Reviewerly/exHarmony huggingface dataset:
Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.
Integrating earthquake early warnings into business continuity and organisational resilience: lessons learned from Mexico City

Earthquake early warning (EEW) is becoming a popular tool for mitigating earthquake‐induced losses. However, the current literature separates EEW technical components and their operational and behavioural implications. This paper investigates how EEW can be integrated into business continuity practices, organisational resilience, and disaster risk reduction (DRR). A mixed methods approach is applied to analyse EEW perceptions in the case‐study context of Mexico City, Mexico, which is characterised by a high level of seismic hazard and social and physical exposure/vulnerability. The dataset includes evidence from 15 semi‐structured interviews with representatives of the public and private sectors, such as governments and enterprises, and 78 valid questionnaires compiled by local organisations, including civil protection and education institutions. The results reveal inconsistencies between technical EEW methodologies and their integration into three core domains of organisational practice: accountability, governance, and jurisdiction; standardisation of plans and procedures; training and education. Finally, open challenges for future research are highlighted.
Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.
Vocational Training in Virtual Reality: A Case Study Using the 4C/ID Model

Virtual reality (VR) is an emerging technology with a variety of potential benefits for vocational training. Therefore, this paper presents a VR training based on the highly validated 4C/ID model to train vocational competencies in the field of vehicle painting. The following 4C/ID components were designed using the associated 10 step approach: learning tasks, supportive information, procedural information, and part-task practice. The paper describes the instructional design process including an elaborated blueprint for a VR training application for aspiring vehicle painters. We explain the model’s principles and features and their suitability for designing a VR vocational training that fosters integrated competence acquisition. Following the methodology of design-based research, several research methods (e.g., a target group analysis) and the ongoing development of prototypes enabled agile process structures. Results indicate that the 4C/ID model and the 10 step approach promote the instructional design process using VR in vocational training. Implementation and methodological issues that arose during the design process (e.g., limited time within VR) are adequately discussed in the article.
Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

In the last decades, data-driven methods have gained great popularity in the industry, supported by state-of-the-art advancements in machine learning. These methods require a large quantity of labeled data, which is difficult to obtain and mostly costly and challenging. To address these challenges, researchers have turned their attention to unsupervised and few-shot learning methods, which produced encouraging results, particularly in the areas of computer vision and natural language processing. With the lack of pretrained models, time series feature learning is still considered as an open area of research. This paper presents an efficient two-stage feature learning approach for anomaly detection in machine processes, based on a prototype few-shot learning technique that requires a limited number of labeled samples. The work is evaluated on a real-world scenario using the publicly available CNC Machining dataset. The proposed method outperforms the conventional prototypical network and the feature analysis shows a high generalization ability achieving an F1-score of 90.3%. The comparison with handcrafted features proves the robustness of the deep features and their invariance to data shifts across machines and time periods, which makes it a reliable method for sensory industrial applications.
Residual attentive feature learning network for salient object detection

Salient object detection is one of the basic challenging problems in the area of computer vision. Traditional saliency models usually utilize handcrafted features and various prior cues to locate and segment salient objects from complicated surroundings. Recently, with the development of convolutional neural networks (CNNs), most of existing approaches explore the combination of multi-level feature maps for improving detection performance. However, how to learn powerful feature is still a challenge, and the multi-level and multi-scale feature maps are usually aggregated without distinction. In this paper, we propose a novel salient object detection approach named residual attentive feature learning (RAFL) to uniformly highlight salient objects and effectively suppress background noises. In our proposed RAFL, a global perception (GP) module is designed to obtain rich global features of input image. A pyramid feature extraction (PFE) module is utilized to capture the rich context of side-output information. Moreover, an attentive feature selection (AFS) module is applied to refine these side-output features and generate attentive features, so as to find out the important feature channels having high responses to the salient objects. Finally, a residual feature learning (RFL) module is proposed to make the shallow layers generate more accurate saliency maps with the help of the deep layers which contain more global semantic information. Experimental results show that our proposed approach performs favorably against 18 state-of-the-art representative methods on five benchmark datasets.
Implications of new hyperspectral satellites for raw materials exploration

Abstract Hyperspectral remote sensing already is important in geoscientific research in the fields of geology, soil, exploration and mining. New hyperspectral satellite systems are already in operation (e.g. PRISMA and DESIS; Caporusso et al. 2020; Alonso et al. (Sensors 19(20):4471–4515, 2019)) and more systems are planned e.g. the European Copernicus Next Generation Hyperspectral Satellite CHIME (Nieke and Rast 2018). The German system EnMAP was successfully launched into space on 1 st of April 2022 (DLR 2022). The potential of hyperspectral airborne and satellite borne data for mining-related applications is discussed. Investigated are the information contents of hyperspectral data for exploration target recognition and their dependency on spatial resolutions of different sensor platforms. Airborne data offer high spatial resolution of 2.5 m with limited areal data acquisition, whereas hyperspectral spaceborne sensors guaranty nearly worldwide data availability with the same spectral characteristics but medium spatial resolution (30 m). The aspects of high spectral resolution and high versus medium spatial resolution targeted mineral mapping is investigated. The methodological concept includes processing aspects, standardized data availability for mineral mapping and mineralization targeting for operational application, to maintain/allow application of hyperspectral data even for non-remote sensing experts. Based on hyperspectral airborne data acquired in the Aggeneys region in South Africa, spaceborne hyperspectral data are simulated following the EnMAP specifications, and the concept for targeted mapping of surface alterations of a lead zinc deposit is discussed.
Sustainable Value Co-Production and Co-Creation in Virtual Reality: An Exploratory Research on Business-to-Business Interactions

