Reviewerly/exHarmony huggingface dataset:
The Survival Analysis of Big Data Application Over Auto-scaling Cloud Environment

The cloud resource provisioning is a mechanism of cloud resources allocation to cloud customers, and cloud customers have to interact with cloud resources using any cloud data center. The workload of the cloud environment consists of the significance of computing resources running situation in the cloud data centers. Cloud resource provisioning has a signification relation with cloud workload. The workload of cloud data centers is not the same all the time. For smooth and effective working of cloud resources at the cloud customer end, scaling of cloud resources required at cloud data center end. The scaling is a primary plan that to manage the extended work-load of the cloud data center. Scaling is implemented by adding additional or increasing computing power and memory capacity. Auto-scaling is one of an essential attribute of cloud computing that facilitates automatic provisioning of computing resources like add, remove, scale-up or scale-down resources depending upon workload. Big data applications associated with the large storage capacity and high processing power, cloud environment is suitable for fulfilling big data application requirement using auto-scaling of resources. In the present study, we have estimated the survival probability of auto-scaled cloud environment in the context of big data applications. Further, we investigated in this paper the importance of cloud resources that are used to build auto-scaling based cloud computing environment.
The southern São Francisco Craton puzzle: Insights from aerogeophysical and geological data

Accretionary orogens are considered as the result of the major crust production process, and terranes are accreted material representing distinctive assemblages regarding age and evolution. Scientific advances in the last years show that the southernmost São Francisco Craton includes Archean, Paleoproterozoic and Neoproterozoic areas. We used aerogeophysical data and field geology to understand the extension of individual pieces of this tectonic puzzle. We described five magnetometric lineaments. A–, B– and C–lineaments are related to dyke swarms of different ages. D– and E–lineaments represent regional-scale tectonic structures. In a tectonic perspective, we have identified the following terranes: (i) the Archean São Tiago crust (2.67 Ga), which is part of the São Francisco proto-craton; two Siderian juvenile arcs, represented by the (ii) Cassiterita (2.47 Ga) and the (iii) Resende Costa/Lagoa Dourada (2.36–2.35 Ga) orthogneisses; and two Rhyacian arcs, the (iv) juvenile Serrinha (2.22–2.20 Ga) and the continental Ritápolis (2.19–2.10 Ga) arcs. Tectonic terranes and five magnetic subdomains were identified on the basis of (i) association of shear zones/faults with quartz veins in the field; (ii) low– and high–intensity magnetic anomalies; (iii) sharp contrast in Euler solution intensities; and (iv) high–contrast in radioelement contents in the gammaspectrometric maps. Processing of aerogeophysical data permitted us to propose a new scenario on the evolution of the southern São Francisco Craton, and in particular of the Mineiro belt. The integration between aerogeophysical, new and compiled geologic information, provides a robust model for the understanding of individual tectonic pieces of the studied area.
Long Sentence Preprocessing in Neural Machine Translation

Machine translation based on neural networks has been shown to produce superior results, compared with other approaches. To build an efficient neural machine translation (NMT) system, it is essential to have an accurate and massive bilingual corpus for training, and ensure the continuous improvement of the methods and techniques used in the translation system. Despite multiple advantages, one challenging issue for current neural network translation system is long sentence processing [1]. In this paper, we propose a method to extract bilingual phrases to build a phrase-aligned bilingual corpus, and the implementation of a long sentence preprocessing technique to be used in the neural machine translation model. Experimental training of the neural machine translation system to translate Vietnamese into English using our proposed technique shows an improvement in BLEU scores.
A General Multi-Source Data Fusion Framework

With the development of the Internet, the increase of information sources and speed of information release and transmission have led to a sharp increase in the amount of information. To enable users finding more accurate and reliable information in the large heterogeneous multi-source data, data fusion technology becomes more and more important. Data fusion technology structuralizes and integrates heterogeneous data from different sources which greatly improves the comprehensiveness, availability and extensibility of data. This paper proposes a general multi-source data fusion framework. The framework transforms multi-source structured data, semi-structured data and unstructured data into unified data format described by RDF (Resource Description Framework) standard, and then realizes information fusion through data fusion algorithm, to solve the heterogeneity and semantic conflict in multi-source data fusion under the big data environment.
A method for strain analyses of surfaces with curved boundaries based on measured displacement fields

