title: Learning in times of lockdown: how Covid-19 is affecting education and food security in India

text: A vast majority of the relief and rehabilitation packages announced in the months following the nationwide lockdown in India have focused on economic rehabilitation. However, the education sector has remained absent from this effort, including in India’s central government’s 250 billion dollar stimulus package. In this paper, we discuss the implications of lockdown-induced school and rural child-care center closures on education and health outcomes for the urban and rural poor. We especially focus on food and nutritional security of children who depend on school feeding and supplementary nutrition programs. We argue that the impacts are likely to be much more severe for girls as well as for children from already disadvantaged ethnic and caste groups. We also discuss ways in which existing social security programs can be leveraged and strengthened to ameliorate these impacts.


title: Discovering Fails in Software Projects Planning Based on Linguistic Summaries

text: Linguistic data summarization techniques help to discover complex relationships between variables and to present the information in natural language. There are some investigations associated to algorithms to build linguistic summaries. But the literature does no report investigations concerned with combination linguistic data summarization techniques and outliers’ mining applied to planning of software project. In particular, outliers’ mining is a datamining technique, useful in errors and fraud detection. In this work authors present new algorithms to build linguistic data summaries from outliers in software project planning context. Besides, authors compare different outliers’ detection algorithms in software project planning context. The main motivation of this work is to detect planning errors in projects, to avoid high cost and time delays. Authors consider that the combination of outliers’ mining and linguistic data summarization support project managers to decision-making process in the software project planning. Finally, authors present the interpretation of obtained summaries and comment about its impact.


title: Sec2graph: Network Attack Detection Based on Novelty Detection on Graph Structured Data

text: Being able to timely detect new kinds of attacks in highly distributed, heterogeneous and evolving networks without generating too many false alarms is especially challenging. Many researchers proposed various anomaly detection techniques to identify events that are inconsistent with past observations. While supervised learning is often used to that end, security experts generally do not have labeled datasets and labeling their data would be excessively expensive. Unsupervised learning, that does not require labeled data should then be used preferably, even if these approaches have led to less relevant results. We introduce in this paper a unified and unique graph representation called security objects’ graphs. This representation mixes and links events of different kinds and allows a rich description of the activities to be analyzed. To detect anomalies in these graphs, we propose an unsupervised learning approach based on auto-encoder. Our hypothesis is that as security objects’ graphs bring a rich vision of the normal situation, an auto-encoder is able to build a relevant model of this situation. To validate this hypothesis, we apply our approach to the CICIDS2017 dataset and show that although our approach is unsupervised, its detection results are as good, and even better than those obtained by many supervised approaches.


title: Agent EXPRI: Licence to Explain

text: Online social networks are known to lack adequate multi-user privacy support. In this paper we present EXPRI, an agent architecture that aims to assist users in managing multi-user privacy conflicts. By considering the personal utility of sharing content and the individually preferred moral values of each user involved in the conflict, EXPRI identifies the best collaborative solution by applying practical reasoning techniques. Such techniques provide the agent with the cognitive process that is necessary for explainability. Furthermore, the knowledge gathered during the practical reasoning process allows EXPRI to engage in contrastive explanations.


title: Explainable Agents as Static Web Pages: UAV Simulation Example

text: Motivated by the apparent societal need to design complex autonomous systems whose decisions and actions are humanly intelligible, the study of explainable artificial intelligence, and with it, research on explainable autonomous agents has gained increased attention from the research community. One important objective of research on explainable agents is the evaluation of explanation approaches in human-computer interaction studies. In this demonstration paper, we present a way to facilitate such studies by implementing explainable agents and multi-agent systems that i) can be deployed as static files, not requiring the execution of server-side code, which minimizes administration and operation overhead, and ii) can be embedded into web front ends and other JavaScript-enabled user interfaces, hence increasing the ability to reach a broad range of users. We then demonstrate the approach with the help of an application that was designed to assess the effect of different explainability approaches on the human intelligibility of an unmanned aerial vehicle simulation.


title: A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research

text: Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.


title: Approximate Reaction Systems Based on Rough Set Theory

text: In this work we investigate how Rough Set Theory could be employed to model uncertainty and information incompleteness about a Reaction System. The approach that we propose is inspired by the idea of an abstract scientific experiment: we define the notion of test, which defines an approximation space on the states of a Reaction System, and observation, to represent the interactive process of knowledge building that is typical of complex systems. We then define appropriate notions of reducts and study their characterization in terms of both computational complexity and relationships with standard definitions of reducts in terms of Information Tables.


title: Similarity Based Granules

text: In the authors’ previous research, a possible usage of the correlation clustering in rough set theory was investigated. Correlation clustering is based on a tolerance relation and its output is a partition. The system of granules can be derived from the partition and as a result, a new approximation space appears. This space focuses on the similarity (represented by a tolerance relation) itself and it is different from the covering type approximation space relying on a tolerance relation. In real-world applications, the number of objects is very high. So it can be effective only if a portion of the data points is used. Previously we provided a method that chooses the necessary number of objects that represent the data set. These members are called representatives and it can be useful to apply them in the approximation of an arbitrary set. A new approximation pair can be defined based on the representatives. In this paper, some very important properties are checked for this approximation pair and the system of granules.


title: A Graph-Based Keyphrase Extraction Model with Three-Way Decision

text: Keyphrase extraction has been a popular research topic in the field of natural language processing in recent years. But how to extract keyphrases precisely and effectively is still a challenge. The mainstream methods are supervised learning methods and graph-based methods. Generally, the effects of supervised methods are better than unsupervised methods. However, there are many problems in supervised methods such as the difficulty in obtaining training data, the cost of labeling and the limitation of the classification function trained by training data. In recent years, the development of the graph-based method has made great progress and its performance of extraction is getting closer and closer to the supervised method, so the graph-based method of keyphrase extraction has got a wide concern from researchers. In this paper, we propose a new model that applies the three-way decision theory to graph-based keyphrase extraction model. In our model, we propose algorithms dividing the set of candidate phrases into the positive domain, the boundary domain and the negative domain depending on graph-based attributes, and combining candidate phrases in the positive domain and the boundary domain qualified by graph-based attributes and non- graph-based attributes to get keyphrases. Experimental results show that our model can effectively improve the extraction precision compared with baseline methods.


title: Putting Attacks in Context: A Building Automation Testbed for Impact Assessment from the Victim’s Perspective

text: Cybersecurity research relies on the reproducibility and deep understanding of attacks to devise appropriate solutions. Different kinds of testbeds are typically used to systematically execute attacks and evaluate defenses. Testbeds are widely used to demonstrate Building Automation and Control System (BACS) attacks and defenses, considered too risky to be executed on real infrastructures. However, those testbeds implement arbitrary configurations of building services that do not resemble real-world deployments. In this work, we present the first BACS testbed specially designed to assess the impact of cyberattacks from the victim’s perspective. It features general purpose building services such as illumination, ventilation, and temperature control, whose configuration is easily adapted to emulate the requirements of real-world locations. In this way, the context added to our testbed allows us to better understand the impact of BACS attacks through concrete and realistic scenarios. Moreover, by analyzing different configurations of the BACS (i.e., contexts), we found out that identical attacks may have dramatically different impacts. Thus, reinforcing our view on the relevance of adding context to BACS testbeds.


title: On the Convergence of Input-Output Fuzzy Cognitive Maps

text: Fuzzy cognitive maps are recurrent neural networks, where the neurons have a well-defined meaning. In certain models, some neurons receive outer input, while other neurons produce the output of the system. According to this observation, some neurons are categorized as input neurons and the others are the state neurons and output neurons. The output of the system is provided as a limit of an iteration process, which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also show up. In this paper, we examine the existence and uniqueness of fixed points for two types of input-output fuzzy cognitive maps. Moreover, we use network-based measures like in-degree, out-degree and connectivity, to express conditions for the convergence of the iteration process.


docid: hwtzbguf

title: An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions

text: The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a characteristic of table data sets that there is usually a fixed decision attribute. Therefore, it is enough for us to handle itemsets with one decision attribute, and we can see that one frequent itemset defines one implication. We make use of these characteristics and reduce the unnecessary itemsets for improving the performance of execution. Some experiments by the implemented software tool in Python clarify the improved performance.


title: Feature and Label Association Based on Granulation Entropy for Deep Neural Networks

text: Pooling layers help reduce redundancy and the number of parameters before building a multilayered neural network that performs the remaining processing operations. Usually, pooling operators in deep learning models use an explicit topological organization, which is not always possible to obtain on multi-label data. In a previous paper, we proposed a pooling architecture based on association to deal with this issue. The association was defined by means of Pearson’s correlation. However, features must exhibit a certain degree of correlation with each other, which might not hold in all situations. In this paper, we propose a new method that replaces the correlation measure with another one that computes the entropy in the information granules that are generated from two features or labels. Numerical simulations have shown that our proposal is superior in those datasets with low correlation. This means that it induces a significant reduction in the number of parameters of neural networks, without affecting their accuracy.


title: On the Hierarchy of Equivalence Classes Provided by Local Congruences

text: In this work, we consider a special kind of equivalence relations, which are called local congruences. Specifically, local congruences are equivalence relations defined on lattices, whose equivalence classes are convex sublattices of the original lattices. In the present paper, we introduce an initial study about how the set of equivalence classes provided by a local congruence can be ordered.


