Reviewerly/exHarmony huggingface dataset:
Household Surface Interactions: Understanding User Input Preferences and Perceived Home Experiences

Households contain a variety of surfaces that are used in a number of activity contexts. As ambient technology becomes commonplace in our homes, it is only a matter of time before these surfaces become linked to computer systems for Household Surface Interaction (HSI). However, little is known about the user experience attached to HSI, and the potential acceptance of HSI within modern homes. To address this problem, we ran a mixed methods user study with 39 participants to examine HSI using nine household surfaces and five common gestures (tap, press, swipe, drag, and pinch). We found that under the right conditions, surfaces with some amount of texture can enhance HSI. Furthermore, perceived good and poor user experience varied among participants for surface type indicating individual preferences. We present findings and design considerations based on surface characteristics and the challenges that users perceive they may have with HSI within their homes.
Science Teachers' Perception of APL

The nature of Science must exist in Science learning. However, the most widely used source, the science textbooks, do not have a similar framework to the nature of Science. Science teachers need to use learning resources that are in line with the nature of Science, one of which is Adapted Primary Literature (APL). This study aims to investigate the science teachers' perceptions of APL and identify the characteristics related to these perceptions. A questionnaire regarding teachers' perceptions had been completed by 29 science teachers in Bandung. The data from the questionnaire indicated that science teachers' perceptions of APL is apparently high. Teachers' scientific publications experience was considered as a characteristic related to their perception of APL. Gender, birth generation, teaching experience, and school status are not regarded as related characteristics on teachers' perceptions of APL. The results suggested that science teachers' training regarding APL use in science class should be carefully designed.
Encoding electromagnetic transformation laws for dimensional reduction

Abstract Electromagnetic phenomena are mathematically described by solutions of boundary value problems. For exploiting symmetries of these boundary value problems in a way that is offered by techniques of dimensional reduction, it needs to be justified that the derivative in symmetry direction is constant or even vanishing. A generalized notion of symmetry can be defined with different directions at every point in space, as long as it is possible to exhibit unidirectional symmetry in some coordinate representation. This can be achieved, for example, when the symmetry direction is given by the direct construction out of a unidirectional symmetry via a coordinate transformation which poses a demand on the boundary value problem. Coordinate independent formulations of boundary value problems do exist but turning that theory into practice demands a pedantic process of backtranslation to the computational notions. This becomes even more challenging when multiple chained transformations are necessary for propagating a symmetry. We try to fill this gap and present the more general, isolated problems of that translation. Within this contribution, the partial derivative and the corresponding chain rule for multivariate calculus are investigated with respect to their encodability in computational terms. We target the layer above univariate calculus, but below tensor calculus.
Cool, a COhort OnLine analytical processing system

With a huge volume and variety of data accumulated over the years, OnLine Analytical Processing (OLAP) systems are facing challenges in query efficiency. Furthermore, the design of OLAP systems cannot serve modern applications well due to their inefficiency in processing complex queries such as cohort queries with low query latency. In this paper, we present Cool, a cohort online analytical processing system. As an integrated system with the support of several newly proposed operators on top of a sophisticated storage layer, it processes both cohort queries and conventional OLAP queries with superb performance. Its distributed design contains minimal load balancing and fault tolerance support and is scalable. Our evaluation results show that Cool outperforms two state-of-the-art systems, MonetDB and Druid, by a wide margin in single-node setting. The multi-node version of Cool can also beat the distributed Druid, as well as SparkSQL, by one order of magnitude in terms of query latency.
Neural Architecture Search Using Multi-objective Evolutionary Algorithm Based on Decomposition

NSGA-Net is a popular method for neural architecture search (NAS). It conducts the improved non-dominated sorting genetic algorithm (NSGA-II) during its search procedure. In this paper, a NAS method using the multi-objective evolutionary algorithm based on decomposition (MOEA/D-Net) is proposed to heighten the running efficiency of NSGA-Net. MOEA/D-Net aims to minimize the number of floating-point operations (FLOPs) and error rate of neural architectures through the multi-objective evolutionary algorithm based on decomposition (MOEA/D) during the search process. It selects parents within the neighborhoods of a subproblem and conducts multi-point crossover and mutation to generate offspring individuals at every generation. Experiment results on the CIFAR-10 image classification dataset indicate that MOEA/D-Net obtains architecture networks with less FLOPs and MOEA/D-Net outperforms NSGA-Net in terms of running efficiency.
Hotel Reviews Analysis based on Sentiment Classification

