Reviewerly/exHarmony huggingface dataset:
Production of Multifunctional Footwear for Prison Police Officers

There is a close connection between the comfort in wearing footwear and the quality of this product.When we refer to the quality of footwear, we must start from the analysis of materials used to make the upper and lower shoe assembly, the analysis of how the joints between the different elements of the assembly behave and finally we must consider the behavior of the shoes as a whole.The footwear must create a climate where temperature, humidity, air circulation and their action on the foot, and on the whole body are correlated.Even at a rapid change of internal or external influences, the wearer must feel good.The so-called triple physiological agreement -temperature, humidity and air circulation -must be adaptable to various requirements during a working day such as: rest, movement, physical effort and climatic conditions.The design of a product is not only related to the aesthetic, artistic aspect, but more to its functionality and engineering (from the correct choice of materials, construction and technological concept, to prototyping).A good example in this regard is the design of multifunctional footwear, for soldiers and police officers.Therefore, designing the right footwear is a big challenge not only for designers, but also for engineers.The paper presents the way in which footwear as a component of the work uniform of prison police officers was made.
An Effective Steganalysis Algorithm for Histogram-shifting Based Reversible Data Hiding

To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether... | Find, read and cite all the research you need on Tech Science Press
Reflections on the Professional Development of College English Teachers under the Internet Hybrid Model

The development of network technology has brought huge changes to all people and industries. In the field of education and professional development of teachers, the application of network technology and the Internet has also created a new opportunity for education at all stages in China. In the reform of education, the related concepts and teaching applications of the Internet hybrid model have gradually begun to be incorporated into college teaching. This article explores the status quo and problems of professional development of college English teachers under the Internet hybrid model, and puts forward suggestions for the professional development of college English teachers under the Internet hybrid model which can be used as reference for the professional development and growth of English teachers.
Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease

Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.
Biomedical Named Entity Recognition Based on Self‐supervised Deep Belief Network

Named entity recognition is a fundamental and crucial issue of biomedical data mining. For effectively solving this issue, we propose a novel approach based on Deep belief network (DBN). We select nine entity features, and construct feature vector mapping tables by the recognition contribution degree of different values of them. Using the mapping tables, we transform words in biomedical texts to feature vectors. The DBN will identify entities by reducing dimensions of vector data. The extensive experimental results reveal that the novel approach has achieved excellent recognition performance, with 69.96% maximum value of F-measure on GENIA 3.02 testing corpus. We propose a self-supervised DBN, which can decide whether to add supervised fine-tuning or not according to the recognition performance of each layer, can overcome the errors propagation problem, while the complexity of model is limited. Test analysis shows that the new DBN improves recognition performance, the F-measure increases to 72.12%.
ELSA: efficient long-term secure storage of large datasets (full version) ∗

Abstract An increasing amount of information today is generated, exchanged, and stored digitally. This also includes long-lived and highly sensitive information (e.g., electronic health records, governmental documents) whose integrity and confidentiality must be protected over decades or even centuries. While there is a vast amount of cryptography-based data protection schemes, only few are designed for long-term protection. Recently, Braun et al. (AsiaCCS’17) proposed the first long-term protection scheme that provides renewable integrity protection and information-theoretic confidentiality protection. However, computation and storage costs of their scheme increase significantly with the number of stored data items. As a result, their scheme appears suitable only for protecting databases with a small number of relatively large data items, but unsuitable for databases that hold a large number of relatively small data items (e.g., medical record databases).In this work, we present a solution for efficient long-term integrity and confidentiality protection of large datasets consisting of relatively small data items. First, we construct a renewable vector commitment scheme that is information-theoretically hiding under selective decommitment. We then combine this scheme with renewable timestamps and information-theoretically secure secret sharing. The resulting solution requires only a single timestamp for protecting a dataset while the state of the art requires a number of timestamps linear in the number of data items. Furthermore, we extend the scheme, that supports a single client, to a multi-client setting. Subsequently, we characterize the arising challenges with respect to integrity and confidentiality and discuss how our multi-client scheme tackles them. We implemented our solution and measured its performance in a scenario where 9600 data items are aggregated, stored, protected, and verified over a time span of 80 years. Our measurements show that our new solution completes this evaluation scenario an order of magnitude faster than the state of the art.
Improved cloud data transfer security using hybrid encryption algorithm

Cloud computing is a model of sophisticated computing which has a strong effect on data innovation. Cloud computing offers remote access to shared computerized assets in the stored cloud. Operationally cloud servers utilize Web services that give enormus advantage to the user in a variety of applications such as banking and finance, storage, social networking and e-mail. Cloud computing accomplishes many of the features interrelated to elasticity, ease of utility, efficiency and performance with low cost. There are a number of potential concerns related to security and privacy since the requirement to protect cloud computing expanded, the encryption algorithms play the key part in data and information security systems, on side these algorithms consume a considerable quantity of computing resource.This paper,presenting a new hybrid encryption algorithm emphasising on AES and Enhanced Homomorphic Cryptosystem (EHC) as a hybrid encryption to guarantee the secure exchange of data between the user and the cloud server, and compression study for two proficient homomorphic encryption techniques for encoding Data Encryption Standard (DES), Advance Encryption Stander (AES).With The proposed techniques an evaluation has been conducted for those encryption algorithms at diverse file sizes of data, to evaluate time taking for encryption and decryption, throughput, memory consumption and power consumption. The major finding was thats the proposed method has the extremity throughput; memory consumption and our proposed work took advantage of the least time taken in sec for encryption and decryption.
UX Fundamentals for Non-UX Professionals: User Experience Principles for Managers, Writers, Designers, and Developers: Edward Stull [Book Review]

