Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-graph Convolutional Network for Unsupervised 3D Shape Retrieval

3D shape retrieval has attracted much research attention due to its wide applications in the fields of computer vision and multimedia. Various approaches have been proposed in recent years for learning 3D shape descriptor from different modalities. The existing works contain the following disadvantages: 1) the vast majority methods rely on the large scale of training data with clear category information; 2) many approaches focus on the fusion of multi-modal information but ignore the guidance of correlations among different modalities for shape representation learning; 3) many methods pay attention to the structural feature learning of 3D shape but ignore the guidance of structural similarity between every two shapes. To solve these problems, we propose a novel multi-graph network (MGN) for unsupervised 3D shape retrieval, which utilizes the correlations among modalities and structural similarity between two models to guide the shape representation learning process without category information. More specifically, we propose two novel loss functions: auto-correlation loss and cross-correlation loss. The auto-correlation loss utilizes information from different modalities to increase the discrimination of shape descriptor. The cross-correlation loss utilizes the structural similarity between two models to strengthen the intra-class similarity and increase the inter-class distinction. Finally, an effective similarity measurement is designed for the shape retrieval task. To validate the effectiveness of our proposed method, we conduct experiments on the ModelNet dataset. Experimental results demonstrate the effectiveness of our proposed method, and significant improvements have been achieved compared with state-of-the-art methods.
Leverage Social Media for Personalized Stress Detection

Timely detection of stress is desirable to address the increasingly serious stress problem. Thanks to the rich linguistic expressions and complete historical records on social media, achieving personalized stress detection through social media is feasible and prominent. We construct a three-leveled framework, aiming at personalized stress detection based on social media. The three-leveled framework learns the personalized stress representations following an increasingly detailed processing, i.e., from the generic mass level, group level, to the final individual level. The first mass-level focuses on mining the generic stress representations from people's linguistic and visual posts with a two-layer attention mechanism. The second group-level adopts the graph neural network to learn the group-wise characteristics of the group where an individual belongs to. The third individual-level analyzes and incorporates individual's personality traits into stress detection. The performance study on the 2,059 microblog users shows that our proposed method can achieve over 90% in detection accuracy. Furthermore, the extended experiment on a harder personalized sub-dataset demonstrates that our method works better in distinguishing personalized expressions with different latent meanings.
Efficient Joint Gradient Based Attack Against SOR Defense for 3D Point Cloud Classification

Deep learning based classifiers on 3D point cloud data have been shown vulnerable to adversarial examples, while a defense strategy named Statistical Outlier Removal (SOR) is widely adopted to defend adversarial examples successfully, by discarding outlier points in the point cloud. In this paper, we propose a novel white-box attack method, Joint Gradient Based Attack (JGBA), aiming to break the SOR defense. Specifically, we generate adversarial examples by optimizing an objective function containing both the original point cloud and its SOR-processed version, for the purpose of pushing both of them towards the decision boundary of classifier at the same time. Since the SOR defense introduces a non-differentiable optimization problem, we overcome the problem by introducing a linear approximation of the SOR defense and successfully compute the joint gradient. Moreover, we impose constraints on perturbation norm for each component point in the point cloud instead of for the entire object, to further enhance the attack ability against the SOR defense. Our JGBA method can be directly extended to the semi white-box setting, where the values of hyper-parameters in the SOR defense are unknown to the attacker. Extensive experiments validate that our JGBA method achieves the highest performance to break both the SOR defense and the DUP-Net defense (a recently proposed defense which takes SOR as its core procedure), compared with state-of-the-art attacks on four victim classifiers, namely PointNet, PointNet++(SSG), PointNet++(MSG), and DGCNN.
Reproducibility Companion Paper: Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network

This companion paper supports the experimental replication of paper "Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network", which is presented at ACM Multimedia 2019. We provide the software package for replicating the implementation of Multi-Layered Comparison Network (MCN), as well as the Polyvore-T dataset and baseline methods compared in the original paper. This paper contains the guides to reproduce the experiment results including outfit compatibility prediction, outfit diagnosis and automatic outfit revision.
Infinity Battle: A Glance at How Blockchain Techniques Serve in a Serverless Gaming System

The blockchain technology provides a data authentication and permanent storage solution to the data volatility issue in peer-to-peer games. In this work, we present the Infinity Battle, a serverless turn-based strategy game supported by a novel Proof-of-Play consensus model. Comprising three major phases: matchmaking, gaming session and global synchronization, the proposed demo game generates a blockchain through distributed storage and processing.
Intra and Inter-modality Interactions for Audio-visual Event Detection

