Reviewerly/exHarmony huggingface dataset:
Semantic Web and Data Visualization

With the terrific growth of data volume and data being produced every second on millions of devices across the globe, there is a desperate need to manage the unstructured data available on web pages efficiently. Semantic Web or also known as Web of Trust structures the scattered data on the Internet according to the needs of the user. It is an extension of the World Wide Web (WWW) which focuses on manipulating web data on behalf of Humans. Due to the ability of the Semantic Web to integrate data from disparate sources and hence makes it more user-friendly, it is an emerging trend. Tim Berners-Lee first introduced the term Semantic Web and since then it has come a long way to become a more intelligent and intuitive web. Data Visualization plays an essential role in explaining complex concepts in a universal manner through pictorial representation, and the Semantic Web helps in broadening the potential of Data Visualization and thus making it an appropriate combination. The objective of this chapter is to provide fundamental insights concerning the semantic web technologies and in addition to that it also elucidates the issues as well as the solutions regarding the semantic web. The purpose of this chapter is to highlight the semantic web architecture in detail while also comparing it with the traditional search system. It classifies the semantic web architecture into three major pillars i.e. RDF, Ontology, and XML. Moreover, it describes different semantic web tools used in the framework and technology. It attempts to illustrate different approaches of the semantic web search engines. Besides stating numerous challenges faced by the semantic web it also illustrates the solutions.
Hybrid parameter adaptation strategy for differential evolution to solve real-world problems

Differential Evolution algorithm (DE) has been investigated in several studies. Indeed, it has been revealed that despite its successful search operators, DE may get trapped in local optimum due to the poor parameter configuration, and the inappropriate search operators. In this study, we introduce a resilient mutation strategy well-suited to real-world problems. Moreover, a machine learning-based parameter adaptation mechanism is proposed to configure DE parameters during the search process. The new adaptive DE has been tested to find the optimal mechanical structure of a novel electric motor topology. Furthermore, the results have been validated using the real-world problems from the CEC 2011 test suite. The results have revealed that the proposal can be competitive compared to recent adaptive DE algorithms.
A Simple Adaptive Tracker with Reminiscences

Correlation filters have provided exceptional results in the field of visual object tracking in the past few years. However, these methods typically learn a single filter to be robust to many different appearance changes, which can be challenging. We propose a simple solution to this problem by utilizing an ensemble method of base trackers trained on different temporal windows of the video history. The proposed tracker, called MTCF, exhibits the following features: i) it can be trained using gradient-based convex optimization; ii) it is robust to short-term and long-term changes in visual appearance. MTCF performs on par with or outperforms state-of-the-art trackers on the OTB and the VOT benchmark datasets. We present an extensive analysis of the performance of MTCF on these benchmark datasets.
Spatial distribution of Heavy Metal contents in farmland soils by different spatial interpolation methods: A case study of a farmland in Lanping County, Yunnan Province

Abstract The spatial interpolation method is of great sign ificance for the spatial distribution of heavy metals in soil and the prevention and control of pollution. However, different interpolation methods are applied to predict the accuracy of different heavy metal pollution distributions in small-scale areas with a single farmland. There is no relevant argument. Taking a farmland (96000m2) in Lanping County, Yunnan Province, Yunnan Province as the research object, 144 surface soil samples were collected and the contents of Pb and Cd in soil were determined by ICP-MS. The deterministic spatial interpolation method and geostatistical Kriskin interpolation method were used to investigate the accuracy of different interpolation methods and the distribution characteristics of Pb and Cd in soil. The results show that the optimal interpolation methods for heavy metals Pb and Cd in polluted farmland are 0-order ordinary Kristen interpolation method, and the content of Pb and Cd in the soil is relatively accurate. This study will provide important basic information for the identification of spatial distribution characteristics of different heavy metals in small-scale areas with a single farmland, which will help to comprehensively evaluate the function of spatial interpolation to identify spatial distribution information of heavy metals.
A Novel Position Concealment Audio Steganography in Insensible Frequency