Advances in technology and in particular web applications that are related to customer relations management have encouraged users to voice their opinions with the rest of the world. Relevant information needs to be sorted within the large amount data that can employed by the organization to ensure optimal corrective actions. Sentimental Analysis is one such technique that is popular due to its ability to distil relevant information from opinionated text. Managing customer satisfaction and expectations are vital components that add credibility and ensures survival of the organization. It is commonly agreed that these are the strategic components enhance customer allegiance and covet retention. Hotels in particular are wary of the fact that opinions do matter for their reputation and credibility. Hence, they strive to seek feedback from their guests. In this paper, the authors aim to develop a model to classify hotel visitors feedback based on their experience. Four different supervised learning approaches were employed and tested on the collected data set. The result indicates Support Vector Machine (SVM) classifier outperformed the rest of the classifiers in term of effectiveness.
Array-based Data Management for Genomics

With the huge growth of genomic data, exposing multiple heterogeneous features of genomic regions for millions of individuals, we increasingly need to support domain-specific query languages and knowledge extraction operations, capable of aggregating and comparing trillions of regions arbitrarily positioned on the human genome. While row-based models for regions can be effectively used as a basis for cloud-based implementations, in previous work we have shown that the array-based model is effective in supporting the class of region-preserving operations, i.e. operations which do not create any new region but rather compose existing ones. In this paper, we remove the above constraint, and describe an array-based implementation which applies to unrestricted region operations, as required by the Genometric Query Language. Specifically, we define a wide spectrum of operations over datasets which are represented using arrays, and we show that the arraybased implementation scales well upon Spark, also thanks to a data representation which is effectively used for supporting machine learning. Our benchmark, which uses an independent, pre-existing collection of queries, shows that in many cases the novel array-based implementation significantly improves the performance of the row-based implementation.
Three-Dimensional Path Planning of Constant Thrust Unmanned Aerial Vehicle Based on Artificial Fluid Method

In this paper, a three-dimensional path planning problem of an unmanned aerial vehicle under constant thrust is studied based on the artificial fluid method. The effect of obstacles on the original fluid field is quantified by the perturbation matrix, the streamlines can be regarded as the planned path for the unmanned aerial vehicle, and the tangential vector and the disturbance matrix of the artificial fluid method are improved. In particular, this paper addresses a novel algorithm of constant thrust fitting which is proposed through the impulse compensation, and then the constant thrust switching control scheme based on the isochronous interpolation method is given. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually. Simulation results demonstrate the effectiveness of this method.
SCoEmbeddings

Contextualized word representations such as ELMo embeddings, can capture rich semantic information and achieve impressive performance in a wide variety of NLP tasks. However, as problems found in Word2Vec and GloVe, we found that ELMo word embeddings also lack enough sentiment information, which may affect sentiment classification performance. Inspired by previous embedding refinement method with sentiment lexicon, we propose an approach that combines contextualized embeddings (ELMo) of the pre-trained model with sentiment information of lexicon to generate sentiment-contextualized embeddings, called SCoEmbeddings. Experimental results show that our SCoEmbeddings achieve higher accuracy than ELMo embeddings, Word2Vec embeddings, and refined Word2Vec embeddings on the SST-5 dataset. Meanwhile, we also visualize embeddings and weights of SCoEmbeddings, demonstrating the effectiveness of our SCoEmbeddings.
Future of Data Hiding: A Walk Through Conventional to Network Steganography

In current scenario where government agencies are supporting digital communication at a heavy rate and data storage and communication time are being improved consequently, as a side effect, bad minds are getting more chances of vulnerabilities. Steganography and cryptography has been leading the front by concealing existence and meaning of the data respectively since a long time. With the promotion of packet switched networks like internet, there opens more doors towards moving to another level of steganography where instead of using traditional digital data as cover file, some network protocol or some other services plays the role of cover channel. In this paper various works recently done in the area of different categories of network security has been discussed and analyzed so as to find scope for future research.
A Framework for Event-oriented Text Retrieval Based on Temporal Aspects

Event, as an important carrier for users to understand the world, has become a special retrieval object. In contrast to traditional text retrieval, Event-oriented Text Retrieval (ETR) can search events by utilizing events knowledge and using events as proxies for information needs. Accordingly, ETR has become the preferred way for users to obtain their interested events from massive web collections. Moreover, it also has already aroused considerable attention from scholars in recent years. However, the retrieval effectiveness of ETR is still subject to the effect of temporal aspects (i.e., temporal dynamics). Thus, in this review, we first analyze three major temporal components in the framework of ETR. After that, we provide a comprehensive overview of state-of-the-art approaches corresponding to such three components. Finally, we summary some ETR-related resources and pinpoint several potential research directions.
Study on Chinese Text Clustering Algorithm Based on K-mean and Evaluation Method on Effect of Clustering for Software-intensive System

