Reviewerly/exHarmony huggingface dataset:
Resilient Dictionaries for Randomly Unreliable Memory

We study the problem of designing a dictionary data structure that is resilient to memory corruptions. Our error model is a variation of the faulty RAM model in which, except for constant amount of definitely reliable memory, each memory word is randomly unreliable with a probability p < 1/2, and the locations of the unreliable words are unknown to the algorithm. An adversary observes the whole memory and can, at any time, arbitrarily corrupt (i.e., modify) the contents of one or more unreliable words.Our dictionary has capacity n, stores N<n keys in the optimal O(N) amount of space, supports insertions and deletions in O(log n) amortized time, and allows to search for a key in O(log n) worst-case time. With a global probability of at least 1-1/n, all possible search operations are guaranteed to return the correct answer w.r.t. the set of uncorrupted keys. The closest related results are the ones of Finocchi et al. [Irene Finocchi et al., 2009] and Brodal et al. [Brodal et al., 2007] on the faulty RAM model, in which all but O(1) memory is unreliable. There, if an upper bound delta on the number of corruptions is known in advance, all dictionary operations can be implemented in Theta(log n + delta) amortized time, thus trading resiliency for speed as soon as delta = omega(log n).Our construction does not need to know the value of delta in advance and remains fast and effective even when up to a constant fraction of the available memory is corrupted. Our techniques can be immediately extended to implement other data types (e.g., associative containers and priority queues), which can then be used as a building block in the design of other resilient algorithms. For example, we are able to solve the resilient sorting problem in our model using O(n log n) time.
The tokenization of assets and property rights

Abstract Blockchain paves the way for new entrepreneurial opportunities for the trust business. Especially in connection with tokenization, asset safekeeping processes can be handled directly by a trustee through the use of a blockchain system. Therefore, a deeper understanding of blockchain, the ways it can be applied, and the relevant tax and legal ramifications are inevitable.
IMPROVING STUDENTS’ ENGLISH SPEAKING ABILITY THROUGH JIGSAW

ABSTRACT The aim of this research is to find out the improvement of students’ speaking ability through Jigsaw method of the Management Study Program students of the second semester in the academic year 2018/2019 at Muhadi Setiabudi University taught by and without using Jigsaw. The data of the current study were gathered at multiple points of time before and after the end of the experiment to determine the improvement of student’s speking ability through Jigsaw speaking skills. In practical terms, the sample’s speaking ability were first examined through an English oral test prior to and after some Jigsaw instructional activities were provided. The data were analyzed using basic and inferential statistical methods including mean scores, standard deviations, paired sample t-test, and effect size. The findings showed a remarkable development in the students’ speaking skills through Jigsaw method. The writer holds an experimental research to apply Jigsaw in teaching English speaking. The population is the second semester students of Muhadi Setiabudi University in the academic year 2018/2019 and the sample are two groups of students placed in experiment and control groups. Using oral test as the instrument, the writer collected the data of the students’ speaking ability. The report of the research concludes that Jigsaw improved the English speaking ability of the second semester students in the academic year 2018/2019. By doing small discussion of Jigsaw method it can motivate to develop the student’s braveries to use their knowledge and their experience to solve a problem without other’s opinion. There may be different of view so the students can give different opinion. Beside that the students are also able to give real opinion orally.Keywords: Teaching Method, Improving Students’ Speaking Ability, Jigsaw
Do We Really Need Another Blockchain Framework? A Case for a Legacy-friendly Distributed Ledger Implementation based on Java EE Web Technologies

Cryptocurrencies, blockchain technology and smart contracts could fundamentally change the way how financial products and financial services are implemented and operated. While many frameworks for implementing such blockchain applications already exist, these are usually implemented using languages either considered “fancy” today, like e.g. Go, or are traditionally used for system software, such as C++. On the other hand, the core business applications e.g. in financial services are typically implemented using enterprise platforms such as Java Enterprise Edition (EE) and/or COBOL. Therefore, to improve the integration of blockchain technology in such applications, in this paper we argue in favor of a legacy-friendly distributed ledger solution by introducing QWICSchain, an implementation build on web services using established open-source enterprise technologies such as Java EE and PostgreSQL. It supports the parallel execution of transactions on the blockchain and in existing legacy applications, thus enabling the blockchain-based modernization of existing IT infrastructures.
RDF Doctor: A Holistic Approach for Syntax Error Detection and Correction of RDF Data

