Reviewerly/exHarmony huggingface dataset:
HRA: An Intelligent Holistic Resource Autoscaling Framework for Multi-service Applications

The elastic cloud applies autoscaling technology to allow users to automatically provision or deprovision resources on demands, attracting many application providers to migrate their applications to the cloud. However, autoscaling multi-service applications are still challenging due to the complex correlations among services. This paper presents HRA, an intelligent, holistic resource autoscaling framework for multi-service applications, utilizing model-based deep reinforcement learning (DRL), mitigating service-level agreements (SLA) violations while saving costs. HRA (i) leverages historical telemetry data and machine learning methods to build a simulated environment adaptively, modeling relations between resources, workloads and performance, (ii) exploits the environment model to drive up training efficiency of DRL agent, and (iii) uses the agent to automatically take actions to scale resources online based on simple low-level features from a monitor instead of elaborate high-level features that are representing the complex correlations and needing much sophisticated prior knowledge. Experiments (i) evaluated the fidelity of the proposed (simulated) environment modeling method, (ii) evaluated the reliability of the resource allocation policy from the simulation to reality, and (iii) compared related autoscaling methods. The evaluation results demonstrate that HRA realizes a more effective resource allocation policy under the limited number of time-consuming interactions and significantly decreases the 32-92% in SLA violation rate at a lower cost compared to other main methods.
Virtual Reality: A Journey From Vision to Commodity

This work is a thorough account of virtual reality's (VR) historical change from vision to commodity. The technology's manifestations are organized into the pre-Sutherland era, beginning approximately in the 18th century, the post-Sutherland milestones of the 70s and the 80s, and the pop culture of the 80s and 90s that triggered its short-lived industrious rise, followed by a spectacular fall. Although the timeline of the evolution of the VR apparatus has been previously documented, little has been said regarding milestone continuity and how technological and market progression relate to the underlying forces that keep propelling VR's slow-burning development and cultural diffusion. Those forces aim toward the “ultimate display”; however, our analysis rejects this goal and examines how, instead, the VR experience has gradually developed.
Progressive Attention-Based Feature Recovery With Scribble Supervision for Saliency Detection in Optical Remote Sensing Image

Salient object detection (SOD) task for optical remote sensing images (RSIs) plays an important role in many remote sensing applications. Most of the existing methods train their networks depending on a large amount of pixel-wise datasets. However, such expensive and time-consuming training setting prevents the approaches becoming flexible and scalable solutions. To this end, we explore efficient SOD for optical RSIs based on easily accessible weak supervision source. In this work, we propose a novel end-to-end progressive attention-based feature recovery framework with scribble supervision. Specifically, to better locate challenging salient objects in optical RSIs, an object position module (OPM) is proposed to capture and enhance the long-range semantic dependence of objects’ position information, which depends on the complementary attention mechanism. And to restore the entire salient objects, a context refinement module (CRM) is proposed, which extract local contextual information for better propagating high-level semantics to low-level details. Moreover, to improve the adaptability of the network to the changing scenarios of optical RSIs, we propose a salient region correcting (SRC) mechanism to help the predicted salient regions rectify their saliency values by constraining the saliency relationship between predictions from different augmentation models. In addition, due to the lack of dataset for weakly supervised SOD for optical RSIs, we relabeled an existing large-scale optical RSIs dataset with scribbles, namely EORSSD-S. Experimental results on benchmark datasets demonstrate that the proposed method can outperform other weakly supervised SOD methods. And the proposed method even outperformed some fully supervised methods. https://github.com/melonless/PAFR.
Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking

3D single object tracking is a key task in 3D computer vision. However, the sparsity of point clouds makes it difficult to compute the similarity and locate the object, posing big challenges to the 3D tracker. Previous works tried to solve the problem and improved the tracking performance in some common scenarios, but they usually failed in some extreme sparse scenarios, such as for tracking objects at long distances or partially occluded. To address the above problems, in this letter, we propose a sparse-to-dense and transformer-based framework for 3D single object tracking. First, we transform the 3D sparse points into 3D pillars and then compress them into 2D bird's eye view (BEV) features to have a dense representation. Then, we propose an attention-based encoder to achieve global similarity computation between template and search branches, which could alleviate the influence of sparsity. Meanwhile, the encoder applies the attention on multi-scale features to compensate for the lack of information caused by the sparsity of point cloud and the single scale of features. Finally, we use set-prediction to track the object through a two-stage decoder which also utilizes attention. Extensive experiments show that our method achieves very promising results on the KITTI and NuScenes datasets.
Privacy-Preserving Multi-Range Queries for Secure Data Outsourcing Services

