Reviewerly/exHarmony huggingface dataset:
Bikeinformatics: An Introduction of Informatics to the Motorcycle Researches and the Development of New Generation Motorcycle-based Personal Vehicles

In this paper, we describe the current situation of the motorcycle industry, the world's markets, and the trend of motorcycle researches. The purpose is to promote researches and developments of motorcycles so that motorcycles become safer and more convenient, and people will obtain good user-experiences (UX) with their motorcycle in their life. We summarize the current research issues and the current social issues about motorcycles, and then we introduce a solution for them with informatics and its related science and engineering. To make a motorcycle safer and to enhance its mobility, it is essential to investigate the motorcycle dynamics. The motion of a rider is also essential, and it affects the motorcycle motion, whereas the motion of a car driver seldom affects the car motion. It is because the weight of a rider is large enough, comparing to the weight of a motorcycle, and a rider usually moves widely to operate the motorcycle. In order to investigate the dynamics of the motorcycle system, which consists of a motorcycle itself and a rider, it is required to obtain appropriate sensing data of both the motorcycle and the rider to improve the knowledge of the dynamics by the data. Such data and knowledge can be applied to other applications and services such as sensing road traffic conditions. In this paper, we introduce the concept of the research project, Bikeinformatics, and the capability of GNSS precise positioning to append adequate labels to measured data with low-cost sensors.
Co-Clustering Triples from Open Information Extraction

Similar facts are often expressed in different ways in natural language text, which introduces the redundancy and ambiguity of Subject-Predicate-Object (SPO) triples in Open Information Extraction (Open IE). This work focuses on canonicalizing such SPO triples. We propose a clustering framework using non-negative matrix tri-factorization that jointly clusters predicate phrases and subject-object pairs, and aligns them in a meaningful manner. The evaluation shows that our co-clustering method outperforms significantly over rule mining and Knowledge-Base-embedding approaches for two existing datasets.
Analysis Cryptographic for Electronic Votes in Systems of Distributed Architectures

It was considered that electronic voting is revolutionary around the world because of the new technological trends that are improving over time with their security and privacy in the different voting platforms; this problem is notable in all underdeveloped countries. The objective is to analyze and determine a cryptographic system to improve the security with efficiency and effectiveness in the electoral processes at the moment of paying and having a vote count, maintaining the transparency and integrity of the data. The deductive method was used to analyze the different cryptographic algorithms used in electronic votes that provide greater knowledge in the new trends of electronic votes. It was determined the operation of the Smartmatic that allows to identify the methods when making the electronic votes around the world, each of these systems implements an exclusive security for their voting systems, so that the voter carries out the action of paying and this vote is keep intact and transparent for the system and for the general public, achieving high reliability and integrity of the data, in addition a change in society will be noted, promoting democracy and honesty. It was concluded that electronic votes show a high level of trust, although this depends on the age and education of the voter who must have some knowledge in the field of computer science, this refers to the elderly who in many cases Sometimes they do not have the comfort and adequate confidence. This system can be an alternative to implement electronic voting in Ecuador to improve democracy.
Multi-Label Classification on Natural Language Sentences for Video Game Design

This paper investigates the effectiveness of multi-label classification (MLC) in classifying controlled natural language sentences that used to write video games. The performance of such multi-label classifying tasks can help us both understand the complexities of program synthesis as well as providing suggestions to the users whenever a sentence is not understood. Real-user data is used in the experiments. The proposed classification paradigm shows that classification and prediction in code generation are feasible, and can offer suggestions to the users when the sentence is unclear. The impact of label correlation is also discussed.
Short Paper: The Impact of Environmental Education with Immersive Environment on Learning Performance and Environmental Identity

Nowadays, the technology of immersive environment and multimodal interaction like the virtual reality (VR) devices provide people with a way to visualize the natural environment. These applications in the field of environmental education will contribute to the study of their positive role in solving societal issues around AI (ethical, legal, and social implications). Users can use it to create a real experience in the environment, not just the images they see. This empirical study suggests that an environmental education programs by immersive and normal environmental films can be effective in helping to improving learning performance and environmental identity. The method of analysis is quantitative analysis, combined with qualitative. The experimental group used immersive applications to view VR video clips while the control group used a desktop computer to observe the same video clips. The subjects were given the same questionnaire after video viewing. The questionnaire included environmental knowledge related to video clips and the Connectedness to Nature scale to measure participants' trait levels of feeling emotionally connected to the natural world. The results answered the research questions well and supported these hypotheses: (1) The use of VR equipment will affect the learning performance of the subjects. The learning effect of the experimental group is better than that of the control group. (2) The use of VR equipment could improve the environmental identity of the subjects. Compared with the control group, the result of scale in the experimental group showed a higher level of environmental identity.
A report on the future of independent and scholarly publishing webinar by the Scholarly Kitchen

