Reviewerly/exHarmony huggingface dataset:
Actions for Sustainability: The implementation of Recycling Thermoplastics in Furniture Design for Painting Community in Jakarta

Abstract Concern for the environment, especially the pile of plastic waste, is an issue that needs attention, especially for designers and the communities. How can both of them contribute to environmental care through the application of sustainable design into existing facilities in the community? The focus of this paper is how the implementation of thermoplastics recycled materials (HDPE) for furniture in the painting community can be an inspiration and educational value for the painting community's action in sustainability. The paper will be discussed the design process of design inspiration with the implementation of thermoplastics (HDPE) as a recycled material that can be an inspiration for the painting community and analyze the application of thermoplastics (HDPE) as recycled materials in the design process carried out. The research was conducted in descriptive qualitative research consists of literature review on sustainable design, thermoplastics (HDPE), recycled materials, and painting community activity and by doing some observation on related design implementation and how the design process could extend the series of furniture for painting community. The results of this paper are how the furniture design can be used by the painting community as an inspiration and educational action for sustainability. The resulting design is expected to be an inspiration and educational value for other communities and society on how thermoplastics (HDPE) as recycled materials from waste products can be implemented for sustainable living.
Political Sentiment Analysis: Case Study of Haryana Using Machine Learning

Textual data is growing rapidly in the world as a culmination use of social media platforms including Twitter, Facebook, Whatsapp, Web 2.0, web articles and micro-blogging site. More than 70% of people spend most of their time on social networking for personal communication and opinion sharing. They share opinions about products, services and general or political events between users. Text data has been used in opinion mining and sentiment classification to know what people thinking about product and political parties. Social networking site produces a huge amount of unstructured text daily. Processing and extraction of information from unstructured text, Machine learning, Natural Language Processing (NLP), information recovery (IR) and data mining are essential. Twitter is the best source of unstructured data and is used for analysis of emotion and extraction of view. The main objective of this study, forecast the result of voting in Haryana from the tweets written in the English language. We used the Twitter TAGS Archiver tool and Twitter API using R to get the tweets. R is a very strong programming language and is most probably utilised in data interpretation and sentiment analysis. This present study uses a corpus base and dictionary-based method to find the sentiment of tweets. This study attempts to ascertain the mood of Twitter users in Haryana in direction of the political party and quantifies the sentiment as positive, negative, or neutral.
Rating the Dominance of Concepts in Semantic Taxonomies

The descriptive concepts of “semantic” taxonomies are assigned to content items of the publishing domain for supporting a plethora of operations, mostly regarding the organization and discoverability of the content, as well as for recommendation tasks. However, either not all publishers rely on such structures, or in many cases employ their own proprietary taxonomies, thus the content is either difficult to be retrieved by the end users or stored in publisher-specific fragmented “data-silos”, respectively. To address these issues, the modular and scalable “Dominance Metric” methodology is proposed for rating the dominance and importance of concepts in semantic taxonomies. Our proposed metric is applied both on the vast multidisciplinary Microsoft Academic Graph Fields of Study taxonomy and the MeSH controlled vocabulary in order for their enhanced and refined versions to be produced. Moreover, we describe the cleansing process of the resulting taxonomy from Microsoft’s structure by deduplicating concepts and refining the hierarchical relations towards the increase of its representation quality. Our evaluation procedure provided valuable insights by showcasing that high volume, namely the number of publications a concept is assigned to, does not necessarily imply high influence, but the latter is also affected by the structural and topological properties of the individual entities.
Thermal Array Sensor Resolution-Aware Activity Recognition using Convolutional Neural Network

Human activity detection and classification (HADC) has become a growing research issue due to the development of sensor technologies, deep learning models, and the need for the safety of people in smart spaces such as buildings and factories. Various researchers have employed sensors with different resolutions for HADC. However, the impact of sensor resolution on the sensor data quality and the accuracy of the deep learning algorithm in this field has been little discussed. In this work, the impact of three different thermal sensor resolutions was investigated while proposing a convolutional neural network (CNN). The results showed that the proposed CNN displayed a resolution-aware performance, being able to contain the impact of a change in thermal sensor resolutions. Although the CNN model had lower accuracy as the resolution was changed from 32x32 to 4x4H and 4x4, respectively. However, it was able to reduce the type errors and maintain an average accuracy of 82.74%.
Hybrid Restricted Boltzmann Machine– Convolutional Neural Network Model for Image Recognition

