Reviewerly/exHarmony huggingface dataset:
A Processing Delay Tolerant Workflow Management in Cloud-Fog Computing Environment (DTWM_CfS)

During the past few years, powerful Fog computing has become a perfect counterpart to the cloud computing where the resources are dealing comfortably with computational, communicational and data needs of recently advanced cloud based Internet of Things (IoT) applications. Studying the interoperability and collaboration among the Fog (Edge) and Cloud (Core) for collective service provisioning has recently gained a considerable attention of researchers. Where, a delay tolerant management of IoT workflows and distributing their tasks among the fog and cloud node was became an important issue. As a possible defeat for this, a successive association among native fog nodes and the hired cloud nodes were exploited. In this work, a heuristic-based delay tolerant task scheduling and workflow management system for Cloud-fog computing (DTWM_CfS) were formulated and proposed for distributing the workflow tasks of IoT applications to the most successive fog or cloud resource nodes for execution. The proposed DTWM_CfS ensures efficient utilization of underlying resources by diminishing the average makespan time and total cost up to 11.23% and 10.6% respectively when compared with other existing methods. Simulation results of DTWM_CfS also ensure a better Cost Makespan Trade-off gain up to 42.6 % when compared with other existing methods.
An Engineer's Brief Introduction to Microwave Quantum Optics and a Single-Port State-Space Representation

Classical microwave circuit theory is incapable of representing some phenomena at the quantum level. To include quantum statistical effects, various theoretical treatments can be employed. Quantum input-output network (QION) theory is one such treatment. Another formalism, called SLH theory, incorporates scattering matrices (S), coupling vectors (L), and system Hamiltonians (H). These theoretical treatments require a reformulation of classical microwave theory. To make these topics comprehensible to an electrical engineer, we demonstrate some underpinnings of microwave quantum optics in terms of microwave engineering. For instance, we equate traveling-wave phasors in a transmission line (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ) directly to bosonic field operators. Furthermore, we extend QION to include a state-space representation and a transfer function for a single port quantum network. This serves as a case study to highlight how microwave methodologies can be applied in open quantum systems. Although the same conclusion could be found from a full SLH theory treatment, our method was derived directly from first principles of QION.
Effect of mono corpus quantity on statistical machine translation Indonesian – Lampung dialect of nyo

Abstract Lampung Province is located on the island of Sumatera. For the immigrants in Lampung, they have difficulty in communicating with the indigenous people of Lampung. As an alternative, both immigrants and the indigenous people of Lampung speak Indonesian. This research aims to build a language model from Indonesian language and a translation model from the Lampung language dialect of nyo, both models will be combined in a Moses decoder. This research focuses on observing the effect of adding mono corpus to the experimental statistical machine translation of Indonesian - Lampung dialect of nyo. This research uses 3000 pair parallel corpus in Indonesia language and Lampung language dialect of nyo as source language and uses 3000 mono corpus sentences in Lampung language dialect of nyo as target language. The results showed that the accuracy value in bilingual evalution under-study score when using 1000 sentences, 2000 sentences, 3000 sentences mono corpus show the accuracy value of the bilingual evaluation under-study, respectively, namely 40.97 %, 41.80 % and 45.26 %.
Exploring the possibilities of Extended Reality in the world of firefighting

Firefighting is a crucial part of the Navy’s training program, as it must ensure the safety on board. This training is dangerous, expensive and environmentally unfriendly. Therefore, the Navy is looking for a safer form of training that can enhance the current one. Extended Reality technology offers new ways of training, with the promise to alleviate issues related to training danger, costs and environmental pollution. In this work, we develop and evaluate a Virtual Reality simulator and a proof of concept of a Mixed Reality simulator, together with a firehose controller adapted to the needs of the Navy’s firefighting training program.
Why Fixed Costs Matter for Proof-of-Work Based Cryptocurrencies

We assess how the cost structure of cryptocurrency mining affects the response of miners to exchange rate fluctuations and the immutability of cryptocurrency ledgers that rely on proof-of-work. We show that the amount of mining power supplied to currencies that rely on specialized hardware, such as Bitcoin, responds less to adverse exchange rate shocks than other currencies respond to such shocks, a fact that is instrumental to avoiding double-spending attacks. The results may change if mining equipment used for one cryptocurrency can be transferred to another. For smaller currencies with low exchange rate correlation, transferability eliminates the protection that fixed costs provide. Our results weaken doomsday predictions for Bitcoin and other cryptocurrencies with declining block rewards.
BotEye: Botnet Detection Technique Via Traffic Flow Analysis Using Machine Learning Classifiers

