Reviewerly/exHarmony huggingface dataset:
Modelling and Analysing Replica- and Fault-aware Management of Horizontally Scalable Applications

Modern enterprise applications integrate multiple interdependent software components, whose management must be suitably coordinated. This must be done by taking into account all inter-component dependencies, the faults potentially affecting them, and the fact that each component can be horizontally scaled, i.e., that multiple instances of each component can be spawned or destroyed, depending on application needs. In this article, we introduce a novel solution for suitably modelling and analysing the replica- and fault-aware management of multi-component applications, based on topology graphs and management protocols. More precisely, we first introduce a compositional model of the management behaviour of the (possibly multiple) instances of the components forming an application, faults included. We then show how this model enables automating various useful analyses, from checking the validity of management plans to automatically determining management plans allowing the instance of an application to reach and maintain a desired target configuration.
Quantitative perspectives in Cognitive Linguistics

As a usage-based approach to the study of language, cognitive linguistics is theoretically well poised to apply quantitative methods to the analysis of corpus and experimental data. In this article, I review the historical circumstances that led to the quantitative turn in cognitive linguistics and give an overview of statistical models used by cognitive linguists, including chi-square test, Fisher test, Binomial test, t-test, ANOVA, correlation, regression, classification and regression trees, naïve discriminative learning, cluster analysis, multi-dimensional scaling, and correspondence analysis. I stress the essential role of introspection in the design and interpretation of linguistic studies, and assess the pros and cons of the quantitative turn. I also make a case for open access science and appropriate archiving of linguistic data.
Enhanced Text Classification Methods to Improve the Performance of the Various Text Mining Processes using Rapid Miner

The approaches used in text mining procedures will be discussed in this study. This study offered a new similarity measure method that outperformed the K-NN and Naive-Bayes approaches. The proposed approach improves the accuracy of text classification methods while also implementing an effective term-weighting strategy that gives the maximum accuracy when compared to other text classification methods. We evaluated the performance of the different TF-IDF models and proposed TF-IDF to perform text classification by comparing performance parameters of KNN and Nave Bayes Classification with different Weighting Methods, Weight Information Gain, SVM, and proposed method and comparing performance parameters of KNN and Nave Bayes Classification with different Weighting Methods, Weight Information Gain, SVM, and proposed method.
Language Learning Strategies: Contrasting English Department and Non-English Department Students

It is a fact that language learning strategies can help learners to learn a language successfully. English learners who employ certain language learning strategy are believed to be able to master the language better and faster than those who do not employ any strategies or those who are not aware of their learning strategies. While learning strategies may affect the success of a language learner, a learner’s ‘choice’ of certain strategy can be influenced by one’s study background. Some previous studies have verified it by examining language learning strategies used by students of English Department and students of non-English Department using Oxford’s Strategy Inventory for Language Learning (SILL). Thus, with the same instrument, this research aims to prove the presence of the students’ differences in using the strategies. This research is under a survey research design with students’ responses as the main data and questionnaires as the instrument. The finding indicates that English Department students make use of social strategies the most, while non-English Department students generally use metacognitive strategies. The implication of this research is to give valuable inputs for English lecturers of both settings in subjecting their students according to the findings of each strategy’s use.
Normalization Issues in Digital Literary Studies: Spelling, Literary Themes and Biographical Description of Writers

Digital literary studies are a branch of digital humanities, which deals with national or world literatures. In this paper, we discuss normalization issues which are crucial for compiling eCulture resources, designed for cultural analytics, social and literary studies, as well as various aspects of digital humanities. One of such resources is the Corpus of Russian short stories of 1900–1930s with the detailed information about Russian writers of the epoch in concern intended for stylometric, linguistic and literary studies of Russian prose. We see our task to create a literary resource based on a system approach to the literature of a certain time period, which implies inclusion into consideration literary texts of the maximum number of writers, who created their works in the given period, both well-known and peripheral. The paper concerns the problem of data normalization, which is a necessary requirement for statistical processing of data of any kind. We describe how we deal with the problem of different spelling, how we normalize manual annotation of literary themes made by an expert and how we tackle the problem of standardization of biographical descriptions of authors. The obtained normalized data can be used for various kinds of research in the field of literary studies, digital humanities, computational linguistics, and cultural heritage studies.
Cloud resource prediction model based on LSTM and RBF

