Search

This article examines convergence by providing a systematic literature review using scientometric method. Despite the steady growth of convergence literature, we still have an incomplete understanding of convergence concepts as well as the processes of industry convergence (IC). Our systematic review highlights several key findings. First, our review highlights six clusters of research on convergence. Second, our analysis suggests that the process of convergence does not necessarily progress in the three- to four-step supply-side process as postulated in the literature, i.e., from scientific convergence, technology convergence, market convergence, and finally to IC. Although existing literature suggests that IC occurs as a result of supply-side convergence, we expand on this proposition and put forth market-driven convergence processes. Third, we provide a typology of the convergence concepts to bring clarity and avoid indiscriminate use of the various convergence concepts. Finally, our article offers new debates on the literature of convergence, providing scholars with research directions that extend beyond the standard convergence processes. Our systematic review is relevant to scholars and managers as the study highlights a taxonomy of convergence scholarship, a typology of convergence concepts, and a re-evaluation of the IC process model to drive the research forward.

Digital electronics course is one of the very fundamental courses for the students of undergraduate programme of electrical and electronic engineering (EEE) and the other undergraduate engineering disciplines. Therefore, "digital electronics" shall be taught effectively, so that students can apply the knowledge learned to solve their real-life engineering problems. A teacher needs to adopt new teaching methodologies to attract current generation of students, and thus, to prepare them with practical knowledge and skills. Skills in the cognitive domain of Bloom's taxonomy revolve around knowledge, comprehension and critical thinking of a particular topic. This makes teaching and learning more effective and efficient. In this paper, the teaching method of 'digital electronics' course for the undergraduate EEE students in the cognitive domain has been described with an example. Class performance evaluation in two different cohorts shows that the students' results improve after using this approach.Keywords: Bloom's taxonomy, cognitive domain, digital electronics course, teaching methods.

Machine learning is the sub field of Artificial Intelligence which is responsible for developing a system that has the capability to learn and improve from past experience. There is an increasing demand for the offensive language detection because there is a large code-mixed text. Sometimes there is a possibility of code-mixed texts which are written using native scripts. If the system is getting trained on the data which is created using single language then it will leads to failure because of complexity. Using online communities and social media platforms has become increasing now. Recently many of the researchers have been investigating different ways of identifying the abusive content and developing systems to detect its different types like aggression identification, cyber bullying detection, hate speech identification, offensive language identification and toxic comments identification. Developing mechanisms to detect this is a challenging task. In the last few years, machine learning techniques and deep learning concept has been playing a significant role in the development of solutions for offensive language detection. So, considerable efforts have also been devoted to the development of machine learning techniques and deep learning models which is used to identify toxic content in recent years. First, this survey provides offensive language taxonomy and detection approaches. Then, the article focuses on the offensive language identification and toxic comments identification approaches. This article reviews various models used, type of learning model, languages for which the developed model is applied, other methods or approaches that were compared against this model, dataset used, offensive language detection type and performance metrics. The findings from the review are as well summarized and they urge that further effort is required to get improvement in the current state-of-the art approaches. This article also focuses on future work to improve the offensive language classification and toxic comments identification.

Cyber security is guarding computer systems, data, network and other resources from unauthorized access and malicious users. There are no direct methods of measuring strength of cyber security. As they say, “You can’t manage what you can’t measure”. One can easily track the efforts taken for security through cyber security metrics. Being a quantifiable measure, metric can be utilized in tracking the status of a specific process and assess its outcomes along with its strength. This work aims to provide taxonomy of cyber security metrics with five basic metrics, along with the tools under Multi Criteria Decision making approach can be used in evaluation of cyber security strength.

SOTER, a cyber security incident management playbook, is developed to provide a comprehensive model to manage cyber security incidents, particularly for the cyber security operations centre. The proposed playbook is adaptive, cross-sectorial, and process driven. Each key components of the incident management playbook are outlined and discussed. Further, a lexicon based on equivalence mapping is developed and used to map existing cyber security incident vocabulary and taxonomy into a common and consistent lexicon to aid understanding among incident management stakeholder communities – national, government and private sectors. A versatile workbook model has been explored which proves to be adaptable to serve a wide range of cases for successfully managing government and private sector security operations centre. Cyber security incident sharing partnership, formalism for metric and measurements of cyber security incident parameters, and cyber security incident classification and prioritisation schemes are presented, and finally, cyber security incident ‘plays’ and playbook templates are discussed.

The Internet of Things (IoT), which describes the equipment of physical objects with sensors, actuators, computing logic, and connectivity, has attracted much attention. Although many facets of the IoT have already been explored, existing works either treat smart things as black box or focus on their technical characteristics. From an engineering management perspective, however, also a profound understanding of nontechnical smart thing characteristics is key. Hence, in this article, we develop and evaluate a corresponding multilayer taxonomy based on the latest IoT literature and a deliberately broad sample of 200 smart things, which covers the diversity of smart things available on the market. Based on 11 dimensions, the taxonomy enables classifying smart things according to the layers of established IoT architectures. Based on our sample, we infer five clusters, each covering a typical combination of nontechnical smart thing characteristics occurring in practice. These results extend our understanding of the IoT by structuring nontechnical characteristics of smart things and by abstracting the diversity of smart things into artefacts with manageable complexity. Our results inspire future research on the adoption, affordances, and design of smart things. Moreover, engineering managers can use our results in early phases of product development and process reengineering projects.

