Reviewerly/exHarmony huggingface dataset:
Integrating remote and biogeochemical data: Analyzing the potential of gamma ray spectrometric data for mapping Cerrado vegetation types

This study integrates remote and chemical data to investigate the potential use of potassium (K), thorium (Th), and uranium (U) radioisotopes for mapping Cerrado physiognomies. Because soil and vegetation are interdependent, the vegetation over the substrate reflects this relationship. Moreover, the physiognomic extracts that accompany the distribution of elements associated with the movement of this substrate in soil can be identified. Gamma ray spectrometric data have been used primarily for mineral exploration; however, the present study addresses the potential of these data for identifying vegetation. Using a multivariate analysis, it is possible to compare radioisotope data and their relationship to vegetation. The current study area is located in the northwest of Goiás, an area of conserved phytophysiognomic diversity that enables studies of the Cerrado vegetation. An initial cluster analysis defined five vegetation cover types: cerradão, cerrado rupestre, cerrado sensu stricto and campo limpo. Dendrograms demonstrated a positive relationship in the radioisotopes absorbed by certain plants; furthermore, their availability in the soil confirmed that it is possible to identify vegetation groups using the same absorption behavior. This soil-plant relationship divides groups that have the capacity for the absorption of radioisotopes with remote gamma-spectrometry data. Even considering that each species might react differently because of absorption and the bioavailability of elements in the soil, a relationship between absorption and remote data was identified. Furthermore, this relationship is reflected in the physiognomy, as confirmed by a multivariate discriminant analysis. The result of the analysis showed the potential of Th and K radioisotopes with regard to phytophysiognomic mapping.
ACO for Image Edge Detection

The text of this chapter is based on []. The aim of the image edge detection is to find the points, in a digital image, at which the brightness level changes sharply. Normally they are curved lines called edges. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. Edge detection may lead to finding the boundaries of objects. It is one of the fundamental steps in image analysis.
Aspect Based Sentiment Analysis for Evaluating Movies and TV series

Aspect-based sentiment analysis (ABSA) is used in different fields for analyzing customer reviews to project an overall customer opinion on certain products. With the expansion of the internet, people are provided with an inexpensive and time-saving method to express their opinion to a larger audience, while various industries are handed with the opportunity to gather free information from it to obtain market value. The implementation of machine learning methods for the evaluation of aspects related to movies and television series has not been commenced, and it could be a new development for the industry. This study focuses on conducting an ABSA on a movie or a television series based on genre, story as well as cast and crew aspects. The data collected from social media through web scraping is processed to produce adequate results to get a broad understanding on how the popularity of the movie or the television series related to above mentioned aspects. Then, each aspect is further analyzed to gather precise information belonging to each aspect. The accuracy of the results of the proposed system has been achieved over 79%. The results proved that the solution is highly successful than the former works with high business value.
Experimental Validation and Noise Assessment of the Thermal-VFM

The virtual fields method is a well-known inverse technique used to identify the constitutive parameters of materials starting from full-field measurement. The method exploits the equilibrium equation, written in the form of the principle of virtual work, to extract the constitutive parameters directly from the measured data. Similar considerations can be done also in the thermal analysis using the law of conservation of thermal energy. In particular, thermography provides a full-field map of the temperature on a specimen surface. From the temperature map, the Thermal Virtual Fields Method (TVFM) is used to extract the thermal properties of the material, like, for instance, the conductivity matrix. Such method was already validated on simulated experiments and the results presented in a previous work. The aim of the present paper is to give a first experimental validation of the method. A suitable experimental set-up was developed using, as target, an aluminum sheet and heater mats to generate the heat flux. The temperature full-field map was detected using a IR camera and the TVFM was accordingly applied to extract the conductivity matrix and the convective heat transfer coefficient. The accuracy of the identification and the impact of the measurement noise is also discussed.
Agricultural Commodity Recommendation based on Emotion Analysis

The traditional ranking algorithm based on product praise rate has obvious defects in processing evaluation data. There is the problem of inaccurate ranking in the small sample data ranking and less valuable comments. In order to solve this problem, this paper introduces the method of emotional analysis of agricultural commodity reviews based on SVM, and proposes an improved algorithm which uses emotional score to replace the positive rate. The algorithm takes into the number of product reviews, the length of comments and the emotional factors of comments, which can effectively solve many problems in the ranking of high praise rate. Experiments on real data show that the improved algorithm can effectively avoid the above problems and provide more reliable ranking results.
AVEI: A Scientific Data Sharing Framework Based on Blockchain

