Reviewerly/exHarmony huggingface dataset:
Simple Three-Dimensional Motion Measurement System using Marker-IMU System

Image-based motion capture system have a limited measurement range, and the inertial motion capture system cannot directly acquire the position information. In addition, simple and robust measurement is required in the realization field, but it is difficult with the conventional motion capture system. Therefore, in this research, we constructed a system that robustly measures human movements by combining images and IMUs. High-accuracy visual markers were used to measure the position and orientation by images. By combining this with an IMU, we have established a robust measurement method even for hiding. In addition, by using the environmental reference marker, it is possible to acquire the absolute position even if the camera moves. In fact, we measured the movement and walking movements of human upper limbs, and realized continuous and smooth movement measurement by this method.
Implementation of Background Subtraction for Counting Vehicle Using Mixture of Gaussians with ROI Optimization

There is an imbalance between the ratio of the number of vehicles of 11% and the addition of new roads or road extensions of 0,01%, especially in Jakarta, Indonesia, which is often an issue that causes traffic problems, one of them is traffic jam. This paper discusses an implementation of a video surveillance system-based method to monitor traffic conditions such as detection, tracking and counting of vehicles in the form of information technology in the form of system simulation using a computer.The objective of this research is the implementation of a video surveillance based system that can detect, track and count the number of vehicles using an image processing method approach. The approach used in this research is Mixture of Gaussians (MOG2) for background subtraction with optimization of Region of Interests (ROI). There are four stages in this method, namely pre-processing, vehicle tracking, vehicle counting, and ROI optimization. The results were obtained in the form of accuracy which is divided into two conditions, namely in the morning and in the daytime. For accuracy, this system has a capability of 86% in the morning and 94,1% in the daytime with each video duration of 30 seconds. This system simulation can be used as a reference for traffic-related bureaus to help manipulate traffic.
Spatial search on Johnson graphs by discrete-time quantum walk

Abstract The spatial search problem aims to find a marked vertex of a finite graph using a dynamic with two constraints: (1) the walker has no compass and (2) the walker can check whether a vertex is marked only after reaching it. This problem is a generalization of unsorted database search and has many applications to algorithms. Classical algorithms that solve the spatial search problem are based on random walks and the computational complexity is determined by the hitting time. On the other hand, quantum algorithms are based on quantum walks and the computational complexity is determined not only by the number of steps to reach a marked vertex, but also by the success probability, since we need to perform a measurement at the end of the algorithm to determine the walker’s position. In this work, we address the spatial search problem on Johnson graphs using the coined quantum walk model. Since Johnson graphs are vertex- and distance-transitive, we have found an invariant subspace of the Hilbert space, which aids in the calculation of the computational complexity. We have shown that, for every fixed diameter, the asymptotic success probability is 1/2 after taking <?CDATA $\pi \sqrt{N}/(2\sqrt{2})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>π</mml:mi> <mml:msqrt> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:msqrt> <mml:mo>/</mml:mo> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mn>2</mml:mn> <mml:msqrt> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msqrt> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> steps, where N is the number of vertices of the Johnson graph.
Differentially Private Ensemble Classifiers for Data Streams

Learning from continuous data streams via classification/regression is prevalent in many domains. Adapting to evolving data characteristics (concept drift) while protecting data owners' private information is an open challenge. We present a differentially private ensemble solution to this problem with two distinguishing features: it allows anunbounded number of ensemble updates to deal with the potentially never-ending data streams under a fixed privacy budget, and it ismodel agnostic, in that it treats any pre-trained differentially private classification/regression model as a black-box. Our method outperforms competitors on real-world and simulated datasets for varying settings of privacy, concept drift, and data distribution.
Strategies in Jpeg Compression Using Convolutional Neural Network(Cnn)

Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly in minimization of information used for the representation of images. With the advances of deep neural networks, image compression can be achieved to a higher degree. This paper describes an overview of JPEG Compression, Discrete Fourier Transform (DFT), Convolutional Neural Network (CNN), quality metrics to measure the performance of image compression, and discuss the advancement of deep learning for image compression mostly focused on JPEG, and suggests that adaptation of model improve the compression.
DeepDiffusion: Unsupervised Learning of Retrieval-Adapted Representations via Diffusion-Based Ranking on Latent Feature Manifold

