Reviewerly/exHarmony huggingface dataset:
A Comparison between Google Cloud Service and iCloud

The availability of high speed networks and low cost storage devices and computer in addition to the adoption of Service-Oriented-Architecture has opened the door to Cloud Storage Services by many providers. During the recent couple of years, more companies are moving towards cloud storage due to the many reasons such as: scalable on demand disk storage space, backup and data replication and the ability to share and access data from anywhere and anytime. The main objective of the paper is to compare different kinds of Cloud storage service providers. It starts by offering a brief introduction to cloud storage followed by an outline of the general history of cloud services and then moves on to the specific history of two major cloud services: iCloud and Google cloud platform. Furthermore, the various features of the two cloud services are explored and a comparison is made between them.
A Machine Learning Algorithm TsF K-NN Based on Automated Data Classification for Securing Mobile Cloud Computing Model

Mobile cloud computing (MCC) is fastest growing technology era in which the research society has recently embarked. Today, Mobile data can include financial transactions such as electronic payments, M-wallets and sensitive multimedia contents. The explosive volumes of mobile devices personal data, bring-up more attention to securely data storage rather than consideration on data privacy and confidentiality levels. In this scenario Machine Leaning (ML) brings an important role in the electronic data management. It is always expensive and hard to manage the data manually without adopting machine learning techniques using metadata. Many Machine Learning algorithms have been proposed to comprehend diverse data management issues, yet the forecast of the top secret data and public data in a document is as yet a challenging exploration task. The contribution of this research article is to demonstrate a securing mobile data storage secrecy and privacy in cloud communication framework in terms of automatic data classification using mobile training datasets with help of Training dataset Filtration Key Nearest Neighbor (TsF-KNN) classifier which classifies the data based on the confidentiality level of the record with higher accuracy and powerful timelines as compared to the traditional K-NN algorithms and securing such confidential data category afterwards by applying various existing cryptographic solutions to assuring data privacy and confidentiality levels and simulation results demonstrates that reducing the overall cost and minimize procedural time, increasing system performance and sustainability.
Stability Assessment of Rubble Mound Breakwaters Using Extreme Learning Machine Models

The stability number of a breakwater can determine the armor unit’s weight, which is an important parameter in the breakwater design process. In this paper, a novel and simple machine learning approach is proposed to evaluate the stability of rubble-mound breakwaters by using Extreme Learning Machine (ELM) models. The data-driven stability assessment models were built based on a small size of training samples with a simple establishment procedure. By comparing them with other approaches, the simulation results showed that the proposed models had good assessment performances. The least user intervention and the good generalization ability could be seen as the advantages of using the stability assessment models.
Adaptive Improved Flower Pollination Algorithm for Global Optimization

In the last few years, meta-heuristic-driven optimization algorithms have been employed to solve several problems since they can provide simple and elegant solutions. In this work, we introduced an improved adaptive version of the Flower Pollination Algorithm, which can dynamically change its parameter setting throughout the convergence process, as well as it keeps track of the best solutions. The effectiveness of the proposed approach is compared against with Bat Algorithm and Particle Swarm Optimization, as well as the naïve version of the Flower Pollination Algorithm. The experimental results were carried out in nine benchmark functions available in literature and demonstrated to outperform the other techniques with faster convergence rate.
Understanding Blockchain

Blockchain is a distributed ledger which works on a peer to peer network, with attributes of decentralized technology it's an emerging technology which will revolutionize the way of management of organizations of public and private sectors in coming decades.it provides transpires, security, trust on the system with its qualities like decentralization, immutability, transparency, and consensus-driven results. It's a buzz word nowadays, receiving massive attention in different areas like government, media, and different public-private sector organization. Blockchain is the future of the administration domain. Studies count it as an essential invention as the internet was. Literature in this significant domain is insufficient, and there are many research gaps which demand attention. This paper is a benefit for those who want to understand or contribute to this domain for research point of view or development. This article presents a general overview of blockchain technology its significant components, existing applications and some limitations of this technology.
Dynamic task scheduling algorithms in cloud computing

Cloud computing refers to dynamically scalable infrastructure and virtualizes resources that allow the application to fulfil infinite demands with inexpensive and reliable services. Therefore, the service provider has to serve a large number of tasks. For elegant service to the tasks, there is a need for an efficient scheduling algorithm. Many algorithms have been developed for scheduling. However, most of the developed algorithm has conflicting objectives and its own limitation. Thus, one is efficient for one type of tasks at the same time inefficient to other kinds of tasks. In this paper, we have proposed a strategy which allocates the algorithms according to the type of task and reduces the conflicting objectives. We have analyzed a number of algorithms such as First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin (RR), Max-Min, Min-Min. Finally, we have shown our proposed strategy which reduces the limitation of other algorithms, the tradeoff between the average waiting time and makespan.
Incrementalization of Vertex-Centric Programs

