Reviewerly/exHarmony huggingface dataset:
Demystifying and Analysing Metaverse Towards Education 4.0

The word Metaverse has influenced many sectors such as healthcare, education, retail and manufacturing and few more industries are there which will be impacted by 2026 as per the research conducted by Gartner. The word “Metaverse” especially in education sector came into existence after the COVID-19 epidemic when the humanity were forced to think about the new methodology of educating and teaching. This ecosphere is the combination of technologies which enables multimodal interactions with artificial environment, electronic library and people such as Virtual Reality (VR) and Augmented Reality (AR). It is believed that metaverse will improve collaboration, training process will be enhanced and most importantly it will create a happier workplace. This is only the reason that many corporate giants like Nvidia, facebook, apple, epic Games and companies has shifted towards this pedagogical ecosystem. This technology has the potential which enables absolute incorporating user conversation in actual-time and compelling interactivity with digital artifact. In this paper, we are addressing metaverse in education along with a detailed framework of metaverse in education. It includes a comparative study of conventional education, online education and metaverse education based on parameters like place of learning, resources used, teaching methodology, learning experience, learning target and learning assessment. Competency based education, energize student and positive attitude towards learning. The various challenges of the metaverse in educational sector are also debated. This paper will help the researcher's fraternity to get a deeper insight along with a clear perception of this ecosystem in education.
Defining the reporting threshold for a cybersecurity incident under the NIS Directive and the NIS 2 Directive

Abstract The NIS Directive and sector-specific cybersecurity regulations require the reporting of (security) incidents to supervisory authorities. Following the risk-based approach adopted in the NIS Directive, the NIS 2 Directive enlists as a basic security element the reporting of significant incidents that (i) have caused or (ii) are capable to cause harm, as well as (iii) notifying the service recipients of cyber threats. Although during the interinstitutional negotiations between the European Commission, the European Parliament, and the Council of the European there was consensus that the NIS Directive’s reporting framework needs to be reformed, views on the determination of what needs to be reported varied. This paper outlines and analyses the different concepts of a report-worthy significant incident that have been proposed during the legislative procedure for the NIS 2 Directive from a legal and policy perspective. Irrespective of further motives that may inhibit reporting, legal compliance is difficult to achieve where legal requirements are vague. In that regard, the difficulties to determine the reporting thresholds in the past and in the future are addressed. In consideration of the increased attack surface and threat scenario, it is argued that incidents where no harm has materialized should not be treated any different than incidents that have actually resulted in harm in order to acquire the envisaged full picture of the threat landscape and create value for business and society.
MILP-Aided Cryptanalysis of the FUTURE Block Cipher

FUTURE is a recently proposed, lightweight block cipher. It has an AES-like, SP-based, 10-round encryption function, where, unlike most other lightweight constructions, the diffusion layer is based on an MDS matrix. Despite its relative complexity, it has a remarkable hardware performance due to careful design decisions. In this paper, we conducted a MILP-based analysis of the cipher, where we incorporated exact probabilities rather than just the number of active S-boxes into the model. Through the MILP analysis, we were able to find differential and linear distinguishers for up to 5 rounds of FUTURE, extending the known distinguishers of the cipher by one round.
Adaptive Event-Triggered Fuzzy Control of State-Constrained Stochastic Nonlinear Systems Using IBLFs

In this article, an adaptive tracking control problem is addressed for nonstrict-feedback stochastic nonlinear systems subject to state constraints. Fuzzy logic systems (FLSs) are used to model unknown nonlinearities and avoid the algebraic loop arising from the system structure. Appropriate integral Barrier Lyapunov functions (IBLFs) are chosen so that time-varying full state constraints can be guaranteed directly rather than by transforming the constraint object. In the framework of backstepping technology, the relative threshold strategy is introduced to modify the adaptive control scheme so that the controller can be updated only after the trigger condition has been met, which reduces the update frequency of the controller and the loss of the actuator. Combined with Lyapunov stability theory, it is shown that all closed-loop signals are bounded in probability, in which the states remain within the specified constraints, and there is no Zeno behavior. A series of simulation results are given to reveal the effectiveness of the constructed control scheme.
Snow Losses for Photovoltaic Systems: Validating the Marion and Townsend Models