Convolutional Neural Networks (CNNs) have become a standard approach to many image processing dilemmas. Consequently, most of the proposed CNN architectures tend to increase the model deepness or layer complexity. Thus, they are composed of many parameters and need considerable computing resources and training examples. However, some recent works show that either shallow neural networks or architectures without convolutions can achieve similar results with these models often being used in systems with limited resources. Consideration of these aspects led us to a relatively simple preprocessing layer that increases the accuracy of CNN or may reduce its complexity. The layer is composed of two parts: the first is used to transform RGB data to binary representation, the second is a neural network that transforms the binary data into a multi-channel, real-value matrix and is trained in a fully unsupervised manner. Our proposal also includes a metric that may be used for measuring the similarity of training data, with the latter proving useful when performing transfer learning. Our experiments show that the resulting architecture not only helps to improve accuracy but is also more robust to image noise, including adversarial attacks, when compared to state-of-the-art models.
Efficiency and Quality-of-Life in Using an Eye-Controlled Wheelchair: A Case Study

Background: Mostly, paralysis individuals are incapable of controlling an electric wheelchair using a usual method that can be a joystick, pressing the key buttons available on the controller to move the wheelchair depending on the level of paralysis. In such a case, an eye-controlled wheelchair can be utilized as it functions to facilitate mobility assistance for paralyzed or elderly individuals with limited movements within their natural environment. Objective: This study aimed to analyze the relationship between the efficiency and quality-of-life of the subjects after using the eye-controlled wheelchair. Method: This case study is participated by 2 paraplegic neurological disorder patients from a local home-care setting. To achieve the research aim, online questionnaires via Google Form were administered verbally after the researcher&#x2019;s eye-controlled wheelchair usage and subjects&#x2019; feedback was filled. The total workload needed for wheelchair usage was measured using the NASA-TLX tool, and, a self-designed questionnaire was developed and validated (face and content validation) before the commencement of the study to measure subjective quality-of-life. Results: In this research, it is found that low exhaustion levels were achieved as a result of low subjective workload when using the eye-controlled wheelchair. Plus, there was a positive correlation between quality-of-life and total subjective workload. Conclusion: The eye-controlled wheelchair is efficient to use and demonstrated that there is a positive impact on paraplegic patients&#x2019; quality-of-life to a certain extent even for short-term usage.
Multimodal Data Fusion

In the era of big data, a large number of multimodal data are constantly generated and accumulated. In the practical application analysis of multimodal big data, the modal incompleteness, real-time processing, modal imbalance and high-dimensional attributes pose severe challenges to the multimodal fusion method. In view of the above characteristics of multimodal data and the shortcomings of existing multimodal fusion algorithms, this chapter studies the multimodal data fusion algorithms from four aspects: incomplete modal analysis fusion, incremental modal clustering fusion, heterogeneous modal migration fusion and low dimensional modal sharing fusion. A series of multimodal data fusion models and algorithms are designed.
Transductive Learning Based on Low-Rank Representation with Convex Constraints

Transductive learning is a problem to predict labels of unlabeled data exploiting both of labeled and unlabeled data. There are various methods for transductive learning, which are often variants of existing machine learning methods. In this paper, we use one of the existing unsupervised methods, row-rank representation (LRR), for transductive learning. The proposed method consists of two phases: clustering and classification. In the clustering phase, we apply a revised LRR to the data set including both of labeled and unlabeled data. Then, we obtain a modification of the data set, which reflects cluster structure behind the data set. In the classification phase, we classify unlabeled data by using the modified data set obtained in the clustering phase. We use a classification method which is inspired by LRR. That is, for each class, we approximate each unlabeled data point by the labeled data set of the class, then classify the point to the class with the smallest approximation error. Finally, we examine performance of the proposed method by numerical experiments.
Residual Channel Attention Connection Network for Reference-based Image Super-resolution

Compared with single image super-resolution (SISR), reference-based image super-resolution (RefSR) utilizes additional references (Ref) to recover more realistic texture details, achieving better reconstruction performance. Most recent works focus on transferring relevant texture features from Ref to low-resolution (LR) images. However, those works ignore the high-frequency information existing in the LR space, leading to performance degradation when irrelevant Ref images are given. To address this issue, we propose a residual channel attention connection network for reference-based image super-resolution (RCACSR), which fuses valuable high-frequency information in LR space with high-resolution (HR) texture details of Ref. Specifically, the proposed residual channel attention connection network (RCACN) can extract more complex features from the LR space. Moreover, an enhanced texture transformer is presented, which can search and transfer texture features more accurately from Ref. Extensive experiments have demonstrated that the proposed RCACSR is superior to the state-of-the-art approaches in the aspects of both quantitative and qualitative measurements.
MetaBayes: A Meta-Learning Framework from a Bayesian Perspective