Software fault continues growing in importance to the factors of equipment failure. Analysis and utilization in amount of text data is important content for research of software failure and reliability of software-intensive system. Algorithm based on k-mean is proposed, in which the calculated cluster-head (CH) out of real text node sample set was replaced with one text node nearest to it in the real text set. Distance between two text nodes is computed by Euclidean distance. Then evaluation method of clustering effect based on Entropy is presented for text clustering algorithm. At last, experiments show that the improved algorithm can solve the problem of empty cluster. Empty cluster means that the calculated CH may be visual text node out of real text node sample set in the progress of k-mean algorithm. Furthermore, the evaluation method can obtain the best clustering result and optimal number of cluster-heads (CHs).
Recognizing a sketched task model by multi-stroke gesture recognition

taskSketch is an eclipse-based task model editor to enable designers to sketch a task model and to recognize it by a multi-stroke gesture recognition of pre-defined shapes and relations. The tool supports three levels of definition (LoF, LoD, LoC) and is linked to other models by model mapping. taskSketch is flexible enough to accommodate variations of the task model notation by defining new shapes and relations, and by searching template and pattern matchings. To demonstrate this capability, we introduce three concepts: the level of fidelity, the level of detail, and the level of criticism. This process is generalizable to other similar models involved in the model-based user interface design.
Implementing Capsule Neural Networks in Traffic Light Image Recognition

Traffic Light Image Recognition is the problem of determining the signal of traffic lights within images taken by an autonomous vehicle. Currently, this is done by Convolutional Neural Network (CNN) machine learning systems. However, CNNs have issues managing positional information and routing data dynamically, so researchers have suggested the use of Capsule Neural Networks (CapsNets) instead. This study compared CapsNets with CNNs as tools in traffic light image recognition by constructing both and testing them on the same data. Both networks reached 100% validation accuracy, but the CapsNet reached that mark slightly faster both in iterations and in real time. While this does not prove benefits of the CapsNet, it supports the continuation of research, development, and optimization of CapsNets in autonomous driving problems.
Hot News Detection System Using Rule-Based Technique

Big data analytics provides the large volumes of data. The data are collected from a various sources including social networks, videos, digital images, sensors, and sales transaction records. Hot news is the information about current news of anywhere in the world. The information is receiving about an event that just happened. Breaking news reported while an event is on-going. In existing system,used CNN based hash tag recommendation along with the increasing requirements. Convolutionalneural networks (CNNs) for many natural language processing tasks, to perform the hashtag recommendation problem. In this work, proposedan efficient rule-based technique for merging news and social streams in real-time, Rule-based classifier makes use of a set of IF-THEN rules for classification. Twitter hash tags function as a key connection between Twitter crowds and the news media. To improve the efficiency and coverage of a state-of-the-art hash tag recommendation model by proposing new techniques for data collection and feature computation
Implications of Eye Tracking Research to Cinematic Virtual Reality

While watching omnidirectional movies via head-mounted displays the viewer has an immersive viewing experience. Turning the head and looking around is a natural input technique to choose the visible part of the movie. For realizing scene changes depending on the viewing direction and for implementing non-linear story structures in cinematic virtual reality (CVR), selection methods are required to select the story branches. The input device should not disturb the viewing experience and the viewer should not be primarily aware of it. Eye- and head-based methods do not need additional devices and seem to be especially suitable. We investigate several techniques by using an own tool for analysing head and eye tracking data in CVR.
Implementation of Facial Feature Extraction Using Viola-Jones Method for Mobile Robot System

Abstract To be able to detect faces, the system must be able to identify the characteristics of the face, so that the camera can distinguish which parts of the face and non-faces. In identifying facial features performed on certain parameters, such as skin color, facial contours, lighting, facial pose. At this time face detection system uses an algorithm Viola-Jones is considered the most accurate in the detection and face tracking. In this research, we will discuss the application of interactions between humans and machines with image media in the form of faces. Interactions between humans and computers can be found in various places. One of the media interaction between humans and computers is with the image. This time the system will indicate to find how r o bot can act through the position of a person’s face recorded on camera robot. This system uses the Viola-Jones algorithm method which is a fairly popular object detection method because the detection process is carried out very quickly. Besides, the bot will also follow the face based on the identity it stores in the database. In recognizing the human face this system uses the Eigenface method. Both of these algorithms will adjust the robot’s motion as it should. The results of the experiment will show the robot can move quite well following the face stored in the database.
Recognition of Image Pattern To Identification Of Braille Characters To Be Audio Signals For Blind Communication Tools

