Reviewerly/exHarmony huggingface dataset:
Data Gathering for Ergonomic and Design Evaluations: Issues and Pitfalls

There are moments within the process of creating an artifact, for instance at the initial requirements gathering or in the assessment phase, that input data is collected from users. There may be an impact directly on the results of the analysis of this data if, for some reason, this data input is not accurate. Another important matter when designing a new artifact is to find out the appropriate number of product reviewers that would better reflect the views of the target population to which the product is intended. In order to contribute to the strategy of increasing the accuracy of the evaluations and minimizing the chance of errors of judgment, this article identifies some case studies where the input from the users is not accurate because of the presence of a phenomenon known as Conjunction Fallacy and also where the number of product reviewers is not calculated properly. This article also presents a possible strategy to minimize errors of judgment caused by the fallacy and also presents a case study where the number of evaluators is calculated correctly by using statistical methods.
Computational Tool for Technical-Economic Analysis of Photovoltaic Microgeneration in Brazil

This paper proposes a new computational tool, called SolarEnergy, which was implemented with an innovative methodology to perform technical-economic analysis of photovoltaic microgeneration in Brazil. SolarEnergy calculates energy flow and energy bill savings based on Brazilian NEM scheme rules. From the maximization of the Net Present Value, SolarEnergy provides the optimal nominal power according to the orientation and slope of the photovoltaic panels and electricity consumption profile. This functionality is essential to avoid undersizing or oversizing, which commonly happens in practice. In addition, the tool provides the following information: i) electricity generation, ii) Net Present Value, iii) Modified Internal Rate of Return, iv) Discounted Payback Time, and v) sensitivity analysis of key parameters such as nominal power, pv system cost, electricity tariff, and minimum attractive rate of return. Currently, such particularities are characterized as relevant diferences in relation to other computational tools. The validation and applicability of SolarEnergy are demonstrated from case studies of photovoltaic systems installed in the city of Goiânia, state of Goiás, Brazil.
A User Interface with Taxonomy Features for Content Curators of Chatbots

A conversational system is a core component needed to create a chatbot, one incredibly popular form of conversational interface, without any programming knowledge, made possible by different toolkits and cloud-based Natural Language Understanding (NLU) services. However, these NLU services provide curatorial tools that do not always follow mental models resembling that of the content curators. Moreover, we present an interface for content curators that are aligned with concepts management embedded in taxonomies. We conducted a workshop to understand content creators' work practices, created a prototype interface, and conducted a user evaluation to identify usability and productivity aspects. Based on our prototype's evaluation insights, we provide implications for designing user interfaces of NLU services to support curators' work practices and enhance collaboration.
Web Brutalism: Assessing Usability of an Ugly Web

Brutalist websites are a contemporary trend that uses anti-aesthetic interface elements to create a high-impact visual product. Since the web is a communications product, this research sought to understand if the aesthetics of Brutalist sites overshadow their usability, defined as the ability of a user to effortlessly navigate through the contents of a digital interface to achieve a goal or retrieve information. Triangulation was applied to obtain a solid understanding of the problem. Consequently, three experiments were conducted on a Brutalist website using a group of thirty-one participants. First, Nielsen's Attributes of Usability Questionnaire (NAU), a test that focuses on five key aspects of usability: learning, efficiency, memorisation, errors, and satisfaction. Second, Brooke's System Usability Scale (SUS) test to gain deeper insight into the users' perspective. Third, the Thinking Aloud (TA) protocol to obtain qualitative information to complement the first two experiments. After analysing the data of the NAU and SUS experiments, a numeric score was given to the perused website to objectively evaluate its usability and assign it to five categories, ranging from Deficient (E) to Excellent (A). Those results were complemented by the TA protocol. The findings highlight difficulties to retrieve information, navigation issues, and a lack of orientation. The use of an unconventional graphical user interface impacts the user experience. This study demonstrates that heuristics are necessary to develop robust interfaces and that deviating from these norms is detrimental to the user experience. The visual features of Brutalist websites, though striking, do not deliver a user-centred project.
Measurements and Interventions to Improve Student Engagement Through Facial Expression Recognition

