Reviewerly/exHarmony huggingface dataset:
Integrate Hybrid Cloud Computing Server With Automated Remote Monitoring for Blockchain as a Service

Blockchain is also used for bitcoin transactions as a technology for accumulating data files in the cloud for key distribution and file manipulation in distributed fashion. It is a service of cloud that manages elasticity of compute cloud, storage, and technology of network security (i.e., secure solution to store and share information by offering a distributed ledger service). In distributed systems, abandoned events are much more frequent than centralized system. This concept causes a number of issues including data reliability, high economical cost, and information system security. In this chapter, the authors present a new framework in blockchain to supervise the cloud server for administration of blockchain, which is verified the transaction reliability in peer-to-peer networks for sharing of data files in centralized manner. Each transaction can be generated keys for server authentication to verify all the connected members for monitoring the web server.
A Study on the Perception and Improvement of Creative-Design Course by Level

In the absence of concrete support plans for extra curriculum programs since the PRIME project has started, the College of Engineering has established regular courses to efficiently operate design programs. It links and operates creative-design curriculum by level: IT-engineering for freshman, basic creative design for sophomore, major creative design for junior, and integrated capstone design for senior. This study aims to find efficient operation and improvement plan for creative-design courses through the questionnaire survey and focused group interview (FGI) on the satisfaction of creative-design courses and the composition of the curriculum for students taking project-oriented creative-design courses. To this end, a survey and group interviews were conducted for 176 students taking creative-design courses, and the survey results and group interview results were analysed. As a result of the analysis, the absolute evaluation method was preferred, and a curriculum that could be done consequently rather than a short-term project. Satisfaction with the creative-design courses was higher in the upper grades than in the lower grades. In addition, when satisfaction for the creative-design courses is high, it showed positive recognition of the satisfaction for the curriculum of creative-design courses. In this paper, I propose four suggestions for efficient operation and improvement for creative-design courses; different goal-setting of creative-design courses at each level, an appropriate form of feedback, the fairness of team setting and evaluation methods, and the grade linkage for creative-design courses. Keywords— Engineering Curriculum; PBL; Higher Education; FGI; Survey.
Alarm text analysis based on convolution neural network and LSTM network feature fusion

Convolutional neural networks (CNN) and recurrent neural networks (RNN) are widely used in natural language processing. However, the natural language has a certain dependence on the structure. A single CNN model leads to ignore the semantic and grammatical information of words. Traditional RNN has gradient disappearance or gradient dispersion problem. For this reason, the paper designs the CNN-BLSTM network in the way of network level combination, introduces Attention mechanism, and proposes a CNN-BLSTM +Attention model. The fusion model effectively deals with the position invariance of local features and extracted efficient local feature information. Furthermore, Attention mechanism is introduced to automatically weigh the output sequence information at each time to reduce the loss of key features when RNNs are used to model the sequence features. The feature extraction in time and space is completed. The experimental results show that the accuracy of the proposed model is 3 to 4 percentage points higher than that of other models. When dealing with alarm text, the model not only guarantees the local correlation of data, but also strengthens the effective combination ability of sequence features.
RECAPITULATION OF GESTURE RECOGNITION SYSTEMRELATED TO DUMP AND DEAF PEOPLE

Sign language is an fast growing and emerging field of research in terms of dump and deafpeople. The conversation among the dumb and deaf people or with the normal people can be done in the form ofNonverbal communication which causes isolation in the society among them. The nonverbal communication canbe done byusing various gestures formed by moving the body parts of a human being i.e. hand , arms or facialexpressions, so the gesture recognition came in to the picture. There are many type of sign languages are therewith in the worldor with in the country like Telugu Sign Language, Tamilnadu Sign Language, Malayalam SignLanguage etc., based on the dialect in their spoken language. So in this paper a recapitulation based on numerousgesture recognition systems for different sign languages.
The new soaking test approach for testing the SONiC network testbed

In the paper, we talked about the new Soaking Test method whose system will run for 7*24 hours under real network load to find out long-term problems, such as memory leak, memory rewrite, kernel resource leak, and measure the stability and reliability of existing network service operation in SONiC network testbed. We illustrate the three main components of the soaking test approach which include: 1) SONiC network testbed 2) Network services 3) Network monitoring. Our illustration shows that, in addition to automated test, RF test and PTF test, the soaking test is a good test approach for testing SONiC network testbed.
An Enhanced 8-Directional Slant Estimation Technique

