Reviewerly/exHarmony huggingface dataset:
A graph based approach for privacy preservation of citizen data in e-governance applications

Online social media provides a platform for the citizens to create profiles and share information. This has also enabled the governments to create their own profiles and promote different e-governance services and policies. It is possible to analyze the responses of citizens available in text form through text mining and infer interesting patterns. Also, governments may ask for citizen profiles and connection structures to strategize decision making. Such analysis on structural information can help governments to identify communities that get benefit from certain government services. Graph is a common way to understand such connections. However, the simple graph model is unable to represent super-dyadic relationship. Hypergraph model is a generalized graph model which can be used in such scenarios to represent complex connections in groups. But, analysis of data irrespective of its representation can raise privacy threats. This paper considers the identity disclosure attack on hypergraph structures. Identity disclosure attacks can disclose the identity of the users leading to disclosure of sensitive information associated with the users. Hence, government must anonymize the data before subjecting it to analysis by any third party. The focus of this paper is hypergraph anonymization where we study a rank-label attack model which is a stronger attack than the existing rank attack model. The results show the vulnerability of rank anonymization against rank-label attack. In the sequel, a Sequential Rank-Label Anonymization (SRLA) approach has been proposed which outperforms the Greedy Rank-Label Anonymization (GRLA).
A normative decision-making model for cyber security

Purpose The purpose of this paper is to investigate security decision-making during risk and uncertain conditions and to propose a normative model capable of tracing the decision rationale. Design/methodology/approach The proposed risk rationalisation model is grounded in literature and studies on security analysts’ activities. The model design was inspired by established awareness models including the situation awareness and observe–orient–decide–act (OODA). Model validation was conducted using cognitive walkthroughs with security analysts. Findings The results indicate that the model may adequately be used to elicit the rationale or provide traceability for security decision-making. The results also illustrate how the model may be applied to facilitate design for security decision makers. Research limitations/implications The proof of concept is based on a hypothetical risk scenario. Further studies could investigate the model’s application in actual scenarios. Originality/value The paper proposes a novel approach to tracing the rationale behind security decision-making during risk and uncertain conditions. The research also illustrates techniques for adapting decision-making models to inform system design.
Learning Non-Stationary Space-Time Models for Environmental Monitoring

One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both space and time and it is imperative to develop a deeper understanding of techniques that can model space-time dynamics accurately. In this paper we propose NOSTILL-GP - NOn-stationary Space TIme variable Latent Length scale GP, a generic non-stationary, spatio-temporal Gaussian Process (GP) model. We present several strategies, for efficient training of our model, necessary for real-world applicability. Extensive empirical validation is performed using three real-world environmental monitoring datasets, with diverse dynamics across space and time. Results from the experiments clearly demonstrate general applicability and effectiveness of our approach for applications in environmental monitoring.
Improvements of Image Retrieval-based Visual Localization Using Structured Database

In this paper, we propose a method to improve image retrieve for visual localization by structuring the database. We are studying cloud-based positioning infrastructure system that we call Universal Map. It can reduce various cost as compared with the conventional technique. However, it takes time to estimate the position because the retrieval process is performed from a large amount of images in the database. To solve this problem, we reduce the retrieval time by structuring the database. We designed feature vector representing each image in the database and classified them using clustering method called K-means. We also made virtual sensing image and measured the Euclidean distance to each cluster in order to evaluate the classification results. As a result, the correct cluster was selected up to the third closest cluster. Therefore, we could reduce the retrieval time to 20% so far.
A Literature Review: Geometric Methods and Their Applications in Human-Related Analysis

Geometric features, such as the topological and manifold properties, are utilized to extract geometric properties. Geometric methods that exploit the applications of geometrics, e.g., geometric features, are widely used in computer graphics and computer vision problems. This review presents a literature review on geometric concepts, geometric methods, and their applications in human-related analysis, e.g., human shape analysis, human pose analysis, and human action analysis. This review proposes to categorize geometric methods based on the scope of the geometric properties that are extracted: object-oriented geometric methods, feature-oriented geometric methods, and routine-based geometric methods. Considering the broad applications of deep learning methods, this review also studies geometric deep learning, which has recently become a popular topic of research. Validation datasets are collected, and method performances are collected and compared. Finally, research trends and possible research topics are discussed.
Distributed outlier detection in hierarchically structured datasets with mixed attributes

Anomaly detection has been extensively studied over the past decades; however, there are still various challenges due to the complex structures of the real-world datasets. First, only a few methods...
Efficient Quantum Algorithms for GHZ and <i>W</i> States, and Implementation on the IBM Quantum Computer (Adv. Quantum Technol. 5‐6/2019)

Entanglement is a fundamental resource in quantum information and technology. In article number 1900015, Clément Javerzac-Galy and co-workers investigate efficient new algorithms to create N-qubit Greenberger–Horne–Zeilinger (GHZ) and W states with time-complexity scaling linearly and logarithmically in N. Quantum circuits are implemented on the IBM quantum computer up to N = 16, and entanglement is investigated through tomography (and other methods) as shown in the cover picture. While the fidelity decreases with increasing N, it does so more slowly for the logarithmic algorithms. (Image design by Chun Lam Chan.)
Making Fast Graph-based Algorithms with Graph Metric Embeddings