For years, peopleMukherjee, Nabanita have devised different techniques for encrypting data while others have attemptedPaul, Goutam to break these encrypted codes. Steganography is a techniqueSaha, Sanjoy Kumar that allows one to hide data within an image, audio, or video. Imperceptibility of noise is an important issue for any steganographic technique. For this reason, we have proposed a new audio steganographic approach without considering the sample value less than 50 Hz and above 5000 Hz, because this range is highly sensible by ear as per biological phenomenon. The novelty of our work is that we have embedded the matching position value (determined from audio sample value itself) in 4 least significant bits rather than direct message bits flipping. Rather than performing sequential embedding, samples are selected nonsequentially based on audio feature itself that helps to withstand statistical attacks. Blind audio steganalysis techniques are hardly found—most of them are either algorithm specific or useful for bit-flipping data hiding scheme. In this context, unlike most common message bit flipping techniques, a novel bit matching and position concealment procedure has been proposed. Satisfactory theoretical and experimental outcomes have been achieved.
Collaborative Filtering Based on Attention Mechanism

Each item has different characteristics. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Therefore, using the traditional method to predict the unknown ratings ignores this difference in importance, resulting in a false assumption that all users have the same attention to different characteristics of the same item. In this paper, we propose a collaborative filtering system based on attention mechanism and design the feature-topic model to extract the characteristics of the item from review texts. Then, we use an attention network to get the importance of the item’s characteristic to the user. Considering the shortcomings of linear interaction features, we adopt the idea of collaboration to predict unknown scores. Extensive experiments on real-world datasets show significant improvements in our proposed CFAM framework over the state-of-the-art methods.
An objective assessment method for video stabilization performance

The intent of video stabilization to remove violent jitter in the video and reduce video distortion. Recently, many excellent video stabilization algorithms have been proposed. However, it is a question to assess the video stabilization performance objectively. In order to solve the problem, we propose a novel objective assessment method for video stabilization performance by computing the distance, distortion and similarity between the stabilized and reference frames. According to published video stabilization database, data sets are built and tested. Finally, the experimental results demonstrate the accuracy of our proposed assessment method and the results are consistent with subjective human eyes.
An adversarial approach to non-uniform blind deblurring with opposite-channel-based discriminative priors

In the traditional uniform blind deblurring methods, we have witnessed the great advances by utilizing various image priors which are expected to favor clean images than blurred images and act through regularizing the solution space. However, these methods failed in dealing with non-uniform blind deblurring because of the inaccuracy in kernel estimation. Learning-based methods can generate clear images in an end-to-end way potentially without an intermediate step of blur kernel estimation. To better deal with the non-uniform deblurring problem in dynamic scenes, in this paper we present a new type of image priors complementary to the deep learning-based blind estimation framework. Specifically, inspired by the interesting discovery of dark and bright channels in dehazing, the opposite-channel-based discriminative priors are developed and directly integrated to the loss of our advocated deep deblurring model, so as to achieve more accurate and robust blind deblurring performance. It deserves noticing that, our deep model is formulated in the framework of the Wasserstein generative adversarial networks regularized by the Liptchitz penalty (WGAN-LP), and the network structures are relatively simpler yet more stable than other deep deblurring methods. We evaluate the proposed method on a large scale blur dataset with complex non-uniform motions. Experimental results show that it achieves state-of-the-art non-uniform blind deblurring performance not only quantitatively but also qualitatively
Media Photo Story dalam Pembelajaran Bahasa Indonesia di Sekolah Dasar

At present the use of learning media is not optimal. Media that is widely used is only limited to visual print media so that learning still cannot bring out the curiosity of students during learning. Even though learning media is one of the most important elements to support the success of the learning process. In the process of Indonesian language learning in elementary school, students are required to be able to develop four language skills, listening, speaking, reading, and writing. The photo story media includes audio visual media that can help students master the four language skills. Photo story media is a picture of a story that has meaning, displayed through video shows using a computer or laptop device. The use of photo story media can stimulate the imagination of students and increase motivation in learning Indonesian. Thus, learning Indonesian in elementary schools by utilizing photo story media is able to accommodate learning to be more meaningful, stimulate the imagination of students, motivate learning activities and of course help students develop their language skills.
The lessons software engineers can extract from painters to improve the software development process

The way software is produced is very similar to an artistic and creative process. This fact is well known and has been acknowledged from the very early era of computers.
Power of Big Data System for Storing and Processing Huge Data

