This paper introduces SCRAG, a prediction frame-work inspired by social computing, designed to forecast community responses to real or hypothetical social media posts. SCRAG can be used by public relations specialists (e.g., to craft messaging in ways that avoid unintended misinterpretations) or public figures and influencers (e.g., to anticipate social responses), among other applications related to public sentiment prediction, crisis management, and social what-if analysis. While large language models (LLMs) have achieved remarkable success in generating coherent and contextually rich text, their reliance on static training data and susceptibility to hallucinations limit their effectiveness at response forecasting in dynamic social media environments. SCRAG overcomes these challenges by integrating LLMs with a Retrieval-Augmented Generation (RAG) technique rooted in social computing. Specifically, our framework retrieves (i) historical responses from the target community to capture their ideological, semantic, and emotional makeup, and (ii) external knowledge from sources such as news articles to inject time-sensitive context. This information is then jointly used to forecast the responses of the target community to new posts or narratives. Extensive experiments across six scenarios on the X platform (formerly Twitter), tested with various embedding models and LLMs, demonstrate over 10% improvements on average in key evaluation metrics. A concrete example further shows its effectiveness in capturing diverse ideologies and nuances. Our work provides a social computing tool for applications where accurate and concrete insights into community responses are crucial.

Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on the scale and quality of multimodal samples. These constraints significantly limit the performance of sensing intelligence in IoT applications, as the heterogeneity and the non-interpretability of time-series signals result in abundant unimodal data but scarce high-quality multimodal pairs. This paper proposes InfoMAE, a cross-modal alignment framework that tackles the challenge of multimodal pair efficiency under the SSL setting by facilitating efficient cross-modal alignment of pretrained unimodal representations. InfoMAE achieves efficient cross-modal alignment with limited data pairs through a novel information theory-inspired formulation that simultaneously addresses distribution-level and instance-level alignment. Extensive experiments on two real-world IoT applications are performed to evaluate InfoMAE's pairing efficiency to bridge pretrained unimodal models into a cohesive joint multimodal model. InfoMAE enhances downstream multimodal tasks by over 60% with significantly improved multimodal pairing efficiency. It also improves unimodal task accuracy by an average of 22%.

Abstract Recent advances in AI culminate a shift in science and engineering away from strong reliance on algorithmic and symbolic knowledge towards new data-driven approaches. How does the emerging intelligent data-centric world impact research on real-time and embedded computing? We argue for two effects: (1) new challenges in embedded system contexts, and (2) new opportunities for community expansion beyond the embedded domain. First, on the embedded system side, the shifting nature of computing towards data-centricity affects the types of bottlenecks that arise. At training time, the bottlenecks are generally data-related. Embedded computing relies on scarce sensor data modalities, unlike those commonly addressed in mainstream AI, necessitating solutions for efficient learning from scarce sensor data. At inference time, the bottlenecks are resource-related, calling for improved resource economy and novel scheduling policies. Further ahead, the convergence of AI around large language models (LLMs) introduces additional model-related challenges in embedded contexts. Second, on the domain expansion side, we argue that community expertise in handling resource bottlenecks is becoming increasingly relevant to a new domain: the cloud environment, driven by AI needs. The paper discusses the novel research directions that arise in the data-centric world of AI, covering data-, resource-, and model-related challenges in embedded systems as well as new opportunities in the cloud domain.

This paper describes Acies-OS, a content-centric platform for edge AI twinning and orchestration that allows easy deployment, re-configuration, and control of edge AI services, augmented by a digital twin. The work is motivated by the proliferation of edge AI in a plethora of IoT applications, ranging from home automation to military defense, and the emergence of digital twins that go beyond monitoring and emulation into configuration management and optimization of edge capabilities. While past work focused on either the edge capabilities themselves or the digital twin, this work focuses on their seamless interactions, offering abstractions that enable the digital twin to manage and optimize an increasingly diverse edge AI system. Acies-OS features a structured namespace, a thin client library with flexible pub/sub-based communication, health monitoring support, and a control plane for twin-based value-added analysis and optimization. To illustrate the use of Acies-OS, we implemented a multi-node multi-modality vehicle classification application and used Acies-OS to interface it to a digital twin. We then deployed the system in the field to showcase run-time twin-based optimizations of inference latency, classification accuracy, and robustness to failures in noisy and challenging conditions.

