arXiv subjects

Ben Kim

Publications and source records attributed to Ben Kim.

6 recordsLinked to original sources

arXiv2026

Towards Knitted Textile Electromechanical Systems

E-textiles and wearable sensing technologies enable flexible, customizable interfaces for human-computer interaction, with capacitive sensing offering precise touch and pressure detection. While machine knitting provides scalable, mechanically tunable structures ideal for such sensors, few studies develop or characterize insulated conductive yarns engineered for knitting's complex structural geometry and high flexure strain. In this work, we present a yarn dip-coating process, driven by an adjusted dip-coating fluid dynamics model, that enables scalable, machine knittable fabrication of capacitive tactile pressure sensing arrays. We establish optimal dip-coating parameters and concentrations of thermoplastic polyurethane (TPU) dissolved in dimethylformamide (DMF) to create knitting-optimized coatings (~630 um thickness). These fabricated yarns are shown to maintain electromechanical characteristics with minimal deviation after knitting and washing, thus allowing the creation of knitted pressure sensors through multi-layered structures. This process demonstrates that machine knitting with insulated yarns is a viable and reliable manufacturing approach to integrate sensing functionality into wearable textiles.

cs.HC↗

arXiv2026

GameEngineBench: Evaluating Coding Agents on Real C++ Runtime Environments

Game engines provide real-time simulation, rendering, physics, interaction, networking, and asset pipelines, making them valuable not only for games but also for 3D applications in healthcare, robotics, architecture, manufacturing, and related domains. Because game development is where these systems are most mature and publicly available, it offers a practical testbed for evaluating coding agents that must modify C++ code within stateful, interactive, real-time systems. We present GameEngineBench, a benchmark for evaluating coding agents on scoped C++ implementation tasks inside Unreal Engine 5 projects, built from nine real-world game repositories. The evaluation set consists of 110 tasks spanning gameplay mechanics, multiplayer behavior, AI and world orchestration, animation and movement, UI and session code, loading behavior, online-service integration, persistence, data serialization, XR behavior, and rendering-oriented plugins. These tasks require models to make native C++ changes that compile and satisfy behavioral tests within executable Unreal Engine projects. Across twelve evaluated configurations, the strongest model reaches 55.5\% pass@1, while 31 tasks remain unsolved by every configuration. Our results demonstrate that frontier coding agents continue to struggle with deeply integrated C++ development for real-time interactive software, highlighting game-engine benchmarks as a valuable complement to existing software engineering evaluations.

cs.SE↗

arXiv2025

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods

Distributed Denial-of-Service (DDoS) attacks remain a serious threat to online infrastructure, often bypassing detection by altering traffic in subtle ways. We present a method using hive-plot sequences of network data and a 3D convolutional neural network (3D CNN) to classify DDoS traffic with high accuracy. Our system relies on three main ideas: (1) using spatio-temporal hive-plot encodings to set a pattern-recognition baseline, (2) applying adversarial training with FGSM and PGD alongside spatial noise and image shifts, and (3) analyzing frame-wise predictions to find early signals. On a benchmark dataset, our method lifts adversarial accuracy from 50-55% to over 93% while maintaining clean-sample performance. Frames 3-4 offer strong predictive signals, showing early-stage classification is possible.

cs.CR↗

arXiv2023

eViper: A Scalable Platform for Untethered Modular Soft Robots

Soft robots present unique capabilities, but have been limited by the lack of scalable technologies for construction and the complexity of algorithms for efficient control and motion, which depend on soft-body dynamics, high-dimensional actuation patterns, and external/on-board forces. This paper presents scalable methods and platforms to study the impact of weight distribution and actuation patterns on fully untethered modular soft robots. An extendable Vibrating Intelligent Piezo-Electric Robot (eViper), together with an open-source Simulation Framework for Electroactive Robotic Sheet (SFERS) implemented in PyBullet, was developed as a platform to study the sophisticated weight-locomotion interaction. By integrating the power electronics, sensors, actuators, and batteries on-board, the eViper platform enables rapid design iteration and evaluation of different weight distribution and control strategies for the actuator arrays, supporting both physics-based modeling and data-driven modeling via on-board automatic data-acquisition capabilities. We show that SFERS can provide useful guidelines for optimizing the weight distribution and actuation patterns of the eViper to achieve the maximum speed or minimum cost-of-transportation (COT).

cs.RO↗

arXiv2021

First-Generation Inference Accelerator Deployment at Facebook

In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network bandwidth requirements. We co-designed a high-performance, energy-efficient inference accelerator platform based on these requirements. We describe the inference accelerator platform ecosystem we developed and deployed at Facebook: both hardware, through Open Compute Platform (OCP), and software framework and tooling, through Pytorch/Caffe2/Glow. A characteristic of this ecosystem from the start is its openness to enable a variety of AI accelerators from different vendors. This platform, with six low-power accelerator cards alongside a single-socket host CPU, allows us to serve models of high complexity that cannot be easily or efficiently run on CPUs. We describe various performance optimizations, at both platform and accelerator level, which enables this platform to serve production traffic at Facebook. We also share deployment challenges, lessons learned during performance optimization, as well as provide guidance for future inference hardware co-design.

cs.AR↗

arXiv2019

An Improved Dipole Extraction Method From Magnitude-Only Electromagnetic-Field Data

Infinitesimal electric and magnetic dipoles are widely used as an equivalent radiating source model. In this paper, an improved method for dipole extraction from magnitude-only electromagnetic-field data based on genetic algorithm and back-and-forth iteration algorithm [1] is proposed. Compared with conventional back-and-forth iteration algorithm, this method offers an automatic flow to extract the equivalent dipoles without prior decision of the type, position, orientation and number of dipoles. It can be easily applied to electromagnetic-field data on arbitrarily shaped surfaces and minimize the number of required dipoles. The extracted dipoles can be close to original radiating structure, thus being physical. Compared with conventional genetic algorithm based method, this method reduces the optimization time and will not easily get trapped into local minima during optimization, thus being more robust. This method is validated by both simulation data and measurement data and its advantages are proved. The potential application of this method in phase retrieval is also discussed.

eess.SP↗