Today, the rapid growth of the internet and advancements in mobile technology and increased internet connectivity have brought us to a data-driven economy where an enormous amount of data is being used to train machine learning models to make strategic decisions. However, in the aftermath of a data breach by Facebook in 2018, there are some serious concerns over user data privacy and security being used to train the Machine Learning models. In this context, a new approach, Federated Machine Learning is now one of the most talked-about and recent approaches. Current research primarily focuses on Federated Learning's advantages over the traditional methods and/or its classification. However, being in a nascent stage of development as a method, certain challenges need to be addressed. This paper intends to address the totality of federated learning with a complete vulnerability assessment. During the study of the literature, it is found that security being promised as one of the key advantages of federated learning can still not be guaranteed because of some issues inherently present, and this can lead to poisoning, inference attacks and insertion of backdoors, etc. This paper intends to provide a complete picture by giving an in-depth and comprehensive analysis of Federated Learning and its taxonomy. It also provides a detailed vulnerability assessment and highlights the challenges faced in the current setting and future research directions to make federated learning a more functional, robust and secure method to train machine learning models.

Graph processing has become an important part of various areas of computing, including machine learning, medical applications, social network analysis, computational sciences, and others. A growing amount of the associated graph processing workloads are dynamic, with millions of edges added or removed per second. Graph streaming frameworks are specifically crafted to enable the processing of such highly dynamic workloads. Recent years have seen the development of many such frameworks. However, they differ in their general architectures (with key details such as the support for the concurrent execution of graph updates and queries, or the incorporated graph data organization), the types of updates and workloads allowed, and many others. To facilitate the understanding of this growing field, we provide the first analysis and taxonomy of dynamic and streaming graph processing. We focus on identifying the fundamental system designs and on understanding their support for concurrency, and for different graph updates as well as analytics workloads. We also crystallize the meaning of different concepts associated with streaming graph processing, such as dynamic, temporal, online, and time-evolving graphs, edge-centric processing, models for the maintenance of updates, and graph databases. Moreover, we provide a bridge with the very rich landscape of graph streaming theory by giving a broad overview of recent theoretical related advances, and by discussing which graph streaming models and settings could be helpful in developing more powerful streaming frameworks and designs. We also outline graph streaming workloads and research challenges. ISSN:1045-9219 ISSN:1558-2183 ISSN:2161-9883

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environments and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing body of work around explainable autonomous driving. First, we open with a motivation for explanations by highlighting and emphasising the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their explanation requirements for AV. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, control, and system management). Finally, we identify pertinent challenges and provide recommendations, such as a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explainability in AVs., Comment: Accepted for publication in IEEE Transactions on Intelligent Transportation Systems

This article seeks to advance the usage-based discourse research that investigates meanings and processes of meaning construction in interaction by elaboratingan empirically grounded interdisciplinary model. The paradigmatic and sequential analysis employed here brings together linguistic discourse analysis with an ethnomethodological perspective, and presents an innovative take on category organization in talk,explaininghow to capture knowledge resources such as asymmetrical category contrast pairs in talk. In analyzing in detail the speaker’s taxonomy constructionin a sample conversation, the papersystematicallyexploresthefollowing two topicsrelated to the speaker: what category characteristic he is orientingto as a resource for his present talkandwhat socialmeaning the speaker’s taxonomizing isconsistentlycommunicatingin the flow of talk. The proposed model captures a color binary–used to categorize people–of the ‘colored’versus the ‘white’, entailed in theexpression ‘a so-called yellowcolored people’,and reveals that the category pair is used as an organizational device in the speaker’s argument.The paper claims that taxonomy analysis in sequence is useful to examine the selected words in relation to their semiotic resources.

International audience; The aim of this article is to complement and refine Ellen Prince's well-known taxonomy of given/new information (Prince 1981, 1992), which distinguishes between discourse-related and assumed familiarity-related newness/givenness. What we suggest is that a new category should be added to the existing hearer new, hearer old, and inferrable information categories, so as to include cases where the informational status of an entity or a propositional content cannot be determined with certainty. We call this new category 'the (hearer) indeterminables', and we justify its existence through a case study on non-restrictive, relevance oriented constructions (appositive relative clauses, non-restrictive pre-modifiers, apposition). We also argue that it is possible for speakers/writers to simulate informational statuses for politeness considerations, and that such simulation should be included in the definition of assumed familiarity.

