Reviewerly/exHarmony huggingface dataset:
An Efficient and Optimized Sematic Web Enabled Framework (EOSWEF) for Google Search Engine Using Ontology

Remarkable growth in the electronics and communication field provides ubiquitous services.It also permits to save huge amount of documents on web.As a result, it is very difficult to search a specific and desired information over the Internet.Classical search engines were unable to investigate the content on web intelligently.The tradition searching results has a lot of immaterial information along with desired one as per user query.To overcome from stated problem many modifications are done in traditional search engines to make them intelligent.These search engines are able to analyze the stored data and reflects only appropriate contents as per users query.Semantic Web is an emerging and efficient approach to handle the searching queries.It gathers appropriate information from web pool based on logical reasoning.It also incorporates rulebased system.Semantic web reasonably scrutinizes webs contents using ontology.The learning process of ontology not only intelligently analyze the contents on web but also improves scrutinizing process of search engine.The paper suggests a new keyword-based semantic retrieval scheme for google search engines.The schemes accelerates the performance of searching process considerably with the help of domain-specific knowledge extraction process along with inference and rules.For this, in ontology the prefix keywords and its sematic association are pre-stored.The proposed framework accelerates the efficiency of content searching of google search engine without any additional burden of end users.
Possible Modifications of RC4 Stream Cipher

In this paper, we consider possible modifications of the stream cipher RC4, which became widespread due to its properties, such as elegant design and an effective software and hardware implementation. We present an enhancement version of the RC4 algorithm, which obtained by adding parallel LFSR and stochastic transformation boxes (R-boxes). The result of proposed modification is the increased complexity of dependence between the key and the initial state of the cipher, as well as increased bit depth of the internal state.
Hierarchy of quantum operations in manipulating coherence and entanglement

Quantum resource theory under different classes of quantum operations advances multiperspective understandings of inherent quantum-mechanical properties, such as quantum coherence and quantum entanglement. We establish hierarchies of different operations for manipulating coherence and entanglement in distributed settings, where at least one of the two spatially separated parties are restricted from generating coherence. In these settings, we introduce new classes of operations and also characterize those maximal, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>i</mml:mi><mml:mo>.</mml:mo><mml:mi>e</mml:mi><mml:mo>.</mml:mo></mml:math>, the resource-non-generating operations, progressing beyond existing studies on incoherent versions of local operations and classical communication and those of separable operations. The maximal operations admit a semidefinite-programming formulation useful for numerical algorithms, whereas the existing operations not. To establish the hierarchies, we prove a sequence of inclusion relations among the operations by clarifying tasks where separation of the operations appears. We also demonstrate an asymptotically non-surviving separation of the operations in the hierarchy in terms of performance of the task of assisted coherence distillation, where a separation in a one-shot scenario vanishes in the asymptotic limit. Our results serve as fundamental analytical and numerical tools to investigate interplay between coherence and entanglement under different operations in the resource theory.
Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm

Proof of work (PoW), the most popular consensus mechanism for Blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-ming, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in Blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our paper is the first work to employ federal learning as the proof of work for Blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.
Solving the Steiner tree problem in graphs by chaotic search

The Steiner tree problem in graphs is an NP-hard combinatorial optimization problem. To solve the NP-hard combinatorial optimization problems, such as the traveling salesman problems, the quadratic assignment problems, and the vehicle routing problems, an algorithm for searching solutions by chaotic dynamics, or the chaotic search, exhibits good performance. From this viewpoint, this paper proposes an algorithm for solving the Steiner tree problem in graphs with chaotic dynamics. Comparing the performance of the chaotic search with that of the tabu search, we analyze the searching characteristics of the chaotic search. From the results of numerical experiments, we found that if parameters of the chaotic search are set to appropriate values, the chaotic search exhibits good performance and the chaotic search outputs optimal solutions more frequently than the tabu search during the searching processes, which implies that the chaotic search has higher ability than the tabu search as metaheuristics.
Structuring and Inspecting 3D Anchors for Seismic Volume into Hyperknowledge Base in Virtual Reality