Unsupervised learning of feature representations is a challenging yet important problem for analyzing a large collection of multimedia data that do not have semantic labels. Recently proposed neural network-based unsupervised learning approaches have succeeded in obtaining features appropriate for classification of multimedia data. However, unsupervised learning of feature representations adapted to content-based matching, comparison, or retrieval of multimedia data has not been explored well. To obtain such retrieval-adapted features, we introduce the idea of combining diffusion distance on a feature manifold with neural network-based unsupervised feature learning. This idea is realized as a novel algorithm called DeepDiffusion (DD). DD simultaneously optimizes two components, a feature embedding by a deep neural network and a distance metric that leverages diffusion on a latent feature manifold, together. DD relies on its loss function but not encoder architecture. It can thus be applied to diverse multimedia data types with their respective encoder architectures. Experimental evaluation using 3D shapes and 2D images demonstrates versatility as well as high accuracy of the DD algorithm. Code is available at https://github.com/takahikof/DeepDiffusion
Realtime Global Attention Network for Semantic Segmentation

In this letter, we proposed an end-to-end realtime global attention neural network (RGANet) for the challenging task of semantic segmentation. Different from the encoding strategy deployed by self-attention paradigms, the proposed global attention module encodes global attention via depthwise convolution and affine transformations. The integration of these global attention modules into a hierarchical architecture maintains high inferential performance. In addition, an improved evaluation metric, namely MGRID, is proposed to alleviate the negative effect of non-convex, widely scattered ground-truth areas. Results from extensive experiments on state-of-the-art architectures for suction region segmentation manifest the leading performance of proposed approaches for robotic monocular visual perception.
Generic Construction for Identity-Based Proxy Blind Signature

Generic constructions of blind signature schemes have been studied since its appearance. Several constructions were made leading to generic blind signatures and achieving other properties such as identity-based blind signature and partially blind signature. We propose a generic construction for identity-based Proxy Blind Signature ( $$\mathsf {IDPBS}$$ ). This combination of properties has several applications in the real world, in particularly in e-voting or e-cash systems and it has never been achieved before with a generic construction. Our construction only requires two classical signatures schemes: a blind EUF-CMA blind signature and a SUF-CMA unique signature. The security of our generic identity-based proxy blind signature is proven under these assumptions.
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients

Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-decentralized FEEL (SD-FEEL) architecture where multiple edge servers collaborate to incorporate more data from edge devices in training. Despite the low training latency enabled by fast edge aggregation, the device heterogeneity in computational resources deteriorates the efficiency. This paper proposes an asynchronous training algorithm to overcome this issue in SD-FEEL, where edge servers are allowed to independently set deadlines for the associated client nodes and trigger the model aggregation. To deal with different levels of model staleness, we design a staleness-aware aggregation scheme and analyze its convergence. Simulation results demonstrate the effectiveness of our proposed algorithm in achieving faster convergence and better learning performance than synchronous training.
Differentially private data publication with multi‐level data utility

Conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive data. Such data publication schemes implicitly assume that all data analysts and users have the same data access privilege levels. However, it is not applicable for the scenario that data users often have different levels of access to the same data, or different requirements of data utility. The multi-level privacy requirements for different authorization levels pose new challenges for private data publication. Traditional PPDP mechanisms only publish one perturbed and private data copy satisfying some privacy guarantee to provide relatively accurate analysis results. To find a good tradeoff between privacy preservation level and data utility itself is a hard problem, let alone achieving multi-level data utility on this basis. In this paper, we address this challenge in proposing a novel framework of data publication with compressive sensing supporting multi-level utility-privacy tradeoffs, which provides differential privacy. Specifically, we resort to compressive sensing (CS) method to project a n-dimensional vector representation of users’ data to a lower m-dimensional space, and then add deliberately designed noise to satisfy differential privacy. Then, we selectively obfuscate the measurement vector under compressive sensing by adding linearly encoded noise, and provide different data reconstruction algorithms for users with different authorization levels. Extensive experimental results demonstrate that ML-DPCS yields multi-level of data utility for specific users at different authorization levels.
Collision Avoidance Using Spherical Harmonics

