Reviewerly/exHarmony huggingface dataset:
Learning-based Gaussian Mixture Reduction for Distributed Bayesian Filter

Based on the Wiener approximation theorem, any distribution can be expressed or approximated by the finite sum of the known Gaussian distribution, so it has been widely used in many scenes. When one applies Gaussian mixture distribution (GMD) to state estimation, an urgent problem to be solved is that the number of Gaussian components will increase exponentially over time, which brings difficulties to the practical application. Therefore, it is particularly important to design appropriate methods to reduce the number of Gaussian components in the application of state estimation and keep the computational complexity at a feasible level. This paper proposes a learning-based improved Expectation Maximization (EM) Gaussian Mixture Reduction (GMR) method. This method is a multi-step optimization method, which can reduce the number of Gaussian components at the expense of a small amount of computational complexity. Finally, the proposed improved EM GMR method is applied to the target tracking scene of the distributed Bayesian filter. Simulation results show that compared with traditional algorithms, the proposed learning-based method can obtain a high estimation accuracy with a low computational cost.
Holder-of-key threshold access token for anonymous data resources

Centralized identity and access management providers (IdAM) are a source of mistrust when deploying federated services. The access token is the main piece that carries a trusted signature between the IdAM and the third-party service. Our holder-of-key access token proposal aims to reduce the risk of token forgery from the IdAM by decentralizing the generation process via (t, n)-threshold cryptography. Nonetheless, implicit consent routes are still a requirement under current legislation; and, non-encrypted personal records are useful and many times required to the third-party service provider. Our token scheme and architecture can grant access to pseudonymised resources via explicit or implicit consents. The access token is publicly verifiable and is bound to a specific pseudonym and secret key. The token has no information that can disclose the true identity behind the pseudonym. The scheme is proven secure under reasonable assumptions and scalable from the experimental results.
Bijective Multi-mode Deraining on Single Image

Since rain streaks distribute non-uniformly in most real-world rain images, to avoid over-deraining or under-deraining in local rain image, we design a rain-density distribution dependent rain model. Base on the model, we propose a two-stage deraining network to remove rain streaks appropriately. In stage-I, we propose a Bijective Attention Multi-mode Deraining Network(BAMDN), including a Residual Streak-detail Attention Module(RSAM) to get the attention mask of the rain streak, removes rain streaks in patches with the determined rain-density label. In stage-II, we recombine the patches and propose an Attention Global Restoring Network(AGRN) including Attention Network-II(AN-II), which provides the details on the junctions of adjacent deraining patches to further derain and recover the lost details in stage-I. Comprehensive experiments on synthetic datasets and real-world datasets demonstrate the effectiveness of our method.
Finite element analysis of a 2D cantilever on a noisy intermediate‐scale quantum computer

Abstract Quantum computing is a promising new computing paradigm that may yield both significant speedup over classical algorithms as well as new ways to think about problems and finding novel solution algorithms. While large‐scale error‐corrected quantum computers are still under development, a selection of noisy intermediate‐scale quantum processing units is readily made available by some companies via cloud access. Despite the lack of error correction, these units can be used to test established quantum algorithms on custom problem setups. Here, we solve the finite element problem of a two‐dimensional cantilever, completely fixed on one side and loaded on the opposite side, on a 15‐qubit QPU from IBM.
Implementation of the hamming code on pascalabc.net while studying the theoretical foundations of informatics

Theoretical Foundations of Informatics is a classic branch of discrete mathematics taught to students in various information, mathematical and technical fields. The presentation of the material is mainly carried out using matrix algebra. This article describes a methodology for teaching the topic of error correcting coding to pedagogical university students as part of the course on theoretical foundations of informatics and considers an algorithm for obtaining a Hamming code through a tabular template. An original implementation of the considered algorithm in PascalABC. NET is proposed. When writing code, modern techniques are used: dynamic arrays, slices, safe slices, conditional (ternary) operation, foreach loop, sequence methods, lambda expressions, tuples, documenting comments, etc. The functions of the School module from the official delivery of the PascalABC.NET compiler are used to work with binary numbers. The described methodology has been tested in teaching 4-year students of the pedagogical direction, the profile "Mathematics and Informatics" of the Orenburg State Pedagogical University and has shown its applicability. In addition, it is possible to use the proposed approach for teaching schoolchildren at a specialized level or in extracurricular work.
Efficient Onboarding and Management of Members in Permissioned Blockchain Networks Utilizing TLS Certificates

