Reviewerly/exHarmony huggingface dataset:
Fed2KD: Heterogeneous Federated Learning for Pandemic Risk Assessment via Two-Way Knowledge Distillation

The world has suffered a lot from the COVID-19 pandemic. Though vaccines have been developed, we still need to be ready for its variants and other possible pandemics in the future. To provide people with pandemic risk assessments without violating privacy, a Federated Learning (FL) framework is envisioned. However, most existing FL frameworks can only work for homogeneous models, i.e., models with the same configuration, ignoring the preferences of the users and the various properties of their devices. To this end, we propose a novel two-way knowledge distillation-based FL framework, Fed2KD. The knowledge exchange between the global and local models is achieved by distilling the information into or out from a tiny model with unified configuration. Nonetheless, the distillation cannot be conducted without a common dataset. To solve this bottleneck, we leverage the Conditional Variational Autoencoder (CVAE) to generate data that will be used as a proxy dataset for distillation. The proposed framework is firstly evaluated on benchmark datasets (MNIST and FashionMNIST) to test its performance against existing models such as Federated Averaging (FedAvg). The performance of Fed2KD improves by up to 30% on MNIST dataset, and 18% on FashionMNIST when data is non-independent and identically distributed (non-IID) as compared to FedAvg. Then, Fed2KD is evaluated on the pandemic risk assessment tasks through a mobile APP we developed, namely DP4coRUna, which provides indoor risk prediction.
Exploring Blockchain-enabled smart contracts technology implementation within ready-mixed concrete plants industry in Saudi Arabia

In recent years, several construction-related services incorporating Blockchain-enabled smart contracts have emerged. Blockchain-enabled smart contracts have the potential to modify the structure of all accessible networks in the near future. The purpose of this study is to explore individuals understanding of smart contracts in Saudi Arabia, identify potential advantages, and assess actors' readiness to implement smart contracts technology in ready-mixed concrete (RMC) plants. Data was acquired by questionnaires distributed to employees from Saudi Arabia's RMC plants companies. Additionally, confirmatory interviews were conducted out to areas of expertise. The study's findings revealed a low level of smart contract knowledge within Saudi Arabia’s RMC plants sector. The readiness to implement smart contracts is extremely low. According to the findings, smart contracts can improve supply chain management and supply chain finance efficiency, but not in the current circumstances in Saudi Arabia owing to insufficient digitization.
SpeechNAS: Towards Better Trade-Off Between Latency and Accuracy for Large-Scale Speaker Verification

Recently, x-vector [1] has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances. Improvement upon the x-vector has been an active research area, and enormous neural networks have been elaborately designed based on the x-vector, e.g., extended TDNN (E-TDNN) [2], factorized TDNN (F-TDNN) [3], and densely connected TDNN (D-TDNN) [4]. In this work, we try to identify the optimal architectures from a TDNN based search space employing neural architecture search (NAS), named SpeechNAS. Leveraging the recent advances in the speaker recognition, such as high-order statistics pooling, multi-branch mechanism, D-TDNN and angular additive margin softmax (AAM) loss with a minimum hyper-spherical energy (MHE), SpeechNAS automatically discovers five network architectures, from SpeechNAS-1 to SpeechNAS-5, of various numbers of parameters and GFLOPs on the large-scale text-independent speaker recognition dataset VoxCelebl. Our derived best neural network achieves an equal error rate (EER) of 1.02% on the standard test set of VoxCelebl, which surpasses previous TDNN based state-of-the-art approaches by a large margin.
Towards Convergence of Blockchain and Self-sovereign Identity for Privacy-Preserving Secure Federated Learning

More and more researchers are eager to train their Machine Learning (ML) model by the distributed dataset in a federated manner. However, there are numerous privacy and security concerns in federated learning, i.e., adversarial attacks, authentication, and model inversion attacks. In this paper, we integrate self-sovereign identity for privacy-preserving secure federated learning. The proposed framework represents identity management and authentication scheme for edge devices, which are participated in FL. It guarantees participants’ privacy, where users’ private data are not shared with the centralized training module. The trust triangle and Blockchain ensure the authenticity of the distributed federated learning, where data privacy is handled with Differential Privacy. Result analysis is done based on measured score and time complexity. In terms of accuracy, BCWD, HDD, and DD achieve $$94.69\%$$ , $$81.05\%$$ and $$78.00\%$$ , respectively on standard ML, whereas $$94.45\%$$ , $$80.59\%$$ and $$76.43\%$$ on $$\epsilon $$ DP-based FL with privacy budget, $$\epsilon $$ = 3. For the time complexity measurement, the total number of transactions employed in the system is 1000. Transactions time and their verification took 3323.15 s. Moreover, the proposed system shows robust performance in both cases.
Representing Graphs via Gromov-Wasserstein Factorization

