Reviewerly/exHarmony huggingface dataset:
Combine availability with user preferences for efficient WSC

The core problems of Web Service Composition (WSC) are to satisfy user preferences while at the same time facilitate reasonable construction for WSC. This poses two problems. First, the user real preferences has not been fully expressed when qualitative preference is used to measure composite servi ce. Second, when considering whether the WSC is reliable, some studies use the trust value as the reference attribute of the composite service. However, this is not sufficient as an evaluation index. To solve these problems, we first use the neural network to adjust initial weights in qualitative preference, making qualitative preference accurately measure user preferences when it changes. Second, we redefine the service invocation structures based on the travel plan. Third, we propose a new indicator: availability. Based on the service invocation structures, global availability of WSC is obtained from availability of a single service. Finally, WSC in three aspects: qualitative, quantitative and availability are used to get the final optimal composite service by multi-objective optimization algorithm. Results show that our method is reasonable and efficient compared with other counterparts.
Clan Culture: Challenges and Potential for University During Transformation Period (Russia)

The clan corporate culture of the university as a culture of collaboration and cooperation is a possible answer to the search for unity between two opposing trends of a modern university.There is a tension between the traditional professorial university culture and innovation and market culture which helps to overcome the contemporary challenges of globalization and competition in the educational market.It is obvious that the clan culture impedes the university transformation into an economic corporation and contributes to the university community preservation and professional and personal identity.The relevance of the study lies in the fact that the university corporate culture configuration as a management tool helps the formation of a common vision of the world-class university.Right now, in a competitive situation within the Russian excellence program 5-100 it is crucial for university to ensure loyalty of employees, students, university partners and university environment.The university clan corporate culture is responsible for introducing innovative brands and for preserving the classical heritage to transforming into new type of the university.
A Provably Secure Scalable Revocable Identity-Based Signature Scheme Without Bilinear Pairings

Revocation functionality is essential for the practical deployment of identity-based cryptosystems because a user’s private key may be corrupted by hacking or the period of a contract expires. Many researchers are focusing on revocable identity-based encryption scheme, while revocable identity-based signature scheme has received limited concentration. Recently, several revocable identity-based signature schemes have been proposed. However, these schemes are not scalable and are vulnerable to signing key exposure attack. In this paper, we first refine the security model of revocable identity-based signature scheme by considering the signing key exposure attack. Then, we propose a scalable revocable identity-based signature scheme with signing key exposure resistance. Finally, we prove the proposed scheme is existentially unforgeable against adaptively chosen message and identity attacks under the standard discrete logarithm assumption in the random oracle model.
Clinical intelligent system for the diagnosis of prostate cancer

A software clinical intelligent system for the diagnosis of prostate cancer is discussed. The system provides assistance to the doctor in the histological diagnosis. The system has the mode of operation of an expert doctor to fill the knowledge base of the system, the mode of assistance to the diagnostician (pathologist) in the formation of requests to the system, the mode of support in decision-making based on the formation of a rating list of possible diagnoses for the case under study, the training mode.
Depolarization of light in optical fibers

The evolution of radiation polarization in a non-uniform medium with a gradient refractive index profile using the quantum mechanical method of coherent states is investigated. It is shown that the degree of polarization of both linearly and circularly polarized radiation decreases with distance due to the interaction between polarization (spin) and trajectory (orbital angular momentum). The effect of asymmetry for the right- and left- circularly polarized radiation takes place in relation to the sign of helicity of the trajectories of sagittal rays. The wave nature of depolarization is emphasized. Depolarization disappears when the wavelength tends to zero &lambda;&rarr;0. Depolarization increases with an increase in the axial displacement of the beam, the gradient parameter of the waveguide and the wavelength of the radiation. The oscillations of the polarization degree of pure diffraction origin in the propagation of radiation in a singlemode optical waveguide are found. It is shown that the increase in the angle of rotation of the polarization plane with the distance is irregular and depends on the axial displacement or the angle of inclination of the beam to the waveguide axis. The fluctuations of Berry’s phase, which have a wave nature, in the propagation of radiation in an inhomogeneous medium are found. It is shown that the dispersion of the angle of rotation of the polarization plane increases with distance and can be determined from measurements of the degree of polarization of radiation.
Object Tracking Algorithm Based on Adaptive Deep Sparse Neural Network