This paper presents a generative data augmentation approach for human activity recognition (HAR) to close the distribution gap between laboratory training and real-world deployment. Despite the recent success of deep learning methods in wearable sensor-based HAR tasks, performance degradation occurs during real-world deployment due to training data scarcity and the vast variability in human activities. In light of this, we aim to enhance the diversity of training datasets by generating new data points within the vicinity of existing samples, as informed by domain expertise. Unlike the commonly utilized methods that augment data by interpolating in data space or feature space, we innovate by applying interpolation in the condition space of a conditional generative model to augment HAR datasets. We use domain-specific knowledge to extract statistical metrics from sensor data, which serve as conditions to direct the generation process. We demonstrate how a conditional generative diffusion model, steered by interpolated conditions, can synthesize realistic new data with various high-level features that benefit the robustness of the downstream HAR models. Our methodology advances the use of interpolation in data augmentation by exploring the capability of a state-of-the-art generative model, offering novel perspectives for bolstering the robustness and generalizability of HAR systems. Experimental results demonstrate that condition space interpolation outperforms the conventional interpolation-based and generative model-based augmentation methods across various datasets and downstream classifier combinations.

Internet of Things (IoT) sensing models often suffer from overfitting due to data distribution shifts between training dataset and real-world scenarios. To address this, data augmentation techniques have been adopted to enhance model robustness by bolstering the diversity of synthetic samples within a defined vicinity of existing samples. This paper introduces a novel paradigm of data augmentation for IoT sensing signals by adding fine-grained control to generative models. We define a metric space with statistical metrics that capture the essential features of the short-time Fourier transformed (STFT) spectrograms of IoT sensing signals. These metrics serve as strong conditions for a generative model, enabling us to tailor the spectrogram characteristics in the time-frequency domain according to specific application needs. Furthermore, we propose a set of data augmentation techniques within this metric space to create new data samples. Our method is evaluated across various generative models, datasets, and downstream IoT sensing models. The results demonstrate that our approach surpasses the conventional transformation-based data augmentation techniques and prior generative data augmentation models.

This paper introduces PhyMask, an adaptive masking paradigm designed to enhance the efficiency and interpretability of Masked Autoencoders (MAEs) in analyzing IoT sensing signals. Different from all mainstream MAEs, which rely on random masking techniques, PhyMask employs an adaptive masking strategy that aligns with critical signal information. Its main contributions are threefold. First, PhyMask leverages the energy significance of frequency components to prioritize information-rich time-frequency regions, improving the reconstruction of original signals. Second, it includes a coherence-based masking component to identify and preserve essential temporal dynamics within the data. Finally, PhyMask integrates these components into an adaptive masking paradigm tailored to optimize the sensing context awareness within the masking configuration, focusing on the most informative parts of the data. This allows PhyMask to mask up to 96% of the input, reducing memory requirements by 14% and accelerating pre-training. Evaluations across two sensing applications, four datasets, and two real-world deployments demonstrate PhyMask's superior performance. PhyMask improves MAE accuracy by 7%, reduces pre-training data requirements by up to 75%, and enhances robustness to domain shifts and signal quality variations, making it of great value to robust and efficient intelligent IoT deployments.

This paper investigates the few-shot temporal reasoning capability within the hyperbolic space. The goal is to forecast future events for newly emerging entities within temporal knowledge graphs (TKGs), leveraging only a limited set of initial observations. Hyperbolic space is advantageous for modeling emerging graph entities for two reasons: First, its geometric property of exponential expansion aligns with the rapid growth of new entities in real-world graphs; Second, it excels in capturing power-law patterns and hierarchical structures, well-suitable for new entities distributed at the peripheries of graph hierarchies and loosely connected with others through few links. We therefore propose a meta-learning framework, MetaHKG, to enable few-shot temporal reasoning within a hyperbolic space. Unlike prior hyperbolic learning works, MetaHKG addresses the challenges of effectively representing new entities in TKGs and adapting model parameters by incorporating novel hyperbolic time encodings and temporal attention networks that achieve translational invariance. We also introduce a meta hyperbolic optimization algorithm to enhance model adaptation by learning both global and entity-specific parameters through bi-level optimization. Comprehensive experiments conducted on three real-world temporal knowledge graphs demonstrate the superiority of MetaHKG over a diverse range of baselines, which achieves average 5.2% relative improvements. Compared to its Euclidean counterpart, MetaHKG operates in a lower-dimensional space but yields a more stable and efficient adaptability towards new entities.

