Reviewerly/exHarmony huggingface dataset:
Autonomous path planning strategy for water-air amphibious vehicle based on improved A* algorithm

In order to solve the problem of autonomous path planning for water-air amphibious unmanned aerial vehicle (UAV), a path planning method combining search strategy and improved A* algorithm has been proposed. In view of factors that affect the performance of amphibious vehicle, the cost function containing various heuristic information has been redefined. The classical A* algorithm has been improved and optimized by changing the corresponding cost weights to adjust the function of heuristic information in path planning. At the same time, the judgment of whether there are obstacles between adjacent nodes is added in the traversal process of the algorithm. The simulation result shows that with different cost weights, appropriate amphibious route can be generated through our improved algorithm. Under different constraints, the three-dimensional (3D) route can meet the needs of unmanned water-air amphibious vehicle.
A survey of RGB-D image semantic segmentation by deep learning

The semantic segmentation task one of a popular difficult problem in computer vision. This task takes great attention to the community of computer vision. Semantic segmentation segments the image which is input into meaningful semantically related regions in another expression, it predicates for each pixel inside image a class label, with appears of depth cameras it provides very useful organized rich information of data depth, RGB-D image not related with illumination when a mixed feature of RGB-D images with depth information features can very improve the accuracy of semantic segmentation. Recently, deep learning satisfied semantic segmentation more easily than traditional methods because of neural networks' power to automatically learn different representations of proper features. This paper survey provides a background for several techniques work on 2.D RGB-D dataset semantic segmentation in various applications on several different out and door free datasets.
Innovative Thinking and Practice of Mobile Interaction Design Teaching in Artificial Intelligence Era

In recent years, the rapid development of artificial intelligence opens a new era and has had a profound influence on the application scenarios, design objects and design methods of interaction design. The intelligent development of mobile applications has become the focus of Internet companies, brings a huge demand for crossover and diversified interaction design talents. For university teachers, it is a task ahead to cultivate high-quality design talents who can adapt to the changing needs of the times. In this context, this paper analyzes the new connotation of interaction design in intelligent era and the new requirements for the knowledge structure and innovative ability of design talents. Based on this, we present new teaching concept for Mobile Interaction Design course and a complete teaching reform plan of optimizing and expanding teaching content modules, adopting thematic task-driven and enterprise-participated teaching mode, which we explore and practice in our course teaching and achieve good results.
A Survey on “Text-to-Speech Systems for Real-Time Audio Synthesis”

Text to Speech (TTS) is a form of speech synthesis wherein the text is converted right into a spoken human-like voice output. The state of the art strategies for TTS employs a neural network based totally method. This paintings pursuits to take a look at a number of the problems and barriers gift inside the contemporary works, especially Tacotron-2, and attempts to in addition enhance its performance by means of editing its structure. till now many papers were published on these topics that display various exceptional TTS structures by means of developing new TTS products. The aim is to have a look at different textual content-to-Speech structures. in comparison to different text-to-Speech systems, Tacotron2 has multiple blessings. In opportunity algorithms like CNN, speedy-CNN the algorithmic program may not investigate the photo fully however in YOLO the algorithmic application check out the picture absolutely by predicting the bounding boxes through using convolutional network and possibilities for those packing containers and detects the image faster in comparison to alternative algorithms.
A Kernel Density Estimation-Based Variation Sampling for Class Imbalance in Defect Prediction

In software engineering, software defect prediction can help to discover the defect modules and improve the quality of software products. However, class imbalance makes the recognition of minority defective class difficult. Resampling on the datasets is a widely used approach to solve this problem and its objective is to balance the numbers of different classes without too much changes on their original distributions. A novel resampling approach called kernel density estimation-based variation sampling (DVS) is proposed to achieve this goal by generating new minority defective samples. The “variation” means that: For each new sample, more than half eigenvalues are directly inherited from the existing defective samples, but others will be varied. The varied eigenvalues can be obtained by sampling from their corresponding probability distribution models based on the kernel density estimation. Finally, the new defective samples different from those in training datasets but consistent with their overall distribution can be generated, which can help the prediction models to recognize various defective modules better. The DVS was compared with K-Means SMOTE, MAHAKIL, RUS and the No sampling approach using total 23 releases of defect datasets from the PROMISE, NASA and Eclipse repositories, and CART was adopted as the classifier. The experiment results indicate that the DVS can obviously improve the Recall and G-means of SDP model without too much loss on Precision.
MULTIVERSE

