Reviewerly/exHarmony huggingface dataset:
Dynamic Explanation of Bayesian Networks with Abductive Bayes Factor Qualitative Propagation and Entropy-Based Qualitative Explanation

The success of Artificial Intelligence (AI) systems as decision aids is often largely contingent on the ability to trust their recommendations. This trust is greatly enhanced when the AI systems are able to provide explanations to justify the presented results which when implemented properly, also serve as a means to better understand unfamiliar domains. Unfortunately, underlying models in such systems can be often non-intuitive for humans and thus hard to interpret and explore. Explainable AI systems allow users to effectively understand, trust, and operate the models. Under this context, a recognizable model for qualitatively displaying probabilistic information is the Bayesian Network (BN), which provides a graphical visualization of quantitative beliefs about the conditional dependence and independence among random variables. We focus primarily on the dynamic explanation, which explains the reasoning process of a BN by exploring the means for analyzing the changes in the model in the light of new evidence. We extend Druzdzel’s concept about qualitative belief propagation, by introducing the idea of qualitative strength of edges in the active path, which is proportional to the Bayes factors of the Most Probable Explanation (MPE), or to the pairwise information entropy of variables (a.k.a. mutual information). We also present a full implementation in Java.
Kommunikation während der Pandemie

Drug Prescribing for Patients with Chronic Kidney Disease in General Practice: a Cross-Sectional Study
A Multi-Kemel Discriminant Analysis Framework for Specific Radar Emitter Identification

Specific radar emitter identification, a.k.a. radar emitter individual identification, plays a momentous role in modern electronic countermeasures. This paper concentrates on the multiple kernel learning (MKL)-based multi-feature fusion methodology, and a multi-kernel discriminant analysis (MKDA) framework is thus proposed for radar individual identification. Based on the Doppler shift slices of ambiguity function (AF) representation, multiple kernels corresponding to the AF slices are constructed firstly. Then MKDA algorithms are utilized to extract the nonlinear discriminative features, followed by the simplest nearest neighbor classifier. Considering the efficiency of real-time systems, we devise two two-stage MKDA algorithms based on the criteria of kernel discriminant ratio (KDR) and kernel between-class scatter degree (KBCSD), respectively. We use two real radar datasets to evaluate the proposed framework. The KDR-MKDA and KBCSD-MKDA algorithms perform best in terms of both time efficiency and identification accuracy.
Cyber Intelligence and Security

Knowledge society is characterized by a hyper-dynamic knowledge, continuously subject to review and discussion, shared, cross-media-based, with multiple reference points and an overload of information. What types of information are at risk? So many, such as power delivery, communications, aviation, financial services, medical records, criminal records, business plans. The threat posed to nations from terrorists is no longer just physical but also expands to our digital world. The benefits of the information age are numerous, but nascent threats like transnational cyber terrorism and information warfare exist alongside the positive aspects of globalization. A new challenge has emerged for free societies: democracies must find ways to strike a balance between allowing internet freedom on one hand and maintaining adequate early warning and monitoring systems on the other. These systems, combined with expanded cybersecurity cooperation across borders, will be integral in detecting suspicious digital activities and countering attempted acts of cyber warfare and cyber terrorism.
COMM-AN Opinion Mining of Customer Feedback

Product reviews have now moved to the online platforms, also the sheer amount of reviews since the past few years has skyrocketed. It is impossible to read and get the overall view of what the customer feels about a product. The idea of this project is to take over this time-consuming job of examining the data fetched and unearthing the underlying meanings of the data. This will be done by first gathering the data, cleaning/pre-processing the data, clustering of data and finally representing the yield in a diagrammatic/graphical format. We aim to design this project for the creators, developers, and producers not for the customers browsing through site(s).
Welcome to the 3rd International Workshop on Learning from Other Disciplines for Requirements Engineering (D4RE'19)

The D4RE workshop brings together a diversity of people who are interested in discussing the question "What can requirements engineering learn from other disciplines?" The overall goal of the workshop is to collaborate with experts from non-SE disciplines in order to develop a body of best practices from these disciplines and identify possible synergies with RE. Moreover, we aim to raise awareness for this interesting topic in the RE community and foster future collaborations across the boundaries of requirements engineering and software engineering. In this third edition, we aim to further enrich our existing body of ideas and identify synergies between non-SE disciplines and RE, inspired by the special theme: "Learning from Fiction - What can RE learn from famous characters or people acting in movies / TV series?"
Is It Privacy or Is It Access Control?

