Reviewerly/exHarmony huggingface dataset:
Improving Few-Shot Learning Through Multi-task Representation Learning Theory

In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task. We start by reviewing recent advances in MTR theory and show that they can provide novel insights for popular meta-learning algorithms when analyzed within this framework. In particular, we highlight a fundamental difference between gradient-based and metric-based algorithms in practice and put forward a theoretical analysis to explain it. Finally, we use the derived insights to improve the performance of meta-learning methods via a new spectral-based regularization term and confirm its efficiency through experimental studies on few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of MTR theory into practice for the task of few-shot classification.
Discharge Test of Unmanned Aerial Vehicles and Live High-voltage Wires

In this paper, tests were carried out on the working state and the dischargestatus of the Unmanned Aerial Vehicles(UAV) motorwith different distances between the UAV and 110kV and 220kV live high-voltage wires. The results showed that the UAV could still keep regular operation when the distance between the 110kV wire and the UAV motor was 0.5m.It ran properly at a distance of 1m from the 220kV wire.The wire suddenly discharged electricity and punched through the UAV when it was boosted to 200kV at a distance of 0.5m from the UAV motor, but the UAV motor was not damaged.
Towards Benchmark Data Generation for Feature Tracking in Scalar Fields

We describe a benchmark data generator for tracking methods for two- and three-dimensional time-dependent scalar fields. More and more topology-based tracking methods are presented in the visualization community, but the validation and evaluation of the tracking results are currently limited to qualitative visual approaches. We present a pipeline for creating different ground truth features that support evaluating tracking methods based on quantitative measures. In short, our approach randomly simulates a temporal point cloud with birth, death, split, merge, and continuation events, where the points are then used to derive a scalar field whose topological features correspond to the points. These scalar fields can be used as the input for different tracking methods, where the computed tracks can be compared against the ground truth feature evolution. This approach facilitates directly comparing the results of different tracking methods, independent of the initial feature characterization.
Deep Multi-Scale Feature Mixture Model for Image Super-Resolution with Multiple-Focal-Length Degradation

Single image super-resolution (SISR) is a classic problem in computer vision. In recent years, deep learning-based models have achieved unprecedented success with this problem. However, most existing deep SR models unavoidably produce degraded results when applied to real-world images captured by cameras with different focal lengths. The degradation in these images is called multiple-focal-length degradation, which is spatially variant and more complicated than the bicubic-downsampling degradation. To address such a challenging issue, we propose a multi-scale feature mixture model in this paper. The proposed model can intensively exploit local patterns from different scales for image super-resolution. To improve the performance, we further propose a novel loss function based on the Laplacian pyramid, which guides the model to separately recover the information of different frequency sub-bands. Comprehensive experiments show that our proposed model has better ability to preserve the structure of objects and generate high-quality images, leading to the best performance compared with other state-of-the-art deep SISR methods.
Adopting formal methods on requirements verification and validation for cyber-physical systems: A systematic literature review

Requirements engineering is a critical activity in developing complex cyber-physical systems. Requirements are usually expressed using natural language, which may be ambiguous, inconsistent, or incomplete. These issues in requirements qualities may introduce errors in system design that lead to high project cost overruns. Hence it is essential to verify the qualities of requirements early. Since formal methods have demonstrated their ability to verify system designs and are increasingly adopted to support requirements engineering for software systems, a question arises about adapting formal methods to account for specific properties of cyber-physical systems. Even if there are many literature reviews concerning requirements engineering, there is a lack of a global view on the reviews that specifically address the issues related to validation and verification (V&V) of requirements. This paper aims to provide an overview of literature reviews related to requirements V&V and mainly investigates the use of formal approaches and models for preventing, detecting, or correcting errors occurring in requirements and identifies the main challenges of adopting formal methods on requirements engineering for cyber-physical systems.
Tai Chi Action Quality Assessment and Visual Analysis with a Consumer RGB-D Camera

Visual-based human action analysis is an important research topic in the field of computer vision, and has great application prospect in intelligent sports. Home-based fitness is increasingly common in recent years, however lacking of accurate feedback and scientific guidance main easily lead to problems such as exercise injuries. In this paper, we propose an analysis system for Tai Chi action quality assessment and visual analysis with a consumer RGB-D camera. The main innovative work is as follows: 1) for home-based fitness action evaluation, we design a real-time intelligent analysis system combined with expert rules through a consumer RGB-D camera; 2) we transform the evaluation of 24-form Tai Chi Chuan into an artificial intelligence (AI) model, and realize action recognition and assessment through computer vision; 2) to train the AI model, we build a new dataset named TaiChi-24, which contains 1,408 samples with RGB-D images and 3D skeletons. We carry out evaluation experiments and analyses, and the experimental results have shown the advantage of applying our evaluation method on the proposed TaiChi-24 dataset.
Area Reduction in Field Programmable Gate Array with Convolutional Neural Network

