Reviewerly/exHarmony huggingface dataset:
Classification On Category Of Public Responses On Television Program Using Naive Bayes Method

Television has high popularity in Indonesia. This matter create a competition to get the highest rating. so that, sometimes the quality of content rarely noticed. This problem, make a lot of public responses likes from official website Komisi Penyiaran Indonesia (KPI), Short Message Service (SMS) or in social media. According that case, this paper using naive bayes method to categorize public responses from two social media likes Twitter and Facebook. This approach inspired from sentiment analysis which has been done before to television program based on public opinions from twitter using naive bayes method. There are several stages in this research start from taking data process from social media based on public opinions, pre-processing data, saving the data to MySQL database, and classification category process with naive bayes method. The researcher process the data responses become two main categories such as station tv category and program category. Output of this apps is list of information about public responses which has been categorized to make easier for KPI or the people to access also choose and determine the best program for their family. From 326 amount of data which has been used as dataset with precentage 80% of training data and 20% of testing data produce an accuracy value of 82%.
Head-Fingers-Arms: Physically-Coupled and Decoupled Multimodal Interaction Designs in Mobile VR

This paper proposes a novel bimanual finger-based gesture called X-Fingers that provides interactive 2D spatial input control using vision-based techniques in mobile VR. This finger-based input modality can be coordinated with the movement of the user's arms or head to provide an additional input modality. The incorporation of the arms or the head provides physically-coupled and physically-decoupled multimodal interactions respectively. Given these two design options, we conducted user studies to understand how the nature of the physical coupling of interactions influences the user's performance and preferences with task consisting of varying degrees of coordination between the modalities. Our results show that physically-decoupled interactions designs are preferred when the degree of coordination is high within the multimodal interaction.
Feed-Forward Neural Networks

This chapter introduces the general features of artificial neural networks. After a presentation of the mathematical neural cell, we focus on feed-forward networks. First, we discuss the preprocessing of data and next we present a survey of the different methods for calibrating such networks. Finally, we apply the theory to an insurance data set and compare the predictive power of neural networks and generalized linear models.
Improvement of Road Surface Discrimination Performance of Movement Support System Using Ultrasonic Sensors

The number of accidents involving elderly individuals has been increasing with the increase of the aging population, and it is a serious problem. Most of the accident are caused by decline in ability of judgment and physical. Therefor, movement supporting tool such as cane for elderly people is used to prevent falls and improve the safety. Individuals moving without the help of a caregiver face a high risk of accidents (such as falls), despite using movement supporting tool. There is a number of studies on support system for elderly people to improve the safety and quality of daily life. They are studies to detect the dangerous road such as a step and obstacle from distance information obtained by an ultrasonic sensor. On the other hand, the actual fall accident has occurred in the road surface, such as grass or gravel not only a step or obstacle. Therefore, there is a need for a system to detect and determine the danger road. We previously showed the effectiveness of the road surface discrimination method using information, the distance to the road surface, the incident angle and the reflection intensity of the ultrasonic sensor. However, it had not been studied the effectiveness of the method at the time of the move. This paper describes the results of the effectiveness of the method at the time of the move.
A multi-Cross Matching Network for Chinese Named Entity Linking in Short Text

Abstract Named entity linking(NEL) for Chinese short text is regard as a ranking task. Current research on NEL is mostly about long text and rich language, which may unfit Chinese short text situation. We propose a multi-cross matching network(CMN) to solve these problems. CMN first matches a candidate and the mention with the text in three different degree, and extracts important matching information through a couple of convolution and pooling layers. Then sequence vectors are accumulated through a Gated Recurrent Unit(GRU) which distill relationships among words in texts. Finally we use dynamic average algorithm to calculate the mention-entity similarity. Experimental results demonstrate that CMN exhibits the outstanding performance on several Chinese datasets for the Chinese short text entity linking problem.
An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research