The objective of this study is to identify the environment for business interactions in virtual reality in the value co-production and co-creation process and to understand how such an environment enables the sustainable development of co-creational activities. This paper takes the concepts of value co-production and co-creation and presence and the Actors, Resources, and Activities Model as conceptual references for the exploration of interactions in virtual reality. Using ten in-depth interviews with senior managers, this paper takes an exploratory case analysis perspective. Results show how the sense of presence—level of immersion to which a medium that produces seemingly accurate representations of objects, events, and people, can submerge the perceptual system of a user—is the primary technical resource enabling the performance of value co-production and co-creational activities inside VR simulations. Additionally, the VR consultants and the hiring firms become the actors, organizing the value-co-production and co-creation practices; those customers and employees are the actors performing five co-creational activities that can result in valuable outcomes for the VR consultants and the hiring firms. Virtual reality is one of the newest vehicles used for the co-production and co-creation of value, for innovating existing products, for sustainable creation of new marketing offerings, or for managing network relations. This study contributes to existent literature by showing the process and practice of value co-production and value co-creation taking place in virtual reality simulations in B2B settings.
Application of Recurrent Neural Networks with Controlled Elements for Accuracy Enhancement in Recognition of Sound Events in a Fog Computing Environment

The article discusses the usage of recurrent neural networks in order to increase the accuracy of determining sound events in intelligent video surveillance systems. New possibilities for sound recognition arise when recurrent neural networks with controlled elements are applied. A new method is proposed for determining sound events by neural networks in a fog computing environment. Developed applications for PCs and smartphones are presented. Efficiency of the proposed method is confirmed by the experimental results.
Non-Fungible Tokens: Might Learning About Them Be Necessary?

Non-fungible tokens (NFTs), as a variant of tokens making up the blockchain, are recently gaining attention with different opportunities for the real and virtual assets and with potential use cases in decentralized markets, arts, gaming and education. This paper gives an overview of the NFT concept and its uses and presents the results of the survey conducted in Croatia in 2021 with the goal of gaining insight into familiarity of respondents with the concepts of the blockchain and NFTs. The results of the survey, especially when compared to publicly available results of an international survey from 2020, indicate that educative efforts might be needed in Croatia towards increasing knowledge about NFTs taking into account their wide potential areas of application.
Deceptive directories and “vulnerable” logs: a honeypot study of the LDAP and log4j attack landscape

The Lightweight Directory Access Protocol (LDAP) has been widely used to query directory services. It is mainly utilized for reading, writing, and searching directory services like the Active Directory. The vast adoption of LDAP for authentication has entailed several attack attempts like injection attacks and unauthorized access due to third-party key storage. Furthermore, recent vulnerabilities discovered in libraries like the Log4j can lead adversaries to obtain unauthorized information from the directory services through pivoting attacks. Moreover, the LDAP can be configured to operate on UDP, motivating adversaries to exploit it for Distributed Reflection Denial of Service attacks (DRDoS). This paper presents a study of attacks on the LDAP by deploying honeypots that simulate multiple profiles that support the LDAP service and correlating the attack datasets obtained from honeypots deployed by the Honeynet Project community. We observe a total of 39,388 malicious events targeting the honeypots and discover 273 unique attack sources performing pivot attacks in a period of one month.
Robotic role theory: an integrative review of human–robot service interaction to advance role theory in the age of social robots

Purpose Social robots increasingly adopt service roles in the marketplace. While service research is beginning to unravel the implications for theory and practice, other scientific disciplines have amassed a wealth of empirical data of robots assuming such service roles. The purpose of this paper is to synthesize these findings from a role theory perspective with the aim of advancing role theory for human–robot service interaction (HRSI). Design/methodology/approach A systematic review of more than 10,000 articles revealed 149 empirical HRSI-related papers across scientific disciplines. The respective articles are analyzed employing qualitative content analysis through the lens of role theory. Findings This review develops an organizing structure of the HRSI literature across disciplines, delineates implications for role theory development in the age of social robots, and advances robotic role theory by providing an overarching framework and corresponding propositions. Finally, this review introduces avenues for future research. Originality/value This study pioneers a comprehensive review of empirical HRSI literature across disciplines adopting the lens of role theory. The study structures the body of HRSI literature, adapts traditional and derives novel propositions for role theory (i.e. robotic role theory), and delineates promising future research opportunities.
Grief-free Atomic Swaps

