journal-q1-polish

name	journal-q1-polish
description	Polish paper for Q1 journal submission (ISI/Scopus). Use after paper draft is complete — handles notation sync with thesis, De-AI/De-translation protocol, and Q1 results table standards.
metadata	{"tags":["research","phd","journal","q1","polish","submission","academic"],"version":"1.0.0","triggers":["polish paper for journal","q1 submission","isi scopus polish","de-ai","de-translation","notation sync","results table std","ablation standard deviation","chuẩn hóa bài báo","đồng bộ ký hiệu","sửa văn phong q1"],"links":["paper-writing","technical-english-cs","experiment-tracking","internal-critique","publication-strategy"]}

Journal Q1 Polish

Purpose: Final polish pass before submitting to Q1 journals (ISI/Scopus indexed). Ensures paper meets top-tier standards for notation consistency, language quality, and experimental rigor.

When to use: After paper draft is complete, before submission. NOT for initial writing — use paper-writing for that.

Links to:

• paper-writing — structure, sections, flow
• technical-english-cs — diction, terminology, IEEE/ACM style
• experiment-tracking — results tables, metrics format
• internal-critique — self-review checklist
• publication-strategy — venue selection, submission prep

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Step 1 — Notation & Complexity Sync (Paper ↔ Thesis)

Paper phải dùng notation và complexity format giống thesis để tránh mâu thuẫn khi defend.

1.1 Notation Table

Tạo bảng notation mapping giữa paper và thesis:

Symbol	Paper	Thesis	Status
Learning rate	$\eta$	$\eta$	✅ Match
Batch size	$B$	$B$	✅ Match
Model params	$\theta$	$\theta$	✅ Match

Checklist:

• Tất cả biến trong paper xuất hiện trong thesis notation table
• Không có symbol conflict (cùng symbol, khác meaning)
• Greek vs Latin letters consistent
• Subscript/superscript convention thống nhất

1.2 Complexity Notation

Q1 journals expect O(·) notation with explicit assumptions:

Time Complexity: O(T · N · d)
  T = number of rounds
  N = number of clients
  d = model dimension
  
Space Complexity: O(N · d)
  Per-client model storage

Rules:

• Always state what each variable represents
• Include amortized complexity if relevant
• Compare with baseline complexity in the same format

1.3 Abstract ↔ Section 3.3 Complexity Sync

Critical for Q1: Complexity stated in abstract must exactly match the analysis in Section 3.3 (or equivalent methodology section). Mismatches are a common reviewer complaint.

Audit template:

Abstract claims: "O(T · N · d) time, O(N · d) space"
Section 3.3 states: "O(T · N · d) time, O(N · d) space"
Status: ✅ Match / ❌ Mismatch → fix: ___

Common mismatch patterns:

• Abstract says "linear time" but Section 3.3 shows O(N²)
• Abstract omits a factor (e.g., forgets communication rounds T)
• Abstract uses different variable names than Section 3.3

1.4 Sampling Ratio Notation Sync

When paper and thesis use different notation for the same concept, create explicit mapping:

Concept	Paper	Thesis	Unified Form
Effective sampling ratio	\min(C.ratio, 75/N)	\min(s_{\text{config}}, T/N)	\min(r_{\text{max}}, S/N)

Rule: Pick one form (prefer thesis notation if already established) and use consistently. Add a footnote in paper: "We use the notation from [thesis citation] for consistency."

1.5 Cross-Reference Audit

• Every equation in paper has matching equation in thesis (or explicit note why different)
• Algorithm pseudocode matches thesis Algorithm chapter
• Complexity claims in paper abstract = complexity in thesis Chapter 3
• Sampling ratio notation unified between paper and thesis

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Step 2 — De-AI / De-translation Protocol

Q1 reviewers increasingly reject papers with AI-generated or machine-translated language. This step strips telltale signs.

