Contents The IEEE Computer Society’s lineup of 12 peer-reviewed technical magazines covers cutting-edge topics ranging from software design and computer graphics to Internet computing and security, from scientific applications and machine intelligence to visualization and microchip design. Here are highlights from recent issues.
Computing in Science & Engineering
Black Hole Physics and Computer Graphics
In this article from the March/April 2022 issue of Computing in Science & Engineering, the authors note that black holes are among the most extreme objects known to exist in nature. As such, they are excellent laboratories for testing fundamental theories and studying matter in conditions that cannot be found anywhere else in the universe. In this article, the authors highlight the relevance of black holes in modern physical and astronomical research and present one of the possible paths to explain observations and probe physics with the aid of numerical simulations. They briefly review dynamical-spacetime general-relativistic magneto-hydrodynamic (GRMHD) calculations as fundamental tools to study the local properties of black holes and the matter around them. Then, the authors discuss the need for general-relativistic radiation transport to propagate the local information about light obtained with GRMHD simulations to their telescopes. Finally, they present accretion onto binary black holes as a key area of study for testing general relativity and plasma physics. The goal of this article is to introduce the reader to some of the methods in current black hole research and to point out how improvements in hardware and software for computer graphics support advancements in the field.
IEEE Annals of the History of Computing
Seeking High IMP Reliability in Maintenance of the 1970s ARPAnet
This article from the April/June 2022 issue of IEEE Annals of the History of Computing describes the first years of ARPAnet operations, a time when computers were not highly reliable, but the network was built from standard computers and was expected to function as a utility with high reliability. The article describes how we managed to achieve the desired reliability, as perceived by ARPAnet users, by making innovations in hardware, maintenance procedures, software, and network operations. This article draws heavily on the personal experiences of the authors, many of which have not been previously reported in the literature. The focus is on the 1969–1975 time period when ARPAnet was the sole responsibility of the Advanced Research Projects Agency (ARPA). A 2018 article (Fidler et al., 2018) discusses ARPAnet maintenance after 1975. The preparation of this article was spearheaded by David Walden. David recruited the other two authors; prepared the outline of the article he envisioned; and drafted some sections. Sadly, David’s deteriorating health prevented him from contributing his usual amount of energy and knowledge, but this article would not have been written without him.
IEEE Computer Graphics and Applications
Bulsarapp: Interactive Visual Analysis for Surname Trend Exploration
The study of surnames for a given population, together with their distribution and spatial patterns identification, has been a long-standing problem in the fields of human biology, public health, and social sciences. The ancestry inferred from surname information can be a useful means to understand the dynamics of human populations. This knowledge allows us to characterize geographically the ethnicity of populations and to understand the complex relationships among identity, migration, and health issues in a demographic view. However, in most cases, a detailed geolocalization of these data can be a daunting task. In this article from the July/August 2022 issue of IEEE Computer Graphics and Applications, the authors propose a visual analytic tool that summarizes the heterogeneous surname and geographic information collected from Argentinean electoral rolls. This tool allows a massive data analysis and facilitates interdisciplinary studies about population dynamics related to ancestry, migration, and health. It also offers an easy-to-use interface that allows interactive exploration of isonymy and surname origins, their distribution, and spatial trends in a high-population-density context.
IEEE Intelligent Systems
Maximizing Fairness in Deep Neural Networks via Mode Connectivity
With frequent reports of biased outcomes of artificial intelligence systems, fairness rightfully becomes an active area of current machine learning research. However, while progress has been made on theoretical analysis and the formulation of fairness as constraints on error probabilities, our ability to design and train modern deep learning models that reach the targeted fairness goals in practice is still limited. The authors of this IEEE Intelligent Systems May/June 2022 article focus on an interesting yet common fairness setting, where multiple samples are collected from each individual, and the goal is to maximally reduce performance disparity among individuals while maintaining overall model performance. To obtain such fair deep learning models, the authors use mode connectivity combined with multiobjective optimization to select the best model out of an identified feasible set of model weight configurations with a similar overall performance but different distributions of performance over individuals. The method is model agnostic and effectively bridges fairness theory and practice
IEEE Internet Computing
Trustworthy Digital Twins in the Industrial Internet of Things With Blockchain
Industrial processes rely on sensory data for critical decision-making processes. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the trustworthiness of data. To this end, in this article in the May/June 2022 issue of IEEE Internet Computing, the authors envision a blockchain-based framework for the Industrial Internet of Things (IIoT) to address the issues of data management and security. Once the data collected from trustworthy sources are recorded in the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, the authors leverage digital twins (DTs) that can draw intelligent conclusions from the data by identifying the faults and recommending precautionary measures ahead of critical events. Furthermore, the authors discuss the integration of DTs and blockchain to target key challenges of disparate data repositories, untrustworthy data dissemination, and fault diagnosis. Finally, the authors identify outstanding challenges faced by the IIoT and future research directions while leveraging blockchain and DTs.
