Reviewerly/exHarmony huggingface dataset:
Sound-Based Thinking and Design Practices with Embodied Extensions

The discourse surrounding intangible materials in interaction design is often directed toward computational materials [2, 9], however, this studio focuses on sonic and electromagnetic fields as intangible materials with distinctive qualities and methods of interaction. Participants explore the notion of extended body by augmenting their natural hearing abilities through body-space-object interactions. Using analog and radio-frequency (RF) sonic extenders, participants direct, block, amplify, and filter sounds, and perceive the surrounding electromagnetic landscape, thereby creating a "super sense" of heightened audition. This sonic experience explores the sensorial possibilities of the future body, where aural augmentation could take place. Using soundwalking and soundmapping as methods, participants explore transitive sonic forms that change their qualities and content over time in downtown Sydney. Participants produce a collective soundmap identifying embodied sonic extensions and acousmatic techniques, along with movements, gestures, and choreographies. This data will be used to stimulate a final discussion.
Novel Method of Knowledge Database Data Mining by Association Rules Extraction Technology in Decision Tree

The extraction method of association rules is to find frequent itemset pattern knowledge from a given dataset. In the decision tree method, the example set is regarded as a discrete information system, and its information is represented by information entropy. In this paper, the theory and algorithm of association rules in data mining technology and decision tree are systematically studied, the theoretical model is established, the corresponding association rules mining algorithms are designed, and the simulation experiments of these algorithms are carried out. The paper presents novel method of knowledge database data mining by association rules extraction technology in decision Tree.
A Comparative Evaluation of Classification Algorithms for Sentiment Analysis Using Word Embeddings

Sentiment Analysis is an increasing field of research that lies at the intersection of many fields such as Natural Language Processing (NLP), Computational Linguistics and Machine Learning. It is concerned with the extraction of sentiment polarity conveyed in a piece of text. Furthermore, one of the most influential recent development in NLP is the use of word embedding or word distributing approach, it is a current and powerful representation to capture the closest words from a contextual text. In this paper, we investigate enhancing sentiment analysis system tailored to the Arabic language by applying word embeddings and evaluating 9 classification algorithms performance (Gaussian Naïve Bayes, Nu-Support Vector, Linear Support Vector, Logistic Regression, Stochastic Gradient Descent, Random Forest, k-nearest neighbors, Decision Tree, AdaBoost). Then the report obtained improved accuracy for Arabic Sentiment Analysis on different datasets. We find that Logistic Regression classifier followed by SVM and AdaBoost classifiers outperforms the other classifiers.
Patient Entity Recognition by Automatic EHR Context Understanding and Deep Learning

Patient entity recognition or patient entity extraction is to detect the relevant electronic health records (EHRs) across multiple data sources belonging to an identical patient, and to link the relevant data together. Patient entity recognition is a useful technology for cross-system electronic health data analysis to define commonality, to synthesize multiple data sources, and to reduce data redundancy. In this paper, we propose a deep learning solution, a sequential LSTM + Word Embedding network model (WE + LSTM) to filter and represent the non-structured electronic health records by measuring their context similarity and link them to the identical patient entities. The text context features are at first filtered by a trained bidirectional LSTM network to filter the irrelevant patient entities, and the related patient information context is estimated by a trained shallow word embedding network for its word vector similarity with the existing entities in the database. Finally, the new input patient data will be linked to the existing patient entity in the dataset with the greatest context similarity. Our hypothesis is that the records pointing to the identical patient have closest context similarity, so the patterns can be encoded by a trained word embedding network. An infection disease registration dataset (5304 patient entities) is used to evaluate the performance of the proposed WE+LSTM model. The classification accuracy is 0.837 and the F score is 0.843, which is the highest compared to the comparators including a single word embedding model, a random forest model, and a conventional neural network model. In addition, the WE + LSTM model has the greatest AUG area when the ROC of the four models are compared. This result indicates the proposed WE + LSTM model provides a feasible solution to correctly recognize the patient identities from electronic records by measure the text context similarity. It provides a solution for patient identity recognition through multi-source health big data integration, which is an urgent task for health big data projects.
A Comparative Study on PID and Interval Type 2 Fuzzy Logic Controllers in a Polypropylene Chemical Plant in India via Simulation

