Reviewerly/exHarmony huggingface dataset:
A New Heuristic Optimization: Search and Rescue Algorithm and Solving the Function Optimization Problems

Heuristic techniques are optimization methods that inspired by nature. Although there are many heuristics in the literature, a new heuristic technique is presented by researchers every day by observing nature-based or living behaviors in nature. In this study, a new heuristic optimization technique inspired by human behavior is proposed. In order to prove the validity of this method called Search and Rescue Optimization Algorithm (AKOA), the technique applied to find the global minimums of function optimization test problems in the literature. As a result of the experiments performed on 21 minimization problems, it has been observed that AKOA is quite competitive when compared to Dynamic Random Search Technique and Random Selection Walk Technique.
Universal scattering with general dispersion relations

Many synthetic quantum systems allow particles to have dispersion relations that are neither linear nor quadratic functions. Here, we explore single-particle scattering in general spatial dimension $D\ensuremath{\ge}1$ when the density of states diverges at a specific energy. To illustrate the underlying principles in an experimentally relevant setting, we focus on waveguide quantum electrodynamics (QED) problems (i.e., $D=1$) with dispersion relation $\ensuremath{\epsilon}(k)=\ifmmode\pm\else\textpm\fi{}|d|{k}^{m}$, where $m\ensuremath{\ge}2$ is an integer. For a large class of these problems for any positive integer $m$, we rigorously prove that when there are no bright zero-energy eigenstates, the $S$ matrix evaluated at an energy $E\ensuremath{\rightarrow}0$ converges to a universal limit that is only dependent on $m$. We also give a generalization of a key index theorem in quantum scattering theory known as Levinson's theorem---which relates the scattering phases to the number of bound states---to waveguide QED scattering for these more general dispersion relations. We then extend these results to general integer dimensions $D\ensuremath{\ge}1$, dispersion relations $\ensuremath{\epsilon}(\mathbit{k})={|\mathbit{k}|}^{a}$ for a $D$-dimensional momentum vector $\mathbit{k}$ with any real positive $a$, and separable potential scattering.
A Crash Course in Creative Commons Licensing

As the number of openly-licensed works continues to grow, so, too, does the need to understand the permissions afforded by the Creative Commons licenses. In this report on a recent program delivered at the North Carolina Serials Conference, the presenters will provide an introduction to Creative Commons, the four conditions upon which the Creative Commons licenses are built, and a description of the six Creative Commons Commons licenses accompanied by a real-life use scenario. A brief discussion of how to locate openly-licensed works will also be addressed.
Communication Assistant Using IoT-Based Device for People with Vision, Speech, and Hearing Disability

According to the census of 2011, about 10.6 million people are visually impaired, 1.2 million people have hearing disability, and 1.6 million people are speech impaired in India. In this paper, we propose an Internet of Thing (IoT)-based integrated device for these people in order to provide them a user-friendly and interactive environment through which they can easily communicate with others and interact with their surroundings. The device provides advanced features like facial recognition, emotion recognition, object recognition, sign language recognition, cloth pattern and color recognition, distance from an object, and optical character recognition. By giving input in the form of voice, text, and sign, one can use different functionalities of the device. Algorithms like image-to-text, text-to-speech, and speech-to-text conversion are implemented to provide intended functionality. Convolutional Neural Network (CNN) is used for feature extraction and classification. The proposed device considers various communications between speech, vision, and hearing impaired people with others.
An Interactive Cooking Support System for Short Recipe Videos based on User Browsing Behavior

Recently, short recipe videos such as Kurashiru and DELISH KITCHEN have become popular. These short recipe videos can help people learn many cooking skills in a brief time. However, it is difficult for users to understand all cooking operations by viewing these videos only once. These short recipe videos do not consider users’ cooking skills (cooking levels) since anyone may view the same video. Therefore, in this work, we propose an interactive cooking support system for short recipe videos by extracting and weighting cooking operations for each cooking genre based on user browsing behavior. The system then recommends various supplementary recipe videos based on the weights of cooking operations and user browsing behavior. Also, the system provides a user interface, called Dynamic Video Tag Cloud for visualizing the supplementary recipe videos, and the supplementary recipe videos can be dynamically changed based on the user browsing behavior. As a result, users can intuitively and easily understand cooking operations suited to their cooking favorites. Finally, we verified the effectiveness of the weighting of cooking operations and discussed the usefulness of our proposed user interface using the SUS score.
Analysis on the Training Mode of Listening and Speaking Skills in College Japanese Education from the Perspective of Rain Classroom

With the rapid development of network information technology, many new teaching models have emerged. As a new teaching model, rain classroom plays a very important role in college Japanese teaching. Based on this, this paper analyzes the problems existing in the training of listening and speaking skills in college Japanese education, and puts forward the training mode of Japanese listening and speaking skills based on rain classroom for reference.
Supporting Today's Multi-Vendor Networks

A multi-vendor environment refers to a computer network that supports tools from more than one manufacturer, vendor or provider. This is often considered to be a hybrid approach where companies add...
Can We Evaluate the Distinguishability of the Opensarurban Dataset?

