Reviewerly/exHarmony huggingface dataset:
From Virtual to Augmented Reality Training System: An IC.IDO-Based Approach

This paper proposes a methodology to exploit virtual prototypes for the development of both VR and AR-based Training systems, with the same Software-Hardware architecture. This architecture provides a unique collaborative environment to enable the interaction between several users even if geographically distant, allowing to switch easily and rapidly between VR, AR and mixed technologies. The proposed Training Systems concern a simple but structured workcycle about the assembly of some Lego bricks. The selected development platform is IC.IDO: a commercial software with an industrial footprint, produced by ESI Group. Despite IC.IDO is originally designed for VR and not compatible for AR applications, it resulted fully compliant with the requisites of the proposed methodology. The paper firstly describes the path outlined from the conversion of a Training session from VR to AR compatible, within IC.IDO. Thanks to the adoption of a colour-filtering software as OBS Studio, the IC.IDO session easily becomes AR-compatible, using a unique hardware architecture: the HTC Vive Pro HMD and a VIVE tracker. In this work, also the Oculus Rift S has been employed for the VR applications to demonstrate the system’s compatibility with other VR devices. For both VIVE and Oculus, the Virtual Desktop feature allowed to display the OBS Desktop window in the HMD. Furthermore, an innovative solution is proposed: thanks to IC.IDO’s Cooperative Environment, trainer and trainee will cooperate within the same scene using simultaneously VR and AR. Finally, the role of the trainer is discussed, to outline a process of progressive independence for the trainee.
Role-Based Sailing Augmented Reality Interface: Three Proposals for Head-Mounted Display

This work is part of a larger project aiming to develop a comprehensive Augmented Reality (AR) interface for recreative and professional nautical sailing navigation. Due to the complexity of the marine environment and the dynamism of the crew members on board, we propose to diversify the display of navigation information concerning the role, position and activity onboard. The interfaces are designed to be viewed by one or more crew members, solving the problem of everyone viewing the same data, and giving each one the information they need based on their role or position. The three novel proposals for sail-specific AR graphic interfaces are designed to be displayed on Head-Mounted Display HMD and each of them differs for the information stabilization: Screen-Stabilized, Body-Stabilized and Boat-Stabilized. Each interface is different from the others differentiated according to the type of navigation and the user who must use that information. These approaches have been prototyped and evaluated by a panel of experts in the field of sailing navigation and were able to showcase their potential for future evaluations in different scenarios.
Random-Value Payment Tokens for On-Chain Privacy-Preserving Payments

Blockchain has been proposed as a trusted execution system, ensuring business process execution integrity and transparency. Smart contracts can manage the task or workflow execution and the allocation of tasks in a decentralized and reliable fashion. Nonetheless, blockchain transactions are public and accessible to their participants, and the issue of privacy is a well-known issue of blockchain systems for business process management. In the example of a service payment occurring after a sealed-bid auction, participants may not be willing to reveal the value of the accepted bid to other competitors. In this paper, we leverage smart contracts and a bank that manages per-collaboration payment tokens. The tokens are backed with fiat money with a conversion rate that is kept secret between payment partners and the bank. Hence, partners benefit from the interests of smart contracts such as autonomous programmable payment while preserving the confidentiality of the payment value. We implement this protocol in a real-world setting to demonstrate the approach’s feasibility, and we carry on quantitative experiments to confirm the validity of the protocol.
Dynamic Forest for Learning from Data Streams with Varying Feature Spaces

In this paper, we propose a new ensemble method, which is called Dynamic Forest, for learning from data streams with varying feature spaces. Unlike traditional online learning where the feature space is static, in varying feature spaces, new features may emerge while others may vanish. This leads to several problems for which state-of-the-art online random forest algorithms are not equipped. We benchmark our proposed method against the state-of-the-art method OLVF on data streams with varying feature spaces and against OLVF and $$OL_{SF}$$ on trapezoidal data streams. These trapezoidal data streams can be considered as a sub-problem of varying feature spaces, where the only characteristic is that new features emerge over time. Our proposed approach dynamically learns and relearns decision stumps while applying a dynamic weighting strategy for the decision stumps. Furthermore, it employs a dynamic strategy for adding and removing weak learners. The proposed method is empirically evaluated by replicating the benchmark of the OLVF algorithm with nine UCI Machine Learning Repository datasets and one real-world dataset. In the experiments, we can show that Dynamic Forest proves to be a good addition to the current state-of-the-art for learning from data streams with varying feature spaces.
A Framework for Constructing Single Secret Leader Election from MPC

