Amodal Instance Segmentation (AIS) aims to segment the regions of both visible and invisible parts of overlapping objects. The mainstream Mask R-CNN-based methods are unsuitable for thin objects with large overlaps because of their object proposal features with bounding boxes for three reasons. First, capturing the entire shapes of overlapping thin objects is difficult. Second, the bounding boxes of close objects are almost identical. Third, a bounding box contains many objects in most cases. In this paper, we propose a box-free AIS method, Seed-to-Mask, for thin objects with large overlaps. The method specifies a target object using a seed and iteratively extends the segmented region. We have achieved better performance in experiments on artificial data consisting only of thin objects.

Artificial intelligence and the introduction of Internet of Things technologies have benefited from technological advances and new automated computer system technologies. Eventually, it is now possible to integrate them into a single offline industrial system. This is accomplished through machine-to-machine communication, which eliminates the human factor. The purpose of this article is to examine security systems for machine-to-machine communication systems that rely on identification and authentication algorithms for real-time monitoring. The article investigates security methods for quickly resolving data processing issues by using the Security operations Center’s main machine to identify and authenticate devices from 19 different machines. The results indicate that when machines are running offline and performing various tasks, they can be exposed to data leaks and malware attacks by both the individual machine and the system as a whole. The study looks at the operation of 19 computers, 7 of which were subjected to data leakage and malware attacks. AnyLogic software is used to create visual representations of the results using wireless networks and algorithms based on previously processed methods. The W76S is used as a protective element within intelligent sensors due to its built-in memory protection. For 4 machines, the data leakage time with malware attacks was 70 s. For 10 machines, the duration was 150 s with 3 attacks. Machine 15 had the longest attack duration, lasting 190 s and involving 6 malware attacks, while machine 19 had the shortest attack duration, lasting 200 s and involving 7 malware attacks. The highest numbers indicated that attempting to hack a system increased the risk of damaging a device, potentially resulting in the entire system with connected devices failing. Thus, illegal attacks by attackers using malware may be identified over time, and data processing effects can be prevented by intelligent control. The results reveal that applying identification and authentication methods using a protocol increases cyber-physical system security while also allowing real-time monitoring of offline system security.

E-learning, which can be used anywhere and at any time, is very convenient and has been introduced to improve learning efficiency. However, securing a completion rate has been a major challenge. Recently, the learning forms of e-learning require learners to be introspective, deliberate, and logical and have proven to be incompatible with many learners with low completion rates. Thus, we developed an e-learning system that incorporates a fill-in-the-blank question-type concept map to deepen learners' understanding of learning contents while watching learning videos. The developed system promotes active learning reflectively and logically by allowing learners to answer blank question labels on concept maps from video content and labels associated with the blank question labels. We confirmed in the laboratory experiment by comparing with a conventional video-based learning system that the developed system encouraged a learner to do more system operations for rechecking the learning content and to better understand the learning contents while watching the learning video. As the next step, a field experiment is needed to investigate the usefulness and effectiveness of the developed system in actual environments in order to boost the practicality of the developed system. In this study, we introduced the developed system into the two class of the uviversity course and investigated the level of understanding to the learning contents, the system operations, and the usefulness of the developed system by comparing with those in the laboratory experiment. The results showed that the developed system provided to support the understanding to learning content and the usefulness of each function in the field experiment, as in the laboratory experiment. On the other hand, the students in the field experiment gave lower usefulness of the developed system than those in the lab experiment, suggesting that the students who attempted to thoroughly understand the learning contents in the field experiment were fewer than those in the lab experiment from their system operations during the learning.

Both group process studies and collective intelligence studies are concerned with “which of the crowds and the best members perform better.” This can be seen as a matter of democracy versus dictatorship. Having evidence of the growth potential of crowds and experts can be useful in making correct predictions and can benefit humanity. In the collective intelligence experimental paradigm, experts' or best members ability is compared with the accuracy of the crowd average. In this research (n = 620), using repeated trials of simple tasks, we compare the correct answer of a class average (index of collective intelligence) and the best member (the one whose answer was closest to the correct answer). The results indicated that, for the cognition task, collective intelligence improved to the level of the best member through repeated trials without feedback; however, it depended on the ability of the best members for the prediction task. The present study suggested that best members' superiority over crowds for the prediction task on the premise of being free from social influence. However, machine learning results suggests that the best members among us cannot be easily found beforehand because they appear through repeated trials.