In Synthetic Aperture Radar (SAR) image classification tasks, the performance depends on both the classifier and the dataset itself. However, in comparison with plenty of SAR classification methods, there is little work aimed at analyzing the distinguishability of the dataset. In the classification dataset, some classes are semantically different but their distinguishability is low, the classes are hard to be classified especially in some more practical cases that there are unknown classes without supervision exist. Referring to open set recognition (OSR), in this paper, we proposed the SAR Distinguishability Analysor (SAR-DA) to evaluate the distinguishability of the OpenSARUrban dataset. By modeling each class as a multivariate Gaussian distribution in latent space, SAR-DA can not only classify the classes having been seen in training phase, but also can recognize unknown samples if a test sample is out of each known distribution. Each class in OpenSARUr-ban is set unknown in turn, then we apply the SAR-DA on the split dataset in OSR and supervised setting. The distinguishability can be reflected by the unknown recognition recall rate. The experimental results show that the unknown recognition recall rate in OSR setting significantly decreased compared with those in supervised setting, indicating that even though the classes in OpenSARUrban are semantically different from each other, the latent distributions of some classes are quite similar and hard to be classified, thus these classes are of low distinguishability.
Positioning of Controlling in the Performance Management System of Cultural Sector Companies

The article studies the specifics of implementation of controlling in cultural sector companies. Hindering factors and grounds for implementation of the controlling system in cultural sector companies were revealed on the basis of the results of empiric studies. The instruments of operating and strategic controlling were considered. Scientific novelty of the study lies in revelation of the factors hindering implementation of the controlling system and setting of the controlling instruments for measurement of the performance efficiency of cultural sector companies.
UAV Visual Target Tracking: Research Status, Algorithms and Future Prospect

Nowadays the unmanned aerial vehicle (UAV) has been widely used in civilian and military fields, such as target search and tracking, owing to its small size and unique flexibility. Although visual target tracking technology has made remarkable progress, it is still a challenging problem to achieve robust and stable tracking due to various factors in actual complex environment. Firstly, this paper introduces the research status of target tracking, and reviews some classical and latest UAV target tracking algorithms, especially single-UAV and multi-UAV cooperative target tracking algorithms. Then, the UAV datasets and evaluation metrics of target tracking have been introduced. Finally, the future directions of UAV target tracking algorithms are proposed.
The Role of Information and Communication Technologies in Human Interactions

The development and diffusion of Information and Communication Technologies (ICT) increased the availability of a large number of communication channels. Some big problems arise when these new tools operate pervasively in our lives. The evolution of these technologies and their development, with the help of uncountable applications, are influencing and modifying our behavior. Unfortunately the respect of human rights is based again on the application of regulations and laws that are formally structured around the human beings. The application of laws is localized into the domain of the national law systems. Furthermore the distance between the quick evolution of the new technical applications and the slow update of the law systems increases constantly. The artificial tools that compose ICT applications are now again out of any regulatory system. In particular the experiences imposed by the diffusion of COVID-19 virus show the great help given by the application of ICT to contrast and react the pandemic diffusion. At the same time the risks to loss the respect of human rights emerge with great evidence.
Large Feature Mining With Ensemble Learning for Image Forgery Detection

The detection of different types of forgery manipulation including seam-carving in JPEG images is a hot spot in image forensics. Seam carving was originally designed for content-aware image resizing. It is also being used for forgery manipulation. It is still very challenging to effectively identify the seam carving forgery under recompression. To address the highly challenging detection problems, this chapter introduces an effective approach with large feature mining. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. The experimental results validate the efficacy of proposed approach to detecting JPEG double compression and exposing the seam-carving forgery while the JPEG recompression is proceeded at the same quality and a lower quality, which is generally much harder for traditional detection methods. The methodology introduced in this chapter provides a strategy and realistic approach to resolve the highly challenging problems in image forensics.
Chaotic systems learning with hybrid echo state network/proper orthogonal decomposition based model

