Reviewerly/exHarmony huggingface dataset:
Multihypothesis Sequential Testing for Illegitimate Access and Collision-Based Attack Detection in Wireless IoT Networks

Jamming or illegitimate wireless network access interferes with legitimate communication sessions by mimicking the legitimate transmissions and degrades the network performance. In this article, we propose a methodology to detect such attacks by implementing a multiple hypotheses sequential testing-based detection framework with variance and channel state information (CSI)-based algorithms. The detection framework focuses on distinguishing between legitimate and illegitimate transmissions and the nature of illegitimate transmissions with a quaternary hypotheses test. The quaternary hypotheses include no transmission, legitimate node transmission, illegitimate node transmission, and collision-based attack. We first devise a sequential testing problem on a ternary hypothesis problem and then tackle the remaining hypothesis with both variance-based approach and CSI-based approach. We devise algorithms based on the same and compare their performance. We also compare our approach with the generalized Neyman-Pearson approach based on detection speed. In addition, we present a multiple sensor-based approach to further improve the detection performance through soft- and hard-decision combining. We conduct extensive performance evaluations based on both simulated and measurement data. The numerical results show fewer sample size requirements for the proposed algorithms, leading to faster detection.
A survey and critical analysis on energy generation from datacenter

A cloud can be considered as a distributed computing environment consists of a set of virtualized and interconnected computers or nodes. Dynamically provisioned cloud presented as consolidated computing resources based on Service Level Agreements (SLA) entrenched through an agreement between the consumers and service provider. The cloud consists of virtualized, distributed datacenters and here the applications are offered on-demand, as a service. Every datacenter needs a very much consistent as well as inexpensive power supply. Usually, this is fulfilled by combining grid electricity, which ensures affordability, and backup diesel generators for an emergency, that assures consistency. Unfortunately, this system has some flaws, relying on increasingly unstable electricity prices, and a very high rate of carbon emissions from the diesel generators, this could direct to the problems of environmental sustainability. We will look into the energy generation and consumption of different components of datacenters. The power sources of these power-hungry datacenters are affecting environmental sustainability. Later, in this chapter we will explore some of the solutions to reduce energy consumption such as green cloud computing, using renewable energy sources, and so on.
A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.
A Framework for Evaluating Personalized Ranking Systems by Fusing Different Evaluation Measures

Personalized ranking systems — also known as recommender systems — use different big data methods, including collaborative filtering, graph random-walks, matrix factorization, and latent-factor models. With their wide use in various social-network, e-commerce, and content platforms, online platforms and developers are in need of better ways to choose the systems that are most suitable for their use-cases. At the same time, the research literature on recommender systems describes a multitude of performance measures to evaluate the performance of different algorithms. For the end-user however, the large number of available measures do not provide much help in deciding which algorithm to deploy. Some of the measures are correlated, while others deal with different aspects of recommendation performance like accuracy and diversity. To address this problem, we propose a novel benchmarking framework that mixes different evaluation measures in order to rank the recommender systems on each benchmark dataset, separately. Additionally, our approach discovers sets of correlated measures as well as sets of evaluation measures that are least correlated. We investigate the robustness of the proposed methodology using published results from an experimental study involving multiple big datasets and evaluation measures. Our work provides a general framework that can handle an arbitrary number of evaluation measures and help end-users rank the systems available to them.
Application of Blockchain Technology in Higher Education

The development of fast-growing blockchain technology is encourages for the review and rethinking of many of the fundamental facts of our traditional education systems. As experience shows, the blockchain increases security and accessibility tremendously, such concepts as confidence, assessment and identity, currently doesn't satisfy users desires and there a reasonable necessity for the improvements. In this paper, we will consider providing of new methods for the database system in education, implementing the new blockchain technology in education since a new scheme can increase transparency and security in the education system.
Leadership Challenges in Agile Environments

