Reviewerly/exHarmony huggingface dataset:
Significance of Network Properties of Function Words in Author Attribution

Author identification or attribution helps in identifying the author of unknown texts and is used in plagiarism detection, identification of writers of threatening documents and resolving disputed authorship of historical documents. Stylometry and machine learning are the most popular approaches to this problem where statistical methods are employed to extract signatures of authors of known texts and these features are used to predict authorship of unknown documents. Complex network approach to feature extraction has focused on content words ignoring function words as noise. In this paper, features of function words of texts are extracted from the word co-occurrence network of texts and used for classification. The results of these experiments are found to have high accuracy. The results of the experiments using function words and content words are compared.
Structure-Borne Sound

The field of structure-borne sound is more complex than fluid acoustics due to the manifold possible degrees of freedom for force and torque and corresponding elastic reactions. In contrast to fluids, solids can react with elastic forces to pure deformation. The number of possible waveforms is therefore higher. And these waveforms can interact and exchange energy, depending on the geometric and elastic coupling in the specific shape of the solid body. Structure-borne sound plays a very important role in building acoustics, in noise control and in vehicle acoustics, for instance. The characterization of structure-borne sources is a very complex field, too. The dynamic behaviour of the source coupled to the structure must be known. Even in the geometrically simple case of a point-like force source, the elementary parameters of the source can be complex since they are related to real and ideal force sources and inner impedances, similar to electric models of voltage sources acting on electric networks. The power output then not only depends on the source itself but also on the kind of attached structure and the impedance coupling between source and structure.
University Social Responsibility Through Marine Environmental Education Projects: A Case Study Based on Survey of Participating Students

Marine environmental education projects have been operated by O-RCC (ocean keeping regional collaboration center) of Pukyong National University, for regional community innovation. These projects can be classified into either of two models i.e. stepping stone–living lab or community innovation project. Of 79 undergraduate and graduate students who carried out the 17 projects, 31 students participated in a three-step survey; hence, the pros and cons of the projects have been revealed. Although, in overall, the students satisfied the project process and outcome, we need to improve the projects and their outcome as follows. First, we need to consider students’ majors in the RCC projects, improve students’ knowledge, and help the students develop or choose their career path. The second one is extending students’ roles such as encouraging the students create new ideas to solve the problems, creating students’ deep involvement into the projects, and making the project outcomes contain a social ripple effect. Third one is taking on-time administrative actions such as promoting students for the participation, giving detail relevant notice, and providing experts for the students to solve the problem. Besides, we found that university image, systematic pre-education and performance inspection, and realizing the six-step spiral model are important to successfully carry out the regional innovation.
A Systematic Overview of Fault Tolerance in Cloud Computing

Emergence of cloud computing technology has created a revolutionary change in the business world where in a central repository of services and resources can be made available to enterprises on demand. The growing popularity as well as demand of cloud services has made this modern technology more challenging in seamless functionality of it. In order to provide the seamless functionality to cloud computing environment, it must be capable of functioning in an error free manner, and moreover, it must be fault tolerant as well. A set of issues and challenges are associated with provisioning of fault tolerance in cloud computing environment, and at the same time, various strategies have been innovated by researchers and scientists in the said filed to make cloud services fault tolerant. In this paper, we have comprehensively outlined the issues and challenges related to fault tolerance of cloud computing environment along with an elaboration of different fault tolerance provisioning techniques.
Underwater Image Enhancement Based on Color Balance and Edge Sharpening

Underwater optical images often suffer from color cast, edge-blurring and low contrast due to the medium absorption and scattering in water. To solve these problems, we propose an effective technique to improve underwater image quality. First, we introduce an effective color balance strategy based on affine transform to address the color distortion. Then we convert the underwater image from RGB color space to CIE-Lab color space for contrast improvement. In ‘L’ component’s nonsubsampled contourlet transform (NSCT) domain, global contrast adjustment and multi-scale edge sharpening are conducted respectively for lowpass and bandpass direction subbands. Finally, a color-corrected and contrast-enhanced output image can be generated by inverse NSCT and conversion back to RGB color space. The propose method is a single image approach that does not require prior knowledge about the underwater imaging conditions. Experimental results show that our method outperforms state-of-the-art methods both in qualitative and quantitative evaluation. It generally results in good perceptual quality, with significant enhancement of the global contrast, the color, and the image structure details.
Research on Virtual Device based Intelligent IoT Management and Test Method of Security Stability Control Systems

