Reviewerly/exHarmony huggingface dataset:
Mining fault association rules in the perception layer of electric power sensor network based on improved Eclat

Aiming at the problem that existing association rule mining algorithms cannot quickly mine faulty association rules in the current perception layer of electric power sensor networks, an improved eclat mining algorithm fast_eclat is proposed. The algorithm combines the characteristics of sparse data and large number of transactions at the perception layer of the power sensor network, and adopts a set intersection strategy based on pruning cross-counting, which reduces the computational complexity and improves the computational efficiency of the algorithm, which can more effectively deal with fault association rules. Comparative analysis through simulation experiments shows that the fast_eclat algorithm has better performance in the face of sparse data and large number of transactions.
Introducing Project-Based Learning to Teams Collaborating Across Disciplines for Innovation in Osaka Electro-Communication University

In this paper, we report a case study of project-based learning (PBL). PBL aims to allow students to experience collaboration across disciplines. The class comprised several teams, each of which consisted of students belonging to different departments. First, the students learned biomimetics prior to PBL and attempted to develop new commercial goods with the knowledge gained. The teams could make full use of the university’s facilities, such as 3D printers, to produce objects precisely. The students’ final results included the development of a propeller for hydropower or a drone. The students evaluated themselves using a rubric that we specifically developed for the class. The evaluation confirmed that the students became aware of the need and importance of teamwork.
Research and application of privacy protection technology based on big data environment

Abstract With the development of the Internet of Things, the popularization of the mobile Internet, and the rapid promotion of social networks, the growth of data has entered a big explosion, and the development of information technology has caused a torrent of big data. The continuous development of technology brings people convenience, speed and comfort, but also hides hidden worries. Combined with differential privacy and clustering, a clustering-based differential privacy universal dataset release method for mixed datasets is proposed: using k-prototype clustering algorithm to group records in the hybrid dataset to reduce differential privacy Query sensitivity and the amount of noise to be added to improve data utility while providing data privacy protection; perform attribute difference calculations for numerical attributes and categorical attributes combined with weights, and measure their information loss separately. Finally, Experimental results show that this algorithm can improve the usability of data publishing.
Research on visibility detection model optimization based on dark channel prior and image entropy and visibility development trend prediction

Abstract In this paper, a visibility detection model based on the dark channel prior and image entropy is established to improve the lane line detection algorithm. Our algorithm does not need to preset the target, nor is it affected by the camera calibration parameters and position. It transforms the visibility calculation problem into the atmosphere transmittance calculation problem and refines the required results through the guided filter, achieving more accurate and stable visibility estimation results. In addition, based on the changing regularity of the visibility over time obtained by the detection model, a mathematical model is established to predict the change of heavy fog. We use ADF to test the visibility obtained in the visibility detection model and calculate the autocorrelation and partial autocorrelation functions. Finding the original sequence non-stationary, we perform the difference on the data, remove all insignificant factors and then incorporate the data into ARIMA model for fitting, finally getting the fitting and prediction results. The results are found similar to the actual situation, indicating that the results obtained by the visibility prediction model are robust and reliable.
Compositional matrix-space models of language: Definitions, properties, and learning methods

Abstract We give an in-depth account of compositional matrix-space models (CMSMs), a type of generic models for natural language, wherein compositionality is realized via matrix multiplication. We argue for the structural plausibility of this model and show that it is able to cover and combine various common compositional natural language processing approaches. Then, we consider efficient task-specific learning methods for training CMSMs and evaluate their performance in compositionality prediction and sentiment analysis.
Elastic Security for Autonomic Computing Using Intelligent Algorithm

There is increasing use of cloud computing in the Industry 4.0 era. Cloud computing provides scalable and highly available resources as per requirement. The resources may be considered as a database, virtual machine, central processing unit, storage, applications, etc. Cloud computing consists of layers such as IaaS, PaaS, and SaaS. With the growing use of cloud computing, it is becoming essential to study the threats and security at each service model. The existing security system cannot be applied as it is to cloud computing due to its characteristics, especially elasticity. Many researchers indicated that security is still a major concern for cloud computing. It is also observed that the main vulnerabilities are bypass authentication, gain information, and gain privileges to get the important credentials. This chapter focuses on the study and analysis of security techniques specifically applied to cloud environments. It is essential to secure the resources in cloud computing using cryptography operations as well as the key that secures the resources. The chapter addresses how key management is playing an important role in providing security of the cloud. The chapter mentions how secret sharing algorithms can be applied for managing the key and achieving elastic security to the cloud. Performance analysis and security analysis are also performed. It is observed due to elastic security applied through secret sharing algorithms, and it is not possible to compromise the system by the attackers. Finally, the chapter concludes with the use of a machine-learning algorithm to identify threat patterns that enable security of the cloud.
Digital technologies and change in education: The arena framework

