Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations. Especially, four kinds of deepfake or face manipulations are reviewed, i.e., identity swap, face reenactment, attribute manipulation, and entire face synthesis. For each category, deepfake or face manipulation generation methods as well as those manipulation detection methods are detailed. Despite significant progress based on traditional and advanced computer vision, artificial intelligence, and physics, there is still a huge arms race surging up between attackers/offenders/adversaries (i.e., DeepFake generation methods) and defenders (i.e., DeepFake detection methods). Thus, open challenges and potential research directions are also discussed. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions.
Processing-in-memory (PIM) is a promising architecture to design various types of neural network accelerators as it ensures the efficiency of computation together with Resistive Random Access Memory (ReRAM). ReRAM has now become a promising solution to enhance computing efficiency due to its crossbar structure. In this paper, a ReRAM-based PIM neural network accelerator is addressed, and different kinds of methods and designs of various schemes are discussed. Various models and architectures implemented for a neural network accelerator are determined for research trends. Further, the limitations or challenges of ReRAM in a neural network are also addressed in this review.
Electrocardiography (ECG) is an essential technique to assess cardiovascular condition and monitor physical activities. While the concept is mature, issues surrounding sampling convenience, device adoption and maintaining signal quality under artifacts remain a problem. In this paper, we present a high performing wearable ECG armband on the upper left arm. It is equipped with miniaturized hardware, capable of data storage and wireless communication. We evaluate different electrode configurations by conducting ECG measurements both at static state and under motion, and using improved algorithms to quantify data quality and assess the agreement between proposed new technique and the gold standard. The optimal electrode position is determined by balancing wearable suitability and signal quality. We propose an electronic textile (E-textile) armband with improved design. It offers favorable wearing comfort and fashionable appearance without sacrificing data quality. Its contact pressure is measured to get a better picture of intimacy and clothing comfort. Our system provides real-time and noise-resilient ECG data without interrupting daily life and can be implemented in use cases that warrant continuous ECG monitoring.
We incorporate deep learning techniques into capacitive images of body parts (ear, four fingers, and thumb) to improve the performance of user authentication in smartphones. Use of a capacitive touchscreen as an image sensor has several advantages, such as it is less sensitive to poor illumination conditions, occlusions, and pose variations. Also, it does not need an additional hardware like iris or fingerprint scanner. Use of capacitive images for user authentication is not new. However, the performance, specially, false reject rates (FRRs) of the state-of-the-art capacitive image-based systems are poor. In this paper, we focus on improving the performance and leverage deep learning. Deep learning techniques demonstrated spectacular performance in previous physical biometrics-based research. However, to our knowledge, effectiveness of deep learning is still unexplored in capacitive touchscreen-based user authentication. In order to bridge this research gap, we devise a multi-modal deep learning model, namely UASNet, and compare its performance with a large set of uni- and multi-modal baselines. Using the UASNet, we achieve an accuracy of 99.77%, an EER of 0.48%, and an FRR of 1.19% at FAR of 0.06%.
Horizontal directional drilling (HDD) is a widely used trenchless method for underground utility connections. The associated ground settlement triggered by HDD depends on the size, types, and surface texture of pipe, diameter of borehole, and soil conditions. The present study investigates the surface settlement due to the construction of a 1067 mm diameter HDD, which will replace an existing sewer siphon under the SR-60 highway in Chino, California using empirical, and numerical methods. Based on the results obtained from the subsurface investigation, an empirical analysis was conducted first. followed by numerical modeling of the HDD using PLAXIS 2D software. A careful comparison between two different methods indicated closer values of surface settlement between the empirical method (7.3 mm) and the numerical modeling (4.6 mm). In addition, the shape of surface settlement and horizontal settlement curves for the empirical and numerical methods was found to be similar. The minor discrepancy between the two methods resulted as the numerical model can host several soil layers whereas the empirical equation can use only one type of soil. Finally, a parametric study was conducted to evaluate the effect of borehole cover depth, size, and soil parameters on surface settlement. It was observed that soil strength parameters yielded a greater effect on surface movement, whereas modulus of elasticity has a relatively smaller influence with zero contribution from Poisson's ratio.
