Reviewerly/exHarmony huggingface dataset:
Knowledge graph extension with a pre-trained language model via unified learning method

Knowledge graphs (KGs) are collections of real-world knowledge that is represented by a structured form of triples. Since they are manually built in their nascent stage, there is a common problem that some links (triples) are missing. Knowledge graph completion (KGC) aims to find those missing links and thereby complete the KGs. However, as knowledge increases through diverse sources, new entities have explosively emerged and they are needed to be connected to existing KGs. Thus, open-world KGC is targeted on extending KGs to those new entities. Dealing with those new entities is challenging because they do not have any connection with entities in the existing KGs. One way to handle the new ones is to embed them with their textual descriptions with pre-trained word embeddings and score them in the graph-vector space with the existing typical KGC models. These models have resulted in meaningful results but there is still a lack of studies on utilizing the latest neural networks, such as pre-trained language models which are known to be better at capturing contexts than pre-trained word embeddings. This paper proposes a novel model that effectively connects new entities and existing KGs through a pre-trained language model. To effectively handle the problem, we utilize two learning methods; one is the classification method of the masked language model (MLM) that predicts a word among a huge vocabulary set with a given context, and the other is multi-task learning based on the Multi-Task for Deep Neural Networks (MT-DNN). Based on the methods, the model first generates an embedding of a new entity using its textual description and then uses the embedding to find one of the existing entities from a KG where the new entity can be connected. The experimental results on three benchmark datasets, DBPedia50k, FB15k-237-OWE, and FB20k, show that the proposed model improves performances by 9.2%p, 4.4%p, and 11.1%p, respectively, and achieves new state-of-the-art performance for all datasets.
The Concept in the Cognitive-Synergetic Aspect

The article considers the concept from a cognitive-synergetic standpoint. The concept is an open, dissipative, non-linear, fractal structure, the development of which is based on the law of spiral fractality and self-similarity. The concept evolves according to the recursive principle, goes in a circle, returning to the starting point on a new round of evolution. The origin of the concept is based on both external necessity and internal need. By the formation of a concept we mean its transition from the mental area, to the language area, and then to the speech area. The concept arises in consciousness in the form of a virtual image as a result of perceptual perception of reality, and is updated, stored in the language and embodied in speech. The concept strives for perfection, but never reaches it, reaching its peak, it starts the process of evolution again in new conditions.
Towards an Analytical Definition of Sufficient Data

We show that, for each of five datasets of increasing complexity, certain training samples are more informative of class membership than others. These samples can be identified a priori to training by analyzing their position in reduced dimensional space relative to the classes’ centroids. Specifically, we demonstrate that for the datasets studied, samples that are nearer to their classes’ centroids are less informative than those that are furthest from them. For all five datasets studied, we show that there is no statistically significant difference between training on the entire training set and when excluding up to 2% of the data nearest to each class’s centroid.
Blockchain-Based Business Process Management (BPM) for Finance: The Case of Loan-Application

Because of the competitive economy, organizations today seek to rationalize, innovate and adapt to changing environments and circumstances as part of business process improvements efforts. The strength of blockchain technology lies in its usage as an apt technology to enhance the efficiency and effectiveness of business processes; furthermore, it prevents the use of erroneous or obsolete data, and allow sharing of confidential data securely. The use of superior technology in the execution and automation of business processes brings opportunities to rethink the specific process itself as well. Business processes modeling and verification are essential to control and assure organizational evolution, therefore, the aim of this paper is three-fold: firstly, to provide business process management patterns in finance, based on blockchain, specifically for the loan-application process, that could be used and customized by companies; secondly, to critically analyze challenges and opportunities from the introduction of such approach for companies; and thirdly to outline how companies can implement the loan business process as a web service.
SSI4Web: A Self-sovereign Identity (SSI) Framework for the Web

