Reviewerly/exHarmony huggingface dataset:
Effectiveness Of Social Media On Youth In Relation To Their Social Life In Kerala

Social Networking has become a global phenomenon. Millions of people now use internet to engage on social networks. As in, there are more than 300 Social Networking Sites (SNS) and the total number of people using Social Networking Sites around the world is 1.73 billion according to 2013 Sensex. In few years, the usage of Social Networking Sites has surprisingly grown. These Social Networking Sites offers a wide variety of resources and services such as messaging, sharing of information, online marketing, blogging, connecting, sharing photos, audios, videos, group discussion, and distribution of applications. Consequently; especially youth, are the avid users of these sites. And, the usage of Social Networking Sites among the youth has increased rapidly throughout the world. Evidently, Social Networking Sites have become significant part of our lives. However, the study has been set forward to analyse the influence of Social Networking Sites on the interpersonal relationships of youth in Kerala state. Humans are social animals and Interpersonal Relationships are a very vital aspect of human existence. Hence, the impact of Social Networking Sites on interpersonal relationships needs to be examined through the lens of Sociology.
Communicative-Function-Based Sentence Classification for Construction of an Academic Formulaic Expression Database

Formulaic expressions (FEs), such as 'in this paper, we propose' are frequently used in scientific papers.FEs convey a communicative function (CF), i.e. 'showing the aim of the paper' in the above-mentioned example.Although CF-labelled FEs are helpful in assisting academic writing, the construction of FE databases requires manual labour for assigning CF labels.In this study, we considered a fully automated construction of a CF-labelled FE database using the top-down approach, in which the CF labels are first assigned to sentences, and then the FEs are extracted.For the CF-label assignment, we created a CF-labelled sentence dataset, on which we trained a SciB-ERT classifier.We show that the classifier and dataset can be used to construct FE databases of disciplines that are different from the training data.The accuracy of in-disciplinary classification was more than 80%, while crossdisciplinary classification also worked well.We also propose an FE extraction method, which was applied to the CF-labelled sentences.Finally, we constructed and published a new, large CF-labelled FE database.The evaluation of the final CF-labelled FE database showed that approximately 65% of the FEs are correct and useful, which is sufficiently high considering practical use.
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase

We introduce a data augmentation technique based on byte pair encoding and a BERTlike self-attention model to boost performance on spoken language understanding tasks.We compare and evaluate this method with a range of augmentation techniques encompassing generative models such as VAEs and performance-boosting techniques such as synonym replacement and back-translation.We show our method performs strongly on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity.
Kepedulian Keamanan Informasi di Pemerintahan: Praktik Manajemen dan Dampaknya

Currently the organization is facing challenges due to the Covid19 pandemic which has caused changes in work patterns for all members of the organization. The priority of management is to give more attention to information security, especially for members and government organizations in general. Various information security practices have been carried out, but there are obstacles that occur such as the lack of awareness of information security, work behavior, work culture and the lack of human resources available and this can have a negative impact on the level of information security concern in the organization. Information security practices carried out by management do not always have an impact on changes in the behavior of organizational members. From the data obtained, there are many information security practices that may have been implemented by management. But do members of the organization care about the practice. Therefore, it is important to conduct research with the aim of knowing the practice of information security concern that has been carried out by management whether it has an impact on organizational members, especially the organization itself. This study used qualitative and quantitative interpretive approaches as a data collection process in three government organizations. Qualitative is used to obtain data from management and processed using the SAP-LAP model through interviews. Then an online survey was conducted with members of the organization to determine the impact of implementing information security care practices on the organization. The results show that organizational members have a high level of concern for the management's information security practices.
Block Chain Technology in Supply Chain Management Using Key Generation

An information system is very important in today's changing world, and therefore choosing the proper system is becoming a very important decision. With the time, the information systems have become more crucial, with multiple problems affecting the supply chain and the continuous writing of performance areas. The principle of chain management aims at chain values. The working of chain management states that chain management enhances the value of the chain by establishing operational units. Traditional manufacturing supply chains bear a lot from additional, costs, delays and information loss due to information middleware’s. Therefore, we propose a blockchain oriented supply chain architecture to reduce intrusion in traditional ones. Current approaches to private-key security include signature schemes based on bio-metrics, index-hidden private key design, and post-quantum block chain schemes. Nevertheless, recovering the lost private key is difficult in all approaches. Here we proposed an optimal key formation procedure using a fitness-based self-adaptive sea lion algorithm for secure information sharing in SCM using block chain.
Identifying the Phishing Websites Using the Patterns of TLS Certificates

