Reviewerly/exHarmony huggingface dataset:
A Logical Analysis of the Interplay Between Social Influence and Friendship Selection

This paper is part of a series of proposals for using logic to analyse social networks. It studies the intertwining of two forms of information dynamics: social influence, through which an agent’s behaviour, opinions or features are affected by those of her social connections, and friendship selection, through which an agent chooses her social connections based on their common behaviour, opinions or features. The text provides a logical analysis of the two forms of dynamics (the main ingredients in the phenomenon known as homophily) as well as of their interaction, discussing also some of their variations.
Spatial Data Visualisation and Beyond

The visualisation of spatial data is no longer limited to a fancy reconstruction on a computer screen. Archaeologists are increasingly recognising that to explore and research space we cannot just concentrate on the visual and topographic aspects of a landscape or a building, we need to understand and represent how humans move through and interact with that space. We are now beginning to understand and explore how we can ‘visualise’ that space with all of the senses, exploring what the space sounded like, smelt like and felt like. This chapter will not describe in detail explicit technological solutions (although they will of course feature), but rather it will describe the kind of workflows we can use to realise the potential of embodied, sensory immersion within GIS and other spatial representations. We will use a number of archaeological case studies that explore multi-sensory immersion in a landscape, traditional and non-traditional ‘visualisation’ of abstract data, and the use of different sensory methods to explore and recognise patterns in various spatial datasets. We will demonstrate that opening up archaeological spatial data to be experienced through other sensory modalities can open our understandings of the past in new ways. It also becomes another vector for knowledge mobilisation, of engaging the researcher and the public with the past in emotional ways.
SceneCam: Improving Multi-camera Remote Collaboration using Augmented Reality

Systems for remote collaboration on physical tasks generally use AR/VR technologies to create a shared visual space for collaborators to perform tasks together. The shared space often comes from a single camera view. Prior research has not reported on the benefits of using multiple cameras for remote collaboration. On the contrary, there seems to be some usability issues, which must be addressed, when designing remote collaboration systems that use multiple cameras to capture different areas and perspectives of a task space. To be usable, a multi-camera remote collaboration system must indicate to the local user which camera the remote user is looking at and vice versa, the system must make it fast and easy for the remote user to obtain the right camera view for a given collaborative task. We present SceneCam, an AR prototype with which we explore different techniques for improving the usability of multi-camera remote collaboration by making camera selection easier and faster. Specifically, SceneCam implements two camera selection techniques. The first technique nudges the remote user to manually select an optimal camera view of the local user's actions. The second technique automatically selects an optimal camera view of the local user and shows it to the remote user. Additionally, SceneCam implements two focus-in-context views (exocentric and egocentric views) that provide the remote user with a spatial overview of the local user's whereabouts in relation to the multiple task space areas and direct visual access to the camera views of said areas. Camera selection techniques (manual point-and-click, nudging, automatic), and focus-in-context views (no focus-in-context view, exocentric, egocentric) make up the two dimensions in a design space for multi-camera remote collaboration. We describe how SceneCam spans this design space. Lastly, as part of future work we discuss some hypotheses regarding the effects of the proposed camera selection techniques, focus-in-context views and combinations hereof on the usability of multi-camera remote collaboration.
Empirical evidence on the short-run &amp; long-run effect of ifrs adoption on FDI in developing economies: The algerian context

This study empirically examines the impact of International Financial Reporting Standards (IFRS) adoption, on the Foreign Direct Investment (FDI) inflows in Algeria, for the period 1970–2017. Although a limited number of empirical studies have been conducted on the association of IFRS adoption with FDI inflows, there has been no prior research in the Algerian context. The analysis was conducted, using the Toda-Yamamoto Granger Causality Approach and the Autoregressive Distributed-lag (ARDL) Bounds Testing Technique. The findings revealed evidence that IFRS adoption did have a significant negative effect on FDI inflows Algeria, both in the long-run as well as in the short-run. The findings offered several significant implications for governments, policymakers, investors, managers, researchers, practitioners and other interested groups, interested in understanding the economic importance of adopting international accounting standards, as one of the driving determinants of FDI inflows in developing countries.
Clustering Analysis and Visualization of Terrorist Attack Data