There are several benefits of the information that is invisible to the human eye. “Invisible” here means that it can be visualized or quantified when using instruments. For example, it can improve security without compromising product design. We have succeeded in making an invisible digital image on a metal substrate using periodic repeatability by thin-film interference of niobium oxides. Although this digital information is invisible in the visible light wavelength range of 400-800nm, but detectable in the infrared light that of 800-1150nm. This technology has a potential to be applied to anti-counterfeiting and traceability.

Thermoelectric effect of Ga-Sn-O (GTO) thin films has been investigated for Internet-of-Things application. It is found that the amorphous GTO thin films provide higher power factors (PF) than the polycrystalline ones, which is because grain boundaries block the electron conduction in the polycrystalline ones. It is also found that the GTO thin films annealed in vacuum provide higher PF than those annealed in air, which is because oxygen vacancies are terminated in those annealed in air. The PF and dimensionless figure of merit (ZT) is not so excellent, but the cost effectiveness is excellent, which is the most important for some examples of the Internet-of-Things application.

Cross-domain data fusion is becoming a key driver in the growth of numerous and diverse applications in the Internet of Things (IoT) era. We have proposed the concept of a new information platform, Geo-Centric Information Platform (GCIP), that enables IoT data fusion based on geolocation, i.e., produces spatio-temporal content (STC), and then provides the STC to users. In this environment, users cannot know in advance “when,” “where,” or “what type” of STC is being generated because the type and timing of STC generation vary dynamically with the diversity of IoT data generated in each geographical area. This makes it difficult to directly search for a specific STC requested by the user using the content identifier (domain name of URI or content name). To solve this problem, a new content discovery method that does not directly specify content identifiers is needed while taking into account (1) spatial and (2) temporal constraints. In our previous study, we proposed a content discovery method that considers only spatial constraints and did not consider temporal constraints. This paper proposes a new content discovery method that matches user requests with content metadata (topic) characteristics while taking into account spatial and temporal constraints. Simulation results show that the proposed method successfully discovers appropriate STC in response to a user request.

We propose a fine structure imaging for the surface and its inside of solid material such as coated drill bits with TiN (Titanium Nitride). We call this method i-MSE (innovative MSE) since the fine structure is visualized with a local mechanical strength (the local erosion rate) which is obtained from a set of erosion depth profiles measured with Micro Slurry-jet Erosion test (MSE). The local erosion rate at any sampling point is estimated from the depth profile using a sliding window regression and for the rest of the 2-dimensional points it is interpolated with the mean value coordinate technique. The interpolated rate is converted to a 2D image (i-MSE image) with a color map. The i-MSE image can distinguish layers if the testing material surface is composed of coats which have different resistance to erosion (erosive wear), while microscopic image such as SEM (Scanning Electron Microscope) and a calotest just provides appearance information, not physical characteristics. Experiments for some layered specimens show that i-MSE can be an effective tool to visualize the structure and to evaluate the mechanical characteristics for the surface and the inside of solid material.

Energy harvesting has been widely investigated as a potential solution to supply power for Internet of Things (IoT) devices. Computing devices must operate intermittently rather than continuously, because harvested energy is unstable and some of IoT applications can be periodic. Therefore, processors for IoT devices with intermittent operation must feature a hibernation mode with zero-standby-power in addition to energy-efficient normal mode. In this paper, we describe the layout design and measurement results of a nonvolatile standard cell memory (NV-SCM) and nonvolatile flip-flops (NV-FF) with a nonvolatile memory using Fishbone-in-Cage Capacitor (FiCC) suitable for IoT processors with intermittent operations. They can be fabricated in any conventional CMOS process without any additional mask. NV-SCM and NV-FF are fabricated in a 180nm CMOS process technology. The area overhead by nonvolatility of a bit cell are 74% in NV-SCM and 29% in NV-FF, respectively. We confirmed full functionality of the NV-SCM and NV-FF. The nonvolatile system using proposed NV-SCM and NV-FF can reduce the energy consumption by 24.3% compared to the volatile system when hibernation/normal operation time ratio is 500 as shown in the simulation.