For northern communities relying on solar photovoltaic (PV) systems to offset the cost of imported diesel fuel, there is an acute need to forecast energy losses due to snow. Two leading snow loss models, developed by Marion et al. and Townsend and Powers, are used widely in the PV industry. In order to test these models, this study compares modeled monthly and annual losses to a best-estimate reference based on PV power output and camera images from an array operating in Quebec, Canada. This study addresses two key areas: the development of an image-analysis algorithm to detect snow on a PV array and the validation of models estimating energy loss. For the PV array in this work, Townsend and Powers is a closer approximation of real annual energy losses than the Marion et al. model. However, to provide communities and PV system developers with accurate snow loss predictions, more work needs to be done to improve these models using arrays at different latitudes. The image analysis algorithm used in this work is 90% accurate in identifying snow. This leads to a difference of about 0.2% points in annual snow loss estimates when using the image analysis algorithm instead of visually inspected photos.
A new support vector machine for categorical features

Support vector machine (SVM) was originally developed to solve binary classification problems for objects with only continuous features, and it often outperforms other classifiers. However, we often encounter datasets with mixed-type features or categorical features only. This study proposes an efficient SVM for dealing with such datasets. The proposed SVM uses a subset of categorical features and it performs well in most cases, including imbalanced and/or high-dimensional categorical datasets. In particular, it is more efficient than existing SVMs in high-dimensional categorical datasets. To validate its performance, it is applied to simulated datasets and various benchmark datasets, and it is also compared to existing SVMs for categorical features.
Intention to Use Blockchain Technologies: Evidence from Two Regions — ASEAN and Middle-East

This study presents an empirically well-versed picture of the intention to use blockchain technologies. Empirical evidence from two different regions — ASEAN and Middle-East (ME) — reveals factors that lead managers to use blockchain technology. Samples of 268 and 224 from two response groups were collected using a Likert-based questionnaire. The proposed model was tested using structural equation modeling with SmartPLS 3.2.9. Results confirm that the design of platforms has positive and significant relationships with consumers’ intention to use blockchain technologies. Furthermore, positive and significant relationships were also found between perceived usefulness and perceived ease of use with consumers’ intention to use blockchain technologies. Our results also indicate that perceived ease of use had a positive and significant effect on perceived usefulness. So far, there is limited standard relationship found between perceived ease of use and consumers’ intention to use blockchain technologies in the data from the ASEAN region. This study contributes to the evidence-based view on the effect of intention to use blockchain technology, on the roles of design, perceived ease of use, and perceived usefulness through proposing a novel framework. It adds to the field of technology acceptance studies by providing fresh insights among managers. The paper also emphasizes practical implications for business leaders who wish to capitalize on BT’s advantages in the industry.
Quantifying body ownership information processing and perceptual bias in the rubber hand illusion

Bodily illusions have fascinated humankind for centuries, and researchers have studied them to learn about the perceptual and neural processes that underpin multisensory channels of bodily awareness. The influential rubber hand illusion (RHI) has been used to study changes in the sense of body ownership - that is, how a limb is perceived to belong to one's body, which is a fundamental building block in many theories of bodily awareness, self-consciousness, embodiment, and self-representation. However, the methods used to quantify perceptual changes in bodily illusions, including the RHI, have mainly relied on subjective questionnaires and rating scales, and the degree to which such illusory sensations depend on sensory information processing has been difficult to test directly. Here, we introduce a signal detection theory (SDT) framework to study the sense of body ownership in the RHI. We provide evidence that the illusion is associated with changes in body ownership sensitivity that depend on the information carried in the degree of asynchrony of correlated visual and tactile signals, as well as with perceptual bias and sensitivity that reflect the distance between the rubber hand and the participant's body. We found that the illusion's sensitivity to asynchrony is remarkably precise; even a 50 ms visuotactile delay significantly affected body ownership information processing. Our findings conclusively link changes in a complex bodily experience such as body ownership to basic sensory information processing and provide a proof of concept that SDT can be used to study bodily illusions.
Estimating Object Perception Performance in Aerial Imagery Using a Bayesian Approach