Due to the complexity of object tracking easy to produce the tracking drift problem, this paper proposes an object tracking algorithm based on deep sparse neural network. In the particle filter framework, using the Rectifier Linear Unit (ReLU) activation function, according to different situations of object to construct a deep sparse neural network structure, through the finite sample label on-line training, this algorithm can get a robust tracking network. The experimental results show that compared with the current mainstream tracking algorithm, the average tracking success rate and accuracy of algorithm are greatly improved, and according to changes in light, occlusion and fast object movement in complex environment, the algorithm can effectively solve the problem of tracking drift, and show good robustness.
Using software to visualize and facilitate the teaching of vowels for Tibetan english majors

As the differences among the English vowels are very subtle, vowels are always difficult to teach. Under the guidance of Automatic Selective Perception Model(ASP), this article has studied how to use modern technology to visualize and facilitate the teaching of vowels for Tibetan English majors and to improve the teaching results.
Geothermal Well Drilling and Deep Fault

Hydrothermal geothermal resources rely on geological structure condition. Deep fault owns important function in geothermal system. It is specially for high temperature geothermal field which located in volcano and recent magma area. Such fractured reservoir depends on the distribution of deep fault.The purpose of geothermal well drilling is to gain thermal energy and fluid as much as possible. High quality geothermal well starts from well drilling design. Such design should select your target fault and decide a certain depth to encounter the fault. When implementation of the drilling it is very important to take care if the drilling touching fault. Over-drilling after penetrating fault will lead reversed result. Many examples have explained such stories.
Review on Current Trends of Deep Learning

Artificial Intelligence is that term which take Science to new horizons. In field of computers, AI is described as where machine acts like human. AI has different fields on the basis of problem i.e. machine learning, natural language processing, computer vision and robotics. To achieve these objectives several approaches are in trend like Symbolic Reasoning, Neural Network, Deep Learning and Evolutionary Algorithms. Out of these, Neural Network and Deep learning are those approaches which attracts the researchers. Both are inspired by biological neural network but Deep learning is more refined neural network in which feature extraction and abstraction is automatic as compared to Neural Network. In this chapter we will emphasise on AI technologies and then focus on recent researches in field of Deep Learning i.e. Sentiment Analysis, WSN etc.
Informational Negligence Law

This article offers an analysis of negligence law in an environment with asymmetric information and costly signaling. We consider three possible variations of the negligence doctrine, based on its two elements—the standard of care and damages. We find that accounting for signaling costs affects the social desirability of the negligence rule. In a nontrivial number of cases, the social costs are lowest under the variation of the negligence regime in which the standard of care is the same for all types of victims but damages vary according to the victim’s type. This analysis provides an efficiency-based justification for the use of negligence doctrine in bodily injuries and wrongful death cases, a practice that has been considered one of the greatest “misalignment puzzles” in negligence law.
Two new constructions of approximately symmetric informationally complete positive operator-valued measures

A symmetric informationally complete positive operator-valued measure (SIC-POVM) is a POVM in [Formula: see text] consisting of [Formula: see text] positive operators of rank one such that all of whose Hermite inner products are equal. SIC-POVMs are important in quantum information theory, which have many applications in quantum state tomography, quantum cryptography and basic research in quantum mechanics. However, it is very difficult to construct SIC-POVMs. Therefore, many scholars have focused on approximately symmetric informationally complete positive operator-valued measures (ASIC-POVMs) for which the Hermite inner products are close to equal. In this paper, two new constructions of ASIC-POVMs are provided by using character sums and some special functions over finite fields.
Prediction of terrorist attacks based on GA-BP neural network

Terrorism is a common threat to mankind. Combating terrorism is also the responsibility that every country should assume. In the face of the development trend of terrorism, in-depth quantitative analysis of data related to terrorist attacks will help deepen people's understanding of terrorism and provide valuable information support for counter-terrorism, and effectively improve the pertinence and efficiency of the anti-terrorism struggle. This paper designs a risk assessment and prediction system for terrorist attacks. The mathematical model calculates the relative risk index of each type of target by factor analysis. The evaluation indicators include three factors: "threat", "vulnerability" and "consequence". Mathematical model uses neural network to evaluate and predict risk index. However, because the BP neural network is easy to fall into the local best, it is difficult to jump out. Therefore, the genetic algorithm (GA) optimizes the initial weight threshold of the BP neural network to enhance the prediction accuracy. Finally, the simulation experiments of 21 main targets are carried out to verify the effectiveness of the model, so as to carry out accurate strategy analysis.
Introducing Weighted Intermediate Recombination in On-Line Collective Robotics, the ( $$\mu /\mu _{\mathrm {W}},1$$ )-On-line EEA