Consortia blockchain networks face the issue of expanding their systems to new members. Onboarding processes are often cumbersome, as they require identifying the new participant, manually setting up rights, exchanging key material, and adding information about the new member to the consensus smart contract. Besides that, these processes are time-consuming and scale poorly. Identifying the members might be faulty as the pre-existing members might be deceived by malicious parties claiming to be someone else. This paper proposes a novel methodology to allow the onboarding of new parties without time-intensive off-chain processes. We establish identities of new consortia members by utilizing TLS certificates bound to publicly known domain names. With this identity scheme in place, the network operators can define rules such as only specific parties are allowed to join the network, e.g., only owners of *.edu domains. This methodology scales well, provides for extensive ruling and monitoring, and helps consortia blockchains to grow faster.
Multi-pedestrian tracking based on hierarchical Siamese Neural Network

This paper proposes a multi-pedestrian tracking method which can effectively track multiple pedestrians in complex and occluded environments. Pedestrian similarity metrics are calculated utilizing a hierarchical Siamese Neural Network (SNN) to provide similarity scores for candidate pedestrian image regions throughout a time-series of images. We calculate inter-frame pedestrian motion affinity using a difference method. This is merged with the similarity metrics to form a Pedestrian Correlation (PeC) term. The PeC results are processed by the principle of maximum correlation, to achieve pedestrian classification and obtain an identity category for each of several pedestrians. This categorization enables each pedestrian to be associated with different tracklets. A second SNN is then used to calculate similarity metrics for sets of small tracklets in a sliding temporal window. Additionally, tracklet motion affinities, based on mean tracklet velocity, are merged with the tracklet similarity metrics to form a Tracklet Correlation (TrC) term. Finally, the TrC results are also processed by a pedestrian classification method, which categories each tracklet by assigning it to a particular pedestrian. All tracklets for each pedestrian category are then merged to create an overall trajectory for each individual pedestrian. We evaluate our method on two publicly available multi-pedestrian tracking datasets, PETS2009- S2L1 and Town Center, which contain complex environments and pedestrian occlusions. Our method significantly outperforms eight other state-of-the-art trackers from the literature, in terms of tracking accuracy on these datasets.
Design and Implementation of Real-time Face Changing based on wrapGAN Model

This research aims to design and implement real-time generation of the target face in the monocular camera to achieve face change using Image-to-Image Translation with Conditional Adversarial Nets. The goal is to perform deep learning based on two sets of image domains and obtain the mapping relationship between input images and output images. However, for many face-changing tasks with specific identities, matching training data cannot be provided. The result realizes the acquisition and production of specific character data sets. The annotation process is reduced by using the modified wrapGAN network model for training and finally pruning the GAN model to leave the generative model. Then, real-time tracking of the face through the monocular camera is realized and the target face according to the generative model is generated. The model in this article retains user identity information, not relying on traditional 3dmm. It uses the wrapGAN model as a general solution for face changing. The data set comes from the network, of which 80% is used for training and 20% is used for testing. Compared with the previous methods, the superiority of our method is proved.
Communicating Performance of Regression Models Using Visualization in Pharmacovigilance

Statistical regression methods can help pharmaceutical organizations improve the quality of their pharmacovigilance by predicting the expected quantity of adverse events during a trial. However, the use of statistical techniques also changes the risk profile of any downstream tasks, due to bias and noise in the model's predictions. That risk profile must be clearly understood, documented, and communicated across many different stakeholders in a highly regulated environment. Aggregated performance metrics such as explained variance or mean average error fail to tell the whole story, making it difficult for subject matter experts to feel confident in deciding to use a model. In this work, we describe guidelines for communicating regression model performance for models deployed in predicting adverse events. First, we describe an interview study in which both data scientists and subject matter experts within a pharmaceutical organization describe their challenges in communicating and understanding regression performance. Based on the responses in this study, we develop guidelines for which visualizations to use to communicate performance, and use a publicly available trial safety database to demonstrate their use.
Movie Rating Estimation Based on Weakly Supervised Multi-modal Latent Variable Model

In this paper, we present a method to estimate movie ratings based on a weakly supervised multi-modal latent variable model. One movie has multiple movie features (e.g., key-frames and descriptions) and one rating annotated by a user. Features included in one movie are not one-to-one correspondence with the rating, and general latent variable models cannot calculate latent variables under such a multiple-instance condition. To solve this problem, we propose a weakly supervised multi-modal label dequantized GPLVM (WmLDGP). WmLDGP can calculate latent variables by estimating label features for each scene based on a label dequantization scheme. The main contribution is introduction of the label dequantization scheme to the multiple-instance condition. Experimental results show the effectiveness of our model.
Smart Waste Segregation