In this work, we present a novel approach for predicting the perception performance of an optical aerial surveillance system. We outline our use case in multi-target tracking motivating this work before giving some background on related work in sensor performance estimation. After explaining the reasoning behind selecting a Bayesian network against other machine learning classifiers, we present relevant underlying concepts of sample-based inference and parameter learning in probabilistic graphical models. We present the modular architecture of our performance estimator and reason through the model input nodes based on system, object, and scene parameters. Further, we describe the extensive flight experiment performed using a crewed ultralight aircraft equipped with a state-of-the-art aerial surveillance gimbal to collect imagery of target vehicles under carefully specified conditions. We describe the implemented post-processing of the recorded data, the method used to sample a reasonably sized dataset for learning, the criteria followed while labeling the dataset, and the technique used to learn a usable model based on a Bayesian network. Next, we analyze the classification performance of the model on an independent validation dataset and compare it against two additional machine-learning classifiers trained on the same dataset. We discuss the limitations of the proposed model and the presented validation. We wrap up by giving an outlook on various opportunities for possible future improvements to enhance the prediction performance of our network. Finally, we present other use cases benefitting from a similarly structured sensor performance prediction model, such as search planning.
Design and Flight Testing of a UAV with a Robotic Arm

Unmanned Aerial Vehicles (UAVs) also commonly known as drones, are increasingly being used in many industries as they capable of accessing areas that are difficult to reach or are remote. However, due to limitations in the payload's reachability the same level work that a human in a similar location cannot be completed. To address this issue, this paper presents the implementation of a robotic manipulator on a UAV which can extend the reachability beyond the normal constraints of the UAV. Many designs of robotic manipulators underneath the UA V have been previously proposed. However, limited designs explore the use of a manipulator mounted to the top of the U A V. This is due to the instabilities that occur as the Centre of Gravity (CoG) is raised. Different methods have been proposed in literature to combat this, ranging from integrating the arm movements with the control systems of the UAV (autopilot), to designs that balance the CoG mechanically. This paper describes the design and implementation of a top mounted pantographic manipulator. Small changes in CoG will be controlled by the UAV's autopilot. The manipulator implemented is a novel design that is able to control the CoG mechanically. The design operates similar to a car jack with the end effector and counterbalance moving in an equal and opposite motion. Through bench and flight testing it was found that the manipulator can mitigate horizontal CoG movements whilst the end effector was moved with an appropriate reachability. The system is demonstrated through flight experiments and shows the feasibility of a top mounted manipulator on a UAV. This aims to provide a benefit to remote sensing and sampling applications without impacting on the flight performance. The full final flight demonstration can be seen in the following link: https://youtu.be/qBzpA1m-dA.
On the Effect of Transitivity and Granularity on Vulnerability Propagation in the Maven Ecosystem

Reusing software libraries is a pillar of modern software engineering.In 2022, the average Java application depends on 40 third-party libraries.Relying on such libraries exposes a project to potential vulnerabilities and may put an application and its users at risk.Unfortunately, research on software ecosystems has shown that the number of projects that are affected by such vulnerabilities is rising.Previous investigations usually reason about dependencies on the dependency level, but we believe that this highly inflates the actual number of affected projects.In this work, we study the effect of transitivity and granularity on vulnerability propagation in the Maven ecosystem.In our research methodology, we gather a large dataset of 3M recent Maven packages.We obtain the full transitive set of dependencies for this dataset, construct wholeprogram call graphs, and perform reachability analysis.This approach allows us to identify Maven packages that are actually affected by using vulnerable dependencies.Our empirical results show that: (1) about 1/3 of packages in our dataset are identified as vulnerable if and only if all the transitive dependencies are considered.(2) less than 1% of packages have a reachable call path to vulnerable code in their dependencies, which is far lower than that of a naive dependency-based analysis.(3) limiting the depth of the resolved dependency tree might be a useful technique to reduce computation time for expensive fine-grained (vulnerability) analysis.We discuss the implications of our work and provide actionable insights for researchers and practitioners.
MulCS: Towards a Unified Deep Representation for Multilingual Code Search

