Reviewerly/exHarmony huggingface dataset:
Bartlett Lewis Rectangular Pulse (BLRP) Approach with Proportional Adjusting Procedure in Rainfall Disagregation Method in Hidrology Laboratory of Brawijaya University Rain Station

Rainfall data is one of the information that can be used in flood prevention, especially low-scale rainfall data (hourly). However, the availability of hourly rainfall data is very limited. It is because not all rain stations have hourly rain gauges which are quite expensive. This research aims to obtain hourly rainfall data based on daily rainfall data with the Bartlett Lewis Rectangular Pulse (BLRP) stochastic disaggregation method. This method was applied to rainfall data at the Hydrology Laboratory of Brawijaya University Malang in 2016. There are six BLRP parameters, namely λ, ν, κ, μx, α, ϕ which are used to produce hourly rainfall data. To maintain the consistency of hourly rainfall data based on daily rainfall data, a proportional adjusting procedure is used. The results showed that the disaggregation method of the BLRP approach can produce good enough hourly rainfall data that is consistent with daily rainfall data, seen from the in-sample MAE value of 0.5348 and 0.3113 for out-sample MAE.
Improving Quality of Service of Border Gateway Protocol Multiprotocol Label Switching Virtual Private Network of EthioTelecom Service Level Agreements

The primary goals of Quality of Service (QoS) are managed bandwidth, controlled jitter, minimized latency, and improved packet loss characteristics to provide satisfactory services for users. Shaping network optimization is crucial for the service provider, too. To implement QoS mechanisms optimizing the current network physical and logical architectures is among the best practices. In this paper, an attempt has been made to investigate the end-to-end QoS parameters of multiprotocol border gateway protocol multiprotocol label switching virtual private network (MP-BGP MPLS VPN) EthioTelecom service level agreement (SLA) customers. That is done using differentiated service (DiffServ) model to manage end-to-end traffic delay, jitter, and packet losses. The traffics are classified and marked depending on their priorities. The proposed network architecture has used weighted fair queueing (WFQ) for congestion management and weighted random early detection (WRED) for congestion avoidance. The Huawei’s Enterprise Network Simulation Platform (eNSP) and Wireshark are used to design, demonstrate and evaluate the network architectures. When the results of the existing network are compared with the proposed network architecture its delay, jitter, packet loss and traffic utilization have shown improvements.
Basic Theory for the Type-2 Fuzzy Sugeno Integral

In this paper we describe the theory and basic concepts of fuzzy logic, aggregation operators and modular neural networks which we use as a basis on this work.
Language support for multiple privacy enhancing technologies

Privacy is one of the critical aspects in the design of computing systems. Various techniques are being used to enforce privacy, such as information flow control, symmetric/asymmetric cryptography, secure computing enclaves, partial homomorphic encryption and differential privacy. These mechanisms should ideally be combined in complex software systems as they offer different properties and performance trade offs. Unfortunately, reasoning about the privacy properties of such combination is still an open research problem.
Lessons Learned from an Action Research Study on the Use of Cloud Computing in Undergraduate Computer Science Courses

Companies have made efforts to migrate their assets to the cloud aiming to use in a more effective way computing resources to provide services to their market share. However, this is not a trivial task due to the lack of information on how to accomplish the migration, which services should be selected and which providers offer the best cost benefit ratio. This paper presents a study from the point of view of undergraduate students enrolled in System Analysis and Operating Systems courses from the University of Salvador (UNIFACS). The goal is to investigate the learning process in these courses using cloud computing scenarios. The results of this study provided initial evidence that the cloud computing capabilities integrated into the content of the above mentioned courses can contribute to motivate and engage students in the proposed activities.
Autocorrelated correlation filter for visual tracking

Recently, discriminative correlation filters (DCF) have achieved enormous popularity in the tracking community due to high efficiency and fair robustness. With a circular structure, DCFs transform computationally consuming spatial correlation into efficient element-wise operation in the Fourier domain. We argue that this element-wise solution can be derived only in the case of single-channel features. In terms of tracking with multichannel features, this element-wise solution trains each feature dimension independently and fails to learn a joint correlation filter. To tackle this problem, we propose a rigorous solution to closed-form correlation filter tracking. This rigorous solution can be computed pixel by pixel from a small linear equation system. Experimental results demonstrate that our rigorous pixel-wise solution achieves better tracking performance than the baseline element-wise solution.
Microscopic Measurement of Strain Fields with Digital Image Correlation and Comparison to Finite Element Modelling

