Reviewerly/exHarmony huggingface dataset:
Real-Time Optimal Trajectory Correction (ROTC) for Autonomous Quadrotor

This research proposes a new algorithm known as Real-Time Optimal Trajectory Correction. The algorithm works if the quadrotor undergoes a deviation, an admissible trajectory path is generated for the quadrotor rapidly return on the straight-line route. There are two stages implicated in the algorithm. The first stage comprises a deviation scheme used to sense a deviation and formulates a vector. The second stage focuses on using the vector to generate a path via a Hermite interpolation technique integrated with time and tangent transformation schemes. A Dead Reckoning navigation technique is employed to steer the quadrotor on the path. The scheme and algorithm have experimented in low, moderate and high categories of deviation acceleration via a customized quadrotor. Comparative performance of quadrotor navigation integrated with and without the algorithm are carried out. The parameters evaluated are focused on distance and time utilized by the quadrotor to return on the route after a deviation happened. The results signify fewer distances and time are consumed by the quadrotor navigation with the algorithm to return to the route after a deviation happened as compared to its navigation without it. Conclusively, the results show the designed algorithm is relevant to assist the quadrotor autonomous navigation in a windy environment.
A neural joint model for rating prediction recommendation

Reviews text reflects user interests and product characteristics, which provide rich useful semantic textual information for modeling user and product. Some existing works improve the performance of rating prediction by distinguishing the usefulness of each review. However, most of them ignore the fact that the usefulness of each review should be dynamic and dependent on the target user-product pair. For example, when we predict the user’s ratings of a restaurant, the user’s previous reviews about restaurants should be more useful. To be more specific, when we model the target user-product pair, the usefulness of each review is associated with the target user and item. To address the above issue, we use a Review-level Dynamic Topic Co-Attention, which combines all reviews written by the user and all reviews that were written for the product to assign attention score for each review collaboratively. Besides, we believe that product category and user co-purchase information can further improve the rating prediction performance. In this paper, we propose a neural joint model called NMRP based on reviews, products category, and users’ co-purchase information for rating prediction recommendation. First, the Review Extraction Module learns user and product information from reviews. Then, HIN Extraction Module extracts the association feature for a given target user-product pair from a heterogeneous information network (HIN). Finally, the two parts of data are connected and input into the feature interaction method Attentional Factorization Machines (AFM), to achieve rating prediction. The experimental study on three datasets from Amazon shows that our model outperforms recently proposed baselines such as DeepCoNN, TransNets, and NARRE.
Fast Tests for Probing the Causal Structure of Quantum Processes

The identification of causal relations is a cornerstone of the scientific method. Traditional approaches to this task are based on classical statistics. However, such classical approaches do not apply in the quantum domain, where a broader spectrum of causal relations becomes accessible. New approaches to quantum causal inference have been developed in recent years, and promising new features have been discovered. In this paper, we review and partly expand the framework and results of Ref. [1], which demonstrated quantum speedups in the identification of various types of causal relations induced by reversible processes.
Anomalous Seismic Events Detection by Build-In Sensors on Smart Phones

Motion sensor equipped smart phones can be operated as the Community Seismic Network for earthquake detection, as the build-in accelerometers share similar principles of those in high precision seismic stations. However, current smart phones often yield poor monitoring quality due to the limited sensing capability of the off-the-shelf sensors and unpredictable dynamics of daily activities of phone users. In this paper, we present a suite of algorithms towards detecting anomalous seismic events from noise contaminated signals, including lightweight signal pre-processing, a two-phase P-phase pickup and timing scheme at the local smartphones, and employing decision fusion to maximize anomaly detection performance at fusion center while meet the requirements on system false alarm rate. We experimentally evaluate the proposed approach on networked smart phones and shake table. The results, including data sampled by smart phones and the numeric simulation, indicate the effectiveness of our scheme in distinguishing anomalous seismic events from motions due to normal daily manipulation.
An Improved Approach to Speaker Recognition for Broadcast News Audio

The goal of this paper is speaker recognition in broadcast news domain. The broadcast news audio is variable in length and accents, and also contains irrelevant signals, such as environment sounds. This paper proposes a two-path neural network to learn the embedding of speakers, one of which focuses on local (detail) features and the other focuses on global features. The features of the two networks will be merged, and new speaker embedding is proposed from these features. For the local network, this paper also aggregates its features by GhostVLAD algorithm. And GhostVLAD algorithm is formalized in this paper. The final result shows that Dual-vector has achieved remarkable results in the field of speaker recognition for broadcast news audio.
Autonomous Scouting Robot Based on Pioneer P3-DX

