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Recommender systems have been successfully applied to assist decision making in multiple domains and applications. Multi-criteria recommender systems try to take the user preferences on multiple criteria into consideration, in order to further improve the quality of the recommendations. Most recently, the utility-based multi-criteria recommendation approach has been proposed as an effective and promising solution. However, the issue of over-/under-expectations was ignored in the approach, which may bring risks to the recommendation model. In this paper, we propose a penalty-enhanced model to alleviate this issue. Our experimental results based on multiple real-world data sets can demonstrate the effectiveness of the proposed solutions. In addition, the outcomes of the proposed solution can also help explain the characteristics of the applications by observing the treatment on the issue of over-/under-expectations. Full article

While several studies have shown how telemedicine and, in particular, home telemonitoring programs lead to an improvement in the patient’s quality of life, a reduction in hospitalizations, and lower healthcare costs, different variables may affect telemonitoring effectiveness and purposes. In the present paper, an integrated software system, based on Sentiment Analysis and Text Mining, to deliver, collect, and analyze questionnaire responses in telemonitoring programs is presented. The system was designed to be a complement to home telemonitoring programs with the objective of investigating the paired relationship between opinions and the adherence scores of patients and their changes through time. The novel contributions of the system are: (i) the design and software prototype for the management of online questionnaires over time; and (ii) an analysis pipeline that leverages a sentiment polarity score by using it as a numerical feature for the integration and the evaluation of open-ended questions in clinical questionnaires. The software pipeline was initially validated with a case-study application to discuss the plausibility of the existence of a directed relationship between a score representing the opinion polarity of patients about telemedicine, and their adherence score, which measures how well patients follow the telehomecare program. In this case-study, 169 online surveys sent by 38 patients enrolled in a home telemonitoring program provided by the Cystic Fibrosis Unit at the “Bambino Gesù” Children’s Hospital in Rome, Italy, were collected and analyzed. The experimental results show that, under a Granger-causality perspective, a predictive relationship may exist between the considered variables. If supported, these preliminary results may have many possible implications of practical relevance, for instance the early detection of poor adherence in patients to enable the application of personalized and targeted actions. Full article

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate. Full article

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel ($CSV{M}_{Pol}$) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models. Full article

Phishing attacks are cybersecurity threats that have become increasingly sophisticated. Phishing is a cyberattack that can be carried out using various approaches and techniques. Usually, an attacker uses trickery as well as fraudulent and disguised means to steal valuable personal information or to deceive the victim into running malicious code, thereby gaining access and controlling the victim’s systems. This study focuses on evaluating the level of cybersecurity knowledge and cyber awareness in Saudi Arabia. It is aimed at assessing end-user susceptibility through three phishing attack simulations. Furthermore, we elaborate on some of the concepts related to phishing attacks and review the steps required to launch such attacks. Subsequently, we briefly discuss the tools and techniques associated with each attack simulation. Finally, a comprehensive analysis is conducted to assess and evaluate the results. Full article

Handgrip strength (HGS) is an indicator of muscle condition and general health wellbeing. Usually, instruments measuring handgrip strength only identify its maximum value. This preliminary study is focused on identifying force vs. time parameters which could contribute to better describe individual strength. They were obtained during a Handgrip strength test of 15 s in a sample group of 94 university students. The tests were conducted with a smart multifunction novel prototype dynamometer, named BodyGrip. Mean values of quantities related to the ability to develop and to maintain strength in percentage of maximum handgrip strength, were extracted from the force vs time profile. Contrary to maximum HGS, such quantities were found to be independent of the participant’s anthropometric characteristics. Individual comparisons based on those quantities are therefore not affected by the anthropometric characteristics. It was possible to identify individuals, differing on the development of HGS. Results suggest that the functionality of the BodyGrip tool enables a more thorough characterization of the time profile of the Handgrip strength that might influence the knowledge of the muscle functions, such as power development and endurance. Full article

Digital platforms have made Generation Z able to listen to the story from both sides, i.e., the brand’s promotional messages, and the consumers’ experiences. To capture an audience’s attention on endless entertainment and informational platforms, narrative advertisement is deployed to trigger emotions and feelings. As digital communities continue to grow, both brands and consumers are using narrative advertising to share their opinions. Hence, the purpose of this research was to investigate how the upcoming generation of consumers will be affected by the two information sources, i.e., the brands via narrative advertising, and peer consumers through electronic word-of-mouth (eWOM). Furthermore, the mediated role of persuasion knowledge was examined among the constructs. Theoretical foundations were empirically tested using quantitative research analysis. The data was collected from a sample of 304 Generation Z respondents from Pakistan. Structural equation modeling (SEM) using AMOS 22.0 was executed to determine cause and effect relationships. This study offers new evidence regarding the effective mobilization of eWOM and narrative advertising in the context of Generation Z. The results indicate narrative advertising to have strong effects on Generation Z purchase intentions as compared to eWOM. Full article

