Censorship and the distribution of false information, tools used to manipulate what users see and believe, are seemingly at opposite ends of the information access spectrum. Most previous work has examined them in isolation and within individual countries, leaving gaps in our understanding of how these information manipulation tools interact and reinforce each other across diverse societies. In this paper, we study perceptions about the interplay between censorship, false information, and influence operations, gathered through a mixed-methods study consisting of a survey (n = 384) and semi-structured interviews (n = 30) with participants who have experienced these phenomena across diverse countries in both the Global South and Global North, including Bangladesh, China, Cuba, Iran, Venezuela, and the United States. Our findings reveal perceptions of cooperation across various platforms between distinct entities working together to create information cocoons, within which censorship and false information become imperceptible to those affected. Building on study insights, we propose novel platform-level interventions to enhance transparency and help users navigate information manipulation. In addition, we introduce the concept of plausibly deniable social platforms, enabling censored users to provide credible, benign explanations for their activities, protecting them from surveillance and coercion.

Recent studies have shown that sponge attacks can significantly increase the energy consumption and inference latency of deep neural networks (DNNs). However, prior work has focused primarily on computer vision and natural language processing tasks, overlooking the growing use of lightweight AI models in sensing-based applications on resource-constrained devices, such as those in Internet of Things (IoT) environments. These attacks pose serious threats of energy depletion and latency degradation in systems where limited battery capacity and real-time responsiveness are critical for reliable operation. This paper makes two key contributions. First, we present the first systematic exploration of energy-latency sponge attacks targeting sensing-based AI models. Using wearable sensing-based AI as a case study, we demonstrate that sponge attacks can substantially degrade performance by increasing energy consumption, leading to faster battery drain, and by prolonging inference latency. Second, to mitigate such attacks, we investigate model pruning, a widely adopted compression technique for resource-constrained AI, as a potential defense. Our experiments show that pruning-induced sparsity significantly improves model resilience against sponge poisoning. We also quantify the trade-offs between model efficiency and attack resilience, offering insights into the security implications of model compression in sensing-based AI systems deployed in IoT environments.

The widespread integration of wearable sensing devices in Internet of Things (IoT) ecosystems, particularly in healthcare, smart homes, and industrial applications, has required robust human activity recognition (HAR) techniques to improve functionality and user experience. Although machine learning models have advanced HAR, they are increasingly susceptible to data poisoning attacks that compromise the data integrity and reliability of these systems. Conventional approaches to defending against such attacks often require extensive task-specific training with large, labeled datasets, which limits adaptability in dynamic IoT environments. This work proposes a novel framework that uses large language models (LLMs) to perform poisoning detection and sanitization in HAR systems, utilizing zero-shot, one-shot, and few-shot learning paradigms. Our approach incorporates \textit{role play} prompting, whereby the LLM assumes the role of expert to contextualize and evaluate sensor anomalies, and \textit{think step-by-step} reasoning, guiding the LLM to infer poisoning indicators in the raw sensor data and plausible clean alternatives. These strategies minimize reliance on curation of extensive datasets and enable robust, adaptable defense mechanisms in real-time. We perform an extensive evaluation of the framework, quantifying detection accuracy, sanitization quality, latency, and communication cost, thus demonstrating the practicality and effectiveness of LLMs in improving the security and reliability of wearable IoT systems.

Separable nonlinear least squares (SNLS)problem is a special class of nonlinear least squares (NLS)problems, whose objective function is a mixture of linear and nonlinear functions. It has many applications in many different areas, especially in Operations Research and Computer Sciences. They are difficult to solve with the infinite-norm metric. In this paper, we give a short note on the separable nonlinear least squares problem, unseparated scheme for NLS, and propose an algorithm for solving mixed linear-nonlinear minimization problem, method of which results in solving a series of least squares separable problems.