The presence of auditory and visual sensory streams enables human beings to obtain a profound understanding of a scene. While audio and visual signals are able to provide relevant information separately, the combination of both modalities offers more accurate and precise information. In this paper, we address the problem of audio-visual event detection. The goal is to identify events that are both visible and audible. For this, we propose an audio-visual network that models intra and inter-modality interactions with Multi-Head Attention layers. Furthermore, the proposed model captures the temporal correlation between the two modalities with multimodal LSTMs. Our method achieves state-of-the-art performance on the AVE dataset.
What Aspect Do You Like: Multi-scale Time-aware User Interest Modeling for Micro-video Recommendation

Online micro-video recommender systems aim to address the information explosion of micro-videos and make the personalized recommendation for users. However, the existing methods still have some limitations in learning representative user interests, since the multi-scale time effects, user interest group modeling, and false positive interactions are not taken into consideration. In view of this, we propose an end-to-end Multi-scale Time-aware user Interest modeling Network (MTIN). In particular, we first present an interest group routing algorithm to generate fine-grained user interest groups based on user's interaction sequence. Afterwards, to explore multi-scale time effects on user interests, we design a time-aware mask network and distill multiple temporal information by several parallel temporal masks. And then an interest mask network is introduced to aggregate fine-grained interest groups and generate the final user interest representation. At last, in the prediction unit, the user representation and micro-video candidates are fed into a deep neural network (DNN) for predictions. To demonstrate the effectiveness of our method, we conduct experiments on two publicly available datasets, and the experimental results demonstrate that our proposed model achieves substantial gains over the state-of-the-art methods.
Transparent object segmentation from casually captured videos

Segmentation of transparent objects from sequences can be very useful in computer vision applications. However, without additional auxiliary information it can be hard work for traditional segmentation methods, as light in the transparent area captured by RGB cameras mostly derive from the background and the appearance of transparent objects changes with surroundings. In this article, we present a from‐coarse‐to‐fine transparent object segmentation method, which utilizes trajectory clustering to roughly distinguish the transparent from the background and refine the segmentation based on combination information of color and distortion. We further incorporate the transparency saliency with color and trajectory smoothness throughout the video to acquire a spatiotemporal segmentation based on graph‐cut. We conduct our method on various datasets. The results demonstrate that our method can successfully segment transparent objects from the background.
Folding patch correspondence for multiview stereo

In this article, we propose the novel folding patch model which can replace the traditional patch model utilized in patch‐based multiview stereo (MVS) methods to significantly improve the reconstruction results. The patch model is applied as an approximation of the scene surface differential in the geometric estimation procedure. By minimizing the photometric discrepancy of the projection of the patch model on multiple source images, patch‐based MVS algorithms optimize the position and normal values for the 3D hypothesis of the target pixel. The optimization is based on the assumption that the patch model can fit the target scene surface perfectly. However, when it comes to complex scenes crowded with sharp edges, splintery surfaces, or round surfaces, the patch model is inherently not suitable since even from the microscopic perspective these surfaces are not entirely flat. We construct the folding patch model by folding the traditional patch model from the middle line. By adjusting the folding angle and direction, the folding patch model can fit complex surfaces more flexibly. We apply our folding patch model to the representative open‐source patch based multiview stereo (PMVS) and COLMAP, and validate the effectiveness on ETH3D benchmark and data sets captured in nature. The results demonstrate that utilizing the folding patch model can significantly improve the behavior of PMVS and COLMAP, especially on data sets mainly consist of complex surfaces from plants.
Power allocation with fairness for non-orthogonal multiple access systems

The non-orthogonal multiple access (NOMA) approach enables a number of users to share the same resource via superposition coding in the transmitter and successive detection in the receiver. In this study a power allocation (PA) approach based on the proportional fair division technique is proposed. In this regard, power is assigned to users such that it ensures the fairness of NOMA follows that of orthogonal multiple access. A straight forward approach that achieves this objective is stated and is followed by proposing three different algorithms. These algorithms loosen the strict proportionality criterion to fulfil the data rate of a specific user. Also, a new fairness index for NOMA based on data rate ratios is defined. Afterwards, a low-complexity strategy based on water filling (WF) for PA amongst resources is proposed. This second PA stage aims at improving the data rate of users. The proposed approaches are applied to NOMA systems with different pairing strategies and results are compared. Results show that the proposed approaches improve the fairness of NOMA and WF increases the total system rate.
Analogue beamforming solution for beam-alignment problem

