Reviewerly/exHarmony huggingface dataset:
PointRend Segmentation for a Densely Occluded Moving Object in a Video

Multimedia information retrieval segmenting of non-semantic and general objects is one of the important task. In particular video object segmentation has been added one more component called temporal, from this temporal component our objective is to find pixels which corresponding to the object in every consecutive frames. In these consecutive frame keenly observes its correlation and segment the instances without overlapping the neighbour objects values or boundary pixels, in this paper, we have elaborated on it. The key technique of our approach is to using PointRend classical subdivision techniques for point estimations and preserve the frame resolution. We will discuss a powerful method for segmenting the most densely occluded objects using PointRend segmentation in video, and we will show how we achieve competitive results on standard benchmark databases.
Dynamic threshold ECDSA signature and application to asset custody in blockchain

The centralized exchange is one of the hottest DeFi applications based on blockchain transaction systems. However, depositing user assets to the exchanges brings the security risks of assets misappropriation. Threshold cryptosystem can effectively solve the drawbacks of centralized hosting by assigning the assets authorization to multiple trustees, but the collusion attack generated by malicious trustees is still unavoidable. In this paper, we propose a new dynamic threshold ECDSA signature scheme which is compatible with current blockchain transaction system. It realizes distributed custody of assets in exchanges, and further achieves a dynamic mechanism allowing user join and drop out to resist collusion attacks. Specifically, we formalize the definition of this system architecture and give its construction based on basic cryptography modules such as ECDSA signature, distributed key generation, and distributed computation. Analysis and experiment results show that our scheme holds protocol security and is more efficient than other threshold ECDSA signature schemes when threshold is less than 200, which makes it applicable to the assets custody scenarios of exchanges.
How HCI Interprets Service Design: A Systematic Literature Review

The scope of Human-Computer Interaction (HCI) research is expanding with regard to the studied systems and stakeholders, and its impact areas. Service design has recently gained tractions in HCI as an approach to deal with these expansions. However, there has been confusion around the definitions and roles of service design in HCI, especially with its overlaps and differences with interaction design. To examine how HCI has adopted service design, this paper presents results from a systematic literature review on 52 papers from the most cited HCI publication venues. Our findings show that the adoption of service design concepts and methods in HCI has been sporadic over the past decade. The term service design has been interpreted as a variety of meanings. The most predominantly observed understandings include service design as a term for designing digital services instead of products, and as an approach providing a journey and system perspective to the design of social computing, Internet of Things, or other complex systems. Only a few studies adopted the fundamental logic of new value exchange or co-creation of systems from service design. We discuss the reasons behind the differing interpretations of service design by HCI and future opportunities for HCI to better benefit from service design.
The Research on Applying Artificial Intelligence Technology to Virtual YouTuber

Nowadays, Virtual YouTuber with its unique characteristics has achieved high popularity in the world. But at the same time, the technology used by the Virtual YouTuber also has a lot of room to improve. For example, today's Live2D technology can't show all the details of characters and perform all kinds of actions just like 3D models, when the cost of 3D modeling of both characters and backgrounds is much higher than 2D. If artificial intelligence technology can be applied to Virtual YouTuber, it is possible to solve these problems. Therefore, this paper explores the possibility of applying artificial intelligence technology to Virtual YouTuber from three aspects: character, motion capture and environment. This paper first proposes to use PIFuHD to transform 2D characters into 3D, then discusses how to use a single camera to achieve motion capture, and finally studies the stylization of background animation. Although there is still a certain distance to realize real low-cost Virtual YouTuber, with the progress of technology, there is still a lot of space to apply artificial intelligence to this industry.
A Generative Adversarial Framework For Optimizing Image Matting And Harmonization Simultaneously

Image matting and image harmonization are two important tasks in image composition. Image matting, aiming to achieve foreground boundary details, and image harmonization, aiming to make the background compatible with the foreground, are both promising yet challenging tasks. Previous works consider optimizing these two tasks separately, which may lead to a sub-optimal solution. We propose to optimize matting and harmonization simultaneously to get better performance on both the two tasks and achieve more natural results. We propose a new Generative Adversarial (GAN) framework which optimizing the matting network and the harmonization network based on a self-attention discriminator. The discriminator is required to distinguish the natural images from different types of fake synthesis images. Extensive experiments on our constructed dataset demonstrate the effectiveness of our proposed method. Our dataset and dataset generating pipeline can be found in https://git.io/HaMaGAN.
SFA-MDEN: Semantic-Feature-Aided Monocular Depth Estimation Network Using Dual Branches