A major challenge for online learning systems is supporting students' engagement. Online systems are sometimes boring, repetitive, and unappealing; external distractions often lead to off-task behavior, and a decline in learning. Student engagement and emotion are also tightly correlated with learning gains because emotion drives attention and attention drives learning. In response to this challenge, we present an exploratory study using computer vision techniques and several engagement strategies to help students re-engage once their attention wanders and their head turns away, within the context of an intelligent tutoring system for mathematics. Initial results indicate that students exposed to our re-engagement strategies were more confident and more persistent. They also responded positively to our avatar or learning companion. We discuss design implications and future work.
Adaptive Resource Allocation Neural Network-Based Mammogram Image Segmentation and Classification

Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases' quality.In Medical Images, detection of breast cancer in its earlier stage is most important in this field.Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists.Hence, improvement of computer-supported strategies has been introduced for breast cancer identification.This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network (ARAN).At first, breast cancer images were taken as input, preprocessing step is utilized to eliminate the noise and unimportant data from the image utilizing a Butterworth filter.Adaptive histogram equalization is utilized to improve the contrast of the image.Multimodal clustering segmentation has been applied, and Tetrolet transformation based feature extraction is applied at various levels, based on this, data classification is implemented.For exact classification, ARAN is utilized to predict if the patient is influenced by breast cancer.Compared with other current research techniques, the proposed strategy predicts the results efficiently.The overall accuracy of ARAN-based mammogram classification is 93.3%.
Can Stock Market Fluctuations Be Predicted? Wig.Games Index Secret on the Example of CD Project

Abstract Do the announcements of listed companies issued under ESPI have an impact on the increase or decrease of stock exchange quotes? Or maybe the visual aspect of the company’s communication is more important? Can we talk about a specific „butterfly effect” when one screen before the game’s premiere or an entry in social media is able to shake the facades of the gaming giant from Poland? The company CD Projekt was subject to the audit. The basic research thesis assumes that the announcement issued by a company as part of the legally established communication of listed companies has an impact on the company’s quotation and capitalization on the market. The selected research method consists of two elements: statistical (quantitative) analysis and qualitative analysis. It is time to start the journey to the world of Cyberpunk 2077 and back.
Ordinal Classification and Regression Techniques for Distinguishing Neutrophilic Cell Maturity Stages in Human Bone Marrow

An automated classification of hematopoietic cells in bone marrow whole slide images would be very beneficial to the workflow of diagnosing diseases such as leukemia. However, the large number of cell types and particularly their continuous maturation process makes this task challenging: the boundaries of cell type classes in this process are fuzzy, leading to inter-rater disagreement and noisy annotations. The data qualifies as ordinal data, as the order of classes is well defined. However, a sensible “distance” between them is difficult to establish. In this work, we propose several classification and regression techniques for ordinal data, which alter the encoding of network output and ground-truth. For classification, we propose using the Gray code or decreasing weights. For regression, we propose encodings inspired by biological properties or characteristics of the dataset. We analyze their performance on a challenging dataset with neutrophilic granulocytes from human bone marrow microscopy images. We show that for a sensible evaluation, it is of utmost importance to take into account the relation between cell types as well as the annotation noise. The proposed methods are straight-forward to implement with any neural network and outperform common classification and regression methods.
Key Frame Extraction for Calisthenics Video by Combining Articulated Model with Dynamic Clustering Algorithm

An ISODATA-based key frame extraction framework for calisthenics video is proposed using pose estimation and feature selection techniques. Our work is motivated by object-based video segmentation. The basic idea is to compute the configuration of a body part in a single frame and to filter out key frames in a clustering-based algorithm. The appearance and deformation of each part is calculated with the mixture-of-parts articulated human model. The motion of body-part is featured with the pose configuration and optical flow-based motion direction estimation of each part together. To mitigate uncertainties of the motion features for higher performance, a feature selection based on Laplacian scoring is conducted. The framework of key frame extraction is formulated as a dynamic ISODATA-based clustering process guided by selecting the frames at the rhythm beat points of calisthenics video as initial cluster centres. The experimental data on different implementations suggest that the proposed algorithm can receive a high accuracy due to the initial cluster centres optimization and Laplacian Scoring based motion feature selection. We also compare our method with the other key frame extraction methods. The results of the experiment demonstrate the benefits of the proposed algorithm.
A Blockchain-Based Decentralized Authentication and Authorization Scheme for Mobile Cloud Computing Services