The amount of waste generated is increasing at a rapid rate. This is due to the growth in population which in turn leads to urbanization and economic development. According to an estimate, the world generates 2.01 billion tonnes of solid waste annually. By 2050, the expected growth in the amount of waste generated will be increased to 3.04 billion. Many countries, at present are facing lots of issues regarding the proper collection, segregation, and disposal of the solid waste generated. Improper methods adopted for the same can lead to various environmental hazards and can impact the health of the inhabitants. The economic value of waste is realized only after it is segregated. As the slogan states “Recycle today, for a better tomorrow”, the most important step in recycling waste is its proper segregation. This is better utilized if the segregation is at the source itself. In the present scenario, there is no one-stop solution that segregates solid waste into categories such as glasses, plastics, metals, and wet waste at the source of their generation itself. Most of the solid waste namely, glass and plastics are recyclable. Therefore, the proposed solution aims to segregate the wastes into the aforementioned categories with the help of various sensors available and automate the process of segregation, thereby reducing the manpower required for segregation which in turn reduces the occupational hazards of the manual workers.
Research on the Application of Machine Learning Technology in the Automatic Recognition System of Engineering Graphics

Based on the analysis of electrical diagram processing, combined with image recognition technology, this article discusses the key to electrical drawing scanning input and recognition-vectorization processing. At the same time, we propose a machine learning algorithm that converts binary dot matrix graphics into vector graphics. Analyze the stitching of multiple graphics. This article provides an important basis for the rapid input of large-format drawings. Many experiments have verified that various algorithms have strong adaptability and anti-interference ability against tracing paper.
A Holistic Systems Approach to Leveraging Heterogeneity

Increasingly, HPC developers are turning to heterogeneity to continue to achieve the performance they desire. Leveraging heterogeneity however is challenging. While an increasing number of applications are starting to gain advantage from heterogeneity, there remains much work before it sees widespread productive use.We believe that a holistic systems approach encompassing both hardware and software is the best path towards productively leveraging heterogeneity. In this paper, we describe the key attributes of a successful hardware approach from a node and system perspective. At the node level, it is important to have components that are pluggable and easily combined in a tightly-coupled manner. From the systems perspective, it is important to ensure resources can be composed in a manner that minimizes unused ("stranded") resources.We describe the importance of a complementary software approach that provides a single development environment, high-lighting the value of software being able to handle heterogeneity from node through system. We detail the vision of oneAPI that addresses these challenges, the resultant programming model, and the advantages for applications.
Communication cost reduction using sparse ternary compression and encoding for FedAvg

Nowadays, Federated Learning (FL), a training paradigm in which data are stored locally and used to train model on client devices has emerged thanks to the growing computational power of client devices as well as the concern about transmitting private information. In FL, multiple parties jointly train a model with their local dataset. By this way, privacy of clients' data is kept. However, FL has to face to communication cost during training as the model and model weights have to be sent to server to update the global model. Several methods have been proposed to address this issue. In this work, we propose a model weight compression and encoding during model uploading for Federated Averaging (FedAvg) - a widely used framework in FL. Our weight compression is inspired by Sparse Ternary Compression algorithm with a modification to be applicable to FedAvg. We also utilize compressed weights' characteristics to encode them hence the communication cost can be reduced. The experimental results on an image classification task with MNIST dataset demonstrate that our method is able to reduce the communication cost without considerably worsening the model accuracy.
Image Analysis of Ventriculomegaly and White Matter Deterioration in Alzheimer's disease

Upgradation in imaging technology has facilitated advanced healthcare provisions in various fields of medicine. Alzheimer's disease (AD) is one such ailment caused by the decaying of brain cells. For early detection, it is necessary to consider MRI (magnetic resonance imaging) of different sections of the brain. In our research, we analyse biomarkers like ventriculomegaly and white matter deterioration in AD brains in comparison to the cognitively normal (CN) brain using the Image Processing Toolbox in MATLAB. Image segmentation and image batch processing is applied to a wide range of image sets and have been used to get a comprehensive result based on the area of the region of interest.
Clustering Optimization using VWK-Means-ALO for Grouping 20 kV Feeders Based on The Reliability Index of Electric Power Distribution System