In the space clustering algorithm, because of the choice of the value of k and the problem of the non-clear "elbow point" of the elbow method, this paper introduces logarithmic function and determines the initial clustering center on the basis of the properties of exponential function, weight adjustment, bigotry term and the basic idea of elbow method, and proposes an improved k-value selection algorithm. Combined with the fully adaptive spectral clustering algorithm, the global terrorist attack data are clustered. It effectively solves the problem that the selection of k value is not clear and the outlier can not be separated in the clustering process. The experimental results show that the clustering method proposed in this paper can not only determine the k value quickly and accurately, but also achieve better clustering effect. CesiumJS, WebVR and speech recognition technology are used to visually display and interact the results.
Regulation Document Search Based on Themes using Cosine Similarity and Naive Bayes

Ministry of Administrative and Bureucratic Reform main task is to make policies. The policies that are produced are Regulations that can be in the form of Undang-Undang (Act), Peraturan Pemerintah (Government Regulation), Peraturan Presiden (Presidential Regulation), and Peraturan Menteri (Ministerial Regulation). In making these regulations, one of the main concern is to look for regulation that has the same theme as the regulation that is still being drafted, so that the regulation that will be produced later, did not conflict or overlap with existing regulation. So far the search process take a long time because the search process is only based on the title, type and year in which the regulation created. Furthermore, the search process will not produce results if you don’t use the right keywords. And after we have succeeded in finding the regulations that we are looking for, in order to facilitate the consultation on these regulations, each regulation document must be classified based on the unit that made it.This paper discusses the search for regulation based on themes using the Cosine Similarity and then classify the regulation document using the Naive Bayes. The result is that the searching process using Cosine Similarity only requires less than 1 second and the classification of the regulations documents using Naive Bayes has an accuracy of 82%. The conclusion is that Cosine Similarity method can be used in the Regulation Document searching process. And Naive Bayes method can also be used in Regulation document classifying process.
Using Brain MRI Images to Predict Memory, BMI & Age

Recently a lot of work has been done on applying deep learning to medical problems. In this paper, we use Brain MRI images to predict parameters like memory scores, body mass index (BMI) and age of a person with simple 3D neural network architectures. We model the problem as both single and multi task. We compare the performance of the two approaches. We also use visualizations to find potential biomarkers for these parameters.
Image-Centric Practices on Instagram

Instagram is an image-centric social media application that allows members to share their smartphone photos with the world. Posts typically combine photographs with a short verbal text, and as such provide fertile ground for multisemiotic analysis. This chapter explores relations between the verbal components in Instagram posts (captions and other metadata), the images they describe, and the creator of the post. Goffman’s (1981) notions of footing and production format are put to use in examining the conflation or separation of speaker roles (as animator, author, and principal) and the effects this has on the production and reception of an Instagram post. A small, specialized set of 92 Instagram posts made in relation to the 2016 Australian federal election, and using the discourse tagging hashtag #dogsatpollingstations, is used as a case study to demonstrate how Instagrammers do or do not align with their audiences.
Using Blockchain for Flow Measurement of 5G Base Station

Abstract 5G is a hot area for its high speed data transmission by millimetre wave. Thus a huge amount of 5G base stations are required to be established, which brings heavy burden to 5G operators. In this paper, we propose a new flow measurement system based on blockchain to settle accounts for base stations belonging to different operators securely and fairly. For the base stations can verify the data flow for each other, only real data transmission can be recorded by blockchain. The proposed system can utilize existing consensus mechanism to make it stable and distributed. A concrete scheme is constructed and the scheme is proved to be secure. With this system, the base stations can be encouraged to be setup by different 5G operators and other companies to gain incentive by transmitting data service. This system can solve the problem of huge demand of base stations, and it’s secure, fair, and suitable for real deployment in practice.
Breast Density Classification Using Multifractal Spectrum with Histogram Analysis

This paper presents a novel method for breast density classification in digital mammograms using multifractal spectrum and histogram analysis. The multifractal spectrum is used to capture fibroglandular texture features in sub-images extracted from the breast region of mammograms, and feature vectors extracted from multifractal spectrum are used to classify sub-images into fat or dense category and to calculate breast percent density. For enhancing texture features and improving classification accuracy of breast density, standard deviation and skewness analysis of histogram on mammograms are considered. A full-field digital mammography (FFDM) dataset INbreast is used to test our proposed method and experimental results of breast density estimation outperform other reported methods, giving a higher classification accuracy (83.33%).
Top­-4 secrets for science literacy promotion

