Fighting Abuse while Promoting Free Speech: Policies to Reduce Opinion Manipulation in Online Platforms

With the rise of misinformation epidemic, this study aims to empirically investigate the consequences of an online commenting platform’s activity-capping policy on abusers’ and regular users’ activities. Utilizing a quasi-experimental setting, we find that restrictive policies not only curtail the activity of the abusers, but also promote the activity of regular users. Results show that the policy has asymmetric effect on abusers and regular users— while it effectively reduces the actions of the malicious users by 1.8%, it promotes the activities of the regular users by 2.2%. To better understand the behavioral change of the regular users, we draw from the rational economic perspective of voting decisions and provide initial evidence that such policy measures reinforce the subjective probability of being influential on the outcome. This study will provide valuable implications to managers and policy makers to estimate the consequences of and to combat against malicious behaviors in online platforms

Leveraging Hidden Positives for Unsupervised Semantic Segmentation

Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we leverage contrastive learning by excavating hidden positives to learn rich semantic relationships and ensure semantic consistency in local regions. Specifically, we first discover two types of global hidden positives, task-agnostic and task-specific ones for each anchor based on the feature similarities defined by a fixed pre-trained backbone and a segmentation head-in-training, respectively. A gradual increase in the contribution of the latter induces the model to capture task-specific semantic features. In addition, we introduce a gradient propagation strategy to learn semantic consistency between adjacent patches, under the inherent premise that nearby patches are highly likely to possess the same semantics. Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets. Our code is available at: https://github.com/hynnsk/HP.Comment: Accepted to CVPR 202

Sub-fingerprint masking for a robust audio fingerprinting system in a real-noise environment for portable consumer devices

author's final draftThe robustness of audio fingerprinting system in a noisy environment is a principal challenge in the area of content-based music retrieval, especially for use in portable consumer devices. Our new audio fingerprint method using sub-fingerprint masking based on the predominant pitch extraction dramatically increases the accuracy of the audio fingerprinting system in a noisy environment, while requiring much less computing power for matching, compared to the expanded hash table lookup method, where the searching complexity increases by the factor of 33 times the degree of expansion.This research was supported by Konkuk University

Total Gastrectomy in Gastric Conduit Cancer

We report a very rare case of surgery on gastric conduit cancer. A 67-year-old male patient underwent esophagectomy and intrathoracic esophagogastrostomy for squamous cell carcinoma of the lower thoracic esophagus 27 months ago. Upon follow-up, a gastric carcinoma at the intra-abdominal part of the gastric conduit was found on an esophagogastroduodenoscopy. We performed total gastrectomy and esophagocolonojejunostomy in the manner of Roux-en-Y anastomosis. The postoperative course was not eventful and an esophagogram on the 10th postoperative day showed no leakage or stenosis of the passage. The patient was discharged on the 17th day with no complications

Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos

Abstract Background Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations. Methods To build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction. Results Overall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos. Conclusion An artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future