With the widespread use of online systems, there is an increasing focus on maintaining the privacy of individuals and information about them. This is often referred to as a need for privacy protection. The author briefly examines definitions of privacy in this context, roughly delineating between keeping facts private and statistical privacy that deals with what can be inferred from data sets. Many of the mechanisms used to implement what is commonly thought of as access control are the same ones used to protect privacy. This chapter explores when this is not the case and, in general, the interplay between privacy and access control on the one hand and, on the other hand, the separation of these models from mechanisms for their implementation.
Human perception-enhanced camera system for web conferences leveraging device motions

We present a demonstration of a human perception-enhanced camera system for web conferencing that protects the user's privacy. Given that people easily forget about their active camera during web conferences, the system advertises the camera's active status via its motions to remind users that they are being watched by others. This prevents inadvertent privacy leakage. The system is developed based on a motorized camera, which moves according to the user's head coordinates just like an eye is looking at the user's face in front of the desk rather than remotely or virtually. The basic idea is to exploit the original human body sense of environmental motions for human-camera interaction, which does not require looking straight at the camera or its LED light to actively check its status. In this demonstration, we showcase our implementation of the human perception-enhanced camera system and invite participants to use the system for web conferences (e.g., Zoom and Google Hangout), which illustrates the system's ability to extend the virtual social interaction to the physical world and the effectiveness of using the camera motion as a non-intrusive awareness indicator.
Super Resolution

This chapter is concerned with super-resolution methods for image enhancement. Development of super-resolution methods is dated back to early 1980s, and the effort intensifies greatly in the past two decades. The essential objective of super-resolution is to enhance the quality of a low-resolution image, using a high-resolution counterpart. The super-resolution research started with combining information content from multiple low-resolution images and fusing them into an output image that can show finer details unavailable in any of the original low-resolution images. This branch of super-resolution research is referred to as multi-frame super-resolution, to be discussed in Sect. 11.1. Then the focus was shifted to the example-based approach or learning-based approach, in which external high-resolution and low-resolution patch pairs are created and a relationship between the high-resolution and low-resolution patch pairs are learned through a training dataset. When given a single new low-resolution image, which may not necessarily be related to any of the images in the training set, the learned algorithm is supposedly able to boost its resolution to a higher level. This second branch of super-resolution research is referred to as single-image super-resolution, to be discussed in Sect. 11.2. In the single-image super-resolution, the low-resolution image in the training set is typically created as a blurred and downsampled version of a high-resolution physical image. In the material science image acquisition, researchers often acquire a pair of images at different physical resolutions by using the same electron microscope over the same material sample. This process produces a pair of physical high-resolution and low-resolution images, rather than a physical image with its downsampled counterpart. If the resolution ratio is two-to-one, then the high-resolution image covers about 25% of the view field of the low-resolution image. The objective is then to enhance the quality of the low-resolution image area where there is no high-resolution counterpart. This last branch of research is referred to as paired images super-resolution, to be discussed in Sect. 11.3.
Sarcasm Detection Using Different Machine Learning Algorithms

Sarcasm is the use of words usually used to indirectly either mock or annoy someone, or for humorous purposes. One of the difficult modes of communication for machines to identify is sarcasm. People often use sarcasm in their daily communication to indirectly annoy people which makes it very important to identify the sentence meaning. There are various machine learning algorithms for sarcasm detection such as Naïve Bayes (NB), Support Vector Machine (SVM), Logistics Regression (LR), Decision Trees (DT).All these algorithm can be used for Sarcasm Detection. The main goal of this paper is to provide various machine learning algorithms for sarcasm detection.
Main objectives of mental education

The relevance of this problem lies in the fact that mental education is an important and at the same time the most difficult section of work in the development of the child, the development of the child occurs both in the course of communication with an adult, playing with pears, and in the process of systematic learning. Themost important role in this is played by this process of mental education carried out in the classroom. The mental upbringing of the child appears not only as the mastery of knowledge and ways of mental activity, but also as the formation of certain personality traits.
Investigating Students' and Teachers' Perceptions of Using the iPad in an Italian English as a Foreign Language Classroom

