Reviewerly/exHarmony huggingface dataset:
Simulation Study on Lightning Protection of New 500 kV Lightning-proof Line Insulator

Line arrester is an important lightning protection method for 500kV extra-high voltage (EHV) transmission lines. The current Zinc Oxide Arresters have shortcomings in performance, installation, in this paper, lightning-proof insulators with excellent lightning protection characteristics are adopted. The ATP-EMTP simulation is used to analyze the transmission line when it was subjected to the most serious conditions of counter-attack and lightning shielding failure, including energy releasing condition of the lightning-proof insulator, and the maximum withstanding lightning current after the lightning-proof insulator replaces the ordinary insulator. The simulation results show that the lightning protection insulator circuit can avoid the grounding fault of the transmission line, especially in the terrain where the grounding resistance is difficult to reduce; In the case of 800A 2ms square wave flow capacity is 1237kJ, the influence of lightning counterattack on the operation of the lightning protection type insulator is negligible with respect to the lightning shielding failure current and the maximum tolerated lightning shielding failure current amplitude of the 500kV line is -84kA ,which meets line lightning protection requirements.
Automatic Synthesis Approach for Unconstrained Face Images Based on Generic 3D Shape Model

Faces captured in unconstrained conditions represent a myriad of challenges like occlusions, non-frontal poses, etc. Compared to the other variations, pose variation is one of the most difficult problems in face recognition. To this end, we propose a new automatic synthesis approach of frontal facing view of unconstrained face images which are based on the use of a generic 3D shape model and accurate localization of facial feature points. On another hand, we have used the same approach as data augmentation technique for enriching a small dataset with important facial appearance variations by manipulating the faces that it contains so as to improve the convolutional neural network (CNN) performances and avoid overfitting. This is done by using a camera model to generate multiple views covering possible poses synthesized from the 3D face model used.
Text Corpus Augmentation to Represent Filled Pause in Indonesian Spontaneous Speech Recognition System

Filled pause is one of the characteristics of spontaneous speech which causes a decrease in recognition accuracy. Including filled pause into language model is one of possible solution. In this case, the limitation of the Indonesian spontaneous text corpus is the main problem. In this paper, we employed augmentation on the non-spontaneous written text by inserting filled pause into non-spontaneous text corpus. Augmentation to the text corpus that will be used to build language model, has an impact on perplexity and WER reduction, about 11,1 % and 2,3% respectively. The augmentation strategy by adding filled pause based on hidden event language model estimation gives the best results both in the perplexity of language model and performance of speech recognition.
Perception-Distortion Trade-Off with Restricted Boltzmann Machines

In this work, we introduce a new procedure for applying Restricted Boltzmann Machines (RBMs) to missing data inference tasks, based on linearization of the effective energy function governing the distribution of observations. We compare the performance of our proposed procedure with those obtained using existing reconstruction procedures trained on incomplete data. We place these performance comparisons within the context of the perception-distortion trade-off observed in other data reconstruction tasks, which has, until now, remained unexplored in tasks relying on incomplete training data.
Multirobot search for a stationary object placed in a known environment with a combination of GRASP and VND

Abstract This paper addresses the problem of multirobot search for a stationary object in an a priori known environment. Two variants of the problem are studied given the working environment represented by a graph. The first variant is an extension of the traveling deliveryman problem for multiple vehicles, while the second variant is a generalization of the graph search problem. A novel algorithm is presented to solve both problems, which is based on a combination of greedy randomized adaptive search procedure with variable neighborhood descent. A set of experimental evaluations were conducted over the benchmark instances derived from the TSPLIB library. For both problems, the results obtained show that the proposed approach is comparable to state‐of‐the‐art heuristics. Moreover, for problems of a few hundred vertices, the solution times suggest that the approach is suitable for online decision‐making in search and rescue.
Mapping the probability of exceeding environmental quality standards for Cd and Pb concentrations in soil of northern Tarim Basin using Bayesian geostatistical model

Abstract Cadmium and Pb are important hazardous trace metals in the biosphere, and they are considered to pose a great threat to environmental health. The probability map exceeding the environmental quality standard (EQS) can be used to measure the uncertainty of the spatial distribution of soil Cd and Pb and provides useful information for decision-making for environmental management. Many researchers have focused on Cd and Pb environmental problems in agricultural soils, and there are few studies that have investigated the Cd and Pb contents exceeding EQS in natural soils of the arid desert areas of northwest China. We collected 150 samples at 0-0.05 m and 0.05-0.1 m depths in the natural solonchak soil of northern Tarim Basin, Xinjiang Province. The average concentrations of Cd and Pb were 0.45 mg/kg and 42.13 mg/kg at 0-0.05 m, and 0.29 mg/kg and 16.88 mg/kg at 0.05-0.1 m, respectively. The soil Cd and Pb concentrations were increased in comparison with the environmental background values. In addition, the soil Cd and Pb concentrations at 0-0.05 m were significantly higher than those at 0.05-0.1 m. We used the Bayesian geostatistical model to predict the spatial distribution of Cd and Pb concentrations and to map the probability of soil Cd and Pb exceeding the EQS. These probability maps are expected to be helpful for soil environmental protection of northern Tarim Basin.
Algorithmic Exploration of American English Dialects