As the graphs in our world become ever larger, the need for programmable, easy to use, and highly scalable graph processing has become ever greater. One such popular graph processing model-the vertex-centric computational model-does precisely this by distributing computations across the vertices of the graph being computed over. Due to this distribution of the program to the vertices of the graph, the programmer “thinks like a vertex” when writing their graph computation, with limited to no sense of shared memory and where almost all communication between each on-vertex computation must be sent over the network. Because of this inherent communication overhead in the computational model, reducing the number of messages sent while performing a given computation is a central aspect of any efforts to optimize vertex-centric programs. While previous work has focused on reducing communication overhead by directly changing communication patterns-by altering the way the graph is partitioned and distributed, or by altering the graph topology itself-in this paper we present a different optimization strategy based on a family of complementary compile-time program transformations in order to minimize communication overhead by changing both the messaging and computational structures of programs. Particularly, we present and formalize a method by which a compiler can automatically incrementalize a vertex-centric program through a series of compile-time program transformations-by modifying the on-vertex computation and messaging between vertices so that messages between vertices represent patches to be applied to the other vertex's local state. We empirically evaluate these transformations on a set of common vertex-centric algorithms and graphs and achieve an average reduction of 2.7X in total computational time, and 2.9X in the number of messages sent across all programs in the benchmark suite. Furthermore, since these are compile-time program transformations alone, other prior optimization strategies for vertex-centric programs can work with the resulting vertex-centric program just as they would a non-incrementalized program.
Cloud Environment for Disseminating NASS Cropland Data Layer

Cropland Data Layer (CDL) is an annual crop-specific land use map produced by the U.S. Department of Agricultural (USDA) National Agricultural Statistics Service (NASS). The CDL products are officially hosted on CropScape website which provides capabilities of geospatial data visualization, retrieval, processing, and statistics based on the open geospatial Web services. This study utilizes cloud computing technology to improve the performance of CropScape application and Web services. A cloud-based prototype of CropScape is implemented and tested. The experiment results show the performance of CropScape is significantly improved in the cloud environment. Comparing with the original system architecture of CropScape, the cloud-based architecture provides a more flexible and effective environment for the dissemination of CDL data.
Video Quality Adaptation for Limiting Transcoding Energy Consumption in Video Servers

Dynamic adaptive streaming over HTTP (DASH) inherently requires many transcoding operations to produce various bit-rate versions. However, transcoding is highly computationally intensive, resulting in significant power consumption in transcoding servers. To limit the amount of energy consumption in transcoding servers, some transcoding tasks can be skipped and executed later, but this poses several questions, such as (1) which versions can be transcoded before an actual streaming session, (2) how to limit total amount of transcoding energy consumption, and (3) how to handle requests to the untranscoded versions. To address this, we first introduce the concept of transcoding gain to express the overall video quality assigned to each transcoded version by considering segment popularity and go on to propose an algorithm that determines when and which versions should be transcoded with the aim of maximizing the overall transcoding gain by limiting energy consumption. To achieve this, it allows lower bit-rate versions to be streamed than are requested, at the cost of minimal video quality degradation by considering the overall video quality. The proposed scheme was implemented on a clustered system where various issues were resolved. The experimental results show that the transcoding selection algorithms can effectively control the amount of power consumption, minimizing the effect of overall video quality degradation. For example, it can save up to 65% energy when video quality degradation is allowed to be 4.3% at maximum compared with the scheme that transcodes all the versions.
Simulation and Visualization of Volcanic Phenomena Using Microsoft Hololens: Case of Vulcano Island (Italy)

This article describes an interactive holographic visualization of volcanic eruption application for Microsoft HoloLens device. The aim of the project is to use this technology to visualize different eruptive phenomena on an active volcano for public education, emergency training, preparedness planning purposes, and raising awareness among tourists. We have selected La Fossa volcano on Vulcano island (Italy) as a case study and, thus, the application is named HoloVulcano. Unity game engine and Microsoft Visual Studio were used to develop the HoloVulcano augmented/virtual reality visualization application. The current version of HoloVulcano visualizes volcanic phenomena typically associated with unrest (fumaroles) and explosive eruptions (e.g. eruptive plumes, ejection of ballistic blocks, bombs, and pyroclastic density currents). The eruption types are developed based on existing literature using Unity game engine's particle systems component. HoloVulcano is a Microsoft HoloLens device application. Wearing the HoloLens, users can interact with the application through voice, gazing, and gestures and view different volcanic phenomena from different sites and angles on the island. HoloVulcano can be used by emergency managers and teachers for training, emergency exercises, and public education.
Genesis of the Selinsing gold deposit, Peninsular Malaysia: Constraints from mineralogy, geochemistry and in situ sulfur isotope compositions of sulfides