Monocular depth estimation based on unsupervised learning has attracted great attention due to the rising demand for lightweight monocular vision sensors. Inspired by multi-task learning, semantic information has been used to improve the monocular depth estimation models. However, multi-task learning is still limited by multi-type annotations. As far as we know, there are scarcely any large public datasets that provide all the necessary information. Therefore, we propose a novel network architecture Semantic-Feature-Aided Monocular Depth Estimation Network (SFA-MDEN) to extract multi-resolution depth features and semantic features, which are merged and fed into the decoder, with the goal of predicting depth with the support of semantics. Instead of using loss functions to relate the semantics and depth, the fusion of feature maps for semantics and depth is employed to predict the monocular depth. Therefore, two accessible datasets with similar topics for depth estimation and semantic segmentation can meet the requirements of SFA-MDEN for training sets. We explored the performance of the proposed SFA-MDEN with experiments on different datasets, including KITTI, Make3D, and our own dataset BHDE-v1. The experimental results demonstrate that SFA-MDEN achieves competitive accuracy and generalization capacity compared to state-of-the-art methods.
Comparative analysis of acquisition methods in digital forensics

This paper presents a comparative analysis of acquisition methods in digital forensic. The author compares the data acquisition and memory acquisition with the help of the Forensic toolkit imager (FTK imager) and ProDiscover tool is open source digital forensic tools. By taking some perimeter for comparison that is generally related to the data acquisition and memory acquisition. In this paper, data acquisition of a 32GB Pen drive is made with the help of both the tools, and the result of the data is taken and captured, which is also presented in this paper with the help of screenshot, and memory acquisition of physical memory (4GB RAM) is also made, and the result is captured and presented with the help of screenshot in this paper. By comparing acquisition methods with the help of these two tools, the author gets that the one tool is better than the other on certain perimeters and the other tool is also better than the first tool in some manner, which will help in the future to the researchers while they are using these two tools.
An efficient perturbation approach for multivariate data in sensitive and reliable data mining

Due to the rapid enhancement of technology, cloud data is increasing rapidly which contains individuals’ sensitive information such as medical diagnostics reports. While extracting knowledge from those sensitive data, both privacy of individuals’ and the utility of data should be preserved which is a crucial concern in data mining related activities. Though therein exist several methods to preserve privacy, a single method can not maintain the harmony interim privacy and data utility. Often achieving individuals’ privacy leads to the loss of the data utility and the opposite is true also. To address the vital issue, a four-stage data perturbation approach, called NRoReM, is proposed in this work based on normalization, geometric rotation, linear regression, and scalar multiplication for sensitive data mining. The proposed approach is experimented with over ten UCI data set using three benchmark classifiers. The empirical exploration of privacy protection, attack resistance, information entropy analysis, data utility, and error analysis exhibits that NRoReM preserves both privacy of individuals’ and data utility on a larger scale for 90% of the data set than 3-Dimensional Rotation Transformation (3DRT) and 2-Dimensional Rotation Transformation (2DRT).
Research and Application of Multifeature Gesture Recognition in Human-Computer Interaction Based on Virtual Reality Technology

Objective. To explore the research and application of multifeature gesture recognition in virtual reality human-computer interaction and to explore the gesture recognition technology scheme to achieve better human-computer interaction experience. Methods. Through the study of the technical difficulties of gesture recognition, comparative static gesture feature recognition and feature fusion algorithms are applied, in the process of research on gesture partition, and adjust the contrast of characteristic parameters, combined with the feature of space-time dynamic gesture tracking trajectory and dynamic gesture recognition and gesture recognition effect under different scheme. Results. The central region was divided into 0 regions, and the central region was divided into 1-4 regions in counterclockwise direction. Compared with the traditional gesture changes, the overlapping problem in the four partition modes was reduced, the gesture was better displayed, and the operation and use of gesture processing were realized more efficiently. Conclusion. Gesture recognition requires the combination of static gesture feature information recognition, gesture feature fusion, spatiotemporal trajectory feature, and dynamic gesture trajectory feature to achieve a better human-computer interaction experience.
Higher-order probabilistic adversarial computations: categorical semantics and program logics

Adversarial computations are a widely studied class of computations where resource-bounded probabilistic adversaries have access to oracles, i.e., probabilistic procedures with private state. These computations arise routinely in several domains, including security, privacy and machine learning. In this paper, we develop program logics for reasoning about adversarial computations in a higher-order setting. Our logics are built on top of a simply typed λ-calculus extended with a graded monad for probabilities and state. The grading is used to model and restrict the memory footprint and the cost (in terms of oracle calls) of computations. Under this view, an adversary is a higher-order expression that expects as arguments the code of its oracles. We develop unary program logics for reasoning about error probabilities and expected values, and a relational logic for reasoning about coupling-based properties. All logics feature rules for adversarial computations, and yield guarantees that are valid for all adversaries that satisfy a fixed resource policy. We prove the soundness of the logics in the category of quasi-Borel spaces, using a general notion of graded predicate liftings, and we use logical relations over graded predicate liftings to establish the soundness of proof rules for adversaries. We illustrate the working of our logics with simple but illustrative examples.
Generalized Zero-Shot Recognition Through Image-Guided Semantic Classification

