Reviewerly/exHarmony huggingface dataset:
Decomposition of Random Vector of GAN and Basis Vector Assembly for Chinese Character Generation

Ian et al published the paper generative adversarial networks (GAN) [1], which consists of two networks one is generator, the other is discriminator. Its principle is that z-vector with a length of 100 then each element along the Gaussian distribution, is inputted into generative network than it makes fake image, which is similar real image what discriminator wouldn't distinguish. In adversarial relation, they are elaborated gradually with training. Finally, discriminator can't distinguish them. In other words, if they have a sufficient training time, z-vector will follow real data set distribution almost exactly. After that, Alec et al's deep convolutional generative adversarial networks (DCGAN) [2] introduced the Vector-Arithmetic, which uses the feature of sufficiently trained GAN's z-vector. It showed that each z-vectors has extraction of image's feature and has relation. However, it revealed that z-vector reflect approximate image characteristics but was not analyzed in practice what z-vector's elements has relation with them. In this paper, we analyzed the relation of z-vector's elements and introduce the Basis Vector Decomposition & Assembly (BVD & BVA).
Machine Learning in Video Games

Machine learning is a part of artificial intelligence in which the learning was done using the data available in the environment. Machine learning algorithms are mainly used in game development to change from presripted games to adaptive play games. The main theme or plot of the game, game levels, maps in route, and racing games are considered as content. Context refers to the game screenplay, sound effects, and visual effects. In any type of game, maintaining the fun mode of the player is very important. Predictable moves by non-players in the game and same type of visual effects will reduce the player's interest in the game. The machine learning algorithms works in automatic content generation and nonpayer character behaviours in gameplay. In pathfinding games, puzzle games, strategy games adding intelligence to enemy and opponents makes the game more interesting. The enjoyment and fun differs from game to game. For example, in horror games, fun is experienced when safe point is reached.
Simplifying Multi-layer and Multi-tenant Support in OpenStack: The SACHER Use Case

The majority of cloud computing deployment environments follow the typical service delivery models, i.e., Software as a Service (SaaS), Platform as a Service (PaaS) and the Infrastructure as a Service (IaaS), that provide specific functionalities to users depending on the service delivery layer and clear resource isolation among different tenants. However, in certain scenarios, such as the Cultural heritage one, it is often required to provide SaaS, IaaS and PaaS cross-functionality support to users and cross-tenant resource visibility among isolated tenants. In the CH scenario, for example, restorers and professionals may often need to connect and to share temporarily data among different tenants or to work with old customized software requiring the possibility to exploit specific management functionalities that are typically available not at the SaaS but at the IaaS or PaaS layers. This paper proposes a middleware called Registry that can provide additional functionalities to different user categories by joining the SaaS with the IaaS layer and that offers also multi-tenancy operations making private data available to certain project on-demand and enabling, in this way, cross-tenant data sharing. The Registry has been designed, developed and tested within the context of the SACHER project that provides a cloud-based infrastructure for the management of the cultural data lifecycle.
S-HAZE: Dataset Consisting of Real World Ground Truth and Hazy Images of Varying Haze Density with No-Sky,Little Sky and Large Sky Regions

De-hazing images captured during inclement weather is highly necessary for proper visibility restoration. In recent years many researchers have proposed various de-hazing algorithms where they mostly portrayed de-hazing as a simple contrast enhancement problem. Proper evaluation of efficiencies of proposed algorithms is often not possible due to lack of sufficient datasets. To overcome this shortcoming, here we have introduced a new dataset S-HAZE, which consists of 60 Ground truth (GT) images with large sky, little sky and no-sky regions. Sky regions are also of different types like clear blue sky, blue sky with clouds, white sky, etc. The reasons behind focusing on sky regions of images while constructing the dataset are as follows: a. Sky regions play a crucial role in the atmospheric light evaluation, whose accurate estimation is highly essential for artifact free high quality image recovery. b. Most of the existing de-hazing algorithms are designed assuming the sky regions of haze free images to be white ideally but in reality this assumption is not always true, so proper restoration of scene color of sky regions in images are often not achieved by these algorithms. That's why to check the capability of true sky color restoration of these algorithms we have included images with different types of sky regions in this dataset. c. Performance of de-hazing algorithms vary according to the variation of haze density in images. So here we have included two hazy images corresponding to each GT image with varying haze density (light haze and dense haze) in the dataset to notice how the performance of algorithms vary with the haze density in same image. Hazy images in the dataset are taken in the presence of real haze created by artificial haze machines. Finally while conducting the quantitative analysis of output images obtained from different de-hazing algorithms using three quantitative parameters like Structural Similarity Index (SSIM), Haze improvement (HI) and Mean square error (MSE) we noticed another interesting finding that de-hazing algorithms giving desired maximum SSIM and minimum MSE values give poor HI value and vice versa. Hence, we concluded that portraying de-hazing as a mere contrast enhancement procedure is incorrect.
A Framework for Building Linux-Based Single-Purpose Appliances Optimized for the Cloud

