Reviewerly/exHarmony huggingface dataset:
PunyVis: A Visual Analytics Approach for Identifying Homograph Phishing Attacks

Attackers seeking to deceive web users into visiting malicious websites can exploit limitations of the tools intended to help browsers translate domain names containing non-ASCII characters, or internationalized domain names (IDNs). These attacks, called homograph phishing, involve registering Unicode domain names that are visually similar to legitimate ones but direct users to distinct servers. Tools exist to identify when domains use non-ASCII characters, which get translated by the Punycode protocol to work with the Domain Name System (DNS); however, these tools cannot automatically distinguish between benign use cases and ones with malicious intent, leading to high rates of false-positive alerts and increasing the workload of analysts looking for evidence of homograph phishing.To address this problem, we present PunyVis, a visual analytics system for exploring and identifying potential homograph attacks on large network datasets. By targeting instances of Punycode that use easily-confusable ASCII characters to spoof popular websites, PunyVis quickly condenses large datasets into a small number of potentially malicious records. Using the interactive tool, analysts can evaluate potential phishing instances and view supporting information from multiple data sources, as well as gain insight about overall risk and threat regarding homograph attacks. We demonstrate how PunyVis supports analysts in a case study with domain experts, and identified divergent analysis strategies and the need for interactions that support how analysts begin exploration and pivot around hypotheses. Finally, we discuss design implications and opportunities for cyber visual analytics.
Artificial intelligence technologies for Maritime Surveillance applications

The use of AI methods is currently evolving tasks done in the past by image analysts. During the last years, technology had helped to jump into fully automatic methods for monitoring and surveillance tasks, such as object detection, change detection and many more. In this work we want to show some of the AI-based models which RHEA Group has been working on which can be applied to the maritime domain, such as ship detection and super-resolution of satellite data. Each of these models can be further extended and specialized into specific monitoring and surveillance tasks, from the detection of ghost ships, measure environmental damage or monitoring of critical infrastructure near harbors or protected areas. In this paper, we illustrate some examples of the status of our research activities and the developments of these prototype applications.
Boosting Feature Matching Accuracy With Pairwise Affine Estimation

Local image feature matching lies in the heart of many computer vision applications. Achieving high matching accuracy is challenging when significant geometric difference exists between the source and target images. The traditional matching pipeline addresses the geometric difference by introducing the concept of support region. Around each feature point, the support region defines a neighboring area characterized by estimated attributes like scale, orientation, affine shape, etc. To correctly assign support region is not an easy job, especially when each feature is processed individually. In this paper, we propose to estimate the relative affine transformation for every pair of to-be-compared features. This "tailored" measurement of geometric difference is more precise and helps improve the matching accuracy. Our pipeline can be incorporated into most existing 2D local image feature detectors and descriptors. We comprehensively evaluate its performance with various experiments on a diversified selection of benchmark datasets. The results show that the majority of tested detectors/descriptors gain additional matching accuracy with proposed pipeline.
Block - NID: A Conceptual Secure Blockchain Based National Identity Management System Model

Blockchain technology is becoming more and more popular day-by-day with an escalating interest in numerous applications ranging from data management, financial services, cyber security, Internet of things (IoT) and food science to healthcare industry and brain research. There has been a remarkable interest witnessed in utilizing applications of blockchain for the delivery of safe and secure National Identity (ID) management. Also, blockchain can reform the traditional ID management practices to a more reliable means in terms of effective use and distribution through safe and secure data sharing. In the future blockchain could be a technology that may potentially help in personalized, authentic, and secure ID management by merging the entire real-time user data of a citizen and presenting it in an up-to-date secure setup. In this paper we review both the existing and latest developments in the field of ID card system by implementing blockchain as a model. We also discuss the applications of blockchain along with the challenges faced and future perspectives.
Recurrent Factorization Machine with Self-Attention for Time-aware Service Recommendation