Code search aims to search for relevant code snippets through queries, which has become an essential requirement to assist programmers in software development. With the availability of large and rapidly growing source code repositories covering various languages, multilingual code search can leverage more training data to learn complementary information across languages. Contrastive learning can naturally understand the similarity between functionally equivalent code across different languages by narrowing the distance between objects with the same function while keeping dissimilar objects further apart. Some works exist addressing monolingual code search problems with contrastive learning, however, they mainly exploit every specific programming language’s textual semantics or syntactic structures for code representation. Due to the high diversity of different languages in terms of syntax, format, and structure, these methods limit the performance of contrastive learning in multilingual training. To bridge this gap, we propose a unified semantic graph representation approach toward multilingual code search called MulCS. Specifically, we first design a general semantic graph construction strategy across different languages by Intermediate Representation (IR). Furthermore, we introduce the contrastive learning module integrated into a gated graph neural network (GGNN) to enhance query-multilingual code matching. The extensive experiments on three representative languages illustrate that our method outperforms state-of-the-art models by 10.7% to 77.5% in terms of MRR on average.
BERT-based Extractive Text Summarization of Scholarly Articles: A Novel Architecture

Currently, there are a variety of extractive summarization approaches available, each with its own set of advantages and disadvantages. However, none of them are ideal, which means there is still room for advancement in this field of automation. BERT is a multilayer transformer network that has been pre-trained for a variety of self-supervised applications. However, because of its input length restriction, it is only appropriate for short text. As a result, we believe that using BERT for long document summarization will be a challenging task. We suggest a novel approach through which BERT can be utilized to summarize long documents. We used the method of dividing a whole document into multiple chunks and each chunk contains one sentence. The basic idea is to get sentence embeddings from BERT and then apply an encoder-decoder model on top of BERT. Experiments are conducted with two scholarly datasets (arXiv and PubMed). The results show that our technique consistently outperform several state-of-the-art models.
Completing and Debugging Ontologies: State-of-the-art and Challenges in Repairing Ontologies

As semantically-enabled applications require high-quality ontologies, developing and maintaining ontologies that are as correct and complete as possible is an important although difficult task in ontology engineering. A key step is ontology debugging and completion. In general, there are two steps: detecting defects and repairing defects. In this paper we discuss the state of the art regarding the repairing step. We do this by formalizing the repairing step as an abduction problem and situating the state of the art with respect to this framework. We show that there are still many open research problems and show opportunities for further work and advancing the field.
A detailed study on trust management techniques for security and privacy in IoT: challenges, trends, and research directions

The Internet of Things is a modern technology that is directed at easing human life by automating most of the things used in daily life. The never-ending dependency on the network for communication is attracting adversaries to exploit the vulnerabilities of IoT. Therefore, this technology is facing some serious issues and challenges concerning security and privacy. These issues and challenges are the real motivation behind considering this study. Hence, this survey includes a discussion about security and privacy challenges as well as available solutions for IoT based wireless sensor networks. This systematic literature review (SLR) focuses particularly on a popular and applicable security approach known as Trust Management System (TMS). Firstly, all aspects of trust management, including trust indicators, trust properties, trust evaluation, trust building, trust models and the importance of those models for security and privacy, trust prediction methodologies, and ultimately trust-based attacks, are covered in this literature. Secondly, trust management schemes are classified into four groups based on the methodology used for trust-based security solutions in the IoT: cryptography-based, computational and probabilistic-based, information theory-based, and others. Then, an understanding of the problems and difficulties with current methodologies is given, along with suggestions for further research. Finally, the SLR is concluded by formulating the desirable characteristics of a trust management system in the IoT and proposing a trust model suitable for IoT networks.
HINRDNet: A half instance normalization residual dense network for passive millimetre wave image restoration

During the imaging process of passive millimeter wave (PMMW), due to the long wavelength of millimeter wave, the small aperture of the antenna, and the introduction of noises, the obtained PMMW image are degraded by blur and noise simultaneously. To address these problems, we propose a passive millimeter-wave image restoration network called as HINRDNet which includes a denoising module (DNM), deblurring and denoising U-Net (DBNU-Net), and an image reconstruction module (RCM). In order to fully utilize local features, the proposed network is designed based on Half Instance Normalization Block, and is mainly composed of the densely connected sum dense HINBlock (SDHB) and the concatenate dense HINBlock (CDHB) block from low step to high step. In addition, the higher level features information is combined with low level features for global feature cross fusion. Finally, we construct remote sensing and infrared simulation datasets to train the network, and test it on simulated datasets and real PMMW degraded images. The experimental results for the simulated degraded images and the real PMMW degraded images demonstrate that our proposed method outperforms the existing state-of-the-art methods in terms of visual perception and evaluation metrics.