title: Attribute Reduction from Closure Operators and Matroids in Rough Set Theory

text: In this paper, we present a new closure operator defined on the set of attributes of an information system that satisfies the conditions for defining a matroid. We establish some basic relationships between equivalence classes and approximation operators where different sets of attributes are used. It is shown that the reducts of an information system can be obtained from dependent sets of a matroid. Finally, we show that the closure operator can be defined at least in three different ways.


title: Quick Maximum Distribution Reduction in Inconsistent Decision Tables

text: Attribute reduction is a key issue in rough set theory, and this paper focuses on the maximum distribution reduction for complete inconsistent decision tables. It is quite inconvenient to judge the maximum distribution reduct directly according to its definition and the existing heuristic based judgment methods are inefficient due to the lack of acceleration mechanisms that mainstream heuristic judgment methods have. In this paper, we firstly point out the defect of judgment method proposed by Li et al. [15]. After analyzing the root cause of the defect, we proposed two novel heuristic attribute reduction algorithms for maximum distribution reduction. The experiments show that proposed algorithms are more efficient.


title: Overlapping Community Detection Using Multi-objective Approach and Rough Clustering

text: The detection of overlapping communities in Social Networks has been successfully applied in several contexts. Taking into account the high computational complexity of this problem as well as the drawbacks of single-objective approaches, community detection has been recently addressed as Multi-objective Optimization Evolutionary Algorithms (MOEAs). One of the challenges is to attain a final solution from the set of non-dominated solutions obtained by the MOEAs. In this paper, an algorithm to build a covering of the network based on the principles of the Rough Clustering is proposed. The experiments in a synthetic networks showed that our proposal is promising and effective for overlapping community detection in social networks.


title: Approximate Decision Tree Induction over Approximately Engineered Data Features

text: We propose a simple SQL-based decision tree induction algorithm which makes its heuristic choices how to split the data basing on the results of automatically generated analytical queries. We run this algorithm using standard SQL and the approximate SQL engine which works on granulated data summaries. We compare the accuracy of trees obtained in these two modes on the real-world dataset provided to participants of the Suspicious Network Event Recognition competition organized at IEEE BigData 2019. We investigate whether trees induced using approximate SQL queries – although execution of such queries is incomparably faster – may yield poorer accuracy than in the standard scenario. Next, we investigate features – inputs to the decision tree induction algorithm – derived using SQL from a bigger associated data table which was provided in the aforementioned competition too. As before, we run standard and approximate SQL, although again, that latter mode needs to be checked with respect to the accuracy of trees learnt over the data with approximately extracted features.


title: Fast and Furious: Outrunning Windows Kernel Notification Routines from User-Mode

text: Modern Operating Systems (OSs) enable user processes to obtain full access control over other processes initiated by the same user. In scenarios of sensitive security processes (e.g., antivirus software), protection schemes are enforced at the kernel level such as to confront arbitrary user processes overtaking with malicious intent. Within the Windows family of OSs, the kernel driver is notified via dedicated routines for user-mode processes that require protection. In such cases the kernel driver establishes a callback mechanism triggered whenever a handle request for the original user-mode process is initiated by a different user process. Subsequently, the kernel driver performs a selective permission removal process (e.g., read access to the process memory) prior to passing a handle to the requesting process. In this paper we are the first to demonstrate a fundamental user-mode process access control vulnerability, existing in Windows 7 up to the most recent Windows 10 OSs. We show that a user-mode process can indeed obtain a fully privileged access handle before the kernel driver is notified, thus prior to the callback mechanism establishment. Our study shows that this flaw can be exploited by a method to (i) disable the anti-malware suite Symantec Endpoint Protection; (ii) overtake VirtualBox protected processes; (iii) circumvent two major video game anti-cheat protection solutions, BattlEye and EasyAntiCheat. Finally we provide recommendations on how to address the discovered vulnerability.


title: Three-Way Decisions Community Detection Model Based on Weighted Graph Representation

text: Community detection is of great significance to the study of complex networks. Community detection algorithm based on three-way decisions (TWD) forms a multi-layered community structure by hierarchical clustering and then selects a suitable layer as the community detection result. However, this layer usually contains overlapping communities. Based on the idea of TWD, we define the overlapping part in the communities as boundary region (BND), and the non-overlapping part as positive region (POS) or negative region (NEG). How to correctly divide the nodes in the BND into the POS or NEG is a challenge for three-way decisions community detection. The general methods to deal with boundary region are modularity increment and similarity calculation. But these methods only take advantage of the local features of the network, without considering the information of the divided communities and the similarity of the global structure. Therefore, in this paper, we propose a method for three-way decisions community detection based on weighted graph representation (WGR-TWD). The weighted graph representation (WGR) can well transform the global structure into vector representation and make the two nodes in the boundary region more similar by using frequency of appearing in the same community as the weight. Firstly, the multi-layered community structure is constructed by hierarchical clustering. The target layer is selected according to the extended modularity value of each layer. Secondly, all nodes are converted into vectors by WGR. Finally, the nodes in the BND are divided into the POS or NEG based on cosine similarity. Experiments on real-world networks demonstrate that WGR-TWD is effective for community detection in networks compared with the state-of-the-art algorithms.


title: The Problem of Finding the Simplest Classifier Ensemble is NP-Hard – A Rough-Set-Inspired Formulation Based on Decision Bireducts

text: We investigate decision bireducts which extend the notion of a decision reduct developed in the theory of rough sets. For a decision table [Formula: see text], a decision bireduct is a pair (X, B), where [Formula: see text] is a subset of attributes which allows to distinguish between all pairs of objects in [Formula: see text] labeled with different values of decision attribute d, and where B and X cannot be made, respectively, smaller and bigger without losing this property. We refer to our earlier studies on deriving bireducts (X, B) from decision tables and utilizing them to construct families of rule-based classifiers, where [Formula: see text] is equal to total support of decision rules built using attributes in [Formula: see text]. We introduce the notion of a correct ensemble of decision bireducts [Formula: see text], where each [Formula: see text] must be validly classified by more than [Formula: see text] of the corresponding models. We show that the problem of finding a correct ensemble of bireducts with the lowest cardinalities of subsets [Formula: see text] is NP-hard.


title: Backstabber’s Knife Collection: A Review of Open Source Software Supply Chain Attacks

text: A software supply chain attack is characterized by the injection of malicious code into a software package in order to compromise dependent systems further down the chain. Recent years saw a number of supply chain attacks that leverage the increasing use of open source during software development, which is facilitated by dependency managers that automatically resolve, download and install hundreds of open source packages throughout the software life cycle. Even though many approaches for detection and discovery of vulnerable packages exist, no prior work has focused on malicious packages. This paper presents a dataset as well as analysis of 174 malicious software packages that were used in real-world attacks on open source software supply chains and which were distributed via the popular package repositories npm, PyPI, and RubyGems. Those packages, dating from November 2015 to November 2019, were manually collected and analyzed. This work is meant to facilitate the future development of preventive and detective safeguards by open source and research communities.


title: Modeling Use-Oriented Attribute Importance with the Three-Way Decision Theory

text: Ranking and measuring attribute importance is one of the key research topics in data mining and machine learning. Most of the existing attribute importance research relying on data-oriented approaches such as statistics and information theory perspectives. User preference, which involves a user specifying his or her preferential attitude towards a set of attributes, is another meaningful perspective. However, the research community has not paid much attention to this perspective. We adopt the three-way decision theory as a framework and concentrate on analyzing attribute importance based on user preference in this paper. In particular, we propose qualitative and quantitative analysis of attribute importance approaches that result a ranking list as well as a set of numerical weights of an attribute set. We then categorize attributes into different groups of importance using qualitative and quantitative analysis results. Finally, a unified model to analyze user-oriented attribute importance is constructed.


title: Rough Sets Meet Statistics - A New View on Rough Set Reasoning About Numerical Data

text: In this paper, we present a new view on how the concept of rough sets may be interpreted in terms of statistics and used for reasoning about numerical data. We show that under specific assumptions, neighborhood based rough approximations may be seen as statistical estimations of certain and possible events. We propose a way of choosing the optimal neighborhood size inspired by statistical theory. We also discuss possible directions for future research on the integration of rough sets and statistics.


title: Weighted Generalized Fuzzy Petri Nets and Rough Sets for Knowledge Representation and Reasoning

text: In this paper, we consider the decision tables provided by experts in the field. We construct an algorithm for executing a highly parallel program represented by a fuzzy Petri net from a given decision table. The constructed net allows objects to be identified in decision tables to the extent that appropriate decisions can be made. Conditional attribute values given by experts are propagated by the net at maximum speed. This is done by properly organizing the net’s work. Our approach is based on rough set theory and weighted generalized fuzzy Petri nets.


docid: kisalznr

title: Representative Set of Objects in Rough Sets Based on Galois Connections

text: This paper introduces a novel definition, called representative set of objects of a decision class, in the framework of decision systems based on rough sets. The idea behind such a notion is to consider subsets of objects that characterize the different classes given by a decision system. Besides the formal definition of representative set of objects of a decision class, we present different mathematical properties of such sets and a relationship with classification tasks based on rough sets.