Several companies in the information technology field are progressively adopting Agile methodologies. This change necessarily transforms the management paradigm and poses new challenges to the leadership process. In this sense, this study seeks to explore the challenges perceived by leaders and perceive the competencies they should have to manage projects and teams in an Agile environment. The study used a survey to obtain data from 387 professionals in the information technology sector. The findings reveal the most important skills include the ability to have people skills, team management competencies, provide feedback on employee performance, and have problem-solving skills. Additionally, this study reveals people's management dimension is key to increasing productivity in Agile environments, and furthermore, respondents' years of experience proved to be discriminating in perceiving the importance of these skills. The outcomes of this study are relevant for companies that are migrating to the agile paradigm and are facing challenges in managing the leadership of these projects.
FabAsset: Unique Digital Asset Management System for Hyperledger Fabric

Business is innovating with the advent of blockchain that tokenizes digital assets. To expand the blockchain's potential, Ethereum, a representative permissionless blockchain platform, supports the fungible token (FT) standard ERC-20 and the nonfungible token (NFT) standard ERC-721. Hyperledger Fabric (Fabric), a representative permissioned blockchain platform, proposed FabToken to support tokens in version 2.0.0 alpha. But FabToken contains only FTs, not NFTs. Given the market share in the enterprise blockchains, Fabric needs to support NFTs as soon as possible. This paper presents a unique digital asset management system called FabAsset so that Fabric can run decentralized applications that require NFTs. This paper describes the design of FabAsset, consisting of chaincode and SDK (Software Development Kit), and the prototype of a decentralized signature service leveraging FabAsset to validate its usefulness.
Foreign bank entry and poverty in Africa: Misaligned incentives?

This study investigates the link between industry reliance on external finance, real activity, and foreign bank entry to poverty reduction. Results show that foreign bank entry widens Africa's poverty gap, notably when financing to high value-added industry sectors remains low. Results are consistent with the cream-skimming intermediation hypothesis, occasioning a contraction of credit. By tendering credit to a broader set of firms in industries disproportionately reliant on external finance to grow, foreign banks may partially offset the welfare-reducing effect. However, foreign banks' role in driving down poverty rates is conditional to the host country's institutional quality – an institutional threshold beyond which foreign banks' entry may induce poverty-reducing effects, necessitating a minimum level enabling institutional environment in the host country.
D-SmartML: A Distributed Automated Machine Learning Framework

Nowadays, machine learning is playing a crucial role in harnessing the value of massive data amount currently produced every day. The process of building a high-quality machine learning model is an iterative, complex and time-consuming process that requires solid knowledge about the various machine learning algorithms in addition to having a good experience with effectively tuning their hyper-parameters. With the booming demand for machine learning applications, it has been recognized that the number of knowledgeable data scientists can not scale with the growing data volumes and application needs in our digital world. Therefore, recently, several automated machine learning (AutoML) frameworks have been developed by automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH). However, a main limitation of these frameworks is that they have been built on top of centralized machine learning libraries (e.g. scikit-learn) that can only work on a single node and thus they are not scalable to process and handle large data volumes. To tackle this challenge, we demonstrate D-SmartML, a distributed AutoML framework on top of Apache Spark, a distributed data processing framework. Our framework is equipped with a meta learning mechanism for automated algorithm selection and supports three different automated hyper-parameter tuning techniques: distributed grid search, distributed random search and distributed hyperband optimization. We will demonstrate the scalability of our framework on handling large datasets. In addition, we will show how our framework outperforms the-state-of-the-art framework for distributed AutoML optimization, TransmogrifAI.
Demand-aware Channel Topologies for Off-chain Payments

Off-chain is a common approach to deal with the scalability problem of blockchain networks. It enables users to execute multiple payments without committing each of them to the blockchain by relying on predefined payment channels. A pair of users can employ a payment even without a direct channel between them, via routing the payment through off-chain channels involving other intermediate users. Users together with the off-chain channels form a graph, known as the off-chain network topology. The off-chain topology and the payment characteristics affect network performance such as the average number of intermediate users a payment is routed through, the amount of fees, or channel capacities needed to successfully route payments. In this paper, we study two basic problems in off-chain network design. First, efficiently mapping users to an off-chain topology with a known structure. Second, constructing a topology of a bounded number of channels that can serve well users with associated payments. We design algorithms for both problems and evaluate them based on real data from Raiden, the off-chain extension for Ethereum.
Local dampening

Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the noisy output of numeric queries (e.g., using the Laplace mechanism), based on the notions of global sensitivity and local sensitivity. However, although there has been some work on generic mechanisms for releasing the output of non-numeric queries using global sensitivity (e.g., the Exponential mechanism), the literature lacks generic mechanisms for releasing the output of non-numeric queries using local sensitivity to reduce the noise in the query output. In this work, we remedy this shortcoming and present the local dampening mechanism. We adapt the notion of local sensitivity for the non-numeric setting and leverage it to design a generic non-numeric mechanism. We illustrate the effectiveness of the local dampening mechanism by applying it to two diverse problems: (i) Influential node analysis. Given an influence metric, we release the top-k most influential nodes while preserving the privacy of the relationship between nodes in the network; (ii) Decision tree induction. We provide a private adaptation to the ID3 algorithm to build decision trees from a given tabular dataset. Experimental results show that we could reduce the use of privacy budget by 3 to 4 orders of magnitude for Influential node analysis and increase accuracy up to 12% for Decision tree induction when compared to global sensitivity based approaches.
Deep Unfolding-Aided Gaussian Belief Propagation for Correlated Large MIMO Detection

This paper proposes a deep unfolding-aided belief propagation (BP) for large multi-user multi-input multi-output (MU-MIMO) detection under correlated fading channels. A BP-based detector is a well-known strategy for realizing large-scale MU detection (MUD) with low-complexity and high-accuracy. However, its convergence property is severely degraded under insufficient large-system conditions and spatial fading correlation among RX antenna elements. To compensate for this drawback, we design a trainable Gaussian BP (T-GaBP) having well-organized trainable internal parameters based on the BP structure. These parameters are optimized by the deep learning techniques in the signal-flow graph of unfolded GaBP; this approach is referred to as data-driven tuning. By training the parameters according to the system model, T-GaBP can maintain the high detection capability even in practical system configurations that differ from the ideal uncorrelated massive MIMO assumption. Numerical results show that the proposed detector improves the convergence property and achieves a comparable detection performance to the cutting-edge expectation propagation (EP) detector in correlated MUD, with a lower computational cost.
A scalable private Bitcoin payment channel network with privacy guarantees

While Bitcoin heavily dominates the cryptocurrency markets, its use in micropayments is still a challenge due to long transaction confirmation times and high fees. Recently, the concept of off-chain transactions is introduced that led to the idea of establishing a payment channel network called Lightning Network (LN), which utilizes multi-hop payments. Off-chain links provide the ability to make instant payments without a need to writing to Blockchain. However, LN's design still favors fees, and it is creating hub nodes or relays that defeat the purpose of Blockchain. In addition, it is still not reliable, as not all transactions are guaranteed to be delivered to their destinations. These issues hinder its wide adoption by retailers. To address this issue, in this paper, we argue that the retailers could create a private payment channel network among them to serve their business needs, just like the concept of private Blockchains. The goal is to build a pure peer-to-peer topology that will prevent the formation of hub nodes while also eliminating the need for any relays to increase the robustness of the payments. Assuming off-chain links as edges and retailers as nodes, we formulate the problem as a multi-flow commodity problem where transactions represent the commodities from various sources to destinations. As the multi-flow commodity problem is NP-Complete, we propose a heuristic approach that utilizes Dijkstra's shortest path algorithm for payments in a dynamic way by updating the edge weights when new paths need to be found. The order of transactions is randomized to provide fairness among the retailers. We further extend this approach to guarantee the privacy of payments by forcing all the payments to travel at least three hops. We utilized k-shortest path algorithm to choose from k options that will meet our criterion. The evaluations indicate that the proposed heuristic comes close to an optimal solution while providing scalability and guaranteeing user privacy.
Becoming #Instafamous