With the emergence of a flood of Web services which makes it difficult for users to find suitable services from a series of similar services. How to provide users with personalized services that utilize sequential historical records has become the key issue in service recommendation. In recent years, Factorization Machine(FM) and long Short Term Memory(LSTM) has been proposed for sequential service recommendation. However, FM ignored dynamic long-term dependencies that change over time between users and services. In addition, LSTM has the problem of vanishing gradient and exploding gradient simultaneously. To address this challenges,we proposed a novel model that combined Gated Recurrent Unit with Self-Attention (SAGRU) and Projected Factorization Machine (PFM), namely Recurrent Factorization Machine (RFM) .The new model comes with three innovations: 1) Self-Attention is considered in our model through assigning different weights for service records for extracting an interpretable user-service-time tensor embedding. 2) A bottleneck structure is designed in downstream candidate output for compressing the encoded information and suppressing the noise. 3) Through comprehensive experiments on real-world dataset, it is proved that this method is significantly better than other state-of-the-art time-aware methods in service recommendation.
STAGES OF MINERALIZATION AND LOCALIZATION FACTORS OF THE AGYURT GOLD-COPPER-MOLYBDENUM DEPOSIT (LESSER CAUCASUS, AZERBAIJAN)

The article considers the stages of mineralization of the Agyurt gold-copper-molybdenum deposit of the Lesser Caucasus. The following mineralization stages were established at the field: 1) quartz-molybdenum; 2) quartz-pyrite-chalcopyrite with gold; 3) quartz-carbonate-sphalerite; 4) quartz-carbonate. Gold ore bodies are mainly composed of aggregates of the second stage of mineralization, which is productive. Its mineral substance is represented by three paragenetic associations: 1) quartz-pyrite; 2) calcite-chalcopyrite-marcasite; 3) gold-telluride-bismuth. Chemical analyzes of pyrites, bismuthin, tellurium bismuthite are given. It has been found that native gold is found in the form of small, simple forms of gold in grains of early pyrite. In veins of chalcopyrite and grains of pyrite, it is usually confined to the marginal parts. The largest amount of gold is in close intergrowth with tellurium-bismuth minerals. It was found that the ore deposition environment (mineral composition, chemistry and structural and texture features of the host rocks) played a decisive role for various types of mineralization. It is established that, in the plan, the Agyurt deposit is localized in the contour of a rock block elongated in the northwest (submeridional) direction, bounded by tectonic zones from the north-north-west and north-east, which also bear a certain imprint of the formation of the structural plan of the ore field with near latitudinal strike of tectonic elements. These structures are most tectonically prepared for the localization of gold-copper-molybdenum mineralization (updated in the pre-ore stages and most permeable for hydrothermal structures), and were the main ore-supplying and ore-locating structural elements. The ore zones represented by hydrothermal-metasomatic formations, as well as quartz veins piercing them and numerous veinlets and sometimes mineralized dykes, are controlled by the Main Ordubad longitudinal (280°∠70–80°NE) and Agyurt-Misdag transverse (40–50°∠70° NE) with discontinuous violations and adjoin the hanging side (northeast flank) of the first. The combination of structural and petrogenetic factors not only predetermined the formation of deposits of the Agyurt type, but also determined the horizontal and vertical zonation of mineralization: an increase in the Mo content and a decrease in Cu with depth are established. The same pattern is observed in the horizontal direction: as you move away from the intrusive massif and the ore-removing channel, there is a transition from Cu-Mo-mineralization to copper and then polymetallic, i.e. the role of Cu increases, then Pb and Zn. The horizontal zoning in Agyurt is expressed in an increase in Au content and the total amount of sulfides with distance from the Main Ordubad Fault, and vertical shows an increase in Au content and decreases in Ag with depth.
Detecting Higher-Order Merge Conflicts in Large Software Projects

Merge conflicts can occur when multiple developers work concurrently on the same source code corpus. Diverging textual changes (in the same lines of code) are typically easy to resolve with help of common tools like Git. More challenging are higher-order merge conflicts. They arise as the result of unintended interactions between changes in different parts of the source code. Higher-order merge conflicts can be caused by a combination of changes, and so even thorough testing of the individual development branches might not be able to identify them. We suggest an approach based on static analysis and a prototypical tool to detect potential higher-order merge conflicts. Our method identifies potentially dangerous dependencies between changed code fragments in a call graph. An evaluation on SAP HANA, a very large industrial product in C++, shows that the approach is able to identify 62% of higher-order merge conflicts causing build failures over 22 months project development time. The same prototype finds no instance of higher-order merge conflicts causing test failures in SAP HANA during a two month development period. In summary, our method scales well and can identify higher-order merge conflicts which escape traditional testing.
An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering

