Reviewerly/exHarmony huggingface dataset:
Current Control of the Permanent-Magnet Synchronous Generator Using Interval Type-2 T-S Fuzzy Systems

The current control of the permanent-magnet synchronous generator (PMSG) using an interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems is designed and implemented. PMSG is an energy conversion unit widely used in wind energy generation systems and energy storage systems. Its performance is determined by the current control approach. IT2 T-S fuzzy systems are implemented to deal with the nonlinearity of a PMSG system in this paper. First, the IT2 T-S fuzzy model of a PMSG is obtained. Second, the IT2 T-S fuzzy controller is designed based on the concept of parallel distributed compensation (PDC). Next, the stability analysis can be conducted through the Lyapunov theorem. Accordingly, the stability conditions of the closed-loop system are expressed in Linear Matrix Inequality (LMI) form. The AC power from a PMSG is converted to DC power via a three-phase six-switch full bridge converter. The six-switch full bridge converter is controlled by the proposed IT2 T-S fuzzy controller. The analog-to-digital (ADC) conversion, rotor position calculation and duty ratio determination are digitally accomplished by the microcontroller. Finally, simulation and experimental results verify the performance of the proposed current control.
Programmatic Link Grammar Induction for Unsupervised Language Learning

Although natural (i.e. human) languages do not seem to follow a strictly formal grammar, their structure analysis and generation can be approximated by one. Having such a grammar is an important tool for programmatic language understanding. Due to the huge number of natural languages and their variations, processing tools that rely on human intervention are available only for the most popular ones. We explore the problem of unsupervisedly inducing a formal grammar for any language, using the Link Grammar paradigm, from unannotated parses also obtained without supervision from an input corpus. The details of our state-of-the-art grammar induction technology and its evaluation techniques are described, as well as preliminary results of its application on both synthetic and real world text-corpora.
Image Segmentation Based on Deep Learning Features

Image segmentation is an important technique in image analysis. Existing methods in image segmentation rely on an artificial neural network to extract the feature of the image. In this study, we propose an image segmentation method based on deep learning features and community detection. We propose the use of a pre-trained convolution neural network (CNN) to extract the deep learning features of the image. The deep CNN is trained on ImageNet dataset and transferred to image segmentations for constructing potentials of super-pixels. We first convert the original image from the pixel level to the region level based on Simple Linear Iterative Clustering super-pixels and then aim at each superpixel region to extract the deep learning features. We combine the color features and deep learning features of the superpixel region. The weights of deep learning features and color features are subsequently adjusted using a random walk algorithm to construct a new similarity matrix. We conduct community detection based on a similarity matrix. To automatically identify the number of image segmentation, we use modularity Q in order to determine the optimal number of associations. To illustrate the effectiveness of our proposed method, we evaluate the BSDS300 dataset and compare the technique with several other wellknown image segmentation methods. The segmentation experiments conducted on different images show that our proposed image segmentation algorithm outperforms other methods.
Stabilization of Traffic Videos Based on Both Foreground and Background Feature Trajectories

This paper considers stabilizing traffic videos, which are recorded by cameras mounted on moving vehicles. Compared with videos captured by hand-held cameras, traffic videos are more difficult to stabilize due to dynamic scenes, higher frequency camera jitter, more moving foreground objects and more serious parallax. The conventional video stabilization methods usually estimate the camera jitter from the background feature trajectories which are mainly determined by the camera motion, and then stabilize videos with the estimated jitter. These methods have to correctly distinguish background and foreground feature trajectories, which is not trivial, and may suffer from performance degradation when large foreground objects exist and no enough number of background feature trajectories can be obtained. To resolve these issues, this paper proposes a novel stabilization method, under which background and foreground feature trajectories are no longer distinguished and work together to yield stabilized trajectories. More specifically, the movement of all feature trajectories is modeled as the summation of the camera motion and the object motion. By solving an optimization problem, we can remove the high frequency components of the camera motion, i.e., the camera jitter, and stabilize videos. Parallax is also treated as object motion and contributes feature trajectories for video stabilization. As our method makes use of both foreground and background feature trajectories, it can outperform the conventional stabilization methods which use only background feature trajectories, especially when there are large foreground objects and the number of extracted background feature trajectories is small. Furthermore, some refinements are proposed to speed up our method and enhance its robustness. Experiments are done and confirm its performance superiority.
A Curatorial Guide to Museum Sound Design

