Reviewerly/exHarmony huggingface dataset:
Parameter estimation of mixed two distributions based on EM algorithm and Nelder-Mead algorithm

As the amount of data increases, the data can obey either a single distribution, two distributions or even multiple distributions. If the distribution parameters and mixing ratio can be estimated for mixed data, it will be beneficial to further analysis and research in practical applications. In this paper, the EM algorithm and the Nelder-Mead(NM) algorithm are applied to the mixed parameter estimation of the two distributions, including mixing two identical distributions, mixing two different distributions, and comparing the EM algorithm and the Nelder-Mead algorithm to estimate the accuracy of the mixed distribution parameter estimation and its initial comparison. The stability of the value and other advantages and disadvantages. A large amount of data simulation results found that the EM algorithm has a good effect on the estimation of mixed distribution parameters, with high accuracy and fast convergence. The initial value selection has little effect on the results, and the preliminary derivation process is more complicated; the Nelder-Mead algorithm has a good effect on the estimation of mixed distribution parameters. High precision, fast convergence speed, the initial value selection has a greater impact on the result, the preliminary derivation process is relatively simple, and the application range is wide. When the initial value is close to the peak value of the real data, the parameter estimation effect is better; the two algorithms have poorer effect on the unimodal distribution parameter estimation for the data, and the bimodal distribution parameter estimation effect is better.
A Library for Visualizing Three-Dimensional Non-Euclidean Spaces

Non-Euclidean spaces and geometries are applied throughout many areas of knowledge, namely, physics and mathematics. Some of their properties, however, may be hard to comprehend and perceive, especially by people who are unfamiliar with the subject area. Using computer graphics to visualize such spaces would help to demonstrate various properties of such spaces, and we would also be able to allow users to examine them interactively. A library that is developed by us and that allows visualizing dynamic and interactive scenes in various three-dimensional non-Euclidean spaces is presented in this paper. For the purpose of non-Euclidean visualization, the method based on exploiting non-Euclidean spaces’ transformation groups has been chosen and is described in the paper. This method allows us to define various transformations of objects present in non-Euclidean spaces using matrices similar to those traditionally used in computer graphics to visualize the Euclidean space. One advantage of it is being able to exploit the capabilities of graphics processing units (GPUs). For that purpose, the Direct3D 11 graphics API has been utilized. Additionally, to simplify navigation in such spaces, we implemented the distance fog effect, which makes it easier to differentiate between nearer and farther objects. The library allows visualizing dynamic and interactive scenes in the spherical, elliptic, hyperbolic and toric spaces. Fields of its possible application are briefly discussed, namely, using the library to visualize the Friedmann model of general relativity.
Nearest Neighbor outperforms Kernel-Kernel Methods for Distribution Regression

We study the distribution regression problem assuming the distribution of distributions has a doubling measure larger than one. First, we explore the geometry of any distributions that has doubling measure larger than one and build a small theory around it. Then, we show how to utilize this theory to find one of the nearest distributions adaptively and compute the regression value based on these distributions. Finally, we provide the accuracy of the suggested method here and provide the theoretical analysis for it.
Secure Trajectory Publication in Untrusted Environments: A Federated Analytics Approach

The increasing awareness of privacy and the adoption of data regulations challenge the traditional trajectory publication framework in which a trusted server has access to the raw data from mobile clients. In the new untrusted environment, the clients call for much stronger data privacy preservation locally without sharing their raw data. Based on the emerging paradigm of federated analytics, we propose a Federated Analytics-based Secure Trajectory PUBlication (FASTPub) mechanism to operate in such untrusted environments. Compared with existing local differential privacy (LDP) methods, FASTPub guarantees LDP and loss-bounded <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-anonymity simultaneously with greatly improved data utility. Specifically, FASTPub works interactively between the server and clients and iteratively builds up the trajectory without exposing raw data. Sampled clients only respond to selected trajectory fragments with randomized answers to preserve privacy as much as possible. The server then intelligently aggregates these randomized responses leveraging the intrinsic Apriori property and a Markov independent assumption of trajectory data to guide further iterations. Extensive experiments on synthetic and real-world datasets on two downstream tasks demonstrate that FASTPub gains a remarkably improved data utility compared to the existing state-of-the-art solutions.
GFENet: Enabling Attention and Adaptive Feature Fusion in Real-time Object Detection

