Reviewerly/exHarmony huggingface dataset:
Distributed Cognition of a Navigational Instrument Display Task

Distributed representation is a representational system that is composed of an integration and exchange of external and internal representational information in a seamless and interweaving manner. The performance of any everyday task utilizes perceptual information obtained from the external environment and internal information retrieved from one’s memory. External representations constructed from information extracted from external objects (such as written symbols) and internal representations (such as schemas) dynamically integrate and interweave to result in a rich pattern of cognitive behavior. The principle of distributed representations is that a distributed cognitive task involves a system of distributed representations that consists of internal and external representations (Zhang & Norman, 1994, 1995). The task is neither exclusively dependent on internally nor exclusively dependent on externally processed information, but rather on the interaction of the two information spaces formed by the internal and external representations.
Dense U-Net for single image super-resolution using shuffle pooling

Single-image super-resolution (SISR) in unconstrained environments is challenging due to various illuminations, occlusions, and complex environments. Recent research has achieved remarkable progress on super-resolution tasks with the development of deep learning in computer vision. We aim to improve the performance of deep-learning-based super-resolution tasks in different scenarios. Existing works are inherently limited to defective transfer structures such as the pooling method and the one-way information transfer between convolutional layers. To address this limitation, we propose a dense U-Net with the shuffle pooling method, a scalable method that can perform a down-sampling method for extracting features from shallow layers. The modified U-Net with dense blocks for SISR introduces a multi-way information transfer between high-to-low and low-to-high convolutional paths. Meanwhile, an innovative shuffle pooling module is designed to retain all information in the feature map of the previous layer, which is effective and easy-to-port. To minimize structural similarity and gradient error, a hybrid loss function was developed that combined mean square error, structural similarity index, and mean gradient error. The proposed method achieves superior accuracy on previous state-of-the-arts on the three benchmark datasets: SET14, Berkeley Segmentation Dataset 300, and International Conference on Document Analysis and Recognition Dataset 2003.
Evaluation of Proximity and Classification of Binary Objects Under Uncertainty

To solve the problems of proximity assessment, classification and recognition of objects with qualitative features, feature coding using a binary alphabet is often used, and then proximity functions are applied the encoded information Russel and Rao, Sokal and Michener, Jaccard and Needhan etc. However, when coding individual features in real conditions, uncertainties may arise. For example, the color of luminous objects can change continuously. The values of the monitored digital signals in the devices, depending on the modes of their operation, can vary over significant ranges, which can lead to uncertainty in determining their true values and requires the use of alphabets with greater significance to encode them or the use of the mathematical apparatus of fuzzy logic. However, the use of fuzzy logic and multivalued alphabets, in turn, requires modification of the known proximity functions and the development of new computer components for their software implementation.
Medical Color Image Encryption Using Chaotic Framework and AES Through Poisson Regression Model

This paper suggests a novel diversion in color medical image encryption using a chaotic framework and Advanced Encryption Standard AES with Poisson regression model. Nowa-days, the remote healthcare monitoring application is getting prominent by providing better assistance to people&#x0027;s life. We proposed a secure color image encryption algorithm for the medical images using the 2D Arnold cat map, AES-128 and Poisson regression. The workflow explained sequentially in this way. First, the plain medical image is decoupled into the corresponding RGB channels. Next, the chaotic map is applied to the plain image for converting it into a scrambled one. This scrambled image is transmitted to the AES-128 encryption block which converts the scrambled image into the encoded text form and encrypted using the hashed symmetric Key. Then the Encrypted image is formed through the Poisson regression model to predict the pixels based on the text encrypted. Finally, the resultant image is transmitted to the receiver with the NPCR score of 99.0174 and average UACI score of 33.0690. The results for the experimental work and its formulated security analyses reveal that this image encryption technique is applicable for medical image encryption and transmission.
Understanding Academics: A UX Ethnographic Research Project at the University of York

In Spring 2016 the University of York launched a research project to better understand academic staff. Ambitiously titled ‘Understanding Academics’ the project centred around the use of specific ethnographic methodologies and in particular two UX techniques: cognitive mapping followed by semi-structured interviews. The use of UX methodologies put the academics at the centre of the interviews, 156focussing on what they wanted to talk about rather than working through a pre-determined set of questions. Following the interviews, a five-stage methodology for managing and analysing the research data was developed. Ultimately, the research has led to a number of key outcomes: a set of ‘quick wins’; a set of longer-term practical recommendations; an evidence-based synthesis which seeks to define and explain academic life and understand the key motivations, frustrations and aspirations for academics; and finally an analysis of the key themes from the interview data.
A USER-CENTERED DESIGN OF A MAJOR AND MINOR INTRON ANNOTATION DATABASE (MMIAD)