Meta-learning is a powerful learning paradigm in which solving a new task can benefit from similar tasks for faster adaption (few shot learning). Stochastic gradient descent (SGD) based meta learning has emerged as an attractive solution in the few-shot learning. However, this approach suffers from significant computational complexity due to the double loop and matrix inversion operations which incurs a significant amount of uncertainty and poor generalization. To achieve lower complexity and better generalization, in this paper, we propose MetaBayes, a novel framework that views the original meta learning problem from a Bayesian perspective where the meta-model is cast as the prior distribution and the task-specific models are viewed as task-specific posterior distributions. The objective amounts to jointly optimizing the prior and the posterior distributions. With this, we obtain a closed-form expression to update the distributions at every iteration, to avoid the high computation cost issue of SGD based meta learning, and produce a more robust and generalized meta-model. Our simulations show that tasks with few training samples achieves higher accuracy when MetaBayes prior distribution is used as an initializer compared to the commonly-used Gaussian prior distribution.
Assessment of an ant-inspired algorithm for path planning

The demand for path planners for a variety of applications has significantly increased over the past decade. The correct choice of a distance metric will be of utmost importance for an efficient path planner. The underlying connectivity of the roadmaps produced by the planner are determined by the metrics. A study was conducted in this chapter for the proper choice of planner metrics. Five metrics from the literature were chosen and implemented in a gain-based ant colony optimization (GACO) algorithm. Results are analyzed against parameters, such as time taken, length of the path, and turn characteristics. Finally, the GACO with the chosen metric was implemented using different satellite images from the International Society for Photogrammetry and Remote Sensing and compared against existing algorithms with respect to performance.
Model selection and explainability in neural networks using a polytope interpolation framework

Modern machine learning systems based on neural networks have shown great success in learning complex data patterns while being able to make good predictions on unseen data points. However, the limited understanding of these systems hinders further progress and application to several domains in the real world. This predicament is exemplified by time consuming model selection and the difficulties faced in predictive explainability, especially in the presence of adversarial examples. In this paper, we take a step towards better understanding of neural networks by introducing a local polytope interpolation method. The proposed Deep Non Negative Kernel regression (NNK) interpolation framework is non parametric, theoretically simple and geometrically intuitive. We demonstrate instance based explainability and develop a method to identify models with good generalization properties using leave one out estimation.
Reversible Audio Data Hiding using Samples Greatest Common Factor and Audio Interpolation

The rapid development of information technology and the massive exchange of information have resulted in crimes like unauthorized access to personal information. This condition sometimes leads to high vulnerability to data theft. Thus, it is necessary to provide more excellent protection for personal data. Consequently, personal data protection has become increasingly important in this digital age. Embedding data into a medium, also known as steganography, is one of the methods for ensuring data security. Some of the different types of data hiding mediums include images, video, and audio. A payload or a secret message is inserted into an audio sample to hide data from listeners. The audio that has been inserted with the message should be of a similar quality to the original audio source. For this purpose, we design a steganography algorithm that uses the Great Common Factor between the original audio sample and the interpolated audio sample to determine where the sample space should be located in the interpolated audio sample. This method results in a higher capacity of an inserted message while maintaining the quality of the media and more efficient in execution time.
Model-based Test Effort Estimator - a Case Study

As per the surveys conducted over a period of time, the top four reasons for failure of software projects are functionality issues, deadline misses, quality issues and budget overruns. Customer goodwill, which is extremely important for a software firm's continued operation, is greatly affected by the product quality. The two main strategies used to achieve quality are, improving the development processes and comprehensive testing of the product based on an accurate test plan. A realistic planning requires a dependable effort estimation of the testing activities. This work presents an automated mechanism for estimation of test effort of Object Oriented Systems (OOS) leveraging the widely accepted Class UML models. Also presented are the steps of estimation for a contemporary web-based case study project undertaken in a large public sector IT firm. The results obtained confirmed the validity and accuracy of this approach.
Mathematical Formula Image Screening Based on Feature Correlation Enhancement