Cloud-specialized OSes improve performance and efficiency of cloud applications by specializing their kernels to their target applications. Existing cloud-specialized OSes such as OSv and Unikernels are developed from the ground up and requires substantial porting efforts, resulting in the following drawbacks: 1) their functionalities are not as rich as general-purpose OSes, 2) some features are not optimized well, 3) the API compatibility with general-purpose OSes is limited, and 4) maintenance independent of existing development communities is required. To mitigate these drawbacks, we propose a Linux-based execution environment that can be specialized in the cloud but keeps the functionalities of Linux. The proposed environment enables "in-kernel" execution of existing applications, and the compile-time "whole-system" optimization beyond the application/kernel boundary. Memcached, running in our environment, shows 10% better throughput than the OSv version.
Verifiable Summation of Read-Once Formula Specified Data

Securely outsourcing computation to untrusted cloud servers has attracted a lot of attention in recent years. In this paper we consider datasets where every item is labeled by a number of attributes and study the problem of securely outsourcing the summation of a subset of the data items. We propose a verifiable summation on outsourced data (VSOD) model and construct two schemes in this model. Our VSOD schemes allow an input client to store a dataset on a cloud server and allow an output client to offload the summation of a subset of the data items to the cloud server, where the subset can be defined by a read-once formula and a number of indicator functions. This kind of computations can capture many real-world queries to datasets. Our schemes are secure such that no malicious server is able to persuade the output client to accept a wrong result. Our schemes are outsourceable such that the client's computation is substantially faster than the native computation of the summation. Compared with the existing solutions, the outsourceability of our schemes is not based on multiple datasets.
Data in DevOps and Its Importance in Code Analytics

Robust DevOps plays a huge role in the health and sanity of software. The metadata generated during DevOps need to be harnessed for deriving useful insights on the health of the software. This area of work can be classified as code analytics and comprises of the following (but not limited to): 1. commit history from the source code management system (SCM); 2. the engineers that worked on the commit; 3. the reviewers on the commit; 4. the extent of build (if applicable) and test validation prior to the commit, the types of failures found in iterative processes, and the fixes done; 5. test extent of test coverage on the commit; 6. any static profiling on the code in the commit; 7. the size and complexity of the commit; 8. many more. This chapter articulates many ways the above information can be used for effective software development.
A Perspective of the Noise Removal for Faster Neural Network Training

Image classification is widely used within image processing area. It is known that insufficient amount of data has negative impact on the training of neural networks in terms of accuracy, convergence speed and in some cases even in the inability to converge. On the other hand, big amount of data significantly increases the training time and costs needed for model creation. Every training sample contains the part valuable for decision (face in case of this paper) and noise, i.e. background of the object. This paper introduces method of iterative noise removal during the training with combination with the transfer learning to optimize the speed of the training process. We show the combination of proposed noise removal and transfer learning leads to more effective training process and enables to learn also from limited data sets. The main contribution of this paper is a proposed method that reduces training time and it is able to accelerate the process in average by 69%. The method was tested on binary classification of two persons from LFW database.
Vision-based Leader-Follower Mobile Robots for Cooperative Object Handling

This paper has addressed the problem of transporting a long object from a given starting location to an end location using leader-follower robot formation in an environment filled with obstacles. Only the leader robot has the authority to make decisions. It possesses the main controller and is capable of navigating through an unknown environment using the data provided by on-board sensors. In the proposed system, a vision-based feedback method is combined with an explicit control method that oversights the virtual trailer between the leader and the follower. The parameters required to maintain a constant distance between the two robots are computed by the leader and transmitted to the follower directly. A fiducial marker is mounted on the front of the follower robot and is captured by a camera attached to the leader robot. Information exchanging methods based only on impedance control can be expensive because of the need of high-level controllers and force/torque sensors for both robots. Furthermore, the visual response scheme adds robustness to the system by reducing the limitations of sole explicit control methods. The proposed control algorithm is implemented on the developed mobile robots. The experimental results suggest that the vision-based feedback method is more effective in cooperate object carrying tasks.
Internet of Things - A Realization

