Reviewerly/exHarmony huggingface dataset:
Visual Tracking Algorithm with Multi-Gaussian Correlation Filters Based on Confidence Feedback Adjustment

In order to improve the speed and robustness of hierarchical convolutional features for visual tracking algorithm, the multi-Gaussian correlation filters for visual tracking based on confidence feedback adjustment is proposed. Our method extracts features for tracking from two appropriate convolutional layers, and proposes the average feature energy ratio to reduce the feature dimensions, along with the sparser model update scheme avoiding over-fitting to improve the tracking speed. Then different Gaussian labeled functions are input to construct the multi-Gaussian correlation filters and adaptively fuse all the predicted results of multiple filters, expecting for higher accuracy. Finally, it introduces the average peak-to-correlation energy and the maximum response ratio to evaluate the tracking results, and adjust the learning rate and search area to further improve the robustness of the tracker. This algorithm is tested on the OTB100 benchmark datasets. The results show that it has an average distance precision of 88.6%, an improvement of 4.9 percentage points over the original hierarchical convolutional features for visual tracking approach, as well an average speed of 29.2 frames per second, 3 times that of the original method. The proposed multi-Gaussian correlation filters can effectively improve the speed and accuracy and has good robustness whatever challenges like occlusion, deformation and similar background interferences subjecting to.
Purely Logical Sequent Calculus

In this chapter we consider a popular version of sequent calculus called G3. In sections 3.2 and 3.3 we focus on different strategies of proving admissibility of cut and some auxiliary results. Section 3.4 is devoted to additional topics connected with different ways of interpretation of sequents. As a by-product of these considerations we will provide a Post-style completeness proof via conjunctive normal forms in the setting of G3. In section 3.5 a general survey of different types of SC, including nostandard and generalised systems, is sketched. Summary of kinds of rules and their properties is provided for better characterization of standard SCs. In the next section we provide three different proofs of the interpolation theorem, all constructive and based on the cut elimination theorem. The remaining sections contain the presentation of tautology elimination rule, which is equivalent to cut, and a proof of a refined version of the strong completeness theorem for nonstandard SC with elimination rules.
Visualizing 3D Data

Humans are creatures living in a three-dimensional world with eyes that only see two dimensions. The brain uses the visual information received from both of our eyes to perceive depth, allowing humans to have stereoscopic vision. Visual cues such as shading, shadows, and perspective also help give us the perception of depth. Our eyes are even able to understand three dimensions when shown an image or video.
Developing a Deep Neural Networks Method based on Sentiment Embeddings for Sentiment Analysis

Most of the existing learning method based on word vector, therefore, only the grammatical context modeling, and ignored the word of emotional information, can't very well solve the emotion classification task. In this paper, for sentiment analysis, an improved Deep Neural Networks method based on sentiment embeddings is proposed. Firstly, two strategies are generated for emotional coding of sentence polarity and contextual word information, and then emotional information in the text is combined with the contextual words of the current word to enhance the emotional information of the word vector. And then, an active learning method under deep confidence network is constructed by the adaptive learning method of deep confidence network, which improves the text classification ability and effectively solve the problem of long text sentiment classification sample selection in semi-supervised learning method. The validity of the proposed model is verified by quantitative experiments on word levels in different language and different dictionary data sets. The correct rate of sentiment classification has been further improved, which further confirms the application value of the model.
Pemetaan Pola Progresi Chord Berdasarkan Kombinasi Interval

Various phenomena, concepts and theories in music can be explained by mathematics. Moreover, it can be an approach in the process of song creation, composition, or music arrangement. Related to this, this study discusses the mapping of chord progressions based on interval combinations in chromatic circles. To be more specific, by connecting the musical notes with lines that represent various combinations of two specific types of intervals into a series of notes (chord progression patterns) in one loop ( starts and ends at note C). The interval combination pattern refers to the smallest size, namely m2 (minor second /minor second, 1 semitone step). This research was conducted to produce various patterns of unconventional chord progression patterns viewed through a tonal harmony system. This research is motivated by the strong influence of the tonal harmony system limiting creativity in the music creation process. Therefore, it takes an approach, method, or other system that needs to be tested to get out of the system. The result of this research is that there are 32 variations of chord progression patterns (series of notes) which are summarized in geometric shapes. By comparing the geometric shapes, the patterns appear mathematically, systematic, and symmetrical. In its application, these patterns seem unconventional, come out of a tonal harmony system (it can still be studied through the system, depending on the chord shape used) and produce a unique sound. Keywords: chord progression, interval, chromatic circle, music creation
Cloud Architecture Implementation