In the modern workplace, technical roles often have overlapping responsibilities. Therefore, it is essential for professional communicators to familiarize themselves with these roles to facilitate a collaborative and cooperative work environment. Stull’s UX Fundamentals for Non-UX Professionals: User Experience Principles for Managers, Writers, Designers, and Developers is a beneficial book for professionals, students, and anyone interested in user-experience (UX) design and research. The book’s purpose is to teach beginners the fundamentals of UX by making the content accessible, although the effort sometimes fails in the denser chapters.
Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning

Abstract- Single Image Super-Resolution (SISR) through sparse representation has received much attention in the past decade due to significant development in sparse coding algorithms. However, recovering high-frequency textures is a major bottleneck of existing SISR algorithms.  Considering this, dictionary learning approaches are to be utilized to extract high-frequency textures which improve SISR performance significantly. In this paper, we have proposed the SISR algorithm through sparse representation which involves learning of Low Resolution (LR) and High Resolution (HR) dictionaries simultaneously from the training set. The idea of training coupled dictionaries preserves correlation between HR and LR patches to enhance the Super-resolved image. To demonstrate the effectiveness of the proposed algorithm, a visual comparison is made with popular SISR algorithms and also quantified through quality metrics. The proposed algorithm outperforms compared to existing SISR algorithms qualitatively and quantitatively as shown in experimental results. Furthermore, the performance of our algorithm is remarkable for a smaller training set which involves lesser computational complexity. Therefore, the proposed approach is proven to be superior based upon visual comparisons and quality metrics and have noticeable results at reduced computational complexity.
Review on Identification of Red Blood Cells by Image Processing

Analysis of blood smear is an important diagnostic test used in the diagnosis of an array of diseases.The method of automatic diagnosis of microscopic blood smear images by identifying and separating it to different categories of cells that are highlighted in the paper.Automation of this process ultimately narrows the scope of possible diseases saving a considerable amount of time.Identification of red blood cells (RBCs) is carried out/done by the system using different techniques of image processing operations like pre-processing, operations for morphology, labelling and extraction of features to calculate shape and size of the RBCs.Morphological properties can provide information regarding shape of the cell.By these operations and calculations, RBCs are classified.There are 2 stages in red cell classification process, first is the separation of RBCs to normal and abnormal followed by abnormal cells classification to three subclasses based on the cell shape and structure.The aim of this system is to assist pathologist by giving quick results by analysing the smear samples.The cure of these diseases is possible, when it is detected at an earlier stage.
Rank-IDF

As big data for text has been one of the core data types in the era of artificial intelligence, feature words selection technique become increasingly important in big data text analysis. The traditional statistical TF-IDF feature words selection algorithm lacks the semantic information extraction ability of text, while the network model Textrank applies the sentence semantic features to feature calculation between words. Network model such as Textrank is very suitable for text feature selection, but it does not take influencing factors of the relationship between documents into consideration, so common words appearing frequently in feature words selected result. Based on the analysis of both feature words selection method, this paper raises a combination of statistical and network model integrated the advantages of Textrank and TF-IDF, and proposes a text feature selection method based on Rank-IDF. The Rank-IDF algorithm has better feature selection and common word filtering effects.
Autocorrelation criterion for quality assessment of random number sequences

The authors analyze different approaches to forming estimates of autocorrelation coefficients of random and pseudorandom number sequences.An integral estimate of normalized autocorrelation coefficients is theoretically obtained.Estimates of some statistical properties of normalized autocorrelation coefficients have been improved.The autocorrelation criterion for quality assessment of time series based on simultaneous analysis of several autocorrelation coefficients has been further developed by adapting it to uniformly distributed random variables.The technique of its implementation is presented.Applying the criterion revealed statistical deviations for some pseudorandom number generators that successfully pass all TestU01 autocorrelation tests.
Multiple Segmentations of Thai Sentences for Neural Machine Translation

Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool.
A Study on the Social Welfare Application of the 4th Industrial Revolution :Focus on Social Welfare Major Student

