Information, Volume 13, Issue 8 (August 2022) – 49 articles

Traditional screening approaches identify students who might be at risk for academic problems based on how they perform on a single screening measure. However, using multiple screening measures may improve accuracy when identifying at-risk students. The advent of machine learning algorithms has allowed researchers to consider using advanced predictive models to identify at-risk students. The purpose of this study is to investigate if machine learning algorithms can strengthen the accuracy of predictions made from progress monitoring data to classify students as at risk for low mathematics performance. This study used a sample of first-grade students who completed a series of computerized formative assessments (Star Math, Star Reading, and Star Early Literacy) during the 2016–2017 (n = 45,478) and 2017–2018 (n = 45,501) school years. Predictive models using two machine learning algorithms (i.e., Random Forest and LogitBoost) were constructed to identify students at risk for low mathematics performance. The classification results were evaluated using evaluation metrics of accuracy, sensitivity, specificity, $F_1$, and Matthews correlation coefficient. Across the five metrics, a multi-measure screening procedure involving mathematics, reading, and early literacy scores generally outperformed single-measure approaches relying solely on mathematics scores. These findings suggest that educators may be able to use a cluster of measures administered once at the beginning of the school year to screen their first grade for at-risk math performance. Full article

Sarcasm is an integral part of human language and culture. Naturally, it has garnered great interest from researchers from varied fields of study, including Artificial Intelligence, especially Natural Language Processing. Automatic sarcasm detection has become an increasingly popular topic in the past decade. The research conducted in this paper presents, through a systematic literature review, the evolution of the automatic sarcasm detection task from its inception in 2010 to the present day. No such work has been conducted thus far and it is essential to establish the progress that researchers have made when tackling this task and, moving forward, what the trends are. This study finds that multi-modal approaches and transformer-based architectures have become increasingly popular in recent years. Additionally, this paper presents a critique of the work carried out so far and proposes future directions of research in the field. Full article

The article presents the building of an event-centric model for the computational representation of crisis events using an ontology encoded in the Web Ontology Language (OWL). The work presented here is done in collaboration with the Leaders and Crisis Management in Ancient Literature. A Comparative Approach (LACRIMALit) project, (2022–2025) hosted at the Institute for Mediterranean Studies/Foundation for Research and Technology (IMS-FORTH). A key outcome of the project is the LACRIMALit ontology that aims principally at the semantic annotation of ancient Greek historiographical texts in open access via Perseus Digital Library. The ontology will facilitate reasoning on and across these documents and enable their semantic querying. The tagset of annotations, concepts, relations, and terms of the ontology will be both human and machine readable, extensible and reusable. The annotated corpus of texts to be produced will be available for sophisticated queries based on the concepts and relations, defined by the ontologies. This will considerably improve the string-based querying of the texts in their present digital format. This article presents the principles of conceptualization of the domain in the three dimensions: domain knowledge (mainly classes illustrated with some individuals), linguistic dimension (terms, proper names, definite descriptions), and references. Full article

Entity linking plays a fundamental role in knowledge engineering and data mining and is the basis of various downstream applications such as content analysis, relationship extraction, question and answer. Most existing entity linking models rely on sufficient context for disambiguation but do not work well for concise and sparse short texts. In addition, most of the methods use pre-training models to directly calculate the similarity between the entity text to be disambiguated and the candidate entity text, and do not dig deeper into the relationship between them. This article proposes an entity linking method for Chinese short texts based on Siamese-like networks to address the above shortcomings. In the entity disambiguation task, the features of the Siamese-like network are used to deeply parse the semantic relationships in the text and make full use of the feature information of the entity text to be disambiguated, capturing the interdependent features within the sentences through an attention mechanism, aiming to find out the most critical elements in the entity text description. The experimental demonstration on the CCKS2019 dataset shows that the F1 value of the method reaches 87.29%, increase of 11.02% compared to the F1 value(that) of the baseline method, fully validating the superiority of the model. Full article

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood. Full article

This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications. Full article

The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality. Full article

In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians’ time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that $3.5\%$ of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research. Full article