Real-time object detection has been a critical component of onboard instrumentation for next-generation autonomous driving. To enable safety and reliability in autonomous driving systems, we must continue advancing its real-time object detection accuracy for future autonomous driving systems in complex environments. Existing works in real-time object detection cannot accurately detect objects with similar appearances and in complex environments. In particular, existing approaches missed many essential features because of the loss of multi-scale contextual features. Therefore, they cannot differentiate objects that have a similar appearance, a low-resolution, and a small size. To solve these issues, and to advance real-time object detection accuracy, we develop GFENet (Global Feature Enhancement Network), an end-to-end real-time object detection framework. First, we introduce a convolutional attention module that integrates spatial and channel-wise features and eliminates ineffective features to enable a strong feature representation of the network, followed by a lightweight residual module that further advances the feature representation by highlighting salient changes in features. Second, we build an adaptive feature fusion module to adaptively learn weights of multi-scale contextual features across layers, aiming to strengthen weights for small-size objects. We comprehensively evaluate GFENet on two leading object detection datasets-PASCAL VOC and MS COCO. The evaluation results (mAP of 82.9 and FPS of 31 on PASCAL VOC; mAP of 39.0 and FPS of 23 on MS COCO) show the outperformance of GFENet compared to competing baseline methods.
Rethinking Language Interaction With Pervasive Applications and Devices

Language-based interaction with pervasive devices today mostly follows a basic command-and-control paradigm, reminiscent of 1960s Star Trek, which is often cumbersome, inadequate, and insufficient. Key causes include an often deceptive portrayal of the language comprehension capabilities of the system and, in many cases, a problematic impersonation of human characters by computers. We argue here that a major challenge of pervasive computing is to rethink language-based interaction with applications and devices. Inspired by the imaginary pervasive conversations of Sci-Fi movies, we suggest a new design principle where machines should only utter statements that they can comprehend, which we call What You Hear Is What You Say (WYHIWYS). We provide some examples of WYHIWYS in language interactions with pervasive applications, including a deployment in an art exhibit, and discuss some key research challenges it poses to HCI, AI, and pervasive computing.
A Digital Filter Bank Based Method for Data Reduction in Spaceborne SAR Electronics

On-board data reduction is a major concern in designing of a spaceborne synthetic aperture radar (SAR) system since the volume of acquired SAR data is generally much higher than the downlink capability to the ground-station. The dechirp-on-receive (DoR) method is considered the state-of-the-art SAR data reduction technique. If there is deramping hardware in the SAR instrument, DoR is an effective method to reduce the volume of images for high resolution modes. In this paper, we use a digital signal processing method which applies a modified discrete Fourier transform (MDFT) filter bank for SAR data reduction. We call this method MDFT band selection filter (MDFT-BSF). This paper presents a quantitative analysis of the MDFT-BSF on data reduction performance. In addition, we provide a relationship between the data reduction performance and the number of sub-bands (M), and show that there is an optimal value of M that minimizes the volume of on-board SAR data. An evaluation of MDFT-BSF including a comparison with the DoR method presents that MDFT-BSF has an advantage for SAR data reduction. We show that MDFT-BSF can be implemented and operated on commercial space-grade FPGAs by the development of a spaceborne SAR digital receiver.
HyperPS: A Virtual-Machine Memory Protection Approach Through Hypervisor's Privilege Separation

The HostOS or Hypervisor constitutes the most important cornerstone of today's commercial cloud environment security. Unfortunately, the HostOS/Hypervisor, especially the QEMU-KVM architecture, is not immune to all vulnerabilities and exploitations. Recently, researchers have put forward lots of schemes to protect Virtual Machines under the compromised HostOS/Hypervisor. However, some of these schemes rely on special hardware facilities, while other (e.g., Nested Virtualization schemes) require large modification to current commercial cloud architecture. In this paper, we present a novel scheme, named HyperPS, to implement virtual machine protection under the compromised HostOS/Hypervisor. The key idea of HyperPS is to deprive the HostOS/Hypervisor of the privileges of managing the physical memory into an isolated and trusted execution environment. HyperPS does not rely customized hardware or extra processor privilege. HyperPS shares the same privilege with the HostOS. We have implemented a fully functional prototype based on the KVM in Intel x86_64 architecture. Experiment results show that HyperPS has achieved an acceptable trade-off between security and performance.
Design and Analysis of Job Assignment in Server Farms of Data Centers

We study the job assignment policy in a data center of diverse processor-sharing servers, and each server has different service rates, energy consumption rates, and buffer sizes. We came up with a job assignment algorithm, called Energy -efficiency available server plus Regularization term takes up Idle energy (ERAIP), that reduces the mean service time while optimizing the energy efficiency. According to Kolmogorov equations which describe the trend of transitions between system states, we prove the optimized performance of a system with two servers. Experiments show that ERAIP can improve energy efficiency while reducing the mean service time.
Data Poisoning Attacks in Internet-of-Vehicle Networks: Taxonomy, State-of-The-Art, and Future Directions