This article informs about new segment of scientific activity, which is developing rapidly through the entire world, namely: science literacy. The results of two international conferences on the subject (Beijing, 2018 and 2019) were presented. Discussions on these conferences gives enough confirmations that science literacy has its own niche (target group, methodology, etc.), which is differ from well-known STEM (science, technology, engineering, mathematics). The main objective of STEM is the selection of talented individuals. The same time certain amount of scientific information is required not only for the mentioned individuals but also for the rest people of our planet. It is impossible to identify correctly what is the percentage of population has interest in specific scientific information. But there are no doubts that all inhabitants of the Earth should have fair information of how science, technology and engineering would change their personal life in the nearest and far future. Climate change is the most evident example. But one also can add artificial intelligence as well as irradiation influence from cell phones on the brain, or shortage of pure water and etc. To cover above demands the specific area of scientific activity should be distinguished. The natural name of this area is science literacy. As the first step of science literacy identification the author gives its own definition of new area of scientific activity. The specific promotion tools for this new area of scientific activity already exist worldwide. They are: popular science books, technical museums and etc. Author analyzed these tools and made the conclusion that for efficient science literacy promotion they should be: a) inexpensive; b) reproducible; c) interesting; d) using modern technologies. The examples, which confirm this conclusion, were listed. The action plan was proposed for more efficient international promotion of science literacy.
Practical Applications of Cryptocurrency Value and 51% Attacks: <i>Evidence from Event Studies</i>

Practical Applications Summary In Cryptocurrency Value and 51% Attacks: Evidence from Event Studies, from the Winter 2020 issue of The Journal of Alternative Investments, authors Savva Shanaev, Arina Shuraeva, Mikhail Vasenin, and Maksim Kuznetsov analyze cryptocurrency altcoins. Their findings suggest that altcoins with certain characteristics tend to be vulnerable to 51% attacks that result in a significant drop in their value on the day of the attack. The characteristic vulnerable altcoin has a low market capitalization and resides on a proof-of-work blockchain with a low hash rate. Furthermore, when the prices of these coins suddenly increase without a clear explanation, it can signal an impending attack. It is notable that the percentage drop in value due to a 51% attack is much larger than the decrease in stock prices that typically occurs when companies experience a data breach or other cybersecurity attack. TOPICS:Currency, risk management, financial crises and financial market history
Computation of Circular Area and Spherical Volume Invariants via Boundary Integrals

We show how to compute the circular area invariant of planar curves, and the spherical volume invariant of surfaces, in terms of line and surface integrals, respectively. We use the divergence theorem to express the area and volume integrals as line and surface integrals, respectively, against particular kernels; our results also extend to higher-dimensional hypersurfaces. The resulting surface integrals are computable analytically on a triangulated mesh. This gives a simple computational algorithm for computing the spherical volume invariant for triangulated surfaces that does not involve discretizing the ambient space. We discuss potential applications to feature detection on broken bone fragments of interest in anthropology.
Automatic classification of manga characters using density-based clustering

Manga (Japanese comic) is a globally popular content. In recent years, sales of e-comics that converted to electronic data from paper-based manga are increasing because of the widespread use of electronic terminals. Against this background, it has been proposed to improve the accessibility of e-comics by tagging manga images with metadata. In order to allocate metadata more efficiently, technology that automatically extracts elements such as character and speech is required. One way to classify characters is to get image features from the character's faces and cluster them. Previous research has shown that using the intermediate output of CNN which fine-tuned with character face images is effective for character face recognition. We proposed a clustering method using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to classify character face images without specifying the number of clusters. However, DBSCAN is greatly affected by the hyperparameter. The purpose of this study is to automatically classify character face images without complicated hyperparameter setting. We examine the application of Ordering Points to Identify the Clustering Structure (OPTICS) and Hierarchical DBSCAN (HDBSCAN), which are density-based clustering algorithms that extend DBSCAN. OPTICS is an algorithm for finding clusters in spatial data, and HDBSCAN is an algorithm extracts flat partition from hierarchical cluster data. We also verify the effective CNN model as the feature extractor of face images. Experimental results showed that HDBSCAN is effective for character face image clustering.
PolSAR Band-to-Band Image Translation Using Conditional Adversarial Networks

