Synthetic Malware Generation using Generative AI

Malware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein GANs to generate synthetic embeddings, specifically testing on eight held-out families not observed during GAN training. To validate utility, we create ten augmented training sets at 100%–10% synthetic ratios and compare four classifiers to 100% real data baselines. Our experiments show that embeddings generated by GANs match the perfor- mance of models trained on real training. Most importantly, the samples generated by GANs generalize across families without overfitting, completing gaps within the data. In our future work, we will test stronger embeddings (e.g., GloVe, Word2Vec, ELMo) and newer adversarial frameworks like WGAN-GP to further enhance malware detection robustness

Optimization of Permutation Flowshop Scheduling Using an Island Genetic Algorithm for Makespan Minimization

The Permutation Flowshop Scheduling Problem is a well-known NP-hard combinatorial optimization problem that involves the sequencing of n jobs across m machines in the same order to minimize the total makespan value. This project proposes a Heterogeneous Island Genetic Algorithm framework (HIGA). Each island represents a group of solutions that evolve in parallel using different initialization heuristics, crossover and mutation operators, and adaptive parameters. A dynamic, stagnation-based migration strategy is proposed to maintain targeted communication between the islands. The proposed HIGA approach was compared against the basic Standard Genetic Algorithm (SGA) and a more advanced Niche-based Genetic Algorithm (NEH-NGA) on Taillard’s benchmark dataset. Experimental results indicate HIGA effectively balances solution quality and efficiency, matching the best-known makespan value or coming close to it, particularly for larger instances, while being several-fold faster than NEH-NGA and achieving significantly better results than the SG

riboMoE: An Application of Mixture of Experts on Artificial Riboswitch Classification

Urban water sewage is a potential health concern due to its possibility to spread contagious RNA viruses such as Coxsackievirus B3. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. To address this, this study proposes a novel viral detection method using synthetic riboswitches that bind to the target virus and trigger a reporter gene, amplifying the detection signals. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features. This model used a sparsely gated Mixture of Expert (MoE) layer to route mixed input to specialized experts, achieving excellent generalization performance. Future work includes improving the biological relevance and interpretability of the MoE model

Translanguaging at School: A Systematic Review of U.S. PK-12 Translanguaging Research

This systematic review considers what is known about translanguaging in relation to teaching and learning, motivated by the concept\u27s popularity and ongoing critiques about its pedagogical utility and transformative potential. Based on thematic analysis of 111 empirical studies on translanguaging in U.S. PK-12 educational settings, we identified the following themes: (1) translanguaging emerges naturally, and purposefully, in classrooms with bi/multilingual learners, (2) translanguaging facilitates student sense-making in support of learning, (3) translanguaging increases student engagement and opportunities for collaboration, (4) translanguaging supports bi/multilingual students\u27 identity development and sense of belonging, (5) translanguaging can create a counterspace that challenges deficit-framed ideologies and cultivates critical consciousness, (6) learning about translanguaging contributes to teachers developing more asset-oriented stances toward bi/multilingual learners and more linguistically responsive pedagogies, and (7) context plays a central role in if and how translanguaging transforms teaching and learning–translanguaging looks different, and has different impacts, across different contexts. We conclude that we know much about the positive ways translanguaging shapes teaching and learning, while also highlighting some challenges and tensions within the extant literature and the need for increased research that attends to the nuances of context, includes more methodological diversity, and centers decolonial and critical perspectives

The Isoperimetric Inequality for Asymptotically CAT(0) Groups

An asymptotically CAT(0) space is one where every ball of radius r is f(r)-CAT(0) for a fixed sublinear function f. An asymptotically CAT(0) group Γ is a group which acts properly and cocompactly by isometries on an asymptotically CAT(0) space X. Fix x0 ∈ X, choose D \u3e 0 such that X = S γ∈Γ �� γ · B �� x0, D 3 ?? , define A = {a ∈ Γ : d(x0, a · x0) ≤ D + 1}, and let R be the set of reduced words in A of length at most 10 that represent the identity in Γ. This gives Γ = ⟨A|R⟩. We prove that for any word w of length m in A which represents the identity, there exists a presentation ⟨A|R⟩ such that Am ⊆ A and the number of relations in Rm needed to express w is at most quadratic with respect to m

Is drainage reorganization a plausible explanation for late Cenozoic incision of Yosemite Valley and within the Kings and Kaweah watersheds (Sierra Nevada, California)?