This contribution deals with investigations of strain fields at the specimen surface. A notched cantilever beam made of epoxy was analyzed. In the notch-surrounding area inhomogeneous strain distributions with large gradients occur. For the detection of these strain fields, high-performance measuring methods and special evaluation methods are required. The detection of in-plane displacement fields was performed using Electronic Speckle Pattern Interferometry (ESPI). Based on B-spline approximation of the displacements, strains were determined. Besides a global evaluation, a parametrization of the notch-surrounding area was performed. The results show significant improvement compared to analyses without using the parametrization.
Security and Cost Optimization Auditing for Amazon Web Services

There are many major security companies involved in developing security features on the cloud. According to Gartner, by 2020, 95% of cloud security errors will be the customer.
Aspect Extraction with Bidirectional GRU and CRF

Opinion mining or sentiment analysis used to understand the community's opinions on a particular product. Sentiment analysis involves building the opinion collection and classification system. One of the most crucial tasks of sentiment analysis is the ability to extract aspects or features that opinions expressed on. There are many approaches and techniques used to explore these features from unstructured comments. We proposed a different approach to the above mentioned aspect extraction task in sentiment analysis using a deep learning model combining Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF). This model is trained on labeled data to extract and classify feature sets in comments. Our model uses a BiGRU neural network with word embeddings achieved by training GloVe on the SemEval 2014 dataset. The SemEval 2014 dataset include 7,686 reviews on two domains, Laptop and Restaurant. Experimental results showed that our aspect extraction model in sentiment analysis using BiGRU-CRF achieved significantly better accuracy than the state-of-the-art methods.
Major factors of teachers’ resistance to innovations

Abstract Modernization and improvement of the quality of general secondary education can be attained while enhancing teachers’ innovative activity. Therefore, it is important to identify and substantiate the factors and reasons for secondary school teachers’ innovative “passivity”, which is the purpose of this work. The study contributes to eliminate these factors during the course of training teachers, preparing students to obtain a degree in education for innovative activities, and improving practicing teachers’ effectiveness. A distinctive feature of the study is the fact that it applies a broad approach that allows identifying social, psychological and professional factors, including relevant interrelated groups of both objective and subjective reasons that predetermine the teachers’ resistance to innovations. The empirical research revealed a decline in the social and psychological factors’ negative effects in the recent years and a substantial moderating influence of the professional factor on teachers’ innovative activities.
Uncovering Social Media Bots: a Transparency-focused Approach

As the Online Social Networks (OSNs) presence continues to grow as a form of mass communication, tensions regarding their usage and perception by different social groups are reaching a turning point. The number of messages that are exchanged between users in these environments are vast and brought a trust problem, where it is difficult to know if the information is from a real person and if what was said is true. Automated users (bots) are part of this issue, as they may be used to spread false and/or harmful messages through an OSN while pretending to be a person. New attempts to automatically identify bots are in constant development, but so are the mechanisms to elude detection. We believe that teaching the user to identify a bot message is an important step in maintaining the credibility of content on social media. In this study, we developed an analysis tool, based on media literacy considerations, that helps the ordinary user to recognize a bot message using only textual features. Instead of simply classifying a user as a bot or human, this tool presents an interpretable reasoning path that helps to educate the user into recognizing suspicious activity. Experimental evaluation is conducted to test the tool's primary effectiveness (classification) and results are presented. The secondary effectiveness (interpretability) is discussed in qualitative terms.
Learning to Map Wikidata Entities To Predefined Topics

Recently much progress has been made in entity disambiguation and linking systems (EDL). Given a piece of text, EDL links words and phrases to entities in a knowledge base, where each entity defines a specific concept. Although extracted entities are informative, they are often too specific to be used directly by many applications. These applications usually require text content to be represented with a smaller set of predefined concepts or topics, belonging to a topical taxonomy, that matches their exact needs. In this study, we aim to build a system that maps Wikidata entities to such predefined topics. We explore a wide range of methods that map entities to topics, including GloVe similarity, Wikidata predicates, Wikipedia entity definitions, and entity-topic co-occurrences. These methods often predict entity-topic mappings that are reliable, i.e., have high precision, but tend to miss most of the mappings, i.e., have low recall. Therefore, we propose an ensemble system that effectively combines individual methods and yields much better performance, comparable with human annotators.
Data Locality Optimization of Depthwise Separable Convolutions for CNN Inference Accelerators

This paper presents a novel framework to maximize the data reusability in the depthwise separable convolutional layers with the Scan execution order of the tiled matrix multiplications. In addition, the fusion scheme across layers is proposed to minimize the data transfer of the intermediate activations, improving both the latency and energy consumption from the external memory accesses. The experimental results are validated against DRAMSim2 for the accurate timing and energy estimation. With a 64K-entry on-chip buffer, our approach can achieve the DRAM energy reduction of 67% on MobileNet V2.
Diversified textual features based image retrieval