With a rapidly escalating number of sophisticated cyber-attacks, protecting Internet of Things (IoT) networks against unauthorized activity is a major concern. The detection of malicious attack traffic is thus crucial for IoT security to prevent unwanted traffic. However, existing traditional malicious traffic detection systems which relied on supervised machine learning approach need a considerable number of benign and malware traffic samples to train the machine learning models. Moreover, in the cases of zero-day attacks, only a few labeled traffic samples are accessible for analysis. To deal with this, we propose a few-shot malicious IoT traffic detection system with a prototypical graph neural network. The proposed approach does not require prior knowledge of network payload binaries or network traffic signatures. The model is trained on labeled traffic data and tested to evaluate its ability to detect new types of attacks when only a few labeled traffic samples are available. The proposed detection system first categorizes the network traffic as a bidirectional flow and visualizes the binary traffic flow as a color image. A neural network is then applied to the visualized traffic to extract important features. After that, using the proposed few-shot graph neural network approach, the model is trained on different few-shot tasks to generalize it to new unseen attacks. The proposed model is evaluated on a network traffic dataset consisting of benign traffic and traffic corresponding to six types of attacks. The results revealed that our proposed model achieved an F1 score of 0.91 and 0.94 in 5-shot and 10-shot classification, respectively, and outperformed the baseline models.

In recent years, there has been a growing demand for urban digitization using cameras and light detection and ranging (LiDAR). Shadows are a condition that affects measurement the most. Therefore, shadow detection technology is essential. In this study, we propose shadow detection utilizing the LiDAR intensity that depends on the surface properties of objects but not on irradiation from other light sources. Unlike conventional LiDAR-intensity-aided shadow detection methods, our method embeds the un-correlation between luminance and LiDAR intensity in each position into the optimization. The energy, which is defined by the un-correlation between luminance and LiDAR intensity in each position, is minimized by graph-cut segmentation to detect shadows. In evaluations on KITTI and Waymo datasets, our shadow-detection method outperformed the previous methods in terms of multiple evaluation indices.

Internet of Things (IoT) devices are widely used in various fields. However, their limited computing resources make them extremely vulnerable and difficult to be effectively protected. Traditional intrusion detection systems (IDS) focus on high accuracy and low false alarm rate (FAR), making them often have too high spatiotemporal complexity to be deployed in IoT devices. In response to the above problems, this paper proposes an intrusion detection model of IoT based on the light gradient boosting machine (LightGBM). Firstly, the one-dimensional convolutional neural network (CNN) is used to extract features from network traffic to reduce the feature dimensions. Then, the LightGBM is used for classification to detect the type of network traffic belongs. The LightGBM is more lightweight on the basis of inheriting the advantages of the gradient boosting tree. The LightGBM has a faster decision tree construction process. Experiments on the TON-IoT and BoT-IoT datasets show that the proposed model has stronger performance and more lightweight than the comparison models. The proposed model can shorten the prediction time by 90.66% and is better than the comparison models in accuracy and other performance metrics. The proposed model has strong detection capability for denial of service (DoS) and distributed denial of service (DDoS) attacks. Experimental results on the testbed built with IoT devices such as Raspberry Pi show that the proposed model can perform effective and real-time intrusion detection on IoT devices.

Involving higher-order thinking in learning activities can produce meaningful learning. It impacts the student's ability to solve problems in new situations. Concept mapping is a learning strategy that has been proven to promote higher-order thinking. Concept map recomposition (KB-mapping) in the Kit-Build system is a closed concept mapping where learners are given concepts and links to build a concept map, and it has advantage that the recomposed map can be automatically diagnosed. It has been proven that KB-mapping improves the students' learning achievement similar to the traditional concept mapping called scratch concept map composition (SC-mapping). However, the study on the effect of KB-mapping in fostering students' higher-order thinking has yet to be evaluated. This study designed and conducted an experiment to compare the impact of KB-mapping and SC-mapping on promoting students' ability in higher-order thinking. Fifty-four undergraduate students were assigned to either KB-Mapping or SC-Mapping for learning activities. The result of this study suggested that students who learn with KB-mapping had better abilities to solve questions of higher-order thinking than those who applied SC-mapping. The findings also suggested that the quality of students' concept maps affected their performance in solving higher-order thinking questions.

