Automatic Examination of Condition of Used Books with YOLO-Based Object Detection Framework

As the demand for used books has grown in recent years,various online/offline market platforms have emerged to support the trade in used books.The price of used books can depend on various factors,such as the state of preservation(i.e.,condition),the value of possession,and so on.Therefore,some online platforms provide a reference document to evaluate the condition of used books,but it is still not trivial for individual sellers to determine the price.The lack of a standard quantitative method to assess the condition of the used book would confuse both sellers and consumers,thereby decreasing the user experience of the online secondhand marketplace.Therefore,this paper discusses the automatic examination of the condition of used books based on deep learning approaches.In this work,we present a book damage detection system based on various You Only Look Once(YOLO)object detection models.Using YOLOv5,YOLOR,and YOLOX,we also introduce various training configurations that can be applied to improve performance.Specifically,a combination of different augmentation strategies including flip,rotation,crop,mosaic,and mixup was used for comparison.To train and validate our system,a book damage dataset composed of a total of 620 book images with 3,989 annotations,containing six types of damages(i.e.,Wear,Spot,Notch,Barcode,Tag,and Ripped),collected from the library books is presented.We evaluated each model trained with different configurations to figure out their detection accuracy as well as training efficiency.The experimental results showed that YOLOX trained with its best training configuration yielded the best performance in terms of detection accuracy,by achieving 60.0%(mAP@.5:.95)and 72.9%(mAP@.5)for book damage detection.However,YOLOX performed worst in terms of training efficiency,indicating that there is a trade-off between accuracy and efficiency.Based on the findings from the study,we discuss the feasibility and limitations of our system and future research directions.

Ethanol extract of Kalopanax septemlobus leaf inhibits HepG2 human hepatocellular carcinoma cell proliferation via inducing cell cycle arrest at G_1 phase

Objective:To investigate the effects of an ethanol extract of Kalopanax septemlobus(Thunb.)Koidz.leaf(EEKS) on cell proliferation in human hepatocellular carcinoma cells and its mechanisms of action.Methods:Cells were treated with EEKS and subsequently analyzed for cell proliferation and flow cytometry analysis.Expressions of cell cycle regulators were determined by reverse transcriptase polymerase chain reaction analysis and Western blotting,and activation of eyclin-associaled kinases studied using kinase assays.Results:The EEKS suppressed cell proliferation in both HepG2 and Hep3 B cells,but showed a more sensitive anli-proliferative activity in HepG2 cells.Flow cytometry analysis revealed an association between the growth inhibitory effect of EEKS and with G_1 phase cell cycle arrest in HepG2 cells,along with the dephosphorylation of retinoblastoma protein(pRB) and enhanced binding of pRB with the E2 F transcription factor family proteins.Treatment with EEKS also increased the expression of cyclin-dependent kinase(CDK) inhibitors,such as p21WAF1/CIP1 and p27KIP1.without any noticeable changes in G_1 cyclins and CDKs(except for a slight decrease in CDK4).Treatment of HepG2 cells with EEKS also increased the binding of p21 and p27 with CDK4 and CDK6.which was paralleled by a marked decrease in the cyclin D- and cyclin E-associated kinase activities.Conclusions:Overall,our findings suggest that EEKS may be an effective treatment for liver cancer through suppression of cancer cell proliferation via G_1,cell cycle arrest Further studies arc required to identify the active compounds in EEKS.

Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of the breast.With an appropriate diagnosis,non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant.With the development of the artificial neural network,the deep discriminative model,such as a convolutional neural network,may evaluate the breast lesion to distinguish benign and malignant cancers frommammogram breast masses images.This work accomplished breastmasses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50(ResNet50)model along with an adaptive gradient algorithm,adaptive moment estimation,and stochastic gradient descent optimizers,as well as data augmentations and fine-tuning methods,were implemented.In addition,a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models.The results of training accuracy,p-value,test accuracy,area under the curve,sensitivity,precision,F1-score,specificity,and kappa for adaptive gradient algorithm 25%,75%,100%,and stochastic gradient descent 100%fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.

Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems.However,previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images.This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics:1)users’continuous movements,2)various lighting conditions,and 3)a limited amount of available data.To address these issues,we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze direction.The proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned conditions.To this end,we implemented a deep learning pipeline that can handle different types and combinations of data.The proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze estimators.Through various experiments,we found that 1)an infrared-modality provides more useful features than a depth-modality,2)multi-view multi-modal approaches provide better accuracy than singleview single-modal approaches,and 3)the proposed estimators achieve a high inference efficiency that can be used in real-time applications.