Ten years ago, creating an application infrastructure was a nightmare. We had the physical servers, where we needed to install step by step each package and application that was needed in our system. There, we need a lot of detailed knowledge in networking, servers, operative, systems and so on. Moreover, if we needed to create multiple infrastructures for multiple environments, this was pretty difficult as we didn't have all the details regarding how that environment was set up – and besides, we did this manually.
THE EFFECT OF THE PICTURE AND PICTURE MODEL ON THE LEARNING OUTCOMES OF GRADE II ELEMENTARY SCHOOL STUDENT

Learning activities achieved affect the level of understanding of students who impact on learning outcomes, especially in writing skills. The purpose of this study was to find out about the results before and after the use of picture and picture models and the influence of the use of picture and picture models on student learning outcomes in narrative story writing skills in class II SDN Ceweng. Data collection techniques used through observation, tests and documentation. The type of approach used is experimental pre-experimental designs with one-group pretest-posttest design. Data analysis techniques include: (1) normality test and (2) Hypothesis test or T-test. The results of this study can be seen from the results of the calculation in the paired samples test table. If the value of tcount&gt; ttable, then Ho is rejected and H1 is accepted. Based on the above analysis, the result of tcount = 21.777 and df = 35 while ttable = 2.04. Shows that the results of testing the hypothesis Ho are accepted and H1 is rejected. With significance 2 tailed 0,000 &lt;0.05. Meaning strong and there is a significant influence on student learning outcomes.
Implementasi Steganography Pada Audio Menggunakan Algoritma End of File (EoF)

Steganography is the art and science of writing hidden messages or hiding messages in a way so that apart from the sender and the recipient no one knows or is aware that there is a secret message. The message is hidden into a media that is audio. One of the methods used in hiding information into audio is the End of File (EOF) method. The purpose of this study is to implement the EOF method to insert messages into audio files. The EOF method works by adding message decimal values to audio files
PENERAPAN MODEL PEMBELAJARAN LANGSUNG DALAM MENINGKATKAN HASIL BELAJAR MATERI PERILAKU TERPUJI PADA PELAJARAN PENDIDIKAN AGAMA ISLAM

This study aims to improve student learning outcomes on the material of commendable behavior in Grade VIIB SMP Negeri 4 Gantung Belitung Timur. This research is Classroom Action Research consisting of two cycles. Each cycle consists of 2 times meeting. The subject of this research is the students of Grade VIIB SMP Negeri 4 Gantung Belitung Timur which amounted to 33 students consisting of 16 men and 17 women. The instruments of data collection in this study are a test, observation, and discussion. After going through the learning process, the results from the end of this study shows that there is an increase both in the student learning activity and student learning outcomes. In the precycles, active students are only about 20%. Then in Cycle 1 the number of active students increased to 40% and in Cycle 2 they increased to 80%. The acquisition of student learning outcomes also experienced an increase from Precycle to Cycle 2, i.e. in the Precycle 45% of students got value above KKM, in Cycle 1 increased to 66%, then in Cycle 2 increased again to 84%. Therefore, the direct learning model has a positive impact on the process of improving student learning outcomes on the materials of commendable behavior in Grade VIIB SMP Negeri 4 Gantung.
INCREASING MOTIVATION AND SOCIAL STUDIES LEARNING OUTCOMES THROUGH VARIED DEMONSTRATION METHODS OF ELEMENTARY SCHOOL STUDENTS

The purpose of this study was to increase the students’ motivation and learning outcomes on the material of documents and valuable objects at grade II elementary school in the academic year 2017/2018. The research was conducted by involving 13 children as the obejcts. The type of this research was classroom action research which was carried out in two cycles. In the first cycle, the varied demonstration method was carried out classically and in the second cycle, it was carried out in groups. Each cycle consisted of four stages: planning the action, implementing the action as planned, observing the results of the action, and reflecting on the implementation of the action. The tools for data collection were the document of students’ grade list, observation sheets, and question sheets since the data were collected using documentation, observation, and test. The results showed that the varied demonstration method improved the students’ learning outcomes in social science subject especially on the material of documents and valuable objects at grade II in the academic year 2017/2018. This was proved by an increase with a success rate of 57.80% in cycle I, and 93.33% in cycle II.
Methodical interpretations on the organization of independent educational classes in the subject of painting