This paper introduces an autonomous scouting robot (ASR) based on Pioneer P3-DX platform that performs efficiently and dynamically in a hazard environment like a fire scene. The autonomous scouting robot works in three different phases. Phase I is scene fetching, where the autonomous scouting robot is given a rotation speed to scan the environment. It will move in the hazard environment and send real-time images to the remote-control computer. Phase II starts with human discovering; skeleton recognition program is running at this phase to find the trapped person. Once there is someone found, the user viewer will show the skeleton of the selected person. In phase III, the firefighters will move to the rescued person's location, and the autonomous scouting robot will follow them by keeping some specific shape or signal on the firefighters' cloth at the center of its vision region while carrying the aid kit. During this phase, the scenario is that once firefighters arrive, the autonomous scouting robot starts directing/following them to the safest place in the scene, by object following function or being sent navigation goal signals on the map from the remote computer operator. Detailed hardware/software developed methodologies and experiment tests results are presented.
A BOW-Based Sentence Embedding Method for Chinese Event Identification

Abstract Event extraction is an important branch of information extraction. It involves two challenging issues: event identification and argument identification. Most approaches of event identification are trigger-based, which suffer a lot from sample imbalance, word ambiguity, low scalability, etc. To solve these problems, we attempt to explore sentence-based event identification, whose main issue is sentence representation. With the success of word embedding, it has attracted much attention to generate semantic embeddings of sentences. In this paper, we propose a simple BOW-based sentence embedding method to represent event sentences for Chinese event identification. We compute embeddings on the dependency tree and map different relations to simple arithmetic operations. We evaluate our method with a dataset of Chinese event identification and compare the result with other BOW-based methods. The results show that our approach significantly outperforms other BOW-based methods.
Population Diversity of Particle Swarm Optimization Algorithm on Solving Single and Multi-Objective Problems

Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: the exploration of new possibilities and the exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained.
A Recursive Algorithm of Digital Polynomial Filtering

The paper considers a class of digital nonlinear filters defined by a discrete truncated Volterra series. Such filters called Volterra filters or polynomial filters are a natural generalization of linear filters. The computational weight of polynomial filters is widely known to exponentially grow with the nonlinearity degree. This article is devoted to design of effective algorithms of polynomial filtering based on the algebraic theory of signals and systems. The implementation of nonlinear filters of this class is based on the procedure of data segmentation and calculation of nonlinear circular convolutions. In the paper we suggest recursive algorithms for calculating nonlinear circular convolutions based on multivariate polynomial transforms and the Chinese remainder theorem. Unlike the discrete Fourier transform, the calculation of polynomial transforms does not require multiplications and is implemented using additions and shifts. It is shown that the calculation of a nonlinear circular convolution of the m- th order can be reduced to the calculation of the convolution of the (m- 1)- th order, the execution of the operation of transition to a single variable in the polynomial region followed by restoring the result using the Chinese remainder theorem. A modification of the recursive algorithm for calculating nonlinear circular convolutions using fast polynomial transform algorithms is proposed. This algorithm allows to reduce the computational costs by using an effective procedure based on the recursive addition of polynomials with exponentially growing bases for the multiplication of polynomials. The article concludes with the assessment of computational complexity of the proposed recursive algorithm and the recommendations for its application.
A framework for the definition of complex structured feature spaces

In this paper, we propose a general framework that, starting from the feature space of an existing base graph kernel, allows to define more expressive kernels which can learn more complex concepts, meanwhile generalizing different proposals in literature. Experimental results on eight real-world graph datasets from different domains show that the proposed framework instances are able to get a statistically significant performance improvement over both the considered base kernels and framework instances previously defined in literature, obtaining state-of-the-art results on all the considered datasets.
Towards Intelligent Waterway Lock Control for Port Facility Optimisation

High tidal ranges pose a significant challenge for affected ports. Waterway locks ensure sufficient water levels but their use often coincides with a loss of water in the harbour basins. As an alternative to energy-intensive pumping stations, it is desirable to fill the port naturally, e.g., by opening the lock gates at high tides. Unfortunately, this is a complex and dynamic scheduling problem due to manifold contributing factors. This paper outlines a novel architecture towards intelligent control for waterway lock operations. The concept employs a multi-agent system to cope with the problem complexity and dynamics. Its software agents represent relevant stakeholders, thereby integrating prediction models derived from machine learning.
ПОГЛЯДИ ПЕДОЛОГІВ НА ПРИЧИНИ ПОЯВИ «МОРАЛЬНО ДЕФЕКТИВНИХ» ДІТЕЙ У ПУБЛІКАЦІЯХ В ПЕДАГОГІЧНИХ ЖУРНАЛАХ (20-30 рр. ХХ ст.)

