Comment on "A special attack on the multiparty quantum secret sharing of secure direct communication using single photons"

In this comment, we show that the special attack [S.-J. Qin, F. Gao, Q.-Y. Wen, F.-C. Zhu, Opt. Commun. 281 (2008) 5472.], which claims to be able to obtain all the transmitted secret message bit values of the protocol of the multiparty quantum secret sharing of secure direct communication using single photons with random phase shift operations, fails. Furthermore, a class of similar attacks are also shown to fail to extract the secrete message.Comment: 2 pages (two-column

Clustering-Based Outlier Detection Technique Using PSO-KNN

In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique. Initially, the data clustering of the considered datasets was carried out using PSO to obtain optimized clusters. In the optimization process, we have adopted Davies-Bouldin (DB) index as a fitness function. The optimized clusters were pruned to exclude densely packed inliers data. Thereafter, the KNN method was employed to detect outliers present in the datasets. Our proposed algorithm was tested for outlier detection on eight different datasets and compared its performance with PSO+K-means, K-means, Local Outlier Factor (LOF), and Local Distance-based Outlier Factor (LDOF) methods. Our results show that the outlier detection efficiency of the proposed method outperforms than other four techniques. We believe that our proposed technique simple and efficient in finding the outliers in various types of datasets and it could be a promising tool for outlier detection in data mining

A solution-based intelligent tutoring system integrated with an online game-based formative assessment: development and evaluation

Nowadays, intelligent tutoring systems are considered an effective research tool for learning systems and problem-solving skill improvement. Nonetheless, such individualized systems may cause students to lose learning motivation when interaction and timely guidance are lacking. In order to address this problem, a solution-based intelligent tutoring system (SITS) is integrated with an online game-based formative assessment game called tic-tac-toe quiz for single-player (TRIS-Q-SP) for learning computer programming. This assessment game combines tic-tac-toe with online assessment, and the rules of tic-tac-toe are revised to stimulate students to use online formative assessment actively. Finally, an experimental study is devised to assess the success of SITS, and significant achievements are observed for the experimental group, besides enjoyment and positive opinions toward the TRIS-Q-SP. Therefore, the practical use of SITS is supported, as the results indicate considerable advantages for the experimental group over the control group. The findings also reveal that immediate elaborated feedback upon answering each question in TRIS-Q-SP is part of an optimal design

Building Unmanned Store Identification Systems Using YOLOv4 and Siamese Network

Labor is the most expensive in retail stores. In order to increase the profit of retail stores, unmanned stores could be a solution for reducing labor cost. Deep learning is a good way for recognition, classification, and so on; in particular, it has high accuracy and can be implemented in real time. Based on deep learning, in this paper, we use multiple deep learning models to solve the problems often encountered in unmanned stores. Instead of using multiple different sensors, only five cameras are used as sensors to build a high-accuracy, low-cost unmanned store; for the full use of space, we then propose a method for calculating stacked goods, so that the space can be effectively used. For checkout, without a checking counter, we use a Siamese network combined with the deep learning model to directly identify products instantly purchased. As for protecting the store from theft, a new architecture was proposed, which can detect possible theft from any angle of the store and prevent unnecessary financial losses in unmanned stores. As all the customers’ buying records are identified and recorded in the server, it can be used to identify the popularity of the product. In particular, it can reduce the stock of unpopular products and reduce inventory

CLCNet: Rethinking of Ensemble Modeling with Classification Confidence Network

In this paper, we propose a Classification Confidence Network (CLCNet) that can determine whether the classification model classifies input samples correctly. It can take a classification result in the form of vector in any dimension, and return a confidence score as output, which represents the probability of an instance being classified correctly. We can utilize CLCNet in a simple cascade structure system consisting of several SOTA (state-of-the-art) classification models, and our experiments show that the system can achieve the following advantages: 1. The system can customize the average computation requirement (FLOPs) per image while inference. 2. Under the same computation requirement, the performance of the system can exceed any model that has identical structure with the model in the system, but different in size. In fact, this is a new type of ensemble modeling. Like general ensemble modeling, it can achieve higher performance than single classification model, yet our system requires much less computation than general ensemble modeling. We have uploaded our code to a github repository: https://github.com/yaoching0/CLCNet-Rethinking-of-Ensemble-Modeling