Search Results (738)

The recruitment headhunting process is time-intensive due to manual candidate searches across multiple job platforms, creating inefficiencies in identifying suitable candidates. Current AI-driven recruitment platforms frequently prioritize accuracy over explainability, limiting transparency for non-technical users such as recruiters. This study streamlines recruitment headhunting by (1) consolidating publicly available candidate data from multiple job portals using a professional data aggregation Application Programming Interface (API), and (2) implementing explainable machine learning for transparent candidate–job matching. We utilized the Coresignal API (v1) to aggregate and standardize candidate profiles (N = 587) sourced from LinkedIn and Indeed, including skills, experience, certifications, and education. Using Term Frequency–Inverse Document Frequency (TF-IDF) feature vectors and regression models (Ridge, Gradient Boosting, Random Forest), we matched and ranked candidates against a standardized Data Scientist job description. Shapash was incorporated to provide interpretable feature importance explanations accessible to non-technical users. Model performance was evaluated using stratified 5-fold cross-validation with statistical significance testing. Ridge Regression achieved superior performance (cross-validated R² = 0.935, bootstrap R² = 0.954, 95% confidence interval [0.939, 0.965], RMSE = 0.025) compared with Gradient Boosting (R² = 0.840) and Random Forest (R² = 0.733). Paired t-tests confirmed significant differences between all model pairs (all ps ≤ 0.001, Bonferroni corrected) with large effect sizes (Cohen’s d ≥ 1.992). Shapash analysis revealed that top-contributing features, such as “engineering”, “data science”, “machine learning”, and “python”, aligned precisely with job description requirements, validating the model’s feature-learning capability. This approach reduces repetitive manual searches across job portals while providing interpretable insights into candidate–job rankings. The methodology’s originality lies in combining professional data aggregation APIs that access publicly available profile data with interpretable models enhanced by user-friendly visualization tools, creating a practical, potentially transferable solution for transparent AI-driven recruitment. Full article

Persons with Disabilities (PWDs) face persistent barriers to healthcare access, welfare services, and timely medical assistance, particularly where hospital information is fragmented across institutions. In Thailand, these challenges are exacerbated by heterogeneous Hospital Information Systems (HISs) across provincial, district, and sub-district hospitals. This study presents the design, implementation, and evaluation of an integrated mobile application and a hybrid Hospital Information Exchange (HIE) system to enhance healthcare accessibility and service coordination for PWDs. The platform integrates a user-centered mobile application (iOS and Android) with a hybrid data exchange architecture (MedEx Hybrid) combining an application programming interface (API) and Message Queuing Telemetry Transport (MQTT). This enables real-time and on-demand data exchange while accommodating hospitals with limited infrastructure. Key functionalities include disability registration, emergency medical service (1669) integration, appointment management, rights notification, service location mapping, teleconsultation, and peer communication. Deployment across 159 hospitals nationwide demonstrates system scalability and interoperability. The system supports secure access to electronic medical records and enables emergency responders to retrieve patient information during SOS events, improving continuity of care. Findings confirm the feasibility of the proposed system and its potential to support inclusive digital health and national healthcare interoperability. Full article

This study presents a hybrid spatio-textual matching approach for integrating historical web-derived address datasets with a municipal reference dataset, using the 2009 administrative delineation of Şişli (Istanbul) as a case study. The proposed approach addresses challenges commonly encountered in data obtained from web archives, such as lexical differences, abbreviations, heterogeneous structures, and missing address information. The methodology consists of three main stages: (i) preprocessing and structuring of web-based address records; (ii) hybrid matching, combining deterministic rules with similarity-based methods; and (iii) post-matching geographic enrichment using an Application Programming Interface (API) to provide supplementary geographic context for matched records. The matching process is conducted exclusively between historical datasets; contemporary geographic information is used only after the completion of the matching process to provide additional contextual information. The methodology integrates token-based, vector-based, and structural similarity measures within a calibrated scoring scheme to improve the matching of ambiguous and inconsistent address records. The results indicate that 65.4% of the records were automatically accepted, 7.3% required manual review, and no suitable candidate was found for 5.4%. Deterministic matching results reveal that strict rule-based approaches are highly sensitive to data integrity and attribute consistency, especially in heterogeneous web-based datasets, highlighting the value of combining multiple similarity measures within a hybrid matching strategy. The API-based enrichment results provide supplementary geographic context regarding the contemporary surroundings of matched records, while historical interpretations remain grounded in the original archival datasets. In this context, the study may contribute to the integration of historical web-based address data with structured municipal datasets under heterogeneous archival data conditions. Full article