This paper presents FreqMAE, a novel self-supervised learning framework that synergizes masked autoencoding (MAE) with physics-informed insights to capture feature patterns in multi-modal IoT sensor data. FreqMAE enhances latent space representation of sensor data, reducing reliance on data labeling and improving accuracy for AI tasks. Differing from data augmentation-based methods like contrastive learning, FreqMAE's approach eliminates the need for handcrafted transformations. Adapting MAE for IoT sensing signals, we present three contributions from frequency domain insights: First, a Temporal-Shifting Transformer (TS-T) encoder that enables temporal interactions while distinguishing different frequency bands; Second, a factorized multi-modal fusion mechanism for leveraging cross-modal correlations and preserving unique modality features; Third, a hierarchically weighted loss function that emphasizes important frequency components and high Signal-to-Noise Ratio (SNR) samples. Comprehensive evaluations on two sensing applications validate FreqMAE's proficiency in reducing labeling needs and enhancing resilience against domain shifts.

This paper introduces SudokuSens, a generative framework for automated generation of training data in machine-learning-based Internet-of-Things (IoT) applications, such that the generated synthetic data mimic experimental configurations not encountered during actual sensor data collection. The framework improves the robustness of resulting deep learning models, and is intended for IoT applications where data collection is expensive. The work is motivated by the fact that IoT time-series data entangle the signatures of observed objects with the confounding intrinsic properties of the surrounding environment and the dynamic environmental disturbances experienced. To incorporate sufficient diversity into the IoT training data, one therefore needs to consider a combinatorial explosion of training cases that are multiplicative in the number of objects considered and the possible environmental conditions in which such objects may be encountered. Our framework substantially reduces these multiplicative training needs. To decouple object signatures from environmental conditions, we employ a Conditional Variational Autoencoder (CVAE) that allows us to reduce data collection needs from multiplicative to (nearly) linear, while synthetically generating (data for) the missing conditions. To obtain robustness with respect to dynamic disturbances, a session-aware temporal contrastive learning approach is taken. Integrating the aforementioned two approaches, SudokuSens significantly improves the robustness of deep learning for IoT applications. We explore the degree to which SudokuSens benefits downstream inference tasks in different data sets and discuss conditions under which the approach is particularly effective.

Recent concepts in defense herald an increasing degree of automation of future military systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge, reducing their network communication footprint, and improving the inference quality of intelligent components in the loop. These requirements pose resource management challenges, calling for operating-system-like constructs that optimize the use of limited computational resources at the tactical edge. This paper describes these challenges and presents IoBT-OS, an operating system for the Internet of Battlefield Things that aims to optimize decision latency, improve decision accuracy, and reduce corresponding resource demands on computational and network components. A simple case-study with initial evaluation results is shown from a target tracking application scenario.

This paper presents a real-time multi-view scheduling framework for DNN-based live video analytics at the edge to minimize frame processing latency. The work is motivated by applications where a higher frame rate is important, not to miss actions of interest. Examples include defense, border security, and intruder detection applications where sensors (in this paper, cameras) are deployed to monitor key roads, chokepoints, or passageways to identify events of interest (and intervene in real-time). Supporting a higher frame rate entails lowering frame processing latency. We assume that multiple cameras are deployed with partially overlapping views. Each camera has access to limited onboard computing capacity. Many targets cross the field of view of these cameras (but the great majority do not require action). We take advantage of the spatial-temporal correlations among multi-camera video streams to perform target-to-camera assignment such that the maximum frame processing time across cameras is minimized. Specifically, we use a data-driven approach to identify objects seen by multiple cameras, and propose a batch-aware latency-balanced (BALB) scheduling algorithm to drive the object-to-camera assignment. We empirically evaluate the proposed system with a real-world surveillance dataset on a testbed consisting of multiple NVIDIA Jetson boards. The results show that our system substantially improves the video processing speed, attaining multiplicative speedups of 2.45× to 6.85×, and consistently outperforms the competitive static region partitioning strategy.