The traditional protected web services rely on a user authentication process. The utilisation of an identifier (e.g. username, email address and so on) and credential (e.g. password) still remains the most widely deployed user authentication process, even though such an authentication process is one of the major sources of security breaches. Moreover, in this traditional setting, the management and sharing of user identity information is cumbersome with limited user controls over their identity data. In recent times, SSI has emerged as a new mechanism for managing and exchanging identity information in a more user-centric and privacy-friendly way. There are many explorations of SSI in different application domains, however, its utility for the web mostly remains unexplored. In this work, we present SSI4Web, a framework for integrating Self-sovereign Identity (SSI) for providing web services in a secure passwordless manner with much more user control and greater flexibility. We provide its architecture, discuss its implementation details, sketch out its use-case with an analysis of its advantages and limitations.
Blockchain Consensus Algorithms: A Survey

Blockchain is one of the emerging technologies based on Distributed peer-to-peer networking. Blockchain gained popularity in 2009 with the launch of Bitcoin. Since, then blockchain has found applications in various domains such as finance, supply chain, health care, agriculture, pharmacy, IOT, automobile, energy, and many more. Blockchain is decentralized in nature which providing an immutable and tamper-proof ledger of transactions that includes data integrity and security. The consensus algorithm is a common agreement between block nodes to become a part or publish a new block in the blockchain. In this study, we survey different consensus algorithms reported in the literature.
Assessing Blockchain Challenges in the Maritime Sector

The maritime sector is the main contributor to global trade but has generally been weakened by its composite anatomy, miscellaneous information sources and systems, and dispersed processes. In this regard, the innovative blockchain technology, characterized by its distinctive features: distribution, immutability, and transparency, promotes great transformative possibilities for the sector. And blockchain applications, use cases, and pilot projects are increasingly emerging in the industry. However, not all applications were successful. And mass adoption of the technology that allows unlocking its full potential in the sector is still in its infancy. This article highlight blockchain’s contribution to the maritime sector and list its applications anchoring literary recommendations for technological advancements in the maritime sector. Second, we unravel the reason that halted Blockchain advancement in the industry and its widespread adoption. Furthermore, we categorize those reasons into challenges, assess them and provide possible solutions.
Fuzzy Gaussian Decision Tree

The Decision Tree algorithm is one of the first machine learning algorithms developed. It is used both as a standalone model and as an ensemble of many cooperating trees like Random Forest, AdaBoost, Gradient Boosted Trees, or XGBoost. In this work, a new version of the Decision Tree was developed for classifying real-world signals using Gaussian distribution functions and a fuzzy decision process. The research was carried out on Power Quality Classification Dataset hosted on the platform kaggle.com for accessing the algorithm’s classification. The proposed algorithm modification produces a sparse tree of multidimensional Gaussian kernels performing fuzzy, proximity-based division of solution space instead of a typical Decision Tree performing definite space restrictions. The machine learning model can cluster samples based on the common pattern and evaluate the input’s similarity in a fuzzified fashion. The studies were conducted for the prediction of 6 classes of current. The proposed modification achieved the best accuracy and F1 score compared to the default Decision Tree and other machine learning algorithms.
Voluntary Data Preservation Mechanism in Base Station-Less Sensor Networks

We consider the problem of preserving a large amount of data generated inside base station-less sensor networks when sensor nodes are controlled by different authorities and behave selfishly. We modify the VCG mechanism to guarantee that each node, including the source nodes with overflow data packets, will voluntarily participate in data preservation. The mechanism ensures that each node truthfully reports its private type and network achieves efficiency for all the preserved data packets. Extensive simulations are conducted to further validate our results.
Optimization assisted frequent pattern mining for data replication in cloud: Combining sealion and grey wolf algorithm

It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel Data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The high-priority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.07%, 34.9%, 30.5%, 29.23%, 24.57%, 16.8%, and 16.85% higher than the existing methods like SMO, LA, ROA, GWO, SLnO, PSO, HCS, respectively.
Older Adults’ Concurrent and Retrospective Think-Aloud Verbalizations for Identifying User Experience Problems of VR Games

