Reviewerly/exHarmony huggingface dataset:
Dynamic State Estimation of Power Systems by $p$ -Norm Nonlinear Kalman Filter

The problem of dynamic state estimation of power systems is relevant to the monitoring of real-time operation of essential power distribution infrastructure. The nonlinear Kalman filter is utilized for dynamic state estimation of power systems based on available measurements from phasor measurement units. However, measurements are corrupted by non-Gaussian noise and exhibit varying levels of sensitivity to outliers, therefore degrading estimation accuracy. This study proposes a robust mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm square root unscented Kalman filter for state estimation of power systems. Unlike traditional nonlinear Kalman filters which utilize the minimum mean square error criterion, the mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm square root unscented Kalman filter utilizes a mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm optimization for weighting the measurement errors to improve robustness against outliers and alleviate the filtering degradation caused by abnormal measurements. The performance of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm square root unscented Kalman filter is demonstrated in the WSCC 3-machine system and the NPCC 48-machine system. Simulation results demonstrate that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -norm square root unscented Kalman filter achieves superior accuracy than the commonly used nonlinear Kalman filters.
Secure IoT Systems Using Raspberry Pi Machine Learning Artificial Intelligence

The Internet of Things (IoT) ML and AI is an enormous gathering of gadgets containing sensors or actuators associated together over wired or remote systems. With a gauge of more than 25 billion gadgets associated together by 2020, IoT using ML and AI has been quickly becoming over the previous decade. Amid the development, security has been distinguished as one of the weakest territories in ML IoT. While actualizing security inside an IoT using AI arrange, there are a few difficulties including heterogeneity inside the framework just as the amount of gadgets that should be tended to. To approach the difficulties in verifying IoT gadgets in ML, we propose utilizing AI inside an IoT door to help secure the framework. We explore utilizing Artificial Neural Networks in a portal to distinguish oddities in the information sent from the edge gadgets. We are persuaded that this methodology can improve the security of IoT frameworks.
Subspace Learning and Feature Selection via Orthogonal Mapping

A plethora of dimensionality reduction (DR) techniques, stemming from statistics, machine learning, or graph theory, has been developed. Their ultimate objective is to eliminate data redundancy without a significant information loss. A general formulation, known as graph embedding, offers a unified framework for describing several well-known DR algorithms. In this paper, the inclusion of several DR algorithms within this unified framework is demonstrated. The enforcement of orthogonality to the projection matrix within this framework is proven to be of vital importance, since Orthogonal Neighborhood Preserving Projections, Orthogonal Locality Preserving Projections, Orthogonal Isometric Projection, Orthogonal Linear Discriminant Analysis, and Orthogonal Local Tangent Space Alignment algorithms outperform their non-orthogonal counterparts, e.g., Neighborhood Preserving Embedding, Locality Preserving Projections, Isometric Mapping, Linear Discriminant Analysis, and Local Tangent Space Alignment. One may simultaneously also impose the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> norm regularization on the projection matrix, seeking for row-sparsity. This leads to the known Joint Feature Selection and Subspace Learning (JFSSL) framework. All DR algorithms are employed within JFSSL. It is proven that the use of orthogonal mapping algorithms within JFSSL against their non-orthogonal counterparts does not improve the recognition rate of NN classifier.
Simultaneous Optimization of Patient–Image Registration and Hand–Eye Calibration for Accurate Augmented Reality in Surgery