The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs.We explore dense vector representations as an effective way to approximate the same information: we introduce a simple yet efficient and effective approach for learning graph embeddings.Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account.We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks and show evaluations on the WordNet graph and two knowledge base graphs.
Implementation Challenges in Information Retrieval System

Natural scenes text recognition has become increasingly attractive in recent years. Several research results have been developed on different languages for the sake of text recognition from natural scenes. However, there is no system for identifying Urdu text from natural scenes. This paper presents a framework steps involved in recognition of Urdu text from landscape images. Furthermore, the paper focus on issues and challenges faced during experimentation and implementation. Challenges includes multi-script text on a single image, different text font with variation in text sizes, light shadow, reflection, text color, text contrast with background, words or ligature missing, blur, low resolution , skew and neon signboard. In the end, we also give a summary of issues and challenges of Urdu Text retrieval system from natural scenes.
Fast density peak-based clustering algorithm for multiple extended target tracking

The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.
A Demonstration of Generative Policy Models in Coalition Environments

Autonomous systems are expected to have a major impact in future coalition operations to assist humans in achieving complex tasks. Policies are typically used by systems to define their behavior and constraints and often these policies are manually configured and managed by humans. This paper presents a demonstration of a recent Generative Policy-based Model (GPM) approach applied to generating coalition policies for asset serviceability. This demonstrates the flexibility of the approach for generating policies in a distributed coalition environment to facilitate effective collaboration between coalition partners.
Supporting Decision Makers with Use Cases; Case Study Results

The benefits of defining Use Cases to support systems development, particularly during the early stages of the lifecycle, is well established in both software and systems engineering. The effective implementation of Use Cases to support stakeholder decision making during system development tends to be uneven, particularly for non-technical decision makers. This paper presents three case studies where Use Cases were developed and successfully used to support a variety of stakeholders in their decision making, two of which featured simulations of the Use Cases. This paper highlights aspects of Use Case development that were most useful to decision makers, what was not as useful, and finally recommends several areas for future research. The goal is not to present a tutorial on Use Case development. Rather, it is to identify factors that developers can consider during Use Case development to better support system visualization and decision making.
Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG

Mammal is a type of animal that has many diverse characteristics, such as vertebrates and breastfeeding. In this study, the HOG feature and the k-NN method were proposed to classify 15 species of mammals. This study uses the LHI-Animal-Faces dataset which has fifteen species of mammals, where each type of mammal has 50 images measuring 100x100 pixels. The image will be conducted the process by the HOG feature extraction process and continued into the classification process using k-Nearest Neighbor. The performance of the HOG and k-NN features that get the best value is in deer and monkey, the best results for precision, recall, and accuracy are at k=3 where HOG feature extraction provides good vector features to be used in the classification process using the k-NN method.
Designing a Virtual Reality Platform to Facilitate Augmented Theatrical Experiences Based on Auralization

In the last few years, the immersive theater has become a new trend for modern performances. Venues increasingly utilize widely available computer technologies, such as virtual/augmented reality and spatial sound, to help facilitate the realization of different ideas. Motivated by this current trend, a prototype platform has been developed that enables the design and implementation of an augmented theatrical experience based on spatial sound immersion. This paper describes the implementation of the platform and, through several use case scenarios, its evaluation. The paper concludes with a discussion of the results and offers some thoughts on future developments.
Deep Teaching: Materials for Teaching Machine and Deep Learning

Machine learning (ML) is considered to be hard because it is relatively complicated in comparison to other topics of computer science. The reason is that machine learning is based heavily on mathematics and abstract concepts. This results in an entry barrier for students: Most students want to avoid such difficult topics in elective courses or self-study. In the project Deep.Teaching we address these issues: We motivate by selected applications and support courses as well as self-study by giving practical exercises for different topics in machine learning. The teaching material, provided as jupyter notebooks, consists of theoretical and programming sections. For didactical reasons, we designed programming exercises such that the students have to deeply understand the concepts and principles before they can start to implement a solution. We provide all necessary boilerplate code such that the students can primarily focus on the educational objectives of the exercises. We used different ways to give feedback for self-study: obscured solutions for mathematical results, software tests with assert statements, and graphical illustrations of sample solutions. All of the material is published under a permissive license. Developing jupyter notebooks collaboratively for educational purposes poses some problems. We address these issues and provide solutions/best practices.
Heráclito: Intelligent Tutoring System for Logic

The present article aims to present the Heraclito environment that has in the Logical Studies and Exercises Logic (LOGOS) an important tool. The Heraclito environment assists students in solving various types of Logic exercises and provides the LOGOS Electronic Notebook to create and edit formulas, truth tables and proofs of Propositional Logic. The LOGOS Electronic Notebook is compatible with tablets, smartphones and PCs. It is currently being used with 1st and 2nd year undergraduate students in curricula in the scientific and technological areas. In addition, the Heraclito environment has an Intelligent Tutor System based on Multiagent Systems that identifies the individual knowledge of each student in the context of Natural Deduction in Propositional Logic.
Data Mining-Based Intelligent Decision Support Systems

