Reviewerly/exHarmony huggingface dataset:
Visual Analysis of Large Multivariate Scattered Data using Clustering and Probabilistic Summaries

Rapidly growing data sizes of scientific simulations pose significant challenges for interactive visualization and analysis techniques. In this work, we propose a compact probabilistic representation to interactively visualize large scattered datasets. In contrast to previous approaches that represent blocks of volumetric data using probability distributions, we model clusters of arbitrarily structured multivariate data. In detail, we discuss how to efficiently represent and store a high-dimensional distribution for each cluster. We observe that it suffices to consider low-dimensional marginal distributions for two or three data dimensions at a time to employ common visual analysis techniques. Based on this observation, we represent high-dimensional distributions by combinations of low-dimensional Gaussian mixture models. We discuss the application of common interactive visual analysis techniques to this representation. In particular, we investigate several frequency-based views, such as density plots in 1D and 2D, density-based parallel coordinates, and a time histogram. We visualize the uncertainty introduced by the representation, discuss a level-of-detail mechanism, and explicitly visualize outliers. Furthermore, we propose a spatial visualization by splatting anisotropic 3D Gaussians for which we derive a closed-form solution. Lastly, we describe the application of brushing and linking to this clustered representation. Our evaluation on several large, real-world datasets demonstrates the scaling of our approach.
IMPROVING QUALITY AND INCLUSIVE EDUCATION ON PHOTOGRAMMETRY: NEW TEACHING APPROACHES AND MULTIMEDIA SUPPORTING MATERIALS

Abstract. The democratization and accessibility of low-cost devices for image acquisition and the development of highly automated procedures for orientation and dense image matching allow almost every person to be a potential producer of photogrammetric models. The diffusion of image-based technologies to produce 3D models amongst wider audiences entails however some risks, as the lack of critical awareness of the final quality of the outputs. Information and education about potentialities and limitations of reality-based digitization by photogrammetry may help spreading procedures and methods for the correct use of this technology. This paper presents the results of one of the funded projects within the 2018 ISPRS Capacity Building Initiatives “Education and training resources on digital photogrammetry”. The production of multimedia material for supporting smart educational teaching and learning approaches will be reported, as well as experiences on their application on case studies. Blended innovative teaching and learning pedagogical approaches have been tested, as Flipped Classroom (FC), Learning-by-doing (LBD), Collaborative Learning (CL), and Challenge-Based Learning (CBL), supported by multimedia tools for capacity-building and knowledge transfer. The implementation of multimedia materials for supporting teaching strategies resulted in the production of updated and engaging resources, as videos, tutorials, and datasets to be used during courses, workshops, and seminars targeted to different user groups. The combination of teaching strategies and multimedia supporting materials were tested within national and international projects, from academic courses to complete non-experts, from activities on the field to online and distance learning.
Intersections between the norm-trace curve and some low degree curves

In this paper we analyze the intersection between the norm-trace curve over Fq3 and the curves of the form y=ax3+bx2+cx+d, giving a complete characterization of the intersection between the curve and the parabolas (a=0), as well as sharp bounds for the other cases. This information is used for the determination of the weight distribution of some one-point AG codes arising from the curve.
Image processing for precise geometry determination

Reliable spatial information can be difficult to obtain in planetary remote sensing applications because of errors present in the metadata of images taken with space probes. We have designed a pipeline to address this problem on disk-resolved images of Jupiter's moon Europa taken with New Horizons' LOng Range Reconnaissance Imager, Galileo's Solid State Imager and Voyager's Imaging Science Subsystem. We correct for errors in the spacecraft position, pointing and the target's attitude by comparing them to the same reference. We also address ways to correct for distortion prior to any metadata consideration. Finally, we propose a vectorized method to efficiently project images pixels onto an elliptic target and compute the coordinates and geometry of observation at each intercept point.
Care Guide System for Caregivers of People with Dementia

In this paper, we introduce a care guide system for caregivers of People with Dementia (PwD) at home or care facility. The system is composed of context data manager, ontological model of caring PwD, and reasoning system that adaptively generates care guides in various circumstances. Caregivers can utilize the proposed system by managing care knowledge through graphical user interface or inquire a care guide through smartphone application for text-based chatting. Knowledge models implemented in the proposed system were evaluated by the experts in caring people with dementia.
Knocking on IPs: Identifying HTTPS Websites for Zero-Rated Traffic