The emergence of distributed digital currencies has raised the need for a reliable consensus mechanism. In proof-of-stake cryptocurrencies, the participants periodically choose a closed set of validators, who can vote and append transactions to the blockchain. Each validator can become a leader with the probability proportional to its stake. Keeping the leader private yet unique until it publishes a new block can significantly reduce the attack vector of an adversary and improve the throughput of the network. The problem of Single Secret Leader Election (SSLE) was first formally defined by Boneh et al. in 2020. In this work, we propose a novel framework for constructing SSLE protocols, which relies on secure multi-party computation (MPC) and satisfies the desired security properties. Our framework does not use any shuffle or sort operations and has a computational cost for N parties as low as O(N) of basic MPC operations per party. We improve the state-of-the-art for SSLE protocols that do not assume a trusted setup. Moreover, our SSLE scheme efficiently handles weighted elections. That is, for a total weight S of N parties, the associated costs are only increased by a factor of $$\log {S}$$ . When the MPC layer is instantiated with techniques based on Shamir's secret-sharing, our SSLE has a communication cost of $$O(N^2)$$ which is spread over $$O(\log {N})$$ rounds, can tolerate up to $$t<N/2$$ of faulty nodes without restarting the protocol, and its security relies on DDH in the random oracle model. When the MPC layer is instantiated with more efficient techniques based on garbled circuits, our SSLE requires all parties to participate, up to $$N-1$$ of which can be malicious, and its security is based on the random oracle model.
Parameter optimization of moving horizon estimation based on hybrid intelligent algorithm

Abstract In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.
fASLR: Function-Based ASLR for Resource-Constrained IoT Systems

The address space layout randomization (ASLR) has been widely deployed on modern operating systems against code reuse attacks (CRAs) such as return-oriented programming (ROP) and return-to-libc. However, porting ASLR to resource-constrained IoT devices is a great challenge due to the limited memory space. We propose a function-based ASLR scheme (fASLR) for IoT runtime security utilizing the ARM TrustZone-M technique and the memory protection unit (MPU). fASLR loads a function from the flash and randomizes its entry address in a randomization region in RAM when the function is called. We design novel mechanisms on cleaning up finished functions from the RAM and memory addressing to deal with the complexity of function relocation and randomization. Compared with related work, a prominent advantage of fASLR is that fASLR can run an application even if the application code cannot be completely loaded into RAM for execution. We test fASLR with 21 applications. fASLR achieves high randomization entropy and incurs runtime overhead of less than 10%.
SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution

This paper presents a novel framework for processing volumetric medical information using Visual Transformers (ViTs). First, We extend the state-of-the-art Swin Transformer model to the 3D medical domain. Second, we propose a new approach for processing volumetric information and encoding position in ViTs for 3D applications. We instantiate the proposed framework and present SuperFormer, a volumetric transformer-based approach for Magnetic Resonance Imaging (MRI) Super-Resolution. Our method leverages the 3D information of the MRI domain and uses a local self-attention mechanism with a 3D relative positional encoding to recover anatomical details. In addition, our approach takes advantage of multi-domain information from volume and feature domains and fuses them to reconstruct the High-Resolution MRI. We perform an extensive validation on the Human Connectome Project dataset and demonstrate the superiority of volumetric transformers over 3D CNN-based methods. Our code and pretrained models are available at https://github.com/BCV-Uniandes/SuperFormer .
Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement

Magnetic resonance imaging (MRI) with gadolinium contrast is widely used for tissue enhancement and better identification of active lesions and tumors. Recent studies have shown that gadolinium deposition can accumulate in tissues including the brain, which raises safety concerns. Prior works have tried to synthesize post-contrast T1-weighted MRIs from pre-contrast MRIs to avoid the use of gadolinium. However, contrast and image representations are often entangled during the synthesis process, resulting in synthetic post-contrast MRIs with undesirable contrast enhancements. Moreover, the synthesis of pre-contrast MRIs from post-contrast MRIs which can be useful for volumetric analysis is rarely investigated in the literature. To tackle pre- and post- contrast MRI synthesis, we propose a BI-directional Contrast Enhancement Prediction and Synthesis (BICEPS) network that enables disentanglement of contrast and image representations via a bi-directional image-to-image translation(I2I)model. Our proposed model can perform both pre-to-post and post-to-pre contrast synthesis, and provides an interpretable synthesis process by predicting contrast enhancement maps from the learned contrast embedding. Extensive experiments on a multiple sclerosis dataset demonstrate the feasibility of applying our bidirectional synthesis and show that BICEPS outperforms current methods.
Efficient Medical Image Assessment via Self-supervised Learning

