Reviewerly/exHarmony huggingface dataset:
Evaluating Ordering Strategies of Star Glyph Axes

Star glyphs are a well-researched visualization technique to represent multi-dimensional data. They are often used in small multiple settings for a visual comparison of many data points. However, their overall visual appearance is strongly influenced by the ordering of dimensions. To this end, two orthogonal categories of layout strategies are proposed in the literature: order dimensions by similarity to get homogeneously shaped glyphs vs. order by dissimilarity to emphasize spikes and salient shapes. While there is evidence that salient shapes support clustering tasks, evaluation, and direct comparison of data-driven ordering strategies has not received much research attention. We contribute an empirical user study to evaluate the efficiency, effectiveness, and user confidence in visual clustering tasks using star glyphs. In comparison to similarity-based ordering, our results indicate that dissimilarity-based star glyph layouts support users better in clustering tasks, especially when clutter is present.
Time performance analysis of advanced encryption standard and data encryption standard in data security transaction

The advancement of the data communication technologies has increased the traffic of data exchange over the internet and at the same time created the opportunity of data attack by various party. This paper present Time Performance Analysis Of Advanced Encryption Standard And Data Encryption Standard in Data Security Transaction&lt;strong&gt;. &lt;/strong&gt;In this study we proposed an AES algorithm with different key size, and different file format. Our aim is to safely to transfer the file for using the AES algorithm. Proposed algorithm has done by analyzing the different time taken for both AES and DES, experiments were done by three different file format which were text, image, and voice. Each file format type was tested with five different file sizes. The result of each experiments were analysed and it was confirmed that the AES algorithm have better performance in term of time taken as compared to DES.
A Dynamic Decision-Making Method Based on Ensemble Methods for Complex Unbalanced Data

Class imbalance has been proven to seriously hinder the precision of many standard learning algorithms. To solve this problem, a number of methods have been proposed, for example, the distance-based balancing ensemble method that learns the unbalanced dataset by converting it into multiple balanced subsets on which sub-classifiers are built. However, the class-imbalance problem is usually accompanied by other data-complexity problems such as class overlap, small disjuncts, and noise instance. Current algorithms developed for primary unbalanced-data problems cannot address the complex-data problems at the same time. Some of these algorithms even exacerbate the class-overlap and small-disjuncts problems after trying to address the complex-data problem. On this account, this study proposes a dynamic ensemble selection decision-making (DESD) method. The DESD first repeats the random-splitting technique to divide the dataset into multiple balanced subsets that contain no or few class-overlap and small-disjunct problems. Then, the classifiers are built on these subsets to compose the candidate classifier pool. To select the most appropriate classifiers from the candidate classifier pool for the classification of each query instance, we use a weighting mechanism to highlight the competence of classifiers that are more powerful in classifying minority instances belonging to the local region in which the query instance is located. Tests with 15 standard datasets from public repositories are performed to demonstrate the effectiveness of the DESD method. The results show that the precision of the DESD method outperforms other ensemble methods.
Essentia: Mining Domain-specific Paraphrases with Word-Alignment Graphs

Paraphrases are important linguistic resources for a wide variety of NLP applications. Many techniques for automatic paraphrase mining from general corpora have been proposed. While these techniques are successful at discovering generic paraphrases, they often fail to identify domain-specific paraphrases (e.g., staff, concierge in the hospitality domain). This is because current techniques are often based on statistical methods, while domain-specific corpora are too small to fit statistical methods. In this paper, we present an unsupervised graph-based technique to mine paraphrases from a small set of sentences that roughly share the same topic or intent. Our system, Essentia, relies on word-alignment techniques to create a word-alignment graph that merges and organizes tokens from input sentences. The resulting graph is then used to generate candidate paraphrases. We demonstrate that our system obtains high quality paraphrases, as evaluated by crowd workers. We further show that the majority of the identified paraphrases are domain-specific and thus complement existing paraphrase databases.
Pagerank Algorithm for Directed Hypergraph

During the last two decades, we easilly see that the World Wide Web's link structure is modeled as the directed graph. In this paper, we will model the World Wide Web's link structure as the directed hypergraph. Moreover, we will develop the PageRank algorithm for this directed hypergraph. Due to the lack of the World Wide Web directed hypergraph datasets, we will apply the PageRank algorithm to the metabolic network which is the directed hypergraph itself. The experiments show that our novel PageRank algorithm is successfully applied to this metabolic network.
PENGARUH RATA-RATA LAMA SEKOLAH KABUPATEN/KOTA TERHADAP PROSENTASE PENDUDUK MISKIN KABUPATEN/KOTA DI PROVINSI JAWA TIMUR TAHUN 2017

