Accurately forecasting the spatiotemporal dynamics of biological systems, such as human pluripotent stem cell (hPSC)-derived cardiac organoids, from microscopy time-series is a critical challenge in biomedicine with profound implications for drug discovery. Existing generative models often fail to capture the intricate dynamics of organoid development, struggling with their irregular morphology, indistinct boundaries, and complex spatiotemporal patterns. To overcome these limitations, we introduce OrgaCast, a novel multimodal conditional diffusion model for high-fidelity organoid forecasting. OrgaCast uniquely conditions the generative process on three synergistic modalities: (i) historical image sequences, captured by a dedicated spatiotemporal control module; (ii) structured numerical metadata defining experimental conditions; and (iii) descriptive text captions summarizing the biological context. This comprehensive conditioning enables the generation of forecasts with high visual accuracy and biological plausibility. Furthermore, to enhance the model's utility in critical research settings, we introduce a post-hoc uncertainty quantification method that produces intuitive confidence maps, bolstering the interpretability and trustworthiness of predictions. Extensive experiments on a challenging cardiac organoid dataset demonstrate that OrgaCast outperforms baselines in metrics such as SSIM, PSNR, and LPIPS. Our framework presents a robust solution for biological forecasting, promising to accelerate research discovery while minimizing experimental costs and manual effort.

Skin-toned emojis are crucial for fostering personal identity and social inclusion in online communication. As AI models, particularly Large Language Models (LLMs), increasingly mediate interactions on web platforms, the risk that these systems perpetuate societal biases through their representation of such symbols is a significant concern. This paper presents the first large-scale comparative study of bias in skin-toned emoji representations across two distinct model classes. We systematically evaluate dedicated emoji embedding models (emoji2vec, emoji-sw2v) against four modern LLMs (Llama, Gemma, Qwen, and Mistral). Our analysis first reveals a critical performance gap: while LLMs demonstrate robust support for skin tone modifiers, widely-used specialized emoji models exhibit severe deficiencies. More importantly, a multi-faceted investigation into semantic consistency, representational similarity, sentiment polarity, and core biases uncovers systemic disparities. We find evidence of skewed sentiment and inconsistent meanings associated with emojis across different skin tones, highlighting latent biases within these foundational models. Our findings underscore the urgent need for developers and platforms to audit and mitigate these representational harms, ensuring that AI's role on the web promotes genuine equity rather than reinforcing societal biases.

Modern vehicles increasingly rely on advanced driver monitoring systems (DMS) to ensure safety and enhance the driving experience. These systems assess driver status to prevent accidents caused by fatigue, inattentiveness, or intoxication. While some DMS applications process video data on vehicle, many rely on edge or cloud-based solutions, raising significant privacy concerns due to the storage of sensor data from vehicles. Existing approaches, such as de-identification and homomorphic encryption, either impose heavy computational overhead on vehicles or insufficiently address privacy. To overcome these limitations, we present the Privacy-preserving Driver Monitoring System (PDMS), a novel framework based on the additive secret sharing theory and privacy-preserving Transformer-based deep learning models. PDMS creates randomized secret shares from driver’s facial video data on vehicle, processes them independently through privacy-preserving Transformer models on edges, and securely aggregates partial results on vehicle, ensuring vehicles’ sensor data and final results remain protected. This approach reduces the computational load on the vehicle, enabling cost-effective and scalable DMS solutions that protect the privacy of the driver both in transit and in processing. Our contributions include the design and optimization of the PDMS system, incorporating privacy-preserving DNN layers that are capable of processing randomized secret shares. Furthermore, we present a practical system that utilizes a vision transformer (ViT)-based gaze estimation model, demonstrating the effectiveness of PDMS through comprehensive experiments.

