Reviewerly/exHarmony huggingface dataset:
A PGD-based Method for Robot Global Path Planning: A Primer

The present paper shows, for the first time, the technique known as PGD-Vademecum as a global path planner for mobile robots.The main idea of this method is to obtain a Vademecum containing all the possible paths from any start and goal positions derived from a harmonic potential field in a predefined map.The PGD is a numerical technique with three main advantages.The first one is the ability to bring together all the possible Poisson equation solutions for all start and goal combinations in a map, guaranteeing that the resulting potential field does not have deadlocks.The second one is that the PGD-Vademecum is expressed as a sum of uncoupled multiplied terms: the geometric map and the start and goal configurations.Therefore, the harmonic potential field for any start and goal positions can be reconstructed extremely fast, in a nearly negligible computational time, allowing real-time path planning.The third one is that only a few uncoupled parameters are required to reconstruct the potential field with a low discretization error.Simulation results are shown to validate the abilities of this technique.
Learning Smart Contracts for Business Environment

Currently the search for a decentralized data model in companies for its big advantage in removing the middleman has been increasing. For that reason, DLT (Distributed Ledger Technology) technologies have gained a lot of visibility in the business world, the most well-known being Blockchain and its emerging Smart Contracts. The identified problem is the lack of knowledge and skill of companies in the domain of the rising Smart Contracts. In this paper, we propose a generic model that could increase the competence of companies in this field by creating a step-by-step tutorial on how to set up the development environment of Smart Contracts.
TwistBytes -- Hierarchical Classification at GermEval 2019: walking the fine line (of recall and precision)

We present here our approach to the GermEval 2019 Task 1 - Shared Task on hierarchical classification of German blurbs. We achieved first place in the hierarchical subtask B and second place on the root node, flat classification subtask A. In subtask A, we applied a simple multi-feature TF-IDF extraction method using different n-gram range and stopword removal, on each feature extraction module. The classifier on top was a standard linear SVM. For the hierarchical classification, we used a local approach, which was more light-weighted but was similar to the one used in subtask A. The key point of our approach was the application of a post-processing to cope with the multi-label aspect of the task, increasing the recall but not surpassing the precision measure score.
Debiasing Personal Identities in Toxicity Classification

As Machine Learning models continue to be relied upon for making automated decisions, the issue of model bias becomes more and more prevalent. In this paper, we approach training a text classifica-tion model and optimize on bias minimization by measuring not only the models performance on our dataset as a whole, but also how it performs across different subgroups. This requires measuring per-formance independently for different demographic subgroups and measuring bias by comparing them to results from the rest of our data. We show how unintended bias can be detected using these metrics and how removing bias from a dataset completely can result in worse results.
Smart Contractsas Techno-Legal Regulation

Smart contracts on a blockchain network can be implemented to control digital value. A key question that arises is the extent to which smart contracts can, or should, operate as “smart legal contracts”. Simply put, can smart contracts meet requirements of validity at law and practical efficacy. In order to achieve the goal of value maximization, the efforts of policy-makers, standards organisations and regulators should be informed by first principles. Standards, and other regulatory activities, must be driven by consideration of the techno-legal functions of contracting. Blockchain-based smart contracts offer the potential to reduce transaction costs through new methods of stateful computation. When applied to commercial transactions, smart contracts can represent enforcement of an executed state. This paper argues that distributed ledger and smart contracts standards should seek to provide sufficient flexibility to facilitate contracting parties to coordinate in an optimal manner.
L2P: An Algorithm for Estimating Heavy-tailed Outcomes

Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, demand for commodities in warehouses, etc. By learning heavy-tailed distributions, big and rare instances (e.g., the best-sellers) will have accurate predictions. Most existing approaches are not dedicated to learning heavy-tailed distribution; thus, they heavily under-predict such instances. To tackle this problem, we introduce Learning to Place (L2P), which exploits the pairwise relationships between instances for learning. In its training phase, L2P learns a pairwise preference classifier: is instance A > instance B? In its placing phase, L2P obtains a prediction by placing the new instance among the known instances. Based on its placement, the new instance is then assigned a value for its outcome variable. Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and ability to reproduce heavy-tailed outcome distribution. In addition, L2P provides an interpretable model by placing each predicted instance in relation to its comparable neighbors. Interpretable models are highly desirable when lives and treasure are at stake.
Bit Allocation Algorithm based on SSIM for 3D Video Coding

