Reviewerly/exHarmony huggingface dataset:
Component-based Implementation of Cyberattack Simulation Models

One of the fundamental concepts of software engineering today is reusability. The capability to reuse selected components and assemble them in such way to satisfy specific user requirements is a challenge that is also faced by the simulation and modeling community. Although the primary interest is in the ability to combine and recombine components, there is also a problem on locating and selecting the components that will best fit the requirements. The component selection process has been proven to be NP-complete. This research focuses on the identification of components specific to Petri Nets with Players, Strategies, and Cost (PNPSC) which are used to model cyberattack patterns. PNPSC models are briefly described and a process of determining their basic components is presented. Such initial decomposition requires the previous knowledge of the intended granularity, level of functionality of the component. This study proposes the granularity at two levels: fine-grain, when a component is defined by a section of an existing PNPSC pattern or coarse-grain, when the component is an existent full PNPSC pattern. Formal specification of the definition of the components, selection and the process of combining them in a new model are defined and a set of examples demonstrates its applicability to cyberattack simulation models.
Background Modeling by Shifted Tilings of Stacked Denoising Autoencoders

The effective processing of visual data without interruption is currently of supreme importance. For that purpose, the analysis system must adapt to events that may affect the data quality and maintain its performance level over time. A methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise, is presented in the paper. The system is based on a stacked denoising autoencoder which extracts a set of significant features for each patch of several shifted tilings of the video frame. A probabilistic model for each patch is learned. The distinct patches which include a particular pixel are considered for that pixel classification. The experiments show that classical methods existing in the literature experience drastic performance drops when noise is present in the video sequences, whereas the proposed one seems to be slightly affected. This fact corroborates the idea of robustness of our proposal, in addition to its usefulness for the processing and analysis of continuous data during uninterrupted periods of time.
A Multicast Scheme Based on Fidelity Metrics in Quantum Networks

A multicast scheme based on fidelity metrics in quantum networks is proposed in this paper. First of all, according to the fidelity of copies in an asymmetric mechanism of 1→ 1+ 1+ 1+1 which is derived, the way of information transmission in the quantum networks is defined: one is one-to-one transmission based on the teleportation and the other is one-to-many lossy transmission based on quantum cloning mechanism. Second, in a specified quantum network which is a connected graph, a source node can randomly assign multicast group members. Then, with constraints of the number of replicas and the maximum number of hops of the shortest path, an optimal multicast tree is constructed to obtain maximum fidelity. Finally, the simulation results show that the proposed scheme can select the optimal path from the source node to the multicast members for the specified quantum network. With the increase of multicast group members, the average value of information fidelity obtained by all members decreases. Compared with the multicast tree constructed by the KMB algorithm, which is used to construct the Steiner tree in classical communication, the proposed schemes make the information fidelity obtained by multicast group members improve significantly.
Partial sub-pixel and pixel-based alteration mapping of porphyry system using ASTER data: regional case study in western Yazd, Iran

This study uses remote sensing image-processing to enhance recognition of areas with mineral abundance halo, which can be associated with porphyry mineralisation of the Urumieh-Dokhtar Magmatic Belt (UDMB) in western Yazd. Granitoid intrusions of Oligocene-Miocene age in UDMB hosts the most important Porphyry Copper Deposits (PCDs) of Iran such as Darezereshk and Aliabad PCDs. Diagnostic spectral absorption features of six shortwave infrared (SWIR) and three visible and near-infrared (VNIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were applied to determine the alteration minerals and their abundance. In this research reflectance data from various representative alteration types throughout the study area were measured by the FieldSpec-3 spectroradiometer and applied for mapping minerals. Different remote sensing analytical techniques, including the Principal Component Analysis (PCA), band ratio, relative absorption band depth, and MTMF, were applied to enhance mapping the alteration minerals. Field works including rock sampling, petrography and field spectroradiometer followed up the alteration mapping. Field observation implies that, the vein-type mineralisation above the cupola of this porphyry system is preserved in the western part, although it was eroded in the eastern area. Consequently, PCDs potential is expected to be higher in the western area.
Multiresolution co-clustering for uncalibrated multiview segmentation