Key points The open access movement continues to gain pace, resulting in increasing regulatory requirements that constrain business models. Plan S itself may only have a modest immediate effect on industry‐wide sustainability but is the clearest indication of the ‘direction of travel’ for our industry yet. Recent negotiations between publishers and consortia represent a ‘bigger deal’ and may therefore lead to even greater economic power for large commercial publishers. Societies have a greater role than merely allowing researchers to get credit for and disseminate their work. A one‐size‐fits‐all model of open access may threaten the sustainability of other community services managed by learned societies. Do not panic about Plan S! Look to the needs of your community and offer a balanced portfolio of publications and services.
Clustering of Web Documents with Structure of Webpages based on the HTML Document Object Model

Web mining is an emerging Data Mining arenathat usesvarious techniques to explore hidden patterns available within the WWW. Clustering has significant applications in Web mining, particularly in grouping Webpages based on their various properties. Literature suggests that clustering applied over Webpages is generally based on the contents of the availableWebpages, thereby focusing on text mining techniques only. But since unlike normal text documents Webpages are structured documents, there is a scope of exploring whether the structural properties of Webpages have any impact on their clustering. This paper aims to apply clustering on Web Documents based on DOM structure of Webpages, where the HTML-DOM structure of each Webpage has been represented as a string of characters, and then applying K-means clustering on the string representation. The same algorithm has been applied with four different distance measures on four different datasets. The clustering output in each case has been evaluated and the results have been compared.
Sentiment analysis of the #MeToo movement using neutrosophy: Application of single-valued neutrosophic sets

Single-valued neutrosophic sets (SVNSs) have been used in scientific problems but not in sociological analysis. This chapter provides a modern-day real-world application of Neutrosophy in sentiment analysis of the #MeToo movement. Sentiment analysis categorizes people's opinions as positive or negative, and the neutral part is generally ignored even in fuzzy sentiment analysis. To capture the prevailing indeterminate feelings, Neutrosophy is used. Over 400,000 tweets of the #MeToo movement were separately represented with positive, indeterminate, negative memberships as an SVNS, which gives an accurate evaluation of the tweets. Clustering of these tuples into three major clusters using a K-means algorithm displays indeterminate as the largest cluster. To increase the accuracy in predicting the indeterminate polarity, the data was further classified into eight classes. Training data was used to model k-nearest neighbor and support vector machine classifiers. A comparative analysis between the classifiers was done.
INFORMATION STEPS OF DEVELOPMENT OF COGNITION. ARTICLE TWO. SPROUTS REPRESENTS THINKING

On the basis of the provisions of the information theory shows that the representation is migrated inward perception. There are analyzed mechanisms of coming into being of represents thinking, the nature and forms of achievements of discovery, judgment modality. Substantiated that in the animal world represents thinking has a random, isolated incidents.
A Human Visual System Based Temporal Model for Semantic Levels Categorization

Humans can categorize an object in three ways. For example, a car can be categorized as vehicles (superordinate), ground vehicles (basic) or car (subordinate). Different semantic levels of categorization is referred to these three categorization modes. There are different speed and accuracy for a similar object in the classification of these levels. However, much research has been done in this context, the trend of these levels is still questionable as to the accuracy and reaction time and the reason for this difference. In this paper, we examine the order of these levels and the reason for their differences. Here we show the superordinate advantage is declared and after this level the base level and the subordinate level are expressed respectively. To this end, first, we design an experiment to examine the semantic levels in the human eye system. The result of this experiment is the superordinate advantage. Actually, at the superordinate level at the same time, the reaction time was lower than other levels, and the efficiency was higher than other levels. In addition, a computational model is introduced that has time and can classify semantic levels. The model is trained for ten categories in this study. These ten categories are considered as subordinate level and five levels of basic and two levels of superordinate are expressed. We found that, at higher levels, such as superordinate, more neurons participate in the clustering task, so outcome is faster and more accurate results. Moreover, the proposed model has been tested for inverted input images to verify the model.
Mammography Image BI-RADS Classification Using OHPLall