Using technology blending for teaching and learning is a complex issue.Niki Davis encourages the reader to interpret this book in term of intellectual journeys.The author, using the Arena Framework as a theoretical tool, draws attention to how technology can enable educators all over the world to be more creative and how to connect teaching with virtual platforms.The Arena's conceptualization is based on human ecology; Davis correlates the adoption of digital tools and the expansion of educational systems as one of coevolution.The keystone species in this theoretical framework is the teacher, as the most important part of the structural construction, but throughout the analyses, stories and case studies, the Arena framework tries to map the educational ecosystem in a global context.As a metaphor, the Arena is a complex structure by the complexity of the interacting layers of ecosystems that this theoretical analysis contains.Speaking about the Arena, the author playfully depicts teachers back in Roman times: "where the enslaved heroes and heroines (teachers) wrangled publicly with captured wild animals (learners) in the hope of winning freedom from the emperor (politician) and his captains (bureaucrats) for both themselves and those whom they struggled to control."(p.162)The author narrates the story of her intellectual journey through case studies and research evidence in which she has been involved, migrating to a new country twice while also collaborating with educators, researchers and scholars worldwide (beginning the journey in the 1990s in UK, in 2000 January moving to Iowa State University and from 2008 to New Zealand).Davis Arena's framework has developed over the years, and this framework became immensely valuable in understanding the local, regional, national and global forces that impact on EdTech.The seven chapters of the book are divided into main parts describing the conceptualization and reflecting on the evolution of new processes in education, applying the Arena as a terminology of the "field of play", a classroom with certain teaching and learning methods.Davis starts her book with an overview in which she highlights the fact that the stories in this book draw out core ideas from a wide range of factors in Australasia, Europe, the United Kingdom and the United States, as well as factors where fewer educational funds can be found.
Application of natural language processing and machine learning to radiology reports

After radiologists perform a set of chest-x-rays (CXRs) they write a short report describing their observations and interpretations. Because these reports are free-text documents, there is the risk of miscommunication, which can result in reduced patient outcomes. We applied text mining methods to radiology reports in the MIMIC Chest X-ray (MIMIC-CXR) database [5], consisting of 227,835 de-identified free-text radiology reports. We selected relevant terms (features) and developed predictive models that take a radiology report as input and return the probability the report describes a positive diagnosis for pneumonia, a common respiratory condition characterized by the accumulation of fluid in the lungs. Subsequently, we evaluated the performance of different predictive models using the area under the curve (AUC) and the Brier Score.
A Walkthrough in Live Migration Strategies for Energy-Aware Resource Management in Cloud

The era of modern computing has fascinated the masses and has outreached to their daily grinds via provisioning day-to-day services for facilitating their work. With this extensive reachability of human populace on service-oriented paradigms, the requirement to manage the existing resources has also escalated. Cloud being the global capturer of pay-per-utility model has become the point of convergence of modern technologies in serving the clients in their day-to-day chores. The extensions of cloud computing, majorly, IoT, Fog Model, and NBIoT, have proliferated across geographical boundaries to every corner of the world. Thus, the exaggerating use of such platforms for services has created a dire need for resource management and is becoming as a major challenge to be addressed before service providers. Besides, the increasing traffic on cloud has also posed a threatening alarm before the cloud human entities and, as per the available statistics, electricity consumption will hike from 632 to 1963 Billion Kilo Watt Hours by the end of 2020 and CO2 emission would be ~1034 megatons. Although it has encapsulated the research focus, and many heuristics have been proposed in this direction, still an infallible solution strategy needs to be derived. Bin packing is an upstanding solution to the problem, and its pragmatic implementation is realized through employing live migration strategies. Consequently, this research study presents an extensive exploration in the direction of resource management and energy management in cloud along with the techniques of live migration as a mitigation.
RSGX: Defeating SGX Side Channel Attack with Return Oriented Programming

