Reviewerly/exHarmony huggingface dataset:
Topic and Sentiment Classification of Streaming Tweets about Tourist Destinations in Thailand

In this research, a website about Thailand's tourist destinations was implemented in a responsive style to support both desktop and mobile displays. It retrieved live streaming tweets about specified destinations and classified them by topic (into News, Foods, Environment, or Traffic) and by sentiment (into Positive, Negative, or Neutral). Using recent state-of-the-art Word2Vec embedding, along with support vector machine classifier, the accuracy of topic classification was 80% and that of sentiment classification was 59%. In addition, based on the website evaluation by 30 users, an average satisfaction score of 4.4 out of 5 was achieved.
Effectivity Photo Story Media Usage In Direct Reading Thinking Activity Methods Towards Ability Understanding Stories Skills of Elementary School Students

Abstract This research is motivated by the importance of reading and understanding a reading for elementary school students. The lack of interest in reading and the difficulty in understanding reading effectively in primary school students in Indonesia has an impact on the quality and resources of the community. Specifically, this study aims to determine the effectiveness of the use of photo story media in the method DRTA (Directed Reading Thinking Activity) in improving the ability to understand stories in fifth grade students of SD Negeri 1 Cengal, Japara District, Kuningan Regency. The subjects of this study were 32 grade V students. This research uses a quantitative approach, the method quasi-experimental and the design chosen is "design one group pretest-posttest. Data collection techniques through tests, interviews, observation and documentation. The results of the study show that the pattern of learning to read a story is quite good as seen from the data posttest that has fulfilled the KKM, the ability to understand stories in fifth grade students of SD Negeri 1 Cengal before and after using photo story media and the method DRTA has changed, seen from the analysis and processing data pretest and posttest , and the process of learning to read and understand stories has been effectively used in fifth grade students of SD Negeri 1 Cengal.
The Deployment View

The term DevOps grew out of conversations that Patrick Debois, a system administrator at the time, and Andrew Shafer, a software developer, were having in 2009 along with others sharing their frustrations in how difficult it was to move development to operations. They observed that the agility, automation, and communication barriers between development and operations teams were common across many organizations. The developers put the responsibility on operations and mismatches in the deployment environment. The operations teams assumed that the problems culminated from the implementation. The teams clearly lacked a common deployment and operations view of the system, one of the key aspects of any system's architecture.
Integral Attack on Reduced-Round BORON Based on Bit-Based Division Property

Abstract BORON is a 64-bit block cipher with 80 and 128-bit key length proposed by Bansod et al (Frontiers of Information Technology &amp; Electronic Engineering). Integral attack is one of the typical cryptographic analysis methods of block cipher. In this paper, we establish a MILP (mixed integer linear programming) model of BORON based on bit-based division. We get a 6-round integral distinguisher, which is the longest integral distinguisher of BORON. Based on 6-round distinguisher, we can attack 7-round BORON with time complex 2 54.19 and data complex 2 54 , 8-round BORON with time complex 2 58.34 and data complex 2 56.32 , and 9-round BORON with time complex 2 94.06 and data complex 2 57.90 . This is the first security assessment of BORON in integral nature.
Impact of Different Domain Inlink, Outlink and Rechability on Relevance of Web Page Using Correlation

Web site users are increasing promptly and because of that web structure mining becomes more difficult. Web structure mining plays a big role in search engine optimization. There are many algorithms which mines the web structures. HITS and PageRank are two basic algorithms which works on web structure mining. Other algorithm uses these algorithms and added different parameters like bookmarked users, number of visits, topic, anchor text, popularity and content to calculate web page score. In this paper we introduce new parameters, inlink, outlink and reachability which helps to reduce mutual reinforcement effect on web page ranking. We collected data set on "abortion" keyword. We analyzed that data set and generated score for reachability and inlink and outlink from different domain. We have observed that data set and found that there is a positive correlation between these parameters and relativity of web page. We used Pearson correlation technique to find correlation co-efficient.
Visualization of Important Human Motion Feature Using Convolutional Neural Network

