Information, Volume 12, Issue 1 (January 2021) – 46 articles

Social media platforms host an increasing amount of costumer reviews on a wide range of products. While most studies on product reviews focus on the sentiments expressed or helpfulness judged by readers and on their impact on subsequent buying this study aims at uncovering the psychological state of the persons making the reviews. More specifically, the study applies a narrative approach to the analysis of product reviews and addresses the question what the narrative construction of product reviews reveals about the level of post-decisional cognitive dissonance experienced by reviewers. The study involved 94 participants, who were asked to write a product review on their recently bought cell phones. The level of cognitive dissonance was measured by a self-report scale. The product reviews were analyzed by the Narrative Categorical Content Analytical Toolkit. The analysis revealed that agency, spatio-temporal perspective, and psychological perspective reflected the level of cognitive dissonance of the reviewers. The results are interpreted by elaborating on the idea that narratives have affordance to express affect. Full article

As a country, Romania tries to communicate abroad its authenticity, intact nature and unique cultural heritage. This message matches perfectly the main attributes associated to Rodna Mountains National Park, as it is the second national park in Romanian. The aim of the research is to identify and analyze the prospects for sustainable development of rural tourism in the area of Rodna Mountains National Park, taking into account its impact on the social and economic life of the inhabitants of the Rodna commune, but also factors that may positively or negatively influence the whole process. From a methodological perspective, quantitative methods were used; a survey-based research was carried out among Romanian mountain tourists, aiming at identifying and analyzing their opinions and suggestions regarding tourism in protected areas in Romania, as well as the impact of the tourist flows generated by the Park upon the surrounding communities. Rodna Mountains National Park seems to be among the favorite destinations of tourists, as the respondents have a good and very good general impression about the interaction with the mountain and protected areas, prefer internal to external destinations regardless of the season, budgets allocated per night, per stay and annually are quite high, so the purchasing power is also high; they constitute a solid foundation for the decisions of the tourist development of the area. The need for holidays and the savings that tourists make throughout the year to go on vacation, regardless of income level, give viability to this opportunity. Other results of this research are related to the problems tourists helped to identify and the solutions they proposed. Full article

This study investigates safety and security from the perspective of Romanian tourists by assessing the level of importance that tourists give to safety and security depending on the level of classification of the services they experienced and the generation to which they belong. We used a quantitative research method in the form of a questionnaire and analysed eight dimensions of safety and security: detectors, emergency preparedness, medical preparedness, staff security, guestroom security, pool and beach security, hotel access control, and cyber security. We identified the differences between tourists’ perception of safety and security depending on the level of classification of accommodation services and on generation. Full article

To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones. Full article

This paper surveys the energy-efficient routing protocols in wireless sensor networks (WSNs). It provides a classification and comparison following a new proposed taxonomy distinguishing nine categories of protocols, namely: Latency-aware and energy-efficient routing, next-hop selection, network architecture, initiator of communication, network topology, protocol operation, delivery mode, path establishment and application type. We analyze each class, discuss its representative routing protocols (mechanisms, advantages, disadvantages…) and compare them based on different parameters under the appropriate class. Simulation results of LEACH, Mod-LEACH, iLEACH, E-DEEC, multichain-PEGASIS and M-GEAR protocols, conducted under the NS3 simulator, show that the routing task must be based on various intelligent techniques to enhance the network lifespan and guarantee better coverage of the sensing area. Full article

Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand the huge amount of text in a short time; this demands the need for an automatic text summarizer. From summaries, the users get the idea of the entire content of the document and can decide whether to read the entire document or not. This work mainly focuses on generating a summary from multiple news documents. In this case, the summary helps to reduce the redundant news from the different newspapers. A multi-document summary is more challenging than a single-document summary since it has to solve the problem of overlapping information among sentences from different documents. Extractive text summarization yields the sensitive part of the document by neglecting the irrelevant and redundant sentences. In this paper, we propose a framework for extracting a summary from multiple documents in the Malayalam Language. Also, since the multi-document summarization data set is sparse, methods based on deep learning are difficult to apply. The proposed work discusses the performance of existing standard algorithms in multi-document summarization of the Malayalam Language. We propose a sentence extraction algorithm that selects the top ranked sentences with maximum diversity. The system is found to perform well in terms of precision, recall, and F-measure on multiple input documents. Full article