Objective: Augmented reality (AR) navigation using a position sensor in endoscopic surgeries relies on the quality of patient-image registration and hand-eye calibration. Conventional methods collect the necessary data to compute two output transformation matrices separately. However, the AR display setting during surgery generally differs from that during preoperative processes. Although conventional methods can identify optimal solutions under initial conditions, AR display errors are unavoidable during surgery owing to the inherent computational complexity of AR processes, such as error accumulation over successive matrix multiplications, and tracking errors of position sensor. Methods: We propose the simultaneous optimization of patient-image registration and hand-eye calibration in an AR environment before surgery. The relationship between the endoscope and a virtual object to overlay is first calculated using an endoscopic image, which also functions as a reference during optimization. After including the tracking information from the position sensor, patient-image registration and hand-eye calibration are optimized in terms of least-squares. Results: Experiments with synthetic data verify that the proposed method is less sensitive to computation and tracking errors. A phantom experiment with a position sensor is also conducted. The accuracy of the proposed method is significantly higher than that of the conventional method. Conclusion: The AR accuracy of the proposed method is compared with those of the conventional ones, and the superiority of the proposed method is verified. Significance: This study demonstrates that the proposed method exhibits substantial potential for improving AR navigation accuracy.
Value Creation From a Decentralized Car Ledger

Blockchain technology is expected to create a variety of new opportunities for businesses. Yet, little is known about how the technology actually enables to create value and how companies will be able to exploit true business value. However, without a clear understanding of the value creation potential from the technology, and corresponding adaption of business practices, the realization of value is doomed to failure. Hence, we contribute to this gap by exploring and explicating the specificities of value creation from blockchain in the ecosystem of a car. In the course of an exploratory case study analysis, over a time period of 2 years, we conducted three iterations of interviews and workshops with industry and blockchain experts from five diverse stakeholder groups. In brief, we provide early evidence that (1) blockchain enables value creation through: Distributed Product Innovation, Shared Operational Efficiency, and Controlled Customer Intimacy. Furthermore, we discuss our learnings for businesses in other domains aiming to leverage value from blockchain technology. We do so, by deriving guidelines for each blockchain value discipline. Furthermore, we give recommendations on how blockchain projects in ecosystems should approach multiple blockchain value potentials.
Automatic Thyroid Ultrasound Image Segmentation Based on U-shaped Network

Automatic tumor segmentation of thyroid ultrasound image is quite challenging due to the poor image quality. Recently the U-shaped network, especially U-Net, has achieved good results in medical image segmentation. In this paper, we proposed a modified U-Net model (ReAgU-Net), which embedded the improved residual units into the skip connection among the encoding and decoding path and introduce the attention gate mechanism to multiply the weight feature maps obtained from shallow layers and deep layers. Also, a hyperparameter is introduced to combine Focal-Tversky Loss, Dice Loss and Cross-entropy Loss to jointly guide the model optimization process. The experimental results demonstrate that the proposed approach outperforms the other U-shaped models.
On the Usability of Consumer Locomotion Techniques in Serious Games: Comparing Arm Swinging, Treadmills and Walk-in-Place

When we refer to locomotion in Virtual Reality (VR) we subtend a vast and variegated number of investigations, solutions and devices coming from both research and industry. Despite this richness, a consolidated methodology for evaluating the many locomotion techniques available is still lacking. The present paper extends a previous work in which authors performed a user study-based comparison between two common locomotion techniques, i.e., Arm Swinging, and an omnidirectional treadmill with a containment ring. In the study, users were engaged in a realistic immersive VR scenario depicting a fire event in a road tunnel. Remaining adherent to the previously defined methodology, the current work widens the comparison to consider two other locomotion methods (keeping results obtained with the former technique above for reference purposes), namely, a different treadmill constraining the user through a top-mounted independent support structure, and Walk-in-Place, a technique which allows the user to move in the virtual environment by performing a natural marching gesture by exploiting two sensors placed on his or her legs.
Mapping Constrained Slot-Placement Problems to Ising Models and its Evaluations by an Ising Machine

Ising machines have attracted attention, which is expected to obtain better solutions of various combinatorial optimization problems at high speed by mapping the problems to natural phenomena. A slot-placement problem is one of the combinatorial optimization problems, regarded as a quadratic assignment problem, which relates to the optimal logic-block placement in a digital circuit as well as optimal delivery planning. Here, we propose a mapping to the Ising model for solving a slot-placement problem with additional constraints, called a constrained slot-placement problem, where several item pairs must be placed within a given distance. Since the behavior of Ising machines is stochastic, the obtained solution does not always satisfy the slot-placement constraint, which is different from the conventional methods such as the conventional simulated annealing. To resolve the problem, we propose an interpretation method in which a feasible solution is generated by post-processing procedures. Using an Ising machine computer, feasible solutions could be obtained up to 50 times faster than the conventional simulated annealing without degrading accuracy for constrained slot-placement problems with 6 × 6 slots and 27 items at the maximum.
Characterization and classification of semantic image-text relations