How to target the users' demands faster and more accurately has become a hot issue in the domain of image retrieval. Most of the existing techniques focus on retrieving the most relevant images to the query, which will reduce the search efficiency and make the retrieval process boring for users. For these reasons, diversity-induced image retrieval is proposed to guarantee that the retrieval results are not only relevant to the query, but also cover the aspects of the query as many as possible. Most of the traditional diversity-induced methods seldom use the multi-modal information to compensate each other. Moreover, the traditional textual features come from the complete textual information which can not highlight the unique property of an image. In this paper, we propose a new retrieval framework that combines visual and textual information in an effective way. Firstly, Latent Dirichlet Allocation (LDA) is applied on visual features to construct topics. Then textual information is used to improve the coherence of the topics. To extract diversified textual features, we propose a new method focusing on the unique words. For topic improvement, we design a new strategy that removes outliers from the constructed graph. Finally, the representative images are selected as retrieval results. The experiments on the database of MediaEval 2013 prove the superiority of our method. Besides, we also illustrate the results of diversified textual feature extraction and similarity graph clustering, which indicates the effectiveness of the proposed method.
Suitability of Singular Value Decomposition for Image Watermarking

Digital images are extensively used and exchanged through internet, which gave rise to the need of establishing authorship of images. Image watermarking has provided a solution to prevent false claims of ownership of the media. Information about the owner, generally in the form of a logo, text or image is imperceptibly hid into the subject. Many transforms have been explored by the researcher community for image watermarking. Many watermarking techniques have been developed based on Singular Value Decomposition (SVD) of images. This paper analyses Singular Value Decomposition to understand its use, ability and limitations to hide additional information into the cover image for Digital Image Watermarking application.
Algorithmically identifying strategies in multi-agent game-theoretic environments

Artificial intelligence (AI) has enormous potential for military applications. Fully realizing the conceived benefits of AI requires effective interactions among Soldiers and computational agents in highly uncertain and unconstrained operational environments. Because AI can be complex and unpredictable, computational agents should support their human teammates by adapting their behavior to the human's elected strategy for a given task, facilitating mutuallyadaptive behavior within the team. While some situations entail explicit and easy-to-understand human top-down strategies, more often than not, human strategies tend to be implicit, ad hoc, exploratory, and difficult to describe. In order to facilitate mutually-adaptive human-agent team behavior, computational teammates must identify, adapt, and modify their behaviors to support human strategies with little or no a priori experience. This challenge may be achieved by training learning agents with examples of successful group strategies. Therefore, this paper focuses on an algorithmic approach to extract group strategies from multi-agent teaming behaviors in a game-theoretic environment: predator-prey pursuit. Group strategies are illuminated with a new method inspired from Graph Theory. This method treats agents as vertices to generate a timeseries of group dynamics and analytically compares timeseries segments to identify group coordinated behaviors. Ultimately, this approach may lead to the design of agents that can recognize and fall in line with strategies implicitly adopted by human teammates. This work can provide a substantial advance to the field of humanagent teaming by facilitating natural interactions within heterogeneous teams.
FPGA-based low-complexity high-throughput real-time hardware accelerator for robust watermarking

This paper presents an FPGA-based hardware accelerator for robust watermarking. By applying some novel transformation techniques, the DCT/IDCT algorithm is simplified in a hardware-friendly way by replacing the original complex multiplication/division and nonlinear function (cosine function) with addition and shift operations. Moreover, the architecture of this real-time accelerator is proposed with pipeline technique to improve throughput and hardware efficiency. The entire design is implemented on FPGA, which achieves up to 3.2 GBps (Giga Bytes per second) throughput. Finally, the experimental results show that the proposed accelerator has good performance.
DTCWT-DCT watermarking method for multimodal biometric authentication

This paper provides a multimodal biometric watermarking algorithm to protect and well authenticate biometric data. It uses the DTCWT-DCT as a combination of two domain transform techniques to fuse face and fingerprint modalities. Two pseudorandom sequences are used to embed the bits of the quantized spectral minutiae representation used as a watermark into the face image. The embedding is done only in high-frequency sub-bands of the DTCWT decomposition, after applying the DCT transform to improve the authentication system security. The conducted experimental results based on ORL face and FVC2002 DB1 fingerprint databases proved the ability of the proposed approach to withstand the conventional digital image watermarking attacks. Also, good performance is showed in term of attaining an accuracy rate of 100%.
CNN classification based on global and local features