Data analyses are based on a series of "decision points" including data filtering, feature operationalization and selection, model specification, and parametric assumptions. "Multiverse Analysis" research has shown that a lack of exploration of these decisions can lead to non-robust conclusions based on highly sensitive decision points. Importantly, even if myopic analyses are technically correct, analysts' focus on one set of decision points precludes them from exploring alternate formulations that may produce very different results. Prior work has also shown that analysts' exploration is often limited based on their training, domain, and personal experience. However, supporting analysts in exploring alternative approaches is challenging and typically requires expert feedback that is costly and hard to scale.
Care-giver alerting for bedridden patients using hand gesture recognition system

Abstract Hand gestures are most intrinsic way of non-verbal communication. It has been difficult task for hospitals to remove the miscommunication between patients and the healthcare providers, when patients ask for help in emergency or in need. In healthcare centers like hospitals, some systems are having complicated device installation which are expensive and quite hard cumbersome. Human Computer Interaction plays a predominant role in Hand Gesture recognition (HCI). Human Computer Interaction made easy to communicate between human and machine which grabs good attention now a days. An alerting system makes a better alarm model which notifies care-givers when patients require assistance by giving defined hand gestures. The study is related to hand gesture based alerting system which uses camera module to capture the hand images. The system uses background subtraction, image recognition and speech conversion to notify the healthcare providers. It can be useful in hospitals, rehabilitation centers, or assisted living areas, where health care providers can take care of their patients more attentively. With this alerting system it increases the operational efficiency, responsiveness, patient’s and other stakeholders’ satisfaction. For recognized hand gesture text and voice is obtained as output which alerts the care-giver to help the bed-ridden patients.
Indian Sign Language Recognition Using Combined Feature Extraction

This research paper aims for the recognition of Indian sign language (ISL). Sign language is a language commonly used by deaf and dumb people to communicate with each other and rest of the world. There is an extensive research carried out for American sign language (ASL), but due to the lack of standard dataset, research for Indian sign language recognition is hampered a lot. This research work focuses on the use of a combined feature extraction technique so as to improve the accuracy and reduce complexity. Histogram of orientation gradient (HOG) and Gabor features are combined and classified using support vector machine (SVM) and K-Nearest neighbor (KNN) with accuracy of 83.92% and 84.92%, respectively.
On Cointegration and Cryptocurrency Dynamics

This paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.
Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction

Relation prediction informed from a combination of text corpora and curated knowledge bases, combining knowledge graph completion with relation extraction, is a relatively little studied task.A system that can perform this task has the ability to extend an arbitrary set of relational database tables with information extracted from a document corpus.OpenKi (Zhang et al., 2019) addresses this task through extraction of named entities and predicates via OpenIE tools then learning relation embeddings from the resulting entityrelation graph for relation prediction, outperforming previous approaches.We present an extension of OpenKi that incorporates embeddings of text-based representations of the entities and the relations.We demonstrate that this results in a substantial performance increase over a system without this information.https://github.com/drevicko/OpenKI
The Signed Cumulative Distribution Transform for 1-D signal analysis and classification

&lt;p style='text-indent:20px;'&gt;This paper presents a new mathematical signal transform that is especially suitable for decoding information related to non-rigid signal displacements. We provide a measure theoretic framework to extend the existing Cumulative Distribution Transform [&lt;xref ref-type="bibr" rid="b29"&gt;29&lt;/xref&gt;] to arbitrary (signed) signals on &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \overline {\mathbb{R}} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. We present both forward (analysis) and inverse (synthesis) formulas for the transform, and describe several of its properties including translation, scaling, convexity, linear separability and others. Finally, we describe a metric in transform space, and demonstrate the application of the transform in classifying (detecting) signals under random displacements.&lt;/p&gt;
Evolutionary Robust Clustering Over Time for Temporal Data