The following topics are dealt with: virtual reality; augmented reality; data visualisation; computer animation; convolutional neural nets; neural nets; learning (artificial intelligence); rendering (computer graphics); Internet; video signal processing.
Load Balancing Based on Optimization Algorithms: An Overview

Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other signiﬁcant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to eﬃciently handle load balancing issues.
Cutoff for exact recovery of Gaussian mixture models

We determine the information-theoretic cutoff value on separation of cluster centers for exact recovery of cluster labels in a $K$-component Gaussian mixture model with equal cluster sizes. Moreover, we show that a semidefinite programming (SDP) relaxation of the $K$-means clustering method achieves such sharp threshold for exact recovery without assuming the symmetry of cluster centers.
Hidden in Plain Sight: Toward a Smart Future in Eindhoven

With the continuous assertion of Eindhoven as a center of innovation, technology and, more specifically, smart systems, the proliferation of “smartness” across the city’s built environment continues to increase in both overt and tacit ways. Traces of such a proliferation of “smartness” appear dotted throughout the city in the form of cameras and sensors, without much disclosure regarding who owns them and what they are used for. As such, Eindhoven’s citizens are subjected to various data-collection systems and living-lab experiments without their knowledge or agreement, as well as without any idea of the potential consequences and implications. This visual essay uncovers some of the traces of “smartness” across Eindhoven to reflect on the pervasiveness of “smartness” not only as an abstract idea, but also as a way of life and, ultimately, question the normalization of its corresponding mass-surveillance regime.
Remittances and bribery in Africa

This chapter examines the effects of remittances on bribe payments to public officials to access public goods and services in Africa. Using Afrobarometer surveys administered in 36 African countries between 2008 and 2016, we find that remittance receivers are more prone to bribe payment than non-receivers. More importantly, we find that individuals who live in countries with higher levels of remittances as a share of gross domestic product (GDP) are more likely to pay bribes than individuals from countries with lower remittances as a share of GDP, which supports the income channel. However, the positive link between remittances and bribery diminishes in countries with a high level of control over corruption. Overall, the main findings of this chapter suggest that policies for an increase in remittances should be coupled with anti-corruption policies. Otherwise, the positive effects of remittances on the economic conditions of the recipients may result in increased corruption. This chapter is linked to the SDG 17.3 that, in one of its indicators, calls for an increase in remittances, and SGD 16.5 on anti-corruption policies.
Model-Based Diagnosis with Uncertain Observations

Classical model-based diagnosis uses a model of the system to infer diagnoses – explanations – of a given abnormal observation. In this work, we explore how to address the case where there is uncertainty over a given observation. This can happen, for example, when the observations are collected by noisy sensors, that are known to return incorrect observations with some probability. We formally define this common scenario for consistency-based and abductive models. In addition, we analyze the complexity of two complete algorithms we propose for finding all diagnoses and correctly ranking them. Finally, we propose a third algorithm that returns the most probable diagnosis without finding all possible diagnoses. Experimental evaluation shows that this third algorithm can be very effective in cases where the number of faults is small and the uncertainty over the observations is not large. If, however, all possible diagnoses are desired, then the choice between the first two algorithms depends on whether the domain's diagnosis form is abductive or consistent.
[Paper] Speech-driven Face Reenactment for a Video Sequence

We present a system for reenacting a person's face driven by speech. Given a video sequence with the corresponding audio track of a person giving a speech and another audio track containing different speech from the same person, we reconstruct a 3D mesh of the face in each frame of the video sequence to match the speech in the second audio track. Audio features are extracted from such two audio tracks. Assuming that the appearance of the mouth is highly correlated to these speech features, we extract the mouth region of the face's 3D mesh from the video sequence when speech features from the second audio track are close to those of the video's audio track. While retaining temporal consistency, these extracted mouth regions then replace the original mouth regions in the video sequence, synthesizing a reenactment video where the person seemingly gives the speech from the second audio track. Our system, coined S2TH (speech to talking head), does not require any special hardware to capture the 3D geometry of faces but uses the state-of-the-art method for facial geometry regression. We visually and subjectively demonstrate reenactment quality.
Dynamic Sampling Network for Semantic Segmentation