Egocentric visual query localization (EgoVQL) focuses on localizing the target of interest in space and time from first-person videos, given a visual query. Despite recent progressive, existing methods often struggle to handle severe object appearance changes and cluttering background in the video due to lacking sufficient target cues, leading to degradation. Addressing this, we introduce PRVQL, a novel Progressive knowledge-guided Refinement framework for EgoVQL. The core is to continuously exploit target-relevant knowledge directly from videos and utilize it as guidance to refine both query and video features for improving target localization. Our PRVQL contains multiple processing stages. The target knowledge from one stage, comprising appearance and spatial knowledge extracted via two specially designed knowledge learning modules, are utilized as guidance to refine the query and videos features for the next stage, which are used to generate more accurate knowledge for further feature refinement. With such a progressive process, target knowledge in PRVQL can be gradually improved, which, in turn, leads to better refined query and video features for localization in the final stage. Compared to previous methods, our PRVQL, besides the given object cues, enjoys additional crucial target information from a video as guidance to refine features, and hence enhances EgoVQL in complicated scenes. In our experiments on challenging Ego4D, PRVQL achieves state-of-the-art result and largely surpasses other methods, showing its efficacy. Our code, model and results will be released at https://github.com/fb-reps/PRVQL.

Medical imaging is foundational to modern diagnostics, with computed tomography (CT) and magnetic resonance imaging (MRI) as standard modalities. However, single-modality imaging lacks the capability to comprehensively represent complex anatomical structures and pathological variations. Recently, multi-source medical image fusion has shown transformative potential in computer-aided diagnosis but faces challenges in integrating heterogeneous multimodal data and fully exploiting its informational richness. To address these challenges, we propose a Federated Dynamic Window Selection Aggregation Method (DWS-FedSeg) to optimize task-specific information while reducing inter-site communication overhead. A frequency-adaptive selection mechanism dynamically prioritizes task-relevant frequency components, improving global model aggregation fidelity. Specifically, a learnable Discrete Cosine Transform (LDCT) transforms spatial features into frequency domain representations, while frequency-adaptive windows isolate high-salience features. This approach enhances data utilization and minimizes communication costs. Extensive experiments on six datasets validate the superior performance and efficiency of the proposed method.

Federated Learning (FL) has become an effective method for improving medical image segmentation performance under privacy protection. However, the heterogeneity between medical image modalities, including background differences, variations in detail presentation, and resolution discrepancies, presents challenges for enhancing the accuracy of multi-modal joint segmentation. These differences between modalities may hinder the effective fusion of information from each modality, thus affecting the accuracy and consistency of segmentation results. To address this issue, this study proposes a novel federated medical image segmentation method, DCT-GMM-FedSeg. This method combines Discrete Cosine Transform (DCT) and Gaussian Mixture Model (GMM) to improve segmentation accuracy while preserving privacy. Specifically, DCT is used to extract background frequency information, which is globally unified and integrated with the original background to alleviate modality-related background differences. GMM is used to model image detail features, and by sharing the distribution information of these detailed regions, it reduces the differences between modalities. In this way, federated learning can extract complementary information from different modalities, enhancing global generalization ability and fully leveraging the advantages of multi-modal segmentation. Throughout the process, key image information is retained locally to prevent privacy leakage. Experimental results demonstrate that DCT-GMM-FedSeg significantly improves performance in four heterogeneous medical image segmentation tasks, particularly exhibiting excellent robustness and generalization ability in Non-IID data environments.

High-performance Transformer trackers have exhibited excellent results, yet they often bear a heavy computational load. Observing that a smaller input can immediately and conveniently reduce computations without changing the model, an easy solution is to adopt a low-resolution input for efficient Transformer tracking. Albeit faster, this hurts tracking accuracy much due to the information loss in low resolution tracking. In this paper, we aim to mitigate such information loss to boost performance of low-resolution Transformer tracking via dual knowledge distillation from a frozen high-resolution (but not a larger) Transformer tracker. The core lies in two simple yet effective distillation modules, including query-key-value knowledge distillation (QKV-KD) and discrimination knowledge distillation (Disc-KD), across resolutions. The former, from the global view, allows the low-resolution tracker to inherit features and interactions from the high-resolution tracker, while the later, from the target-aware view, enhances the target-background distinguishing capacity via imitating discriminative regions from its high-resolution counterpart. With dual knowledge distillation, our Low-Resolution Transformer Tracker, dubbed LoReTrack, enjoys not only high efficiency owing to reduced computation but also enhanced accuracy by distilling knowledge from the high-resolution tracker. In extensive experiments, LoReTrack with a 2562resolution consistently improves baseline with the same resolution, and shows competitive or better results compared to the 3842high-resolution Transformer tracker, while running 52% faster and saving 56% MACs. Moreover, LoReTrack is resolution-scalable. With a 1282resolution, it runs 25 fps on a CPU with SUC scores of 64.9%/46.4% on LaSOT/LaSOText, surpassing other CPU real-time trackers. Code is released at https://github.com/ShaohuaDong2021/LoReTrack.