Digital Image Correlation (DIC) has become more and more important in the field of material characterization and research, especially for strongly anisotropic fiber reinforced materials. Its big advantage over the conventional methods like strain gauges or point based video-extensometers is the full field strain and displacement measurement and the ability to analyze three-dimensional displacements. Although theoretically, the concept of the DIC as a pure image-based method allows it to work on every imaginable scale, its main field of application is in the range, where the region of interest (ROI) has a size between 10 −2 m to 10 −1 m. In this case, imaging is accomplished with the use of high-resolution black and white digital cameras. This work is focused on a smaller scale with ROI sizes between 10 −4 m to 10 −3 m, where a digital microscope is used to create the images. The innovative idea behind this work is using the natural surface structure of a polished carbon fiber reinforced Polyamide-6 sample, produced by automated fiber placement, as a statistical pattern instead of the usual speckle pattern applied to the area to be investigated. This way the stress and strain distributionin different regions of the investigated sample area can be evaluated and displayed, while the sample is exposed to an increasing mechanical load in form of a three-point bending test. The resulting strain and displacement fields are compared to finite element modeling of the ROI. To provide an accurate model, the image of the sample is first segmented into fiber, matrix and voids using “Trainable Weka Segmentation” and the resulting phases mapped with the corresponding material properties. To compute the resulting strains in the sample, the measured displacements from the DIC on the edges of the ROI were used as boundary conditions for the simulation. Simulation and experimental results clearly point out the inhomogeneity of the strain field in these samples. Due to the presence of fiber rovings and the presence of voids, local strain values exceed the global average by up to 4 %.
SKAITMENINIO KŪRYBIŠKUMO TAIKYMAS GENERUOJANT ŽAIDIMO OBJEKTUS / COMPUTATION CREATIVITY USING PROCEDURAL GENERATION FOR GAME OBJECT GENERATION

One of the hardest task for a machine is creativity. Computational creativity defines creative task completion for a machine. Three main creative content generation methods are: exploratory, combinatorial and transformational. Video game content can be generated using procedural generation. Computational creativity, procedural generation, and application are explained in this paper. Procedural level generator is used as a base and additional features are built on top of it. The main goal of this research and modification is to increase application creative value, variety and expression. Additional functionality consists of tree and texture generation.
Enhancing Feature Representation for Saliency Detection

Current detectors for saliency detection adopt deep convolutional neural networks to continuously improve accuracy, but the results are still not satisfactory. We propose Multiple Receptive Field Aggregating Module (MRFAM) that can capture abundant context information to enhance feature representation. We assemble it into a novel network to predict saliency maps. Extensive experiments on six benchmark datasets demonstrate that the module is efficient and our proposed network can accurately capture salient objects with sharp boundaries in complex scene, performing favorably against the state-of-the-art methods in term of different evaluation metrics.
A Serious Game to Promote Compliance with Hand Hygiene Among Healthcare Workers: Results from User Research Stage

Hand hygiene has been recognized over the past two decades as the most effective method of preventing healthcare-associated infections, which are responsible for increased morbidity, mortality and costs of healthcare per year, worldwide. Although hand hygiene practice is considered critical, compliance with the “Five Moments for Hand Hygiene”, proposed by the World Health Organization, is still considerably below the expected rate. Thus, keeping healthcare workers motivated and engaged in maintaining the best hand hygiene practices remains a challenge for the community. Based on previous cases suggesting a good potential of games for achieving behavioural changes in various health domains, this paper reports the creation of Personas for the development of a Serious Games aiming to promote hand hygiene in a hospital context. This is part of the knowledge/information and ideation phases (user research), which involved data collection and interaction with healthcare workers; i.e., physicians, nurses, technicians and medical auxiliaries from a hospital in Lisbon area.
An outlier-robust Kalman filter with mixture correntropy

We consider the robust filtering problem for a nonlinear state-space model with outliers in measurements. A novel robust cubature Kalman filtering algorithm is proposed based on mixture correntropy with two Gaussian kernels. We have formulated the robust filtering problem by employing the mixture correntropy induced cost to replace the quadratic one in the conventional Gaussian approximation filter for the measurement fitting error. In addition, a tradeoff weighting coefficient is introduced to make sure the proposed approach can provide reasonable state estimates in scenarios with small measurement fitting errors. The robust filtering problem is iteratively solved by using the cubature Kalman filtering framework with a reweighted measurement covariance. Numerical results show that the proposed method can achieve a performance improvement over existing robust solutions.
The ASSL Approach to Formal Specification of Self-managing Systems

