Physical layer security (PLS) has emerged as a promising technology to protect critical and sensitive information against unauthorized devices. To address the key challenge of acquiring channel state information (CSI) of passive eavesdroppers in PLS implementation, we propose a novel sensing-assisted PLS scheme with the aid of reflecting reconfigurable intelligent surface (STAR-RIS). It employs a self-refine sensing scheme utilizing the artificial noise (AN) signals to iteratively estimate the eavesdroppers’ positions for CSI calculation. We aim to maximize the secrecy capacity based on the sensing-estimated CSI while tracking the eavesdroppers in full-duplex (FD) mode with integrated sensing and communication (ISAC) signals comprising artificial noise (AN). This is achieved by jointly designing the beamforming vector of information signals, the beamforming vector of AN signals, and the coefficients of the STAR-RIS. To optimize these coupled variables, we introduce an alternating optimization (AO) scheme to solve the problem recursively. In particular, we tackle the non-convexity of the beamforming optimizations for information and AN signals with the successive convex approximation (SCA) scheme and adopt a semi-definite relaxation (SDR) scheme to design the reflection and refraction coefficients of the STAR-RIS. The numerical results validate that the proposed scheme ensures secure communications against multiple eavesdroppers without any prior eavesdropper channel information. In addition, the proposed scheme can significantly improve SC performance by up to 66. 7% compared to the benchmarks without the sensing-assisted function.

Traditional self-interference cancellation (SIC) methods are common in full-duplex (FD) integrated sensing and communication (ISAC) systems. However, exploring new SIC schemes is important due to the limitations of traditional approaches. With the challenging limitations of traditional SIC approaches, this paper proposes a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled FD ISAC system, where STAR-RIS enhances simultaneous communication and target sensing and reduces self-interference (SI) to a level comparable to traditional SIC approaches. The optimization of maximizing the sensing signal-to-interference-plus-noise ratio (SINR) and the communication sum rate, both crucial for improving sensing accuracy and overall communication performance, presents significant challenges due to the non-convex nature of these problems. Therefore, we develop alternating optimization algorithms to iteratively tackle these problems. Specifically, we devise the semi-definite relaxation (SDR)-based algorithm for transmit beamformer design. For the reflecting and refracting coefficients design, we adopt the successive convex approximation (SCA) method and implement the SDR-based algorithm to tackle the quartic and quadratic constraints. Simulation results validate the effectiveness of the proposed algorithms and show that the proposed deployment can achieve better performance than that of the benchmark using the traditional SIC approach without STAR-RIS deployment.

In recent years, using Al to create an engaging vocabulary learning experience has been a prominent topic. Studies have shown Al can provide educational affordances for enhancing vocabulary learning. However, the appropriation of these affordances varies depending on teachers' use. This paper presents a case study on six teachers appropriated an Al-powered vocabulary learning system, particularly focusing on the affordances of monitoring and regulation, and engaging co-construction enhanced by Al-enabled automatic feedback and recommendations. By examining teachers' beliefs and knowledge of self-regulation and collaborative learning, the study details how they appropriated the affordances in their classes. The study provides suggestions for the design of Al in education and teachers' professional development during the implementation of Al-supported learning system.

This panel discussion explores the theory-informed utilization of smart technologies in language learning, focusing on contextualized, authentic, and communicative approaches. Integrating advanced smart technologies such as Artificial Intelligence, Augmented Reality, Virtual Reality, learning analytics, and robotics into language education offers new avenues for enhancing learner engagement and outcomes. Panelists will present and discuss studies involving various pedagogical strategies that align with contemporary language learning theories. Case studies and research findings will illustrate effective smart technology integration in language classrooms and beyond, addressing practical considerations, challenges, and strategies for leveraging these tools to support second language acquisition and other language learning contexts. The panel aims to provide a comprehensive understanding of how to harness smart technologies to create engaging, effective, and theory-aligned language learning experiences, contributing to the academic discourse on the transformative potential of these tools in fostering meaningful language education.