At least half of a protostar's mass is accreted in the Class 0 phase, when the central protostar is deeply embedded in a dense, infalling envelope. We present the first systematic search for outbursts from Class 0 protostars in the Orion clouds. Using photometry from Spitzer/IRAC spanning 2004 to 2017, we detect three outbursts from Class 0 protostars with $\ge 2$ mag changes at 3.6 or 4.5 $\mu$m. This is comparable to the magnitude change of a known protostellar FU Ori outburst. Two are newly detected bursts from the protostars HOPS 12 and 124. The number of detections implies that Class 0 protostars burst every 438 yr, with a 95% confidence interval of 161 to 1884 yr. Combining Spitzer and WISE/NEOWISE data spanning 2004-2019, we show that the bursts persist for more than nine years with significant variability during each burst. Finally, we use $19-100$ $\mu$m photometry from SOFIA, Spitzer and Herschel to measure the amplitudes of the bursts. Based on the burst interval, a duration of 15 yr, and the range of observed amplitudes, 3-100% of the mass accretion during the Class 0 phase occurs during bursts. In total, we show that bursts from Class 0 protostars are as frequent, or even more frequent, than those from more evolved protostars. This is consistent with bursts being driven by instabilities in disks triggered by rapid mass infall. Furthermore, we find that bursts may be a significant, if not dominant, mode of mass accretion during the Class 0 phase.

A social network is a social structure made up of individuals or organizations called nodes, which are connected by one or more specific types of interdependency, such as friendship, common interest, and exchange of finance, relationships of beliefs, knowledge or prestige. A cyber threat can be both unintentional and intentional, targeted or non targeted, and it can come from a variety of sources, including foreign nations engaged in espionage and information warfare, criminals, hackers, virus writers, disgruntled employees and contractors working within an organization. Social networking sites are not only to communicate or interact with other people globally, but also one effective way for business promotion. In this paper, we investigate and study the cyber threats in social networking websites. We go through the amassing history of online social websites, classify their types and also discuss the cyber threats, suggest the anti-threats strategies and visualize the future trends of such hoppy popular websites.

The Super High Momentum Spectrometer (SHMS) has been built for Hall C at the Thomas Jefferson National Accelerator Facility (Jefferson Lab). With a momentum capability reaching 11 GeV/c, the SHMS provides measurements of charged particles produced in electron-scattering experiments using the maximum available beam energy from the upgraded Jefferson Lab accelerator. The SHMS is an ion-optics magnetic spectrometer comprised of a series of new superconducting magnets which transport charged particles through an array of triggering, tracking, and particle-identification detectors that measure momentum, energy, angle and position in order to allow kinematic reconstruction of the events back to their origin at the scattering target. The detector system is protected from background radiation by a sophisticated shielding enclosure. The entire spectrometer is mounted on a rotating support structure which permits measurements to be taken with a large acceptance over laboratory scattering angles from 5.5 to 40 degrees, thus allowing a wide range of low cross-section experiments to be conducted. These experiments complement and extend the previous Hall C research program to higher energies.

We study the processes $\gamma \gamma \to K^+ K^- \eta$ and $\gamma \gamma \to K^+ K^- \pi^0$using a data sample of 519$fb^{-1}$ recorded with the BaBar detector operating at the SLAC PEP-II asymmetric-energy $e^+ e^-$ collider at center-of-mass energies at and near the $\Upsilon(nS)$ ($n = 2,3,4$) resonances. We observe $\eta_c \to K^+ K^- \eta$ and $\eta_c \to K^+ K^- \pi^0$ decays, measure their relative branching fraction, and perform a Dalitz plot analysis for each decay. We observe the $K^*_0(1430) \to K \eta$ decay and measure its branching fraction relative to the $K \pi$ decay mode to be ${\cal R}(K^*_0(1430)) = \frac{{\cal B}(K^*_0(1430) \to K \eta)}{{\cal B}(K^*_0(1430) \to K \pi)} = 0.092 \pm 0.025^{+0.010}_{-0.025}$. The$\eta_c \to K^+ K^- \eta$ and $K^*_0(1430) \to K \eta$ results correspond to the first observations of these channels. The data also show evidence for $\eta_c(2S) \to K^+ K^- \pi^0$ and first evidence for $\eta_c(2S) \to K^+ K^- \eta$.

