Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA

Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.
Application of the Pattern-Mixture Latent Trajectory Model in an Epidemiological Study with Non-Ignorable Missingness

In longitudinal studies where the same individuals are followed over time, bias caused by unobserved data raises a serious concern, particularly when the data are missing in a non-ignorable manner.One approach to deal with non-ignorable missing data is a pattern mixture model.In this paper, we combine the pattern mixture model with latent trajectory analysis using the SAS TRAJ procedure, which offers a practical solution to many problems of the same nature.Our model assumes a stochastic process that categorizes a relative large number of missing-data patterns into several latent groups, each of which has unique outcome trajectory, which allows patterns with missing values to share information with patterns with more data points.We estimated the longitudinal trajectories of a memory test over 12 years of follow-up, using data from the prospective epidemiological study of dementia.Missing data patterns were created conditional on survival, and final marginal response was obtained by excluding those who had died at each time point.The approach presented here is appealing since it can be easily implemented using common software.
Outlines of Pyrrhonism

The first Ukrainian translation of the classic work of ancient skepticism, Sextus Empiricus’ Outlines of Pyrrhonism, made by D. of Sc. Philology Lesia Zvonska under the scientific editorship of Dr. of Sc. in Philosophy. Oleg Khoma.
Predictability of ICO Success and Returns

Initial coin offerings (ICOs) provide a platform to issue tokens to the public. This study analyses the determinants of ICO success and listing day returns using a dataset of 341 executed ICOs. Funds raised under an ICO are negatively related to target funding size and positively related to Cryptocurrency usage and ICO quality. ICO listing day returns are negatively related to the amount raised, the time period of the offer and time delay to listing. In contrast, ICO listing day returns are positively related to Blockchain usage, ICO quality and the ICO success to raise the target level of funds.
Deep Neural Network for Better Face Processing

This Paper represents the face detection using advanced method deep neural network which uses deep learning frame work. The old models used to detect the faces were like Haar-cascade method which detect the faces with good approaches but there is some uncertainty in the accuracy of the old models, so in this system we will use the latest deep neural network model which is embedded with latest open cv and by using the deep learning model frame work which is weighted with some other files. By using this model, we can achieve the better accuracy in face detection which can be used for further purposes like auto focus in cameras, counting number of people etc. This model detects the faces accurately and paves the way for better recognition systems which can be used in many face biometric applications. For this purpose, low-cost computer board Raspberry Pi and Camera Sensor will be used.
The Effect of Augmented Reality Application toward Student’s Learning Performance in PC Assembly

Assembly is a process of combine two or more component together through particular sequence and operation.Now, lab instruction was utilizing as main tools to guide computer assembly in computer hardware course.However, this lab instruction contains too much text and it makes student takes time to visual the process and interpret the step.Numerous study has reported Augmented Reality (AR) has a great impact in improving student learning as well as assist in assembly task by means as visualization tool.This study aimed to investigate the effect of AR application on student learning performance in term of pc assembly knowledge.To meet the purpose, a marker-based AR application, AR PC Assembly was developed to provide a visualization in 3D on pc assembly.A quasi-experimental pre-test and post-test control group design was used as a research design.The participants were 68 first-year diploma students, where they were allotted to either an experimental or a control group.The control group learned the theoretical and perform the assembly task about personal computer assembly through textbooks while students in the experimental group used AR PC Assembly application for the same purpose.The experimental result reveal that using AR application can enhance the learning performance of the students.From the result of analysis, post-test score in experimental group was higher 20 % compared to control group.Considering these result, it can conclude that AR application is an effective and beneficial tool to be applied in education.
Robotics and Generation Z - apprehension or attachment

Developments in technology resulted in rapid change over the past decades. The purpose of this exploratory research is to discover the positive emotional response of attachment and apprehension towards robotics of Generation Z. This exploratory study contributes to existing knowledge as it is based in the UAE and also studies gender differences within Generation Z. The theoretical framework is the change theory by Lewin (1951). A quantitative methodology was adopted. In total, 116 participants filled in a questionnaire consisting of the negative attitudes towards robots scale (NARS), robot anxiety scale (Nomura et al., 2006) and an eight-item scale of product emotional attachment by Schifferstein and Zwartkruis-Pelgrim (2008). The main finding is that Generation Z participants are in a process of change as they find themselves in a state of ambiguity and are undecided about their emotions towards robots in the workplace, however lean more towards feeling apprehensive, fearful and detached.
Xilinx Based Electronic Voting Machine

