX tests were repeated across a 10-day burst 2.5-4 months later (n = 323). The mean age of participants was 20.3 years (SD = 1.8; range = 16-25 years), and 50.1% identified as female. Burst administration scores improved for one working memory test (Memory Matrix) but declined for a response inhibition test (Quick Tap 2). Positive associations were observed between mobile test performance and tablet-based neuropsychological scores across both testing formats, with an association identified between the Toolbox Fluid Cognitive Composite and NeuroUX Composite Score (β = 0.04, 95% confidence interval [0.03, 0.05]). Demographic trends indicated that younger, male, and more educated participants performed better on some tests on both testing platforms. Mobile cognitive testing shows considerable promise for assessing cognition among Spanish-speaking Latin American adolescents and young adults, revealing significant associations with tablet-based cognitive assessments, and may be generalizable to other Latin American populations. These findings underscore the value of mobile cognitive tests as a viable alternative or complement to lab-based assessments, especially in culturally diverse and rural populations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The generation of high-quality test cases remains challenging due to combinatorial explosion and difficulty balancing exploration-exploitation in complex parameter spaces. This paper presents a novel Hybrid Artificial Bee Colony (ABC) algorithm that uniquely combines ABC optimization with Simulated Annealing temperature control and adaptive scout mechanisms for automated test case generation. The approach employs a four-tier categorical fitness function discriminating between boundary-valid, valid, boundary-invalid, and invalid values, with first-occurrence bonuses ensuring systematic exploration. Through comprehensive empirical validation involving 970 test suite generations across 97 parameter configurations, the hybrid algorithm demonstrates 68.3% improvement in fitness scores over pairwise testing (975.9 ± 10.6 vs. 580.0 ± 0.0, p < 0.001, d = 42.61). Statistical analysis identified three critical parameters with large effect sizes: MutationRate (d = 106.61), FinalPopulationSelectionRatio (d = 42.61), and TotalGenerations (d = 19.81). The value discrimination system proved essential, uniform weight configurations degraded performance by 7.25% (p < 0.001), while all discriminating configurations achieved statistically equivalent results, validating the architectural design over specific weight calibration.

The Black-winged Kite Algorithm (BKA) is a novel heuristic optimization algorithm proposed in 2024, which has demonstrated superior optimization performance on most CEC benchmark functions and several engineering problems. To further enhance its convergence accuracy and solution quality, this paper proposes a Modified Black-winged Kite Algorithm (MBKA). First, a three-phase attacking strategy is designed to replace the original BKA’s attacking mechanism, thereby enhancing population diversity and improving solution quality. Additionally, a Lévy–Cauchy migration strategy is incorporated to achieve a more effective balance between exploration and exploitation. The effectiveness of MBKA is assessed through extensive experiments on 18 classical benchmark functions, the CEC-2017 and CEC-2022 test suites, and two real-world engineering optimization problems. The results indicate that MBKA consistently outperforms the original BKA and several state-of-the-art algorithms in both convergence accuracy and convergence speed across most test cases.

In the era of pervasive multimedia communication, image data has become a dominant form of information exchange across embedded, mobile, and IoT platforms. This surge in visual data transmission introduces critical challenges related to confidentiality, authenticity, and tamper resistance particularly in resource-constrained environments where conventional cryptographic solutions may prove computationally intensive. To address these challenges, lightweight cryptographic algorithms tailored for image protection are essential, balancing rigorous security requirements with efficient hardware and software implementation. This paper proposes a novel lightweight block cipher optimized for image encryption, employing a multi-stage internal Addition-Rotation-XOR (ARX) structure within each round to enhance confusion and diffusion. The cipher operates on 64-bit plaintext blocks with a 64-bit master key and utilizes a customized key schedule mechanism that generates five distinct subkeys per round through bit-swapping, modular addition, and XOR operations. The cryptographic properties of the proposed cipher were evaluated using the NIST SP 800-22 statistical test suite, confirming high key randomness. Further analysis demonstrated robust security with a 50% average avalanche effect, a maximum differential probability of approximately lesssim 2^{-32}, and a maximum linear bias below lesssim 2^{-8}. The cipher achieves strong resistance to differential and linear cryptanalysis within five rounds, offering an optimal balance between security and efficiency. Comprehensive statistical analysis using various input images are analyzed and demonstrate the cipher’s robustness in securing visual data. The encryption algorithm was further implemented on an Artix-7 FPGA, and synthesis results confirmed its suitability for resource constrained environments. The results indicate that the proposed cipher offers a secure and efficient solution to modern image security challenges.

Abstract To address the limitations of the Beluga Whale Optimization (BWO) algorithm, including insufficient population diversity, susceptibility to local optima, and room for improvement in convergence speed, this paper proposes a multi-strategy enhanced Beluga Whale Optimization (EBWO). The EBWO integrates three improvement strategies: (1) An elite set strategy to preserve high-quality individuals, thereby maintaining diversity and guiding the search; (2) Adaptive Cauchy mutation to introduce perturbations, enhancing global exploration, preventing premature convergence, and balancing exploration and exploitation; (3) Incorporation of a differential evolution mutation mechanism, leveraging global best individual information and differential vector perturbations to accelerate convergence and improve computational efficiency. Comprehensive validation using the CEC-2022 benchmark test suite, statistical analysis, and real-world engineering optimization problems demonstrates that, compared to the original BWO, the EBWO achieves significantly higher convergence accuracy, markedly faster convergence speed, and exhibits superior global search capability and stability. The EBWO provides a more efficient and stable novel approach for solving complex optimization problems.