Biologists and bioinformaticians heavily rely on data portals and repositories accessible through web application.While they mostly agree that the data is valuable, they find the interfaces hard to use and non-intuitive.In this paper we present a user-centered design of a database for classification and annotation for major and minor introns in various species.Our design is based on surveying and interviewing minor intron researchers and comparing the features of existing databases.In addition to its ease of use, the proposed database, Major and Minor Intron Annotation Database (MMIAD) offers high flexibility in querying and downloading subsets of information that interest the user in multiple commonly used file formats.
Application of Tamil Syntax based on Authorship Attribution Using Neural Network and Classification Methods

Application of Neural Networks with regard to author attribution as a problem of pattern recognition and proven results of their applications make them as promising techniques for the future in continuing authorship determination.Learning Vector Quantization (LVQ) is a neural network technique that develops a codebook of quantization vectors and makes use of these vectors to encode any input vector.In this paper an attempt is made to identify authorship attribute of disputed articles using LVQ and verify with the results obtained by traditional canonical discriminant analysis.This study demonstrates that statistical methods of attributing authorship can be clubbed successfully with neural networks to produce a powerful classification tool.Comparisons are made using means of sixteen articles of syntax identified from thirty -one articles written in Tamil language by three contemporary scholars namely, Mahakavi Bharathiar (MB), Subramaniya Iyer (SI) and T. V. Kalyanasundaranar (TVK) of identical repute to determine the authorship of twenty-three un-attributed articles pertaining to the same period.
A Phenomenological Study on the Challenges and Problems of New Social Studies Teachers

To enhance the teaching-learning process in Social Studies, this study explored the challenges and problems encountered by the new graduate Social Studies Teachers' at the selected private schools in one of the provinces of the Philippines.Using the qualitative methodology, the researchers interviewed five (5) participants who were purposively selected using the identified criteria.A series of procedures were employed that helped to code and categorize the details of participants' narratives and stories.To get the themes of their accounts, the analyses of meanings were performed.Findings revealed five (5) important themes that may help teachers and educators to enhance the Social Studies Education.Finally, a discussion of concepts and major recommendations was presented in light of the recently established facts from literature and studies.
Vocabulary Learning Strategies, Vocabulary Skills, and Integrative Motivation Levels among University Students

The Basic elements that contribute to the English proficiency are motivation and vocabulary learning strategies. Yet these two elements are generally acknowledged among language teachers and learners as an essential factor in language competence, but paid less attention in the learning process practicality. This research aimed to examine vocabulary learning strategies, integrative motivation, and vocabulary skills among university students in Batam, Indonesia. Vocabulary learning strategies and integrative motivation were independent variables, while vocabulary skills were the dependent variable. This research used quantitative approach. Two questionnaires were used to measure vocabulary learning strategies and integrative motivation, while a test was conducted to measure the level of vocabulary skills. The questionnaire and test were adapted and modified from previous studies. The validity of the data in this research has been verified by language experts and the reliability by pilot test. There were 314 samples. The research revealed the finding on the vocabulary learning strategies through integrativeness and attitudes to learning situation toward vocabulary skills. The research proposes a new model of vocabulary learning strategies through integrativeness and attitudes to learning situation toward vocabulary skills. The students with the higher strategies to expand the knowledge of new vocabulary items can ideally have higher level of integrativeness and attitudes to learning situation to achieve higher vocabulary skills. Overall as a conclusion, to master the vocabulary, leaners should focus on vocabulary learning strategies as well as integrative motivation.
Robust countermeasure against detector control attack in a practical quantum key distribution system: reply

We reply to the comment on our robust countermeasure against the detector control attack [ Optica 6 , 1178 ( 2019 ) OPTIC8 2334-2536 10.1364/OPTICA.6.001178 ] and explain that the concerns arising from an over-interpretation of our countermeasure are one-sided and untenable.
Dynamic key password authentication