Due to their largely non-routinized forms and their not being retrievable in computerised corpus searches, refusals have hitherto not been examined from a diachronic perspective. The present paper presents an inventory of refusal strategies in Early Modern English drama texts. Five comedies from two periods (1560–1599 and 1720–1760), respectively, taken from the Corpus of English Dialogues 1560–1760 (Kytö and Culpeper 2006) were examined manually and analysed qualitatively and quantitatively. The analysis lead to an alternative classification of refusals which differs considerably from the frequently used taxonomy by Beebe, Takahashi, and Uliss-Weltz (1990). The proposed classification takes into account three levels of analysis: the propositional content of the utterance, the functional super-strategy, and the speaker’s stance. The development of refusal within the period under investigation partially matches findings regarding related speech acts that show a development towards increased indirectness (Culpeper and Demmen 2011, Pakkala-Weckström 2008, Del Lungo Camiciotti 2008).

Laughter is a crucial signal for communication and managing interactions. Until now no consensual approach has emerged for classifying laughter. We propose a new framework for laughter analysis and classification, based on the pivotal assumption that laughter has propositional content. We propose an annotation scheme to classify the pragmatic functions of laughter taking into account the form, the laughable, the social, situational, and linguistic context. We apply the framework and taxonomy proposed in a multilingual corpus study (French, Mandarin Chinese and English), involving a variety of situational contexts. Our results give rise to novel generalizations about the range of meanings laughter exhibits, the placement of the laughable, and how placement and arousal relate to the functions of laughter. We have tested and refuted the validity of the commonly accepted assumption that laughter directly follows its laughable. In the concluding section, we discuss the implications our work has for spoken dialogue systems. We stress that laughter integration in spoken dialogue systems is not only crucial for emotional and affective computing aspects, but also for aspects related to natural language understanding and pragmatic reasoning. We formulate the emergent computational challenges for incorporating laughter in spoken dialogue systems.

With the continuous development of Artificial Intelligence (AI), autonomous driving has become a popular research area. AI enables the autonomous driving system to make a judgment, which makes studies on autonomous driving reaches a period of booming development. However, due to the defects of AI, it is not easy to realize a general intelligence, which also limits the research on autonomous driving. In this paper, we summarize the existing architectures of autonomous driving and make a taxonomy. Then we introduce the concept of hybrid human-artificial intelligence (H-AI) into a semi-autonomous driving system. For making better use of H-AI, we propose a theoretical architecture based on it. Given our architecture, we classify and overview the possible technologies and illustrate H-AI's improvements, which provides a new perspective for the future development. Finally, we have identified several open research challenges to attract the researchers for presenting reliable solutions in this area of research.

The Internet of Things (IoT) is an emerging technology that has earned a lot of research attention and technical revolution in recent years. Significantly, IoT connects and integrates billions of devices and communication networks around the world for several real-time IoT applications. On the other hand, cybersecurity attacks on the IoT are growing at an alarming rate since these devices are vulnerable because of their limited battery life, global connectivity, resource-constrained nature, and mobility. When attacks on IoT networks go undetected within a speculated period, such security attacks may prompt severe threats and disruptive behavior inside the network and make the network unavailable to the end-user. Hence, it is quintessential to design an intelligent and robust security approach that promptly detects potential attack surfaces in a dynamic IoT network. This paper investigates a comprehensive survey of machine learning, deep learning, and reinforcement learning-based intelligent intrusion detection techniques for securing IoT. Also, this paper thoroughly illustrates the implementation of various categories of security threats in IoT with a neat diagram. Significantly, we classify the threats into two broad categories: Wireless Sensor networks (WSN) inherited security attacks and Routing Protocol for Low Power and Lossy Networks (RPL) specific security attacks in IoT. Finally, we present potential research opportunities and challenges in intelligent intrusion detection approaches in future IoT security.

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications. Third, we comprehensively collect and organize widely used public datasets in the existing literature to facilitate other researchers. Furthermore, we give an evaluation and analysis by conducting extensive experiments to compare the performance of different methods on a real-world public dataset. Finally, we discuss open challenges in this field., to be published in IEEE Transactions on Intelligent Transportation Systems

The modeling of atmospheric clouds is one of the crucial elements in the natural phenomena visualization system. Over the years, a wide range of approaches has been proposed on this topic to deal with the challenging issues associated with visual realism and performance. However, the lack of recent review papers on the atmospheric cloud modeling methods available in computer graphics makes it difficult for researchers and practitioners to understand and choose the well-suited solutions for developing the atmospheric cloud visualization system. Hence, we conducted a comprehensive review to identify, analyze, classify, and summarize the existing atmospheric cloud modeling solutions. We selected 113 research studies from recognizable data sources and analyzed the research trends on this topic. We defined a taxonomy by categorizing the atmospheric cloud modeling methods based on the methods' similar characteristics and summarized each of the particular methods. Finally, we underlined several research issues and directions for potential future work. The review results provide an overview and general picture of the atmospheric cloud modeling methods that would be beneficial for researchers and practitioners.