This study entails a state-of-the-art quantitative modeling approach to latent class analysis (i.e., marketing segmentation and targeting analysis) of American, French, and French-Canadian consumers’ perception of American and French products based on their demographics and individual level cultural values. It identifies ‘hidden’ segments of consumers in the American/French/French-Canadian cultures and subcultures using their perception on US and France’s level of competitiveness, similarities between the French, American, and French-Canadian cultures, and understanding of these consumers towards France and US. We unveil these segments for three major French and American product categories. For the former, we study car, wine, and perfume, where car represents durable, wine represents shopping, and perfume covers luxury products; and for the latter we include large electronics (durables), apparel (shopping), and designer sunglasses (luxury). To the best of our knowledge, this is the first study investigating such a segmentation research question for French and American products by marrying analytics/modeling with individual level cultural values and the international business field. Results of this analysis have implications for marketing French and American products in the US, France, and French-Canadian markets and have applications for managers in improving the effectiveness of their segmentation/targeting processes, helping them get better responses as they target different segments, hence, reaching higher sales levels. This analysis contributes to academics and practitioners in at least three major levels: (1) it extends the marketing and international business literature by empirically unveiling hidden segments of American/French/French-Canadian consumers based on their demographics towards American and French products, (2) it offers managerial implications to those managers selling American and French products in the US/French/French-Canadian markets helping them better choose their target markets, and (3) by lowering targeting/advertising costs, it sets the ground for higher profits. The paper also further develops and updates the globalization and cultural change theory in additional markets and provides insights into the evolution of globalization and cosmopolitanism in consumer behavior.
Unresolved states of mind regarding experiences of loss/abuse (U/d) are identified through lapses in the monitoring of reasoning, discourse, and behavior surrounding loss/abuse in response to the Adult Attachment Interview. Although the coding system for U/d has been widely used for decades, the individual indicators of unresolved loss/abuse have not been validated independently of the development sample. This study examined the psychometric validity of U/d, using individual participant data from 1,009 parent–child dyads across 13 studies. A latent class analysis showed that subsets of commonly occurring U/d indicators could differentiate interviewees with or without unresolved loss/abuse. Predictive models suggested a psychometric model of U/d consisting of a combination of these common indicators, with disbelief and psychologically confused statements regarding loss being especially important indicators of U/d. This model weakly predicted infant disorganized attachment. Multilevel regression analysis showed no significant association between ratings of unresolved other trauma and infant disorganized attachment, over and above ratings of unresolved loss/abuse. Altogether, these findings suggest that the coding system of U/d may have been overfitted to the initial development sample. Directions for further articulation and optimization of U/d are provided.
We advance the argument that a culture-laden retail website design elicits multiple mental categorizations in consumers: categorizations in relation to the consumers’ own culture and categorizations in relation to the product category. We study interactions of these mental categorizations and examine which of the two types dominates the other in terms of the strength of its impact on attitude toward the website and purchasing intentions. Further, we challenge the relevance of the well-known “identity accessibility effect” as we demonstrate that successful activation of a chronic cultural identity through exposure to a culture-laden website design is contingent on the suitability of the product category to accommodate cues of that culture. The tests of the study’s hypotheses rely on data from an initial thought listing test, and a subsequent experiment using data gathered from French mainstream and Maghrebian minority consumers living in France.
Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinson’s disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each other’s field, leading to fruitful collaborations and effective solutions.
Fast-growing and evolving online content has enabled responsive curriculum updates to support students’ learning of how to apply concepts to understand and solve real world problems. Written case studies have long served this purpose in a wide variety of disciplines, and many educators have seen value in presenting cases through the use of online resources under the assumption that this approach will result in positive learning outcomes. Studies designed to compare the two methods have had mixed results with regard to student performance. This paper shows comparison results of using case study assignments presented either as traditional written descriptions or as a combination of online resources including video clips, blogs, and vlogs. Research results indicate that learning outcomes for case study assignments presented through online resources improved significantly for the learners. Various types of students gained benefits differently. Higher achievers gained more than average achievers, and struggling students obtained necessary gains sufficient to pass the case study assignments. Marketing educators should consider incorporating modern online content in curriculum design components such as case study assignments to improve student learning.
The diagnosis of fracture nonunion following plate osteosynthesis is subjective and frequently ambiguous. Initially following osteosynthesis, loads applied to the bone are primarily transmitted through the plate. However, as callus stiffness increases, the callus is able to bear load proportional to its stiffness while forces through the plate decrease. The purpose of this study was to use a “smart” fracture plate to distinguish between phases of fracture healing by measuring forces transmitted through the plate. A wireless force sensor and small adapter were placed on the outside of a distal femoral locking plate. The adapter converts the slight bending of the plate under axial load into a transverse force which is measurable by the sensor. An osteotomy was created and then plated in the distal femur of biomechanical Sawbones. Specimens were loaded to simulate single-leg stance first with the osteotomy defect empty (acute healing), then sequentially filled with silicone (early callus) and then polymethyl methacrylate (hard callus). There was a strong correlation between applied axial load and force measured by the “smart” plate. Data demonstrate statistically significant differences between each phase of healing with as little as 150 N of axial load applied to the femur. Forces measured in the plate were significantly different between acute (100%), early callus (66.4%), and hard callus (29.5%). This study demonstrates the potential of a “smart” fracture plate to distinguish between phases of healing. These objective data may enable early diagnosis of nonunion and enhance outcomes for patients.
