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We provide an overview of the INTERSECT project which delivers research software engineering training to intermediate and advanced developers of research software through week-long bootcamps. In addition to providing a project overview, we describe the first summer bootcamp, provide links to materials, and discuss observations from bootcamp participants. [ABSTRACT FROM AUTHOR]

Maintenance of military equipment plays a vital role in achieving higher war readiness. The vast number of military equipment and their deployment on distinct locations make their maintenance a challenging task. Decision-makers are always interested in knowing the latest status of equipment, develop effective maintenance strategies, and predict mission reliability as accurately as possible. To suffice this need, maintenance data of military equipment must be maintained with high granularity and accuracy, including maintenance information about every lowest maintainable unit of every military equipment. There is a profound need for a comprehensive maintenance management framework, which addresses several military-specific issues with equipment maintenance and stores the maintenance data in the utmost secure environment. In this article, we attempt to provide a novel blockchain enabled framework to make military maintenance management comprehensive and future ready. This article first provides a representation of the maintenance scenario of military equipment and later discusses the several choices made available within the blockchain technology, along with the architecture of the proposed blockchain. The proposed framework uses smart contracts to make monitoring and validation more uncompromising and with minimum human intervention. Some pragmatic analytics are presented in this article, along with the inherent blockchain advantages. [ABSTRACT FROM AUTHOR]

Background: We conducted an exploratory study to test the delivery of technical instructions built on the principles of minimalism. The aim was to investigate how we could support target users’ skill levels in a context-sensitive manner. Literature review: Related work examines minimalism, user needs and profiling, and industrial maintenance and technician experience. Research questions: 1. How can the semantic structure of DITA XML be utilized in delivering technical information to users based on their skill levels? 2. How would a layered system of information support the principles of minimalism? Methodology: We created material and tested the concept in user studies with maintenance personnel in three countries. We collected feedback through participant observation, interviews, and questionnaires. Results and discussion: The minimalist approach of delivering information to maintenance technicians was well received and supported users with varying skill levels. Conclusion: The context-sensitive level of expertise concept empowers users to decide on the depth of technical information that they require to complete the task at hand. The semantic structure of DITA XML works well in the delivery of technical information to the users based on their skill levels. Many of the key principles of minimalism are applicable to hardware maintenance instructions. [ABSTRACT FROM AUTHOR]

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset. [ABSTRACT FROM AUTHOR]

The Internet of Things envisions billions of physical devices connecting over the Internet to provide a near real-time view of the state of the world. These devices' capabilities can be abstracted as IoT services and provided on demand. To enable quality-aware service provision, Service Level Agreements (SLA) are widely used as legally binding contracts to obligate service providers to comply with a pre-negotiated Quality of Service (QoS). With a possible ever-increasing number of service providers in an IoT environment, multi-bilateral SLA negotiation is likely to be prohibitively time-consuming without an a-priori process to select trusted candidate providers with whom to negotiate. In this article, a trust model is proposed to identify trusted service providers in a dynamic IoT environment before attempting to negotiate an SLA. A trust credit that indicates both the SLA’s fulfillment and the possible negotiation success rate is derived based on historical information relating to a service’s previous negotiations and its monitored run-time performance. Indiscernibility analysis in Rough Set theory is used to predict the negotiation success rate, while Bayesian inference is applied to deduce the possibility of SLA violation according to the monitored data. The simulation results demonstrate the feasibility and efficiency of the proposed trust model. [ABSTRACT FROM AUTHOR]

Recent trends in the Web of Things (WoT) have led to data explosion. Data lake (DL), as a flexible on-demand heterogeneous data management architecture, has become a feasible solution in data management. Metadata modeling for DLs is the key basis for smart analysis and processing. However, the varieties in structures and semantics of industrial WoT data hinder metadata modeling and maintenance. Moreover, the lack of textual descriptions and the semantics hidden in value streams make it hard to automatically construct semantic metadata. The dynamic nature of WoT requires on-time evolution on metadata. To overcome these challenges, we propose an automated bottom-up metadata generation approach for DL of WoT applications. Applying a data-driven framework, raw data are notated as linked data and self-organizing map-based online clustering is applied to real timely extract data characteristics. To recognize entities, concepts and relations, semantics-based entity discovery approach from short texts is proposed according to the feature of WoT data. The numerical analysis is performed to find the hidden relations from raw values. Full-dimensional metadata with rich semantic knowledge are finally built. Experiments on a real-world dataset are conducted to verify the effectiveness of methods and a case study on an energy WoT system is provided to demonstrate the feasibility of the approach. [ABSTRACT FROM AUTHOR]