Polypropylene (PP) underwent phenomenal growth in production and use throughout the world during the latter half of the 20th century. In this paper, we have developed a comparative revision for simulation models proportional-integral-derivative (PID) and Fuzzy type-2 controllers (FT2) during PP production. During PP production, the temperature is maintained by men or workers. That work will be fulfilled by PID and FT2 controllers. The proposed models consist of two inputs (temperature and flow-rate) parameters and two outputs (cold water valve, Hot water valve), which are controlled by PID and FT2 controllers. A proportional study has been made first time in the field of PP production and statistical data analysis is performed with some sensitivity analyses.
Making Sense of Economics Datasets with Evolutionary Coresets

Machine learning agents learn to take decisions extracting information from training data. When similar inferences can be obtained using a small subset of the same training set of samples, the subset is called coreset. Coresets discovery is an active line of research as it may be used to reduce the training speed as well as to allow human experts to gain a better understanding of both the phenomenon and the decisions, by reducing the number of samples to be examined. For classification problems, the state-of-the-art in coreset discovery is EvoCore, a multi-objective evolutionary algorithm. In this work EvoCore is exploited both on synthetic and on real data sets, showing how coresets may be useful in explaining decisions taken by machine learning classifiers.
Multiple Face Image Feature Extraction Using Geometric Moment Invariants Method

Research on human facial expression recognition has become a growing field. One important step in the recognition of facial expressions is feature extraction. This research uses Geometric Moment Invariants (GMI) as a feature extraction method. Research on facial expression recognition using either the GMI method or another method use single face image as the dataset. Therefore, in this study uses GMI feature extraction to classify facial expressions on multiple face images. Face detection process uses Viola-Jones method on OpenCV and classification process uses Multi Class SVM method. The results are features for each expression and a small average accuracy of 5 times. Therefore, the classification is also done with the k-fold cross validation technique with another classification method. The average accuracy results are still small. It caused by the training image also using outer area of face in the image, so the background included as the image features. It is tested from k value 2 to10, and produce Multi Class SVM 10.2%, Decision Tree Classifier 14.73%, Random Forest Classifier 14.78%, Gaussian Naive Bayes 14.73%, Nearest Centroid 14.66%, MLP Classifier 11.09%, and Stochastic Gradient Descent Classifier 14.19%. The highest accuracy result is Random Forest Classifier method 14.78%. In Random Forest method, the best k value obtained is 4 with an average accuracy 16.18%.
Integrate and Apply the Recommendation System of Agile Methods

Software development is an organized process that thrives to deliver products in faster, better and cheaper ways. There have been many studies and suggestion in improving the development process. Recently, this interest has paved way to a new software development method called Agile Software Development. Agile methods strive to deliver small sets of software features to customers as quickly as possible in short iterations. As part of this article scope most commonly used methods will examined from the angle of their applicability, strengths and weaknesses and their adoption in industry. This will lead us to find benefits, limitations and difficulties in agile software development.
Snake Fruit Classification by Using Histogram of Oriented Gradient Feature and Extreme Learning Machine

Snake fruit, or most famous as Salak, is Indonesian local fruit. Salak is also one of fruit commodity from Indonesia. To perform export on Salak, rigid sortation is performed. The sortation is usually done manually. This study will implement digital image processing technique to differentiate Salak quality for export purpose. Salak sample were taken from Magelang district, one of the largest Salak producer. The feature used in this study is Histogram of Oriented Gradient. The classification used is Extreme Learning Machine (ELM). It is shown in this study that by using ELM, the highest accuracy can be achieved is 95%. A comparison classifier, SVM, is also used in this study. In this case SVM is able to achieve highest accuracy of 97.3%, which is still higher than ELM result.
The digital Hopf construction

Various concepts and constructions in homotopy theory have been defined in the digital setting. Although there have been several attempts at a definition of a fibration in the digital setting, robust examples of these digital fibrations are few and far between. In this paper, we develop a digital Hopf fibration within the category of tolerance spaces. By widening our category to that of tolerance spaces, we are able to give a construction of this digital Hopf fibration that mimics the smooth setting.
A scheme for anomalous RFID trajectory detection based on improved clustering algorithm under digital-twin-driven

