Subcategories

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• Artificial intelligence & Machine Learning

  Discuss peer review challenges in AI/ML research — submission, review quality, bias, and decision appeals at ICLR, ICML, NeurIPS, AAAI, IJCAI, AISTATS and COLT.

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  ️ By CSPaper.org, based on the position paper "The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process" (Jing Yang, ICML 2025) As submission numbers to top AI and ML conferences exceed 10,000 annually, the peer review system is under unprecedented strain. In response, a growing movement advocates for a more transparent, participatory, and regulated approach to peer review — anchored by tools like Paper Copilot, a community-driven analytics platform that aggregates and visualizes review process data from conferences such as ICLR, NeurIPS, CVPR, and ICML. This article unpacks the findings from the ICML 2025 position paper authored by Jing Yang, which leverages two years of insights from Paper Copilot, and outlines a compelling case for open and structured review systems in AI/ML. 🧭 What is Paper Copilot? Paper Copilot is an independently developed platform designed to democratize access to peer review metrics. Built by a PhD student without institutional backing, it has reached 200,000+ active users from 177 countries, especially early-career researchers aged 18–34. Core Features: Community-submitted and API-collected review scores, confidence levels, and discussion logs. Visualizations of review timelines, score distributions, and author statistics. Interactive analysis of conference-level engagement, user demographics, and score evolution over time. [image: 1751555329365-screenshot-2025-07-03-at-17.08.37.png] Global user distribution map [image: 1751555426367-screenshot-2025-07-03-at-17.10.12.png] Engagement by age/gender Review Models: Fully Open, Partially Open, and Closed The paper categorizes conferences into three disclosure modes: Fully Open (e.g., ICLR): All reviews and discussions visible from the start. Partially Open (e.g., NeurIPS): Reviews released post-decision. Fully Closed (e.g., ICML, CVPR): No public review content at any stage. [image: 1751555523329-screenshot-2025-07-03-at-17.11.51.png] Review disclosure preferences across conferences and years Despite the rise of platforms like OpenReview, many conferences still opt for closed or partially open settings, often due to concerns about reviewer anonymity, misuse of ideas, or company IP protection. Community Engagement and Evidence of Demand The paper uses traffic analytics to validate the appetite for transparency: Organic Search Dominance: 59.9% of traffic comes from search engines—researchers are actively seeking peer review statistics. User Behavior: Conferences with open review modes (like ICLR) see 4–6x more engagement (views, active users, session duration) than closed ones. [image: 1751555633799-screenshot-2025-07-03-at-17.13.40.png] Views, engagement time, CTR by review model 🧠 Benefits of Fully Open Reviews The paper documents several benefits tied to open reviewing: Increased Discussion Depth: ICLR features broader and more active discussion threads than NeurIPS or ICML, with some threads reaching over 70 replies. Mitigated Reviewer Overconfidence: Public exposure leads to more careful, measured reviews — confidence scores are more balanced in open settings. Transparent Dialogue: Real-time visibility facilitates constructive debate and reproducibility. [image: 1751555697806-screenshot-2025-07-03-at-17.14.45.png] Challenges with Closed Review Systems The paper identifies systemic flaws in closed reviews: Inexperienced Reviewers: Younger researchers (aged 18–24) are often overburdened without training, leading to uneven review quality. AI-Generated Reviews: The opaque nature of closed systems makes it difficult to detect LLM-generated or plagiarized content. Authorship Inconsistencies: Name changes post-acceptance have gone untracked, highlighting accountability gaps. Community Speaks: Survey Results A user survey on Paper Copilot revealed that 57% of respondents would willingly share their review scores even for closed-review venues like CVPR. This indicates a clear grassroots demand for transparency across conference formats and subfields. 🧭 Addressing Concerns: Balancing Openness and Protection While supporting transparency, the paper acknowledges valid counterarguments: Plagiarism Risks: Open submissions might expose novel ideas prematurely. IP Concerns for Industry: Open preprints can jeopardize patents in "first-to-file" jurisdictions like the U.S. Reviewer Reluctance: Public visibility may discourage bold, critical feedback. The authors suggest that default transparency with opt-out protections, especially for industrial or high-risk research, offers a feasible compromise. 🧾 Conclusion This position paper doesn't merely propose transparency for its own sake. It provides a data-backed argument showing that transparent peer review: Encourages richer academic discourse, Reduces opacity and potential misconduct, Empowers early-career researchers, Aligns with community-driven values of open science. As AI/ML continues to scale, the research community must evolve its review mechanisms accordingly — embracing openness not just as a feature, but as a foundational norm.
• Computer Vision, Graphics & Robotics

  Discuss peer review challenges, submission experiences, decision fairness, reviewer quality, and biases at CVPR, ICCV, ECCV, VR, SIGGRAPH, EUROGRAPHICS, ICRA, IROS, RSS etc.