Blockchain technology is now being used in every aspect of human life. It appears like everyone is racing to develop apps that run on top of the blockchain technology available today. Indeed, this is owing to the fact that it has distinct qualities and a distinctive design. However, it is not well suited to all applications of blockchain technology. Not merely altering the blockchain protocols would make it suitable, but instead redesigning its architecture is required to make it ideal for different applications. This paper describes the overview of blockchain technology thoroughly. It provides a detailed discussion on the prevalent core-oriented and client-oriented attacks on blockchain technology and the vulnerabilities exploited by them. It also presents the possible countermeasure to these attacks. Moreover, it provides insight into the creation of a better version of the blockchain that is more suitable for different types of blockchain applications.

As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). To enable in-depth understanding of deep SOD, in this paper, we provide a comprehensive survey covering various aspects, ranging from algorithm taxonomy to unsolved issues. In particular, we first review deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection. Following that, we summarize and analyze existing SOD datasets and evaluation metrics. Then, we benchmark a large group of representative SOD models, and provide detailed analyses of the comparison results. Moreover, we study the performance of SOD algorithms under different attribute settings, which has not been thoroughly explored previously, by constructing a novel SOD dataset with rich attribute annotations covering various salient object types, challenging factors, and scene categories. We further analyze, for the first time in the field, the robustness of SOD models to random input perturbations and adversarial attacks. We also look into the generalization and difficulty of existing SOD datasets. Finally, we discuss several open issues of SOD and outline future research directions., Published on IEEE TPAMI. All the saliency prediction maps, our constructed dataset with annotations, and codes for evaluation are publicly available at \url{https://github.com/wenguanwang/SODsurvey}

This study describes an attempt to develop an integrative model of reciprocity, specifically as it is used in the organizational behavior field. Three issues have been identified in the literature: (i) the overlap of different meanings for existing taxonomy of the norm of reciprocity; (ii) an underestimation of some aspects of reciprocity; and (iii) imprecise descriptions and predictions of constructs and behaviors inside the organizations. A systematic and rigorous literature review of the designated manuscripts is performed. Nine organizational constructs that use the norm of reciprocity as a critical mechanism to explain social relationships within the workplace have been identified. A comprehensive classification for the notion and features of reciprocity is offered, which helps deliver a precise and novel map of the field. Moreover, it isolates three main distinctions, namely contents of the acts that are given and received; the type of exchange; and the subjects to whom reciprocity is directed, offering a novel interpretative framework of the norm of reciprocity. This research tries to overcome the ambiguity that characterizes how reciprocity is defined, classified, and used in the organizational behaviors field. Finally, it analyzes how different organizational behavior constructs use the different notions and features of reciprocity. This endeavor helps to create a precise map of the field of organizational behavior through a novel framework highlighting underdeveloped areas for future research attention.

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed., Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation Systems

Humans engage in a wide variety of different actions and activities. These range from simple motor actions like reaching for an object, to complex activities like governing a nation. Navigating everyday life requires people to make sense of this diversity of actions. We suggest that the mind simplifies this complex domain by attending primarily to the most essential features of actions. Using a parsimonious set of action dimensions, the mind can organize action knowledge in a low-dimensional representational space. In seven studies, we derive and validate such an action taxonomy. Study 1 uses large-scale text analyses to generate and test potential action dimensions. Study 2 validates interpretable labels for these dimensions. Studies 3-5 demonstrate that these dimensions can explain human judgments about actions. We perform model selection on data from these studies to arrive at the optimal set of six psychological dimensions, together forming the Abstraction, Creation, Tradition, Food, Animacy, Spiritualism Taxonomy (ACT-FAST). Study 6 demonstrates that ACT-FAST can predict socially relevant qualities of actions, including how, when, where, why, and by whom they are performed. Finally, Study 7 shows that ACT-FAST can explain action-related patterns of brain activity using naturalistic functional MRI (MRI). Together, these studies reveal the dimensional structure the mind applies to organize action concepts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A concern that people ought to be given what they deserve, in both positive and negative senses, lies deep within the human psyche. Views on the level of reward or punishment that a person deserves for their actions will differ across persons, places, and time, but, I argue in this article, depend substantively upon some combination of intentions and outcomes. Using these characteristics, I propose a taxonomy of actions, ordered from most to least blameworthy, with, for example, it being suggested that for any particular level of harm an intentional yet unrealized harm is more blameworthy than an unintentional yet realized harm (a similar taxonomy can be developed for the positive domain of praiseworthy actions). The taxonomy is focused upon people's actions toward others, but I finish the article with a discussion of desert in relation to people's intentions toward themselves. Ultimately, I contend that the strength and sustainability of public sector services and welfare systems, not to mention our private relationships, rely upon the recognition that desert underpins our notion of justice.

This article examines the connected histories of racial science and colonial geography in Island Southeast Asia. By focusing on the island of Timor, it explores colonial boundaries as modes of arranging racial classifications, and racial typologies as forms of articulating political geography. Portuguese physical anthropologist António Mendes Correia's work on the ethnology of East Timor is examined as expressive of these productive connections. Correia's classificatory work ingeniously blended political geography and racial taxonomy. Between 1916 and 1945, mainly based on data from the Portuguese enclave of Oecussi and Ambeno, he claimed a distinct Malayan racial type for the whole colony of ‘Portuguese Timor’. Over the years he developed an anthropogeographical theory that simultaneously aimed to reclassify East Timor and to revise the racial cartography of the Malay Archipelago, including Wallace's famous ethnological line.