A phishing attack is one of the less complicated ways to circumvent sophisticated technical security measures. It is often used to exploit psychological (as as well as other) factors of human users to succeed in social engineering attacks including ransomware. Guided by the state-of-the-arts in a phishing simulation study in healthcare and after deeply assessing the ethical dilemmas, an SMS-based phishing simulation was conducted among healthcare workers in Ghana. The study adopted an in-the-wild study approach alongside quantitative and qualitative surveys. From the state-of-the-art studies, the in-the-wild study approach was the most commonly used method as compared to laboratory-based experiments and statistical surveys because its findings are generally reliable and effective. The attack results also showed that 61% of the targeted healthcare staff were susceptible, and some of the healthcare staff were not victims of the attack because they prioritized patient care and were not susceptible to the simulated phishing attack. Through structural equation modelling, the workload was estimated to have a significant effect on self-efficacy risk (r = 0.5, p-value = 0.05) and work emergency predicted a perceived barrier in the reverse direction at a substantial level of r = −0.46, p-value = 0.00. Additionally, Pearson’s correlation showed that the perceived barrier was a predictor of self-reported security behaviour in phishing attacks among healthcare staff. As a result, various suggestions including an extra workload balancing layer of security controls in emergency departments and better security training were suggested to enhance staff’s conscious care behaviour. Full article

The inclusion of information and communication technologies in education has become a priority for all universities. To meet this need, there are several research works that have dealt with the subject for several decades. However, for its inclusion, the analysis of each institution is necessary since the needs of the university population and the resources for its application change according to each situation. This work seeks to create a method that allows establishing the needs and doubts of students about the use of educational technologies in the classroom without affecting their performance. For this, a process has been designed that identifies learning needs, through the validation of data obtained from surveys and the monitoring of the academic efficiency and learning of a cohort of students. The follow-up includes a period of four years from 2019 to 2022. This follow-up allowed establishing three different realities, in 2019 the academic data was analyzed in a face-to-face education model, from 2020 to 2021 the follow-up was included in a remote model with the use of technologies as a communication channel and in 2022 these were included as a learning component, which marked an in-depth analysis of student performance and how technology affected their learning. Full article

Digital inclusive finance, as a vital engine for the country’s high-quality growth, provides new impetus and prospects for encouraging economic development during the looming economic downturn. SMEs play a significant role in economic growth and development, particularly in developing countries. However, value promoting financial inclusion for SMEs through digitalization is still understudied. The objectives aimed at by this investigation were: to study the impact of financial inclusion on SME performances, to observe the influence of digital financing on financial inclusion and SME performance association as a mediator and to examine how the Technology Acceptance Model (TAM) supports financial inclusion and SME performance. A well-structured questionnaire using a quantitative research approach was utilized to gather data from 366 owner-managers among Sri Lankan SMEs. The study’s findings are presented: financial inclusion, digital financing and TAM play influential roles in SME performance. More precisely, digital financing and TAM mediate positively the relationship between financial inclusion and performance in SMEs. The findings of this research endeavor to shed light on developing and popularizing digital financing by providing services which are cheap, secure and low risk from a supply-side perspective, as well as adopting and adjusting digital financing by enhancing financial literacy, which would be necessary from the demand-side perspective. Full article

A new era of the fifth-generation (5G) networks is realized to satisfy user demands on higher data rate and massive connectivity for information sharing and utilization. The vertical applications such as vehicle-to-everything (V2X) communications, industrial automation, smart factory, smart farm and smart cities require ultra-fast communications and wide service range. Coverage extension is a key issue to support the required demands on higher performance, but requires an additional deployment of base or relay stations. Therefore, an efficient solution needs to be cost-effective and easy, in order to deploy more stations. An unmanned aerial vehicle (UAV) has been considered as a candidate to overcome these issues because it is much more cost-effective than the ground stations and does not require network or cell replanning, thereby enhancing the network coverage without additional excessive deployment procedures of the existing networks. UAVs will play important roles in 5G and beyond networks assisting as macro base stations, relay stations, small cells, or a moving aggregator. The performance of UAV wireless networks highly depends on the position or the trajectory of UAVs and the resource managements of entire networks. Recently, there have been extensive studies on performance analysis, UAV deployment, UAV trajectory and resource management of UAV wireless networks to achieve the required demands on performance. This paper surveys research conducted for the UAV deployment and trajectory to construct UAV wireless networks for the coverage extension, the throughput improvement and the resource management for different use cases and scenarios, so as to encourage further studies in this area. Full article

