In the sphere of Cone Beam Computed Tomography (CBCT), acquiring X-ray projections from sufficient angles is indispensable for traditional image reconstruction methods to accurately reconstruct 3D anatomical intricacies. However, this acquisition procedure for the linear accelerator-mounted CBCT systems in radiotherapy takes approximately one minute, impeding its use for ultra-fast intra-fractional motion monitoring during treatment delivery. To address this challenge, we introduce the Patient-specific Cycle-domain Geometric-integrated Denoising Diffusion Probabilistic Model (CG-DDPM). This model aims to leverage patient-specific priors from patient's CT/4DCT images, which are acquired for treatment planning purposes, to reconstruct 3D CBCT from a single-view 2D CBCT projection of any arbitrary angle during treatment, namely single-view reconstructed CBCT (svCBCT). The CG-DDPM framework encompasses a dual DDPM structure: the Projection-DDPM for synthesizing comprehensive full-view projections and the CBCT-DDPM for creating CBCT images. A key innovation is our Cycle-Domain Geometry-Integrated (CDGI) method, incorporating a Cone Beam X-ray Geometric Transformation Module (GTM) to ensure precise, synergistic operation between the dual DDPMs, thereby enhancing reconstruction accuracy and reducing artifacts. Evaluated in a study involving 37 lung cancer patients, the method demonstrated its ability to reconstruct CBCT not only from simulated X-ray projections but also from real-world data. The CG-DDPM significantly outperforms existing V-shape convolutional neural networks (V-nets), Generative Adversarial Networks (GANs), and DDPM methods in terms of reconstruction fidelity and artifact minimization. This was confirmed through extensive voxel-level, structural, visual, and clinical assessments. The capability of CG-DDPM to generate high-quality reconstructed CBCT from a single-view projection at any arbitrary angle using a single model opens the door for ultra-fast, in-treatment volumetric imaging. This is especially beneficial for radiotherapy at motion-associated cancer sites and image-guided interventional procedures.

In this work, we propose ‘Network Sentinel’, a root cause analysis (RCA) model aiming at detecting silent failures in a network. Even nowadays AI comes to aid, CSPs still rely on the network management system (NMS) to remotely collect data or alarms from the network to perform fault detection or RCA. Consequently, the NMS itself can be the ‘Achilles heel’ of the network. A silent failure emerges when the NMS breaks down or disconnects so that we cannot determine the real state of the Network according to the deficient observed symptoms. Network Sentinel utilizes uncertainty levels of topologically neighboring nodes of that possible silently failed node to identify the failure. It is best embedded into a Digital Twin Network (DTN) that can predict and simulate traffic changes of the links of its physical twin.

In recent years, Internet Service Providers (ISPs) are expected to enable various practical services. To meet the requirements of services, the infrastructure of networks has become more and more complex. In telecom networks, the complex infrastructure implies that it would be difficult to analyze the root causes and to locate the faults. In the telecom companies, network maintenance staffs need to spend a lot of time to trace the root cause and solve the network problems. An intelligent fault location approach allows ISPs to be cost-effective, and can assist humans in decision-making and increase automation. To automatically locate the faults, we apply both the Ant Colony Optimization (ACO) algorithm and fuzzy logic methods, and the main contributions of this paper are threefold: (1) we apply the pheromones of the ACO algorithm to quantify the risks that network devices might fail; (2) based on the risks, we leverage fuzzy logic, including the fuzzy relation matrix and the max-min composition method, to infer the fault location; (3) for improving autonomous network, we implemented and evaluated the proposed intelligent fault location approach using the real data in telecom networks.

In recent years, IP surveillance networks are expected to enable various practical applications, such as finding suspects, monitoring pedestrians, and securing societies (e.g., securing a city, a company and a data center). With these applications, IP surveillance network is regarded as one of the potential technologies toward developing smart cities. To support the concept of IP surveillance networks, automatic attribute recognition systems have emerged as a promising intelligent management system. To automatically recognize attributes of pedestrians (e.g., gender and clothing), we apply deep convolutional neural networks (CNNs), and the main contributions of this paper are threefold: (1) we proposed a practical system architecture for intelligent management of surveillance networks; (2) we implemented different deep CNNs, and an ensemble-learning method that leverages these multiple deep-learning models; (3) we evaluated the models using the real data of IP surveillance networks.

The proposed sinusoidal-wave synthesis (SWS) and the optimized THD control (OTC) improve power factor (PF) and total harmonic distortion (THD) over a wide line voltage range. The SWS detects the input line root-mean-square (rms) voltage to generate the digital equivalent code to the OTC for optimizing the THD by tuning the on-time value. Besides, the SWS and the OTC provide a feedforward path to reduce the ripple of the feedback voltage for further improving the THD. Therefore, the PFC converter can keep high PF and low THD over a wide line voltage. Experimental results demonstrate the PF is higher than 0.99 and the THD is 1.7% at VACof 90–110V by the test circuit fabricated in TSMC 800V UHV process.

To reduce the time-to-market and photomask cost for advanced process technologies, metal-only economic cooperation organization (ECO) has become a practical and attractive solution to handle incremental design changes. Due to limited spare cells in metal-only ECO, the new added netlist may often violate the input-slew and output-loading constraints and, in turn, delay or even fail the timing closure. This paper presents a framework, named metal-only ECO slew/cap solver (MOESS), to resolve the input-slew and output-loading violations by connecting spare cells onto the violated nets as buffers. MOESS performs two buffer-insertion schemes in a sequential manner to first minimize the number of inserted buffers and then resolve timing violations, if any. The experimental results based on industrial designs demonstrate that MOESS can resolve more violations with fewer inserted buffers and less central processing unit runtime compared to an electronic design automation vendor's solution.