The rapid growth of new computing models that exploit the cloud continuum has a big impact on the adoption of microservices, especially in dynamic environments where the amount of workload varies over time or when Internet of Things (IoT) devices dynamically change their geographic location. In order to exploit the true potential of cloud continuum computing applications, it is essential to use a comprehensive set of various intricate technologies together. This complex blend of technologies currently raises data interoperability problems in such modern computing frameworks. Therefore, a semantic model is required to unambiguously specify notions of various concepts employed in cloud applications. The goal of the present paper is therefore twofold: (i) offering a new model, which allows an easier understanding of microservices within adaptive fog computing frameworks, and (ii) presenting the latest open standards and tools which are now widely used to implement each class defined in our proposed model. Full article

The destination image is a complex construction in which the different sources of information consulted are decisive. Among these information sources, we can find films, which have a relevant function to create destination images. Films are one of the factors that most influences people in their motivation to travel to a destination. The academic literature on film tourism has grown considerably in recent years, but an updated revision is needed. Due to this fact, the main contribution of this paper is the proposal of an updated review of the academic literature focused on film tourism and destinations and their management implications. The general objective of this research is to analyse the existing literature, systematising the relationships that exist between films, film tourism, destination image, and tourist destination. To this end, a systematic review of the literature has been carried out, with two aims: on the one hand, a review of the existing literature from a descriptive perspective, showing the main publications, authors and topics, and on the other hand, a review from a thematic perspective. The results of the latter will be addressed in the discussion and conclusions. The results of the descriptive analysis highlight the significant growth in the literature analysed over the last 45 years, especially between 2005 and 2015. They also underline that the most studied topics are film destinations, marketing and destination image. The results of the thematic analysis highlight the importance of films and series as important information sources on the creation of destination images. The thematic analysis also provides information on the negative and positive aspects of film destination management. Full article

The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities. Full article

The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people’s behaviors. Misleading information refers to false information made up by some malicious marketer to create panic and seek benefits. In particular, when emergency events break out, many users may be misled by the misleading information on the Internet, which further leads them to buy things that are not in line with their actual needs. We call this kind of human activity ‘emergency consumption’, which not only fails to reflect users’ true interests but also causes the phenomenon of user preference deviation, and thus lowers the accuracy of the personal recommender system. Although traditional recommendation models have proven useful in capturing users’ general interests from user–item interaction records, learning to predict user interest accurately is still a challenging problem due to the uncertainty inherent in user behavior and the limited information provided by user–item interaction records. In addition, to deal with the misleading information, we divide user information into two types, namely explicit preference information (explicit comments or ratings) and user side information (which can show users’ real interests and will not be easily affected by misleading information), and then we create a deep social recommendation model which fuses user side information called FSCR. The FSCR model is significantly better than existing baseline models in terms of rating prediction and system robustness, especially in the face of misleading information; it can effectively identify the misleading users and complete the task of rating prediction well. Full article

Security patterns encompass security-related issues in secure software system development and operations that often appear in certain contexts. Since the late 1990s, about 500 security patterns have been proposed. Although the technical components are well investigated, the direction, overall picture, and barriers to implementation are not. Here, a systematic literature review of 240 papers is used to devise a taxonomy for security pattern research. Our taxonomy and the survey results should improve communications among practitioners and researchers, standardize the terminology, and increase the effectiveness of security patterns. Full article

There is a long-standing debate about the effects of media-generated stereotypes on receivers’ trust and attitude. However, there is insufficient consensus about their influence on the media receiver’s ecological perspective in determining their extent of trust and attitudes. Drawing an analogy from Differential Susceptibility to Media Effect Model (hereafter DSMM) notion that media effects are conditional and are contingent on differential-susceptibility, this study examines the influence of dispositional and social susceptibility to media. To do so, the study validates the influence of media user’s gender (dispositional susceptibility) and ethnicity (social susceptibility) in determining the outcomes of media-generated stereotypes, media trust (MT), and attitude towards media organization (AO). The survey method has been employed to collect data through a self-administered questionnaire from 1061 university students in public sector institutions in Pakistan. The results provide empirical evidence that media-generated stereotypes are a substantially negative predictor of media trust and attitudes towards the media organization. The results also validate that the influence of the stereotyping manifested by the receiver’s ecological perspective such as ethnicity and gender are crucial determinants of the receiver’s trust and attitudes. Managerially, the study urges that journalistic practices must be more ethnoculturally inclusive, to cope with the contemporary media landscape. Full article

Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. The collected data are, then, combined to handle inconsistencies, whereas machine learning techniques are applied to detect trends and outliers. In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial transactions investigation. Full article

