Reviewerly/exHarmony huggingface dataset:
An Exploratory Method for Smooth/Transient Decomposition

We consider a separation problem where the observation consists of the sum of a high amplitude smooth signal and a low amplitude transient signal. We propose a method for decomposition that relies on solving instances of a `constrained filtering problem', which is posed as a convex minimization problem. We provide a fast algorithm for solving the minimization problem, and demonstrate the effectiveness of the scheme for a vital signs monitoring experiment using radar.
SCADA System Framework for Monitoring, Controlling and Data Logging of Industrial Processing Plants

In this research, an advancement of an open source administrative control and information obtaining Supervisory Control and Data Acquisition Systems (SCADA) framework is presented. The framework utilizes the universally useful programming platform, Python. The SCADA framework offers correspondence capacities through an open source OPC-UA worker, which comprehends information trade with control gadgets, for example, PLC, PAC, and so on. The framework additionally gives adaptation to non-critical failure highlights because of the execution of a functioning shortcoming open minded control (AFTC) engineering. The proposed approach plays out a three-layer connection of the CIM model, and offers comparable capacities of business SCADA frameworks.
Secure Gamma Generation For Stream Cipher based on Fuzzy Logic

In this paper, we proposed a new approach for secure bit stream gamma generation based on the concept of fuzzy logic and linear feedback shift registers (LFSRs). We used early-suggested pseudorandom number generator based on fuzzy logic (FPRNG) and suggested the updated version of FPRNG. We improved gamma security by aggregation the seeds with some valuable secure information about FPRNG parameters. We discussed the relationship between the size of the buffer and the degree of the primitive characteristic polynomial of the attached LFSR. We also made an advanced study of the FPRNG membership functions tuning process. We can increase the gamma security as well as stream cipher security by keeping these configurations in secret additionally to seeds.
A Blockchain Architecture for SDN-enabled Tamper-Resistant IoT Networks

In this paper we introduced the design of a novel Blockchain-based IoT network architecture that leverages Software Defined Network (SDN) and Network Function Virtualization (NFV) to secure IoT transactions. We developed an intrusion detection system in a form of Virtualized Network Functions (VNFs) that improves both the scalability and performance of IoT networks. We show how the design of our IoT-focused smart contract can prevent DAO induction attacks in distributed IoT network. We introduce a novel Proof-of-Authority (PoA) consensus algorithm to detect and report suspected IoT nodes and mitigate malicious traffic.
A Blockchain Consensus Mechanism for Marine Data Management System

As the underlying technology of Bitcoin, blockchain has become increasingly mature in financial, medical, logistics and other commercial fields, and has great application potential in the marine field. Its “decentralized” feature can maintain data security and reliability through decentralized methods. Marine data management system is one of the specific application scenarios that blockchain technology is used to protect marine network data information. The consensus algorithm and decentralized idea possessed by the blockchain technology can effectively guarantee the information collaboration of the marine network, and help to improve the collaboration efficiency among multiple parties involved in the safe sharing of marine data. In this paper, according to the analysis of the construction demands of the marine data management system, based on the blockchain technology, a distributed cross-chain transaction, called the global blockchain, is structured, which integrates the marine data collaborative heterogeneous blockchain network. On this basis, aimed at the problems of the existing PBFT consensus algorithm, such as poor dynamic addition and deletion of nodes, and large communication overhead, etc., a global consensus algorithm adapted to the marine data global blockchain network is designed for optimization of the checkpoint mechanism and view change mechanism as well as reduction of the amount of transmitted information in these two processes of the system. The simulation results show that the algorithm can effectively guarantee the consensus efficiency and the trustworthiness of the proxy nodes, realize the efficient sharing of marine data, and support the design and implementation of the blockchain-based marine data management system.
Interactive Text Graph Mining with a Prolog-Based Dialog Engine

On top of a neural network-based dependency parser and a graph-based natural language processing module we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document. We reorganize dependency graphs to focus on the most relevant content elements of a sentence and integrate sentence identifiers as graph nodes. Additionally, after ranking the graph we take advantage of the implicit semantic information that dependency links and WordNet bring in the form of subject-verb-object, is-a and part-of relations. Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements. The open-source code of the integrated system is available at https://github.com/ptarau/DeepRank . Under consideration in Theory and Practice of Logic Programming (TPLP).
WITHDRAWN: Sports image recognition based on FPGA and Machine Learning

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Cloud Allocation Technique: A Comparative Study