Human motion feature extraction is necessary for robot motion generation. In particular, feature extraction methods related to non-periodic motion should be proposed. Recently, the number of the human motion recognition studies utilizing Convolutional Neural Network (CNN) is increasing due to the brilliant ability of extracting and identifying features. CNN has the same or better discrimination ability than humans so that the features extracted by CNN are thought to be useful for human motion understanding. However, since the internal structure of CNN is like a black box, it is difficult to understand the extracted features. In order to understand the features, some visualization methods which are utilized in the image field are applied in this paper. Furthermore, the many conventional studies utilizing the gradient are not preferred for visualizing CNN which recognizes human motion because human motion is sensitive for the object which treated. That is, the gradient method does not work because the value varies greatly depending on the environment even in the same motion. Therefore, new visualizing method without using the gradient is proposed in this paper. The proposed method visualizes CNN focusing part by following the neuron in CNN. Since this method does not require the gradient, the stable and accurate visualization can be performed. The effectiveness is shown by experiments.
Digital Forensics in Vessel Transportation Systems

Large vessels are safety-critical systems where operations, performance and component availability are continuously monitored by means of multiple sensors producing large amount of data. Relevant information is preserved in Event Data Recorders that are fundamental for the reconstruction of scenarios related to serious malfunctions and incidents in technical and legal terms. By considering the state-of-the-art and two important naval accidents we evidence some issues related to the exploitation of recorded data in reconstructing the events timeline and the semantics of the scenarios. These studies motivate our proposal that aims to guarantee strong data integrity and availability of all information registered in Event Data Recorders. Our results are fundamental for the precise identification of the sequences of events and for the correct attribution of human and/or machine responsibilities.
Exploration of Text Mining and Lexical Analysis Using a Novel Approach

To analyze a massive and extremely large amount of text information efficiently we use text mining techniques. It has been received a lot of attention because of the increasing demands of various organizations and companies to manage the large datasets of information which are available in text documents. In this digital world, text mining is very essential. The main goal is to discover information which is unknown, which is something till now not aware of anyone and so it has not been written down. Text mining is much similar to the web search but there are minor differences between the two of them. The major difference between text mining and web search is that in web search the browser searches the information which the user wants and that is already known that is it is written by someone else whereas in text mining we don't know exactly what we are looking for. The main objective of text mining is to discover new patterns and new trends among data, associations in entities and predictive rules, etc which combine together to extract information which is required by the user. This research is based on the exploration of the capabilities of text mining using R language which is an open source software which is basically used for statistical computing and graphics.
Development of an Ankle Detection Platform for Foot Drop Rehabilitation

With the aging of China's social population, the incidence of stroke has also increased and the sequelae such as hemiplegia have become a problem that cannot be ignored. Timely detection, accessing to patients with walking hemiplegia gait information is the therapist for the patient to develop an important part of the rehabilitation plan. At present, the newer gait detection systems mostly use three-dimensional analysis, the three-dimensional gait analysis system is larger in size, high in cost and inconvenient in operation, and has great limitations in active rehabilitation training. In this paper, a gait detection system for the ankle joint intelligent rehabilitation robot is designed and developed based on the method of detecting in electrical signal. The system can judge the motion state of the ankle joint in real time by measuring the plantar pressure of patient, the measured state of the patient can be analyzed further. The gait situation of the object can also meet the requirements of the new ankle-assisted rehabilitation robot system which can be controlled by the subjective awareness of patients, and provide theoretical support for the design of the new ankle rehabilitation robot.
The Language and Style of Early Shakespeare

This chapter illuminates the authenticity and variety of the rhetorical styles of writing in a selection of Shakespeare’s earliest plays and the epyllion, Venus and Adonis. Focusing on bombast and repetition as two of the most frequent and representative rhetorical techniques that stand out in Shakespeare’s early writing, and addressing the earliest attack on Shakespeare’s writing craft, the chapter explores different ways in which Shakespeare turns rhetoric into an instrument that produces meaning. Critical attention is paid on examining how Shakespeare produces originality in collaborative and solo works and on radical uses of rhetoric for unrhetorical purpose. Comparing Shakespeare to some of the contemporaries that inspired his writing, like Marlowe, Greene, and Lodge, the chapter offers an insight into forms of imitation and creative resistance to existing models, on examples that have not yet been explored, and within the debate about styles in poetic and rhetorical treatises of the late Elizabethan period.
Seriation and Matrix Reordering Methods for Asymmetric One-Mode Two-Way Datasets

Analyzing asymmetric datasets and finding novel insights from traces of asymmetries is a non-trivial challenge in data analysis. We propose a novel use of seriation and matrix reordering methods to find insights from asymmetric one-mode two-way datasets. This article addresses the following research questions: How to use seriation and matrix methods with asymmetric one-mode two-way matrices? What insights and patterns can be found from asymmetric structure using such an approach?
Google Colaboratory for Quantifying Stomata in Images