Web Application Programming Interfaces (Web APIs) have become fundamental components of modern software ecosystems. At the same time, they have emerged as major attack surfaces in web applications and distributed services. Although many web API vulnerabilities are well documented, a critical gap remains in understanding how insecure development practices, usability limitations, and developer-related issues contribute to recurring API security problems. To address this gap, this study presents a systematic review of web API security research using a PRISMA-guided methodology and a taxonomy-driven analytical approach. The review synthesizes findings from 50 selected studies covering web API architectural styles, usability concerns, authentication and access-control weaknesses, and common vulnerabilities. These vulnerabilities include SQL Injection (SQLi), Cross-Site Scripting (XSS), Broken Authentication, and Denial-of-Service (DoS) attacks within the context of the OWASP API Security Top 10 framework. The findings indicate that recurring web API vulnerabilities are associated not only with technical weaknesses but also with API usability issues, insecure development practices, inconsistent security guidance, and increasing implementation complexity. The review also identifies persistent research gaps involving usability-security integration, API evolution, secure-by-design development practices, and empirical validation of security tools and frameworks. By synthesizing these dimensions into a unified conceptual perspective, this study provides researchers and practitioners with a clearer understanding of the factors contributing to web API insecurity. The study also highlights directions for developing more resilient and developer-aware API security practices. Full article

This study addresses the early-age brittleness and performance limitations of sustainable cement soil. While prior works optimized the baseline compressive strength using recycled sand and nanoclay, the multi-scale synergistic effects of fibers and nanomaterials on the post-peak deformation remain underexplored. To address this gap, a composite modification system incorporating recycled sand, nanoclay, polypropylene fibers, and graphene derivatives was developed. The experimental program comprised standard specimen fabrication, early-age curing, and unconfined compressive strength (UCS) testing, supplemented by RBF neural network curve fitting and quantitative ArcGIS digital image processing of scanning electron microscopy (SEM) micrographs. The results demonstrate that optimizing the fiber parameters (0.6% content with 6 mm length) successfully increases the early UCS to 2263.2 kPa, which is further elevated to a peak of 2755.0 kPa upon co-incorporation with 0.05% small-sized graphene oxide. Correspondingly, a newly introduced ductility index quantitatively confirms that the single-fiber reinforcement yields an index of 1.93, which is further enhanced to 2.02 by the graphene composite system. Microstructure tracking and digital image extraction revealed that the SEM-derived surface porosity decreased significantly, exhibiting a clear inverse relationship with the macroscopic mechanical strength. These quantitative microstructural shifts confirm that graphene effectively filled micropores and reinforced the fiber–matrix interface, establishing a dense matrix network with enhanced interfacial bonding. This multi-scale approach offers a sustainable strategy for green geotechnical applications. Full article

This work proposes a vendor-independent integration method for International Electrotechnical Commission (IEC) 61850 Manufacturing Message Specification (MMS) communication protocol into Programmable Logic Controller (PLC) systems that support an open network communication interface available for the PLC program. IEC 61850 is globally well accepted for electrical substation control, and the protocol MMS is used for integrating the electrical substation bay level into the station level, where the PLC orchestrates the process level of the substation and parallel processes. This method was created because most PLCs lines do not natively support any protocol of IEC 61850, although it often needs to be used for the control of electrical substations. For the development of the prototype presented in this paper, PLCs from the Siemens AG families S7-1500 and S7-410, which support open communication over Transmission Control Protocol/Internet Protocol (TCP/IP) with external systems, were used for validation. Different results regarding network communication and PLC program performance are presented in this paper. The implemented solution presents a meaningful implementation of the MMS application layer into the PLC program and was successfully validated with real industrial, single and redundant PLC systems. Full article

