Reviewerly/exHarmony huggingface dataset:
FSCC: Flexible Smart Contract Interaction with Access Control for Blockchain

Smart contract (SC) is a user-defined program code over blockchain, which holds the characteristics of immutability and auditability without requiring trust third party and is crucial to provide in-network computation for green applications. Herein we investigate the interactions among the SCs, where the functions defined in one SC are called by another one. The existing work, Ethereum name service (ENS), provides a method to map human-readable names to machine-readable addresses. However, it still suffers the problems induced from the ossification of SC, such as no mechanism to call functions in a non-existing SC, to update SC, to acquire the SC information (e.g. contract application binary interface and address), and to achieve access control. To solve these problems, we propose a flexible smart contract interaction framework with access control (FSCC), where SC name and information are separately stored at blockchain and the distributed off-chain storage to reduce on-chain storage overhead. With the FSCC, interactions with non-existing SC, update of SC, and access control of SC can be achieved. Furthermore, performance evaluations show that on-chain storage overhead can be greatly reduced with keeping the communication delay at a low level.
Mitigating Attacks on Fake News Detection Systems using Genetic-Based Adversarial Training

The study of adversarial effects on AI systems is not a new concept, but much of the research has been devoted to deep learning. In this paper we explore the effects of adversarial examples on 4 machine learning classifiers and measure the effectiveness of adversarial training. Additionally, we present a novel method for selecting adversarial training examples that lead to a more robust machine learning system. Our results suggest that adversarial examples can significantly hinder the classification performance and that adversarial training is an effective defensive counter-measure.
Features of Single Value Coordinate System (SVCS) for Earthquake Forecasting using Single Layer Hierarchical Graph Neuron (SLHGN)

In order to increase its accuracy, the earthquake forecasting using the Single Layer Hierarchical Graph Neuron (SLHGN) requires particular capabilities related to earth coordinates. As the current coordinate system cannot provide the SLHGN with such capabilities, this work has developed the Single Value Coordinate System (SVCS) as a solution. The main principles of the SVCS that are different to the current coordinate system are: 1) The SVCS coordinate points to an area, not to a dot (point), therefore it is suitable for the SLHGN; 2) The SVCS only utilizes basic mathematical operations, therefore it is fast; 3) The SVCS treats each area on the earth equally, therefore it is reliable. Based on these, the work has successfully discovered three main features of the SVCS that will be beneficial for the SLHGN. The SVCS provides earthquake forecasting using the SLHGN with these three main features: 1) Conversion from current coordinates to the SVCS coordinates; 2) Shortest distance calculation in different areas; 3) Simplest direction formulation between two areas.
GA-Based Iterative Optimization System to Supervise Adaptive Workflows in Cloud Environment

Due to enormous data generation at rapid rate, processing of large volumes of data becomes a challenging assignment. This leads to many issues in computing environment since it requires complex data analysis. Issues related to performance and cost may certainly arise due to heterogeneity of the systems which are connected in the distributed environment. Various consumers submit their request to the dynamic cloud environment depending on their demand and requirement. To manage these volatile workloads, an efficient workflow management system is proposed in this paper. In order to deal with these vital issues, a novel design of adaptive workflow management system is described to support on demand service management in the cloud. This mechanism includes workflow scheduler and iteration controller to optimize the data processing through iterative workflow task scheduling mechanism. Upgrade fit—is an optimization technique that dynamically and continuously reallocates multiple types of cloud resources to fulfill the performance and cost requirements of cloud resources. Using this algorithm, iterative workflow tasks can be executed repetitively for data processing and workload execution. The performance of the algorithm is evaluated by using weather forecast workflow. Since the performance of the algorithm is showing considerable efficiency levels, the same algorithm can be used in real time large volume data processing. The evaluation results indicate that the system can effectively handle multiple types of cloud resources and optimize the performance iteratively. The present paper has implemented the algorithm in cloud computing environment using CloudSim that has proved that the completion time is being minimized with maximization of resource allocation.
Fault-tolerant syndrome extraction and cat state preparation with fewer qubits