In this paper, a method based on virtual devices for testing and managing security stability control systems through intelligent IoT is studied. With the aid of the cloud computing intelligent IoT platform, stability control virtual devices are built on the basis of stability control devices in each substation through virtual technology, consisting a complete security stability control virtual system, to carry out management on operating and maintaining of the security stability control system. Through the virtual test of the stability control virtual devices, the large and complex stability control system strategy of the power grid can be effectively, reliably, complete, and efficiently joint commissioned without dropping out the actual devices in the security stability control virtual system. Furthermore, remote testing of security stability control systems can also be enabled by inputting test commands to different security stability control devices through virtual devices synchronously.
On Using Meta-Features to Learn Under Class Skew in Biomedical Domains

In many real world medical datasets learning under class imbalance can limit the performance of most supervised algorithms, resulting in a low recognition rate for samples belonging to the minority class. Within the wide literature on this issue the class of techniques that goes under the name of ensemble methods have been less investigated, despite their effectiveness. In this panorama, we present here an original strategy to construct the training set of each base classifier in the ensemble: it exploits information in the feature space that can give rise to unreliable classifications, which are determined by a novel algorithm here introduced. Our proposal is compared against multiple standard ensemble approaches on 18 publicly available biological datasets, showing promising results.
Deep Learning System for Image Retrieval

In the modern era of digital photography and advent of smartphones, millions of images are generated every day and they represent precious moments and events of our lives. As we continue to add images to our digital storehouse, the management and access handling of the images becomes a daunting task and we lose track unless properly managed. We are in essential need of a tool that can fetch images based on a word or a description. In this paper, we try to build a solution that retrieves relevant images from a pool, based on the description by looking at the content of the image. The model is based on deep neural network architecture and attending to relevant parts of the image. The algorithm takes a sentence or word as input and obtains the top images which are relevant to the caption. We obtain the representation of the sentence and image in a higher dimension, which enables us to compare the two and find the similarity level of both to decide on the relevance. We have conducted various experiments to improve the representation of the image and the caption obtained in the latent space for better correlation, for e.g. use of bidirectional sequence models for better textual representation, use of various baseline convolution-based stacks for better image representation. We have also tried to incorporate the attention mechanism to focus on only the relevant parts of the image and the sentence, thereby enhancing the correlation between the two spaces.
Refinement of Cost Models for Cloud Deployments through Economic Models Addressing Federated Clouds

Ten different cloud deployment models (e.g. public clouds, private clouds, and federated clouds) can be selected by users, depending on their requirements for executing their applications. As each of these deployment models comes with certain costs and benefits, they need to be understood by users to ensure they make an optimal selection. In order to support this decision making process, a comprehensive cost model that comprises all relevant cost factors is needed. The aim of this chapter is to present such a comprehensive cost model comprising all relevant cost factors and an economic models underlying the various cloud deployment models. In particular, an architecture for managing federated clouds using an economic model is discussed in the outlook on future research.
ESSA (Enhanced speech synthesis approach) for Building Punjabi Voice Model

This Paper presents the text to speech synthesis model using Random Forest Technique along with mixed excitation approach for decision making. Base model is developed by extracting the various voice features types (segment features, phoneme identity etc.) in statistical parametric synthesis approach, which is further enhanced with Random Forest criteria to redevelop the voice model. Twenty cluster trees are generated in Random forest from which one best is selected and used to create a voice model.In this paper for each developed text to speech model, the Mel-cepstral distortion scores are evaluated for comparative study.
Optimal temperature estimation for a XXZ spin-1 2 chain coupled locally to independent thermal baths

Temperature as an environmental parameter influences the evolution of an open quantum system. In detail, temperature lies in Lindblad operator of quantum master equation that the evolution of an open quantum system follows. Hence, one can implement a temperature estimation of thermal baths through a measurement of quantum Fisher information about temperature brought from quantum states. Such a method by calculating quantum Fisher information about a parameter to estimate its value avoids measuring the parameter directly and it does not change the value of the parameter due to making measurements. In this paper, we consider a model consisting of a XXZ spin-[Formula: see text] chain coupled locally to independent thermal baths with different temperature. Based on the model, we investigate optimal temperature estimation for thermal baths with respect to an open quantum system subjected to non-steady states. We first study optimal probe time for temperature estimation in the case of non-steady states and find that the optimal time shows different features for different types of system variables. It proves that in a certain duration there exists a tradeoff between the trial times and the attaining amount of Fisher information in each trial. In addition, we pay attention to an issue on optimal probe states. We demonstrate that in many cases the optimal states are not always the maximally entangled states and even maybe the separable states, which is related with the measuring time, system couplings.
Reduction coefficient and electric field near plane electrode with geometric heterogeneity

