Reviewerly/exHarmony huggingface dataset:
Pengembangan Bahan Ajar Menulis Fabel dengan Model Sinektik pada Peserta Didik Kelas VII SMP Muhammadiyah 1 Balikpapan

Learning method, especially at fabel writing is less less variative; as a result, learning process cannot explore students’ writing skill. Therefore, the writer found that it needs to do the synectics learning method development in writing skill learning. This research purpose is to develop the synectics method development in writing learning process. This research uses research and Development (R&amp;D) method, which is stated by Brog and Gall that it consists of ten steps of source development, which in this research, is VII grade students of SMP Muhammadiyah 1 Balikpapan. Data collection is done by doing the questionnaire, observing, interviewing and documenting. This learning method development is declared very feasible by three validators; material expert validator, media expert validator and learning pratitioner. Based on test result, the synectics model can imrpove the students’ score through the test process. The observation result of the learning activity is done by observer I and II and is declared very good.
A multi-functional BCI system for exigency assistance and environment control based on ML and IoT

Brain-Computer Interface (BCI) is a modality to create an interface which sustains bidirectional communication between the brain and computers. Major disadvantages in implementing such systems are the bulky design and system cost. This study implements a simple multifunction BCI system for the environment control and exigency assistance by just using single channel Electroencephalogram (EEG). In the proposed model, the environment is controlled through Internet of Things (IoT) as per individual's cognitive state while for exigency assistance served as per Event Related Potential (ERP) observed during oddball paradigm. Arduino microcontroller (AMC) hardware is designed for controlling environment. Different Machine Learning (ML) algorithms observed for training the classifiers. Weighted k-Nearest Neighbour (Wk-NN) algorithm trained classifier delivers the best result, with accuracy of 98.3% to detect ERP and 95% accuracy for cognitive state detection. The simple, low cost prototype system was tested for environment control and assistance.
Visual Communications of Love in Athens and Venice: Plato and Tomas Mann

The article is devoted to the study of ontological, aesthetical and anthropological features of visual communication in Plato and Thomas Mann. I compare T. Mann’s Death in Venice artistic conception with the philosophical understanding of the image, contemplation, beauty and Eros of Plato, primarily in the dialogues Phaedrus and Symposium (with some reference to F. Nietzsche and the film by L. Visconti). The author explores the specifics of the visual-plastic worldview and the contemplative cognition of being, determined by the erotic foundation of the aesthetic contemplation of the human image as phenomenal manifestation of truth, fundamentally different from the one revealed in speech communication, and even capable of being autonomous from it, and represents the philosophical and artistic phenomenology of the development, transformation and implementation of such consciousness. The role of Athens and Venice as particularly significant historical-cultural topoi of visual communication is specially emphasized.
USING PERSONAL JOURNAL TO IMPROVE WRITING ABILITY IN RECOUNT TEXT

The application of learning through the use of personal journal is related to the learning of the "Recount text", where this text leads to the process of retelling about experiences in the past to provide information or to entertain the reader. Based on several previous researchers, it has been suggested that learning writing through a personal journal can become a habit that has an impact on increasing students' interest in writing. The application of this learning is aimed to find an increase in learning ability of writing skills, especially in writing "Recount text". This research is a quasi-experimental design with involving experiment 1 and 2. The experiment 1 was taught with the use of a personal journal while the experiment 2 was taught with the learning as applied by the teacher. Data collection for student learning outcomes is done by giving a test of learning outcomes after being treated in each group. The findings in this study indicate that there is an increase in students' writing Recount through the use of personal journal. The mean score in the pre-test of the Experiment 1 there was an increase of 11.13%. In the experimental 2 there was an increase of 10.16%. This proves that the use of personal journal is more effective.
Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.
Verifiable Security Templates for Hardware

High-level synthesis (HLS) research generally focuses on transferring "software engineering virtues" (e.g., modularity, abstraction, extensibility, etc.) to hardware development with the ultimate goal of making hardware development as agile as software development. And recent HLS research has focused on transferring ideas and techniques from high assurance software formal methods to hardware development. Just as it has introduced software engineering virtues, we believe HLS can also become a vector for adapting software formal methods to the challenge of high assurance security in hardware. This paper introduces the Device Calculus and its mechanization in the Agda proof checking system. The Device Calculus is a starting point for exploring the formal methods and security of high-level synthesis flows. We illustrate the Device Calculus with a number of examples of formally verifiable security templates-i.e., functions in the Device Calculus that express common security structures at a high-level of abstraction.
A Comparative Analysis on Hand Gesture Recognition using Deep Learning