Abstract While virtual reality (VR) games are beneficial for older adults to improve their physical functions and cognitive abilities, VR research often does not include older adults. Our review of the proceedings of major HCI conferences (i.e. ASSETS, CHI, CHI PLAY, CSCW and DIS) between 2016 and 2020 shows that only three out of 352 VR-related papers involved older adults. Consequently, older adults tend to encounter user experience (UX) problems with VR. One common way to identify UX problems is to conduct usability testing with think-aloud (TA) protocols. As VR games tend to be perceptually and physically demanding, older adults might need to allocate more resources to VR content and interaction and thus have fewer resources for thinking aloud. This raises the question of whether TA protocols are still a viable approach to detecting UX problems of VR games for older adult participants. To answer this question, we conducted usability testing with older adults who played two common types of VR games (i.e. the exergame and experience game) using concurrent and retrospective TA protocols (i.e. CTA and RTA), which are widely used in the industry. We analyzed participants’ TA verbalizations and uncovered how different categories of verbalizations indicate UX problems. We further show how older adults perceived the effects of thinking aloud on their game experiences in two TA protocols and offer design implications.
Effective Malware Detection Approach based on Deep Learning in Cyber-Physical Systems

Cyber-physical Systems based on advanced networks interact with other networks through wireless communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of privacy and security as third-party users should authenticate the network. If it fails, it will create extensive damage and threat to the established network and makes the hacker malfunction the network services efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyberphysical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps to improve the privacy and authentication of the network by improving the data accuracy and Quality of Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the network reliability and security compared to some of the existing SVM-based and Apriori-based detection approaches.
Building linked lexicography applications with LexO-server

Abstract The adoption of Semantic Web technologies in the lexicographic field, has been driven by the need to ensure the construction of lexical resources that are interoperable and can be shared and reused by the scientific communities. In this context, the OntoLex W3C working group proposed the OntoLex-Lemon model aimed at providing rich linguistic grounding for ontologies. It includes the representation of morphological and syntactic properties of lexical entries as well as their meaning with respect to an ontology or vocabulary. This article aims at presenting LexO-server, a set of REST services for the management of OntoLex-Lemon modeled lexical resources. LexO-server comes as a software backend providing data access and manipulation to frontend developers. The set of services are general enough to make possible the construction of applications oriented at different tasks, such as editing, linking, dictionary making, linguistic annotation, or NLP ones. As a demonstration of the versatility and the potential of LexO-server, we will present three web applications that rely on it.
Confronting the Carbon-Footprint Challenge of Blockchain

The distributed consensus mechanism is the backbone of the rapidly developing blockchain network. Blockchain platforms consume vast amounts of electricity based on the current consensus mechanism of Proof-of-Work (PoW). Here, we point out a different consensus mechanism named Proof-of-Stake (PoS) that can eliminate the extensive energy consumption of the current PoW-based blockchain. We comprehensively elucidate the current and projected energy consumption and carbon footprint of the PoW- and PoS-based Bitcoin and Ethereum blockchain platforms. The model of energy consumption of PoS-based Ethereum blockchain can lead the way toward the prediction of other PoS-based blockchain technologies in the future. With the widespread adoption of blockchain technology, if the current PoW mechanism continues to be employed, the carbon footprint of Bitcoin and Ethereum will push the global temperature above 1.5 °C in this century. However, a PoS-based blockchain can reduce the carbon footprint by 99% compared to the PoW mechanism. The small amount of carbon footprint from PoS-based blockchain could make blockchain an attractive technology in a carbon-constrained future. The study sheds light on the urgency of developing the PoS mechanism to solve the current sustainability problem of blockchain.
Fighting for a future free from violence: A framework for real-time detection of “Signal for Help”