Botnet is a prevalent threat among the Internet that always keep on proliferating. They can mow down an entire network within a blink of an eye. Different detection techniques have been proposed to detect botnets but botmasters always keep on revamping these botnets making it onerous for detection techniques that are based on command and control (C&C) protocols and structures. Botnets also utilize encrypted communication during their propagation. As a result, a technique irrespective of the protocols and propagation mechanisms used needs to be developed. Also, the technique should be able to detect encrypted botnets. In this paper, BotEye is proposed that is a botnet detection technique based on the traffic flow behavior of the network. The fringe benefit of using a flow-based approach is that only a fraction of the total network traffic flow needs to be analyzed. The technique suggested is heedless towards the C&C protocols and structures used. It can even detect encrypted botnets as it is independent of the payload information. BotEye makes use of four features to differentiate between malicious and benign traffic. Furthermore, BotEye is evaluated against the CTU-13 dataset, using three different machine learning classifiers that incorporates a stratified 10-fold cross-validation technique. The evaluation process shows that BotEye achieved the best results, i.e., 98.5% accuracy along with a low false-positive rate when the time window is set at 240s.
Privacy Preserving Architecture for Healthcare Information Systems

Protecting patient private data in the healthcare industry is an important subject. However, it is not an easy task. Patient data is important in the process of providing quality healthcare. Therefore, Healthcare regulatory bodies issued regulations that require securing patient information and making it accessible only by authorized users. Today, many digital information systems handle patients' data. However, they introduce some privacy and security concerns. This issue is going to be the focus of this project. In this paper, we design and test a system that allows the sharing of patients' data while addressing privacy concerns by designing a digital information system that restricts access to patients' information only when the patient mobile approves that access. Moreover, the proposed architecture is designed to protect the patients' data from breaches by taking advantage of the availability and flexibility of cloud computing. To test the proposed architecture, a prototype is built and tested. The experiments' results were satisfactory. This project tackles to a big problem in the healthcare industry by utilizing the latest technologies in mobility and cloud computing and data protection.
Collisionless Fast Pattern Formation Mechanism for Dynamic Number of UAVs

Unmanned Aerial Vehicle (UAV) is an emerging technology that assists in various automated activities where human involvement is minimal. Though individual UAVs are extremely useful entities, their productivity can further be increased by deploying multi-UAVs. Pattern formation among multi-UAVs is one of the key functionalities in a swarm environment that is essential for several UAV missions namely military expedition, search and rescue operations, drone based delivery mechanisms etc. In this paper, to facilitate pattern formation among UAVs in an effective manner, a Time-Interleaved Pattern Formation (TIPF) Mechanism is proposed. The existing systems work for a fixed number of drones whose pattern switching mechanisms are preprogrammed. However, the TIPF mechanism enables switching patterns among dynamic number of drones (UAVs) on the fly by inducing a small delay between each UAV movement. The TIPF mechanism avoids collision, which occurs due to the simultaneous movement of UAVs. The proposed TIPF mechanism encompasses a Centralised Coordinate Calculation (CCC) algorithm to easily calculate the coordinates of UAVs in a given pattern. Further, this mechanism has also been simulated and tested in our proposed virtual IP based Software In The Loop (V-SITL) environment. This proposed V-SITL environment offers increased scalability on account of the entire UAV system being simulated in a single computer. The TIPF mechanism has been simulated for 8 drones in a dynamic manner for square and triangle patterns. The simulation results show that the pattern formation time avoids collision in a time interleaving rate of 52.63%.
PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>ϵ</mi> </mrow> <mrow> <mtext>total</mtext> </mrow> </msub> </math> -PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme.
Training set fuzzification based on histogram to increase the performance of a neural network

This article describes a new approach which uses a histogram to fuzzify variables. We used a linguistic expression to form a training set output vector. The whole fuzzification process of the training set output vector is described in detail. This proposed method was verified on a real data set. We found out that the adaptation of a neural network by fuzzified output vectors has a considerably lower prediction error rate compared with another one without such transformation. Another advantage of the fuzzification approach is that only one neural network can be used for more various data sets with a high range of data attributes (units, thousands, millions). The proposed improvements increase the performance of neural networks, which is presented in the final part.
Exploiting Time in Adaptive Learning from Educational Data