PolSAR image captured at different frequency bands contains varied information of the same target object. It has been reported that multi-frequency PolSAR data incurs high acquisition costs and computational requirements. In this paper, we put forward a novel concept of PolSAR band-to-band image translation to synthesize multi-frequency PolSAR images from a single frequency PolSAR image. Our proposed method uses PolSAR images captured at a particular frequency to generate its representation in different frequency bands based on image representation and target understanding. We leverage a deep neural network, particularly conditional adversarial network to perform the task. Our proposed framework shows promising results on AIRSAR dataset both qualitatively in terms of visual similarity and quantitatively in terms of root mean square error(RMSE).
Society publishing: It's all about the community you serve

Key points Put your audience first; factor community needs into every editorial and business decision. Do not confuse ‘open’ with ‘free’ access; be as open as possible but maintain subscription models where those who value quality information share the cost of creating it. Carefully choose your technology partner; select a firm with a disciplined approach to developing infrastructure that benefits all publishers on the platform. Keep print as a distribution option; retain this medium with its documented advantages over digital publication in the acquisition and retention of knowledge. Think twice about consolidation; consider, evaluate, and adopt the business model that best serves your community.
Achieving Privacy-Preserving Edit Distance Query in Cloud and Its Application to Genomic Data

Edit distance is one of the most frequently used metrics for sequence comparison in many fields, including genomic data computation. With growing availability of cloud computing, data owners tend to outsource to cloud servers for edit distance computation. However, directly outsourcing and computing edit distance on the cloud inevitably raises concerns over privacy. Therefore, in this paper, we propose a secure and efficient one-server model for achieving privacy-preserving edit distance queries in the cloud. The proposed scheme combines BGN encryption and improved data generation technique to securely and efficiently process queries without revealing the original data to the cloud. In addition, we modify our scheme with a block-wise edit distance approximation for privacy-preserving genomic data comparison. A detailed security analysis proves that our proposed scheme is secure and privacy preserving and performance evaluations indicate the efficiency of the scheme.
Research on Anomaly Detection of Civil Aircraft Hydraulic System Based on Multivariate Monitoring Data

The hydraulic system is one of the most important systems of an aircraft, and the stainability and reliability of this complex dynamical system directly affect operation and safety. To secure normal operation of the hydraulic system and to assure successful missions, accurate health monitoring, rapid fault diagnosis and isolation, and effective maintenance measures are needed. As a highlight of the new civil aircraft model, some methods based on analysis of a single parameter have been implemented to anomaly detection in Aircraft Condition Monitoring System (ACMS) records for early warning of out-of-gauge events and trend monitoring of performance parameters. Unfortunately, because of the complexity and the coupling relationship between components of the hydraulic system, it is almost impossible to monitor the health state of the hydraulic system via a single variable. To serve the purpose, a comprehensive anomaly detection method considering multivariate monitoring data is proposed in this paper. The unsupervised autoencoder (AE) model with a multi-layer network structure is used for feature extraction of multivariable time series data, and the distance based on similarity is measured to characterize the difference between observations and reconstructions. A health baseline is obtained from a large number of normal training samples and used for anomaly detection and analysis of a new observation. In this paper, a case of driving pump anomaly detection of the hydraulic system is used to illustrate the effectiveness of the proposed method.
CogniPGA: Longitudinal Evaluation of Picture Gesture Authentication with Cognition-Based Intervention

Abstract There is evidence that the visual behavior of users when creating graphical passwords affects the password strength. Adopting a cognitive style perspective in the interpretation of the results of recent studies revealed that users, depending on their cognitive style, follow different visual exploration paths when creating graphical passwords which affected the password strength. To take advantage of the inherent abilities of people, we proposed CogniPGA , a cued-recall graphical authentication scheme where a cognition-based intervention using gaze data is applied. This paper presents the longitudinal evaluation of the proposed scheme in terms of security, memorability, and usability from a cognitive style perspective. Results strengthen the assumptions that understanding and using the inherent cognitive characteristics of users could enable the design of user-first authentication schemes, where no compromises need to be made on security for benefiting usability or the other way around.
Towards the Named Entity Recognition Methods in Biomedical Field

Natural Language Processing (NLP) is very important in modern data processing taking into consideration different sources, forms and purpose of data as well as information in different areas our industry, administration, public and private life. Our studies concern Natural Language Processing techniques in biomedical field. The increasing volume of information stored in medical health record databases both in natural language and in structured forms is creating increasing challenges for information retrieval (IR) technologies. The paper presents the comparison study of chosen Named Entity Recognition techniques for biomedical field.