Over the years, the demand for interoperability support between diverse applications has significantly increased. The Resource Definition Framework (RDF), among other solutions, is utilized as a data modeling language which allows for encoding the knowledge from various domains in a unified representation. Moreover, a vast amount of data from heterogeneous data sources are continuously published in documents using the RDF format. Therefore, these RDF documents should be syntactically correct in order to enable software agents performing further processing. Albeit, a number of approaches have been proposed for ensuring error-free RDF documents, commonly they are not able to identify all syntax errors at once by failing on the first encountered error. In this paper, we tackle the problem of simultaneous error identification, and propose RDF-Doctor, a holistic approach for detecting and resolving syntactic errors in a semi-automatic fashion. First, we define a comprehensive list of errors that can be detected along with customized error messages to allow users for a better understanding of the actual errors. Next, a subset of syntactic errors is corrected automatically based on matching them with predefined error messages. Finally, for a particular number of errors, customized and meaningful messages are delivered to users to facilitate the manual corrections process. The results from empirical evaluations provide evidence that the presented approach is able to effectively detect a wide range of syntax errors and automatically correct a large subset of them.
PELATIHAN PEMANFAATAN BARANG BEKAS UNTUK PEMBUATAN BUKET BUNGA DAN CARA PEMASARANNYA

Waste is one of the problems that often arise in life society. However, many ways are considered wiser in solving problems with garbage. One of them is by using it to produce new products that have economic value. Not only can it reduce the garbage stack, but this method can also bring additional income. This service activity was carried out in the village of Bulu Lor, Jambon Subdistrict, Ponorogo Regency. Aim to provide training in making a bouquet by utilizing used items to homemakers and their youth. Besides, it also teaches how to market handicraft products to be known to more quickly, so the possibility of selling products is getting bigger. The result of this activity is to increase entrepreneurial skills to the community by providing capabilities to homemakers and youth in making flower bouquets and teaching how to market product results online. Keywords: Waste, bucket flower, entrepreneurship, online marketing
Comparison of Artificial Neural Network and Regression Models for Prediction of Body Weight in Raini Cashmere Goat

The artificial neural networks (ANN) are the learning algorithms and mathematical models, which mimic the information processing ability of human brain and can be used to non linear and complex data. The aim of this study was to compare artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. The data of 1389 goats for body weight, height at withers (HAW), body length (BL) and chest girth (CG) were used. Different regression models with all fixed factors were calculated for the most possible states and with different degrees and two artificial neural networks with different hidden layers, learning functions and transform functions were used. Finally, Multilayer perceptron model with one hidden layer along with neurons was selected and used. Correlation between body weight and its measurements showed that it is possible to use body measurements for prediction of body weight though prediction of body weight can be improved when more measurements are used. Based on R2 andmean square error (MSE) parameters, the best fitted regression equation for prediction of body weight using body measurements was selected. While all three measurements had a significant effect in the model (P<0.0001), height at wither had the highest correlation coefficient (0.65), hence may have the greatest effect on prediction. Comparing two models indicated that both models can predict body weight well and near to actual body weight, but the capability of artificial neural network model is higher (R2=0.86 for ANN and 0.76 for multiple regression analysis (MRA)) and closer to actual body weight. However, if more related measurements are recorded, ANN can give the desirable results. Therefore, it is possible to apply artificial neural networks, instead of customary procedures for prediction of actual body weight using body measurements.
From Face Recognition to Facial Pareidolia: Analysing Hidden Neuron Activations in CNNs for Cross-Depiction Recognition

The imagination of non-existent faces in random patterns, clouds and rock formations is known as facial pareidolia. We show that facial pareidolia also occurs naturally in a standard Convolutional Neural Network (CNN) trained on face recognition. For achieving this we propose a new method to analyse CNNs that combines feature visualisation and dimensionality reduction methods to cluster the hidden neuron activations in convolutional layers into groups with discriminative roles. The main contributions of the present paper are 1.) an approach that uses a CNN trained on human face detection for facial pareidolia simulation without any additional training on a target image set of abstract facial patterns and 2.) a novel way of improving the generalisation capacity of a CNN for cross-depiction recognition and domain adaptation scenarios using features learned by hidden neurons.
Zero-Shot Object Detection for Indoor Robots