A super-resolution algorithm is proposed to solve the problem of measuring the thin thickness of oil slick using compressed sensing theory. First, a mathematical model of a single pulse underwater ultrasonic echo is established. Then, the estimation model of the transmit time of flight (TOF) of ultrasonic echo within oil slick is given based on the sparsity of echo signals. At last, the super-resolution TOF value can be obtained by solving the sparse convex optimization problem. Simulations and experiments are conducted to validate the performance of the proposed method.

We propose a non-photorealistic rendering method to automatically generate reaction-diffusion-pattern-like images from photographic images. The proposed method uses smoothing filter with a circular window, and changes the size of the circular window depending on the position in photographic images. By partially changing the size of the circular window, the size of reaction-diffusion patterns can be changed partially. To verify the effectiveness of the proposed method, experiments were conducted to apply the proposed method to various photographic images.

Direct-current superconducting quantum interference device (dc-SQUID) based on intrinsic Josephson junction (IJJ) has been fabricated using Bi2Sr2CaCu2O8+δ (Bi-2212) films grown on MgO substrates with surface steps. The superconducting loop parallel to the film surface across the step edge contains two IJJ stacks along the edge. The number of crystallographically stacked IJJ for each SQUIDs were 40, 18 and 3. Those IJJ SQUIDs except for one with 40 stacked IJJs revealed clear periodic modulation of the critical current for the flux quanta through the loops. It is anticipated that phase locking of IJJ has an effect on the modulation depth of the IJJ dc-SQUID.

Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

It is now becoming important for mobile cellular networks to accommodate all kinds of Internet of Things (IoT) communications. However, the contention-based random access and radio resource allocation used in traditional cellular networks, which are optimized mainly for human communications, cannot efficiently handle large-scale IoT communications. For this reason, standardization activities have emerged to serve IoT devices such as Cellular-IoT (C-IoT). However, few studies have been directed at evaluating the performance of C-IoT communications with periodic data transmissions, despite this being a common characteristic of many IoT communications. In this paper, we give the performance analysis results of mobile cellular networks supporting periodic C-IoT communications, focusing on the performance differences between LTE and Narrowband-IoT (NB-IoT) networks. To achieve this, we first construct an analysis model for end-to-end performance of both the control plane and data plane, including random access procedures, radio resource allocation, establishing bearers in the Evolved Packet Core network, and user-data transmissions. In addition, we include the impact of the immediate release of the radio resources proposed in 3GPP. Numerical evaluations show that NB-IoT can support more IoT devices than LTE, up to 8.7 times more, but imposes a significant delay in data transmissions. We also confirm that the immediate release of radio resources increases the network capacity by up to 17.7 times.

This paper proposes a reference-clock-less quick-start-up CDR that resumes from a stand-by state only with a 4-bit preamble utilizing a phase generator with an embedded Time-to-Digital Converter (TDC). The phase generator detects 1-UI time interval by using its internal TDC and works as a self-tunable digitally-controlled delay line. Once the phase generator coarsely tunes the recovered clock period, then the residual time difference is finely tuned by a fine Digital-to-Time Converter (DTC). Since the tuning resolution of the fine DTC is matched by design with the time resolution of the TDC that is used as a phase detector, the fine tuning completes instantaneously. After the initial coarse and fine delay tuning, the feedback loop for frequency tracking is activated in order to improve Consecutive Identical Digits (CID) tolerance of the CDR. By applying the frequency tracking architecture, the proposed CDR achieves more than 100bits of CID tolerance. A prototype implemented in a 65nm bulk CMOS process operates at a 0.9-2.15Gbps continuous rate. It consumes 5.1-8.4mA in its active state and 42μA leakage current in its stand-by state from a 1.0V supply.

The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.