Intel SGX provides a new method to protect software privacy data, but it faces the security risk of side channel attack. In our opinion, SGX side channel attack depend on the implicit mapping between control flow and data flow to infer privacy data indirectly with control flow. For this reason, we propose code reuse to construct dynamic control flow software. In this method, by loading a large number of related gadgets in advance, the software reset the software control data according to the original software semantics at runtime, so that the software control flow can change dynamically heavily. Based on code reuse, we make the software control flow change dynamically, and the mapping between control flow and data flow more complex and difficult to determine, which can increase the difficulty of SGX side channel attack.
Characterizing Input-sensitivity in Tightly-Coupled Collaborative Graph Algorithms

This paper conducts a study of input-sensitivity in collaborative graph algorithms for CPU-GPU systems with support for Unified Memory. The study, conducted on an extensive set of real-world graphs from the Koblenz Network, identifies three main sources of performance inefficiencies that are influenced by characteristics of the input graph. We develop autotuning methods to specifically address these inefficiencies. We then explore machine learning approaches to characterize the relationship between input graph properties, performance, and optimization parameters. In applying our learned models to a test dataset of 70 real-world graphs on the problems of breadth-first search (BFS) and single-source shortest path (SSSP), we are able to attain 96.33% of the peak performance on BFS, and 99.40% on SSSP when using the top-3 predictions of a neural network. We also attain 95.63% of the peak performance on BFS when using three decision trees trained on different categories of graphs. The performance of the learned models is superior to selecting the most frequent optimal configuration, indicating that the machine learning models were able to successfully correlate our selected configuration parameters with graph attributes.
A Biologically-Inspired Model for Mass Extinction in Genetic Algorithms

Mass extinction events have previously been shown to be a catalyst for accelerating the rate of evolution in genetic algorithms. This increased evolution rate combined with a destabilization of the dynamic equilibrium of the genetic algorithm can allow the algorithm to overcome local maxima in the solution space; however, most implementations of mass extinction squander the full benefit of the increased evolutionary rate by relying upon random chance to generate better solutions in the post-extinction generations. This paper examines the hypothesis that, by increasing variability and decreasing selection pressure immediately after an extinction event, the algorithm has a higher chance of yielding more fit individuals and overcoming local maxima in the post-extinction generations. We compare the performance of a genetic algorithm without extinction with the two approaches described above on a problem with a large search space containing many local optima, point scattering in the unit circle, and examine the effects of extinction methods on performance. The preliminary results presented here show that decreasing selection pressure and increasing variability help the genetic algorithm achieve higher success rates than with standard mass extinction.
Large-Scale Studies of the Repetition Characteristic for Different Models of Symbolic Sequences

Using a standard suffix-tree algorithm, we study the repetition characteristic v(t), which has been introduced by F. Golcher, for different models of random symbolic sequences and compare it with the corresponding data obtained for natural-language texts and program codes. The character of v(t) function, the saturated repetition parameter v <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> averaged at large enough times t and the appropriate standard deviation Δv <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> are examined for 144 natural, random and randomized texts of different types. The main peculiarities of repetitions peculiar for the Simon, Markov and Miller's monkey text-generating models are analyzed. The results obtained for these analytically tractable models can be useful for developing mathematical fundamentals of the repetition characteristic.
Use Case Prediction Using Deep Learning

Research into utilising text classification to analyse product reviews from e-commerce websites has increased tremendously in recent years. Machine Learning and Deep Learning classifiers have been utilised to organise, categorise and classify product reviews, enabling the identification of polarity and sentiment within product reviews. In this paper, we propose a methodology to classify product reviews using machine learning and deep learning with the intention to identify and predict the activity (use case) in which the consumer used the product they have reviewed.
Parallel Security Video Streaming in Cloud Server Environment