Most existing voltage optimization approaches of low voltage distribution networks (LVDNs) assume users follow the regulations unconditionally, which is not always true. To tackle this, we propose a real-time peer to peer flexibility trading scheme for LVDNs. Besides distributed optimization, our scheme offers a way to let users trade their flexibility, in case some users do not want to follow the system regulations at some point. The scheme consists of flexibility allocation and trading. Player compatibility equilibrium and Bernoulli trials are employed to approximate the Pareto optimum in flexibility allocation, while consensus and voltage reference are used to provide real-time offers for flexibility trading. The proposed scheme is computationally tractable and can work with limited communication in a decentralized manner. All the user data is used locally so that privacy is well protected. A network based on a real Belgian semiurban LVDN is employed to validate the proposed scheme with three different scenarios. Besides, the flexibility allocation is benchmarked with a centralized ACOPF algorithm. The case studies result clearly prove the effectiveness and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Intelligent reflective surface (IRS) technology is emerging as a promising performance enhancement technique for next-generation wireless networks. Hence, we investigate the physical layer security of the downlink in IRS-aided non-orthogonal multiple access networks in the presence of an eavesdropper, where an IRS is deployed for enhancing the quality by assisting the cell-edge user to communicate with the base station. To characterize the network's performance, the expected value of the new channel statistics is derived for the reflected links in the case of Nakagami- $m$ fading. Furthermore, the performance of the proposed network is evaluated both in terms of the secrecy outage probability (SOP) and the average secrecy capacity (ASC). The closed-form expressions of the SOP and the ASC are derived. We also study the impact of various network parameters on the overall performance of the network considered. To obtain further insights, the secrecy diversity orders and the high signal-to-noise-ratio (SNR) slopes are obtained. We finally show that: 1) the expectation of the channel gain in the reflected links is determined both by the number of IRS elements and by the Nakagami- $m$ fading parameters; 2) If the Nakagami- $m$ parameter is no less than 2, the SOP of both User 1 and User 2 becomes unity, when the number of IRS elements tends to infinity; 3) The secrecy diversity orders are affected both by the number of IRS elements and by the Nakagami- $m$ fading parameters, whereas the high-SNR slopes are not affected by these parameters. Our Monte-Carlo simulations perfectly demonstrate the analytical results. [ABSTRACT FROM AUTHOR]

The dual-deck architecture with aligned upper and lower decks is considered a promising technology to meet the demand of increasing word-line (WL) layers of 3D NAND flash. However, the relevant reliability studies are still lacking for the dual-deck 3D NAND array. In this work, it is reported an abnormal program disturbance phenomena of the bottom WLs in the upper-deck, and the physical mechanisms were studied. According to experimental analysis and TCAD simulations, the un-programmed dummy WLs at the joint region can introduce excessive joint residual electrons in the channel before the program, resulting in insufficient channel self-boosting potential, which is responsible for the degraded program disturbance. Thus, a novel program scheme is proposed to alleviate the program disturbance, which has been validated by experiments. [ABSTRACT FROM AUTHOR]

Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This article presents the overall system—design, hardware, and software—for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating noncontact human–robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform. [ABSTRACT FROM AUTHOR]

Improving the resilience of complex engineered and engineering systems (CES) includes planning for complex resilience situations, in which there may be multiple threats, interactions, and disruptions. One challenge in the modeling of CES is the identification of how interactions in a complex situation occur and their combined influence on CES resilience. This article presents a resilience holon that can be used to analyze complex resilience situations. It is made up of 24 elements (defining types of resilience, threats, interactions, and disruptions), which have varying importance to specific situations. Holons can be linked together hierarchically or in a network. An application of the resilience holon to a documented real-world resilience situation, widespread flooding in a city, illustrates its use. Pathways taken by threats and disruptions, as the flood effects cascaded through the city, are shown as connections between holons. The resilience holon could be used to decompose diverse resilience situations involving CES, to identify where critical vulnerability points are and how the whole resilience situation could be improved. The visual nature of the resilience holon could be used in an interactive way, allowing stakeholders to better understand the full resilience picture of CES that they use or operate. [ABSTRACT FROM AUTHOR]

Studies show that reduced literacy skills can negatively influence a child's self-esteem and future career opportunities. Literacy is significantly affected when problems exist in understanding the phonological component or sound structure of language, i.e., phonological awareness. Children with dyslexia in particular experience difficulties in spelling and reading accuracy due to a deficit in this phonological component of language. To support children with dyslexia and reduced literacy skills, intervention programs that focus on phonological awareness elements are recommended. Studies show that game-based learning interventions can enhance learning for children with dyslexia. The purpose of this pilot study was to partner with children with dyslexia aged between 9 and 12 years, to develop a game toolkit called Cosmic Sounds, and to support the teaching of phonological awareness skills. The content for the Cosmic Sounds games was informed by a pedagogical expert in dyslexia. This pilot study addressed the following: “Can a toolkit of games, codesigned by children with dyslexia improve the teaching of phonological awareness skills?” Our findings showed that by including children and their teacher as part of the design team, they were more invested in using the games for learning. Furthermore, when children with dyslexia played Cosmic Sounds, there was a positive impact on their phonological awareness skills progress while their engagement in learning also increased. [ABSTRACT FROM AUTHOR]

The ChaLearn large-scale gesture recognition challenge has run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. [ABSTRACT FROM AUTHOR]

This year marks the 25th edition of ISWC—International Symposium on Wearable Computers—which is the leading research venue for all the topics related to wearables. The conference was held September 21st—24th, 2021, with workshops dedicated to specialized research topics further extending into the 26th. ISWC was co-hosted with the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing, as has been the case for a few years. [ABSTRACT FROM AUTHOR]

