User Need Research Articles

UX (USER EXPERIENCE) EVALUATION OF THE OPENLEARNING SYSTEM AT UNIVERSITAS MUHAMMADIYAH SURAKARTA USING HEURISTIC EVALUATION AND USABILITY TESTING

Since the COVID-19 pandemic in 2020, the learning process at the University of Muhammadiyah Surakarta (UMS) has been carried out online using the OpenLearning platform. The use of OpenLearning media has provided an easy learning process for both lecturers and students. Although there are many conveniences and benefits offered, usability problems still arise, for example, there is no distinction between courses that have ended and those that are still active, the "Edit Page Header" feature on student users, and misperceptions in the progress bar in the Course menu. In addition, previous research has evaluated the UMS OpenLearning platform using the System Usability Scale (SUS) technique and scored 61 (grade scale D) and indicates that the system is well received. To identify more detailed problems related to user experience (UX) for the OpenLearning system applied at UMS, it is necessary to evaluate the UX with the other approaches. In this study, the Heuristic Evaluation method based on 10 principles of Heuristic and Usability Testing based on 5 Usability criteria are implemented to evaluate. The results of this study are a system prototype according to the results of heuristic evaluation. The prototype was evaluated using SUS method on 380 respondents. The SUS calculation yields a value of 69, which indicates that the system is classified to the grade scale category D. This insignificant increase in the SUS score indicates that the UX improvements have not been optimally related to usability due to some factors such as the complexity of the system when used, inconsistencies, and the need for users to adapt to using the system. This finding can be considered by the system development team to fix the weaknesses so as to create an online learning platform with a better UX.

The effect of social media environments on online emotional disclosure: tie strength, network size and self-reference

PurposeMotivating users to self-disclose online is significant to the long-term development of social media. Therefore, research on emotional disclosure, a significant form of self-disclosure, is required. By developing a stimulus-organism-response model, this study aims to investigate the mechanisms by which the social media environment affects users' online emotional disclosure.Design/methodology/approachThis paper proposes two dimensions of social media environments, the online interpersonal environment (tie strength and network size) and the online information environment (self-reference). They are hypothesized to stimulate users' internal psychological needs (image management and emotional expression) which in turn will influence their emotional disclosure intentions. Using data from 489 users of WeChat Moments, the authors conduct partial least squares analysis to validate the research model.FindingsThe findings show that users' intrinsic psychological needs are stimulated by social media environments, but network size is not correlated with the need for emotional expression. The user's need for emotional expression promotes both positive and negative emotional disclosure intentions. The need for image management has a positive impact on positive emotion disclosure intentions but has a negative impact on negative emotion disclosure intentions.Originality/valueThis study contributes to the understanding of online emotional disclosure. It can also help social media managers create efficient plans to encourage users to create content.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2022-0245.

MediBERT: A Medical Chatbot Built Using KeyBERT, BioBERT and GPT-2

The emergence of chatbots over the last 50 years has been the primary consequence of the need of a virtual aid. Unlike their biological anthropomorphic counterpart in the form of fellow homo sapiens, chatbots have the ability to instantaneously present themselves at the user's need and convenience. Be it for something as benign as feeling the need of a friend to talk to, to a more dire case such as medical assistance, chatbots are unequivocally ubiquitous in their utility. This paper aims to develop one such chatbot that is capable of not only analyzing human text (and speech in the near future), but also refining the ability to assist them medically through the process of accumulating data from relevant datasets. Although Recurrent Neural Networks (RNNs) are often used to develop chatbots, the constant presence of the vanishing gradient issue brought about by backpropagation, coupled with the cumbersome process of sequentially parsing each word individually has led to the increased usage of Transformer Neural Networks (TNNs) instead, which parses entire sentences at once while simultaneously giving context to it via embeddings, leading to increased parallelization. Two variants of the TNN Bidirectional Encoder Representations from Transformers (BERT), namely KeyBERT and BioBERT, are used for tagging the keywords in each sentence and for contextual vectorization into Q/A pairs for matrix multiplication, respectively. A final layer of GPT-2 (Generative Pre-trained Transformer) is applied to fine-tune the results from the BioBERT into a form that is human readable. The outcome of such an attempt could potentially lessen the need for trips to the nearest physician, and the temporal delay and financial resources required to do so.

