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With the rapid development and wide application of cloud computing and 5G communication, the number of mobile users is increasing rapidly, meaning that cloud storage services are receiving more and more attention. The equality test technology of retrievable encrypted data has become a hot research topic among scholars in recent years. In view of the problem of offline keyword-guessing attacks (KGAs) caused by collusion between internal servers and users, a public key encryption with equality test scheme (RKGA-CET) with higher security against KGAs is proposed. Based on the assumed difficulty of the discrete logarithm problem (DLP) and the properties of bilinear mapping, a specific encryption algorithm that encrypts the keyword twice is designed. In the first encryption stage, we convert the keyword according to the property of isomorphism of a finite field. In the second encryption stage, we encrypt the converted keyword vector and embed the user’s private key, and then perform the equality test. The algorithm ensures that the adversary cannot generate legal ciphertexts and implement KGAs when the secondary server is offline. At the same time, the algorithm also supports two authorization modes, in which case users can flexibly choose the corresponding authorization mode according to their own needs. Performance analysis shows that this scheme has overall superiority compared with other similar ones. Full article

How to achieve secure content distribution and accountability in information-centric networking (ICN) is a crucial problem. Subscribers need to verify whether the data came from a reliable source, rather than from a spoofing adversary. Public key cryptography was introduced to achieve a method of authentication that binds the data packet to its owner. In existing prototypes, PKIs, identity-based signatures (IBSs) and recommendation networks are the common schemes used to ensure the authenticity and availability of public keys. However, CA-based PKIs and KGC-based IBSs have been proven to be weak when it comes to resisting security attacks, with recommendation networks being too complex to deploy. In this respect, we designed a novel distributed authentication model as a secure scheme to support public key cryptography. Our model establishes a decentralized public key infrastructure by combining the smart contracts of blockchain and optimized zero-knowledge proof-verifiable presentations by utilizing the DID project, which realizes the management of public key certificates through blockchain and ensures the authenticity and availability of public keys in decentralized infrastructure. Our scheme fundamentally solves the issues of security and feasibility in existing schemes and provides a more scalable solution with respect to authenticating data sources. An experiment demonstrated that our proposal is 20% faster than the original zero knowledge proof scheme in registration. Full article

Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device’s data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies. Full article

G protein-coupled receptors (GPCRs) control the response of cells to many signals, and as such, are involved in most cellular processes. As membrane receptors, they are accessible at the surface of the cell. GPCRs are also the largest family of membrane receptors, with more than 800 representatives in mammal genomes. For this reason, they are ideal targets for drugs. Although about one third of approved drugs target GPCRs, only about 16% of GPCRs are targeted by drugs. One of the difficulties comes from the lack of knowledge on the intra-cellular events triggered by these molecules. In the last two decades, scientists have started mapping the signaling networks triggered by GPCRs. However, it soon appeared that the system is very complex, which led to the publication of more than 320,000 scientific papers. Clearly, a human cannot take into account such massive sources of information. These papers represent a mine of information about both ontological knowledge and experimental results related to GPCRs, which have to be exploited in order to build signaling networks. The ABLISS project aims at the automatic building of GPCRs networks using automated deductive reasoning, allowing to integrate all available data. Therefore, we processed the automatic extraction of network information from the literature using Natural Language Processing (NLP). We mainly focused on the experimental results about GPCRs reported in the scientific papers, as so far there is no source gathering all these experimental results. We designed a relational database in order to make them available to the scientific community later. After introducing the more general objectives of the ABLISS project, we describe the formalism in detail. We then explain the NLP program using the finite state methods (Unitex graph cascades) we implemented and discuss the extracted facts obtained. Finally, we present the design of the relational database that stores the facts extracted from the selected papers. Full article

Supply chains have received significant attention in recent years. Neural networks (NN) are a technique available in artificial intelligence (AI) which has many supporters due to their diverse applications because they can be used to move towards complete harmony. NN, an emerging AI technique, have a strong appeal for a wide range of applications to overcome many issues associated with supply chains. This study aims to provide a comprehensive view of NN applications in supply chain management (SCM), working as a reference for future research directions for SCM researchers and application insight for SCM practitioners. This study generally introduces NNs and has explained the use of this method in five features identified by supply chain area, including optimization, forecasting, modeling and simulation, clustering, decision support, and the possibility of using NNs in supply chain management. The results showed that NN applications in SCM were still in a developmental stage since there were not enough high-yielding authors to form a strong group force in the research of NN applications in SCM. Full article

