Reviewerly/exHarmony huggingface dataset:
Accurate and Serialized Dense Point Cloud Reconstruction for Aerial Video Sequences

Traditional multi-view stereo (MVS) is not applicable for the point cloud reconstruction of serialized video frames. Among them, the exhausted feature extraction and matching for all the prepared frames are time-consuming, and the scope of the search requires covering all the key frames. In this paper, we propose a novel serialized reconstruction method to solve the above issues. Specifically, a joint feature descriptors-based covisibility cluster generation strategy is designed to accelerate the feature matching and improve the performance of the pose estimation. Then, a serialized structure-from-motion (SfM) and dense point cloud reconstruction framework is designed to achieve high efficiency and competitive precision reconstruction for serialized frames. To fully demonstrate the superiority of our method, we collect a public aerial sequences dataset with referable ground truth for the dense point cloud reconstruction evaluation. Through a time complexity analysis and the experimental validation in this dataset, the comprehensive performance of our algorithm is better than the other compared outstanding methods.
Is embodied interaction beneficial? A study on navigating network visualizations

Network visualizations are commonly used to analyze relationships in various contexts. To efficiently explore a network visualization, the user needs to quickly navigate to different parts of the network and analyze local details. Recent advancements in display and interaction technologies inspire new visions for improved visualization and interaction design. Past research into network design has identified some key benefits to visualizing networks in 3D versus 2D. However, little work has been done to study the impact of varying levels of embodied interaction on network analysis. We present a controlled user study that compared four environments featuring conditions and hardware that leveraged different amounts of embodiment and visual perception ranging from a 2D visualization desktop environment with a standard mouse to a 3D visualization virtual reality environment. We measured the accuracy, speed, perceived workload, and preferences of 20 participants as they completed three network analytic tasks, each of which required unique navigation and substantial effort. For the task that required participants to iterate over the entire visualization rather than focus on a specific area, we found that participants were more accurate using a VR and a trackball mouse than conventional desktop settings. From a workload perspective, VR was generally considered the least mentally demanding and least frustrating in two of our three tasks. It was also preferred and ranked as the most effective and visually appealing condition overall. However, using VR to compare two side-by-side networks was difficult, and it was similar to or slower than other conditions in two of the three tasks. Overall, the accuracy and workload advantages of conditions with greater embodiment in specific tasks suggest promising opportunities to create more effective environments in which to analyze network visualizations.
SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network

Data publishing techniques have led to breakthroughs in several areas. These tools provide a promising direction. However, when they are applied to private or sensitive data such as patient medical records, the published data may divulge critical patient information. In order to address this issue, we propose a differential private data publishing method (SSKM_DP) based on the SFLA-Kohonen network, which perturbs sensitive attributes based on the maximum information coefficient to achieve a trade-off between security and usability. Additionally, we introduced a single-population frog jump algorithm (SFLA) to optimize the network. Extensive experiments on benchmark datasets have demonstrated that SSKM_DP outperforms state-of-the-art methods for differentially private data publishing techniques significantly.
Information set supported deep learning architectures for improving noisy image classification

Deep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures with four variants are developed by integrating information set theory and deep learning principles to address the critical problem of the absence of robust deep learning models. There is a description of the ISDL architectures, learning algorithms, and analytic workflows. The performance of the ISDL models and standard architectures is evaluated using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures.
Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations

We propose gradient-based simulation-optimization algorithms to optimize systems that have complicated stochastic structure. The presence of complicated stochastic structure, such as the involvement of infinite-dimensional continuous-time stochastic processes, may cause the exact simulation of the system to be costly or even impossible. On the other hand, for a complicated system, one can sometimes construct a sequence of approximations at different resolutions, where the sequence has finer and finer approximation resolution but higher and higher cost to simulate. With the goal of optimizing the complicated system, we propose algorithms that strategically use the approximations with increasing resolution and higher simulation cost to construct stochastic gradients and perform gradient search in the decision space. To accommodate scenarios where approximations cause discontinuities and lead path-wise gradient estimators to have an uncontrollable bias, stochastic gradients for the proposed algorithms are constructed through finite difference. As a theory support, we prove algorithm convergence, convergence rate, and optimality of algorithm design under the assumption that the objective function for the complicated system is strongly convex, whereas no such assumptions are imposed on the approximations of the complicated system. We then present a multilevel version of the proposed algorithms to further improve convergence rates, when in addition the sequence of approximations can be naturally coupled. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1279 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0289 ) at ( http://dx.doi.org/10.5281/zenodo.7485443 ).
Localized Simple Multiple Kernel K-Means Clustering with Matrix-Induced Regularization