The term "Convolutional Neural Networks" (CNN) refers to a technique that is widely used in a variety of industries and has achieved promising results in recent years. Over ninety percent of the jobs that are performed by a CNN are convolutions. The most effective convolutional neural networks, or CNNs, often consist of a very large number of layers and filter weights, which results in a significant increase in the amount of work required for computation and communication. The present CNN-explicit accelerator is substantially more productive than its predecessors; nonetheless, it is often rigid or calls for a sophisticated regulator to manage the computation and data transmission motion of multiple CNNs. Utilizing the striding value will allow us to accomplish our primary goal, which is to minimize the height and breadth of the picture while simultaneously increasing the pixel intensity. Employing the stride values also helps to lessen the computational imbalance and the overlap that occurs as a result of using them. The suggested CNN accelerator based on FPGAs is composed of three primary levels, which are the convolution layer, the pooling layer, and the activation layer. Pixels that have been taken from the picture serve as the input for the accelerator. The design of the proposed FPGA-based CNN accelerator has been completed, and the performance parameters of the FPGA accelerator show that the novel multiplier approach improves the accelerator's speed by 55,43 percent while simultaneously reducing its power consumption by 64,12 percent in exchange for a larger footprint. The findings of the experiments indicate that the magnitude of the picture may be reduced by the incorporation of a multiplier array-based convolver into the architecture of the proposed accelerator.
Machine Learning and Deep Transfer Learning approaches were used to create a Face Mask Identification model for COVID-19

COVID-19 pandemic has led to an international health emergency the WHO considers wearing a face mask an appropriate form of public health protection. This work will describe a face mask identification model that incorporates both deep and traditional machine learning techniques. Parts of the suggested model can be divided into two. Using Resnet50, the initial part of the system is set up for feature extraction. The second component classifies face masks using decision trees, support vector machines (SVMs), and the ensemble approach. The research will focus on three face-masked datasets. The Real-World Masked Face Dataset Includes three datasets: real-world masked faces, simulated faces, and wild faces (LFW). 99.64% of RMFD's SVM classifier is accurate throughout testing.. It achieved a 99.49% accuracy rate in SMFD and a 100% accuracy rate in LFW.
Towards an Improved Bi-GAN-Based End-to-End One-Class Classifier for Anomaly Detection in Cloud Data-Centers

With the continuous popularization of cloud computing services, ensuring the stable operation of cloud data-centers has become a hot research issue. Efficient anomaly detection for a running cloud data-center and timely judgment of the cause of anomalies will help to fundamentally improve the reliability of cloud data-center infrastructures. Nevertheless, traditional anomaly identification approaches are challenging to meet the requirements of cloud data-centers with increasingly complex system structures. Due to the low feasibility of labeling data, machine learning methods based on supervised learning also make it difficult to perform efficient anomaly detection in cloud data-centers. This work exploits novel GAN-based generative models and end-to-end one-class classification for optimizing unsupervised anomaly identification. A new Bi-GAN-based Heterogeneous Anomaly-reconstruction One-class Classifier (BG-HA-OC) is developed optimizing a one-class classifier and an anomaly scoring function. The Generator-Encoder-Discriminator Bi-GAN is capable of performing practical anomaly score computation and capturing fine temporal features. In the empirical study, we demonstrate that our proposed framework outperforms its peers upon third-party anomaly detection methods on anomaly benchmarks and synthetic datasets.
Scheduling and Tasking of Autonomous Systems for Collaborative Missions

This paper describes the GALILEO Decision Aid Artificial Intelligence (AI) agent, an intelligent agent developed under the Defence Science and Technology Laboratory (Dstl)’s Intelligent Ship Project, which aims at enhancing the command team’s cognitive ability to understand a rapidly changing operational environment and decide the appropriate Course of Action (CoA). Given high level operational tasking orders, GALILEO builds concrete mission plans that optimally allocate available resources to mission tasks. This work discusses the two-level optimisation that was developed to goal-decompose complex commands into actionable tasks that can be sent to human operators or other autonomous agents (or any combination of the two) for execution. Initial results are provided from deploying the agent with operators at Dstl’s Command Lab.
Cultivating Nationalism and National Insight Through Film for Students at MTs Miftahul Ulum, Gowa Regency, South Sulawesi