Many rumors spread quickly and widely on social media, affecting social stability. The rumors of most current detection methods only use textual information or introduce external auxiliary information (such as user information and propagation information) to enhance the detection effect, and the inherent statistical features of the corpus have not been fully used and compared with the external auxiliary features; in addition, statistical features are more certain and can only be obtained from textual information. Therefore, we adopted a method based on the adaptive fusion of word frequency distribution features and textual features to detect rumors. Statistical features were extracted by encoding statistical information through a variational autoencoder. We extracted semantic features and sequence features as textual features through a parallel network comprising a convolutional neural network and a bidirectional long-term memory network. In addition, we also designed an adaptive valve to only fuse useful statistical features with textual features according to the credibility of textual features, which can solve the over-fitting problem caused by the excessive use of statistical features. The accuracy of the model in three public datasets (Twitter15, Twitter16, and Weibo) reached 87.5%, 88.6%, and 95.8%, respectively, and the F1 value reached 87.4%, 88.5%, and 95.8%, respectively, showing that the model can effectively improve the performance of rumor detection. Full article

The negation of probability distribution is a new perspective from which to obtain information. Dempster–Shafer (D–S) evidence theory, as an extension of possibility theory, is widely used in decision-making-level fusion. However, how to reasonably construct the negation of basic probability assignment (BPA) in D–S evidence theory is an open issue. This paper proposes a new negation of BPA, logarithmic negation. It solves the shortcoming of Yin’s negation that maximal entropy cannot be obtained when there are only two focal elements in the BPA. At the same time, the logarithmic negation of BPA inherits the good properties of the negation of probability, such as order reversal, involution, convergence, degeneration, and maximal entropy. Logarithmic negation degenerates into Gao’s negation when the values of the elements all approach 0. In addition, the data fusion method based on logarithmic negation has a higher belief value of the correct target in target recognition application. Full article

Class imbalance is one of the significant challenges in classification problems. The uneven distribution of data samples in different classes may occur due to human error, improper/unguided collection of data samples, etc. The uneven distribution of class samples among classes may affect the classification accuracy of the developed model. The main motivation behind this study is the design and development of methodologies for handling class imbalance problems. In this study, a new variant of the synthetic minority oversampling technique (SMOTE) has been proposed with the hybridization of particle swarm optimization (PSO) and Egyptian vulture (EV). The proposed method has been termed SMOTE-PSOEV in this study. The proposed method generates an optimized set of synthetic samples from traditional SMOTE and augments the five datasets for verification and validation. The SMOTE-PSOEV is then compared with existing SMOTE variants, i.e., Tomek Link, Borderline SMOTE1, Borderline SMOTE2, Distance SMOTE, and ADASYN. After data augmentation to the minority classes, the performance of SMOTE-PSOEV has been evaluated using support vector machine (SVM), Naïve Bayes (NB), and k-nearest-neighbor (k-NN) classifiers. The results illustrate that the proposed models achieved higher accuracy than existing SMOTE variants. Full article

Multi-label learning has become a hot topic in recent years, attracting scholars’ attention, including applying the rough set model in multi-label learning. Exciting works that apply the rough set model into multi-label learning usually adapt the rough sets model’s purpose for a single decision table to a multi-decision table with a conservative strategy. However, multi-label learning enforces the rough set model which wants to be applied considering multiple target concepts, and there is label correlation among labels naturally. For that proposal, this paper proposes a rough set model that has multiple target concepts and considers the similarity relationships among target concepts to capture label correlation among labels. The properties of the proposed model are also investigated. The rough set model that has multiple target concepts can handle the data set that has multiple decisions, and it has inherent advantages when applied to multi-label learning. Moreover, we consider how to compute the approximations of GMTRSs under a static and dynamic situation when a target concept is added or removed and derive the corresponding algorithms, respectively. The efficiency and validity of the designed algorithms are verified by experiments. Full article

Social media has become a very commonplace way for many people to have social interactions. The role of social media has changed from what was originally only a way to bridge social interactions, to becoming a business tool in various industries, one of which is the tourism industry. The interaction between social media users can create new ways to increase public awareness of existing tourist objects. One way to achieve that goal is by utilizing social media influencers. This study aims to identify the factors that influence the intention of the followers to follow the travel recommendations given by the influencer. This study uses the theory of follower-influencer experience and the theory of emotional dimensions, as well as their effect on the level of commitment and intention to follow the recommendation. This research was conducted by distributing surveys through social media and we managed to obtain a total of 203 valid respondents. The results of the study were analyzed using structural equation modeling (SEM), which showed that information experience and homophily experience had a significant effect on pleasure, arousal, and dominance. Pleasure and dominance have a significant effect on commitment, and commitment has a significant effect on the intention to follow the recommendation. Full article