Multikernel clustering achieves clustering of linearly inseparable data by applying a kernel method to samples in multiple views. A localized SimpleMKKM (LI-SimpleMKKM) algorithm has recently been proposed to perform min-max optimization in multikernel clustering where each instance is only required to be aligned with a certain proportion of the relatively close samples. The method has improved the reliability of clustering by focusing on the more closely paired samples and dropping the more distant ones. Although LI-SimpleMKKM achieves remarkable success in a wide range of applications, the method keeps the sum of the kernel weights unchanged. Thus, it restricts kernel weights and does not consider the correlation between the kernel matrices, especially between paired instances. To overcome such limitations, we propose adding a matrix-induced regularization to localized SimpleMKKM (LI-SimpleMKKM-MR). Our approach addresses the kernel weight restrictions with the regularization term and enhances the complementarity between base kernels. Thus, it does not limit kernel weights and fully considers the correlation between paired instances. Extensive experiments on several publicly available multikernel datasets show that our method performs better than its counterparts.
PSSGAN: Towards spectrum shift based perceptual quality enhancement for fluorescence imaging

Fluorescence imaging has demonstrated great potential for malignant tissue inspection. However, poor imaging quality of medical fluorescent images inevitably brings challenges to disease diagnosis. Though improvement of image quality can be achieved by translating the images from low-quality domain to high-quality domain, fewer scholars have studied the spectrum translation and the prevalent cycle-consistent generative adversarial network (CycleGAN) is powerless to grasp local and semantic details, leading to produce unsatisfactory translated images. To enhance the visual quality by shifting spectrum and alleviate the under-constraint problem of CycleGAN, this study presents the design and construction of the perception-enhanced spectrum shift GAN (PSSGAN). Besides, by introducing the constraint of perceptual module and relativistic patch, the model learns effective biological structure details of image translation. Moreover, the interpolation technique is innovatively employed to validate that PSSGAN can vividly show the enhancement process and handle the perception-fidelity trade-off dilemma of fluorescent images. A novel no reference quantitative analysis strategy is presented for medical images. On the open data and collected sets, PSSGAN provided 15.32% ∼ 35.19% improvement in structural similarity and 21.55% ∼ 27.29% improvement in perceptual quality over the leading method CycleGAN. Extensive experimental results indicated that our PSSGAN achieved superior performance and exhibited vital clinical significance.
Probabilistic knowledge infusion through symbolic features for context-aware activity recognition

In the general machine learning domain, solutions based on the integration of deep learning models with knowledge-based approaches are emerging. Indeed, such hybrid systems have the advantage of improving the recognition rate and the model’s interpretability. At the same time, they require a significantly reduced amount of labeled data to reliably train the model. However, these techniques have been poorly explored in the sensor-based Human Activity Recognition (HAR) domain. The common-sense knowledge about activity execution can potentially improve purely data-driven approaches. While a few knowledge infusion approaches have been proposed for HAR, they rely on rigid logic formalisms that do not take into account uncertainty. In this paper, we propose P-NIMBUS, a novel knowledge infusion approach for sensor-based HAR that relies on probabilistic reasoning. A probabilistic ontology is in charge of computing symbolic features that are combined with the features automatically extracted by a CNN model from raw sensor data and high-level context data. In particular, the symbolic features encode probabilistic common-sense knowledge about the activities consistent with the user’s surrounding context. These features are infused within the model before the classification layer. We experimentally evaluated P-NIMBUS on a HAR dataset of mobile devices sensor data that includes 14 different activities performed by 25 users. Our results show that P-NIMBUS outperforms state-of-the-art neuro-symbolic approaches, with the advantage of requiring a limited amount of training data to reach satisfying recognition rates (i.e., more than 80% of F1-score with only 20% of labeled data).
Non-Parallel Voice Conversion Using Cycle-Consistent Adversarial Networks with Self-Supervised Representations

Numerous voice conversion techniques using non-parallel data have been presented. Among these, there are many algorithms related to style transfer. This is because the voice conversion problem can be determined as a style transfer problem, where the linguistic and speaker information can be regarded as domains and styles, respectively. Here, the group of CycleGAN-VC series has considerable achievement, and thus we examine the feasibility of CycleGAN-VC for self-supervised representations. In other words, we incorporate analysis features extracted from wav2vec into the CycleGAN-VC model. Objective experiments showed that the quality of the converted speech is comparable to that of the original speech, and the source speech was successfully transformed into the voice of the target speech while preserving the linguistic information.
On the Decentralization of Health Systems for Data Availability: a DLT-based Architecture

Mobile devices entered people's lives by leaps and bounds, offering various applications relying on private thirdparty entities to manage their users' data.Centralized control of personal health data endangers the privacy of the users directly involved.In the future, there will likely be a trend toward decentralizing the health data collection, relieving central entities of this task.This comes with several challenges in a decentralized environment, such as avoiding a single point of failure to guarantee data availability.The following work proposes an architecture based on Distributed Ledger Technology to allow users to decide on their data while ensuring availability by employing social networks.We will outline the mechanisms behind data storage and the implications of using smart contracts in the architecture.In concluding the work, we show the developed architecture and results deriving from its assessment, highlighting possible use cases applied to the specific health data management context.
Edge-Assisted Multi-User 360-Degree Video Delivery

