Reviewerly/exHarmony huggingface dataset:
Comparison of feed forward, Cascade forward and Layer Recurrent Algorithm model for breast cancer prediction

Abstract The number of cases of Breast cancer seems to be constantly increasing worldwide in the recent years. According to 2012 world cancer report, the breast cancer incidence rate in Asia appears to be 37.5 and the mortality rate is 13.21. As per the recent statistics report of 2018, it has been estimated that the number of new cases of breast cancer is 14% in India. The percentage is higher when compared to lung cancer, oral cancer and other types of cancers. It has been identified that it occurs mostly in the age group between 41-50 years and the distribution of this is found to be 42%, whereas it is 18%, 24% for the age groups of 31-40 and 51-60 respectively. The five stages of survival include the survival rate of 100%, 98%, 88%, 52%, 16% for each stage respectively. Diagnosis in the early stage can reduce the death rate. Even during the stage 3 if the cancer is predicted, it can be treated. Machine learning using Artificial Neural Network(ANN) techniques can be effectively utilised for the prediction of cancer. In the proposed method, networks designed using various ANN Algorithm are used to predict the breast cancer if it is benign or malignant. Three different networks such as feedforward back prop, Cascade forward and layer recurrent has been implemented. The performance measures such as Accuracy, error, specificity, sensitivity, positive predictive value and negative predictive value are obtained. From the table 2, it is inferred that among these algorithms, feed-forward algorithm has better performance compared to other two algorithms.ANN has been a powerful tool for analyzing the data when there are non-linear interactions between the input and the output to be predicted. The results show that the accuracy of ANN for the prediction was better than other approaches. The Wisconsin breast cancer database has been used for the analysis. The results are very competitive and can be used for diagnosis, prognosis, and treatment.
An Approach to Intelligent Distributed Scanning and Analytical Processing of the Internet Inappropriate Information

The paper presents a new approach to implementation of intelligent distributed scanning and analytical processing of Web-content for detection of the Internet inappropriate information. The offered architecture of the component of the intelligent distributed scanners eliminates restrictions on capacity and localization of scanners in network by decentralized management of loading. The offered architecture of the component of analytical processing uses a set of the binary qualifiers based on machine learning methods. Experimental assessment of the proposed solutions showed their high efficiency on the classifier training time and on the completeness and the accuracy of inappropriate information detection.
Graph-Based Arabic Key-phrases Extraction

This paper proposes Arabic key-phrases extraction using graph representation. The proposed approach based on representing the text of an individual document as a graph, where the nodes within the graph hold the words' stem and the edges represent the co-occurrence relation between stems in specific window size. After building the graph, graph-based centrality measures were used in ranking the nodes according to their importance. Then the ranking results are sorted decently to determine the top n nodes. The stems that are represented by the top n nodes will be considered as the key-stems of the individual document. The performance of our work is measured using the three accuracy measures: Precision, Recall, and F-Measure. The obtained result reached 54%, 82% and 64% for Precision, Recall, and F-measure respectively.
Mastering 2.0: The Real or Perceived Threat of DIY Mastering and Automated Mastering Systems

This article explores mastering–the final stage of the music production process, which until recently has proven resistant to the democratizing characteristics of “Music 2.0.” Musicians are now able to forgo the services of professional engineers in favor of plug-ins such as iZotope’s Ozone and online automated services such as LANDR. This article draws on survey responses from mastering engineers to address the nature and extent of disruption to the mastering profession. The engineers surveyed here suggest that certain parts of the mastering process may eventually be replaced by automated algorithms but argue that there are distinct advantages to having an objective “set of ears” assess the final recording.
Combining URL and HTML Features for Entity Discovery in the Web

The web is a large repository of entity-pages. An entity-page is a page that publishes data representing an entity of a particular type, for example, a page that describes a driver on a website about a car racing championship. The attribute values published in the entity-pages can be used for many data-driven companies, such as insurers, retailers, and search engines. In this article, we define a novel method, called SSUP , which discovers the entity-pages on the websites. The novelty of our method is that it combines URL and HTML features in a way that allows the URL terms to have different weights depending on their capacity to distinguish entity-pages from other pages, and thus the efficacy of the entity-page discovery task is increased. SSUP determines the similarity thresholds on each website without human intervention. We carried out experiments on a dataset with different real-world websites and a wide range of entity types. SSUP achieved a 95% rate of precision and 85% recall rate. Our method was compared with two state-of-the-art methods and outperformed them with a precision gain between 51% and 66%.
AR Training for Paragliding Pilots: An Investigation of User Experience and Requirements