In recent years, the vision-based innovation of hand motion acknowledgment is a significant piece of human computer interaction (HCI).In the last decades; keyboard and mouse play a significant role in human-computer interaction. However, owing to the rapid development of hardware and software, new types of HCI methods have been required. In particular technologies, such as speech recognition and gesture recognition receive great attention in the field of HCI. Hand gesture recognition is very significant for human-computer interaction. On survey many models were used to recognize hand gestures with different custom images from various datasets captured by camera. One of the approaches that highly used in image feature extraction is Convolution neural network (CNN). In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand gesture is detected from the trained dataset of images. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 2569 images show that our method performs well and is highly efficient. Finally we have shown the comparison among the CNN with Softmax and KNN classifier on the images which leads accurate result.
A Differentially Private Classification Algorithm With High Utility for Wireless Body Area Networks

The advancement of the wireless body area networks (WBAN) and sensor technologies allows us to collect a variety of physiological and behavioral data from human body. And appropriate application of machine learning methods can greatly promote the development of e-health. Nevertheless, the collected data contains personal privacy information. When using the machine learning methods to analyze the collected data, some information of the training data will be stored in the learning models unconsciously. To handle such information disclosure problem, we propose a differentially private classification algorithm based on ensemble decision tree with high utility for wireless body area networks. In order to improve the accuracy and stableness of classification, the bagging framework of ensemble learning is used in our algorithm. We aggregate the results of multiple private decision trees as the final classification in a weight-based voting way. For each private decision tree trained on the bootstrap samples, we offer a novel privacy budget allocation strategy that allows the nodes in larger depth to get more privacy budget, which can mitigate the problem of excessive noise introduced to leaf nodes to some extent. The better classification accuracy and stableness of this new algorithm, especially on small dataset, are demonstrated by simulation experiments.
Insult detection using a partitional CNN-LSTM model

Recently, deep learning has been coupled with notice- able advances in Natural Language Processing related research. In this work, we propose a general framework to detect verbal offense in social networks comments. We introduce a partitional CNN-LSTM architecture in order to automatically recognize ver- bal offense patterns in social network comments. Specifically, we use a partitional CNN along with a LSTM model to map the social network comments into two predefined classes. In particular, rather than considering a whole document/comments as input as performed using typical CNN, we partition the comments into parts in order to capture and weight the locally relevant information in each partition. The resulting local information is then sequentially exploited across partitions using LSTM for verbal offense detection. The combination of the partitional CNN and LSTM yields the integration of the local within comments information and the long distance correlation across comments. The proposed approach was assessed using real dataset, and the obtained results proved that our solution outperforms existing relevant solutions.
De-Identifying Swedish EHR Text Using Public Resources in the General Domain.

Sensitive data is normally required to develop rule-based or train machine learning-based models for de-identifying electronic health record (EHR) clinical notes; and this presents important problems for patient privacy. In this study, we add non-sensitive public datasets to EHR training data; (i) scientific medical text and (ii) Wikipedia word vectors. The data, all in Swedish, is used to train a deep learning model using recurrent neural networks. Tests on pseudonymized Swedish EHR clinical notes showed improved precision and recall from 55.62% and 80.02% with the base EHR embedding layer, to 85.01% and 87.15% when Wikipedia word vectors are added. These results suggest that non-sensitive text from the general domain can be used to train robust models for de-identifying Swedish clinical text; and this could be useful in cases where the data is both sensitive and in low-resource languages.
Use of Mobile Health in Infant and Young Child Nutrition: A Formative Study in Rural Maharashtra