: Analogue only beamforming generally refers to a structure of analogue phase shifters that have no control over the amplitude of the signals and are utilised in millimetre-wave massive multi-input multi-output systems to reduce the cost and power consumption. Conventional analogue only beamforming methods are based on beam-training and subspace sampling where the transmitter and receiver select the best beamformer and combiner from a list of predefined beam patterns to maximise the beamforming gain. In this study, first, the beam-alignment problem is formulated as a quadratic optimisation problem with quadratic constraints. Then, a practical solution for analogue only beamforming is proposed to maximise beamforming gain. The proposed method has no assumption on the array structure or isotropic antenna elements in the array. Also, a new method is proposed to design sub-codebooks to reduce the construction time of the codebook. Simulation results also corroborate the analytical results and performance of the proposed method.
SPCOR: a secure and privacy-preserving protocol for mobile-healthcare emergency to reap computing opportunities at remote and nearby

This study proposes a secure and privacy-preserving protocol for outsourcing health data processing operations during the emergency in the mobile healthcare network. The proposed protocol provides a practical solution to utilise smartphone resources at both remote and nearby for processing the overwhelming personal health information (PHI) of a user in healthcare emergency opportunistically and securely. The patients with symptoms matching with those of the user in an emergency are considered as opportunities to minimise the privacy disclosure of the user. Opportunities at both remote and nearby are exploited with the help of a base station in the 4G network. Moreover, novel and efficient outsourced privacy access control schemes are developed to minimise the power drain of the user in an emergency without compromising his privacy. The outsourced privacy access control is facilitated through the design of innovative schemes for outsourced attribute-based access mechanism and an outsourced privacy-preserving scalar product computation. Detailed performance evaluations through implementations on Raspberry Pi 3B + board and simulations using NS3 network simulator and Scyther tool confirm the efficiency of the proposed protocol in providing highly reliable PHI processing and transmissions with reasonably low delay and energy consumption while maintaining user privacy.
Direct and indirect selection on mate choice during pollen competition: Effects of male and female sexual traits on offspring performance following two‐donor crosses

Mate choice in plants is poorly understood, in particular its indirect genetic benefits, but also the direct benefits of avoiding harmful matings. In the herb Collinsia heterophylla, delayed stigma receptivity has been suggested to enhance pollen competition, potentially functioning as a female mate choice trait. Previous studies show that this trait can mitigate the cost of early fertilization caused by pollen, thus providing a direct benefit. We performed two‐donor pollinations during successive floral stages to assess how this stigma receptivity trait and two pollen traits known to affect siring success influence indirect benefits in terms of offspring performance. We also investigated differential resource allocation by studying the influence of sibling performance in the same capsule. Offspring performance in terms of flower number was mainly affected by parental identities and differential resource allocation. Offspring seed production showed some influence of resource allocation, but was also affected by pollen donor identity and varied positively with late stigma receptivity. However, the effect of late stigma receptivity on offspring seed production was weakened in matings with pollen that advanced stigma receptivity. In conclusion, delayed stigma receptivity may be selected through both direct and indirect fitness effects in C. heterophylla, where pollen‐based delay on stigma receptivity might act as a cue for mate choice. However, selection may also be counteracted by antagonistic selection on pollen to advance stigma receptivity. Our results highlight the challenges of studying indirect genetic benefits and other factors that influence mate choice in plants.
Assessing the Impact of Factors that Influence the Ketogenic Response to Varying Doses of Medium Chain Triglyceride (MCT) Oil

