Purdue University Fort Wayne

= 260) perceptions of their own and their children's use of social media and other types of communication technologies in the beginning stages of coronavirus disease 2019 (COVID-19) related sanctions (e.g., social distancing) in the United States. We also examined associations between social media and technology use and anxiety. On average, parents reported that both they and their children (especially teenagers aged 13-18) had increased technology and social media use since the beginning of social distancing. Moreover, even after controlling for demographic factors, structural equation models showed that parents and children with higher levels of anxiety (as reported by parents) were more likely to increase their technology use and use social media and phones to connect. Among parents, higher anxiety was related to using social media for both social support and information seeking. Based on these results, we advocate for the utilization of social media by public health officials for collecting, collating, and dispersing accurate crisis-related information. As social media use is widespread, and there is potential for false rumors to cause erroneous behavioral action and/or undue stress and anxiety, we also suggest that social media campaigns be thoughtfully designed to account for individual differences in developmental stages and psychological vulnerabilities.

There are few certainties in our visions of post-COVID-19 careers, but change is inevitable. This article will explore how HRD can be proactive in addressing the immediate needs of the post-pandemic workforce and workplaces, as they strive to recover and resume a productive future. Uncertainties about employment and employability, how workplaces will be configured, the future of some careers and the possibilities for new opportunities will weigh heavily on individuals as they navigate these challenges. Drawing on the career shock, resilience, and sustainable careers literature, we consider how both individual and contextual factors will impact people and their occupations moving forward.

Mediated Discourse ( MD ) reports a longitudinal study of the development of a one-year-old child, focusing on a single, narrowly defined social practice – handing, or “the simple practice of giving an object to another person” (p. 12). In this book, R. Scollon proposes a framework of mediated discourse to address social (practice) theory, which was “badly in need of an ontogenetic view of social practice” (vii). Although Ron Scollon is an author of both books under review, the second differs from the first, theoretical, work in focusing on practical application. Professional communication in international settings ( PCIIS ) is an excellent book that is much needed by professionals, researchers, trainers, or anyone else involved in business or professional communication across cultural boundaries. Different from many other books on the same subject, its goal is to offer a method for effective communication, and it can be used as either a textbook or a reference.

Abstract The collection and dissemination of vertebrate ichnological data is struggling to keep up with techniques that are becoming commonplace in the wider palaeontological field. A standard protocol is required to ensure that data is recorded, presented and archived in a manner that will be useful both to contemporary researchers, and to future generations. Primarily, our aim is to make the 3D capture of ichnological data standard practice, and to provide guidance on how such 3D data can be communicated effectively (both via the literature and other means) and archived openly and in perpetuity. We recommend capture of 3D data, and the presentation of said data in the form of photographs, false‐colour images, and interpretive drawings. Raw data (3D models of traces) should always be provided in a form usable by other researchers (i.e. in an open format). If adopted by the field as a whole, the result will be a more robust and uniform literature, supplemented by unparalleled availability of datasets for future workers.

Identifying the neural mechanisms underlying spatial orientation and navigation has long posed a challenge for researchers. Multiple approaches incorporating a variety of techniques and animal models have been used to address this issue. More recently, virtual navigation has become a popular tool for understanding navigational processes. Although combining this technique with functional imaging can provide important information on many aspects of spatial navigation, it is important to recognize some of the limitations these techniques have for gaining a complete understanding of the neural mechanisms of navigation. Foremost among these is that, when participants perform a virtual navigation task in a scanner, they are lying motionless in a supine position while viewing a video monitor. Here, we provide evidence that spatial orientation and navigation rely to a large extent on locomotion and its accompanying activation of motor, vestibular, and proprioceptive systems. Researchers should therefore consider the impact on the absence of these motion-based systems when interpreting virtual navigation/functional imaging experiments to achieve a more accurate understanding of the mechanisms underlying navigation.