Big data uses storage of huge data with some approaches and techniques to manage and process them. During the past few years the number of persons using internet, email and other internet-based applications has been growing tremendously. Big Data is mainly characterized by 3V’s (Volume, Velocity and, Variety). The Big Data Architecture Framework (BDAF) is proposed to address all aspects of the Big Data Ecosystem. BDAF includes components such as Big Data Infrastructure, Big Data Analytics, Data structures &amp; models, Big Data Lifecycle Management and Big Data Security. Nowadays the volume of data used by the people throughout the world is increasing enormously and exponentially. So, the need for storing, processing and protecting large volume of data has been becoming a great challenge in the modern hyper-connected world. On the basis of work from home concept lot of software professionals are doing their jobs with their internet connected systems for development, implementation, testing and maintenance of various softwares. These professionals and experts are sending and receiving lot of data to various locations to their clients, higher authorities and other officials frequently depending upon their requirements. The traditional data management models are not efficient for today’s exponentially growing data from variety of industries. This challenging task of storing and managing huge volume of data is achieved in Big Data Systems. In this paper we try to give an overview of Big Data Analytics system for storing and processing huge volume of various types of data. Overwhelming the security threats due to various factors like viruses, worms, etc are also great challenges to protect huge volume of data in a big data system.
3D geological model of Nahr Umr/Ratawi oil field in Basra

Abstract The study includes building a 3-D geological model, which involves get the Petrophysical properties as (porosity, permeability and water saturation). Effective Porosity, water saturation results from log interpretation process and permeability from special correlation using core data and log data. Clay volume can be calculated by six ways using IP software v3.5 the best way was by using gamma Ray. Also, Water Resistivity, flushed zone saturation and bulk volume analysis determined through geological study. Lithology determined in several ways using M-N matrix Identification, Density-Neutron and Sonic-Neutron cross plots. The cut off values are determined by Using EHC (Equivalent Hydraulic Unit) method. The full grid of the model was 454308 cells. Layering of units depend on the importance of these units and scale up had done using several methods depend on property (Arithmetic for porosity and water saturation and geometric for permeability). Petrophysical properties distributed using Gaussian statistical approach. Original oil in place calculated for Nahr Umr which equal to 460 m3.
Enhancing the Cryptographic Strength of the LSB Steganographic Method with Pixel Sieving

In this paper an enhancement is proposed for the LSB (Least Significant Bits) type steganographic method based on the pixel sieve cryptographic primitive. The novelty of the method, the added security, resides in the fact that the useful data is hidden not in one digital image, but in several carrier images. To reconstruct the original data not only all the carrier images are required, but also the correct “encryption key” used to spread the data in the shares.
On-chip Generation, Coherent Control and Processing of Complex Entangled Photon States

We demonstrate the on-chip generation of time-bin entangled two- and multi-photon qubit states, as well as high-dimensional frequency-entangled photon pairs. Combining time and frequency entanglement, we generate high-dimensional optical cluster states and implement proof-of-concept high-dimensional one-way quantum computing. This, by using standard, fiber-based telecommunication components.
Estimate's Statistics in the Performance Evaluation of Extended Object Tracker

This paper considers the statistical parameters of all characteristic points of a target's estimate, when assessing the performance of extended object tracker. For this, the established optimal subpattern assignment metric is modified by replacing the Euclidean base distance between the true and the estimated target state-vectors with the Mahalanobis distance. It represents the distance between a deterministic ground truth target position and its corresponding estimate, which is characterized by a multivariate Gaussian distribution. The necessity of considering the track covariance into the metric is proven by experimental results, obtained with a Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD). Adding the track uncertainty to the OSPA metric definition for extended object tracker leads to a fast and intuitive assessment method for modern tracking algorithms, as used for example in automotive lidar sensors, radar sensors or future traffic control systems for flying taxis.
DEEM: Enabling Microservices via DEvice Edge Markets

Native applications running over handheld devices have an irreplaceable role in users' daily activities. That said, recent studies show that users download on average zero new applications on monthly basis, which suggests that new apps can face discoverability issues. In this work, we aim for a web-based, download/installation-free access to native application features through microservices (μ Services)that are shared between user devices in a peer-to-peer (P2P)manner. Such a P2P approach is self-scalable and requires no investment for μ Service deployment, unlike mobile edge computing or Data Centre. We introduce DEEM, a DEvice Edge Market design that enables device-hosted μServices to end-users. In DEEM, μ Service-based markets act as rendezvous points between available μ Service instances and clients. DEEM ensures the i) assignment of instances to the users that value them the most, in terms of QoS gain, and ii) devices' income maximisation. Our evaluation on synthetic settings demonstrates DEEM's capability in exploiting the pool of device instances for improving the application QoS in terms of latency.
Development of Goals Achievement Sharing System Based on Approach Goals in Positive Psychology