The transition to digital society is characterised by the development of new methods and tools for big data processing. New technologies have a substantial impact on the education sector. The article represents the results of applying big data to analyse and transform the learning content of Moscow’s schools. The analysis of the school curriculum comprised the following: (a) identifying one-topic lesson scripts, (b) analysing cross-disciplinary connections between subjects, (c) verifying the compliance of the lesson script digital content to the Federal Educational Standards. The analysed material included 36,644 lesson scripts. The analysis has been conducted using specifically designed digital tools featuring data mining algorithms. The article considers the issue of applying data mining algorithms to analyse school curriculum for the improvement of its quality. Full article

With the passing of the last testimonies, Holocaust remembrance and Holocaust education progressively rely on digital technologies to engage people in immersive, simulative, and even counterfactual memories of the Holocaust. This preliminary study investigates how three prominent Holocaust museums use social media to enhance the general public’s knowledge and understanding of historical and remembrance events. A mixed-method approach based on a combination of social media analytics and latent semantic analysis was used to investigate the Facebook, Twitter, Instagram, and YouTube profiles of Yad Vashem, the United States Holocaust Memorial Museum, and the Auschwitz–Birkenau Memorial and Museum. This social media analysis adopted a combination of metrics and was focused on how these social media profiles engage the public at both the page-content and relational levels, while their communication strategies were analysed in terms of generated content, interactivity, and popularity. Latent semantic analysis was used to analyse the most frequently used hashtags and words to investigate what topics and phrases appear most often in the content posted by the three museums. Overall, the results show that the three organisations are more active on Twitter than on Facebook and Instagram, with the Auschwitz–Birkenau Museum and Memorial occupying a prominent position in Twitter discourse while Yad Vashem and the United States Holocaust Memorial Museum had stronger presences on YouTube. Although the United States Holocaust Memorial Museum exhibits some interactivity with its Facebook fan community, there is a general tendency to use social media as a one-way broadcast mode of communication. Finally, the analysis of terms and hashtags revealed the centrality of “Auschwitz” as a broad topic of Holocaust discourse, overshadowing other topics, especially those related to recent events. Full article

This paper proposes a method to ensure compatibility between physical devices for implementing a service design. The method supports the relaxation of strict implementation. It allows a set of compatible devices to be implemented instead of specifying specific devices in the service design. This paper’s main contribution is the formalization of device constraints using the device’s attributes and a method to check the compatibility between devices. The method’s characteristic is that a service designer can decide the level of strictness and abstractness of the design by adjusting the compatibility rate. We show the feasibility of the proposed method to achieve the goal of “Design Once, Provide Anywhere” with an application example. We also evaluated the quality of service of the implemented IoT service in a different environment using a platform called Elgar. Full article

In the context of few-shot learning, one cannot measure the generalization ability of a trained classifier using validation sets, due to the small number of labeled samples. In this paper, we are interested in finding alternatives to answer the question: is my classifier generalizing well to new data? We investigate the case of transfer-based few-shot learning solutions, and consider three settings: (i) supervised where we only have access to a few labeled samples, (ii) semi-supervised where we have access to both a few labeled samples and a set of unlabeled samples and (iii) unsupervised where we only have access to unlabeled samples. For each setting, we propose reasonable measures that we empirically demonstrate to be correlated with the generalization ability of the considered classifiers. We also show that these simple measures can predict the generalization ability up to a certain confidence. We conduct our experiments on standard few-shot vision datasets. Full article

Two robots and a bike are initially placed at the origin of an infinite line. The robots are modelled as autonomous mobile agents whose communication capabilities are either in the wireless or face-to-face model, while the bike neither can move nor communicate on its own. Thus, the bike is not autonomous but rather requires one of the robots to ride it. An exit is placed on the line at distance d from the origin; the distance and direction of the exit from the origin is unknown to the robots. Only one robot may ride the bike at a time and the goal is to evacuate from the exit in the minimum time possible as measured by the time it takes the last robot to exit. The robots can maintain a constant walking speed of 1, but when riding the bike they can maintain a constant speed $v>1$ (same for both robots). We develop algorithms for the evacuation of the two robots from the unknown exit and analyze the evacuation time defined as the time it takes the second robot to evacuate. In the wireless model we present three algorithms: in the first the robots move in opposite direction with max speed, in the second with a specially selected “optimal” speed, and in the third the robot imitates the biker (i.e., robot riding the bike). We also give three algorithms in the Face-to-Face model: in the first algorithm the robot pursues the biker, in the second the robot and the biker use zig-zag algorithms with specially chosen expansion factors, and the third algorithm establishes a sequence of specially constructed meeting points near the exit. In either case, the optimality of these algorithms depends on $v>1$. We also discuss lower bounds. Full article