In the article, recommendations were developed on the planning of Independent Education of students on the subject of painting, its organization and creating the necessary conditions for this, as well as teaching the students to study more in classrooms, showing ways of obtaining knowledge and organizing independent educational sessions
Real-Time Person Segmentation – Based on Body Pix

In this paper, we propose a novel semantic segmentation-based on the body pix module of the Tensor flow.js which can keep up with the accuracy of the state-of-the art approaches while running in real time. The solution follows the convolutional neural networks, each step in the workflow being enhanced by additional information from semantic segmentation. Therefore, we introduce several improvements to computation, aggregation, and optimization by adapting existing techniques to integrate additional surface information given by each semantic class. Using the body pix model which is trained using CNN, the ResNET50, this network can work with more than 150 layers, removing the problem of vanishing gradients. Using this network our body pix module, creates a more accurate and defined segmentation, and also supports multi-person segmentation.
Blockchain Applications In The Insurance Industry

The report surveys the possible applications of blockchain technology in the insurance industry. Special attention is paid to its potential to increase the competitiveness of insurers by making their operations more transparent and effective. The Blockchain will have a long-term effect on the insurance business, as it automates the processing of risk data on a consensus-based algorithm, supports the detectability of insurance fraud and speeds up the payments of insurance indemnities through encrypted transfers. The proof of this is the increasing demand for blockchain technologies from insurance companies and their loyal clients in recent years. Some of blockchain innovative applications are smart insurance contracts, P2P insurance and index-based insurance.
Development of an Expert System for Early Detection of Heart Disease Using The Bayes Theorem Method

Coronary heart disease (CHD) is the leading cause of death and disability in developed and developing countries. In 2014 it was also reported that coronary heart disease is one of the causes of death for both men and women, starting in the 25-29 age group and increasing with age. The highest prevalence of coronary heart disease as a cause of death in the 60-64 year age group and in the 70-74 year age group. The development of science and technology has made information technology usable in various fields including health. So it is hoped that by making an application that can make it easier for users to detect heart disease early and independently. The purpose of this study is to build an application by implementing an expert system using the website-based Bayes Theorem method as a tool for diagnosing heart disease. This application was built using the web programming language & MySQL database. Expert system application to help detect heart disease early.
Dominasi Struktur Kalimat dan Pilihan Kata dalam Esai Mahasiswa Asing pada Tes Kemampuan Berbahasa Indonesia

This study aims to describe the sentence structure and choice of words in foreign students' essays on the Indonesian language proficiency test. This research is qualitative with descriptive method. The data source of this research is the essay of foreign students who took the Indonesian language proficiency test. The research data are Indonesian words and sentences contained in the student essays. The data analysis technique was carried out through the steps, namely (a) data identification, (b) data classification, and (c) data interpretation. The results showed that foreign students were able to write simple sentences in Indonesian, but had difficulty understanding the affix process.
KEYNOTE SPEECH I AI and Machine Learning for Disease Incidence Modeling

Vector borne diseases have been a serious concern all over the world. Due to various factors, South Asian and South East Asian region have seen a surge in incidences of dengue, malaria, chicken guinea, encephalitis etc. Many of such diseases are having geographic significance and many times have been found to be seasonal. Earlier researches have indicated the influence of climatic factors over appearance of disease cases and outbreaks. This study investigated the influence of climate factors on the incidence of malaria in the in India. WEKA machine learning tool with two classifier techniques, Multi-Layer Perceptron (MLP) and J48 were used with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model amongst the techniques concerning the prediction accuracy of malaria in the context of varying climate. The results suggested J48 had exhibited better skill to MLP with the 10-fold cross-validation method had better performance over the percentile Spilt and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had better association with malaria incidents compared to rainfall and the performance was better during the monsoon and post-monsoon when the incidents are at the peak.
Emotional Programmer's Behavior to Respond Problems by Using a Decision Tree

