Reviewerly/exHarmony huggingface dataset:
Application of Ozone Plasma Technology for Treating Peat Water into Drinking Water

Abstract The need of clean water will continue to increase along with the increase of population. However, not all regions in Indonesia have a potential for raw water sources that meet standards to be treated into clean/drinking water, so an alternative water treatment needs to be developed. Peat water is one of water sources that is often found in lowland swamps or in tidal conditions which is much located in Sumatera and Kalimantan. Peat water requires an appropriate technology to be able to treat water into clean water. Peat water treatment using ozone plasma technology is one of technological breakthroughs that can treat peat water into clean water effectively and economically. Peat water used for this study is taken from Sintang, West Kalimantan. Tested Parameters of water quality will be compared with the Regulation of Minister of Health No. 416 Year of 1990. In this study, four parameters in raw water are carried out to be tested after treating with variations in length of contact time of 10, 12, and 15 minutes. Water quality parameters to be tested are turbidity, color, coliform, and permanganate with final quality of treating for each parameter are 5.8 NTU, 1.1 Pt-Co, below 2 MPN/100 mL, and 11.8 mg/L. There is significant decrease in a value of four parameters, which three parameters meeting the maximum limit. Permanganate as parameter that exceed the maximum limit, have high probability of meeting this limit if contact time of treating peat water with ozone plasma is extended.
Practical Research on Integration of Information Technology and Kindergarten Music Teaching

With the rapid development and popularization of information technology, people's life and work have been thoroughly changed. In the field of education, the mode of education has also changed from traditional education to modern education. As the starting point of education, early childhood education is the most urgent task to cultivate children's cultural literacy. Among all kinds of activities in kindergartens, music is an important part. Music enables children to intuitively feel an emotion, learn to appreciate others, and develop good personality and habits. Therefore, it is particularly necessary to integrate information technology with kindergarten music teaching. This paper takes action to study the application of information technology in kindergarten teaching and the application of information technology in kindergarten music teaching. According to the situation of the deep integration of information technology and kindergarten music teaching, combined with the mode of the deep integration of information technology and education teaching, the teaching mode suitable for this study and the corresponding teaching activity flow are designed.
Deducing Qualitative Capabilities with Generic Ontology Design Patterns

Generic Ontology Design Patterns, GODPs, encapsulate design details, hiding the complexity of modeling. As a separation of concerns, they are to be developed by ontology experts, while their safe (re-)use is entrusted to domain experts, who may focus on appropriate instantiations; these effectively document the design decisions. The deployment to the domain of robotics is demonstrated by simple GODPs for adding new objects, relating them consistently to a given ontology. Advanced GODPs show how semantic device properties such as qualitative capabilities can be deduced from quantitative data in a general way.
Noise-Robust Wagon Text Extraction Based on Defect-Restore Generative Adversarial Network

Wagon text extraction mainly depends on manual identification of relevant information, which is laborious, time consuming, monotonous and error-prone. To address this concern, we develop a two-stage wagon text extraction system based on the combination of transfer learning and defect-restore generative adversarial network (GAN). Considering the limited number of wagon images and vast computer resource required, wagon texts are first detected via refined connectionist text proposal network. In this study, we focus on text recognition and develop a generic strategy comprising an adversarial learning. The generator is made up of encoder-decoder-encoder sub-networks, enabling it to learn discriminative representations from the intermediate layer. In addition, by adding a random mask block prior to the generator and rebuilding the encoder with full convolution structure, the proposed strategy is able to significantly reduce the noise sensitivity of the encoder. The generated images are of high quality, even when the image is masked. The propose strategy yields an accuracy of 97.76% on 2682 real-world test sub-images, remarkably outperforms the prior arts. Furthermore, the proposed defect-restore GAN model performs well on synthetic images contaminated by three types of noise, which evidently verifies its robustness.
Examining the Capability of GANs to Replace Real Biomedical Images in Classification Models Training

In this paper, we explore the possibility of generating artificial biomedical images that can be used as a substitute for real image datasets in applied machine learning tasks. We are focusing on generation of realistic chest X-ray images as well as on the lymph node histology images using the two recent GAN architectures including DCGAN and PGGAN. The possibility of the use of artificial images instead of real ones for training machine learning models was examined by benchmark classification tasks being solved using conventional and deep learning methods. In particular, a comparison was made by replacing real images with synthetic ones at the model training stage and comparing the prediction results with the ones obtained while training on the real image data. It was found that the drop of classification accuracy caused by such training data substitution ranged between 2.2% and 3.5% for deep learning models and between 5.5% and 13.25% for conventional methods such as LBP + Random Forests.
Efficient Mining of High Average-Utility Sequential Patterns from Uncertain Databases