In cloud computing, the allocation of resources is a foremost alarm. As the cloud is heterogeneous, so many parameters can injure the virtual machine like cost, consumption of time, quality of services etc. When a huge request comes, the grouping of a virtual machine is important to execute the job. In our work, different resource allocation strategies have been discussed and their comparison with optimal cost. How the physical mapping of resources is completed over virtual resources is also considered. This work also depicts the different challenges involved in modern-day cloud computing.
Performance Evaluation of Viral Infection Diagnosis using T-Cell Receptor Sequence and Artificial Intelligence

The adaptive immune system expresses millions of different receptors that detect and fight pathogens encountered throughout life. These receptors are encoded by unique DNA sequences that allow immune cells to express millions of different receptors. High-throughput sequencing and analyses of immune cell receptor sequences present a unique opportunity to inform our understanding of immunological responses to infections and to evaluate vaccine efficacy. Even after the infection is eliminated, pathogen-specific immune cells and their receptor sequences are present at higher frequencies than prior to infection, and their increase in frequency prevents secondary infections. As a result of their persistence in the body, they may be useful for diagnosing infections and evaluating vaccine efficacy as a stable biomarker. However, this process requires thorough analysis of massive datasets at an accuracy beyond traditional statistical tests to diagnose infectious statuses based on sequence analyses. Here we evaluate various machine learning and deep learning algorithms to measure the performance of the identification and diagnosis of specific viral infections or vaccination statuses using the publicly available mouse (monkeypox infection and smallpox vaccination) and human (cytomegalovirus serostatus) T-cell receptor sequenced datasets. Our intensive experiments hold the potential for effective screening of disease status, including recently encountered strains like the ongoing SARS-CoV-2 pandemic.
Based on the Research of Texture Image Restoration Technology

Nowadays people have higher and higher requirements for image quality, and computer-aided assistance can restore the original image information more precisely and accurately. This article focuses on the repair of partial information damage in complex images. It uses texture-based image repair algorithms to repair images with large damaged areas. By improving the CIEI (Cross Isophotes Exemplar-based Inpainting) algorithm, it makes it suitable for Texture-rich image restoration has better applicability.
Deciphering Cryptocurrencies by Reverse Analyzing on Smart Contracts

As the initial application of blockchain, Bitcoin is the most famous blockchain application as cryptocurrency and it has led to a misconception that blockchain can only be used to issue cryptocurrency. Meanwhile, the growing number of companies and organizations issuing their own cryptocurrencies based on blockchain has led to more confirmation that the main role of blockchain is to be used to issue cryptocurrency. However, blockchain is originally intended to achieve decentralized application, and cryptocurrency is just a byproduct of it. In this paper, we figured out how many smart contracts on Ethereum are involved issuing cryptocurrency, how many people are using these smart contracts and what is the revenue status of these smart contracts. To address these question, we collect more than 140,000 open source smart contracts and 16 million closed source smart contracts from Ethereum, and then identify the smart contracts that involve issuing cryptocurrency (called IC-contract) by determining whether the contract implements ERC20 standard. For the closed source smart contracts, we propose a reverse hash mapping method to determine the IC-contract at bytecode level. Additionally, we also obtain the transactions of all the IC-contracts and find that the open source IC-contracts have more users. Analyzing the IC-contracts based on transactions reveals that most of the IC-contracts have exceptionally low user activity, but most of them are profitable actually.
Usability Study of Electronic Product with Healthy Older Adults Based on Product Semantic

As a typical example of the electronic products with multi- and high- functions, integrated- function microwave oven is said to bring a much more convenient and brand-new lifestyle; however, in the meanwhile, it also compels their users to face many problems in using due to its multifunction. In this study, we conducted a usability test with healthy older adults to determine how well the elderly users can use the microwave oven, and what kinds of processes they are taking as their problem-solving. Based on the results of the usability test, we summarized several types of difficulties-in-use and tried to provide the design solutions based on product semantics, especially focusing on the process of transition between exploration and reliance.
Multimodal Affordances of Immersive Virtual Reality for Visualising and Learning Molecular Interactions