Recently, developing students’ thinking, especially critical thinking (CT), has become a hot issue. Critical thinking has been claimed to have an important impact on learners’ reading comprehension because it can help them analyze, evaluate, construct their thinking, solving problems and reasoning (Ennis, 1989). However, the extent that teachers’ classroom activities contribute to developing students’ critical thinking has rarely been researched. The current case study was conducted with six EFL high school teachers and 10 reading lessons in Vietnam to explore the teachers’ use of questions and to analyze if these questions could facilitate the students’ critical thinking. Classroom observations and the cognitive domain of Bloom’s taxonomy were adapted as the research instruments. The study results reveal common types of questions are often used by high school teachers in their reading lessons. Suggestions are made on types of questions that teachers should function more in their class in order to enhance students’ critical thinking.

This study is aimed to know the grammatical errors found in the articles’ abstracts of scholarly journals published by one of Indonesian Islamic State Colleges in 2008-2010. The theory used to analyze the data in this case study is Burt and Kiparsky’s theory, namely Surface Strategy Taxonomy. This theory devides errors into errors of omission, errors of addition, errors of misformation and errors of misordering. This results of the study show that there are 172 items of grammatical errors. The most frequent type of grammatical error is omission with the total number is 72 items or 41.9%. The second is errors of misformation which consist of 57 items or 33.1%. The next is errors of addition (27 items or 15.7%) and finally is errors of misordering as the least number of errors with 16 items or 9.3%. Keywords: grammatical errors, surface strategy taxonomy, journals abstract

This study is aimed to find out the types of errors made by the presenters of thesis proposal seminar based on Communicative Effect Taxonomy and to find out whether or not global errors of Communicative Effect Taxonomy made by the presenters of thesis proposal seminar significantly hinder communication between the presenters and the audiences. This study used qualitative approach since the data of this study was taken from 4 students’ presentations which were in the form of words rather than number and statistic. This study revealed that there were 188 errors committed by the students in which 179 and 9 erroneous utterances fall under local error and global error respectively. The local errors consist of 45 lexical errors, 93 morphological errors, and 41 syntactical errors. The global errors consist of 2 wrong order of major constituents, 1 missing, wrong, or misplaced sentence connectors, 1 missing, wrong, or misplaced sentence connectors, and 5 uncategorized global errors. As for the effect of global error to the audience, the writer found out that the global errors did not significantly hinder communication to the audience. Keywords: Error Analysis, thesis proposal seminar, Communicative Effect Taxonomy

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

This article presents a re-classification of information seeking (IS) tasks, concepts, and algorithms. The proposed taxonomy provides new dimensions to look into information seeking tasks and methods. The new dimensions include the number of search iterations, search goal types, and procedures to reach these goals. Differences along these dimensions for the information seeking tasks call for suitable computational solutions. The article then reviews machine learning solutions that match each new category. The paper ends with a review of evaluation campaigns for IS systems., Comment: Accepted to ACM TOIS in Nov 2021

An intelligent assistant (IA) is a computer system endowed with artificial intelligence and/or machine learning techniques capable of intelligently assisting people. IAs have gained in popularity over recent years due to their usefulness, significant commercial developments, and a myriad of scientific and technological advances resulting from research efforts by the computer science community. In particular, these efforts have led to an increasingly extensive and complex state of the art of IAs, making evident the need to carry out a review in order to identify and catalogue the advances in the construction of IAs as well as to detect potential areas of further research. This paper presents a systematic review aiming to (i) describe, classify, and organize recent advances in IAs; (ii) characterize IAs’ objectives, application domains, and workings; (iii) analyze how IAs have been evaluated; and (iv) identify what artificial intelligence and machine learning techniques are used to enable the intelligence of IAs. As a result of this systematic review, it is also proposed a taxonomy of IAs and a set of potential future research directions to further improve IAs. A set of research questions was formulated to guide this systematic review and address the proposed objectives. Well-known scientific databases were searched for articles on IAs published from January 2015 to June 2021 using 172 search strings. A total of 22,554 articles were retrieved and after applying inclusion, exclusion, and quality criteria, an overall number of 99 articles were selected, which are the basis for this systematic review.

Background: Advancement in sequencing technology yields a huge number of genomes from a multitude of organisms on our planet. One of the fundamental tasks for processing and analyzing these sequences is to organize them in the existing taxonomic orders. Method: Recently, we proposed a novel approach, GenFooT, for taxonomy classification using the concept of genomic footprint (GFP). The technique is further refined and enhanced in this work leading to improved accuracies in the task of taxonomic classification based on various benchmark datasets. GenFooT maps a genome sequence in a 2D coordinate space and extracts features from that representation. It uses two hyper-parameters, namely block size and number of fragments of genomic sequence while computing the feature. In this work, we propose an analysis of choosing values of those parameters adaptively from the sequences. The enhanced version of GenFooT is named GenFooT2. Results and Conclusion: We have tested GenFooT2 on ten different biological datasets of genomic sequences of various organisms belonging to different taxonomy ranks. Our experimental results indicate a 3% improved classification performance of the proposed GenFooT2 featured with a Logistic regression classifier as compared to GenFooT. We also performed the statistical test to compare the performance of GenFooT2 to the state-of-the-art methods including our previous method, GenFooT. The experimental results as well as the statistical test exhibit that the performance of the proposed GenFooT2 is significantly better.

Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods., Comment: Accepted by IEEE/CAA Journal of Automatica Sinica

The study of sedentary behaviors requires taxonomies (classification schemes) to standardize data collection, measurements, and outcomes. Three taxonomies of sedentary behaviors have been identified, but none address an important challenge in sedentary behavior research, which is to distinguish between beneficial and detrimental health effects of various sedentary behaviors. Some sedentary behaviors (e.g., reading) are associated with positive health outcomes, whereas other sedentary behaviors (e.g., television viewing) are associated with adverse health outcomes. To address directly this complexity and present a different conception and understanding of discrepant findings related to health outcomes, a new taxonomy is needed. The development of the new taxonomy is guided by analysis of literature and selection of a relevant and informative behavioral sciences theoretical framework (i.e., self-determination theory). Because older adults are an increasing percentage of the population and report a high prevalence of sedentary behaviors, the new taxonomy was designed for older adults with potential application to all age groups. Taylor’s taxonomy of sedentary behaviors is parsimonious with four domains: social interaction (i.e., not solitary, companionship, interacting, and connecting with others); novelty (i.e., refreshingly new, unusual, or different); choice (i.e., volition, preferred option or alternative, the power, freedom, or decision to choose); and cognition (i.e., mentally stimulating and engaging).

The large-scale protein-protein interaction (PPI) data has the potential to play a significant role in the endeavor of understanding cellular processes. However, the presence of a considerable fraction of false positives is a bottleneck in realizing this potential. There have been continuous efforts to utilize complementary resources for scoring confidence of PPIs in a manner that false positive interactions get a low confidence score. Gene Ontology (GO), a taxonomy of biological terms to represent the properties of gene products and their relations, has been widely used for this purpose. We utilize GO to introduce a new set of specificity measures: Relative Depth Specificity (RDS), Relative Node-based Specificity (RNS), and Relative Edge-based Specificity (RES), leading to a new family of similarity measures. We use these similarity measures to obtain a confidence score for each PPI. We evaluate the new measures using four different benchmarks. We show that all the three measures are quite effective. Notably, RNS and RES more effectively distinguish true PPIs from false positives than the existing alternatives. RES also shows a robust set-discriminating power and can be useful for protein functional clustering as well.

Instruction manuals need to explain specialized technical information to readers of the general public. Using metadiscourse effectively, i.e. engaging with readers and guiding them through the text, is therefore crucial for successful communication. An increased awareness of how these features are used in instruction manuals would therefore help technical writers write reader-friendly texts. This study investigates metadiscourse in ten instruction manuals in English using Hyland’s (2005) taxonomy. It was found that the interactive metadiscourse is predominated by frame markers and code glosses, which reflects the purpose of instruction manuals to explain clearly and efficiently how a machine works. The interactional metadiscourse reflects the twofold relationship between the writers and their readers: on the one hand, that of instructor and inexperienced users of the product and, on the other, that of manufacturer and customer. The former is reflected by the frequent usage of necessity attitude markers and boosters to reinforce instructions and precautions. The latter is reflected by the usage of politeness markers and the hedging of problems and risks. The visual presentation of the manuals, i.e. its segmentation, typographical variation, punctuation and illustrations, was also shown to express metadiscourse meanings.

International audience; Mixed Integer Linear Programs (MILPs) are usually NP-hard mathematical programming problems, which present difficulties to obtain optimal solutions in a reasonable time for large scale models. Nowadays, metaheuristics are one of the potential tools for solving this type of problems in any context. In this paper, we focus our attention on MILPs in the specific framework of Data Envelopment Analysis (DEA), where the determination of a score of technical efficiency of a set of Decision Making Units (DMUs) is one of the main objectives. In particular, we propose a new hyper-matheuristic grounded on a MILP-based decomposition in which the optimization problem is divided into two hierarchical subproblems. The new approach decomposes the model into discrete and continuous variables, treating each subproblem through different optimization methods. In particular, metaheuristics are used for dealing with the discrete variables, whereas exact methods are used for the set of continuous variables. The metaheuristics use an indirect representation that encodes an incomplete solution for the problem, whereas the exact method is applied to decode the solution and generate a complete solution. The experimental results, based on simulated data in the context of Data Envelopment Analysis, show that the solutions obtained through the new approach outperform those found by solving the problem globally using a metaheuristic method. Finally, regarding the new hyper-matheuristic scheme, the best algorithm selection is found for a set of cooperative metaheuristics ans exact optimization algorithms.