2.1 AI Smell Detection (0-5 Score)

Audit your paper for these AI-generated patterns:

Signal	Description	Example	Fix
Symmetrical structure	All bullets/paragraphs start with same word pattern	"Enhance X...", "Enhance Y...", "Enhance Z..."	Vary sentence openings
Abstract noun stacking	Chaining abstract nouns	"utilization of optimization strategies"	"using optimization"
Generic intro/outro	Vague opening/closing	"In today's rapidly evolving world..."	Start with specific problem
Excessive hedging	Too many qualifiers	"It could potentially be argued that..."	State directly: "X shows..."
Repetitive paraphrasing	Same idea restated differently	"This is important. This matters. This is significant."	State once, move on
Triple adjective stacking	3+ adjectives before noun	"novel comprehensive robust framework"	Pick one: "robust framework"
Passive voice overuse	>50% passive sentences	"It was found that..."	"We found..."
Connector overuse	furthermore, moreover, additionally every paragraph	"Furthermore, ... Moreover, ..."	Vary or restructure

Scoring:

0/5 — Natural human writing
1/5 — Minor AI痕迹, light edit needed
2/5 — Noticeable patterns, targeted fixes
3/5 — Multiple signals, significant revision
4/5 — Heavy AI smell, major rewrite
5/5 — Obviously AI-generated, total rewrite

Target: 0-1/5 for Q1 submission.

2.2 Hype Words Blacklist

BAN these words/phrases entirely:

Banned	Replace with
delve into	examine, investigate, explore
leverage	use, apply, employ
robust	reliable, stable, consistent
cutting-edge	current, recent, state-of-the-art
novel	(just delete — let the work speak)
groundbreaking	(delete)
paradigm	approach, framework, method
landscape	field, domain, area
tapestry	(delete)
meticulous	careful, thorough
furthermore	(use "Additionally" or restructure sentence)
moreover	(same)
in conclusion	(delete — just end the section)
it is worth noting	(delete — if worth noting, just state it)
comprehensive	complete, full, extensive
innovative	(delete or specify what's new)
significant improvement	improvement of X% (be specific)
state-of-the-art	current best, recent methods (cite them)
demonstrates	shows, indicates, confirms
facilitates	enables, allows, supports
enhances	improves, increases
utilizing	using
aforementioned	(delete — restructure)
subsequently	then, next, after
preliminary	initial, early

2.2 Passive → Active Voice

Q1 journals prefer active voice. Scan for passive patterns:

Passive (bad):

The model was trained on the dataset.
Experiments were conducted to evaluate performance.
It was found that the method outperforms baselines.

Active (good):

We trained the model on the dataset.
We evaluated performance through experiments.
The method outperforms baselines.

Exception: Methods section can use passive for standard procedures ("The dataset was split into 80/20 train/test").

2.4 Quantify Hype with Experimental Data

Rule: When encountering hype words, replace with specific numbers from your results.

Hype phrase	Bad (vague)	Good (data-driven)
"extremely fast"	"The method is extremely fast"	"The method converges in 43 rounds vs. 67 for FedAvg (35.8% reduction)"
"significantly better"	"Our method performs significantly better"	"Our method achieves 94.3% accuracy, outperforming the best baseline by 2.2 percentage points (p < 0.001)"
"absolutely robust"	"The approach is absolutely robust to noise"	"Accuracy degrades by only 0.8% when noise increases from 0% to 30%"
"vastly superior"	"Our framework is vastly superior"	"Our framework reduces communication cost by 35% while maintaining comparable accuracy"
"extremely efficient"	"The algorithm is extremely efficient"	"The algorithm runs in O(N log N) time, 2.3× faster than the O(N²) baseline"

Workflow:

1. Search document for: extremely, vastly, absolutely, incredibly, remarkably, substantially, considerably
2. For each hit, locate the corresponding result in your experiments
3. Replace with: metric + value + comparison/baseline

2.5 De-translation Patterns

If paper was translated from Vietnamese:

Vietnamese structure	English fix
"The method has the ability to..."	"The method can..."
"In order to..."	"To..."
"Due to the fact that..."	"Because..."
"At the present time"	"Currently" / "Now"
"In the event that"	"If"
"Despite the fact that"	"Although" / "Despite"
"On a daily basis"	"Daily"
"Has the potential to"	"Can" / "May"

2.6 Sentence Length Audit

• Target: 15-25 words per sentence
• Hard max: 35 words (split if longer)
• Check: any paragraph with 3+ consecutive sentences > 25 words → rewrite

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Step 3 — Q1 Results Table Standard

Q1 journals expect rigorous experimental reporting. This is the #1 rejection reason for ML/AI papers.