Industrial heritage reflects the development track of human production activities and witnessed the rise and fall of industrial civilization. As one of the earliest countries in the world to start the Industrial Revolution, Belgium has a rich industrial history. Over the past years, a set of industrial heritage renewal projects have emerged in Belgium in the process of urban regeneration. In this paper, we introduce the basic contents of the related terms of industrial heritage, examine the overall situation of protection and renewal in Belgium. The industrial heritage in Belgium shows its regional characteristics, each region has its representative industrial heritage types. In the Walloon region, it is the heavy industry. In Flanders, it is the textile industry. In Brussels, it is the service industry. The kinds of industrial heritages in Belgium are coordinate with each other. Industrial heritage tourism is developed, especially on eco-tourism, experience tourism. The industrial heritage in transportation and mining are the representative industrial heritages in Belgium. There are a set of numbers industrial heritages are still in running based on a successful reconstruction into industrial tourism projects. Due to the advanced experience in dealing with industrial heritage, the industrial heritage and the city live together harmoniously. Full article

Since the outbreak of COVID-19, in addition to the continuous increment in the number of infected patients, the number of COVID-19-related papers has also increased significantly. According to the statistics, its number even exceeds the research of some research fields over many years. Similar to COVID-19, the related research on COVID-19 also seems highly infectious. What causes this situation? By crawling the data of COVID-19-related papers from web of Sciences this year, we found that there are three mechanisms to promote the rapid growth of the number of COVID-19 papers: incentive mechanism, cross-field collaboration mechanism, and potential impact mechanism of writing papers. To understand the impact of COVID-19 on cross-domain paper network further, we proposed a new construction method of multi-field paper association structure network based on COVID-19. The paper association mechanism and the wall breaking principle between multiple research fields were found through the experiments. Then, combined with the constructed network, we gave the knowledge dissemination model of the new discoveries in multiple fields and obtained some relevant new findings. Full article

Attribute reduction is commonly referred to as the key topic in researching rough set. Concerning the strategies for searching reduct, though various heuristics based forward greedy searchings have been developed, most of them were designed for pursuing one and only one characteristic which is closely related to the performance of reduct. Nevertheless, it is frequently expected that a justifiable searching should explicitly involves three main characteristics: (1) the process of obtaining reduct with low time consumption; (2) generate reduct with high stability; (3) acquire reduct with competent classification ability. To fill such gap, a hybrid based searching mechanism is designed, which takes the above characteristics into account. Such a mechanism not only adopts multiple fitness functions to evaluate the candidate attributes, but also queries the distance between attributes for determining whether two or more attributes can be added into the reduct simultaneously. The former may be useful in deriving reduct with higher stability and competent classification ability, and the latter may contribute to the lower time consumption of deriving reduct. By comparing with 5 state-of-the-art algorithms for searching reduct, the experimental results over 20 UCI data sets demonstrate the effectiveness of our new mechanism. This study suggests a new trend of attribute reduction for achieving a balance among various characteristics. Full article

This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predictive data mining applications that aim to forecast the value of a particular target feature of unseen data instances, answering questions such as whether a credit card transaction is fraudulent or not. A real-time, expressive and descriptive Data Mining technique for streaming data has not been previously established as part of the DSM toolkit. This has motivated the work reported in this paper, which has resulted in developing and validating a Generalised Rule Induction (GRI) tool, thus producing expressive rules as explanations that can be easily understood by human analysts. The expressiveness of decision models in data streams serves the objectives of transparency, underpinning the vision of ‘explainable AI’ and yet is an area of research that has attracted less attention despite being of high practical importance. The algorithm introduced and described in this paper is termed Fast Generalised Rule Induction (FGRI). FGRI is able to induce descriptive rules incrementally for raw data from both categorical and numerical features. FGRI is able to adapt rule-sets to changes of the pattern encoded in the data stream (concept drift) on the fly as new data arrives and can thus be applied continuously in real-time. The paper also provides a theoretical, qualitative and empirical evaluation of FGRI. Full article

