A Lightweight Privacy-Preserving Fair Meeting Location Determination Scheme

Equipped with mobile devices, people relied on location-based services can expediently and reasonably organize their activities. But location information may disclose people\u27s sensitive information, such as interests, health status. Besides, the limited resources of mobile devices restrict the further development of location-based services. In this paper, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy-preserving under our defined threat models. Besides, the presented solution only needs to calculate n Euclidean distances, and hence, our scheme has linear computation and communication complexity

Sphere-shaped Mn3O4 catalyst with remarkable low-temperature activity for Methyl-Ethyl-Ketone combustion

Mn3O4, FeMnOx, and FeOx catalysts synthesized via a solvothermal method were employed for catalytic oxidation of methyl−ethyl−ketone (MEK) at low temperature. Mn3O4 with sphere-like morphology exhibited the highest activity for MEK oxidation, over which MEK was completely oxidized to CO2 at 200 °C, and this result can be comparable to typical noble metal loaded catalysts. The activation energy of MEK over Mn3O4 (30.8 kJ/mol) was much lower than that of FeMnOx (41.5 kJ/mol) and FeOx (47.8 kJ/mol). The dominant planes, surface manganese species ratio, surface-absorbed oxygen, and redox capability played important roles in the catalytic activities of catalysts, while no significant correlation was found between specific surface area and MEK removal efficiency. Mn3O4 showed the highest activity, accounting for abundant oxygen vacancies, low content of surface Mn4+ and strong reducibility. The oxidation of MEK to CO2 via an intermediate of diacetyl is a reaction pathway on Mn3O4 catalyst. Due to high efficiency and low cost, sphere-shaped Mn3O4 is a promising catalyst for VOCs abatement

StarNet: Style-Aware 3D Point Cloud Generation

This paper investigates an open research task of reconstructing and generating 3D point clouds. Most existing works of 3D generative models directly take the Gaussian prior as input for the decoder to generate 3D point clouds, which fail to learn disentangled latent codes, leading noisy interpolated results. Most of the GAN-based models fail to discriminate the local geometries, resulting in the point clouds generated not evenly distributed at the object surface, hence degrading the point cloud generation quality. Moreover, prevailing methods adopt computation-intensive frameworks, such as flow-based models and Markov chains, which take plenty of time and resources in the training phase. To resolve these limitations, this paper proposes a unified style-aware network architecture combining both point-wise distance loss and adversarial loss, StarNet which is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network that can effectively disentangle the Gaussian prior from input's high-level attributes in the mapped latent space to generate realistic interpolated objects. Experimental results demonstrate that our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks, but is more lightweight in model size, requires much fewer parameters and less time for model training

Algebraic Number Precoded OFDM Transmission for Asynchronous Cooperative Multirelay Networks

This paper proposes a space-time block coding (STBC) transmission scheme for asynchronous cooperative systems. By combination of rotated complex constellations and Hadamard transform, these constructed codes are capable of achieving full cooperative diversity with the analysis of the pairwise error probability (PEP). Due to the asynchronous characteristic of cooperative systems, orthogonal frequency division multiplexing (OFDM) technique with cyclic prefix (CP) is adopted for combating timing delays from relay nodes. The total transmit power across the entire network is fixed and appropriate power allocation can be implemented to optimize the network performance. The relay nodes do not require decoding and demodulation operation, resulting in a low complexity. Besides, there is no delay for forwarding the OFDM symbols to the destination node. At the destination node the received signals have the corresponding STBC structure on each subcarrier. In order to reduce the decoding complexity, the sphere decoder is implemented for fast data decoding. Bit error rate (BER) performance demonstrates the effectiveness of the proposed scheme

Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?

Advances in Large Language Models (e.g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts. However, this progress poses security and privacy concerns, necessitating effective solutions for distinguishing deepfake texts from human-written ones. Although prior works studied humans' ability to detect deepfake texts, none has examined whether "collaboration" among humans improves the detection of deepfake texts. In this study, to address this gap of understanding on deepfake texts, we conducted experiments with two groups: (1) nonexpert individuals from the AMT platform and (2) writing experts from the Upwork platform. The results demonstrate that collaboration among humans can potentially improve the detection of deepfake texts for both groups, increasing detection accuracies by 6.36% for non-experts and 12.76% for experts, respectively, compared to individuals' detection accuracies. We further analyze the explanations that humans used for detecting a piece of text as deepfake text, and find that the strongest indicator of deepfake texts is their lack of coherence and consistency. Our study provides useful insights for future tools and framework designs to facilitate the collaborative human detection of deepfake texts. The experiment datasets and AMT implementations are available at: https://github.com/huashen218/llm-deepfake-human-study.gitComment: Accepted at The 11th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2023

Accelerated colorimetric immunosensingusing surface-modified porous monolithsand gold nanoparticles

A rapid and sensitive immunoassay platform integrating polymerized monoliths and gold nanoparticles (AuNPs) has been developed. The porous monoliths are photopolymerized in situ within a silica capillary and serve as solid support for high-mass transport and high-density capture antibody immobilization to create a shorter diffusion length for antibody–antigen interactions, resulting in a rapid assay and low reagent consumption. AuNPs are modified with detection antibodies and are utilized as signals for colorimetric immunoassays without the need for enzyme, substrate and sophisticated equipment for quantitative measurements. This platform has been verified by performing a human IgG sandwich immunoassay with a detection limit of 0.1 ng ml−1. In addition, a single assay can be completed in 1 h, which is more efficient than traditional immunoassays that require several hours to complete

Deficiency of Mkrn2 causes abnormal spermiogenesis and spermiation, and impairs male fertility.

Although recent studies have shed insights on some of the potential causes of male infertility, new underlining molecular mechanisms still remain to be elucidated. Makorin-2 (Mkrn2) is an evolutionarily conserved gene whose biological functions are not fully known. We developed an Mrkn2 knockout mouse model to study the role of this gene, and found that deletion of Mkrn2 in mice led to male infertility. Mkrn2 knockout mice produced abnormal sperms characterized by low number, poor motility, and aberrant morphology. Disruption of Mkrn2 also caused failure of sperm release (spermiation failure) and misarrangement of ectoplasmic specialization (ES) in testes, thus impairing spermiogenesis and spermiation. To understand the molecular mechanism, we found that expression of Odf2, a vital protein in spermatogenesis, was significantly decreased. In addition, we found that expression levels of Odf2 were decreased in Mkrn2 knockout mice. We also found that MKRN2 was prominently expressed in the sperm of normal men, but was significantly reduced in infertile men. This result indicates that our finding is clinically relevant. The results of our study provided insights into a new mechanism of male infertility caused by the MKRN2 downregulation