This study explores the learning impact of introducing vlogs and blogs to graduate international business case studies. Data were collected over a four-year period from graduate business programs to compare assignment performances from students when case studies were presented with versus without the inclusion of vlogs and blogs. A rubric measuring six criteria was used to evaluate case study submissions and identify areas drawing upon specific learning skills. A crosswalk between the rubric and Bloom’s taxonomy of cognitive levels linked student performance and these skills. Group comparisons of the 535 observations showed the inclusion of vlogs and blogs led to statistically significant improvement in students’ learning outcomes in overall scores, failing rates, and high-score achievements. A performance examination of th3e international business case studies against the six specific grading criteria also showed marked advances in students’ higher-level thinking skills of analysis, evaluation, and synthesis or creation, which have been linked to developing cross-cultural awareness. The study indicates that instructors should thoughtfully curate sets of vlogs and blogs pertaining to products, services, values, cultures, and economic systems from multiple diverse origins to use in administering international business case studies used in their course curricula.
While prior research shows that atmospheric cues such as visual design trigger customers' cognition and emotions, thus leading to approach-avoidance responses, this article proposes self-congruity as a mediator, paralleling cognitive evaluation (i.e., perceived quality). More specifically, this article, situated in the context of the coffee shop industry in China, investigates how perceived luxuriousness, reflected from the service provider's visual design, affects customers' willingness to pay a price premium (WTPP). The findings show that perceived luxuriousness leads to customers' inferences of high quality of the coffee and high self-congruity, thus increasing WTPP. Further, cosmopolitanism moderates the effect of perceived luxuriousness only via self-congruity, but not via perceived quality. This article contributes to the existing literature on atmospherics, self-congruity, brand equity, and cosmopolitanism. More importantly, this article provides managerial implications for global coffee/food brands that aim to set up their chain outlets and expand rapidly in China, one of the largest emerging markets.
While prior research on animation effects focused on Web advertising, this article focuses on online retailing and identifies animated images as an important online atmospheric cue. Using an extended Stimulus-Organism-Response (SOR) model, this article explores animation effects on emotional and cognitive processes. Across two studies, the findings show that compared with static images, animated images elicit greater pleasure, which in turn induces more favorable website attitudes, and lead to higher purchase intentions. Further, this serial mediation effect holds across different types of products. These findings, from the perspective of online atmospherics, deepen our understanding of animation effects on consumer approach-avoidance responses.
An artificial pancreases (AP) is a device for managing diabetes through automated insulin infusion. The control algorithm is the heart of the AP that computes the insulin infusion based on blood glucose measurements. In this article, we investigate the role of multimedia data to enable the advanced control techniques that could personalize AP in elderly type 2 diabetes patients. The performance is evaluated through in silico studies on a patient simulator wherein the patient model is computed based on the data collected from clinical studies.
The use of Unmanned Aerial Vehicles (UAVs) for collecting data from remotely located sensor systems is emerging. The data can be time-sensitive and require to be transmitted to a data processing center. However, planning the trajectory for a swarm of UAVs depends on multi-fold constraints, such as data collection requirements, UAV maneuvering capacities, and budget limitations. Since a UAV may fail or be compromised, it is important to provide necessary resilience to such contingencies, thus ensuring data security. It is important to provide the UAVs with efficient spatio-temporal trajectories so that they can efficiently cover necessary data sources. In this work, we present Synth4UAV, a formal approach for automated synthesis of efficient trajectories for a UAV swarm by logically modeling the aerial space and data point topology, UAV moves, and associated constraints in terms of the turning and climbing angle, fuel usage, data collection point coverage, data freshness, and resiliency properties. We use efficient, logical formulas to encode and solve the complex model. The solution to the model provides the routing and maneuvering plan for each UAV, including the time to visit the points on the paths and corresponding fuel usage such that the necessary data points are visited while satisfying the resiliency requirements. We evaluate the proposed trajectory synthesizer, and the results show that the relationship among different parameters follows the requirements while the tool scales well with the problem size.
Overall, 5G networks are expected to become the backbone of many critical IT applications. With 5G, new tech advancements and innovation are expected; 5G currently operates on software-defined networking. This enables 5G to implement network slicing to meet the unique requirements of every application. As a result, 5G is more flexible and scalable than 4G LTE and previous generations. To avoid the growing risks of hacking, 5G cybersecurity needs some significant improvements. Some security concerns involve the network itself, while others focus on the devices connected to 5G. Both aspects present a risk to consumers, governments, and businesses alike. There is currently no real-time vulnerability assessment framework that specifically addresses 5G Edge networks, with regard to their real-time scalability and dynamic nature. This paper studies the vulnerability assessment in the 5G networks and develops an optimized dynamic method that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the hexagonal fuzzy numbers to accurately analyze the vulnerabilities in 5G networks. The proposed method considers both the vulnerability and 5G network dynamic factors such as latency and accessibility to find the potential attack graph paths where the attack might propagate in the network and quantifies the attack cost and security level of the network. We test and validate the proposed method using our 5G testbed and we compare the optimized method to the classical TOPSIS and the known vulnerability scanner tool, Nessus.
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