This paper addresses the user requirements of implementing augmented reality smart glasses in the paragliding training process aiming to enhance the learning outcomes and safety of pilots. Focusing on improving the thermalling skills of pilots, we developed a prototype application using the Microsoft HoloLens and conducted a field test with six paragliding instructors and ten pilots during a training course in Lüsen, South Tyrol, Italy. The exploratory study consisted of testing the device and a prototype at the launching site and semi-structured qualitative interviews. Our results show that integrating AR technology into a pilots helmet would be most beneficial in paragliding training. Users assign great importance to an unrestricted field of view, the simplicity of the user interface and positional accuracy of the virtual elements. The main benefit of the application is that pilots and instructors can share the experience in real-time and that it may enhance their learning outcomes and performance.
Tiek: Two-tier Authentication and Key Distribution for Wearable Devices

Wearable devices, such as implantable medical devices and smart wearables, are becoming increasingly popular with applications that vary from casual activity monitoring to critical medical uses. Unsurprisingly, numerous security vulnerabilities have been found in this class of devices. Yet, research on physical measurement-based authentication and key distribution assumes that body-worn devices are benign and uncompromised. Tiek is a novel authentication and key distribution protocol which addresses this issue. We utilize two sources of randomness to perform device authentication and key distribution simultaneously but through separate means. This creates a two-tier authorization scheme that enables devices to join the network while protecting them from each other. We describe Tiek and analyze its security.
Robust Bayesian Transfer Learning Between Kalman Filters

Bayesian transfer learning typically requires complete specification of the stochastic dependence between source and target domains. Fully probabilistic design-based Bayesian transfer learning-which transfers source knowledge in the form of a probability distribution-obviates these restrictive assumptions. However, this approach has suffered from negative transfer when the source knowledge is imprecise. We propose a scale variable relaxation to transfer all source moments successfully, achieving robust transfer (i.e. rejection of imprecise source knowledge). A recursive algorithm is recovered via local variational Bayes approximation. The solution offers positive transfer of precise source knowledge, while rejecting it when imprecise. Experiments show that the technique is competitive with or equivalent to alternative methods.
Recovering Human Motion Patterns from Passive Infrared Sensors: A Geometric-Algebra Based Generation-Template-Matching Approach

The low-cost, indoor-feasibility, and non-intrusive characteristic of passive infrared sensors (PIR sensors) makes it widely used in human motion detection, but the limitation of its object identification ability makes it difficult to further analyze in the field of Geographic Information System (GIS). We present a template matching approach based on geometric algebra (GA) that can recover the semantics of different human motion patterns through the binary activation data of PIR sensor networks. A 5-neighborhood model was first designed to represent the azimuth of the sensor network and establish the motion template generation method based on GA coding. Full sets of 36 human motion templates were generated and then classified into eight categories. According to human behavior characteristics, we combined the sub-sequences of activation data to generate all possible semantic sequences by using a matrix-free searching strategy with a spatiotemporal constraint window. The sub-sequences were used to perform the matching operation with the generation-templates. Experiments were conducted using Mitsubishi Electric Research Laboratories (MERL) motion datasets. The results suggest that the sequences of human motion patterns could be efficiently extracted in different observation periods. The extracted sequences of human motion patterns agreed well with the event logs under various circumstances. The verification based on the environment and architectural space shows that the accuracy of the result of our method was up to 96.75%.
A Practical Use Case of Homomorphic Encryption

This paper is a proof of concept that homomorphic encryption can be deployed in practice and can be used by the industry to ensure security and computation of customer data. In this paper, we present a concrete use case of homomorphic encryption that is not considered in literature, using Fan and Vercauteren (FV) cryptosystem, and we propose a practical implementation of FV cryptosystem and its deployment in an IoT use case.
Evaluation on Neural Network Models for Video-Based Stress Recognition

We examined neural network models to perform video-based stress recognition using ANUStressDB data set [6]. Recent works on video-based stress recognition [6, 11] requires feature engineering process, which is time consuming and expensive. The neural network (NN) model aims to reduce this process. In this work, we set a baseline Feed-Forward Neural Network (FFNN) and extended the model using Feature Selection Technique, namely magnitude measure technique [3] and $$\ell $$-1 regularisation [9]. Subsequently, we performed extensive evaluation between those models with the Long Short-Term Memory (LSTM) [5] model, which are designed to store state information for time-series data. We show that feature selection technique model used significantly less parameters compare to the LSTM model with the expense of small accuracy loss. We also show that the NN models performed well in video-based stress recognition task as compare to the previous work with hand-crafted feature engineering from experts.
Association Analysis of Abnormal Behavior of Electronic Invoice Based on K-Means and Skip-Gram