World is transforming to its digital counterpart at a faster pace. With the advent of high speed internet, the present day world witnessed a disruptive change in the means by which data is being collected, transmitted and analyzed. This paper presents an in-depth study of the Internet of Things technology and how it is transforming the world. Also, this paper provides an overview of the functional view, different architectures, technologies, application areas and hardware design concepts of the Internet of Things. The effect of more future technologies like machine learning and artificial intelligence is also discussed here.
Exploring Humanities Research Data in Figshare

Purpose/Thesis: The purpose of this study is to explore the uptake and use of Figshare by humanities community and to know if opening of the research data is enough to make them visible and findable.Approach/Methods: The overall research design was to gather a sample of humanities artifacts found on Figshare through its API, and then to analyze such indicators as the number of articles by discipline, the evolution of contribution per year, and the number of views and downloads by affiliation to an institution.Results and conclusions: The humanities community engagement with Figshare varies depending on the discipline; in addition, even when research data is published, 50% is rarely viewed. This means that making research data open is not enough to make it visible and to ensure their reuse. The final conclusion is that articles affiliated to an institution have more chance to be visible than unaffiliated ones.Research limitations: The limit of our study is that it can only analyze data present in Figshare and not the data that is missing, so we cannot know the reasons for the small visibility of humanities research data; the study of other data warehouses is necessary to give more explanation, but the results con­form to the pattern visible in the data presented in several studies of research management practices.Originality/Value: The first study that explores the humanities contents the multidisciplinary data repository Figshare.
Review of Development and Application of Future Network (IPV9)

Sciendo provides publishing services and solutions to academic and professional organizations and individual authors. We publish journals, books, conference proceedings and a variety of other publications.
ClassFlow: Performance Indicator Based on Learning Routes

At present, education is subject to a series of changes that generally affects the exact sciences such as physics and mathematics. This makes the methodological or pedagogical process always the same, the same sequence, the same series and some teachers generally accept any process as long as I came to the answer, although in some cases there is resistance from the students. Although most teachers prefer the traditional and reproduce only what he learned during his training, this type of thinking limits learning to the student and makes him a mechanic or a memorist. During the investigation, it has been considered what needs to change the mentality of human behavior in teaching students, transform their method, their thinking in a pedagogical way, using necessary face-to-face resources that facilitate the teacher to make an interactive class, attacking the different styles of learning that exist within a classroom and therefore covering some special educational needs that may or may exist within the classroom. The ClassFlow tool is a teaching support resource that helps and greatly facilitates understanding to foster the ideology that exact sciences can also be practiced online or in real time, inside or outside a laboratory, using the resources or mobile devices that Students have, like their internet data, ClassFlow is a powerful tool that helps control any type of hyperactivity or learning problem within the classroom. Therefore, ClassFlow is being used in several institutions worldwide to improve the student learning process. The Promethean ClassFlow teacher learning platform allows you to plan classes more efficiently and make the classes a more interesting experience for your students.
Ontological model of a production shop

Abstract The paper presents an ontological model of a production shop. The necessity of such approach to domain modeling has been identified and justified. Based on the model obtained, the author suggests using it for further design of the production planning information system.
Practical Data-in-Use Protection Using Binary Decision Diagrams

Protection of data-in-use, contrary to the protection of data-at-rest or data-in-transit, remains a challenge. Cryptography advances such as Fully Homomorphic Encryption (FHE) provide theoretical, albeit impractical, solutions to functionally-complete computation over encrypted operands, necessary for general-purpose computation. In this work, we propose a practical data-in-use protection mechanism that, contrary to application-specific homomorphic encryption approaches, focuses on arbitrary computation native to established programming languages, such as C++. Therefore, our work provides a more efficient alternative to FHE schemes that can be used for general-purpose computation. Specifically, we use Binary Decision Diagrams (BDD) to transform high-level programming operations to their equivalents working on protected data. To automate this, we develop a framework that allows automatic conversion of program expressions over encrypted operands into efficient circuits that are reduced using BDDs and can simulate corresponding composed operations. Our experimental results show that our methodology is orders of magnitude faster than state-of-the-art FHE schemes and enables execution of real C++ applications with practical overheads. Our framework is complemented with security analysis proving resistance to different attack methods.
A scheduling algorithm to maximize storm throughput in heterogeneous cluster