Anomaly analysis of trajectories is one of the means to maintain indoor safety. Effective track anomaly detection should be based on the current road network structure. However, the indoor environment usually contains many obstacles flexible to move. The changes in the positions of obstacles frequently cause the changes of the road network structure. When the road network changes, it takes time and effort to manually redraw the road network and simultaneously losses the guarantee of real-time performance. At the same time, manual drawing is forbidden in some confidential places. In our paper, the scheme of RFID track anomaly detection combined with digital-twin technology is proposed to provide a real-time actual road network for anomaly analysis. The accurate mapping and virtual simulation functions of digital-twin technology are used to achieve the dynamic real-time rendering and the maintenance of the indoor road network structure. We also improve a clustering algorithm to make it suitable for indoor RFID track clustering. In our paper, deviation thresholds and digital-twin model are used to detect anomalies. The trajectory is judged to be abnormal when it appears in the restricted areas or exceeds the deviation thresholds in any terms of position, velocity, or direction. We use an improved scan-line algorithm and F1-scores to determine the threshold values. Our proposed anomaly detection method provides more effective and intuitive results for researchers to analyze.
A Highly-Effective Approach for Generating Delaunay Mesh Models of RGB Color Images

A highly effective method for generating Delaunay mesh models of RGB (i.e., red-green-blue) color images, known as CMG, is proposed. This method builds on ideas from the previously-proposed GPRFSED method of Adams for grayscale images to produce a method that can handle RGB color images. The key ideas embodied in our CMG method are Floyd-Steinberg error diffusion with improved initial-condition selection and greedy-point removal. Through experimental results, our CMG method is shown to outperform several competing methods that are based on a straightforward extension of grayscale mesh generators to color, with our method yielding meshes of vastly better quality at lower or comparable computational/memory cost.
Design of Radiation-Hardened Image Compressor Based on Lossless JPEG-LS

Image compressor in aerospace application is susceptible to single event effect, causing encoder logic errors and system errors. Therefore, the image compressor in aerospace application must be specially designed to improve the reliability of image information. Compression efficiency of JPEG-LS is higher than JPEG2000 and compression effect is better than lossless JPEG. Especially, JPEG-LS algorithm has the characteristics of low complexity and easy hardware implementation, which will save hardware resources of spaceborne system. An image compressor based on lossless JPEG-LS algorithm is designed in this paper, which can correctly compress 512×512 8-bit wide grayscale images. It is hardened by three-mode redundancy, Hamming code, timeout detection, one-hot code, and inter-process independently. The results point out that the error rate caused by SEU is significantly reduced 19.48% using the hardened compressor in this paper.
An Approach for Image Search and Retrieval by Cluster-Based Indexing of Binary MKSIFT Codes

Abstract Image search is an information retrieval approach that gained remarkable attention in the areas like multimedia and computer vision. The first work presented a cross-indexing-based approach for image retrieval using multiple kernel scale-invariant feature transform (MKSIFT), where the key point descriptor was calculated using two kernel functions. Previous work had complexity issues while dealing with the large databases, and hence, to avoid this, cluster-based indexing of binary MKSIFT codes is presented. The proposed cluster-based indexing scheme uses the MKSIFT feature extraction and the Particle Swarm-Fractional Bacterial foraging optimization algorithm for extracting the useful features from the images. Also, the Bayesian fuzzy clustering scheme is employed for grouping the images in the database into several clusters. The search index is constructed for the user query, and Bhattacharya distance between the cluster centroids and the search index is calculated to identify the optimal cluster. Then, finally, the images present in the optimal centroid are retrieved. The experimentation of the proposed cluster-based indexing scheme is analysed for the various query from the users. From the analysis, it is evident that the proposed cluster-based indexing scheme has achieved improved performance with the mean values of 0.9656, 0.9489 and 0.9049, for precision, recall and F measure, respectively.
Interpolatory filter banks and interpolatory wavelet packets

In classic wavelet sampling, research is generally focused on interpolatory bases in individual spaces. Consideration is seldom given to relations among different interpolatory bases, including those potentially related by filter banks. In this paper, we show that when an interpolatory basis exists in a space with a multiresolution analysis, this space can be decomposed orthogonally into two subspaces in which interpolatory bases also exist. Furthermore, similarly to standard orthogonal bases, these interpolatory bases can be related by pairs of filters, called interpolatory filter banks. For such interpolatory filter banks, it is shown that existence of interpolatory scaling functions generally leads to that of interpolatory wavelets and interpolatory wavelet packets. With this result, we further propose new algorithms for constructing such interpolatory wavelets/wavelet packets from corresponding scaling functions. In examples, our algorithms are applied to some typical wavelet spaces, demonstrating our theorems for interpolatory wavelets, interpolatory wavelet packets and interpolatory filter banks.
EDUCATION IN BHUTAN: AN OBSERVATION FROM SUSTAINABILITY PERSPECTIVE