Electronic invoice is the product of the information age. The issuance of electronic invoice greatly saves the cost of enterprises and provides more convenient services for consumers. At present, our country is vigorously promoting electronic invoices, but there are also some abnormal behaviors in the process of circulation of electronic invoices, such as false invoices reimbursement, over-quota invoices and so on. This paper designed and implemented a deep fusion analysis method of electronic invoices based on K-means and Skip-gram. On the basis of abnormal behavior of electronic invoices, it carries out correlation analysis, excavates abnormal relationship between users and enterprises in the process of electronic invoices circulation, and provides support for the normal operation of electronic invoice service system. In this paper, the model is trained on the abnormal behavior data of electronic invoices. The accuracy of the model is 76% in the inter-enterprise association analysis and 85% in the inter-user association analysis.
EOG-based interface and speech recognition for wheelchair control

Mobility problems are a growing international medical concern. We developed a prototype to help patients with paralysis of the lower extremities to control a wheelchair using Electrooculography (EOG) signals and the voice signal. EOG signal can control the direction of movement, while the voice signal processing controls the on and off the wheelchair. The prototype is a scalable, portable, compact and inexpensive with acceptable results with a noninvasive design.
User Input Based Style Transfer While Retaining Facial Attributes

There is an increase of focus on both style transfer and user interaction based image editing. Commercial apps provide users means to edit their images to their liking. Even though two-step methods exist that allow the user to perform signif-icant changes to their images and then perform style trans-fer, these methods tend to distort the image and remove theaesthetic beauty the user desires. We propose a new Genera-tive Adversarial Network (GAN), called FRGAN, that maintains the user suggested changes to the image and performs style transfer retaining the changes. We qualitatively demonstrate the efficacy of FRGAN formulation over various two-step GAN methods and traditional style transfer methods. We utilize the Mean of Opinion Score (MOS) metric to quantify our proposed models' performance.
PENDIDIKAN BERBASIS TEKNOLOGI INFORMASI DAN KOMUNIKASI

In the present time, the development of information technology, especially in Indonesia, is growing. With the existence of information and communication technology can make it easier for us to learn and get information that we need from anywhere, anytime, and from anyone. In the education world, the development of information technology has begun to have a positive impact because the development of information technology in the education world has begun to show significant changes. Nowadays distance and time are not significant problems to gain knowledge, various applications are created to facilitate them. Information technology is a technology used to process data, including processing, obtaining, compiling, storing, manipulating data in various ways to produce quality information that is relevant, accurate, and timely information, which is used for personal, business, and government and is strategic information for decision making.
Multimodel clustering of social networks in social dampening applying BIG DATA (acquiring knowledge from data)

The developed methodology provides a solution to two essential tasks, thereby revealing the gnoseological potential of Big Data technology: social forecasting in the three most significant areas of the information society based on a model which identifies conditions for social resonance; successful implementation of the social dampening procedure based on the use of appropriate management options using multimodal clusterization of social networks based on Big Data technology. The article suggests the tool that helps to increase work efficiency in the sphere of social dampening in the region. The proposed method of regulation may be efficient when it comes to the control of the regional social dampening processes which have variety of forms and broad range of elements and factors, as well as growth dynamics and active transformation of life activities. At the same time using modern products make it possible to evaluate and show changes on a real-time basis which can be useful for local government authorities.
Impact of Digital Technology and the Use of Blockchain Technology from the Consumer Perspective

The Blockchain concept, promising breakthrough innovations has captured interest in the business world and also among potential investors and consumers. Block chain technology can be used for various purposes in the finance industry, public sector, smart contracts and internet of things, shared economy and financial transformation. However, from the individual consumer side, it is mainly linked to the cryptocurrency. Cryptocurrency is seen as a subset of alternative currencies from a general perspective and as a subset of digital currencies in a narrow sense. According to Gartner Hype Cycle for Emerging Technologies 2017, block chain technology is in the peak of inflated expectations stage. There are 5–10 years for maturity and widespread usage. Many consulting firms share the same insight, stating that block chain technology will be a main technological platform by 2025. Blockchain technology is a relatively new area of study mainly analyzed by engineering and finance scholars. There are very few academic studies including the consumers’ standpoint. Analyzing a new business idea or a technological novelty without considering the consumer side will be ungrounded. In this chapter, crypto currencies, specifically Bitcoin and the underlying technology Blockchain, will be studied from the consumer point of view. Awareness of crypto currencies, attitudes toward it, purchase intentions, user profiles, usage motivation around the world and in Turkey will be discussed. Following this section, the adoption of Blockchain technology and Bitcoin adoption will be analyzed by different technology acceptance and adoption models.
Temporal Mapping of Hyperspectral Data