The way a person thinks in dealing with a problem is greatly influenced by the knowledge of the person. The main foundation of knowledge is through education. With the high level of education, there are more choices for people to live more prosperously. measure the magnitude and direction of the relationship between the average length of school, residence, and the percentage of the poor, (2) measure the magnitude of the influence of the average length of school, residence of the population on the percentage of poor people. This research method uses statistical Pearson correlation and linear regression. this study shows a strong negative relationship between the average length of school and the percentage of poor people. This very strong negative relationship explains that the higher the average school year, the lower the percentage of poor people. In addition to being strongly related, the average length of school also significantly affects the percentage of poor people. However, other factors need to be sought that can reduce the percentage of poor people more quickly.
Design and exploration of virtual marine ship engine room system based on Unity3D platform

In order to enable the students to experience the actual engine room working environment, have a full understanding of the Marine ship equipment, and have the same operating experience as the working environment in the real ship cabin to improve the effect of education and training, the rudder cabi n of a Dalian ocean 10000-ton super large oil tanker “Yuanshan lake” is used as the virtual design object. First, the mathematical model of the rudder is set up on the basis of introducing the related concepts of the ship system. Then, the layout of the actual cabin is analyzed, and a complete three-dimensional model of the rudder cabin is established with 3ds Max. Finally, the three-dimensional model of the cabin is introduced into the virtual reality engine Unity3D, which realizes the interactive operation of the cabin equipment. An extended user interface is designed to complete the generation and release of the final virtual scene program. The research and design of this subject has certain reference significance in applying virtual reality technology to the various different compartments of the whole ship and broadening its application to different ship types.
Adaptation of flexible project management models based on Scrum and Kanban technologies

This paper shows the conduct and result of an experiment regarding combining two software development technologies – Scrum and Kanban – into one technology that will take the best sides of these methodologies and will be convenient and efficient to use. Scrum and Kanban development methodologies are the object of study. Existing flexible development methodologies are analyzed, including XP, Lean, FDD, as well as Scrum and Kanban in more detail. A comparative table of the last two methodologies is also drawn up, reflecting their differences in a number of criteria. These methodologies are found to have some drawbacks and could be removed in the new methodology, thus improving existing methodologies for specific purposes. The new methodology should be fairly flexible and adaptable for all members of the software development team. This methodology should be easy to use and have some set of rules. An experiment is conducted to demonstrate how to combine the methodology with maximum efficiency using Petri nets. First, the Scrum and Kanban methodologies were modeled. Then, based on these two models, as well as the new rules, a new model for the methodology was formed. After conducting the experiment, the model reveals what the proposed development methodology should look like. It can be used in software development. Creating this methodology on the basis of two existing ones is a very challenging task today, as it has 3 tasks – to improve the existing methodologies of Scrum and Kanban, to create a set of rules for the new methodology, and to make it as flexible, adaptive and useful as possible.
A Study on the Gaze Range Calculation Method During an Actual Car Driving Using Eyeball Angle and Head Angle Information

Car operation requires advanced brain function. Currently, evaluation of the motor vehicle driving ability of people with higher brain dysfunction is medically unknown and there are few evaluation criteria. The increase in accidents by elderly drivers is a social problem in Japan, and a method to evaluate whether elderly people can drive a car is needed. Under these circumstances, a system to evaluate brain dysfunction and driving ability of elderly people is needed. Gaze estimation research is a rapidly developing field. In this paper, we propose the gaze calculation method by eye and head angles. We used the eye tracking device (TalkEyeLite) made by Takei Scientific Instruments Cooperation. For our image processing technique, we estimated the head angle using the template matching method. By using the eye tracking device and the head angle estimate, we built a system that can be used during actual on-road car operation. In order to evaluate our proposed method, we tested the system on Japanese drivers during on-road driving evaluations at a driving school. The subjects were one instructor of the car driving school and eight general drivers (three 40–50 years old and five people over 60 years old). We compared the gaze range of the eight general subjects and the instructor. As a result, we confirmed that one male in his 40s and one elderly driver had narrower gaze ranges.
Iterative construction of Gaussian process surrogate models for Bayesian inference

A new algorithm is developed to tackle the issue of sampling non-Gaussian model parameter posterior probability distributions that arise from solutions to Bayesian inverse problems. The algorithm aims to mitigate some of the hurdles faced by traditional Markov Chain Monte Carlo (MCMC) samplers, through constructing proposal probability densities that are both, easy to sample and that provide a better approximation to the target density than a simple Gaussian proposal distribution would. To achieve that, a Gaussian proposal distribution is augmented with a Gaussian Process (GP) surface that helps capture non-linearities in the log-likelihood function. In order to train the GP surface, an iterative approach is adopted for the optimal selection of points in parameter space. Optimality is sought by maximizing the information gain of the GP surface using a minimum number of forward model simulation runs. The accuracy of the GP-augmented surface approximation is assessed in two ways. The first consists of comparing predictions obtained from the approximate surface with those obtained through running the actual simulation model at hold-out points in parameter space. The second consists of a measure based on the relative variance of sample weights obtained from sampling the approximate posterior probability distribution of the model parameters. The efficacy of this new algorithm is tested on inferring reaction rate parameters in a 3-node and 6-node network toy problems, which imitate idealized reaction networks in combustion applications.
Can Microkernels Mitigate Microarchitectural Attacks?