Cultural heritage inventories have been created to collect and preserve the culture and to allow the participation of stakeholders and communities, promoting and disseminating their knowledges. There are two types of inventories: those who give data access via web services or open data, and others which are closed to external access and can be visited only through dedicated web sites, generating data silo problems. The integration of data harvested from different archives enables to compare the cultures and traditions of places from opposite sides of the world, showing how people have more in common than expected. The purpose of the developed portal is to provide query tools managing the web services provided by cultural heritage databases in a transparent way, allowing the user to make a single query and obtain results from all inventories considered at the same time. Moreover, with the introduction of the ICH-Light model, specifically studied for the mapping of intangible heritage, data from inventories of this domain can also be harvested, indexed and integrated into the portal, allowing the creation of an environment dedicated to intangible data where traditions, knowledges, rituals and festive events can be found and searched all together. Full article

Semantic segmentation on Landsat-8 data is crucial in the integration of diverse data, allowing researchers to achieve more productivity and lower expenses. This research aimed to improve the versatile backbone for dense prediction without convolutions—namely, using the pyramid vision transformer (PRM-VS-TM) to incorporate attention mechanisms across various feature maps. Furthermore, the PRM-VS-TM constructs an end-to-end object detection system without convolutions and uses handcrafted components, such as dense anchors and non-maximum suspension (NMS). The present study was conducted on a private dataset, i.e., the Thailand Landsat-8 challenge. There are three baselines: DeepLab, Swin Transformer (Swin TF), and PRM-VS-TM. Results indicate that the proposed model significantly outperforms all current baselines on the Thailand Landsat-8 corpus, providing F1-scores greater than 80% in almost all categories. Finally, we demonstrate that our model, without utilizing pre-trained settings or any further post-processing, can outperform current state-of-the-art (SOTA) methods for both agriculture and forest classes. Full article

The pathway through which behavior change techniques have an effect on the behavior of an individual is referred to as the Mechanism of Action (MoA). Digitally enabled behavior change interventions could potentially benefit from explicitly modelling the MoA to achieve more effective, adaptive, and personalized interventions. For example, if ‘motivation’ is proposed as the targeted construct in any behavior change intervention, how can a model of this construct be used to act as a mechanism of action, mediating the intervention effect using various behavior change techniques? This article discusses a computational model for motivation based on the neural reward pathway with the aim to make it act as a mediator between behavior change techniques and target behavior. This model’s formal description and parametrization are described from a neurocomputational sciences prospect and elaborated with the help of a sub-question, i.e., what parameters/processes of the model are crucial for the generation and maintenance of motivation. An intervention scenario is simulated to show how an explicit model of ‘motivation’ and its parameters can be used to achieve personalization and adaptivity. A computational representation of motivation as a mechanism of action may also further advance the design, evaluation, and effectiveness of personalized and adaptive digital behavior change interventions. Full article

There are still some shortcomings in the latest version of the 5G authentication and key agreement (AKA) protocol, which is specified by the third-generation partnership project (3GPP). To overcome these shortcomings, an improved primary authentication and key agreement protocol for 5G networks (5G-IPAKA) were proposed. However, one of the shortcomings of the 5G AKA protocol has not been completely overcome in the 5G-IPAKA protocol, resulting in denial of service (DoS) attacks against both the serving network (SN) and the home network (HN). In addition, the 5G AKA protocol has large communication and computation overhead, while the 5G-IPAKA protocol has an even larger communication and computation overhead. These will lead to a great deal of energy consumption. To solve these problems, a secure 5G authentication and key agreement protocol, with less communication and computation overhead (5GAKA-LCCO) is proposed. Then, the 5GAKA-LCCO protocol is proven secure in both the strand space model and the Scyther tool. Further discussion and comparative analysis show that the 5GAKA-LCCO protocol can completely overcome the shortcomings of the latest version of the 5G AKA protocol and is better than the recently improved 5G AKA protocols in overcoming these shortcomings. Additionally, the 5GAKA-LCCO protocol has less communication and computation overhead than the 5G AKA protocol and the recently improved 5G AKA protocols. Full article

Any information is (transported as) a selection from an ordered set, which is the “domain” of the information. For example, any piece of digital information is a number sequence that represents such a selection. Its senders and receivers (with software) should know the format and domain of the number sequence in a uniform way worldwide. So far, this is not guaranteed. However, it can be guaranteed after the introduction of the new “Domain Vector” (DV) data structure: “UL plus number sequence”. Thereby “UL” is a “Uniform Locator”, which is an efficient global pointer to the machine-readable online definition of the number sequence. The online definition can be adapted to the application so that the DV represents the application-specific, reproducible features in a precise (one-to-one), comparable, and globally searchable manner. The systematic, nestable online definition of domains of digital information (number sequences) and the globally defined DV data structure have great technical potential and are recommended as a central focus of future computer science. Full article