As a renowned orogenic lode gold deposit, the Selinsing gold deposit is located in the Central Belt of Peninsular Malaysia. Geologically, the gold deposit is characterized by auriferous quartz veins and fine-grained gold minerals coexisted with sulfides, and the ore body is hosted by low-grade metamorphic sedimentary rocks and volcanic rocks. Furthermore, gold mineralization is closely related to tectonic deformation. Four main stages of vein formation have been identified, namely, (1) quartz-pyrite-arsenopyrite vein, (2) quartz-pyrite-gold vein, (3) quartz-stibnite-pyrite vein, and (4) quartz-chlorite vein. The occurrence of gold includes two types, i.e., visible gold and invisible gold. The former is featured by coarse-grained gold (millimeter-scale, such as auriferous quartz veins) and fine-grained gold (micron-scale, such as native gold, calaverite, and electrum), the latter is contained in pyrite and arsenopyrite in the form of nano-scale native gold particles (Au0) and lattice gold (Au1+). The geothermometer of arsenopyrite and chlorite as well as temperature-sulfur fugacity diagram indicate that the temperature and sulfur fugacity of hydrothermal fluid decrease gradually. Considering that the sulfur fugacity drops gradually and aqueous inclusions coexist with carbonic inclusions, it is inferred that sulfuration reaction in which sulfur is consumed to produce sulfides and fluid immiscibility or phase separation may be responsible for gold precipitation. Tested by LA-MC-ICP-MS, the in-situ sulfur isotope compositions of sulfides formed in different stages are homogenous (about −1 to 1‰) and the calculated H2S-dominated hydrothermal fluid is also consistent (about −2 to 0‰) in sulfur isotope compositions, which reflect the mantle or ultrabasic rock and basic rock or magmatic rocks may be a potential source of sulfur. Combined with possible source and tectonic evolution of Peninsular Malaysia, possible metallogenic processes are proposed to explain genesis of the Selinsing gold deposit and other gold deposits in the Central Belt, i.e., ore-forming fluid was differentiated from oceanic crust and/or fertilized mantle wedge, or ore-forming fluid may be sourced from concealed magmatic rocks that formed by subduction of the Palaeo-Tethys toward the East Malaysia block.
Deep Learning for Consumer Devices and Services 2—AI Gets Embedded at the Edge

The recent explosive growth of deep learning is enabling a new generation of intelligent consumer devices. Specialized deep learning inference now provides data analysis capabilities that once required an active cloud connection, while reducing latency and enhancing data privacy. This paper addresses current progress in Edge artificial intelligence (AI) technology in several consumer contexts including privacy, biometrics, eye gaze, driver monitoring systems, and more. New developments and challenges in edge hardware and emerging opportunities are identified. Our previous article, “Deep learning for consumer devices and services,” introduced many of the basics of deep learning and AI. In this paper, we explore the current paradigm shift of AI from the data center into CE devices-“Edge-AI”.
Analysis of the Backpropagation Algorithm in Viewing Import Value Development Levels Based on Main Country of Origin

Abstract In this study, we will discuss the development of import values in general based on the main country of origin in units of net weight (tons). The import value to be discussed is focused on the value of imports in North Sumatra Province, whose data is obtained from the Central Bureau of Statistics in 2009-2014. The algorithm that will be used to see and measure the level of development of import values is the backpropagation algorithm. This algorithm is one part of the Artificial Neural Network algorithm that is quite reliable in solving problems related to times series data. With the backpropagation algorithm, 5 architectural models will be formed that will be used to measure the level of development of import values, namely 4-12-1 architectural models, 4-15-1 models, 4-18-1 models, 4-19-1 models and model 4-20-1. From the 5 models, one of the best that will be used as a reference to measure the development of import values will be chosen. After analyzing the best models were obtained 14-19-1 with the level of truth produced more than 90%. With this architectural model, calculations will be made on the level of development of import values based on the main country of origin for 2015 to 2020.
Security and Privacy of AWS S3 and Azure Blob Storage Services