Microarchitectural attacks such as Meltdown and Spectre have attracted much attention recently. In this paper we study how effective these attacks are on the Genode microkernel framework using three different kernels, Okl4, Nova, and Linux. We try to answer the question whether the strict process separation provided by Genode combined with security-oriented kernels such as Okl4 and Nova can mitigate microarchitectural attacks. We evaluate the attack effectiveness by measuring the throughput of data transfer that violates the security properties of the system. Our results show that the underlying side-channel attack Flush+Reload used in both Meltdown and Spectre, is effective on all investigated platforms. We were also able to achieve high throughput using the Spectre attack, but we were not able to show any effective Meltdown attack on Okl4 or Nova.
Adding the power-set to description logics

We explore the relationships between Description Logics and Set Theory. The study is carried on using, on the set-theoretic side, a very rudimentary axiomatic set theory Ω, consisting of only four axioms characterizing binary union, set difference, inclusion, and the power-set. An extension of ALC, dubbed ALCΩ, is defined in which concepts are naturally interpreted as sets living in Ω-models. In ALCΩ not only membership between concepts is allowed—even admitting membership circularity—but also the power-set construct is exploited to add metamodelling capabilities. We investigate translations of ALCΩ into standard description logics as well as a set-theoretic translation. A polynomial encoding of ALCΩ in ALCOI proves the validity of the finite model property as well as an ExpTime upper bound on the complexity of concept satisfiability. We develop a set-theoretic translation of ALCΩ in the theory Ω, exploiting a technique proposed for translating normal modal and polymodal logics into Ω. Finally, we show that the fragment LCΩ of ALCΩ not admitting roles and individual names, is as expressive as ALCΩ.
A framework for cryptographic problems from linear algebra

Abstract We introduce a general framework encompassing the main hard problems emerging in lattice-based cryptography, which naturally includes the recently proposed Mersenne prime cryptosystem, but also problems coming from code-based cryptography. The framework allows to easily instantiate new hard problems and to automatically construct plausibly post-quantum secure primitives from them. As a first basic application, we introduce two new hard problems and the corresponding encryption schemes. Concretely, we study generalisations of hard problems such as SIS, LWE and NTRU to free modules over quotients of ℤ[ X ] by ideals of the form ( f , g ), where f is a monic polynomial and g ∈ ℤ[ X ] is a ciphertext modulus coprime to f . For trivial modules (i.e. of rank one), the case f = X n + 1 and g = q ∈ ℤ &gt;1 corresponds to ring-LWE, ring-SIS and NTRU, while the choices f = X n – 1 and g = X – 2 essentially cover the recently proposed Mersenne prime cryptosystems. At the other extreme, when considering modules of large rank and letting deg( f ) = 1, one recovers the framework of LWE and SIS.
Artificial Intelligence Recruitment Analysis

With the rapid development of the computer industry, the computer processing performance has been continuously improved, and artificial intelligence has been continuously developed and innovated from the beginning of the concept. Artificial intelligence has become an indispensable part of the computer industry. China’s domestic emphasis on the artificial intelligence industry continues to increase, supporting science and technology enterprises to develop artificial intelligence in depth, and the number of artificial intelligence recruitment in China has been exploding. Nowadays, with the rapid development of artificial intelligence, it is reasonable to analyze the demand for talents in the market, and combine the domestic research status of artificial intelligence to make reasonable suggestions for those who are about to enter the artificial intelligence market.
Using Mobile Learning Tools in Higher Education: A UAE Case

Research indicates that mobile learning (ML), has the potential to transform teaching and learning. Despite its benefits, mobile learning adoption is a challenging process which requires support to facilitate its integration. The focus of this article is to investigate the factors that could affect ML acceptance. The quantitative and qualitative data collected from the surveys revealed additional information regarding the pedagogical benefits of and obstacles to mobile learning integration. Even though participants in this study reported to be late adopters of technology, they maintained that ML is useful for learning, specifically ubiquitous learning. Teachers also noted that mobile learning integration is more successful when aligned with local cultural norms. Mobile learning tools that might be culturally appropriate in one context may not always be accepted in another. Findings of this study provide several implications and recommendations for future research on mobile learning implementation. Further research could explore the development and utilization of a rubric with specific criteria for evaluating mobile learning applictations.&lt;strong&gt; � &lt;/strong&gt;
Desain Sistem On-Grid Energi Terbarukan Skala Rumah Tangga Menggunakan Perangkat Lunak HOMER