In this paper, we propose a novel optimization-based trajectory planner that utilizes spherical harmonics to estimate the collision-free solution space around an agent. The space is estimated using a constrained over-determined least-squares estimator to determine the parameters that define a spherical harmonic approximation at a given time step. Since spherical harmonics produce star-convex shapes, the planner can consider all paths that are in line-of-sight for the agent within a given radius. This contrasts with other state-of-the-art planners that generate trajectories by estimating obstacle boundaries with rough approximations and using heuristic rules to prune a solution space into one that can be easily explored. Those methods cause the trajectory planner to be overly conservative in environments where an agent must get close to obstacles to accomplish a goal. Our method is shown to perform on-par with other path planners and surpass these planners in certain environments. It generates feasible trajectories while still running in real-time and guaranteeing safety when a valid solution exists.
Design of Network Education Platform Based on Cloud Computing Technology

With a quick growth of the Internet, all walks of life are striving to integrate with the increasingly mature “cloud computing” (CC) model. Modern online distance education has not only greatly expanded the time and space of education, but also caused the reorganization of the education system and the redistribution of resources. It is a very promising distance education model. This article is based on cloud computing technology, and the purpose of designing an online education platform (EP) is to solve the current shortage of talents and insufficient capital resources in most urban and rural education informatization. In this regard, this study uses a combination of theoretical analysis and empirical research to select participating teachers as interview subjects to understand their feelings when using the online training platform for learning. It analyzes and summarizes the influential factors, and then give students and teachers who use the online training platform questionnaire surveys and uses the returned results of the questionnaire as the source of data analysis for the empirical research part. Experimental research results show that 91.3% of people are accustomed to using the internet for learning, so online EP are expected to continue to develop.
Deformable Convolution Based No-Reference Stereoscopic Image Quality Assessment Considering Visual Feedback Mechanism

Simulation of human visual system (HVS) is very crucial for fitting human perception and improving assessment performance in stereoscopic image quality assessment (SIQA). In this paper, a no-reference SIQA method considering feedback mechanism and orientation selectivity of HVS is proposed. In HVS, feedback connections are indispensable during the process of human perception, which has not been studied in the existing SIQA models. Therefore, we design a new feedback module (FBM) to realize the guidance of the high-level region of visual cortex to the low-level region. In addition, given the orientation selectivity of primary visual cortex cells, a deformable feature extraction block is explored to simulate it, and the block can adaptively select the regions of interest. Meanwhile, retinal ganglion cells (RGCs) with different receptive fields have different sensitivities to objects of different sizes in the image. So a new multi receptive fields information extraction and fusion manner is realized in the network structure. Experimental results show that the proposed model is superior to the state-of-the-art no-reference SIQA methods and has excellent generalization ability.
Method for extracting the full-field dispersion data of birefringent objects based on the digital RGB photoelasticity

In this paper, we are presenting a new photoelastic pattern analysis technique to find the point-wise distribution of the dispersion data of birefringent materials. This technique can be applied to profile the dispersion data in three dimensions during static, online, real-time, and dynamic investigations using the well-known digital photoelasticity technique. This method is applied to characterize the dispersion parameters of monofilament isotactic polypropylene fibers. The photoelastic patterns are included for illustration.
AMoC: A Multifaceted Machine Learning-based Toolkit for Analysing Cybercriminal Communities on the Darknet

There is an increasing demand for expert analysis of cybercriminal communities. Cybercrime is continually becoming more complex due to the rapid development of digital technologies, on the one hand, in new types of criminal activity, such as hacking, distributing malware and DDoS attacks, and on the other hand, in digitised forms of more traditional crimes, such as email scams, phishing, identity theft, and cryptographically secured black markets. Tackling this broad array of behaviour requires tool support for multi-disciplinary investigations, and a connecting framework that can adjust flexibly to changes in the populations being studied. In this work, we present AMoC, a multi-faceted machine learning toolkit that combines structured queries, anomaly detection, social network analysis, topic modelling and accounts recognition to enable comprehensive analysis of cybercriminal communities and users. The toolkit enables the extraction of findings regarding the motivations, behaviour and characteristics of offenders, and how cybercriminal communities react to interventions such as arrests and take-downs. In our demonstration, the toolkit is deployed to analyse over 150,000 accounts from 35 underground marketplaces.
Tulip: An Authenticated Encryption Algorithm with Trusted Identity