To shorten the time-to-market and reduce the expensive cost of photomasks in advance process technologies, metal-only ECO has become a practical and attractive solution to handle incremental design changes. Due to limited spare cells in metal-only ECO, the new added netlist may often violate the input-slew and outputloading constraints and, in turn, delay or even fail the timing closure. This paper proposes a framework, named MOESS, to solve the input-slew and output-loading violations by connecting spare cells onto the violated nets as buffers. MOESS provides two buffer insertion schemes performed sequentially to minimize the number of inserted buffers and then to solve timing violations if there is any. This framework has been silicon-validated through industrial designs with more than 1-million instances. The experimental results demonstrate that MOESS can solve more violations with less inserted buffers and less CPU runtime compared to an EDA vendor's solution. The whole framework is built based on a commercial APR tool and can be ported to any other APR tool offering open access to its design database.

Intention is an important factor to drive human behavior. With a certain intention, an action becomes more natural and reasonable. In this paper, an agent with intention, which not only possesses facial expressions, hand gestures, body motions, but also holds memory and personality, is presented. The agent exploits the social dialogue to show intention and its care for students in a learning system. The agent will purposefully guide students in the learning process, care about their needs during learning, provide empathy feedback in order to lessen their negative emotions, and offer appropriate reproach or encouragement to help them overcome the obstacles to English learning. A preliminary experiment will be done to show that this agent can interact with students more naturally and meanwhile strengthen their will to learn as well as make them more willing to accept the opinions from the agent.

In recent years, combining the empathic virtual human with the learning system is one of the most popular topics. However, there are few studies on designing pedagogical agents which have different empathic strategies to interact with learners. In this paper, we have described our approach to design an interactive empathic agent with different empathic strategies. We created an empathic interaction model according to Davis' empathic processes and Lester's empathic model. We invited 12 students to participate in this experiment. We had found some interactive suggestions when students suffered some negative or positive emotions during the learning process. These suggestions can make students feeling concerned and more engaged in learning.

From the review of many literatures, it is considered that there are still prevailing obstacles for second language learners due to insufficient vocabulary. Fortunately, there is a way to increase onepsilas vocabulary by means of extensive reading realizing stories. The research tries to utilize robot as a medium to design a pressure-free and intriguing learning system. It is a learning method imitating TPRS that enables learners to design a personal storytelling robot. The learners proceed with extensive reading during the process of designing a storytelling robot and achieve vocabulary acquisition through designing the movements of robots corresponded to the vocabulary by themselves. Our research is in a preliminary stage of realizing userspsila responses. Ten learners from graduate school students of National Central University Department of Computer Science and Information Engineering, the result of tests and interviews will be the basis of further mass amount experiments and improvement.

Inter-trust is one of the important factors in collaborative learning. In the collaborative learning process, if the relationship between group members lacks trust to each other, the collaboration performance will be limit. If we can obtain the inter-trust of group members by the computer assist, teachers can take the information about the cooperation in team any time. Then teachers would have the ability to intervene and guide students to co-work at the right moment. In this study, a trust management system was proposed on Web site, and could provide trust score and personal advices to each student.

Feeling cared for has important influence on physiology, cognition and emotional states. It plays a very crucial role in education and medical professions to help people deal with emotional problems. In this paper, we present an interactive caring agent which uses facial expressions, hand gestures, body movements, and social dialogue to show empathy and active listening for interacting with students in a learning system. The caring agent would listen to learners' social and affective needs, provide empathic feedback to recover from negative emotions (such as frustration), and encourage learners to overcome academic problems. The preliminary results revealed that the caring agent may not only increase the interaction between students and the learning system, but also have positive effects on students' emotions to make them more engaged in learning.

We are developing a storytelling robot which learns by children's teaching. The robot is used on learning of the second language. It can reduce the negative emotion due to the anxiety of speaking foreign language, which thus motivates learning and enhances the speaking and listening abilities. Presently, our system is still at the initial stage of research. Therefore, we only interviewed and experimented on some of the children now. These results will be used to improve our system.

This paper introduces software which enables teachers create robot as a partner easily and find out how to help a teacher difficulties when using storytelling lessons. First of all, we must understand the relations between storytelling and language learning, and the benefit of a robot being the assistance of a teacher during the lesson. In the next step, let teachers use the "programming a robot into a storytelling tool" and design the "story time" during English class. According to the evaluation result, there was highly satisfied in the new system implementation and effectively improved teacher's organization and technique of difficulties in the storytelling lesson. In addition, tester also had the confidence to improve students' participation and atmosphere during the storytelling activities.

Relational robot is an intelligent conversational agent designed to be a learning secretary for student to notify student of using our learning Web site on time. In this paper we discuss some modules of relational robot to let it play a learning secretary role; it contains interaction module (verbal communication unit, nonverbal communication unit and process unit), internet interface, and communication interface with structure dialogue

Advancement in robotic research enables robot can assist human in many way. However, few researches have been done on applying on education. This paper reports field trials of using robot in an English learning classroom. In our experiment, the robot plays as a partner of a teacher. Five models of collaboration between teachers and robot are proposed. Teachers design the course flow in advance based on the learning content and these five models. Formative evaluations of the experiments are presented to show the impact of adopting robot in the classroom