We propose a technique for coherently co-clustering uncalibrated views of a scene with a contour-based representation. Our work extends the previous framework, an iterative algorithm for segmenting sequences with small variations, where the partition solution space is too restrictive for scenarios where consecutive images present larger variations. To deal with a more flexible scenario, we present three main contributions. First, motion information has been considered both for region adjacency and region similarity. Second, a two-step iterative architecture is proposed to increase the partition solution space. Third, a feasible global optimization that allows to jointly process all the views has been implemented. In addition to the previous contributions, which are based on low-level features, we have also considered introducing higher level features as semantic information in the co-clustering algorithm. We evaluate these techniques on multiview and temporal datasets, showing that they outperform state-of-the-art approaches.
Trustworthy Product Lifecycle Management Using Blockchain Technology—Experience from the Automotive Ecosystem

Rooted on the principle “from cradle to grave”, the lifecycle-driven approach to managing products like automobiles and related services has been recognised as a pivotal approach in research and practice [5, 15]. Digital technologies have continuously fostered the further development of product lifecycle management (PLM) in recent decades [17]. Nowadays, novel disruptive technologies offer even more important advances for providers and users of such solutions alike [14]. For the case of the automotive industry, intelligent products have created seamless visibility over the vehicle operations [9], big data techniques allow for the creation of sound insights [10], and blockchain technology holds the potential for trustworthy vehicle data management [2, 7]. The economic potential of preventing fraud and providing correct data is vast. Solely for the case of mileage manipulation, financial damage of around 9 billion Euro is estimated for the European Union [3]. Accurate data establishing the basis for digital services potentially delivers a global revenue in the 100 billion Euro range [11]. While these benefits of decentralised and encrypted data management are clear in theory [6, 18], less knowledge is available about the practical implementation of such blockchain-based solutions [2, 7]. The purpose of this case study [19] is to reflect experiences from a project in the setting of a leading automotive player which targets development and roll out of a trustworthy product lifecycle management using blockchain technology. Specifically, the study at hand mirrors insights from the automotive ecosystem focusing on the business-to-business context, involving fleets, OEMs, and repair shops. Such a case study seems valuable as research and practice call for real-world insights on blockchain applications especially outside the financial industry [1]. After this abstract, the second part of the case study provides a sketch of product lifecycle management and blockchain technology itself. In the third part, further details on the case of vehicle operations in the automotive ecosystem are given. The fourth part illustrates findings in terms of experience from the realisation of trustworthy product lifecycle management. In the fifth part, a discussion on the diverse and relevant hurdles to overcome is followed by a description of limitations and a view towards the future.
Digital image correlation for strain measurement in plastic film blowing process

Surface deformation study of a polymer film during plastic film blowing (PFB) extrusion process though is considered essential, is difficult to execute as the process is fast and continuous. Digital image correlation (DIC) is an optical technique that is non-destructive, highly accurate and designed to measure deformations from micro to macro scale. DIC can be proposed as a reliable tool to understand the relationship of polymer structure-processing-properties. In this literature, digital image correlation, its basic principle of operation and reliability guided DIC for continuous large deformation are comprehensively explored. Versatility of DIC, and the conditions that affect the measurement algorithm as well as preceding use of DIC in polymer processes are reviewed. The implementation and potential use of DIC in PFB are described. This review was submitted as part of the 2019 Materials Literature Review Prize of the Institute of Materials, Minerals and Mining run by the Editorial Board of MST. Sponsorship of the prize by TWI Ltd is gratefully acknowledged.
Interpolation and Beth Definability in Default Logics

We investigate interpolation and Beth definability in default logics. To this end, we start by defining a general framework which is sufficiently abstract to encompass most of the usual definitions of a default logic. In this framework a default logic $$\mathscr {D}\mathfrak {L}$$ is built on a base, monotonic, logic $$\mathfrak {L}$$ . We then investigate the question of when interpolation and Beth definability results transfer from $$\mathfrak {L}$$ to $$\mathscr {D}\mathfrak {L}$$ . This investigation needs suitable notions of interpolation and Beth definability for default logics. We show both positive and negative general results: depending on how $$\mathscr {D}\mathfrak {L}$$ is defined and of the kind of interpolation/Beth definability involved, the property might or might not transfer from $$\mathfrak {L}$$ to $$\mathscr {D}\mathfrak {L}$$ .
RedLeaf