Abstract Drawing on Goffman’s (1990 [1959]) metaphor of stage, this paper considers Instagram a frontstage environment where users are cautious of being watched and attune their performance to how they want to be perceived via strategic self-presentation. This understanding of online performance is particularly pertinent in the discussions of bloggers who turn to Instagram to promote their work to new audiences. Examining the self-presentation practices of three fashion bloggers, this paper argues that to gain popularity on Instagram, bloggers utilize the features of formality and informality in the construction of an authentic and likable self-image. Since in the photographs the bloggers’ professional life is usually depicted as distant from their audience’s reality, the accompanying textual caption serves as a means of providing balance for the overall image the poster seeks to present. Consequently, the caption abounds with features of informality, which connote linguistic immediacy and imitate an intimate conversation with peers.
Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions

Anomaly detection focuses on identifying examples in the data that somehow deviate from what is expected or typical. Algorithms for this task usually assign a score to each example that represents how anomalous the example is. Then, a threshold on the scores turns them into concrete predictions. However, each algorithm uses a different approach to assign the scores, which makes them difficult to interpret and can quickly erode a user's trust in the predictions. This paper introduces an approach for assessing the reliability of any anomaly detector's example-wise predictions. To do so, we propose a Bayesian approach for converting anomaly scores to probability estimates. This enables the anomaly detector to assign a confidence score to each prediction which captures its uncertainty in that prediction. We theoretically analyze the convergence behaviour of our confidence estimate. Empirically, we demonstrate the effectiveness of the framework in quantifying a detector's confidence in its predictions on a large benchmark of datasets.
Crafting Adversarial Examples for Deep Learning Based Prognostics

In manufacturing, unexpected failures are considered a primary operational risk, as they can hinder productivity and can incur huge losses. State-of-the-art Prognostics and Health Management (PHM) systems incorporate Deep Learning (DL) algorithms and Internet of Things (IoT) devices to ascertain the health status of equipment, and thus reduce the downtime, maintenance cost and increase the productivity. Unfortunately, IoT sensors and DL algorithms, both are vulnerable to cyber attacks, and hence pose a significant threat to PHM systems. In this paper, we adopt the adversarial example crafting techniques from the computer vision domain and apply them to the PHM domain. Specifically, we craft adversarial examples using the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) and apply them on the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) based PHM models. We evaluate the impact of adversarial attacks using NASA's turbofan engine dataset. The obtained results show that all the evaluated PHM models are vulnerable to adversarial attacks and can cause a serious defect in the remaining useful life estimation. The obtained results also show that the crafted adversarial examples are highly transferable and may cause significant damages to PHM systems.
Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey

Deep learning (DL) has demonstrated superior success in various of applications, such as image classification, speech recognition, and anomalous detection. The unprecedented performance gain of DL largely depends on tremendous training data, high-performance computation resources, and well-designed model structures. However, privacy concerns raise from such necessities. First, as the training data are usually distributed among multiple parties, directly exposing and collecting such large amount of data could violate the laws especially for private information, such as personal identities, medical records, and financial profiles. Second, locally deploying advantageous computation resources is costly for individual party having partial data. Third, direct release of well-trained model parameters threatens the information about training data or the intellectual property of model owners. Therefore, individual party prefers outsourcing computation (data) in a secure way to powerful cloud servers such as Microsoft Azure, and how to enable the cloud servers to perform DL algorithms without revealing data owners&#x2019; private information and model owners&#x2019; valuable parameters is emerging as an urgent task, which is termed as privacy-preserving (outsourcing) DL. In this article, we review the state-of-the-art researches in privacy-preserving DL based on multiparty secure computation with data encryption and summarize these techniques in both training phase and inference phase. Specifically, we categorize the techniques with respect to the linear and nonlinear computations, which are the two basic building blocks in DL. Following a comprehensive overview of each research scheme, we present primary technical hurdles needed to be addressed and discuss several promising directions for future research.