Encrypted range query schemes that enable range-based searches over encrypted data have become an effective solution for secure data outsourcing services. However, existing schemes are still inadequate on desired functionality and security. Specifically, supporting efficient multi-range queries while hiding the data ordering leakage remains a challenging research problem. Existing works on order-hiding query schemes only work for encrypted single-range search and incur significant computational overhead due to the protection of the ordering information. In this paper, we present a privacy-preserving multi-range query scheme that can address the above problems simultaneously. It not only enables efficient multi-range queries over encrypted data but also guarantees the privacy of ordering information. To protect the ordering leakage, our design adopts an Order-hiding Encoding (OHE) scheme to support multi-range obfuscation. In addition, a novel order-hiding KD-tree index structure is designed as the core technique underlying our scheme, which accelerates efficient multi-range queries and obfuscates ordering information of encrypted values. Finally, the formal security analysis confirms that our proposed multi-range query scheme is secure in the random oracle model. The extensive experimental results conducted on real-world datasets demonstrate the practicality of our design.
Your Labels are Selling You Out: Relation Leaks in Vertical Federated Learning

Vertical federated learning (VFL) is an emerging privacy-preserving paradigm that enables collaboration between companies. These companies have the same set of users but different features. One of them is interested in expanding new business or improving its current service with others’ features. For instance, an e-commerce company, who wants to improve its recommendation performance, can incorporate users’ preferences from another corporation such as a social media company through VFL. On the other hand, graph data is a powerful and sensitive type of data widely used in industry. Their leakage, e.g., the node leakage and/or the relation leakage, can cause severe privacy issues and financial loss. Therefore, protecting the security of graph data is important in practice. Though a line of work has studied how to learn with graph data in VFL, the privacy risks remain underexplored. In this paper, we perform the first systematic study on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relation inference attacks</i> to reveal VFL's risk of leaking samples’ relations. Specifically, we assume the adversary to be a semi-honest participant. Then, according to the adversary's knowledge level, we formulate three kinds of attacks based on different intermediate representations. Particularly, we design a novel numerical approximation method to handle VFL's encryption mechanism on the participant's representations. Extensive evaluations with four real-world datasets demonstrate the effectiveness of our attacks. For instance, the area under curve of relation inference can reach more than 90%, implying an impressive relation inference capability. Furthermore, we evaluate possible defenses to examine our attacks’ robustness. The results show that their impacts are limited. Our work highlights the need for advanced defenses to protect private relations and calls for more exploration of VFL's privacy and security issues.
Numerical simulation of performance of magnonic reservoir computer based on active-ring oscillator

We carry out numerical simulation of the performance of a physical reservoir computer based on a magnonic active ring oscillator. Our numerical model is based on the theory of nonlinear spin-wave transient processes in magnetic films. We employ-two metrics to evaluate the performance, namely the short-term memory and parity-check capacities. Obtained results demonstrate that there exists high potential for improvement of performance characteristics of the reservoir computer. Finally, we formulate optimal physical parameters for the magnonic reservoir. Our numerical model shows that the optimized reservoir will be able to increase its short-term memory and parity check capacities by two times with respect to the experimentally demonstrated in recent papers.
Episode-based personalization network for gaze estimation without calibration

Appearance-based methods employing deep convolutional architectures have achieved impressive performance for unconstrained gaze estimation. However, due to inter-personal anatomical differences, existing methods always get biased results for person-independent tasks. Generally, improvements can be achieved by adapting the models to a specified person with few calibration samples. Nevertheless, the calibration-based strategies are likely to collapse to high errors as over-parameterized neural networks easily fall into over-fitting. In this paper, we propose an E pisode- b ased P ersonalization N etwork (EbPN) for person-independent gaze estimation, which does not rely on any calibration samples. Firstly, inspired by multi-task learning, we propose a two-branch gaze estimation network, including appearance-aware network and personalization-aware network. Secondly, during training, our model is optimized with a collection of episodes, each of which is designed to imitate a few-shot gaze estimation task. Through multiple episodes, our EbPN model progressively accumulates the experience on capturing appearance and personalized information of each image, which would help our model get lower errors and generalize well on any new persons. Experiments on GazeCapture, MPIIGaze and ETH-XGaze datasets demonstrate the effectiveness of our method.
Robust and Scalable Process Isolation Against Spectre in the Cloud