We present a new visual-semantic embedding method for generalized zero-shot learning. Different to existing embedding-based methods that learn the correspondence between an image classifier and its class prototype for each class, we learn the mapping between an image and its semantic classifier. Given an input image, the proposed method creates a label classifier and applies it to all label embeddings to determine whether a label belongs to the input image. Therefore, a semantic classifier is image conditioned and is generated during inference. We validate our approach with four standard benchmark datasets.
Machine-Learning Based Secondary Transform for Improved Image Compression in JPEG2000

This paper proposes a convolutional neural network (CNN) based secondary transform for not only improved coding efficiency in the JPEG2000 image compression format, but also to produce more appealing approximation sub-bands at different resolutions. The CNN in this work exploits information in detail sub-bands to predict some of the aliasing information in the corresponding approximation or low-pass sub-band; this reduction in aliasing, although not perfect, improves the compressibility of the “cleaned” approximation subband. This process is repeated in subsequent wavelet decomposition levels to further improve coding efficiency. Experimental results show that, at high bit rates, the proposed network outperforms conventional JPEG2000 compression framework by up to 1.2 dB, especially for images with strong geometric flow.
Design and modeling of multi-spectral polarimetry streak tube imaging LiDAR

A new Multi-Spectral Polarimetry Streak Tube Imaging LiDAR (MSP-STIL) system is proposed, with mathematical model of the system established and verified. Multiple experiments are conducted before drawing conclusions. Intensity images and degree of polarization (DOP) images at three wavelengths and a depth image of the target area can be obtained by the single imaging mode of the system, and Mueller matrices at three wavelengths of the target area can be obtained by the twice imaging mode of the system. Meanwhile, the proposed system utilizes the fiber optic delay device to alleviate the contradiction of traditional streak tube imaging LiDAR system among the large number of echo signals, the depth resolution and the depth of field. Therefore, the proposed system can perform multi-spectral polarimetry imaging and three-dimensional imaging with higher depth resolution and larger depth of field. Compared with the traditional single-wavelength streak tube imaging LiDAR system, the image contrast of the target area can be improved by fusing the spectral information, polarization information and three-dimensional information obtained by the proposed system, and the high-contrast image makes it easier to distinguish the target.
Smart Pointers and Shared Memory Synchronisation for Efficient Inter-process Communication in ROS on an Autonomous Vehicle

Despite the stringent requirements of a real-time system, the reliance of the Robot Operating System (ROS) on the loopback network interface imposes a considerable overhead on the transport of high bandwidth data, while the nodelet package, which is an efficient mechanism for intra-process communication, does not address the problem of efficient local inter-process communication (IPC). To remedy this, we propose a novel integration into ROS of smart pointers and synchronisation primitives stored in shared memory. These obey the same semantics and, more importantly, exhibit the same performance as their C++ standard library counterparts, making them preferable to other local IPC mechanisms. We present a series of benchmarks for our mechanism - which we call LOT (Low Overhead Transport) - and use them to assess its performance on realistic data loads based on Five’s Autonomous Vehicle (AV) system, and extend our analysis to the case where multiple ROS nodes are running in Docker containers. We find that our mechanism performs up to two orders of magnitude better than the standard IPC via local loopback. Finally, we apply industry-standard profiling techniques to explore the hotspots of code running in both user and kernel space, comparing our implementation against alternatives.
Privacy Preserving Association Rule Mining on Distributed Healthcare Data: COVID-19 and Breast Cancer Case Study

Association rule mining can be used in healthcare data mining to provide solutions to life-threatening diseases like recent COVID-19. Due to healthcare data privacy concerns, privacy preserving distributed healthcare data mining becomes the primary focus of medical science research. Recently, Chahar et al. (Sādhanā 42:1997–2007, 2017) proposed privacy preserving distributed association rule mining scheme with insecure communication channels. They used the concept of an elliptic curve-based paillier cryptosystem to achieve privacy, authenticity, and integrity. We observed some security vulnerabilities in their privacy preserving association rule mining scheme when implemented with insecure communication channels. We observed that the security vulnerabilities will result in the disclosure of private data of sites (or participants). Furthermore, we propose a secure version of their scheme to solve the security vulnerabilities with insecure communication channels. Theoretical and experimental analysis shows that the proposed scheme has almost equal computation and communication complexities with better securities. A case study on the effectiveness of the proposed approach in combating COVID-19 coronavirus and Breast Cancer is also discussed.
GoSeed: Optimal Seeding Plan for Deduplicated Storage