In many clustering scenes, data samples' attribute values change over time. For such data, we are often interested in obtaining a partition for each time step and tracking the dynamic change of partitions. Normally, a smooth change is assumed for data to have a temporal smooth nature. Existing algorithms consider the temporal smoothness as an a priori preference and bias the search toward the preferred direction. This a priori manner leads to a risk of converging to an unexpected region because it is not always the case that a reasonable preference can be elicited given the little prior knowledge about the data. To address this issue, this article proposes a new clustering framework called evolutionary robust clustering over time. One significant innovation of the proposed framework is processing the temporal smoothness in an a posteriori manner, which avoids unexpected convergence that occurs in existing algorithms. Furthermore, the proposed framework automatically infers the a posteriori preference to temporal smoothness without data's affinity matrix and predefined parameters, which holds better applicability and efficiency. The effectiveness and efficiency of the proposed framework are confirmed by comparing with state-of-the-art algorithms on both synthetic and real datasets.
Financial Bubbles for Economic Development: Implications Regarding ICOs

Abstract Most people consider financial bubbles as the result of speculations and manipulations, and that less of them will make a market economy more efficient. While speculative bubbles no doubt increase the risks and uncertainties of investors, there are many episodes in human civilization where financial bubbles are commensurate with real positive contribution to real development of an economy. This paper analyzes this issue from theoretical perspectives and in the context of recent new financial instruments created from the technology of blockchain.
What Type of Asset is Bitcoin? An Answer from the Stock Market

In recent years, bitcoin has been at the center of a debate: is it a financial asset or not? If it is an asset, what kind of asset is it? Is it a tradable, speculative asset, or is it a long-term investment? The debate has largely unfolded around the real nature of Bitcoin and its correlation with other asset classes. Some scholars have discussed the correlation coefficient of bitcoin using the S& P 500 Index, the Volatility Index, and gold. However, the debate is now sufficiently mature to discuss the reasons determining its correlation with other assets. We study the correlations of Bitcoin and one benchmark, a stock market index-specifically, the Nasdaq 100 Index. Our focus is on the correlation between Bitcoin and the Nasdaq 100 as a proxy for the most representative technology stocks. We show that the correlation between the two has grown substantially since 2018, and has increased more since the beginning of the COVID-19 pandemic.
Simulation of Obstacle Detection of An Autonomous Car

Perceiving the surroundings accurately and quickly is one of the most essential and challenging tasks for systems such as self-driving cars. view to the car making it more informed about the environment than a human driver. To build a fully virtual self-driving car, we have to build two things, Self-driving car software and virtual Self-driving car. Self-driving software can do two things one is based on video input of the road, the software can determine how to safely and effectively steer the car another is based on video input of the road, the software can determine how to safely and effectively use the car’s acceleration and braking mechanisms.
A Lightweight Saliency Prediction Model for Omnidirectional Images

At present, most high-performing saliency prediction models for omnidirectional images (ODIs) depend on deeper or wider convolutional neural networks (CNNs), benefiting from their superior feature representation capability but suffering from high computational costs. To address this issue, we propose a novel lightweight saliency prediction model to predict the eye fixations on ODIs. Specifically, our proposed model consists of three modules: a lightweight feature representation module, a supervised attention module, and a dynamic convolution aggregation module. Different from the existing saliency prediction models, our proposed model is the first to introduce the dynamic convolution into the saliency prediction and aggregate multiple parallel convolution kernels dynamically based on their attention. Such a dynamic convolution operation is not only computationally efficient (small kernel size), but also increases the feature representation capability since these convolution kernels are aggregated in a non-linear manner via attention. Experimental results on two benchmark datasets show that our model is lightweight and outperforms other state-of-the-art methods.
Generating Response-Specific Elaborated Feedback Using Long-Form Neural Question Answering

In contrast to simple feedback, which provides students with the correct answer, elaborated feedback provides an explanation of the correct answer with respect to the student's error. Elaborated feedback is thus a challenge for AI in education systems because it requires dynamic explanations, which traditionally require logical reasoning and knowledge engineering to generate. This study presents an alternative approach that formulates elaborated feedback in terms of long-form question answering (LFQA). An off-the-shelf LFQA system was evaluated by human raters in a 2x2x2x2 ablation design that manipulated the context documents given to the LFQA model and the post-processing of model output. Results indicate that context manipulations improve performance but that post-processing can have detrimental results.
Application of Matter Element Analysis in Power Grid Development Diagnosis