How can this study apply to the Fourth Industrial Revolution and its technologies in social welfare?As a follow-up of the research on the 4th Industrial Revolution, to find out the implications for the application of social welfare through Focus Group Interview and literature studies on how various technologies can be applied to the social welfare site.According to the purpose of this study, two research methods were used for literature data related to the Fourth Industrial Revolution and Focus Group Interview research methods for exploratory research.As a result of the Focus Group Interview, the AI section raised the possibility of promoting resources and transferring information between communities.As a result of the Focus Group Interview, the AI section raised the possibility of promoting resources and transferring information between communities.The development of robots has a positive effect on the rehabilitation of clients and the guarantee of the right to move.The development of 3D printers is said to be easy for customizing the necessary equipment and providing the necessary goods for welfare centers.In the case of drones, they showed closeness with the site.In the big data and sharing economy, it is expected to quickly find out the welfare blind spots based on artificial intelligence big data and prevent them from being excluded.Was raised.In the future, social welfare expertise in the welfare field will not only be a convergence technology of various technologies, but also the reinforcement of expertise to practice client-centered human emotions, empathy, and emotion, and strengthen the skills of face-to-face service, creativity and management according to future changes.It was found that more and more necessary.
A Sentiment-Based Hotel Review Summarization Using Machine Learning Techniques

With the advent of social media, there is a data abundance so that analytics can be reliably designed for ultimately providing valuable information towards a given product or service. In this paper, we examine the problem of classifying hotel critiques using views expressed in users’ reviews. There is a massive development of opinions and reviews on the web, which invariably include assessments of products and services, and beliefs about events and persons. In this study, we aim to face the problem of the forever increasing amount of opinionated data that is published in a variety of data sources. The intuition is the extraction of meaningful services despite the lack of sufficient existing architectures. Another important aspect that needs to be taken into consideration when dealing with brand monitoring, relates to the rapid heterogeneous data processing, which is vital to be implemented in real-time in order for the business to react in a more immediate way.
Methodological development of educator’s information and communication mobility as adaptive ability of educational and developing e-environment self-organization

The phenomenon of globalization and the active use of the capabilities of information and communication technologies affect the social atmosphere and the economic situation in society. The aim of the study is to determine the adaptability of a teacher to modern challenges in the context of improving the human potential, the innovativeness of the educational environment and the process of work.The actualization of the self-development value of a person is caused by the emergence of new forms of learning, advanced training and ways of modern communication. There is a public need for educators with a high level of information and communication mobility, able to provide a quality learning process in an educational setting.The article describes the approaches to the organization of teacher development in the educational environment with the use of information and communication technologies. Methodical approaches to the development of teacher mobility as an adaptive capacity are indicated. Information and communication mobility is defined as the adaptive capacity of educational and development e-environment self-organization, its structure and levels. The methodology of teacher’s information and communication mobility development, tasks that are realized in the educational e-environment of postgraduate education are characterized.To carry out the experimental work the author's program of development of the teacher's adaptive ability was developed, which envisages preparatory, activity, creative stages of realization.The educational and methodological support for the implementation of the author's methodology for the development of the teacher's ICM provides the developed electronic educational and development environment.The educational e-environment envisages increased opportunities for the individual’s activity, development of new ways of interaction with the environment, represented by life events, focus on ensuring access of adults to the flow of knowledge, focus on instrumental equipment and expanding their own educational space as a component of continuous education.The integrated use of theoretical and empirical methods (testing, conversation, observation, interviewing, self-assessment, self-evaluation) allowed us to find out the initial level of information and communication mobility of a teacher, to determine the impact of the educational environment of postgraduate education and the development of teacher’s adaptive ability.The research allowed to determine the features of educational environment design: continuity, multilevel, developmental character of learning, openness, in which such educational values of lifelong adult education, such as self-organization, self-development, self-education, self-control, are actualized.Prospects for the study of the problem are associated with the development of conditions for the development of information and communication mobility in the electronic environment of inclusive education
PocketView: A Concise and Informative Data Summarizer

A data summarization for the large table can be of great help, which provides a concise and informative overview and assists the user to quickly figure out the subject of the data. However, a high quality summarization needs to have two desirable properties: presenting notable entities and achieving broad domain coverage. In this demonstration, we propose a summarizer system called PocketView that is able to create a data summarization through a pocket view of the table. The attendees will experience the following features of our system:(1) time-sensitive notability evaluation - PocketView can automatically identify notable entities according to their significance and popularity in user-defined time period; (2) broad-coverage pocket view - Our system will provide a pocket view for the table without losing any domain, which is much simpler and clearer for attendees to figure out the subject compared with the original table.
Improved Quantum-Behaved Particle Swarm Algorithm Based on Levy Flight

To improve convergence speed and search accuracy, this paper proposes an improved quantum-behaved particle swarm optimization algorithm based on Levy flight. The improved algorithm reduces the probability of a local optimal solution through Levy flight and enhances the accuracy of the later search through a postsearch strategy. During the search process, the probability of quantum behavior is retained and the directivity of the particles is strengthened. According to the simulation comparison results, the improved quantum-behaved particle swarm algorithm exhibits faster convergence speed and higher accuracy.
Simple and Highly Secure, Efficient and Accurate Method (SSEAM) to Encrypt-Decrypt Color Image

Digital color image is very famous and important data type; it is used in many important vital applications such as banking systems, protection and security systems, so image protection is required.In this research paper we will introduce a simplified method of color image encryption-decryption; the method will be tested and implemented using various color images.The issues of security, efficiency and accuracy will be discussed; the obtained experimental results will be analyzed in order to raise some judgments.