Abstract The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics Cross-Modal Mutual Information and Semantic Correlation in order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight image-text classes. Experimental results show the feasibility of the approach, whereby the predict-all approach outperforms the cascaded approach of the metric classifiers.
Deep tone mapping network in HSV color space

Tone mapping operators can convert high dynamic range (HDR) images to low dynamic range (LDR) images so that we can enjoy the informative contents of HDR images with LDR devices. However, current state-of-the-art tone mapping algorithms mainly focus on the luminance mapping while neglecting the color component. Meanwhile, they often suffer from halo artifacts and over-enhancement. In this paper, we propose a tone mapping network (TMNet) in Hue-Saturation-Value (HSV) color space to obtain better luminance and color mapping. We adopt the improved Wasserstein generative adversarial network (WGAN-GP) as the basic architecture and further introduce several improvements. A meticulously designed loss function is adopted to push tone mapped image to the natural image manifold. What’s more, we create a tone mapped image dataset in which the label images are manually adjusted by photographers. Compared with some state-of-the-art tone mapping methods, the proposed method can achieve better performance in both subjective and objective evaluations.
Asymmetric response aggregation heuristics for rating prediction and recommendation

User-based collaborative filtering is widely used in recommendation systems, which normally comprises three steps: (1) finding the nearest conceptual neighbors, (2) aggregating the neighbors’ ratings to predict the ratings of unrated items, and (3) generating recommendations based on the prediction. Existing algorithms mainly focus on steps 1 and 3 but neglect subtle treatments of aggregating neighbors’ suggestions in step 2. Based on the discovery of psychology that (i) users’ responses to positive and negative suggestions are different, and (ii) users may respond differently from one another, this paper proposes a Personal Asymmetry Response-based Suggestions Aggregation (PARSA) algorithm, which first uses a linear regression method to learn each user’s response to negative/positive suggestions from neighbors and then uses a gradient descent algorithm for optimizing them. In addition, this paper designs an Identical Asymmetry Response-based Suggestions Aggregation (IARSA) baseline algorithm, which assumes that all the users’ responses to suggestions are identical as references to verify the key contribution of the heuristics employed in our PARSA algorithm that user may responses differently to positive and negative suggestions. Three sets of experiments are designed and implemented over two real-life datasets (i.e., Eachmovie and Netflix) to evaluate the performance of our algorithms. Further, in order to eliminate the influence of different similarity measures, this paper selects three kinds of similarity measures to discover neighbors. Experimental results demonstrate that most people indeed pay more attention to negative suggestions and our algorithms achieve better prediction and recommendation performances than the compared algorithms under various similarity measures.
A lifelong spam emails classification model

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift” . In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.
Input initialization for inversion of neural networks using k-nearest neighbor approach

Inversion of neural networks aims to find optimal input variables given a target output, and is widely applicable in an industrial field such as optimizing control variables of complex systems in manufacturing facilities. To achieve optimal inputs using a standard first-order optimization technique, proper initialization of input variables is essential. This paper presents a new initialization method for input variables of neural networks based on k-nearest neighbor (k-NN) approach. The proposed method finds inputs which resulted in an output close to a target output in a training dataset, and combine them to form initial input variables. Experiments on a toy dataset demonstrate that our method outperforms random initialization. Also, we introduce an exhaustive case study on power scheduling of a heating, ventilation, and air conditioning (HVAC) system in a building to support the effectiveness of the algorithm.
Cost-driven scheduling of service processes in hybrid cloud with VM deployment and interval-based charging