Weighted intermediate recombination has been proven very useful in evolution strategies. We propose here to use it in the case of on-line embodied evolutionary algorithms. With this recombination scheme, solutions at the local populations are recombined using a weighted average that favors fitter solutions to produce a new solution. We describe the newly proposed algorithm which we dubbed ( $$\mu /\mu _{\mathrm {W}},1$$ )-On-line EEA, and assess it performance on two swarm robotics benchmarks while comparing the results to other existing algorithms. The experiments show that the recombination scheme is very beneficial on these problems.
Optimizing Accuracy of the Variety Bitspaces for Weather Forecasting for Early Flight Safety

Different types of images when testing will give different results. Between still images and moving images requires speed and accuracy in the computation process. This is due to the availability of time in processing which tends to narrow at any time along with environmental changes. Retesting of processors that have been built through previous research requires the selection of new image data and processes. The selection of the new image refers to a different image and has never been used before. Next, for the new process by applying gaussian filtering selection. The results of the initial stage in testing some new images obtained that the bitspace adder/sub 32 bit (single-operand) accuracy value for cloud image data test without selector was 97.91% in the first cycle and without using filters block. While the architecture with the least significant bit method only produces an accuracy of 0.01% so that there is a very significant difference in accuracy, amounting to 97.90%. The last result if using filters block, the accuracy value is 98.75% at the first cycle with using filters block.
Selecting Optimal Proposal Number for Image-based Object Detection

In order to balance the detection time and accuracy, the state-of-the-art region-based detectors use a fixed number of proposals to obtain detection results in the inference phase. However, in surveillance scenes, object population varies in different images, causing the fixed proposal number becomes an undeterminable hyper-parameter, which needs to be correspondingly adjusted to maintain high recall. To solve this problem, we propose two image-level optimal proposal number selection methods called linear proposal number (LPN) selection method and adaptive proposal number (APN) selection method respectively, both aiming at selecting an optimal proposal number for each image to adapt both the images with sparsely and densely distributed objects. In LPN selection method, we introduce a linear weighting hyper-parameter to formulate the relationship between the actual object number and proposals' scores to obtain the optimal proposal number. To avoid setting the hyper-parameter manually, we further propose another APN selection method where the optimal proposal number of each image is selected by exploring the distribution of the proposals' scores. Results obtained from the UA-DETRAC car dataset and self-built bird dataset (BSBDV 2017) show that our proposed methods can largely improve the detection performance in terms of detection time and accuracy without any re-training process.
USING ALGODOO© SOFTWARE IN PHYSICS EDUCATION: STATIC FRICTION AS AN EXAMPLE

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 6569-6574ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1593Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
KEARIFAN LOKAL DALAM NOVEL DAWUK KARYA MAHFUD IKHWAN SERTA RELEVANSINYA SEBAGAI MATERI PEMBELAJARAN SASTRA DI SMA

This study aims to describe and explain (1) the form of local wisdom in the novel Dawuk by Mahfud Ikhwan; and (2) the relevance of Mahfud Ihkwan's Dawuk novel as a literature learning material in high school. This research is a qualitative descriptive study using a literary anthropology approach. The anthropological approach to literature is used to analyze the form of local wisdom in the novel Dawuk. The sampling technique is done using purposive sampling. Data collection techniques are carried out by content analysis or documents and interviews. The data analysis technique in this study uses an interactive analysis model. The results of this study indicate that: (1) the form of local wisdom in the Dawuk novel includes life tools, livelihoods, social systems, language systems, arts, knowledge systems, and religious systems; and (2) Dawuk's novel by Mahfud Ikhwan is relevant as literature learning material in high school for class XII with basic competencies (KD), among others: (a) interpreting the author's view of life in a novel that is read; and (b) analyze the content and language of the novel. Novel Dawuk is also relevant because it has fulfilled three novel selection criteria, namely language, student psychology, and student cultural background.
Data clustering in distributed monitoring systems