Decentralized Storage for Educational Resources

The growth of blockchain over the last ten years has been astounding: from bitcoin to over 2,000 altcoins, and from decentralized electronic payments to programmable transactions, by complicated tokens and smart contracts controlled by autonomous entities. The technological aspects of blockchain are developing at the same time as the new generation of blockchain applications are also developing. This paper considers one such sphere, to develop a decentralized solution for accessing Educational Resources which will alleviate the issues due to the single point of failure of centralized systems, democratize access to educational content and provide a facile experience for users who are not exposed to web3 technologies. This platform was developed by implementing Smart Contracts using Solidity and deploying them on the Ethereum blockchain to store educational resource metadata and files on the Inter Planetary File System (IPFS). Interfacing of blockchain and the frontend is done using the web3.js. The platform is more tenacious and fault-tolerant than systems using a centralized architecture and provides a better user experience by incorporating a search mechanism for the files uploaded to the platform. This paper proposes a decentralized platform that allows users to upload educational resources that can be accessed by others and implemented a file meta-data-based search feature to remove the need for users to remember the IPFS hash of a file.

Intelligent Vision Based Decision Making System for Aviation Accidents and Incidents

Safety has become the primary concern for the air transportation system nowadays primarily due to increasing air traffic throughout the world. Various regulatory bodies have been maintaining enormous amount of aviation accidental data repositories. This past data is highly complex because of its many temporal and geographical components along with multiple variables. To be able to analyze this past data, there is always a need of user friendly and GUI based System. This article has proposed an intelligent vision-based decision-making system for the exploration of past aviation accidents and incidents dataset. The proposed visual query-based model is capable to analyse the major factors like flight phases, human factors, weather conditions and faulty components in particular aircraft models which are responsible for those unsafe events and may claim life of many passengers who are traveling and crew personnels. This model enables the users to express “what” visuals should be created instead of “how” to create them. Various case studies conducted through visual queries have proved that the system will be highly able to improve situational awareness regarding flight conditions to the crew members and air traffic controllers along with aviation authorities so that they are able to take timely decisions and deciding on what kind of training staff members need to reduce the consequences of such accidents and incidents.

Pengaruh Tingkat Pemahaman Akuntansi, Fungsi Badan Pengawas Dan Profesionalisme Terhadap Kualitas Laporan Keuangan Lembaga Perkreditan Desa (LPD) Di Kecamatan Abiansemal Kabupaten Badung

Each LPD certainly provides financial reporting that can provide the information users need. This study was conducted to determine the effect of the level of understanding of accounting, the function of the supervisory body and professionalism on the quality of financial reports in LPDs in Abiansemal sub-district. The population used in this study were 33 LPDs in Abiansemal District. The number of samples in this study was 199. The analytical technique used was multiple linear regression analysis. Based on the results of the analysis, it is known that the level of understanding of accounting, the function of the supervisory body and professionalism has a positive effect on the quality of financial reports at the Village Credit Institution or LPD in Abiansemal District.

Moderating effects of self-perceived knowledge in a relevance assessment task: An EEG study

Relevance assessment, a crucial Human-computer Information Retrieval (HCIR) aspect, denotes how well retrieved information meets the user's information need (IN). Recently, user-centred research benefited from the employment of brain imaging, which contributed to our understanding of relevance assessment and associated cognitive processes. However, the effect of contextual aspects, such as the searcher's self-perceived knowledge (SPK) on relevance assessment and its underlying neurocognitive processes, has not been studied. This work investigates the impact of users' SPK about a topic (i.e. ‘knowledgeable’ vs. ‘not knowledgeable’) on relevance assessments (i.e. ‘relevant’ vs. ‘non-relevant’). To do so, using electroencephalography (EEG), we measured the neural activity of twenty-five participants while they provided relevance assessments during the Question and Answering (Q/A) Task. In the analysis, we considered the effects of SPK and specifically how it modulates the brain activity underpinning relevance judgements. Data-driven analysis revealed significant differences in cortical electrical activity modulated by searchers' SPK in the context of relevance assessment, suggesting that SPK affects cognitive processes associated with attention, semantic integration and categorisation, memory, and decision formation that underpin relevance assessment formation. Our findings are an important step toward a better understanding of the role users' SPK plays during relevance assessment.

Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples

Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild". Our survey of real spreadsheet-tables and web-tables shows that over 30% of such tables do not conform to the relational standard, for which complex table-restructuring transformations are needed before these tables can be queried easily using SQL-based tools. Unfortunately, the required transformations are non-trivial to program, which has become a substantial pain point for technical and non-technical users alike, as evidenced by large numbers of forum questions in places like StackOverflow and Excel/Tableau forums. We develop an Auto-Tables system that can automatically synthesize pipelines with multi-step transformations (in Python or other languages), to transform non-relational tables into standard relational forms for downstream analytics, obviating the need for users to manually program transformations. We compile an extensive benchmark for this new task, by collecting 244 real test cases from user spreadsheets and online forums. Our evaluation suggests that Auto-Tables can successfully synthesize transformations for over 70% of test cases at interactive speeds, without requiring any input from users, making this an effective tool for both technical and non-technical users to prepare data for analytics.

THE IMPACT OF MACROECONOMIC INDICATORS ON BITCOIN: A CASE STUDY ON PAKISTAN

Modern-day revolution is brought on by cryptocurrencies. Both the terms cryptocurrency & currency describe means of exchanging goods & services. In system that uses cryptocurrencies, every transaction is preprogrammed. Underlying blockchain technology governs transactions by using consensus mechanisms, hash algorithms, and public and private key encryption, doing away with the need for user to have faith in the counterparty. In this study, impact of macro-factors on bitcoin in Pakistan was examined. The relevance of this study is that the majority of people are ignorant of bitcoin to spread awareness of bitcoin and how to use them among the general audience. The macroeconomic indicators (GDP, INF, Exchange, & Gold) were monitored on a quarterly basis between JAN 2016 and DEC 2021. The E-view program and numerous tests on secondary data were also used in this inquiry. In Pakistan, there is no long-term link. Bitcoin's short-term relationships with Pakistan's GDP, INF, ER, and gold. In short term, there is a link between bitcoin and the exchange rate.

Query Refinement into Information Retrieval Systems

Query, expressing the user need and requirement, has an important role, in aninformation retrieval system, for reaching a high accuracy search. In this paper, wepresent an overview of the different refinement operations that the query may undergo,in the sake to enhance performance of an information retrieval system, such as:automatic query formulation through words prevision, query reformulation, queryexpansion, and query optimization.

An Evolutionary Game Analysis of Shared Private Charging Pile Behavior in Low-Carbon Urban Traffic

Choosing new energy vehicles for travel, especially electric vehicles, is an important component of building a low-carbon urban transportation system. However, the charging need of electric vehicle users is still constrained by the unreasonable layout and insufficient supply of public charging piles in cities. Private charging pile sharing, as an alternative policy tool, can play a beneficial role in solving this problem. However, it needs decision-makers in urban transportation to take corresponding measures to promote. This paper constructs an evolutionary game model to study the decision behavior of participants in a private pile-sharing platform. Through numerical simulation analysis, it is found that under most parameter conditions, the government tends to establish a shared charging pile platform based on public interests. Private charging pile owners are influenced by the relationship between the cost of supply modification and revenue, and they tend to join the shared platform when they expect to recover the modification cost. The research conclusions of this paper will provide support for exploring how participants make decisions to maximize overall benefits in the development of low-carbon urban transportation.

A human-centred design strategy for self-training healthcare service among visually impaired persons

Challenges faced by healthcare services for visually impaired persons are becoming increasingly complex. There is a lack of interventions for people in the transitional stage of blindness (the process of vision loss). The present study described a human-centred design approach to help visually impaired persons to better adapt to the transitional stage. Fifteen visually impaired persons and their relatives were interviewed to understand user behaviors and need in the empathize phase. After discussing with stakeholders, we defined user personas and problem statements. In the ideate phase, a healthcare service, including a self-training space and a health management app, was developed to help visually impaired persons to train their auditory, tactile, and directional walking abilities in different environments through sensory compensation. The results showed a high usability score, which revealed how the human-centred design approach could help visually impaired persons to train themselves during the transitional stage effectively.

EXPANDING USER NEED FINDING THROUGH ABDUCTIVE REASONING

AbstractPrior research has shown the importance of latent user needs for enabling innovation in early product development phases. The success of a product is largely dependent on to what extent the product satisfies customer needs, and latent user needs play a significant role in impacting the way the product or service unexpectedly delights the user. Complications arise because traditional need finding methods are not able to account for the nuances of latent user needs. A user's need is multidimensional while traditional methods are built on deductive reasoning. The traditional method isolates parts of the user's needs, only pointing to what is deducible within its search space. To address this, we introduce abduction as a way to broaden traditional need finding methods. From a logic based argument it is shown that abduction accounts for the dimensionality of user needs by integrating various traditional need finding theories using design knowledge to isolate the latent need. This theoretical development shows that latent need finding must go beyond a deductive focus, to developing methods that are able to conjecture with the deduced facts in order to abduce the latent user need.