RedLeaf is a new operating system being developed from scratch to utilize formal verification for implementing provably secure firmware. RedLeaf is developed in a safe language, Rust, and relies on automated reasoning using satisfiability modulo theories (SMT) solvers for formal verification. RedLeaf builds on two premises: (1) Rust's linear type system enables practical language safety even for systems with tightest performance and resource budgets (e.g., firmware), and (2) a combination of SMT-based reasoning and pointer discipline enforced by linear types provides a unique way to automate and simplify verification effort scaling it to the size of a small OS kernel.
Using YOLO-based pedestrian detection for monitoring UAV

Pedestrian detection (PD) is an important application domain in computer vision and pattern recognition. Conventional PD in real life scene is usually based on a fixed camera, which can detect and track the pedestrians in the monitoring region. However, when the pedestrian leaves the visible area of the fixed camera, it is usually difficult, if not impossible, to monitor the pedestrian. In response to the limitations of the conventional pedestrian detection application scenarios, a four-rotor unmanned aerial vehicle (UAV) system equipped with a high-definition (HD) camera is designed and implemented to detect human targets. Considering the size of human body in aerial image is small and easily to be occluded, we draw on the advanced research results in the field of target detection and propose a robust pedestrian detection method based on YOLO (You Only Look Once) network. The flow of the proposed approach is as follows. Firstly, the HD camera, which is installed on the monitoring UAV, is used for capturing images of the designated outdoor area. Secondly, image sequences are collected and processed using the airborne embedded NVIDIA Jason TX1 and Ubuntu as the core and operating system, respectively. Finally, YOLO is used to train the pedestrian classifier and perform the pedestrian detection. Experimental results show that our method has good detection results under the complicated conditions of detecting small-scale pedestrians and pedestrian occlusion.
Individual identification Based on Cascaded PCANet from ECG Signal

In this paper, we propose a Cascaded PCA Network (CPCANet) based on principal component analysis network (PCANet) for Individual identification of electrocardiogram (ECG). The CPCANet consists of three stages. In the first stage, ECG signals are preprocessed by normalization and spike removal. The R peak points in the preprocessed ECG signals are detected. Then, ECG signals are transformed into two-dimensional images to use as input of CPCANet. Here, we perform patch-mean removal and PCA algorithm as the same manner as PCANet from the transformed two-dimensional images. Also, we verified the influence of these parameters by modifying the key parameters of the experiment. The experiments are performed by the variation of patch size, number of filters, size of a block, and ratio of the lap from two databases. For this, we use a PTB ECG database which is benchmarking database. The experimental results clearly revealed that the proposed method showed good performance and effectiveness when compared with conventional algorithms such as PCA, auto-encoder (AE), extreme learning machine (ELM) and ensemble extreme learning machine (EELM).
Automating Autoencoder Architecture Configuration: An Evolutionary Approach

Learning from existing data allows building models able to classify patterns, infer association rules, predict future values in time series and much more. Choosing the right features is a vital step of the learning process, specially while dealing with high-dimensional spaces. Autoencoders (AEs) have shown ability to conduct manifold learning, compressing the original feature space without losing useful information. However, there is no optimal AE architecture for all datasets. In this paper we show how to use evolutionary approaches to automate AE architecture configuration. First, a coding to embed the AE configuration in a chromosome is proposed. Then, two evolutionary alternatives are compared against exhaustive search. The results show the great superiority of the evolutionary way.
The Evolution of Blockchain Virtual Machine Architecture Towards an Enterprise Usage Perspective

Virtualization in the context of blockchain systems represents an essential phase in the development and migration of services from public chains to enterprise logic. Most of the ongoing blockchain uses-cases are using the existing public ledgers, but for business products and services, there is a need for custom tailored solutions to ensure flexibility and security. The Ethereum Virtual Machine has opened new ways to solve problems that require a public proof by executing logic on a decentralized ecosystem. In a natural evolutive process, virtualization logic was shaped by numerous architectures and business requirements. Beside performance and scalability, enterprise virtual machines are starting to focus on features like: formal verification, execution of business logic, cross-domain rules, upgradable smart contracts, data privacy politics and audit tools. The current paper presents an introduction, an architectural walkthrough of the most relevant virtual machines that currently exist in the enterprise blockchain ecosystem and an analytical perspective on the future of industry ready VM’s.
A hybrid neural network approach for fine-grained emotion classification and computing