Background: Computer programming faces persistent problems of inequity. In response, bootcamps and workshops have rapidly responded by offering an introduction to coding literacy intended to increase access and representation in the tech industry. Literature review: Prior research on software development focuses primarily on workplace contexts. This study considers bootcamps and workshops outside the workplace for minoritized and marginalized software developers to better understand the institutional contexts in which programming is taught and made. In so doing, it contributes to ongoing conversations on strategies for social justice in technical communication. Research questions: 1. How do bootcamps and workshops function as sites of coding education? 2. What strategies do activist programmers use in bootcamps and workshops to work towards social justice goals? Methodology: For this research, I interviewed organizers, instructors, and participants at three coding workshops and bootcamps for marginalized communities. I also conducted participant observation of the workshops, collected educational materials, and analyzed the interview transcripts using a grounded theory approach. Results and discussion: This analysis revealed how coding workshops and bootcamps operate as literacy sponsors, contributing to a transformative access for participants. More specifically, my research describes how activist programmers craft open, inclusive, and culturally aware pedagogies by attending to access, representation, community, and active learning, ultimately facilitating an affective coding literacy. [ABSTRACT FROM AUTHOR]

This article discusses the impact and significance of the autonomous science movement and the role and potential uses of intelligent technology in DAO-based decentralized science (DeSci) organizations and operations. What is DeSci? How does it relate the science of team science? What are its potential contributions to multidisciplinary, interdisciplinary, and/or transdisciplinary studies? Does it have any correspondence to the social movement organizations in traditional social sciences or the cyber movement organizations in the new digital age? Particularly, issues on DeSci to current professional communities, such as IEEE and its societies, conferences, and publications, are addressed, and the effort for the framework and process of DAO-based DeSci for free, fair, and responsibility sensitive sciences is reviewed. [ABSTRACT FROM AUTHOR]

In the beginning of traditional roadmapping workshops, brainstorming activities are conducted so that experts can populate roadmap templates. However, when large groups are involved, information management and facilitation can become time-consuming and difficult. An alternative option for improving brainstorming with large expert groups is the individual collection of information before the workshop through interviews. This article analyzes three strategic roadmapping projects in which experts were engaged using a hybrid approach, based on interviews and workshops. Based on the data collected, this alternative design was assessed in terms of impact on roadmapping design, facilitation, and experts’ contribution. As a result, despite adding a relevant effort to roadmapping teams, the hybrid strategic roadmapping approach seems to better manage experts’ involvement. In addition, individual interviews are shown to support the effective data gathering in large groups of experts, leading to richer and focused workshops in terms of information, involvement of participants, and decision-making. [ABSTRACT FROM AUTHOR]

Roadmapping is a workshop-based tool that draws on diverse sets of knowledge from individuals to create shared knowledge in the form of a roadmap for navigating a strategic landscape. Theoretical explanations of knowledge sharing in roadmapping are underdeveloped, with only limited insights into the relationship between individuals’ knowledge (at the micro level) and organizational-level outcomes. This article uses a multilevel lens to examine knowledge sharing in two roadmapping projects empirically. The analysis identifies five mechanisms that enable or constrain knowledge sharing in roadmapping: 1) a neutral space for face-to-face interactions between individuals that fosters; 2) a positive disposition of individuals to share knowledge, together with (3) imperatives to trigger knowledge contributions. Individually shared knowledge is transformed into collective outcomes via 4) shared meanings and 5) shared commitment to act. The analysis also highlights the important role of the facilitator in relation to knowledge sharing. This article extends the theoretical understanding of individuals and their behavior in the roadmapping process. In doing so, it demonstrates how microlevel actions relate to macrolevel outcomes. Practitioners, including facilitators, can use these insights as they design and deliver roadmapping interventions to improve knowledge sharing and, therefore, better strategic outcomes. [ABSTRACT FROM AUTHOR]

How to adapt and still get the full potential of roadmapping to the context of Digital Entrepreneurship (DE)? To answer this question and design an agile roadmapping approach to support highly incremental planning done by startups, this article unfolded through a three-phase action research program that lasted 40 months and involved three startups and two startup accelerators. Typically, startups are not complex organizations but operate in turbulent markets—requiring constant changes in strategy, organization, and product decisions. Such an environment is notably adherent to the assumptions of agile methods, but not so to roadmapping, an approach typically tuned for larger companies. Thus, the DE context is explored to adapt the roadmapping approach to agile principles aiming at offering a nimble, living, and integrated process that can enhance business, product, and organizational decisions in startups. Finally, the resulting method helps to support 1) strategic to operational management by continuous planning of small teams dealing with constant changes; 2) better communication between investors, technical, and business members; 3) better absorption of roadmapping and its benefits by teams already proficient in agile approaches, and 4) better balance between plan and action in startups. [ABSTRACT FROM AUTHOR]

Much attention has been paid internationally to the adoption of sustainable development goals to achieve sustainable outcomes. Although roadmapping is widely used by companies and other organizations to plan long-term strategies, relatively few studies have examined the development of roadmapping methods aiming at sustainability. To address this challenge, in this article, a backcasting-based method to design roadmaps that could be used to facilitate decision making and plan sustainable futures is proposed. By drawing on the concept of backcasting, the proposed roadmap design method consists of two phases: defining a sustainable vision, and describing the pathways that are required to realize that vision. In order to develop pathways that bridge the gap between the present and the vision, we develop a roadmap template called a “four-arrow model.” To demonstrate the proposed method, roadmaps are developed for Japanese manufacturing from the present to 2050 by organizing an expert workshop. As a result, two different roadmaps that connected sustainable visions and associated pathways are successfully developed. The number of ideas generated through the workshop indicates that the proposed method encouraged brainstorming and concept development. Future research will focus on making the roadmap design process more comprehensive by conducting industrial case studies. [ABSTRACT FROM AUTHOR]