Authentication and authorization constitute the essential security component, access control, to prevent unauthorized access to cloud services in the mobile cloud computing (MCC) environments. Traditional centralized access control models relying on the trusted third party face a critical challenge due to high trust cost and single point of failure. Blockchain can achieve distributed trust without centralized authorities for access control designs in a mutual untrustworthy scenario, but it leads to expensive storage overhead. Considering the above issues, this work constructs a decentralized authentication and authorization scheme based on blockchain that can provide a dynamic update of access permissions. To improve storage efficiency, only one transaction is required to be stored in blockchain to record a userâ€™s access privileges on different cloud service providers (CSPs). Besides, mobile users in the proposed scheme are able to register with an arbitrary CSP once, and then utilize the same credential to access different CSPs with different access levels. Security analysis indicates that the proposed scheme is secure under the random oracle model. Performance analysis clearly shows that the proposed scheme has superior computation and communication efficiency, and requires low blockchain storage capacity for accomplishing necessary functions to provide scalability.
Factors Influencing Women's Participation in Economic Activities in Uganda

The main objectives of the study were to find out factors influencing women’s participation in economic activities in Uganda. Catering for both the positive and the negative factors. The positive factors such as age, education level of the women, accessibility to land, access to capital, and access to health facilities of women encouraged women to participate in economic activities while the negative factors such as illiteracy, lack of capital, men refusing their wives to work hindered women from participating in economic activities. However, despite government’s efforts to promote women emancipation and gender equality, women are still lagging behind in the participation of economic activities. It was found out that age, education, and access to land positively influenced the participation of women in economic activities. This was mainly because women who were mature in age had more responsibilities as compared to those who were young in age. Secondly, women who were educated had more knowledge and skills to enable them participate in economic activities as compared to the uneducated. Lastly, access to land increased productivity and was used as collateral to acquire credit. However, despite access to capital being one of the major factors, it negatively influenced women’s participation in economic activities since women feared these loans because of high interest rates and miss-use by their husbands. Therefore, Government should emphasize more facilitation of women’s education to enable them acquire the necessary skills that would help them participate fully in economic activities and enable them empower the girl child who has dropped out of school to further her education.
Multi-user Multi-platform xR Collaboration: System and Evaluation

Virtual technologies (AR/VR/MR, subsumed as xR) are used in many commercial applications, such as automotive development, medical training, architectural planning, teaching and many more. Usually, existing software products offer either VR, AR or a 2D monitor experience. This limitation can be a hindrance. Let’s draw a simple application example: Users at a university shall join an xR teaching session in a mechanical engineering lecture. They bring their own xR device, join the session and experience the lecture with xR support. But users may ask themselves: Does the choice of my own xR device limit my learning success? In order to investigate multi-platform xR experiences, a software framework was developed and is presented here. This allows one shared xR experience for users of AR smartphones, AR/MR glasses and VR-PCs. The aim is to use this framework to study differences between the platforms and to be able to research for better quality multi-user multi-platform xR experiences. We present results of a first study that made use of our framework. We compared user experience, perceived usefulness and perceived ease of use between three different xR device types in a multi-user experience. Results are presented and discussed.
A Survey: Handwriting Analysis Software Using Image Preprocessing and Machine Learning

This study is about an application that will perform Handwriting Analysis using Machine Learning. This application aims to input an image of a handwritten text and perform Handwriting Analysis on it to produce a brief report about the writer's personality traits. There are many areas where Handwriting Analysis is required, but it is not possible to consult a professional. This application will be used as a substitute for such situations.
Improving Document Evolvability Based on Normalized Systems Theory

During the last decade, there was a huge shift in the problem of evolvability. However, still, the problem remains fully unresolved. Our focus is to improve evolvability in the domain of data stewardship planning. The main problem causing the low evolvability is the use of traditional documents – data management plans. Our approach redesigns the workflow of creating the data management plan with a possibility to extend an application to similar well-structured documents in different domains. We base our approach on principles and recommendations from the Normalized Systems Theory and on the usage of ontologies. The result is a new workflow that should increase the evolvability compared to the current state.
Explanation using model-agnostic methods

Interpretable machine learning has become a hot topic as the complexity and capacity of machine learning models grow. However, these big and complex models, often referred to as black box models because their essence is difficult to understand, still boast high performance that cannot be given up. Model-agnostic methods are an approach to understand the predictive response of a black box model, instead of the response from the original dataset. This chapter first categorizes model-agnostic methods in two ways. They are first categorized by the goal of understanding such as analyzing marginal effect, measuring contribution of each feature, and finding a surrogate model. Then, they are categorized into global or local area of interest. This chapter discusses the focus and strategy in each of six domains. Representative outputs in six domains are demonstrated through a lab session using the statistical software R with the sophisticated package iml.
Panoramic Stitching of 4-Channel Dynamic Video Platform Based on Pinhole Lens