This paper presents AdaMask, a machine-centric video streaming framework for remote deep neural network (DNN) inference. The objective is to optimize the accuracy of downstream DNNs, offloaded to a remote machine, by adaptively changing video compression control knobs at runtime. Our main contributions are twofold. First, we propose frame masking as an effective mechanism to reduce the bandwidth consumption of video stream, which only preserves regions that potentially contain objects of interest. Second, we design a new adaptation algorithm that achieves the Pareto-optimal tradeoff between accuracy and bandwidth by controlling the masked portions of frames together with conventional H.264 control knobs (eg. resolution). Through extensive evaluations on three sensing scenarios (dash camera, traffic surveillance, and drone), frame masking saves the bandwidth by up to 65% with < 1% accuracy degradation, and AdaMask improves the accuracy by up to 14% over the baselines against the network dynamics.

This paper reviews the novel concept of a controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offers useful extensions and applications. ControlVAE is a new variational autoencoder (VAE) framework that combines automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) controller, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows us to precisely control the KL-divergence to a desired value (set point) that is effective in avoiding posterior collapse and learning disentangled representations. While prior work developed alternative techniques for controlling the KL divergence, we show that our PI controller has better stability properties and thus better convergence, thereby producing better disentangled representations from finite training data. In order to improve the ELBO of ControlVAE over that of the regular VAE, we provide a simplified theoretical analysis to inform the choice of set point for the KL-divergence of ControlVAE. We evaluate the proposed method on three tasks: image generation, language modeling, and disentangled representation learning. The results show that ControlVAE can achieve much better reconstruction quality than the other methods for comparable disentanglement. On the language modeling task, our method can avoid posterior collapse (KL vanishing) and improve the diversity of generated text. Moreover, it can change the optimization trajectory, improving the ELBO and the reconstruction quality for image generation.

This paper presents a contrastive self-supervised representation learning framework that is new in being designed specifically for deep learning from frequency domain data. Contrastive self-supervised representation learning trains neural networks using mostly unlabeled data. It is motivated by the need to reduce the labeling burden of deep learning. In this paper, we are specifically interested in applying this approach to physical sensing scenarios, such as those arising in Internet-of-Things (IoT) applications. Deep neural networks have been widely utilized in IoT applications, but the performance of such models largely depends on the availability of large labeled datasets, which in turn entails significant training costs. Motivated by the success of contrastive self-supervised representation learning at substantially reducing the need for labeled data (mostly in areas of computer vision and natural language processing), there is growing interest in customizing the contrastive learning framework to IoT applications. Most existing work in that space approaches the problem from a time-domain perspective. However, IoT applications often measure physical phenomena, where the underlying processes (such as acceleration, vibration, or wireless signal propagation) are fundamentally a function of signal frequencies and thus have sparser and more compact representations in the frequency domain. Recently, this observation motivated the development of Short-Time Fourier Neural Networks (STFNets) that learn directly in the frequency domain, and were shown to offer large performance gains compared to Convolutional Neural Networks (CNNs) when designing supervised learning models for IoT tasks. Hence, in this paper, we introduce an STFNet-based Contrastive Self-supervised representation Learning framework (STF-CSL). STF-CSL takes both time-domain and frequency-domain features into consideration. We build the encoder using STFNet as the fundamental building block. We also apply both time-domain data augmentation and frequency-domain data augmentation during the self-supervised training process. We evaluate the resulting performance of STF-CSL on various human activity recognition tasks. The evaluation results demonstrate that STF-CSL significantly outperforms the time-domain based self-supervised approaches thereby substantially enhancing our ability to train deep neural networks from unlabeled data in IoT contexts.