Research projects in the security domain often aim to develop innovative technology-based solutions for end users (e.g., situational awareness tools, crisis management tools). The pandemic crisis hit hard and without warning, not only influencing our everyday life but also the scientific community. To continue applied research projects during a pandemic, work structures needed to be adapted (e.g., user requirements collection, use case development), as face-to-face events were impossible but crucial to collect high quality requirements with a variety of different stakeholders. To ensure continued multi-stakeholder engagement we developed an overarching framework for collecting user requirements and use cases in an online setting and applied the framework within two research projects. The framework consists of four steps with the aim to assure high quality user requirements and use case collection (first analysis, stakeholder consultation, evaluation and prioritization, technical evaluation). The two projects presented in this paper provide insight on the potential of the framework. The framework offers a structured approach that fits for many different security research projects in terms of the easy application and its transferability. The main advantages (e.g., easily adaptable, reduced workshop time, no need to travel, suitability for different contexts and project types, etc.) and drawbacks (e.g., organization of online events, feedback collection time, etc.) of the framework are presented and discussed in this paper to offer increased stakeholder engagement. Empirical testing of the framework is proposed. Full article

The tourism and hospitality sectors contribute significantly to the Indonesian economy. Meanwhile, COVID-19 affects these sectors. During the pandemic, the Indonesian government applied quarantine regulations at designated hotels to support its tourism industry. Since COVID-19 is becoming endemic and travel bans are being relaxed, hotel satisfaction becomes a crucial factor in quarantine hotels. If guests have a positive experience while staying at these hotels, they are likely to return for a staycation or vacation in the near future. The study examined 4856 reviews from Google reviews on 15 quarantine hotels in Indonesia. Following word frequency calculations in a matrix, UCINET 6.0 is used to analyze the network centrality and perform CONCOR analysis. The CONCOR analysis categorizes the review data into five categories. As quantitative analysis was performed, exploratory factor analysis was grouped into six variables: tangible, assurance, frontline, accommodation, quarantine, and location. As a result, tangible, assurance, and frontline negatively impacted guest satisfaction. Furthermore, three other variables: accommodation, quarantine, location, which have a positive influence, will lead to increased trust from inbound travelers. For managerial implication, results allow managers of quarantine hotels in Indonesia to focus more on improving tangible, assurance, and frontline factors. Full article

Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study aims to define the factors affecting digital transformation strategies and present a decision-making model required for digital transformation strategies based on the definition. It also reviews previous AI technology and digital transformation strategies and draws influence factors. The research model drew four evaluation areas, such as subject, environment, resource, and mechanism, and 16 evaluation factors through the SERM model. After the factors were reviewed through the Delphi methods, a questionnaire survey was conducted targeting experts with over 10 years of work experience in the digital strategy field. The study results were produced by comparing the data’s importance using an Analytic Hierarchy Process (AHP) on each group. According to the analysis, the subject was the most critical factor, and the CEO (top management) was more vital than the core talent or technical development organization. The importance was shown in the order of resource, mechanism and environment, following subject. It was ascertained that there were differences of importance in industrial competition and market digitalization in the demander and provider groups. Full article

Deceptive online content represents a potentially severe threat to society. This content has shown to have the capability to manipulate individuals’ beliefs, voting and activities. It is a demonstrably effective way for foreign adversaries to create domestic strife in open societies. It is also, by virtue of the magnitude of content, very difficult to combat. Solutions ranging from censorship to inaction have been proposed. One solution that has been suggested is labeling content to indicate its accuracy or characteristics. This would provide an indication or even warning regarding content that may be deceptive in nature, helping content consumers make informed decisions. If successful, this approach would avoid limitations on content creators’ freedom of speech while also mitigating the problems caused by deceptive content. To determine whether this approach could be effective, this paper presents the results of a national survey aimed at understanding how content labeling impacts online content consumption decision making. To ascertain the impact of potential labeling techniques on different portions of the population, it analyzes labels’ efficacy in terms of income level, political party affiliation and online usage time. This, thus, facilitates determining whether the labeling may be effective and also aids in understating whether its effectiveness may vary by demographic group. Full article