title: Towards Demystifying Subliminal Persuasiveness: Using XAI-Techniques to Highlight Persuasive Markers of Public Speeches

text: The literature provides evidence for the importance of non-verbal cues when it comes to persuading other people and developing persuasive robots. Mostly, people use these non-verbal cues subconsciously and, more importantly, are not aware of the subliminal impact of them. To raise awareness of subliminal persuasion and to explore a way for investigating persuasive cues for the development of persuasive robots and agents, we have analyzed videos of political public speeches and trained a neural network capable of predicting the degree of perceived convincingness based on visual input only. We then created visualizations of the predictions by making use of the explainable artificial intelligence methods Grad-CAM and layer-wise relevance propagation that highlight the most relevant image sections and markers. Our results show that the neural network learned to focus on the person, more specifically their posture and contours, as well as on their hands and face. These results are in line with existing literature and, thus, show the practical potential of our approach.


title: A Situation Awareness-Based Framework for Design and Evaluation of Explainable AI

text: Recent advances in artificial intelligence (AI) have drawn attention to the need for AI systems to be understandable to human users. The explainable AI (XAI) literature aims to enhance human understanding and human-AI team performance by providing users with necessary information about AI system behavior. Simultaneously, the human factors literature has long addressed important considerations that contribute to human performance, including how to determine human informational needs. Drawing from the human factors literature, we propose a three-level framework for the development and evaluation of explanations about AI system behavior. Our proposed levels of XAI are based on the informational needs of human users, which can be determined using the levels of situation awareness (SA) framework from the human factors literature. Based on our levels of XAI framework, we also propose a method for assessing the effectiveness of XAI systems.


title: fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

text: We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters.


title: Efficient Context-Sensitive CFI Enforcement Through a Hardware Monitor

text: Recent works on Control-Flow Integrity (CFI) have mainly focused on Context-Sensitive CFI policies to provide higher security guarantees. They utilize a debugging hardware feature in modern Intel CPUs, Processor Trace (PT), to efficiently collect runtime contextual information. These PT-based CFI mechanisms offload the processing of the collected PT trace and CFI enforcement onto idle cores. However, a processor does not always have idle cores due to the commonly-used multi-threaded applications such as web browsers. In fact, dedicating one or more cores for CFI enforcement reduces the number of available cores for running user programs. Our evaluation with a state-of-the-art CFI mechanism ([Formula: see text]CFI) shows that the performance overhead of a CFI mechanism can substantially increase (up to 652% on a single-core processor) when there is no idle core for CFI enforcement. To improve the performance of [Formula: see text]CFI, we propose to leverage a hardware monitor that unlike PT does not incur trace processing overhead. We show that the hardware monitor can be used to efficiently collect program traces (<1% overhead) in their original forms and apply [Formula: see text]CFI. We prototype the hardware-monitor based [Formula: see text]CFI on a single-core RISC-V processor. Our analysis show that hardware-monitor based [Formula: see text]CFI incurs, on average, 43% (up to 277%) performance overhead.


title: The Reduct of a Fuzzy [Formula: see text]-Covering

text: This paper points some mistakes of three algorithms of updating the reduct in fuzzy [Formula: see text]-covering via matrix approaches while adding and deleting some objects of the universe, and gives corrections of these mistakes. Moreover, we study the reduct of a fuzzy [Formula: see text]-covering while adding and deleting objects further.


title: Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review

text: In this work we introduce a framework, based on three-way decision (TWD) and the trisecting-acting-outcome model, to handle uncertainty in Machine Learning (ML). We distinguish between handling uncertainty affecting the input of ML models, when TWD is used to identify and properly take into account the uncertain instances; and handling the uncertainty lying in the output, where TWD is used to allow the ML model to abstain. We then present a narrative review of the state of the art of applications of TWD in regard to the different areas of concern identified by the framework, and in so doing, we will highlight both the points of strength of the three-way methodology, and the opportunities for further research.


title: On Positive-Correlation-Promoting Reducts

text: We introduce a new rough-set-inspired binary feature selection framework, whereby it is preferred to choose attributes which let us distinguish between objects (cases, rows, examples) having different decision values according to the following mechanism: for objects u1 and u2 with decision values [Formula: see text] and [Formula: see text], it is preferred to select attributes a such that [Formula: see text] and [Formula: see text], with the secondary option – if the first one is impossible – to select a such that [Formula: see text] and [Formula: see text]. We discuss the background for this approach, originally inspired by the needs of the genetic data analysis. We show how to derive the sets of such attributes – called positive-correlation-promoting reducts (PCP reducts in short) – using standard calculations over appropriately modified rough-set-based discernibility matrices. The proposed framework is implemented within the RoughSets R package which is widely used for the data exploration and knowledge discovery purposes.


title: In-Time Explainability in Multi-Agent Systems: Challenges, Opportunities, and Roadmap

text: In the race for automation, distributed systems are required to perform increasingly complex reasoning to deal with dynamic tasks, often not controlled by humans. On the one hand, systems dealing with strict-timing constraints in safety-critical applications mainly focused on predictability, leaving little room for complex planning and decision-making processes. Indeed, real-time techniques are very efficient in predetermined, constrained, and controlled scenarios. Nevertheless, they lack the necessary flexibility to operate in evolving settings, where the software needs to adapt to the changes of the environment. On the other hand, Intelligent Systems (IS) increasingly adopted Machine Learning (ML) techniques (e.g., subsymbolic predictors such as Neural Networks). The seminal application of those IS started in zero-risk domains producing revolutionary results. However, the ever-increasing exploitation of ML-based approaches generated opaque systems, which are nowadays no longer socially acceptable—calling for eXplainable AI (XAI). Such a problem is exacerbated when IS tend to approach safety-critical scenarios. This paper highlights the need for on-time explainability. In particular, it proposes to embrace the Real-Time Beliefs Desires Intentions (RT-BDI) framework as an enabler of eXplainable Multi-Agent Systems (XMAS) in time-critical XAI.


title: Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets

text: In this paper authors propose a new algorithm for linguistic data summarization based on hybridization of rough sets and fuzzy sets techniques. The new algorithm applies rough sets theory for feature selection in early stages of linguistic summaries’ generation. The rough sets theory was used to reduce on significant way, the amount on summaries obtained by others algorithms. The algorithm combines lower approximation, k grade dependency and fuzzy sets to get linguistic summaries. The results of proposed algorithm are compared with association rules approach. In order to validate the algorithm proposed, authors apply both qualitative and quantitative methods. Authors used two databases in order to validate the algorithm; theses databases belong to “Repository of Project Management Research”. The first database is associated to personality traits and human performance in software projects. The second database is associated to analysis of revenue assurance in different organization. Considering quantitative approach, the algorithm proposed, obtains better results than the algorithm based on association rules; while regards execution time, the best algorithm was the algorithm based on association rules, because rough sets theory was high time-consuming technique.


title: Object Oriented Protoconcepts and Logics for Double and Pure Double Boolean Algebras

text: The notion of a protoconcept in the framework of Yao’s object oriented concepts is proposed. Approximations by object oriented concepts are defined and these ‘object oriented protoconcepts’ are characterized using them. It is shown that the object oriented protoconcepts form a double Boolean algebra, and any double Boolean algebra is quasi-embedded in an algebra of protoconcepts. A logic DBL for the class of double Boolean algebras is proposed along with an extension PDBL for the class of pure double Boolean algebras. Utilizing the representation result for (pure) double Boolean algebras, it is established that DBL (PDBL) is sound and complete with respect to a semantics based on object oriented protoconcepts (semiconcepts).


title: Detecting Overlapping Communities Using Distributed Neighbourhood Threshold in Social Networks

text: In this work, we have proposed a simple overlapping community detection algorithm based on a distributed neighbourhood threshold method (DNTM). DNTM uses pre-partitioned disjoint communities and then analyzes the neighbourhood distribution of boundary nodes in disjoint communities to detect overlapping communities. It is a form of seed-based global method since boundary nodes are considered as seeds and become the starting point for detecting overlapping communities. Threshold value for each boundary node is used as minimum influence by the neighbours of a node in order to determine its belongingness to any community. The effectiveness of the DNTM algorithm has been demonstrated by testing on fifteen real-world datasets and compared with seven overlapping community detection algorithms. DNTM outperforms comparable algorithms with 10 out of 15 datasets and gives comparable results for the remaining 5 datasets in terms of the extended modularity [Formula: see text] measure. Experiments with various disjoint algorithms on 15 datasets reveal that DNTM with tolerance community detection (TCD) as a preprocessing algorithm gives the best result.


title: Agent-Based Explanations in AI: Towards an Abstract Framework

text: Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable and explainable. Unfortunately, researchers are struggling to converge towards an unambiguous definition of notions such as interpretation, or, explanation—which are often (and mistakenly) used interchangeably. Furthermore, despite the sound metaphors that Multi-Agent System (MAS) could easily provide to address such a challenge, and agent-oriented perspective on the topic is still missing. Thus, this paper proposes an abstract and formal framework for XAI-based MAS, reconciling notions, and results from the literature.


title: Distributed Heterogeneous N-Variant Execution

text: N-Variant Execution (NVX) systems utilize artificial diversity techniques to enhance software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their diverging behavior on a malicious input. Existing NVX systems execute diversified program variants on a single host. This means the level of inter-variant diversity will be limited to what a single platform can offer, without costly emulation. This paper presents DMON , a novel distributed NVX design that executes native program variants across multiple heterogeneous hosts. Our approach greatly increases the level of diversity between the simultaneously running variants that can be supported, encompassing different ISAs and ABIs. Our evaluation shows that DMON can provide comparable performance to traditional, non-distributed NVX systems, while enhancing security.