Internet of Things (IoT) refers to networks with billions of physical devices for collecting, sharing, and utilizing data in the virtual world. Most of IoT applications centralize security assurance in creating, authenticating, transferring, or delating system components. However, the centralization exposes its limitations to meet security needs of a rapidly growing number of things world-widely. How to scale up the applications with assured security becomes a critical challenge. Blockchain technology (BCT) is a promising solution to provide security and protect privacy in a large scale; especially, smart contracts offer opportunities to improve the reliability of IoT applications. Smart contracts establish trusts for both of data and executed processes. Recently, many literature surveys and positioning articles have been published on the integration of BCT with IoT, but they are limited to superficial discussions of technical potentials, and very few of them have a thorough exploration of the challenges in developing BCT for IoT at technical levels. This paper uses the system design approach to scrutinize the state of the art of study on BCT-based applications and clarify critical research areas of enabling BCT for security assurance: 1) the relations of BCT and IoT are modeled and discussed; 2) the needs of eliminating threats in IoT-based applications are defined as functional requirements (FRs), existing works on enabling technologies of BCT are defined as the physical solutions (PSs); and 3) the mappings between FRs and PSs are established to identify the limitations and the critical areas for the applications of BCT in large-scale distributed environment.

Abstract The analysis of sentiments is essential in identifying and classifying opinions regarding a source material that is, a product or service. The analysis of these sentiments finds a variety of applications like product reviews, opinion polls, movie reviews on YouTube, news video analysis, and health care applications including stress and depression analysis. The traditional approach of sentiment analysis which is based on text involves the collection of large textual data and different algorithms to extract the sentiment information from it. But multimodal sentimental analysis provides methods to carry out opinion analysis based on the combination of video, audio, and text which goes a way beyond the conventional text‐based sentimental analysis in understanding human behaviors. The remarkable increase in the use of social media provides a large collection of multimodal data that reflects the user's sentiment on certain aspects. This multimodal sentimental analysis approach helps in classifying the polarity (positive, negative, and neutral) of the individual sentiments. Our work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve human–machine interaction and challenges involved in analyzing them. A detailed survey on sentimental dataset, feature extraction algorithms, data fusion methods, and efficiency of different classification techniques are presented in this work. This article is categorized under: Commercial, Legal, and Ethical Issues > Social Considerations

Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.

Along with the development of Information Technology, Online Social Networks (OSN) are constantly developing and have become popular media in the world. Besides communication enhancement benefits, OSN have such limitations on rapid spread of false information as rumors, fake news, and contradictory news. False information spread is collectively referred to as misinformation which has significant on social communities. The more sources and topics of misinformation are, the greater the number of users are affected. Therefore, it is necessary to prevent the spread of misinformation with multiple topics within a given period of time. In this paper, we propose a Multiple Topics Linear Threshold model for misinformation diffusion, and define a misinformation blocking problem based on this model that takes account of multiple topics and budget constraint. The problem is to find a set of nodes that minimizes the impact of misinformation at an allowed cost when blocking them from the network. We prove that the problem is NP-hard and the time complexity of the objective function calculation is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\#P$ </tex-math></inline-formula> -hard. We also prove that the objective function is monotone and submodular. We propose an approximation algorithm with approximation ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-1/\sqrt {e})$ </tex-math></inline-formula> based on these attributes. For large networks, we propose an extended algorithm by using a tree data structure for quickly updating and calculating the objective function. Experiments conducted on real-world datasets show efficiency and effectiveness of our proposed algorithms in comparison with other state-of-the-art algorithms.

Cow behavior recognition systems support the assessment of cows’ condition by providing their behavior information. Accelerometers are particularly suited for a non-invasive solution for the development of these monitoring systems. They are cheap and simple in setting up and providing high-performance recognition when using machine learning algorithms. The activity complexity of animals brings challenges in real context applications because different behaviors may have similar acceleration data. The reason is that they contain similar gestures, for example, feeding and standing. In our previous work, we proposed a cows’ behavior classifier based on leg-mounted acceleration data. The distinguishing of similar behaviors, such as feeding and standing, is limited by the data. This study presents a new efficient cow behavior recognition system based on combining leg-mounted and collar-mounted accelerometers. Significantly, the acceleration data from these two sensors were synchronized. Therefore, we can substantially expand the amount of information for classification purposes. Our approach identifies four cow behaviors: walking, feeding, lying, and standing. Random Forest algorithm with our extracted features (root mean square, standard deviation, and mean) and 16-second data window (a sample/second) offer excellent performance when identifying all concerning behaviors: feeding (0.914 accuracy, 0.884 sensitivity, 0.956 positive predictive value), lying (0.998, 0.996, 1), standing (0.88, 0.928, 0.842), and walking (0.998, 0.996, 0.998). These performances are better than other existing works, especially in our experiments with free-grazing cows.