A retail business is a network of similar-format grocery stores with a sole proprietor and a well-established logistical infrastructure. The retail business is a stable market, with low growth, limited customer revenues, and intense competition. On the system level, the retail industry is a dynamic system that is challenging to represent due to uncertainty, nonlinearity, and imprecision. Due to the heterogeneous character of retail systems, direct scenario modeling is arduous. In this article, we propose a framework for retail system scenario planning that allows managers to analyze the effect of different quantitative and qualitative factors using fuzzy cognitive maps. Previously published fuzzy retail models were extended by adding external factors and combining expert knowledge with domain research results. We determined the most suitable composition of fuzzy operators for the retail system, highlighted the system’s most influential concepts, and how the system responds to changes in external factors. The proposed framework aims to support senior management in conducting flexible long-term planning of a company’s strategic development, and reach its desired business goals. Full article

Recently, Transformer-based models have shown promising results in automatic speech recognition (ASR), outperforming models based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs). However, directly applying a Transformer to the ASR task does not exploit the correlation among speech frames effectively, leaving the model trapped in a sub-optimal solution. To this end, we propose a local attention Transformer model for speech recognition that combines the high correlation among speech frames. Specifically, we use relative positional embedding, rather than absolute positional embedding, to improve the generalization of the Transformer for speech sequences of different lengths. Secondly, we add local attention based on parametric positional relations to the self-attentive module and explicitly incorporate prior knowledge into the self-attentive module to make the training process insensitive to hyperparameters, thus improving the performance. Experiments carried out on the LibriSpeech dataset show that our proposed approach achieves a word error rate of 2.3/5.5% by language model fusion without any external data and reduces the word error rate by 17.8/9.8% compared to the baseline. The results are also close to, or better than, other state-of-the-art end-to-end models. Full article

Current state-of-the-art neural machine translation (NMT) architectures usually do not take document-level context into account. However, the document-level context of a source sentence to be translated could encode valuable information to guide the MT model to generate a better translation. In recent times, MT researchers have turned their focus to this line of MT research. As an example, hierarchical attention network (HAN) models use document-level context for translation prediction. In this work, we studied translations produced by the HAN-based MT systems. We examined how contextual information improves translation in document-level NMT. More specifically, we investigated why context-aware models such as HAN perform better than vanilla baseline NMT systems that do not take context into account. We considered Hindi-to-English, Spanish-to-English and Chinese-to-English for our investigation. We experimented with the formation of conditional context (i.e., neighbouring sentences) of the source sentences to be translated in HAN to predict their target translations. Interestingly, we observed that the quality of the target translations of specific source sentences highly relates to the context in which the source sentences appear. Based on their sensitivity to context, we classify our test set sentences into three categories, i.e., context-sensitive, context-insensitive and normal. We believe that this categorization may change the way in which context is utilized in document-level translation. Full article

In this research, we propose a GIS-based framework implementing a fuzzy-based document classification method aimed at classifying urban areas by the type of criticality inherent or specific problems highlighted by citizens. The urban study area is divided into subzones; for each subzone, the reports of citizens relating to specific criticalities are analyzed and documents are created, and collected by topic and by temporal extension. The framework implements a model applied to the multiclassification of the documents in which the topic to be analyzed is divided into categories and a dictionary of terms connected to each category is built to measure the relevance of the category in the document. The framework produces, for each time frame, thematic maps of the relevance of a category in a time frame in which a subzone of the study area is classified based on the classification of the corresponding document. The framework was experimented on to analyze and monitor over time the relevance of disruptions detected by users in entities that make up urban areas, such as: roads, private buildings, public buildings and transport infrastructures, lighting networks, and public green areas. The study area is the city of Naples (Italy), partitioned in ten municipalities. The results of the tests show that the proposed framework can be a support for decision makers in analyzing the relevance of categories into which a topic is partitioned and their evolution over time. Full article

The health sector is one of the most knowledge-intensive and complicated globally. It has been proven repeatedly that Business Intelligence (BI) systems in the healthcare industry can help hospitals make better decisions. Some studies have looked at the usage of BI in health, but there is still a lack of information on how to develop a BI system successfully. There is a significant research gap in the health sector because these studies do not concentrate on the organizational determinants that impact the development and acceptance of BI systems in different organizations; therefore, the aim of this article is to develop a framework for successful BI system development in the health sector taking into consideration the organizational determinants of BI systems’ acceptance, implementation, and evaluation. The proposed framework classifies the determinants under organizational, process, and strategic aspects as different types to ensure the success of BI system deployment. Concerning practical implications, this paper gives a roadmap for a wide range of healthcare practitioners to ensure the success of BI system development. Full article