title: Web Runner 2049: Evaluating Third-Party Anti-bot Services

text: Given the ever-increasing number of malicious bots scouring the web, many websites are turning to specialized services that advertise their ability to detect bots and block them. In this paper, we investigate the design and implementation details of commercial anti-bot services in an effort to understand how they operate and whether they can effectively identify and block malicious bots in practice. We analyze the JavaScript code which their clients need to include in their websites and perform a set of gray box and black box analyses of their proprietary back-end logic, by simulating bots utilizing well-known automation tools and popular browsers. On the positive side, our results show that by relying on browser fingerprinting, more than 75% of protected websites in our dataset, successfully defend against attacks by basic bots built with Python scripts or PhantomJS. At the same time, by using less popular browsers in terms of automation (e.g., Safari on Mac and Chrome on Android) attackers can successfully bypass the protection of up to 82% of protected websites. Our findings show that the majority of protected websites are prone to bot attacks and the existing anti-bot solutions cannot substantially limit the ability of determined attackers. We have responsibly disclosed our findings with the anti-bot service providers.


title: It Never Rains but It Pours: Analyzing and Detecting Fake Removal Information Advertisement Sites

text: Fake antivirus (AV) software is a serious threat on the Internet to make users install malware and expose their personal information. Fake removal information advertisement (FRAD) sites, which introduce fake removal information for cyber threats, have emerged as platforms for distributing fake AV software. Although FRAD sites seriously threaten users who have been suffering from cyber threats and need information for removing them, little attention has been given to revealing these sites. In this paper, we propose a system to automatically crawl the web and identify FRAD sites. To shed light on the pervasiveness of this type of attack, we performed a comprehensive analysis of both passively and actively collected data. Our system collected 2,913 FRAD sites in 31 languages, which have 73.5 million visits per month in total. We show that FRAD sites occupy search results when users search for cyber threats, thus preventing the users from obtaining the correct information.


title: On the Behavior of Fuzzy Grey Cognitive Maps

text: Fuzzy Cognitive Maps (FCMs) are recurrent neural networks made up of well-defined neurons and causal relations. Fuzzy Grey Cognitive Maps (FGCMs) are an extension of FCMs, intended to surpass the intrinsic uncertainties modeling real-world problems by means of Grey theory. Despite the rising number of studies about FGCM-based models, little has been investigated with regard to the convergence of such networks. In this paper, we build a mathematical basis to uncover the behavior FGCM-based models equipped with transfer F-functions. To do so, we propose sufficient conditions for the existence and unicity of fixed-point attractors. Also, the results reported in the literature on the convergence of FGCMs, are compared with ours. Furthermore, we elucidate the reach and depth of our findings, especially and not exclusive to the prediction of FCMs’ behavior.


docid: ipudahyn

title: Understanding Android VoIP Security: A System-Level Vulnerability Assessment

text: VoIP is a class of new technologies that deliver voice calls over the packet-switched networks, which surpasses the legacy circuit-switched telecom telephony. Android provides the native support of VoIP, including the recent VoLTE and VoWiFi standards. While prior works have analyzed the weaknesses of VoIP network infrastructure and the privacy concerns of third-party VoIP apps, no efforts were attempted to investigate the (in)security of Android’s VoIP integration at the system level. In this paper, we first demystify Android VoIP’s protocol stack and all its four attack surfaces. We then propose a novel vulnerability assessment approach that assembles on-device Intent/API fuzzing, network-side packet fuzzing, and targeted code auditing. By testing Android from version 7.0 to the recent 9.0, we have discovered 8 zero-day Android VoIP vulnerabilities, all of which were confirmed by Google with bug bounty awards. The security consequences are serious, including denying voice calls, caller ID spoofing, unauthorized call operations, and remote code execution. To mitigate these vulnerabilities and further improve Android VoIP security, we uncover a new root cause that requires developers’ attention during their design and implementation.


docid: frrxbklw

title: Concept Analysis Using Quantitative Structured Three-Way Rough Set Approximations

text: One important topic of concept analysis is to learn an intension of a concept through a given extension. In the case where an exact intension cannot be formulated due to limited information, rough set theory introduces approximations to roughly learn the intension. Pawlak originally proposes a qualitative formulation of approximations which allows no error in the learned intension. Various quantitative formulations have been studied as generalizations, most of which use probabilistic measures. In contrast, non-probabilistic formulations have not been fully investigated. On the other hand, three-way approximations and structured approximations have been proposed to emphasize the semantics of approximations for the purpose of learning and interpreting intension. To combine the benefits of these two directions of generalizations, this paper investigates quantitative structured three-way approximations based on both probabilistic and non-probabilistic measures in the context of both complete and incomplete information.


title: Rough Net Approach for Community Detection Analysis in Complex Networks

text: Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.


title: Fuzzy FCA Attribute Reduction Properties in Rough Set Theory

text: Formal concept analysis and rough set theory are two of the most important mathematical tools for the treatment of information collected on relational systems. In particular, the idea of reducing the size of a database is widely studied in both theories separately. There are some papers that studied the reduction of a formal context by means of reducts from rough set. In this paper, we are focused in the reduction obtained in an information system considering the FCA reduction mechanism.


title: Automated CPE Labeling of CVE Summaries with Machine Learning

text: Open Source Security and Dependency Vulnerability Management (DVM) has become a more vital part of the software security stack in recent years as modern software tend to be more dependent on open source libraries. The largest open source of vulnerabilities is the National Vulnerability Database (NVD), which supplies developers with machine-readable vulnerabilities. However, sometimes Common Vulnerabilities and Exposures (CVE) have not been labeled with a Common Platform Enumeration (CPE) -version, -product and -vendor. This makes it very hard to automatically discover these vulnerabilities from import statements in dependency files. We, therefore, propose an automatic process of matching CVE summaries with CPEs through the machine learning task called Named Entity Recognition (NER). Our proposed model achieves an F-measure of 0.86 with a precision of 0.857 and a recall of 0.865, outperforming previous research for automated CPE-labeling of CVEs.


title: Multi-granularity Complex Network Representation Learning

text: Network representation learning aims to learn the low dimensional vector of the nodes in a network while maintaining the inherent properties of the original information. Existing algorithms focus on the single coarse-grained topology of nodes or text information alone, which cannot describe complex information networks. However, node structure and attribution are interdependent, indecomposable. Therefore, it is essential to learn the representation of node based on both the topological structure and node additional attributes. In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. Experiments show that our method can not only capture indecomposable multi-granularity information, but also retain various potential similarities of both topology and node attributes. It has achieved effective results in the downstream work of node classification and the link prediction on real-world datasets.


title: Explainable Agents for Less Bias in Human-Agent Decision Making

text: As autonomous agents become more self-governing, ubiquitous and sophisticated, it is vital that humans should have effective interactions with them. Agents often use Machine Learning (ML) for acquiring expertise, but traditional ML methods produce opaque results which are difficult to interpret. Hence, these autonomous agents should be able to explain their behaviour and decisions before they can be trusted by humans. This paper focuses on analyzing the human understanding of the explainable agents behaviour. It conducts a preliminary human-agent interaction study to investigate the effect of explanations on the introduced bias in human-agent decision making for the human participants. We test the hypothesis where different explanation types are used to detect the bias introduced in the autonomous agents decisions. We present three user groups: Agents without explanation, and explainable agents using two different algorithms which automatically generate different explanations for agent actions. Quantitative analysis of three user groups (n = 20, 25, 20) in which users detect the bias in agents’ decisions for each explanation type for 15 test data cases is conducted for three different explanations types. Although the interaction study does not give significant findings, but it shows the notable differences between the explanation based recommendations and non-XAI recommendations in human-agent decision making.


title: Decision Theory Meets Explainable AI

text: Explainability has been a core research topic in AI for decades and therefore it is surprising that the current concept of Explainable AI (XAI) seems to have been launched as late as 2016. This is a problem with current XAI research because it tends to ignore existing knowledge and wisdom gathered over decades or even centuries by other relevant domains. This paper presents the notion of Contextual Importance and Utility (CIU), which is based on known notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.


title: Towards Student Centric Rough Concept Inventories

text: In the context of education research, a concept inventory is an instrument (that consists of a number of multiple-choice questions) designed to test the understanding of concepts (and possibly the reasons for failure to understand) by learners. Subject to a few caveats they are known to be somewhat effective in non student centric learning environments. In this research the issue of adapting the subject/concept-specific instruments to make room for diverse response patterns (including vague ones) is explored in some detail by the present author. It is shown that higher granular operator spaces (or partial algebras) with additional temporal and key operators are well suited for handling them. An improved version of concept inventory called rough concept inventory that can handle vague subjective responses is also proposed in this research.