Zero-rating is a technique where internet service providers (ISPs) allow consumers to utilize a specific website without charging their internet data plan. Implementing zero-rating requires an accurate website identification method that is also efficient and reliable to be applied on live network traffic. In this paper, we examine existing website identification methods with the objective of applying zero-rating. Furthermore, we demonstrate the ineffectiveness of these methods against modern encryption protocols such as Encrypted SNI and DNS over HTTPS and therefore show that ISPs are not able to maintain the current zero-rating approaches in the forthcoming future. To address this concern, we present “Open-Knock,” a novel approach that is capable of accurately identifying a zero-rated website, thwarts free-riding attacks, and is sustainable on the increasingly encrypted web. In addition, our approach does not require plaintext protocols or preprocessed fingerprints upfront. Finally, our experimental analysis unveils that we are able to convert each IP address to the correct domain name for each website in the Tranco top 6000 websites list with an accuracy of 50.5% and therefore outperform the current state-of-the-art approaches.
Quantitative Analysis of the Morphological Complexity of Malayalam Language

This paper presents a quantitative analysis on the morphological complexity of Malayalam language. Malayalam is a Dravidian language spoken in India, predominantly in the state of Kerala with about 38 million native speakers. Malayalam words undergo inflections, derivations and compounding leading to an infinitely extending lexicon. In this work, morphological complexity of Malayalam is quantitatively analyzed on a text corpus containing 8 million words. The analysis is based on the parameters type-token growth rate (TTGR), type-token ratio (TTR) and moving average type-token ratio (MATTR). The values of the parameters obtained in the current study is compared to that of the values of other morphologically complex languages.
On the Complexity of BWT-Runs Minimization via Alphabet Reordering

The Burrows-Wheeler Transform (BWT) has been an essential tool in text compression and indexing. First introduced in 1994, it went on to provide the backbone for the first encoding of the classic suffix tree data structure in space close to entropy-based lower bound. Within the last decade, it has seen its role further enhanced with the development of compact suffix trees in space proportional to r, the number of runs in the BWT. While r would superficially appear to be only a measure of space complexity, it is actually appearing increasingly often in the time complexity of new algorithms as well. This makes having the smallest value of r of growing importance. Interestingly, unlike other popular measures of compression, the parameter r is sensitive to the lexicographic ordering given to the text’s alphabet. Despite several past attempts to exploit this fact, a provably efficient algorithm for finding, or approximating, an alphabet ordering which minimizes r has been open for years. We help to explain this lack of progress by presenting the first set of results on the computational complexity of minimizing BWT-runs via alphabet reordering. We prove that the decision version of this problem is NP-complete and cannot be solved in time poly(n)⋅ 2^o(σ) unless the Exponential Time Hypothesis fails, where σ is the size of the alphabet and n is the length of the text. Moreover, we show that the optimization variant is APX-hard. In doing so, we relate two previously disparate topics: the optimal traveling salesperson path of a graph and the number of runs in the BWT of a text. In addition, by relating recent results in the field of dictionary compression, we illustrate that an arbitrary alphabet ordering provides an O(log² n)-approximation. Lastly, we provide an optimal linear-time algorithm for a more restricted problem of finding an optimal ordering on a subset of symbols (occurring only once) under ordering constraints.
A Comparative study using Machine Learning and Data Mining Approach for Leukemia

Leukemia is a type of blood cancer which impacts the white blood cells and damages the bone marrow. Usually the Complete Blood Count (CBC) and bone marrow get affected. It can be a fatal disease if not identified at the earliest level. In exercise, manual microscopic assessment of the sample slide is used for analysis of Leukemia. But manual diagnostic strategies are time taking, less in accuracy, and often have errors due to diverse human elements like pressure, fatigue, and so on. To retrieve the possible faults, errors and assisting the doctors, clustering and classification techniques are required. So, the authors have done rigorous analysis and study of many research articles based on Leukemia diseases interesting majorly in the classification algorithm. The prime objective of this review article is to analyse and compare techniques on Leukemia disease.
A Legendre Neural Network for Credit Card Fraud Detection

Every year heavy financial losses are caused due to fraudulent activities in credit card transactions. These losses can be reduced through the development of efficient fraud detection algorithm. However, most transaction data are highly non-stationary and have unbalanced class distributions. Hence, the design of efficient fraud detection algorithm is always a challenging task for the fraud investigators. In this paper, a credit card fraud detection (CCFD) framework is proposed using a Legendre neural network (LENN). The model is validated by analyzing its performance on two credit card datasets. Result analysis clearly illustrates the better performance of LENN compared to a Chebyshev functional link artificial neural network (CHFLANN), multilayer perceptron (MLP) and a decision tree (DT)-based fraud detection framework.
From Combined to Hybrid: Making Feedback-based AE even Smaller