In this paper, we address the limitation for mining of high-utility sequential-pattern mining from uncertain databases and present a probabilistic high average-utility sequential pattern mining framework for discovering the set of probabilistic high average-utility sequential patterns from uncertain databases. A level-wise algorithm and three pruning strategies are introduced to mine the set of the desired patterns. Several experiments are then evaluated to show that the proposed algorithm achieves promising performance.
MGD: Mask Guided De-occlusion Framework for Occluded Person Re-identification

Person re-identification (ReID) is a challenging task in computer vision area due to the dramatic changes across different non-overlapping camera views, e.g., lighting, view angle, and pose, among which occlusion is one of the hardest challenges. Recently, occluded person re-identification (Occluded-ReID) is proposed to address this problem. Nevertheless, current occluded-ReID methods focus on how to learn a matching function between partial-body images and full-body images while ignore the structural information of the full body. To handle this problem, we propose a novel framework called Mask Guided De-occlusion (MGD) for occluded Person Re-identification. The MGD mainly consists of three components, i.e., a Coarse-to-Fine Mask Generation (CFMG) module, a Person De-Occlusion (PDO) module and a Person Feature Extractor (PFE). The key module CFMG aims to locate the occlusion areas by manipulating the instance segmentation masks through a two-stage process. The proposed PDO module is to reconstruct the occluded pedestrian. After that, all the images are fed into the PFE module to obtain their feature vectors. With PDO and CFMG modules, the proposed method MGD reduces the impact of occlusions and thus improves the performance of Occluded-ReID. The extensive experiments conducted on several public occluded ReID datasets show that our method is effective and outperforms the state-of-the-art methods.
Bridging the Gap between Smart Home Platforms and Machine Learning using Relational Reference Attribute Grammars

Machine learning improves the development of self-adaptive systems, i.e., to react to unforeseen situations. However, state-of-the-art smart home platforms lack the ability to integrate machine learning models directly into their knowledge base. While there are machine learning approaches for smart home environments, they are usually not integrated nor represented in the middleware of smart environments. Instead, they rely on data obtained previously and use it in a form prepared for a single use case. To remedy this, our aim is to extend existing middleware platforms to integrate machine learning algorithms as first-class concepts of the knowledge base. To achieve this, we employ relational Reference Attribute Grammars to design and implement an integrated runtime model, where machine learning models can be represented and related to elements of the extended knowledge base, e.g., physical entities, location, users, and activities. Consequently, this enables using a state-of-the-art middleware to build a self-adaptive system, which integrates machine learning algorithms enabling both context-awareness and self-awareness. To showcase the feasibility of our approach, we implemented a small smart home scenario using openHAB as a middleware, in which the system learns the preferences of a user using neural networks.
Method for Behavior Normalization to Enable Comparative Understanding of Interactions of Elderly Persons with Consumer Products using a Behavior Video Database

Consumer product safety for dementia sufferers is a global problem. To develop products that can be safely used by elderly people with degradation of physical and cognitive functions (e.g., people with dementia), it is necessary to measure the product use behavior of elderly people using them in various environments and quantitatively analyze behavioral changes during product use due to changes in the functions. Recent developments in smart home technology are opening a new path for quantifying the behavior of elderly people in daily environments. This proposes a new method for comparative understanding of the elderly’s interactions with consumer products. The method employs three functions: robust pose estimation, behavior normalization method, and clustering. This paper also describes the evaluation of the developed functions and report their application to analyzing an elderly behavior library, which is an RGB-D database for elderly product use in daily environments.
On catching and reversing a quantum jump mid-flight

We present results that experimentally demonstrate that the quantum jump from the ground state to an excited state of a superconducting artificial three-level atom can be tracked, caught, and reversed as it follows a predictable "flight."
Survey: Utilizing excess cloud resources to reduce the cost and time of distributed processing