We present BitConduite, a visual analytics approach for explorative analysis of financial activity within the Bitcoin network, offering a view on transactions aggregated by entities, i.e., by individuals, companies, or other groups actively using Bitcoin. BitConduite makes Bitcoin data accessible to nontechnical experts through a guided workflow around entities analyzed according to several activity metrics. Analyses can be conducted at different scales, from large groups of entities down to single entities. BitConduite also enables analysts to cluster entities to identify groups of similar activities as well as to explore characteristics and temporal patterns of transactions. To assess the value of our approach, we collected feedback from domain experts. [ABSTRACT FROM AUTHOR]

The benefits of user-centered and participatory design have been widely acknowledged for the development of technologies that are likely to be appropriated by the product’s stakeholders. While participatory design has been applied to some affective technologies, the technical and algorithmic complexity of those based on semi-intelligent information filters (SIIFs) pose distinct challenges. Coincidentally, these technologies raise important and distinct ethical issues that make stakeholder input critical during product design. We present a framework for fostering genuine engagement from stakeholders through the case example of biomusic - a SIIF-based affective technology that translates emotion-related physiological changes into sound. During a 3-day workshop, ethnographic methods were used to collect data about the interface between biomusic and individuals on the autism spectrum. From these data, emergent themes, such as such as privacy, data security, conceptions of assistive technology and representation of emotions were analyzed using a grounded theory approach. In order to illuminate distinct design decisions implicated by these complex and interwoven ethical issues, we propose a design framework consisting of a technological, a human-centered and an ecological lens. This framework and recommendations provide a concrete praxis for engaging stakeholders in the complex issues associated with the design of SIIF-based emotion-oriented systems. [ABSTRACT FROM AUTHOR]

Administrative justice concerns the relationships between individuals and the state. It includes redress and complaints on decisions of a child's education, social care, licensing, planning, environment, housing and homelessness. However, if someone has a complaint or an issue, it is challenging for people to understand different possible redress paths and explore what path is suitable for their situation. Explanatory visualisation has the potential to display these paths of redress in a clear way, such that people can see, understand and explore their options. The visualisation challenge is further complicated because information is spread across many documents, laws, guidance and policies and requires judicial interpretation. Consequently, there is not a single database of paths of redress. In this work we present how we have co-designed a system to visualise administrative justice paths of redress. Simultaneously, we classify, collate and organise the underpinning data, from expert workshops, heuristic evaluation and expert critical reflection. We make four contributions: (i) an application design study of the explanatory visualisation tool (Artemus), (ii) coordinated and co-design approach to aggregating the data, (iii) two in-depth case studies in housing and education demonstrating explanatory paths of redress in administrative law, and (iv) reflections on the expert co-design process and expert data gathering and explanatory visualisation for administrative justice and law. [ABSTRACT FROM AUTHOR]

We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301. [ABSTRACT FROM AUTHOR]

A stacked-coil technology is demonstrated that realizes size-adjustable coil systems to compensate the notorious problem of lateral misalignment in nonradiative wireless power transfer (WPT) systems. The proposed system has a simple structure that consists of multiple layers of coils with various sizes. Depending on the misalignment, the coil pair that provides the best transfer efficiency is switched on. Thus, not only the transfer null is removed, but also a very high transfer efficiency is maintained even with extreme misalignment. Experimental results at 6.78 MHz show the effectiveness of the stacked coil for both short- and mid-range wireless power transfer systems. The transfer efficiency maintains abovementioned 80% up to 87% and 65% lateral misalignment when the separation between Tx and Rx coils are 10% and 50%, respectively, relative to the largest dimension of the coil. The three-layer short-range system successfully removes the transfer null to maintain a minimum transfer efficiency of 39.7% up to 126% misalignment. The two-layer mid-range system maintains transfer efficiency abovementioned 50% up to 103% misalignment. Furthermore, the performance is virtually the same even in an asymmetric WPT system, i.e., when the technique is applied to only one of the two coils. A detailed design procedure is also provided. [ABSTRACT FROM AUTHOR]

This paper presents signal processing methods to enhance the energy vs. accuracy trade-off of in-memory computing (IMC) architectures. First, an optimal clipping criterion (OCC) for signal quantization is proposed in order to minimize the precision of column analog-to-digital converters (ADCs) at iso-accuracy. For a Gaussian distributed signal, the OCC is shown to reduce the column ADC precision requirements by 3 bits at a signal-to-quantization noise ratio (SQNR) of $\text{22.5}\,dB$ over the commonly used full range (FR) quantizer. Next, the input-sliced weight-parallel (ISWP) IMC architecture is presented as a generalization of the popular bit-serial bit-parallel (BSBP) architecture. Quantization noise analysis of the ISWP indicates that its accuracy is comparable to BSBP while providing an order-of-magnitude reduction in energy consumption due to fewer array invocations and smaller ADC precision. Combining OCC and ISWP noise analysis, we map popular DNNs such as VGG-9 (CIFAR-10), ResNet-18 (CIFAR-10), and AlexNet (ImageNet) on a OCC-enabled ISWP architecture and show a reduction in energy consumption by an order-of-magnitude at iso-accuracy over the BSBP architecture that employs FR-based ADCs. [ABSTRACT FROM AUTHOR]