The 3D video system has broad application prospects and has become a new research hotspot at home and abroad in the video field.However, there are still many problems in multi-view video coding rate control in a three-dimensional video system.Therefore, this paper proposes a rate allocation algorithm based on structural similarity index measurement (SSIM) for 3D video coding.In this paper, we first analyze the correspondence between the weights of inter-view rate and the correlation between viewpoints, and then we establish the multi-view video main view and non-main view bit allocation calculation model.Finally, the view layer, frame layer, and macro block layer respectively perform bit allocation and rate control.The experimental results show that the proposed method can effectively control the bit rate of multi-view video coding while maintaining the multi-view video coding quality under limited bandwidth compared with the existing view layer fixed ratio allocation rate.
Trend Topic Analysis using Latent Dirichlet Allocation (LDA) (Study Case: Denpasar People’s Complaints Online Website)

According to the publication of the Central Bureau of Statistics 2017, the population of Denpasar people has increased to 914,300 people. The Increasing number of the population raises various problems that must be faced by the Denpasar’s Government. The variety of problems is in line with the increase in complaints data posted through Denpasar people’s complaints online website, which made it difficult to know the main topics of the problems. The purpose of this research is to find the main topics of complaints Denpasar residents quickly and efficiently. The method used to achieve the objective of the research is Latent Dirichlet Allocation topic models with Gibbs sampling parameter estimation. The number of topics obtained through the highest log-likelihood value -42,528.84, the value is in the number of topics 19. The trending topic was based on the highest topic probability, topic 4, with a topic probability value 0.055. Based on these results, the trend of a topic is on topic 4 which can be interpreted that many residents of Denpasar complained about damaged roads and requested to fix the roads.
What is needed for simple spatial language capabilities in VQA?

Visual question answering (VQA) comprises a variety of language capabilities. The diagnostic benchmark dataset CLEVR has fueled progress by helping to better assess and distinguish models in basic abilities like counting, comparing and spatial reasoning in vitro. Following this approach, we focus on spatial language capabilities and investigate the question: what are the key ingredients to handle simple visual-spatial relations? We look at the SAN, RelNet, FiLM and MC models and evaluate their learning behavior on diagnostic data which is solely focused on spatial relations. Via comparative analysis and targeted model modification we identify what really is required to substantially improve upon the CNN-LSTM baseline.
Individualised modelling of affective data for intelligent tutoring systems: lessons learned

One on one tutoring from human expert tutors to human students is the most effective form of instruction found to date. There are many actions that human tutors perform which make them remarkably effective, including the attention that they pay to the cognitive and affective states of the human students that they tutor, and the use of this knowledge to modify the way that they instruct the material. According to theoretical models, learner state data is used to inform instructional data and decisions, which then influences the learning of the student. Naturally, the data about student state must be available in order to be used to adjust the instruction. Success amongst operational systems, however, has not been observed with generalised modelling techniques. Individualised and adaptive modelling techniques from other domains in the literature present an alternative to the approach which is not observing significant operational success. This work investigates individualised adaptive models, validates the approach, and shows that it can produce models of acceptable quality, but that doing so does not obviate the experimenter from creating quality generalised models prior to individualising.
DEVELOPING ENGLISH SUPPLEMENTARY LOCAL WISDOM-BASED LISTENING MATERIALS USING NATURAL READER 14 APPLICATION