Distributed computing requires to have large resources in order to perform. The distributed computing system performance is proportionally increasing along with the increasing of the available resources. A work obtained from a single computer in one hundred hours can be achieved within one hour by integrating a hundred computers together. This makes the distributed computing a preferred choice for applications that need to work on big data or intensive parallel processing. Maintaining a high number of resources is considered a costly issue, especially for the applications that operate in an infrequent period. On the other hand, the cloud service providers invested in building their data centers and preparing them to handle dynamically changing loads. By adding extra resources, the cloud providers successfully provide scalability features to their customers. Such excess resources can be leased with a relatively low cost compared with other services, but at the same time, it comes with some risks. Deploying the distributed computing on the cloud using the excess resources will help in solving many issues. From one point, it will help to minimize the total costs of provisioning which the distributed computing suffering from. Another point is that the cloud providers maintain large resources which can be used to make the process faster, while relaying on the distributed computing system to manage the risks associated with the excess resources allocation. In this paper, a survey of implementing distributed computing integrated with cloud is introduced. The paper shows some benefits and problems associated with such integration. The survey shows that utilizing the cloud excess recourses can reduce the total provisioning cost by 90% or more, but it has to be done using an enhanced version which is designed to tolerate the dynamic cloud changing.
EBNO: Evolution of cost‐sensitive Bayesian networks

Abstract The last decade has seen an increase in the attention paid to the development of cost‐sensitive learning algorithms that aim to minimize misclassification costs while still maintaining accuracy. Most of this attention has been on cost‐sensitive decision tree learning, whereas relatively little attention has been paid to assess if it is possible to develop better cost‐sensitive classifiers based on Bayesian networks. Hence, this paper presents EBNO, an algorithm that utilizes Genetic algorithms to learn cost‐sensitive Bayesian networks, where genes are utilized to represent the links between the nodes in Bayesian networks and the expected cost is used as a fitness function. An empirical comparison of the new algorithm has been carried out with respect to (a) an algorithm that induces cost‐insensitive Bayesian networks to provide a base line, (b) ICET, a well‐known algorithm that uses Genetic algorithms to induce cost‐sensitive decision trees, (c) use of MetaCost to induce cost‐sensitive Bayesian networks via bagging (d) use of AdaBoost to induce cost‐sensitive Bayesian networks, and (e) use of XGBoost, a gradient boosting algorithm, to induce cost‐sensitive decision trees. An empirical evaluation on 28 data sets reveals that EBNO performs well in comparison with the algorithms that produce single interpretable models and performs just as well as algorithms that use bagging and boosting methods.
A signal adaptive diffusion filter for video coding: improved parameter selection

Abstract In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE)-based methods. The central idea of the signal adaptive filters is explained and demonstrated geometrically. The meaning of particular parameters is discussed in detail. Furthermore, thorough parameter tests are introduced which improve the overall bitrate savings. It is shown that these filters enhance the rate-distortion performance of the state-of-the-art hybrid video codecs. In particular, based on mathematical denoising techniques, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime increase of 19% and 17% decoder runtime increase. For UHD (Ultra High Definition) test sequences, bitrate savings of up to 7.36% for Random Access are accomplished.
It all starts with entities: A Salient entity topic model

Abstract Entities play an essential role in understanding textual documents, regardless of whether the documents are short, such as tweets, or long, such as news articles. In short textual documents, all entities mentioned are usually considered equally important because of the limited amount of information. In long textual documents, however, not all entities are equally important: some are salient and others are not. Traditional entity topic models (ETMs) focus on ways to incorporate entity information into topic models to better explain the generative process of documents. However, entities are usually treated equally, without considering whether they are salient or not. In this work, we propose a novel ETM, Salient Entity Topic Model, to take salient entities into consideration in the document generation process. In particular, we model salient entities as a source of topics used to generate words in documents, in addition to the topic distribution of documents used in traditional topic models. Qualitative and quantitative analysis is performed on the proposed model. Application to entity salience detection demonstrates the effectiveness of our model compared to the state-of-the-art topic model baselines.
Geometric probabilistic evolutionary algorithm

