Reviewerly/exHarmony huggingface dataset:
Virtual Machine Migration Approach in Cloud Computing Using Genetic Algorithm

The technology of cloud computing is decentralized in nature due to which various issues in the network get raised which reduce its efficiency. The cloud computing technology is applied to fulfill the demands of hosts over the internet. For the purpose of using or sharing, the resource cloud computing can be used. The virtual machine migration is the major issue of cloud computing, and it gets raised when uncertainty get happened in the network. Due to extensive use of the virtual machine resources, machine gets overloaded which increase delay for the cloudlet execution. In the base paper, the threshold algorithm has been proposed which assign task to most capable machine and hosts maintain checkpoints on the virtual machines. When the virtual machine get overloaded, the task needs to migrate to another VM (Virtual Machine). In this study, weight-based technique will be proposed which migrate cloudlet from one virtual machine to another.
Whole Database Retrieval Method of General Relational Database Based on Lucene

Aiming at the needs of full-database retrieval of multi-table massive data, a general-purpose, lightweight, non-invasive full-database retrieval method was proposed and implemented. The JDBC technology was used to complete the content extraction of the general database, and the mapping relationships of common database data types such as Mysql, Sqlserver, and Oracle in the Type class were summarized. Based on Lucene, The full-text indexing of the database content is implemented. From the result of experiment, it could be demonstrated that the proposed method has good performance and stability and is well appropriate for construction of index system for common relational database.
A Cloud SecDevOps Methodology: From Design to Testing

DevOps is becoming one of the most popular software development methodologies, especially for cloud-based applications. In spite of its popularity, it is still difficult to integrate non-functional requirements, such as security, in the full application development life-cycle. In some recent works, security DevOps (or SecDevOps) has been introduced, in order to enable the adoption of Security-by-Design principles in DevOps processes. In [4], a novel SecDevOps methodology was proposed to exploit such integration, but the security assessment and testing were performed with a static approach. In this paper, we propose to extend the SecDevOps methodology with the adoption of a novel security testing technique in order to dynamically test security properties in the operational phase, too. In order to validate the proposed approach, a cloud application case study involving the WordPress software module is presented and analyzed.
On the Temporality of Introducing Code Technical Debt

Code Technical Debt (TD) is intentionally or unintentionally created when developers introduce inefficiencies in the codebase. This can be attributed to various reasons such as heavy work-load, tight delivery schedule, unawareness of good practices, etc. To shed light into the context that leads to technical debt accumulation, in this paper we investigate: (a) the temporality of code technical debt introduction in new methods, i.e., whether the introduction of technical debt is stable across the lifespan of the project, or if its evolution presents spikes; and (b) the relation of technical debt introduction and the development team’s workload in a given period. To answer these questions, we perform a case study on twenty-seven Apache projects, and inspect the number of Technical Debt Items introduced in 6-month sliding temporal windows. The results of the study suggest that: (a) overall, the number of Technical Debt Items introduced through new code is a stable metric, although it presents some spikes; and (b) the number of commits performed is not strongly correlated to the number of introduced Technical Debt Items.
IS FUTURE A RULE OF DIGITAL CURRENCY???

Digital Currency (DC) is a form of currency that is available in digital or electronic form and not in physical form. Digitalization has remodeled money and payments systems. Although digital money itself is not new to modern economies, digital currencies now facilitate spontaneous peer-to-peer transfers of value in a way that was formerly impossible. Digital currency has already materialized in a variety of contexts. Digital Currency is an extent put away in a dispersed database on the Internet. This study is toted with the objective to highlight the concept of digital currency, its various forms, evolution and growth, global impact, impact during COVID-19 and the future of digital currency. This is an historical descriptive study which flashes the opinions given by distinctive researchers and disparate financial consultants and central banks.
A Low-Cost Embedded Computer Vision System for the Classification of Recyclable Objects