ASSL (Autonomic System Specification Language) is a framework dedicated to the development of self-managing systems whereby developers are helped with problem formation, system design, system analysis and evaluation, and system implementation. The bottom line is a special multi-tier approach to specification exposing a rich set of constructs allowing a system to be modeled by emphasizing different key aspects, but centering the model around special self-management policies. This article presents in detail the aforementioned mechanism together with the underlying semantics. As a case study, we also present ASSL specifications of self-managing behavior of prospective autonomous NASA space exploration missions.
A Study on Monitoring Coastal Areas for Having a Better Underwater Surveillance Perspective

Surveillance systems for coastal areas especially monitoring ecosystems are becoming more important these days. It is becoming a necessary requirement for coastal areas to protect from various threats such as illegal smuggling, immigration, underwater cyber threats, illegal trafficking and especially preserving the marine habitats. Also, it is very important to identify and separate the marine habitats from the submerged objects for security reasons. Detecting different marine habitats such as fish objects plays a significant role in this area, as it helps to differentiate between habitats (fish) and other objects that are threats to us. In this paper, we propose an approach to detect fish objects, which will provide a better solution in advance to different security solutions in underwater surveillance applications. However, detecting fish objects is not an easy task, as there are many challenges to overcome, and underwater turbulence is one of the biggest factors that hinders this process. Thus, we take this issue into account and resolve this problem through patch-wise deconvolution to restore the image prior to object detection. Next, a saliency-based approach is considered for object detection. The problem is analyzed with a recently developed method and considering a real-world dataset. Computational results show significant improvement over the considered method.
Representation of Quantum States of Two-Dimensional simple Harmonic oscillator by convolution Neural Network

It is proposed to train a deep convolution neural network (CNN) to learn the mapping relation between two-dimensional electrostatic potential and ground state energy eigenvalue by using the feature extraction and function fitting ability of machine learning (ML), so as to avoid the difficulty of strict solution of Schrodinger equation. The potential energy function is processed by CNN image processing, and the projection of the two-dimensional simple harmonic oscillator potential energy function on the two-dimensional plane is taken as input. Experimental results verify the validity of the method: the average absolute error is 0.0372eV, The Standard Deviation was 0.429eV, and the Relative Error was 1.042%.
A Pipeline Approach in Identifying Important Input Features from Neural Networks

Neural networks are well-known for their powerful capability in producing high prediction accuracy. However, due to the non-linear calculations in the network, it is very difficult for users to understand which input features are important in leading to final predictions. In this study, we propose a two-step pipeline approach that uses two sets of linear models to estimates feature importance in the input dataset X that leads to the class prediction specified in Y. More specifically, the first linear regression model derives the feature importance in X in explaining the Z-code that was extracted from any hidden layer of a trained neural network. The second linear classification model captures the importance in the Z- code in predicting the target class Y. We then combine the first X to Z importance with the second Z to Y importance together to approximate the non-linear importance from X to Y. The experiments conducted in this study also show that our method is sound and stable in selecting the truly important features from input datasets regardless how a neural network was constructed with different parameters such as activation functions or the number of hidden layers.
An on-demand service aggregation and service recommendation method based on RGPS

“Internet plus” application service recommendation is challenged by two issues: One is the increase in service volume and the disorderliness of the service organizations. A second is the diversification of user requirements. The research focus of this study was to investigate how to achieve more or dered aggregation and recommend services that meet the individualized requirements of users. This paper addresses the disorderliness of conventional service aggregation and considers the aggregation requirements of QoS weights with non-functional targets. Based on semantic relevance using the role (R), goal (G), process (P), service (S) demand metamodel, an RGPS association is proposed that is a weighted network for ordered QoS service aggregation. An individualized service recommendation method then is provided, based on an LSTM neural network with role and target backstepping using RGPS association network, that can achieve a high-quality precision service. Finally, a simulation experiment was carried out on service recommendations in the tourism domain, which verified the precision, effectiveness and application value of the service recommendation method.
Electronic Expertise Using Distributed Ledger Technology

This paper examines how to implement electronic expertise based on the Corda platform. The paper aims to implement such a system, taking as an example the organization of the online contest. A draft solution is created for this task, which demonstrates the necessary tools to solve it and key capabilities that will be required from the platform for the implementation of the electronic expertise system. It justifies the choice of the Corda platform and describes the solution with its use. The main advantages and disadvantages of this approach to the implementation of electronic expertise when using Blockchain are highlighted.
Spectrally controlled interferometry for high numerical aperture spherical cavity measurements