Abstract Based on experimental studies and the theory of the electrode effect, the dependences of the reduction coefficient used in the processing of atmospheric-electric observation data on the geometry of the sensors and the size of the measured electric field are studied. In the approximation of the classical electrode effect, analytical expressions are obtained for the potential and electric field distributions near a flat electrode with a spherical inhomogeneity.
Research and Technology Development of University Campus Sports Data Platform

This article introduces a sports platform APP developed based on Android system. The APP contains multiple sections, users can perform autonomous extracurricular exercises according to the goals specified by professional teachers; users can query the fitness test results through the APP; professional teachers can use the APP to collect users'exercise data and physical fitness data, and develop course goals of different exercise intensity and exercise load, guide users to choose a suitable course and through the data analysis system in the APP, provide hierarchical guidance to user groups. In the Android Studio environment, the team uses Java to write the client APP; In the Net beans IDE environment, the client APP is written by Java to respond to the request; the C/S architecture is implemented, and it cooperates with other tools like the MySql database, Tomcat server, and Python program. Then the basic functions of the software can work well.
Tight Approximation for the Minimum Bottleneck Generalized Matching Problem

We study a problem arising in statistical analysis called the minimum bottleneck generalized matching problem that involves breaking up a population into blocks in order to carry out generalizable statistical analyses of randomized experiments. At a high level the problem is to find a clustering of the population such that each part is at least a given size and has at least a given number of elements from each treatment class (so that the experiments are statistically significant), and that all elements within a block are as similar as possible (to improve the accuracy of the analysis). More formally, given a metric space $$(V, d)$$ , a treatment partition $$\mathcal {T} = \{T_1, \ldots , T_k\}$$ of $$V$$ , and a target cardinality vector $$(b_0, b_1, \ldots , b_k) \in Z_+^{k+1}$$ such that $$b_0 \ge \sum _{j=1}^k b_j$$ . The objective is to find a partition $$M_1, \ldots , M_\ell $$ of V minimizing the maximum diameter of any part such that for each part we have $$|M_i| \ge b_0$$ and $$|M_i \cap T_j| \ge b_j$$ for all $$j=1, \ldots , k$$ . Our main contribution is to provide a tight 2-approximation for the problem. We also show how to modify the algorithm to get the same approximation ratio for the more general problem of finding a partition where each part spans a given matroid.
Design and Application of RFID-based Management System in Public Laboratory

Due to the large space, large number of users, and high mobility of public laboratories in public laboratories, laboratory management often has problems with inaccurate data records on the use of laboratory benches, which makes it difficult for further laboratory management. Aiming at this situation, this paper designs a set of RFID-based laboratory management system. The system consists of human body sensor, RFID active tag, MCU, high-frequency card reader and host computer software system. The system has stable performance, high cost performance, and good operability and scalability. The experiments show that the application of the system can improve the accuracy of the laboratory's use data, and can also provide location information for the maintenance of experimental instruments in a timely manner.
Towards a Secure Quantum Communication Implementation for OSI Layer 2 Devices

The threat caused by the ubiquitous nature of the internet and quantum-computer-assisted cryptanalysis amongst the networked devices, organizations and users seeks a redress within the security community to apply innovative ways in the development of information communication protocols. Quantum teleportation (QT) has emerged as a technology that promise unconditional security and providing new ways to design and develop frameworks that operate based on the principles of quantum physics such as entanglement and no-cloning. This study applied an evaluation approach to empirically investigate the effect of QT on the improvement of networked device communication security on layer 2 using a simulated experiment. Software Defined Network's OpenFlow, Mininet-Wifi and Simulator for Quantum Networks and Channels (SQUANCH) were considered as our coding approach. The empirical evidence revealed that QT has the ability to make it harder for anyone trying to listen-in, eavesdrop or tap into data in-transit. QT despite its known challenges i.e., interference, freespace loss, transmission loss etc., when implemented properly within reasonable scope can improve security for data in-transit. This will become an advantage when designing frameworks to adopt quantum technology for implementation.
Analysis of Undergraduate Education Enrollment and Cultivation Mode According to Big Academic Subjects