In this paper, we use a novel algorithmic approach to explore dialectal variation in American English speech. Without the need for human phonemic annotations, we are able to use an existing corpus transcribed in text form only. Our results show that, in general, American English dialects can be divided into two larger groups: dialects of the South (Texas to North Carolina except for peninsular Florida), and the rest of the country. Our results confirm some well-known results from dialectology, such as the pin-pen merger, but show that some other ones, such as the cot-caught merger, may be losing their isogloss boundaries. Moreover, we demonstrate that our algorithm can extend to dialectal features in other languages.
Going the Distance: Incorporating Collection Maintenance into Technical Services in an Academic Library

ABSTRACT Incorporating shelving and stacks maintenance into technical services puts responsibility for the full life cycle of physical materials in one place. Purchasing, cataloging, processing, shelving, repair, and deaccessioning are done by a single department. With changes in use patterns and retention, it makes sense to closely ally stacks maintenance with other technical services functions. This strategy also creates a pool of trained staff and students who can work on related projects as needed, from shifting to transfers to withdrawals. Public services staff can refocus on customer service rather than managing the flow of books.
On Exponentially Consistency of Linkage-Based Hierarchical Clustering Algorithm Using Kolmogrov-Smirnov Distance

This paper focuses on performance analysis of linkage-based hierarchical agglomerative clustering algorithms for sequence clustering using the Kolmogrov-Smirnov distance. Data sequences are assumed to be generated from unknown continuous distributions. The goal is to group the data sequences whose underlying generative distributions belong to one cluster without a priori knowledge of both the underlying distributions as well as the number of clusters. Upper bounds on the clustering error probability are derived. The upper bounds help establish the fact that the error probability decays exponentially fast as the sequence length goes to infinity and the obtained error exponent bound has a simple form. Tighter upper bounds on the error probability of single-linkage and complete-linkage algorithms are derived by taking advantage of the simplified metric updating for these two special cases. Simulation results are provided to validate the analysis.
Parts of Speech Tagging for Punjabi Language Using Supervised Approaches

This paper discusses various parts of speech tagging approaches used in machine translation systems to analyse the structure of the Punjabi sentence. The objective of this paper is to give detailed knowledge of parts of supervised parts of speech tagging techniques in order to generate tree structures for sentences. Data is increasing globally in different languages which are leading to a great impact on machine translation systems. Various approaches for parts of speech tagging are also defined in the paper. Rule based parts of speech tagging approach and Statistical based parts of speech tagging approach have been defined briefly.
The Embedded Design of Barcode Coding and Display

People have already got the habit of scanning code nowadays. In order to provide an efficient barcode-scanning function for the traditional equipment, an embedded barcode coding and display device was designed. System structure, hardware and software of the design were described in this paper. A Cortex-A9 core MCU is applied to the hardware. The software run in ARM embedded software and PC host computer, Android APK interface, image processing, data transmitting, barcode encoding and decoding, and the tests of the designs were given. According to externally input data, the system can provide the functions, including barcode access and display. An embedded device solution for the dynamic barcodes generation and display was given. And it can provide a convenient code-scanning interface for various interactive devices such as automatic vending machine, automatic teller machine and POS.
Radio Antennas for a Forecast of Solar Activity

Simultaneous observations of three small radio telescopes RT-3, RT-2 and RTM, covering the range from 0.66 cm to 1.05 m, will allow obtaining information for analysis of energy release in the solar atmosphere, diagnostics of eruptive solar plasma and short-term forecast of solar activity. The implementation of this radio astronomy project allowed Ukraine to enter the international network of the Sun Service. Detected acceleration of growth the surface temperature of the earth in "Nikitsky Botanical Garden" after 1988. Based on the analysis of ground measurements of temperature: the air at a height of 2 m and the Earth's surface for the period from 1930 by 2014, discusses the possible causes of the acceleration. The tendency of variation of the secular variations of the geomagnetic declination in the Kara-Dag point is determined similar to the tendency of the northern component of the velocity of the northern geomagnetic pole to change: reaching a maximum value of about 2000, the trend sign changes to the opposite one.
An Event-Based Architecture for Cross-Breed Multi-population Bio-inspired Optimization Algorithms