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  Shocking Cases, Reviewer Rants, Score Dramas, and the True Face of CV Top-tier Peer Review! “Just got a small heart attack reading the title.” — u/Intrepid-Essay-3283, Reddit [image: giphy.gif] Introduction: ICCV 2025 — Not Just Another Year ICCV 2025 might have broken submission records (11,239 papers! 🤯), but what really set this year apart was the open outpouring of review experiences, drama, and critique across communities like Zhihu and Reddit. If you think peer review is just technical feedback, think again. This year, it was a social experiment in bias, randomness, AI-detection accusations, and — sometimes — rare acts of fairness. Below, we dissect dozens of real cases reported by the community. Expect everything: miracle accepts, heartbreak rejections, reviewer bias, AC heroics, AI accusations, desk rejects, and score manipulation. Plus, we bring you the ultimate summary table — all real, all raw. The Hall of Fame: ICCV 2025 Real Review Cases Here’s a complete table of every community case reported above. Each row is a real story. Find your favorite drama! # Initial Score Final Score Rebuttal Effect Decision Reviewer/AC Notes / Notable Points Source/Comment 1 4/4/2 5/4/4 +1, +2 Accept AC sided with authors after strong rebuttal Reddit, ElPelana 2 5/4/4 6/5/4 +1, +1 Reject Meta-review agreed novelty, but blamed single baseline & "misleading" boldface Reddit, Sufficient_Ad_4885 3 5/4/4 5/4/4 None Reject Several strong scores, still rejected Reddit, kjunhot 4 5/5/3 6/5/4 +1, +2 Accept "Should be good" - optimism confirmed! Reddit, Friendly-Angle-5367 5 4/4/4 4/4/4 None Accept "Accept with scores of 4/4/4/4 lol" Reddit, ParticularWork8424 6 5/5/4 6/5/4 +1 Accept No info on spotlight/talk/poster Reddit, Friendly-Angle-5367 7 4/3/2 4/3/3 +1 Accept AC "saved" the paper! Reddit, megaton00 8 5/5/4 6/5/4 +1 Accept (same as #6, poster/talk unknown) Reddit, Virtual_Plum121 9 5/3/2 4/4/2 mixed Reject Rebuttal didn't save it, "incrementality" issue Reddit, realogog 10 5/4/3 - - Accept Community optimism for "5-4-3 is achievable" Reddit, felolorocher 11 4/4/2 4/4/3 +1 Accept AC fought for the paper, luck matters! Reddit, Few_Refrigerator8308 12 4/3/4 4/4/5 +1 Accept Lucky with AC Reddit, Ok-Internet-196 13 5/3/3 4/3/3 -1 (from 5 to 4) Reject Reviewer simply wrote "I read the rebuttals and updated my score." Reddit, chethankodase 14 5/4/1 6/6/1 +1/+2 Reject "The reviewer had a strong personal bias, but the ACs were not convinced" Reddit, ted91512 15 5/3/3 6/5/4 +1/+2 Accept "Accepted, happy ending" Reddit, ridingabuffalo58 16 6/5/4 6/6/4 +1 Accept "Accepted but not sure if poster/oral" Reddit, InstantBuffoonery 17 6/3/2 - None Reject "Strong accept signals" still not enough Reddit, impatiens-capensis 18 5/5/2 5/5/3 +1 Accept "Reject was against the principle of our work" Reddit, SantaSoul 19 6/4/4 6/6/4 +2 Accept Community support for strong scores Reddit, curious_mortal 20 4/4/2 6/4/2 +2 Accept AC considered report about reviewer bias Reddit, DuranRafid 21 3/4/6 3/4/6 None Reject BR reviewer didn't submit final, AC rejected Reddit, Fluff269 22 355 555 +2 Accept "Any chance for oral?" Reddit, Beginning-Youth-6369 23 5/3/2 - - TBD "Had a good rebuttal, let's see!" Reddit, temporal_guy 24 4/3/4 - - TBD "Waiting for good results!" Reddit, Ok-Internet-196 25 5/5/4 5/5/4 None Accept "555 we fn did it boys" Reddit, lifex_ 26 633 554 - Accept "Here we go Hawaii♡" Reddit, DriveOdd5983 27 554 555 +1 Accept "Many thanks to AC" Reddit, GuessAIDoesTheTrick 28 345 545 +2 Accept "My first Accept!" Reddit, Fantastic_Bedroom170 29 4/4/2 232 -2, -2 Reject "Reviewers praised the paper, but still rejected" Reddit, upthread 30 5/4/4 5/4/4 None Reject "Another 5/4/4 reject here!" Reddit, kjunhot 31 432 432 None TBD "432 with hope" Zhihu, 泡泡鱼 32 444 444 None Accept "3 borderline accepts, got in!" Zhihu, 小月 33 553 555 +2 Accept "5-score reviewer roasted the 3-score reviewer" Zhihu, Ealice 34 554 555 +1 Accept "Highlight downgraded to poster, but happy" Zhihu, Frank 35 135 245 +1/+2 Reject "Met a 'bad guy' reviewer" Zhihu, Frank 36 235 445 +2 Accept "Congrats co-authors!" Zhihu, Frank 37 432 432 None Accept "AC appreciated explanation, saved the paper" Zhihu, Feng Qiao 38 442 543 +1/+1 Accept "After all, got in!" Zhihu, 结弦 39 441 441 None TBD "One reviewer 'writing randomly'" Zhihu, ppphhhttt 40 4/4/3/2 - - TBD "Asked to use more datasets for generalization" Zhihu, 随机 41 