Graph representation is a challenging and significant problem for many real-world applications. In this work, we propose a novel paradigm called “Gromov-Wasserstein Factorization” (GWF) to learn graph representations in a flexible and interpretable way. Given a set of graphs, whose correspondence between nodes is unknown and whose sizes can be different, our GWF model reconstructs each graph by a weighted combination of some “graph factors” under a pseudo-metric called Gromov-Wasserstein (GW) discrepancy. This model leads to a new nonlinear factorization mechanism of the graphs. The graph factors are shared by all the graphs, which represent the typical patterns of the graphs’ structures. The weights associated with each graph indicate the graph factors’ contributions to the graph's reconstruction, which lead to a permutation-invariant graph representation. We learn the graph factors of the GWF model and the weights of the graphs jointly by minimizing the overall reconstruction error. When learning the model, we reparametrize the graph factors and the weights to unconstrained model parameters and simplify the backpropagation of gradient with the help of the envelope theorem. For the GW discrepancy (the critical training step), we consider two algorithms to compute it, which correspond to the proximal point algorithm (PPA) and Bregman alternating direction method of multipliers (BADMM), respectively. Furthermore, we propose some extensions of the GWF model, including (i) combining with a graph neural network and learning graph representations in an auto-encoding manner, (ii) representing the graphs with node attributes, and (iii) working as a regularizer for semi-supervised graph classification. Experiments on various datasets demonstrate that our GWF model is comparable to the state-of-the-art methods. The graph representations derived by it perform well in graph clustering and classification tasks.
Global Plus Local Jointly Regularized Support Vector Data Description for Novelty Detection

In many practice application, the cost for acquiring abnormal data is quite expensive, thus the one-class classification (OCC) problem attracts great attention. As one of the solutions, support vector data description (SVDD) gains a continuous focus in outlier detection since it is based on the data description. For the sphere obtained by SVDD, both the center and the volume (or radius) strongly depend on the support vectors, while the support vectors are sensitive to the tradeoff parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> . Hence, how to select this parameter is a rather challenging problem. In order to address this problem, we define several distance metrics relative to the image region in Gaussian kernel space. With the distance metrics, two probability densities relative to the global region and the local region are designed, respectively. Then, the information quantity and the information entropy are developed for regularizing the tradeoff parameter. This novel SVDD is called global plus local jointly regularized support vector data description (GL-SVDD), in which both the global region information and the local image region information jointly penalize the images as possible outliers. Finally, we use the UCI dataset and the hyperspectral data of cherry fruit to evaluate the performance of several OCC approaches. Experimental results show that GL-SVDD is encouraging.
Dynamic Quantum Network: from Quantum Data Centre to Quantum Cloud Computing

This paper presents challenges and solutions for creating a dynamic entangled quantum network as the main technology enabler for realizing scalable quantum data centres and future quantum cloud computing infrastructure serving a large number of users.
Using current research information systems to investigate data acquisition and data sharing practices of computer scientists

Without sufficient information about research data practices occurring in a particular research organisation, there is a risk of mismatching research data service efforts with the needs of its researchers. This study describes how data acquiring and data sharing occurring within a particular research organisation can be investigated by using current research information system publication data. The case study organisation’s current research information system was used to identify the sample of investigated articles. A sample of 193 journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a classification of the main study types was developed to accommodate the multidisciplinary nature of the case department’s research agenda. Furthermore, a coding framework was developed to capture the key elements of data acquiring and data sharing. The articles representing life sciences and computational research relatively frequently reused open data, whereas data acquisition of experimental research, human interaction studies and human intervention studies often relied on collecting original data. Data sharing also differed between the computationally intensive study types of life sciences and computational research and the study types relying on collection of original data. Research data were not available for reuse in only a minority of life science ( n = 2; 7%) and computational research ( n = 15; 14%) studies. The study types of experimental research, human interaction studies and human intervention studies less frequently made their data available for reuse. The findings suggest that research organisations representing computer sciences may include different subfields that have their own cultures of data sharing. This study demonstrates that analyses of publications listed in current research information systems provide detailed descriptions how the affiliated researchers acquire and share research data.
Implementation of Blockchain Technology in Education System