This study aims to provide a bibliometric analysis of the literature on thriving at work in psychology and business/management produced between 2001 and 2021, using the Web of Science (WoS) database. The analyses allowed us to identify, through 190 documents, the emergence of the concept of thriving at work and its development. The main research variables related to this concept and its methodology were identified. Likewise, the most influential authors, the most cited articles, the more frequently cited journals, and the countries contributing to developing this construct are analyzed. In addition, an analysis of co-citation, co-occurrences, and bibliographic coupling was conducted. Finally, content analysis of the most popular keywords and the co-citation of cited references are conducted. These analyses allow the identification of the main developments in the topic of thriving at work. The theoretical and practical implications of this bibliometric analysis are discussed. Full article

The Controller Area Network (CAN) is a bus protocol widely used in Electronic control Units (ECUs) to communicate between various subsystems in vehicles. Insecure CAN networks can allow attackers to control information between vital vehicular subsystems. As vehicles can have lifespans of multiple decades, post-quantum cryptosystems are essential for protecting the vehicle communication systems from quantum attacks. However, standard CAN’s efficiency and payload sizes are too small for post-quantum cryptography. The Controller Area Network Flexible Data-Rate (CAN-FD) is an updated protocol for CAN that increases transmission speeds and maximum payload size. With CAN-FD, higher security standards, such as post-quantum, can be utilized without severely impacting performance. In this paper, we propose PUF-Based Post-Quantum Cryptographic CAN-FD Framework, or PUF-PQC-CANFD. Our framework provides post-quantum security to the CAN network while transmitting and storing less information than other existing pre-quantum and post-quantum CAN frameworks. Our proposal protects against most cryptographic-based attacks while transmitting (at up to 100 ECUs) 25–94% less messages than existing pre-quantum frameworks and 99% less messages than existing post-quantum frameworks. PUF-PQC-CANFD is optimized for smaller post-quantum key sizes, storage requirements, and transmitted information to minimize the impact on resource-restricted ECUs. Full article

Accurate and timely traffic information is a vital element in intelligent transportation systems and urban management, which is vitally important for road users and government agencies. However, existing traffic prediction approaches are primarily based on standard machine learning which requires sharing direct raw information to the global server for model training. Further, user information may contain sensitive personal information, and sharing of direct raw data may lead to leakage of user private data and risks of exposure. In the face of the above challenges, in this work, we introduce a new hybrid framework that leverages Federated Learning with Local Differential Privacy to share model updates rather than directly sharing raw data among users. Our FL-LDP approach is designed to coordinate users to train the model collaboratively without compromising data privacy. We evaluate our scheme using a real-world public dataset and we implement different deep neural networks. We perform a comprehensive evaluation of our approach with state-of-the-art models. The prediction results of the experiment confirm that the proposed scheme is capable of building performance accurate traffic predictions, improving privacy preservation, and preventing data recovery attacks. Full article

The world of video games has become one of the most important entertainment niches for society. In the last decade it has surpassed in turnover audio-visual markets such as cinema and music, driving the development of a new form of communication. The increase in the number of female gamers has highlighted the need to discover differences and similarities between players, both in habits and motivations. We present a study based on a survey procedure for the completion of a questionnaire that aimed to cover the age range of 18 to 30 years of Spanish youngsters and that reached a total of 711 valid responses. The results showed that there were no significant differences in terms of hours spent playing video games between the two genders, although there were motivational differences in the reasons for playing, specifically in terms of competition and challenge. The discussion of the results was carried out by means of a comparative statistical analysis of means to confirm the hypotheses and meet the objectives. Despite the existence of significant differences between genders, these were not as notable as might be expected. When it comes to gaming, as we have detected in our study, there were some consumption habits with differentiated gender patterns; however, in relevant indicators such as hours of consumption, increase in lockdown consumption, and spending, there were no significant differences. The gender gap that existed a few years ago between video gamers is becoming progressively narrower. Full article