In this paper we introduce a crossover operator and a mutation operator, called Bernoulli Reflection Search Operator (BRSO) and Cauchy Distributed Inversion Search Operator (CDISO) respectively, in order to define the search mechanism of a new evolutionary algorithm for global continuous optimisation, namely the Geometric Probabilistic Evolutionary Algorithm (GPEA). Both operators have been motivated by geometric transformations, namely inversions with respect to hyperspheres and reflections with respect to a hyperplanes, but are implemented stochastically. The design of the new operators follows statistical analyses of the search mechanisms (Inversion Search Operator (ISO) and Reflection Search Operator (RSO)) of the Spherical Evolutionary Algorithm (SEA). From the statistical analyses, we concluded that the non-linearity of the ISO can be imitated stochastically, avoiding the calculation of several parameters such as the radius of hypersphere and acceptable regions of application. In addition, a new mutation based on a normal distribution is included in CDISO in order to guide the exploration. On the other hand, the BRSO imitates the mutation of individuals using reflections with respect to hyperplanes and complements the CDISO. In order to evaluate the proposed method, we use the benchmark functions of the special session on real-parameter optimisation of the CEC 2013 competition. We compare GPEA against 12 state-of-the-art methods, and present a statistical analysis using the Wilcoxon signed rank and the Friedman tests. According to the numerical experiments, GPEA exhibits a competitive performance against a variety of sophisticated contemporary algorithms, particularly in higher dimensions.
UPISketch: The UPIC idea and its current applications for initiating new audiences to music

With the invention of UPIC by Iannis Xenakis in 1977, for the first time one could achieve the sonic realisation of drawn musical ideas by a computer. Since then, no one seems to have proposed an equivalent tool, providing usability for both beginners and professionals. We will discuss how lively the UPIC idea can still be today, and why we should pay attention to this approach. Indeed, there has been a relative quietness in the domain of computer music specifically regarding how we arrange graphical curves to describe the evolution of sound parameters and create a musical form. This ascertainment led to the birth of our project UPISketch, which aims at the same goal, using today’s technology. We hope that this first version can reintroduce the concept of a ‘computer-assisted tool for sound composition, assisted by drawing’ to a global audience. The program being available since June 2018, we will evaluate the results of the first workshops with various publics and take a glimpse at the next developments.
A two-stage sizing method of standalone solar lighting systems

This paper deals with a two-stage method for the sizing of two standalone solar lighting systems. Where, each of these systems uses a solar panel, a battery backup, a DC lamp and a charge controller. These remotely controlled systems are intended to be used in public places and streets of Biskra and Skikda for replacing the traditional grid-powered lighting systems. The first stage is a pre-sizing step in which the consumed daily energy, the system autonomy, the peak sun hours and the system losses are considered for the sizing of the battery and the photovoltaic generator. In the second stage, the PVSYST software is used to evaluate and optimize the size of the PV generator by simulating the systems over a whole year.The obtained results show the importance of the tilt angle choice in the production production and the efficiency of the solar panel. Indeed, as the value of this angle is approximately equal to the geographical value of the latitude of the area in which the solar system is used. They obtained results show also that the application of the presented solar lighting is more favorable for the site of Biskra in term of cost and efficiency.
Challenge-response behavioral mobile authentication

The most researched behavioral biometrics for mobile device authentication involves the use of touch gestures as the user enters a graphical pattern password (like the one used on Android) or otherwise interacts with the device. However, due to the inherent static nature of these schemes, they are vulnerable to impersonation attacks. In this paper, we investigate challenge-response mechanisms to address this security vulnerability underlying the traditional static biometric schemes. We study the performance, security, and usability of two schemes of such challenge-response interactive biometric authentication geared for mobile devices and contrast them to static graphical pattern based biometrics. The first scheme is based on random graphical patterns. The second scheme, recently introduced for PC class of devices (not mobile), is based on a simple cognitive game involving semantic interactive random challenges. Our results show that the accuracy of user identification with these approaches is similar to static pattern based biometric scheme. Finally, we argue that utilizing interactivity and randomization significantly enhance the security against impersonation attacks. As an independent result, our work demonstrates that the use of motion sensors available on mobile device serves to improve the identification accuracy of schemes that only use touch-based gestures (static and interactive).
Mid-air Motion Planning of Floating Robot Using Heat Flow Method

We address in this paper the problem of planning motion of free floating robot. Due to the conservation of angular and linear momentum during the in-air motion, the problem is two-fold: one needs to design, in addition to the in-air motion, the initial impulse that imparts the robot with the appropriate momentum to perform a desired motion. We propose in this paper a new method to address this problem. The method is a homotopy method which deforms an arbitrary path connecting the desired initial and final states to a feasible trajectory between these states, that is a trajectory that meets the system’s dynamics and constraints. From this trajectory, the method extracts both the controls and initial momentum needed to perform the motion. We illustrate the method by designing somersault motions for a diver robot.