Patient data or information collected from public health and health care surveys are of great research value. Usually, the data contain sensitive personal information. Doctors, nurses, or researchers in the public health and health care sector do not analyze the available datasets or survey data on their own, and may outsource the tasks to third parties. Even though all identifiers such as names and ID card numbers are removed, there may still be some occasions in which an individual can be re-identified via the demographic or particular information provided in the datasets. Such data privacy issues can become an obstacle in health-related research. Statistical disclosure control (SDC) is a useful technique used to resolve this problem by masking and designing released data based on the original data. Whilst ensuring the released data can satisfy the needs of researchers for data analysis, there is high protection of the original data from disclosure. In this research, we discuss the statistical properties of two SDC methods: the General Additive Data Perturbation (GADP) method and the Gaussian Copula General Additive Data Perturbation (CGADP) method. An empirical study is provided to demonstrate how we can apply these two SDC methods in public health research.
Nonlinguistic Aspects of Linguistic Contexts

Our paper works on a proposal recently put forward by Hunter, Asher and Lascarides (2018) on the use of events in discourse context. We basically accept their view and their proposal of using events as explanation in discourse context. However we think that a stricter connection with demonstrations and causal reasoning in everyday conversation is a necessary step in a coherent view of discourse context. We will not deal with any particular formalism, but only with the general problem of taking into account some elements that may simplify or explain what is taken for granted in some steps of our inferences. A central concept used in these setting is the concept of “explanation” as a way to give coherence to the discourse context. This kind of explanation is also based, besides elements of a general encyclopedic knowledge, on default assumptions derived by the ontology present in the lexicon as Asher (2011) has abundantly shown. However, the steps to recover such coherence would gain clarity with a better specification of causal explanation and with a more precise account of the relation between demonstrative and demonstrations in discourse context. On these two aspects we give some suggestions.
Analyzing Quality of Software Requirements; A Comparison Study on NLP Tools

For analyzing the quality of software requirements and performing natural language (NL) processing, only few of many tools are currently adopted by the industry. Most of these tools are no longer available and are obsoleted due to their complexity, less accuracy and high cost. The only few remaining tools exhibit either optimum functionality or at sub-optimal level. Some tools are limited to processing NL whereas the rest check for grammatical mistakes. First, this paper performs comparison on five existing tools named as QVscribe, QuOD, Innoslate, RAT and RQA in which we compare features, functionality and quality indicators addressed by these tools. Second, we propose a new tool named as Requirement Assessment Tool (RAT) for analyzing quality of individual requirements and specification document. Finally, check the performance of these tools for automatically detecting ambiguity, deviations and quality affecting defects on the basis of real-world natural language requirements set i.e. golden standard data. The results of these tools concluded that RAT gives more accurate results for detecting defects in all categories except looking for negative sentences.
Magnetic field effect on the dynamics of entanglement for time-dependent harmonic oscillator

We investigate the dynamics of entanglement, uncertainty and mixedness by solving time-dependent Schrödinger equation for two-dimensional harmonic oscillator with time-dependent frequency and coupling parameter subject to a static magnetic field. We compute the purities (global/marginal) and then calculate explicitly the linear entropy [Formula: see text] as well as logarithmic negativity [Formula: see text] using the symplectic parametrization of vacuum state. We introduce the spectral decomposition to diagonalize the marginal state and get the expression of von Neumann entropy [Formula: see text] and establish its link with [Formula: see text]. We use the Wigner formalism to derive the Heisenberg uncertainties and show their dependencies on both [Formula: see text] and the coupling parameters [Formula: see text] [Formula: see text] of the quadrature term [Formula: see text]. We graphically study the dynamics of the three features (entanglement, uncertainty, mixedness) and present a similar topology with respect to time. We show the effects of the magnetic field and quenched values of [Formula: see text] and [Formula: see text] on these three dynamics, which lead eventually to control and handle them.
Comparison of C4.5 algorithm with naive Bayesian method in classification of Diabetes Mellitus (A case study at Hasanuddin University hospital Makassar)

Abstract Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. Indonesia ranks fourth in the world with the highest DM after the United States, India and China. The method used in this study is data collection, variable selection, classification methods, validation and evaluation and decision making. The algorithm used in this study is C4.5 Algorithm and Naive Bayesian Method using a dataset obtained from the results of Hasanuddin University hospital medical records. The results of calculations that have been done obtained accuracy on the C4.5 algorithm of 100% and on the Bayesian naive method obtained at 90%. From these results it can be concluded that to diagnose DM disease it is recommended to use the C4.5 Algorithm .
Parking Space Information Monitoring by Millimeter Wave SAR Based on Unmanned Aerial Vehicle