Sampling is a basic operation of modern convolutional neural networks (CNN) since down-sampling operators are employed to enlarge the receptive field while up-sampling operators are adopted to increase resolution. Most existing deep segmentation networks employ regular grid sampling operators, which can be suboptimal for semantic segmentation task due to large shape and scale variance. To address this problem, this paper proposes a Context Guided Dynamic Sampling (CGDS) module to obtain an effective representation with rich shape and scale information by adaptively sampling useful segmentation information in spatial space. Moreover, we utilize the multi-scale contextual representations to guide the sampling process. Therefore, our CGDS can adaptively capture shape and scale information according to not only the input feature map but also the multi-scale semantic context. CGDS provides a plug-and-play module which can be easily incorporated in deep segmentation networks. We incorporate our proposed CGDS module into Dynamic Sampling Network (DSNet) and perform extensive experiments on segmentation datasets. Experimental results show that our CGDS significantly improves semantic segmentation performance and achieves state-of-the-art performance on PASCAL VOC 2012 and ADE20K datasets. Our model achieves 85.2% mIOU on PASCAL VOC 2012 test set without MS COCO dataset pre-trained and 46.4% on ADE20K validation set. The codes will become publicly available after publication.
Prediction of solar energy radiation using adaptive neuro fuzzy inference system in the tropical region

The research aims to develop a prediction system of solar energy radiation in the tropical region for home energy needs. The tropics get sunlight all year round. The Solar energy radiates abundantly in the tropic. The Solar radiation Energy is influenced by weather factors. The input data in this study use weather factors such mximum temperature, minimum temperature, Precipitation, wind speed and relative humidity. The input data retrieves in 3 regions are Klaten, Sragen and Sukoharjo. The prediction system was built by using soft computing. The method used in this prediction system is an adaptive neuro fuzzy inference system (ANFIS) using hybrid Hybrid optimization method was chosen because it produces a calculation of RMSE (root mean square error) which is lower than backpropagation. methods. Hybrid optimization method was chosen because it produces a calculation of RMSE (root mean square error) which is lower than backpropagation. The prediction results were the values of solar energy in each region.
Risk Level of Asthma and Chronic Obstructive Pulmonary Disease Through Design of an Intelligent Type-2 Fuzzy Expert System

Diagnosis of Asthma during the early stages plays a primary role in providing effective medical treatment.Different opinions may be given by different doctors for a patient having the same set of symptoms.This opinion is based on the type or degree of the disorder.There can be scenarios where the same doctor may diagnose differently for two different patients having similar symptoms.Hence, it can be concluded that the thought process of a doctor is fuzzy in nature.In any medical field, accuracy of diagnosis is extremely important as it concerns the life of a patient.Besides, with increase in number of hospitals and staff, the amount of measured data is extremely huge.Off late, mistakes have tended to creep during recording of measurements.Mistakes include carelessness, inaccurate measurements and failure in recording take place from time to time.Taking these points into consideration, the focus of the work has shifted to providing an improvement in the accuracy levels of detection.In this connection, a Type-2 Fuzzy model has been designed.
Searchable Public Key Encryption Supporting Simple Boolean Keywords Search

Searchable encryption with advanced query function is an important technique in today's cloud environment.To date, in the public key setting, the best query function supported by the previous schemes are conjunctive or disjunctive keyword search, which are elementary but not enough to satisfy the user's query requirements.In this paper, we make a progress for constructing a searchable public key encryption scheme with advanced query function called simple Boolean keyword search.To create our scheme, we proposed a keywords conversion method that projects the index and query keywords into a group of vectors.Based on a combination of these obtained vectors and an adaptively secure inner product encryption scheme, a public key encryption with simple Boolean keyword search scheme is proposed.We also present both theoretical and experimental analysis to show the effectiveness of this scheme.To the best of our knowledge, it is the first time to give a searchable public key encryption scheme supporting queries like q 1 op 1 q 2 op 2 • • • op i-1 q i op i • • • op n-1 q n , where op i is a logical operator which can be and(∧) or or(∨) and q i is a keyword.
Bartleby, the IoT, and Flat Ontology: How Ontology is Written in the Age of Ubiquitous Computing