Re-Purposing the Physical Space of an Academic Library in the Digital Era: A Case Study of Jawaharlal Nehru University and Delhi University, New Delhi (India)

Objectives – The issue/return of books and other reading materials available on shelves in academic libraries has declined. Round-the-clock availability of information resources, high-speed internet, cheaper cost of data download, laptops, and smartphones are some of the reasons behind decreased usage of print reading materials. Users are spending more time studying, accessing e-resources, socializing with friends, and discussing with peer groups in the library building. Libraries in developed countries have already recognized the need of the hour and redesigned their spaces to create a variety of reading spaces, creative spaces, quiet spaces, and so forth in the existing building. But the libraries of the higher education institutions in India have not considered the users' needs concerning the library space. No such study has been conducted at large universities in India. This study was conducted with library users of the central library of two large universities located in New Delhi to find users' opinions and views to re-purpose the library space to meet users' needs. Methods – The survey method has been used to understand the use of the existing format of knowledge resources and the need of library users. The central point of the research questionnaire is to optimize the library space of the central libraries of the universities under study. Printed questionnaires were distributed to the users present in the reading halls of the libraries at Jawaharlal Nehru University (JNU), New Delhi, and Delhi University (DU), New Delhi, and online questionnaires were distributed to faculty members. The collected data were analyzed with Microsoft Excel, and various hypotheses were tested using non-parametric tests such as the sign test, Mann-Whitney U test, and Kruskal-Wallis test. Results – Students and research scholars visit daily and spend more than four hours in the library building. They use their time in study, accessing e-resources in the reading halls of the library more than any other place. The users opined the reading halls should have wi-fi facilities, a power source to charge laptops and mobile phones, washrooms near every reading hall, a quiet reading space, and a clean ambient environment for long hours of study in the library building. Conclusion – Library users in the 21st century want more physical space to study, concentrate, socialize, and learn in the informal learning environment. They need library space with the latest infrastructure to connect to the digital world to retrieve study materials and print copies of study materials. Educational institutions should create more varied reading spaces for serious reading, pleasure reading, in-depth reading, and interactive learning spaces in new library buildings.

On enhancing transmission performance for iov based on improved greedy algorithm

Nowadays, the development of 5G and IoT technology can be observed from abundance of their applications, such as VR/AR/MR, communication, healthcare, transportation and so on. Some of these applications are in need of quality improvement like delay and resource consumption decrease since the need of users and devices about the relatively developed technique gradually increase as well. About this kind of QoS improvement in the field of VANET, this paper first has a brief review on this concept and related key word and makes use of the concept of Edge Computing, eventually provides information about the research that focuses on the optimization of Task Offloading function by proposing a new algorithm. The new algorithm consists of 3 C++ programs and determines the relatively effective task offloading strategy by taking both calculating resource consumption and communication latency into consideration. In order to prove the developed performance of the proposed algorithm, paper uses other two kinds of algorithms to have comparison, finally reaches the conclusion that proposed algorithm has the best system cost performance among the comparison objects.

Extremely high thermal conductive cement-based composites with diamond/ZnO/expanded graphite thermal conductivity network for cooling road

In recent years, the deterioration of traffic safety due to high road temperatures and the increase in transport costs have led to an urgent need for road users to reduce the temperature of the road surface. In this paper, high thermal conductivity diamond/ZnO/expanded graphite (D/ZnO/EG)11Diamond: D; Expanded graphite: EG. cement-based composites were prepared by constructing and optimizing a thermal conductivity network in a cement matrix to achieve road cooling. Thermal network structure and porosity’s impact on thermal conductivity had both been investigated, and the mechanism governing thermal conductivity had also been analyzed. With a thermal conductivity of 6.338 W/(m·K), 732 % of the cement matrix, the result showed that the expanded graphite (EG) thermal conductivity network and D/ZnO significantly enhanced heat transfer. The excellent thermal transfer capacity of D/ZnO/EG cement-based composites was also shown to be beneficial in reducing pavement temperatures in the temperature distribution simulations at the road surface under solar irradiation. The above results illustrated the EG network thermal’s conductivity, the impact of D in shortening the EG spacing, and the impact of ZnO in filling the gaps, all of which were effective in enhancing thermal conductivity. In addition, D/ZnO/EG prevented crack extension and thus attenuated the damage to thermal conductivity caused by freeze–thaw cycles. This work confirms that the D/ZnO/EG cement-based composite can achieve effective heat dissipation from pavements, thereby improving traffic safety and reducing the long-term costs of road traffic.