The Blockchain for Education platform helps us to make the tamper-proof certificates and their correct and the overall permanent allocation of these certificates to learners, as well as verification of certificates. It can reduce the overall frauds and tampering of the degrees and certificates. Blockchain technology can be used to solve many educational problems and can help educators as well as learners to monitor the learning outcomes. The data can be stored securely and tamper proof format when it’s stored onto the blockchain network. Here smart contracts can be designed and deployed on to the Ethereum blockchain that can be designed using the solidity programming language. Blockchain can be applied to private, public and consortium sectors depending upon the usage and the scope of the blockchain. Education system can take benefit of this scalability of the blockchain and can be effectively useful in the educational institutions.
Security component fundamentals for assessment

The key to the management, oversight, and governance of the security components and program in the organization is the understanding of the risks involved and how each of it treated and tolerated by the organization. As the assessor for a US Governmental system, it is important to grasp and work with the fundamental requirements for these systems. With the SP 800-53 structured approach to security controls, the assessor can review each management, technical, and operational area of security directly. NIST SP 800-53, rev. 4, is divided into 18 control families comprising three security classes of controls viz. management, operational, and technical controls. Each of the controls and the management areas of consideration is discussed in detail in this chapter.
Introduction

column Share on Introduction Authors: Shaimaa Lazem City of Scientific Research and Technology Applications (SRTA-City) City of Scientific Research and Technology Applications (SRTA-City)View Profile , Ebtisam Alabdulqader Newcastle University and King Saud University Newcastle University and King Saud UniversityView Profile , Mohamed Khamis University of Glasgow University of GlasgowView Profile Authors Info & Claims InteractionsVolume 26Issue 3May - June 2019 pp 41–43https://doi.org/10.1145/3320109Published:23 April 2019Publication History 3citation502DownloadsMetricsTotal Citations3Total Downloads502Last 12 Months137Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Cryptocurrencies: the communication inside blockchain technology and the cross-border tax law

This research aims to analyse the social event that is modifying the traditional financial system since the blockchain technology and cryptocurrencies came up. Taxation in international scenario were examined by the ideal from governments in a democratic system as an instrument that materialises human rights. The social observation approaches the legacy of Emile Durkheim philosophy who established a power relationship between social fact and coercion. Taxation has been the focal point in smart economy to juridical scientists and everyone involved in the digital economy. This research conducted tests and researches from historical and social method, seeking for laws, doctrine, jurisprudence and concrete case analysis in front of the philosophical school of logical-semantic constructivism as a suitable means to verify the possibility of constructing a matrix rule of tax incidence. This research was enough to show that the blockchain technology and cryptocurrencies fulfils its humanitarian role in the smart economy.
Performance evaluation of 2.25 kWp rooftop solar PV plant based on experimental measurements in the desert environment, case study for Ghardaia, Algeria

The aim of the present study was to assess the electrical performance and the suitability of a of 2.25 kWp photovoltaic plant placed on the roof of a cars parking located at Ghardaia (Algerian desert). Performance parameters have been experimentally studied in the year 2017. The main goal is to show the change in energy production and to present the impact of weather and sky conditions based on site measurements. The experimental results show that the lowest value of the performance ratio and the maximum value in reference yield, array yield, final yield, the capture and system losses was in clear day due to high ambient temperature. The minimum value of reference yield, array yield, and final yield were in cloudy sandstorm day due to low level of daily solar irradiation and sand dust particles. The PV system performance was comparable to that of other installed systems in different countries.
The Meaning of Silence in Cyberspace

It has been argued in the literature on hate speech and subordination theory that even ordinary hate speakers who are not “figures of authority” in the conventional sense can possess the power or authority to subordinate (rank as inferior, deny rights and powers, or legitimate discrimination against) the targets of their hate speech in virtue of the fact that when witnesses to the hate speech remain silent they “license” or grant authority to the hate speaker. A typical example is when a hate speaker targets a victim on public transport and other passengers remain silent. The aim of this chapter is to examine the extent to which this account of licensing is applicable to online hate speech or cyberhate. More generally, the chapter explores whether the potentially distinctive nature of online communication changes the meaning of silence such that it becomes difficult to interpret silence in cyberspace as acquiescence, licensing, or complicity.
Velocity Model Building from Raw Shot Gathers Using Machine Learning