The Internet of Things, as the object-oriented reconstruction of the traditional internet, is characterized by its smart objects freely inter-operating without being necessarily under human control. Re-building the internet's information economy from the data captured by and communicated through these autonomous objects, the IoT operationalizes a sort of flat ontology, which recent realist philosophers suggest as a means to speculate about the world-making activities of nonhumans not necessarily correlated to human subjects. This paper examines the coincidence of recent interest in these nonhuman world-making processes drawn by two traditionally distinctive but now converging fields: computer engineering and philosophical ontology.
Meta-analysis Study of Achievement Motivation and Academic Achievement

Academic achievement becomes something important for students.Academic achievement has a relationship with achievement motivation.Based on the results of a review of the literature, there is an inconsistency in the results of research on the relationship of academic achievement with achievement motivation.Therefore, the author conducted a meta-analyst with a significant effect size correlation with academic achievement (r = 0.334508) and significant with CI (0.170255; 0.480675).The results of the I2 statistical test showed a high inconsistency with a value of 98.7%.There is also high heterogeneity in the results of this meta-analysis with the value of Cohcran Q showing 1,029.604289.The relationship between the two variables also shows the bias by looking at the results of the egger, which is 9.311441.The results of statistical tests show that achievement motivation and academic achievement have a fairly high effect size correlation.The heterogeneity in this meta-analysis is high and there is publication bias.
The Cardboard VR Game Development Tool

Due to the technological development of the head-mounted device, the user can experience the world of virtual reality, and the immersive experience combined with the gameplay of the game can provide a new experience for the player. Google Cardboard is cheap and easy to use, providing a simpler method to experience VR games. Cardboard VR games make game design more difficult due to the functional limitations of the device, so there are few related games. However, it is easier to play such games. Designing a good game will have good market value. When students make this kind of hard -to-design game, if their pressure on programming can be reduced, students can focus on game design to design better ones. This research develops development tools for Cardboard games to provide game designers arising the quality of the games they develop.
LMLFM: Longitudinal Multi-Level Factorization Machine

We consider the problem of learning predictive models from longitudinal data, consisting of irregularly repeated, sparse observations from a set of individuals over time. Such data often exhibit longitudinal correlation (LC) (correlations among observations for each individual over time), cluster correlation (CC) (correlations among individuals that have similar characteristics), or both. These correlations are often accounted for using mixed effects models that include fixed effects and random effects, where the fixed effects capture the regression parameters that are shared by all individuals, whereas random effects capture those parameters that vary across individuals. However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables. We propose Longitudinal Multi-Level Factorization Machine (LMLFM), to the best of our knowledge, the first model to address these challenges in learning predictive models from longitudinal data. We establish the convergence properties, and analyze the computational complexity, of LMLFM. We present results of experiments with both simulated and real-world longitudinal data which show that LMLFM outperforms the state-of-the-art methods in terms of predictive accuracy, variable selection ability, and scalability to data with large number of variables. The code and supplemental material is available at https://github.com/junjieliang672/LMLFM.
The Strategy of Disaster Mitigation Literacy through Problem-Based Learning (PBL) in the School Prone to Tidal Floods

A disaster becoming a great issue and it always happens nowadays.A school becomes an appropriate tool for spread knowledge of disaster widely that happens in environs.SMA N 10 Semarang is a school that located in the prone area of tidal floods.However, the lesson material of disaster and students' literacy capabilities still restricted.Hence, disaster mitigation literacy must be owned by students.The aim of this study is to present the strategy disaster mitigation literacy through PBL learning in the school prone to tidal floods.This research has been done in SMA Negeri 10 Semarang.The method that used was mixed method.The data collection used with observation, test, and interview.The technique of analyzing the data that used in this research is descriptive analysis.The results showed that the disaster mitigation literacy in the majority experiment class was at level 5 and was better than the control class with the majority being at level 3.In detail, the literacy improvement strategy for the students is; 1) giving disaster mitigation of literacy education through PBL learning; 2) increase the quality of science literacy though various readings; and 3) encourage improvement of awareness and skills of individual about disaster that concerns in the surrounding environment.The results of this study can be used as a reference in integrating disaster education in particular the mitigation of disasters in school learning so that the school formed disaster preparedness with a strong generation of disasters.