Abstract We explore the possibility of combining a knowledge-based reduced order model (ROM) with a reservoir computing approach to learn and predict the dynamics of chaotic systems. The ROM is based on proper orthogonal decomposition (POD) with Galerkin projection to capture the essential dynamics of the chaotic system while the reservoir computing approach used is based on echo state networks (ESNs). Two different hybrid approaches are explored: one where the ESN corrects the modal coefficients of the ROM (hybrid-ESN-A) and one where the ESN uses and corrects the ROM prediction in full state space (hybrid-ESN-B). These approaches are applied on two chaotic systems: the Charney–DeVore system and the Kuramoto–Sivashinsky equation and are compared to the ROM obtained using POD/Galerkin projection and to the data-only approach based uniquely on the ESN. The hybrid-ESN-B approach is seen to provide the best prediction accuracy, outperforming the other hybrid approach, the POD/Galerkin projection ROM, and the data-only ESN, especially when using ESNs with a small number of neurons. In addition, the influence of the accuracy of the ROM on the overall prediction accuracy of the hybrid-ESN-B is assessed rigorously by considering ROMs composed of different numbers of POD modes. Further analysis on how hybrid-ESN-B blends the prediction from the ROM and the ESN to predict the evolution of the system is also provided.
Deep Embedded Knowledge Graph Representations for Tactic Discovery

Using unsupervised Machine Learning (ML) techniques for the discovery of commonalities within a dataset is a well-established approach. However, existing clustering methods require relationships to be represented within the data, making discovery difficult a priori since unlabeled classes are discovered through exploratory clustering. To circumvent exploratory class labeling, we propose a feature-rich, connected structure (i.e., semantic graph), rather than a feature-engineered vectorization, enabling the encoding of maximal information despite class uncertainty. Expanding upon previous tactics discovery work, the authors present a systematic approach using knowledge graph representations and graph embeddings to discover tactics ab initio from data characteristics (DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. (OPSEC #4189)).
Explanations of Non-monotonic Inference in Admissibility-Based Abstract Argumentation

In this paper, we introduce a formal framework for explaining change of inference in abstract argumentation, in particular in the context of iteratively drawing inferences from a sequence of normal expansions, with a focus on admissible set-based semantics. We then conduct a formal analysis, showing that given an initial argumentation framework and an extension that has been inferred from it, we can guarantee the existence of explanation arguments for the violation of monotony when inferring an extension from a normal expansion of the initial argumentation framework.
On the Detecting Anomalies within the Clickstream Data: Case Study for Financial Data Analysis Websites

In current systems, each user behaves uniquely and within certain behavior boundaries. Behaviors that go beyond these boundaries are called anomaly behavior. Detection of such behaviors is very important, especially in the financial sector. The most important reason for this is that behaviors that are out of normal may also threaten the existence of the system, so they have the potential to cause economic damage to the company. Previous studies have been done without using big data platforms and have problems with big data processing. In our solution, we performed machine learning algorithms on big data platforms for this process. This solution includes extracting features from user behavior data, detecting outliers with the help of boxplot analysis, and then processing these data with machine learning on big data processing platforms. At the end of our study, success and performance comparisons of machine learning algorithms and big data processing platforms were made.
t-SNE-Based K-NN: A New Approach for MNIST

K-nearest neighbors (K-NN) is an effective lazy and instance-based learner. It is natural to scale the K-NN to big data. In this paper, we propose to conduct spectral clustering through t-distributed stochastic neighboring embedding (t-SNE) and then apply k-nearest neighbor to improve its performance and accuracy on big data.
Improved gesture detection algorithm based on YOLOv3

In order to solve the problem of low accuracy and serious redundancy of traditional gesture detection algorithms in first-view and multi-gesture scenarios, this paper proposes an improved gesture detection algorithm based on the YOLOv3 network framework. First, use the K-means++ clustering algorithm instead of K-means to re-cluster the scale of the training set to obtain a set of Priors that are more in line with the gesture distribution; then, the Focal Loss is introduced to improve the loss function to solve the problem of extremely imbalanced positive and negative samples in the detection process, and let the network pay more attention to samples that are difficult to train; finally, improve the non-maximum suppression algorithm, get a result closer to ground truth by weighting the candidate boxes, and remove redundant candidate boxes by judging the positional relationship between the candidate bounding boxes. Experiments in the Egohands data set show that the algorithm proposed in this paper has a greater improvement in accuracy and mAP compared to the original YOLOv3 algorithm.
An Adaptive Iterative Inpainting Method with More Information Exploration

The CNN-based image inpainting methods have achieved promising performance because of its outstanding semantic understanding and reasoning potentialities. However, previous works could not get satisfied results in some situations because information is not fully explored. In this paper, we propose a new method by combining three innovative ideas. First, to increase the diversity of the semantic information obtained by the network in image synthesis, we propose a multiple hidden space perceptual (MHSP) loss, which extracts high-level features from multiple pre-trained autoencoders. Second, we adopt an adaptive iterative reasoning (AIR) stategy to reduce the calculations under small-hole circumstances while ensuring the performance in large-hole circumstances. Third, we find that color inconsistencies occasionally occurred in the final image merging process, so we add a novel interval maximum saturation (IMS) loss to the final loss function. Experiments on the benchmark datasets show our method performs favorably against state-of-the-art approaches. Code is made publicly available at: https://github.com/IC-LAB/adaptive_iterative_inpainting.
The limitations, dangers, and benefits of simple methods for testing identifiability