Today's scientific applications running on supercomputers produce large volumes of data, leading to critical data storage and communication challenges. To tackle the challenges, error-bounded lossy compression is commonly adopted since it can reduce data size drastically within a user-defined error threshold. Previous work has shown that compression techniques can significantly reduce the storage and I/O overhead while retaining good data quality. However, the existing compressors are mainly designed for CPU and GPU. As new AI chips are being incorporated into supercomputers and increasingly used for accelerating scientific computing, there is a growing demand for efficient data compression on the new architecture. In this paper, we propose an efficient lossy compressor, CereSZ, based on the Cerebras CS-2 system. The compression algorithm is mapped onto Cerebras using both data parallelism and pipeline parallelism. In order to achieve a balanced workload on each processing unit, we propose an algorithm to evenly distribute the pipeline stages. Our experiments with six scientific datasets demonstrate that CereSZ can achieve a throughput from 227.93 GB/s to 773.8 GB/s, 2.43x to 10.98x faster than existing GPU compressors.

Multimodal (e.g., RGB-Depth/RGB-Thermal) fusion has shown great potential for improving semantic segmentation in complex scenes (e.g., indoor/low-light conditions). Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion. To address this issue, we propose a surprisingly simple yet effective dual-prompt learning network (dubbed DPLNet) for training-efficient multimodal (e.g., RGBD/T) semantic segmentation. The core of DPLNet is to directly adapt a frozen pre-trained RGB model to multimodal semantic segmentation, reducing parameter updates. For this purpose, we present two prompt learning modules, comprising multimodal prompt generator (MPG) and multimodal feature adapter (MFA). MPG works to fuse the features from different modalities in a compact manner and is inserted from shallow to deep stages to generate the multi-level multimodal prompts that are injected into the frozen backbone, while MFA adapts prompted multimodal features in the frozen backbone for better multimodal semantic segmentation. Since both the MPG and MFA are lightweight, only a few trainable parameters (3.88M, 4.4% of the pre-trained backbone parameters) are introduced for multimodal feature fusion and learning. Using a simple decoder (3.27M parameters), DPLNet achieves new state-of-the-art performance or is on a par with other complex approaches on four RGB-D/T semantic segmentation datasets while satisfying parameter efficiency. Moreover, we show DPLNet is general and applicable to other multimodal segmentation tasks. Without special design, DPLNet outperforms many complicated models. The source code can be found at https://github.com/ShaohuaDong2021/DPLNet.

Recent years have witnessed growing concerns over demographic biases in image-centric applications, including image search engines and generative systems. While the advent of generative AI offers a pathway to mitigate these biases by producing underrepresented images, existing solutions still fail to precisely generate images that reflect specified demographic distributions. In this paper, we propose PreciseDebias, a comprehensive end-to-end framework that can rectify demographic bias in image generation. By leveraging fine-tuned Large Language Models (LLMs) coupled with text-to-image generative models, PreciseDebias transforms generic text prompts to produce images in line with specified demographic distributions. The core component of PreciseDebias is our novel instruction-following LLM, meticulously designed with an emphasis on model bias assessment and balanced model training. Extensive experiments demonstrate the effectiveness of PreciseDebias in rectifying biases pertaining to both ethnicity and gender in images. Furthermore, when compared with two baselines, PreciseDebias illustrates its robustness and capability to capture demographic intricacies. The generalization of PreciseDebias is further illuminated by the diverse images it produces across multiple professions and demographic attributes. To ensure reproducibility, we will make PreciseDebias openly accessible to the broader research community by releasing all models and code.