High-performance deep learning methods typically rely on large annotated training datasets, which are difficult to obtain in many clinical applications due to the high cost of medical image labeling. Existing data assessment methods commonly require knowing the labels in advance, which are not feasible to achieve our goal of ‘knowing which data to label.’ To this end, we formulate and propose a novel and efficient data assessment strategy, EXponentiAl Marginal sINgular valuE ( $$\textsf{EXAMINE}$$ ) score, to rank the quality of unlabeled medical image data based on their useful latent representations extracted via Self-supervised Learning (SSL) networks. Motivated by theoretical implication of SSL embedding space, we leverage a Masked Autoencoder [8] for feature extraction. Furthermore, we evaluate data quality based on the marginal change of the largest singular value after excluding the data point in the dataset. We conduct extensive experiments on a pathology dataset. Our results indicate the effectiveness and efficiency of our proposed methods for selecting the most valuable data to label.
Verifying the Quality of Outsourced Training on Clouds

Deep learning training is often outsourced to clouds due to its high computation overhead. However, clouds may not perform model training correctly due to the potential violations on Service Level Agreement (SLA) and attacks, incurring low quality of outsourced training. It is challenging for customers to understand the quality of outsourced training on clouds. They cannot measure the quality by simply testing the trained models because the testing performance is impacted by various factors, e.g., the quality of training and testing data. In order to address these issues, in this paper, we propose a novel framework that allows customers to verify the quality of outsourced training without modifying the processes of model training. Particularly, our framework achieves black-box verification by utilizing an extra training task that can be learned by the model only after the model converges on the original training task. We construct well-designed extra training tasks according to the original tasks, and develop a training quality verification method to measure the model performance on the extra task with a hypothesis testing-based threshold. The experiment results show that the models passing the quality verification achieve at least 96% of their best performance with negligible accuracy loss, i.e., less than 0.25%.
Virtual Reality in E-commerce: A Study

Online shopping has made the hassles of stepping out of homes to purchase items a thing of the past. With various organizations running their businesses online and with the onset of the pandemic, relying on e-commerce sites has simplified our lives. Although these applications provide details of products through the use of images and text, consumers are still wary and skeptical about the quality or fit of a product before making the purchase. In this work, we have discussed the use of virtual reality (VR) in these applications, to create a shopping environment for the consumers from the comfort of their homes. We have also proposed an architecture framework to implement VR on e-commerce. We provide a use case of online shopping which involves a product such as apparels along with certain future research directions.
Implementation of Open Domain Question Answering System

The development of an open domain question answering system that can automatically find and give the answer for any question in the given context is always an open challenge for the researcher. This paper discusses a web-based application that accepts a media file from the user and generates a span of context for the same. Then the users can pose any open domain question which gets mapped with the context and an appropriate answer is provided as output. SQuAD 2.0 dataset is used for training the model. The pre-trained models of ALBERT with different parameter sizes are implemented to perform the media file content context matching with the input question and the most optimized one is selected as an output. To measure the performance of the system, the Exact Matching (EM) score and F1 score are calculated.
Application of Watershed Algorithm in Digital Image Processing

Segmentation of image is the method of separating objects from its background. It is helpful finding and deciding about which pixel belong to which objects. It is the process of making pixels of every region has similar visual characteristics. The watershed algorithm provides a complementary approach to the segmentation of an object. It is essential for segmenting objects where they touch their boundaries. Watershed is a dividing ridge between drainage areas. In digital image processing, the banks are the watershed lines and the drainage areas are catchment basins. Watershed is the representation of the grayscale image as topographic relief. Watersheds with adjacent catchment basins are being built during the repeated flooding of the gray value relief. This flooding method is performed on the gradient image. The magnitude of gradient value is intensely sensitive to image noise. So noise value plays a vital role in watershed processing. In computer-world watershed is a classical algorithm used for image segmentation. In this paper, we will witness the approaches of the watershed transformation in an image with a proper analysis of its advantages over other methods.
Estimation of Rayleigh flat channel coefficients using deep learning