Introduction: The traditional methods of analyzing distributed data sources typically use centralized data warehouses and haveseveral disadvantages associated with confidentiality, a high cost of centralized data storage, a limited bandwidth and a high loadon telecommunications networks. Techniques for a decentralized analysis do not take into account the data distribution type andthe features of the selected algorithm. This reduces the performance and accuracy, or can be impracticable under certain conditions.Purpose: Studying and analyzing the features of distributed monitoring systems and data mining algorithms. Results: For clusteringbased on distributed data sources, requirements were set for an algorithm in distributed monitoring systems: single pass, supportof different types of input data, online operation and adaptation to data when the environment changes, large data scaling, analysiswithout assumptions about the input data distribution, analysis of data on information sources without involving a third party. Twomain ways of data distribution on sources in heterogeneous systems are defined: vertical and horizontal ones. The methods are classifiedaccording to their basic principle of cluster delimitation. The classification includes the main clustering algorithms, their operationprinciples, advantages and disadvantages. A review of the existing clustering methods has shown that in distributed monitoringsystems, Kohonen’s neural networks are most efficient. The algorithm of Kohonen self-organizing maps was decomposed, and blocksfor data processing were determined: the calculation of a winner neuron and the adjustment of neuron weights. Two strategies have beenproposed for clustering distributed data. Practical relevance: The proposed strategies allow you to perform clustering in systems withdistributed sources taking into account the characteristics of the environment, without transferring all the data.
Influence of Local Selection and Robot Swarm Density on the Distributed Evolution of GRNs

Distributed Embodied Evolution (dEE) is a powerful approach to learn behaviors in robot swarms by exploiting their intrinsic parallelism: each robot runs an evolutionary algorithm, and locally shares its learning experience with other nearby robots. Given the distributed nature of this approach, dEE entails different evolutionary dynamics when compared to standard centralized Evolutionary Robotics. In this paper, we investigate the distributed evolution of Gene Regulatory Networks (GRNs) as controller representation to learn swarm robot behavior, which have been extensively used for the evolution of single-robot behavior with remarkable success. Concretely, we use dEE to evolve fixed-topology GRN swarm robot controllers for an item collection task; this constitutes the first work to evolve GRNs in distributed swarm robot settings. To improve our understanding of such distributed GRN evolution, we analyze the fitness and the behavioral diversity of the swarm over generations when using 5 levels of increasing local selection pressure and 4 different swarm sizes, from 25 to 200 robots. Our experiments reveal that there exist different regimes, depending on the swarm size, in the relationship between local selection pressure, and both behavioral diversity and overall swarm performance, providing several insights on distributed evolution. We further use a metric to quantify selection pressure in evolutionary systems, which is based on the correlation between number of offspring and fitness of the behaviors. This reveals a complex relationship on the overall selection pressure between the ability or ease to spread genomes (or environmental pressure), and the fitness of the behavior (or task-oriented (local) pressure), opening new research questions. We conclude the paper by discussing the need for developing specialized statistical tools to facilitate the analysis of the large and diverse amount of data relevant to distributed Embodied Evolution.
The effectiveness of using smartphone-based e-book in increasing students’ learning outcomes in science learning

In science learning, new innovative breakthroughs need to be sought as renewal effort to comply with the advancement of science and technology. One of the innovative applications is selection of smartphone-based e-book equipped with videos and images since it can be taken and used anywhere. The purpose of this research was to investigate the effectiveness of using smartphone-based e-book in improving students' learning outcomes in science learning. This research used classroom action research method in science learning in three schools. Data of this research were collected using tests, observations, and questionnaires. The results of this research showed that the use of smartphone-based e-book can improve students' learning outcomes. In schools 1, 2, and 3, the students' learning outcomes increased by 27%, 15%, and 18%, respectively. The use of smartphone-based e-book also increased the students' learning motivation by 12.8%, the students' learning activities by 8% and the originality of the students' creative thinking ability by 14.26%.
Highly versus Normally Attractive Person in Picture Reviews

This research explored the impact of person's physical attractiveness in the picture reviews on women's clothing purchase intention.The results showed that the physical attractiveness of the person in the picture reviews significantly affected the purchase intention of consumers, and the highly attractive person (HAP) could promote the purchase behavior of consumers.Body image was a mediator in the relationship between person's physical attractiveness in picture reviews and consumers' purchase intention, that is, people's physical attractiveness indirectly influenced consumers' purchase intention through body image.Finally, the influence of person's physical attractiveness on consumers' purchase intention was moderated by perceived similarity.When the consumers perceived that the people in the pictures were similar, HAP would promote their buying behavior.However, when the consumers perceived that the people in the pictures were dissimilar, HAP would reduce their buying behavior.