The surveillance system composed of a single camera has problems such as limited field of view and too large blind spots. The key task of this technology is to complete the real-time splicing of videos captured by multiple cameras. For this reason, we propose an algorithm for a multi-channel video splicing and fusion system, the basic idea is to use the camera as the center point and a virtual unit sphere or cylinder. The images formed by each lens are projected onto the virtual surface according to the principle of “pinhole imaging”, and the overlapping areas are merged to form a complete three-dimensional image. Then it is converted into a flat image representation through the 3D to 2D projection method we designed to form a surround-view image. Considering that real-time fusion of multi-channel video requires heavy computation consumption, we chose HiSilicon’s new generation of on-chip processing system chip (SOC) Hi3519A100 as the computing platform. This chip has powerful video encoding/decoding capabilities and supports up to 8192 * 8192 resolution encoding, supports up to 5 video inputs. In addition, we will introduce the selection of other main components later.
Effect on Positive Behavioral Movement Expression Activities of People with Intellectual Developmental Disabilities Using Kinect in VR (Virtual Reality)

The purpose of this study is to investigate the effect of using Kinect in VR (virtual reality) on the positive behavior and movement expression activities of intellectually disabled people. The game design and production was carried out using the Kinect movement expression activity program. Game animation images, musical rhythms, and videos were used, and the researcher set up the movement analysis work himself. For 10 weeks, 10 people with intellectual disabilities participated, and positive behaviors of resilience participated in the self-evaluation of the participants. The results of the difficulty and satisfaction of the movement (out of 1 to 5) showed high difficulty with 3 points and satisfaction with 4.6 points. These results show that the participants' concentration and the interaction of positive movement expression activities with the people around them are well represented as a program approach to the participants' favorite game.
Soft Splicing Model: Bridging the Gap between Composite Model and Finite Mixture Model

Considerations of both the heavy-tail phenomenon and multi-modality of a claim severity distribution have been challenging in the actuarial literature and practices. In this article, we develop a novel class of soft splicing models that bridges the gap between pre-existing methods for handling the issues above. The proposed method is flexible enough to incorporate tail-heaviness and multi-modality with computational efficiency and nests finite mixture models and splicing models as its special and/or limiting cases. The soft splicing model is also more robust in extrapolating the tail-heaviness of distribution subject to model contamination. According to simulation studies and real insurance claim data analyses, it is shown that the proposed soft splicing model provides superior goodness-of-fit and more accurate estimates of tail risk measures than both finite mixture and composite models.
Realtime Activity Recognition Using LSTM and Smartwatch Sensor Data

The aim of this work is to gain knowledge about finding, providing, and classifying interaction and health data during the course of disease of people suffering from dementia. In the following, we present a prototype that records interaction data of dementia patients using the smartwatch “Apple Watch Series 6” and that uses a recurrent neural network to provide information about the respective activity in real time. Based on three very similar activities, we systematically compare the prediction accuracy of different sensors in combination with a recurrent neural network.
An End-to-end Weakly-supervised News Aggregation Framework

On the Internet era, there are plenty of important events hidden in the mass of news media. Using automated tools to aggregate the valuable media news relative to those events is meaningful for specific media data. In this paper, we present an end-to-end weakly-supervised news aggregation framework that enables tracking the evolution of topics from news. The framework combines Snorkel-based weakly-supervised classification, Latent Dirichlet Allocation (LDA) topic modeling, and topic signal detection model to classify and aggregate unlabeled news texts and ultimately generate visualized results containing news categories, news topics, and temporal topic relationships. This paper uses constructed knowledge thesaurus and the Snorkel method to weakly supervise the classification of unlabeled news with no manual tagging. Subsequently, we utilize LDA to generate the topics and obtain the signal value of each topic based on the topic signal detection function. Finally, we establish the temporal topic relationships and get the visualized results of news aggregation. One news dataset from one channel of Sina.com is used to test the framework. We obtain both the classification and aggregation of news, and generate the visualized results of the topics with topic signal and temporal evolution relationships.