Recent research indicates that mobile technologies can support second language learning. However, studies focused on the use of the iPad and teaching in schools is still scarce. This study reports on an action research project that investigated the use of the iPad in the English as a foreign language (EFL) context in an Italian school. The study sought to investigate learners' and teachers' perceptions of mobile learning through the use of the iPad. The data was collected through a survey (N=41), classroom observations (N=4), interviews (N=20), and recorded teacher meetings (N=5). Results show a positive impact on student motivation and on the approach to second language learning tasks. We found that within the duration of the study students and teachers became increasingly independent in the use of the iPad for English language learning and teaching. This study provides educators with hints on how to start integrating mobile devices to perform specific language learning/teaching tasks.
Ten-Minute Prediction of Solar Irradiance Based Cloud Detection and a Long Short-Term Memory (LSTM) Model

The intermittency and volatility of solar radiation cause irregular disturbances to the power output, which has been the main challenge for large-scale photovoltaic grid-connected power generation. Accurate short-term prediction of solar irradiance is essential for the planning and operation of power grids. The existing short-term solar irradiance prediction method seldom consider the clouds, which are the major influencing factor for variations in solar irradiance on a minute scale. In this study, in addition to meteorological factors and historical global horizontal irradiance (GHI), the cloud cover was also considered to construct a short-term solar irradiance prediction model ten minutes in advance using the Bayesian Optimisation-Long Short-Term Memory (BO- LSTM) method. The cloud amount was obtained from ground-based cloud images using the hybrid cloud detection method, and other meteorological inputs were determined by the linear correlation analysis (LCA). The proposed model outperforms persistent model (PM), autoregressive model (AR), and support vector regression (SVR) under all sky conditions. The forecast uncertainty is also assessed separately for clear, cloudy and partly cloudy days. Particularly, in the partly cloudy condition the proposed model performs better than the other models with an nRMSE of 16.32% which indicates that this proposed model can better capture the volatility and intermittency of GHI. These results show that in challenging highly variable weather conditions the proposed model can forecast GHI accurately.
Quantification

Although propositional logic provides a formal account of a wide class of valid arguments, its explanatory power is limited. Many arguments are valid in virtue of formal properties that do not depend on the truth-functional structure of their premises and conclusion, so their validity is not explainable in propositional logic.
Blockchains Explained: As new technologies present opportunities, they also unleash competitive threats to incumbents and eventually reshape the future. So is the promise of blockchain and distributed ledger technologies. But what do we know about them? Could they be used in international trade to make the movement of goods across country borders more efficient? Some commentators seem to be very clued up on what this technology is and how it will revolutionise the world, whereas others are …

As new technologies present opportunities, they also unleash competitive threats to incumbents and eventually reshape the future. So is the promise of blockchain and distributed ledger technologies. But what do we know about them? Could they be used in international trade to make the movement of goods across country borders more efficient? Some commentators seem to be very clued up on what this technology is and how it will revolutionise the world, whereas others are struggling to understand the basics. This paper offers a description of the technology and how it could be applied to international trade and lists some challenges to its implementation.
Data Visualization Strategies for Computer Simulation in Bioelectromagnetics

Bioelectromagnetics is a branch of science that examines how living organisms interact with electromagnetic fields. In a broader sense, it is a discipline that spans the fields of signal processing, electromagnetics, medical imaging, physiology, medical physics, anatomy, occupational and environmental health, behavioral, and computer sciences. Due to its provision of interactive and flexible programming environment, this chapter describes a LabVIEW-based data visualization system that has been implemented and used in bioelectromagnetics simulations. The graphical nature of the LabVIEW programming environment makes it very easy to create a sophisticated application in a minimum amount of time, especially when compared to designing graphical user interfaces in a low-level programming language.
A satellite-based quantum key distribution using decoy-state protocol

Abstract An ideal QKD implementation that provides the promising unconditional security using single photon laser source is not practically easy, also sending these photons over a fiber channel affects the maximum distance of link up to few hundreds of kilometers because of the receiver’s detectors losses. Therefore, the achievement of a global quantum network is not easy, and the need to use LEO satellites [1,2] for establishing quantum links makes the job easier as quantum free space link provides much less losses to the link than fiber links. Nowadays, a quantum communication link can be established by satellites through free space. in this paper, we propose an implementation [3] of a satellite-based quantum key distribution using decoy-state protocol [4], we then compare this protocol with the BB-84 protocol [3] against security and the key length of the generated shared key.
A Modified Deep Convolutional Neural Network for Brian Abnormalities Detection

Cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection.. However, for analyzing brain tumor without human intervention is considered as a significant area of research because the extracted brain images need to be optimized using segmentation algorithm which should have high resilient towards noise and cluster size sensitivity problem with automatic region of Interest (ROI) detection. In this research, Deep Convolutional Neural Networks (DCNN) is one of the widely used deep learning networks for any practical applications. The accuracy is generally high and the manual feature extraction process is not necessary in these networks. However, the high accuracy is achieved at the cost of huge computational complexity. The complexity in DCNN is mainly due to: (a) Increased number of layers between input and output layers and (b) Two set of parameters (one set of filter coefficients and another set of weights in the fully connected network need to be adjusted. In this work, the second aspect is targeted to reduce the computational complexity of conventional DCNN. Suitable modifications are performed in the training algorithm to reduce the number of parameter adjustments. The weight adjustment process in the fully connected layer is completely eliminated in the proposed modified approach. Instead, a simple assignment process is used to find the weights of this fully connected layer. Thus, the computational complexity is significantly reduced in the proposed approach. The application of Modified DCNN (MDCNN) is explored in the context of Magnetic Resonance (MR) brain tumor image classification. Abnormal brain tumor images from four different classes are used in this work. This research pays its proficiency in the field of brain abnormality detection and analysis in health care sector without human intermediation.
Cloud Computing Economics

Cloud services and technologies are currently receiving increased attention from the industry mostly due to business-driven promises and expectations. Significant innovations in virtualization and distributed computing, as well as improved access to high-speed Internet and a weak economy, have accelerated interest in cloud computing. However, is the migration to the Cloud the most profitable option for every business? Enterprise adoption of cloud computing often requires a significant transformation of existing Information Technology (IT) systems and processes. To justify such a change, a viable business case must be made based on the economics of transformation. This chapter presents a study of the basic parameters for estimating the potential infrastructure and software costs deriving from building and deploying applications on cloud and on-premise assets. Estimated user demand and desired quality attributes related to an application are also addressed in this chapter as they are aspects of the decision problem that also influence the choice between cloud and in-house solutions.
A Thorough Insight into Theoretical and Practical Developments in MultiAgent Systems

Multiagent systems have been a fascination for research community and are often seen as an intelligent solution to many complex real world problems. Researchers have been active in the domain since last three decades and many developments pertaining to theoretical design and practical developments of multiagent systems are worth appreciating. The growth in MAS is multidirectional ranging from conceptual ideas to practical implementations and from the wide range of applications; it appears that multiagent systems are proving to be universal. The paper presents a concise survey of developments in MAS highlighting the important contributions in the field and also questions the universal applicability of agents.
Tailoring Privacy-Aware Trustworthy Cooperating Smart Spaces for University Environments

The more information users disclose to pervasive systems or social media, the better quality and enhanced experience they enjoy for a wider variety of personalised services. However, the privacy concerns of individuals that use such systems have dramatically risen the last years, especially after several events of massive security breaches in various computing or communication systems that have reached the news. This chapter presents the approach being employed by the SOCIETIES project to protect the privacy of sensitive user data and ensure the trustworthiness of delivered services via social and pervasive computing systems. This framework has already been designed, implemented and evaluated via real user trials engaging wide and heterogeneous user populations. In addition to the respective requirements, architecture and features discussed herewith, this chapter elaborates on the user trial that has been conducted in university settings to validate this system focusing on the privacy and trust evaluation results obtained.
Supplemental Material: Geophysical analysis of the 30 July 1972 Sitka, Alaska, earthquake sequence

Supplemental Material 1 contains relocated aftershocks of 30 July 1972 sequence. Supplemental Material 2 contains relocation parameters used in double-difference algorithm (HYPODD).&lt;br&gt;
Moving Collections

Once the candidates for the storage facility have been selected, the transferring library will start the actual processing of materials. The preparations for transferring these materials should comply with the agreement made with the storage facility regarding the condition and format of the selected items, the verification of necessary physical identifiers such as unique barcodes, the packing and shipping of the materials. While the staff in the transferring library will update the item records in their local catalog, they will also prepare the records with the required information about the materials and communicate them to the storage facility, likely in the form of batch records for ease of processing once received.