Physical layer security (PLS) technologies are expected to play an important role in the next-generation wireless networks, by providing secure communication to protect critical and sensitive information from illegitimate devices. In this paper, we propose a novel secure communication scheme where the legitimate receiver use full-duplex (FD) technology to transmit jamming signals with the assistance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) which can operate under the energy splitting (ES) model and the mode switching (MS) model, to interfere with the undesired reception by the eavesdropper. We aim to maximize the secrecy capacity by jointly optimizing the FD beamforming vectors, amplitudes and phase shift coefficients for the ES-RIS, and mode selection and phase shift coefficients for the MS-RIS. With above optimization, the proposed scheme can concentrate the jamming signals on the eavesdropper while simultaneously eliminating the self-interference (SI) in the desired receiver. To tackle the coupling effect of multiple variables, we propose an alternating optimization algorithm to solve the problem iteratively. Furthermore, we handle the non-convexity of the problem by the the successive convex approximation (SCA) scheme for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-RIS, as well as the phase shifts optimizations for the MS-RIS. In addition, we adopt a semi-definite relaxation (SDR) and Gaussian randomization process to overcome the difficulty introduced by the binary nature of mode optimization of the MS-RIS. Simulation results validate the performance of our proposed schemes as well as the efficacy of adapting both two types of STAR-RISs in enhancing secure communications when compared to the traditional self-interference cancellation technology.

In this paper, we propose a secure unmanned aerial vehicle (UAV) communication system with the assistance of a reconfigurable intelligent surface (RIS), where UAV trajectory design and artificial noise are incorporated to prevent eavesdropping from multiple colluding curious users. To maximize the secrecy rate of the proposed system, we undertake a joint optimization process that encompasses the trajectory of the UAV, the RIS phase shifts, and the beamforming vectors for both information and artificial noise signals, considering the constraints of the UAV transmit power, UAV flying speed and the phase shifts. To address the non-convex nature of the joint problem and handle the coupling effects of multiple parameters, we conduct the problem decomposition by using the block coordinate descent (BCD) method, combined with an alternating algorithm to optimize the decomposed sub-problems. To further tackle the non-convexity in sub-problems, we apply the successive convex approximation (SCA) method to circumvent the trajectory optimization problem and to optimize the beamformers of information and artificial noise signals, while a majorization-minimization (MM) based scheme is adopted for the RIS phase shifts optimization. Numerical simulation results substantiate the convergence and effectiveness of the proposed algorithm through the comparison with benchmark methods, and our proposed scheme is proven to achieve a significant improvement in average secrecy rate across various conditions.

5G NR is an emerging technology to overcome the mobile traffic explosion which is triggered by various new services. Radio Access Network (RAN) sharing between different Mobile Network Operators (MNOs) is expected to be an efficient method to reduce the CAPEX and OPEX while at the same time increase the flexibility of networks. This paper studies the radio unit (RU) sharing between MNOs. We propose a resource allocation scheme to overcome the deterioration of performance induced by limited bandwidth of fronthaul and increase of inter-RU interference. In our proposed scheme, whole radio-frequency bandwidths are divided into subbands and the number of MIMO layers per subband are controlled. We reveal the effectiveness of our proposed method by utilizing system level simulations.

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients, and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

In recent years, Artificial Intelligence (AI) has significantly increased in digital second language (L2) learning, particularly in supporting vocabulary acquisition. However, research on how AI might facilitate collaborative vocabulary learning is still in its new stage. This study works on investigating the effectiveness of AI- recommended contexts in fostering collaborative language learning among young learners. The research employed a self-developed AI-empowered Chinese vocabulary learning system called ARCHe, which was implemented in primary schools in Singapore. A mixed-methods case study approach was conducted with the 2nd-grade students who spoke English as their first language. Preliminary findings indicate that learning Chinese with ARCHe effectively enhances the academic performance of young learners, with the AI-empowered self-generated contexts feature exhibiting a positive impact on collaborative language learning performance. The study offers insights into the integration of AI in digital language learning, with the potential to enhance L2 learning outcomes for young learners.

The logographic nature of Chinese script is a major dissuading factor for learning handwriting. The challenge is the complex psycholinguistic process behind handwriting. Thus, we developed AI-Strokes, a Chinese handwriting learning tool that assists teachers in facilitating students’ handwriting practice in various modalities, and provides personalized feedback for the students. By leveraging a trainable Machine Learning back-end framework, the tool diagnoses and scores students’ handwriting errors. This paper reports a pilot study in a Singapore primary school with an early prototype of AI-Strokes. Two classes of students went through AI-Strokes-based Chinese handwriting lessons (the experimental group) and conventional lessons (the control group) respectively. Pre- and post-tests were administered, and their handwriting processes were analyzed regarding errors in stroke orders, extra/missing strokes, and errors in stroke directions. The results show that the experimental group has yielded significantly better learning gains than the control group. It is posited that the personalized feedback of AI-Strokes has formed a feedback loop to support students’ trial-and-error process in improving their handwriting skills. The multimodal handwriting task design may have also fostered their orthographic awareness through the activation of alternative psycholinguistic pathways during their handwriting lessons.

Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission, which can realize secure communications against the external passive eavesdropping, is considered one of the most practicable PLS technologies. In this paper, we reveal that the conventional AN scheme has a severe vulnerability when the eavesdropper adopts Independent Component Analysis (ICA)-based attack, by which the AN-aided transmission will be cracked in a high probability even with most transmission power allocated to AN signals. In order to ensure secure communications, we propose a robust AN scheme focused on the ICA's ability of blind source separation. In our proposed scheme, AN signals are generated with the same modulation scheme and a certain correlation to information signals, thus to introduce irremovable correlation to degrade the ICA's separation performance. Furthermore, we derive the optimal correlation factors from the relation between an information-AN correlation and the ICA's performance. Numerical simulation results with the comparison to the conventional AN scheme show that, our proposed scheme can ensure secure communications by degrading eavesdropper's success probability of attack from 96% to lower than 1%.

Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission is considered as one of the most practicable PLS technologies, as it can realize secure transmission independent of the eavesdropper's channel status. In this paper, we reveal that AN transmission has the dependency of eavesdropper's channel condition by introducing our proposed attack method based on a supervised-learning algorithm which utilizes the modulation scheme, available from known packet preamble and/or header information, as supervisory signals of training data. Numerical simulation results with the comparison to conventional clustering methods show that our proposed method improves the success probability of attack from 4.8% to at most 95.8% for the QPSK modulation. It implies that the transmission to the receiver in the cell-edge with low order modulation will be cracked if the eavesdropper's channel is good enough by employing more antennas than the transmitter. This work brings new insights into the effectiveness of AN schemes and provides useful guidance for the design of robust PLS techniques for practical wireless systems.

In this paper, we propose a novel radio access technology (RAT) selection method that can maximize total throughput by simultaneously selecting optimal RATs for a group of user equipments (UEs). To perform simultaneous selection, we also propose a novel throughput estimation method based on not wireless information collected from the existing network equipment but the accurate estimation of the amount of radio resources available for the UEs. Throughputs of all the UEs are estimated for all RAT selection patterns, by using the estimated available radio resources. The RAT selection method then selects a RAT selection pattern that gives the maximum total throughput. We conduct both simulations with a network simulator and field experiments with a commercially operating LTE network. The field experiments confirm that the proposed throughput estimation method can achieve an average estimation error of about 10%. The simulations confirm that the proposed method with a 10% throughput estimation error can achieve 96% throughput compared to that with perfect estimation. A conventional successive RAT selection method takes 1–10 minutes to achieve the same throughput as the proposed method, while the proposed simultaneous RAT selection method only takes about 1–10 seconds. Moreover, the conventional method achieves only 71% throughput of the proposed method with same processing time as the proposed method.

Increasing use of IEEE 802.11 WLAN specifications has resulted in dense deployment of access points (AP) to provide service to stations (STA). Dynamic sensitivity control (DSC) technology, which adjusts the carrier sense threshold (CST) to encourage more APs and STAs to transmit simultaneously, is considered an effective approach to improve the throughput in densely-deployed WLAN networks. The DSC algorithm should take into account both the increase of transmission opportunity, and the possible transmission rate degradation of related links caused by the consequent interference. Conventional algorithms, which set the CST value to allow simultaneous transmission only when a required signal to interference plus noise ratio (usually for a high transmission rate) can be guaranteed, are too conservative to improve the throughput. In this paper, we propose a novel DSC algorithm that enables simultaneous transmissions which lead to improved system throughput even when the transmission rate is degraded. Since whether system throughput can be improved by simultaneous transmission is different for each communication link, we set different CST values for different links instead of setting just one common CST value. We first formulate the throughput of each downlink as a function of related links' CST, and then search a set of CST values for these links to maximize the system throughput. A heuristic procedure is adopted in our algorithm to reduce calculation complexity. From numerical simulation results, it is shown that our algorithm can improve the system throughput by up to 30% when compared to a conventional algorithm.

Various radio access technology (RAT) selection methods between LTE and wireless LAN (WLAN) have been proposed, which aim to improve the capacity of the cellular network and to improve user satisfaction through mobile network operators. However, studies from the viewpoint of mobile virtual network operators (MVNOs) have not yet been investigated sufficiently. We propose a novel RAT virtualization system suitable for MVNO composed of a new network-initiated RAT selection method and RAT selection algorithms. The system conceals a change of IPs associated with a change of RATs and offers a context-aware virtualized RAT for the application server and user application. We demonstrate a new RAT selection method by developing a prototype of the proposed RAT selection system. We also evaluate a system-level performance by using a network simulator (NS-3) and show that our proposed algorithm outperforms conventional algorithms in terms of the WLAN usage ratio and user satisfaction.