Objectives, Design, Setting The ketogenic effect of medium chain triglyceride (MCT) oil offers potential for Alzheimer’s disease prevention and treatment. Limited literature suggests a linear B-hyroxybutyrate (BHB) response to increasing MCT doses. This pharmacokinetic study evaluates factors affecting BHB response in three subject groups. Participants Healthy subjects without cognitive deficits <65years, similarly healthy subjects >=65years, and those with Alzheimer’s Disease were assessed. Intervention Different doses (0g,14g, 28g, 42g) of MCT oil (99.3% C8:0) were administered, followed by fasting during the study period. Measurements BHB measured by finger prick sampling hourly for 5 hours after ingestion. Each subject attended four different days for each ascending dose. Data was also collected on body composition, BMI, waist/hip ratio, grip strength, gait speed, nutrient content of pre-study breakfast and side effects. Results Twenty-five participants: eight healthy; average age of 44yr (25–61), nine healthy; 79yr (65–90) and eight with AD; 78.6yr (57–86) respectively. Compiled data showed the expected linear dose response relationship. No group differences, with baseline corrected area under the blood vs. time curve (r 2 =0.98) and maximum concentrations (r 2 =0.97). However, there was notable individual variability in maximum BHB response (42g dose: 0.4 −2.1mM), and time to reach maximum BHB response both, within and between individuals. Variability was unrelated to age, sex, sarcopenic or AD status. Visceral fat, BMI, waist/hip ratio and pretest meal CHO and protein content all affected the BHB response (p<0.001). Conclusion There was a large inter-individual variability, with phenotype effects identified. This highlights challenges in interpreting clinical responses to MCT intake.
SPECIAL ISSUE ON INTRASPECIFIC VARIATION IN PLANT FUNCTIONAL TRAITS Intraspecific trait variability shapes leaf trait response to altered fire regimes

• Background and Aims Understanding impacts of altered disturbance regimes on community structure and function is a key goal for community ecology. Functional traits link species composition to ecosystem functioning. Changes in the distribution of functional traits at community scales in response to disturbance can be driven not only by shifts in species composition, but also by shifts in intraspecific trait values. Understanding the relative importance of these two processes has important implications for predicting community responses to altered disturbance regimes. • Methods We experimentally manipulated fire return intervals in replicated blocks of a fire-adapted, longleaf pine (Pinus palustris) ecosystem in North Carolina, USA and measured specific leaf area (SLA), leaf dry matter content (LDMC) and compositional responses along a lowland to upland gradient over a 4 year period. Plots were burned between zero and four times. Using a trait-based approach, we simulate hypothetical scenarios which allow species presence, abundance or trait values to vary over time and compare these with observed traits to understand the relative contributions of each of these three processes to observed trait patterns at the study site. We addressed the following questions. (1) How do changes in the fire regime affect community composition, structure and community-level trait responses? (2) Are these effects consistent across a gradient of fire intensity? (3) What are the relative contributions of species turnover, changes in abundance and changes in intraspecific trait values to observed changes in community-weighted mean (CWM) traits in response to altered fire regime? • Key Results We found strong evidence that altered fire return interval impacted understorey plant communities. The number of fires a plot experienced significantly affected the magnitude of its compositional change and shifted the ecotone boundary separating shrub-dominated lowland areas from grass-dominated upland areas, with suppression sites (0 burns) experiencing an upland shift and annual burn sites a lowland shift. We found significant effects of burn regimes on the CWM of SLA, and that observed shifts in both SLA and LDMC were driven primarily by intraspecific changes in trait values. • Conclusions In a fire-adapted ecosystem, increased fire frequency altered community composition and structure of the ecosystem through changes in the position of the shrub line. We also found that plant traits responded directionally to increased fire frequency, with SLA decreasing in response to fire frequency across the environmental gradient. For both SLA and LDMC, nearly all of the observed changes in CWM traits were driven by intraspecific variation.
Effects of different smoking materials and methods on the quality of Chinese traditional bacon (larou).

Larou is a traditional smoked meat product in China. In this experiment, larou was processed with different smoking materials and methods evaluating whether the control of processing methods could balance the quality of larou and the content of carcinogens. Pork bellies were marinated, dried and divided into four groups then directly smoked by four different smoking materials for 40 minutes. The best index smoking material would be brought into the indirect smoking device for larou single-factor variable indirect smoking with nano-activated carbon fiber. Results showed that the specific surface area of the nano-activated carbon fiber was 978.00 m 2 /g, and such fiber could effectively adsorb the smoking ash particles. In pomelo skins group (PS), the highest phenols content and variety were found as 4.48% and 11, which increased the smoking flavor most significantly. The moisture, SA, LAB, Y&M values were 32.64%, 0.98 log 10 CFU/g, 1.10 log 10 CFU/g, 0.59 log 10 CFU/g, which could best inhibit harmful bacteria and benefit microbial environment for larou fermentation maturity. The benzo[a]pyrene (B[a]P) content of indirect smoking device (IN) by PS was 1.82 μg/kg, whereas the B[a]P content of direct smoking by PS was 36.1 μg/kg, which indicated a significant difference ( p < 0.01). These findings suggested that pomelo skins with indirect smoking device of larou could effectively maintain the inspection indexes and reduce the content of B[a]P. The control of this processing method can be used for the actual production of meat.