The structure and form of modern power grids are becoming more and more complex, and there are many indicators and factors that need to be considered to evaluate the development status of the power grid. In order to diagnose the development level of power grid, the index system is reconstructed, which includes seven dimensions: the coordination of power grid development speed, the coordination of power grid development scale, the coordination of power grid structure, the security and reliability of power grid, the level of equipment, the utilization efficiency of power grid and the development benefit of power grid, the construction principle and index selection basis are given. When dealing with contradictory problems in reality, methods combining quantitative and qualitative can be applied to study. The matter element analysis method is applied to power grid development diagnosis, and a model of power grid development diagnosis based on matter element analysis is established, the model can not only diagnose the development level of power grid, but also determine the key factors that affect the diagnosis results, and give suggestions for improvement. The practical example verifies the feasibility and rationality of the method, which has good evaluation accuracy and prediction effect.
Study on Lightning inrush overvoltage of 500 kV GIL-GIS Hydropower Station

Abstract Aiming at the overvoltage of the equipment in GIL-GIS system when the tower outside the station strikes back, a calculation model based on EMTP program is proposed to analyze the influence of GIL distance length, lightning tower position and tower name on the lightning inrush overvoltage. Combined with the calculation results, it shows that the lightning inrush overvoltage level varies with GIL distance length, lightning tower position and tower name The variation law of tower nominal height can provide reference for overvoltage calculation and electromagnetic disturbance source calculation of hydropower plant.
An adversarial sample defense model based on computer attention mechanism

The traditional neural network can not realize the invariance of image rotation and distortion well, so the attacker can cheat the neural network by adding tiny disturbance on the image. If signposts are attacked, automatic driving will be misguided, leading to disastrous consequences. This paper proposes a new adversarial sample defense model based on attention mechanism, which used LSTM network to get the key features. Firstly, we describe our model which recognizes an object through enough main characteristics. It can discard large parts of the noise distributed throughout the image area. Secondly, we show recognition process of the model, receiving adversarial samples and then outputting the salient mask image which can be classified correctly by neural networks in deep learning. At last, we designed experiments to test model effect on the traffic sign and the general picture. The experimental results show that our defense model can strongly defend adversarial examples.
Developmental Network-2: The Autonomous Generation of Optimal Internal-Representation Hierarchy

It is very challenging for machine learning methods to reach the goal of general-purpose learning since there are so many complicated situations in different tasks. The learning methods need to generate flexible internal representations for all scenarios met before. The hierarchical internal representation is considered as an efficient way to build such flexible representations. By hierarchy, we mean important local features in the input can be combined to form higher level features with more context. In this work, we analyze how our proposed general-purpose learning framework-the developmental network-2 (DN-2)-autonomously generates internal hierarchy with new mechanisms. Specifically, DN-2 incrementally allocates neuronal resources to different levels of representation during learning instead of handcrafting static boundaries among different levels of representation. We present the mathematical proof to demonstrate that optimal properties in terms of maximum likelihood (ML) are established under the conditions of limited learning experience and resources. The phoneme recognition and real-world visual navigation experiments that are of different modalities and include many different situations are designed to investigate general-purpose learning capability of DN-2. The experimental results show that DN-2 successfully learns different tasks. The formed internal hierarchical representations focus on important features, and the invariant abstract arise from optimal internal representations. We believe that DN-2 is in the right way toward fully autonomous learning.
Selective inference for latent block models

Model selection in latent block models has been a challenging but important task in the field of statistics. Specifically, a major challenge is encountered when constructing a test on a block structure obtained by applying a specific clustering algorithm to a finite size matrix. In this case, it becomes crucial to consider the selective bias in the block structure, that is, the block structure is selected from all the possible cluster memberships based on some criterion by the clustering algorithm. To cope with this problem, this study provides a selective inference method for latent block models. Specifically, we construct a statistical test on a set of row and column cluster memberships of a latent block model, which is given by a squared residue minimization algorithm. The proposed test, by its nature, includes and thus can also be used as the test on the set of row and column cluster numbers. We also propose an approximated version of the test based on simulated annealing to avoid combinatorial explosion in searching the optimal block structure. The results show that the proposed exact and approximated tests work effectively, compared to the naive test that did not take the selective bias into account.
Model of the Cold-Start Recommender System Based on the Petri-Markov Nets

The article describes a model for constructing a cold-start recommendation system based on the mathematical apparatus of Petri-Markov nets. The model combines stochastic and structural approaches to building recommendations. This solution allows you to differentiate recommendation objects by their popularity and impose restrictions on the available latent data about the user.