We have shown previously that the dynamics of isolated oil slicks on the water's surface after spills is significantly influenced by surface-wave motion practically at the onset of the spreading process. In this work, we draw our attention to another practical scenario of the oil slick's behaviour in semi-confined geometries under the action of surface waves and in the presence of oil leakage source. We hypothesize that the geometric constraints should qualitatively change the oil layer response to the wave motion leading to localization of oil spill domains even at modest levels of the surface-wave perturbations. Several realistic cases were rigorously explored, with special attention paid to the interplay, the combined effect of the two factors, oil influx and wave motion. It was demonstrated quantitatively how the spreading process can be either facilitated or suppressed leading to potential confinement.

As the cost of information processing and Internet accessibility falls, most organizations are becoming increasingly vulnerable to potential cyber threats which its rate has been dramatically increasing every year in recent times. In this paper, we study, discuss and classify the most significant malicious software: viruses, Trojans, worms, adware and pornware which have made step forward in the science of Virology.

Quantum communication demands efficient distribution of quantum entanglement across a network of connected partners. The search for efficient strategies for the entanglement distribution may be based on percolation theory, which describes evolution of network connectivity with respect to some network parameters. In this framework, the probability to establish perfect entanglement between two remote partners decays exponentially with the distance between them before the percolation transition point, which unambiguously defines percolation properties of any classical network or lattice. Here we introduce quantum networks created with local operations and classical communication, which exhibit non-classical percolation transition points leading to the striking communication advantages over those offered by the corresponding classical networks. We show, in particular, how to establish perfect entanglement between any two nodes in the simplest possible network -- the 1D chain -- using imperfect entangled pairs of qubits.

We propose a 2D graphene structure containing atomic ensemble as a platform for implementing nanoscale enhanced coherent interactions of plasmonic fields with resonant atomic systems. We determine the graphene surface plasmon modes, and the properties of its electromagnetic fields, and emphasize the role of graphene sheet separation on the interaction with atomic systems for various dipole orientations and positions between the graphene sheets. We analyze the conditions for implementation of coherent interaction of SP mode with resonant atomic ensembles. By solving the Maxwell-Bloch equations that govern the resonant interaction of surface plasmons with atoms, we derive the modified area theorem, which makes it possible to identify the most common nonlinear patterns in the behavior of plasmons under the studied conditions. We obtain analytical and numerical solutions of the area theorem, and find the possibility of stable propagation of isolated SP pulses of graphene surface plasmon modes at "fractional" pulse area values relative to $\pi$. We show that the coherent dynamics of SP fields can be realized in nanoscale design and we highlight the possibilities of using this scheme of coherent dynamics for implementing compact multimode nanoscale quantum memory and its integration with other quantum devices on the proposed platform.

We present a systematic study of $\Lambda$ hyperon's polarization observables using event-by-event (3+1)D relativistic hydrodynamics. The effects of initial hot spot size and QGP's specific shear viscosity on the polarization observables are quantified. We examine the effects of the two formulations of the thermal shear tensor on the polarization observables using the same hydrodynamic background. With event-by-event simulations, we make predictions for the Fourier coefficients of $\Lambda$'s longitudinal polarization $P^z$ with respect to the event planes of different orders of anisotropic flow. We propose new correlations among the Fourier coefficients of $P^z$ and charged hadron anisotropic flow coefficients to further test the mapping from fluid velocity gradients to hyperon's polarization. Finally, we present a system size scan with Au+Au, Ru+Ru, and O+O collisions at $\sqrt{s_\mathrm{NN}} = 200$ GeV to study the system size dependence of polarization observables at the Relativistic Heavy-ion Collider.

This paper describes the Leray spectral sequence associated to a differential fibration. The differential fibration is described by base and total differential graded algebras. The cohomology used is noncommutative differential sheaf cohomology. For this purpose, a sheaf over an algebra is a left module with zero curvature covariant derivative. As a special case, we can recover the Serre spectral sequence for a noncommutative fibration.

ADHM Yang-Mills instantons are extended field theoretical objects. These are more general than the more familiar 't Hooft Yang-Mills instantons. Their counter parts exist in string theory in terms of sigma models. Nearly three decades ago Witten constructed a (0,4) supersymmetric linear sigma model incorporating ADHM instantons. Witten's construction was in component form. Galperin and Sokatchev constructed the corresponding off-shell supersymmetric version using the harmonic superspace. Recently Ali and Ilahi constructed an ADHM instanton linear sigma model that is complementary, in the sense of being dual, to the original model constructed by Witten. Full (0,4) supersymmetric off-shell harmonic superspace formalism for Ali-Ilahi's complementary ADHM instanton sigma model is developed in this note.