Due to rapid urbanization, increasing population and industrialization, there has been a sharp rise in solid waste pollution across the globe. Here, we present a novel solution to this inefficiency, by the use of embedded computer vision (CV) in the material recovery facilities (MRF). The proposed architecture employs software (i.e., TensorFlow and OpenCV) and hardware (i.e., Raspberry Pi) as an embedded platform in order to classify daily life objects according to their visual aspect. The CV system is trained using modules contained within the TensorFlow API with two datasets, namely the TrashNet and a combination of the TrashNet and a set of Web images. This solution allows greater accuracy, with a baseline performance of 90% which drops to 70% when deployed on the embedded platform, due to the quality of the images taken by an integrated camera for the real-time classification. The speed results are also promising with a baseline speed of 10 FPS at simulation level, which drops to 1. 4fps when running on the platform. Such a system is cheap at less than £100, it is perfectly adequate to be used to identify recyclables in the MRF for sorting.
Understanding the hermeneutics of digital materiality in contemporary architectural modelling: a material engagement perspective

Abstract This article develops a framework for analysing how digital software and models become mediums for creative imagination in architectural design. To understand the hermeneutics of these relationships, we develop key concepts from Material Engagement Theory (MET) and Postphenomenology (PP). To push these frameworks into the realm of digital design, we develop the concept of Digital Materiality. Digital Materiality describes the way successive layers of mathematics, code, and software come to mediate enactive perception, and the possibilities of creative material engagement actualised in work with software. Just as molecular materials come to transform action with material objects, so digital materiality comes to enable and transform creative practices with computers. Digital architectural design form a new space for ongoing enactive discovery and creativity through manipulation of digital models and their underlying software environments. By shifting relationships within their digital models, architects can direct their attention, intention, and imagination towards widely different aspects of the model. Here, creative imagination becomes a fundamentally situated activity where mind emerges through dynamic interaction between a variety of embodied, material, and cultural domains.
Secure OMP Computation Maintaining Sparse Representations and Its Application to EtC Systems

In this paper, we propose a secure computation of sparse coding and its application to Encryption-then-Compression (EtC) systems. The proposed scheme introduces secure sparse coding that allows computation of an Orthogonal Matching Pursuit (OMP) algorithm in an encrypted domain. We prove theoretically that the proposed method estimates exactly the same sparse representations that the OMP algorithm for non-encrypted computation does. This means that there is no degradation of the sparse representation performance. Furthermore, the proposed method can control the sparsity without decoding the encrypted signals. Next, we propose an EtC system based on the secure sparse coding. The proposed secure EtC system can protect the private information of the original image contents while performing image compression. It provides the same rate-distortion performance as that of sparse coding without encryption, as demonstrated on both synthetic data and natural images.
Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification

In this article, we presented a novel spectral estimation method, the dyadic aggregated autoregressive model (DASAR), that characterizes the spectrum dynamics of a modulated signal.DASAR enhances automatic modulation classification (AMC) on environments where new or unknown modulation techniques are introduced, and only size-restricted data is accessible to train classification algorithms.A key component for obtaining efficient machine learning-based classification is the development of valuable knowledge-descriptive features.DASAR constructs a multi-level spectral representation by subdividing a signal into successive dyadic segments where each partition is modeled as an aggregation of single-frequency autoregressive processes.Thus, the model ensures a robust representation at the segment level, while the multi-level decomposition can capture time-varying spectra.As a feature extraction model, DASAR can provide useful learning features related to signals with complex spectra.The effectiveness of our model was tested on a dataset comprised of 11 different modulation techniques and realistic transmission medium characteristics.Using only 200 128-point samples per modulation scheme (1% of the available signal samples) and a proper selection of a classification algorithm, DASAR reaches accuracy up to 70.96% compared with a maximum accuracy of 43.62% using the state-of-art methods tested under the same conditions.
Enhancing CluStream Algorithm for Clustering Big Data Streaming over Sliding Window

Data stream mining becomes a hot research issue in the ongoing time. The main challenge in data stream mining is the knowledge extraction in real-time from an immense, data stream in only one scan. Data stream clustering demonstrates an significant task in data stream processing. This paper introduces SCluStream an algorithm for determining clusters over a sliding window to manage such challenges. The algorithm is an improvement over CluStream which does not involve this sliding window concept. In the sliding window model, only the most recent data is utilized while the old data is eliminated, which allows for faster execution. A better clustering technique is also involved which managed to contribute to accuracy improvement. The proposed algorithm has been tested on two real datasets; charitable donation data set and forest cover type data set. The results showed that comparing SCluStream to CluStream has proven that the former algorithm is more efficient for clustering big data streams in regard to the accuracy as well as the utilized time and memory usages.
Methodology for Building Synthetic Datasets with Virtual Humans

Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset. Repeatability of data is another challenge as it is not possible to exactly recreate `real-world' acquisition conditions outside of the laboratory. In this work, we explore a framework to synthetically generate facial data to be used as part of a toolchain to generate very large facial datasets with a high degree of control over facial and environmental variations. Such large datasets can be used for improved, targeted training of deep neural networks. In particular, we make use of a 3D morphable face model for the rendering of multiple 2D images across a dataset of 100 synthetic identities, providing full control over image variations such as pose, illumination, and background.
The effect of coefficients on the continuous attractors in coupled Highway Neural Networks

Inspired by the recurrent highway networks, we study the coupled networks as a modular behavior of the networks, which can better improve the performance of the system. For a neural network, the non-zero continuous attractors play an important role to decode the information. The emphasis of this paper is to study the conditions for the generation of non-zero continuous attractors in the coupled new system theoretically. By exploring the relationship between network parameters, the conditions for generating non-zero continuous attractors in the new model are successfully determined. In order to verify the correctness and validity of the proposed conclusions, the results of this paper are further verified by simulation.
Construction of hierarchical knowledge graph based on deep learning

With the continuous deepening of knowledge graph research, more and more knowledge is softened together, and the knowledge in professional fields is also emerging. Although people can quickly identify the knowledge they need based on their needs, machines cannot. There are many problems in the organization and application of traditional graphs, such as the inaccuracy of knowledge representation, which makes it difficult to obtain. The lack of clear knowledge layers causes a lot of irrelevant knowledge to appear after the query. The chaotic structure of knowledge in the graph causes query time-consuming. Therefore, considering the different layers of knowledge representation and the knowledge used to solve complex engineering problems, we propose to divide knowledge into three layers - basic knowledge, deep knowledge, and application knowledge and an agent-based hierarchical knowledge graph construction framework and methodology. The deep learning model method is used in the classification agent to realize the automatic division of knowledge type, pass the classification results to the corresponding knowledge agent. This knowledge agent is able to construct the hierarchical knowledge graph by the same layer knowledge or cross-layer knowledge. This method of constructing the hierarchical knowledge graph has practical significance in the application of the knowledge graph, which makes the knowledge graph have a wider application and practical value.
Features of Speech Audio for Accent Recognition

An accent is the distinctive way words are pronounced. Every speaker has an accent, which varies by gender, age, formality, social class, geographical region, and native language. Accent recognition is an important task as it can help improve Automatic Speech Recognition by first identifying the accent and then transferring to a Speech Recognizer that is trained for a particular accent group. Accent recognition is a complex problem due to the numerous characteristics that set accents apart. Accents differ by voice quality, phoneme pronunciation, and prosody. Since it is difficult to extract these exact features, existing work uses alternate features such as spectral features, which captures the frequency of speech. Such features include the Mel-Frequency Cepstral Coefficient (MFCC), Spectrogram, Chromagram, Spectral Centroid, and Spectral Roll-off, which are extracted from raw audio samples. Previous work has not made clear which features yield the highest accuracy for an accent classification task. In this work, these five features were used to train a 2-layer Convolution Neural Network on a dataset of five distinct language-accents, namely, Arabic, English, French, Mandarin, and Spanish. The accuracy of each feature was evaluated and compared. The MFCC yielded the highest accuracy.
Evidence for Attentional Phenotypes in Infancy and Their Role in Visual Cognitive Performance

Infant visual attention rapidly develops during the first year of life, playing a pivotal role in the way infants process, learn, and respond to their visual world. It is possible that individual differences in eye movement patterns shape early experience and thus subsequent cognitive development. If this is the case, then it may be possible to identify sub-optimal attentional behaviors in infancy, before the emergence of cognitive deficit. In Experiment 1, a latent profile analysis was conducted on scores derived from the Infant Orienting with Attention (IOWA) task, a cued-attention task that measures individual differences in spatial attention and orienting proficiency. This analysis identified three profiles that varied substantially in terms of attentional efficiency. The largest of these profiles ("high flexible", 55%) demonstrated functionally optimal patterns of attentional functioning with relatively rapid, selective, and adaptive orienting responses. The next largest group ("low reactive", 39.6%) demonstrated low attentional sensitivity with slow, insensitive orienting responses. The smallest group ("high reactive", 5.4%) demonstrated attentional over-sensitivity, with rapid, unselective and inaccurate orienting responses. A linear mixed effect model and growth curve analysis conducted on 5- to 11-month-old eye tracking data revealed significant stable differences in growth trajectory for each phenotype group. Results from Experiment 2 demonstrated the ability of attentional phenotypes to explain individual differences in general cognitive functioning, revealing significant between-phenotype group differences in performance on a visual short-term memory task. Taken together, results presented here demonstrate that attentional phenotypes are present early in life and predict unique patterns of growth from 5 to 11 months, and may be useful in understanding the origin of individual differences in general visuo-cognitive functioning.
Cinematographic Shot Classification through Deep Learning