This study aimed at a) describing the topics needed to be developed for English supplementary local wisdom-based listening materials for the eighth grade students based on Curriculum 2013 at SMP Negeri 2 Singaraja; b) describing the process of developing the English supplementary local wisdom-based listening materials for the eighth grade students at SMP Negeri 2 Singaraja; c) assessing the quality of the developed product. This study implemented Design and Development research by Richey and Klein (2007). The phases were analysis, design, development, and evaluation. The data were obtained from document study, observation sheet, interview guide, questionnaire and a scoring rubric. The data were analyzed qualitatively and quantitatively. The results of this study show that (a) the developed topics are (1) giving and responding to compliment, (2) asking and giving opinions, (3) asking and giving information about ability and willingness, (4) asking and giving information about a must, prohibition, and suggestions, (5) asking and giving instruction, invitation, and permission, (6) asking and giving information about special events or days, (7) asking and giving information about the location, things, and animals; (b) the processes of developing the materials are analyzing the English syllabus, four potencies of local wisdom of Bali, and present situation at SMPN 2 Singaraja, designing a blue print of the product, developing a blue print into a complete listening materials, and evaluating the developed materials by four expert judges; (c) the result of product evaluation done by the four expert judges shows that the developed product is categorized as excellent materials. The outcome of this study is the product which is in the form of audio recording (CD-RW) and the printed listening materials along with the answer key and listening scripts.
RANCANG BANGUN VENTURI SCRUBBER TERHADAP PENURUNAN EMISI JELAGA

The industrial world and vehicles that currently exist make the pollution level increase. Often we see thick smoke rising out of the factory chimney, the thick smoke is a waste of gas released by the environmental factory. The scrubber system is a collection of various air pollution control devices that can be used for particles and / or gases from industrial output gas streams. The term scrubber is also used to describe a system that injects or enters a dry active ingredient or liquid into a dirty gas stream to clean acid gas. The design of the venturi scrubber aims to design a venturi scrubber to reduce soot emissions on direct injection diesel engines, get a standard venturi scrubber design and reduce soot levels. This design begins with determining the alternative design, determining the venturi dimensions and the material that will be used to build the venturi scrubber. The result of this step is a working drawing that is ready to be realized through machining. Next is to make the components based on the design results in the previous step. These components are then assembled into a tool. The final step is to do the venturi design test according to the venturi type standard and the size requirements that are in the ISO 5167-4 standard. Making a venturi scrubber design program using Delphi-7 software with pressure drop data, liquid to gas ratio, diameter ratio, pipe diameter, throat diameter, renold number and type of flow that occurs and design using Solidworks 2010.
Tracking Algorithm Based on Dual Residual Network and Kernel Correlation Filters

Visual target tracking is a target detection task for a period of time. During this period, the tracking target will undergo significant appearance changes due to deformation, sudden movement, complex background and occlusion. These changes make visual target tracking challenging. In this paper, a target tracking algorithm based on dual residual neural network and kernel correlation filters is proposed, which mainly solves the problems of inaccurate tracking. This method combines depth residual neural network with kernel correlation filter tracking algorithm. The template matches the depth residual feature to determine the location of the target and the kernel correlation filter based on residual feature is designed to detect the target, finally, the template matching result and kernel correlation filtering result are fused to determine the location of the target. The experimental results on a large-scale standard data set show that the proposed algorithm has the advantages of high accuracy. Compared with the previous algorithm, the proposed algorithm has good performance in tracking visual deformation, occlusion and fuzzy video objects.
Object Tracking and Identification by Quantum Radar

Quantum Radar is a promising technology that could have a strong impact on the civilian and military realms. In this study we introduce a new concept design for implementing a Quantum Radar, based on the time and polarization correlations of the entangled photons for detection and identification and tracking of high-speed targets. The design is focused on extracting high resolution details of the target with precision timing of entangled photons that provides important operational capabilities like distinguishing a target from a decoy. The quantum entanglement properties guarantee the legitimacy of the photons captured by the search telescope. Time correlations of the photon detection events can be extracted via cross-correlation operation between two sets of photon detection time-tags for the entangled photons. The fact that the wavelengths of the entangled photons can be tuned also makes the Quantum Radar concept an enticing candidate for tracking stealth objects. We present the proof-of-principle test results of the Quantum Radar and discuss the technical challenges and limitations of the design.
A Frame Work for Hospital Readmission Based on Deep Learning Approach and Naive Bayes Classification Model

From the last few years has seen an eruption in the quantity of digital data stored in electronic health records (EHR).Presently the most difficult thing in world that was facing by patients and doctors was when to join hospital.And how to start treatment for patent.If a person was sick, he wants to go which hospital and met which kind of doctor for his problem.When a doctor diagnoses a person for some decease he needs his past medical history.Previously, these medical prescriptions were written on papers.But these papers are no longer available.Presently, the better choice is Electronic health records (EHR).But EHR data is a heterogeneous in nature, in order to process heterogeneous data we use deep learning based feature extraction method, and for predation we use navie basian classifier to make prediction.Here in this paper we propose a frame work based on VAR and Skipgram method to take features using those feature we use basian classification to predict the readmission into hospital.
Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability

Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues. However, existing methods either take too many manual efforts (e.g. reinforcement learning methods) or cannot guarantee the dialogue efficiency (e.g. sequence-to-sequence methods). In this paper, we address this problem by proposing a novel end-to-end learning model to train a dialogue agent that can look ahead for several future turns and generate an optimal response to make the dialogue efficient. Our method is data-driven and does not require too much manual work for intervention during system design. We evaluate our method on two datasets of different scenarios and the experimental results demonstrate the efficiency of our model.
A Simple Recommender Engine for Matching Final-Year Project Student with Supervisor

This paper discusses a simple recommender engine, which can match final year project student based on their interests with potential supervisors. The recommender engine is constructed based on Euclidean distance algorithm. The initial input data for the recommender system is obtained by distributing questionnaire to final year students and recording their response in CSV format. The recommender engine is implemented using Java class and application, and result of the initial tests has shown promises that the project is feasible to be pursued as it has the potential of solving the problem of final year students in finding their potential supervisors.
Regression on imperfect class labels derived by unsupervised clustering

Outcome regressed on class labels identified by unsupervised clustering is custom in many applications. However, it is common to ignore the misclassification of class labels caused by the learning algorithm, which potentially leads to serious bias of the estimated effect parameters. Due to their generality we suggest to address the problem by use of regression calibration or the misclassification simulation and extrapolation method. Performance is illustrated by simulated data from Gaussian mixture models, documenting a reduced bias and improved coverage of confidence intervals when adjusting for misclassification with either method. Finally, we apply our method to data from a previous study, which regressed overall survival on class labels derived from unsupervised clustering of gene expression data from bone marrow samples of multiple myeloma patients.
Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models

In this chapter, we propose an image segmentation method based on a spatially constrained inverted Beta-Liouville (IBL) mixture model for segmenting medical images. Our method adopts the IBL distribution as the basic distribution, which can demonstrate better performance than commonly used distributions (such as Gaussian distribution) in image segmentation. To improve the robustness of our image segmentation method against noise, the spatial relationship among nearby pixels is imposed into our model by using generalized means. We develop a variational Bayes inference algorithm to learn the proposed model, such that model parameters can be efficiently estimated in closed form. In our experiments, we use both simulated and real brain magnetic resonance imaging (MRI) data to validate our model.
Communication on Requirements Elicitation and Interaction Design through SPIDe (S)

Participatory Design has a large number of techniques that can be used for requirements elicitation and interaction design.However, choosing a technique (or set of them) suitable for both processes can be challenging.In this sense, in this paper, we present a semio-participatory methodological process -SPIDe -for requirements elicitation integrated with interaction design, by means of a case study, with the objective of investigate if the results of SPIDe application satisfies the needs and desires of users, and how communication process occurs during SPIDe application.This paper contributes to the use of Semiotics and Participatory Design for requirements engineering and interaction design, to the integration of both areas, and to the SPIDe application.
Significance of the Background Image Texture in CBIR System

The Content Based Image Retrieval (CBIR) using local texture feature is extensively studied for decades. The local texture features such as LBP, LTP, LDP, and other derived local texture features uniquely describes the content of an image. The performances of the local texture features with different classifiers are well exposed by the researchers. The purpose of this paper is to explore the effect of the LBP texture feature due to simple and complex background in the image. The experiments are done with the Wang database using various distance measures. The results are promising and it shows the LBP feature is biased by the complex background and it affects the performance of the CBIR system.
Towards Efficient Discriminative Pattern Mining in Hybrid Domains

Discriminative pattern mining is a data mining task in which we find patterns that distinguish transactions in the class of interest from those in other classes, and is also called emerging pattern mining or subgroup discovery. One practical problem in discriminative pattern mining is how to handle numeric values in the input dataset. In this paper, we propose an algorithm for discriminative pattern mining that can deal with a transactional dataset in a hybrid domain, i.e. the one that includes both symbolic and numeric values. We also show the execution results of a prototype implementation of the proposed algorithm for two standard benchmark datasets.