Automatic plant classification is challenging due to the vast biodiversity of the existing plant species in a fine-grained scenario. Robust deep learning architectures have been used to improve the classification performance in such a fine-grained problem but usually build models that are highly dependent on a large training dataset and are not scalable. This paper proposes a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained plant species recognition. It uses the botanical taxonomy as a basis for a coarse-to-fine strategy applied to identify the plant genus and species. The two-view representation provides complementary global and local features of leaf images. A deep metric based on Siamese Convolutional Neural Networks is used to reduce the dependence on many training samples and make the method scalable to new plant species. The experimental results on two challenging fine-grained datasets of leaf images (i.e., PlantCLEF 2015 and LeafSnap) have shown the proposed method’s effectiveness, which achieved recognition accuracy of 0.87 and 0.96, respectively.

Hierarchical multilabel classification (HMC) assigns multiple labels to each instance with the labels organized under hierarchical relations. In ship classification in remote sensing images, depending on the expert knowledge and image quality, the same type of ships in different remote sensing images may be annotated with different class labels from coarse to fine levels such as merchant ship (MS) or container ship (CTS). In this article, we propose a novel deep network with two output channels and their associated loss functions to learn an HMC classifier using samples labeled at different levels in the hierarchy. In the proposed network, a hierarchy and exclusion (HEX) graph is introduced to model the label hierarchy, which satisfies hierarchical constraints by encoding semantic relations between any two labels. The output nodes of the first channel are organized according to the HEX graph, and its corresponding probabilistic classification loss is built to reflect the hierarchical structure of the HEX graph. On the other hand, the output nodes of the second channel only represent the finest grained (last level in the hierarchy) classes, and its multiclass cross-entropy loss is designed to enhance the discriminative power of the HMC classifier on the last level labels, which is also compatible with constraints in the HEX graph. The combination of these two losses from two output channels can effectively transfer the hierarchical information of ship taxonomy during network training. Experimental results on two commonly used ship datasets demonstrate that the proposed method outperforms the state-of-the-art HMC approaches, and is especially advantageous when trained with fewer fine-grained samples.

Recent years have illustrated the significantly pervasive interest in transparent antennas. The number and the popularity of transparent antenna applications have escalated dramatically. Although antenna applications are diverse and available across multiple platforms, some of these antennas are unsuitable for practical usage, particularly in cases associated with renewable energy. As such, this study presents the review of related articles pertaining to transparent antennas across a range of platforms to identify their best practices. The methods applied in prior research work within this domain were identified. Relevant articles published between 2015 and September 2020 were gathered from four major databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. The identified indicators were considered as broad and reliable to cover this field of literature. The articles (n=81) were selected based on inclusion and exclusion criteria. This paper concentrates on the present views and opportunities to further understand the research segment of transparent antenna.

Xylariomycetidae ( Ascomycota ) is a highly diversified group with variable stromatic characters. Our research focused on inconspicuous stromatic xylarialean taxa from China, Italy, Russia, Thailand and the United Kingdom. Detailed morphological descriptions, illustrations and combined ITS-LSU- rpb 2- tub 2- tef 1 phylogenies revealed 38 taxa from our collections belonging to Amphisphaeriales and Xylariales . A new family ( Appendicosporaceae ), five new genera ( Magnostiolata , Melanostictus , Neoamphisphaeria , Nigropunctata and Paravamsapriya ), 27 new species ( Acrocordiella photiniicola , Allocryptovalsa sichuanensis , Amphisphaeria parvispora , Anthostomella lamiacearum , Apiospora guiyangensis , Ap. sichuanensis , Biscogniauxia magna , Eutypa camelliae , Helicogermslita clypeata , Hypocopra zeae , Magnostiolata mucida , Melanostictus longiostiolatus , Me. thailandicus , Nemania longipedicellata , Ne. delonicis , Ne. paraphysata , Ne. thailandensis , Neoamphisphaeria hyalinospora , Neoanthostomella bambusicola , Nigropunctata bambusicola , Ni. nigrocircularis , Ni. thailandica , Occultitheca rosae , Paravamsapriya ostiolata , Peroneutypa leucaenae , Seiridium italicum and Vamsapriya mucosa ) and seven new host/geographical records are introduced and reported. Divergence time estimates indicate that Delonicicolales diverged from Amphisphaeriales + Xylariales at 161 (123–197) MYA. Amphisphaeriales and Xylariales diverged 154 (117–190) MYA with a crown age of 127 (92–165) MYA and 147 (111–184) MYA, respectively. Appendicosporaceae ( Amphisphaeriales ) has a stem age of 89 (65–117) MYA. Ancestral character state reconstruction indicates that astromatic, clypeate ascomata with aseptate, hyaline ascospores that lack germ slits may probably be ancestral Xylariomycetidae having plant-fungal endophytic associations. The Amphisphaeriales remained mostly astromatic with common septate, hyaline ascospores. Stromatic variations may have developed mostly during the Cretaceous period. Brown ascospores are common in Xylariales , but they first appeared in Amphisphaeriaceae , Melogrammataceae and Sporocadaceae during the early Cretaceous. The ascospore germ slits appeared only in Xylariales during the Cretaceous after the divergence of Lopadostomataceae . Hyaline, filiform and apiospores may have appeared as separate lineages providing the basis to Xylariaceae , which may have diverged independently. The future classification of polyphyletic xylarialean taxa will not be based on stromatic variations, but the type of ring, the colour of the ascospores, and the presence or absence of the type of germ slit.