An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. This EA facilitates a different search of the fitness landscape, i.e., the likelihood surface, utilizing both crossover and mutation. Furthermore, this EA represents an efficient approach to "hard" model-based clustering and so it can be viewed as a sort of generalization of the k-means algorithm, which is itself equivalent to a restricted Gaussian mixture model. The EA is illustrated on several datasets, and its performance is compared with that of other hard clustering approaches and model-based clustering via the EM algorithm.
Exploring the relationship between voice similarity estimates by listeners and by an automatic speaker recognition system incorporating phonetic features

The present study investigates relationships between voice similarity ratings made by human listeners and comparison scores produced by an automatic speaker recognition system that includes phonetic, perceptually-relevant features in its modelling. The study analyses human voice similarity ratings of pairs of speech samples from unrelated speakers from an accent-controlled database (DyViS, Standard Southern British English) and the comparison scores from an i-vector-based automatic speaker recognition system using ‘auto-phonetic’ (automatically extracted phonetic) features. The voice similarity ratings were obtained from 106 listeners who each rated the voice similarity of pairings of ten speakers on a Likert scale via an online test. Correlation analysis and Multidimensional Scaling showed a positive relationship between listeners’ judgements and the automatic comparison scores. A separate analysis of the subsets of listener responses from English and German native speaker groups showed that a positive relationship was present for both groups, but that the correlation was higher for the English listener group. This work has key implications for forensic phonetics through highlighting the potential to automate part of the process of selecting foil voices in voice parade construction for which the collection and processing of human judgements is currently needed. Further, establishing that it is possible to use automatic voice comparisons using phonetic features to select similar-sounding voices has important applications in ‘voice casting’ (finding voices that are similar to a given voice) and ‘voice banking’ (saving one's voice for future synthesis in case of an operation or degenerative disease).
Towards a Cloud Native Big Data Platform using MiCADO

In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware (e.g., cluster nodes) and software (e.g., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms (e.g., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoopbased big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling).
State of the Art of Extended Reality Tools and Applications in Business

Over the past years, extended reality (XR) technologies, such as virtual, augmented, and mixed reality, have become more popular in business. Through immersive extended reality experiences, businesses of all shapes and sizes are waking up to the possibilities of using this emerging technology to create new opportunities and support their digital transformation. The aim of this chapter is to present the state of the art of extended reality devices, tools, and applications currently available.
Speedup of Grover’s search algorithm and closed timelike curves

Abstract The quadratic reduction of query complexity of Grover’s search algorithm (GA), while significant, would not be enough to enjoy exponentially fast data searching in large-scale quantum computation. One of the ways to enhance the speedup in the framework of Grover’s algorithm is to employ a novel quantum operation, i.e., inversion against an unknown state; however, this is not possible at least in quantum theory. We thus extend the Grover algorithm assisted by closed timelike curves (CTCs), in which the unknown-state inversion is achievable by combining the superposition of two unknown states with cloning. We dubbed this refined algorithm CTC-assisted Grover algorithm (CTC-GA). We show that the CTC-GA can vastly reduce the query complexity compared to the original algorithm; remarkably, from polynomial to poly-logarithmic. These results will provide a broader intuition for exponential quantum speedup of data searching problems.
Achieving cultural relevance in technomediated platforms: instant cultural branding and controversial clicktivism

Cultural branding acknowledges brands’ power as cultural artifacts that infuse culture with meanings and absorb new meanings from culture. The last mile of brand humanization sees brands act as citizens that construct their meanings through taking active part in socio-political discourse. This participation has been magnified and challenged by digital platforms that activate clicktivism practices to propagate cultural values. Our study is aimed at exploring how clicktivism is currently being leveraged by brands to achieve cultural relevance. We set our study in the clicktivist engagement solicited by the political mandate of Donald Trump, as numerous brands saw in Trump’s contested political agenda an opportunity to stand up and achieve cultural relevance. We term this new brand activism and its temporal dynamics as “instant cultural branding”, pointing at branding initiatives that apply fast, real time communication logics to get organically embedded in technomediated conversations with consumers, leveraging on cogent socio-cultural and political issues. Our findings reveal how, despite ambivalence of perceived authenticity of brand discourse can cause strong consumer criticism in digital conversations, deliberate ambiguity of instant cultural branding proves to be a trigger of clicktivist engagement. Ambiguity in taking socio-political stance resonates with the cultural norms of contemporary shifting consumers’ activism and ideology. Hence, controversial clicktivism emerges as a new dimension of cultural relevance in the fast temporality of technomediated discourse.
Tangle the Blockchain: Toward IOTA and Blockchain Integration for IoT Environment