This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for other potential users and predict the corresponding user rankings. Inferring user interests from diffusion data lies the foundation of diffusion prediction, because users often forward the information in which they are interested or the information from those who share similar interests. Their interests also evolve over time as the result of the dynamic social influence from neighbors and the time-sensitive information gained inside/outside the social media. Existing works fail to model users' intrinsic interests from the diffusion data and assume user interests remain static along the time. DyDiff-VAE advances the state of the art in two directions: (i) We propose a dynamic encoder to infer the evolution of user interests from observed diffusion data. (ii) We propose a dual attentive decoder to estimate the propagation likelihood by integrating information from both the initial cascade content and the forwarding user sequence. Extensive experiments on four real-world datasets from Twitter and Youtube demonstrate the advantages of the proposed model; we show that it achieves 43.3%relative gains over the best baseline on average. Moreover, it has the lowest run-time compared with recurrent neural network based models.

Much work on social media opinion polarization focuses on a flat categorization of stances (or orthogonal beliefs) of different communities from media traces. We extend in this work in two important respects. First, we detect not only points of disagreement between communities, but also points of agreement. In other words, we estimate community beliefs in the presence of overlap. Second, in lieu of flat categorization, we consider hierarchical belief estimation, where communities might be hierarchically divided. For example, two opposing parties might disagree on core issues, but within a party, despite agreement on fundamentals, disagreement might occur on further details. We call the resulting combined problem a hierarchical overlapping belief estimation problem. To solve it, this paper develops a new class of unsupervised Non-negative Matrix Factorization (NMF) algorithms, we call Belief Structured Matrix Factorization (BSMF). Our proposed unsupervised algorithm captures both the latent belief intersections and dissimilarities, as well as hierarchical structure. We discuss properties of the algorithm and evaluate it on both synthetic and real-world datasets. In the synthetic dataset, our model reduces error by 40%. In real Twitter traces, it improves accuracy by around 10%. The model also achieves 96.08% self-consistency in a sanity check.

This paper develops a novel detection system of possibly fake accounts on public social media, called FADE, that uses features based on group behaviors to identify suspicious groups. The work is motivated by the prospect of mitigating misinformation campaigns on social media, where malicious entities on directional social networks pose as credible sources and coordinate the spreading of highly corroborated false information. Instead of account-level detection, this paper aims to detect the very group activity that underlies misinformation campaigns; namely, the coordinated spreading of messages to boost (misinformation) visibility. The existing group detection methods group users into two clusters (fake or not) and directly produce clusters of fake accounts. Conversely, we group users into many clusters based on information propagation patterns and user features and then classify them. The benefit of multiple clusters is that we can detect suspicious behavior more easily from cluster-wide statistics. In order to improve clustering accuracy, we analyze and select the most important features for clustering based on Bayesian optimization instead of using all the features. Accordingly, similarity metrics are defined that allow clustering of individually plausible accounts in a manner that enables one to detect suspicious clusters of activity. Cluster-level features are then used to decide if the cluster is benign. We further explore the cost of adversarial attacks on our detection model. Evaluation results on Twitter data sets demonstrate that our proposed approach outperforms state-of-the-art baselines in detecting accounts created for information manipulation campaigns. In addition, we show that the cost of subverting detection (without reducing the effectiveness of the attacker's campaign) is high.

The paper presents a real-time computing framework for intelligent real-time edge services, on behalf of local embedded devices that are themselves unable to support extensive computations. The work contributes to a new direction in realtime computing that develops scheduling algorithms for machine intelligence tasks that enable anytime prediction. We show that deep neural network workflows can be cast as imprecise computations, each with a mandatory part and (several) optional parts whose execution utility depends on input data. With our design, deep neural networks can be preempted before their completion and support anytime inference. The goal of the realtime scheduler is to maximize the average accuracy of deep neural network outputs while meeting task deadlines, thanks to opportunistic shedding of the least necessary optional parts. The work is motivated by the proliferation of increasingly ubiquitous but resource-constrained embedded devices (for applications ranging from autonomous cars to the Internet of Things) and the desire to develop services that endow them with intelligence. Experiments on recent GPU hardware and a state of the art deep neural network for machine vision illustrate that our scheme can increase the overall accuracy by 10% ~ 20% while incurring (nearly) no deadline misses.