Scientific data sharing faces some dilemmas in process of practice. Blockchain technology has the characteristics of decentralization, openness, independence, security and anonymity, it provided a new solution to solve the dilemmas of scientific data sharing. So, exploring the application of blockchain technology in scientific data sharing is of great significance to expand the applications of blockchain and improve the effectiveness of scientific data sharing. This paper introduced the basic concepts of blockchain, summarized the practical dilemmas faced by scientific data sharing, analyzed the coupling between blockchain and scientific data sharing. Finally, we proposed a scientific data sharing framework which called AVEI based on blockchain, and analyzed the performance of AVEI from three perspective. AVEI covers three processes, they are user identity role authenticate process, data verify process and data exchange process, and we also introduced incentive system to enhance the enthusiasm of participating nodes. In theory, AVEI can solve practical dilemmas such as inconsistent standards, violation of privacy and security, insufficient motivation, etc., and it has a good performance in data quality, data security, and sharing effects.
Security Considerations on Three-Factor Anonymous Authentication Scheme for WSNs

Wireless sensor networks (WSNs) are used to monitor various environmental conditions including movement, pollution level, temperature, humidity, and etc. Secure authentication is very important for the success of WSNs.Li et al.proposed a three-factor anonymous authentication scheme in WSNs over Internet of things (IoT).They argued that their authentication scheme achieves more security and functional features, which are required for WSNs over IoT.Especially, they insisted that their user authentication scheme provides security against sensor node impersonation attack, and resists session-specific temporary information attack and various other attacks.However, this paper shows some security weaknesses in Li et al.'s scheme, especially focused on sensor node masquerading attack, known session-specific temporary information attack and deficiency of perfect forward secrecy.Especially, security considerations are very important to the modern IoT based applications.Thereby, the result of this paper could be very helpful for the IoT security researches.
A Flexible Access Control with User Revocation in Fog-Enabled Cloud Computing

The major challenging task in the fog-enabled cloud computing paradigm is to ensure the security for accessing the data through cloud and fog nodes. To solve this challenge, a Flexible Access Control using Elliptic Curve Cryptography (FAC-ECC) protocol has been developed in which the user data are encrypted by multiple asymmetric keys. Such keys are handled by both users and fog nodes. Also, data access is controlled by encrypting the data through the user. However, the main problem is to guarantee the privacy and security of resources after processing of User Revocation (UR) by data owners. The issue of UR is needed to consider for satisfying the dynamic change of user access in different applications like healthcare systems, e-commerce, etc. Therefore in this article, a FAC-UR-ECC protocol is proposed to control the data access and realize the UR in fog-enabled cloud systems. In this protocol, a revocable key aggregate-based cryptosystem is applied in the fog-cloud paradigm. It is an extension of the key-aggregate cryptosystem such that a user is revoked if his/her credential is expired. First, the subset-cover model is combined into FAC-ECC protocol to design an efficient revocable key-aggregate encryption depending on multi-linear maps which realizes the user's access control and revocation. It can simplify the user's key management efficiently and delegate various clients with decryption permission. Also, it can accomplish revocation of user access privileges and the FAC efficiently. By using this protocol, both the user's secret key and the ciphertext are preserved in a fixed size. The security of accessing the data is highly enhanced by updating the ciphertext through the data owners successfully. At last, the experimental results exhibit the efficiency of FAC-UR-ECC compared to the FAC-ECC protocol.
Brief Introduction in Applications of Other Groups

rom previous Chaps. 2–11, the wavelet-based solutionWavelet-based solution methodSolution methods and its applications are detail introduced to what were conducted by the author and his colleagues. In this chapter, we give a brief introduction of those applications done by else groups using the generalized CoifletsGeneralized Coiflets and relevant method proposed by the author’s group.
Development of a Neural Network Library for Resource Constrained Speech Synthesis

Machine learning frameworks, like Tensorflow and PyTorch, use GPU hardware acceleration to deliver the needed performance. Since GPUs require a lot of power (and space) to operate, typical use cases involve high-performance servers, with the final deployment available as a cloud service. To address limitations of this approach, AI Accelerators have been proposed. In this context, we have designed and implemented a library of neural network algorithms, to efficiently run on “edge devices”, with AI Accelerators. Moreover, a unified interface has been provided, to allow easy experimentation with various neural networks applied to the same dataset. Here, let us stress that we do not propose new algorithms, but port known ones to, resource restricted, edge devices. The context is provided by a speech synthesis application for edge devices that is deployed on an NVIDIA Jetson Nano. This application is to be used by social robots for real-time off-cloud text-to-speech processing.
User Experience Research of Queuing System based in Chinese Smart Bank Branches