With AI systems widely deployed in societal applications, the fairness of these models is of increasing concern, for instance, hiring systems should recommend applicants impartially from different demographic groups, and risk assessment systems must eliminate racial inequity in the criminal justice system. Therefore, ensuring fairness in these models is crucial. In this paper, we propose Task-Free Fairness-Aware Adversarial Perturbation (TF-FAAP), a flexible approach for improving the fairness of black-box deployed models by adding perturbations on input samples that blind their fairness-related attribute information without modifying the model's parameters or structures. The proposed TF-FAAP consists of a discriminator and a generator to create universal fairness-aware perturbations for a variety of tasks. The former aims to distinguish fairnessrelated attributes, and the latter generates perturbations to make the discriminator's prediction distribution of fairness-related attributes uniform. To preserve the utility of perturbed samples, we maximize the mutual information between their representations and corresponding original samples, retaining more original samples' information. In addition, the perturbation generated by TF-FAAP has a high transferability, i.e., the perturbations learned on one dataset can also alleviate the unfairness of a model trained on a different dataset. The extensive experimental evaluation demonstrated the effectiveness and superior performance of our method.

Transferability of adversarial examples is critical for black-box deep learning model attacks. While most existing studies focus on enhancing the transferability of untargeted adversarial attacks, few of them studied how to generate transferable targeted adversarial examples that can mislead models into predicting a specific class. Moreover, existing transferable targeted adversarial attacks usually fail to sufficiently characterize the target class distribution, thus suffering from limited transferability. In this paper, we propose the Transferable Targeted Adversarial Attack (TTAA), which can capture the distribution information of the target class from both label-wise and feature-wise perspectives, to generate highly transferable targeted adversarial examples. To this end, we design a generative adversarial training framework consisting of a generator to produce targeted adversarial examples, and feature-label dual discriminators to distinguish the generated adversarial examples from the target class images. Specifically, we design the label discriminator to guide the adversarial examples to learn label-related distribution information about the target class. Meanwhile, we design a feature discriminator, which extracts the feature-wise information with strong cross-model consistency, to enable the adversarial examples to learn the transferable distribution information. Furthermore, we introduce the random perturbation dropping to further enhance the transferability by augmenting the diversity of adversarial examples used in the training process. Experiments demonstrate that our method achieves excellent performance on the transferability of targeted adversarial examples. The targeted fooling rate reaches 95.13% when transferred from VGG-19 to DenseNet-121, which significantly outperforms the state-of-the-art methods.

This paper presents a publicly accessible large-scale geo-tagged Twitter dataset, comprising 95.8 million tweets from 247 countries, spanning from Jan. 2012 to Dec. 2021. To systematically extract this dataset from over 57.18 TB of raw tweets, we employed parallel computing on a 40-node cluster with 480 CPU cores. Distinguishing it from most existing Twitter datasets, our dataset includes four-level granularity tweet locations, two-level granularity user profile locations, and tweet text languages, enabling personalized queries. To enhance the open accessibility of our dataset, we have designed an innovative interactive online query system (https://sigspatial.yunhefeng.me) and provided free-to-use JSON APIs (https://github.com/ResponsibleAILab/unt-geotweet-api) for customized queries to retrieve tweet IDs in tweet coordinate, tweet text-based location, and user location modes. Then users can use https://github.com/ResponsibleAILab/unt-tweet-rehydration to download complete tweet information. Furthermore, we have demonstrated the practical utility of our dataset through two applications: human movement modeling and geo-aware Large Language Model (LLM) tuning. Our geo-tagged Twitter dataset, along with the accompanying query system and APIs, contributes to the research community and opens up avenues for multidisciplinary investigations and the advancement of knowledge.