With the rapid development of various multimedia, an increasing volume of multimedia data are being generated. Such data may include sensitive medical, commercial, and military imaging information. As the technology used to provide image information in a cloud based web system is being developed, important images that require protection are provided through internal intra networks without using the cloud server; the demand for the service is increasing. In terms of video security, several encryption algorithms have been applied to secure transmissions in recent years. A large number of multimedia encryption schemes are also used in real products. However, there is a problem where a third party can acquire an image by exploiting a security vulnerability in a cloud-based image transmission. We have focused our research on a cloud server environment that provides real-time service to transmit secure video with desirable encryption speed. However, security levels and video streaming quality should also be considered. Real-time streaming technology that provides secure real-time service for encrypted and unencrypted video without image degradation has not yet been reported. We obtained satisfactory results in the parallel security streaming development environment. The results of this study are evaluated and analyzed in the manuscript.
ANDROID FRAMEWORK FOR SECURED BIOMETRIC SYSTEM

Authentication is the key parameter to speak the accuracy of an attribute claimed by the real entity. There are many ways to strengthen research and biometrics is one of them. Over the past decade, biometric technology has become widely adopted and accepted to check human identity. Also the technology used overcomes the limitations of traditional authentication systems such as password-related information and signatures for human authentication. Because of the development of mobile technology, smartphones based on Android OS are bringing a lot of market share. The encrypted culture of the technology could not solve the problem with the removal of popular information, as well as the Android testing system showing biometric compliance to provide more reliable assurance. In this research, we summarize many biometrics that provide their characteristics. This study develops an android biometric framework that uses a key generated without a user identifying it or its key when you sign up with a certificate and it will be used to accomplish a record to use for that uses biometric authentication.
A virtual learning environment for a remedial differential calculus course

Information and communication technologies have led to the development of new learning modalities and techniques. This research is based on the design of a remedial course for the subject of Differential Calculus and its corresponding method of evaluation in a virtual learning environment, which allows the student practical and autonomous learning. Its elaboration consists of two parts: the instructional design associated with the learning topics identified as necessary for the learning of the Differential Calculus and the evaluation method for the achievement of the learnings. This course is expected to assist the student in the possible learning lags of the Differential Calculus to successfully complete the Integral Calculus learning unit.
Attributed Network Embedding via Edge Information Enhancing for Wireless Communication Network

The continuous development of wireless communication networks makes the interaction between users become frequent, and the analysis of network users has become a hot topic. Attributed network embedding combines user relationship and user attributes in the network to learn high-quality node representations. However, Existing attributed network embedding method ignore the edge information and the information enhancement of node attributes on the edge, so ultimately high-quality node representation cannot be obtained. In order to make full use of node attributes to enhance network edge information, we propose an Attributed Network Embedding via Edge Information Enhancing(ANEE) method. Our method uses random walk based on network structure and node attributes to obtain sequences. After that, In node sequence learning and edge sequence learning these two sequence learning, we adopt Self-Attention mechanism in multiple latent spaces to learn multiple latent correlation information in the sequence, so as to obtain better node representations. In the node sequence learning, the position information in the sequence is added as a constraint, and the node representation with a reasonable position is learned. In edge sequence learning, the attribute relation of adjacent nodes is added to edge sequence relation as additional information to enhance edge information. The performance of model proposed in this paper has been verified by experiments on several real data sets. The result shows that the model is superior to the state-of-the-art algorithm in link prediction task.
Architechnopreneurship: instrument calibration

Abstract Jakarta as the capital city of Indonesia has the potential to be prone to flooding duringthe rainy season. Geographical conditions, dense settlements, drainage systems and river flow conditions are various causes that require complex handling. Therefore, it needs synergy from various professions and expertise to be able to overcome problems that have social and economic impacts. Architechnopreneurship is a multidisciplinary concept developed as an effort to provide solutions for communities to survive in areas that are potentially affected by flooding. An architect’s work that has a content of beauty provides education for the public to take advantage of housing as a place of refuge equipped with technology as a warning system as well as providing insights to bring the value of entrepreneurship to life. To achieve this goal, an instrument will be made that will be distributed to the wider community to assess this architechnopreneurship concept building sketch. This study aims to describe the results of the architechnopreneurship instrument calibration. The research method used is the Neuroresearch method, a mixed method with an emphasis on the exploratory and explanatory research stages. The result of this research is an architechnopreneurship standard instrument that can be used to measure the architechnopreneurship concept building sketch.