IEEE Micro
Maya: Using Formal Control to Obfuscate Power Side Channels
The security of computers is at risk because of information leaking through their power consumption. Attackers can use advanced signal measurement and analysis to recover sensitive data from this side channel. To address this problem, the authors of this July/August 2022 IEEE Micro article present Maya, a simple and effective defense against power side channels. The idea is to use formal control to reshape the power dissipated by a computer in an application-transparent manner—preventing attackers from learning any information about the applications that are running. With formal control, a controller can reliably keep power consumption close to a desired target function even when runtime conditions change unpredictably. By selecting the target function intelligently, the controller can make power to follow any desired shape, appearing to carry activity information, which, in reality, is unrelated to the application. Maya can be implemented in privileged software, firmware, and hardware. The authors implement Maya on three machines using only privileged threads against machine learning-based attacks and show its effectiveness and ease of deployment. Maya has already thwarted a newly developed remote power attack.
IEEE MultiMedia
Why VR Games Sickness? An Empirical Study of Capturing and Analyzing VR Games Head Movement Dataset
Virtual reality (VR) technology is gaining popularity in a variety of fields, including education, games, movies, medicine, and engineering, to name a few. The authors state that 360° VR video could provide an immersive experience and attract more researchers’ and developers’ attention. Some literature focused on the head movement when users watched 360° videos and released head tracking datasets. With the popularity of VR games, how the game contexts influence players’ head movement and the effect of head movement on VR sickness is a topic worth studying. In this article from the April/June 2022 issue of IEEE MultiMedia, the authors collected a head movement dataset of 30 participants while playing five different VR games (Aircar, Beat Saber, Moss, Arizona Sunshine, and SUPERHOT), and the participants filled the Simulator Sickness Questionnaire (SSQ) after playing the VR games. They then analyzed the SSQ scores and the impact of VR games on VR sickness. To the best of their knowledge, this is the first available head movement trajectory dataset based on playing several types of VR games.
IEEE Pervasive Computing
Long–Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-Worn Sensors
Manic episodes of bipolar disorder can lead to uncritical behavior and delusional psychosis, often with destructive consequences for those affected and their surroundings. Early detection and intervention of a manic episode are crucial to prevent escalation, hospital admission, and premature death. However, people with bipolar disorder may not recognize that they are experiencing a manic episode, and the symptoms, such as euphoria and increased productivity, can also deter affected individuals from seeking help. In this article in the April/June 2022 issue of IEEE Pervasive Computing, the authors propose to perform user-independent, automatic mood-state detection based on actigraphy and electrodermal activity acquired from a wrist-worn device during mania and after recovery (euthymia). This article proposes a new deep learning-based ensemble method leveraging long (20-h) and short (5-min) time intervals to discriminate between the mood states. When tested on 47 bipolar patients, the proposed classification scheme achieves an average accuracy of 91.59% in euthymic/manic mood-state recognition.
IEEE Security & Privacy
Measures to Ensure Cybersecurity of Industrial Enterprises: A Legal Perspective
The spread of digital technologies in production has increased production capacity and enterprise potential. Practice shows the negative consequences associated with the leakage of confidential information. The cyberattacks on the industrial sector demonstrate that information security is the most important strategic task at the international level. In this IEEE Security & Privacy article in the July/August 2022 issue, the authors consider the legal regulation of the cybersecurity of industrial enterprises in certain foreign countries and Russia. The aim of the study is to analyze the situation in the industrial segment of the sufficiency and effectiveness of legal instruments and mechanisms; to identify threats and attacks; and to eliminate their consequences.
IEEE Software
OSSARA: Abandonment Risk Assessment for Embedded Open Source Components
Software needs to be continuously updated and maintained to continue being useful. This is particularly true for open source software (OSS) components and libraries, which are increasingly integrated into large and complex systems. For companies developing long-term projects, all embedded OSS components should guarantee lengthy life expectancies and be maintained as long as systems are in service. Systems with unmaintained embedded OSS components are vulnerable to severe risks. In this July/August IEEE Software article, the authors introduce the OSS Abandonment Risk Assessment model to help companies avoid potentially dire consequences.
IT Professional
5G/SDR-Assisted Cognitive Communication in UAV Swarms: Architecture and Applications
Unmanned aerial vehicles (UAVs) are in general faster to deploy and easier to operate due to off-the-shelf guidance, navigation, and control solutions. Such solutions usually support short-range line-of-sight operations. However, operating a swarm of UAVs for surveillance purposes in diverse terrains requires cost-effective wireless connectivity, infrastructure-less operation, adaptive mobility models, multiclass routing solutions, and a tailored ground control station. In this May/June IT Professional article, the authors address the challenge of UAV swarm communications by presenting a framework that offers an open-interface communication and networking solution for surveillance operations in urban/outreach areas. It is based on a hybrid connectivity module that can enable the coexistence of 5G infrastructures; adaptive multiband software-defined radio (SDR) waveforms empowered with cooperative communication capacities; and satellite communications for continuous swarm operation in any demographic area. In addition, they also discuss some of the current and futuristic applications and scenarios that can benefit from the provided solution.