Immersive Virtual Reality (VR) is increasingly being used as an educational tool to help students visualise complex structures and phenomena. The use of VR is particularly useful for science animation that involves sub-microscopic and abstract processes in a 3D environment, such as the interactions of molecules, electrons and forces. However, few research studies have explored how immersive VR can support science visualisation compared to existing tools, notably computer animation on a flat screen. The study in this chapter used a semiotic perspective to examine the multimodal affordances of a 'virtual' mode of representation made possible by VR technology. The study involved the design and development of a VR animation to show the structure and reaction of an enzyme called acetylcholinesterase. A group of chemistry undergraduates was recruited to take part in a learning activity using the VR animation in pairs. Based on an analysis of the learners interacting with the VR animation, this chapter reports on the affordances of VR compared to other tools in five specific areas: viewing, sequencing, modelling, scaling and manipulating. Through the use of case studies, it also illustrates and discusses how these affordances facilitate student learning of molecular interactions.
Guest Editor’s Introduction: Software Mathematical Models for Human Understanding

The unrelenting trend of larger and larger sizes of Software Systems and data has made software comprehensibility an increasingly difficult problem. However, a tacit consensus that human understanding of software is essential for most software related activities, stimulated software developers to embed comprehensibility in their systems’ design. On the other hand, recent empirical successes of Deep Learning neural networks, in several application areas, seem to challenge the tacit consensus: is software comprehensibility a necessity, or just superfluous? This introductory paper, to the 2020 special issue on Theoretical Software Engineering, offers reasons justifying our standpoint on the referred controversy. This paper also points out to specific techniques enabling Human Understanding of software systems relevant to this issue’s papers.
Evaluation of security risks using Apriori algorithm

The progress of IT technologies offers many means to collect and store an extremely large quantity of data and conveys a prodigious quantity of information in several sectors of activity. However, this progress is not only exposed to classic operational risks such as fire or blackouts, but also to various viruses and data theft. These extremely technologically complex risks have risen a big challenge at responding to a large-scale of intangible threats within an industry of perpetual change. Wherefore, the value of Security Risk Assessment "SRA" at ensuring the protection of the organizations' business services. However, conducting SRA is difficult and time-consuming and its results may not project the risky behaviors which often leads to unnecessary controls being implemented. Therefore, we tolerate using the Apriori algorithm as a prominent approach accurately determining the threat sources emerging within the risky behaviors.
Integrative Deep Learning for PanCancer Molecular Subtype Classification Using Histopathological Images and RNAseq Data

Deep learning has recently become a key methodology for the study and interpretation of cancer histology images. The ability of convolutional neural networks (CNNs) to automatically learn features from raw data without the need for pathologist expert knowledge, as well as the availability of annotated histopathology datasets, have contributed to a growing interest in deep learning applications to histopathology. In clinical practice for cancer, histopathological images have been commonly used for diagnosis, prognosis, and treatment. Recently, molecular subtype classification has gained significant attention for predicting standard chemotherapy's outcomes and creating personalized targeted cancer therapy. Genomic profiles, especially gene expression data, are mostly used for molecular subtyping. In this study, we developed a novel, PanCancer CNN model based on Google Inception V3 transfer learning to classify molecular subtypes using histopathological images. We used 22,484 Haemotoxylin and Eosin (H&E) slides from 32 cancer types provided by The Cancer Genome Atlas (TCGA) to train and evaluate the model. We showed that by employing deep learning, H&E slides can be used for classification of molecular subtypes of solid tumor samples with the high area under curves (AUCs) (micro-average= 0.90; macro-average=0.90). In cancer studies, combining histopathological images with genomic data has rarely been explored. We investigated the relationship between features extracted from H&E images and features extracted from gene expression profiles. We observed that the features from these two different modalities (H&E images and gene expression values) for molecular subtyping are highly correlated. We, therefore, developed an integrative deep learning model that combines histological images and gene expression profiles. We showed that the integrative model improves the overall performance of the molecular subtypes classification ((AUCs) micro-average= 0.99; macro-average=0.97). These results show that integrating H&E images and gene expression profiles can enhance accuracy of molecular subtype classification.
An Improvement for Capsule Networks Using Depthwise Separable Convolution

Capsule Networks face a critical problem in computer vision in the sense that the image background can challenge its performance, although they learn very well on training data. In this work, we propose to improve Capsule Networks' architecture by replacing the Standard Convolution with a Depthwise Separable Convolution. This new design significantly reduces the model's total parameters while increases stability and offers competitive accuracy. In addition, the proposed model on $64\times64$ pixel images outperforms standard models on $32\times32$ and $64\times64$ pixel images. Moreover, we empirically evaluate these models with Deep Learning architectures using state-of-the-art Transfer Learning networks such as Inception V3 and MobileNet V1. The results show that Capsule Networks can perform comparably against Deep Learning models. To the best of our knowledge, we believe that this is the first work on the integration of Depthwise Separable Convolution into Capsule Networks.
Suitability of calibrated X-ray fluorescence core scanning for environmental geochemical characterisation of heterogeneous sediment cores