In CHES 2017, Chakraborti et al. proposed COFB, a rate-1 sequential block cipher-based authenticated encryption (AE) with only 1.5n-bit state, where n denotes the block size. They used a novel approach, the so-called combined feedback, where each block cipher input has a combined effect of the previous block cipher output and the current plaintext block. In this paper, we first study the security of a general rate-1 feedback-based AE scheme in terms of its overall internal state size. For a large class of feedback functions, we show that the overlying AE scheme can be attacked in 2r queries if the internal state size is n + r bits for some r ≥ 0. This automatically shows that a birthday bound (i.e. 2n/2 queries) secure AE scheme must have at least 1.5n-bit state, whence COFB is almost-optimal (use 1.5n-bit state and provides security up to 2n/2/n queries). We propose a new feedback function, called the hybrid feedback or HyFB, which is a hybrid composition of plaintext and ciphertext feedbacks. HyFB has a key advantage of lower XOR counts over the combined feedback function. This essentially helps in reducing the hardware footprint. Based on HyFB we propose a new AE scheme, called HyENA, that achieves the state size, rate, and security of COFB. In addition, HyENA has significantly lower XOR counts as compared to COFB, whence it is expected to have a smaller implementation as compared to COFB.
Geostationary Operational Environmental Satellite-R series advanced baseline imagery artifacts

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion. Yet, there are rare times when visual artifacts, or anomalies, occur. Our study highlights and explains a number of these artifacts, some of which are traditional imagery defects for imagers such as striping and stray light, and colorfully named artifacts such as “caterpillar tracks” and “shark fins.” In addition, multiple resources are presented for more information about image quality and near-real-time image monitoring.
Bidirectional Attention Network for Monocular Depth Estimation

In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks. The structure of this mechanism derives from a strong conceptual foundation of neural machine translation, and presents a light-weight mechanism for adaptive control of computation similar to the dynamic nature of recurrent neural networks. We introduce bidirectional attention modules that utilize the feed-forward feature maps and incorporate the global context to filter out ambiguity. Extensive experiments reveal the high degree of capability of this bidirectional attention model over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets -- KITTI and DIODE. We show that our proposed approach either outperforms or performs at least on a par with the state-of-the-art monocular depth estimation methods with less memory and computational complexity.
Single particle entanglement of a massive relativistic particle: Dirac bispinors and spin 1/2 states

Abstract In this paper we study the single particle entanglement induced by a Lorentz boost. We described the particle as a solution of the free Dirac equation, a Dirac bispinor, and compare the induced momentum-spin entanglement with the result obtained in the widely considered framework of relativistic spin 1/2 states. The spin linear entropy for both approaches agree in the ultra relativistic limit. We also verify that the spin-momentum entanglement differers from the spin entropy for the bispinorial case, indicating a true multipartite entanglement involving the degrees of freedom of a Dirac bispinor state: momentum, spin and intrinsic parity. The fact that Dirac bispinors belongs to an irreducible representation of the complete Lorentz group, which also included parity as a symmetry, is the ultimate reason behind such non-trivial structure.
Implementing a Quantum Coin Scheme

Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. Quantum computing also has the power to support stronger more resistant technologies. In this paper, we describe a digital cash scheme created by Dmitry Gavinsky, which utilises the capability of quantum computing. We contribute by setting out the methods for implementing this scheme. For both the creation and verification of quantum coins we convert the algebraic steps into computing steps. As part of this, we describe the methods used to convert information stored on classical bits to information stored on quantum bits.
COGNITIVE AND METACOGNITIVE ASPECTS OF THE DEVELOPMENT OF LIFELONG LEARNING COMPETENCIES IN LAW STUDENTS

