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A systematic research flow was applied to the Southern Metropolis in Taiwan not only to recount residents’ considerations in this cultural area but also to compare them with those of other metropolitans on the island in relation to general housing concerns. The constructs and factors in housing decision-making were justified using the literature, confirmed with experts in the field, and organised as a decision hierarchy that formed the foundation of a survey. The investigation combined the analytic hierarchy process and Student’s t-test, both of which are credible methods, to facilitate a grounded process for mind mining. The importance of constructs/factors were thus assessed on a numerical basis, and a set of unforeseen insights were explored for the different parties of interest (e.g., buyers, construction companies, agents, asset managers, etc.). Opinion gaps between different sample groups were identified. This set of empirical knowledge filled the gap in the literature. It is noteworthy that among the constructs in the region studied, (housing) ‘conditions’ dominated ‘price’, while ‘location and transport’ was the least important. A ‘non-paradigmatic shift’ in people’s total housing preference structure, which changed gradually with decreasing population density and increasing plain geography from the north to the south between nearby metropoles, was observed, despite the niche but commensurable cultural norm in East Asia being the overall scenario of the island. Some existing claims about the housing preferences in this area were also either supported or rebutted by the quantitative evidence(s).

In the information era, information security monitoring tools would be helpful for enterprises/organizations to monitor employees’ computer usage behaviors and improve their information security protection. The Windows-based operating systems have the largest market share in the world. Therefore, the study target is the development of a Windows-based information security monitoring tool in this study. We proposed an assessment model for developing an information security tool in this study to explore the significances of functionalities in a Windows-based information security monitoring tool and the decision-makers’ decision opinions. We adopted four steps with four study methods: the literature study method, the Delphi method, the analytic hierarchy process (AHP) method, and the analysis methods related to data-driven decision-making in the proposed model. In Step 1, we studied some literature about information security monitoring, and we discovered 26 functionalities as the decision criteria in this study. In Step 2, using the Delphi method, we confirmed the decision criterion set with potential decision-makers and organized the decision criteria hierarchy. In Step 3, we designed an AHP questionnaire to get the criterion weight vectors from the 12 decision-makers. With the AHP method, this study received the weights of the decision criteria and found that the 16 functionalities among the 26 functionalities should receive their corresponding developing priority in a Windows-based information security monitoring tool. Finally, we used the Pearson correlation coefficient and cosine distance to explore the correlations and similarities among the decision-makers’ decision opinions. This study found the relevance among the decision-makers’ decision opinions in a Windows-based information security monitoring tool developed with the Pearson correlation coefficients/the cosine distances among all pairs of decision-makers’ decision opinions.

Recent neuroimaging studies revealed that the dominant frequency of neural oscillations is brain-region-specific and can vary with frequency-specific reorganization of brain networks during cognition. In this study, we examined the dominant frequency in low-frequency neural oscillations represented by oxygenated hemoglobin measurements after the hemodynamic response function (HRF) deconvolution. Twenty-nine healthy college subjects were recruited to perform a serial finger tapping task at the frequency of 0.2 Hz. Functional near-infrared spectroscopy (fNIRS) was applied to record the hemodynamic signals over the primary motor cortex, supplementary motor area (SMA), premotor cortex, and prefrontal area. We then explored the low frequency steady-state brain response (lfSSBR), which was evoked in the motor systems at the fundamental frequency (0.2 Hz) and its harmonics (0.4, 0.6, and 0.8 Hz). In particular, after HRF deconvolution, the lfSSBR at the frequency of 0.4 Hz in the SMA was identified as the dominant frequency. Interestingly, the domain frequency exhibited the correlation with behavior data such as reaction time, indicating that the physiological implication of lfSSBR is related to the brain anatomy, stimulus frequency and cognition. More importantly, the HRF deconvolution showed its capability for recovering signals probably reflecting neural-level events and revealing the physiological meaning of lfSSBR.