Today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost. However, existing lossy compressors for scientific data cannot achieve a high compression ratio and throughput simultaneously, hindering their adoption in many applications requiring fast compression, such as in-memory compression. To this end, in this work, we develop a fast and high- ratio error-bounded lossy compressor on GPUs for scientific data (called FZ-GPU). Specifically, we first design a new compression pipeline that consists of fully parallelized quantization, bitshuffle, and our newly designed fast encoding. Then, we propose a series of deep architectural optimizations for each kernel in the pipeline to take full advantage of CUDA architectures. We propose a warp-level optimization to avoid data conflicts for bit-wise operations in bitshuffle, maximize shared memory utilization, and eliminate unnecessary data movements by fusing different compression kernels. Finally, we evaluate FZ-GPU on two NVIDIA GPUs (i.e., A100 and RTX A4000) using six representative scientific datasets from SDRBench. Results on the A100 GPU show that FZ-GPU achieves an average speedup of 4.2× over cuSZ and an average speedup of 37.0× over a multi-threaded CPU implementation of our algorithm under the same error bound. FZ-GPU also achieves an average speedup of 2.3× and an average compression ratio improvement of 2.0× over cuZFP under the same data distortion.

Network pruning aims to compress models while minimizing loss in accuracy. With the increasing focus on bias in AI systems, the bias inheriting or even magnification nature of traditional network pruning methods has raised a new perspective towards fairness-aware network pruning. Straightforward pruning plus debias methods and recent designs for monitoring disparities of demographic attributes during pruning have endeavored to enhance fairness in pruning. However, neither simple assembling of two tasks nor specifically designed pruning strategies could achieve the optimal trade-off among pruning ratio, accuracy, and fairness. This paper proposes an end-to-end learnable framework for fairness-aware network pruning, which optimizes both pruning and debias tasks jointly by adversarial training against those final evaluation metrics like accuracy for pruning, and disparate impact (DI) and equalized odds (DEO) for fairness. In other words, our fairness-aware adversarial pruning method would learn to prune without any handcraft rules. Therefore, our approach could flexibly adapt to variate network structures. Exhaustive experimentation demonstrates the generalization capacity of our approach, as well as superior performance on pruning and debias simultaneously. To highlight, the proposed method could preserve the SOTA pruning performance while significantly improving fairness by around 50% as compared to traditional pruning methods.

Machine learning (ML) technologies are known to be riddled with ethical and operational problems, however, we are witnessing an increasing thrust by businesses to deploy them in sensitive applications. One major issue among many is that ML models do not perform equally well for underrepresented groups. This puts vulnerable populations in an even disadvantaged and unfavorable position. We propose an approach that leverages the power of web search and generative models to alleviate some of the shortcomings of discriminative models. We demonstrate our method on an image classification problem using ImageNet's People Subtree subset, and show that it is effective in enhancing robustness and mitigating bias in certain classes that represent vulnerable populations (e.g., female doctor of color). Our new method is able to (1) identify weak decision boundaries for such classes; (2) construct search queries for Google as well as text for generating images through DALL-E 2 and Stable Diffusion; and (3) show how these newly captured training samples could alleviate population bias issue. While still improving the model's overall performance considerably, we achieve a significant reduction (77.30%) in the model's gender accuracy disparity. In addition to these improvements, we observed a notable enhancement in the classifier's decision boundary, as it is characterized by fewer weakspots and an increased separation between classes. Although we showcase our method on vulnerable populations in this study, the proposed technique is extendable to a wide range of problems and domains.

Gender bias is one of the most common and well-studied demographic biases in information retrieval, and in general in AI systems. After discovering and reporting that gender bias for certain professions could change searchers' worldviews, mainstreaming image search engines, such as Google, quickly took action to correct and fix such a bias. However, given the nature of these systems, viz., being opaque, it is unclear if they addressed unequal gender representation and gender stereotypes in image search results systematically and in a sustainable way. In this paper, we propose adversarial attack queries composed of professions and countries (e.g., 'CEO United States') to investigate whether gender bias is thoroughly mitigated by image search engines. Our experiments on Google, Baidu, Naver, and Yandex Image Search show that the proposed attack can trigger high levels of gender bias in image search results very effectively. To defend against such attacks and mitigate gender bias, we design and implement three novel re-ranking algorithms -- epsilon-greedy algorithm, relevance-aware swapping algorithm, and fairness-greedy algorithm, to re-rank returned images for given image queries. Experiments on both simulated (three typical gender distributions) and real-world datasets demonstrate the proposed algorithms can mitigate gender bias effectively.