&#x0D; &#x0D; &#x0D; &#x0D; In the article based on the analysis of publications in pedagogical journals of the 20-30s of the XX century (“The Way of Education”, “The Way of Enlightenment”, “Pedology”, “Our School”) the views of well-known psychologists and educationists on the social causes of the emergence of “morally defective children” have been generalized.It has been found that the term “morally defective child” was used to refer to children who suffered from character and emotional-will disorders, and who were prone to aggressive and unlawful behavior or were pedagogically neglected, homeless, etc.The ambiguity of approaches to the interpretation of the causes of the emergence of “morally defective” children in the pedagogy and psychology of the studied period has been proved. In particular, A. Zalkind, L. Vygotskyi, O. Mukhina, M. Tarasevich in their works substantiated the role of the social environment as a factor of morally defective childhood.It has been proved the interconnection of moral deficiency with the socio- economic and cultural-pedagogical conditions of the development and upbringing of a child.&#x0D; &#x0D; &#x0D; &#x0D;
Hand Gesture Classification based on Short-Time Fourier Transform of Inaudible Sound

As IoT devices and wearable devices are increasing in number, researches for recognizing and classifying human action or behavior as a method for controlling them have become important. This study proposes a method to classify hand gestures by one smartphone using high frequency sound which is inaudible. While generating the sound from the smartphone and conducting hand gestures, we have classified the reflected sound. The reflected data differs between the hand gestures because of the Doppler effect and we have analyzed this data in the time domain and frequency domain with short-time Fourier transform. In this paper, we have presented a convolutional neural network model that classifies 8 hand gestures, and the model showed 94.25 % classification accuracy.
Motion compensation method for video encoding

A motion compensation method for video coding is proposed. The method is based at the estimation of motion vectors along the bit sheets of the most informative high order bits and the adaptive technology for selecting block sizes for motion compensation. Evaluation of motion vectors along the bit sheets of the higher bits will significantly reduce the computational complexity and enumeration of possible blocks. An adaptive technology of selecting block sizes for motion compensation will allow to select large blocks for homogeneous areas of the frame, and smaller blocks for images with complex motion. The proposed method can effectively eliminate temporary redundancy and increase the compression ratio of video frames with minimal computational resources. The results of modeling and evaluating the effectiveness of the developed algorithm are presented.
Selected and refined local attention module for object detection

Lacking enough feature expression on the shallow part of the network always hinders the object detection results by missing the position of small instances. To address this, the authors propose a selected and refined local attention module (SRLAM) for object detection. SRLAM tries to improve the feature expression of local areas by establishing a relationship graph between different channels. Inspired by the classical non-local neural networks for video classification, they present the local attention module (LAM) to more effectively use remote information. The LAM can suppress the influence of irrelevant areas for improving detection accuracy. Moreover, for further information utilisation, the weight map generated by LAM is integrated together with the original feature map and the information from the higher layer by a feature fused module. Experiments on the PASCAL VOC2007 show that the authors’ model has good detection performance, the proposed model can achieve 81.6 mAP when the size of the input image is 300 × 300, that outperforms many other mainstream detectors.
Analyzing journeys in sound: usability of graphical interpolators for sound design

Abstract Graphical interpolation systems provide a simple mechanism for the control of sound synthesis by providing a level of abstraction above the engine parameters, allowing users to explore different sounds without awareness of the underlying details. Typically, a graphical interpolator presents the user with a two-dimensional pane where a number of synthesizer presets, each representing a collection of synthesis parameter values, can be located. Moving an interpolation cursor within the pane results in the calculation of new parameter values, based on its position, the relative locations of the presets, and the mathematical interpolation function, thus generating new sounds. These systems supply users with two sensory modalities in the form of sonic output and visual feedback from the interface. A number of graphical interpolator systems have been developed over the years, with a variety of user-interface designs, but few have been subject to formal user evaluation. Our testing studied both user interaction with, and the perceived usability of, graphical interpolation systems by comparing alternative visualizations in order to establish whether the visual feedback provided by the interface aids the locating of sounds within the space. The outcomes of our study may help to better understand design considerations for graphical interpolators and inform future designs.
The Application Design of Nifasku Based On Android for Postpartum Care and Newborn

