Skill UI

This skill provides comprehensive guidelines for submitting manuscripts to IEEE Transactions on Biomedical Engineering (TBME). It covers the required balance between a sound engineering method and rigorous biomedical validation, specific scope areas (e.g., biosignal processing, neural engineering), structural requirements, and official submission checklists. It helps authors re-frame their work to meet the journal's high standards.

A comprehensive guide for authors structuring signal processing manuscripts for top-tier venues like IEEE Transactions on Signal Processing. It details the necessary theoretical depth, the requirement for rigorous analysis (e.g., convergence, bounds), correct baseline selection, and adherence to specific academic formatting. This tool helps authors reframe general machine learning applications into rigorous, theory-driven scientific methods.

This comprehensive guide outlines best practices for designing, evaluating, and reporting experimental results in speech processing research (ASR, TTS, Speaker Verification, etc.). It details required metrics (WER, MOS, EER), establishing robust baselines, conducting significance testing (bootstrap, CI), and ensuring comprehensive condition coverage (speakers, noise, languages) to enhance the scientific rigor of academic submissions.

A comprehensive guide for designing and auditing rigorous experiments for sensing systems and information processing claims. It emphasizes the necessity of measuring performance on real hardware (not simulation), accurately instrumenting ground truth, quantifying energy consumption, latency, and physical footprint, and establishing fair, skeptical baselines. This ensures scientific rigor and reproducibility in research submissions.

This guide models the rigorous, double-blind, and per-track evaluation process for submissions to the IPSN conference. It details how papers are judged based on the soundness of information-processing methods, the reality of collected evidence (e.g., measured energy, latency), and the novelty of sensing approaches. It also clarifies key differences from other academic venues like SenSys and OpenReview, guiding authors on what evidence is crucial for acceptance.

This guide helps researchers determine the primary focus of their work—whether it lies in Information Processing (algorithms, estimation) or Sensor Platforms, Tools, and Design Methods (hardware, deployment). It provides a structured methodology for selecting the most appropriate academic venue (e.g., SenSys, MobiSys, INFOCOM) based on the project's technical contribution and maturity.

This guide provides structured rules for authors writing sensor-systems or information-processing papers. It emphasizes leading with the physical sensing problem, requiring real-hardware measurements for energy, latency, and ground truth, and adhering to strict academic formatting constraints. Use this when revising submissions to ensure technical depth and practical deployability.

A comprehensive guide on achieving scientific reproducibility for complex computer architecture and system simulations. It details how to manage and pin all dependencies—including simulator commit hashes, local patches, workload binaries, hardware states, and post-processing scripts—to ensure that every reported figure and number can be accurately regenerated, regardless of time or environment changes.

A comprehensive guide for authors submitting research to sensor network and information processing conferences. It instructs authors on how to write the 'Related Work' section by focusing on 'delta-first contrast' rather than simple citation listing, ensuring novelty is measured against both system-level and theoretical prior art.

A comprehensive guide for researchers aiming to submit manuscripts to the Journal on Communications (JOC). It helps users assess if their topic fits the core communication discipline (theory, wireless, networking, security, signal processing) and distinguishes it from related academic journals. The guide also assists in selecting the appropriate article type and ensuring sufficient innovation and empirical rigor before submission.

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

This guide outlines the stringent standards for ensuring the reproducibility of Natural Language Processing (NLP) research. It teaches authors how to treat the Responsible NLP checklist as a binding contract by meticulously 'pinning' every variable—including model identifiers, API access dates, prompt strings, random seeds, and evaluation metrics. It details creating a comprehensive reproducibility manifest to make research results verifiable and defensible.