React has become the most widely used web front-end framework, enabling the creation of user interfaces in a declarative and compositional manner. Hooks are a set of APIs that manage side effects in function components in React. However, their semantics are often seen as opaque to developers, leading to UI bugs. We introduce React-tRace, a formalization of the semantics of the essence of React Hooks, providing a semantics that clarifies their behavior. We demonstrate that our model captures the behavior of React, by theoretically showing that it embodies essential properties of Hooks and empirically comparing our React-tRace-definitional interpreter against a test suite. Furthermore, we showcase a practical visualization tool based on the formalization to demonstrate how developers can better understand the semantics of Hooks.

REST APIs form the backbone of modern interconnected systems by providing a language-agnostic communication interface. REST API specifications should clearly describe all response types, but automatically generating specifications is difficult with existing tools. We present REST𝜋, a type inference engine capable of automatically generating REST API specifications. The novel contribution of REST𝜋 is our use of path-sensitive type inference, which encodes symbolic path-constraints directly into a type system. This allows REST𝜋 to enumerate all response types by considering each distinct execution path through an endpoint implementation. We implement path-sensitive type inference for Ruby, a popular language used for REST API servers. We evaluate REST𝜋 by using it to infer types for 132 endpoints across 5 open-source REST API implementations without utilizing existing specifications or test suites. We find REST𝜋 performs type inference efficiently and produces types that are more precise and complete than those obtained via an HTTP proxy. Our results suggest that path-sensitivity is a key technique to enumerate distinct response types for REST endpoints.

We present cosmic shear constraints from the completed Kilo-Degree Survey (įds), where the cosmological parameter S_8 Ω_ ̊m m is found to be in agreement ($0.73σ$) with results from the Legacy cosmic microwave background experiment. The final KiDS footprint spans $1347$ square degrees of deep nine-band imaging across the optical and near-infrared (NIR), along with an extra 23-square degrees of KiDS-like calibration observations of deep spectroscopic surveys. Improvements in our redshift distribution estimation methodology, combined with our enhanced calibration data and multi-band image simulations, allowed us to extend our lensed sample out to a photometric redshift of z_ ̊m B łeq2.0. Compared to previous įds analyses, the increased survey area and redshift depth results in a ∼32% improvement in constraining power in terms of Σ_8 ̊m m /0.3̊ight)^α = 0.821^ where α = 0.58 has been optimised to match the revised degeneracy direction of σ_8 and Ω_̊m m for our current survey at higher redshift. We adopted a new physically motivated intrinsic alignment (IA) model that jointly depends on the galaxy sample’s halo mass and spectral type distributions, and which is informed by previous direct alignment measurements. We also marginalised over our uncertainty on the impact of baryon feedback on the non-linear matter power spectrum. Compared to previous KiDS analyses, we conclude that the increase seen in S_8 primarily results from our improved redshift distribution estimation and calibration, as well as a new survey area and improved image reduction. Our companion paper presents a full suite of internal and external consistency tests (including joint constraints with other datasets), finding the įdslegacy dataset to be the most internally robust sample produced by įds to date.

The R programming language is primarily designed for statistical computing and mostly used by researchers without a background in computer science. R provides a wide range of dynamic features and peculiarities that are difficult to analyze statically like dynamic scoping and lazy evaluation with dynamic side effects. At the same time, the R ecosystem lacks sophisticated analysis tools that support researchers in understanding and improving their code. In this paper, we present a novel static dataflow analysis framework for the R programming language that is capable of handling the dynamic nature of R programs and produces the dataflow graph of given R programs. This graph can be essential in a range of analyses, including program slicing, which we implement as a proof of concept. The core analysis works as a stateful fold over a normalized version of the abstract syntax tree of the R program, which tracks (re-)definitions, values, function calls, side effects, external files, and a dynamic control flow to produce one dataflow graph per program. We evaluate the correctness of our analysis using output equivalence testing on a manually curated dataset of 779 sensible slicing points from executable real-world R scripts. Additionally, we use a set of systematic test cases based on the capabilities of the R language and the implementation of the R interpreter and measure the runtimes well as the memory consumption on a set of 4,230 real-world R scripts and 20,815 packages available on R’s package manager CRAN. Furthermore, we evaluate the recall of our program slicer, its accuracy using shrinking, and its improvement over the state of the art. We correctly analyze almost all programs in our equivalence test suite, preserving the identical output for 99.7% of the manually curated slicing points. On average, we require 576ms to analyze the dataflow and around 213kB to store the graph of a research script. This shows that our analysis is capable of analyzing real-world sources quickly and correctly. Our slicer achieves an average reduction of 84.8% of tokens indicating its potential to improve program comprehension.