Denial of service (DoS) attacks play a significant role in contemporary cyberspace scenarios. A variety of different DoS attacks pollute networks by exploring various vulnerabilities. A group of DoS called application DoS attacks explore application vulnerabilities. This work presents a tool that detects and blocks an application DoS called Slowloris on wireless mesh networks (WMNs). Our tool, called SDToW, is designed to effectively use the structure of the WMNs to block the Slowloris attack. SDToW uses three different modules to detect and block the attack. Each module has its specific tasks and thus optimizes the overall detection and block efficiency. Our solution blocks the attacker on its first WMN hop, reducing the malicious traffic on the network and avoiding further attacks from the blocked user. The comparison results show that SDToW performs with 66.7% less processing consumption and 89.1% less memory consumption than Snort. Our solution does not limit the number of parallel connections per user. Hence, by avoiding this limitation, SDToW has a lower incidence of false positive errors than Snort. Full article

The intention of this article is to share research taken from the initial training of secondary education pre-service teachers, in which college professors from mathematics and social sciences education participate. There are numerous studies regarding curriculum integration. However, there is insufficient research that offers insight into how different forms of disciplinary thought may come together in the design and development in practice for an educational purpose. The research professors carried out a qualitative investigation, working with two pre-service teacher groups from the fields of mathematics and social sciences. We used a validated research instrument to analyze how interdisciplinary educational proposals are developed, and how said proposals can be used to understand our social reality. Through an integrated project, pre-service teachers addressed sexist hate speech taken from different contexts. They designed didactic proposals, with mathematical contents, which allow high school students to argue with data and create counter-narratives, which softened or eliminated the sexist hate speech Among the conclusions, we may emphasize the value of pre-service teachers identifying their strengths in order to use the mathematical and social thought processes in an autonomous and creative way, thereby developing the instrumental, functional, and formative character in mathematics education. Full article

An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike’s Information Criterion technique (AIC) has also shown that the proposed model has a higher performance. Full article

The Internet of Things (IoT) is a key enabler of the digital mutation of our society. Driven by various services and applications, Machine Type Communications (MTC) will become an integral part of our daily life, over the next few years. Meeting the ITU-T requirements, in terms of density, battery longevity, coverage, price, and supported mechanisms and functionalities, Cellular IoT, and particularly Narrowband-IoT (NB-IoT), is identified as a promising candidate to handle massive MTC accesses. However, this massive connectivity would pose a huge challenge for network operators in terms of scalability. Indeed, the connection to the network in cellular IoT passes through a random access procedure and a high concentration of IoT devices would, very quickly, lead to a bottleneck. The latter procedure needs, then, to be enhanced as the connectivity would be considerable. With this in mind, we propose, in this paper, to apply the access class barring (ACB) mechanism to regulate the number of devices competing for the access. In order to derive the blocking factor, we formulated the access problem as a Markov decision process that we were able to solve using one of the most advanced deep reinforcement learning techniques. The evaluation of the proposed access control, through simulations, shows the effectiveness of our approach compared to existing approaches such as the adaptive one and the Proportional Integral Derivative (PID) controller. Indeed, it manages to keep the proportion of access attempts close to the optimum, despite the lack of accurate information on the number of access attempts. Full article

Commercial bibliometric databases, and the quantitative indicators presented by them, are widely used for research assessment purposes, which is not fair for the humanities. The humanities are different from all other areas by nature in many aspects. This study aimed to show the extent of the difference in terms of five size-independent bibliometric indicators, based on citations and collaborations. We used categorical InCites data (1980–2020) to compare six main Organisation for Economic Co-operation and Development (OECD) subject areas, and the 45,987 sources of humanities, to make a comparison for subareas of the humanities. Results showed that the humanities are statistically different from all other areas, including social sciences, with high effect sizes in terms of the five indicators taken into consideration. Besides that, all the subareas of the humanities differ from each other. This main finding indicates that the humanities do not need new indicators for quantitative evaluation, but different approaches for assessment, such as bottom-up approaches. Full article