Conventional paper based voting procedure was terribly long process and extremely prone to errors. Polling by Electronic Voting Machine (EVM) is easy, safe and secure methodology that takes minimum of our time .In order to perform this mechanism, there were several phases in the design process such as designing a flow chart, algorithm and simultaneously the code is developed to implement &amp; stimulate the logic. The proposed digital EVM was designed on Xilinx ISE using verilog HDL and can also be implemented on FPGA board for real time purpose. The proposed method consists of 3 stages, in the first stage we decide the total no. of voters and the total number of contestants taking part in the election process .we have assigned Voting enable which is active high input signal for the voter in order to cast his vote by using voter switch input signal for making this election process more secure and safe. In stage two, voting process begins when the voter casts his vote to a particular party or contestants the polled vote is registered in the individual contestant registry. In stage three after completion of voting process the votes are validated by comparing the votes polled to the contestants in their registries after which the election process ends by declaring the winner. The above proposed method can be implemented on FPGA board for real time applications ranging from university level elections to Assembly and LokSabha elections, as it has the advantage that it can be reprogrammed over and over for various tasks according to their requirement which helps in reducing the expenditure.
Query Expansion using Semantic Network for Information Retrieval in Telugu Language

Now-a-days digital documents are playing a major role in all the areas /web, as such all the information is digitalised. Queries are used by the search engines to retrieve the information. Query plays a major role in information retrieval system, as a result relevant and non relevant documents are retrieved. Query expansion techniques will better the performance of the information retrieval system. Our proposed query expansion technique is Word Sense Disambiguation. This is to find the correct sense of the ambiguous word in regional Telugu language. In Query expansion, if the added query term is an ambiguous word, accuracy of relevant documents will be very less. So to avoid this, proposed method Word Sense Disambiguation (WSD) is used, which is related to NLP Natural Language Processing and Artificial Intelligence AI. WSD improves the accuracy of information retrieval system.
Intelligent Interactive Voice Response Systems and Customer Satisfaction

This study intends to investigate the customer satisfaction on "Intelligent Interactive Voice Response Systems" which are the extension of the traditional "Interactive Voice Response Systems (IVRS)", and play important role in many organizations to provide more complex and personalized customer service.A theoretical model is developed basing on literature.Data are collected from 239 people with a survey questionnaire for testing the proposed model.Structural equation modelling is used as the statistical method in order to analyze data.As a result, the constructs which can affect the customer satisfaction are specified for the improvement of IVRS and guiding companies.
Smart Voting System using Facial Detection

India being a democracy, that too world’s largest, still conducts its elections using either Secret Ballet Voting or Electronic Voting Machines (EVM) both of which involve high costs, manual labor and are inefficient. So, the system must be optimized to be made efficient which would not leave room for unwanted means of voting. The current system requires the physical presence of every individual which is inconvenient to many people. This paper focuses on a system that uses faces to unlock the voting system just like in your phone and does not require physical presence to cast a vote as the traditional system does. The process is time-consuming as well. The entirely web-based system enables people to cast their votes from anywhere in the world. Using detection of faces decreases the chance of duplicating a vote and those who are registered prior to the election and are recognized by the system will be allowed to vote. Just like fingerprints, every face also has unique features like the distance between the eyes and eyebrows that remain unchanged with growing age which makes the system more secure. Hence, the approach makes the system the best way to vote.
Attendance Management System using Deep Learning and Facial Recognition Technique

Big data could be a term acclimated converse with the investigation and utilizations of data sets that are unreasonably entangled for conventional information handling application programming framework to sufficiently alter. AI is a use of (AI) that enables frameworks to precisely take in and improve from aptitude while not being explicitly customized. AI centers around the occasion of pc programs which will get to data and use it learn for themselves. The technique for learning starts with perceptions or data, similar to precedents, direct skill, or guidance, in order to appear for examples in data and manufacture higher decisions inside the future bolstered the models that square measure gave. One in everything about applications is programmed face acknowledgment bunch activity framework to a great extent used in Workplaces or gifted workspaces. ID or Verification done by catching an image of client's face through a camera that progressively stamps bunch activity. This propelled face acknowledgment bunch activity biometric framework enlists the particular and changeless facial fine purposes of staff and records them inside the data as stencils. When the enrollment strategy is finished, you just should investigate the camera to confirm your character and furthermore the programmed face acknowledgment bunch activity framework precisely denotes your gathering activity for your sake. Face acknowledgment bunch activity framework is a right innovation for overseeing bunch activity since it scarcely gives mistakes.
HealthMedia'19

Managing one's health is among the most personal and most important challenges. HealthMedia can be viewed as one response from the multimedia community to rise to this challenge. There is an increasing number of research work that shows how core multimedia research is becoming an important enabler for solutions with applications and relevance for the societal questions of health. Within this workshop, we continue to explore the relevance, contribution and future directions of multimedia to health care and personal health. This workshop brings together researchers from diverse topics such as multimedia, tracking, lifelogging, accessibility, HCI, but also health, medicine, and psychology to address challenges and opportunities of multimedia in and for health.
Sentiment Analysis