Many information access and machine learning systems, including recommender systems, lack transparency and accountability. High-quality recommendation explanations are of great significance to enhance the transparency and interpretability of such systems. However, evaluating the quality of recommendation explanations is still challenging due to the lack of human-annotated data and benchmarks. In this paper, we present a large explanation dataset named RecoExp, which contains thousands of crowdsourced ratings of perceived quality in explaining recommendations. To measure explainability in a comprehensive and interpretable manner, we propose ExpScore, a novel machine learning-based metric that incorporates the definition of explainability from various perspectives (e.g., relevance, readability, subjectivity, and sentiment polarity). Experiments demonstrate that ExpScore not only vastly outperforms existing metrics and but also keeps itself explainable. Both the RecoExp dataset and open-source implementation of ExpScore will be released for the whole community. These resources and our findings can serve as forces of public good for scholars as well as recommender systems users.

Effective and efficient animal disease detection and control have drawn increasing attention in smart farming in recent years. It is crucial to explore how to harvest data and enable data-driven decision making for rapid diagnosis and early treatment of infectious diseases among herds. This article proposes an IoT-based animal social behavior sensing framework to model mastitis propagation and infer mastitis infection risks among dairy cows. To monitor cow social behaviors, we deploy portable GPS devices on cows to track their movement trajectories and contacts with each other. Based on those collected location data, we build directed and weighted cattle social behavior graphs by treating cows as vertices and their contacts as edges, assigning contact frequencies between cows as edge weights, and determining edge directions according to contact spatial-temporal information. Then, we propose a flexible probabilistic disease transmission model, which considers both direct contacts with infected cows and indirect contacts via environmental contamination, to estimate and forecast mastitis infection probabilities. Our model can answer two common questions in animal disease detection and control: 1) which cows should be given the highest priorities for an investigation to determine whether there are already infected cows on the farm and 2) how to rank cows for further screening when only a tiny number of sick cows have been identified. Both theoretical and simulation-based analytics of in-the-field experiments (17 cows and more than 70-h data) demonstrate the proposed framework’s effectiveness. In addition, somatic cell count (SCC) mastitis tests validate our predictions as correct in real-world scenarios.

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers’ sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers’ different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets.

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in decompression as multidimensional partial-sum computation and propose a fine-grained Lorenzo reconstruction algorithm for GPU architectures. (3) We carefully optimize each of CUSZ kernels by leveraging state-of-the-art CUDA parallel primitives. (4) We evaluate CU SZ+ using seven real-world HPC application datasets on V100 and A100 GPUs. Experiments show CUSZ+ improves the compression throughputs and ratios by up to 18.4× and 5.3×, respectively, over CUSZ on the tested datasets.

The micromobility is shaping first- and last-mile travels in urban areas. Recently, shared dockless electric scooters (e-scooters) have emerged as a daily alternative to driving for short-distance commuters in large cities due to the affordability, easy accessibility via an app, and zero emissions. Meanwhile, e-scooters come with challenges in city management, such as traffic rules, public safety, parking regulations, and liability issues. In this paper, we collected and investigated 5.8 million scooter-tagged tweets and 144,197 images, generated by 2.7 million users from October 2018 to March 2020, to take a closer look at shared e-scooters via crowdsourcing data analytics. We profiled e-scooter usages from spatial-temporal perspectives, explored different business roles (i.e., riders, gig workers, and ridesharing companies), examined operation patterns (e.g., injury types, and parking behaviors), and conducted sentiment analysis. To our best knowledge, this paper is the first large-scale systematic study on shared e-scooters using big social data.