This paper proposes a parking space information monitoring system by millimeter wave synthetic aperture radar (SAR) based on unmanned aerial vehicle (UAV). Parking space information that people are concerned about includes vacant parking place, parking place occupied by obstacles and place parked by vehicles. Specially, the free parking space detection is an important module for the parking guidance system (PGS) that can help drivers to find parking space efficiently. In this system, we obtain high resolution SAR images of parking lots at first. Then, in order to define the free parking space, Maximally Stable Extremal Region (MSER) method is exploited to leach the candidate regions occupied by vehicles from millimeter wave SAR images. Next, the system utilize visual saliency detection method to extract obstacles from the non-parked parking space acquired by pre-detection. Ultimately, the three types of information have been determined, including vacant parking space, parking space occupied by obstacles and the parked place. Experimental results prove that the integrated scheme performs well in parking information determination.
Comparison of Machine Learning Classification Algorithms in Sentiment Analysis Product Review of North Padang Lawas Regency

The growth of SMEs in Indonesia, which has increased by 6% every year, is driven by continued growth by many parties, including the government and private institutions that often conduct business coaching and assistance. Problems that are often encountered are the lack of willingness of MSME business practitioners to apply information technology and the internet, besides that most of them live in rural areas with very limited internet access and many are not yet digital-literate, adequate digital technology utilization capabilities and the will of business people For SMEs to understand customer needs, a service that is consistent with standard service procedures will give a good impression and pay attention to customer feedback. This research was conducted by collecting data on MSME products obtained from the North Padang Lawas District Trade Industry Office followed by the development of a Paluta Market website as a marketplace for media promotion and marketing of MSME products in North Padang Lawas by applying a sentiment analysis approach using machine learning classification algorithm to produce product rating values based on public opinion of MSME products contained on the website, in addition the system is able to classify consumer comment data on MSME products from various sources from the umkm web, so that it becomes useful information for MSME businesses especially in North Padang Lawas Regency and the community at large. The results of the application of sentiment analysis of a product on the Paluta Market website can be used as a reference in improving service and product quality, so as to create a variety of new opportunities that are profitable for MSME businesses.
The determinants of individual cyber security behaviours: Qualitative research among French students

The aim of the article is to study the determinants of individual cyber security behaviours, using a qualitative approach with face-to-face interviews of students in France. Based on our interviews, we will stress that cyber security behaviours could be explained by: self-efficacy, perceived personal susceptibility to digital threat, guardianship and conformity.
Insta-Search

Knowledge Graphs (KGs) are used to store heterogenous information in the form of graphs. One flexible and non-expert way to query these KGs is to use relationship queries or keyword search. The user can specify a query using keywords referring to entities in the graph. The system then returns a set of relationships among the queried entities. However, effectively querying these graphs is still challenging for a new user. She is not familiar with the entities and relationships in the graph and hence, her queries could often return empty or too few answers. We demonstrate a system called Insta-Search which facilitates effective exploration of KGs using relationship queries. Insta-Search helps the user by giving autocomplete keyword suggestions for partially typed words. It also displays an estimated number of answers that the current query would fetch along with few approximate top-scoring answers. The users also get entity suggestions so that they can iteratively reformulate the query until they find the query with the expected results. On submitting the query, the system returns ranked query results, grouped on the basis of similar information content to enhance result interpretation. No prerequisite knowledge of the data is required by the user to be able to use the system.
Task Scheduling for Streaming Applications in a Cloud-Edge System

With the increasing popularity of ubiquitous smart devices, more and more IoT (Internet of Things) data processing applications are deployed. Due to the inherent defects of traditional data transmission networks and the low latency requirement of applications, effective use of bandwidth computing resources to support the efficient deployment of applications has become a very important issue. In this paper, we focus on how to deploy multi-source streaming data processing applications in a cloud-edge collaborative computing network and pay attention to make the overall application data processing delay lower. We abstract the application into a form of streaming data processing, formalize it as a Stream Processing Task Scheduling Problem. We present an efficient algorithm to solve the above problem. Simulation experiments show that our approach can significantly reduce the end-to-end latency of applications compared to commonly used greedy algorithms.
A Class Activation Mapping Guided Adversarial Training Method for Land-Use Classification and Object Detection