Electricity demand increases along with an increasing population. Renewable energy power plants are experiencing an increase in their use. This increase occurred because the world's electricity needs are rising every year, so the development of renewable energy power plants continues. Indonesia's state-owned power plants supply electricity more from non-renewable energy sources than renewable energy sources. Therefore, there is a need for renewable energy sources that can supply electricity in Indonesia. This research discusses an efficient renewable energy system for residential and the total installation costs for on-grid systems in Bandung, Indonesia. The research method used is collecting solar radiation data, equipment specifications and other data needed and then optimized. The simulation model uses HOMER software. HOMER is used to determine the best technically estimated cost, payback period, and NPC. Based on the optimization results, the system configuration can supply the electricity load 45.5% of daily load consumption with a total NPC cost is 75,300,000 million with a payback period of 7 years. In addition, the on-grid system produces 1400 kg of carbon dioxide (CO2) emissions per year from diesel generators, lower than the CO2 emissions from systems that only comprise diesel generators reaching 114 tons per year.
Aerial Image and Map Synthesis Using Generative Adversarial Networks

Accurate automatic conversion between aerial images and maps is a valuable and challenging task in computer vision and computer graphics. Deep convolutional neural networks (CNN) have achieved promising results on this task but the results accuracy is not ideal. In this paper, we propose a solution to improve the precision and quality of the transforming results. The core learning method is based on generative adversarial networks (GANs). A novel generator and a multi-scale discriminator are introduced in our network. The generator operates at the progressive method to gurantee the spatial consistency between the inputs and outputs, and our multi-scale discriminator focuses on increasing the capacity of the network and guides the generator to generate better results. In particular, our architecture can also be used as a general neural network for style translation. Analytic experiments on the aerial-to-map dataset show that our network outperforms the existing method, advancing both accuracy and visual appearance.
Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model

This paper presents research on word familiarity rate estimation using the 'Word List by Semantic Principles'.We collected rating information on 96,557 words in the 'Word List by Semantic Principles' via Yahoo!crowdsourcing.We asked 3,392 subject participants to use their introspection to rate the familiarity of words based on the five perspectives of 'KNOW', 'WRITE', 'READ', 'SPEAK', and 'LISTEN', and each word was rated by at least 16 subject participants.We used Bayesian linear mixed models to estimate the word familiarity rates.We also explored the ratings with the semantic labels used in the 'Word List by Semantic Principles'.
A Higher Level Security Protocol for Cloud Computing

In this work, we construct a cloud-independent protocol which allows any public cloud system to be used as the private. While designing the protocol, we aim to provide both more secure mechanism as the private cloud and the advantages of the public cloud. We consider the data owner in a public cloud a company consisting of several units. The hierarchical structure of a company divided into organizational units offers many advantages for designing a higher level security protocol. We provide each user of an organizational unit of the company with secure access to the public cloud from both inside and outside the network. We also present a higher level security protocol based on location- and cloud-independent infrastructures.
A fast algorithm for a total variation based phase demodulation model

Abstract In this paper we introduce fast numerical algorithms for the solution of the model. For each variable, background illumination, amplitude modulation and phase map, we develop a fixed point method. Then, we write all three algorithms in the same framework and analyze their convergence rates, local smoothing factors by means of local Fourier analysis and present experimental evidence of their performance on synthetic and real world problems.
Collision Avoidance System with Obstacles and Humans to Collaborative Robots Arms Based on RGB-D Data

The collaboration between humans and machines, where humans can share the same work environment without safety equipment due to the collision avoidance characteristic is one of the research topics for the Industry 4.0. This work proposes a system that acquires the space of the environment through an RGB-Depth sensor, verifies the free spaces in the created Point Cloud and executes the trajectory of the collaborative manipulator avoiding collisions. It is demonstrated a simulated environment before the system in real situations, in which the movements of pick-and-place tasks are defined, diverting from virtual obstacles with the RGB-Depth sensor. It is possible to apply this system in real situations with obstacles and humans, due to the results obtained in the simulation. The basic structure of the system is supported by the ROS software, in particular, the MoveIt! and Rviz. These tools serve both for simulations and for real applications. The obtained results allow to validate the system using the algorithms PRM and RRT, chosen for being commonly used in the field of robot path planning.