Background/objectives: Opinions and its related concepts such as sentiments, evaluations, attitudes, and emotions are the subjects of study of sentiment analysis and opinion mining. Methods/findings: This research introduced the field of sentiment analysis and opinion mining and surveyed the current state-of-the-art. After that, we discussed the widely studied topic sentiment analysis based on Twitter and need of opinion spam detection. We discussed “Sentiment Analysis on section-377”, in which we would discuss about the LGBT community, about the pride parades, about the action taken by the Supreme Court, and about the sentiments or opinion of people towards the section-377 through a poll.Keywords: Sentimental Analysis, Pride Parade, Twitter, Opinion Spamming, LGBT Community
Analysis of smooth pursuit eye movements in a clinical context by tracking the target and eyes

In the evaluation of smooth pursuit eye movements (SPEMs), recording the stimulus onset time is mandatory. In the laboratory, the stimulus onset time is recorded by electrical signal or programming, and video-oculography (VOG) and the visual stimulus are synchronized. Nevertheless, because the examiner must manually move the fixation target, recording the stimulus onset time is challenging in daily clinical practice. Thus, this study aimed to develop an algorithm for evaluating SPEMs while testing the nine-direction eye movements without recording the stimulus onset time using VOG and deep learning-based object detection (single-shot multibox detector), which can predict the location and types of objects in a single image. The algorithm of peak fitting-based detection correctly classified the directions of target orientation and calculated the latencies and gains within the normal range while testing the nine-direction eye movements in healthy individuals. These findings suggest that the algorithm of peak fitting-based detection has sufficient accuracy for the automatic evaluation of SPEM in clinical settings.
Association between lowering restriction levels during the coronavirus outbreak and physical activity among adults: a longitudinal observational study in Brazil.

The COVID-19 pandemic has had significant consequences on public health and lifestyle and has negatively affected mental health and the level of physical activity worldwide. This study examined the impact of reopening fitness centers and nonessential services and introducing flexible measures to ensure social distancing on physical activity and mental health.This was a longitudinal study. A self-administered questionnaire, including personal, behavioral, physical activity, perception of health, and mood state disorder information, was answered by 128 Brazilians in June 2020 (during severe restrictive measures) and again in April 2021 (after fitness centers and nonessential services were reopened).The restriction level adopted in April 2021 was significantly lower than that in June 2020 (p<0.001). The level of physical activity (p<0.001) and health status perception (p<0.001) decreased from June 2020 to April 2021. The median values for depression and anxiety did not differ across the study period.The level of physical activity was reduced during the COVID-19 pandemic, and anxiety and depression were not improved following less restrictive social distancing measures and the reopening of fitness centers. Thus, the return to a prepandemic level of physical activity and mental health status may not be automatic. The results presented herein suggest that the decrease in physical activity observed in the population may be challenging in the postpandemic period.
What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges

From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was using sensor data from one location to recognize activities with sensors in another location, while in the following year, the main challenge was using the sensor data of one person to recognize the activities of other persons. We use these two challenge scenarios as a framework in which to analyze the effectiveness of different components of a machine-learning pipeline for activity recognition. We show that: (i) selecting an appropriate (location-specific) portion of the available data for training can improve the F1 score by up to 10 percentage points (p. p.) compared to a more naive approach, (ii) separate models for human locomotion and for transportation in vehicles can yield an increase of roughly 1 p. p., (iii) using semi-supervised learning can, again, yield an increase of roughly 1 p. p., and (iv) temporal smoothing of predictions with Hidden Markov models, when applicable, can bring an improvement of almost 10 p. p. Our experiments also indicate that the usefulness of advanced feature selection techniques and clustering to create person-specific models is inconclusive and should be explored separately in each use-case.
Privacy: An Axiomatic Approach

The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what exactly is being protected, that is, of privacy itself. In this work, we formulate an axiomatic definition of privacy based on quantifiable and irreducible information flows. Our definition synthesizes prior work from the domain of social science with a contemporary understanding of PETs such as differential privacy (DP). Our work highlights the fact that the inevitable difficulties of protecting privacy in practice are fundamentally information-theoretic. Moreover, it enables quantitative reasoning about PETs based on what they are protecting, thus fostering objective policy discourse about their societal implementation.
Nukhada USV: a Robot for Autonomous Surveying and Support to Underwater Operations

The Technology Innovation
ClinicalPath: A Visualization Tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians’ daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients’ history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians’ experience in decision-making tasks, made with more confidence and better usage of the physicians’ time, allowing them to take other needed care for the patients.