Medical image analysis and classification, using machine learning, particularly Convolutional Neural Networks, have demonstrated a great deal of success. Research into mammography image classification tended to focus on either binary outcome (malignancy or benign) or nominal (unordered) classification for multiclass labels [1]. The industry standard metric for radiologist's classification of mammography images is a rating scale called BI-RADS (Breast Imaging Reporting and Data System), where values 1 through 5 are a distinct progression of assessment that are intended to denote higher risk of a malignancy, based on the characteristics of anomalies within an image [1][2][3]. The development of a classifier that predicts BI-RADS 1-5, would provide radiologists with an objective second opinion on image anomalies. In this paper, we applied a novel Deep Learning method called OHPLall (Ordinal Hyperplane Loss - all centroids), which was specifically designed for data with ordinal classes, to the predictions of BI-RADS scales on mammography images. Our experimental study demonstrated promising results generated by OHPLall and great potential of using OHPLall models as a supplemental diagnostic tool.
A Resource-Efficient Weight Sampling Method for Bayesian Neural Network Accelerators

The main problems of deep learning are requiring a large amount of data for learning, and prediction with excessive confidence. A Bayesian neural network (BNN), in which a Bayesian approach is incorporated into a neural network (NN), has drawn attention as a method for solving these problems. In a BNN, the probability distribution is assumed for the weight, in contrast to a conventional NN, in which the weight is point estimated. This makes it possible to obtain the prediction as a distribution and to evaluate how uncertain the prediction is. However, a BNN has more computational complexity and a greater number of parameters than an NN. To obtain an inference result as a distribution, a BNN uses weight sampling to generate the respective weight values, and thus, a BNN accelerator requires weight sampling hardware based on a random number generator in addition to the standard components of a deep learning neural network accelerator. Therefore, the throughput of weight sampling must be sufficiently high at a low hardware resource cost. We propose a resource-efficient weight sampling method using inversion transform sampling and a lookup-table (LUT)-based function approximation for hardware implementation of a BNN. Inversion transform sampling simplifies the mechanism of generating a Gaussian random number from a uniform random number provided by a common random number generator, such as a linear feedback shift register. Employing an LUT-based low-bit precision function approximation enables inversion transform sampling to be implemented at a low hardware cost. The evaluation results indicate that this approach effectively reduces the occupied hardware resources while maintaining accuracy and prediction variance equivalent to that with a non-approximated sampling method.
Graph Processing System with Multi-level Architecture

The paper describes multi-level graph processing system, which allows to develop graph algorithms independently of underlying computational architecture and details of graph storage format. This system is designed to be used in network science and other research fields involving processing of large graphs with complex algorithms.
A Neural Network-Based Teaching Style Analysis Model

Human motion detection and behavior analysis have become research hotspots in the field of artificial intelligence. This paper proposes a teaching style analysis model based on neural networks to solve the problem that the teaching evaluation of colleges and universities is not objective and not comprehensive and the students select their courses blindly. The model uses OpenPose to extract the coordinates of the key points and the human-body skeleton diagram, and then uses the DenseNet to classify the actions. The action activity evaluation model is then used to evaluate the teachers' activity level during the lectures. And the emotion analysis model of Microsoft is used to analyze the emotions of the teachers during lectures. We use the self-made dataset to test and analyze the model, and the results fully prove the validity of the model.
Comparative Study between Different Image Compression Algorithms

This term paper presents a comparison between compression algorithms.The two types of image compression, which are considered in this paper, are the lossless and lossy image compression algorithms.First we compare between two lossless image compression methods: Run Length Encoding and Huffman, then we compare between two lossy image compression methods: Discrete Cosine Transform and Wavelets.
Review of Decision Tree-Based Binary Classification Framework Using Robust 3D Image and Feature Selection for Malaria-Infected Erythrocyte Detection

We start with a famous proverb 'health is wealth.' Malaria is one of the most rapidly spreading and contagious diseases, mostly spread through microbes. Efficient treatment of the disease requires early and accurate estimation to ensure control from spreading and treatment in early phases. Accordingly, several studies have been put forward during the past decade. Analyzing the blood smear's images is one of the prominent works proposed in this context. This manuscript attempts to automate the process of diagnosis through machine learning techniques. The algorithm trains the model through different selected features of the input images and thereby uses the learning experience to classify the blood smears as disease prone or healthy. The cuckoo search algorithm is used for designing a heuristic scale, which is further assessed through multiple experiments to evaluate its accuracy. Different performance evaluation measures like precision, sensitivity, specificity, and accuracy are used to assess the robustness of the model toward early identification of malaria in the premature stage.
AffectiveNemo