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. Training wide and deep neural networks require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. Traditional memory saving techniques either suffer from performance overhead or are constrained by limited interconnect bandwidth or specific interconnect technology. In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger models or to accelerate training. Our framework purposely adopts error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we perform a theoretical analysis on the compression error propagation from the altered activation data to the gradients, and empirically investigate the impact of altered gradients over the training process. Based on these analyses, we optimize the error-bounded lossy compression and propose an adaptive error-bound control scheme for activation data compression. Experiments demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5X over the baseline training and 1.8X over another state-of-the-art compression-based framework, respectively, with little or no accuracy loss.

Truth discovery is an effective tool to unearth truthful answers in crowdsourced question answering systems. Incentive mechanisms are necessary in such systems to stimulate worker participation. However, most of existing incentive mechanisms only consider compensating workers' resource cost, while the cost incurred by potential privacy leakage has been rarely incorporated. More importantly, to the best of our knowledge, how to provide personalized payments for workers with different privacy demands remains uninvestigated thus far. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in crowdsourced question answering systems, named PINTION, which provides personalized payments for workers with different privacy demands as a compensation for privacy cost, while ensuring accurate truth discovery. The basic idea is that each worker chooses to sign a contract with the platform, which specifies a privacy-preserving level (PPL) and a payment, and then submits perturbed answers with that PPL in return for that payment. Specifically, we respectively design a set of optimal contracts under both complete and incomplete information models, which could maximize the truth discovery accuracy, while satisfying the budget feasibility, individual rationality and incentive compatibility properties. Experiments on both synthetic and real-world datasets validate the feasibility and effectiveness of PINTION.

It is a fundamental issue to achieve efficient information collection in Internet of Things (IoT), where random (channel) access plays an indispensable role, especially when coordination among IoT end nodes is unachievable. Compressive sensing (CS) has been widely used in random access to facilitate energy efficient and accurate data collection. However, a joint sparsity structure, which commonly exists among signals acquired by different end nodes, has been long ignored by existing CS-based random access schemes, leading to insufficient energy efficiency and accuracy. In this article, capitalizing on this joint sparsity structure, we propose a structured compressive random access (SCRA) mechanism in order to achieve maximum energy efficiency with accuracy guarantee for data collection. Specifically, we first model the data loss induced by packet collisions during random access as an independent CS measurement process for each node, where the corresponding CS projection matrix is determined by the data loss pattern. Furthermore, in order to control the amount of data transmitted in the channel and alleviate the packet collisions, we employ the concept of sensing probability to perform random subsampling at each end node before transmission, where the optimal sensing probability is derived. Finally, we propose to jointly recover the set of original signals at all nodes based on the concept of group sparsity by formulating the data collection process as a single-measurement-vector problem in CS. The evaluation results validate the effectiveness of SCRA in utilizing the joint sparsity structure to obtain superior performance compared to the benchmark methods.

Deep learning has great potential to reshape manufacturing industries. In this article, we present DeepWelding, a novel framework that applies deep learning techniques to improve gas tungsten arc welding process monitoring and penetration detection using multisource sensing images. The framework is capable of analyzing multiple types of optical sensing images synchronously and consists of three deep learning enhanced consecutive phases: image preprocessing, image selection, and weld penetration classification. Specifically, we adopted generative adversarial networks (pix2pix) for image denoising and classic convolutional neural networks (AlexNet) for image selection. Both pix2pix and AlexNet delivered satisfactory performance. However, five individual neural networks with heterogeneous architectures demonstrated inconsistent generalization capabilities in the classification phase when holding out multisource images generated with specific experimental settings. Therefore, two ensemble methods combining multiple neural networks are designed to improve the model performance on unseen data collected from different experimental settings. We have also found that the quality of model prediction is heavily influenced by the data stream collection environment. We think these findings are beneficial for the broad intelligent welding community.

Recently, there has been a significant interest in the study of the community search problem in large- scale graphs. K-truss as a community model has drawn increasing attention in the literature. In this work, we extend our scope from the community search problems to a more generalized local community query problem based on a triangle- connected k-truss community model. We classify local community query into two categories, community-level and edge-level query, based on the information required to process a given query. We design a two-level index structure that supports both types of queries with multiple query vertices and arbitrary cohesiveness criteria. We conduct extensive experiments using real-world large-scale graphs and compare with the state-of-the-art methods of k-truss community search. The results show that our method outperforms the state-of-the- art works in various types of local k-truss community queries.