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one of the potential solutions. Distributed intelligence can greatly relieve server pressures via plenty of terminal devices, and these devices collaboratively perceive and handle the mass data to improve the reliability, s-calability and security of industrial IoT systems. As future IoT system will embrace more wireless sensors and devices, the high-performance computing, high-bandwidth and low-latency communication are excessively required, many new research opportunities and challenges for distributed intelligence over Internet of things have arisen. To promote the development of distributed intelligence technology, this special section (SS) focuses on various technologies and platforms regarding industrial IoT systems. This special section received nearly 50 submitted manuscripts, out of which 10 of them have been accepted after a rigorous peer review. Each manuscript is reviewed by multiple rounds of review with at least three or four reviewers, the problems to be solved and the innovation of each manuscript are mainly concerned. Then the accepted papers are summarized as follows in details. Considering the joint optimization of the offloading decision and resource allocation under limited resource constraints in collaborative edge computing networks with multiple IIoT devices and MEC servers, an improved differential evolution algorithm [7] is proposed to minimize the weighted sum of cost of energy consumption and time delay, which can effectively reduce the system delay and energy consumption. In order to improve the performance of task scheduling in cloud computing, Attiya et al. [1] propose a novel hybrid swarm intelligence method MRFOSSA, which uses a modified Manta-Ray Foraging Optimizer (MRFO) and the Salp Swarm Algorithm (SSA). MRFOSSA is superior to other methods in terms of makespan time and cloud throughput. The research goal of the paper [5] is to design an intelligent computing offloading strategy for industrial applications in order to optimize costs and mitigate energy losses. Then the paper proposes to combine a fog controller and AI-based learning techniques so that the fog controller can intelligently assign tasks to the most appropriate fog devices and find the appropriate path to the target. Considering the resource utilization efficiency under dynamic overload requests and network states in IIoT, Chen et al. [2] propose DRL-based intelligent SFC orchestration scheme and jointly optimize the VNF deployment and SFC embedment by the improved DDQN algorithm, which can improve the performance of resource utilization rate, execution cost and delay compared with other representative schemes. To solve the problem of resource allocation and energy cost in Internet of Vehicles, Kong et al. [8] design a joint computing and caching framework and formulate the problem as a reinforcement learning problem to minimize the energy cost. On this basis, the optimization algorithm based on DDPG is proposed, which can effectively decrease energy costs. To reduce the query numbers of the object model when constructing adversarial examples, Zhang et al. [10] propose generating adversarial examples with shadow model (GASM), i.e., transfering the query operations to the designed shadow model, which can achieve high attack success rates. Chen et al. [3] revise a Decentralized-Wireless-Federated-Learning algorithm (DWFL) which utilizes the superposition property of the analog scheme. It can solve the problem of single failure, limited bandwidth resource and privacy protection in wireless federated learning algorithm, which can be applied widely in wireless IoT networks. To reduce the resource consumption in CNN-based applications, Jia et al. [6] propose the CNN-based Resource Optimization APProach which utilizes model compression and computation sharing to optimize inner-model and inter-model respectively, and the comparison results show the superior performance in scalability and the decrease of resource cost. In mobile crowdsensing activities, Gao et al. [4] propose a differential Location Privacy-preserving Mechanism based on Trajectory obfuscation (LPMT) to protect the location privacy of mobile users, which includes three operations: stay points extraction, stay points obfuscation and stay points sampling. In order to mimic the task-free bottom-up visual attention process by predicting salient regions on natural images, Umer et al. [9] propose a Pseudo Knowledge Distillation (PKD) model based on knowledge distillation and pseudo labelling technique, which is computationally efficient and suitable for real-time on-device saliency prediction. [ABSTRACT FROM AUTHOR]

In this article, we report on our experiences of running visual design workshops within the context of a master's level data visualization course, in a remote setting. These workshops aim to teach students to explore visual design space for data by creating and discussing hand-drawn sketches. We describe the technical setup employed, the different parts of the workshop, how the actual sessions were run, and to what extent the remote version can substitute for in-person sessions. In general, the visual designs created by the students as well as the feedback provided by them indicate that the setup described here can be a feasible replacement for in-person visual design workshops. [ABSTRACT FROM AUTHOR]

We present how to integrate Design Sprints and project-based learning into introductory visualization courses. A design sprint is a unique process based on rapid prototyping and user testing to define goals and validate ideas before starting costly development. The well-defined, interactive, and time-constrained design cycle makes design sprints a promising option for teaching project-based and active-learning-centered courses to increase student engagement and hands-on experience. Over the past five years, we have adjusted the design sprint methodology for teaching a range of visualization courses. We present a detailed guide on incorporating design sprints into large undergraduate and small professional development courses in both online and on-campus settings. Design sprint results, including quantitative and qualitative student feedback, show that design sprints engage students and help practice and apply visualization and design skills. We provide design sprint teaching materials, show examples of student-created work, and discuss limitations and lessons learned. [ABSTRACT FROM AUTHOR]