This study aims to foster the spirit of nationalism and or national insight in students at MTs Miftahul Ulum, Gowa Regency through Indonesian genre films. Film is a medium that is easily understood and digested by students compared to other media that tend to be conventional. The method used is watching Indonesian genre films. Through watching this film, students gain an aspect of consciousness, so that after watching the film, students feel and bring up in their minds the importance of protecting and loving Indonesia, both as a homeland and as a nation. The results showed that after they finished watching several films such as: Denias, Senandung di Atas Awan and Tanah Surga “Katanya”, it showed that they were able to have knowledge and understanding that nationalism and national insight were not only about fighting the colonialists (Dutch and Japanese Colonial). They have an understanding that nationalism can be expressed in different forms according to the context of the era. Students love and are proud of Indonesia.
Ensembles of Gaussian process latent variable models

In this paper, we address the classification and dimensionality reduction via ensembles of Gaussian Process Latent Variable Models (GPLVMs). The underlying idea is to have a diverse representation of latent spaces represented by an ensemble of GPLVMs. Each GPLVM of the ensemble has its own projections of the high dimensional observed data on a low dimensional latent space. These models are weighted using importance sampling. Since in practical settings, neither the kernel of the GPLVM nor the dimension of the latent space is known, it is logical to engage an ensemble of GPLVMs based on different kernels and for each of them estimate the dimension of the lower dimensional space. We demonstrate the advantage of working with ensembles for classification and show the performance of dimensionality reduction of our method with numerical simulations.
Virtual Reality Streaming for Remote Medical Training: Evaluation of Interface Usability with Physicians

A novel virtual reality (VR) system for streaming medical training was designed and evaluated from a user-centered perspective. We developed the VR user interface for remote medical training based on interviews with physicians who instruct medical simulation sessions. We then conducted usability tests with eleven board-certified physicians who specialize in Emergency Medicine or Obstetrics and Gynecology (OBGYN) Physicians used the novel VR system to watch stereoscopic video of a training simulation for an obstetrica emergency: management of shoulder dystocia. Physicians then rated the usability of the VR system. Th average usability rating was above an "A+" grade average on an industry benchmark scale.
Causality Analysis Method and Model Related to Why-Question Answering in Business Intelligence Context

Data warehouses are the key Business Intelligence technologies that effectively contribute to decision-making. Querying Data Warehouses is increasingly performed with natural language. In order to analyse the data in the warehouse, the decision-maker can express his requirement with respect to a Why question. Answering this question needs to provide explanations determined by identifying causal correlations between the phenomenon highlighted in the Why-Question and factors located in the data warehouse as well as in external sources. Hence, we propose in this paper a causality model followed by a statistical method that enables causal influence computing. To evaluate the relevance of our proposal, we present an experimental study that shows encouraging results.
Amanuensis: provenance, privacy, and permission in TEE-enabled blockchain data systems

Blockchain technology is heralded for its ability to provide transparent and immutable audit trails for data shared among semi-trusted parties. With the addition of smart contracts, blockchains can track and verify arbitrary computations – which enables blockchain users to verify the provenance of information derived from data through the blockchain. This provenance comes at the cost of data confidentiality and user privacy, however, which is unacceptable for many sensitive applications. The need for verifiable yet confidential data sharing and computation has led some to add trusted execution environment (TEE) hardware to blockchain platforms. By moving sensitive operations (e.g., data decryption and analysis) off of the blockchain and into a TEE, they get both the confidentiality of TEEs and the transparency of blockchains without the need to completely trust any one party in the data-sharing ecosystem.In this paper, we build on our TEE-enabled blockchain data-sharing system, Amanuensis, to ensure the freshness of access-control lists shared between the blockchain and TEE, and to improve the privacy of users interacting within the system. We also detail how TEE-based remote attestation help us to achieve information provenance – specifically, how to achieve information provenance in the context of the Intel SGX trusted execution environment. Finally, we present an evaluation of our system, in which we test several real-world machine-learning applications (logistic regression, kNN, SVM) to determine the run-time overhead of information confidentiality and provenance. Each machine-learning program exhibited a slowdown between 1.1 and 2.8x when run inside of our confidential environment, and took an average of 59 milliseconds to verify the provenance of an input data set.
Colorization of Black &amp; White Videos &amp; Photographs