Undernutrition is a major public health problem for under 5 years of age children in India. Approximately 41% and 21% of under 5 years of age children are stunted and wasted respectively. Despite the known importance of age appropriate infant and young child feeding practices for child nutrition, the rates of these practices remain poor in India. The major determinants for inappropriate IYCF practices are beliefs and knowledge of parents and caregivers. These can be effectively addressed through counselling by mobile Health technology as mobile phones are widely available and have a high penetration across the country. This formative research explored the perceptions of caregivers regarding infant feeding practices, feasibility of mobile phone for counselling and targeted messaging to mothers on appropriate infant feeding. We conducted in-depth interviews, focus group discussions and key informant interviews. The data were translated, transcribed and analysed using a thematic approach. We found that rural households have at least one mobile phone with good network connectivity. Utilizing mobile phones for counselling was found to be acceptable in the community, provided that the advice given is affordable, tailored to their cultural beliefs and socio-economic status.
Reasoning about Partial Correctness Assertions in Isabelle/HOL

Hoare Logic has a long tradition in formal verification and has been continuously developed and used to verify a broad class of programs, including sequential, object-oriented and concurrent programs. The purpose of this work is to provide a detailed and accessible exposition of the several ways the user can conduct, explore and write proofs of correctness of sequential imperative programs with Hoare logic and the ISABELLE proof assistant. With the proof language Isar, it is possible to write structured, readable proofs that are suitable for human understanding and communication.
Human face recognition methods based on principle component analysis (PCA), wavelet and support vector machine (SVM) : a comparative study

&lt;p class="JESTECAbstract"&gt;Human face Recognition systems are increasingly gaining more importance and can be utilized throughout many applications like video surveillance, Security, human-computer intelligent interaction, etc. this paper presents performance comparison between three feature extraction techniques for an automatic face recognition system. In the first step, we benefit from wavelet Transforms, Principal Component Analysis (PCA) and combining Wavelet with PCA as feature extracting methods. After feature vectors generation, linear and nonlinear Support Vector Machines (SVM) are usually used for implementing the classification or recognition step. These methods are compared on accuracy in an ORL database for face recognition applications including 400 images of 40 people.&lt;/p&gt;
From Fully-Supervised, Single-Task to Scarcely-Supervised, Multi-Task Deep Learning for Medical Image Analysis

Author(s): Imran, Abdullah-Al-Zubaer | Advisor(s): Terzopoulos, Demetri | Abstract: Image analysis based on machine learning has gained prominence with the advent of deep learning, particularly in medical imaging. To be effective in addressing challenging image analysis tasks, however, conventional deep neural networks require large corpora of annotated training data, which are unfortunately scarce in the medical domain, thus often rendering fully-supervised learning strategies ineffective.This thesis devises for use in a variety of medical image analysis applications a series of novel deep learning methods, ranging from fully-supervised, single-task learning to scarcely-supervised, multi-task learning that makes efficient use of annotated training data. Specifically, its main contributions include (1) fully-supervised, single-task learning for the segmentation of pulmonary lobes from chest CT scans and the analysis of scoliosis from spine X-ray images; (2) supervised, single-task, domain-generalized pulmonary segmentation in chest X-ray images and retinal vasculature segmentation in fundoscopic images; (3) largely-unsupervised, multiple-task learning via deep generative modeling for the joint synthesis and classification of medical image data; and (4) partly-supervised, multiple-task learning for the combined segmentation and classification of chest and spine X-ray images.
Overview of Moving Object Detection and Tracking

Moving object detection and tracking is one of the critical areas of research for various applications of computer vision and image processing such as pedestrian detection, traffic monitoring, security surveillance and so forth. Object tracking is the key step in video surveillance, public safety, and traffic analysis, remote sensing applications. Though the latest moving object detection methods provide promising results, accurate detection is still tedious task because of various challenges like illumination issues, occlusion, and background objects in an uncontrolled environment. Here going to discuss the most common challenges of accurately detecting moving objects, gives an overview of existing methods for detecting moving objects and tracking.
The Dynamic Time Warping Distance Measure as Q U BO Formulation