In a wireless convergecast network, load-balancing routing contributes to improving energy-efficiency by eliminating the difference of relaying load among one-hop nodes. Load-balancing algorithms can be operated by centralized and distributed implementation. Centralized implementation needs to collect global neighborhood information, which leads to a large amount of signaling overhead. On the contrary, distributed implementation can overcome such issue. However, it only achieves sub-optimal load-balancing. Thus, it is obscure that which type of implementation is more energy-efficiency. In this paper, a novel distributed implementation for load-balancing routing is proposed. We make a series of analyses of comparing the energy-efficiency between centralized and distributed implementation. The results show that, the overhead for constructing load-balancing routing via distributed implementation is always less than that via centralized implementation. However, the heaviest one-hop node in distributed implementation suffers from more loads so that according to its energy consumption, distributed implementation can maintain energy-efficiency only in the condition that routing update cycle is shorter than a threshold.

Automated interference detection is required for operation and management services in wireless networks as Wireless Local Area Networks (WLANs) based on IEEE 802.11 Standard since co-channel interference from other wireless transmitters occurs irregularly. We propose a new approach for detecting the presence of interference on 802.11 links. Our solution focuses on the Access Point (AP)-initiated monitoring approach without Wireless Station (STA)-initiated additional function and sniffer devices. Our approach uses the geographical distribution of the response delay of the Acknowledgment packet (ACK) to the downlink packet that AP transmitted to STAs. The proposed method consists of a continuous monitoring step and a periodical diagnosis step to measure network statistics by ACK response delay between an AP and STAs and to detect whether and where interference occurs respectively. Comprehensive simulation results show that the proposed approach has a good accuracy and low misdetection ratio for diagnosing the interference existence and location in IEEE 802.11 WLANs.

Recently, wireless networks using ISM band have been attracting a lot of attention. In these systems using carrier sense multiple access with collision avoidance (CSMA/CA), the packet collision and recollision between hidden nodes occur frequently and drastically decrease the network (NW) capacity. In this paper, we propose a packet retransmission protocol to avoid the packet recollision in 1 hop many-to-one network, in which all nodes send their packets to an access point (AP). In our proposed retransmission protocol, hidden nodes whose first transmitted packet is lost control their retransmission timing autonomously to avoid recollision with each other by using time lag between the finish time of their first transmitted packet and the received time of a collision inform signal, which is sent by AP to inform nodes about packet collision. The NW capacity using the proposed retransmission protocol is evaluated by computer simulation. And it is shown that the maximum network capacity of our proposed method is about 1.7 times better than that of the conventional method.

This case study investigates the appropriation of a representational tool by students in small groups in the context of collaborative second language writing. The functions of inscriptional devices in second language classroom learning are identified: (1) referencing, (2) pinpointing, (3) accumulating, (4) prompting notice, (5) realizing parallels, and (6) promoting synergy. The study explores the beneficial affordances of the representational tool that supplement face-to-face communication for second language learning, and draws some implications for the design of collaborative L2 learning in networked classrooms.

One serious challenge of wireless multi-hop ad hoc networks is frequent link and node failures due to lossy wireless channels and low-cost hardware. Failure detection plays an important role in network maintenance. In order to reduce communication overhead, local-based failure detection methods conduct diagnosis processes in a local area. However, the overhead of them is still very large because several redundant detection processes are triggered for one failure by several nodes. In this paper, we present a new failure detection approach named Root Node Reduction (RNR), which can reduce the number of detection processes by a backoff scheme. Furthermore, we propose two additional strategies to improve diagnosis accuracy, including multi fusion which guarantees complete evidence collection, and multi report which improves the reception ratio of diagnosis conclusion report. The comparison results show that the performance of communication overhead, diagnosis accuracy and data packet reception is improved by these schemes.

Understanding the significance of argumentation in the learning and doing of science, the community of computer-supported collaborative learning has developed an increasing interest in argumentation. To empower the teaching and learning of science in real classrooms, a collaborative argumentation tool (called AppleTree) embedding three scaffolding mechanisms, namely, dual representational and interactional spaces, automated assessment for learning, and staged-based collaboration scripts, has been designed and developed using a design research approach. This paper presents the design rationale of the system and its realized prototype. A pilot study in a secondary science grade 1class is also reported. Preliminary data analysis results point towards validation of the effectiveness of the system on empowering learning and its usability.