Multi-population methods can combine multiple algorithms, with different parameters, interacting with each other at the same time. For instance, a genetic algorithm could find a promising global solution that is not optimal while another algorithm, more suitable for a local search, finds the global optimum. This approach has been followed extensively in recent years, with success. Moreover, there is a need for frameworks, architectures, and implementation models that can allow researchers the development of new parallel, asynchronous, heterogeneous, and parameter-free algorithms in a scalable way. In this work, we present an event-driven architecture, designed to distribute the processing of population-based algorithms asynchronously. The search algorithm uses a multi-population approach, creating multiple populations with different parameters of execution, allowing the implementation of multiple algorithms. In this work, we cross-breed Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). Experiments show that the framework allows the combined algorithms outperform, with a high probability, single-algorithm versions. The framework we provide also has few parameters to tune since single-algorithm parameters are selected randomly; in general, this will boost the diversity of every algorithm by itself.
Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking

In this paper, we extend previous particle filtering methods whose states were constrained to the (real) Stiefel manifold to the complex case. The method is then applied to a Bayesian formulation of the subspace tracking problem. To implement the proposed particle filter, we modify a previous MCMC algorithm so as to simulate from densities defined on the complex manifold. Also, to compute subspace estimates from particle approximations, we extend existing averaging methods to complex Grassmannians. As we verify via numerical simulations, the proposed method is advantageous over traditional SVD-based subspace tracking algorithms for scenarios with low signal-to-noise ratio.
An algorithmic approach for optimising biometric systems using liveness and coercion detection

This paper looks at the possibility of creating an algorithm that will combine liveness and coercion modalities, along with organisational factors such as workforce composition. The hypothesis is that the algorithm can produce a value that can further be used to compare different setups of biometric security for self-optimisation by taking in the context of technique compatibility and user requirements. To this end, the algorithm focuses on four main aspects: time, participants, anomalous user-bases, and device redundancy inside a typical organisation. An experimental methodology has been used, focusing on the development of the algorithm, its associated effects, and how different parameters can be reliably estimated. After testing, the algorithm is proved to work as it creates an appropriate value, called the security value, which can be used to discover the best combinations of modalities for fusion development or practical installation for a given situation. There are some issues with this primarily due to data provision, the requirements for more data to parse through the algorithm, and finally, the need for a suitable interface, otherwise it may be too complex for efficient usage in a traditional security environment. There are potential implications within a general security application such as liveness and coercion multimodal fusion and autonomous system development and pervasive environments, allowing dynamic security systems to be developed. However, the main focus of this algorithm is to highlight the fusion of liveness and coercion detection and how they can be best applied to specific security scenarios.
Color Stabilization for Multi-Camera Light-Field Imaging

By capturing a more complete rendition of scene light than standard 2D cameras, light-field technology represents an important step towards closing the gap between live action cinematography and computer graphics. Light-field cameras accomplish this by simultaneously capturing the same scene under different angular configurations, providing directional information that allows for a multitude of post-production effects. Among the practical challenges related to capturing multiple images simultaneously, a very important problem is the fact that the different images do not perfectly match in terms of color, which severely complicates all further processing. In this work we adapt and extend to the light-field scenario a color stabilization method previously proposed for standard multi-camera shoots, and demonstrate experimentally that it provides an improvement over the state-of-the-art techniques for light-field imaging.
Dynamic Routing in Machine Reading Comprehension

Dynamic routing mechanism was introduced by Geoffrey E Hinton et al. in 2017, which is a new information flow mechanism applied in the deep neural networks (Capsule Networks) for the first time, and achieves state-of-the-art performance on MNIST. The typical mark of CapsuleNet “Vector in Vector out” is a natural property in NLP models (word embedding). So, be inspired by this, we introduce the dynamic routing mechanism in NLP tasks. We focus on machine reading comprehension (MRC) task. On MRC tasks, by introducing the dynamic routing mechanism into BiDAF and BERT, our two models DR-BiDAF and DR-BERT were formed. Experiments on SQuAD2.0 dataset and Facebook bAbI project (The (20) QA bAbI tasks) show that the accuracy and robustness of DR-BiDAF and DR-BERT have a significant improvement compared with their counterpart original models. Besides, the process of model training shows that the convergence speed of new models with dynamic routing is faster than original models. It indicates the optimization problem has also been improved.
Clustering of Nonnegative Data and an Application to Matrix Completion

In this article, we propose a simple algorithm to cluster nonnegative data lying in disjoint subspaces. We analyze its performance in relation to a certain measure of correlation between said subspaces. We use our clustering algorithm to develop a matrix completion algorithm which can outperform standard matrix completion algorithms on data matrices satisfying a certain natural low rank condition.
Research on Image Denoising Based on Gamma Transformation

