Vikas Ashok

Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a multimodal LLM (e.g., recognition of common meme templates and cultural references) to convey intent, timing, and implicit meaning through conversational interaction. We evaluate MemeBuddy in a user study with 14 blind participants. Results show that dialog-style meme representations consistently improve engagement and user satisfaction compared to caption-style descriptions, while maintaining comparable comprehension.

Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Jessica M. Johnson et al.

Statistical visualization is usually treated as a visual medium, but data can also be touched. Three dimensional printed tactile graphs let blind and low vision students feel distributions, trace trends, and explore relationships through direct haptic interaction. Yet classroom scale use remains limited because producing each graph in CAD software requires specialized skill and hours of manual work. We address this bottleneck as a software problem through a three layer reusable pipeline in about 1500 lines of JavaScript. The first layer derives tactile design parameters automatically from plate dimensions using tactile perception research. The second provides shared chart scaffolding and five modular builders for scatter, bar, histogram, line, and box plots. The optional third layer uses a multi-modal large language model to extract structured chart specifications from uploaded images, with mandatory teacher review before print generation. The pipeline produces print ready binary Standard Tessellation Language files in under 250 milliseconds. We present the design, performance, and limitations.

Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Vikas Ashok et al.

Reliable and reproducible radio-frequency (RF) measurements in real-world environments are essential for characterizing spectrum behavior across unlicensed ISM and WiFi bands, licensed mid-band allocations, and emerging next-generation wireless deployments. Existing measurement platforms are often laboratory-grade, cost-prohibitive, or dependent on fixed infrastructure, limiting their practicality for rapid, distributed, or long-duration field campaigns. This paper presents a compact, battery-powered RF capture system integrating a HackRF One software-defined radio, Raspberry Pi 5, GNSS receiver, regulated battery supply, and high-speed solid-state storage. The platform records continuous IQ data at up to 20 Msps in SigMF format with per-segment location and timing metadata for reproducible spectrum analysis. Field experiments at 2.45 GHz in dense foliage, urban outdoor, and indoor office environments reveal distinct propagation signatures. Foliage measurements remain near the noise floor at -76 to -82 dBFS with limited spectral structure, consistent with strong canopy attenuation. Urban measurements show multipath activity across a 30 dB dynamic range, overlapping WiFi channels, and frequent ISM-band interference. Indoor measurements show dominant WiFi channels, an estimated 20 to 25 dB building entry loss relative to outdoor conditions, and an 8 to 10 dB higher interference floor caused by structural reflections. The system sustained 75 to 85 MB/s write throughput with no dropped samples or buffer underruns, while GNSS synchronization remained below one second with meter-level positioning. These results show that a portable, cost-effective SDR platform can produce high-fidelity, geotagged IQ datasets for spectrum characterization, interference analysis, radio environment mapping, and environment-aware wireless research.

Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Jessica M. Johnson et al.

Emotion recognition in natural language is a foundational challenge in affective computing, with critical implications for human-computer interaction, mental health support, and conversational AI. This paper presents a rigorous, unified zero-shot evaluation of three leading commercial large language models: Claude (claude-sonnet-4-6), ChatGPT (GPT-5.4), and Gemini (gemini-2.5-flash). The models were queried through their respective production APIs as of April 2026 on a fine-grained 13-class emotion classification task. Using a stratified 1,000-sentence sample from the boltuix/emotions dataset, which comprises 131,306 sentences across 13 categories, a single uniform prompt with no exemplars was applied identically across all models. Gemini achieves the highest accuracy (39.9%) and macro-F1 score (0.363), followed by GPT-5.4 (38.8%, macro-F1 = 0.291) and Claude (38.0%, macro-F1 = 0.159). All models excel on sarcasm and desire while consistently failing on love, confusion, and shame. McNemar tests reveal no statistically significant pairwise differences (p > 0.10), suggesting convergence at a shared zero-shot ceiling. Claude's markedly lower macro-F1 score exposes a class-imbalance prediction bias. These findings highlight the current limitations of frontier AI systems in zero-shot fine-grained emotion classification.