The increasing popularity of hyperspectral sensors is dramatically increasing the temporal availability of data. To date, algorithms struggle to compare hyperspectral data collected across dates due to different environmental conditions during collection. In this work, we develop a temporal mapping in order to map data collected from one year to a different year. We investigated both conditional generative adversarial networks (cGANs) as well as affine transformations to perform this mapping. Both methods showed an improvement over using data from past collections without mapping, with cGANs outperforming the affine transformation.
Mobile Learning Systems’ Functionalities in Higher Education Institutions in Tanzania: Teachers and Students’ Readiness at the College of Business Education

Mobile learning (m-learning) through mobile devices has been playing a significant role in enhancing teaching and learning in higher education institutions (HEIs). However, little is known about the readiness of both teachers and students to extend the traditional face-to-face pedagogy into using their mobile devices for mobile learning. This study, therefore, aimed at investigating the readiness of both teachers and students at the College of Business Education (CBE) for m-learning and the suitable m-learning systems’ functionalities to enhance the teaching and learning. The study applied a survey strategy to obtain the views of both the teachers and students. Using the random sampling technique, 35 teachers and 141 third year bachelor students were selected for the study. The analysis of data was done using statistical package for social sciences (SPSS) descriptive statistics. Results showed the majority of teachers (Mean = 4.5143) and students (Mean = 4.3551) are ready for mobile learning.
Accounting and Taxation of Crypto Currencies in Emerging Markets

With the development of the Internet and e-commerce, the fact that cryptocurrencies are transferred between accounts using crypts and not controlled by central banks has remained on the agenda in recent years, and the increase in the number of people and entities investing heavily particularly in cryptocurrency production has forced governments to take certain steps to regulate this structure. In many countries, the trading volumes of cryptocurrencies, which became popular with Bitcoin, have reached a significant level. As a result of the increasing interest in such currencies and their widespread usage, it is also necessary to analyze the taxing, legal and accounting aspects of these currencies. Thus, this study aimed to discuss the accounting and taxation of cryptocurrencies, whose use has increased rapidly in recent years. To that end, the study first addresses the question of why cryptocurrencies are needed in the first place, and then, attempts to introduce blockchain technology, used to create new cryptocurrencies, and Bitcoin, the most renowned and valuable cryptocurrency of this technology. The study then makes recommendations on the accounting and taxation of cryptocurrencies by providing examples of accounting records. Finally, the study recommends that common definitions are made for these new assets throughout the world, and globally accepted international cryptocurrency standards for the accounting and taxation of these currencies are established and implemented.
Re-identifying people from anonymous histories of their activities

Privacy problems are major obstacles to collecting and using big data because, in many cases, big data reflects a person's history of activities, such as moving around a city, buying goods, surfing the Web, and posting content on social media. Although anonymization is an effective technical measure for alleviating privacy concerns, we must be aware of two problems that could infringe privacy: re-identifying the people represented by the data despite anonymization and profiling people from the data. In this paper, we first survey reidentification techniques developed for various areas, clarify the relationship between re-identification and profiling, and mathematically model the re-identification problem. We then present methods for re-identifying social media accounts and location histories and present the results of evaluations demonstrating their effectiveness.
Consent Verification Under Evolving Privacy Policies

Personal data provides important business value, for example, in the personalization of services. In addition, companies are moving toward new business models, in which products and services are offered without charge to users, but in exchange for targeted advertising revenue. New privacy regulations require organizations to explicitly state their data practices in privacy policies, including which data types will be collected. By consenting to data collections described in a policy, the user acknowledges that he or she is granting the company the authorizations needed to access their data. When data practices change, a new version of the policy is released. This release can occur a few times a year, when requirements are rapidly changing for the collection and processing of personal data. Furthermore, the user may change his or her privacy consent by opting in or out of the policy. We propose a formal framework to support companies and users in their understanding of policies evolution under consent regime that supports both retroactive and non-retroactive consent and consent revocation. Preliminary results include an ontology for policy evolution, expressed in Description Logic, that can be used to formalize consent and data collection logs and then query for which data types can be legally accessed.