In order to solve the problem that the neural network style transfer method produces a lot of noise in the process of image synthesis, a method of neural network image denoising based on gamma transform is presented in this paper. The VGG -19 network is used to obtain the composite images of each round, and the pixels of the images are modified by gamma transformation to achieve the purpose of image demolishing. Taking the gamma transformation as a part of the loss function of neural network, and combining the content loss function and style function, the weighted algebra of the three loss functions is taken as the total loss function of neural network. The experimental results show that the noise of the composite image is successfully suppressed and the number of interactions of image generation is reduced.
Multiprocessor Task Programming and Flexible Load Balancing for Time-Stepping Methods on Heterogeneous Cloud Infrastructures

This article considers the execution of CSE applications on cloud infrastructures with heterogeneous hardware elements. For an efficient and reliable execution of long-running, time-consuming CSE applications, we pursue a programming model of multiprocessor-tasks (M-Tasks) which can be flexibly mapped onto the resources of the heterogeneous cloud infrastructure, thus supporting scalability. To capture the heterogeneity, we introduce variants of M-tasks and types for M-task as well as for hardware resources. Especially, we propose a two-stage procedure consisting of an offline mapping algorithm for typed M-tasks variants onto the typed hardware resources and an online execution protocol. The online execution protocol accompanies the actual execution by a monitoring of the execution time and the execution success as well as a bookkeeping for the successful and efficient execution of M-tasks. The approach is especially suited for time-stepping methods, since an adaptation of the execution strategy can be performed between time steps in case of failure or inefficient execution.
An Autonomous Navigation and Exploration System for Search and Rescue Robots in Mine Incidents

A team of unmanned vehicles for mine incidents is being developed as part of the INDIRES project, funded by the European Research Fund for Coal and Steel (RFCS). The goal of this team is to rapidly acquire and to provide information before human rescuers can safely be deployed in serious mining accidents. In this paper, we present the navigation and exploration architecture of the robotic team consisting of a ground robot, an aerial vehicle and a teleoperated snake-like robot. This architecture has two levels: a higher level with a team manager based on a Finite State Machine, which is in charge of coordinating the action of the vehicles; and a lower level which implements the particular functionalities of each vehicle. The current status of the development and the future plans are explained.
INTERDISCIPLINARITY IN THE DEVELOPMENT OF POST-CLASSICAL ART EDUCATION

The interdisciplinarity of art education is the universality of the art influence upon all the people’s cognitive processes and an artistically imaginative embodiment ability of any topic and plot that relates to the person — the artist and the recipient. The origins of interdisciplinarity as a scientific paradigm are in the theory of communication. The basis of interdisciplinarity lies in scientific semantics: interdisciplinarity plays a syntactic role, on the one hand, and, on the other hand, promotes the growth of semantic links in schemes and transitions between different subject areas. The promising strategic direction is to increase the cultural intensity of all academic disciplines. Simplified-practical use of art as an illustration, “figurative confirmation” of life’s phenomena ignores its high purpose
Multiagent System Development for Cooperative Multiplayer Video Game Using Deep Q-Network

In this work, a multiagent system was developed for cooperative multiplayer video game. One of the cooperative popular multiplayer video games when this paper is written is Overcooked 2. For that reason, the game is chosen as the development environment for this work. Because Overcooked 2 is not an open-source software and does not provide APIs for reading information and accept inputs, the built multiagent system is not applied directly to Overcooked 2. A game with similar mechanics is used to represent Overcooked 2 instead. The game has simplified display and features of the game it is based on. Agents in the system use deep q-network to decide what action to be taken. The neural network takes the game state which consist of information from the game and the coordination protocol i.e. blackboard system as inputs. The outputs of the neural network are estimated q-values of every actions. The developed system is then compared with a system where its agents choose actions randomly. Both systems played the developed game for certain amount of episodes, then the average score of the systems are compared. Based on the comparison result, the developed system has better average score.
Semi-Supervised Learning Based on Local Adaptive Kernels

This paper presents a novel semi-supervised learning algorithm with local adaptive kernels named local feature adaptive learning (LFAL) for data classification. We employ multiple kernels to explore the local feature of data distribution and learn a novel Hilbert space from the kernel matrix of data derived from multiple kernels to extract more data information. Next, we construct a new semi-supervised learning problem with the learned Hilbert space under the general framework of semi-supervised learning. The advantage of the LFAL is that it learns more local data feature from data samples, and provides an adaptive way to learn the solution space with multiple kernels for learning problem. Experimental results on four real world data sets show the effectiveness of our algorithm.