The use of blockchain (BC) technology for IoT-based collaboration platforms is still hindered by its inherent properties, namely, the need for ever-increasing storage and the low scalability. In this paper, we investigate the interoperability requirements between BC and tangle distributed technologies. We propose a new decentralization architecture in which BC and tangle are combined in such a way that the IoT functionality is increased, and storage is enhanced while keeping a high level of reliability, data accessibility, integrity, and security. In our architecture, a BC-based platform installed in the backend, used primarily for data storage and smart contract. In the frontend, the applications are running on a Tangle-based platform so that it fits IoT devices. The architecture, when implemented, will reap the advantages of both technologies while limiting the drawbacks of them. The main part of the proposed approach has been implemented and tested using a GPS data emulator connected to an IOTA node. The received data have been propagated and stored in the deployed BC and IOTA.
A Fuzzy Cognitive Mapping Approach to the Conference Selection Problem

Academic conferences are popular platforms for academicians to share their research with colleagues, get feedback, and stay up to date on recent academic studies. Conferences also provide opportunities for the participants to express themselves, expand their network, and become socialized. However, academicians are forced to choose a limited number of conferences to participate due to several different factors such as time required for preparing a research, traveling and lodging expenses, and conference fees. At this multi-criteria decision problem, relevant factors can be used to evaluate the alternatives (i.e., academic conferences to participate) and prioritization of these factors would be necessary in advance. To address this issue, this study suggests an improved fuzzy cognitive mapping (FCM) approach to analyze factors affecting the choice of academic conferences to participate. The classical FCM allows to observe the dynamic behavior of complex systems during time. While the approach is widely used in different areas, it has considerable drawbacks: (i) producing same steady state values under different initial conditions and (ii) yielding completely different steady state values when different threshold functions are used. The new approach provides a mathematical formulation that produces steady state values sensitive to initial conditions. Since the selection of the threshold function in classical FCM is a highly subjective choice, the proposed approach offers an alternative way to obtain comparable values.
Automating Detection of Occurrences of PostgreSQL Database Design Problems

SQL is a very resilient and widely used software language. In case of building a SQL database, one has to design schemas of the database so that the database management system (DBMS) can enforce these. The result of designing a database schema is a technical artifact, which may have technical debt. The debt makes it more difficult to understand, maintain, extend, and reuse the artifact. Smells are the signs of technical debt. Many database design smells manifest the same problems as code smells. It could also be that a database schema makes incorrect statements about the domain of the database or lacks necessary elements, i.e., is incomplete. Thus, database schemas can have numerous problems and finding these is a prerequisite of improving the schemas. The paper introduces a catalog of open-source SQL queries that have been designed for finding the occurrences of design problems in PostgreSQL databases ( https://github.com/erki77/database-design-queries ). Most of the queries help us to detect the occurrences of database design smells. The queries are for a specific although popular DBMS. However, most of the problems that occurrences these help us to find can appear in any SQL database, regardless of the used DBMS.
Efficient resource prediction model for small and medium scale cloud data centers

By leveraging the performance of small and medium-scale data centers (SMSDCs), which are involved in high-performance computing, data centers are central to the current modern industrial business world. Extensive enhancements in the SMSDC infrastructure comprise a diverse set of connected devices that disseminate resources to the end users. The high certainty workloads of end users and over resource provisioning result in high power consumption in SMSDCs, which are pivotal factors contributing to high carbon footprints from SMSDCs. The excessive emission of CO2 is higher in SMSDCs compared with that of hyperscale data centers (HSDCs). An exorbitant amount of electricity is utilized by 8.6 million data centers worldwide, and is expected to increase by up to 13% in 2030. The power requirement of an SMSDC domain is expected to be 5% of the global power production. However, the power consumption of SMSDCs changes annually. To aid SMSDCs, machine learning prediction is deployed. Literature review indicates that many studies have focused on the recurring issues of HSDCs rather than those of SMSDC. Herein, a regressive predictive analysis, i.e., multi-output random forest regressor, is proposed to forecast the resource usage and power utilization of virtual machines. These prediction results in diminishes the power utilization of SMSDC whilst reduces the CO2 emission from SMSDC. The obtained result shows that the proposed approach yields better predictions than other single-output prediction methods for future resource demand from end users.