With the recent rise of HTTPS adoption on the Web, attackers have begun “HTTPSifying” phishing websites. HTTPSifying a phishing website has the advantage of making the website appear legitimate and evading conventional detection methods that leverage URLs or web contents in the network. Further, adopting HTTPS could also contribute to generating intrinsic footprints and provide defenders with a great opportunity to monitor and detect websites, including phishing sites, as they would need to obtain a public-key certificate issued for the preparation of the websites. The potential benefits of certificate-based detection include (1) the comprehensive monitoring of all HTTPSified websites by using certificates immediately after their issuance, even if the attacker utilizes dynamic DNS (DDNS) or hosting services; this could be overlooked with the conventional domain-registration-based approaches; and (2) to detect phishing websites before they are published on the Internet. Accordingly, we address the following research question: How can we make use of the footprints of TLS certificates to defend against phishing attacks? For this, we collected a large set of TLS certificates corresponding to phishing websites from Certificate Transparency (CT) logs and extensively analyzed these TLS certificates. We demonstrated that a template of common names, which are equivalent to the fully qualified domain names, obtained through the clustering analysis of the certificates can be used for the following promising applications: (1) The discovery of previously unknown phishing websites and (2) understanding the infrastructure used to generate the phishing websites. Furthermore, we developed a real-time monitoring system using the analysis techniques. We demonstrate its usefulness for the practical security operation. We use our findings on the abuse of free certificate authorities (CAs) for operating HTTPSified phishing websites to discuss possible solutions against such abuse and provide a recommendation to the CAs.
Chaotic Duck Traveler Optimization (cDTO) Algorithm for Feature Selection in Breast Cancer Dataset Problem

Objective: 1 of every 3 individuals will be determined to have malignancy in the course of their life. Currently, there are more than 3.8 million ladies who have been determined to have breast malignancy in the United States. 2021 is practically around the bend, yet there's still an ideal opportunity to help ladies confronting breast malignancy in 2020. In this paper, chaotic based duck travel optimization (cDTO) meta-heuristic algorithm is introduced to classifying the input images from Mammogram Image Analysis Society (MIAS) database. Methods: Linear Discriminant Analysis is used to extract the mammogram image features. (cDTO-LDA) is an intrinsic algorithm to remove irrelevant features and select the optimal features by using wavelet families Haar (harr), db4 (daubechies), bior4.4 (Biorthogonal), Symlets (SYM8), “Discrete” FIR approximation of Meyer wavelet (dmey) features. Results: These selected features are evaluated by the quality measures such as accuracy, sensitivity, specificity, error rate that are clearly shows the high exactness of cDTO classifier is 98.5%. CSA-LDA classifier has the minimum exactness. Conclusion: Algorithm efficiency is proved by the promising results achieved by the proposed algorithm for selecting the best feature of breast cancer classification.
Detection of Moving Vehicles on Highway using Fuzzy Logic for Smart Surveillance System

The computer vision incorporates a prominent performance in developing models for medical, security and lot more concepts and we need to deal with detecting and tracking the moving objectsuch as moving vehicle or person. Various challenges are available due to environmental issues, illumination variation, or fast motion etc. This paper work has developed a fuzzy logic based method for identifying the moving vehicles though bounding boxas depicted in the green colour. The paper depictsa fuzzy based method to detect and track the object around the location until the specific secured dimension of the device. The extended recognition (EIR) is the proposed method which works on the automatic fuzzy set creation.The EIR consists of the pixels of the surroundings and recognize them with the predefined inputs we have with the EIR algorithm in the repository. This methodology successfully identified and detects vehicles. It can track the instances within that visible region and this is working as a human eye mechanism.
Pengenalan Citra Huruf Hijaiah Menggunakan Metode Gray Level Co-Occurrence Matrices (Glcm) Dengan 4 Sudut Orientasi Dan Jaringan Syaraf Tiruan Backpropagation