In order to respond to resource usage "in advance" and to dynamically, timely and accurately schedule and allocate resources based on predicted values, a cloud resource prediction model based on Long Short Term Memory (LSTM) neural network and Radial Basis Function (RBF) neural network is proposed after investigating the current state of research on the cloud container resource prediction problem, and the parameters are trained using back propagation (BP) neural network to find the optimal combination of parameters. The LSTM can avoid the long-term dependency problem, and remembering information from very early moments is the default behavior of the LSTM without the use of a back propagation (BP) neural network. The LSTM can avoid the long-term dependency problem, and remembering information from very early moments is the default behavior of the LSTM without paying a great price for it specifically. The transformation of RBF from input space to hidden space is nonlinear, while the transformation from hidden space to output space is linear, greatly speeding up learning and avoiding the local minima problem .The experimental results show that the average error ratios of the LSTM single prediction model and RBF single prediction model are 16.38% and 19.36%, respectively, while the average error ratio of the proposed model is 4.51%, which shows that the model proposed in this paper has better prediction performance and prediction accuracy compared with other models.
The Impact of Network Design Interventions on CPS Security

We study a game-theoretic model of the interactions between a Cyber-Physical System’s (CPS) operator (the defender) against an attacker who launches stepping-stone attacks to reach critical assets within the CPS. We consider that, in addition to optimally allocating its security budget to protect the assets, the defender may choose to modify the CPS through network design interventions. In particular, we propose and motivate four ways in which the defender can introduce additional nodes in the CPS: these nodes may be intended as additional safeguards, be added for functional or structural redundancies, or introduce additional functionalities in the system. We analyze the security implications of each of these design interventions, and evaluate their impacts on the security of an automotive network as our case study. We motivate the choice of the attack graph for this case study and elaborate how the parameters in the resulting security game are selected using the CVSS metrics and the ISO-26262 ASIL ratings as guidance. We then use numerical experiments to verify and evaluate how our proposed network interventions may be used to guide improvements in automotive security.
Divergent Projection Analysis for Unsupervised Dimensionality Reduction

Principal component analysis (PCA) has been extended to a series of classical methods in dimensionality reduction for unsu¬pervised learning. However, the differences among the projected data samples may not be sufficient by PCA, causing problems both in data visualization and recognition. Though some nonlinear dimensionality reduction methods have been proposed to preserve the samples’ differences, their nonlinear mappings hide the relationships among the data features, e.g., Isometric mapping (Isomap) and Locally Linear Embedding (LLE). In this paper, we propose a linear dimensionality reduction method, called Divergent Projection Analysis (DPA), to preserve the largest differences among the samples. Our DPA projects the sam¬ples into a low-dimensional space such that any two projected samples have a certain distance, resulting in solving a nonconvex optimization problem. The nonconvex problem is solved by the subgradient descent algorithm and particle swarm optimiza¬tion algorithm, and their solution qualities are compared in the experiments. Experiments on a synthetic dataset and three face recognition problems confirm the effectiveness of the proposed method compared with other state-of-the-art methods.
Adaptive Streaming and Rendering of Static Light Fields in the Web Browser

Static light fields are an image-based technology that allow for the photorealistic representation of inanimate objects and scenes in virtual environments.As such, static light fields have application opportunities in heterogeneous domains, including education, cultural heritage and entertainment.This paper contributes the design, implementation and performance evaluation of a web-based static light field consumption system.The proposed system allows static light field datasets to be adaptively streamed over the network and then to be visualized in a vanilla web browser.The performance evaluation results prove that real-time consumption of static light fields at AR/VR-compatible framerates of 90 FPS or more is feasible on commercial off-the-shelf hardware.Given the ubiquitous availability of web browsers on modern consumption devices (PCs, smart TVs, Head Mounted Displays, . . .), our work is intended to significantly improve the accessibility and exploitation of static light field technology.The JavaScript client code is open-sourced to maximize our work's impact.
Devanagari Handwritten Compound Character Recognition Using Various Machine Learning Algorithms

Handwriting is unique for each person. The formation of letters, compound characters, spaces and numbers by each person is so unique that handwriting is considered as legal evidence in forgery cases. Hence accurate recognition of handwritten characters holds huge significance. Especially compound characters are differing in writing ways and there are some characters which can be written in many ways so it is complex to recognize. Segmentation is another issue occurs due to structure of the character is very complex. This paper proposes a method to recognize compound handwritten Devanagari characters using several machine learning algorithms like K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM). Techniques used for extracting features from the images are playing a significant role in the identification of compound characters. In this paper, the Zoning feature extraction technique is used combined with other features like eccentricity, equivalent diameter and angle. We created a new dataset of 45 compound characters. Using 10 cross-validation we achieved the highest 96.62% accuracy by SVM classifier with training time 1.56 seconds.
A Fusion Architecture Model for Human Activity Recognition