The traditional sentiment classification focuses more on the three polarities of sentiments, which are not fine-grained enough to fully characterize the overall evolution of sentiments and face the problem of sparse features. Aiming at these limitations, we propose a novel method to handle this pr oblem: a hybrid neural network model for fine-grained emotion classification and computing. First, a sentiment dictionary is constructed by using the sentiment lexical ontology. Then, according to dependency parsing, a textual sentiment classifier is built with the aid of long short term memory network technologies; fine-grained netizen sentiment index is calculated. Finally, our approach was applied to practical business problem–exploring the interactions between the netizen sentiment index and the stock return in order to test its reliability. The experimental results show that compared with traditional methods, this approach improves the accuracy of sentiment classification, possess higher classification performance, reduces the number of iterations and saves computing resources. The empirical analysis demonstrate that hybrid method is rapid, effective and feasible, could be more suitable for fine-grained emotion computing.
Heuristically Reducing the Cost of Correlation-based Feature Selection

Feature selection plays a critical role in processing today's massive datasets. A popular wrapper-based feature selection technique, Correlation-based Feature Selection (CFS), heuristically searches the exponential number of possible feature subsets to find a subset with a high predictive power. In this work, we examine the ability of multi-task learning and heuristic subset decomposition to reduce the cost of using CFS. To do this, we describe and evaluate two algorithms, Multi-Task Correlation-based Feature Selection (MT-CFS) and Heuristic Decomposition Correlation-based Feature Selection (HD-CFS). Both algorithms utilize CFS on feature subspaces reduced by either a technique inspired by multi-task learning or a novel technique that heuristically subdivides the feature space for faster execution with minimal losses in accuracy. We conclude that HD-CFS shows promise as a standalone feature selection technique.
Systematic Generation of Conditional Knowledge Bases up to Renaming and Equivalence

A conditional of the form “If A then usually B” establishes a plausible connection between A and B, while still allowing for exceptions. A conditional knowledge base consists of a finite set of conditionals, inducing various nonmonotonic inference relations. Sets of knowledge bases are of interest for, e.g., experimenting with systems implementing conditional reasoning and for empirically evaluating them. In this paper, we present an approach for systematically generating knowledge bases over a given signature. The approach is minimal in the sense that no two knowledge bases are generated that can be transformed into each other by a syntactic renaming or that are elementwise equivalent. Furthermore, the approach is complete in the sense that, taking renamings and equivalences into account, every consistent knowledge base is generated.
Self-Adaptive System Verification based on SysML

In the development of current technology, systems are being used in various fields. The system may be newly configured depending on the environment, and may be added to the existing environment. There are also cases where the systems need to collaborate with each other due to environmental changes. Especially, in real-time systems, it is necessary to cope with continuously changing environment. And, you should check whether the result of the correspondence adversely affects other systems. Recognition requirements engineering has been studied for self-adaptive system configuration. A variety of self-adaptation frameworks have also been studied. Formal verification techniques are also being studied to verify the self-adaptive system. However, there is a lack of research to verify self-adaptive systems based on requirements. In this paper, we propose a self-adaptive system verification scheme using Requirement Diagram and State Diagram used in SysML. Requirement analysis contents are expressed as Requirement Diagram and system state information is displayed as State Diagram to establish the basis for verifying whether the requirements are functionally satisfied. Also, based on the Awareness Requirement, the requirements specification technique is used to provide a basis for expressing non-functional requirements.
Story co-segmentation of Chinese broadcast news using weakly-supervised semantic similarity

This paper presents lexical story co-segmentation, a new approach to automatically extracting stories on the same topic from multiple Chinese broadcast news documents. Unlike topic tracking and detection, our approach needs not the guidance of well-trained topic models and can consistently segment the common stories from input documents. Following the MRF scheme, we construct a Gibbs energy function that feasibly balances the intra-doc and inter-doc lexical semantic dependencies and solve story co-segmentation as a binary labeling problem at sentence level. Due to the significance of measuring lexical semantic similarity in story co-segmentation, we propose a weakly-supervised correlated affinity graph (WSCAG) model to effectively derive the latent semantic similarities between Chinese words from the target corpus. Based on this, we are able to extend the classical cosine similarity by mapping the observed words distribution into the latent semantic space, which leads to a generalized lexical cosine similarity measurement. Extensive experiments on benchmark dataset validate the effectiveness of our story co-segmentation approach. Besides, we specifically demonstrate the superior performance of the proposed WSCAG semantic similarity measure over other state-of-the-art semantic measures in story co-segmentation.