Personality is an important concept in psychology that reflects individual differences in thinking and behavior, and has significant applications across various fields.Most existing personality analysis methods address this issue at the bag level, treating the entire corpus gathered from one individual as a single unit for classification.However, this paradigm presents several challenges.From the data perspective, collecting a large corpus for each individual and performing comprehensive annotations pose significant difficulties in both data collection and labeling.On the application side, concentrating on classifying the entire corpus limits its applicability in more common single-instance scenarios.To address these issues, we propose a new task paradigm in text-based personality representation learning.Specifically, we construct a triplet personality trend comparison dataset to learn single-sentence personality embeddings with desirable metric properties.This approach removes the traditional constraints on data sources, facilitating dataset expansion, and can leverage the transfer capabilities of embeddings to easily adapt to various downstream tasks.Our experiments show that the learned embeddings significantly boost performance by a relative 10% across various applications, including personality detection, personality retrieval, and emotion translation prediction.The code and dataset are available at https://github.com/zjutangk/PTCD.

Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks.While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention.It is still underexplored how to reduce the annotation cost in the LLMs era.To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs.During collaborative training, an LLM serves as an active annotator inculcating its coarse-grained knowledge, while a downstream SLM is incurred as a student to filter out high-quality in-context samples to feedback LLM for the subsequent label refinery.Extensive experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision.The code is available at https: //github.com/Justherozen/FreeAL.

Distributed cloud computing environments rely on sophisticated communication and sharing paradigms for ease of access, information processing, and analysis. The challenging characteristic of such cloud computing environments is the concurrency and access as both the service provider and end-user rely on the common sharing platform. In this article, retrieval and storage-based indexing framework (RSIF) is designed to improve the concurrency of user and service provider access to the cloud-stored healthcare data. Concurrency is achieved through replication-free and continuous indexing and time-constrained retrieval of stored information. The process of classifying the constraints for data augmentation and update is performed using deep learning for all the storage instances. Through conditional assessment, the learning process determines the approximation of indexing and ordering for storing and retrieval, respectively. This helps to reduce the time for access and retrieval concurrently, provided the process is not dependent. The simulation analysis using the metrics discontinuous indexing, replicated data, retrieval time, and cost proves the reliability of the proposed framework.

The indoor positioning method based on received signal strength indication (RSSI) ranging is a lack of systematic quantitative research on the factors affecting the characteristics of indoor RSSI, which affect the accuracy of positioning. This paper quantitatively analyzes the characteristics of the 2.4 GHz RSSI collected in indoor scenes from the following four aspects: antenna orientation of the receiver, type of wireless network interface card (NIC) of the receiver, time period of the data measurement and height difference between the transmitting and receiving antenna. The experimental results show that: (i) the RSSI value measured is the strongest when the antenna of the receiver is vertically oriented to the antenna of the transmitter, while the weakest when the antenna is vertically back‐facing and the difference between the strongest signal and the weakest signal is 20–25% at the same test point; (ii) the wider the measurement range of NIC, the more conducive to data collection; (iii) the distribution of RSSI signals generated by an access point at a fixed position is inconsistent with time; (iv) when the height of receiving antenna is 0.5 m different from each other, the path loss index of ranging model produces a deviation of about 10%.

Abstract This paper presents a novel system that enables a fully automatic modeling of both 3D geometry and functionality of a mechanism assembly from a single RGB image. The resulting 3D mechanism model highly resembles the one in the input image with the geometry, mechanical attributes, connectivity, and functionality of all the mechanical parts prescribed in a physically valid way. This challenging task is realized by combining various deep convolutional neural networks to provide high‐quality and automatic part detection, segmentation, camera pose estimation and mechanical attributes retrieval for each individual part component. On the top of this, we use a local/global optimization algorithm to establish geometric interdependencies among all the parts while retaining their desired spatial arrangement. We use an interaction graph to abstract the inter‐part connection in the resulting mechanism system. If an isolated component is identified in the graph, our system enumerates all the possible solutions to restore the graph connectivity, and outputs the one with the smallest residual error. We have extensively tested our system with a wide range of classic mechanism photos, and experimental results show that the proposed system is able to build high‐quality 3D mechanism models without user guidance.

This paper presents a method to reconstruct a functional mechanical assembly from raw scans. Given multiple input scans of a mechanical assembly, our method first extracts the functional mechanical parts using a motion-guided, patch-based hierarchical registration and labeling algorithm. The extracted functional parts are then parameterized from the segments and their internal mechanical relations are encoded by a graph. We use a joint optimization to solve for the best geometry, placement, and orientation of each part, to obtain a final workable mechanical assembly. We demonstrated our algorithm on various types of mechanical assemblies with diverse settings and validated our output using physical fabrication.