Object detection is one of the most crucial tasks for robotic systems. Therefore, a plethora of researches have explored the traditional object detection, where the model must be trained on instances of all objects of interest. However, this is not a very realistic setting because of the huge number of classes in real-world settings and the high cost and time associated with annotating data. Thus, it is almost impossible to train a model in all possible classes. This led to the introduction of the concept of zero-shot object detection, in which a model is trained on some seen classes and then tries to use the obtained knowledge to be able to detect novel unseen classes. Despite the fact that, this setting is more realistic, but very few researchers have explored it. Furthermore, to the best of our knowledge, no previous work has explored it specifically for indoor robots. In fact, indoor environments can be more challenging, since the number of objects of interests is way larger compared to other applications like self-driving cars for instance. In this work, we explore zero shot object detection for indoor robots by embedding deep features and class labels to a shared semantic space. We use the famous SUN RGB-D dataset [1] and our own collected dataset for training and evaluation, and we propose two novel splits for SUN RGB-D dataset especially for zero-shot object detection.
Relevancy Scoring for Knowledge-based Recommender Systems

Knowledge-based recommender systems are well suited for users to explore complex knowledge domains like iconography without having domain knowledge. To help them understand and make decisions for navigation in the information space, we can show how important specific concept annotations are for the description of an item in a collection. We present an approach to automatically determine relevancy scores for concepts of a domain model. These scores represent the importance for item descriptions as part of knowledge-based recommender systems. In this paper we focus on the knowledge domain of iconography, which is quite complex, difficult to understand and not commonly known. The use case for a knowledge-based recommender system in this knowledge domain is the exploration of a museum collection of historical artworks. The relevancy scores for the concepts of an artwork should help the user to understand the iconographic interpretation and to navigate the collection based on personal interests.
Towards Multiple Instance Learning and Hermann Weyl’s Discrepancy for Robust Image-Guided Bronchoscopic Intervention

This paper proposes an advantageous approach that introduces multiple instance learning (MIL) and Hermann Weyl’s discrepancy (HWD) to improve image-guided bronchoscopic intervention. Numerous 2D-3D registration methods used for bronchoscopic navigation suffer from problematic bronchoscopic video images (e.g., bubbles and collision) that easily collapse the registration optimization since these images remain challenging to precisely calculate the similarity between bronchoscopic real images and virtual renderings generated from CT slices, resulting in inaccurate bronchoscopic navigation. To address this limitation, we develop a new navigation framework that employs a MIL-driven image classification strategy to remove problematic frames and then performs a HWD-enhanced 2D-3D registration procedure. We validate our framework on patient data. The experimental results demonstrate that our effective and accurate navigation method outperforms others approaches. In particular, the average navigation accuracy of position and orientation was improved from (6.8, 18.0) to 3.5 mm, 9.4$$^{\circ }$$).
Investigating the Role of Code Smells in Preventive Maintenance

The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. We examine the Eclipse software with four versions (3.2, 3.3, 3.6, and 3.7) for metrics and smells. Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. This model is then used to detect the absence of defects in the four Eclipse versions. The effect of balanced and unbalanced datasets is also examined for these four versions. The results suggest that the code smells can be a valuable feature in discriminating absence of defects in a software.
A Theoretical Study on the Benefits of Integrating GNSS and Collaborative Relative Ranges

Previous research contributions addressed the definition of a Cramer Rao Lower Bound (CRLB) as a theoretical tool to investigate the performance of unbiased position estimators for deterministic non linear systems. Moving from such theoretical findings, this work aims at investigating the CRLB of hybrid estimators integrating auxiliary measurements obtained from the combination of satellite-based range measurements shared among pairs of connected receivers. The study is conceived to inspect the benefits of this GNSS-based collaborative positioning approach in terms of precision improvement. The analysis of such a theoretical limit aims at identifying when the use of cooperative ranges is beneficial despite of their correlation with the individual measurements, according to the geometry of satellites and terrestrial agents w.r.t. a target agent. The theoretical analysis is validated by simulation by demonstrating when the collaborative measurements increases the precision of the position estimates. The study practically provides a methodology for the selection of the best cooperating agents in multi-agent frameworks.
Short-Term Photovoltaic Power Forecasting Using Artificial Neural Network Considering Multiple Time Lags

This paper presents the artificial neural network (ANN) used to perform the short-term photovoltaic power forecasting (STPPF) for the next 24 hours. The input data of ANN is comprising with the multiple time lags of hourly data of power, current, temperature, solar irradiance and hour that have been denoised by using the wavelet decomposition. The multiple time lags are used to improve the input data while wavelet decomposition removes the noises available in the data which will then significantly improve the STPPF results. The data of a solar photovoltaic (PV) system in the year 2015 and 2016 obtained from the Green Energy Research (GERC), UiTM, Shah Alam, Malaysia. The results have shown that the ANN provides relatively accurate results of STPPF and able to forecast the PV output power for the next 24 hours with less error.
Bayesian Volumetric Autoregressive Generative Models for Better Semisupervised Learning

Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of volumetric data, exhibit convergence problems, and offer no robust indices of model uncertainty. By comparison, the autoregressive generative model PixelCNN can be extended to volumetric data with relative ease, it readily attempts to learn the true underlying probability distribution and it still admits a Bayesian reformulation that provides a principled framework for reasoning about model uncertainty. Our contributions in this paper are two fold: first, we extend PixelCNN to work with volumetric brain magnetic resonance imaging data. Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low. We quantify this improvement across classification, regression, and semantic segmentation tasks, training and testing on clinical magnetic resonance brain imaging data comprising T1-weighted and diffusion-weighted sequences.
Controlling a motorized electric wheelchair based on face tilting

Abstract Disability, specifically impaired upper and/or lower limbs, has a direct impact on the patients’ quality of life. Nowadays, motorized wheelchairs supported by a mobility-aided technique have been devised to improve the quality of life of these patients by increasing their independence. This study aims to present a platform to control a motorized wheelchair based on face tilting. A real-time tracking system of face tilting using a webcam and a microcontroller circuit has been designed and implemented. The designed system is dedicated to control the movement directions of the motorized wheelchair. Four commands were adequate to perform the required movements for the motorized wheelchair (forward, right, and left, as well as stopping status). The platform showed an excellent performance regarding controlling the motorized wheelchair using face tilting, and the position of the eyes was shown as the most useful face feature to track face tilting.
CoNN: Collaborative Neural Network for Personalized Representation Learning with Application to Scalable Task Classification

Inspired by the personalized recommendation system, the Collaborative Neural Network (CoNN) is proposed to solve the problem of insufficient personalization of the existing representation learning methods. According to the input sample, CoNN generates personalized feature extractor (PFE), which is used for personalized representation learning. Specifically, we present two variants of CoNN: Unconditional Collaborative Neural Network (U-CoNN) and Conditional Collaborative Neural Network (C-CoNN). They are suitable for single-task and multi-task personalized representation learning, respectively. So based on CoNN we can solve the problem of personalized representation learning in the Scalable Task scenario. To evaluate CoNN, we experimented with two datasets, MNIST and Fashion-MNIST, which are commonly used in image classification problems. The results showed that U-CoNN improved the classification accuracy of 4.26% in a single task classification, and C-CoNN improved the classification accuracy by 1.25% in multiple task classification.
Ethnicity Recognition Under Difficult Scenarios Using HOG

With the rapid advance of globalization, analyzing nationality and race/ethnicity groups is becoming an emerging research topic that has multi-disciplinary real-world applications such as surveillance systems and targeted advertisements. This paper presents an approach to automatically predict the ethnicity groups of individuals based on their facial characteristics. Several ethnicity groups are considered in this study including: Asian, Indian, and others (like Hispanic, Latino and Middle Eastern). The proposed approach extracts features based on the Histogram of Oriented Gradients (HOG) texture descriptor. Then, it trains a support vector machine (SVM) to detect ethnicity with promising achievable results when evaluated on a publicly available dataset of labelled images.
Estimation of Capacitance

The vast majority of microphones that are produced each year utilize capacitive sensing either through the use of a charged electret material or an applied bias voltage. The design of these microphones, in essentially all cases, relies on simple formulas to estimate the capacitance of parallel plate electrodes. This greatly limits the designs to follow the ubiquitous parallel plate configuration, with all of its common design challenges. In an attempt to overcome this limitation, in the following we review the basic principles of sensing charge and examine a capacitive sensing geometry that overcomes several of the common limitations encountered in capacitive microphone design. Our approach follows that described in [1], which presents an electrostatic sensing scheme that results in a minimum of electrostatic force and stiffness on the moving electrode.
A 3D reconstruction method using PMVS for a limited number of view points

Patch-based Multi-View Stereo (PMVS), which is an accurate 3D reconstruction method, can reconstruct the accurate and dense 3D shape from enough number of viewpoints. On the other hand, its accuracy is degraded when using only a limited number of viewpoints. Addressing the above problem, this paper improves the PMVS so as to reconstruct the 3D shape from a limited number of viewpoints. Through a set of experiments, we demonstrate that the proposed method exhibits the efficient performance on 3D reconstruction such difficult situations.
Unsupervised Neural Machine Translation, a new paradigm solely based on monolingual text

UnsupNMT is a project funded by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2017-91692-EXP).