High numerical aperture optical elements are relied on for the most demanding applications in optical imaging but pose a significant challenge for conventional metrology techniques. Laser Fizeau interferometers provide a flexible measurement platform for measuring spherical optics by offering a common path configuration to test spherical optics against a convex reference surface. However, in this configuration, traditional piezoelectric transducer (PZT) based phase shifters produce non-uniform phase shifts which vary across the aperture as the spherical reference surface is translated along the optical axis. While these errors are negligible for low numerical aperture optics, the phase shift errors quickly become significant for high numerical aperture optics. The phase shift nonuniformity results in fringe print through and phase ripple artifacts which limit overall accuracy of phase shifted interferometry (PSI) measurements. Spectrally controlled interferometry (SCI) is a method which produces localized, high contrast interference fringes in non-zero optical path length cavities through tailored control of the sources spectral distribution. In addition to fringe location, fringe phase is also controlled through spectrum manipulation without mechanical motion or compensation. As a consequence, the SCI method produces uniform, full-aperture phase shifts with a high degree of linearity regardless of numerical aperture; thus, phase shift errors associated with traditional PZTs can be eliminated. Furthermore, because SCI is a source driven method, it can easily be integrated with any Fizeau interferometer. In this paper, we present the fundamental background for SCI and the advantages of the method as they apply to the measurement of high numerical aperture spherical optics. Additionally, we compare PSI measurements between a traditional laser Fizeau interferometer with PZT based phase shifters and an SCI Fizeau interferometer. Existing methods to this problem are discussed and compared with the presented SCI method, as well.
Proposal of Information Analysis System on Latent Human Error

Latent Human Error information is gathered in companies, but there is a problem that it cannot be used well. The problem behind it is lack of knowledge on trouble analysis and insufficient analysis on text data. Therefore, in this research, we propose an information analysis system on latent human error. In the analysis system, the following three important elements are included. The first point is knowledge of Human Factors which is useful for trouble analysis. The second point is natural language processing technology which processes text information on trouble. The third point is statistics and DeepLearning technology using processed text information. Based on these factors, we aim to build an analysis support system that can be used by people who are not familiar with trouble analysis at the company site.
2018 NEC C&amp;C Prize Ceremony

Presents the recipient of the NEC Computers and Communications Foundation Award for 2018.
Maximum Entropy Calculations for the Probabilistic Description Logic $$\mathcal {ALC}^{\mathsf{ME}}$$

The probabilistic Description Logic extends the classical Description Logic with probabilistic conditionals of the form (D|C)[p] stating that “D follows from C with probability p.” Conditionals are interpreted based on the aggregating semantics where probabilities are understood as degrees of belief. For reasoning with probabilistic conditional knowledge bases in , the principle of maximum entropy provides a valuable methodology following the idea of completing missing information in a most cautious way. In this paper, we give a guidance on calculating approximations of the maximum entropy distribution for -knowledge bases. For this, we discuss the benefits of solving the dual maximum entropy optimization problem instead of the primal problem. In particular, we show how representations of approximations of the maximum entropy distribution can be derived from the dual problem in time polynomial in the size of the underlying domain. The domain is the crucial quantity in practical applications. For a compact representation of the objective function of the dual maximum entropy optimization problem, we apply the principle of typed model counting.
Mobile Teleradiology Simulation Using Dicom Files Reader Application on Android Smartphones

Abstract The rapid development of digital technology has given birth to the industrial revolution 4.0, where every line of human life cannot be separated from the existence of smartphones. The development has spread to all fields including in the field of radiology. The development of Android-based applications that are able to open Dicom files, makes it easier for doctors to analyze patients’ diseases without limited time and location. In this study mobile teleradiology simulation was carried out by sending dicom files that were archived in zip from the scanning results using mobile x-ray and DR films via email, files which were received using an android smartphone and then extracted. By activating the dicom file reader application on the smartphone an image that can be adjusted to brightness, sharpness, contrast so that it can be further analyzed by the recipient. With this simulation, it is analogous if the file sent is a scanning of a patient, and the recipient is a doctor, then the doctor can analyze the disease of the patient.
Cache-Efficient FM-Index Variants for Mapping of DNA Sequences

Mapping reads to a reference genome is the first and very important step in genome analysis. One of the most used data structure for DNA read mapping is the FM-index. To this day many different variants of the FM-index exist. In the proposed paper we introduce two new variants of the FM-index suitable especially for DNA sequences. Proposed variants decrease the number of cache misses by efficiently interleaving auxiliary data structures of the FM-index thus increasing the searching speed. Experimental results have shown that the proposed variants are about two times faster than other variants, while having comparatively low memory requirements.Source code is available at https://github.com/xsitarcik/DNASeqMap .