Many users around the world use cloud-computing services, and cloud services providers such as Amazon, Microsoft and Google. One of the commonly used services is the storage service. The service allows users to store and retrieve data online from any location. However remotely stored data that is maintained by a service provider raises concerns related to the security and privacy of the data. This paper compares the security and privacy of Amazon AWS S3 and Microsoft Azure Blob. The comparison will focus on their security and privacy compliance programs and how the access control behaves on securing the user's data.
Analysis of Automated Web Application Security Vulnerabilities Testing

In recent years, the global spread of web risks have proposed an immediate demand for security models and prevention mechanisms. This study preliminary findings analyze an extensive literature review on web application vulnerabilities security testing. Out of an initial set of 237 studies, 30 studies were finally included as Primary Research Studies (PRS) by assuring two research questions. The results reveals that SQL injection followed by XSS and Sensitive data exposure are the most recurring risks of web applications. Similarly, Invalidated Redirects and Forwards/Under Protected APIs have received little attention by research community. The scope of this study is also extended to web application vulnerabilities testing and identification of relevant data sets. This paper also recommends future possibilities to enhance the security approaches for protection against the risks.
PageRank and criticality of architectural smells

Architectural smells represent symptoms of problems at architectural level that can negatively impact internal software qualities and lead to architectural debt. Detecting architectural smells and identifying the most critical ones is the key to prioritize refactoring efforts and prevent software architecture erosion. In this paper we focus our attention on three architectural smells based on dependency issues, called Cyclic Dependency, Unstable Dependency, and Hub-Like Dependency. Moreover, we evaluate the PageRank and Criticality of these smells through the analysis of six projects. PageRank is a measure that estimates whether an architectural smell is located in an important part of the project, where the importance is evaluated according to how many parts of a project depend on the one involved in the architectural smell. We describe a case study on the analysis of the possible relation existing between the PageRank and Criticality of the smells. The results show how the two measures can be both used to locate the architectural smells that need particular inspections and attention.
Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

This paper addresses the resilient $H_{\infty }$ state estimation problem under variance constraint for discrete uncertain time-varying recurrent neural networks with randomly varying nonlinearities and missing measurements. The phenomena of missing measurements and randomly varying nonlinearities are described by introducing some Bernoulli distributed random variables, in which the occurrence probabilities are known a priori. Besides, the multiplicative noise is employed to characterize the estimator gain perturbation. Our main purpose is to design a time-varying state estimator such that, for all missing measurements, randomly varying nonlinearities and estimator gain perturbation, both the estimation error variance constraint and the prescribed $H_{\infty }$ performance requirement are met simultaneously by providing some sufficient criteria. Finally, the feasibility of the proposed variance-constrained resilient $H_{\infty }$ state estimation method is verified by some simulations.
Active Image Synthesis for Efficient Labeling

The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities. However, the limited availability of data and high costs of data collection are the major challenges for the applications in those fields. We propose in this work AISEL, an active image synthesis method for efficient labeling, to improve the performance of the small-data learning tasks. Specifically, a complementary AISEL dataset is generated, with labels actively acquired via a physics-based method to incorporate underlining physical knowledge at hand. An important component of our AISEL method is the bidirectional generative invertible network (GIN), which can extract interpretable features from the training images and generate physically meaningful virtual images. Our AISEL method then efficiently samples virtual images not only further exploits the uncertain regions but also explores the entire image space. We then discuss the interpretability of GIN both theoretically and experimentally, demonstrating clear visual improvements over the benchmarks. Finally, we demonstrate the effectiveness of our AISEL framework on aortic stenosis application, in which our method lowers the labeling cost by 90 percent while achieving a 15 percent improvement in prediction accuracy.
A generalized semantic representation for procedural generation of rooms

Procedural generation of rooms aims to create virtual environments that mimic common patterns found in real-world indoor locations, like offices or bedrooms. Graph-based models (e.g. factor graphs or Bayesian networks) have often been used to represent typical location's objects and their occurrence likelihood (nodes), as well as their inter-relationships (edges). Previous methods have struggled to represent object semantics in their graph nodes; specifically, they fail to fully and effectively support notions as abstractions (e.g. generic seat instead of chair) and replication (e.g. cups instead of cup). We propose a generalized representation and use for object semantics that overcomes the above limitations of graph-based models in the procedural generation of rooms. This node representation handles semantics as attributes, and clearly distinguishes the contribution of the attributes on the node from the potential effects of the node on the whole graph. We illustrate the additional expressive power of the resulting graph-based model for room generation, and show that it subsumes previous models as particular cases.