Cryptanalysis and improvement of controlled secure direct communication

This paper points out that, due to a flaw in the sender＇s encoding, the receiver in Gao et al.＇s controlled quantum secret direct communication （CQSDC） protocol [Chin. Phys. 14 （2005）, No. 5, p. 893] can reveal the whole secret message without permission from the controller. An improvement is proposed to avoid this flaw.

Performance Analysis of Intelligent Neural-Based Deep Learning System on Rank Images Classification

The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Various research has been done for pornographic image detection and classification.Most of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine learning.Therefore,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic vs.Nonpornographic)classification in images.The simulation result shows that VGG-16 performed better than the used model in this study without augmented data.The VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,0.16.The precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,0.11.The f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&0.46.The f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of 0.47.The f-measure,recall,and precision values for explicit and non-explicit images are(0.86,0.97,0.91)and(0.86,0.93,0.89).Where else without augmented data the VGG-16 model reached a training and validation accuracy of 0.997,0.986 with a loss of 0.008,0.056.The f-measure,recall,and precision values for explicit and non-explicit images are(0.94,0.99,0.97)and(0.99,0.93,0.96)which outperforms the used models with the augmented dataset in this study.

A Health Monitoring System Using IoT-Based Android Mobile Application

Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done different work in the field of health monitoring with sensor networks.Different researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study development.While no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring systems.Therefore,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body temperature.The microcontroller’s Wi-Fi device is used to manage wireless data transport.The findings of the patient are saved on the Firebase server for further usage in the mobile app.The performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and SpO2.This system uses temperature,pulse,ECG,blood pressure,and eye blink sensors.This device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also included.With the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase server.To check the performance of the proposed system first,the BPM parameter was checked on the cardiac monitor.Then,the proposed model is tested on different patients age-wise.The simulation result shows that the BPM reading is not much different than the BPM of the cardiac monitor.According to the simulation findings,the proposed model achieved the best performance as compared to commercially available devices.

A novel algorithm for establishing taxonomic relation of Chinese ontology

Both a general domain-independent bottom-up multi-level model and an algorithm for establishing the taxonomic relation of Chinese ontology are proposed.The model consists of extracting domain vocabularies and establishing taxonomic relation,with the consideration of characteristics unique to Chinese natural language.By establishing the semantic forests of domain vocabularies and then using the existing semantic dictionary or machine-readable dictionary(MRD),the proposed algorithm can integrate these semantic forests together to establish the taxonomic relation.Experimental results show that the proposed algorithm is feasible and effective in establishing the integrated taxonomic relation among domain vocabularies and concepts.

Vision-Based Vehicle Detection for a Forward Collision Warning System

A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition;2) the local features are mutually processed to improve the reliability of vehicle detection;3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions;4) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.

An Adaptive Particle Filter Based Method for Real Time Face Tracking

The video surveillance systems of recent years, usually major focus on the Human-Face of observation and detection. Human-Face is the most characteristic and prominent feature of a human, therefore, detection and tracking of Human-Face has become an important indicator of the study. This paper discusses video surveillance of public places and majors in?automated face detection and face tracking. The main detection method is the use of Haar-Like Feature-based and through the Cascade classifier of the Adaboost face detection. In the tracking mechanism is based on particle filter and we modified SURF (Speeded Up Robust Features) particle filter tracking, and thus enhance the detection and tracking accuracy.

Perspective:Societally connected multimedia across cultures

The advance of the Internet in the past decade has radically changed the way people communicate and col- laborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences （at the individual, community, as well as societal levels） still govern human-human interactions and must be con- sidered and leveraged in the online world. The rapid deployment of high-speed lnternet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ＇connected multimedia＇, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.