Aquarium fish is one of the most common household companion animals for humans. Keeping and appreciating aquarium fish enriches us and provides comfort to our lives. However, difficulty in communicating and understanding their fish may negatively impact the person's motivation to continuously take care of their fish. In order to enhance human-fish interaction, we propose "AffectiveNemo", an ACI system for aquarium fish, that estimates and displays "observed emotion" (defined as fish's emotional states that the system estimates based on observations by humans), via speech bubbles shown on an LCD display placed behind the aquarium. The system estimates the observed emotion by analyzing the movement of the fish in the aquarium, and combining this data along with the emotions that the humans perceive the fish to have. We evaluated the accuracy of each module as a preliminary experiment, conducted a user study on the influence of displaying observed emotion, and evaluated the utility of the system. Our findings concluded that the system can correctly detect the observed emotion, further enhance viewer sensitivity by displaying the observed emotion of aquarium fish that humans are perceptive, and promote the interest in aquarium fish.
Sensitivity analysis of atmospheric spectral irradiance model

Many Radiative Transfer Models (RTM) have been developed to simulate and estimate solar irradiance. Theirs accuracy is well documents in literature nonetheless the effect of the parameters uncertainties on the established models has not been well studied yet. This work focuses on implementing a RTM based on the models found in the literature along with some updates, with the aim to study the sensitivity of the model towards the variations of the input parameters. The parameters studied in this paper are: the day of the year, the solar zenith angle, the local atmospheric pressure, the local temperature, the relative humidity, the height of ozone layer concentration, the ozone concentration, the single scattering albedo, the ground albedo, the Ångström’s exponent and the aerosol optical depth. The sensibility analysis is achieved using the Normalized Root Mean Square Error (NRMSE) as an independent function, calculated with a set of simulated measurements of spectral global solar irradiance and a reference spectrum generated with a group of standard input parameters.
Personalization and the Conversational Web

Hyper-personalization intends to maximize the opportunities a marketer has to tailor content that fits each and every customer’s wants and needs. Naturally, gathering and analyzing more data is the key to those opportunities. This is were the “Conversation Web” comes in, which in the near future is expected to transform to so much more than just conversational interfaces (chat-bots). In a truly Conversation Web, websites and users implicitly “discuss” in the form of clicks, mouse scrolls and movements, as well as page views and product purchases. Websites use this information for decoding user interests and profile and provide customized one-to-one services. In this work we proposed an integrated architecture for the conversational Web; consequently we propose a novel hybrid approach for recommendations using offline and online analysis, as well as we propose a novel personalized search strategy that takes into account the strict time performance limitations applied in e-commerce. We evaluate the proposed methods on three different datasets and we show that our personalized search approach provides considerably improvements in search results while being suitable for near real-time search in commercial environments. Regarding personalized recommendations, the proposed approach outperforms current state-of-art methods in small-medium datasets and improves performance in large datasets when combined with other methods.
Research on Access Control Model Based on RBAC Model in Microservice Environment

Abstract With the development of cloud platform technology, micro-service has become a very popular software development architecture mode. The micro-service architecture reduces the coupling between application modules, which is conducive to the development, deployment and operation of complex application systems, but also brings many system security problems. Access control model is an important guarantee. This paper discusses the basic requirements of access control in micro-service environment. Through the research of traditional access control technology, the limitations and shortcomings of access control in micro-service environment are analyzed, and an optimization model of access control based on RBAC is proposed.
Context Aware Web Service Recommender Supported by User-Based Classification

Generally, the recommendation systems aim to present the items for the users based on their preferences that take from past behavior. However, Context-sensitive recommender systems have been tailored the recommendations according to a specific situation. In other words, it uses contextual information for making better recommendations. Context modeling is one approach of context-sensitive recommender systems that incorporate context directly into the recommendation process. In particular, the context in the web services recommender system is no less important from that in marketing. The proposed work includes two stages; the first one related to suggest services by using user-based classification that depends on majority voting. As for the second stage, related to suggest a services with contextual information using the method based on neighborhoods. In fact, the latter works at least on three dimensions namely; user, service, and context. The proposed work has been constrained with only services that be suggested from the first stage, hence, it would be narrowed the domain of services that forms the bulk. The proposed method has been applied on a real-world dataset; WS-DREAM, where the method records success percentage significantly, between 76% to 81% for response time and 50% to 87% for throughput. Worth to mention, the comparison has been conducted with the latest work such as; WSPred.