Abstract Entering the puerperium, the mother will experience changes both physically and psychologically. This period is still a vulnerable period for the survival of the mother. Beside, maternal needs during the puerperium will also be different in each woman and also in each period of the puerperium. The implementation of Mobile Health Information Technology (mHealth) in improving health services for postpartum mothers is one alternative solution that can help in providing information and health education for postpartum mothers. For this reason, a health promotion media is designed in the form of applications according to the needs and contains information about childbirth care and newborn care without being limited by space, time, without having to face to face with health workers, especially midwives as milestones in service delivery. The system development method used is Extreme Programming (XP) which consists of planning, design, coding, test. This research has successfully developed an Android-based application design for postpartum care and a newborn care that can facilitate postpartum care in obtaining information about postnatal care and newborn so that the information obtained is easier and more effective.
Can You Patent a 3D Printable File? (and Why it Matters)

Chapter 4 analyzes the doctrine of patentable subject matter. Delving into American, European, and Japanese patent jurisprudence, it first describes how these legal systems handle software-related inventions in general. Next, it applies that jurisprudence to 3D printable files to demonstrate why only one of the three 3D printing file formats is likely to constitute patentable subject matter. More intriguingly, it turns out that this file format is of least interest to would-be patent holders. In other words, a patent protection gap exists. Chapter 4 also analyzes jurisdictions’ differential treatment of patent claims directed to electronic signals. The Japanese and European patent systems consider these claims to be patentable subject matter, whereas the U.S. system does not. The upshot is that patent protection for software and 3D printable files is weaker in the United States because most 3D printable files are sold as internet signal transmissions. I argue that the United States should provide protection for signal claims.
The optimal numerical wavelet based integration of probability density function by chebyshev wavelet method

This paper presents, numerical evaluation of probability density functions gamma, exponential, weibull, normal distributions with different parameters by wavelet based integration approach of chebyshev wavelet method, we illustrate in an numerical examples that proposed method results in a much lower computation time and complexity than the existing numerical methods and comparable to the performance of those method.
Visualization Techniques for Outlier Data

When deep learning models classify the actual data and outlier data, most of them do not have enough information about the outlier, which might cause misclassification. Therefore, there is a need for an efficient way to analyze outlier data through visualization. We propose a visualization method combining LBP, LLE and SMOTE for outlier data detection. Furthermore, we introduce a new confusion visualization method that uses the similarity of pixel density distributions. We also present a new histogram visualization method using the frequency of pixel position's distribution in EDA. To validate its effectiveness, we compared the proposed method with UMAP and LLE. In evaluation, the outlier data is generated by three types of GAN (Vanilla GAN, DRAGAN, and EBGAN). The results of the proposed visualization method show its usefulness for outlier detection.
Semi-Automatic Sensor Ontology Matching Based on Interactive Multi-Objective Evolutionary Algorithm

Since different sensor ontologies are developed independently and for different requirements, a concept in one sensor ontology could be described with different terminologies or in different context in another sensor ontology, which leads to the ontology heterogeneity problem. To bridge the semantic gap between the sensor ontologies, authors propose a semi-automatic sensor ontology matching technique based on an Interactive MOEA (IMOEA), which can utilize the user's knowledge to direct MOEA's search direction. In particular, authors construct a new multi-objective optimal model for the sensor ontology matching problem, and design an IMOEA with t-dominance rule to solve the sensor ontology matching problem. In experiments, the benchmark track and anatomy track from the Ontology Alignment Evaluation Initiative (OAEI) and two pairs of real sensor ontologies are used to test performance of the authors' proposal. The experimental results show the effectiveness of the approach.
Image description by harmonic Fourier moment

Abstract In this paper, Two kinks of orthogonal moments are constructed with Trigonometric functions, named Harmonic-Fourier moments (HFMs), which include Cosine-Fourier moments (CFMs) and Sine-Fourier moments (SFMs). These kinds of moments can be used in image and object analysis. The radial functions of HFMs have more zeros than Zernike polynomials of the same degree so that the property makes HFMs have stronger ability in image description. Furthermore, the formulas of HFMs are extremely simple. This property makes it possible that HFMs can be computed at a higher speed.