Slant estimation and correction is an integral part of offline handwritten character recognition system. Broadly, it is a part of successful OCR generation. Various techniques such as 4-connectivityand 8-connectivity are being used for the same purpose. However, it is found that majority of these methods are not capable of 100% slant estimation. In this paper, an enhanced slant estimation technique based on 8-directional Freeman chain code is proposed. The proposed technique is tested with Assamese handwritten texts. Results found are found satisfactory and improving.
다문화 유관기관의 온라인 기록물 관리에 관한 연구* - 대구경북을 중심으로 -

The aim of this study is to investigate management of online archive of multicultural agencies and organizations in Daegu and Kyungbook Area of Korea. To do this, 48 online archives and websites from academic research institute, governmental agencies and NGOs were selected and analyzed. As a result, most agencies and organizations run websites and some run even internet clubs and blogs instead of online archives. The majority of contents were notices, reports and press reports about their programs and activities. The types of digital media were texts and photos but videos and audios only had a relatively small portion. There were some problems with poor data structuralization, omission of contents files, and low level of file format with a recommended standard. Also, the recency and the retrospectiveness were limited but continuity was relatively good. Finally, some suggestions were discussed in order to make current situation better.
Sophroneo: Fear not. A VR Horror Game with Thermal Feedback and Physiological Signal Loop.

We present ”Sophroneo: Fear not”, a VR horror experience with thermal feedback interface. To emphasize the supernatural side of the experience we introduce several innovative approaches, such as long intense cold feedback, liminal audio and physiological feedback loops. We built a wearable thermal feedback setup with water-cooled Peltier TEC elements for long cold sensations to improve the immersion and to increase the sense of fear and unease. At several points of the experience the player will have to close their eyes and interact with the VR environment depending on their other senses alone.
A lattice-based representation of independence relations for efficient closure computation

Independence relations in general include exponentially many statements of independence, that is, exponential in the number of variables involved. These relations are typically characterised however, by a small set of such statements and an associated set of derivation rules. While various computational problems on independence relations can be solved by manipulating these smaller sets without the need to explicitly generate the full relation, existing algorithms for constructing these sets are associated with often prohibitively high running times. In this paper, we introduce a lattice structure for organising sets of independence statements and show that current algorithms are rendered computationally less demanding by exploiting new insights in the structural properties of independence gained from this lattice organisation. By means of a range of experimental results, we subsequently demonstrate that through the lattice organisation indeed a substantial gain in efficiency is achieved for fast-closure computation of semi-graphoid independence relations in practice.
Multi-Label Entropy-Based Feature Selection with Applications to Insurance Purchase Prediction

Insurance purchasing has been receiving increasing attention nowadays. Facing various insurance products, customers prefer the convincing recommendation by machine learning algorithms. The amount of personal information explosively grows due to the development of network and storage. Redundant features within the personal information may lead to reduced prediction accuracy and low efficiency. As a result, it is important to select effective features for multi-product prediction. Many current multi-label feature selection methods ignore the correlation between either features or labels. Thus, the selected features cannot comprehensively represent the leading factors of each label. In this paper, we propose a Multi-label Entropy-based Feature selection (MEF) method, aiming to provide a clear and reasonable explanation for insurance purchase prediction. Our method considers the relationship between features and removes redundant features. We compare our MEF model with 3 previously proposed multi-label feature selection methods on the Insurance Company Case datasets from CoIL Challenge 2000. The experiment results show that the MEF algorithm outperforms other methods with fewer features by 6 evaluation indexes and can effectively improve the classification performance of the multi-label classifier.
El diario como instrumento para potenciar la habilidad de escritura del idioma inglés en universitarios