The nuclear dependence of the inclusive inelastic electron scattering cross section (the EMC effect) has been measured for the first time in $^{10}$B and $^{11}$B. Previous measurements of the EMC effect in $A \leq 12$ nuclei showed an unexpected nuclear dependence; $^{10}$B and $^{11}$B were measured to explore the EMC effect in this region in more detail. Results are presented for $^9$Be, $^{10}$B, $^{11}$B, and $^{12}$C at an incident beam energy of 10.6~GeV. The EMC effect in the boron isotopes was found to be similar to that for $^9$Be and $^{12}$C, yielding almost no nuclear dependence in the EMC effect in the range $A=4-12$. This represents important, new data supporting the hypothesis that the EMC effect depends primarily on the local nuclear environment due to the cluster structure of these nuclei.

One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a sentence and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the author addresses the negation problem of machine learning-based sentiment classification for a colloquial Arabic language. To this end, we propose a simple rule-based algorithm for handling the problem; the rules were crafted based on observing many cases of negation. Additionally, simple linguistic knowledge and sentiment lexicon are used for this purpose. The author also examines the impact of the proposed algorithm on the performance of different machine learning algorithms. The results given by the proposed algorithm are compared with three baseline models. The experimental results show that there is a positive impact on the classifiers accuracy, precision and recall when the proposed algorithm is used compared to the baselines.

The calibration of the EFL teaching and learning approaches with Artificial Intelligence can potentially facilitate a smart transformation, fostering a personalized and engaging experience in teaching and learning among the stakeholders. The paper focuses on developing an EFL Big Data Ecosystem that is based on Big Data, Analytics, Machine Learning and cluster domain of EFL teaching and learning contents. Accordingly, the paper uses two membranes to construe its framework, namely (i) Open Big Data Membrane that stores random data collected from various source domains and (ii) Machine Learning Membrane that stores specially prepared structured and semi-structured data. Theoretically, the structured and semi structured data are to be prepared skill-wise, attribute-wise, method-wise, and preference-wise to accommodate the personalized preferences and diverse teaching and learning needs of different individuals. The ultimate goal is to optimize the learning experience by leveraging machine learning to create tailored content that aligns with the diverse teaching and learning needs of the EFL communities.

The hospitality industry in the Arab world increasingly relies on customer feedback to shape services, driving the need for advanced Arabic sentiment analysis tools. To address this challenge, the Sentiment Analysis on Arabic Dialects in the Hospitality Domain shared task focuses on Sentiment Detection in Arabic Dialects. This task leverages a multi-dialect, manually curated dataset derived from hotel reviews originally written in Modern Standard Arabic (MSA) and translated into Saudi and Moroccan (Darija) dialects. The dataset consists of 538 sentiment-balanced reviews spanning positive, neutral, and negative categories. Translations were validated by native speakers to ensure dialectal accuracy and sentiment preservation. This resource supports the development of dialect-aware NLP systems for real-world applications in customer experience analysis. More than 40 teams have registered for the shared task, with 12 submitting systems during the evaluation phase. The top-performing system achieved an F1 score of 0.81, demonstrating the feasibility and ongoing challenges of sentiment analysis across Arabic dialects.

Online abuse, a persistent aspect of social platform interactions, impacts user well-being and exposes flaws in platform designs that include insufficient detection efforts and inadequate victim protection measures. Ensuring safety in platform interactions requires the integration of victim perspectives in the design of abuse detection and response systems. In this paper, we conduct surveys (n = 230) and semi-structured interviews (n = 15) with students at a minority-serving institution in the US, to explore their experiences with abuse on a variety of social platforms, their defense strategies, and their recommendations for social platforms to improve abuse responses. We build on study findings to propose design requirements for abuse defense systems and discuss the role of privacy, anonymity, and abuse attribution requirements in their implementation. We introduce ARI, a blueprint for a unified, transparent, and personalized abuse response system for social platforms that sustainably detects abuse by leveraging the expertise of platform users, incentivized with proceeds obtained from abusers.