We reduce the extra qubits needed for two fault-tolerant quantum computing protocols: error correction, specifically syndrome bit measurement, and cat state preparation. For distance-three fault-tolerant syndrome extraction, we show an exponential reduction in qubit overhead over the previous best protocol. For a weight-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>w</mml:mi></mml:math> stabilizer, we demonstrate that stabilizer measurement tolerating one fault needs at most <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo fence="false" stretchy="false">&amp;#x2308;</mml:mo><mml:msub><mml:mi>log</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>&amp;#x2061;</mml:mo><mml:mi>w</mml:mi><mml:mo fence="false" stretchy="false">&amp;#x2309;</mml:mo><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:math> ancilla qubits. If qubits reset quickly, four ancillas suffice. We also study the preparation of entangled cat states, and prove that the overhead for distance-three fault tolerance is logarithmic in the cat state size. These results apply both to near-term experiments with a few qubits, and to the general study of the asymptotic resource requirements of syndrome measurement and state preparation.With <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>a</mml:mi></mml:math> flag qubits, previous methods use <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>a</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> flag patterns to identify faults. In order to use the same flag qubits more efficiently, we show how to use nearly all <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mi>a</mml:mi></mml:msup></mml:math> possible flag patterns, by constructing maximal-length paths through the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>a</mml:mi></mml:math>-dimensional hypercube.
How pandemic and the industry 4.0 era constraint: the perspective of authentic leadership on innovation

Abstract The COVID-19 crisis brought the importance of interdisciplinary knowledge to the forefront. Scholars carry out online communication and learning, instead of relying on face-to-face meetings at the conference, and are more proactive in disseminating their research online, which will achieve more success due to the higher number of readers citations. Remote work behaviour facilitated technology adoption in the era of industry 4.0 will affect company innovation. Then the authentic leaders who have the vision and views in industry 4.0 and are faced with pandemics will increase the level of innovation of these leaders. This research cannot be generalized to the industry in general but can be a reference for future researchers. 4.0 and corporate culture
Advancing Genetic Programming via Information Theory

Genetic Programming (GP) is a powerful tool often used to solve optimization problems where analytical methods are unusable. While the general technique is well understood, there exist deficiencies in the multitude of implementations currently widely available. The primary areas of improvement are computation time, search space reduction, and accuracy. Despite significant advances in GP systems, a key deficiency remains in the structural randomization of symbolic GP trees. Our initial assumptions regarding the formation of expression trees in symbolic GP trees is at best highly limited and normally simply non-existent. In this paper, we introduce a new GP methodology that incorporates both current cutting- edge GP system solutions as well as an information-theoretic approach to expression tree initialization. Through a more informed initial tree construction, this approach reduces the search space and model complexity. We introduce in this work the methodology as well as the accompanying theoretical component and comparison benchmarks from tests. A key advantage of the algorithm proposed is its high parallelization potential which is highlighted in further discussion. The method consists of two parts. The first is a variable-interaction system termed Entropy Shaving that is used for both variable selection and initial expression structure generation. The second is a GP system that utilizes the variable-interaction system as input to determine a final solution.
Live VR end-to-end workflows: real-life deployments and advances in VR and network technology

Live VR sports events are gaining traction among viewers since immersive services add a closer experience than watching on a TV providing even better point of views than being at the event. The content acquisition of the Live VR feed is captured using additional cameras dedicated for this purpose (in addition to the traditional cameras). In the venue, there might be several locations where the VR content is captured. The director may choose which Live VR feeds are distributed to home delivery, either by producing one single VR feed, or by giving the choice to the end user to select which one to watch, or a by offering a combination of a produced feed and individual cameras. — In this paper, we describe and analyze the live VR workflows from an end-to-end perspective such as production, contribution, distribution and consumption aspects and describe how this can be complementary to the traditional broadcast services with a 4K like experience. We also describe how the recent advances in VR technology can improve VR experience in an optimal bandwidth using view port dependent technologies (for instance using MPEG OMAF format) and how VR production techniques (such as volumetric) provide advance tools for capturing footage from many angles for producing live free-point of view consumption and volumetric replays. This paper also presents Live VR service deployments using those technologies and it will describe the technical challenges from an end-to-end perspective on how to achieve high quality (VR360 8K equivalent) considering aspects such as bit rate, latency, CDN configurations and device capabilities. Finally, we also address how any high speed broadband infrastructure like 5G, Fiber of DOCSIS 3.1 infrastructure and new 5G devices with their built in 8K capabilities can simplify the Live workflow, and how it can accelerate the 8K VR deployments.
Software-driven Security Attacks: From Vulnerability Sources to Durable Hardware Defenses