Lifelong learning is one of the main trends in educational and social policy in Europe, aimed at ensuring professional realization and social integrity of an individual. The article describes cognitive and metacognitive aspects of the formation of learning competence, as well as predisposition of law students to lifelong learning. A survey of 218 students and masters was conducted, data on the most popular and effective forms of knowledge acquisition (experience of formal, nonformal and informal learning over the past year), plans for further education and career development were collected. The dynamics of learning at different years of study is described. A group of students with pronounced learning and focus on lifelong learning (45% of the sample) was identified. A comparative analysis of two samples was carried out and qualities that could be considered markers of the LLL orientation were identified: a high level of metacognitive knowledge and metacognitive activity, reflective competence (at the cognitive, metacognitive and personal level), internal involvement in learning, focus on achieving professionalism and personal development, general self-efficacy, the prevalence of progressive and creative motives over consumer ones. Conclusions are drawn about the predominant role of metacognitions and personality determinants in the formation of lifelong learning competency among students. Reflection aimed at recognizing one’s own cognitive processes and understanding their contribution to personal development is the main mechanism for the formation of meta-cognitive abilities. The results obtained in the study determine the ways of pedagogical support and psychological intervention to ensure the development of lifelong learning competencies in different categories of students.
Multi-objective Magnitude-Based Pruning for Latency-Aware Deep Neural Network Compression

Layer-wise magnitude-based pruning is a popular method for Deep Neural Network (DNN) compression. It has the potential to reduce the latency for an inference made by a DNN by pruning connects in the network, which prompts the application of DNNs to tasks with real-time operation requirements, such as self-driving vehicles, video detection and tracking. However, previous methods mainly use the compression rate as a proxy for the latency, without explicitly accounting for latency in the training of the compressed network. This paper presents a new layer-wise magnitude-based pruning method, namely Multi-objective Magnitude-based Latency-Aware Pruning (MMLAP). MMLAP captures latency directly and incorporates a novel multi-objective evolutionary algorithm to optimize both accuracy of a DNN and its latency efficiency when designing compressed networks, i.e., when tuning hyper-parameters of LMP. Empirical studies show the competitiveness of MMLAP compared to well-established LMP methods and show the value of multi-objective optimization in yielding Pareto-optimal compressed networks in terms of accuracy and latency.
Actor-based risk analysis for blockchains in smart mobility

Blockchain technology is a crypto-based secure ledger for data storage and transfer through decentralized, trustless peer-to-peer systems. Despite its advantages, previous studies have shown that the technology is not completely secure against cyber attacks. Thus, it is crucial to perform domain specific risk analysis to measure how viable the attacks are on the system, their impact and consequently the risk exposure. Specifically, in this paper, we carry out an analysis in terms of quantifying the risk associated to an operational multi-layered Blockchain framework for Smart Mobility Data-markets (BSMD). We conduct an actor-based analysis to determine the impact of the attacks. The analysis identified five attack goals and five types of attackers that violate the security of the blockchain system. In the case study of the public-permissioned BSMD, we highlight the highest risk factors according to their impact on the victims in terms of monetary, privacy, integrity and trust. Four attack goals represent a risk in terms of economic losses and one attack goal contains many threats that represent a risk that is either unacceptable or undesirable.
Exploring Artificial Jabbering for Automatic Text Comprehension Question Generation

Many educational texts lack comprehension questions and authoring them consumes time and money. Thus, in this article, we ask ourselves to what extent artificial jabbering text generation systems can be used to generate textbook comprehension questions. Novel machine learning-based text generation systems jabber on a wide variety of topics with deceptively good performance. To expose the generated texts as such, one often has to understand the actual topic the systems jabbers about. Hence, confronting learners with generated texts may cause them to question their level of knowledge. We built a novel prototype that generates comprehension questions given arbitrary textbook passages. We discuss the strengths and weaknesses of the prototype quantitatively and qualitatively. While our prototype is not perfect, we provide evidence that such systems have great potential as question generators and identify the most promising starting points may leading to (semi) automated generators that support textbook authors and self-studying.
Teacher Perceptions of Training and Pedagogical Value of Cross-Reality and Sensor Data from Smart Buildings

This paper discusses elementary, and secondary (K-12) teachers’ perceptions of cross-reality (XR) tools for data visualization and use of sensor data from the built environment in classroom curricula. Our objective was to explore the use of sensor-informed XR in the built environment and civil engineering (BECE) field to support K-12 science, technology, engineering, and mathematics (STEM) experiential learning and foster BECE-related career awareness. We conducted surveys and informal questionnaires with 33 primary and secondary teachers attending an annual two-day university-based teacher professional development workshop as part of a statewide STEM afterschool program serving students in rural communities. We assessed teachers’ familiarity with, knowledge about, and appraisal of using cross-reality platforms and sensor data in classrooms and after school curricula. Findings show that, while all teachers reported relatively high interest in learning about sensor applications and innovative interactive techniques, middle school teachers in particular were most likely to see value in using these applications for teaching and learning. Implications for teacher professional development are discussed.