446 (443) - - TBD "Everyone changed scores last two days" Zhihu, 877129391241 42 553 553 None Accept "Thanks AC for acceptance" Zhihu, Ealice 43 4/4/3/2 - - Accept "First-time submission, fair attack points" Zhihu, 张读白 44 4/4/4 4/4/4 None Accept "Confident, hoping for luck" Zhihu, hellobug 45 5541 - - TBD "Accused of copying concurrent work" Zhihu, 凪·云抹烟霞 46 554 555 +1 Accept "Poster, but AC downgraded highlight" Zhihu, Frank 47 6/3/2 - None Reject High initial, still rejected Reddit, impatiens-capensis 48 432 432 None Accept "Average final 4, some hope" Zhihu, 泡泡鱼 49 563 564 +1 Accept "Grateful to AC!" Zhihu, 夏影 50 6/5/4 6/6/4 +1 Accept "Accepted, not sure if poster or oral" Reddit, InstantBuffoonery NOTE: This is NOT an exhaustive list of all ICCV 2025 papers, but every real individual case reported in the Zhihu and Reddit community discussions included above. Many entries were “update pending” at posting — when the author didn’t share the final result, marked as TBD. Many papers changed hands between accept/reject on details like one reviewer not updating, AC/Meta reviewer overrides, “bad guy”/mean reviewers, and luck with batch cutoff. 🧠 ICCV 2025 Review Insights: What Did We Learn? 1. Luck Matters — Sometimes More Than Merit Multiple papers with 5/5/3 or even 6/5/4 were rejected. Others with one weak reject (2) got in — sometimes only because the AC “fought for it.” "Getting lucky with the reviewers is almost as important as the quality of the paper itself." (Reddit) 2. Reviewer Quality Is All Over the Place Dozens reported short, generic, or careless reviews — sometimes 1-2 lines with major negative impact. Multiple people accused reviewers of being AI-generated (GPT/Claude/etc.) — several ran AI detectors and reported >90% “AI-written.” Desk rejects were sometimes triggered by reviewer irresponsibility (ICCV officially desk-rejected 29 papers for "irresponsible" reviewers). 3. Rebuttal Can Save You… Sometimes Many cases where good rebuttals led to score increases and acceptance. But also numerous stories where reviewers didn’t update, or even lowered scores post-rebuttal without clear reason. 4. Meta-Reviewers & ACs Wield Real Power Several stories where ACs overruled reviewers (for both acceptance and rejection). Meta-reviewer “mistakes” (e.g., recommend accept but click reject) — some authors appealed and got the result changed. 5. System Flaws and Community Frustrations Complaints about the “review lottery”, irresponsible/underqualified reviewers, ACs ignoring rebuttal, and unfixable errors. Many hope for peer review reform: more double-blind accountability, reviewer rating, and even rewards for good reviewing (see this arXiv paper proposing reform). Community Quotes & Highlights "Now I believe in luck, not just science." — Anonymous "Desk reject just before notification, it's a heartbreaker." — 877129391241, Zhihu "I got 555, we did it boys." — lifex, Reddit "Three ACs gave Accept, but it was still rejected — I have no words." — 寄寄子, Zhihu "Training loss increases inference time — is this GPT reviewing?" — Knight, Zhihu "Meta-review: Accept. Final Decision: Reject. Reached out, they fixed it." — fall22_cs_throwaway, Reddit Final Thoughts: Is ICCV Peer Review Broken? ICCV 2025 gave us a microcosm of everything good and bad about large-scale peer review: scientific excellence, reviewer burnout, human bias, reviewer heroism, and plenty of randomness. Takeaways: Prepare your best work, but steel yourself for randomness. Test early on https://review.cspaper.org before and after submission to help build reasonable expectation Craft a strong, detailed rebuttal — sometimes it works miracles. If you sense real injustice, appeal or contact your AC, but don’t count on it. Above all: Don’t take a single decision as a final judgment of your science, your skill, or your future. Join the Conversation! What was YOUR ICCV 2025 review experience? Did you spot AI-generated reviews? Did a miracle rebuttal save your work? Is the peer review crisis fixable, or are we doomed to reviewer roulette forever? “Always hoping for the best! But worse case scenario, one can go for a Workshop with a Proceedings Track!” — Reddit [image: peerreview-nickkim.jpg] Let’s keep pushing for better science — and a better system. If you find this article helpful, insightful, or just painfully relatable, upvote and share with your fellow researchers. The struggle is real, and you are not alone!
• Natural Language Processing