Stomata are pores in the epidermal tissue of plants formed by specialized cells called occlusive cells or guard cells. Analyzing the number and behavior of stomata is a task carried out by studying microscopic images, and that can serve, among other things, to better manage crops in agriculture. However, quantifying the number of stomata in an image is an expensive process since a stomata image might contain dozens of stomata. Therefore, it is interesting to automate such a detection process. This problem can be framed in the context of object detection, a task widely studied in computer vision. Currently, the best approaches to tackle object detection problems are based on deep learning techniques. Although these techniques are very successful, they might be difficult to use. In this work, we face this problem, specifically for the detection of stomata, by building a Jupyter notebook in Google Colaboratory that allows biologists to automatically detect stomata in their images.
Enroll, and Authentication Will Follow

High-assurance user identification and credentials provisioning are crucial for accessing digital services. Usability, service customization, and security should be carefully balanced to offer an appropriate user experience. We propose an eID-based enrollment approach for tailoring authentication to the particular needs of the service provider and strike a good trade-off between usability and security via the registration of authenticators, artifacts providing identity proofs. We demonstrate the practicality of our approach in the case of patient access to Electronic Health Records (EHR) through an Android application: enrollment is done by using the Italian national eID card to register the mobile authenticator, unlocked by the user's fingerprint, customized to interact with the identity and access management system of the EHR.
Tensor Decomposition for Poisson Image Denoising

In this paper, we provide a new iterative algorithm via the weighted low-rank tensor model for Poisson image denoising. The noise variance of the scaled Poisson data is transformed at each iteration through the Anscombe transformation obtaining a signal where the noise is as additive white Gaussian noise. Then this Gaussian noise is removed using the weighted low-rank tensor filter with the local and nonlocal geometry information because of natural images containing patches with high similarity. Finally, an exact unbiased inverse is constructed to estimate the signal of interest. Experimental results demonstrate the superior performance of our proposed method compared with other Poisson denoising algorithms on the ten test images.
Competitive Coevolution for Color Image Steganography

The need for more secure and advanced dissemination is ever increasing since advancement in digital transmission has also posed equally challenging threats. Steganography has been around for a while now, with frequent updates in the traditional methods. In the past one year, with the rise in research in deep learning, Steganography and Steganalysis have emerged as a prime application. The paper has further worked on it, with introducing a competitive coevolution learning approach wherein Generative Adversarial Networks(GANs) are used for performing steganography along with steganalysis to improve the overall results. RGB color images are used as cover and secret images. A mixed loss function was used which is a weighted sum of Mean Squared Error(MSE), Structural Similarity Index(SSIM) and Mean Structural Similarity Index(MSSIM) for generating the stego image and extracting the secret image. The major improvements proposed in this paper are the introduction of attention based Squeeze and Excitation-Inception modules in the hiding and the reveal networks, which enhance spatial encoding and increase the representational power of the network, thereby giving better overall results.
Capturing Trends in Industrial-Organizational Psychology: A Shiny Web Application

The present paper describes the purpose, and documents the primary functions of an open-source, Shiny web application developed for anyone interested in industrial-organizational psychology (trends.j-rigby.com). The application allows users to query the abstracts of over 150,000 peer-reviewed publications in 85 academic journals published in 1950 and thereafter that were sourced from Elsevier’s citation database, Scopus. Future goals include the incorporation of different frequency metrics and a peer-reviewed outlet recommendation system. Publication- and citation-related trends are plotted in accordance with the user-specified query, and publications meeting one’s query are displayed in tabular form, with an option to download a .csv of said table. All of the code and related materials, including the full database, are available via GitHub (https://github.com/jimmyrigby94/trends_in_io).
An overview of machine learning techniques applicable for summarisation of characters in videos

Machine Learning and its applications have been developed and utilised in implementing a wide array of functionalities. Its usage in the area of image and video processing have resulted in an improved mechanism for video analysis. It can further be used in the development of a system that helps summarise the role of characters in a video. In this paper we analyse the existing techniques that can help formulate the required model.
Methodology of developing an ontological model for the formation of content and assessment of ICT competencies of university students