La habilidad de escribir en un idioma extranjero, es de vital importancia en este mundo globalizado, la comunicacion escrita juega un papel fundamental en la sociedad; asi como en el ambito educativo. Mas aun, cuando las nuevas regulaciones universitarias demandan a los estudiantes obtener el nivel B1 para poder graduarse. El objetivo del presente estudio fue explorar la efectividad de los diarios como herramienta para fomentar el desarrollo de la habilidad de escritura en los estudiantes del primer semestre de la universidad de Guayaquil, y aportar algo de luz que permita mejorar los procesos de ensenanza aprendizaje. Como soporte de esta investigacion, se tomo una posicion epistemologica basada en el enfoque constructivista y una metodologia cuali-cuantitativa fue utilizada para la recoleccion y analisis de datos. Se realizo un pre y una post prueba de escritura, lo cual permitio comparar la efectividad de los diarios, cuyos resultados mostraron que la aplicacion de los diarios como recurso pedagogico, ayudaron a mejorar el nivel de los estudiantes para escribir y que son una herramienta efectiva dentro del aula. Palabras clave: Habilidad de escritura, diario, estrategia. ABSTRACT The ability to write in a foreign language is of a pivotal importance in this globalized world. Written communication plays a fundamental role in society as well as in the educational field. And even more, when new university regulations will sue students to approve the B1 level before graduating. The objective of this study was to explore the effectiveness of the journals as a tool to promote the development of writing skills on the first semester students of the University of Guayaquil, and to shed some light on the teaching and learning processes. To support this research, an epistemological position based on the constructivist approach was taken and a qualitative-quantitative methodology was used for data collection and analysis. In addition, a pre- and post-writing test was conducted, which allowed to compare the effectiveness of the journals. The results showed that the application of the journals as a pedagogical resource, helped to improve the level of students to write and that they are an effective tool within the classroom. Keywords: Writing skills, journal, strategy.
ARchitecture — Insights From Theory and Practice

Augmented Reality (AR) applications are part of scientific and industrial discussion for several years. However, only partial architectures tailored for specific use cases exist. Thus, the practical implementation of an AR system as well as the theoretical discussion lacks a common ground with consolidated recent aspects and requirements. To fill the gap, we conducted a structure literature review, combined the findings with aspects from our professional context (being a software company) and constructed a consolidated AR system architecture. So, our contribution is a reference architecture for future implementations and theoretical developments.
Art of Style Transfer Using Convolutional Neural Network: A Deep Learning Approach

In the present scenario, many difficulties are being faced while rendering the images with different styles. This makes the image analysis a bit difficult process. The major limiting factor, in this case, is the lack of image representation that explicitly represents the semantic information, which complicates the representation of the image in a way that could further be used to separate the image’s content part from the whole image. To resolve this issue, image representation derived from convolution neural networks (CNN) is used. In the current study, there is an advantage that CNN is optimized for object recognition, which enables it to make high-level image information explicit. This opens up the possibility for the application of a neural algorithm in artistic style which can separate and then recombine the image’s content and its style. This enables us to combine information of random images with well-defined artworks and give it an artistic look that can further be used for different purposes. This deep analysis of images and its information is done by using CNN, and it portrays their capability of high-level image synthesis and image manipulation. The intermediate results in CNN can also be used for feature and content extraction from images.
FPGA Implementation of WRVFLN for Classification

Now-a-days, field programmable gate array (FPGA) is used in various domain of research to design a computer-aided-diagnosis (CAD) system to classify the data in real-time. In this paper, weighted random vector functional link network (WRVFLN) is presented and implemented on a fast FPGA hardware platform to classify the data. The WRVFLN classifier produces better data classification accuracy in comparison to other existing prevalent methods. The remarkable data classification accuracy and faster learning speed of the WRVFLN classifier facilitates the hardware implementation of WRVFLN classifier. Moreover, the developed digital architecture of WRVFLN classifier is employed on a Xilinx Virtex-5 (ML506) FPGA hardware environment, aid to construct an embedded system for real-time data classification.
2-tuple fuzzy linguistic perceptions and probabilistic awareness-based heuristics for modeling consumer purchase behaviors

Agent-based modeling (ABM) is a simulation paradigm to model complex systems by defining heterogeneous individual-level behaviors in a bottom-up approach. ABM is typically employed to simulate markets to study consumer decisions and to see how consumers make their purchase decisions. In this work, we present a marketing ABM where consumer perceptions are modeled using 2-tuple fuzzy linguistic variables. These variables represent the opinions the consumers have on the different features of every product, which drive their decisions (e.g., price or quality). In contrast to numerical or crisp values, fuzzy linguistic variables are a realistic representation of these qualitative aspects. In our ABM, agents use a decision-making heuristic to select a product, which is based on those perceptions and a probabilistic utility maximization rule. This process requires a fuzzy aggregation of the perceptions of every product, based on an ordered weighted average (OWA). In addition, consumers can be aware or unaware of each product in the market. In our ABM, we model this information by introducing a brand awareness filter when applying the decision-making heuristic. Thus, consumer agents can only select those products they are aware of. Our experimental results show that our realistic representation of the consumer preferences is more accurate than other existing approaches.
Tree-Based Multi-Keyword Rank Search Scheme Supporting Dynamic Update and Verifiability upon Encrypted Cloud Data