Cinematographic shot classification assigns a category to each shot on the basis of the field size, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. This task is very important in the context of the creative field and can help freelancers in their daily activities when it is performed automatically. Novel and effective approaches capable of processing large volumes of images/videos and analyzing them effectively are becoming increasingly important. This paper presents a data-driven methodology to automatically classify cinematographic shots through deep learning techniques. In our study, we consider four classes of film shots: full figure, half figure, half torso and close up and we discuss three different scenarios in which the proposed work can be helpful. A new dataset of images was created to evaluate performances of the proposed methodology and to compare them with state-of-the-art techniques. Experimental results demonstrate the effectiveness of the proposed approach in performing the classification task with good accuracy.
Design and implementation of convenient and compulsory voting system using finger print sensor and GSM technologies

India is a developing country, where still the electronic voting system is under threat due to the rigging in elections. The future of country is decided mainly by the elections, so the electronic voting machine must be trustworthy. The existing voting system, doesn't have person identification, so that there are chances to misuse the others rights. Fingerprint based authentication for voting is proposed in this paper, which eliminates the misuse in voting. GSM module is incorporated with finger print sensor to collect the data before election. The proposed model is implemented in Arduino for effective authentication and quick process with more flexibility. GSM module is used to transfer the collected information to near by taluk office or district office so that the authorized persons will access the information.
Overt attentional correlates of memorability of scene images and their relationships to scene semantics

Computer vision-based research has shown that scene semantics (e.g., presence of meaningful objects in a scene) can predict memorability of scene images. Here, we investigated whether and to what extent overt attentional correlates, such as fixation map consistency (also called inter-observer congruency of fixation maps) and fixation counts, mediate the relationship between scene semantics and scene memorability. First, we confirmed that the higher the fixation map consistency of a scene, the higher its memorability. Moreover, both fixation map consistency and its correlation to scene memorability were the highest in the first 2 seconds of viewing, suggesting that meaningful scene features that contribute to producing more consistent fixation maps early in viewing, such as faces and humans, may also be important for scene encoding. Second, we found that the relationship between scene semantics and scene memorability was partially (but not fully) mediated by fixation map consistency and fixation counts, separately as well as together. Third, we found that fixation map consistency, fixation counts, and scene semantics significantly and additively contributed to scene memorability. Together, these results suggest that eye-tracking measurements can complement computer vision-based algorithms and improve overall scene memorability prediction.
Drink2Vec: Improving the classification of alcohol-related tweets using distributional semantics and external contextual enrichment

The hazardous and harmful use of alcohol has become a public health issue worldwide. Social media has emerged as a reliable source to extract information on alcohol consumption at low cost and latency. The automatic classification of tweets related to alcohol consumption can help to understand the factors related to alcohol abuse. In this paper, we propose Drink2Vec, a method aimed at improving the classification of alcohol-related tweets by exploring two forms of contextual information: distributional semantics and external contextual enrichment. The core of Drink2Vec is a convolutional neural network that learns domain-specific word embedding representations that capture vocabulary related to alcohol consumption. Drink2Vec builds on Drink2Symbol, a method that finds relevant symbolic features on external sources (e.g., Semantic Web) to provide meaning and generalization to the terms present in tweets. Based on five datasets and three classification algorithms, our experiments show that external enrichment improves the recall by the addition of generalization features, while distributional semantics improves the precision mainly by characterizing terms according to their usage. A stacking ensemble of these classifiers establishes a proper balance between the advantages of each contextual enrichment technique. Our experiments also suggest that the task-specific embeddings produced by Drink2Vec capture more nuances of the informal vocabulary related to alcohol consumption (e.g., slangs, events, misspelled words) and yield better results compared to other strategies (e.g., pre-trained embeddings and generic algorithms).