The double-inverted pendulum (DIP) constitutes a classical problem in mechanics, whereas the control methods for stabilizing around the equilibrium positions represent the classic standards of control system theory and various control methods in robotics. For instance, it functions as a typical model for the calculation and stability of walking robots. The present study depicts the controlling of a double-inverted pendulum (DIP) on a cart using a fuzzy logic controller (FLC). A linear-quadratic controller (LQR) was used as a benchmark to assess the effectiveness of our method, and the results showed that the proposed FLC can perform significantly better than the LQR under a variety of initial system conditions. This performance is considered very important when the reduction of the peak system output is concerned. The proposed controller equilibration and velocity tracking performance were explored through simulation, and the results obtained point to the validity of the control method. Full article

Introduction: Convergence of linguistics and semiotics materializes in the text not only the conceptual content that is expressed through codes, but the message also underlies the realism of the communicative intentions of the issuing agent in a specific context and the value of the interactions of the actors of the communicative act. Methodology: The vision of reality that is established in the collective imaginary must be analyzed from the interpretation of society and culture, the laws of operation and their constituent parts. For this, this research approached two newspapers of national circulation, five general radio channels of national coverage, and two television newscasts of private channels, and through the use of quantitative instruments, the posts, tweets or videos were reviewed in order to analyze the constituent elements of the discourse—text, images, hashtags, or keywords—which are appreciated from the syntactic and semantic perspectives (structural) and pragmatics (functional). Results: The communication process in its social context denotes the intervention of nonlinguistic elements of sociocultural order that demarcate the generation and interpretation of the meanings and senses of linguistic expressions. Discussion: The linguistic structure offers conditions for communication, but any generation and transmission of meanings is a product of the intention of the subjects who use it for specific purposes and in specific communicative situations within a social context. Conclusions: Intensive use of digital technology and social networks naturalized a relationship of proximity and familiarity in the communication process, satisfaction of needs in the multiplicity of information that is created and distributed in the network bundled to mobile devices and the political and social ecosystem of the nation. Full article

Shadow removal is a fundamental task that aims at restoring dark areas in an image where the light source is blocked by an opaque object, to improve the visibility of shadowed areas. Existing shadow removal methods have developed for decades and yielded many promising results, but most of them are poor at maintaining consistency between shadowed regions and shadow-free regions, resulting in obvious artifacts in restored areas. In this paper, we propose a two-stage (i.e., shadow detection and shadow removal) method based on the Generative Adversarial Network (GAN) to remove shadows. In the shadow detection stage, a Recurrent Neural Network (RNN) is trained to obtain the attention map of shadowed areas. Then the attention map is injected into both generator and discriminator to guide the shadow removal stage. The generator is a dual encoder-decoder that processes the shadowed regions and shadow-free regions separately to reduce inconsistency. The whole network is trained with a spatial variant reconstruction loss along with the GAN loss to make the recovered images more natural. In addition, a novel feature-level perceptual loss is proposed to ensure enhanced images more similar to ground truths. Quantitative metrics like PSNR and SSIM on the ISTD dataset demonstrate that our method outperforms other compared methods. In the meantime, the qualitative comparison shows our approach can effectively avoid artifacts in the restored shadowed areas while keeping structural consistency between shadowed regions and shadow-free regions. Full article

Urban mobility is evolving today towards the concept of Mobility as a Service (MaaS). MaaS allows passengers to use different transport services as a single option, by using a digital platform. Therefore, according to the MaaS concept, the mobility needs of passengers are the central element of the transport service. The objective of this paper is to build an updated state-of-the-art of the main disaggregated and aggregated variables connected to travel demand in presence of MaaS. According to the above objective, this paper deals with methods and case studies to analyze passengers’ behaviour in the presence of MaaS. The methods described rely on the Transportation System Models (TSMs), in particular with the travel demand modelling component. The travel demand may be estimated by means of disaggregated, or sample, surveys (e.g., individual choices) and of aggregate surveys (e.g., characteristics of the area, traffic flows). The surveys are generally supported by Information Communication System (ICT) tools, such as: smartphones; smartcards; Global Position Systems (GPS); points of interest. The analysis of case studies allows to aggregate the existing scientific literature according to some criteria: the choice dimension of users (e.g., mode, bundle and path, or a combination of them); the characteristics of the survey (e.g., revealed preferences or stated preferences); the presence of behavioural theoretical background and of calibrated choice model(s). Full article