There are mathematical formula images or other images in scientific and technical documents or on web pages, and mathematical formula images are classified as either containing only mathematical formulas or formulas interspersed with other elements, such as text and coordinate diagrams. To screen and collect images containing mathematical formulas for others to study or for further research, a model for screening images of mathematical formulas based on feature correlation enhancement is proposed. First, the Feature Correlation Enhancement (FCE) module was designed to improve the correlation degree of mathematical formula features and weaken other features. Then, the strip multi-scale pooling (SMP) module was designed to solve the problem of non-uniform image size, while enhancing the focus on horizontal formula features. Finally, the loss function was improved to balance the dataset. The accuracy of the experiment was 89.50%, which outperformed the existing model. Using the model to screen images enables the user to screen out images containing mathematical formulas. The screening of images containing mathematical formulas helps to speed up the creation of a database of mathematical formula images.
A Quantitative Performance Evaluation Of Machine Learning Algorithms For Analysing Sentiments Of Emoticons

Twitter is one among the most popular platforms for people to express their opinions on a certain issue or product. As it consists of a multitude of users, Twitter is a significant source of data for companies to analyze product reviews, or general public opinion. On Twitter, a tweet can consist of 280 characters. Due to this limitation, users have resorted to using emoticons in a more efficient fashion. This has been achieved by replacing multiple words that indicate an emotion, with a single emoji. Traditional sentiment analysis algorithms and classifiers are highly efficient for usage on text data that does not contain any emojis. This paper evaluates the existing sentiment analysis algorithms in their capability to analyse emoticons in text.
Beyond the Hausdorff metric in digital topology

Two objects may be close in the Hausdorff metric, yet have very different geometric and topological properties. We examine other methods of comparing digital images such that objects close in each of these measures have some similar geometric or topological property. Such measures may be combined with the Hausdorff metric to yield a metric in which close images are similar with respect to multiple properties.
Aging in Smart Buildings: How Can Smart Building Technologies Support the Cognitively Impaired Aging Population?

In recent years, emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) have broadened the horizon for innovative applications of smart buildings, supporting people with cognitive impairment to live more safely and independently. Currently, researchers and industry professionals need a state-of-the-art overview of the smart building innovations that can be used to facilitate the independence of the cognitively impaired population. This paper aims to evaluate and summarize the existing and potential smart building applications that can improve the welfare of cognitively impaired occupants by offering them supports in their daily lives. This review finds that most contributions have been toward data analytics for behavioral recognition to identify assistive tasks and to provide guidance. More smart innovations of the built environment are in need to provide assistive services and compensate for the intelligence shortfall of occupants who are suffering from cognitive-related dysfunctions. Especially, user-centered designs are needed to improve the smart buildings’ acceptability and usability.
Interpretable deep learning models for better clinician-AI communication in clinical mammography

There is increasing interest in using deep learning and computer vision to help guide clinical decisions, such as whether to order a biopsy based on a mammogram. Existing networks are typically black box, unable to explain how they make their predictions. We present an interpretable deep-learning network which explains its predictions in terms of BI-RADS features mass shape and mass margin. Our model predicts mass margin and mass shape, then uses the logits from those interpretable models to predict malignancy, also using an interpretable model. The interpretable mass margin model explains its predictions using a prototypical parts model. The interpretable mass shape model predicts segmentations, fits an ellipse, then determines shape based on the goodness of fit and eccentricity of the fitted ellipse. While including mass shape logits in the malignancy prediction model did not improve performance, we present this technique as part of a framework for better clinician-AI communication.
Managing Increased Cognitive Load in a Guided Search

In the Sternberg item recognition task and its variants, an individual's mean reaction time increases with the number of items to be retained in the memory set. An increase in reaction time has also been seen when a secondary task was added. The usual interpretation for this increased reaction time is that adding cognitive load makes tasks more difficult. In a series of three experiments, we manipulated cognitive load through increases in the memory set or through a second task. In each experiment, high cognitive load was associated with higher mean response times but a reduced slope, based on the target position in a series of probes. Thus, in a Sternberg task with multiple word targets and multiple word probes, participants searched more efficiently per probe under high load than under low load. This pattern was replicated with the addition of a working memory task requiring participants to calculate a cumulative price based on the price per target word item. By considering both initial response times and reaction time slopes in large memory sets, this study provides a challenge to the traditional interpretation of cognitive load effects on search performance.