Due to large scale use and many applications of cloud maximum data, owners upload the information at cloud space to save time and local disk space. Here, the authors provided a TBMKRS (Tree-Based Multi-Keyword Rank Search) scheme which also supports dynamic update (insert/delete) and verifiability of encrypted information upon cloud. For the evaluation of performance and analysis of result, the Enron data set have been used by the authors.
Managing the Challenges Facing the Adoption of E-books: A Case of UKZN

The invention of the web and evolution of mobile technology has brought about changes in today's teaching and learning styles. They have also changed the dynamics of how resources such as study materials for students learning, are being disseminated. With these invention and evolution, study materials are now increasingly being disseminated in electronic format popularly referred to as eBook. E-books are gradually becoming the popular means of sharing learning information with students, and hence, they are becoming easy to access within most educational institutions. However, despite its benefits, studies have shown that students are facing challenges with regards to its adoption. This study was conducted to identify the challenges faced by students in the adoption of e-books. A quantitative research design was employed in the study; hence 250 questionnaires were distributed to students. The findings show that students prefer e-books due to their accessibility, cost effectiveness and ease of use. However, the findings also showed that digital rights, page navigation, poor screen presentation and resolution are some of the challenges students face in the adoption of e-books. The study concludes by proposing some possible means of managing the identified challenges.
AutoDLCon: An Approach for Controlling the Automated Tuning for Deep Learning Networks

Neural networks have become the main building block on revolutionizing the field of artificial intelligence aided applications. With the wide availability of data and the increasing capacity of computing resources, they triggered a new era of state-of-the-art results in diverse directions. However, building neural network models is domain-specific, and figuring out the best architecture and hyper-parameters in each problem is still an art. In practice, it is a highly iterative process that is very time-consuming, requires substantial computing resources, and needs deep knowledge and solid technical skills. To tackle this challenge, we introduce a new gradient-based technique, AutoDLCon, that aims to automate the design process of neural network architecture for the given classification task and dataset within a specified time budget using a controller neural network. In particular, the controller network predicts how good a model is and suggests trying an optimized model by back-propagating from a loss function through the controller network to the controller's weights one time and to the controller's inputs at another time. This approach mimics the exploration of the search space in a more efficient way by reducing the number of trials to the minimum. As a consequence, it can significantly reduce the time budget and computing resources to a minimum and controllable level. We evaluate our approach using MNIST and CIFAR-10 datasets with different settings based on the difficulty of the problem. The results of our experiments show that our approach is able to generate child models that are good enough to obtain competitive results on the validation data after only a few trials.
Space and spectral domain relative entropy for homogeneous random fields

A classical result in spectral estimation establishes that the relative entropy rate between two zero-mean stationary Gaussian processes can be computed explicitly in terms of their spectral densities, hence inducing a pseudo-distance in the cone of positive definite spectra. Relying on the properties of multi-level circulant and multi-level Toeplitz matrices, we establish a similar theory for homogeneous Gaussian random fields, both periodic and non-periodic. As a consequence we derive a natural entropic pseudo-distance on the cone of positive definite multi-dimensional spectra. We also define the spectral relative entropy rate as an entropic divergence index between the spectral measures associated to two homogeneous Gaussian random fields and we show that the relative entropy rate and the spectral relative entropy rate are in fact equal.
Classification by Learning of Wavelet and Texture Features

Image classification is one among the significant tasks that can benefit industry through advancement in science and technology. Texture images possess a specific pattern that can be utilized to uniquely identify a texture class. Classification of Texture images can be done through proper selection of parameter that uniquely identifies an image in a group containing images of different classes. Machine learning approaches are currently the choice of implementation to provide accuracy and robustness in various fields like automatic image recognition, registration, and analysis. This work addresses machine learning methods to classify learnt texture samples through feature fusion of parameters obtained through wavelet and texture analysis.