This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise information systems are viewed as information systems to acquire, process, and utilise data in decision-making supports at all levels and domains of businesses, and Internet of things (IoT), big data analytics (BDA), and digital manufacturing (DM) are introduced as representative enabling technologies for data collection, processing, and utilisation in manufacturing applications. The historical development of manufacturing technologies is examined to understand the evolution of system paradigms. The Shannon entropy is adopted to measure the complexity of systems and illustrate the roles of EAs in managing system complexity and achieving system stability in the long term. It is our argument that existing EAs sacrifice system flexibility, resilience, and adaptability for the reduction of system complexity; note that higher adaptability is critical to make a manufacturing system successfully. New EA is proposed to maximise system capabilities for higher flexibility, resilience, and adaptability. The potentials of the proposed EA to modern manufacturing are explored to identify critical research topics with illustrative examples from an application perspective.

In this study, the performance of a secondary network in cognitive radio (CR) is studied in the context of vehicle-to-everything (V2X). The non-orthogonal multiple access (NOMA) is effectively applied in this new system model, namely CR-assisted NOMA-V2X, and it is beneficial to serve group of vehicles. In our proposed system, two schemes related to vehicle-to-vehicle (V2V) transmissions are further considered to enhance performance of the vehicle that needs higher quality of service (QoS). However, the degradation performance can be predicted by evaluating downlink under impacts from interference from the primary network, imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC). The outage performance gap among two vehicles exists since different power allocation factors were assigned to them. To validate the system performance, the outage probability is first derived in exact and approximate forms and then the throughput can be further achieved. The optimal throughput can be obtained by numerical simulations. Simulation results are provided to verify the correctness of the derived expressions and it exhibits advantages of the proposed CR-assisted NOMA-V2X system in terms of outage probability and the throughput.

A computationally efficient method for image registration is investigated that can achieve an improved performance over the traditional two-dimensional (2-D) cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise. The method relies on transforming each image in the sequence of frames into two vector projections formed by accumulating pixel values along the rows and columns of the image. The vector projections corresponding to successive frames are in turn used to estimate the individual horizontal and vertical components of the shift by means of a one-dimensional (1-D) cross-correlation-based estimator. While gradient-based shift estimation techniques are computationally efficient, they often exhibit degraded performance under noisy conditions in comparison to cross-correlators due to the fact that the gradient operation amplifies noise. The projection-based estimator, on the other hand, significantly reduces the computational complexity associated with the 2-D operations involved in traditional correlation-based shift estimators while improving the performance in the presence of temporal and spatial noise. To show the noise rejection capability of the projection-based shift estimator relative to the 2-D cross correlator, a figure-of-merit is developed and computed reflecting the signal-to-noise ratio (SNR) associated with each estimator. The two methods are also compared by means of computer simulation and tests using real image sequences.

Internet of Things (IoT) is able to integrate the computation and physical processes as services in the social world. The number of services at the edge of IoT is rising rapidly due to the prevalent uses of smart devices and cyber-physical systems (CPSs). To explore the promising applications of IoT services, one of the challenges is to enable the interoperability of the services in a decentralized environment. The blockchain technology (BCT) has been proven as a promising solution to establish the trust of data and call for executions; theoretically, it can be used to support the interoperability of services. BCT verifies data or a process and stores it as a transaction in a distributed ledger. Similar to the topology to IoT, applying BCT at the edges of the network exhibits the distributed characteristic. However, currently, BCT is still facing the challenges for interoperability due to a number of factors such as consensus protocols, block sizes, and interval of blocks. Prominent protocols such as proof-of-work (PoW) may cause excessive delays in finality settlement. One promising protocol Practical Byzantine Fault Tolerant offers a fast finality settlement and uses hyperledger to support the scalability; however, the trust might also be a concern if the validators are chosen improperly. This paper discusses the interoperability of IoT services and the challenges and proposes an architecture solution by integrating BCT, service-oriented architecture (SoA), and enablers of key performance indicators (KPIs) and service selections. The proposed architecture aims to solve both interoperability and trust issues for IoT services. The feasibility of the proposed method is validated by the examples of smart contract implementations.