With the popularity of social networking services, big social data analytics emerged in various applications, such as discovering trending topics, monitoring public sentiment, and identifying human mobility patterns. In this paper, we take the opportunity of The 2017 Great American Eclipse, a once-in-a-lifetime event, to look into its potential social, emotional, and human movement impacts at the national level. Specifically, we collected more than five million English eclipse- mentioning tweets in a real-time manner using Twitter Streaming APIs. Then we profiled spatio- temporal distributions of the data, extracted both hashtagged and latent topics, analyzed emotions using polarized words, emojis and emoticons, and revealed both interstate and intrastate eclipse- chasing travel patterns. Our study provides a comprehensive example of understanding big social data and its associated influence from diverse perspectives.

As emojis become prevalent in personal communications, people are always looking for new, interesting emojis to express emotions, show attitudes, or simply visualize texts. In this study, we collected more than thirty million tweets mentioning the word “emoji” in a one-year period to study emoji requests on Twitter. First, we filtered out bot-generated tweets and extracted emoji requests from the raw tweets using a comprehensive list of linguistic patterns. Then, we examined patterns of new emoji requests by exploring their time, locations, and context. Finally, we summarized users' advocacy behaviors and identified expressions of equity, diversity, and fairness issues due to unreleased but expected emojis, and concluded the significance of new emojis on society. To the best of our knowledge, this paper is the first to conduct a systematic, large-scale study on new emoji requests.

This paper introduces a novel hand-free human-computer interaction system called AirMouse, which turns a common pair of glasses into a mouse to enable the interaction between computers and humans, especially for disabled people. The basic idea is to simulate mouse operations with head activities without using hands. To this end, an embedded device is attached to a pair of glasses, which leverages the gyroscope to accurately detect head activities and map them to corresponding mouse operations on devices (e.g., computers and smart TVs). In particular, AirMouse uses activities to simulate mouse operations instead of tracking the gaze or the head movements in the real-time manner. This provides flexibility to users allowing them to control devices even far away or not at front of the devices. We implement a prototype of AirMouse with the personalized pretraining module and the motion detection module, which is featured with low-cost, accurate, easy-to-use and real-time interaction, and evaluate AirMouse with 20 volunteers. The experimental results show that AirMouse achieves accurate, reliable, and real-time activity recognition and interaction. Specially, the technique of AirMouse can be integrated into wearable devices (e.g., smart glasses) to enrich their interaction functionalities.

With the technology enhanced, 3D mobile light detection and ranging (LiDAR) can produce more accurate 3D information for the objects. Meanwhile, hyperspectral remote sensing has more number of wavelengths and provides a higher resolution spectrum of objects. This paper proposes a multi-sensor platform to provide these two data for health detection at the individual tree level in urban environments. We firstly locate and segment the suspected tree objects by ground removal and Euclidean distance clustering. Then we take use of spectrum to remove non-tree objects, e.g., buildings, light poles. After that, we use LiDAR data to compute the geometric parameters of each tree and hyperspectral data to analyze its health situation.

Finding approximated shortest paths for extremely large-scale complex networks is a challenging problem, where existing works require large overhead to achieve high accuracy and diversity for estimated paths, especially for large graphs with millions of vertices. In this paper, we propose an online search approach based on preprocessed indexes, to approximate point-to-point shortest paths. The approach is able to find more accurate and diverse paths with limited index overhead and requires low search overhead. Furthermore, a new path degree based index construction algorithm is introduced that can greatly increase the approximation accuracy and involve no additional index overhead. To handle extreme size graphs, we build a query processing system with our algorithm on distributed graph processing platforms. The system also supports parallel processing of online searches to achieve high throughput for a large number of queries. We evaluate our algorithm on various real-world graphs from different disciplines with up to billions of edges, and we demonstrate that our system can process hundreds of thousand queries per second on these graphs with reduced overhead.