Dynamic Time Warping (DTW) is a representative of a distance measure that is able to calculate the distance between two time series. It is often used for the recognition of handwriting or spoken language. The metaheuristic Quantum Annealing (QA) can be used to solve combinatorial optimization problems. Similar to Simulated Annealing it seeks to find a global minimum of a target function. In order to use specialized QA hardware, the problem to be optimized needs to be translated into a Quadratic Unconstrained Binary Optimization (QUBO) problem. With this paper we investigate whether it is possible to transfer the DTW distance measure into a QUBO formulation. The motivation behind is the hope on an accelerated execution once the QA hardware scales up and the aspiration of gaining benefits due to quantum effects that are not given in the classical calculation. In principle, we find that it is possible to transform DTW into a QUBO formulation suitable for executing on QA hardware. Also, the algorithm returns not only the minimum total distance between two sequences, but also the corresponding warping path. However, there are several difficulties that make a manual intervention necessary.
Human-machine sense making in context-based computational decision

In this chapter, we present what should be the inner structure of a decision algebra whose motivation is to fill a causal break induced by context invalidity to ultimately permit human-machine interactions. It turns out that such a decision structure can be qualified using the context change arrow as a disruptive process or as a phase transition according to the geometrical representation of computational contexts as double S-curves. In particular a computational context is always characterized by a shift between sense making and temporal causality. That is, once a causal break occurs, it has to be filled locally by a decision. A causal break is always continuous and never discrete; in effect, in the discrete case, combinatoric analysis causes unwanted complexity due to a lack of knowledge, whereas we need full knowledge thanks to a very precise semantic of a causal break. Intuitively, rather than separating brutally models and counter-models as a proof can do by setting a strong negation operator, we prefer to use the continuous inference as an implementation of sense making. Sense is that way taken as the rationality of the transition. Full knowledge requires a special structure, a Hopf algebra in which the continuous property we cannot implement is replaced by the computable co-continuous property in the co-algebraic component of the decision Hopf algebra. We hope that thanks to co-continuous structures and to co-dimensional exterior algebras, we’ll be able to find out a representation of the continuous inference able to compute a decision rather than admitting definitely and desperately that “such a mechanism is totally out of bounds” and will never ever concern a machine.
Survey on Image Forgery Detection using Machine Learning Classifier

Image is a very powerful media of communication. Nowadays, most of the people like to take pictures and manipulate the picture using free editing software for example is Photoshop, picsart, etc. Images play very important part in our daily life and it is not much of complex to make edited image using software and it's not required any particular knowledge but it is very difficult to recognize a forged image. We can also see bad image editing in social media, photography. It comes under illegal activities. So image forgery detection is very important. In this paper, we have compared the different techniques for forgery detection of image and machine learning classifiers.
DUOLINGO AS AN EDUCATIONAL LANGUAGE TOOL TO ENHANCE EFL STUDENTS’ MOTIVATION IN WRITING

Duolingo is one of the modern applications that facilitate acquiring a second language.This research aims at invesitigating students’ motivation in writing through Duolingo as one of educational language tool. This study used descriptive qualitative method to explore and describe the phenomenon. Subjects of this study were 40 students of English department, which consisted of 10 male and 30 female. Data collection techniques in this study were interviews, while the data analysis techniques in this study were data reduction, data presentation, and conclusions. The study revealed that the use of Duolingo application was quite effective to enhance the students’ motivation in writing. This study showed that Duolingo is an app where every beginner can start learning a second language as it motivates him / her to keep on making progress. Nevertheless, Duolingo also needs improvement to cover more vocabulary, grammar rules and languages, especially English, since it is the lingua franca of the world.
Corpus Development for Malay Sentiment Analysis Using Semi Supervised Approach

Research on sentiment analysis have gained so much interest currently. However, research on Malay sentiment analysis and the availability of the resources is still lacking. The aim of this study is to develop a Malay sentiment corpus using a semi-supervised approach. Data from Twitter have been used in this corpus development. The corpus is developed using the combination of lexicon and machine learning approach. The sentiment lexicon will be used to build the seed of training data from unlabelled data resources. In addition, sentiment emoticons are used to compare the accuracy of the lexicon-based approach. After preparation of training data set, the process of adding new training data instances will be carried out using the seed set and machine learning classification method. The process of classification using machine learning approach consists of pre-processing, feature extraction and classification. Five types of classifiers are considered for the classification task. Based on the experimental results, the lexicon-based approach and Multinomial Naïve Bayes algorithm is the best classifier for Malay sentiment corpus development.