International audience; Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities.

Interval-valued intuitionistic fuzzy set (IVIFS) is a flexible method to deal with uncertainty and fuzziness. For the past few years, extensive researches about the multi-attribute group decision making (MAGDM) problems based on IVIFSs has been extensively studied in many fields. In this study, the Taxonomy method based on IVIFSs (IVIF-Taxonomy) was proposed for MAGDM problems. For the sake of the objectivity of attribute weight, entropy is introduced into the proposed model. The IVIF-Taxonomy method fully considers the weight of the decision makers (DMs) and the homogeneity of the chosen alternatives, making it more realistic. In addition, we apply IVIF-Taxonomy method to fund selection to verify the validity of IVIF-Taxonomy method. Finally, the trustworthy of IVIF-Taxonomy method is proved by comparing with the aggregate operator, IVIF-TOPSIS method, IVIF-GRA method and modified IVIF-WASPAS method.

This paper proposes a synthesis of the taxonomy, phylogeny, palaeogeographic distribution, regional biostratigraphy, and palaeobiogeographic faunal development of Carboniferous fusuline foraminifers. They appeared in the latest Tournaisian and comprised a small-sized, morphologically conservative taxonomic group during the Mississippian. Fusulines became larger and prevailed in Pennsylvanian foraminiferal assemblages. Carboniferous fusulines consist of Ozawainellidae, Staffellidae, Schubertellidae, Fusulinidae, and Schwagerinidae, in which 95 genera are considered as valid taxonomically. Upsizing their shells throughout the Pennsylvanian is likely related to symbiosis with photosynthetic microorganisms, which was accelerated by the acquisition of a keriothecal wall in Late Pennsylvanian schwagerinids. Regional fusuline succession data from 40 provinces provide a refined biostratigraphy, enabling zonation and correlation with substage- or higher-resolution precision in the Pennsylvanian. Their spatio-temporal faunal characteristics show that fusulines had a cosmopolitan palaeobiogeographic signature in Mississippian time, suggesting unrestricted faunal exchange through the palaeoequatorial Rheic Ocean. After the formation of Pangaea, Pennsylvanian fusulines started to show provincialism, and their distributions defined the Ural–Arctic Region in the Boreal Realm, Palaeotethys, Panthalassa, and North American Craton regions in the Palaeoequatorial Realm, and Western Gondwana and Eastern Peri-Gondwana regions in the Gondwana Realm. The Western Palaeotethys and East European Platform Subregions maintained higher generic diversity throughout the Pennsylvanian.

Background A central construct in Schema Therapy (ST) is that of a schema mode, describing the current emotional-cognitive-behavioral state. Initially, 10 modes were described. Over time, with the world-wide increasing and broader application of ST to various disorders, additional schema modes were identified, mainly based on clinical impressions. Thus, the need for a new, theoretically based, cross-cultural taxonomy of modes emerged. Methods An international workgroup started from scratch to identify an extensive taxonomy of modes, based on (a) extending the theory underlying ST with new insights on needs, and (b) recent research on ST theory supporting that modes represent combinations of activated schemas and coping. Results We propose to add two emotional needs to the original five core needs that theoretically underpin the development of early maladaptive schemas (EMSs), i.e., the need for Self-Coherence, and the need for Fairness, leading to three new EMSs, i.e. Lack of a Coherent Identity, Lack of a Meaningful World, and Unfairness. When rethinking the purpose behind the different ways of coping with EMS-activation, we came up with new labels for two of those: Resignation instead of Surrender, and Inversion instead of Overcompensation. By systematically combining EMSs and ways of coping we derived a set of schema modes that can be empirically tested. Conclusions With this project, we hope to contribute to the further development of ST and its application across the world.

Theory of Mind (ToM), the ability to represent the mental states of oneself and others, is an essential social skill disrupted across many psychiatric conditions. The transdiagnostic nature of ToM impairment means it is plausible that ToM impairment is related to alexithymia (difficulties identifying and describing one’s own emotions), as alexithymia is seen across psychiatric conditions. Whilst many studies have examined links between alexithymia and ToM, results are mixed. Therefore, the purpose of this systematic review is to provide a taxonomy of ToM tests and assess their relationship with alexithymia. Tests are grouped according to whether they assess propensity to engage spontaneously in ToM or accuracy of ToM inferences, with tests further subdivided into those that do, and do not, require emotion recognition. A review of 63 suitable studies suggests that alexithymia is often associated with reduced ToM, and inaccurate ToM when tasks require emotion recognition. This latter finding appears due to impaired emotion recognition, rather than ToM impairment per se. Further directions and considerations for future research are discussed.