title: HGAR: Hybrid Granular Algorithm for Rating Recommendation

text: Recommendation algorithms based on collaborative filtering show products which people might like and play an important role in personalized service. Nevertheless, the most of them just adopt explicit information feedback and achieve low recommendation accuracy. In recent years, deep learning methods utilize non-linear network framework to receive feature representation of massive data, which can obtain implicit information feedback. Therefore, many algorithms are designed based on deep learning to improve recommendation effects. Even so, the results are unsatisfactory. The reason is that they never consider explicit information feedback. In this paper, we propose a Hybrid Granular Algorithm for Rating Recommendation (HGAR), which is based on granulation computing. The core idea is to explore the multi-granularity of interaction information for both explicit and implicit feedback to predict the users ratings. Thus, we used Singular Value Decomposition model to get explicit information and implicit information can be received by multi-layer perception of deep learning. In addition, we fused the two part information when the two models are jointly trained. Therefore, HGAR can explore the multi-granularity of interaction information which learned explicit interaction information and mined implicit information in different information granular level. Experiment results show that HGAR significantly improved recommendation accuracy compared with different recommendation models including collaborative filtering and deep learning methods.


title: Towards the Role of Theory of Mind in Explanation

text: Theory of Mind is commonly defined as the ability to attribute mental states (e.g., beliefs, goals) to oneself, and to others. A large body of previous work—from the social sciences to artificial intelligence—has observed that Theory of Mind capabilities are central to providing an explanation to another agent or when explaining that agent’s behaviour. In this paper, we build and expand upon previous work by providing an account of explanation in terms of the beliefs of agents and the mechanism by which agents revise their beliefs given possible explanations. We further identify a set of desiderata for explanations that utilize Theory of Mind. These desiderata inform our belief-based account of explanation.


title: On the Security of Application Installers and Online Software Repositories

text: The security of application installers is often overlooked, but the security risks associated to these pieces of code are not negligible. Online public repositories have been one of the most popular ways for end users to obtain software, but there is a lack of systematic security evaluation of popular public repositories. In this paper, we bridge this gap by analyzing five popular software repositories. We focus on their software updating dynamics, as well as the presence of traces of vulnerable and/or trojanized applications among the top-100 most downloaded Windows programs on each of the evaluated repositories. We analyzed 2,935 unique programs collected in a period of 144 consecutive days. Our results show that: (i) the repositories frequently exhibit rank changes due to applications fast climbing toward the first positions; (ii) the repositories often update their payloads, which may cause the distribution of distinct binaries for the same intended application (binaries for the same applications may also be different in each repository); (iii) the installers are composed by multiple components and often download payloads from the Internet to complete their installation steps, posing new risks for users (we demonstrate that some installers are vulnerable to content tampering through man-in-the-middle attacks); (iv) the ever-changing nature of repositories and installers makes them prone to abuse, as we observed that 30% of all applications were reported malicious by at least one AV.


title: Comparative Approaches to Granularity in General Rough Sets

text: A number of nonequivalent perspectives on granular computing are known in the literature, and many are in states of continuous development. Further related concepts of granules and granulations may be incompatible in many senses. This expository paper is intended to explain basic aspects of these from a critical perspective, their range of applications and provide directions relative to general rough sets and related formal approaches to vagueness. General granular principles related to knowledge are also mentioned.


title: Functional Extensions of Knowledge Representation in General Rough Sets

text: A number of low and high-level models of general rough sets can be used to represent knowledge. Often binary relations between attributes or collections thereof have deeper properties related to decisions, inference or vision that can be expressed in ternary functional relationships (or groupoid operations) – this is investigated from a minimalist perspective in this research by the present author. General approximation spaces and reflexive up-directed versions thereof are used by her as the basic frameworks. Related semantic models are invented and an interpretation is proposed in this research. Further granular operator spaces and variants are shown to be representable as partial algebras through the method. An analogous representation for all covering spaces does not necessarily hold. Applications to education research contexts that possibly presume a distributed cognition perspective are also outlined.


title: Short Paper - Taming the Shape Shifter: Detecting Anti-fingerprinting Browsers

text: When it comes to leaked credentials and credit card information, we observe the development and use of anti-fingerprinting browsers by malicious actors. These tools are carefully designed to evade detection, often by mimicking the browsing environment of the victim whose credentials were stolen. Even though these tools are popular in the underground markets, they have not received enough attention by researchers. In this paper, we report on the first evaluation of four underground, commercial, and research anti-fingerprinting browsers and highlight their high success rate in bypassing browser fingerprinting. Despite their success against well-known fingerprinting methods and libraries, we show that even slightest variation in the simulated fingerprint compared to the real ones can give away the presence of anti-fingerprinting tools. As a result, we provide techniques and fingerprint-based signatures that can be used to detect the current generation of anti-fingerprinting browsers.


title: Rough Forgetting

text: Recent work in the area of Knowledge Representation and Reasoning has focused on modification and optimization of knowledge bases (KB) through the use of forgetting operators of the form [Formula: see text], where [Formula: see text] is a set of relations in the language signature used to specify the KB. The result of this operation is a new KB where the relations in [Formula: see text] are removed from the KB in a principled manner resulting in a more efficient representation of the KB for different purposes. The forgetting operator is also reflected semantically in terms of the relation between the original models of the KB and the models for the revised KB after forgetting. In this paper, we first develop a rough reasoning framework where our KB’s consist of rough formulas with a semantics based on a generalization of Kleene algebras. Using intuitions from the classical case, we then define a forgetting operator that can be applied to rough KBs removing rough relations. A constructive basis for generating a new KB as the result of applying the forgetting operator to a rough KB is specified using second-order quantifier elimination techniques. We show the application of this technique with some practical examples.


title: HAEPG: An Automatic Multi-hop Exploitation Generation Framework

text: Automatic exploit generation for heap vulnerabilities is an open challenge. Current studies require a sensitive pointer on the heap to hijack the control flow and pay little attention to vulnerabilities with limited capabilities. In this paper, we propose HAEPG, an automatic exploit framework that can utilize known exploitation techniques to guide exploit generation. We implemented a prototype of HAEPG based on the symbolic execution engine S2E [15] and provided four exploitation techniques for it as prior knowledge. HAEPG takes crashing inputs, programs, and prior knowledge as input, and generates exploits for vulnerabilities with limited capabilities by abusing heap allocator’s internal functionalities. We evaluated HAEPG with 24 CTF programs, and the results show that HAEPG is able to accurately reason about the type of vulnerability for 21 (87.5%) of them, and generate exploits that spawn a shell for 16 (66.7%) of them. All the exploits could bypass NX [25] and Full RELRO [28] security mechanisms.


title: Algebraic Structure of Adjoint Triples Generating a Weak Negation on the Unit Interval

text: This paper presents a theoretical research about the relationship between weak negations and adjoint negations. Adjoint negations are a generalization of residuated negations built from the implications of an adjoint triple. Specifically, this work shows how to build adjoint triples on the unit interval such that their adjoint negations coincide with a given weak negation. Moreover, the algebraic structure formed by these adjoint triples is also investigated.


title: Engaging the Media: Telling Our Operations Research Stories to the Public

text: Academic research occasionally captures the attention of the media. When this happens, there is a small window of opportunity to disseminate the real-world impact of our research and the value of our operations research and analytics expertise to the public. To do so, we must package our messages for public consumption. In this article, I summarize principles for interacting with the media, describe what various media interactions are like, and offer tips for capitalizing on one’s expertise. Finally, I reflect on what we have to offer to journalists and the value of telling our stories to the public.


title: Seeing and Viewing Through a Postdigital Pandemic: Shifting from Physical Proximity to Scopic Mediation

text: This paper addresses a particular area of concern regarding our habits that pertains to embodied experience and education through the Covid-19 pandemic: the shift from comfort to discomfort regarding face-to-face social interaction within the same physical space. To explore this transition, I use the related concepts of seeing and viewing from Isaac Asimov’s novel, The Naked Sun (1957), which are useful tools for investigating a probable collateral effect of rapid social distancing for the sake of avoiding contagion and includes replacing physically proximate interaction and procedures with a scopic mediation. Seeing and viewing provide concepts for understanding how values change in the midst of fears concerning contagion through physical contact that are mollified through the use of technology analogous to video conferencing. Postdigital education and the concepts of we-think, we-learn, and we-act provide critical tools for helping us understand this transition of perspective regarding educational and social practices in the midst of a pandemic.