The aim of this study was to perform discriminant analysis of voice commands in the presence of an unmanned aerial vehicle equipped with four rotating propellers, as well as to obtain background sound levels and speech intelligibility. The measurements were taken in laboratory conditions in the absence of the unmanned aerial vehicle and the presence of the unmanned aerial vehicle. Discriminant analysis of speech commands (left, right, up, down, forward, backward, start, and stop) was performed based on mel-frequency cepstral coefficients. Ten male speakers took part in this experiment. The unmanned aerial vehicle hovered at a height of 1.8 m during the recordings at a distance of 2 m from the speaker and 0.3 m above the measuring equipment. Discriminant analysis based on mel-frequency cepstral coefficients showed promising classification of speech commands equal to 76.2% for male speakers. Evaluated speech intelligibility during recordings and obtained sound levels in the presence of the unmanned aerial vehicle during recordings did not exclude verbal communication with the unmanned aerial vehicle for male speakers. Full article

This article numerically analyzes the distribution of the zeros of Riemann’s zeta function along the critical line (CL). The zeros are distributed according to a hierarchical two-layered model, one deterministic, the other stochastic. Following a complex plane anamorphosis involving the Lambert function, the distribution of zeros along the transformed CL follows the realization of a stochastic process of regularly spaced independent Gaussian random variables, each linked to a zero. The value of the standard deviation allows the possible overlapping of adjacent realizations of the random variables, over a narrow confidence interval. The hierarchical model splits the $\zeta$ function into sequential equivalence classes, with the range of probability densities of realizations coinciding with the spectrum of behavioral styles of the classes. The model aims to express, on the CL, the coordinates of the alternating cancellations of the real and imaginary parts of the $\zeta$ function, to dissect the formula for the number of zeros below a threshold, to estimate the statistical laws of two consecutive zeros, of function maxima and moments. This also helps explain the absence of multiple roots. Full article

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving. Full article

The need to fight the progressive negative impact of fake news is escalating, which is evident in the strive to do research and develop tools that could do this job. However, a lack of adequate datasets and good word embeddings have posed challenges to make detection methods sufficiently accurate. These resources are even totally missing for “low-resource” African languages, such as Amharic. Alleviating these critical problems should not be left for tomorrow. Deep learning methods and word embeddings contributed a lot in devising automatic fake news detection mechanisms. Several contributions are presented, including an Amharic fake news detection model, a general-purpose Amharic corpus (GPAC), a novel Amharic fake news detection dataset (ETH_FAKE), and Amharic fasttext word embedding (AMFTWE). Our Amharic fake news detection model, evaluated with the ETH_FAKE dataset and using the AMFTWE, performed very well. Full article

Modern facial recognition algorithms make it possible to identify system users by their appearance with a high level of accuracy. In such cases, an image of the user’s face is converted to parameters that later are used in a recognition process. On the other hand, the obtained parameters can be used as data for pseudo-random number generators. However, the closeness of the sequence generated by such a generator to a truly random one is questionable. This paper proposes a system which is able to authenticate users by their face, and generate pseudo-random values based on the facial image that will later serve to generate an encryption key. The generator of a random value was tested with the NIST Statistical Test Suite. The subsystem of image recognition was also tested under various conditions of taking the image. The test results of the random value generator show a satisfactory level of randomness, i.e., an average of 0.47 random generation (NIST test), with 95% accuracy of the system as a whole. Full article

A crisis is an exceptional event that causes damage and negative impacts on organizations. For this reason, crisis management is considered as a significant action needed to follow crisis causes and consequences for preventing or avoiding these exceptional events from occurring again. Studies have devoted their efforts to proposing methods, techniques, and approaches in the crisis management direction. As a result, it is critical to provide a consolidated study that has an integrated view of proposed crisis management methods, crisis impacts, and effective response strategies. For this purpose, this paper first highlights the proposed techniques used in crisis management and presents the main objective behind each technique. Second, the risks and impacts resulting from a crisis are highlighted. Finally, crisis response strategies are discussed. The major contribution of this study is it can guide researchers to define research gaps or new directions in crisis management and choose the proper techniques that cope with their research problems or help them discover new research problems. Full article

Contemporary assistance systems support a broad variety of tasks. When they provide information or instruction, the way they do it has an implicit and often not directly graspable impact on the user. System design often forces static roles onto the user, which can have negative side effects when system errors occur or unique and previously unknown situations need to be tackled. We propose an adjustable augmented reality-based assistance infrastructure that adapts to the user’s individual cognitive task proficiency and dynamically reduces its active intervention in a subtle, not consciously noticeable way over time to spare attentional resources and facilitate independent task execution. We also introduce multi-modal mechanisms to provide context-sensitive assistance and argue why system architectures that provide explainability of concealed automated processes can improve user trust and acceptance. Full article