Interpretation of convolutional neural networks (CNNs) critically influence our understanding of deep learning models’ internal dynamics. In this paper, we demonstrate an interpretable training method, namely class activation mapping guided adversarial training (CAMAT), for two typical remote sensing tasks, land-use classification and object detection. We first generate class activation maps of the current batch training samples. Class activation map is a kind of class-specific saliency map that quantifies the contributions of a particular region in the image to the CNN prediction result. Then, high contribution regions in the training samples are occluded, and we leverage the partial masked images as the inputs for network training. Following this paradigm, the key areas for network learning and decision making are purposefully disturbed in the training phase, thus the trained model could have better performance in robustness and generalization. Experiments conducted on classic remote sensing datasets verified the outperforming effectiveness and efficiency of the proposed CAMAT.
What to Expect When You Are Simulating?

While simulation is a proven teaching strategy, it has not had much development in the training of teachers. However, it has a lot of potential: risk elimination, high quality learning, reflective practice, didactic use of error, transition between theory, and practice. In addition to it, educational technology further enhances this potential: immersive digital environments enhance the realism of the simulations, help reflection, help overcome the time-space limitations, etc. To know the state of the matter on the potentialities of digital simulations in the training of teachers, a systematic review of the literature is organized, which allows us to know both the advances and the limitations in the use of this didactic strategy. On the one hand, it has been shown that digital simulations are effective in teacher training and that help cover current gaps; on the other hand, it is evident that there are technological limitations and shortcomings in research.
Interactive real time fuzzy class level gesture similarity measure based sign language recognition using artificial neural networks

Vision-based Sign Language Recognition has been an open research problem since decades. Many existing methods for sign recognition works well under restricted laboratory conditions but failed to support real-time scenarios because extraction of manual and non-manual movements with constantly chang ing shapes of signs are considered as tedious problem in machine vision and machine learning. To overcome these shortcomings, an interactive real time class level gesture similarity based sign recognition using Artificial Neural Network is presented in this paper. The method uses the sign images and starts with enhancing the image quality. The quality enhancement is performed by equalizing the histograms of luminance and contrast. The features of hand as subunits from quality improved image have been extracted by template matching techniques. Extracted features are used to generate neural network and trained with different class of signs. The classification is performed by measuring the class level gesture similarity measure towards each class of signs and images. Based on the measure estimated, the method classifies the image and sign. The result produced to the user has been iterated based on the actions provided by the user. The method is capable of iterating the result and recognition till the user gets satisfied. The method produces higher accuracy in sign recognition and reduces the false ratio.
A Model of Information Security Policy Compliance for Public Universities: A Conceptual Model

The university is an organization that manages much public information, and therefore, information security policies are developed to ensure data security. However, during implementation still founded disobey behavior user and has an impact on data security. The previous research has been conducted to find influencing factor user comply with information security, although some model and theories still limited to implementation. There is a lack of researchers combine behavioral theory and organizational theory to develop models and previous model inadequate to universities that have unique characteristics. This study aims to explore and identify factors that influence information security compliance and continue to develop conceptual models for assessing information security policies. This conceptual model creates based on a systematic literature review and preliminary study. The results in the conceptual model found several variables, namely habits, attitudes, moral beliefs, self-efficacy from behavioral theories and human culture, commitment, rewards, costs can be used to evaluate user compliance with information security policies. Conceptual will be tested further to contribute to help universities to ensure and assess users to comply with information security policies.
Interactive Multi-Grained Joint Model for Targeted Sentiment Analysis

In this paper, we propose an interactive multi-grained joint model for targeted sentiment analysis. Firstly, different from previous works, we leverage the correlation between target and sentiment clues and deeply strengthen interaction between them because targets are highly related to the sentiment clues in a sentence. Moreover, we apply a multi-layer structure to consider multi-grained target and sentiment tagging information more comprehensively. Also, we design two specific loss functions to prevent a word from being both part of a target and a sentiment clue simultaneously, and to align the boundary information of two labeling subsystems. We conduct experiments on English and Spanish datasets and the experimental results show that our approach substantially outperforms a variety of previous models and achieves new state-of-the-art results on these datasets.