We have finally reached the point of introducing the cookbook of intelligent decision-support systems. In the previous chapters you have already caught a glimpse of concepts like data mining, neural networks, deep learning, evolutionary algorithms, etc. In what follows, we shall present step-by-step each method, so that you could understand the theoretical concepts better in order to make your very own intelligent decision support system.
Robustness of Quantum Memories: An Operational Resource-Theoretic Approach

Quantum memory -- the capacity to store and faithfully recover unknown quantum states -- is essential for any quantum technology to exhibit operational advantage. There is thus great value in identifying physically meaningful means to benchmark candidate quantum memories against each other across diverse operational settings. Here we introduce the robustness of quantum memory, and prove that it satisfies all the mathematical properties we expect of a bona-fide resource-theoretic measure of a system's capacity to retain non-classical information. We demonstrate its operational meaning as a resource in three different settings: (1) the resource cost of synthesising the memory with idealised qubit memories, (2) the resource cost of simulating the memory's observational statistics with classical resources, and (3) the performance of the memory in discriminating different input states. We illustrate an efficient semi-definite program to estimate the robustness of quantum memory, and show that it can be lower bounded with a single witness or purity measurement. Our results thus provide an experimentally accessible and operationally meaningful quantifier of the resources behind preserving quantum information.
Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks

In this paper, we present an approach for classifying news articles as biased (i.e., hyperpartisan) or unbiased, based on a convolutional neural network.We experiment with various embedding methods (pretrained and trained on the training dataset) and variations of the convolutional neural network architecture and compare the results.When evaluating our best performing approach on the actual test data set of the SemEval 2019 Task 4, we obtained relatively low precision and accuracy values, while gaining the highest recall rate among all 42 participating teams.
The Moderating Effect of Electronic-HRM on Training and Employee Performance Relationship: A Moderated Model

The current study addressed the effect of electronic-human resource management (e-HRM) as a moderating variable on training practices and employee-level performance relationship, as guided by the contingency theory and social exchange theory in the context of a developing country. Through the application of a self-administrated questionnaire, data were collected from respondents (n = 592) operating in the private sector of Jordan. Notably, structural equation modelling (SEM) with smart partial least square (PLS) was applied; the results confirmed the hypothesised model, as training practices and e-HRM use significantly affect employee performance. Further, e-HRM was seen to significantly moderate the relationship. In addition, discussion for the implications, direction and suggestions for the future body of literature were introduced.
Different InterCriteria Analysis of Variants of ACO algorithm for Wireless Sensor Network Positioning

Wireless sensor networks areRoeva, Olympia formed by spatially distributed sensors, which communicate in a wireless way. This network can monitor various kinds of environment and physical conditions like movement, noise, light, humidity, images, chemicalFidanova, Stefka substances etc. A given area needs to be fully covered with minimal number of sensors and the energy consumption of the network needs to be minimal too. We propose several algorithms, based on Ant Colony Optimization, to solve the problem. We study the algorithms behavior when the number of ants varies from 1 to 10. We apply InterCriteria analysis to study relations between proposed algorithms and number of ants and analyse correlation between them. Four different algorithms of ICrA— $$\mu $$ -biased, Balanced, $$\nu $$ -biased and Unbiased—are applied. The obtained results are discussed in order to find the stronger correlations between considered hybrid ACO algorithms.
Initial Steps Towards a Family of Regular-Like Plan Description Logics

A wide range of ordinary Description Logics (DLs) have been explored by considering collections of concept/role constructors, and types of terminologies, yielding an array of complexity results. Representation and reasoning with plans is a very important topic in AI, yet there has been very little work on finding and studying DL constructors for plan concepts. We start to remedy this problem here by considering Plan DLs where concept instances are sequences of action instances, and hence plan concepts can be viewed as analogues of formal languages, describing sets of strings. Inspired by the clasp system, we consider using regular-like expressions, obtaining a rich variety of Plan DLs based on combinations of regular-like expression constructors, including sequence (concatenation), alternation (union, disjunction), looping (Kleene star), conjunction (intersection), and complement. To model the important notion of concurrency, we also consider interleaving. We present results from the formal language literature which have immediate bearing on the complexity of DL-like reasoning tasks. However, we also focus on succinctness of representation, and on expressive power, issues first studied by Franz Baader for ordinary DLs.
Basic Visual Cryptography Algorithms

This chapter aims to help the readers to get familiar with the classical VC algorithms. These algorithms are also stepstones for developing more complicated algorithms in later chapters. First, we introduce the general framework of VC, such as access structure and general requirements (security and contrast). Then, we introduce the classical deterministic VC with pixel expansion as developed in Naor and Shamir’s seminal work. Next, the size-invariant algorithms are summarized, including the probabilistic VC and random grid VC. Some examples will be discussed for the readers that are new to this topic. Finally, various security levels in visual cryptography will be discussed, including strong security and several weak securities.