Arabic is one of the international languages according to the United Nations (UN) which was adopted by General Council resolution 3190 (XXVIII) as the official language and working language of the General Council and Main Committees on 18 December 1973. Arabic can be found in the holy book Al - Qur'an. For a Muslim, it is obligatory to learn and master Arabic in order to read and understand the contents of the Al-Qur'an. job applicant from Indonesia is also have to learn Arabic. The Hijaiiyah letter has the same role as the alphabet, which is to compose every word and sentence. Humans have a natural intelligence to be able to recognize each Hijaiiyah letter based on the special characteristics or patterns contained in each letter. However, natural intelligence has deficiencies such as inconsistencies in assessing the similarity of each handwritten Hijaiiyah letter from different people. Therefore this research will develop a system for identifying or recognizing Hijaiiyah handwritten patterns using the Gray Level Co-occurrence Matrices (GLCM) method with 4 orientation angles and Backpropagation Artificial Neural Network (ANN). Data was collected using the Autodesk Sketchbook application so that can reduce the noise. The purpose of this research is to know the level of accuracy and precision of the classification of the Hijaiiyah letter pattern. In this research, the amount of data used was 1500 images of Hijaiiyah letters. The highest accuracy is 45.1111% with a precision 45.1111%.
Development process of multiagent system for glycemic control of intensive care unit patients

This article presents the entire process of developing an agent-based system for the glycemic control of patients in the Intensive Care Unit (ICU). The agent’s goal is to monitor and recommend treatment to keep the patient’s blood glucose within the target range, avoiding complications in the health of patients and even decreasing rates of morbidity and mortality in the ICU. The process of developing the agent-based solution was presented, starting from the understanding of the problem, including a brief review of the literature, going through the pre-project and modelling through the Tropos methodology, until the implementation. The agent inference mechanism is based on production rules and intuitionistic fuzzy logic. An illustration of use, with the collaboration of a specialist intensive care physician, shows how agents behave in a real situation of monitoring and controlling the blood glucose of patients admitted to the ICU, interacting with all elements of the proposed architecture. Finally, feedback from health professionals indicate the system can assist in the glycemic control of patients in the ICU having advantages over traditional monitoring systems.
DESIGNING THE INFORMATION SYSTEM FOR DATA COLLECTION OF COVID-19 SYMPTOMS FOR INDONESIAN CITIZENS

Information systems are the result of rapidly emerging technological developments because human needs as information users of technology are very strong and rapid. Begin with the very rapid growth of computer technology, computers such as smartphones, tablets, notebooks and others are limited in scale. This small computer device must be connected to the Internet so that it can be used properly. And the information system plays its role as a provider of information that computer users want to access when the computer device is linked to the Internet. The information system developed in this paper is the Citizen Data Collection Information System, this system was created using the YII Framework. The YII framework uses the MVC method which makes it easier to develop a Citizen Data Collection Information System. With this Information System, information about the latest condition of the residents can be easily accessed. So that the Kelurahan, Kecamatan, RW, and RT will have the latest information about their residents and easily anticipate if needed. In the development of this system, it has staged such as literature study, observation, interviews, analysis, design, testing, and implementation. The conclusion is that this information system can carry out the desired function when it is implemented.
Exploring Supervised and Unsupervised Rewards in Machine Translation

Reinforcement Learning (RL) is a powerful framework to address the discrepancy between loss functions used during training and the final evaluation metrics to be used at test time.When applied to neural Machine Translation (MT), it minimises the mismatch between the cross-entropy loss and non-differentiable evaluation metrics like BLEU.However, the suitability of these metrics as reward function at training time is questionable: they tend to be sparse and biased towards the specific words used in the reference texts.We propose to address this problem by making models less reliant on such metrics in two ways: (a) with an entropy-regularised RL method that does not only maximise a reward function but also explore the action space to avoid peaky distributions; (b) with a novel RL method that explores a dynamic unsupervised reward function to balance between exploration and exploitation.We base our proposals on the Soft Actor-Critic (SAC) framework, adapting the off-policy maximum entropy model for language generation applications such as MT.We demonstrate that SAC with BLEU reward tends to overfit less to the training data and performs better on out-of-domain data.We also show that our dynamic unsupervised reward can lead to better translation of ambiguous words.
Current Progress on Passion Efficient Cluster Based Routing Protocols of Wireless Sensor Networks for Medicine Systems

Detected modified works by getting to cellar organizes as clear in A measure of doctor's facilities and therapeutic focuses are investigating utilizations of remote sensor course of action (WSN) innovation to a propelled ambit of restorative applications, including pre-healing center, and in-clinic crisis mind, misfortune reaction, and accomplishment obliging recovery. Current Healthcare Wireless Sensor Networks (HCWSN) examination patterns concentrate on patients, dependable correspondence, obliging versatility, and vitality proficient steering, a some portion of others. The reviews concentrate on modified bunch based procurement conventions which are accustomed in gradual addition movement capacity of WSN for medicinal services apparatus and to call attention to imperative concerns in group established obtaining (CBR) understanding that counselor to propel them in acclimation to develop their machine go. Today, Wireless Sensor Networks are adequate acknowledged and flourishing securing conventions acknowledge been proposed in theory with an attention on the various leveled directing. This cardboard overviews the WSNs vitality productive CBR strategies that are accustomed for Healthcare Advice framework. Late progress and restrictions of forerunner studies were highlighted. The procurement conventions are classified by comparing action proficiency. We mean for counselors to bound investigate territories that hunger for added retention and to illustrate an atypical arrangement for healing the capacity of outright conventions.
Towards the design of a quick and universal questionnaire to assess the intuitiveness of products