Data acquisition systems play an important role in several industries. Because highly scalable ad-hoc networks with multi-hop transmissions can be easily constructed at low cost, such networks are considered suitable for data acquisition systems. However, a lack of centralized control makes it difficult to respond to congestion when system capacity is exceeded. Therefore, the estimation of system capacity is a critical issue for system design. In this paper, we propose a novel zone division model to analyze the capacity of multi-hop data acquisition systems using Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols. We divide the one-hop area to a gateway (GW) into two zones: (1) an outer zone where an access node (AN) can relay packets from multi-hop ANs; and (2) an inner zone where ANs cannot relay packets. Using this approach, we calculate packet loss for each zone, considering the difference in the communication range of the GW and ANs, as well as the collision with hidden nodes. Simulation results show that our model achieves higher estimation accuracy than conventional methods, indicating that we have successfully modeled phenomena of the real system more accurately.

Good learning activity designs do not guarantee effective classroom orchestration by the teacher. Enactments of the same learning activity design may vary greatly among different teachers. This study compares two teachers’ enactments of a collaborative learning activity in a L2 writing classroom supported by a networked technology called Group Scribbles (GS). Plausible factors of teacher’s moves and actions that impact the different enactments are identified and discussed, including articulating the objective of activity explicitly, providing improvised formative assessment and scaffolding to support students’ work on an ongoing basis, and controlling the tempo of the activity and maintaining students’ enthusiasm.

This paper reports a comparative case study to explore the discrepancies in learning outcomes attained by two student groups in an identical CSCL activity in the language classroom and the differences in group behaviors that contributed to these discrepancies. From micro-analysis, group behaviors that are desired for language development are identified. The findings can inform future pedagogical and technological design to improve language learning in classrooms.

Recently, wireless many-to-one networks have been attracting much attention and wireless sensor ad-hoc networks are applied to various applications. In ad-hoc networks using carrier sense multiple access with collision avoidance (CSMA/CA), the capacity is drastically decreased by the hidden node problem. In this paper, we assume a many-to-one network and propose a transmit power control (TPC) algorithm to avoid the hidden node problem. In our proposed TPC algorithm, a 1-hop area, a forced multi-hop area, and a multi-hop area are defined. All nodes in the 1-hop area adjust their transmit power as they can sense each other's transmit data, and all nodes in the forced multi-hop area and multi-hop area control their transmit power as they connect to a gateway (GW) with multi-hop and reduce the interference to nodes in the 1-hop area. The network capacity using the proposed TPC algorithm is evaluated by computer simulation. By this computer simulation, it is shown that the network capacity of TPC is better by about 2.6 times than that of No-TPC.

Given that real-world classification tasks always have irrelevant or noisy features which degrade both prediction accuracy and computational efficiency, feature selection is an effective data reduction technique showing promising performance. This paper presents a cooperative coevolution framework to make the feature selection process embedded into the classification model construction within the genetic-based machine learning paradigm. The proposed approach utilizes the divide-and-conquer strategy to manage two populations in parallel, corresponding to the selected feature subsets and the rule sets of classifier respectively, in which a memetic feature selection algorithm is adopted to evolve the feature subset population while a Pittsburgh-style learning classifier system is used to carry out the classifier evolution. These two coevolving populations cooperate with each other regarding the fitness evaluation and the final solution is obtained via collaborations between the best individuals from each population. Empirical results on several benchmark data sets chosen from the UCI repository, together with a non-parametric statistical test, validate that the proposed approach is able to deliver classifiers of better prediction accuracy and higher stability with fewer selected features, compared with the original learning classifier system. In addition, the incorporated feature selection process is shown to help improve the computational efficiency as well.

Effective task scheduling, which is essential for achieving high performance of parallel processing, remains challenging despite of extensive studies. In this paper, a heuristic-based hybrid Genetic Algorithm (GA) is proposed for solving the heterogeneous multiprocessor scheduling problem. The proposed algorithm extends traditional GA-based approaches in three aspects. First, it incorporates GA with Variable Neighborhood Search (VNS), a local search metaheuristic, to enhance the balance between global exploration and local exploitation of search space. Second, two novel neighborhood structures, in which problem-specific knowledge concerned with load balancing and communication reduction is utilized, are proposed to improve both the search quality and efficiency of VNS. Third, the use of GA is restricted to map tasks to processors while an upward-ranking heuristic is introduced to determine the task sequence assignment in each processor. Simulation results indicate that our proposed algorithm consistently outperforms several state-of-art scheduling algorithms in terms of the schedule quality while maintaining high performance within a wide range of parameter settings. Further experiments are carried out to validate the effectiveness of the hybridized VNS.