Webseite für innovative Versorgungskonzepte


"Wir waren nicht im Shutdown": Erste KBV-VV 2020 betont Stärke des ambulanten Systems


CoronaBest und PsoBest Bioscience: Neue Ausbaustufen beim Deutschen Psoriasis-Register


COVID-19: Honorarrückgang im privatärztlichen Bereich


Finanzielle Hilfen für Vertragsärzte: Weitere KVen haben HVM-Änderungen ausgearbeitet


Abwertung bei PCR-Tests: KBV reicht Klage ein


title: Pressure Support Ventilation (PSV) versus Neurally Adjusted Ventilatory Assist (NAVA) in difficult to wean pediatric ARDS patients: a physiologic crossover study

text: BACKGROUND: Neurally adjusted ventilatory assist (NAVA) is an innovative mode for assisted ventilation that improves patient-ventilator interaction in children. The aim of this study was to assess the effects of patient-ventilator interaction comparing NAVA with pressure support ventilation (PSV) in patients difficult to wean from mechanical ventilation after moderate pediatric acute respiratory distress syndrome (PARDS). METHODS: In this physiological crossover study, 12 patients admitted in the Pediatric Intensive Care Unit (PICU) with moderate PARDS failing up to 3 spontaneous breathing trials in less than 7 days, were enrolled. Patients underwent three study conditions lasting 1 h each: PSV1, NAVA and PSV2. RESULTS: The Asynchrony Index (AI) was significantly reduced during the NAVA trial compared to both the PSV1 and PSV2 trials (p = 0.001). During the NAVA trial, the inspiratory and expiratory trigger delays were significantly shorter compared to those obtained during PSV1 and PSV2 trials (Delay(trinsp)p < 0.001, Delay(trexp)p = 0.013). These results explain the significantly longer Time(sync) observed during the NAVA trial (p < 0.001). In terms of gas exchanges, PaO(2) value significantly improved in the NAVA trial with respect to the PSV trials (p < 0.02). The PaO(2)/FiO(2) ratio showed a significant improvement during the NAVA trial compared to both the PSV1 and PSV2 trials (p = 0.004). CONCLUSIONS: In this specific PICU population, presenting difficulty in weaning after PARDS, NAVA was associated with a reduction of the AI and a significant improvement in oxygenation compared to PSV mode. TRIAL REGISTRATION: ClinicalTrial.gov Identifier: NCT04360590 “Retrospectively registered”.


title: Software-Illustrated Explanations of Econometrics Contributions by CR Rao for his 100-th Birthday

text: Since the vast array of scientific contributions by Dr. C. R. Rao are difficult to summarize in a short paper, we focus on (1) concepts relevant for applied statisticians including econometricians, (2) using the R software to explain the concepts to be better understood by Economics students who may lack sufficient background in mathematical statistics, (3) topics that are covered in publicly and freely available R packages. The range of R packages influenced by Rao’s research is impressive. Dr. Rao will be 100 years old in 2020, and it is high time the Economics profession recognizes and honors his foundational contributions to econometrics.


Prolonged shedding of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) RNA among patients with coronavirus disease 2019 (COVID-19)


title: Impact of lockdown on air quality in India during COVID-19 pandemic

text: First time in India, total lockdown was announced on 22 March 2020 to stop the spread of COVID-19 and the lockdown was extended for 21 days on 24 March 2020 in the first phase. During the total lockdown, most of the sources for poor air quality were stopped in India. In this paper, we present an analysis of air quality (particulate matter-PM(2.5), Air Quality Index, and tropospheric NO(2)) over India using ground and satellite observations. A pronounced decline in PM(2.5) and AQI (Air Quality Index) is observed over Delhi, Mumbai, Hyderabad, Kolkata, and Chennai and also a declining trend was observed in tropospheric NO(2) concentration during the lockdown period in 2020 compared with the same period in the year 2019. During the total lockdown period, the air quality has improved significantly which provides an important information to the cities’ administration to develop rules and regulations on how they can improve air quality.


title: How green is green enough? The changing landscape of financing a sustainable European economy

text: Changing narratives surrounding the climate and environmental crisis have shaped the degree of ambition in the approach of EU policy-makers towards the sustainability transition and sustainable finance. This paper presents the evolution of EU sustainable finance policies between 2015 and 2019, focusing on the EU Taxonomy, arguing that as European narratives have shifted towards highlighting the severity of the climate and environmental crises, EU sustainable finance policies have also accelerated. The paper considers the IPCC SR1.5, published in October 2018, as one catalyst for this shift in narratives and the subsequently stronger policy responses, culminating in the European Green Deal.


title: Expert consensus on protocol of rehabilitation for COVID‐19 patients using framework and approaches of WHO International Family Classifications

text: Coronavirus disease 2019 (COVID‐19) has widely spread all over the world and the numbers of patients and deaths are increasing. According to the epidemiology, virology, and clinical practice, there are varying degrees of changes in patients, involving the human body structure and function and the activity and participation. Based on the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) and its biopsychosocial model of functioning, we use the WHO Family of International Classifications (WHO‐FICs) framework to form an expert consensus on the COVID‐19 rehabilitation program, focusing on the diagnosis and evaluation of disease and functioning, and service delivery of rehabilitation, and to establish a standard rehabilitation framework, terminology system, and evaluation and intervention systems based the WHO‐FICs.


title: Tourism and the Highlands: A cross-sectional study on trauma and orthopaedic service use by tourists in 2017

text: BACKGROUND: NHS Highland covers a wide geographical region encompassing the Isle of Skye, and Ben Nevis. Tourism is a significant contributor to the local economy and in 2017 the Highlands welcomed 534,000 visitors. Health services across the region treat tourists in addition to the local population. We investigated how many tourists accessed the Trauma and Orthopaedic (T&O) Department at Raigmore Hospital, Inverness. METHODS: We conducted a cross-sectional study with data collected over one year (2017). The number of tourists referred to T&O including patient demographics, country of origin, type of injury and their clinical outcome were recorded. A freedom of information (FOI) request to NHS Highland was sought to investigate associated costs incurred by tourists. RESULTS: 376 tourists accessed T&O services in 2017. Country of origin: 47 (12.5%) Scotland; 177 (47.1%) rest of UK; 74 (19.7%) EU; 45 (12.0%) non-EU. Highest referral month August (61), lowest referral month November (8). Injuries: 224 (59.6%) fracture; 62 (16.5%) soft tissue injury; 20 (5.3%) laceration. Commonest sites of injury were ankle, distal radius and finger. Outcomes: 28 (7.4%) Virtual clinic; 137 (36.4%) hospital admission; 193 (51.3%) advice to referring team and discharge; 13 (3.5%) direct discharge by T&O; 4 (1.1%) missing. No. of trauma cases booked: tourists 133 (9%), local population 1415 (91%). CONCLUSIONS: Tourists account for fewer than ten percent of the T&O surgical workload over one year with common injuries being fractures affecting the extremities. Seasonal variation was observed with more referrals occurring in the summer months. Just under half of tourists originated from outside the UK and EU. Health boards should consider increasing resources over the summer months to deal with expected increases in tourist numbers and should be able to recover the cost of treatment from the patient or their travel insurance companies directly at point of care.


Critical care nursing workforce: Global imperatives, innovations and future-proofing – A call for papers


A proof of concept study: Esophagogastroduodenoscopy is an aerosol-generating procedure and continuous oral suction during the procedure reduces the amount of aerosol generated


Supporting a Work-Life Balance for Radiology Resident Parents


title: Down Under: Shark attack

text: Op het moment gelden ook in Australië forse restricties om de COVID-19 besmettingen onder controle te houden, al is het hier zo dunbevolkt dat het even zoeken is naar iemand die je op een afstand van 1,5 meter kunt houden. Hier kennen we andere gevaren.


title: Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity

text: The Covid-19 pandemic has raised significant challenges for the higher education community worldwide. A particular challenge has been the urgent and unexpected request for previously face-to-face university courses to be taught online. Online teaching and learning imply a certain pedagogical content knowledge (PCK), mainly related to designing and organising for better learning experiences and creating distinctive learning environments, with the help of digital technologies. With this article, we provide some expert insights into this online-learning-related PCK, with the goal of helping non-expert university teachers (i.e. those who have little experience with online learning) to navigate in these challenging times. Our findings point at the design of learning activities with certain characteristics, the combination of three types of presence (social, cognitive and facilitatory) and the need for adapting assessment to the new learning requirements. We end with a reflection on how responding to a crisis (as best we can) may precipitate enhanced teaching and learning practices in the postdigital era.


docid: cqffgnpw

title: IR voices about COVID 19


docid: yalhkgcl

title: Dexamethasone to the rescue


Operating Room Protocols in OMFS During Corona virus (Covid-19) Pandemic


Hydroxychloroquine – An Antimalarial to Fight COVID-19?


α-Ketoamide Inhibitors of SARS-CoV-2 Main Protease


docid: ftcvfgij

title: Azithromycin – An Antibiotic to Fight COVID-19?


Non-neoplastic diseases of the testis


COVID-19 in Adult Patients with Hematological Disease: Analysis of Clinical Characteristics and Outcomes


docid: rruzmyrs

title: High-resolution CT features of COVID-19 pneumonia in confirmed cases

text: BACKGROUND: Coronavirus (COVID-19) pneumonia emerged in Wuhan, China, in December 2019. It was highly contagious spreading all over the world, with a rapid increase in the number of deaths. COVID-19 is characterized by fever, fatigue, dry cough, and dyspnea with variable chest imaging features which have been detected. In our study, we shared our experience of CT findings in proven cases of COVID-19 to recognize the different CT patterns to help in proper and accurate diagnosis. RESULTS: The most common CT features detected in COVID-19 cases were ground glass patches (93.3%) followed by subpleural linear abnormality (53.3%), air bronchogram (23.3%), and consolidation patches (23.3%), as well as bronchial wall thickening (16.7%), crazy paving pattern (13.3%), and discrete nodules surrounded by ground glass appearance (10%). Only one case had pleural effusion (3.3%). No cavitary lesions or specific lymph nodes were detected in any of the examined patients. The lung lesions showed typical diffuse, basal, and subpleural involvement with less affection of the upper lobes. CONCLUSION: CT imaging findings of COVID-19 can help in early and accurate diagnosis of COVID-19 and proper assessment of the severity of the disease.


docid: peywlrxd

title: Seuchengeschichte in der deutschsprachigen Urologie

text: Pandemics are relevant for many fields of medicine from microbiology to economics and epidemiology. Many medical specialties which developed during the 19th century, e. g., urology, have had much impact on diagnostics and therapy, such as during the treatment of tuberculosis and sexually transmitted diseases. For some of them, including urology, treatment of, for example, sexually transmitted diseases, was constitutional and differed between countries.