With the unprecedented development of deep learning, autonomous vehicles (AVs) have achieved tremendous progress nowadays. However, AV supported by DNN models is vulnerable to data poisoning attacks, hindering the large-scale application of autonomous driving. For example, by injecting carefully designed poisons into the training dataset of the DNN model in the traffic sign recognition system, the attacker can mislead the system to make targeted misclassification or cause a reduction in model classification accuracy. In this article, we conduct a thorough investigation of the state-of-the-art data poisoning attacks and defenses against AVs. According to whether the attacker needs to manipulate the data labeling process, we divide the state-of-the-art attack approaches into two categories, i.e., dirty-label attacks and clean-label attacks. We also differentiate the existing defense methods into two categories based on whether to modify the training data or the models, i.e., data-based defenses and model-based defenses. In addition to a detailed review of attacks and defenses in each category, we also give a qualitative comparison of the existing attacks and defenses. Besides, we provide a quantitative comparison of the existing attack and defense methods through experiments. Last but not least, we pinpoint several future directions for data poisoning attacks and defenses in AVs, providing possible ways for further research.
A Matrix-based Satellite Cloud Tracking Algorithm

Solar power generation is heavily reliant on the availability of direct solar irradiation. Intermittent appearances of clouds over a solar plant introduce high variability to solar power output and result in high forecast errors. This warrants the study of different cloud patterns. In this paper, we present a novel approach to track different cloud patterns throughout their lifetimes. We define a matrix, called the Association Matrix, to describe the relationship between different clusters and put forth 7 different conditions for identifying the different cloud phenomena. We use the cloud mask data from the INSAT-3D satellite for our analysis and comprehensively describe the operation of the proposed algorithm in this paper.
Asymmetric Generative Adversarial Networks with a New Attention Mechanism

In this paper, a new residual decoding network is proposed to solve the problem that semantic label maps are transformed into real images in image processing tasks, which is a very challenging and difficult task. Since the input semantic label map lacks rich detailed information, it will generate blurry, low-detailed, and color-distorted images during con-version. We propose a new residual decoding network to solve the above problems, calling AsymmetricGAN. Compared with the traditional upsampling network, our proposed new residual module with skip and attention connections can better preserve the information in the original image to avoid the loss of details. We also propose a new module with channel and spatial attention mechanism as the main component of the network which can better retain useful information and make the edges of the synthesized image clearer and more abundant in the details. The experimental results on Cityscapes and ADE20K datasets demonstrate the advantage of AsymmetricGAN over the state-of-the-art approaches, regarding both visual quality and the representative evaluating criteria.
Speeding up Graph Matching by Means of Systematic Graph Reductions Using Centrality Measures

Comparing large graphs using graph edit distance is a time-consuming or even intractable problem. This is mainly due to the exponential time complexity of graph edit distance w.r.t. to the size of the underlying graphs. Nevertheless, considering its great adaptability, graph edit distance is a popular metric in structural pattern recognition and has found widespread applications. Main contribution of the present paper is to substantially speed up the computation of graph edit distance by initially reducing the graphs to the most important substructures. The graph downsizing is based on centrality measures - e.g. Page Rank -. That is, we systematically delete nodes including their edges from our graphs that offer the least level of influence to the overall graph structure. We empirically validate the benefit of our reduction method by classifying reduced graphs from a broad range of applications with a distance-based classifier. The main finding is that we can reduce the computation time by a wide margin while maintaining satisfying classification accuracy.
An Algorithm for Terrain Recognition of Heavy Duty Diesel Vehicles Using Chinese Regulatory Standard Data

An algorithm for terrain recognition of heavy-duty diesel vehicles using Chinese regulatory standard data is proposed in this paper. First of all, clean the data; Then, select and calculate the required eigenvalues according to the different characteristics of diesel vehicles on different terrain; Finally, an improved depth learning network based on multi-headed self-attention is designed to estimate the proportion of driving mileage on different terrain. In addition, a terrain database of heavy diesel vehicles driving roads is constructed. The experimental results show that the proposed algorithm has low computational complexity, universal applicability, and good recognition performance on the constructed terrain database. At present, there is no similar algorithm for terrain recognition of heavy-duty diesel vehicles using Chinese regulatory standards data. The algorithm in this paper is the first in the industry.
Verification Method of Hypothesis Based on Different EEG Data Statistical Processing