Immune checkpoint modulation has emerged as a promising strategy in cancer therapy, including the treatment of aggressive tumors such as glioblastoma. Among these targets, programmed death-ligand 1 (PD-L1) plays a key role in tumor immune evasion and represents an attractive target for small-molecule inhibitor development. In this study, a virtual screening approach was applied to identify potential PD-L1 modulators within a library of nucleoside-related compounds and structurally similar molecules. A dataset of 400 compounds was evaluated using molecular docking to predict their binding affinity (free energy values and binding pose) toward PD-L1. The resulting complexes were analyzed to identify nonbond interactions within the hydrophobic pocket formed at the PD-L1 dimer interface. In addition to docking results, physicochemical descriptors associated with drug-likeness and blood-brain barrier penetration were calculated, including lipophilicity, molecular weight, hydrogen bond donors and acceptors, as well as topological polar surface area. To integrate these parameters, a multiparameter optimization (MPO) score was implemented. Finally, molecular dynamics simulations of protein-ligand interactions were performed to explore the structural stability for 100 ns using the most promising ligands. The analysis revealed that several top-ranked compounds exhibited favorable docking scores and physicochemical properties compatible with drug-like behavior. Interestingly, BMS-1, a known PD-L1 inhibitor, was identified among the highest-scoring compounds, supporting the reliability of the MPO protocol. Furthermore, multiple candidates displaying nucleoside-like scaffolds combined with reduced polarity and moderate lipophilicity emerged as promising molecules according to the MPO ranking. Overall, the results suggest that nucleoside-derived scaffolds may represent a viable starting point for the development of small-molecule PD-L1 modulators with potential applicability in glioblastoma therapy. Full article

The of personal computer (PC) tasks represents a systems-level challenge that integrates natural language processing, visual perception and mouse–keyboard action control. While existing approaches mainly focus on the application programming interface (API)-based or terminal-based automation, which are incompatible with the majority of applications for the lack of accessible interface. In this article, we propose PCLLM, a novel end-to-end system that automates PC operations by integrating large language models (LLMs) with computer vision techniques to directly control the mouse and keyboard. First, a software knowledge-based prompt engineering method is developed to comprehend software architecture and operational sequences. Second, template matching techniques are integrated for precise element localization, allowing the system to accurately identify and interact. Third, a dual-LLM pipeline is designed to automatically generate the test data, where a questioner LLM generates diverse task commands and the PCLLM executes these tasks, the corresponding process data are recorded automatically for performance evaluation. Finally, PCLLM is further validated through three typically PC applications (Notepad, Wordpad and Calculator), demonstrating its flexible and robust performance towards intelligent PC automation. To evaluate the proposed system, we adopt task completion rate as the primary metric. Experimental results show that PCLLM achieves the highest completion rates of 98.59%, 95.77%, and 52.11% on Notepad for basic, intermediate, and advanced tasks respectively when powered by GPT-4o, outperforming the CogAgent baseline. These results demonstrate the effectiveness of our approach for PC task automation. Full article

Ransomware is malicious software that encrypts user files, rendering them inaccessible unless a ransom is paid to the attacker. Ransomware detection has been a challenge recently. In this paper, we present a comprehensive analysis of ransomware detection using machine learning (ML) on Application Program Interface (API) calls made by malicious and non-malicious processes. The analysis is conducted on different ML models, including Random Forest, Long Short-Term Memory, Support Vector Machine, K-Nearest Neighbors, XGBoost, Linear Discriminant Analysis, and an ensemble model. The models were analyzed using two datasets: a primary public dataset and a zero-day dataset representing previously unseen real-world ransomware generated with no overlap with the primary dataset. For the primary dataset, the results indicate high detection accuracy of 99.82% for the ensemble model. On the other hand, testing on the zero-day ransomware results in a 96.3% accuracy while imposing minimal overhead on the CPU and memory of the machine. To boost the confidence in the detection method, we analyzed the detection time and the percentage of files saved because of the timely response of the proposed detection method. The results indicate that detection was done in a timely manner, saving on average 93% of the files under ransomware attack. Full article

With the rapid evolution of cyberattack techniques, identifying dynamic behavioral intents from Application Programming Interface call sequences has become a fundamental modality for ensuring reliable malware detection and information security. However, existing detection methods face the dual challenges of semantic sparsity and inadequate spatial dependency modeling when processing these sequences, which fundamentally undermines their stability against complex structural variations and in-the-wild evasive patterns. To address these critical vulnerabilities, we propose LLM-SGCF, a highly effective malware detection framework that jointly models deep behavioral semantics and spatial structures. Specifically, our framework leverages generative Large Language Models, which are subsequently encoded by BERT, to transform sparse API calls into rich and contextualized descriptions. Concurrently, it employs a novel Spatially Guided Convolution (SGC) module to localize critical malicious segments and extract cross-position dependencies in a two-dimensional semantic space. Extensive experiments on the public Aliyun and Catak datasets demonstrate that LLM-SGCF exhibits exceptional resilience to real-world structural complexity and significantly outperforms state-of-the-art baselines, achieving a peak binary-classification accuracy of 95.82%. Further ablation analyses confirm that the synergistic fusion of semantic enhancement driven by Large Language Models and spatial structural modeling dramatically improves the resilience of the framework against complex attack chains, providing a highly reliable paradigm for next-generation malware recognition systems. Full article