Human Activity Recognition (HAR) is a domain of increasing interest with several two-stream architectures being suggested in recent years. However, such models have a huge number of parameters and storage needs due to the presence of a dedicated temporal stream. In this paper, we propose an architecture comprising of the weighted late fusion between the Softmax scores of the spatiotemporal stream (I3D) and another 2D convolutional neural network stream (Xception). We show that our model produces competitive performance w.r.t to other existing spatial and two-stream architectures along with reducing the number of parameters significantly and minimizing storage costs.
Channel Split Convolutional Neural Network for Single Image Super-Resolution (CSISR)

Recently, deep convolutional neural networks have achieved remarkable performance for the task of Single Image Super-Resolution (SISR); however these models set huge amount of computational complexity to achieve such performance. Hence, computationally efficient and low memory models are needed for SISR if such models are deployed on resources with low-computational devices (for instance, Mobile). We propose a novel approach referred to as Channel Split Convolutional Neural Network for Single Image Super-Resolution (CSISR). The proposed work aims to create a light-weight but efficient network to enhance SR performance. It employs a unique channel splitting alongside channel reduction blocks, leading to more effectiveness with a less computational burden to obtain state-of-the-art accuracy. Further, we suggest a new strategy for Channel Attention (CA) using a combination of global average and standard deviation pooling accompanied by conventional non-linear mapping layers to improve the learning. The performance of the proposed network is validated on various benchmark testing datasets for the SISR task, which shows its superiority over other existing methods.
Cost-Efficient Shuffling and Regrouping Based Defense for Federated Learning

Federated learning (FL) enables multiple user de-vices to collaboratively train a global machine learning (ML) model by uploading their local models to the central server for aggregation. However, attackers may upload tampered local models (e.g., label-flipping attack) to corrupt the global model. Existing defense methods focus on outlier detection, but they are computationally intensive and can be circumvented by advanced model tampering. We employ a shuffling-based defense model to isolate the attackers from ordinary users. To explore the intrinsic properties, we simplify the defense model problem and formulate it as a Markov Decision Problem (MDP) to find the optimal policy. Then, we introduce a novel notion, (re)grouping, into the defense model to propose a new cost-efficient defense framework termed SAGE. Experiment results manifest that SAGE can effectively mitigate the impact of attacks in FL by efficiently decreasing the ratio of attacker devices to ordinary user devices. SAGE increases the testing accuracy of the targeted class by at most 40&#x0025;.
Modeling Big Data-centric Services using Knowledge Graphs

Big services have recently emerged from the synergy between big data and cloud computing paradigms. This new big data-centric service model aims to provide customer-oriented massive services by combining both physical and virtualized resources from different domains. Although such complex ecosystem is able to process, encapsulate and offer huge volumes of data as services, its management operations are beyond the ability of human administrators, due to several challenges including the big services&#x2019; large-scale nature and complexity, the heterogeneity of their components (e.g., services, data sources, connected things), the dynamicity and uncertainty of their hosting cloud environments. To cope with the lack of understanding regarding big services capabilities, we propose to describe them using a novel meta-model for the quality of big services (QoBS). We also take advantage of a recent technology called knowledge graphs, to represent the big service information (service descriptions, services&#x2019; and data sources&#x2019; quality levels, management policies) as a heterogeneous information network. Finally, a multi-view representation learning approach is proposed to infer additional knowledge regarding big services capabilities.
Digital Footprint Analysis Technology: Some Aspects of Its Application in Recruitment

In the developing trend of digital transformation, which is accelerating under the influence of the Covid-19 pandemic, digital HR technologies are being developed, the use of which is necessary in the conditions of increasing competition in the fight for the best employees. This study is designed to identify the features and consequences of using the technology of analyzing the digital footprint of a job seeker in the recruitment process. Based on the methods of content analysis, statistical analysis of information, as well as system analysis and synthesis, the author conducted a study of the problem field of contradictions between the interests of employers and job seekers arising as a result of the use of digital footprint determining technology. Attention is paid to the ethical aspects of using the technology of tracking and analyzing the digital footprint in social media and the need to build a transparent recruitment process for job seekers, which will lead to the formation of a digital environment based on a more responsible approach.
QSpark: Distributed Execution of Batch &amp; Streaming Analytics in Spark Platform