title: Flexibler Entscheidungsalgorithmus zum innerklinischen stationären Patient*innen-Management unter wechselnden Anforderungen der SARS-CoV-2-Pandemie

text: After the initial fulminant outbreak, the SARS-CoV‑2 pandemic has now taken a more protracted course which, nevertheless, challenges hospitals in returning to a “normal” mode and in preparing for a worst-case scenario of a second wave. Not only the organization of the first contact with the patient and the admission in the emergency department but also the admission as an in-patient and the subsequent management requires both flexibility and clear directions of action for the medical personnel involved. The aim of the algorithm was to develop a structured, easy to implement and easy to follow guideline while simultaneously preserving resources. The algorithm covers some key points of decision making such as clinical signs, first contact, admission for in-patient treatment, consequences of swab and computed tomography (CT) results, and allocation and isolation measures within the hospital. The algorithm is not intended to guide diagnostics, decisions and treatment in the narrower medical sense but to provide more general instructions for the management of in-patients considering specific aspects of SARS-CoV‑2.


title: CT chest of COVID-19 patients: what should a radiologist know?

text: BACKGROUND: The aim of current work is to review the CT findings of COVID-19 in a pictorial study to help the radiologists to be familiar to imaging findings of COVID-19. MAIN BODY: Coronavirus disease 2019 (COVID-19) is a pandemic highly infectious disease which is first reported in December 2019 in Wuhan, China, and then had its outbreak leads to a global public health emergency. Real-time reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is considered as the reference standard for COVID-19 diagnosis; however, recent studies showed the importance of CT chest in the diagnosis of COVID-19 with high sensitivity. The CT hallmarks of COVID-19 were bilateral peripheral ground-glass opacities, consolidation with the advance of the disease more consolidation is there with linear opacities and crazy-paving pattern as well as halo and reverse halo sign. CONCLUSION: Early identification of COVID-19 cases is vital. The radiologist should be familiar with the possible findings. Further future studies with pathological correlation will help for more understanding of the imaging findings and its value in assessing of prognosis.


title: Transaction Management

text: In the first step of the E2E M&A Process Design, the Embedded M&A Strategy aligned with the overarching corporate and SBU strategies was defined. Based on this framework of the Embedded M&A Strategy a distinguished shortlist of suitable M&A targets with a strong financial, strategic, Business and Culture Design fit have been distilled. The Transaction Management, as the follow-on module of the E2E M&A Process Design, is focused on a specific transaction with a selected target company or merger partner. Core parts of the Transaction Management are the valuation of the target company (Standalone Value) and the potential synergies (Integrated Value), the Due Diligence of the target company which should identify the risks and upsides of the potential transaction, as well as the blending of the Standalone Business and Culture Designs and the Redrafting of the Joint Culture and Business Designs according to the Due Diligence outcomes. Supplementary parts of the Transaction Management as the negotiation of a share or asset purchase agreement, the acquisition financing, and the Purchase Price Allocation (PPA), will be not discussed in detail. (These parts will be incorporated in the second edition).


Increasing Immunity to Fight against Novel COVID-19: Noninvasive Public Health Approach


docid: dejnfrku

title: Zukunft des ASCO Annual Meetings


title: Enhancing the antimalarial activity of artesunate

text: The global challenge to the treatment of malaria is mainly the occurrence of resistance of malaria parasites to conventionally used antimalarials. Artesunate, a semisynthetic artemisinin compound, and other artemisinin derivatives are currently used in combination with selected active antimalarial drugs in order to prevent or delay the emergence of resistance to artemisinin derivatives. Several methods, such as preparation of hybrid compounds, combination therapy, chemical modification and the use of synthetic materials to enhance solubility and delivery of artesunate, have been employed over the years to improve the antimalarial activity of artesunate. Each of these methods has advantages it bestows on the efficacy of artesunate. This review discussed the various methods employed in enhancing the antimalarial activity of artesunate and delaying the emergence of resistance of parasite to it.


Infection as a trigger for cardiovascular disease


docid: zwqditpw

title: After the pandemic: the role of science in the future of the countries


title: Hand-held point of care ultrasound probes – the new generation of POCUS

text: Recent advances in ultrasound technology have made ultrasound equipment more versatile, portable and accessible than ever. Modern hand-held, ultra-portable ultrasound devices have been developed by multiple companies and are contributing to make bedside ultrasound evaluation a practice available to all physicians. The significance of making point-of-care ultrasound a common practice that all physicians can eventually use in the evaluation of their patients is changing the way medicine is practiced, allowing physicians to quickly obtain valuable information to complement the traditional physical exam. Despite the proven benefits of using bedside ultrasound imaging as a part of the patient evaluation and for procedure guidance, adoption of this technology is still not widespread among anesthesiology clinicians, nor is there uniform teaching of ultrasound skills to anesthesia residents and faculty. Among obstacles that have been identified as precluding achievement of the goal of widespread utilization of POCUS among anesthesia professionals and trainees is the availability of equipment for all physicians when it is needed and lack of instructor supervision for trainees who desire to use ultrasound but do not always have an instructor knowledgeable in POCUS with them when an US exam is warranted. We analyze the characteristics, advantages and limitations of available ultra-portable, hand-held ultrasound devices, with a focus on the Butterfly IQ pocket-probe, which is available at our institution, and how some of its features, like the capacity to emulate multiple transducers and its cloud-sharing and teleguidance technology, may contribute to increase the availability and utilization of POCUS by anesthesia clinicians.


COVID-19 and the Radiology Match: A Residency Program's Survival Guide to the Virtual Interview Season


title: Biological Biosensors for Monitoring and Diagnosis

text: Quantification and detection of various contaminants in the ecosystem have become critically important in the past few decades due to their exhaustive use in soil and aquatic ecosystems. The contamination by both organic and inorganic contaminants in the ecosystem has drawn attention due to their persistence, biological accumulation, and toxicity. Organic contaminants reach the air, water, food, soil, and other systems through drift mechanism and have detrimental effect on various life systems after entering the food chain, thus interfering the normal biological process of the ecosystem. Inorganic contaminants have less solubility, primarily get adsorbed, and accumulate on lower sediments. The sources of both organic and inorganic contaminants include anthropogenic activities which dispose industrial and sewage effluent directly into water bodies. Most of the contaminants are very much toxic and have tumorigenic, carcinogenic, and mutagenic effect on various life-forms. Biosensors have various prospective and existing applications in the detection of these compounds in the environment by transducing a signal. It also has immense applications in the detection of different contaminants in the food industry, environmental monitoring, disease diagnosis, etc. where reliable and precise analyses are required. This chapter points out a comprehensive glimpse on different biosensors and their characteristics, operating principles, and their designs, based on transduction types and biological components. Efforts have been made to summarize various applications of biosensors in food industry, environmental monitoring, drug delivery systems, and clinical diagnostics etc.


title: Acute Stroke Management During the COVID-19 Pandemic: Does Confinement Impact Eligibility for Endovascular Therapy?

text: During the coronavirus disease 2019 (COVID-19) pandemic, the World Health Organization recommended measures to mitigate the outbreak such as social distancing and confinement. Since these measures have been put in place, anecdotal reports describe a decrease in the number of endovascular therapy (EVT) treatments for acute ischemic stroke due to large vessel occlusion. The purpose of our study was to determine the effect on EVT for patients with acute ischemic stroke during the COVID-19 confinement. In this retrospective, observational study, data were collected from November 1, 2019, to April 15, 2020, at 17 stroke centers in countries where confinement measures have been in place since March 2020 for the COVID-19 pandemic (Switzerland, Italy, France, Spain, Portugal, Germany, Canada, and United States). This study included 1600 patients treated by EVT for acute ischemic stroke. Date of EVT and symptom onset-to-groin puncture time were collected. Mean number of EVTs performed per hospital per 2-week interval and mean stroke onset-to-groin puncture time were calculated before confinement measures and after confinement measures. Distributions (non-normal) between the 2 groups (before COVID-19 confinement versus after COVID-19 confinement) were compared using 2-sample Wilcoxon rank-sum test. The results show a significant decrease in mean number of EVTs performed per hospital per 2-week interval between before COVID-19 confinement (9.0 [95% CI, 7.8–10.1]) and after COVID-19 confinement (6.1 [95% CI, 4.5–7.7]), (P<0.001). In addition, there is a significant increase in mean stroke onset-to-groin puncture time (P<0.001), between before COVID-19 confinement (300.3 minutes [95% CI, 285.3–315.4]) and after COVID-19 confinement (354.5 minutes [95% CI, 316.2–392.7]). Our preliminary analysis indicates a 32% reduction in EVT procedures and an estimated 54-minute increase in symptom onset-to-groin puncture time after confinement measures for COVID-19 pandemic were put into place.


title: Breast Feeding in Suspected or Confirmed Cases of COVID 19–a New Perspective

text: The encounter with the rampant novel Corona virus infection has led the healthcare system across the world to update and modify its tools to fight this pandemic. Pregnancy, childbirth and breast feeding are a set of special situations to be dealt in women afflicted with Covid-19. Currently there is no universal consensus on managing the issue of breast feeding with rooming-in of the neonates in women with suspected or confirmed Covid-19. Literature is still evolving with contradictory guidelines from various authorities across the globe. This review intends to analyse the available evidence on managing breast feeding in such women and to derive a practically plausible approach in handling such situations.