Abstract Channel estimation is a significant prerequisite in wireless communication, especially where the multipath radio propagation incurs significant fading in a noisy environment. In such a scenario, Rayleigh fading model is traditionally adopted to represent the communication channel. In this article, a new method of getting the Rayleigh flat channel coefficients using deep learning is presented. Here, we presume that the channel state and the corresponding channel coefficients remain constant in a given communication context which depends on the locations of transmitter/receiver, time of the day and the communication environment. When the context changes, the channel state also changes and the corresponding coefficients switch to the respective matching values. Thus the channel coefficients can have several possible realizations or classes. In our scheme, the deep learning network, after training, acts as a classifier to detect the class or the context of the channel state and based on that determines the corresponding channel coefficients. In our proposed method, the percentage reduction in the percentage error is about 26% of that of its nearest competitor when the channel SNR is 10 dB.
Verifiable dynamic search over encrypted data in cloud‐assisted intelligent systems

The cloud-assisted intelligent systems have attracted extensive attention due to their powerful data analysis and computation capabilities. However, how to handle encrypted data remains a challenging problem in intelligent systems. A promising solution is searchable symmetric encryption (SSE), which enables a client to privately outsource their data to the cloud while preserving keyword search functionality. In practice, dynamic SSE is more practical and supports efficient data addition and deletion. Unfortunately, data update will leak some additional information which can be exploited to break data privacy. To address this issue, forward and backward secure SSE schemes are proposed to reduce the leakage of data update. That is, forward security guarantees that the newly updated documents cannot reveal the previously searched keywords, while backward security guarantees that the server cannot recover the deleted documents. However, the existing forward and backward secure SSE schemes mainly consider curious-but-honest server. How to verify the soundness and completeness of search results is still a challenge. In this paper, we propose a noninteractive verifiable dynamic SSE scheme with forward and backward security from two universal accumulators. Specifically, the server in our scheme only needs one roundtrip to return the nondeleted search results to the client, which saves the communication overhead dramatically. Besides, our scheme can achieve public verification that anyone can verify the search results but not only the client who has the private key. Finally, we give a formal security analysis and compare the proposed scheme with other related work, the results show that our scheme can achieve the desired security properties with practical efficiency.
Regularized online tensor factorization for sparse knowledge graph embeddings

Abstract Knowledge Graphs represent real-world facts and are used in several applications; however, they are often incomplete and have many missing facts. Link prediction is the task of completing these missing facts from existing ones. Embedding models based on Tensor Factorization attain state-of-the-art results in link prediction. Nevertheless, the embeddings they produce can not be easily interpreted. Inspired by previous work on word embeddings, we propose inducing sparsity in the bilinear tensor factorization model, RESCAL, to build interpretable Knowledge Graph embeddings. To overcome the difficulties that stochastic gradient descent has when producing sparse solutions, we add $$l_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>l</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> regularization to the learning objective by using the generalized Regularized Dual Averaging online optimization algorithm. The proposed method substantially improves the interpretability of the learned embeddings while maintaining competitive performance in the standard metrics.
Multimodal Super Resolution with Dual Domain Loss and Gradient Guidance

Spatial resolution plays a crucial role in quantitative assessment of various structures in brain MRI. Super resolution (SR) as a post-processing tool holds promise for restoring the high frequency details lost in a low resolution (LR) acquisition with no additional scan time. Prior multicontrast deep learning SR approaches are mostly in 2D and operate in a pre-upsampling or progressive setting. Here we propose an efficient shallow 3D projection based post-upsampling network for anisotropic SR of brain MRI. The network is optimized using losses in the spatial and frequency domains and a complementary high resolution (HR) input to inform SR of the low resolution (LR) input with tighter integration of features. We investigated the benefit of different feature aggregation strategies such as concatenation and multiplicative attention and gradient guidance from the HR target or the additional HR input. The models were trained and evaluated on diverse datasets and performed comparably with MINet, another recently developed multimodal SR model, with approximately half the number of model parameters. The model generalized well to an external test set; performed satisfactorily on acquired LR MRI volumes despite the LR input being simulated from HR volumes during training and resulted in lower high frequency error norm. From the ablation studies, we note that a multimodal network noticeably improves SR compared to a unimodal network and feature aggregation using concatenation and multiplicative attention performed equally well. We also highlight the leakage of information from the complementary HR input to the SR output volume and the limited value of PSNR and SSIM as evaluation metrics in such cases.