Which on-device LLM best fits a given user? 2B-class edge models exhibit user-specific behavioral variation: one may better align with genre-sensitive recommendations, another with procedural instructions, and no single model dominates across all users or domains. We present PEARL (Profile-based Explainable Agent for edge-model Recommendation via Latent Decomposition), which selects the best-fitting 2B-class edge LLM from a structured user profile and generates a natural-language explanation grounded in interpretable latent alignment dimensions. Its core, Explainable Latent Decomposition (ELD), maps per-model profile-alignment fingerprints into a shared low-correlation latent subspace for direct profile-to-model matching. On PersonaLens (200 profiles, 8,521/1,974 TSD/TMD dialogues), PEARL achieves P = 2.23 \pm .02 on TSD, outperforming the best single-fixed edge model by +0.11 and closing 35.0% of the Oracle-UB gap; on TMD, PEARL achieves P = 2.14 \pm .03 (+0.11, 34.0% Oracle-UB gap closure). A perturbation analysis shows that masking the top-1 ELD dimension alters 60% of recommendations (Δ P = -0.43), with a modest 1.3× specificity ratio over a random-dimension control.

Modern web interfaces increasingly support complex decision workflows, such as travel planning and multi-criteria selection, yet remain largely static and insensitive to users' moment-to-moment cognitive states during interaction. Travel planning, in particular, requires users to synthesize dispersed information under multiple constraints, making it a representative high-load interactive decision task. This study presents MACA (Multi-Agent Cognitive Adaptation), a framework that enables real-time cognitive adaptation in web-based decision environments by integrating hierarchical Monte Carlo Tree Search with a Planner–Critic–Executor multi-agent architecture. MACA continuously estimates users' emotional and attentional states using facial expression analysis (ResEmoteNet) and gaze stability tracking (MediaPipe), and uses these signals to regulate agent collaboration, reasoning depth, and feedback pacing during interaction. We evaluated MACA in a 2×2 within-subject study (N = 30) comparing Single versus Multi-agent and Fixed versus Adaptive configurations. Results show that the Multi-Adaptive condition significantly improved decision quality (F(3,116) = 2.96, p = 0.035) while reducing mental effort (F(3,116) = 2.82, p = 0.042), yielding a 10.7% gain in decision efficiency without increasing cognitive burden. These findings demonstrate that multimodal user-state sensing combined with cooperative multi-agent reasoning can enhance interactive web-based decision making while maintaining user well-being.

Internet Gaming Disorder (IGD) is officially recognized as a mental health condition by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), a standard for diagnosing mental health conditions. However, current diagnostic methods rely on subjective self-reports, which lack objectivity and consistency. This study proposes the Gaming Disorder Detection (GDD) framework, using data from Reddit’s StopGaming subreddit to provide an objective and scalable IGD detection approach. Sentences extracted from Reddit posts were labeled using a Large Language Model (LLM) based on DSM-5 and IGD-20 criteria, reducing bias and improving diagnostic consistency. The labeled dataset was then analyzed with a Graph Neural Network (GNN) to predict IGD patterns in new data. By combining LLM-based labeling and GNN-based analysis, the study shows the potential of integrating DSM-5 and IGD-20 criteria into a data-driven framework for IGD detection, offering a novel tool for clinicians and experts to objectively and systematically evaluate gaming disorder.

Surfacing peers' workload and speed can reshape collaboration in VR, yet prior work often conflates social cues with environmental load. We isolate the social channel with TRACE-VR, which holds geometry, physics, rules, and timing constant while independently manipulating effort identifiability (traceable vs. anonymous) and peer effort (pace) (high vs. low). In a 2×2 within-subjects study $(n=32)$, each participant worked with nine scripted co-actors for three minutes, transporting 16 crates along a self-chosen path between fixed pickup/drop points; co-actors followed fixed pre-authored routes. We measured intrinsic motivation, social-evaluative load, NASA-TLX, and behavioral/process outcomes. Our findings show that identifiability and higher peer effort (pace) each modestly increased completion rates, with asymmetric effects suggesting partial substitution between accountability cues and normative pace. Perceived monitoring and temporal demand depended on their combination-identifiability raised monitoring at low pace and amplified time pressure at high pace-while composite TLX and finishers' times remained comparable across conditions. Thus, these cues chiefly determine who finishes rather than how fast finishers move. Motivation did not uniformly increase under higher pace. We frame identifiability and pace as partially substitutable levers for shaping social-evaluative experience and facet-level motivation, and outline tempo-aware, accountability-aware guidance for collaborative VR.

