Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remains challenging due to contextual ambiguity, co-occurrence, and semantic overlap.We propose a novel framework that combines Large Language Models (LLMs) with a Multiple-Instance Learning (MIL) architecture to enhance interpretability and expression-level reasoning.Each utterance is decomposed into Emotion, Logic, and Behavior (ELB) components, which are processed by LLMs to infer multiple distortion instances, each with a predicted type, expression, and model-assigned salience score.These instances are integrated via a Multi-View Gated Attention mechanism for final classification.Experiments on Korean (KoACD) and English (Therapist QA) datasets demonstrate that incorporating ELB and LLMinferred salience scores improves classification performance, especially for distortions with high interpretive ambiguity.Our results suggest a psychologically grounded and generalizable approach for fine-grained reasoning in mental health NLP.The dataset and implementation details are publicly accessible 1 .

Edge computing has emerged as a viable solution to bridge the gap between distributed Internet of Things (IoT) devices and centralized distant clouds. In particular, small-scale servers are deployed at the edge of network (i.e., edge servers) to `help' cloud servers process data IoT devices constantly generate. However, these edge servers often struggle to deal with emerging applications that require real-time data processing in situ, such as real-time facial recognition. In this paper, we present iEdge as an IoT-assisted edge computing framework that enables the seamless execution of applications across an edge server and nearby IoT devices. The seamless execution in essence has been realized by transforming platform-dependent monolithic applications to cross-platform composite applications and offloading some tasks/functions of these composite applications to IoT devices considering device context. We have evaluated iEdge using a prototype implementation with a real-time facial recognition application. Experimental results show that iEdge effectively harnesses smart IoT devices as a consolidated edge computing execution environment and enables such an application to process more video streams than typical `edge-only' computing.

The object storage systems are used to store and manage unstructured data. Most object storage systems provide the replication policy (REP) or erasure code policy (EC) to ensure the reliability and availability of data. In this paper, we study the trade-off between performance and storage efficiency of these policies with respect to different data sizes of user requests. To this end, we present a hybrid policy management system that takes advantage of both policies by automatically changing policy based on data size. We have implemented the hybrid system in OpenStack Swift. Our evaluation results show the throughput of GET request increases up to 36% while improving storage efficiency by up to 53% compared to that using the REP policy.

The collective use of ever connected and pervasive mobile devices has been increasingly sought for in mobile collaboration, such as multiplayer mobile gaming and distributed processing. The current model of mobile collaboration requires each device to install a particular, `full' mobile app for a respective collaboration. Besides, collaboration functionalities are typically implemented at application level. In this paper, we present Single Application Multiple Device (SAMD) as a platform-level mobile collaboration framework. A mobile app developed using SAMD is capable of fine-grained application sharing. In particular, SAMD enables devices, agreed to participate in collaboration, to get portions of the app on-the-fly and run them without the prior installation. To achieve this, we have developed three solutions as core functionalities of SAMD: 1) Controller packaging, 2) lookahead transfer and 3) code adaptation. We have implemented SAMD on Android as a proof-of-concept prototype. Our experimental results demonstrate SAMD can provide fine-grained sharing of latency-insensitive applications.

Resistance-based memory devices are considered as a strong candidate for next-generation nonvolatile memories as well as potential application in high density embedded cache. These devices can be programmed to different resistance states by applying electrical bias. Read operation then senses the programmed state by discharging a shared data line through the memory cell. However, as the dimensions of the device scale down, its resistance increases and the distribution widens, which leads to reduced sensing margins and degradation in read performance. In the proposed dual-data line (DDL) read scheme, we recycle current flowing through the memory cell during the read operation to create an additional voltage swing on a secondary data line, and combine it with the signal on the original data line to reduce sensing time and energy. Performance comparison with the conventional read scheme is derived theoretically by using circuit analysis and verified through simulation on an array critical path constructed in 65-nm technology. Results show that the DDL scheme can improve sensing margins by an average of 86%, which translates to a sensing time reduction of 47%, across various device conditions. For the same read performance, the sensing energy is decreased by 48%.

Computing resources including mobile devices at the edge of a network are increasingly connected and capable of collaboratively processing what's believed to be too complex to them. Collaboration possibilities with today's feature-rich mobile devices go far beyond simple media content sharing, traditional video conferencing and cloud-based software as a services. The realization of these possibilities for mobile edge computing (MEC) requires non-trivial amounts of efforts in enabling multi-device resource sharing. The current practice of mobile collaborative application development remains largely at the application level. In this paper, we present CollaboRoid, a platform-level solution that provides a set of system services for mobile collaboration. CollaboRoid's platform-level design significantly eases the development of mobile collaborative applications promoting MEC. In particular, it abstracts the sharing of not only hardware resources, but also software resources and multimedia contents between multiple heterogeneous mobile devices. We implement CollaboRoid in the application framework layer of the Android stack and evaluate it with several collaboration scenarios on Nexus 5 and 7 devices. Our experimental results show the feasibility of the platform-level collaboration using CollaboRoid in terms of the latency and energy consumption.

A word line pulse (WLP) circuit scheme is proposed toward the implementation of magnetoelectric random access memory (MeRAM). The circuit improves the write error rate (WER) and cell area efficiency by generating a better write pulse compared to conventional bitline pulse (BLP) techniques in terms of the pulse slew rate and amplitude. For the voltage-controlled magnetic anisotropy-induced precessional switching of the magnetic tunnel junction (MTJ), the write pulse shape has a large impact on the switching probability. Typically, a square shape pulse results in higher switching probability compared to that of a triangular shape pulse with long rise and falling edges, since the square shape pulse causes a more stable precessional trajectory of the free layer magnetization by providing a relatively constant in-plane-dominant effective field. Compared to the BLP scheme, the WLP can generate a better square shape pulse by eliminating discharge paths under the pulse condition, using the gain of the access transistor, and effectively diminishing the capacitive loading which needs to be driven. A macrospin compact model of voltage-controlled MTJ shows that the WLP can improve WER by${10}^{7}$times and allow MeRAM to have four-time improvement in area efficiency of driver circuits compared to the BLP.

Spin-transfer torque random access memory (STT-RAM) and magnetoelectric random access memory (MeRAM) are promising non-volatile memory technologies. But STT-RAM and Me RAM both suffer from high write error rate due to thermal fluctuation of magnetization. Temperature and wafer-level process variation significantly exacerbate these problems. In this paper, we propose a design that adaptively selects optimized write pulse for STT-RAM and MeRAM to overcome ambient process and temperature variation. To enable the adaptive write, we design specific MTJ-based variation monitor, which precisely senses process and temperature variation. The monitor is over 10X faster, 5X more energy-efficient, and 20X smaller compared with conventional thermal monitors of similar accuracy. With adaptive write, the write latency of STT-RAM and MeRAM cache are reduced by up to 17% and 59% respectively, and application run time is improved by up to 41%.

A non-volatile spintronic programmable logic (SPL), based on a 3-teriminal magnetic tunnel junction (MTJ), is presented and simulated using a compact device model. The SPL structure is compatible with CMOS technology and can be fabricated in the back end of line (BEOL). The proposed SPL exploits the gate-voltage-modulated spin Hall effect (V-SHE) switching, which combines the voltage controlled magnetic anisotropy (VCMA) effect and SHE, as a parallel configuration method. The VCMA modulates the coercivity of the MTJ, reducing the critical current for the SHE to change the state of MTJs. This allows the SPL to achieve 100x faster configuration speed due to the parallel configuration, and 32% area reduction because of minimized transistors in the write circuit, compared to conventional spin transfer torque memory (STT-RAM) based programmable logic.