Virtual Learning Environments (VLEs) are web platforms where educational content is delivered, along with tools to support individual study. Logs that record how students interact with the platform are collected daily, so automated methods can be used to extract useful knowledge from these data. All stakeholders involved in the learning activities of the VLEs, especially students and teachers, can benefit from the insights derived from the educational data and valuable information can be extracted using machine learning algorithms. Usually, educational data are examined as stationary data using conventional batch methods. However, these data are non-stationary by nature and could be better treated as data streams. This paper reports the results of a classification study in which Random Forests, applied in both batch and adaptive mode, are used to build a model for predicting student exam failure/success. In addition, an analysis of the most important features is performed to detect the most discriminating attributes related to the student’s result. Experiments conducted on a subset of the Open University Learning Analytics (OULAD) dataset demonstrate the reliability of the adaptive version of Random Forest in accurately classifying the evolving educational data.
OSLDetector

Using open-source libraries can provide rich functions and reduce development cost. However, some critical issues have also been caused such as license conflicts and vulnerability risks. In this paper, we design and implement an open-source libraries detection tool OSLDetector which uses methods of matching features to detect third-party libraries for multi-platform software in binaries. We took a series of methods such as filtering features and novelty building an internal clone forest to cope with the challenge of feature duplication. The tool can also provide the conflict of licenses and identify possible corresponding vulnerabilities, so these potential risks can be resolved and avoided. To evaluate the efficiency of OSLDetector, we collect 5K libraries containing 9K versions and manage their respective license type and existing vulnerabilities. The experimental results with a precision of 96% and recall of 92.3% show that OSLDetector is effective and outperforms similar tools.
Waveform Classification in Radar-Communications Coexistence Scenarios

In this paper the problem of recognizing waveform and modulation is addressed in radar-communications coexistence and shared spectrum scenarios. We propose a deep learning method for waveform classification. A hierarchical recognition approach is employed. The received complex-valued signal is first classified to single carrier radar, communication or multicarrier waveforms. Fourier synchrosqueezing transformation (FSST) time-frequency representation is computed and used as an input to a convolutional neural network (CNN). For multicarrier signals, key waveform parameters including the cyclic prefix (CP) duration, number of subcarriers and subcarrier spacing are estimated. The modulation type used for subcarriers is recognized. Independent component analysis (ICA) is used to enforce independence of I- and Q-components, and consequently significantly improving the classification performance. Simulation results demonstrate the high classification performance of the proposed method even for orthogonal frequency division multiplexing (OFDM) signals with high-order quadrature amplitude modulation (QAM).
On the Role of Explainable Machine Learning for Secure Smart Vehicles

The concept of mobility is experiencing a serious transformation due to the Mobility-as-a-Service paradigm. Accordingly, vehicles, usually referred to as smart, are seeing their architecture revamped to integrate connection to the outside environment (V2X) and autonomous driving. A significant part of these innovations is enabled by machine learning. However, deploying such systems raises some concerns. First, the complexity of the algorithms often prevents understanding what these models learn, which is relevant in the safety-critical context of mobility. Second, several studies have demonstrated the vulnerability of machine learning-based algorithms to adversarial attacks. For these reasons, research on the explainability of machine learning is raising. In this paper, we then explore the role of interpretable machine learning in the ecosystem of smart vehicles, with the goal of figuring out if and in what terms explanations help to design secure vehicles. We provide an overview of the potential uses of explainable machine learning, along with recent work in the literature that has started to investigate the topic, including from the perspectives of human-agent systems and cyber-physical systems. Our analysis highlights both benefits and criticalities in employing explanations.
Characterization of EM faults on ATmega328p

We investigate the effects of EM pulses on an ATmega328p 8-bit microcontroller. We establish which areas of the chip are sensitive to EM pulse injection and describe the fault model for these sensitive areas. Furthermore, we compare our results to those of a previous study, which examined the effects of laser fault injection on the same device.
Indistinguishability Operators via Yager t-norms and Their Applications to Swarm Multi-Agent Task Allocation