The development of science and technology is very rapidly, resulting in employees having to change their working systems in based on demand that exist today. In an increasingly complex modern life, humans will tend to experience emotion when humans are less able to adapt their desires to the reality, both the reality inside and outside themselves. All kinds of emotional forms are basically caused by a lack of understanding of human beings on their own limitations. Emotional behavior of employees occur at PO Pambudi Jaya is mainly part of Programmer staff. Programmers alwayswork with high pressure and tight schedules. Programmers always had been relationship with other employees and where is often misunderstandings from it. Whereas a programmer always have problem with their emotional, especially if the Programmer is under high pressure and other employees are trying to ask about the problem. In this study the application of data mining using the decision tree method and J48 algorithm to determine the emotional behavior of the programmer in response to problems. The data mining analysis process in this study uses WEKA 3.9.3 data mining application software. The J48 algorithm classification results in classifying data achieve accuracy above 80% with educational attributes are attributes that have the highest Gain value.
Efficient Volumetric Mapping Using Depth Completion With Uncertainty for Robotic Navigation.

In robotic applications, a key requirement for safe and efficient motion planning is the ability to map obstacle-free space in unknown, cluttered 3D environments. However, commodity-grade RGB-D cameras commonly used for sensing fail to register valid depth values on shiny, glossy, bright, or distant surfaces, leading to missing data in the map. To address this issue, we propose a framework leveraging probabilistic depth completion as an additional input for spatial mapping. We introduce a deep learning architecture providing uncertainty estimates for the depth completion of RGB-D images. Our pipeline exploits the inferred missing depth values and depth uncertainty to complement raw depth images and improve the speed and quality of free space mapping. Evaluations on synthetic data show that our approach maps significantly more correct free space with relatively low error when compared against using raw data alone in different indoor environments; thereby producing more complete maps that can be directly used for robotic navigation tasks. The performance of our framework is validated using real-world data.
Automating the censorship arms race

Evading oppressive internet censorship is possible, but discovering how is difficult and time-consuming for humans. Geneva is a genetic algorithm that automatically discovers and implements censorship circumvention strategies---many of which were long thought impossible.
A Robust Trajectory Similarity Measure Method for Classical Trajectory

In view of the great difference between classical trajectory and real-time trajectory, a robust trajectory similarity measurement method is proposed based on the longest common subsequence theory. Firstly, the distance between point and line segment is used to judge whether the point of classical trajectory is consistent with the line segment of real-time trajectory. Secondly, the longest common subsequence length between classical trajectory and real-time trajectory is calculated by using the improved multi-to-one longest common subsequence algorithm. Finally, the ratio of the longest common subsequence length to the number of points of the classical trajectory is taken as the similarity between the classical trajectory and the real-time trajectory. Experiments show that the algorithm is robust and can effectively solve the problem of similarity measurement between the classical trajectories and real-time trajectories.
Research work in the Applied Cognitive Modelling Lab

Dr Junya Morita is based at the Applied Cognitive Modelling Laboratory (ACML) within the Department of Behavior Informatics at Shizuoka University in Japan. His team is conducting investigations that use computational models in an effort to improve our understanding of human minds and their inner workings. There are currently two directions of study underway at ACML. The first is concerned with theoretical studies of cognitive modelling, where the team try to construct models that explain human minds as computational and algorithmic levels. The second direction of study is the application of computational cognitive models. Morita and his team believe that there are fundamental values within the basic endeavours of cognitive science and are working to prove these values exist and are valid. Current topics of application include education, driving, entertainment, graphic design, language development, web navigation and mental illness.
Mapping SETMAT practices to SPEM

ESSENCE, the Software Engineering Method and Theory (SEMAT) extensible Kernel, allows description of software development initiatives. One of the extensions is SETMAT, the Software Engineering Teaching Method and Theory, which provides a set of new sub-alphas and activity spaces aiming at the description of software engineering teaching initiatives such as software-related university courses. Different alternatives for describing the elements of SETMAT would eventually provide more freedom for adopters by widening the representations options. In this paper we propose mapping SETMAT elements to SPEM (the Software Process Engineering Meta-model) to provide another representation alternative for SETMAT practices. We also provide an example by making use of EPF Composer and SPEM to map a previously created SETMAT practice for a Software Analysis and Design course. We also argue that by making use of a platform like EPF Composer both software engineering professors and practitioners will be able to instrument and enact the practices as concrete processes.