Abstract In the most popular distributed stream processing frameworks (DSPFs), programs are modeled as a directed acyclic graph. Using this model, a DSPF can benefit from the parallelism capabilities of distributed clusters. Choosing a reasonable number of vertices for each operator and mapping the vertices to the appropriate processing resources significantly affect the overall system performance. Due to the simplicity of the current DSPF schedulers, these frameworks perform poorly on large-scale clusters. In this paper, we present a heterogeneity-aware scheduling algorithm that finds the proper number of the vertices of an application graph and maps them to the most suitable cluster node. We begin with a pre-processing step which allocates the vertices to the given cluster nodes using profiling data. Then, we gradually increase the topology input rate in order to scale up the application graph. Finally, using a CPU utilization model which predicts the CPU workload based on the input rate to vertices and the processing node’s CPU characteristics, we identify the bottlenecked vertices and allocate new instances derived from them to the least utilized processing resource. Our experimental results on Storm Micro-Benchmark show that (1) the prediction model estimate CPU utilization with 92% accuracy. (2) Compared to the default scheduler of Storm, our scheduler provides 7 to 44% throughput enhancement. (3) The proposed method can find the solution within 4% (worst case) of the optimal scheduler, which obtains the best scheduling scenario using an exhaustive search over problem design space.
Mathematical Fundamentals I: Number Theory

There are many cryptosystems that are based on modular arithmetic (also known in some contexts as residue arithmetic); examples of such systems are given in the next chapter. This chapter covers some of the fundamentals of modular arithmetic and will be a brief review or introduction, according to the reader’s background. The first section of the chapter gives some basic definitions and mathematical properties. The second section is on the basic arithmetic operations, squares, and square roots. The third section is on the Chinese Remainder Theorem, a particularly important result in the area. And the last section is on residue number systems, unconventional representations that can facilitate fast, carry-free arithmetic.
Categorization of Multilingual Text on Languages of Indic Script

Due to the social media revolution in the world wide web of internet, a new aspect of language mixing and the ensuing language processing has opened up. There are a lot of messengers, applications and social websites that backup various languages for posting our messages and comments. Language detection and identification is a task of supervised machine learning which maps a text onto a unique language from among a set of trained languages. People many often use mixed languages in their communications. Identification of a native language from the mixed data is important as this conveys useful information. The present work explores Supervised Learning Method like K-Nearest Neighbor (KNN) technique, and character level n-gram method for the purpose of categorization of English, Bengali, Assamese, Marathi and Hindi web documents.
A New Model for Predicting Node Type Based on Deep Learning

With the development of the Internet, a large number of data sets are generated, which contain valuable resources. Meanwhile, there are various graphical representations in real life, such as social networks, citation networks, and user networks. For user networks, there also exists rich information about entities except the network structure. Therefore, predicting the type of nodes in the network can help us quickly identify user type, citations type etc. In this paper, a new method based on deep learning is proposed to predict the class of node. Two public data sets are used as training sets. First, the node features are embedded to pre-train the neighbor’s neighborhood structure features, then the pre-trained data is used to input to the classification model, and the structural feature parameters are loaded. The final result shows that the prediction accuracy is increased by nearly 25% higher than the baseline model. The F1 scores of the model tested on the two data sets are 83.5% and 80.2%, respectively.
Salient Object Detection Based on Deep Multi-level Cascade Network

Fully convolutional neural networks (FCNs) have played an important role in current saliency detection task, since the multi-layer structure describe the depth features of an image in different scales. To reasonably and effectively aggregate and utilize the hierarchical features, we propose a novel multi-level convolution feature cascade model with an end-to-end way in this paper. Our model consists of two modules: one is the multi-level depth feature extraction module via our improved FCNs; the other module aim at combining the characteristics of multiple resolutions with coarse semantics and fine details via $$\mathbf {C}$$ascade, $$\mathbf {U}$$psampling and $$\mathbf {D}$$econvolution operations, named as CUD module. Finally, the output of CUD module is used to predict the saliency map through further learning. The proposed model can efficiently and flexibly aggregate multi-layer convolution features and provides accurate saliency maps. The extensive experiments show that our method achieves satisfactory results compared with some current representative methods.