This study is a qualitative study on GNH based education policy of Bhutan based on literature review. Modernization of Bhutan despite being a land-locked country is due to its paradigm shift from Monastic education system to modern education system based on GNH principles and policy. Most of the GNH values align with SDG laid down by UN. However it is also seen that GNH education policy is a continuous attempt to protect and preserve cultural integrity of Bhutan. It has special importance as the society is exposed to forces of westernization through mass-media and internet. This study is an attempt to analyze how far Bhutan has achieved in terms of providing equitable and quality education. This study is also an inquiry into whether education has equipped its youths with minimum knowledge and skills to compete in the global markets. The continuation of green school initiatives is bound to have a positive impact on environment sustainability which is one of key pillars of a sustainable society.
Adversarial Multi-task Label Embedding for Text Classification

Multi-task learning makes use of the potential correlation among related tasks to perform well in text classification. However, in the most multi-task works, labels are converted to meaningless one-hot vectors, which cause the loss of label semantics closely related to text semantics. Besides, shared and private features captured by previous shared-private multi-task learning framework are usually confused by the fact that the shared unit simply shares the parameters. In this paper, we propose the Adversarial Multi-task Label Embedding model. In this model, we integrate label semantics and improve the performance of multi-task learning. We introduce adversarial training and orthogonal constraints into the multi-task learning framework to prevent shared features and private features from interacting with each other. Extensive experimental results on six benchmark datasets demonstrate that our proposed approach is superior to the state-of-the-art multi-task text classification methods.
Twitter Sentiment Analysis About Public Opinion on 4G Smartfren Network Services Using Convolutional Neural Network

Sentiment Analysis was a process for identifying whether a source of text contains certain opinions, emotions, and polarity. Twitter Sentiment Analysis was a process for identifying sentiment and polarity on tweet. Twitter Sentiment Analysis provided a way for did survey about public sentiment to product, or particular even through collections of tweet. Main problem in sentiment identifying was how to determine classification model that gave high accuracy to classifying sentiment of tweet. One of the method for classifying sentiment of tweet was Deep Learning. Convolutional Neural Network (CNN) was special type of architecture from Deep Learning that its architecture had convolution layer. Convolution layer was important for extract relevant feature from text for classifying sentiment. The objective of this research was for found out the best CNN model for classifying sentiment of tweet. By using a dataset of tweets about public opinion on the Smartfren 4G network service, we searched the best CNN model using 6 combination parameters, that is the computational eficiency method, window size, and dimension of word embedding for parameters in Word2Vec Skip-gram model, then activation function in convolution layer, dropout rate, and pool size in pooling layer for parameters in CNN. The test is done using 10-fold cross validation for each search for the best parameter value and produced the best CNN model with an accuracy value of 88,21%.
Ultra-compact 3D measurement module using silica-based PLC

We fabricated the world's smallest optical module using a silica-based planar lightwave circuit for performing three-dimensional measurements. The module with a fiber, a camera and a lens is so small as to be attached to a 2.5 mm tube. We demonstrated accurate shape measurements with a variation of 0.1 mm.
Improved Varient for FOC-based Adaptive Filter for Chaotic Time Series Prediction

An improved normalized fractional least mean square (iNFLMS) has been proposed in this study. Least mean square (LMS) and fractional LMS (FLMS) are both prone to the problem of sensitivity to the input. In the proposed algorithm, the sensitivity of the FLMS to the input is reduced by normalization. The summation of the fractional and conventional gradients is made convex to obtain better convergence rate and keeping minimum error in steady state. To make the algorithm less computationally expensive, the gamma function is now absorbed into the fractional learning rate. Through the experiment it is quite clear that the efficacy of the proposed method is promising considering the parameters of steady-state error and convergence rate when compared to that of LMS, FLMS, MFLMS and NFLMS algorithm.
An Extended Protected Secret Sharing Scheme

Designing a protocol for the protection of sensitive information from possible thefts, losses, and damages is an important area of research in information security. Secret sharing scheme (SSS) allows a secret to be recovered from a specific set of information. The idea of secret sharing, is to divide the secret into several pieces (or shares) so that secret can be recovered from distributed shares. When shares are released to the network, there is chance to get shares by the adversaries. One of the solution of this problem is protected secret sharing (PSS). In the PSS, shares are used to reconstruct the secret and to establish a secure key between every pair of shareholders. In this paper, we have studied and analyzed the protected secret sharing scheme, and we have proposed an extension of the PSS scheme by using the bivariate polynomial. Where, each shareholder has a pair of shares - one is to reconstruct the secret, and another is for the verification of adversaries.