In this paper, we propose a family of indistinguishability operators, that we have called Yager Possibilitic Response Functions (YPRFs for short), as an appropriate tool for allocating tasks to a collective of agents. In order to select the best agent to carry out each task, we have used the so-called response threshold method, where each agent decides the next task to perform following a probabilistic Markov process and, in addition, involves a response function which models how appropriate the task is for the agent. In previous works, we developed a new response threshold method which incorporates the use of indistinguishability operators as response functions and possibility theory instead of probability, for task allocation from a very general perspective without taking into account the specific characteristics of the agents except their limitations to carry out a task. Such an allocation is modelled by means of possibilistic, instead of probabilisitic, Markov chains. We show that possibilistic Markov chains outperform its probabilistic counterparts for the aforementioned propose. All the indistinguishability operators considered in previous papers were not able to take into account the agents’ restrictions for moving from a task to another one, or equivalently to carry out a task instead of another one. In order to avoid this handicap, we introduce a new kind of response functions, YPRFs, which are modelled by means of indistinguishability operators obtained via Yager t-norms. This new type of response functions drops to zero when an agent, due to its limitations, is not able to execute a task and, therefore, is able to model a generic multi-agent system with restrictions. The performed simulation, under Matlab, allows us to compare the results obtained using the new YPRFs with those obtained applying celebrated response functions also generated via indistinguishability operators (that we call Original Possibilitic Response Functions, OPRFs for short). Moreover, the results confirm that the YPRFs are able to take into account agent’s restrictions while the OPRFs are not able. Finally, in the light of the experimental results, we can confirm that those systems modelled.
Dexter the College FAQ Chatbot

Chatbot is a new and upcoming technology that has great demand in various industries. The main goal of a chatbot is to create a human-like conversation between a human and a machine, to reduce the work stress. The chatbot can be implemented in any industry easily, unlike any other product where the products need to be developed and tested before switching platforms. In colleges, especially during the time of admission, reception gets crowded and people have to wait to get their queries solved. If any person wants to know about the college, then he/she has to travel to college. Although every college has its website, not everybody can find the answer to their query. Colleges are not working on weekends, so if someone wants to visit or call reception to get their query answered they will have to wait until any working weekday. To solve these problems, we will create an AI chatbot. This chatbot will be embedded on the college website and will be able to answer any college-related query easily. Chabot will be able to answer multiple persons at the same time, people don't have to visit the college to get their query solved and it will be available 24/7.
AutAvailChain: Automatic and Secure Data Availability through Blockchain

The trust centralization in current data sharing systems restricts the owner's control over their data. Furthermore, the owner's intervention to authorize his/hers data access for each request makes frequent access to popular data tiresome. In this paper, we propose AutAvailChain, an architecture based on software defined networking (SDN) and blockchain to provide secure, automatic, and distributed sharing of IoT data. We develop a prototype using the Hyperledger Fabric platform to implement the blockchain and a smart contract. The results show a quick, secure, and excellent performance of dozens of transactions per second.
IIoT Benefits and Challenges with BlockChain

The millions of people in the world will be using billions of devices in the future Internet and they all are controlled by a centralized or decentralized system. The Industrial Internet of Things (IIoT) provides an era of huge information sharing, storage and processing. The limitations with IIoT are security and reliability of data. Blockchain technology has emerged as the key player to provide the solution to data reliability and security. The data sharing between IIoT-based distributed centres will be enormous in the upcoming internet future and will be the bottleneck for most of the industries implementing IIoT. In this paper, we focus on the relationship between blockchain-IIoT integration challenges and optimal solutions for the improvement of IIoT services using IoT boards comparison.
Is BlockChain Mining Profitable in the Long Run?

Blockchain technologies are at the heart of digital innovation and are a harbinger of Industry 4.0.Consequently, popular press and academic researchers alike have focused on its importance.Yet blockchain technologies' most promising efforts, cryptocurrency and smart contracts, are underpinned by blockchain mining.The blockchain mining service is undergoing change, cryptocurrencies like Ethereum and others are nearing the end of their minting.Smart contracts are in their infancy.The financial impetus for providing the mining service has changed.Here, we add to the literature through a deep financial analysis of blockchain mining regarding its long-term financial viability.Our methods include a financial cost analysis and an analysis of the financial viability of cryptocurrency through focus on Ethereum.It is found that blockchain miners, despite initial profitability, cannot maintain sustainable financial viability without substantial fees.This work is important to those academics who focus on understanding how service technologies and products underpin Industry 4.0.Finally, this paper contributes to the practitioners' decision-making process to embrace blockchain mining as a technological entrepreneur.