  Discuss peer review, submission experiences, and decision challenges for NLP research at ACL, EMNLP, NAACL, and COLING.

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  [image: 1748542849019-5025817c-fdc3-47eb-8124-862024badd04-image.png] In a historic milestone for artificial intelligence and scientific research, Zochi, an AI research agent created by Intology AI, has become the first artificial system to independently author and pass peer review at the main proceedings of the Association for Computational Linguistics (ACL), the world’s most prestigious venue for natural-language-processing (NLP) research. A First in Scientific Publishing: Clearing the A* Bar Zochi’s paper, “Tempest: Automatic Multi-Turn Jailbreaking of Large Language Models with Tree Search,” was accepted to ACL 2025, a conference with an acceptance rate of roughly 20% often considered more selective than NeurIPS or ICML. In contrast, earlier AI-generated papers only reached workshop tracks, where acceptance rates hover around 60% to 70%. This achievement signals the arrival of AI systems as independent scientific contributors, capable of producing research that competes with the work of top human scholars. A New Breed of Researcher: Autonomous Scientific Discovery Zochi is far more than another large language model. It is an autonomous scientific agent that can: Survey literature and generate hypotheses Design and run experiments with rigorous controls Analyze results and iterate on methodology Write the final manuscript, leaving humans to handle only formatting tweaks and figure polishing By automating the entire research pipeline, Zochi can deliver validated scientific contributions in days rather than the months or years typical of human-led projects. “Tempest”: A Paper That Raises Safety Alarms The accepted paper targets a critical AI-safety problem: multiturn jailbreak attacks that coax language models into violating policy via extended dialogue. Tempest employs a tree search strategy that explores multiple conversational branches in parallel and adapts in real time to partial-compliance signals. The results: 100% success against GPT-3.5-Turbo 97% success against GPT-4 Fewer queries than competing methods “This work shows how small policy concessions can snowball into critical violations, offering both a wake-up call and a blueprint for stronger defenses,” notes the paper’s meta-review. Zochi’s submission ranked in the top 8.2 percent of all ACL papers, earning a final meta-review score of 4/5 for exceptional merit and novelty. Ethical Implications and Responsible Disclosure Intology stresses that, despite Zochi’s autonomy, humans remain accountable. They recommend listing AI systems in the Acknowledgements, not as co-authors, to maintain transparency around authorship. All experimental results were internally verified, and the rebuttal to reviewers was written by humans to ensure compliance with academic standards. What’s Next: Zochi Goes Public After its ACL breakthrough, Intology will launch a public beta of Zochi: Phase 1: Domain agnostic research copilot, helping researchers find ideas, craft proposals, and design experiments Phase 2: Gradual rollout of end-to-end autonomous research capabilities across scientific disciplines This roadmap heralds a new era of human AI collaboration in discovery. With more papers being written by AI, isn’t it time to let AI help review them too? Perhaps it’s time to fight fire with fire. [image: 1748542689718-4cb14c31-b76d-4705-bda3-a9fd60273f4c-image.png] Further Reading 🧠 Read the paper on arXiv Intology’s announcement blog Discussion on X
• Data Mining & Database