Impact of SARS CoV-2 in Hemoglobinopathies with Immune Disfunction and Epidemiology. A Protective Mechanism from Beta Chain Hemoglobin Defects?


docid: blongyrr

title: Focusing On A Unique Innate Memory Cell Population Of Natural Killer Cells In The Fight Against COVID-19: Harnessing The Ubiquity Of Cytomegalovirus Exposure


title: Children in Coronaviruses’ Wonderland: What Clinicians Need to Know

text: Human coronaviruses (HCoVs) commonly cause mild upper-respiratory tract illnesses but can lead to more severe and diffusive diseases. A variety of signs and symptoms may be present, and infections can range in severity from the common cold and sore throat to more serious laryngeal or tracheal infections, bronchitis, and pneumonia. Among the seven coronaviruses that affect humans (SARS)-CoV, the Middle East respiratory syndrome (MERS)-CoV, and the most recent coronavirus disease 2019 (COVID-19) represent potential life-threatening diseases worldwide. In adults, they may cause severe pneumonia that evolves in respiratory distress syndrome and multiorgan failure with a high mortality rate. Children appear to be less susceptible to develop severe clinical disease and present usually with mild and aspecific symptoms similar to other respiratory infections typical of childhood. However, some children, such as infants, adolescents, or those with underlying diseases may be more at-risk categories and require greater caution from clinicians. Available data on pediatric coronavirus infections are rare and scattered in the literature. The purpose of this review is to provide to clinicians a complete and updated panel useful to recognize and characterize the broad spectrum of clinical manifestations of coronavirus infections in the pediatric age.


Management of Pediatric Rheumatological Diseases During the Outbreak of COVID-19: Our Experience


Pidotimod in Paucisymptomatic SARS-CoV2 Infected Patients


Severe Autoimmune Hemolytic Anemia in COVID-19 İnfection, Safely Treated with Steroids


Spontaneous and Severe Haematomas in Patients with COVID-19 on Low- Molecular-Weight Heparin for Paroxysmal Atrial Fibrillation


title: Kawasaki Disease as the Immune-Mediated Echo of a Viral Infection

text: Although the etiology of Kawasaki disease (KD) remains elusive, the available evidence indicates that the primum movens may be a dysregulated immune response to various microbial agents, leading to cytokine cascade and endothelial cell activation in patients with KD. Documented infections by different viruses in many individual cases have been largely reported and are discussed herein, but attempts to demonstrate their causative role in the distinctive KD scenario and KD epidemiological features have been disappointing. To date, no definite link has been irrefutably found between a single infection and KD.


title: Preliminary Data on COVID-19 in Patients with Hemoglobinopathies: A Multicentre ICET-A Study

text: OBJECTIVES: This study aims to investigate, retrospectively, the epidemiological and clinical characteristics, laboratory results, radiologic findings, and outcomes of COVID-19 in patients with transfusion-dependent β thalassemia major (TM), β-thalassemia intermedia (TI) and sickle cell disease (SCD). DESIGN: A total of 17 Centers, from 10 countries, following 9,499 patients with hemoglobinopathies, participated in the survey. MAIN OUTCOME DATA: Clinical, laboratory, and radiologic findings and outcomes of patients with COVID-19 were collected from medical records and summarized. RESULTS: A total of 13 patients, 7 with TM, 3 with TI, and 3 with SCD, with confirmed COVID-19, were identified in 6 Centers from different countries. The overall mean age of patients was 33.7±12.3 years (range:13–66); 9/13 (69.2%) patients were females. Six patients had pneumonia, and 4 needed oxygen therapy. Increased C-reactive protein (6/10), high serum lactate dehydrogenase (LDH; 6/10), and erythrocyte sedimentation rate (ESR; 6/10) were the most common laboratory findings. 6/10 patients had an exacerbation of anemia (2 with SCD). In the majority of patients, the course of COVID-19 was moderate (6/10) and severe in 3/10 patients. A 30-year-old female with TM, developed a critical SARS-CoV-2 infection, followed by death in an Intensive Care Unit. In one Center (Oman), the majority of suspected cases were observed in patients with SCD between the age of 21 and 40 years. A rapid clinical improvement of tachypnea/dyspnea and oxygen saturation was observed, after red blood cell exchange transfusion, in a young girl with SCD and worsening of anemia (Hb level from 9.2 g/dl to 6.1g/dl). CONCLUSIONS: The data presented in this survey permit an early assessment of the clinical characteristics of COVID 19 in different countries. 70% of symptomatic patients with COVID- 19 required hospitalization. The presence of associated co-morbidities can aggravate the severity of COVID- 19, leading to a poorer prognosis irrespective of age.


A Socioeconomic Paradox in the COVID-19 Pandemic in Italy: a Call to Study Determinants of Disease Severity in High and Low-Income Countries


Guidance for Facing Dilemmas of Hematopoietic Stem Cell Transplant Clinicians in the COVID-19 Pandemic: An Iranian Consensus


title: Successful Outcomes of Severe COVID-19 in Patient with Chronic Lymphocytic Leukemia: Diagnostic Challenges in Immunocompromised Hosts

text: The emergence and spread of 2019 novel coronavirus have led to an unprecedented public health crisis around the globe, threatening the lives of millions of people. We report a severe case of COVID-19 in a patient with chronic lymphocytic leukemia and describe primarily the clinical presentation and the challenges encountered in the COVID-19 diagnosis, treatment, and specimens sampling pitfalls. This case highlights the importance of a comprehensive diagnostic approach of pneumonia in immunocompromised hosts, including timely and safe bronchoscopy, because of the broad differential diagnosis, more challenging with the current outbreak of COVID-19.


Ozone Alerts and Respiratory Emergencies: The Environmental Protection Agency’s Potential Biological Pathways for Respiratory Effects


Air pollution in Africa


Personal care


BM1: Events Guide


New products and investments target the laundry sector


Integrating Biodiversity Infrastructure into Pathogen Discovery and Mitigation of Emerging Infectious Diseases


On the use of lymphocyte to neutrophil ratios in laboratory medicine


title: Impact of the Coronavirus Infection in Pregnancy: A Preliminary Study of 141 Patients

text: BACKGROUND: The novel coronavirus disease (COVID-19) is the most challenging health crisis that we are facing today. Against the backdrop of this pandemic, it becomes imperative to study the effects of this infection on pregnancy and its outcome. Hence, the present study was undertaken to evaluate the effects of COVID-19 infection on the maternal morbidity and mortality, the course of labour as well as the neonatal outcome. MATERIALS AND METHODS: A total of 977 pregnant women were included in the study, from 1st April to 15th May 2020 at a tertiary care hospital. There were 141 women who tested COVID positive and remaining 836 patients were included in the COVID negative group. Findings were compared in both the groups. RESULTS: The incidence of COVID positive pregnant women was found to be 14.43%. More patients delivered by LSCS in the COVID positive and the COVID negative group (50%) as compared to COVID negative group (47%), (p > 0.05). Low APGAR score (0-3) was observed in 2(1.52%) neonates of COVID positive mothers and in 15 (1.91%) neonates of COVID negative mothers. Overall most of the babies were healthy. Out of all babies tested, 3 were detected positive initially which were retested on day 5 and were found to be negative. CONCLUSION: There is no significant effect of COVID infection on maternal and foetal outcome in pregnancy and there is no evidence of vertical transmission of the COVID-19 infection but long-term follow-up of these babies is recommended.


Prepare for Post-COVID-Pandemic Polio Problems


Theme: Immunization


title: Guillain-Barré syndrome and SARS-CoV-2

text: Since January 2020, after Chinese health authorities identified a new type of coronavirus (SARS-CoV-2), the virus has spread throughout China and consecutively throughout the whole world. The most common symptoms include fever and respiratory tract symptoms. Nevertheless, some patients show less common symptoms such as gastrointestinal or neurological manifestations. This article presents the case of a 65-years old man who was presumptively infected with SARS-CoV-2 during his ski vacation in Austria in March 2020 and acutely presented with typical symptoms of Guillain-Barré syndrome.


title: Security of passenger transport in the Baltic Sea in the context of foreign terrorist fighters

text: The Baltic Sea basin is one of the busiest areas in Europe in terms of the passenger transport and is considered as a domestic sea of the European Union, very important for the development and prosperity of all citizens. A high number of ferries connections along with exclusive cruise ships, visiting the Baltic countries, make the sea very busy. At the same time the security of lines of communications has become the highest priority, especially that a possibility of terrorist attacks still poses a serious threat. Although terrorism has been being fought intensively since 11th September, terrorist organizations such as Al-Qaeda or Islamic State of Iraq and the Levant, are still able to attack. The article considers the threat of organizing a terrorist attack posed by Foreign Terrorist Fighters returning home to the Baltic countries. Due to the fairly high number of Foreign Terrorist Fighters in some countries around the Baltic Sea, very intense passenger traffic in the area, and the appeals which have been proclaimed by Al-Qaeda and Islamic State leaders, there is a possibility of a terrorist attack occurrence in the near future. Cited facts lead to the conclusion that some precautions should be taken both in terms of technical and organizational measures to make passenger traffic secure.