Analysis of Mobile Malware: A Systematic Review of Evolution and Infection Strategies

The open-source and popularity of Android attracts hackers and has multiplied security concerns targeting devices. As such, malware attacks on Android are one of the security challenges facing society. This paper presents an analysis of mobile malware evolution between 2000-2020. The paper presents mobile malware types and in-depth infection strategies malware deploys to infect mobile devices. Accordingly, factors that restricted the fast spread of early malware and those that enhance the fast propagation of recent malware are identified. Moreover, the paper discusses and classifies mobile malware based on privilege escalation and attack goals. Based on the reviewed survey papers, our research presents recommendations in the form of measures to cope with emerging security threats posed by malware and thus decrease threats and malware infection rates. Finally, we identify the need for a critical analysis of mobile malware frameworks to identify their weaknesses and strengths to develop a more robust, accurate, and scalable tool from an Android detection standpoint. The survey results facilitate the understanding of mobile malware evolution and the infection trend. They also help mobile malware analysts to understand the current evasion techniques mobile malware deploys

Multi-agent reinforcement learning for character control

Simultaneous control of multiple characters has been a research topic that has been extensively pursued for applications in computer games and computer animations, for applications such as crowd simulation, controlling two characters carrying objects or fighting with one another and controlling a team of characters playing collective sports. With the advance in deep learning and reinforcement learning, there is a growing interest in applying multi-agent reinforcement learning for intelligently controlling the characters to produce realistic movements. In this paper we will survey the state-of-the-art MARL techniques that are applicable for character control. We will then survey papers that make use of MARL for multi-character control and then discuss about the possible future directions of research.

A Compendium of Infinite Group Theory: Part 1—Countable Recognizability

Countably recognizable group classes were introduced by Reinhold Baer and provide a very ingenious way to study large groups through the properties of their countable subgroups. This is the reason we have chosen the countable recognizability to start this series of survey papers on infinite group theory.

A Review: Point Cloud-Based 3D Human Joints Estimation

Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.

The current state of the methods for calculating global illumination in tasks of realistic computer graphics

Modern realistic computer graphics are based on light transport simulation. In this case, one of the main and difficult to calculate tasks is to calculate the global illumination, i.e. distribution of light in a virtual scene, taking into account multiple reflections and scattering of light and all kinds of its interaction with objects in the scene. Hundreds of publications and describing dozens of methods are devoted to this problem. In this state-of-the-art review, we would like not only to list and briefly describe these methods, but also to give some “map” of existing works, which will allow the reader to navigate, understand their advantages and disadvantages, and, thereby, choose a right method for themselves. Particular attention is paid to such characteristics of the methods as robustness and universality in relation to the used mathematical models, the transparency of the method verification, the possibility of efficient implementation on the GPU, as well as restrictions imposed on the scene or illumination phenomena. In contrast to the existing survey papers, not only the efficiency of the methods is analyzed, but also their limitations and the complexity of software implementation. In addition, we provide the results of our own numerical experiments with various methods that serve as illustrations for the conclusions.

A critical (and interdisciplinary) survey of popular music genre theories

Genres are among the most discussed topics in popular music studies. The attempt to explain issues as complex and layered as how musical genres are born, how they work and what they ontologically are cannot avoid opening a box full of theoretical problems, questions and tools that need to be understood and used in order to say something significant on genre today. Despite the long story of this theoretical debate (roots of which can be traced back to ancient Greece) and the variety of disciplines involved (e.g. literature, music and film studies, but also philosophy, sociology, cultural studies and semiotics), it is difficult to find survey papers that can give an overview of such a rich research environment. This paper attempts to fill that void by trying to systematize the main (contemporary) perspectives on musical genre, in particular non-essentialist theories coming from the overlapping fields of musicology and sociology. Most importantly, its overview stresses the necessity of an interdisciplinary study of musical genre, which – as an exemplum of extraordinarily layered phenomenon of the human production of culture – intertwines technical, social, discursive, commercial, historical and other elements, thus requiring an approach capable of accounting for as much of its many layers of meaning as possible.

Camouflaged Object Detection and Tracking: A Survey

Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.

Explanation Perspectives from the Cognitive Sciences---A Survey

With growing adoption of AI across fields such as healthcare, finance, and the justice system, explaining an AI decision has become more important than ever before. Development of human-centric explainable AI (XAI) systems necessitates an understanding of the requirements of the human-in-the-loop seeking the explanation. This includes the cognitive behavioral purpose that the explanation serves for its recipients, and the structure that the explanation uses to reach those ends. An understanding of the psychological foundations of explanations is thus vital for the development of effective human-centric XAI systems. Towards this end, we survey papers from the cognitive science literature that address the following broad questions: (1) what is an explanation, (2) what are explanations for, and 3) what are the characteristics of good and bad explanations. We organize the insights gained therein by means of highlighting the advantages and shortcomings of various explanation structures and theories, discuss their applicability across different domains, and analyze their utility to various types of humans-in-the-loop. We summarize the key takeaways for human-centric design of XAI systems, and recommend strategies to bridge the existing gap between XAI research and practical needs. We hope this work will spark the development of novel human-centric XAI systems.