With the development of Internet finance, customer behavior and consumption habits have transformed and brought new challenges to commercial banks' traditional branches in China. Bank branches face the pressure of rising operating costs, declining profits, and being gradually replaced by online services. Under the smart economic trend, bank branches are facing significant transforming and upgrading. Branches will gradually transform their roles from traditional business transaction centers to product marketing and customer experience centers. The intelligent upgrading and transformation of branches play an essential role in accelerating the process. In this paper, our research focuses on user experience in the smart branch and adjust with the smart society development trend. We study smart diversions' pre-processing at smart branches, establish smart queuing models, and mobile appointment systems to control the queue. We compare the smart model with the traditional under the bank's virtual simulation system. Use customer waiting time for service and employee work intensity as essential indicators of the queuing model. The simulation results show that the branch uses intelligent distribution is much more efficient than the traditional one. Customer experience and service efficiency are greatly improved.
Discipleship in Three Dimensions: Implications for Home, School and Church as Learning Institutions

This article intended to explore the three mentoring aspects as discipleship dimensions within the three Training avenues, attempting to show the significance of each one. ¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬Literary method was engaged in this study whereas bibliographical data were collected to placate the concern. The article was divided into two major parts. The first part disclosed the prominence of the three mentoring dimensions; rational, relational and missional which began with evaluation aspect. In this study, the three dimensions work as catalysts for the growth of any Christ’s follower. The second part discussed the prominent training avenues for the faithful and trustworthy disciples. This part displayed the family, the church and the school as the precious avenues for mentoring and discipleship. It is anticipated that this paper will contribute to the knowledge and skills on how to enhance students’ commitment to faith in Christian learning institutions. As far as discipleship is concerned, the study is in harmony with the following statement, “prevention is better than cure.” For the bright future of the Christian church, students need to be guarded morally before it is too late.
Interference identification and removal of seismic precursor observation data based on singular spectrum analysis method

Abstract The singular spectrum analysis (SSA) method has the characteristics that it is not affected by the noise spectrum distribution, and is superior to the traditional denoising method. Moreover, it has been commonly used in signal analysis in the fields of oceanography, machinery, and electronic technology. This paper systematically introduces the basic principle and implementation process of singular spectrum analysis method. Then the singular spectrum analysis method is applied to the interference identification and removal of seismic precursor observation data. Taking deformation data and geoelectric field observation data as an example, the results show that the singular spectrum analysis method can effectively separate the rainfall interference in deformation observation data, and has important guiding significance for the subsequent removal of rainfall interference. In addition, the singular spectrum analysis method can be used to extract and remove the interference of HVDC and subway from the geoelectric field observation data, and the effect is obvious, which can effectively improve the data quality and better serve the earthquake analysis and prediction.
Optimization of the Database Function Transactions by using the Fireworks Algorithm

Abstract Inspired by the fireworks flare-up at night, the old algorithm of fireworks (FWA) developed during 2010. Since then there have been some changes or unfinished programs intended to enhance FWAS affectivity. The conventional fireworks algorithm is a forward summary and reviewed in this article, or is presented below three elevated fireworks algorithms. By swapping the methods for numbers measured and instead of the amplitudes of the sparks between the Outbreak of fireworks, the high FWA algorithms turn out to be more lifelike or explainable. The multi-purpose fireworks algorithm, although the image processing (GPU) designed fireworks algorithm is provided as well, the GPU mostly based the algorithm of fireworks is capable of speeding up the optimization. Large experiments on thirteen benchmark services show that the sum of the 3 algorithms multiplied by fireworks dramatically increases the propriety of found solutions but strongly decreases the walking time. Lastly, the fireworks algorithm’s limited capabilities are concisely defined, while its inadequacies are acknowledged, then future lookup advices.
Sequence-Based Filtering for Visual Route-Based Navigation: Analyzing the Benefits, Trade-Offs and Design Choices

Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place using visual information under environmental, viewpoint and appearance changes. An emerging trend in VPR is the use of sequence-based filtering methods on top of single-frame-based place matching techniques for route-based navigation. The combination leads to varying levels of potential place matching performance boosts at increased computational costs. This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods? How does sequence matching length affect the performance curve? Which specific combinations provide a good trade-off between performance and computation? However, there is lack of previous work looking at these important questions and most of the sequence-based filtering work to date has been used without a systematic approach. To bridge this research gap, this paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods. It analyzes individual trade-offs, properties and limitations for different combinations of single-frame-based and sequential techniques. A number of state-of-the-art VPR methods and widely used public datasets are utilized to present the findings that contain a number of meaningful insights for the VPR community.
Information-Flow Control by Means of Security Wrappers for Active Object Languages with Futures

This paper introduces a run-time mechanism for preventing leakage of secure information in distributed systems. We consider a general concurrency language model where concurrent objects interact by asynchronous method calls and futures. The aim is to prevent leakage of secure information to low-level viewers. The approach is based on a notion of security wrappers, where a wrapper encloses an object or a component and controls its interactions with the environment. Our run-time system automatically adds a wrapper to an insecure component.The wrappers are invisible such that a wrapped component and its environment are not aware of it. The security policies of a wrapper are formalized based on a notion of security levels. At run-time, future components will be wrapped upon need, and objects of unsafe classes will be wrapped, using static checking to limit the number of unsafe classes and thereby reducing run-time overhead. We define an operational semantics and sketch a proof of non-interference. A service provider may use wrappers to protect its services in an insecure environment, and vice-versa: a system platform may use wrappers to protect itself from insecure service providers.
Adaptability analysis of solar water heating system in Chongqing area

Abstract According to the simulation software TRNSYS, based on meteorological parameters and and the distribution of solar energy resources in Chongqing area, the dynamic simulation method is used to analyze the application effect of the solar water heating system in Chongqing area. The results show that the application of solar water heating system in Chongqing area has potential, but it has obvious seasonal characteristics, with sufficient solar radiation in summer, followed by spring and autumn, and the least in winter. The simulation calculation shows that the monthly thermal efficiency of the collector fluctuates between 0.2 and 0.7 throughout the year, with an average value of 0.51. The solar fraction basically increases with the increase of solar radiation, which also has obvious seasonal characteristics.
Machine learning studies for the effects of probes and cavity on quantum synchronization

Abstract As an important technology of the quantum detection, the quantum synchronization detection is always used in the detection or measurement of some quantum systems. A probing model is established to describe the probing of a qubit system in the cavity field and to reveal the effect of the environment (cavity) on the quantum synchronization occurrence, as well as the interactions among environment, a qubit system, and probing equipment. By adjusting the frequency of the probe, the in-phase, anti-phase, and out-of-phase synchronization can be achieved. Simultaneously, the effect of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:mrow> <m:msub> <m:mi>γ</m:mi> <m:mn>3</m:mn> </m:msub> </m:mrow> </m:math> ${\gamma }_{3}$ which describes the interaction strength between the probe and environments for quantum synchronization is discussed under different Ohmic dissipation index s . Finally, the machine learning method is applied to present an optimization for classification and regression of synchronization transition dependent on s and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:mrow> <m:msub> <m:mi>γ</m:mi> <m:mn>3</m:mn> </m:msub> </m:mrow> </m:math> ${\gamma }_{3}$ .
Resistance to Change in Software Process Improvement - An Investigation of Causes, Effects and Conducts

Context: Resistance to change can affect the effectiveness of software process improvement initiatives as well as the maintenance of improvements after an official assessment. However, the inherent attributes of resistance to change are not entirely clear, for instance: its effects as well as actions to mitigate its occurrence. Objective: We aim to investigate potential causes and effects of resistance to change as well as conducts that can mitigate its occurrence on software processes improvement initiatives. Method: We performed a qualitative study in which we interviewed 21 practitioners and specialists in software process improvement. Results: We have identified 32 causes, 16 effects, and 29 conducts related to resistance to change. For instance, resistant senior management can sabotage the process by creating resistance to other stakeholders who want to implement improvements. Conclusion: We found that resistance to change in software process improvement initiatives also includes resistance to follow the processes. A significant part of the conducts comprises preventive and low-cost actions. Additionally, the results pointed out situations in which given conduct to mitigate resistance can generate other positive impacts like collaborative work fostering continuous improvements. Also, we observed that individuals are more affected by resistance to change than the projects. Our findings and their interconnections expose a diverse set of circumstances that can be explored to help achieve the objectives of a software process improvement initiative.