A significant portion of research work in the past decade has been devoted on developing resource allocation and task scheduling solutions for large-scale data processing platforms. Such algorithms are designed to facilitate deployment of data analytic applications across either conventional cluster computing systems or modern virtualized data-centers. The main reason for such a huge research effort stems from the fact that even a slight improvement in the performance of such platforms can bring a considerable monetary savings for vendors, especially for modern data processing engines that are designed solely to perform high throughput or/and low-latency computations over massive-scale batch or streaming data. A challenging question to be yet answered in such a context is to design an effective resource allocation solution that can prevent low resource utilization while meeting the enforced performance level (such as 99-th latency percentile) in circumstances where contention among applications to obtain the capacity of shared resources is a non negligible performance-limiting parameter. This paper proposes a resource controller system, called QSpark, to cope with the problem of (i) low performance (i.e., resource utilization in the batch mode and p-99 response time in the streaming mode), and (ii) the shared resource interference among collocated applications in a multi-tenancy modern Spark platform. The proposed solution leverages a set of controlling mechanisms for dynamic partitioning of the allocation of computing resources, in a way that it can fulfill the QoS re-quirements of latency-critical data processing applications, while enhancing the throughput for all working nodes without reaching their saturation points. Through extensive experiments in our in-house Spark cluster, we compared the achieved performance of proposed solution against the default Spark resource allocation policy for a variety of Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) applications. Experimental results show the effectiveness of the proposed solution by reducing the p-99 latency of high priority applications by 32 % during the burst traffic periods (for both batch and stream modes), while it can enhance the QoS satisfaction level by 65 % for applications with the highest priority (compared with the results of default Spark resource allocation strategy).
SUBLμME: Secure Blockchain as a Service and Microservices-based Framework for IoT Environments

IoT applications have extended new concepts to smart technologies in different vital sectors such as healthcare, education, agriculture, energy management and control, etc. IoT aims to interconnect several intelligent devices over the Internet to control, store, exchange, and analyze collected data. However, the main problem in IoT environments is that they present numerous potential vulnerabilities that can be the origin of security attacks. In this context, this paper proposes a novel framework, named SUBLμME, which stands for SecUre BLockchain as a service and μservices-based fraMework for IoT Environments. This framework integrates BaaS and microservices technologies that have the predominant characteristic of offering reusable and reconfigurable security features implemented as independent services that can be reused for multiple IoT applications. SUBLμME is implemented and validated by a simulated smart home environment. The results of the simulation experiments showed that SUBLμME brings several security improvements in terms of performance, data integrity, data packages’ validation, access control, and efficient and secure data transmission and storage.
DD-Locker: Blockchain-based Decentralized Personal Document Locker

Document verification is the first step whenever we enter any organization or institute. In any organization, it is essential to track, verify, and check the person’s background who will become a part of the organization. This process is very time-consuming and hectic for both parties involved. Various governments provide cloud-based digital locker services for the citizens storing the public document on a centralized server. But due to its centralized nature, this type of service is weak against information breaches and Denial of Service (DoS) attacks. Also, there are some privacy concerns with such centralized digital locker services as the stored documents may contain users’ crucial personal information. This paper proposes a blockchain-based digital locker in a decentralized application using Ethereum Blockchain to securely store personal documents with high availability. The proposed solution also verifies documents with ease, confidentiality, access control, data privacy, authenticity, and maintaining the integrity of documents.
Multimodal Variational Autoencoders for Sensor Fusion and Cross Generation

The cognitive system of humans, which allows them to create representations of their surroundings exploiting multiple senses, has inspired several applications to mimic this remarkable property. The key for learning rich representations of data collected by multiple, diverse sensors, is to design generative models that can ingest multimodal inputs, and merge them in a common space. This enables to: i) obtain a coherent generation of samples for all modalities, ii) enable cross-sensor generation, by using available modalities to generate missing ones and iii) exploit synergy across modalities, to increase reconstruction quality. In this work, we study multimodal variational autoencoders, and propose new methods for learning a joint representation that can both improve synergy and enable cross generation of missing sensor data. We evaluate these approaches on well-established datasets as well as on a new dataset that involves multimodal object detection with three modalities. Our results shed light on the role of joint posterior modeling and training objectives, indicating that even simple and efficient heuristics enable both synergy and cross generation properties to coexist.