In April 2020, by the start of isolation all around the world to counter the spread of COVID-19, an increase in violence against women and kids has been observed such that it has been named The Shadow Pandemic. To fight against this phenomenon, a Canadian foundation proposed the “Signal for Help” gesture to help people in danger to alert others of being in danger, discreetly. Soon, this gesture became famous among people all around the world, and even after COVID-19 isolation, it has been used in public places to alert them of being in danger and abused. However, the problem is that the signal works if people recognize it and know what it means. To address this challenge, we present a workflow for real-time detection of “Signal for Help” based on two lightweight CNN architectures, dedicated to hand palm detection and hand gesture classification, respectively. Moreover, due to the lack of a “Signal for Help” dataset, we create the first video dataset representing the “Signal for Help” hand gesture for detection and classification applications which includes 200 videos. While the hand-detection task is based on a pre-trained network, the classifying network is trained using the publicly available Jesture dataset, including 27 classes, and fine-tuned with the “Signal for Help” dataset through transfer learning. The proposed platform shows an accuracy of 91.25% with a video processing capability of 16 fps executed on a machine with an Intel [email protected] GHz CPU, 31.2 GB memory, and NVIDIA GeForce RTX 2080 Ti GPU, while it reaches 6 fps when running on Jetson Nano NVIDIA developer kit as an embedded platform. The high performance and small model size of the proposed approach ensure great suitability for resource-limited devices and embedded applications which has been confirmed by implementing the developed framework on the Jetson Nano Developer Kit. A comparison between the developed framework and the state-of-the-art hand detection and classification models shows a negligible reduction in the validation accuracy, around 3%, while the proposed model required 4 times fewer resources for implementation, and inference has a speedup of about 50% on Jetson Nano platform, which make it highly suitable for embedded systems. The developed platform as well as the created dataset are publicly available.
Jury-Trial Story Construction and Analysis Using Goal-Directed Answer Set Programming

Answer Set Programming (ASP) is a well known paradigm for knowledge representation and for automating commonsense reasoning. Query-driven implementations of Predicate ASP, e.g., the s(CASP) system, permit top-down execution of answer set programs without the need for grounding them. In this paper we consider the problem of analyzing a jury trial for the crime of homicide. We demonstrate how knowledge related to reaching a guilty/not-guilty verdict can be modeled in ASP, and how s(CASP) can be used to analyze the situations under various assumptions. Detailed justification can be provided for each possible verdict, thanks to s(CASP). The goal, and the main contribution, of this paper is to show how a complex situation—whose analysis has been traditionally considered to be within the purview of only humans—can be automatically analyzed using a query-driven predicate ASP system such as s(CASP). For illustration, we use the well-known fictional case of Commonwealth of Massachusetts v. Johnson used in the cognitive science literature. Frank Johnson and Alan Caldwell had a quarrel, which was later followed by Frank Johnson stabbing Alan Caldwell. However, this seemingly simple case was complex in many ways: the lack of critical information led to different verdicts from different jurors. This paper attempts to formalize the logic behind each story construction using s(CASP) and expanding it to similar situations.
Tutor activity in the system of education

The purpose of the article is determined by the increasing importance and necessity of tutor activity in the context of digital transformation in education system, also in the educational process of students with disabilities. The article provides a retrospective analysis of tutor activity overseas and in national education system. Authors consider different approaches to the definition of the tutors concept and the content of tutor activity at various historical stages, changing of the tutors role in the conditions of institutional and social changes in society, methodological aspects at organization of different types of education. The article highlighted the main problems of professional training of tutors and prospects for the development of tutor activity
Spatial Perception Imperatives in Virtual Environments: Understanding the Impacts of View Usage Patterns on Spatial Design Decisions in Virtual Reality Systems

Spatial perception in virtual reality systems relies on a number of variables, including how users explore, observe and perceive the spatial factors of a virtual environment. In virtual reality systems, users can assume different types of views for spatial decision-making about the sizes and scales of and relationships among virtual spaces. This research explored the role of view usage patterns in spatial cognition and decision-making in a fully immersive virtual reality system and monitor-based virtual reality system. The focus of study was the impact of using the eye-level view as the only view type in a fully immersive virtual reality system on actual and perceived view usage patterns in a monitor-based virtual reality system. In addition, users’ spatial decision-making results were compared with regards to system usage sequence and view type. Quantitative and qualitative data, descriptive and inferential statistical comparisons, and testing of both systems were used to determine the participants’ performances concerning view usage patterns and the design outcomes. The results showed a moderate association between the view type utilized for spatial perception in a monitor-based virtual reality system and variations in system usage sequence. In addition, for both systems, variations in system usage sequence, space type, and other characteristics all affected the strength of the linear regressions of the sizes and scales of the design outcomes.