Nowadays, Python is very popular as the first programming language for novices, including high school students, to learn due to its short code features with rich libraries. Thus, it is important to provide a learning environment supporting studies starting from the fundamentals, since students have no knowledge on how a program runs on a computer. Previously, we have developed a web-based programming learning assistant system (PLAS) to allow the self-study of major programming languages, including Python, by university students. It offers several types of exercise problems that have different learning goals and levels for step-by-step study. Any student answer is automatically marked at the answer interface for quick feedback. However, PLAS has not implemented functions to assist the learning needs of high school-level students. In this paper, we propose a novice-friendly answer interface for a Python programming learning assistant system (PyPLAS) that introduces a code behavior visualization and an AI assistant with learning logs. The visualization allows learners to observe the changes in variable states and the control flow. The assistant provides multi-level hints during learning and reflective feedback after it by analyzing the logs based on engagement, reasoning strategies, learning pace, and tool usage. For evaluation, we implemented the proposed interface using Python Flask for the web platform and Ollama as a locally deployed AI model. A pilot application was conducted with high school students solving introductory Python exercises in PyPLAS. The results showed high task completion, positive questionnaire responses toward embedded visualization and interface usability, and teacher-observed usefulness of the four-dimensional learning analytics for interpreting learner behaviors. These findings provide preliminary evidence for the feasibility and practical value of the proposed interface, while larger controlled studies are required to validate its instructional effectiveness. Full article

DTDL (Digital Twins Definition Language) provides no mechanism for logical reasoning or constraint checking over digital twin models. We integrate DTDL with ASP Chef, a web-based Answer Set Programming (ASP) platform, via a structured DTDL-to-ASP mapping and three dedicated operations: @DTDL/Parse for fact generation, @DTDL/Analysis for structural metrics, and @DTDL/Debug for symbolic validation with LLM-guided repair. The key design decision is that error detection is symbolic and deterministic within the implemented set of constraint classes; a language model is invoked only after the ASP layer has produced a concrete, grounded diagnostic, keeping the correctness boundary with the symbolic layer. Soundness and completeness guarantees are scoped to these constraint classes; a formal proof is left as future work. We illustrate the framework on two agricultural use cases and report a proof-of-concept assessment on 99 diagnostics spanning 21 error classes across four domains. Three binary metrics are used: json_valid and entity_recall are computed mechanically; fix quality (judge_correct) is assessed by an independent LLM judge (Claude Sonnet 4.6). The complete grounded workflow achieves 90% judge_correct and 86% json_valid; a fair ablation baseline—same LLM and system message, but error type and entity name in natural language without structured diagnostics—achieves 77% and 75%, respectively. The gap is consistent across three independent judges and statistically significant (McNemar $p<0.01$), but the inter-judge reliability of judge_correct is limited ($\kappa$ ranging from 0.00 to 0.44), so results should be read as directional evidence rather than precise effect estimates. Excluding the dominant isolated_interface class ($n=28$, ceiling score), the conservative estimate is 87% vs. 79% on the remaining 71 diagnostics. These results constitute a preliminary proof-of-concept limited to a small number of models, a few application domains, and a single LLM configuration; results do not generalize beyond this specific setting. The judge_correct metric is assessed by LLM-as-judge and does not carry a perfect inter-annotator agreement. Full article