There is an increasing body of work in the area of hardware defenses for software-driven security attacks. A significant challenge in developing these defenses is that the space of security vulnerabilities and exploits is large and not fully understood. This results in specific point defenses that aim to patch particular vulnerabilities. While these defenses are valuable, they are often blindsided by fresh attacks that exploit new vulnerabilities. This article aims to address this issue by suggesting ways to make future defenses more durable based on an organization of security vulnerabilities as they arise throughout the program life cycle. We classify these vulnerability sources through programming, compilation, and hardware realization, and we show how each source introduces unintended states and transitions into the implementation. Further, we show how security exploits gain control by moving the implementation to an unintended state using knowledge of these sources and how defenses work to prevent these transitions. This framework of analyzing vulnerability sources, exploits, and defenses provides insights into developing durable defenses that could defend against broader categories of exploits. We present illustrative case studies of four important attack genealogies—showing how they fit into the presented framework and how the sophistication of the exploits and defenses have evolved over time, providing us insights for the future.
University Network Capital as an Element of the City’s Social Capital

The formation and development of network capital in the university brings up the issue of its influence on characteristics of the city’s social capital. The basic provisions of the network approach and the theory of social capital are used as theoretical grounds to identify such an impact. The article reveals the features of a modern university as a complex network object and the characteristics of interpersonal relationships that arise in its socio-cultural environment. These include the cultural homogeneity, formation of a joint reality, optimization of communication processes, the need for cooperation. Taking into account the revealed specificity, the network capital available to the subjects of social interaction is characterized and studied through the categories of trust and solidarity. It is determined that trust and solidarity are not only central, but necessary structural elements of the university’s network capital, first of all, because the peculiarities of the organization of network interaction dictate the preferred strategies of network behavior, into which these phenomena are embedded. Thus, being the holder of intellectual resources and a network of interpersonal connections, the university produces the effective ways to expand the city’s social capital, firstly, through a unique social network organization capable of rapid mobilization. It provides access to the formation of temporary teams with deep and versatile competencies that generate “quick trust”. Secondly, it expands the city’s social capital through impersonal trust, which convinces society that the university has some universal competence related to the life of the city and its people.
How firms strategically navigate informal and formal copyright practices: insights from Nollywood

Purpose The purpose of this study is to explain how and why firms configure copyright practices when confronted with state-sanctioned laws and informal customs projected by local ethnic or religious communities. Design/methodology/approach A multi-case inductive study of four film-producing organizations within the Nigerian film industry (i.e. Nollywood) was conducted. Specifically considered were firms that started their operations around the same time with similar founding conditions, experiences, resources and technical competencies. Field observations and multiple rounds of in-depth interviews were conducted to achieve the research objectives. Findings The study found that firms adopted dominant or hybrid configurations when interacting with informality and formality. Dominant configurations represent the exclusive adoption of informal copyright practices while hybrid configurations refer to the blended use of informal and formal copyright practices. The second set of findings revealed that each firm’s strategic intent affected the type of interactional configuration that unfolded in the firm. Specifically, firms with social intents tended to adopt dominant configurations, whereas firms with socio-economic intents tended to adopt hybrid configurations. Practical implications The study implies that firms may profit from strategically focusing on when and in what circumstances to adopt informality. Strategic intents that blend social and economic rationales may secure more positive interactive outcomes from internal and external stakeholders promoting formality and informality. Social implications This study highlights the fact that firms embedded in local religious and ethnic communities use organizational practices to solve social and institutional problems of their members. The copyright practices of these organizations encourage apprenticeship, youth empowerment and entrepreneurship in Nigeria. Originality/value To the best of the authors’ knowledge, this is the first study that goes beyond macro-level analysis to investigate the interactional dynamics between formality and informality at the firm, community, and state levels. The study is also first of its kind to use copyright practices as an analytical lens to explore the interaction between informality and formality.
Secure Asset Tracking in Manufacturing through Employing IOTA Distributed Ledger Technology

Today, manufacturing industry is increasingly embracing new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increase information visibility, improve production performance, and gain competitive advantages in the global market. These advances are rapidly enabling a new generation of smart manufacturing, i.e., a cyber-physical system tightly integrating manufacturing enterprises in the physical world with virtual enterprises in cyberspace. To a great extent, realizing the full potential of cyber-physical systems depends on the development of new methodologies on the Internet of Manufacturing Things (IoMT) for data-enabled engineering innovations. This article presents a real implementation of IOTA Tangle architecture for data transactions extended with HMAC signing through using STM32 (F7 CPU) IoT devices. The evaluation results show promising with 32 light nodes to exceed 28 transactions per second by using 4 full nodes, thus making IOTA-based distributed ledger an effective solution for IoT-based manufacturing environments with zero-value (data) transactions.
Fine-Grained Image Classification Based on Target Acquisition and Feature Fusion