In this paper, the authenticated encryption algorithm (called Tulip) is integratedlydesigned based on the straightforward combination of CFB mode and CBC-MAC mode of blockcipher. CFB mode is called once to generate ciphertext, and CBC-MAC mode is called twice to process trusted identity and ciphertext in order to generate authentication code. The algorithm is suitable for short message authenticated encryption. The authenticated encryption algorithm only uses the encryption mode of block cipher, and only needs one circuit for hardware implementation. Theoretical analysis shows that if the block cipher is pseudo-random permutation, and the adversary is nonce-respecting, the privacy and authenticity of the authenticated encryption algorithm will be provably secure.
A Machine Learning Consensus Based Light-Weight Blockchain Architecture for Internet of Things

Blockchain is considered as an important technique for maintaining the integrity of data in enterprises. However, the computationally intensive proof-of-work forms a major bottleneck in adopting blockchains to energy-constrained environments such as Internet of Things (IoT). In this paper, we propose a Machine learning Consensus based Light-weight Blockchain (MCLB) for resource-constrained edge devices in IoT to detect malicious data besides providing the consensus for maintaining the integrity of data. Our light-weight approach reduces the overhead by eliminating the nonce of traditional blockchains, thereby bringing down the delay in arriving at a consensus. Each edge device is equipped with a machine learning algorithm to classify data and the predicted classifications are used to arrive at the consensus. The use of different machine learning algorithms at the edge nodes improves the robustness of our system. Our framework is realized on a sensor network consisting of four edge devices and the results show that MCLB outperforms existing blockchain models in terms of computational delay, communication overhead, and block addition delay.
A Less Traditional Approach to Biomedical Signal Processing for Sepsis Prediction

Most of the data generated by monitors in a clinical setting represent time series data which can be visualized and subsequently used for decision making. This usually is the simplest part. A more challenging aspect is using this data for more complex task like machine learning with the same goal – computer assisted decisions. Within this challenge raw biomedical signal data need to be preprocessed before being passed to the machine learning algorithm. This can be done by a multitude of methods. A number of such methods comes from the field of Algorithmic Complexity and although of a promising nature, these particular methods are poorly explored yet. The current research presents an example of applying the Block Decomposition Method to data routinely generated by patients in a modern Intensive Care Unit. The final goal of a larger research, the actual research being part of, is building a system for early sepsis prediction.
Views Meet Labels: Personalized Relation Refinement Network for Multiview Multilabel Learning

Multiview multilabel (MVML) learning deals with objects with diverse feature vectors and rich semantics. Existing methods are built on a shared latent space among multiple views. However, they do not well capture semantic consistency and view interactions. What’s more, they neglect different contributions of each view to multilabel learning. To address these issues, a novel Personalized RElation rEfinement Network (PREEN) is proposed to sufficiently exploit associated relationships. First, common and specific information is learned in an adversarial way to prevent them from interfering with each other. Then, we adapt the standard transformer to capture cross-view interactions. Similarly, we design label-specific transformers to model label-view dependence, which associates each label with relevant views separately. Eventually, we develop an interlabel attention mechanism to exploit label correlations and refine complementary information from other labels dynamically. Extensive experiments on one real and seven public MVML datasets validate the effectiveness of our proposed PREEN.
Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

In existing methods for segmented images, either edge point extraction or preservation of edges, compromising contrast images is so sensitive to noise.The Degeneration Threshold Image Detection (DTID) framework has been proposed to improve the contrast of edge filtered images.Initially, DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images.This filter decomposes input images into base layers in the DTID framework.With minimal filtering time, Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation.In the DTID framework, Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise.This Shift-Invariant Filtering estimates value across edges for removing outliers (i.e., noise preserving base layers of the contrast image).The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter (SIDF).At last, Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image.It normalizes the translation and rotation of images.With this, Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy, rate, and filtering time on contrast images.Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54% and improves average contrast enhancement quality by 27% compared to GUMA, HMRF, SWT, and EHS.It provides better performance on the enhancement of average contrast enhancement quality by 28%, detection accuracy rate by 26%, and reduction in filtering time taken on contrast images by 30% compared to state-of-art methods.