Cost-driven scheduling of service processes mainly focuses on task allocation to achieve the cost minimization from a user's perspective. As an important challenge, cost-driven scheduling of service processes in hybrid cloud has been widely studied. However, existing researches seldom precisely formulate the virtual machine resources and their capacity constraints in hybrid cloud, which may reduce the elasticity or lead to unfeasible solutions. Besides, they do not comprehensively consider the interval-based charging, which cannot precisely calculate and reduce the cost. In this paper, we propose a more practical model for service processes scheduling in hybrid cloud, in which resource constraints are defined more precisely without reducing the elasticity by virtual machine deployment, and a comprehensive interval-based charging including billing cycle and sustained-use discount is considered. This problem is solved by an improved fireworks algorithm based approach, in which specific representation, decoding and fireworks updating strategies are proposed for the mechanisms of virtual machine deployment and interval-based charging. Experiments are conducted using service processes with different problem sizes, and results show that our approach can find scheduling solutions with lower costs than existing algorithms.
Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0

Abstract Indonesia is one of the developing countries that have serious problems with poverty. The still many poor people in Indonesia encourage the government to make and determine the right policies so that the problem of poverty can be overcome and not drag on. Therefore, the authors conducted this study to try to help the government conduct an analysis in predicting the level of development of the poor in Indonesia. The prediction method used is the Bayesian Regulation artificial neural network. This method is a development of the backpropagation method that is often used to predict data. The data used are data on poor people in Indonesia in 2012-2018, which are sourced from the Indonesian Central Bureau of Statistics. Based on this data a network architecture model will be formed and determined using the Bayesian Regulation method, including 10-5-10-2, 10-10-10-2, 10-10-15-2, 10-10-20-2, 10-15-10-2, 10-15-15-2, 10-15-20-2, 10-20-20-2, 10-25-25-2 and 10-30-30-2. From these 10 models after training and testing, the results show that the best architectural model is 10-25-25-2. The accuracy of the architectural models is 94.1% and 61.8% with MSE values of 0,00013571 and 0,00005189. The results of this study are the prediction of the poor for the next 5 years.
Snapshot phase-shifting lateral shearing interferometer

We propose a novel, low-cost snapshot phase-shifting lateral shearing interferometer (SPLSI) with full-range continuously adjustable lateral shear ratio. A tunable shear plate, consisting of a polarizing beam splitter (PBS) plate and a reflective mirror, separates the orthogonally polarization states of the test wavefront into original and sheared wavefronts. The lateral shear ratio can be tuned by adjusting the spacing between PBS plate and the mirror. A pixelated polarization camera simultaneously captures four π/2 phase-shifted interferograms for phase-shifting interferometric measurement. In addition to tunable shear ratio and snapshot capture of phase-shifted interferograms, one more key feature is that SPLSI can measure the wavefront of low-coherence light. The feasibility of the proposed system is demonstrated experimentally. The proposed system provides a simple, compact, and fast way to achieve the instantaneous phase-shifting lateral shearing interferometric testing of wavefronts with minimal impact from environmental disturbance.
Occlusion-aware UAV Path Planning for Reconnaissance and Surveillance in Complex Environments

This paper concerns the reconnaissance and surveillance problem for multi-rotor aerial drones or unmanned aerial vehicles (UAVs) flying over geometrically complex environments, such as mountainous terrains and urban regions. In contrast to the existing literature, our approach takes both UAV kinematics constraints and camera sensing constraints into consideration. We present a two-stage strategy to solve this UAV surveillance problem with the given terrain information. In the first stage, the challenge is to find a set of camera locations called the vantage waypoint set to provide full coverage of the area of interest, which can be viewed as a 3D Art Gallery Problem using drones as the observers. In the second stage, one or several UAVs are determined to conduct the full coverage reconnaissance and surveillance duty along individual routes respecting their kinematic constraints in the optimization criterion (the shortest time possible). This variant of the combinatorial traveling salesman problem is solved by introducing unsupervised learning and Bézier curves. The reported results support the feasibility of the proposed solution.