The enrollment and cultivation mode of undergraduate according to the big academic subjects in colleges and universities is presented in this paper. Four kinds of big academic subjects enrollment and cultivation modes are introduced. The advantages of this kind of mode for the reform of high education are expounds. The expected effects of this kind of enrollment and cultivation mode are analyzed. It is concluded that the mode of enrollment and cultivation is the fundamental reform of the original mode of talent training in colleges and universities. This mode has become a trend of college enrollment and training, which has important practical significance for talent training system.
Powerless Security

Power line communication (PLC) allows home users and industries to transfer data over power cables. Protection of transmitted data is crucial because signals are not limited to “one’s own four walls”. We provide a detailed and structured security analysis of the currently most widely used in-Home PLC standard, namely the Broadband-PLC specification HomePlug AV2 (part of IEEE 1901), and present a design weakness in the pairing process as well as a new offline dictionary attack that can be used to compute the main network key efficiently. We evaluated our attacks on 13 widely used PLC devices and found all of them be vulnerable. We provide different countermeasures and discuss their advantages and disadvantages. We responsibly disclosed the vulnerabilities and are currently supporting the vendors in fixing these issues.
Ontology-Driven Sentiment Analysis in Indian Healthcare Sector

In today’s world, social media platforms have emerged as one of the most prominent media for expressing opinions. Sentiment analysis for utilizing the big data socially available over the web has become one of the most researched areas. Sentimental analysis is used to get the public perception over any event, topic or subject matter by classifying data into various polarity categories like positive, neutral, negative, etc. Various computational techniques such as machine learning and deep learning are used to perform polarity analysis and to find the best classifiers. Traditional techniques tend to create feature vectors in order to quantify the data, but these feature vectors are often very large for tweets collected over a particular domain and that leads to a performance reduction. This paper proposes a combined approach which utilizes domain ontology with machine learning methods in order to reduce the size of feature vector to increase the performance for all machine learning models. The observations state that our technique provides a significant advantage over the traditional methods.
Analysis of Software Development Process Based on Software Components

Although software component technology has been used, it lacks a systematic method to support software development from the perspective of software engineering. With the development of science and technology in our country, the development of software is also in the stage of continuous innovation. The concept, process and related construction technology of software development are also constantly developing and perfecting. With the increasing scale and complexity of software, the traditional software development model is facing unprecedented challenges. People begin to explore new software development technologies to meet the requirements of software development. How to strictly control the software development process, improve the success rate of software system and user satisfaction, and reduce the development cost has become the focus of theoretical research in the software industry. Component technology plays an important role in the whole process of software development. Using software component technology to develop, it can greatly reduce the workload, and has many application advantages.
The adaptive equalization compensation algorithm for DVI video signal

In order to meet the requirements for collecting video signals of airborne avionics systems and improve the signal transmission quality of airborne video distributors, an adaptive equalization compensation method is proposed and applied to the airborne DVI (Digitial Visual Interface) video signal distributor. The advantages of this method are: using hardware pre-emphasis and equalization technology to solve the problem of distortion of DVI signals during long cable transmission; using a double histogram equalization method based on a weighted probability equalization model to further improve image contrast and sharpness. Finally, experiments show that this method can improve the clarity and recognition of video images.
A Study on SF6 Gas Sensor on MEMS

This paper aims to realize miniaturized, highly sensitive passive gas sensors. The basic operation principle of the sensor is illustrated below. Light is coupled from the source into the on-chip waveguide and attenuated at selected wavelengths due to evanescent wave interactions with surrounding molecules. Such wavelength-specific absorption can be used to identify and quantify gas molecules in the environment. According to the Lambert-Beer Law, optical absorption scales with the gas concentration as well as the optical path length. This dependence forms the basis for quantitative gas analysis using optical absorption spectroscopy. The main technical challenges for this sensor design include: 1) realizing low-loss on-chip optical waveguides in the long-wave infrared (LWIR, 8-14 um); and 2) optical packaging of LWIR sensor chip. Both challenges are beyond the capabilities of existing technologies, and therefore we will need to pioneer new innovative solutions above and beyond the current state-of-the-art. Successful completion of the project will likely involve not-yet-invented technologies and thus represent a major technical triumph in the field of infrared integrated photonics. It will also have broad impacts on application arenas including environmental monitoring, industrial process control, as well as security and defense.