Viewport-based 360-degree video delivery is a typical method to reduce video traffic for virtual reality (VR) applications. However, viewport-based solutions cause some key issues in multi-user VR applications, including large video traffic volumes owing to redundant video transmission across multiple headset users and quality degradation owing to view-port transition. In this study, we propose a 360-degree video delivery scheme for multi-user VR applications. To overcome the abovementioned issues, the proposed approach includes JPEG-XS-based video recompression at the edge server to follow the viewport transition and hybrid unicast and multicast tile delivery to prevent redundant transmissions. Moreover, we present the results of evaluations using 360-degree video and corresponding fixation points of multiple users to show that the proposed scheme prevents redundant transmissions across multiple headset users and provides better video quality for each user than existing solutions for the same video traffic.
I See What You're Watching on Your Streaming Service: Fast Identification of DASH Encrypted Network Traces

In recent years, concerns about the privacy of users data have raised as testified by the wide adoption of the HTTPS protocol over its unencrypted predecessor. This work demonstrates, however, that the encryption used in HTTPS does not guarantee that the user's data is hidden when streaming videos using the DASH protocol. We show that the encryption can be bypassed by exploiting recognizable and predictable patterns produced by DASH in side-channels. To demonstrate our attack, we have collected 100k fingerprints from the SVT Play streaming platform, and have shown that encrypted videos can reliably and quickly be identified by capturing streamed HTTPS traffic and comparing it against the fingerprint database. Compared with previous work, our evaluation demonstrates the superior accuracy in our approach as well as its capacity of swiftly identify videos that are playing from an arbitrary timestamp. Our prototype is, to the best of our knowledge, the fastest and most accurate video streaming recognizer to date, only requiring as little as 12 seconds of network traffic to infer a video title with more than 98% accuracy among a catalogue of 20k videos. Our results call for future updates in the DASH protocol designed to circumvent the privacy leak we have shown. An open-source implementation of our prototype is publicly available at https://github.com/embeage/streaming-identification.
Online Federated Learning based Object Detection across Autonomous Vehicles in a Virtual World

Federated Learning (FL) enables collaborative training of machine learning models for edge devices (e.g., mobile phones) over a network without revealing raw data of the participants. The existing FL benchmarks mostly assume static distribution of local data over time failing to capture the behavior of real-world applications with space-time varying data (e.g., autonomous cars). Our framework addresses this limitation by leveraging popular open source physics simulator (CARLA) and FL framework (OpenFL) to allow collection of streaming data from the mobile agents and feeding them to a practically deployable FL engine for online collaborative training. It also provides the FL researchers with the ability to model data heterogeneity, annotate data with practically zero cost, and perform reproducible continual FL experiments. We believe that this is one of the first attempts to demonstrate online FL on realistic streaming datasets from a virtual world. The demo showcases this framework using a popular object detection use case.
Hide &amp; Seek: Traffic Matrix Completion and Inference Using Hidden Information

Traffic matrices are used for many network management operations, from planning to repairing. Despite years of research on the topic, their estimation and inference on the Internet are still challenging and error-prone. For example, missing values are unavoidable due to flaws in the measurement systems and possible failure in data collection systems. It is thus helpful for many network operators to recover the missing data from the partial direct measurements. Some existing matrix completion methods do not fully consider network traffic behavior and hidden traffic characteristics, showing the inability to adapt to multiple scenarios. Others instead make assumptions on the matrix structure that may be invalid or impractical, curtailing the applicability. In this paper, we propose Hide & Seek, a novel matrix completion and prediction algorithm based on a combination of generative autoencoders and Hidden Markov Models. We demonstrate with an extensive experimental evaluation on real-world datasets how our algorithm can accurately reconstruct missing values while predicting their short-term evolution.
Version++: Cryptocurrency Blockchain Handshaking With Software Assurance

Cryptocurrency software implements the cryptocurrency operations, including the distributed consensus protocol and the peer-to-peer networking. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight due to the dynamic nature of the peer connections and the large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. In addition, the Version++ verification overhead is small compared to the overall handshaking process; our measured overhead of 2.22% with minimal networking latency between the virtual machines provides an upper bound in the real-world networking with greater handshaking duration, i.e., the relative Version++ overhead in the real world with physically separate machines will be smaller.
Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection.
Compressing Model before Federated Learning by Transferrable Surrogate Lottery Ticket

Federated learning (FL) is a promising technique to collaboratively train a model with distributed users and datasets. To develop communication-efficient FL systems, model-size reduction by using a winning ticket of the lottery ticket hypothesis has been proposed and investigated; however, it is still an issue how to discover winning tickets in FL systems, which incur communication costs for model training to find the winning ticket. To address this issue, we propose a method of using a surrogate dataset that can be synthetically generated at an FL server, instead of the distributed target datasets found at the clients, for finding the winning ticket. The method is based on observations that winning tickets obtained from a large dataset could be transferable to other tasks. The performance evaluations using a subset of the FEMNIST (as target) and Chars 74K (as emulated surrogate) datasets show that the proposed method reduces communication cost in FL by about 80%.