Delineating spatial boundaries that accurately encompass complex, often cryptic, life histories of highly migratory marine megafauna can be a significant conservation challenge. For example, marine turtles range across vast ocean basins and coastal areas, thus complicating the evaluation of relative impacts of multiple overlapping threats and the creation of coherent conservation strategies. To address these challenges, spatially explicit ‘regional management units’ (RMUs) were developed in 2010 for all marine turtle species, globally. RMUs were intended to provide a consistent framework that organizes conspecific assemblages into units above the level of nesting rookeries and genetic stocks, but below the species level, within regional entities that may share demographic trajectories because they experience similar environmental conditions and other factors. From their initial conception, RMUs were intended to be periodically revised using new information about marine turtle distributions, life history, habitat use patterns, and population structure. Here, we describe the process used to update the 2010 RMU framework by incorporating newly published information and inputs from global marine turtle experts who are members of the IUCN Marine Turtle Specialist Group. A total of 48 RMUs for 6 of 7 marine turtle species and 166 distinct genetic stocks for all 7 species are presented herein. The updated RMU framework reflects a significant advance in knowledge of marine turtle biology and biogeography, and it provides improved clarity about the RMU concept and its potential applications. All RMU products have been made open access to support research and conservation initiatives worldwide.

Abstract Modern malware evolves various detection avoidance techniques to bypass the state‐of‐the‐art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning models to classify and detect malware. However, existing works in this field only perform simple image transformation methods. These simple transformations have not considered color encoding and pixel rendering techniques on the performance of machine learning classifiers. In this article, we propose a novel approach to encoding and arranging bytes from binary files into images. These developed images contain statistical (eg, entropy) and syntactic artifacts (eg, strings), and their pixels are filled up using space‐filling curves. Thanks to these features, our encoding method surpasses existing methods demonstrated by extensive experiments. In particular, our proposed method achieved 93.01% accuracy using the combination of the entropy encoding and character class scheme on the Hilbert curve.

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.

Fingerprint image enhancement is a key aspect of an automated fingerprint identification system. This paper describes an effective algorithm based on a novel lighting compensation scheme. The scheme involves the use of adaptive higher-order singular value decomposition on a tensor of wavelet subbands of a fingerprint (AHTWF) image to enhance the quality of the image. The algorithm consists of three stages. The first stage is the decomposition of an input fingerprint image of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2M\times 2N$ </tex-math></inline-formula> into four subbands at the first level by applying a two-dimensional discrete wavelet transform. In the second stage, we construct a tensor in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{M\times N\times 4}$ </tex-math></inline-formula> space. The tensor contains four wavelet subbands that serve as four frontal planes. Furthermore, the tensor is decomposed through higher-order singular value decomposition to separate the fingerprint’s wavelet subbands into detailed individual components. In the third stage, a compensated image is produced by adaptively obtaining the compensation coefficient for each frontal plane of the tensor-based on the reference Gaussian template. The experimental results indicated that the quality of the AHTWF image was higher than that of the original image. The proposed algorithm not only improves the clarity and continuity of ridge structures but also removes the background and blurred regions of a fingerprint image. Therefore, this algorithm can achieve higher fingerprint classification accuracy than related methods can.

Most of today's smart grids are highly vulnerable to cascading failure attacks in which the failure of one or more critical components may trigger the sequential failure of other components, resulting in the eventual breakdown of the whole system. Existing works design different ranking methods for critical node or link identifications that fail to identify potential cascading failure attacks. In this work, we first consider the system from the attacker's point of view with a limited attack budget to study the smart grid vulnerability, referred to as Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. We propose an efficient algorithm by considering the interdependency property of the system, called Greedy Based Partition Algorithm (GBPA) to solve the MICLLB problem. In addition, we design an algorithm, namely Homogeneous-Equality Based Defense Algorithm (HEBDA) to help reduce damages in case the system is suffering from the cascading failure attacks. Through rigorous theoretical analysis and experimentation, we demonstrate that the investigated problem is NP-complete problem and our proposed methods perform well within reasonable bounds of computational complexity.

Sea turtles are an iconic group of marine megafauna that have been exposed to multiple anthropogenic threats across their different life stages, especially in the past decades. This has resulted in population declines, and consequently many sea turtle populations are now classified as threatened or endangered globally. Although some populations of sea turtles worldwide are showing early signs of recovery, many still face fundamental threats. This is problematic since sea turtles have important ecological roles. To encourage informed conservation planning and direct future research, we surveyed experts to identify the key contemporary threats (climate change, direct take, fisheries, pollution, disease, predation, and coastal and marine development) faced by sea turtles. Using the survey results and current literature, we also outline knowledge gaps in our understanding of the impact of these threats and how targeted future research, often involving emerging technologies, could close those gaps.