In the quest for efficiency and performance, edge-computing providers replace process isolation with sandboxes, to support a high number of tenants per machine. While secure against software vulnerabilities, microarchitectural attacks can bypass these sandboxes. In this paper, we present a Spectre attack leaking secrets from co-located tenants in edge computing. Our remote Spectre attack, using amplification techniques and a remote timing server, leaks $${2}\,\mathrm {bit/min}$$ . This motivates our main contribution, DyPrIs, a scalable process-isolation mechanism that only isolates suspicious worker scripts following a lightweight detection mechanism. In the worst case, DyPrIs boils down to process isolation. Our proof-of-concept implementation augments real-world cloud infrastructure used in production at large scale, Cloudflare Workers. With a false-positive rate of only $${0.61}\,{\%}$$ , we demonstrate that DyPrIs outperforms strict process isolation while statistically maintaining its security guarantees, fully mitigating cross-tenant Spectre attacks.
Flexibly Graded Monads and Graded Algebras

When modelling side-effects using a monad, we need to equip the monad with effectful operations. This can be done by noting that each algebra of the monad carries interpretations of the desired operations. We consider the analogous situation for graded monads, which are a generalization of monads that enable us to track quantitative information about side-effects. Grading makes a significant difference: while many graded monads of interest can be equipped with similar operations, the algebras often cannot. We explain where these operations come from for graded monads. To do this, we introduce the notion of flexibly graded monad, for which the situation is similar to the situation for ordinary monads. We then show that each flexibly graded monad induces a canonical graded monad in such a way that operations for the flexibly graded monad carry over to the graded monad. In doing this, we reformulate grading in terms of locally graded categories, showing in particular that graded monads are a particular kind of relative monad. We propose that locally graded categories are a useful setting for work on grading in general.
Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain

Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concerns, data ownership, and the cost of acquisition, thus limiting our ability to understand the human body. A possible solution to this issue is the creation of a model able to learn and then generate synthetic images of the human body conditioned on specific characteristics of relevance (e.g., age, sex, and disease status). Deep generative models, in the form of neural networks, have been recently used to create synthetic 2D images of natural scenes. Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations. This work proposes a generative model that can be scaled to produce anatomically correct, high-resolution, and realistic images of the human brain, with the necessary quality to allow further downstream analyses. The ability to generate a potentially unlimited amount of data not only enables large-scale studies of human anatomy and pathology without jeopardizing patient privacy, but also significantly advances research in the field of anomaly detection, modality synthesis, learning under limited data, and fair and ethical AI. Code and trained models are available at: https://github.com/AmigoLab/SynthAnatomy .
Half-Aggregation of Schnorr Signatures with Tight Reductions

An aggregate signature (AS) scheme allows an unspecified aggregator to compress many signatures into a short aggregation. AS schemes can save storage costs and accelerate verification. They are desirable for applications where many signatures need to be stored, transferred, or verified together, including blockchain systems, sensor networks, certificate chains, network routing, etc. However, constructing AS schemes based on general groups, only requiring the hardness of the discrete logarithm problem, is quite tricky and has been a long-standing research question. Recently, Chalkias et al. [6] proposed a half-aggregate scheme for Schnorr signatures. We observe the scheme lacks a tight security proof and does not well support incremental aggregation, i.e., adding more signatures into a pre-existing aggregation. This work's contributions are threefold. We first give a tight security proof for the scheme in [6] in the ROM and the algebraic group model (AGM). Second, we provide a new half-aggregate scheme for Schnorr signatures that perfectly supports incremental aggregation, whose security also tightly reduces to Schnorr's security in the AGM+ROM. Third, we present a Schnorr-based sequential aggregate signature (SAS) scheme that is tightly secure as Schnorr signature scheme in the ROM (without the AGM). Our work may pave the way for applying Schnorr aggregation in real-world cryptographic applications.
What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity

Abstract We present an analysis of eight measures used for quantifying morphological complexity of natural languages. The measures we study are corpus-based measures of morphological complexity with varying requirements for corpus annotation. We present similarities and differences between these measures visually and through correlation analyses, as well as their relation to the relevant typological variables. Our analysis focuses on whether these ‘measures’ are measures of the same underlying variable, or whether they measure more than one dimension of morphological complexity. Principal component analysis indicates that the first principal component explains 92.62 percent of the variation in eight measures, indicating a strong linear dependence between the complexity measures studied.
Image-Based Automatic Energy Meter Reading Using Deep Learning

We propose to perform an image-based framework for electrical energy meter reading.Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units.Combining the readings of serial numbers and energy meter units, an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup.However, such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display, orientation and scale variations caused by camera positioning, and non-uniform illumination caused by shades.To this end, our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once (YOLO ) along with traditional handcrafted features for text extraction and recognition.Our image dataset contains 10,000 images of electrical energy meters and is further expanded by data augmentation such as in-plane rotation and scaling to make the deep learning algorithms robust to these image variations.For training and evaluation, the image dataset is annotated to produce the ground truth of all the images.Consequently, YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98% for all the digits.It proves to be robust against the mentioned image variations compared with the traditional handcrafted features.Our proposed method can be highly instrumental in reducing the time and effort involved in the current meter reading, where workers visit door to door, take images of meters and manually extract readings from these images.
A Survey on Image Semantic Segmentation Using Deep Learning Techniques

Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc.In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture.This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation.Firstly, the commonly used image segmentation datasets are listed.Next, extensive pioneering works are deeply studied from multiple perspectives (e.g., network structures, feature fusion methods, attention mechanisms), and are divided into four categories according to different network architectures: CNN-based architectures, transformer-based architectures, MLP-based architectures, and others.Furthermore, this paper presents some common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used datasets.Finally, possible future research directions and challenges are discussed for the reference of other researchers.
Incentive EMR Sharing System Based on Consortium Blockchain and IPFS

Electronic medical records (EMRs) are extremely private data in the medical industry. Clinicians use the patient data that the EMR stores to quickly assess a patient's status and save diagnostic information. In the conventional medical model, it is easy for duplicate exams, medical resource waste, or the loss of medical records to happen when a patient is transferred between several medical facilities due to problems with data sharing and exchange, inadequate data privacy, security, confidentiality, and difficulties with data traceability. This paper recommends a Hyperledger Fabric-based strategy to promote the exchange of EMR models. With the use of Hyperledger Fabric, EMR stakeholders can be brought into the channel to facilitate data sharing. Attribute-based access control (ABAC) allows users to design the data access control policy, and the data access control may improve security. Any record stored in the blockchain can be viewed using the Hyperledger Fabric feature and it cannot be altered or destroyed, ensuring data traceability. Through proxy re-encryption, which makes sure that the data is not leaked during data exchange, data secrecy can be ensured. A module for medical tokens has now been added. Many foreign medical institutions currently use the medical token system, and the system described in this paper can use the tokens to pay for some medical expenses. The tokens are obtained by the patient's initiative to share their EMR with the medical institution for research, which is how many foreign medical institutions currently use the medical token mechanism. This paradigm can encourage the growth of medical data by enabling stakeholders to collaborate and share EMR trust.
Deep Learning Approaches for Long-Term Global Horizontal Irradiance Forecasting for Microgrids Planning

Providing sustainable energy to rural communities is considered in Sustainable Development Goal 7. Off-grid renewable energy systems arise as an affordable solution due to their portability and the availability of renewable sources for rural communities. In this work, to deal with the uncertainties of solar resources, we employ two deep learning models (feed forward and recurrent neural networks) to predict renewable sources in a long-term horizon. To this aim, the approach presented takes into account the necessity of a high enough resolution in the forecasting output. As a case study, we employ open source data for a location in Michoacan, Mexico as well as open source programming frameworks to ensure the replicability of the numerical experiments. The results show that our prediction model performs excellently with respect to the baseline methods (ARIMA, exponential smoothing, and seasonal naive) in terms of the evaluation metrics MASE (18.5% of reduction with respect to seasonal naive), RMSE (24.7%), WAPE (13.1%), MAE (12.9%), and APB (8.9%).