3.1 Mandatory Columns

Column	Required	Notes
Method	✅	Full name + citation
Metric(s)	✅	Primary metric bolded
Mean	✅	Arithmetic mean
Std Dev	✅	MANDATORY for ablation studies
# Seeds	✅	Minimum 3, recommend 5
p-value	⚠️	Required if claiming "significant improvement"

3.2 Ablation Study Format

⚠️ CRITICAL: Every cell in an ablation table must include mean ± standard deviation. No exceptions.

WRONG (rejection risk):

| Method    | Accuracy |
|-----------|----------|
| Baseline  | 85.2     |
| + Module A| 87.1     |
| + Module B| 88.3     |

RIGHT (Q1 standard):

| Method     | Accuracy       | # Seeds | p-value  |
|------------|----------------|---------|----------|
| Baseline   | 85.2 ± 0.3     | 5       | —        |
| + Module A | 87.1 ± 0.4     | 5       | 0.002*   |
| + Module B | 88.3 ± 0.2     | 5       | <0.001*  |

* Statistically significant (paired t-test, α=0.05)

Rule: If a result cell shows only a single number (e.g., 85.2), it is incomplete. Every value must be mean ± std format.

3.3 Seed Count Justification

Include in experimental setup:

We report mean ± standard deviation over N independent runs with
different random seeds. We use [5/10/30] seeds to ensure reliable
statistical inference and reproducibility of our results.

Seed count guidelines:

Domain	Minimum Seeds	Justification
Deep Learning (deterministic)	3-5	Low variance, GPU determinism
Deep Learning (stochastic)	5-10	Moderate variance
Federated Learning	5-10	Client sampling variance
Metaheuristic / Evolutionary	30	Central Limit Theorem (n ≥ 30 for normality)
Reinforcement Learning	10-30	High variance, environment stochasticity

Why CLT matters for n ≥ 30:

• Sampling distribution of mean approximates normality regardless of population distribution
• Enables parametric tests (t-test, ANOVA) even for non-normal results
• Standard expectation in empirical research methodology

Template:

We report mean ± standard deviation over [N] independent runs
(seed ∈ {42, 123, 456, ...}). [N] seeds ensure [reliable statistical
inference / CLT normality / reproducibility] per standard empirical
methodology.

3.4 Statistical Tests

Claim	Required Test
"outperforms" / "better than"	Paired t-test or Wilcoxon signed-rank
"comparable" / "similar"	Equivalence test or confidence interval overlap
"robust to hyperparameter"	Sensitivity analysis table

3.5 Results Table Checklist

• All cells in ablation study show mean ± std (not just main results table)
• Seed count stated for each experiment (minimum 30 for metaheuristics per Đurasević & Jakobović [2023])
• Statistical significance marked with asterisk + footnote
• Best result bolded, second-best underlined
• Baseline results from original paper cited, not re-implemented (unless stated)
• Hardware spec mentioned (GPU type, RAM) for reproducibility
• Runtime/latency comparison if claiming efficiency

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Final Polish Checklist

From internal-critique

• Self-review using internal-critique checklist
• Address all "fatal flaw" items before submission

From technical-english-cs

• Diction matches IEEE/ACM standards
• No Vietnamese-English translation artifacts

From paper-writing

• Section structure follows target venue template
• Abstract within word limit
• References formatted correctly

From publication-strategy

• Selected venue matches paper scope
• Checked recent acceptance rate
• Reviewed 2-3 recent papers from target venue for style

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Integration Flow

paper-writing (draft complete)
    ↓
journal-q1-polish (this skill)
    ├── Step 1: notation-sync
    ├── Step 2: de-ai-protocol
    └── Step 3: q1-results-standard
    ↓
internal-critique (final review)
    ↓
publication-strategy (venue selection + submission)