Deduplication decreases the physical occupancy of files in a storage volume by removing duplicate copies of data chunks, but creates data-sharing dependencies that complicate standard storage management tasks. Specifically, data migration plans must consider the dependencies between files that are remapped to new volumes and files that are not. Thus far, only greedy approaches have been suggested for constructing such plans, and it is unclear how they compare to one another and how much they can be improved. We set to bridge this gap for seeding —migration in which the target volume is initially empty. We prove that even this basic instance of data migration is NP-hard in the presence of deduplication. We then present GoSeed, a formulation of seeding as an integer linear programming (ILP) problem, and three acceleration methods for applying it to real-sized storage volumes. Our experimental evaluation shows that, while the greedy approaches perform well on “easy” problem instances, the cost of their solution can be significantly higher than that of GoSeed’s solution on “hard” instances, for which they are sometimes unable to find a solution at all.
Identification of a Set of Variables for the Classification of Páramo Soils Using a Nonparametric Model, Remote Sensing, and Organic Carbon

Páramo ecosystems harbor important biodiversity and provide essential environmental services such as water regulation and carbon sequestration. Unfortunately, the scarcity of information on their land uses makes it difficult to generate sustainable strategies for their conservation. The purpose of this study is to develop a methodology to easily monitor and document the conservation status, degradation rates, and land use changes in the páramo. We analyzed the performance of two nonparametric models (the CART decision tree, CDT, and multivariate adaptive regression curves, MARS) in the páramos of the Chambo sub-basin (Ecuador). We used three types of attributes: digital elevation model (DEM), land use cover (Sentinel 2), and organic carbon content (Global Soil Organic Carbon Map data, GSOC) and a categorical variable, land use. We obtained a set of selected variables which perform well with both models, and which let us monitor the land uses of the páramos. Comparing our results with the last report of the Ecuadorian Ministry of Environment (2012), we found that 9% of the páramo has been lost in the last 8 years.
A Fuzzy-Based Adaptation Controller For Low Latency Live Video Streaming

HTTP adaptive streaming with chunked transfer encoding can provide low latency live video streaming experience. However, providing high and smooth quality video streaming experience under low latency conditions poses a great challenge for Adaptive BitRate (ABR) algorithm design due to the shorter time to react to bandwidth fluctuations. To address the problem, we design a Fuzzy logic controller based ABR for low latency live video streaming with Chunked Transfer Encoding (FCTE). We take player buffer size, throughput mean, and throughput standard deviation as inputs to make bitrate decisions. By making full use of network information and dealing with uncertainties using fuzzy language, FCTE can make quick and robust bitrate decisions in the complicated network environment. We evaluate FCTE over five network scenarios released by Twitch. FCTE achieves average QoE improvement by 129%, 338%, and 127% compared with three representative algorithms: L2A, LoL, and Stallion, respectively.
A constraint-based algorithm for the structural learning of continuous-time Bayesian networks

Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention. In this paper, we propose the first implementation of a constraint-based algorithm for learning the structure of continuous-time Bayesian networks. We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence. Furthermore, we analyze and discuss the computational complexity of the best and worst cases for the proposed algorithm. Finally, we validate its performance using synthetic data, and we discuss its strengths and limitations comparing it with the score-based structure learning algorithm from Nodelman et al. [23]. We find the latter to be more accurate in learning networks with binary variables, while our constraint-based approach is more accurate with variables assuming more than two values. Numerical experiments confirm that score-based and constraint-based algorithms are comparable in terms of computation time.
An Algorithm for the Sequence Alignment with Gap Penalty Problem using Multiway Divide-and-Conquer and Matrix Transposition

We present a cache-efficient parallel algorithm for the sequence alignment with gap penalty problem for shared-memory machines using multiway divide-and-conquer and not-in-place matrix transposition. Our r−way divide-and-conquer algorithm, for a fixed natural number r≥2, performs Θ(n3) work, achieves Θ(nlogr⁡(2r−1)) span, and incurs O(n3/(BM)+(n2/B)log⁡M) serial cache misses for n>γM, and incurs O((n2/B)log⁡(n/M)) serial cache misses for αM<n≤γM, where, M is the cache size, B is the cache line size, and α and γ are constants.