Multi-element analysis of discrete samples via X-Ray fluorescence or inductively coupled plasma spectrometry is commonly used to characterise the composition of solid geo-materials for environmental geochemical characterisation. Conventional geochemical analysis of individual samples is time consuming and costly, which often results in low-resolution sampling with the danger of missing crucial information. X-ray fluorescence Core Scanning (XCS) provides an alternative method to obtain elemental information, which can be potentially used quantitatively when combined with the Multivariate Log-ratio Calibration (MLC) approach. The suitability of the XCS-MLC method was tested for environmental geochemical characterisation on four terrestrial Holocene-Pleistocene sediment cores that have a variable lithology (clay, sand, peat, with variable calcareous content), were stored at ambient room conditions and scanned post sampling. Element contents based on XCS-MLC and conventional geochemical analysis proved to be comparable (R2 > 0.5) for Al, Ca, Cr, Fe, K, Sr, Mn, Ni, Pb, Rb, S, Si, Ti, Zn, Zr, and also for Br as proxy for organic matter. For As, Cu and Ba the correlations were less satisfactory (R2 < 0.4) partially due to the low concentration ranges present in these sediments. For some samples aberrant high values for Ca, Fe, S, and Zn were introduced by application of the MLC method due to extrapolation outside the MLC-calibrated range. Similar to the conventional element analyses the XCS-MLC approach has the ability to retrieve quantitative element contents and enable the calculation of mineral phases such as calcium carbonate and reactive iron species. Both conventional and XCS-MLC methods reproduced the average contents per sediment layer and mostly their ranges. Nevertheless, local features such as diagenetic enrichments were not always evident from the discrete samples. Thus, a better understanding of the spatial heterogeneity in geochemical and mineralogical contents within the sediment layers was obtained with the additional XCS-MLC data. Our study shows that also cores stored under unfavourable conditions can be reliably re-analysed with XCS to generate high resolution records.
Mapping of the 2+1 Dirac–Moshinsky Oscillator Coupled to an External Isospin Field Onto the Jaynes–Cummings Model

We map the 2+1 Dirac–Moshinsky oscillator (2+1 DMO) coupled to an external isospin field onto the Jaynes–Cummings model (JCM), which describes the interaction between two two-level systems and a quantum single-mode field. We obtain the time-dependent wave function and the density matrix in two cases. As an initial state, we consider the quantum number state in the first case and the coherent state in the second case. We study the effect of the detuning parameter and the coherence angle on the entanglement and the population inversion. The obtained results show that the coherent state provides a better description than the number state for both the entanglement and the population inversion.
A Secure Algorithm for Deep Learning Training under GAN Attacks

Deep neural networks have outperformed traditional machine learning approaches for many tasks, and are the tool of choice in many fields. However, directly applying these techniques in fields that deal with private data is challenging. The reason is that a third-party, which organizations may not trust, usually needs to centrally collect private data. To overcome this challenge, researches have proposed distributed training algorithms that allow multiple users to collaboratively train their local deep learning models without sharing private datasets. However, these approaches are vulnerable to recently proposed attacks where a malicious user can replicate private data from another user by compromising the collaborative training algorithm. In this paper, we propose a privacy-preserving distributed deep learning algorithm that allows a user to leverage the private datasets from a group of users while protecting its privacy. Our algorithm prohibits this user from ever sharing the parameters of its model, and thus it prevents malicious users from compromising the training and replicating the user's private data. We conduct extensive experiments and observe that our algorithm can achieve a model accuracy of 95.18 %, which is the same accuracy that previous approaches that are vulnerable to attacks can achieve.
Fragments online: virtual performatives in recreational discourse

Virtual performatives constitute conspicuous digital fragments as they appear across modes of computer-mediated communication and social media platforms, irrespective of whether these are text-only or multimodal, and whether reciprocity is expected. Typical instances include typographically signalled, stand-alone predications in the third-person simple present tense which refer to the technology user. For instance, by typing in a performative predication such as *waves*, the user is instantaneously enacting the virtual action of waving. This article examines the form and function of such fragments in light of their use in two modes of recreational online discourse in English: discussion boards and microblogging. The study adds to the knowledge of textual fragments and has implications for the understanding of the intricacies of online discourse.