In their Commentary paper, Villaverde and Massonis (On testing structural identifiability by a simple scaling method: relying on scaling symmetries can be misleading) have commented on our paper in which we proposed a simple scaling method to test structural identifiability. Our scaling invariance method (SIM) tests for scaling symmetries only, and Villaverde and Massonis correctly show the SIM may fail to detect identifiability problems when a model has other types of symmetries. We agree with the limitations raised by these authors but, also, we emphasize that the method is still valuable for its applicability to a wide variety of models, its simplicity, and even as a tool to introduce the problem of identifiability to investigators with little training in mathematics.
Multi-output Infinite Horizon Gaussian Processes

Learning the uncertain dynamical environments for online learning and prediction from noisy sensory measurement streams is essential for various tasks in robotics. Recently, Gaussian process (GP) online learning such as an infinite-horizon Gaussian process (IHGP) has shown effectiveness to cope with non-stationary dynamical random processes in learning hyperparameters online by reducing the computational cost. However, the IHGP was originally proposed to deal with only a single-output. Therefore, to tackle complex real-world problems, we propose a multi-output infinite-horizon Gaussian process (MOIHGP) that generalizes the single-output IHGP to deal with multiple outputs for better prediction. Our approach allows us to consider correlations between multiple outputs for better prediction, even with occlusions in a Bayesian way. Finally, we successfully demonstrate the effectiveness of our approach by benchmark and experimental results. For simulated benchmark experiments with high noise levels, our approach reduced 16.6% of the averaged RMSE value achieved by the single-output IHGP.
Multiscale Hyperspectral Imaging of Hydrothermal Alteration in Yellowstone National Park, USA

Imaging spectroscopy (hyperspectral imaging) data have mainly been used to map surface materials covering relatively small areas from airborne sensors over the past 20+ years. As part of the U.S. Geological Survey Integrated hyperspectral, geophysical and geochemical studies of Yellowstone National Park hydrothermal systems project, we have collected multiscale imaging spectrometer data including borehole core, field, and airborne data. These data give us the unique opportunity to map subsurface and surface alteration of shallow epithermal systems at scales ranging from microns to meters per pixel. Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), Corescan HCI-3, HySpex VNIR-1800 and SWIR-384 imaging spectrometers, and a Riegl VZ-2000i terrestrial lidar system were used in this study. Maps utilizing spectral analysis of multiscale hyperspectral data indicate the presence of mineral assemblages consistent with epithermal deposits. Minerals such as alunite, hydrated silica, and kaolinite typically form in steam-heated cap systems and can be found in core and AVIRIS data. At depth, higher temperature fluids that are less acidic produce mixed-layer kaolinite with clay/muscovite and then montmorillonite and muscovite towards the bottom of the borehole. This transition can be seen in AVIRIS and field data at the Grand Canyon of the Yellowstone and in borehole Corescan data at Y-12 Norris-Ii R874. Instrument specifications, collection parameters, and setup for data acquisition will be discussed as well as mapping software and preliminary results.
Aggregating Inter-viewpoint Relationships of User’s Review for Accurate Recommendation

User reviews contain rich information about user interests in items. Recently, many deep learning methods have attempted to integrate review contents into user preference rating prediction, helping to solve data sparseness problems. However, existing methods suffered from an inherent limitation that many reviews are noisy and contain non-consecutive viewpoints, besides, they are insufficient to capture inter-viewpoint relationships. Incorporating useful information is helpful for more accurate recommendations. In this paper, we propose a neural recommendation approach with a Diversity Penalty mechanism and Capsule Networks, named DPCN. Specifically, the diversity penalty component employs weight distributed matrices with the penalization term to capture different viewpoints in textual reviews. The capsule networks are designed to aggregate individual viewpoint vectors to form high-level feature representations for feature interaction. Then we combine the review feature representations with the user and item ID embedding for final rating prediction. Extensive experiments on five real-world datasets validate the effectiveness of our approach.
A Robust Blade Profile Feature Parameter Identifying Method

The blade is the key part of aero-engine. The detection of the blade profile feature parameters is an important aspect in blade manufacturing. However, the surface of the blade is free-form surface. It is difficult to extract feature parameters. In this paper, a robust feature identify method is proposed. First, the theoretical profile is segmented to concave, convex, leading edge and trailing edge by a regional identifying method. Second, the actual profile is segmented to four parts through the theoretical profile. According to the actual segmentation results, the profile feature parameter is solved. The simulation and experiment manifest that the proposed method is well robustness.