Fine-grained images classification aims to analyze visual objects from subordinate categories, e.g., models of cars or species of dogs, which is challenging due to the small inter-class variations and the large intra-class variations. Recent research has focused on extracting distinguishable local features via part-based model or attention networks to locate discriminative regions/parts, which highly relies on the part annotation, incurs high background noise and ignores the semantic information during the learning. In this paper, we present a fine-grained image classification model, RPN-SCA-BCNN (RSCAB), based on target acquisition and feature fusion. In RSCAB, we first adopt a new Soft-NMS enhanced Region Proposal Network (RPN) for weak supervised object detection to separate the distinguishable object and its background to remove the background noise and costly annotation requirement. Then, we provide a new attention mechanism Spatial-Channel Attention (SCA) to focus on the spatial discriminative parts of the image to reduce the feature redundancy. Based on SCA, we further construct a Bilinear Convolutional Neural Network (BCNN) to fuse the high and low dimensional features by leveraging two networks obtaining more abundant multi-dimensional attention features for classification. The experimental results show that the proposed RSCAB model achieves higher classification accuracy over the state-of-the-art algorithms.
Towards a Translation-Based Method for Dynamic Heterogeneous Network Embedding

Network embedding, which aims to map the discrete network topology to a continuous low-dimensional representation space with the major topological properties preserved, has emerged as an essential technique to support various network inference tasks. However, incorporating both the evolutionary nature and the network's heterogeneity remains a challenge for existing network embedding methods. In this study, we propose a novel Translation-Based Dynamic Heterogeneous Network Embedding (TransDHE) approach to consider both the aspects simultaneously. For a dynamic heterogeneous network with a sequence of snapshots and multiple types of nodes and edges, we introduce a translation-based embedding module to capture the heterogeneous characteristics (e.g., type information) of each single snapshot. An orthogonal alignment module and RNN-based aggregation module are then applied to explore the evolutionary patterns among multiple successive snapshots for the final representation learning. Extensive experiments on a set of real-world networks demonstrate that TransDHE can derive the more informative embedding result for the network dynamic and heterogeneity over state-of-the-art network embedding baselines.
RKT2FCM: RBF Kernel-Based Type-2 Fuzzy Clustering

Fuzzy clustering is used to cluster data such that each point may pertain to a number of clusters. Fuzzy clustering has applications in the areas like pattern recognition, medical segmentation, recommender systems etc. Kernel based clustering algorithms are performing better as in Kernel based algorithms, clustering is performed by mapping data to high dimension space. RBF kernel type-2 fuzzy-c-means (RKT2FCM) algorithm has been proposed using the RBF function in this paper. A few standard datasets are used to better analysis and compare performance of proposed algorithm with Fuzzy-C-Means (FCM), Type-2 Fuzzy-C-Means (T2FCM), Kernalized Fuzzy-C-Means (KFCM), FCM with new distance metric (FCM-Ïƒ). KT2FCM algorithm uses RBF mapping function and hence gives better performance. Experimental analysis of the proposed algorithm has been given in this paper.
A Domain-Independent Ontology Learning Method Based on Transfer Learning

Ontology plays a critical role in knowledge engineering and knowledge graphs (KGs). However, building ontology is still a nontrivial task. Ontology learning aims at generating domain ontologies from various kinds of resources by natural language processing and machine learning techniques. One major challenge of ontology learning is reducing labeling work for new domains. This paper proposes an ontology learning method based on transfer learning, namely TF-Mnt, which aims at learning knowledge from new domains that have limited labeled data. This paper selects Web data as the learning source and defines various features, which utilizes abundant textual information and heterogeneous semi-structured information. Then, a new transfer learning model TF-Mnt is proposed, and the parameters’ estimation is also addressed. Although there exist distribution differences of features between two domains, TF-Mnt can measure the relevance by calculating the correlation coefficient. Moreover, TF-Mnt can efficiently transfer knowledge from the source domain to the target domain and avoid negative transfer. Experiments in real-world datasets show that TF-Mnt achieves promising learning performance for new domains despite the small number of labels in it, by learning knowledge from a proper existing domain which can be automatically selected.