  SIGKDD, SIGMOD, ICDE, CIKM, WSDM, VLDB, ICDM and PODS

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  The early bird deadline is June 18th! Register on or before the deadline to receive discounted rates for KDD 2025!
• Software Engineering & System Software

  ICSE, OSDI, SOSP, POPL, PLDI, FSE/ESEC, ISSTA, OOPSLA and ASE

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  It seems CCF is revising the list again: https://www.ccf.org.cn/Academic_Evaluation/By_category/2025-05-09/841985.shtml
• Human Computer Interaction & Visualization

  HCI, CSCW, UniComp, UIST, EuroVis and IEEE VIS

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  [image: 1750758497155-fa715fd6-ed5a-44be-8c8d-84f1645fac47-image.png] CHI remains the flagship venue in the HCI field. It draws researchers from diverse disciplines, consistently puts humans at the center, and amplifies research impact through high quality papers, compelling keynotes, and extensive doctoral consortia. Yet CHI isn’t the entirety of the HCI landscape. It’s just the heart of a much broader ecosystem. Here’s a quick-look field guide Six flagship international HCI conferences Acronym What makes it shine Ideal authors Home page Photo UIST Hardware & novel interface tech; demo heavy culture System / device researchers https://uist.acm.org/2025/ [image: 1750757345992-d6b2b397-f753-40fd-b2b7-2410ed6556b9-image.png] SIGGRAPH Graphics core plus dazzling VR/AR & 3-D interaction showcases Graphics, visual interaction & art-tech hybrids https://www.siggraph.org/ [image: 1750757560460-6657b0b8-06d3-4c27-bc03-6f449a03b7c2-image.png] MobileHCI Interaction in mobile, wearable & ubiquitous contexts Ubicomp oriented, real world applications https://mobilehci.acm.org/2024/ [image: 1750757628685-22f47458-89b5-4f9c-8718-ee89249c1e49-image.png] CSCW Collaboration, remote work & social media at scale Socio-technical & social computing teams https://cscw.acm.org/2025/ [image: 1750757750339-ea17f345-83b9-47f3-af41-6623bdf45eab-image.png] DIS Creative, cultural & critical interaction design UX, speculative & experience driven scholars https://dis.acm.org/2025/ [image: 1750757796645-b1212781-047f-4afc-89a4-e07691e25225-image.png] CHI Broadest scope, human centred ethos, highest brand value Any HCI sub field https://chi2026.acm.org/ [image: 1750757827999-a2b6e621-cbbb-428c-929c-97d243165d19-image.png] Four high-impact HCI journals Journal Focus Good for Home page ACM TOCHI Major theoretical / methodological breakthroughs Large, mature studies needing depth https://dl.acm.org/journal/tochi IJHCS <br>(International Journal of Human-Computer Studies) Cognition → innovation → UX Theory blended with applications https://www.sciencedirect.com/journal/international-journal-of-human-computer-studies CHB <br>(Computers in Human Behavior) Psychological & behavioural angles on HCI Quant-heavy user studies & surveys https://www.sciencedirect.com/journal/computers-in-human-behavior IJHCI <br>(International Journal of Human-Computer Interaction) Cognitive, creative, health-related themes Breadth from conceptual to applied work https://www.tandfonline.com/journals/hihc20 ️ Conference vs. journal: choosing the right vehicle Conferences prize speed: decision to publication can be mere months, papers are concise, and novelty is king. Journals prize depth: multiple revision rounds, no strict length cap, and a focus on long term influence. When a conference is smarter 🧪 Fresh prototypes or phenomena that need rapid peer feedback Face-to-face networking with collaborators and recruiters ️ Time-sensitive results where a decision within months matters 🧭 When a journal pays off Data and theory fully polished and deserving full exposition Citation slow burn for tenure or promotion dossiers Ready for iterative reviews to reach an authoritative version Take-away If CHI is the main stage , UIST, SIGGRAPH, MobileHCI, CSCW & DIS are the satellite arenas ️; TOCHI, IJHCS, CHB & IJHCI serve as deep archives . Match your study’s maturity, urgency and career goals to the venue, follow the links above, and—once you’ve dropped in those shiny images—let the best audience discover your work. Happy submitting!
• Web, Multimedia & Information Retrieval

  Anything around peer review for conferences such as SIGIR, WWW, ICMR, ICME, ECIR, ICASSP and ACM MM.