With recent advances, neural networks have become a crucial building block in intelligent IoT systems and sensing applications. However, the excessive computational demand remains a serious impediment to their deployments on low-end IoT devices. With the emergence of edge computing, offloading grows into a promising technique to circumvent end-device limitations. However, transferring data between local and edge devices takes up a large proportion of time in existing offloading frameworks, creating a bottleneck for low-latency intelligent services. In this work, we propose a general framework, called deep compressive offloading. By integrating compressive sensing theory and deep learning, our framework can encode data for offloading into tiny sizes with negligible overhead on local devices and decode the data on the edge server, while offering theoretical guarantees on perfect reconstruction and lossless inference. By trading edge computing resources for data transmission time, our design can significantly reduce offloading latency with almost no accuracy loss. We build a deep compressive offloading system to serve state-of-the-art computer vision and speech recognition services. With comprehensive evaluations, our system can consistently reduce end-to-end latency by 2X to 4X with 1% accuracy loss, compared to state-of-the-art neural network offloading systems. In conditions of limited network bandwidth or intensive background traffic, our system can further speed up the neural network inference by up to 35X 1.

GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release repositories of source code related to their research papers in order to attract more people to follow their work. Motivated by this trend, we describe a novel item-item cross-platform recommender system, paper2repo, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic. The key challenge is to identify the similarity between an input paper and its related repositories across the two platforms, without the benefit of human labeling. Towards that end, paper2repo integrates text encoding and constrained graph convolutional networks (GCN) to automatically learn and map the embeddings of papers and repositories into the same space, where proximity offers the basis for recommendation. To make our method more practical in real life systems, labels used for model training are computed automatically from features of user actions on GitHub. In machine learning, such automatic labeling is often called distant supervision. To the authors’ knowledge, this is the first distant-supervised cross-platform (paper to repository) matching system. We evaluate the performance of paper2repo on real-world data sets collected from GitHub and Microsoft Academic. Results demonstrate that it outperforms other state of the art recommendation methods.

The deep neural network becomes an increasingly crucial component in recent intelligent applications. The excessive resource consumptions of state-of-the-art neural networks, however, remains a huge impediment towards their widespread deployment in the Internet of Things (IoT). In this paper, we propose an IoT-oriented deep learning serving system, Stardust, that accelerates the neural network inference to improve the quality of IoT services. Stardust integrates several joint contributions from both the system and AI perspectives, including system performance predictor, model compression, and compressive offloading. On one hand, the performance predictor profiles and predicts the runtime characteristics of neural network operations on a particular device with the targeted runtime environment, which enables a hardware and software oriented performance optimization during model compression and offloading. On the other hand, the model compression minimizes the computation time of neural networks on different devices, and the compressive offloading diminishes the network data transferring time during the mobile-edge offloading. Moreover, all these optimizations can be done with almost no compromise on inference accuracy. The integration of these modules, therefore, collaboratively reduce the end-to-end latency of serving deep learning services that reside across embedded/mobile devices and edge servers. We deploy illustrative applications on Stardust, performing human perception tasks with on-device camera microphone and motion sensors to demonstrate the capability of Stardust serving system.

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better footprints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments on a wide range of sensing inputs, including motion sensors, WiFi, ultrasound, and visible light. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs 1.

A trend in nowadays data centers is that heterogeneous storage devices are deployed to meet different storage demands of various big data workloads. For example, many nodes are equipped with both SSDs and HDDs. And HDFS has introduced the heterogeneous-storage-aware feature to adapt to such hybrid storage clusters. However, current task scheduler on big data processing platforms (such as Hadoop and Spark) only considers the overhead of network data transmission by exploiting the data locality principle. On heterogeneous storage clusters, task completion time is also affected by the speed of storage devices (SSDs and HDDs) where the data are stored. Ignoring the different speed of storage devices results in poor utilization of high speed devices such as SSD. In this paper, we propose a task scheduling strategy for heterogeneous storage clusters called H-Scheduler. The key idea of H-Scheduler is to differentiate speeds of storage devices by storage types. It classifies the tasks by both data locality and storage types, and redefines the priorities of different classes of tasks by both storage device speed and data locality to reduce job execution time. We implemented H-Scheduler in Spark, and the experiment results show that H-Scheduler can reduce job execution time by up to 73.6 %, depending on the workload characteristics and data distribution among different types of storage devices.