Today’s society is facing new challenges and opportunities that demand professional profiles specialized in problem solving, with the ability to innovate and exploit the possibilities offered by information and communication technologies (ICTs). Far from being a novelty, the term STEM was coined in the mid-1990s. From then until now, there are a multitude of initiatives focusing on working STEM education with students. In recent years, the use of the arts as an enhancer of the educational experience has been incorporated into STEM education. There has also been a focus on involving the student in the educational process. Despite this, few experiences have been detected in which parents are involved in the educational process. Throughout this work, it is shown the pilot experience which has been developed to motivate parents to be part of the learning process in science, technology, engineering, art, and mathematics (STEAM) subjects. [ABSTRACT FROM AUTHOR]

The Technical Committee on Biological and Medical Measurements (TC-25) aims at establishing, developing, promoting and supporting cooperation among researchers in the different fields of biological and medical measurements. Sensors for the measurement of clinical and/ or biological parameters, instrument and procedure characterization, instrument interoperability and interconnection, and data processing are some of the topics of interest to the TC-25. To those aims, the technical committee designs and realizes joint activities, like study groups aimed at standardization, such as the Subcommittee on Blood Pressure Measurement (SCOBPM); conferences or special sessions, such as the IEEE International Symposium on Medical Measurements and Applications (MeMeA); and several special sessions during the annual IEEE Instrumentation and Measurement Technical Conferences. TC-25 is also continuously engaged to maintain liaisons with other committees, groups, societies and organizations working on topics related to the TC scope. This paper presents the latest updates about the main activities of the TC-25. Some information about the TC-25 activities and targets is briefly presented, then the objectives and the efforts of the SCOBPM are summarized, and finally, the history and latest updates of our main conference, the MeMeA Symposium, are reported. [ABSTRACT FROM AUTHOR]

This article analyzes a survey delivered in 2019 to 259 experts in engineering education that asked them to forecast information and communication technologies, which were most likely to impact the practice of engineering education based on the expert's discipline (electrical, electronics, mechanical, telecommunications engineering, computer science, etc.) and region. The analysis was performed from different perspectives. First, a descriptive approach was used to analyze technologies that are considered as the most important for the experts and a nonparametric perspective approach was adopted to evaluate variation in the responses according to discipline, region, and years of experience. Second, a decision tree technique was used to establish technology profiles based on when the experts expected that the technologies will impact mass engineering education and the challenges or requirements needed to get widespread use. Third, the Spearman correlation coefficient was used to determine the value of social interest, measured through Google Trends, as a predictor of expert ratings of different technologies. Results indicate adaptive and personalized learning technologies, learning analytics, and open educational resources are the three most promising technologies that will impact engineering education in the short term. Results also show a relation between the predictions made by the experts in 2019 and the social interest in the Google Trends education category. [ABSTRACT FROM AUTHOR]

Over the recent winter holidays, in a casual conversation with some engineering friends, I brought up the role of engineers in the Cahora Bassa Dam of Mozambique, a project I highlighted at the recent IEEE International Symposium on Technology and Society 2020 (ISTAS20) Conference. During the construction of the dam, local knowledge of drainage and seasonal river patterns were ignored by European and American engineers, which resulted in tragic consequences for both the environment and the welfare of the indigenous villages along the wide and mountainous Zambezi River. In response, one of my (anonymous) friends declared, “Well, that’s not the engineer’s fault. The engineers were asked to optimize the placement of the dam, so they optimized it. Those consequences are the fault of the manager who scoped out the project!” [ABSTRACT FROM AUTHOR]

Kunpeng 920 is the second generation server processor designed by HiSilicon based on ARM architecture. Kunpeng 920 is able to achieve cost efficiency for various workloads through using a variety of chiplets and hybrid process technologies. The unique recomposition(s) of these flexible chipsets allows new designs to be created. The Kunpeng series processors combine technology innovations from various levels to improve efficiency, eliminate bottlenecks, and deliver value and performance. Its key features are as follows: The Kunpeng 920 core is specifically designed with superscalar architecture with the support of vector extension to provide leading features for high-performance computing applications; the coherent cache subsystem is created to integrate multicores into single chiplet (e.g., 7-nm process node) with a ring design that is ultralow-latency ( [ABSTRACT FROM AUTHOR]

Resilience of a distribution system can be enhanced by efficient restoration of critical load following a major outage. Existing models include optimization approaches that consider available information without incorporating the inherent asynchrony of data arrival during execution of the restoration plan. Failure to consider the asynchronous nature of information arrival can lead to underutilization of critical resources. Moreover, analytical models become computationally inefficient for large scale systems. On the other hand, artificial intelligence (AI)-based tools have demonstrated efficient results for power system applications. In this paper, it is proposed a Reinforcement Learning (RL) model that learns how to efficiently restore a distribution system after a major outage. The proposed approach is based on a Monte Carlo Tree Search to expedite the training process. The proposed model strategy provides a robust decision-making tool for asynchronous and partial information scenarios. The results, validated with the IEEE 13-bus test feeder and IEEE 8500-node distribution test feeder, demonstrate the effectiveness and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

Quantum computing is experiencing an inflection point. This technology is likely to facilitate great advances in many fields including logistics, chemistry simulation, or even risk analysis. It is attractive for large hardware and software companies in addition to start-up entrepreneurial enterprises. In this article, we summarize interesting insights into this field related to possibilities related to quantum computing and entrepreneurship. [ABSTRACT FROM AUTHOR]