Abstract Sound is increasingly recognized as an important aspect of museum design. Yet there remain few resources available to curators on how to incorporate sound design principles into their exhibition projects. To address this need, this paper outlines a process‐based approach to museum sound design. This approach builds on, but also differs from, existing scholarly approaches in that it is written specifically from the perspective of integrating sound design principles into the exhibition development process. In this way, it aims to make sound design more attainable to working curators in the field, while also grounding museum sound theory more firmly in the realities of everyday curatorial practice.
Detecting Time-Fragmented Cache Attacks Against AES Using Performance Monitoring Counters

Cache timing attacks use shared caches in multi-core processors as side channels to extract information from victim processes. These attacks are particularly dangerous in cloud infrastructures, in which the deployed countermeasures cause collateral effects in terms of performance loss and increase in energy consumption. We propose to monitor the victim process using an independent monitoring (detector) process, that continuously measures selected Performance Monitoring Counters (PMC) to detect the presence of an attack. Ad-hoc countermeasures can be applied only when such a risky situation arises. In our case, the victim process is the Advanced Encryption Standard (AES) encryption algorithm and the attack is performed by means of random encryption requests. We demonstrate that PMCs are a feasible tool to detect the attack and that sampling PMCs at high frequencies is worse than sampling at lower frequencies in terms of detection capabilities, particularly when the attack is fragmented in time to try to be hidden from detection.
FABRIC: Framework for Agent-Based Robot Control Systems

The paper presents FABRIC, a framework and a toolchain that facilitates semi-automatic generation of agent-based control systems for robots. The presented approach combines agent-based formal specification with implementation employing component-based frameworks. The input for the generation method consists of the formal specification of the designed control system expressed in a DSL and source code encapsulated in components. Decomposition of a formal specification into a hierarchical structure of patterns and their parameters is employed. It implies a division of the specification into a number of items, produced using a DSL and source code expressed in a universal programming language. The method of generating a working agent-based system, out of its specification and selected components, is verified on a service robot that executes complex tasks, i.e. door opening.
Autonomous Design Space Exploration of Computing Systems for Sustainability: Opportunities and Challenges

Editor's note: Data-driven design methods are a promising way to design computing systems at various scales. This article presents a survey regarding data-driven techniques to design sustainable computing systems. -Partha Pratim Pande, Washington State University
Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes

Runtime models support decision-making and reasoning for self-adaptation based on both design-time knowledge and information that may emerge at runtime.In this paper, we demonstrate a novel use of Partially Observable Markov Decision Processes (POMDPs) as runtime models to support the decision-making of a Self Adaptive System (SAS) in the context of the MAPE-K loop.The trade-off between the non-functional requirements (NFRs) has been embodied as a POMDP in the context of the MAPE-K loop.Using Bayesian learning, the levels of satisficement of the NFRs are inferred and updated during execution in the form of runtime models in the Knowledge Base.We evaluate our work with a case study of the networking application domain.
Application Research of Artificial Intelligent Technology in Substation Inspection Tour

At present, the management and behavior control of substation field operating persons rely entirely on manual verification and supervision. There are hidden dangers of inadequate site control. And the fire, foreign body floating and other abnormal situations, unexpected defects in the scene cannot be automatically identified through the substation video monitoring system. Aiming at the problems existing in substation inspection tour, this paper applies artificial intelligent technology to it. Through the research of intelligent identification monitoring and image intelligent discrimination technology, it can realize the intelligent control of field personnel and operation behavior, automatic confirmation and alarm of abnormal situation such as foreign body, smoke and flame in station area, realize the automatic layout function of electronic fence, and automatically identify and warn the situation of intrusion, crossing fence, mistakenly entering live area, etc. Improve the quality of site safety control and inspection efficiency.
Reliable Presence and Intrusion Detection with Collaborative Sensor Modules in Electronic Property Protection Systems

This article deals with components (that effectuate the electronic protection) of property protection systems. Within this, mostly with to delineate the electronic outdoor protection, the intrusion detection system and some possible additional functions of the access control system. The solutions presented here are aimed to keep the cost low from the viewpoint of hardware requirements, together with to take advantage of the software opportunities.
Towards Seamless Configuration Tuning of Big Data Analytics