The paper describes a method for hypotheses proving based on statistical processing of heterogeneous EEG data by means of mediation analysis. The methodological research basis is a multilevel mediation analysis, in which the independent variable is the emotional stimulus category, the mediator is the spectral power of the brain activity sources, and the dependent variable is the behavioral response to the stimulus. In this article there are described the following methods: primary processing of the obtained data (artifacts removing, unification, interpolation) by the toolset of MATLAB scripts software packages (EEGLAB, Mediation toolbox, SPM12, Neuroelf); localization of EEG-signal sources using the sLoreta package; mediation analysis (multilevel mediation analysis toolbox MATLAB), including regression analysis (linear regression method, SPSS Statistics; statistical analysis by statistical parametric mapping using the SPM12 software package; multivariate analysis (ANOVA method, SPSS Statistics); methods of structural programming for creating scenarios (scripts) of group data processing in the MATLAB software package.
Review of Lighting Systems of Buildings as Active Energy Users

In this paper, a review study on energy efficiency in lighting systems is conducted. The study is a preparation for a case study on the lighting efficiency of education buildings. A review of lighting systems is given. In the review, the educational buildings in the Marmara region of Turkey were primarily investigated. In addition, with the zero energy building approach, how to provide the amount of light required for lighting with the least possible energy according to the requirements in the legislation were explained. The key parameters of lighting educational facilities are introduced.
A correlation filter tracking algorithm with adaptive model update strategy

For the traditional correlation filtering algorithm, the model tracking performance degrades when dealing with deformation, rotation and fast movement, etc. In this paper, we propose a tracking algorithm with multi-feature fusion and adaptive model updating strategy. The algorithm fuses fHOG features and color features with their respective response percentages as weighted features, and adaptively adjusts the learning rate of the model for the next frame according to the magnitude of the mean square difference between the current frame and the previous 5 frames of history. The experimental results show that the average overlap rate and the average accuracy of the algorithm in this paper are high. On the OTB100 dataset, the algorithm in this paper improves 6.3&#x0025; over Staple in terms of accuracy, 10.5&#x0025; over SAMF, 11.9&#x0025; over DCF&#x005F;CA, and 13.9&#x0025; over fDSST. it improves 5.9&#x0025; over Staple, 11.2&#x0025; over SAMF, 19.7&#x0025; over DCF&#x005F;CA, and 11.2&#x0025; over fDSST in terms of overlap rate. It can be seen that this strategy of adaptively updating the model can improve the performance of the algorithm,to some extent,can solve the problems caused by deformation, in-plane rotation out-of-plane rotation, and fast movement to some extent.
Adaptive Multi-tasking Framework for Video Action Proposal Localization

This paper focuses on the improvement of the accuracy of activity detection for multi-camera/extended video stream. Most existing methods typically sample frames from video, which is applied by sliding window method. Action localization in video can be divided into different phases: temporal proposal generation and action classification. In the part of proposal generation stage, most of the works choose the static sampling method, that is, in the evaluation stage, the same sampling rules are followed for any input videos. We figure that there is also guidance information for generating a proposal in the training data. In this paper, We propose a Adaptive Multi-tasking Framework, to deliver proposals according to the input video automatically. For each video, we can first establishes a mapping from visual signals to proposal bounding, the starting and ending frames for the proposal, and then combine the generated proposal with state-of-art model SlowFast to finish the action classification task. The framework in this practice was defined as Adaptive Proposal Generation Network(APGN). We train and test our model on the VIRAT dataset, which consists of real outdoor video with non-actors actions. We hope that the accuracy of activity detection will be enhanced by combining our model with some existing activity detection network which based on the old fashion methods. By testing with SlowFast network, we achieve the improvement of Mean Average Precision(mAP) by more than 10 percent. We believe that by replacing the typical sliding window framework with our proposed framework, other models can enhance accuracy and performance, which we will explore more in the future work.
Methodology for the Synthesis of Acceptable Options for Organizational Functional Structure of the Security Management System of a Significant Object of Critical Information Infrastructure

The technologies inevitably development gives rise to new threats, and requires an approaches revision for both the security systems themselves and their management. A special place in the fight against threats to information security is occupied by the cybersecurity of critical facilities for the country, called critical information infrastructure common objects. The security of critical information infrastructure facilities is provided by heterogeneous means of information protection. This, in turn, creates a paradox in which ensuring the country’s cybersecurity critical facilities is not possible with the information security management system introduction. Such a system should provide the ability to manage heterogeneous information security tools. Any information security management system has a number of limitations. The main limitation in this case is the need to build an automated information security management system that manages a variety of heterogeneous information security tools, possibly incompatible with each other. In this paper, the authors solve the problem of synthesizing acceptable options for the organizational and functional structure of the critical facility security management system, which can ultimately ensure the information security.