Increases in the rates of river incision can be driven by a variety of causes, such as tectonic uplift, changes in sediment supply, changes in climate, and drainage reorganization. Here, I evaluate whether drainage reorganization may have driven late Cenozoic episodes of accelerated river incision in the Sierra Nevada (California) that have previously been attributed to uplift. I propose several lines of evidence useful for distinguishing between these two potential drivers of incision and apply them at three sites with well-documented histories of late Cenozoic incision: Yosemite Valley and Tenaya Canyon, along the South Fork Kings River, and in the Kaweah River watershed. These lines of evidence include the spatial patterns of knickpoints and relief, the presence of paleochannels, the morphologies of alluvial fans, and results from numerical modeling. The analyses suggest that a river draining a Miocene–Pliocene volcanic edifice along the crest of the northern Sierra Nevada temporarily extended into Tenaya Creek, greatly increasing the latter’s erosive power and incising Tenaya Canyon and Yosemite Valley. This hypothesis explains previously unsolved issues, such as the presence of voluminous andesitic volcaniclastic deposits in the Merced River paleofan, despite their absence in the Merced watershed, and the discrepancy in relief between Tenaya Canyon and neighboring Little Yosemite Valley. Results for the Kings River drainage show that deep incision since the mid-Pliocene has been confined to the upper portion of its watershed and that it occurred in a rapid pulse, consistent with an episode of stream piracy. Albeit limited, the geomorphic evidence for drainage reorganization in the Kaweah watershed includes a 1800-m-high convex knickpoint on a trunk stream and an anomalously large alluvial fan. This study demonstrates the importance of considering drainage reorganization as an alternative to uplift when investigating abrupt and short-lived increases in river incision rates

A Frontal Ablation Dataset for 49 Tidewater Glaciers in Greenland

Frontal ablation at tidewater glaciers, which comprises iceberg calving and submarine and subaerial melting, is a key boundary condition for numerical ice sheet models but remains difficult to measure in-situ. Although previous studies have provided frontal ablation estimates over a range of spatiotemporal scales, most use ice discharge as an approximation, thereby neglecting the influence of terminus position change. Here, we present a dataset of frontal ablation estimates for 49 tidewater glaciers in Greenland that have reliable near-terminus bathymetry data. Near-terminus volume change over the period 1987–2020 is determined using previously published datasets of terminus positions (TermPicks) together with ice thicknesses estimated from ArcticDEM, AeroDEM, and Bedmachine v5 bed topography. Assuming a vertical terminus geometry and uniform ice density, we estimate frontal ablation as the residual between mass flux towards the terminus taken from a published dataset and mass change due to changes in terminus position. The frontal ablation dataset offers opportunities for developing new insights into ice dynamics, including helping to improve numerical model hindcasting and projections

Reinforcement Learning Methods for Assistive and Rehabilitation Robotic Systems: A Survey

Advancements in robotic systems aimed at improving mobility for individuals with disabilities have required more sophisticated control and navigation methods. Traditional control approaches often lack the complexity and adaptability needed for the high-dimensional nature of human activities. Consequently, reinforcement learning (RL) has emerged as a dynamic and effective framework for managing robotic actions in complex and unpredictable human environments. This article reviews the integration of RL in robotic systems for enhancing the mobility of individuals with disabilities, addressing the limitations of traditional control methods in complex and unpredictable environments. We critically analyze various RL algorithms, discussing their advantages and challenges in assistive and rehabilitation applications. The study highlights the ongoing development of these algorithms, presenting current research directions, future prospects, and key challenges to achieving higher autonomy in assistive robots. Our findings underscore the potential of RL to improve adaptability and effectiveness in robotic control and navigation, offering insights into advancing these technologies for practical implementations

A Place for Ancient Philosophy in Axial Age Historiography

Long-standing debates over historiographical approaches to the Axial Age have distracted the history of philosophy from its own disciplined inquiry into the breadth and depth of ancient thought beyond the Greeks. The philosopher Karl Jaspers offered a vista for seeing commonalities among ancient innovations and discerning continuities along history to modern times. That dual agenda divided Axial historiography with the question of whether axiality reflects creativities of ancient systems or have modern reflections created images of axiality. A singular chronology for humanity encourages a mode of philosophical history open to providential designs, epochal turns, spiritual evolutions, psychological leaps, or cognitive revolutions. History of philosophy and religion, with the advice of theology and social history herein solicited, can reformulate a stricter and sounder historiography more congenial to a broad scope for ancient philosophy. In particular, arrivals of axiality would appear in distinct stages at different times across separate regions as a matter of responding creatively to changing socio-historical conditions. Twelve candidates for Axial phases across Eurasia during the early Iron Age are accordingly proposed, which include oft-mentioned philosophies and religions as well as overlooked systems that were no less Axial