Atomic Swaps enable exchanging crypto-assets with-out trusting a third party. To enable these swaps, both parties lock funds and let their counterparty withdraw them in exchange for a secret. This leads to the so-called griefing attack, or the emergence of an American Call option, where one party stops participating in the swap, thereby making their counterparty wait for a timelock to expire before they can withdraw their funds. The standard way to mitigate this attack is to make the attacker pay a premium for the emerging American Call option. In these premium-paying approaches, the premium itself ends up being locked for possibly an even longer duration than the swap amount itself. We propose a new Atomic Swap construction, where neither party exposes itself to a griefing attack by their counterparty. Notably, unlike previous constructions, ours can be implemented in Bitcoin as is. Our construction also takes fewer on-chain transactions and has a lower worst-case timelock.
Building structural analysis based Internet of Things network assisted earthquake detection

Analyzing the patterns of earthquakes and creating a system to detect them, can help to minimize loss of lives. Existing sensor based detection works failed to distinguish between earthquakes and other external vibrational noises that may be caused due to construction or other external sources. In the proposed work, to overcome this drawback, first in-depth structural analysis of shear walls is done to find a structurally suitable location to install the sensor in a building. Further, MPU6050 sensor is used to reduce the complexity and make the circuit less bulky. This sensor consists of an accelerometer which measures the change in acceleration and a gyroscope which records the shaking pattern of the ground. Since this shaking pattern is exhibited exclusively by an earthquake occurrence, the proposed model distinguishes between earthquake and other non-hazardous vibrations efficiently. With the considered model for machine learning algorithms, we were able to achieve 99.82% of accuracy, which is an important factor for earthquake detection.
Modality-Specific Segmentation Network for Lung Tumor Segmentation in PET-CT Images

Lung tumor segmentation in PET-CT images plays an important role to assist physicians in clinical application to accurately diagnose and treat lung cancer. However, it is still a challenging task in medical image processing field. Due to respiration and movement, the lung tumor varies largely in PET images and CT images. Even the two images are almost simultaneously collected and registered, the shape and size of lung tumors in PET-CT images are different from each other. To address these issues, a modality-specific segmentation network (MoSNet) is proposed for lung tumor segmentation in PET-CT images. MoSNet can simultaneously segment the modality-specific lung tumor in PET images and CT images. MoSNet learns a modality-specific representation to describe the inconsistency between PET images and CT images and a modality-fused representation to encode the common feature of lung tumor in PET images and CT images. An adversarial method is proposed to minimize an approximate modality discrepancy through an adversarial objective with respect to a modality discriminator and reserve modality-common representation. This improves the representation power of the network for modality-specific lung tumor segmentation in PET images and CT images. The novelty of MoSNet is its ability to produce a modality-specific map that explicitly quantifies the modality-specific weights for the features in each modality. To demonstrate the superiority of our method, MoSNet is validated in 126 PET-CT images with NSCLC. Experimental results show that MoSNet outperforms state-of-the-art lung tumor segmentation methods.
Evaluation of synthetic aerial imagery using unconditional generative adversarial networks

Image generation techniques, such as generative adversarial networks (GANs), have become sufficiently sophisticated to cause growing concerns around the authenticity of images in the public domain. Although these generation techniques have been applied to a wide range of images, including images with objects and faces, there are comparatively few studies focused on their application to the generation and subsequent evaluation of Earth Observation (EO) data, such as aerial and satellite imagery. We examine the current state of aerial image generation by training state-of-the-art unconditional GAN models to generate realistic aerial imagery. We train PGGAN, StyleGAN2 and CoCoGAN models using the Inria Aerial Image benchmark dataset, and quantitatively assess the images they produce according to the Fréchet Inception Distance (FID) and the Kernel Inception Distance (KID). In a paired image human detection study we find that current synthesised EO images are capable of fooling humans and current performance metrics are limited in their ability to quantify the perceived visual quality of these images.
Carboncoin: Blockchain Tokenization of Carbon Emissions with ESG-based Reputation

Recent blockchain-based carbon markets focus on permit-based trading requiring manual application processes to grant the right for carbon emission. A decentralized blockchain-based carbon market without relying on off-chain permits is yet to be explored. In this paper, we present a new design of blockchain-based carbon trading through the introduction of Carboncoin – a blockchain asset which tokenizes the right of energy producers to emit carbon. Instead of relying on off-chain and centralized permits, producers are allowed to freely exchange Carboncoin with each other for fiat currency. By using an on-chain asset, carbon production can be automatically expensed whenever a producer records new energy production which is certified on the blockchain. Moreover, the proposed design enables generic ESG (Environmental, Social and Governance) data to be used to provide a more holistic reputation score inclusive of ESG initiatives undertaken by market participants. We conclude that entirely blockchain-based carbon markets can be made more comprehensive using ESG data and on-chain assets, but at the cost of reduced performance.