In order to solve the problem of the slow transmission rate of high-frequency information in smart grid and improve the efficiency of information transmission, a research method of high-frequency information transmission in smart grids based on the CNN-LSTM model is proposed. It effectively combines the superiority of the CNN algorithm for high-frequency information feature extraction and the learning ability of the LSTM algorithm for global features of high-frequency information. Meanwhile, the client buffer is divided by the VLAN area division method, which avoids the buffer being too large due to line congestion. The intelligent control module is adopted to change the traditional control concept. In addition, the neural network optimization control module is used for intelligent control, which ensures the feedback speed of the control terminal and avoids the problem of increasing the buffer area caused by the feedback time difference. The experimental results show that via the method in this paper, the total efficiency of single-channel transmission reaches 96% and the transmission rate reaches 46 bit/s; the total efficiency of multiplex transmission is 89% and the transmission rate reaches 75 bit/s. It is verified that the method proposed in this paper has a fast transmission rate and high efficiency. Full article

The distributor management system has long been a challenge for many organizations and companies. Overall, successful distribution involves several moving entities and methods, requiring a resilient distribution management strategy powered by data analysis. For nonprofit organizations, the distribution system requires efficient distribution and management. This includes minimizing time, distance, and cost. As a consequence, service quality and financial efficiency can be achieved. This paper proposes a methodology to tackle the vehicle routing problems (VRP) faced by nonprofit organizations. The methodology consists of four subsequent approaches—greedy, intraroute, interroute, and tabu search—to improve the functionality and performance of nonprofit organizations. The methodology was validated by applying it to a real nonprofit organization. Furthermore, the proposed system was compared to another state-of-the-art system; the achieved results were satisfactory and suggest that this methodology is capable of handling the VRP accordingly, improving the functionality and performance of nonprofit organizations. Full article

Online purchasing has developed rapidly in recent years due to its efficiency, convenience, low cost, and product variety. This has increased the number of online multi-category e-commerce retailers that sell a variety of product categories. Due to the growth in the number of players, each company needs to optimize its own business strategy in order to compete. Customer lifetime value (CLV) is a common metric that multi-category e-commerce retailers usually consider for competition because it helps determine the most valuable customers for the retailers. However, in this paper, we introduce two additional novel factors in addition to CLV to determine which customers will bring in the highest revenue in the future: distinct product category (DPC) and trend in amount spent (TAS). Then, we propose a new framework. We utilized, for the first time in the relevant literature, a multi-output deep neural network (DNN) model to test our proposed framework while forecasting CLV, DPC, and TAS together. To make this outcome applicable in real life, we constructed customer clusters that allow the management of multi-category e-commerce companies to segment end-users based on the three variables. We compared the proposed framework (constructed with multiple outputs: CLV, DPC, and TAS) against a baseline single-output model to determine the combined effect of the multi-output model. In addition, we also compared the proposed model with multi-output Decision Tree (DT) and multi-output Random Forest (RF) algorithms on the same dataset. The results indicate that the multi-output DNN model outperforms the single-output DNN model, multi-output DT, and multi-output RF across all assessment measures, proving that the multi-output DNN model is more suitable for multi-category e-commerce retailers’ usage. Furthermore, Shapley values derived through the explainable artificial intelligence method are used to interpret the decisions of the DNN. This practice demonstrates which inputs contribute more to the outcomes (a significant novelty in interpreting the DNN model for the CLV). Full article

During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. Full article

During the last two decades, many products, research projects and prototypes were announced with different characteristics and capabilities, all adopting the “smart” qualifier word. If a smartness property could not be defined simply as true or false, defining a suitable range was not an easy task. This issue led to the proposal of some classification models, frameworks and taxonomies for project classifications, but none of them provide a clear and pragmatic smartness scale able to classify smart artifacts and serve as a reference. This paper aims to propose a smartness scale to help research and non-research communities to better quantify and easily understand the features and autonomy of smart artifacts. The proposed smartness scale considers the main function of physical-device components in smart systems. The provided smartness scale is based on a uni-dimensional typology that defines 12 different smartness levels, created based on our definition of “smart artifact” and by following an evolutionary set of capabilities ranging from traceable-only and sensing-capable artifacts to autonomous, adaptable and self-driven artifacts. In order to show the feasibility of the proposed smartness scale, an analytic model was defined and applied to several research- and market-based artifacts tagged as smart in order to extract their smartness levels. Full article