The relevance of the work is confirmed by the need to ensure that vocational training meets the requirements of formal and non-formal, difficult to coordinate requirements of the education system and the labor market. The article proposes one way to solve this problem in the framework of control theory and knowledge engineering. A methodology for developing an ontological model is proposed and described for the formation of the content and assessment of ICT competencies of university students, based on two basic ideas: first, the definition of the boundaries of the subject area of competencies, for which the content and means of assessment are formed on the basis of harmonized regulatory framework (boundaries are defined by invariant and variant documents.); secondly, a mechanism for taking into account personal characteristics and needs in order to build an individual trajectory of professional education and self-training should be conceptualized and preliminarily designed. An ontological model developed based on this technique is presented for forming the content and evaluating the competencies of IT specialists using the example of the subject field “IT solutions for business on the 1C: Enterprise platform”. The development of the research topic is possible in the direction of expanding the functionality of the ontological model in terms of preparing for certification exams from venders, adding other areas of preparation and adding other subject areas. The knowledge base created based on the ontological model can become part of intelligent information systems of decision support for education systems and corporate training, as well as HR management. This article will be of interest to specialists involved in the development of new tools and methods for managing the educational process and self-learning processes in distance education systems.
Self-protection against business logic vulnerabilities

Attacks against business logic rules occur when the attacker exploits the domain rules in a malicious way. Such attacks have not received sufficient attention in research so far. In this paper, we propose a novel self-protecting approach that defends a system against the exploitation of business logic vulnerabilities. The approach empowers a system with a self-protecting layer to protect it against attacks aimed at misusing business logic rules. The approach maintains up-to-date domain knowledge which is analyzed using runtime verification to detect logical attacks. When attacks are discovered they are dynamically mitigated by applying proper system reconfigurations at runtime. We evaluate the approach using a case from the domain of hotel booking systems.
The Effectiveness of the NHT Model in Improving Social Skills and Social Studies Learning Outcomes in Primary Schools

Abstract This study aims to determine the effectiveness of the application of the NHT model to improve social skills and social studies learning outcomes of grade IV at SDN Kembangan Utara 01. The design of this study uses Quasi Experimental (Nonequivalent Control Group). The research instrument used social skills observation and learning achievement tests. Data were analyzed using quantitative analysis. The results obtained by an average value of 83.86% observation of social skills with a good category. The NHT model of social skills is declared effective because it gets an average percentage score ≥ 76%. Improved learning outcomes based on the acquisition of a normalized gain value with 0.83 high criteria. The NHT model of learning outcomes in the category is quite effective with an average N-Gain value of 74.6%. The conclusion of this study is that the NHT model has proven to be effective in improving social skills and social studies learning outcomes.
Aligned Intrinsic User Factors Knowledge Transfer for Cross-domain Recommender Systems

Matrix factorization model of collaborative filtering has been proven to be an effective approach to provide personalized recommendations to users. However, It does not guarantee to obtain high prediction accuracy due to the availability of sparse user-item matrix. One of the leading solutions to overcome this problem is knowledge transfer from other related source domain. Previous work only uses the concept of pooling, i.e., all available domains' training data into a single one, and apply traditional techniques of recommender systems. In this paper, we propose a novel method for cross-domain recommender systems by adapting the learned knowledge, in terms of aligned intrinsic user factors, from existing source domain. We apply matrix factorization to estimate aligned intrinsic user factors and item factors of both source and target domains independently. Thereafter we calculate permutation matrix to align intrinsic user factors of source domain to target domain. At lest, prediction is estimated by multiplying of corresponding intrinsic user factors of source domain, permutation matrix and item factors of target domain. Evaluating the effectiveness of the proposed method, several experiments are done on Amazon co-purchasing dataset. The obtained results demonstrate the improvements of proposed method over other state-of-the-art methods.
Using a logical derivative to analyze data outlier

In this paper, we propose a method for applying continuous logic to detect data outliers in order to get rid of noise and erroneous values, as well as a method for their logical systematization and their analysis based on the Boolean derivative.
Optimized Image Restoration Based On Residual Cascade Convolution Neural Networks

Images, while acquiring, or by passing over analog media, suffers degradation, which may directly affect the quality of scenes taken. Despite numerous image restoration methods proposed, effective image restoration is a further challenging problem. In recent years, the wide application of Convolution Neural Networks (CNN) facilitates more effective image restoration methods. In this work, a novel Residual Cascade Convolution Neural Network (R-CCNN) is proposed for high quality image restoration. The proposed method considers a single trained model which can be considered as a filter for blind image de noising capable of removing all types of noises. The method has the advantage of being computationally less complex. The previous proposed neural network models for restoration requires more number of parameters which makes it relatively more complex. The proposed R-CCNN method assigns an optimal value for these parameters and proposes a method with minimal regularization and hence we need only a less number of parameters. The optimal values for these parameters are found using a global optimization algorithm, Flower Pollination Algorithm (FPA). Results obtained shows that the proposed model is more advantageous than other state-of-the-art restoration methods by checking peak signal-to-noise-ratio (PSNR) and structure similarity index metrics (SSIM) values.