arXiv subjects

Sadman Sakib

Publications and source records attributed to Sadman Sakib.

5 recordsLinked to original sources

arXiv2026

A Taxonomy of Construction Task Activities for Robot Workers

Recent vision-language-action models offer a path toward robots with broader repertoires than conventional task-specific systems. Construction deployment, however, requires a precise inventory of worker activities and the capabilities needed to execute them. We present TARCAT, an occupation-grounded taxonomy derived from 91 O*NET tasks across seven high-employment construction occupations and 30 instructional videos of physical work. TARCAT defines 41 action primitives in 12 groups and three classes and provides a mechanism for composing parameterized primitive sequences into reusable skills. This human-interpretable structure can organize demonstrations, specify robot requirements, and support coding agents that retrieve and extend skill libraries. We also demonstrate selected primitives on a DOBOT CR3 arm with a CRAFT hand. TARCAT thereby provides a common vocabulary for analyzing human work and developing general-purpose construction robots. Annotations are available at https://github.com/AICPS/TARCAT-Taxonomy.

cs.RO↗

arXiv2026

FAMOSE: A ReAct Approach to Automated Feature Discovery

Feature engineering remains a critical yet challenging bottleneck in machine learning, particularly for tabular data, as identifying optimal features from an exponentially large feature space traditionally demands substantial domain expertise. To address this challenge, we introduce FAMOSE (Feature AugMentation and Optimal Selection agEnt), a novel framework that leverages the ReAct paradigm to autonomously explore, generate, and refine features while integrating feature selection and evaluation tools within an agent architecture. To our knowledge, FAMOSE represents the first application of an agentic ReAct framework to automated feature engineering, especially for both regression and classification tasks. Extensive experiments demonstrate that FAMOSE is at or near the state-of-the-art on classification tasks (especially tasks with more than 10K instances, where ROC-AUC increases 0.23% on average), and achieves the state-of-the-art for regression tasks by reducing RMSE by 2.0% on average, while remaining more robust to errors than other algorithms. We hypothesize that FAMOSE's strong performance is because ReAct allows the LLM context window to record (via iterative feature discovery and evaluation steps) what features did or did not work. This is similar to a few-shot prompt and guides the LLM to invent better, more innovative features. Our work offers evidence that AI agents are remarkably effective in solving problems that require highly inventive solutions, such as feature engineering.

cs.LG↗

arXiv2024

FlexBSO: Flexible Block Storage Offload for Datacenters

Efficient virtualization of CPU and memory is standardized and mature. Capabilities such as Intel VT-x [3] have been added by manufacturers for efficient hypervisor support. In contrast, virtualization of a block device and its presentation to the virtual machines on the host can be done in multiple ways. Indeed, hyperscalers develop in-house solutions to improve performance and cost-efficiency of their storage solutions for datacenters. Unfortunately, these storage solutions are based on specialized hardware and software which are not publicly available. The traditional solution is to expose virtual block device to the VM through a paravirtualized driver like virtio [2]. virtio provides significantly better performance than real block device driver emulation because of host OS and guest OS cooperation. The IO requests are then fulfilled by the host OS either with a local block device such as an SSD drive or with some form of disaggregated storage over the network like NVMe-oF or iSCSI. There are three main problems to the traditional solution. 1) Cost. IO operations consume host CPU cycles due to host OS involvement. These CPU cycles are doing useless work from the application point of view. 2) Inflexibility. Any change of the virtualized storage stack requires host OS and/or guest OS cooperation and cannot be done silently in production. 3) Performance. IO operations are causing recurring VM EXITs to do the transition from non-root mode to root mode on the host CPU. This results into excessive IO performance impact. We propose FlexBSO, a hardware-assisted solution, which solves all the mentioned issues. Our prototype is based on the publicly available Bluefield-2 SmartNIC with NVIDIA SNAP support, hence can be deployed without any obstacles.

cs.NI↗

arXiv2021

Federated 3GPP Mobile Edge Computing Systems: A Transparent Proxy for Third Party Authentication with Application Mobility Support

Multi-Access or Mobile Edge Computing (MEC) is being deployed by 4G/5G operators to provide computational services at lower latencies. Federating MECs across operators expands capability, capacity, and coverage but gives rise to two issues - third-party authentication and application mobility - for continuous service during roaming without re-authentication. In this work, we propose a Federated State transfer and 3rd-party Authentication (FS3A) mechanism that uses a transparent proxy to transfer the information of both authentication and application state across operators to resolve these issues. The FS3A proxy is kept transparent, with virtual counterparts, to avoid any changes to the existing MEC and cellular architectures. FS3A provides users with a token, when authenticated by an MEC, which can be reused across operators for faster authentication. Prefetching of subscription and state is also proposed to further reduce the authentication and application mobility latencies. We evaluated FS3A on an OpenAirInterface (OAI)-based testbed and the results show that token reuse and subscription prefetching reduce the authentication latency by 53-65%, compared to complete re-authentication, while state prefetching reduces application mobility latency by 51-91%, compared to no prefetching. Overall, FS3A reduces the service interruption time by 33%, compared to no token reuse and prefetching.

cs.CR↗

arXiv2021

Provisioning Fog Services to 3GPP Subscribers: Authentication and Application Mobility

Multi-Access Edge computing (MEC) and Fog computing provide services to subscribers at low latency. There is a need to form a federation among 3GPP MEC and fog to provide better coverage to 3GPP subscribers. This federation gives rise to two issues - third-party authentication and application mobility - for continuous service during handover from 3GPP MEC to fog without re-authentication. In this paper, we propose: 1) a proxy-based state transfer and third-party authentication (PS3A) that uses a transparent proxy to transfer the authentication and application state information, and 2) a token-based state transfer and proxy-based third-party authentication (TSP3A) that uses the proxy to transfer the authentication information and tokens to transfer the application state from 3GPP MEC to the fog. The proxy is kept transparent with virtual counterparts, to avoid any changes to the existing 3GPP MEC and fog architectures. We implemented these solutions on a testbed and results show that PS3A and TSP3A provide authentication within 0.345-2.858s for a 0-100 Mbps proxy load. The results further show that TSP3A provides application mobility while taking 40-52% less time than PS3A using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service via tokens and prefetching.

cs.CR↗