K-Means is a simple and computationally efficient clustering algorithm. However, K-Means has some weaknesses; i.e., the number of k clusters must be determined by the user; the initial centroid is determined randomly; cannot handle outlier data and the contribution of all variables in cluster formation is treated the same in the clustering process. This research proposes VWK-Means-ALO to optimize the clustering model. Optimization is performed by determining the number of k clusters with the Dunn Index, processing the initial centroid with the antlion optimizer, and using variable weighting in the clustering process. The research was conducted using a 20 kV feeder dataset from an electric power distribution system. The proposed method performance is compared with VWK-Means and K-Means-ALO based on the average intracluster and intercluster distances.
Rotation transformation-based selective ensemble of one-class extreme learning machines

Extreme learning machine (ELM) possesses merits of rapid learning speed and good generalization ability. However, due to the random initialization of connection weights, the network outputs of ELM are usually unstable. Similar to ELM, one-class ELM (OCELM) also has the disadvantage of output instability. To enhance the stability and generalization performance of OCELM, a selective ensemble of OCELMs based on rotation transformation is proposed. First, principal component analysis (PCA)-based rotation transformation is utilized to construct different transformed training sets. Furthermore, several component OCELMs are trained independently on these training sets. Second, a dissimilarity measure based on angle cosine is used to evaluate the dissimilarity between each pair of OCELMs. The diversity of each component OCELM in the obtained ensemble can be further achieved. Thereafter, the component OCELMs with lower value of diversity are removed from the original ensemble. Finally, the voting strategy is utilized to determine that testing samples belong to the target class or the non-target class. Experimental results on 15 UCI benchmark data sets and one handwritten digit data set show that the proposed method is superior to its related approaches.
ITLCS Based on OpenCV Image Processing Technology

Abstract With the rapid growth of the number of motor vehicles in the city, traffic congestion is more serious every day, part of it is caused by the coding delay with the red light on, not real traffic jams, now we need a control system that can really change the traffic flow. In this paper, ITLCS (intelligent traffic signal control system) based on OpenCV image processing technology is proposed to adjust the timing of traffic signal according to road density, instead of setting a level that is balanced with other lanes, so that high-load traffic lanes can be used for a long time. The camera facing the roadway in the system takes pictures of the driving route, then takes pictures of the driving density of pedestrians and vehicles, and compares each image through processing technology, after the system is processed, the traffic light signal timing can be adjusted immediately, which greatly reduces the time spent on the inactive green light and can effectively deal with the traffic congestion problem.
Defense Against Image Captioning Attacks via A Robust and Stable Recurrent Neural Network

This paper proposes a defense method against image captioning attacks via a robust and stable recurrent neural network (RNN). The improved RNN is constructed based on the dynamical system theory. The recurrent unit in this RNN has a very high ability to express hidden states. From the perspective of the theory, the improved RNN has global exponential stability. Then we conduct noun-type attack experiments on image captioning systems for comparing a modified vanilla RNN with the improved RNN, respectively. The experimental results fully illustrate the effectiveness and robustness of the improved RNN for the image captioning system.
A Modified Minibatch Sampling Method for Parameter Estimation in Hidden Markov Models using Stochastic Variational Bayes

Abstract Parameter estimation using stochastic variatonal Bayes (SVB) under a mean field assumption can be carried out by sampling a single data point at each iteration of the optimization algorithm. However, when latent variables are dependent like in hidden Markov models (HMM), a larger sample is required at each iteration to capture that dependence. We describe a minibatch sampling procedure for HMMs where the emission process can be segmented into independent and identically distributed blocks. Instead of sampling a block and using all elements within it, we divide the block into subgroups and sample subgroups from different blocks using simple random sampling with replacement. Simulation results are provided for an HMM for precipitation data, where each block of 90 days represents 3 months of wet season data. SVB based on the proposed sampling method is shown to provide parameter estimates comparable with existing methods.
A Study on Video Streaming Quality of DASH Scheme in Multimedia Services

In data communication networks and the Internet, there is a growing need for more transmission resources due to the massive and increasing consumption of video transmission services. An alternative to reduce this requirement is the application of video coding such as MPEG-DASH. In this study, an evaluation of the performance of the DASH mechanism over streaming video is performed. For this, the structural similarity index is used, a WAN distribution scenario is implemented through the WANem platform and the procedure is validated with a quality of experience (QoE) evaluation model. The results obtained show a high efficiency of DASH coding in video transmission.