When a gray image is given as an input, this paper is an offensive problem guessing the audio version of the image. This problem is under the ban, so the previous methods have worked. It may rely on significant user interaction or result in a dull color rendering. We suggest a fully automatic method that produces brightness and realistic coloring. We accept the subtle uncertainty of the problem by setting it as a division function and applying class re-equation to training time to increase color variation in effect. The system is used as a feed pass on CNN during testing and so on trained in over a million color photographs. We test our calculation utilizing a "colorization Turing test," requesting that human members pick between the variety picture of the delivered truth and the base. Our approach is deceptively successful in 32% of the survey, much higher than previous methods. In addition, we show that color can be a powerful motivator learning feature we watch and acts as a multichannel connector. This methodology results in a modern application to several feature learning benchmark.
Application of an Improved Multimodal Multiobjective Algorithm in Feature Selection

Feature selection is the process of screening the original data set and selecting the most effective features to reduce the data dimension. In the process of feature selection, when selecting the same number of features, there may be different combinations of features, making the final obtained classification results very similar. This is a multimodal multiobjective (MMO) feature selection optimization problem. In this paper, an improved multimodal multiobjective evolutionary algorithm based on uniform crossover is proposed for MMO feature selection optimization problem. The algorithm uses the strategy based on the niche of decision space and the uniform crossover strategy, which has better global optimization ability and can find more equivalent feature subsets. The algorithm is tested on seven feature selection datasets and compared with the existing multimodal multiobjective feature selection algorithms. Experimental results show that the algorithm can find more equivalent feature combinations without reducing the classification accuracy.
Hiding Function with Neural Networks

In this paper, we show that neural networks can hide a specific task while finishing a common one. We leverage the excellent fitting ability of neural networks to train two tasks simultaneously. In a classification example, the first task is normally trained by the traditional crossentropy loss. The output of the second task can be obtained by the product of the penultimate layer and a predefined random matrix. A well-trained network looks like a benign classifier and will not arouse suspicion. For certain people, they can use a predefined random matrix to turn on the second covert function of the neural network. Experimental results demonstrate that the hidden tasks can achieve satisfactory performance without affecting the original tasks.
Gaussian Process-based Amortization of Variational Message Passing Update Rules

Variational Message Passing facilitates automated variational inference in factorized probabilistic models where connected factors are conjugate pairs. Conjugate-computation Variational Inference (CVI) extends the applicability of VMP to models comprising both conjugate and non-conjugate factors. CVI makes use of a gradient that is estimated by Monte Carlo (MC) sampling, which potentially leads to substantial computational load. As a result, for models that feature a large number of non-conjugate pairs, CVI-based inference may not scale well to larger model sizes. In this paper, we propose a Gaussian Process-enhanced CVI approach, called GP-CVI, to amortize the computational costs caused by the MC sampling procedures in CVI. Specifically, we train a Gaussian process regression (GPR) model based on a set of incoming outgoing message pairs that were generated by CVI. In operation, we use the “cheaper” GPR model to produce outgoing messages and resort to the more accurate but expensive CVI message only if the variance of the outgoing message exceeds a threshold. By experimental validation, we show that GP-CVI gradually uses more fast memory-based update rule computations, and less sampling-based update rule computations. As a result, GP-CVI speeds up CVI with a controllable effect on the accuracy of the inference procedure.
Heuristics to Solve a Team Orienteering Problem

Online-Labs in Education , Seite 307 - 326
RDF Query Processing: Relational Vs. Graph Approach

The data volume for the resource description framework (RDF) is growing rapidly. To query this large amount of data, two types of query processing approaches are there: the relational approach and graph-based approach. The relational approach offers better scalability and good security, but it suffers from query joins. The graph-based approach helps to eliminate query joins. Both relational and graph-based approach have their own advantages. This paper presents the performance analysis of relational database management system (RDBMS) and graph database management system (GDBMS) in terms of query processing. The evaluation of query execution time (QET) is carried out for the same benchmark linked observation data (LOD). A detailed comparison is carried out using two popular graph-based tools, Neo4j, and DGraph with relational database PostgreSQL. Neo4j uses cipher-query language, and DGraph uses GraphQL+−, and PostgreSQL uses standard SQL language for the RDBMS. The experiments are performed for four different types of queries. It has been observed that RDBMS outperforms GDBMS for star and range queries, and graph database works well with join and projection queries. We have extended our evaluation for the comparison of available graph tools, and it has been found that DGraph outperforms Neo4j for all the types of queries and works 2.5% faster for the query execution time as well.