The execution of distributed data processing workloads (such as those running on top of Hadoop or Spark) in cloud environments presents a unique opportunity to explore multiple trade-offs between elasticity (and types of resources being allocated), overall runtime and total costs. However, beyond high-level constraints and objectives, it's not the end-users who should be mainly concerned with those optimizations, but the cloud providers. They have both the vantage point to collect actionable information, economies of scale and position to adjust parameters when dynamic conditions change, in order to fulfil SLOs that go beyond classic measures of latency and throughput. This is at odds with the existing approach of making software (including the interfaces to the cloud and the processing frameworks) as configurable as possible. We propose that rather than configurability, self-tunability (or the illusion of it as far as the end-user is concerned) is a better long-term goal.
Text Detection Algorithm based on Improved YOLOv3

Focusing on scene text detection, this paper adopts an improved scene text detection algorithm based on YOLOv3 network. Firstly, for a single detection target, the training speed of Darknet53 backbone network used by YOLOv3 is slow due to too many layers, so that this paper proposes a method for replacing it by Darknet19. Secondly, multi-scale detection was retained in the original network, and three anchors of different sizes were used to predict the bounding boxes. The experiment results show that the training speed of this algorithm is greatly improved and the detection effect is accurate and stable.
Teamscale: Tackle Technical Debt and Control the Quality of Your Software

Teamscale is a software intelligence platform, that is, it creates transparency on code quality and the underlying software development process. This makes it possible for developers, testers and managers to better understand and control technical debt of their systems. In this paper, we give an overview of Teamscale and how this tool can be used in practice to control and lower technical debt in the long run. We explain which code analyses can be used to identify and address technical debt. Teamscale is available for free for research and teaching purposes at www.teamscale.io.
Assessment of Virtual Guides’ Credibility in Virtual Museum Environments

Immersive virtual museum environments can play a crucial role for communicating cultural information in an engaging and educational way, especially when storytelling is involved. In this paper we explore and assess the impact that the status of three types of avatars (embodiments of a museum curator, a museum security guard and a museum visitor, respectively) may have on the credibility of their storytelling and the emotions they evoke to virtual visitors. Preliminary results derived from an experiment provide evidence that supports our initial hypothesis that the status of the avatar may indeed influence their credibility and the participants’ emotions of sadness/worry, distress, shame, anger, relief and admiration.
Node Embedding with Adaptive Similarities for Scalable Learning over Graphs

Node embedding is the task of extracting informative and descriptive features over the nodes of a graph. The importance of node embedding for graph analytics as well as learning tasks, such as node classification, link prediction, and community detection, has led to a growing interest and a number of recent advances. Nonetheless, node embedding faces several major challenges. Practical embedding methods have to deal with real-world graphs that arise from different domains, with inherently diverse underlying processes as well as similarity structures and metrics. On the other hand, similar to principal component analysis in feature vector spaces, node embedding is an inherently unsupervised task. Lacking metadata for validation, practical schemes motivate standardization and limited use of tunable hyperparameters. Finally, node embedding methods must be scalable in order to cope with large-scale real-world graphs of networks with ever-increasing size. The present work puts forth an adaptive node embedding framework that adjusts the embedding process to a given underlying graph, in a fully unsupervised manner. This is achieved by leveraging the notion of a tunable node similarity matrix that assigns weights on multihop paths. The design of multihop similarities ensures that the resultant embeddings also inherit interpretable spectral properties. The proposed model is thoroughly investigated, interpreted, and numerically evaluated using stochastic block models. Moreover, an unsupervised algorithm is developed for training the model parameters effieciently. Extensive node classification, link prediction, and clustering experiments are carried out on many real-world graphs from various domains, along with comparisons with state-of-the-art scalable and unsupervised node embedding alternatives. The proposed method enjoys superior performance in many cases, while also yielding interpretable information on the underlying graph structure.
High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks

Abstract Dynamic optical imaging (e.g. time delay integration imaging) is troubled by the motion blur fundamentally arising from mismatching between photo-induced charge transfer and optical image movements. Motion aberrations from the forward dynamic imaging link impede the acquiring of high-quality images. Here, we propose a high-resolution dynamic inversion imaging method based on optical flow neural learning networks. Optical flow is reconstructed via a multilayer neural learning network. The optical flow is able to construct the motion spread function that enables computational reconstruction of captured images with a single digital filter. This works construct the complete dynamic imaging link, involving the backward and forward imaging link, and demonstrates the capability of the back-ward imaging by reducing motion aberrations.
Application of addition HClO4 to improve the dissolution process and sensitivity of Os for low-concentration of Os in pyrite