This study introduces an informational and empathetic chatbot for childhood cancer survivors. As the survival rates for childhood cancer around the world have increased, survivors often face various psychosocial challenges during and after cancer treatment. However, they rarely seek support from psychosocial professionals due to the low availability of resources and stigma toward cancer survivors in countries like South Korea. This study aimed to develop a chatbot tailed to the unique characteristics of childhood cancer survivors in need of informational and emotional support. Given the limited availability of empirical data on childhood cancer survivors, quotes from survivors were gathered from academic articles and social media, then large language models were employed to generate appropriate responses. Furthermore, we incorporated domain learning techniques to ensure a more tailored and suitable model for addressing the needs of survivors.

With the advantage of practical way to experiment with new network protocols in realistic settings, NOX and OpenFlow switch networks are becoming extremely popular. However, because of basic characteristics of NOX and OpenFlow switch based on the separation between control and data plane, every OpenFlow switch faces a long transmission and retransmission delay when it fails to transmit its data. Until now, the virtualized programmable networks only consider how to achieve the throughput for the direct link between OpenFlow switches. Since wireless channel experiences different conditions and NOX and OpenFlow switch networks supports the maximum flow size threshold, the aggregated flow size of a neighbor OpenFlow switch may be delivered faster than through the direct link if the neighbor link has higher RSS (Received Signal Strength). In this paper, we propose a flow-based hybrid mechanism to improve performance in NOX and wireless OpenFlow switch networks. The main idea of this scheme is that when the transmission of a OpenFlow switch fails, one of neighbor OpenFlow switches with better channel condition transmits the lost frame as well as the own data using flow aggregation scheme. To do so, every OpenFlow switch should manage overhear table to buffer the transmitted packets that is not yet acknowledged. We also present algorithms to retransmit lost packets, to maintain the overhear table and to compensate for the retransmission of packets of other OpenFlow switches. Simulation results show that the proposed flow-based hybrid mechanism can significantly improve the system throughput and the throughput gain.

Network coding is a promising technology that increases the system throughput via reducing the number of transmissions for the packets delivered from the source node to the destination node in the saturated traffic scenario. Nevertheless, some packets can suffer from the metric of end-to-end delay. Since it takes the queuing delay in the intermediate node to wait for other packets to be encoded with (XOR). Therefore, in this paper, we analyze the delay according to the packet arrival rate and propose a new network coding scheme, iXOR (Intelligent XOR). It reduces the average delay even unsaturated traffic load through the Holding-χ strategy. Through an analysis and extensive simulations, we show that iXOR is better than the general forwarding scheme (FWD) without XOR and XOR without the holding-χ strategy, χ=0, in aspect of the average delay as well as the delivery ratio.

With the rapidly increasing demand of traffic applications, the need to support seamless multimedia services in the Vehicular Wireless Networks and Vehicular Intelligent Transportation Systems (V-WINET/V-ITS) is growing. Several mobility support protocols such as the Mobile IPv6 (MIPv6) and the fast handover for the MIPv6 (FMIPv6) have been developed to support seamless handover. However, MIPv6 depreciates Quality-of-Service (QoS) especially for multimedia service applications due to the long handover latency and the packet loss problem. FMIPv6 tries to solve these problems of MIPv6 through handover prediction but the high speed and sudden direction change of vehicles make predictions inaccurate. In this paper, we propose a seamless and robust handover scheme that supports multimedia services in V-WINET/V-ITS. Unlike MIPv6 or FMIPv6 where a new Care-of-Address (nCoA) has to be configured every time when a vehicle meets a new AR (nAR), the proposed scheme continuously maintains the original CoA (oCoA) configured at original Access Router (oAR) and reduces the handover delay caused by the Duplicate Address Detection (DAD). While a vehicle maintains its oCoA, the data packet destined to the vehicle is forwarded from the oAR to the nAR, and finally to the vehicle. At the intersection, the vehicle creates a nCoA to limit the packet forwarding hops between the oAR and the nAR. However, our background DAD scheme reduces the DAD delay at the intersection and also reduces the number of Home Agent (HA) binding updates. Through extensive simulations, we show that the proposed scheme significantly reduces the average handover latency by up to 40%.

An auto-mated network management has been not only critical but also difficult in the network research area. Among the artificial intelligent techniques, traditional supervised learning techniques are not appropriate for an auto-mated network management and specially to detect temporal changes in network intrusion patterns and characteristics. The reason is that supervised learning needs the manager. Therefore, unsupervised learning techniques such as SOM (self-organizing map) are more appropriate for an auto-mated network management such as configuration, performance and anomaly detection. In this paper, we propose an auto-mated network management based on hierarchical SOM that groups similar data and visualize their clusters. Our system labels the map produced by SOM using correlations between features for an auto-mated network management. We experiments our system with KDD Cup 1999 data set. Our system yields the reasonable misclassification rates and takes 0.5 seconds to decide whether a behavior is normal or attack.