Service oriented architecture (SOA) provides a flexible platform to build collaborative Internet applications by composing existing self-contained and autonomous services. However, the implicit interactions among the concurrently provisioned services may introduce interference to Internet applications and cause them behave abnormally. It is thus desirable to isolate services to safeguard their application consistency. Existing approaches mostly address this problem by restricting concurrent execution of services to avoid all the implicit interactions. These approaches, however, compromise the performance and flexibility of Internet applications due to the long running nature of services. This paper presents Sifter, a new service isolation strategy for Internet applications. We devise in this strategy a novel static approach to analyze the potential implicit interactions among the services and their impacts on the consistency of the associated Internet applications. By locating only those afflicted implicit interactions that may violate the application consistency, a novel approach based on exception handling and behavior constraints is customized to involved services to eliminate their impacts. We show that this approach exempts the consistency property of Internet applications from being interfered at runtime. The experimental results show that our approach has a better performance than existing solutions. [ABSTRACT FROM AUTHOR]

We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years. [ABSTRACT FROM AUTHOR]

This article presents the experience of a longlasting collaboration in robotics between the countries of Japan and Italy. The diversity of the approaches to robotics research and technology in the two countries and, at the same time, the similarity of some social drives for robotics progress have created a stimulus for generating new thinking about robotics. Not only has this occurred at the main participant institutions of Waseda University in Tokyo and Scuola Superiore Sant?Anna in Pisa, Italy, but it has also happened worldwide. The authors present a summary of the Italy-Japan workshops, exploring topics on 1) humanoid robotics, 2) the application of robotics for exploration and rescue, 3) biorobotics, 4) the use of robots in education, 5) the ethics of robotics, 6) robotics for sports, and 7) robotics for the arts. The experience of the workshops represents an example of an open-minded approach to robotics that is grounded on social challenges, explored through multidisciplinary discussion. Such interdisciplinary research efforts deserve to be shared with the international robotics community. Indeed, this approach could be used as a base model to foster further international collaborations among countries as well. [ABSTRACT FROM AUTHOR]

The input data of photometric stereo have three dimensions: one photometric dimension of different lights and two spatial dimensions, i.e., rows and columns in the image coordinate. Recent deep-learning-based photometric stereo algorithms usually use 2-D/3-D convolutions to process the input with three dimensions. Assumptions that violate the natural characters of photometric stereo problem, e.g., spatial pixel interdependence and light permutation invariance, have to be made due to the dimension mismatch. In this article, we propose a self-attention photometric stereo network (SPS-Net), which can exploit the information in all three dimensions without violating these natural characters. In SPS-Net, the spatial information is extracted by convolutional layers and the photometric information is aggregated by the proposed photometric fusion blocks based on the self-attention mechanism. Extensive experiments on both synthetic and real-world data sets are conducted. The proposed SPS-Net achieved higher performance than the state-of-the-art algorithms photometric stereo task with dense lightings. Without any changes, the proposed algorithm also outperformed the benchmarks in sparse and light-information-robust photometric stereo tasks. [ABSTRACT FROM AUTHOR]

Scientific software underpins a large and growing number of scientific discoveries and engineering advances. At the same time, it is a small portion of the total software produced, has little literature, or focus on improvement and community building relative to other, larger software domains. The Collegeville Workshop Series on Scientific Software provides a platform for scientific software teams and stakeholders to exchange information on the nature, challenges, and technical and cultural approaches to improve key elements of the scientific software enterprise. By soliciting white papers, posters, and tea-time themes, these workshops gather content to provide a three-day live event, plus recorded presentations and interviews that enable community members to learn from each other. The outcomes from the first two workshops, in 2019 and 2020, have provided a body of knowledge and experience that expands the size and recognition of software as an important part of the scientific enterprise. The 2021 workshop, focused on software teams, intends to further expand the conversation, and continue to build community around scientific software to improve its impact on science and engineering. [ABSTRACT FROM AUTHOR]

With the increasing demand for distributed big data analytics and data-intensive programs which contribute to large volumes of packets among processing elements (PEs) and memory banks, we witness a pressing need for new mathematical models and algorithms that can engineer a brain-inspired plasticity into the computing platforms by mining the topological complexity of high-level programs (HLPs) and exploiting their self-similar and fractal characteristics for designing reconfigurable domain-specific computing architectures. In this article, we present Plasticity-on-Chip (PoC) by engineering plasticity into ”artificial brains” to mine and exploit the self-similarity of HLPs. First, we present a communication modeling of HLPs (e.g., C/C++ implementations of various applications) that relies on static and dynamic compiler analysis of programs with varying input seeds, performing comprehensive program analysis of all traces, and representing the HLPs as weighted directed acyclic graphs while capturing the intrinsic timing constraints and data/control flow requirements. Second, we propose a rigorous mathematical framework for determining the optimal parallel degree of executing a set of interacting HLPs (by partitioning them into clusters of densely interconnected supernodes - tasks) which helps us decide the number of available heterogeneous PEs, the amount of required memory and the structure of the synthesized deadlock-free irregular NoC topology that offers an efficient communication medium. These clusters serve as abstract models of computation for the synthesized PEs within the parallel execution model. Finally, exploiting the fractal and complex networks concepts, we extract in-depth features from graphs that serve as inputs for distributed reinforcement learning. Our experimental results on synthesized PEs and NoCs show performance improvements as high as 7.61x when compared to the traditional NoC and 2.6x compared to gem5-Aladdin. [ABSTRACT FROM AUTHOR]