Summary We present a machine learning setup that can estimate a velocity model from raw seismic shot gathers without the need for an initial velocity model. Our setup is based on a convolutional neural network (CNN) trained on pairs of random generated synthetic velocity models and corresponding forward modelled synthetic shot gathers. The network is trained to predict the correct velocity model for a given input shot gather. We evaluate the performance of the trained network on both synthetic and real seismic data and observe that the system is able to estimate background velocity trends directly from the raw shot gathers without any need for preprocessing or preconditioning. Once trained, the network is very fast to run, and can deliver a velocity model in seconds running on a single GPU. The precision and resolution of the estimated velocity models is not on par with state of the art velocity model building techniques such as FWI and reflection tomography but shows that machine learning can extract meaningful velocity information from real shot gathers, and that there is potential in using such methods for velocity model building.
K-means clustering algorithm for data distribution in cloud computing environment

This study analyses the data structure in cluster analysis. It is a clustering method that randomly selects a known number of points and then continues to expand. Through the comparative experiments on the clustering accuracy of different similarity matrices, the experimental analysis on the effectiveness of the model, the distribution of e-commerce data under cloud computing and the calculation time of different clustering algorithms, we can better understand the K-means clustering algorithm and the status of e-commerce in cloud computing environment. The experimental results show that if the appropriate similarity function is selected, the result of spectral clustering is usually not lower than that of simple K-means clustering. When the number of users reaches 4000, the list reading time of the K-means clustering algorithm is 3.15 s, while the other three algorithms consume more time.
Neural Network

How are you able to recognize different digits written in a different way? Well, all of these crazy smart activities are done by our visual cortex and are inherited (mimicked) by the means of neural network (Python Deep Learning: Exploring deep learning techniques, neural network architectures and GANs with PyTorch, Keras and TensorFlow. [Authors: Ivan Vasilev, Daniel Slater, GianmarioSpacagna, Peter Roelants, Valentino Zocca]). In other words, neural networks are inspired by the human brain that contains neurons and synapses to perform their own task (Hands-On Transfer Learning with Python Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras [Authors: DipanjanSarkar, Raghav Bali, TamoghnaGhosh]).
Contributions of the internet of things in education as support tool in the educational management decision-making process

The aim of this paper is to understand the contributions that the development of IoT provides to education, particularly in assisting the decision-making process and addressing some of the challenges that the educational system faces. The study adopts a qualitative approach and applies bibliographic overview procedures that substantiate 'internet of things' concepts in education and the decision-making process in education, as well as analysing the phenomena in the light of contingency theory precepts. Therefore, this study investigated several contexts for the application of this technology in learning and a more efficient management of educational organisation structure, in addition to indicating spaces for the construction of alternatives when making decisions hitherto compromised by the limitation of technological resources. Finally, IoT tools applied to the educational context were identified and they can radiate benefits to the decision making process in education, based on the collection of information generated in this environment.
Python Basics

This chapter will get you started with Python, using it as a calculator, managing Python packages, and helping you familiarize yourself with the Jupyter notebook. As you will see, Python has a great set of packages with expansive sets of functionalities. At the end of this chapter, you should be able to execute commands in Python, install and manage the Python packages in Jupyter notebook, and use Python's basic mathematical functions.
Converting Text to Features

This chapter covers basic to advanced feature engineering (text to features) methods. By the end of the chapter, you will be comfortable with the following recipes.
Changing the logic of replication: A case from infant studies

Infant research is making considerable progresses. However, among infant researchers there is growing concern regarding the widespread habit of undertaking studies that have small sample sizes and employ tests with low statistical power (to detect a wide range of possible effects). For many researchers, issues of confidence may be partially resolved by relying on replications. Here, we bring further evidence that the classical logic of confirmation, according to which the result of a replication study confirms the original finding when it reaches statistical significance, could be usefully abandoned. With real examples taken from the infant literature and Monte Carlo simulations, we show that a very wide range of possible replication results would in a formal statistical sense constitute confirmation as they can be explained simply due to sampling error. Thus, often no useful conclusion can be derived from a single or small number of replication studies. We suggest that, in order to accumulate and generate new knowledge, the dichotomous view of replication as confirmatory/disconfirmatory can be replaced by an approach that emphasizes the estimation of effect sizes via meta-analysis. Moreover, we discuss possible solutions for reducing problems affecting the validity of conclusions drawn from meta-analyses in infant research.