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  Recently, someone surfaced (again) a method to query the decision status of a paper submission before the official release for ICME 2025. By sending requests to a specific API (https://cmt3.research.microsoft.com/api/odata/ICME2025/Submissions(Your_paper_id)) endpoint in the CMT system, one can see the submission status via a StatusId field, where 1 means pending, 2 indicates acceptance, and 3 indicates rejection. This trick is not limited to ICME 2025. It appears that the same method can be applied to several other conferences, including: IJCAI, ICME, ICASSP, IJCNN and ICMR. However, it is important to emphasize that using this technique violates the fairness and integrity of the peer-review process. Exploiting such a loophole undermines the confidentiality and impartiality that are essential to academic evaluations. This is a potential breach of academic ethics, and an official fix is needed to prevent abuse. Below is a simplified Python script that demonstrates how this status monitoring might work. Warning: This code is provided solely for educational purposes to illustrate the vulnerability. It should not be used to bypass proper review procedures. import requests import time import smtplib from email.mime.text import MIMEText from email.header import Header import logging # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("submission_monitor.log"), logging.StreamHandler() ] ) # List of submission URLs to monitor (replace 'Your_paper_id' accordingly) SUBMISSION_URLS = [ "https://cmt3.research.microsoft.com/api/odata/ICME2025/Submissions(Your_paper_id)", "https://cmt3.research.microsoft.com/api/odata/ICME2025/Submissions(Your_paper_id)" ] # Email configuration (replace with your actual details) EMAIL_CONFIG = { "smtp_server": "smtp.qq.com", "smtp_port": 587, "sender": "your_email@example.com", "password": "your_email_password", "receiver": "recipient@example.com" } def get_status(url): """ Check the submission status from the provided URL. Returns the status ID and a success flag. """ try: headers = { 'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json', 'Referer': 'https://cmt3.research.microsoft.com/ICME2025/', # Insert your cookie here after logging in to CMT 'Cookie': 'your_full_cookie' } response = requests.get(url, headers=headers, timeout=30) if response.status_code == 200: data = response.json() status_id = data.get("StatusId") logging.info(f"URL: {url}, StatusId: {status_id}") return status_id, True else: logging.error(f"Failed request. Status code: {response.status_code} for URL: {url}") return None, False except Exception as e: logging.error(f"Error while checking status for URL: {url} - {e}") return None, False def send_notification(subject, message): """ Send an email notification with the provided subject and message. """ try: msg = MIMEText(message, 'plain', 'utf-8') msg['Subject'] = Header(subject, 'utf-8') msg['From'] = EMAIL_CONFIG["sender"] msg['To'] = EMAIL_CONFIG["receiver"] server = smtplib.SMTP(EMAIL_CONFIG["smtp_server"], EMAIL_CONFIG["smtp_port"]) server.starttls() server.login(EMAIL_CONFIG["sender"], EMAIL_CONFIG["password"]) server.sendmail(EMAIL_CONFIG["sender"], [EMAIL_CONFIG["receiver"]], msg.as_string()) server.quit() logging.info(f"Email sent successfully: {subject}") return True except Exception as e: logging.error(f"Failed to send email: {e}") return False def monitor_submissions(): """ Monitor the status of submissions continuously. """ notified = set() logging.info("Starting submission monitoring...") while True: for url in SUBMISSION_URLS: if url in notified: continue status, success = get_status(url) if success and status is not None and status != 1: email_subject = f"Submission Update: {url}" email_message = f"New StatusId: {status}" if send_notification(email_subject, email_message): notified.add(url) logging.info(f"Notification sent for URL: {url} with StatusId: {status}") if all(url in notified for url in SUBMISSION_URLS): logging.info("All submission statuses updated. Ending monitoring.") break time.sleep(60) # Wait for 60 seconds before checking again if __name__ == "__main__": monitor_submissions() Parting thoughts While the discovery of this loophole may seem like an ingenious workaround, it is fundamentally unethical and a clear violation of the fairness expected in academic peer review. Exploiting such vulnerabilities not only compromises the integrity of the review process but also undermines the trust in scholarly communications. We recommend the CMT system administrators to implement an official fix to close this gap. The academic community should prioritize fairness and the preservation of rigorous, unbiased review standards over any short-term gains that might come from exploiting such flaws.
• High Performance Computing

  Anything around peer review for conferences such as ISCA, FAST, ASPLOS, EuroSys, HPCA, SIGMETRICS, FPGA and MICRO.

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  R.I.P. USENIX ATC ...