An improved Kloubuchar ionospheric correction model for single frequency GNSS receivers

Abstract The enhancement of positional accuracy of single frequency ionospheric correction models is an urgent need for low-cost and smartphone GNSS users. The available single frequency ionospheric correction models such as Klobuchar, NeQuick-G, NTCM, and Klob-BDS are providing ionospheric corrections for multi GNSS systems such as GPS, Galileo, BDS, and NAVIC systems. Otherwise, Global Ionospheric Map (GIM) Total Electron Content (TEC) corrections are also available to the GNSS users. In this letter, an improved Klobuchar ionospheric model is implemented. The slant TEC of dual Frequency GPS TEC observations is considered a reference. The Klobuchar model slant TEC observations are improved by taking the grid-based residual TEC corrections with the Adjusted Spherical Harmonic Function model. The Single frequency users can improve the ionospheric delay estimation using Klobuchar model and grid-based TEC residual corrections. The improved ionospheric correction model is tested under the biggest geomagnetic storm conditions during 24th Solar cycle that occurred in March 2015 over India. The proposed hybrid slant ionospheric TEC algorithms are evaluated with individual single frequency ionospheric models (Klobuchar, and GPS TEC) under adverse space weather conditions.

Used Car Price Prediction using Multiple Linear Regression

Abstract: Car price prediction is an active research topic that requires a lot of data and effort. The car market is a significant industry as a result of the tremendous expansion in vehicle production in recent years. The recent emergence of online marketplaces has strengthened the need for users and sellers to stay informed about the most recent market trends related to the subject. Our goal is to create a reliable and accurate model to forecast the cost of a used car based on a set of characteristics. To this end, we are utilizing the machine learning algorithm linear regression. When estimating the potential price of a car, we consider factors such as the company name, model name, year of purchase, fuel type, and number of miles driven.

Kovai Spot Bus - Bus Recommendation System

Abstract: Kovai spot bus is a real time project which is useful for the people who is the facing problems with the current manual work of bus searching. Kovai spot bus system contains all bus with their route details. The main purpose of this software is to reduce the manual errors involved in the travel process and make it convenient for the people to manage their bus details. This project has been made simpler and interactive. This is an android application used to find out the bus number from one place to another place. User need to give the details of source and destination.

ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification

Background and ObjectiveHistopathology is the gold standard for diagnosis of many cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for many tasks, including the detection of immune cells and microsatellite instability. However, it remains difficult to identify optimal models and training configurations for different histopathology classification tasks due to the abundance of available architectures and the lack of systematic evaluations. Our objective in this work is to present a software tool that addresses this need and enables robust, systematic evaluation of neural network models for patch classification in histology in a light-weight, easy-to-use package for both algorithm developers and biomedical researchers. MethodsHere we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible evaluation toolkit that is a one-stop-shop to train and evaluate deep neural networks for patch classification. ChampKit curates a broad range of public datasets. It enables training and evaluation of models supported by timm directly from the command line, without the need for users to write any code. External models are enabled through a straightforward API and minimal coding. As a result, Champkit facilitates the evaluation of existing and new models and deep learning architectures on pathology datasets, making it more accessible to the broader scientific community. To demonstrate the utility of ChampKit, we establish baseline performance for a subset of possible models that could be employed with ChampKit, focusing on several popular deep learning models, namely ResNet18, ResNet50, and R26-ViT, a hybrid vision transformer. In addition, we compare each model trained either from random weight initialization or with transfer learning from ImageNet pretrained models. For ResNet18, we also consider transfer learning from a self-supervised pretrained model. ResultsThe main result of this paper is the ChampKit software. Using ChampKit, we were able to systemically evaluate multiple neural networks across six datasets. We observed mixed results when evaluating the benefits of pretraining versus random intialization, with no clear benefit except in the low data regime, where transfer learning was found to be beneficial. Surprisingly, we found that transfer learning from self-supervised weights rarely improved performance, which is counter to other areas of computer vision. ConclusionsChoosing the right model for a given digital pathology dataset is nontrivial. ChampKit provides a valuable tool to fill this gap by enabling the evaluation of hundreds of existing (or user-defined) deep learning models across a variety of pathology tasks. Source code and data for the tool are freely accessible at https://github.com/SBU-BMI/champkit.