The rhenium (Re) and osmium (Os) isotope systems have long been recognized as effective tools to directly date mineralization and trace the source of metals. In general, 0.4 g of pyrite was dissolved by 11 mL of inverse aqua regia in a 30-mL Carius tube. Larger volumes of acids and a larger Carius tube were required if the sample weight was over 0.4 g. Herein we present an improved dissolution process of the Carius tube digestion method by adding HClO4 to dissolve larger sample weights in smaller volumes of acid. This approach is more suitable for determining low concentrations of Os in metal sulfides. In detail, 0.4 g of pyrite was completely dissolved in a mixture of 1 mL of HClO4 and 3 mL of inverse aqua regia. Notably, addition of 1 mL of HClO4 to 8 mL of inverse aqua regia significantly increases the amount of sample from 0.4 to 0.8 g. Based on our data, while the sample weight was no >0.8 g, addition of 1 mL of HClO4 to inverse aqua regia can not only improve the dissolving capacity significantly, but also avoid Carius tube explosion. Furthermore, the dissolving capacity of HClO4-inverse aqua regia method is much higher than that of H2O2-inverse aqua regia method, which is a typical digestion method for Re-Os dating. This method was certified by reference materials (HLP and JDC). In brief, this new method allows dissolution of larger amounts of sample while consuming less acid. Therefore, this method has more potential for determining low concentrations of Re and Os in metal sulfides.
Machine Learning Techniques to Reduce Error in the Internet of Things

Over the years, Machine Learning (ML) techniques have been implemented to improve data processing speed and result in Internet of Things (IoT) devices. Some of the most common ML algorithms include Bayesian Statistics, Neural Networks, K Nearest Neighbors (KNN), Support Vector Machines (SVM), K means Clustering, Genetic Algorithms, decision trees, Principal Component Analysis (PCA), Random Forest, Regression Analysis. The aforementioned algorithms have been used for classification for various uses such as finding faults in the data, improve speeds by forming clusters of similar data points. ML can be used in varied fields such as to predict heart diseases, energy consumption, the state and location of IoT devices etc. This paper details the techniques and their usage in IoT.
Research on 3D Human Pose Estimation Using RGBD Camera

To aim at the problem of many researchers have only focused on recovering 3D human body information from color images, which is not accurate, causing great ambiguity and slow. we propose a new method for 3D human pose estimation. We get color images and depth images through RGBD camera. we use convolutional neural networks for 2D human pose estimation to get joint points coordinates in color image and then map the returned results to corresponding depth image to obtain 3D joint points information. For 2D human pose estimation, we improve the accuracy of the stacked hourglass network using Faster-RCNN and residual structure Resnet50 as the human target extractor. During the mapping process, a sparse feature point matching method based on the SURF algorithm is used to determine the calibration parameters of color images and depth images.
ComDia+: An Interactive Visual Analytics System for Comparing, Diagnosing, and Improving Multiclass Classifiers

Performance analysis is essential for improving classification models. However, existing performance analysis tools do not provide actionable insights such as the cause of misclassification. Machine learning practitioners face difficulties such as prioritizing model, looking over confusion between classes. In addition, existing performance analysis tools that provide feature-level analysis are difficult to apply to image classification problems. This study has been proposed to solve these difficulties. In this paper, we present an interactive visual analytics system for diagnosing the performance of multiclass classification models. Our system is able to compare multiple models, find weaknesses, and obtain actionable insights for improving models. Our visualization consists of three views for analyzing performance at the class, confusion, and instance levels. We demonstrate our system using MNIST handwritten digits data.
Facial Image Inpainting Using Multi-level Generative Network

Facial image inpainting is a challenging task due to the loss of key components in faces such as eyes and nose. In this paper, we propose a simple yet efficient method to inpaint missing information from a facial image. We build an end-to-end multi-level generative network to capture features at various levels while reducing training time. We adopt multi-scale feature maps to produce natural-looking faces with realistic texture. To optimize the parameters of the proposed network, we use two kinds of losses: content and texture. The former loss consists of mean absolute error (MAE) and multi-scale structural similarity (MS-SSIM) losses to minimize distortion in content, while the latter one contains style and adversarial losses to facilitate texture synthesis. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative measurements. The code is available at https://github.com/JieLiu95/MLGN.
Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm

The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and communication paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed paradigm is demonstrated through a set of experiments.