While industrial firms realize the importance of Industry 4.0, many have not yet started implementing the technologies required to harvest the benefits. To enable the adoption of artificial intelligence applications for Industry 4.0 and to address the gap between advances in technologies and their adoption in industry, this article presents a team-based workshop to capture organizational knowledge for identifying relevant artificial intelligence proof-of-value (AI POV) projects based on system engineering knowledge management tools. The aim of this one-day workshop is to identify a potential AI POV within an organization, including its starting point and initial expected savings. [ABSTRACT FROM AUTHOR]

Infographics are frequently promoted for their ability to communicate data to audiences affectively. To facilitate the creation of affect-stirring infographics, it is important to characterize and understand people's affective responses to infographics and derive practical design guidelines for designers. To address these research questions, we first conducted two crowdsourcing studies to identify 12 infographic-associated affective responses and collect user feedback explaining what triggered affective responses in infographics. Then, by coding the user feedback, we present a taxonomy of design heuristics that exemplifies the affect-related design factors in infographics. We evaluated the design heuristics with 15 designers. The results showed that our work supports assessing the affective design in infographics and facilitates the ideation and creation of affective infographics. [ABSTRACT FROM AUTHOR]

TO BUILD a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare develop products for specialized medical applications. Using IoT-Health for data collection and workflow automation is a great way to reduce waste and minimize human errors. However, the security of healthcare information is a major concern, and cybersecurity has become a significant threat for healthcare providers as well as governments to achieve sustainable city milestones. IT professionals must continually resolve health data security issues to help patients and the damage that healthcare security breaches can have on their lives. [ABSTRACT FROM AUTHOR]

The Portland International Conference for Management of Engineering and Technology (PICMET) has become a world-leading organization in the field of management of engineering and technology management (MET) since its inception in 1989. PICMET provides a strong platform for academics, industry professionals, and government representatives to exchange new knowledge in the field. To celebrate its 30-year journey, this article examines 20 conferences organized by PICMET covering 6601 accepted papers in order to show the trends in MET research and implementation through topics, authors, journals, and countries. In addition to the analysis of the PICMET data, the article delves into the past ten years (2009–2018) to carry out an in-depth bibliometric analysis of the citations of more than 3000 PICMET papers available at Scopus. The detailed analysis sheds light on how PICMET has developed a rich network of researchers and practitioners through its conferences over time. PICMET contributes to the interdisciplinary nature of the MET field and is also affected by the changes of the field. The article ends with key observations and a few suggestions for further studies. [ABSTRACT FROM AUTHOR]

Numbers of citations of outstanding conference papers (OCPs) of IEEE Nuclear and Space Radiation Effects Conferences (NSRECs) from 1966 to 2017 are compared with citations of the most highly cited papers (HCPs) of the corresponding special issues of the IEEE TRANSACTIONS ON NUCLEAR SCIENCE (TNS), as well as average numbers of citations and cumulative $h$ index, $h_{\mathrm {cum}}$ , for all TNS papers published in the same year. 85% of NSREC OCPs are cited at a higher rate than the average for all TNS articles in that year. 55% of NSREC OCP citation totals are higher than $h_{\mathrm {cum}}$ for TNS in that year; these rank among the ~8% most highly cited TNS papers for that year. In ~10% of cases, the NSREC OCP is also the most HCP. Similarly high citation impact is found for papers nominated as candidates for OCPs. Radiation effects papers published in annual NSREC special issues comprise a significant fraction of all TNS articles cited $h_{\mathrm {cum}}$ times or more. These results support the use of $h_{\mathrm {cum}}$ as a metric of citation impact in this work and show that the NSREC OCP process generally meets awards criteria guidelines for impact. Potential reasons for variations in citation rates are discussed, and recommendations are provided to enhance the awards process. [ABSTRACT FROM AUTHOR]

Introduction: Engineering programs must take creative approaches to ensure that their students receive needed communication instruction in curricula constantly experiencing pressures of accreditation, state, and industry requirements: expectations for students’ knowledge and skills increase although curricula are compressed. Situating the case: Technical communication and engineering education scholarship describe multiple models for integrating writing instruction into engineering curricula: 1. writing across the curriculum, 2. partnership models, 3. embedded models, and 4. support models. About the case: Technical and professional writing and engineering faculty collaborated to develop a hybrid model, which borrows from multiple existing models for integrating technical writing education throughout the engineering curriculum, both in and outside of courses, including collaborative workshops, specialized writing center support, and other interventions. Methods/approach: Survey research was conducted with students on the effectiveness of multiple writing interventions. Results/discussion: The hybrid model enables students to experience a variety of writing interventions; students perceived them as beneficial. Students found most effective writing interventions occurred in the context of their engineering coursework. Faculty and administrators found the approach beneficial because of its collaborative nature and because it balanced instructional time with external support methods. Conclusions: Local solutions to universal problems must take many variables into consideration: people and programmatic cultures, disciplinary and institutional contexts, and curricular, regulatory, and funding constraints. The authors’ hybrid model for integrating technical writing into the engineering curriculum represents a flexible, sustainable approach adaptable to meet specific needs in specific environments at different institutions. [ABSTRACT FROM AUTHOR]