A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

A Survey of Deep Active Learning

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions.

Mobile Edge Computing to Assist the Online Ideological and Political Education

With the rapid development of mobile Internet technology, mobile network data traffic presents an explosive growth trend. Especially, the proportion of mobile video business has become a large proportion in mobile Internet business. Mobile video business is considered as a typical business in the 5G network, such as in online education. The growth of video traffic poses a great challenge to mobile network. In order to provide users with better quality of experience (QoE), it requires mobile network to provide higher data transmission rate and lower network delay. This paper adopts a combined optimization to minimize total cost and maximize QoE simultaneously. The optimization problem is solved by ant colony algorithm. The effectiveness is verified on experiment.

Ransomware Attacks in History of Cyber World

Technology advancement since last few decades creates cyber attack a critical issue. Cyber security has become an important part today. It has also become an important and crucial subject in the field of forensic science. Increased in the growth of internet technology and internet devices have increased the risk of cyber attack. Almost every organization today are depends on the internet and devices. There are many types of cyber attack. This paper is the detailed review about Ransomware attack. This paper is consisted about vast of the information about What is Ransomware Attack, how does it work, how ransomware attack emerged. After reading this paper you will learn about the ransomware attacks in history of cyber world. This will help you to learn and understand about ransomware attack, how to prevent yourself from ransomware attack. As a forensic science student, it is always important to be aware about the attacks that have happened in the history of cyber world. Before writing this paper, I have read and analyze many research paper and internet articles, so that I can write a detailed review paper which can help students and for the forensic awareness. Keywords: Cyberattack, Hacking, Ransomware, cyberworld, cyber security, ransomware, forensic, network security

Internet technology as one of distance education during pandemic

The article aims at allowing you to deepen the knowledge of the program material on computer science and, as a consequence, will increase the efficiency of the educational process as a whole during a pandemic, will increase the motivation of students to the subject; will allow developing students' abilities for self-development, self-education. Based on the purpose, subject, hypothesis of the research, the following tasks were solved: scientific methodological and pedagogical literature on the topic of research was studied; revealed the content and essence of Internet technologies; an Internet resource has been developed that allows organizing the process of distance learning in informatics; tested the effectiveness of its use in the process of teaching computer science to students. The practical significance of the study lies in the creation of an educational Internet resource that allows students to acquire the skills to independently create a Web site. This Internet resource can be used for forms of work in informatics in any educational institution during a pandemic.

Knowledge Economy: Model Construction and Development Trend of Online Education

In regard to knowledge economy, the current concept in the model construction of online education, including distance education and online learning, generally refers to a kind of network-based learning behavior, similar to the concept of online training. Compared with traditional offline education methods, through the application of information technology and internet technology for content dissemination and rapid learning, online education has the characteristics of high efficiency, convenience, low threshold, and rich teaching resources. Online education covers a wide range of people, different forms of learning, and its classification methods are more diverse. Online education services are the fastest growing field of education informatization. At the moment, the most pressing problems include effectively integrating educational resources with internet technology, launching online education services and products that are highly interactive and would encourage personalized learning, increasing user stickiness, as well as avoiding trend-following and conceptualized investment.

Contemporary information social risks in the post-modern era

The article analyses the global social risks related to the expansion of information technologies, mass digitalisation, and the accessibility of sources of all information. The possibility of risky situations arising in different areas of society under postmodern conditions has been demonstrated. This is due to the massive spread of information and Internet technology, global changes in the structure of values of modern society, and the reassessment of a number of historical events and characters by some social groups. The focus is on the destruction of traditional mechanisms for transmitting social experience and memory and the transformation of perceptions of history through the use of virtual forms of communication. A different, own interpretation of historical events, the liberation of historical knowledge from politicisation and mythologisation can lead to risks of distortion of historical memory and even to conflicting situations of interpretation of the past. Case studies show that this, in turn, can lead to a set of risks in the economic sphere, for example: the risk of a situation of global redistribution of economic resources, the risk of losing the source of legitimacy of an economic resource, the risk of loss the reputation of a memory entity. These processes negatively affect social stability in society and distort the integrity of historical memory.Particular attention is paid to the topic of cognitive transformation risk related to the mass use of virtual media in the educational process. On the one hand, they are an effective teaching tool based on rapid search, transformation and storage of learning information. But, on the other hand, practice shows that knowledge loses its consistency and becomes “mosaic”, “clichéd”. The consequences of these processes are of a lasting nature and require further in-depth study by the scientific community, including psychologists, educators, and sociologists.

Semantic-guided autoencoder adversarial hashing for large-scale cross-modal retrieval

With the vigorous development of mobile Internet technology and the popularization of smart devices, while the amount of multimedia data has exploded, its forms have become more and more diversified. People’s demand for information is no longer satisfied with single-modal data retrieval, and cross-modal retrieval has become a research hotspot in recent years. Due to the strong feature learning ability of deep learning, cross-modal deep hashing has been extensively studied. However, the similarity of different modalities is difficult to measure directly because of the different distribution and representation of cross-modal. Therefore, it is urgent to eliminate the modal gap and improve retrieval accuracy. Some previous research work has introduced GANs in cross-modal hashing to reduce semantic differences between different modalities. However, most of the existing GAN-based cross-modal hashing methods have some issues such as network training is unstable and gradient disappears, which affect the elimination of modal differences. To solve this issue, this paper proposed a novel Semantic-guided Autoencoder Adversarial Hashing method for cross-modal retrieval (SAAH). First of all, two kinds of adversarial autoencoder networks, under the guidance of semantic multi-labels, maximize the semantic relevance of instances and maintain the immutability of cross-modal. Secondly, under the supervision of semantics, the adversarial module guides the feature learning process and maintains the modality relations. In addition, to maintain the inter-modal correlation of all similar pairs, this paper use two types of loss functions to maintain the similarity. To verify the effectiveness of our proposed method, sufficient experiments were conducted on three widely used cross-modal datasets (MIRFLICKR, NUS-WIDE and MS COCO), and compared with several representatives advanced cross-modal retrieval methods, SAAH achieved leading retrieval performance.