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare. Full article

While increased household income is associated with overall decreased screen time for children, less is known about the effect of racial variation on this association. According to Minorities’ Diminished Returns (MDRs) theory, family income and other economic resources show weaker association with children’s developmental, behavioral, and health outcomes for racialized groups such as black families, due to the effect of racism and social stratification. In this study, we investigated the association, by race, between family income and children’s screen time, as a proxy of screen time. This longitudinal study followed 15,022 American children aged 9–11 over a 1-year period. The data came from the baseline of the Adolescent Brain Cognitive Development (ABCD) study. The independent variable was family income, and it was categorized as a three-level nominal variable. The dependent variable, screen time, was a continuous variable. Ethnicity, gender, parental education, and marital status were the covariates. The results showed that family income was inversely associated with children’s screen time. However, there was a weaker inverse association seen in black families when compared with white families. This was documented by a significant statistical interaction between race and family income on children’s screen time. Diminished association between family income and children’s screen time for black families, compared with white families, is similar to MDRs and reflects a health risk to high-income black children. In a society where race and skin color determine opportunities and treatment by society, children from middle class black families remain at risk across multiple domains. We should not assume that income similarly promotes the health of all racial and ethnic groups. Addressing health and behavioral inequalities requires interventions that go beyond equalizing socioeconomic resources for black families. Marginalization, racism, and poverty interfere with the normal family income-related development of American children. Full article

The rapid move to digitalization and usage of online information systems brings new and evolving threats that organizations must protect themselves from and respond to. Monitoring an organization’s network for malicious activity has become a standard practice together with event and log collection from network hosts. Security operation centers deal with a growing number of alerts raised by intrusion detection systems that process the collected data and monitor networks. The alerts must be processed so that the relevant stakeholders can make informed decisions when responding to situations. Correlation of alerts into more expressive intrusion scenarios is an important tool in reducing false-positive and noisy alerts. In this paper, we propose correlation rules for identifying multi-stage attacks. Another contribution of this paper is a methodology for inferring from an alert the values needed to evaluate the attack in terms of the attacker’s skill level. We present our results on the CSE-CIC-IDS2018 data set. Full article

Automatic document summarization is a field of natural language processing that is rapidly improving with the development of end-to-end deep learning models. In this paper, we propose a novel summarization model that consists of three methods. The first is a coverage method based on noise injection that makes the attention mechanism select only important words by defining previous context information as noise. This alleviates the problem that the summarization model generates the same word sequence repeatedly. The second is a word association method to update the information of each word by comparing the information of the current step with the information of all previous decoding steps. According to following words, this catches a change in the meaning of the word that has been already decoded. The third is a method using a suppression loss function that explicitly minimizes the probabilities of non-answer words. The proposed summarization model showed good performance on some recall-oriented understudy for gisting evaluation (ROUGE) metrics compared to the state-of-the-art models in the CNN/Daily Mail summarization task, and the results were achieved with very few learning steps compared to the state-of-the-art models. Full article

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model. Full article

Today’s applications and providers are very interested in knowing the social aspects of users in order to customize the services they provide and to be more effective. Among the others, the most frequented places and the paths to reach them are information that turns out to be very useful to define users’ habits. The most exploited means to acquire positions and paths is the GPS sensor, however it has been shown how leveraging inertial data from installed sensors can lead to path identification. In this work, we present a Computationally Efficient algorithm to Reconstruct Vehicular Traces (CERT), a novel algorithm which computes the path traveled by a vehicle using accelerometer and magnetometer data. We show that by analyzing data obtained through the accelerometer and the magnetometer in vehicular scenarios, CERT achieves almost perfect identification for medium and small sized cities. Moreover, we show that the longer the path, the easier it is to recognize it. We also present results characterizing the privacy risks depending on the area of the world, since, as we show, urban dynamics play a key role in the path detection. Full article

Cybersecurity vulnerabilities in industrial control systems have been steadily increasing over the last few years. One possible way to address this issue is through raising the awareness (through education) of software developers, with the intent to increase software quality and reduce the number of vulnerabilities. CyberSecurity Challenges (CSCs) are a novel serious game genre that aims to raise industrial software developers’ awareness of secure coding, secure coding guidelines, and secure coding best practices. An important industry-specific requirement to consider in designing these kinds of games is related to the whole event’s duration and how much time it takes to solve each challenge individually—the challenge solve time. In this work, we present two different methods to compute the challenge solve time: one method based on data collected from the CSC dashboard and another method based on a challenge heartbeat. The results obtained by both methods are presented; both methods are compared to each other, and the advantages and limitations of each method are discussed. Furthermore, we introduce the notion of a player profile, which is derived from dashboard data. Our results and contributions aim to establish a method to measure the challenge solve time, inform the design of future challenges, and improve coaching during CSC gameplay. Full article