The Effect of Response Conditions on Food Images-Evoked Emotions Measured Using the Valence × Arousal Circumplex-Inspired Emotion Questionnaire (CEQ).

In 2020, a single-response-based, valence × arousal circumplex-inspired emotion questionnaire (CEQ) was developed. Using a between-participants design, previous studies have found that a multiple response (MR) condition better discriminated test samples (e.g., written food names) based on their evoked emotions than a single response (SR) condition. This research, comprising Studies 1 and 2, aimed to determine the effect of response conditions (i.e., SR vs. MR) on emotional responses to food image samples, using a within-participants design. In Study 1, 105 Korean participants were asked to select a pair of emotion terms (i.e., SR condition) or select all pairs representing their evoked emotions (i.e., MR condition) from a list of 12 pairs of emotion terms of the CEQ, in response to the 14 food images. Both SR and MR conditions were tested within a remote (online) session. To minimize both a potential carry-over effect of the "within-participants design" and an influence of environmental factors in the remote testing, Study 2 asked 64 U.S. participants to do so over two separated sessions on two different days in a controlled laboratory setting. In both Studies 1 and 2, participants selected the CEQ's emotion-term pairs in the MR condition more frequently than in the SR condition, leading to the MR condition's higher capacity to discriminate test samples. While the configurations of the correspondence analysis biplots drawn in the SR and MR conditions were similar, those in the MR condition were more likely to be similar to the configurations of the principal component analysis biplots drawn from the ratings of valence and arousal for food image samples. In conclusion, this study provides robust empirical evidence that the MR condition can perform better in capturing sample differences in food-evoked emotions, while the SR condition is also effective in characterizing emotional profiles of test samples. Our findings will provide practical insights to sensory professionals, enabling them to effectively leverage the CEQ or its variants when measuring food-evoked emotions.

Secure Collaborative Platform for Health Care Research in an Open Environment: Perspective on Accountability in Access Control.

BACKGROUND: With the recent use of IT in health care, a variety of eHealth data are increasingly being collected and stored by national health agencies. As these eHealth data can advance the modern health care system and make it smarter, many researchers want to use these data in their studies. However, using eHealth data brings about privacy and security concerns. The analytical environment that supports health care research must also consider many requirements. For these reasons, countries generally provide research platforms for health care, but some data providers (eg, patients) are still concerned about the security and privacy of their eHealth data. Thus, a more secure platform for health care research that guarantees the utility of eHealth data while focusing on its security and privacy is needed. OBJECTIVE: This study aims to implement a research platform for health care called the health care big data platform (HBDP), which is more secure than previous health care research platforms. The HBDP uses attribute-based encryption to achieve fine-grained access control and encryption of stored eHealth data in an open environment. Moreover, in the HBDP, platform administrators can perform the appropriate follow-up (eg, block illegal users) and monitoring through a private blockchain. In other words, the HBDP supports accountability in access control. METHODS: We first identified potential security threats in the health care domain. We then defined the security requirements to minimize the identified threats. In particular, the requirements were defined based on the security solutions used in existing health care research platforms. We then proposed the HBDP, which meets defined security requirements (ie, access control, encryption of stored eHealth data, and accountability). Finally, we implemented the HBDP to prove its feasibility. RESULTS: This study carried out case studies for illegal user detection via the implemented HBDP based on specific scenarios related to the threats. As a result, the platform detected illegal users appropriately via the security agent. Furthermore, in the empirical evaluation of massive data encryption (eg, 100,000 rows with 3 sensitive columns within 46 columns) for column-level encryption, full encryption after column-level encryption, and full decryption including column-level decryption, our approach achieved approximately 3 minutes, 1 minute, and 9 minutes, respectively. In the blockchain, average latencies and throughputs in 1Org with 2Peers reached approximately 18 seconds and 49 transactions per second (TPS) in read mode and approximately 4 seconds and 120 TPS in write mode in 300 TPS. CONCLUSIONS: The HBDP enables fine-grained access control and secure storage of eHealth data via attribute-based encryption cryptography. It also provides nonrepudiation and accountability through the blockchain. Therefore, we consider that our proposal provides a sufficiently secure environment for the use of eHealth data in health care research.

Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities.

With the adaptation of video surveillance in many areas for object detection, monitoring abnormal behavior in several cameras requires constant human tracking for a single camera operative, which is a tedious task. In multiview cameras, accurately detecting different types of guns and knives and classifying them from other video surveillance objects in real-time scenarios is difficult. Most detecting cameras are resource-constrained devices with limited computational capacities. To mitigate this problem, we proposed a resource-constrained lightweight subclass detection method based on a convolutional neural network to classify, locate, and detect different types of guns and knives effectively and efficiently in a real-time environment. In this paper, the detection classifier is a multiclass subclass detection convolutional neural network used to classify object frames into different sub-classes such as abnormal and normal. The achieved mean average precision by the best state-of-the-art framework to detect either a handgun or a knife is 84.21% or 90.20% on a single camera view. After extensive experiments, the best precision obtained by the proposed method for detecting different types of guns and knives was 97.50% on the ImageNet dataset and IMFDB, 90.50% on the open-image dataset, 93% on the Olmos dataset, and 90.7% precision on the multiview cameras. This resource-constrained device has shown a satisfactory result, with a precision score of 85.5% for detection in a multiview camera.

A Comprehensive Survey on Security and Privacy for Electronic Health Data.

Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.

Self-Adaptive Framework Based on MAPE Loop for Internet of Things.

The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.

An Interoperable Access Control Framework for Diverse IoT Platforms Based on OAuth and Role.

Due to the rapid development of Internet of Things (IoT), IoT platforms that can provide common functions for things are becoming increasingly important. However, access control frameworks in diverse IoT platforms have been developed for individual security goals, designs, and technologies. In particular, current OAuth-based access control frameworks that are widely used in IoT research have not been providing interoperability among IoT platforms even though sharing resources and services is a critical issue for IoT platforms. Therefore, we analyze the main requirements for an IoT access control framework to properly design our framework and propose an interoperable access control framework based on OAuth 2.0 and Role. Our approach describes a new extended authorization grant flow to issue an Interoperable Access Token (IAT) that has a global access scope across IoT platforms using multiple pairs of clients' credentials. With the IAT and proposed framework, we can access client-specific domains in heterogeneous IoT platforms, then valuable resources (e.g., data and services) in the domains can be accessed by validating the roles, which will greatly simplify permission management. Furthermore, IAT supports a simple token management (e.g., token issuance, refreshing, and revocation) by managing only one token for diverse IoT platforms. In addition, we implement our interoperable access control framework on Mobius and FIWARE, which are promising open-source IoT platforms, and test an interoperability scenario to demonstrate our approach with the implementation. Furthermore, the proposed framework is compared with other IoT access control approaches based on the selected requirements in this paper.

Device Identification Interoperability in Heterogeneous IoT Platforms.

With the continuous improvement of Internet of Things (IoT) technologies, various IoT platforms are under development. However, each IoT platform is developed based on its own device identification system. That is, it is challenging to identify each sensor device between heterogeneous IoT platforms owing to the resource request format (e.g., device identifier) varying between platforms. Moreover, despite the considerable research focusing on resource interoperability between heterogeneous IoT platforms, little attention is given to sensor device identification systems in diverse IoT platforms. In order to overcome this problem, the current work proposes an IoT device name system (DNS) architecture based on the comparative analysis of heterogeneous IoT platforms (i.e., oneM2M, GS1 'Oliot', IBM 'Watson IoT', OCF 'IoTivity', FIWARE). The proposed IoT DNS analyzes and translates the identification system of the device and resource request format. In this process, resource requests between heterogeneous IoT platforms can be reconfigured appropriately for the resources and services requested by the user, and as a result, users can use heterogeneous IoT services. Furthermore, in order to illustrate the aim of the proposed architecture, the proposed IoT DNS is implemented and tested on a microcomputer. The experimental results show that a oneM2M-based device successfully performs a resource request to a Watson IoT and FIWARE sensor devices.