In this study, we investigated how shapes are classified based on local and global features by four representative convolutional neural networks (CNNs), i.e., AlexNet, VGG, ResNet and Inception. While the local features are based on simple components, such as orientation of line segment, the global features are based on the whole object, such as whether an object has a hole. For example, solid triangles and solid squares are differentiated by local features, solid circles and rings are differentiated by a global feature. Two sets of experiments were performed in this research. In the first experiment, we examined how the four CNNs pre-trained on ImageNet (with transfer learning) learned to differentiate the regular shapes (equilateral-triangles, squares, circles and rings). Our results showed that the pre-trained CNNs exhibited faster learning rates in the tasks discriminating the local features than in the tasks discriminating the global feature. However, the transfer learning of discriminating the global feature in regular shapes were better generalized to irregular shapes than the transfer learning of discriminating local features. In the second experiment, the CNNs were trained from scratch (with random weights initialization) to discriminate local and global features in regular and irregular shapes. Different from the transfer learning, the CNNs exhibited faster learning rates in discriminating the global feature than the local features. Similar to transfer learning, the CNNs exhibited excellent learning generalization to discriminating the global feature of irregular shapes, but poor learning generalization to discriminating the local features in the irregular shapes. The overarching goal of this research is to create a paradigm and benchmark to directly compare how the CNNs and primate visual systems process geometrical invariants. In contrast to the ImageNet approach which employs natural images to train CNNs, we employed the “ShapeNet” approach which features geometrical shapes with well-defined properties. The ShapeNet approach will not only help elucidate the strengths and limitations of CNN computation, but also provide insights into visual information processing in the primates.
Optimal Design of Lead Compensator Using Nature-Inspired Algorithms

There are numerous algorithms for designing lead compensators, some of which are graphical whereas others are analytical. When designing a lead compensator, the parameters of the compensator are considered as an optimization problem which aims at getting the required time and frequency specifications. This paper presents a comparison between lead compensators designed by nature-inspired algorithms against those designed by conventional algorithms for various types of systems. The nature-inspired algorithms considered in this paper are the genetic algorithm (GA), which is built on the concept of natural selection process which imitates biological evolution, and the particle swarm optimization (PSO), which is stimulated by social behavior of fish schooling or bird flocking. In this paper, two different examples are considered to demonstrate the comparison between the design methods. The simulation results of these examples show that the nature-inspired algorithms provided better transient response due to reduced settling and rise times and provided better relative stability due to zero overshoot and higher phase margin.
Can Blockchain Leverage for New Public Governance?

New Public Governance (NPG) as a paradigm for collaborative forms of public service delivery and Blockchain governance are trending topics for researchers and practitioners alike. Thus far, each topic has, on the whole, been discussed separately. This paper presents the preliminary results of ongoing research which aims to shed light on the more concrete benefits of Blockchain for the purpose of NPG. For the first time, a conceptual analysis is conducted on process level to spot benefits and limitations of Blockchain-based governance. Per process element, Blockchain key characteristics are mapped to functional aspects of NPG from a governance perspective. The preliminary results show that Blockchain offers valuable support for governments seeking methods to effectively coordinate co-producing networks. However, the extent of benefits of Blockchain varies across the process elements. It becomes evident that there is a need for off-chain processes. It is, therefore, argued in favour of intensifying research on off-chain governance processes to better understand the implications for and influences on on-chain governance.
A UML Profile for Multi-Cloud Service Configuration (UMLPMSC) in Event-driven Serverless Applications

The rapid evolution in cloud computing leads to a rising elegance of serverless cloud-based software architectures which primarily focuses on providing the software developers a great potential for executing different arbitrary functions having minor overhead in server management as FaaS (Function-as-a-service). These FaaS applications are a set of stateless functions that are triggered by events defined by the cloud provider. However, the service configuration of such event-driven serverless applications is a complex process. Particularly, the changing configuration requirements on multiple clouds create low-level implementation challenges. Therefore, this article introduces UMLPMSC (UML Profile for Multi-Cloud Service Configuration) for event driven serverless applications to model the service configuration design requirements at high abstraction level. This leads to transform the high-level source UMLPMSC models into low level serverless framework implementations for the service configuration. The applicability of the profile has been validated through two case studies for AWS and Azure serverless function providers. It has been concluded that UMLPMSC significantly simplifies the multi-cloud service configuration process for event-driven serverless applications.