Inter-satellite link (ISL) spoofing in Low Earth Orbit (LEO) constellations poses a growing yet underexplored security challenge. This letter presents EMMA, a lightweight Multi-task Learning (MTL)-based framework for ISL authentication and spoof detection using simple physical behavior-based features. It also presents the first benchmark simulation dataset on ISL security. EMMA achieves detection rates of 94.75% and 94.46% on registered and spoofing satellite samples, respectively, at low inference latency ( 10(-4) seconds) and low memory overhead (3.5 KB). We also compare EMMA with existing works to demonstrate its comparative effectiveness.
Dyslexia screening remains a challenging clinical problem due to its multimodal cognitive manifestations, reliance on specialist assessment, and limited scalability in conventional healthcare settings. While the Intelligent Internet of Medical Things (IIoMT) enables data-driven dyslexic pattern assessment through distributed sensing, existing solutions often lack adaptive intelligence, contextual continuity, and integrated security. This article presents a secure multi-agentic hybrid IIoMT framework for multimodal dyslexia screening, combining agent-to-agent (A2A) intelligence, Model Context Protocol (MCP)-based orchestration, and longitudinal cognitive reasoning. The proposed framework integrates four complementary screening tasks—face drawing, clock drawing, handwriting analysis, and reading assessment with gaze tracking—captured via heterogeneous IIoMT endpoints. Specialized AI agents analyze each modality and collaborate through A2A coordination to enable multimodal feature fusion and unified dyslexia risk assessment. The use of persistent contextual intelligence is a key innovation, enabling the retention of clinically relevant interaction context across sessions for personalized and progressive screening. Security and privacy are embedded as system properties, addressing data protection, multi-agentic AI model trustworthiness, and secure, ethical, and authenticated inter-agent collaboration. Experimental evaluation on dyslexia screening demonstrates achieving 94.5% accuracy in face drawing analysis, 82.7% in clock drawing, 96.7% in handwriting assessment, and 83.3% accuracy in reading fluency detection (n = 120). The proposed framework advances dyslexia screening toward scalable, intelligent, and longitudinal digital assessment.
Automated short-answer grading research has historically focused on exams consisting solely of questions pertaining to a single topic. Automatic grading of exams containing questions about more than one topic remains less explored. In this work, a Graph-Retrieval Automated Scoring Pipeline (GRASP) is introduced for grading label-free multi-topic science exams. Label-free exams are short-answer exams in which a student's responses to several distinct topics are merged into a single paragraph, with no markup labels or segmentation indicating which span answers which question. Reference answers for each question are encoded into a FAISS vector index via Sentence-BERT, and a semantic similarity graph is constructed over this set of reference answers. At grading time, sentence count heuristics, with a large language model used to resolve ambiguous cases, are first applied to predict how many distinct topics were answered in the student essay. This process is performed without training data or domain-specific example essays. Candidate reference nodes, each storing one (question, reference answer, concatenation of both) from the reference index, are then retrieved through cosine similarity based Retrieval-Augmented Generation (RAG) and Graph Retrieval-Augmented Generation (GRAG). GRAG operates by taking the top cosine matches as seed nodes and then performing a graph traversal over strong edges to find additional reference nodes that may have been missed by RAG. The Hungarian algorithm is then used to optimally assign one reference node per question segment such that no reference is duplicated. Each segment is then graded against its assigned reference independently using GPT-4.1-mini. This experiment is performed to show the effect of retrieval quality on grading accuracy and the benefit of graph-augmented retrieval versus strict cosine similarity methods at various levels of essay complexity.
We address a practical failure mode in cameraLiDAR fusion for urban driving: extremely small image disparities that, when converted directly to metric depth, yield unstable estimates and derail the front end before LiDAR cues can anchor the pose. We propose a minimal, intrinsics-aware conditioning step prior to fusion that rescales disparity using the true focal length and baseline and enforces a small minimumdisparity floor to bound depth, complemented by conservative dynamic-object masking and a LiDAR-anchored back end. On representative sequences, we verify that the data operate in a “small-disparity” regime and show that conditioning restores usable depth maps and allows the fusion pipeline to proceed. We further report preliminary trajectory metrics on KITTI Odometry Sequence 00 using standard absolute and relative pose errors: a LiDAR ICP baseline (filtered point-to-plane) achieves APE RMSE $28.14 \mathbf{m}$ and RPE ($\Delta=10$) translation/rotation RMSE $17.37 \mathbf{m} / 23.58^{\circ}$. A disparity-conditioned point-selection variant yields comparable $\operatorname{RPE}(\Delta=10)\left(17.04 \mathbf{~ m} / 23.42^{\circ}\right)$ but higher APE RMSE ($46.67 \mathbf{m}$), suggesting that disparity-based gating alone can reduce long-range geometric constraints needed for global consistency. These results quantify end-to-end operability and highlight where conditioning must be integrated as an explicit residual/prior (rather than selection-only) to improve trajectory accuracy.
Small and medium-sized enterprises (SMEs) face increasing pressure to navigate digital transformation, yet many struggle to convert digital initiatives into sustainable organisational capability development and growth outcomes. This paper develops the Exponential SME Framework, an integrated conceptual framework that explains how SMEs may progress from digital capability development to non-linear growth potential. Drawing on literature relating to Digital Dynamic Capabilities (DDC), digital transformation practices, organisational transformation, Exponential Organization (ExO) attributes, and SME growth, the framework synthesises previously fragmented streams of research into a coherent conceptual structure. The framework proposes that Digital Dynamic Capabilities provide the foundation for Digital Transformation Practices (DASAT), which contribute to transformations across key organisational domains, including business models, value chains, customer relationships, and organisational culture. These transformations may support the adoption of ExO-like attributes, specifically experimentation, dashboards, autonomy, and community engagement, that enhance organisational adaptability, learning, and responsiveness. Through the interaction of these organisational elements, SMEs may strengthen the conditions associated with non-linear growth potential. The paper contributes to the SME and digital transformation literature by integrating multiple streams of research into a unified framework, clarifying the relationships between capabilities, practices, organisational transformation, and growth-enabling mechanisms, and providing a foundation for future empirical research. The paper also offers managerial and policy implications for strengthening SME digital transformation and growth readiness.