The attention network test (ANT) is a reliable tool to detect the efficiency of alerting, orienting, and executive control networks. However, studies using the ANT obtained inconsistent relationships between attention networks due to two reasons: on the one hand, the inter-network relationships of attention subsystems were far from clear; on the other hand, ANT scores in previous studies were disturbed by possible inter-network interactions. Here we proposed a new computing method by dissecting cue-target conditions to estimate ANT scores and relationships between attention networks as pure as possible. The method was tested in 36 participants. Comparing to the original method, the new method showed a larger alerting score and a smaller executive control score, and revealed interactions between alerting and executive control and between orienting and executive control. More interestingly, the new method revealed unidirectional influences from alerting to executive control and from executive control to orienting. These findings provided useful information for better understanding attention networks and their relationships in the ANT. Finally, the relationships of attention networks should be considered with more experimental paradigms and techniques.

This study explores the experts’ opinions during the consultation stage before law-making for civilian drones. A thorough literature study is first undertaken to have the set of influencing factors that should be suitable for the investigation from the perspective of designing and selecting civilian drones. Several rounds of surveys using the Delphi method, followed by an analytic hierarchy process (AHP), are performed to conform to the organized tree structure of constructs and factors and to obtain the knowledge about the opinions of the expert groups, with the expert sample being intentionally partitioned into three opinion groups at the beginning: academia (A), industry (I), and research institutes (R). Doing so facilitates a “mind-mining” process using the triple helix model (THM), while the opinions across the groups can also be visualized and compared. This exploits a new set of knowledge for the design and selection of civilian drones on a scientific yet empirical basis, and the observed differences and similarities among the groups may benefit their future negotiations to propose the drafts for regulating the design, manufacturing, and uses of civilian drones. As several significant implications and insights are also drawn and gained from the abovementioned results eventually, some possible research directions are worthwhile. The proposed hybrid methodological flow is another novelty.

Neuroimaging studies have revealed that insomnia is characterized by aberrant neuronal connectivity in specific brain regions, but the topological disruptions in the white matter (WM) structural connectivity networks remain largely unknown in insomnia. The current study uses diffusion tensor imaging (DTI) tractography to construct the WM structural networks and graph theory analysis to detect alterations of the brain structural networks. The study participants comprised 30 healthy subjects with insomnia symptoms (IS) and 62 healthy subjects without IS. Both the two groups showed small-world properties regarding their WM structural connectivity networks. By contrast, increased local efficiency and decreased global efficiency were identified in the IS group, indicating an insomnia-related shift in topology away from regular networks. In addition, the IS group exhibited disrupted nodal topological characteristics in regions involving the fronto-limbic and the default-mode systems. To our knowledge, this is the first study to explore the topological organization of WM structural network connectivity in insomnia. More importantly, the dysfunctions of large-scale brain systems including the fronto-limbic pathways, salience network and default-mode network in insomnia were identified, which provides new insights into the insomnia connectome. Topology-based brain network analysis thus could be a potential biomarker for IS.

For an R&D institution to design a specific high investment cost product, the budget is usually ‘large but limited’. To allocate such budget on the directions with key potential benefits (e.g., core technologies) requires, at first and at least, a priority over the involved design criteria, as to discover the relevant decision knowledge for a suitable budgeting plan. Such a problem becomes crucial when the designed product is relevant to the security and military sustainability of a nation, e.g., a next generation fighter. This study presents a science education framework that helps to obtain such knowledge and close the opinion gaps. It involves several main tutorial phases to construct and confirm the set of design criteria, to establish a decision hierarchy, to assess the preferential structures of the decision makers (DMs) (individually or on a group basis), and to perform some decision analyses that are designed to identify the homogeneity and heterogeneity of the opinions in the decision group. The entire framework has been applied in a training course hold in a large R&D institution, while after learning the staff successfully applied these knowledge discovery processes (for planning the budget for the fighter design works and for closing the opinion gaps present). With the staffs’ practical exercises, several empirical findings except for the budgeting priority (e.g., the discrimination between ‘more important criteria’ against the less important ones) are also interesting. For some examples (but not limited to these), it is found that the results from using two measures (statistical correlation vs. geometrical cosine similarity) to identify the opinion gaps are almost identical. It is found that DMs’ considerations under various constructs are sometimes consistent, but often hard to be consistent. It is also found that the two methods (degree of divergence (DoD) vs. number of observed subgroups (NSgs)) that are used to understand the opinions’ diversity under the constructs are different. The proposed education framework meets the recent trend of data-driven decision-making, and the teaching materials are also some updates to science education.