Abstract The five senses are a source of information in humans. The sense that is the main source of information is the sense of sight. Some humans are created with limited sense of sight. The blind performs reading and writing activities using Braille characters. Braille characters are writing or printed systems for the visually impaired in the form of codes consisting of six dots in various combinations that are highlighted on the paper so that they can be touched. Those with significant visual impairment need special education or learning services. Information is very important for everyone, including blind people. Submission of information is done through various media. One media that is often used is print media such as books. But books available on the market do not adapt the way for blind people to capture information. This research offers alternative solutions to overcome the above problems, namely communication aids for reading the blind. The implementation is in the form of a scanner and webcame that is equipped with a braille character text to speech system as an alternative to the lack of blind reading media, especially braille print books. The method, the reading of Braille character scripts by studying braille characters from a to z. First, a webcame or scanner captures braille characters. Second, the system will convert Braille characters and translate Braille characters into alphabetical form through Optical Character Recognition (OCR) image processing. Recognition of Braille character patterns in written text using Artificial Neural Networks (ANN). The results of research on braille character testing are in the form of alphabetical texts a to z, and audio signals of alphabet pronunciation. The results of testing the introduction of braille character patterns using a scanner for training data 100% and for testing data 90.38 % and 96.15 %.
Analysis of Player Behavior and EEG Readings in a Cybersecurity Game

This paper describes a study of player behavior and electroencephalography (EEG) headset readings while playing a cybersecurity educational video game. While difficulty was progressively increased, player actions and EEG readings were recorded, along with a pre- and post-test of student knowledge and opinions regarding information security awareness and perceived immersion. This study employed Brute Force, a tower defense game that teaches players to choose strong, unique, and memorable passwords. Participants reported significantly more responsible attitudes regarding the importance of strong, unique passwords. More successful players who played the full fifteen minutes tended to improve at identifying strong passwords from a list. After playing the game, participants were most likely to add password length and uniqueness as important password strategies.
MCMC for Bayesian Uncertainty Quantification from Time-Series Data

In computational neuroscience, Neural Population Models (NPMs) are mechanistic models that describe brain physiology in a range of different states. Within computational neuroscience there is growing interest in the inverse problem of inferring NPM parameters from recordings such as the EEG (Electroencephalogram). Uncertainty quantification is essential in this application area in order to infer the mechanistic effect of interventions such as anaesthesia. This paper presents software for Bayesian uncertainty quantification in the parameters of NPMs from approximately stationary data using Markov Chain Monte Carlo (MCMC). Modern MCMC methods require first order (and in some cases higher order) derivatives of the posterior density. The software presented offers two distinct methods of evaluating derivatives: finite differences and exact derivatives obtained through Algorithmic Differentiation (AD). For AD, two different implementations are used: the open source Stan Math Library and the commercially licenced tool distributed by NAG (Numerical Algorithms Group). The use of derivative information in MCMC sampling is demonstrated through a simple example, the noise-driven harmonic oscillator. And different methods for computing derivatives are compared. The software is written in a modular object-oriented way such that it can be extended to derivative based MCMC for other scientific domains.
The Analysis of Fuzzy Logic Method and Multiple Linear Regression in Determining National Rice Production to Improve Food Self-Sufficiency in Indonesia

Abstract Indonesia in 2023 targets rice self-sufficiency. To be included in the category of rice self-sufficiency, Indonesia’s total rice production should be 82 million tons in accordance with Government Regulation No.2 of 2015. The amount of rice production is uncertain. One way to calculate uncertainty in forecasting is by using the Fuzzy Logic of the Mamdani method and the Linear Regression of the Least Squares method. In the Fuzzy Logic Mamdani method there are 4 steps namely; 1) Fuzzy set formation; 2) Application of Implication functions; 3) Composition of Rules; and 4) Defuzzification. Rice Harvested Area, Rice Production and National Rice Consumption are the input variables and National Rice Production is the output variable. The data used are secondary data from the Central Statistics Agency (BPS) and the Ministry of Agriculture Annual Report 2006 - 2017. The data analysis process also uses matlab and minitab software. From the research results obtained that the Fuzzy Logic of the Mamdani method and the Linear Regression of the Smallest Square method can be used to predict the amount of national rice production. For the results of predictions of Fuzzy Logic the Mamdani method has a relatively large error compared to the Linear Regression of the Smallest Squares method. Furthermore it is also known that there is a significant relationship between harvested area and consumption. These two variables will be the determining factors for realizing rice self-sufficiency in Indonesia.