Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Double Medical Images Zero-Watermarking Algorithm Based on the Chaotic System and Ternary Accurate Polar Complex Exponential Transform

Journal of Mathematical Imaging and Vision - Different from the traditional watermarking schemes, zero-watermarking schemes are lossless embedding methods, which are applicable to be used in...     

A conditional classification recurrent RBM for improved series mid-term forecasting

In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.

     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...     

A graph-based QoS prediction approach for web service recommendation

Applied Intelligence - With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in...     

AES算法的硬件实现分析与设计

分析了AES算法的结构特点,对算法的不同硬件实现方式进行了对比分析,分析结果表明,不同的实现方式在算法应用模式支持、运行频率、资源占用、吞吐量方面各有优缺点,需要根据具体应用需求采用合适的实现方式。对AES算法不同实现方式的分析以及提出的设计结构对于其他类似的分组密码算法实现也具有参考价值。     

面向IC装备功能仿真平台的多模式测试方法

为了实现全面对IC装备以及装备功能仿真平台进行功能测试,通过分析装备命令响应特点,提出了包含单指令测试、逻辑指令序列测试以及随机指令序列测试的多模式测试方法;单指令测试验证设备以及由相关设备组成的系统的单指令响应正确性;逻辑指令序列从控制系统角度验证设备或系统的连续动态运行性能;随机指令序列测试验证设备或系统的动态随机响应性能;实际应用表明,提出的测试方法可以满足IC装备功能仿真平台的测试需求。     

Customer knowledge discovery from online reviews

The explosive growth of Chinese electronic market has made it possible for companies to better understand consumers?? opinion towards their products in a timely fashion through their online reviews. This study proposes a framework for extracting knowledge from online reviews through text mining and econometric analysis. Specifically, we extract product features, detect topics, and identify determinants of customer satisfaction. An experiment on the online reviews from a Chinese leading B2C (Business-to-Customer) website demonstrated the feasibility of the proposed method. We also present some findings about the characteristics of Chinese reviewers.     

Fast full‐color reflective display via photoluminescent enhancement

Reflective displays are advantageous in applications requiring low power or daylight readability. However, there are no low‐cost reflective technologies capable of displaying bright colors. By employing photoluminescence to more efficiently use ambient light, we created a prototype display that provides bright, full color in a simple, low‐cost architecture. This prototype includes a novel electrokinetic shutter, a layer that incorporates patterned luminescent red, green, and blue sub‐pixel elements, and a novel optical out‐coupling scheme. The luminescent elements convert otherwise‐wasted portions of the incident spectrum to light in the desired color band, resulting in improved color saturation and lightness. This prototype provides a color gamut that is superior to competing reflective display technologies that utilize color filters in single‐layer side‐by‐side sub‐pixel architectures. The current prototype is capable of switching in <0.5 s; future displays based on an alternative electro‐optic shutter technology should achieve video rate operation. A transflective version of this technology has also been prototyped. The transflective version utilizes its backlight with a power efficiency that is at least three times that of a conventional liquid crystal display. These photoluminescence‐based technologies enable a host of applications ranging from low‐power mobile products and retail pricing signage to daylight readable signage for outdoor advertising segments.     

Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment

We study the group decision making problem under intuitionistic fuzzy environment. Based on entropy and cross entropy, we give two methods to determine the optimal weights of attributes, and develop two pairs of entropy and cross entropy measures for intuitionistic fuzzy values. Then, we discuss the properties of these measures and the relations between them and the existing ones. Furthermore, we introduce three new aggregation operators, which treat the membership and non-membership information fairly, to aggregate intuitionistic fuzzy information. Finally, several practical examples are presented to illustrate the developed methods.