Recently, GNH (Gross National Happiness) has been emphasized as a policy goal of the national and local governments. In positive psychology, people feel the happiness in the process towards the goal achievement. Additionally, the goal is classified as approach and avoidance goal. And to achieve the goal, determination of approach goal is effective. In this study, we developed a goals achievement sharing system based on approach goals in positive psychology to investigate the change in the goal achievement rate and happiness scale in order to share the type of goal.
Analysis of the Process Capability Index According to the Sample Size of Multi-Measurement

This study is about the process capability index (PCI). In this study, we introduce several indices including the index and present the...
Ensemble Regression Kernel Extreme Learning Machines for Multi-Instance Multi-Label Learning

The multi-instance multi-label learning (MIML) framework is an extension of the multi-label learning, where each object in MIML is represented by a multi-instance bag and associated with a multi-label vector. Recently, extreme learning machine (ELM) has been widely used in multi-instance multi-label classification due to its short runtime. Simultaneously, ELM also has good classification accuracy compared to other neural network models. However, this type of ELM-based MIML classification algorithms can easily lead to overfitting problems during training and the basic ELM algorithm with random initial weights and biases is not stable. In order to solve the above problems, ensemble learning is used to overcome overfitting problems and regression kernel extreme learning machine (RKELM) as classifier instead of the basic ELM effectively can solve the problem of instability of training. In this paper, Bagging-based RKELM (BRKELM) and AdaBoost-based RKELM (ARKELM) for MIML classifications are proposed. The comparison with other state-of-the-art multi-instance multi-label learning algorithms shows that the BRKELM and ARKELM are highly efficient, feasible and stable algorithms.
Bridging the Gap between High-Performance, Cloud and Service-Oriented Computing

Modern large-scale computing systems are often naturally assumed to be based on the cloud provisioning paradigm. While this model is very flexible and often more cost effective than the actual ownership of physical servers, it also comes at a price, i.e., abstraction by virtualization. Even today, there are many fields of application in which any kind of performance degradation would be considered harmful. These types of applications are what we consider to be the core of classical high-performance computing. Bridging the gap between this low level performance optimization oriented execution model, higher level but more flexible cloud-based Infrastructure-as-aService provisioning and finally service-oriented execution models on the same physical hardware is a very challenging task. It requires tailor-made solutions to connect and combine the various independent software layers and abstraction stacks into a single manageable platform. In this paper we present our approach for the integration of all three tiers into one large scale hardware and software framework. Based on our existing solution for the simultaneous operation of cluster and cloud infrastructure on the same physical hardware, we provide an outlook on our current work-in-progress and vision for the coupling of two remote locations into a single distributed system for service-oriented applications.
Environmental quality index modeling in Indonesia using ordinal probit regression approach for panel data with random effect

Abstract Environmental issues are one of the important global issues and are of concern because they are considered to require a solution. The Ministry of Environment and Forestry has developed an Environment Quality Index (EQI) that provides rapid information of an environmental condition with river water quality, air quality, and forest as indicator. This study aims to model EQI in Indonesia using ordinal probit regression for panel data with random effects. Parameter estimation of ordinal probit regression model for panel data with random effect using the marginal likelihood function, and maximize that function using M-point Gauss-Hermite Quadrature method. The data that be used in this study is EQI from 33 Provinces in Indonesia during 2011 to 2016 which are categorized into three category i.e less good, good and very good. The result of modelling is gotten variable that influence to EQI is Human Development Index (HDI). EQI opportunities in DKI Jakarta province in the year 2016 in the category of less good is high enough, that is equal to 99.89%. This may be due to high population density and less balanced with awareness of the population to preserve the environment. The accuracy of classification of modeling result is 70.202%.
A Novel Modified Electromagnetism-like Algorithm for Solving Constrained Optimization Problem

Abstract The original Electromagnetism-like mechanism (EM) is a meta-heuristic algorithm utilizing an attraction-repulsion mechanism (called as force F) to move sample points towards optimality in global optimization problems. To solve the potential problem of stagnation happening in the original algorithm, new searching procedures were proposed in modified algorithm to determine the next search direction and step length of points in the paper. Compared to the original algorithm, tri-factors of the best historical visited positions of each point, best point and total force F were considered in the improved searching procedures. Moreover, the feasibility and dominance (FAD) rules were incorporated to extend the proposed algorithm to solve constrained optimization problem. Finally, Preliminary experiments verified the effectiveness of the proposed algorithm.