Passwords still remain the most popular method of user authentication. Passwords appear to be the easiest way of registration and logging into remote services such as websites. However, passwords also appear to be the most insecure authentication method. One of the most popular attack techniques aimed at compromising passwords is to leak their hashes directly from their storage location to be cracked offline. The paper presents an authentication method with passwords, which complicates carrying out the attacks that succeed in extracting information sufficient for password cracking. The authentication method is called dynamic key password authentication (DKAuth). The method is based on a password 'blurring' using a number of network hosts. The 'blurring' is performed by encryption of password hash with a key that is not stored anywhere. The key is divided into parts and distributed among a number of different hosts. The key is modified for every password and changes due to change of the number of hosts in the system. Storage and authentication of a dynamic key is arranged so that it can never be recovered completely, that is even assuming cracking or rearrangement of each and every host where DKAuth key data is stored, an adversary will not be able to recover hashes and will have to crack them by brute-force attack. Practical implementation of DKAuth as an authentication service for external websites demonstrated low time and computational requirements for user registration and authentication.
Using diverse set of features to design a content-based video retrieval system optimised by gravitational search algorithm

This paper explains about the content-based video retrieval approach (CBVR) using four varieties of features and 12 distance measurements, which is optimised by gravitational search algorithm (GSA). Initially, CBVR technique extracts five kinds of features such as colour, texture, shape, image and audio features that belong to each frame. Consequently, it emerges particular distance measurements for every sort of features to compute the similarity between query frame and remaining in the database frame. In this paper, we have used GSA to find the nearly optimal combination between the features and their respective similarity measurements. At last, from the video database, the query-based videos are recovered. For experimentation, here we used two types of databases such as sports video and UCF sports action datasets. The experimental results demonstrate that the proposed CBVR method shows better performance when contrasted with other existing methods.
Customer Perception towards Online Banking Service Quality – With Reference to Public Sector Banks

This research study was aimed to explore the customer perception towards online banking adoption in Chennai city. This research study was adopted empirical and exploratory research design and structured questionnaire was used to gather the primary information from the public sector bank customers. The data collected were analysed with the help of PSPP software and statistical tools such as, percentage analysis, descriptive statistics, and multiple regression analysis has been applied to draw meaningful solutions to the research problem. The empirical evidences reveal that youngsters have higher online banking service quality perception as compared to elderly peoples. Those who are earning more income and having good occupation have higher online banking service quality perception as compared to other income and occupation groups. Public sector bank customers those who are using online banking practices before demonetization have higher service quality perception in online banking as compared to users started using aftermath of demonetisation.
To Understand the Team Cohesiveness, Teamwork Quality and Team Performance in Employee Performance in University

The purpose of this research is to seek and identify the impact teamwork has on organizational performance. The research to understand the teamwork quality, team cohesiveness and team performance in employee performance, identify the positive or negative effects of teamwork on employees and to examine the impact of teamwork on organization performance. Therefore, it is to determine how teamwork could effect and improve University employee’s performance within the organization. In fact, it is also to understand on how methods like teamwork quality, team cohesiveness, and teamwork performance have could be used to have positive effects in employee overall performance. The research study used correlation techniques in order to analyze the relationship between two variables that was Teamwork and Organization Performance. In many of the journals and article that was collected, there was clear evidence that teamwork and other measures of team performance are positively related with organization performance. The study of the research shown that there was a significant positive impact of teamwork on organizational performance and employee’s overall performance.
Influence of Social Marketing on Rain Water Harvesting Practices for Water Recycling System

Water is a limited natural resource for nourishing human life. Drinking water scarcity is one of the globally facing emerging issues. Harvesting is one among the major solution for this problem. There are three different ways of harvesting techniques like rainwater, ground water and flood water. Human health is intrinsically linked to the environment and water resources. Increasing water demand affecting more than majority of people throughout the world every year and are considered as a most critical risk to human sustainability. In the recent years social marketing is applied for general issues that are facing by public. This paper mainly focuses on the level of knowledge on water recycling. The study area is Sivagangai district. The sample size is 518 based on proportionate random sampling method. From this analysis they identified female are more aware on water harvesting
NBP-IWT-DCT-SVD-PSO Based Secure Image Watermarking