Autonomous ground vehicle navigation in dynamic real-world environments demands path planning systems that simultaneously accommodate real-time environmental hazards and diverse user-defined objectives requirements that classical algorithms, with their static, single-objective cost functions, cannot fulfil. This paper presents the Semantic Personalised Path Planning (SPPP) system, centred on a novel Semantic Personalised Cost (SPC) algorithm that augments the A* search framework with a dynamically computed personalised cost term. The SPC function integrates eight real-time semantic obstacle categories including traffic congestion, weather severity, road surface conditions, and construction activity with eight user-defined preference dimensions spanning safety, travel time, emergency response, comfort, and battery efficiency. An adaptive scaling mechanism amplifies obstacle penalties near the goal, and a gradient-based weight evolution rule refines preference weights iteratively over successive route segments. The user-defined preference activation directly personalises the routing objective to individual operational needs, with the gradient-based evolution further refining preference alignment over successive route segments. Experiments were conducted in two phases: 500 randomised obstacle configurations on a controlled $8\times 8$ grid, and a real 847-node road graph extracted from OpenStreetMap around SRM Institute of Science and Technology, Kattankulathur, representing a single 1.4 km urban corridor, with obstacle scores derived from live Mapbox Traffic and OpenWeatherMap application programming interface data. Under the full emergency preference scenario, SPPP achieves 94.3% obstacle avoidance versus 31.7% for the Euclidean distance threshold A* baseline, a difference statistically significant at $p < 0.001$ under the Wilcoxon signed-rank test with Cohen’s d ≈ 18.9. Real-world computation time of 1.91 ms on a standard laptop and 3.76 ms on a Raspberry Pi 4 confirms deployability on embedded autonomous vehicle hardware. Full article

This paper investigates whether a low-code workflow engine can operate as practical Industrial Internet of Things (IIoT) middleware at the edge when cloud application programming interface (API) rate limits make direct telemetry upload unsustainable. The main contribution is a dual-path architecture in which a Hot Path persists all telemetry locally, while a Cold Path selectively forwards only anomalous or summary events to cloud services. The architecture is implemented as a lightweight containerized stack based on n8n, Eclipse Mosquitto, InfluxDB, and Grafana, and evaluated on a Raspberry Pi 4 under baseline, cloud-only saturation, and edge-filtered stress scenarios. Under the cloud-only condition, the external endpoint is throttled to approximately 60 requests/min, yielding a rejection rate of 98.0% (95% Wilson confidence interval: 97.43–98.44%). Under the dual-path condition, the same inbound load is fully retained locally while outbound cloud traffic is reduced by 98.0%, thereby avoiding throttling without sacrificing edge-side data fidelity. The measured Hot Path processing latency remains around 5 ms on average, with observed peaks below 10 ms, which is compatible with soft real-time monitoring workloads. Compared with more established low-code tools such as Node-RED, the novelty of the study is not the existence of visual orchestration itself, but the combination of containerized deployment, explicit hot/cold decoupling, and an empirical rate-limit mitigation analysis focused on low-cost edge hardware. Full article

The design of differentiated fiscal instruments for industrial sustainability requires robust, data-driven tools capable of capturing the heterogeneity of environmental performance across manufacturing units—a challenge that conventional econometric approaches address only partially, given the non-linear nature of operational–environmental interactions in reconfigurable production systems. This study introduces a two-phase computational framework that integrates unsupervised machine learning and supervised classification to generate evidence-based sustainability profiles for fiscal policy targeting. Its principal contribution is the combination of K-Means clustering with a binary artificial neural network (ANN) classifier, operationalized through an accessible decision-support interface that enables differentiated incentive allocation without requiring programming expertise from policymakers. A dataset of 1000 manufacturing records comprising seven operational and technological input variables—material usage, production capacity, reconfiguration time, downtime, AI optimization, IoT connectivity, and predictive maintenance—and three environmental output indicators—energy consumption, carbon emissions, and waste generation—was analyzed. In Phase One, K-Means segmentation with k = 6, selected through multi-criteria convergence (Silhouette = 0.102; Elbow, Davies–Bouldin, and Calinski–Harabasz indices), identified six distinct sustainability profiles with marked environmental differentiation. In Phase Two, a binary ANN classifier (architecture: 7 → 64 → 32 → 1 neurons; ReLU and sigmoid activations) was trained to distinguish the reference cluster C0 (low environmental impact: energy 145.1 kWh, emissions 45.2 CO₂-eq) from the high-impact cluster C1 (emissions 67.8 CO₂-eq, waste 41.5 kg). The trained classifier achieved an overall accuracy of 75.4% and an AUC-ROC of 0.774 on the held-out test set, with a macro-averaged F1-score of 0.753 and a Cohen’s kappa coefficient of 0.508, indicating moderate-to-substantial agreement beyond chance. Class C1 (high-impact establishments) achieved a precision of 0.794 and a recall of 0.730, supporting reliable identification of manufacturing units that would most benefit from targeted fiscal support. The framework is deployed through a Gradio-based graphical interface incorporating a traffic-light sustainability classification (green/yellow/red), enabling direct and interactive application by tax authorities and industrial policymakers. The modular architecture supports adaptation to larger or sector-specific datasets, making it transferable across industrial policy contexts. Full article