The integration of the low Earth orbit (LEO) satellite and the terrestrial networks has extended the coverage of the Internet of Things (IoT) from densely populated areas to the entire globe. Random access plays an important role in LEO satellite-based IoT (SIoT) since many sensors on the ground need to send the data back to the LEO satellites with a stringent delay requirement. Due to the significant difference in inherent characteristics between the LEO satellite-based systems and the terrestrial networks, the factors of consideration for random access are quite different. First and foremost, a LEO satellite has limited resources, and the coverage is rather dynamic. Secondly, the services provided require scalability and differentiated quality of service (QoS). Thirdly, the received packets are sporadic and sparse at the satellites. In this paper, we propose using a deep neural network box (DNNB) to resolve collisions for resource reservation in the SIoT. An active sensor node sends a reservation packet, which contains a randomly generated ticket number and a password with a checksum. The former is converted into a signature by the mapping of the Finite Projective Plane (FPP). The resource allocator (RA) at the LEO satellite uses the output of the DNNB to determine the active sensor nodes of the reservation packet and assign resources accordingly. The confirmation of resource reservation is doubly checked by the integrity of passwords, placed independently and sequentially in the password section. Through such a dual checking system, the RA at the LEO satellite-based system can take either a conservative policy, an aggressive policy, or a hybrid policy in allocating resources. The reservation-based random access with the assistance of machine learning (ML) can provide high throughput, high scalability, differentiated QoS, and age of information (AoI). In the performance evaluation, we analyze the expected throughput and mean delay for the reservation-based system, and compare the proposed DNNB with CRDSA and IRSA. Lastly, we provide the design of a multi-class QoS mechanism.

Random access plays a critical role in massive machine type communication networks that consist of a myriad number of heterogeneous Internet of Things devices to meet the demand of applications in a large geographical area. In this paper, we propose using packet length alignment for framed slotted Aloha, where packets are assigned chunks of time slots according to the packet lengths generated by the devices. The packet length-dependent alignment ensures that the collision windows are relatively small, whereas the devices with varying packet lengths view the frame partitioned into chunks of time slots differently. In this paper, packet length alignment is used in combination with packet squeezing to enhance the throughput in framed slotted Aloha. Furthermore, the age of information (AoI) can be easily included in the proposed approach to emphasize the importance of data freshness. In the performance evaluation, the analytical model of the proposed packet length alignment along with packet squeezing demonstrate that the throughput of framed slotted Aloha in the context of massive machine type communications in 5G can reach up to 0.9. A simplified AoI model can be easily implemented with the proposed approach.

The multimedia Tactile Internet is a network that provides ultra-low latency and ultra-high reliability for two-way multimedia services. Since random access in uplink dominates the delay and there exist variations in target delays and traffic volumes among connections, it is much more efficient to perform a joint uplink and downlink resource allocation. In this paper, we provide uplink random access schemes with joint uplink and downlink resource allocation with multichannel architecture for multimedia Tactile Internet. By doing so, one can significantly increase the resource utilization efficiency and improve the reliability of the multimedia Tactile Internet as well. The cores of resource management are two-fold. First, the joint resource allocation dynamically adjusts the ratio of the number of uplink channels to the number of downlink channels and performs access control based on the collision ratios and the queue lengths. Machine learning techniques such as deep reinforcement learning can be applied to maximize the throughput. Secondly, the efficient uplink random access for multichannel architecture increases the throughput, where each transmitting node selects several time slot/channel pairs based on either theory of the Finite Projective Plane, the random numbers, or the hybrid. In the performance evaluation, we compare the performance of different selection schemes for the multichannel architecture in terms of throughput for the multimedia Tactile Internet through simulation runs and analysis. The hybrid selection scheme performs the best since it has the advantages of both the Finite Projective Plane structure and random numbers. Lastly, we compare and contrast the proposed joint uplink/downlink random access scheme with other known schemes.