This paper proposes energetic photograph watermarking approach contingent upon entire wide variety Wavelet alternate, Discrete cosine change, unique well really worth Disintegration and Molecule Swarm Streamlining. PSO is implemented to get an extremely good watermarking predictable. further, to make the watermarked picture continually fiery to disenchanted and flipping assaults,in advance than introducing the Standardized square system is achieved to the host image. because the population duration assembles, GA cannot overhaul the gadget snappy. consequently the proposed watermarking plan mulled over PSO is for improve. diverse investigations have been pushed on the proposed manner to manipulate analyzethe execution. The got results display that the proposed system is higher than customary strategies and is the general public of the more capably comfortable over Gaussian confusion, Salt and Pepper Commotion, middle Sifting, Trimming, Pivot, Complexity development, Scaling and Histogram Leveling attacks.
Large-scale global optimisation using cooperative co-evolution with self-adaptive differential grouping

Cooperative co-evolution (CC) provides the divide-and-conquer framework for solving large-scale global optimisation (LSGO) problems. Identification of variable interactions is the main challenge in CC. Differential grouping (DG) is a competitive approach to find the identification and Global DG (GDG) is its improvement by introducing the global information. In this paper, a self-adaptive DG (SDG) is proposed for further improving the grouping accuracy of GDG. The threshold for grouping in SDG can adjust adaptively along with the magnitude of different functions and is determined by only two points which is a randomly sampled point and its corresponding opposite point. A self-adaptive pyramid allocation (SPA) for computational resources is also studied. The proposed algorithm, with SDG, SPA, and the optimiser SaNSDE, is used to solve the CEC'2010 LSGO benchmark suite. Experimental results show that SDG achieved ideal decomposition for all the functions and the proposed algorithm obtained competitive optimisation performance.
Uncertain Data Outlier Detection Algorithm based on iForest and LOF

With the advancement of technology and the deepening of people's understanding of data acquisition and processing technologies, as a new type of data model, uncertainty data is widely used in finance, military, logistics, telecommunications and other fields.The traditional data management technology can not manage the uncertain data effectively.The management technology for uncertain data has become a research hotspot in the field of data mining.Many traditional data mining technologies have been extended and applied to uncertain data.Outlier detection is an important technology in the field of data mining.It is used to discover objects whose behavior is different from other data objects and has high application value in network intrusion and sensor network detection.Outlier detection for deterministic data has been studied in depth, but it is a new topic in the new area of uncertain data.We study a possible world model based on attribute-level uncertain data and proposes a new outlier detection method.Firstly, iForest's anomaly score calculation method was improved to make it applicable to uncertain data.Second, the local outliers of uncertain data are redefined.Then, we use iForest and K nearest neighbor query optimization to reduce the candidate set of data without expanding the possible world.Finally, experiments verify the performance of the algorithm.Experimental results show that the proposed algorithm can effectively improve the accuracy, reduce the time complexity, and improve the outlier detection performance of uncertain data.
Decentralizing Privacy: Protecting Personal Data by Blockchain

the security of users are questioned when security breaches occur in data when third parties are incorporated for collecting and controlling huge amount of personal data. A decentralized network of peers are accompanied by a public ledger and it has demonstrated bitcoin in the financial space that trusted and auditable computing. This paper describes a decentralized personal data management system for ensuring users control over their data. A protocol is implemented that is capable of turning a blockchain into an automated access-control manager that is not requiring trust in a third party. There are no strict financial transactions in our system. They are used for carrying instructions like querying, storing and sharing data. Finally, some possible blockchain extensions are discussed that are able to harness them into a well-rounded solution for faithful computing problems in society.
University Counterpart Support Information Management System Based On Artificial Intelligence Technology

Counterpart support is an inter-regional coordinated development mechanism with Chinese characteristics, and the education sector is an important part of it. In the process of government support for the development of frontier ethnic regions, the main purpose is to improve the economy and people's lives in ethnic minority areas, improve the scientific, technological and cultural qualities of the people of all ethnic groups, and cultivate high-level talents. In recent years, education-related assistance has mainly been related to Tibet, Xinjiang, and Qingdao. Through continuous practice and research, a relatively stable working model has been established. However, with the development of the socialist market economy, education security services are also in a new development situation. The relevant information of various schools has increased significantly, but information management is relatively scattered. The emergence of counterpart support information management systems for universities is inevitable, so this article focuses on research on the college counterpart support information management system based on artificial intelligence technology, understand the relevant theories of the information management system on artificial intelligence technology on the basis of relevant literature materials, and then design the college counterpart support information management system based on artificial intelligence technology, and test the designed system, the test result shows that the average response time of the system in this paper is below 3s, which basically meets the requirements of the system.