The goal of this research is to design a tool to assess intuitiveness of products. Existing scales such as INTUI (Diefenbach and Ullrich 2015 Diefenbach, S. , and D. Ullrich . 2015. “An Experience Perspective on Intuitive Interaction: Central Components and the Special Effect of Domain Transfer Distance.” Interacting with Computers 27 (3): 210–234. doi:https://doi.org/10.1093/iwc/iwv001.[Crossref], [Web of Science ®] , [Google Scholar]) show some limitations when used by children and/or for evaluating non-digital products. We aim to obtain a more universal questionnaire tool. INTUI questionnaire measures the components of an intuitive interaction (Effortlessness, Gut feeling, Magical experience, Verbalizability and Intuitiveness) through 17 items. After removing or rephrasing several items, we tested a revised version (8 items) with 68 participants (children and adults) who performed a task (a drawing for children and subtractions for adults) with a digital device (tactile tablet for children and smartphone for adults) and a non-digital device (paper and pencil). The results led us to remove the “Verbalizability” and “Gut feeling” dimensions which were difficult to understand and inconsistent with the conceptual model. The final version of the questionnaire (5 items) including three dimensions (Effortlessness, Magical Experience and Intuitiveness) was tested with 69 adults to evaluate a Coffee Dispenser Machine. Both “Effortlessness” and “Magical experience” dimensions seem to reliably predict intuitiveness.
If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations

Fine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by the difficulty of annotation.Targeted sentiment analysis, on the other hand, is a more narrow task, focusing on extracting sentiment targets and classifying their polarity.In this paper, we explore whether incorporating holder and expression information can improve target extraction and classification and perform experiments on eight English datasets.We conclude that jointly predicting target and polarity BIO labels improves target extraction, and that augmenting the input text with gold expressions generally improves targeted polarity classification.This highlights the potential importance of annotating expressions for fine-grained sentiment datasets.At the same time, our results show that performance of current models for predicting polar expressions is poor, hampering the benefit of this information in practice.
The Effects of Reading Activity on Episodic Memory in Elderly

Abstract: The aging process is a natural event that cannot be avoided. Especially in the elderly, this is accompanied by a decrease in episodic memory function, which can reduce the quality of life. Therefore, efforts are needed to maintain cognitive function in elderly, one of which is through routine cognitive activities such as reading which can affect memory function in elderly through neuroplasticity. This study aims to determine the effect of reading activity on episodic memory function in the elderly. This research is a quasi-experimental study with a pre-test post-test control group design method. The total research subject were 30 people in Desa Demangan Siman, Kabupaten Ponorogo and Kelurahan Ngadirejo, Kota Kediri, with a sample technique using random purposive sampling. The research subjects were then divided into 3 groups with random allocation techniques using microsoft excel. Group 1 was the control group, group 2 was the 15-minute reading aloud intervention group, and group 3 was the 30-minute reading aloud intervention group. The results of the Dependent T-test showed that there was a significant difference in mean scores between the pre-test and post-test scores in group 2 (p = 0.000) and group 3 (p = 0.000), but not significant in group 1 (p = 0.111). The One Way ANOVA test results showed a significant difference in post-test scores in the three groups (p = 0.043), and the Post-hoc LSD test found a significant difference in scores between the post-test scores in group 1 with group 2 (p = 0.025) and group 1 with group 3 (p = 0.034), but there was no significant difference between the post-test scores in group 2 with group 3 (p = 0.883). It can be concluded, there is a significant effect between the duration of reading activity and episodic memory function in the elderly. Keywords: aging, elderly, episodic memory, reading, neuroplasticity.
Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies

In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for optimal weights and structure of the model. The difference is determined according to the innovation numbers assigned to each node and connection, allowing tracking the changes. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses a set of mutation operators, including connections merging and deletion. The algorithm is tested on a set of classification problems and the rotary inverted pendulum control problem. The comparison is performed between the basic approach and modified versions. The sensitivity to parameter values is examined. The experimental results prove that the newly developed operator delivers significant improvements to the classification quality in several cases, and allow finding better control algorithms.