Reviewerly/exHarmony huggingface dataset:
On the design of fixed-gain tracking filters by pole placement: Or an introduction to applied signals-and-systems theory for engineers

The Kalman filter computes the optimal variable-gain using prior knowledge of the initial state and random (process and measurement) noise distributions, which are assumed to be Gaussian with known variance. However, when these distributions are unknown, the Kalman filter is not necessarily optimal and other simpler state-estimators, such as fixed-gain ({\alpha}, {\alpha}-\b{eta} or {\alpha}-\b{eta}-{\gamma} etc.) filters may be sufficient. When such filters are used as low-complexity state-estimators in embedded tracking systems, the fixed gain parameters are usually set equal to the steady-state gains of the corresponding Kalman filter. An alternative procedure, that does not rely prior distributions, based on Luenberger observers, is presented here. It is suggested that the arbitrary placement of closed-loop state-observer poles is a simple and intuitive way of tuning the transient and steady-state response of a fixed-gain tracking filter when prior distributions are unknown. All poles are placed inside the unit circle on the positive real axis of the complex z-plane at p for a well damped response and a configurable bandwidth. Transient bias errors, e.g. due to target manoeuvres or process modelling errors, decrease as p=0 is approached for a wider bandwidth. Steady-state random errors, e.g. due to sensor noise, decrease as p=1 is approached for a narrower bandwidth. Thus the p parameter (with 0<p<1) may be interpreted as a dimensionless smoothing factor. This tutorial-style report examines state-observer design by pole placement, which is a standard procedure for feedback controls but unusual for tracking filters, due to the success and popularity of the Kalman filter. As Bayesian trackers are designed via statistical modelling, not by pole-zero placement in the complex plane, the underlying principles of linear time-invariant signals and systems are also reviewed.
Learning Parametrised Graph Shift Operators

In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represented using a graph shift operator (GSO) with the most common choices being the adjacency, Laplacian matrices and their normalisations. In this paper, a novel parametrised GSO (PGSO) is proposed, where specific parameter values result in the most commonly used GSOs and message-passing operators in graph neural network (GNN) frameworks. The PGSO is suggested as a replacement of the standard GSOs that are used in state-of-the-art GNN architectures and the optimisation of the PGSO parameters is seamlessly included in the model training. It is proved that the PGSO has real eigenvalues and a set of real eigenvectors independent of the parameter values and spectral bounds on the PGSO are derived. PGSO parameters are shown to adapt to the sparsity of the graph structure in a study on stochastic blockmodel networks, where they are found to automatically replicate the GSO regularisation found in the literature. On several real-world datasets the accuracy of state-of-the-art GNN architectures is improved by the inclusion of the PGSO in both node- and graph-classification tasks.
Scalable computation of predictive probabilities in probit models with Gaussian process priors

Predictive models for binary data are fundamental in various fields, and the growing complexity of modern applications has motivated several flexible specifications for modeling the relationship between the observed predictors and the binary responses. A widely-implemented solution is to express the probability parameter via a probit mapping of a Gaussian process indexed by predictors. However, unlike for continuous settings, there is a lack of closed-form results for predictive distributions in binary models with Gaussian process priors. Markov chain Monte Carlo methods and approximation strategies provide common solutions to this problem, but state-of-the-art algorithms are either computationally intractable or inaccurate in moderate-to-high dimensions. In this article, we aim to cover this gap by deriving closed-form expressions for the predictive probabilities in probit Gaussian processes that rely either on cumulative distribution functions of multivariate Gaussians or on functionals of multivariate truncated normals. To evaluate these quantities we develop novel scalable solutions based on tile-low-rank Monte Carlo methods for computing multivariate Gaussian probabilities, and on mean-field variational approximations of multivariate truncated normals. Closed-form expressions for the marginal likelihood and for the posterior distribution of the Gaussian process are also discussed. As shown in simulated and real-world empirical studies, the proposed methods scale to dimensions where state-of-the-art solutions are impractical.
Centralized vs Decentralized Targeted Brute-Force Attacks: Guessing with Side-Information

According to recent empirical studies, a majority of users have the same, or very similar, passwords across multiple password-secured online services. This practice can have disastrous consequences, as one password being compromised puts all the other accounts at much higher risk. Generally, an adversary may use any side-information he/she possesses about the user, be it demographic information, password reuse on a previously compromised account, or any other relevant information to devise a better brute-force strategy (so called targeted attack). In this work, we consider a distributed brute-force attack scenario in which $m$ adversaries, each observing some side information, attempt breaching a password secured system. We compare two strategies: an uncoordinated attack in which the adversaries query the system based on their own side-information until they find the correct password, and a fully coordinated attack in which the adversaries pool their side-information and query the system together. For passwords $\mathbf{X}$ of length $n$, generated independently and identically from a distribution $P_X$, we establish an asymptotic closed-form expression for the uncoordinated and coordinated strategies when the side-information $\mathbf{Y}_{(m)}$ are generated independently from passing $\mathbf{X}$ through a memoryless channel $P_{Y|X}$, as the length of the password $n$ goes to infinity. We illustrate our results for binary symmetric channels and binary erasure channels, two families of side-information channels which model password reuse. We demonstrate that two coordinated agents perform asymptotically better than any finite number of uncoordinated agents for these channels, meaning that sharing side-information is very valuable in distributed attacks.
DPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees

We propose a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form. At the center of our framework are project-join trees, which specify efficient project-join orders to apply additive projections (variable eliminations) and joins (clause multiplications). In this framework, model counting is performed in two phases. First, the planning phase constructs a project-join tree from a formula. Second, the execution phase computes the model count of the formula, employing dynamic programming as guided by the project-join tree. We empirically evaluate various methods for the planning phase and compare constraint-satisfaction heuristics with tree-decomposition tools. We also investigate the performance of different data structures for the execution phase and compare algebraic decision diagrams with tensors. We show that our dynamic-programming model-counting framework DPMC is competitive with the state-of-the-art exact weighted model counters cachet, c2d, d4, and miniC2D.
Structured (De)composable Representations Trained with Neural Networks

The paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing to decompose the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data).
Learning Inconsistent Preferences with Gaussian Processes

We revisit widely used preferential Gaussian processes by Chu et al.(2005) and challenge their modelling assumption that imposes rankability of data items via latent utility function values. We propose a generalisation of pgp which can capture more expressive latent preferential structures in the data and thus be used to model inconsistent preferences, i.e. where transitivity is violated, or to discover clusters of comparable items via spectral decomposition of the learned preference functions. We also consider the properties of associated covariance kernel functions and its reproducing kernel Hilbert Space (RKHS), giving a simple construction that satisfies universality in the space of preference functions. Finally, we provide an extensive set of numerical experiments on simulated and real-world datasets showcasing the competitiveness of our proposed method with state-of-the-art. Our experimental findings support the conjecture that violations of rankability are ubiquitous in real-world preferential data.
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the same time. Simultaneously, novel machine learning algorithms have boosted the performance of image analysis approaches. In this paper, we focus on a particularly powerful class of architectures, called Generative Adversarial Networks (GANs), applied to histological image data. Besides improving performance, GANs also enable application scenarios in this field, which were previously intractable. However, GANs could exhibit a potential for introducing bias. Hereby, we summarize the recent state-of-the-art developments in a generalizing notation, present the main applications of GANs and give an outlook of some chosen promising approaches and their possible future applications. In addition, we identify currently unavailable methods with potential for future applications.
Recognizing Single-Peaked Preferences on an Arbitrary Graph: Complexity and Algorithms

This paper is devoted to a study of single-peakedness on arbitrary graphs. Given a collection of preferences (rankings of a set of alternatives), we aim at determining a connected graph G on which the preferences are single-peaked, in the sense that all the preferences are traversals of G. Note that a collection of preferences is always single-peaked on the complete graph. We propose an Integer Linear Programming formulation (ILP) of the problem of minimizing the number of edges in G or the maximum degree of a vertex in G. We prove that both problems are NP-hard in the general case. However, we show that if the optimal number of edges is m-1 (where m is the number of candidates) then any optimal solution of the ILP is integer and thus the integrality constraints can be relaxed. This provides an alternative proof of the polynomial-time complexity of recognizing single-peaked preferences on a tree. We prove the same result for the case of a path (an axis), providing here also an alternative proof of polynomiality of the recognition problem. Furthermore, we provide a polynomial-time procedure to recognize single-peaked preferences on a pseudotree (a connected graph that contains at most one cycle). We also give some experimental results, both on real and synthetic datasets.
Enterprise API Security and GDPR Compliance: Design and Implementation Perspective

With the advancements in the enterprise-level business development, the demand for new applications and services is overwhelming. For the development and delivery of such applications and services, enterprise businesses rely on Application Programming Interfaces (APIs). In essence, API is a double-edged sword. On one hand, API provides ease of expanding the business through sharing value and utility, but on another hand it raises security and privacy issues. Since the applications usually use APIs to retrieve important data, therefore it is extremely important to make sure that an effective access control and security mechanism are in place , and the data does not fall into wrong hands. In this article, we discuss the current state of the enterprise API security and the role of Machine Learning (ML) in API security. We also discuss the General Data Protection Regulation (GDPR) compliance and its effect on the API security.
The Capacity of Quantum Neural Networks

A key open question in quantum computation is what advantages quantum neural networks (QNNs) may have over classical neural networks (NNs), and in what situations these advantages may transpire. Here we address this question by studying the memory capacity $C$ of QNNs, which is a metric of the expressive power of a QNN that we have adapted from classical NN theory. We present a capacity inequality showing that the capacity of a QNN is bounded by the information $W$ that can be trained into its parameters: $C \leq W$. One consequence of this bound is that QNNs that are parameterized classically do not show an advantage in capacity over classical NNs having an equal number of parameters. However, QNNs that are parametrized with quantum states could have exponentially larger capacities. We illustrate our theoretical results with numerical experiments by simulating a particular QNN based on a Gaussian Boson Sampler. We also study the influence of sampling due to wavefunction collapse during operation of the QNN, and provide an analytical expression connecting the capacity to the number of times the quantum system is measured.
PEMBINAAN KEMAHIRAN BERBAHASA INGGRIS PROFESI DAN LITERASI HUKUM KOMUNIKASI BAGI GURU DAN SISWA DI MTS MANBAA’UL NURUL HIDAYAH BATAM

The ability to speak English is one of the skills that must be possessed by students as their expertise that can be used in the field of work and is also important for teachers to teach and practice in the learning process. In the world of work, children who have graduated are required to have skills in communicating in English, and communication students are also expected to understand communication literacy. Many are good at speaking English but sometimes lack direction and lack of knowledge about communication literacy. On this occasion, the lecturers of Putera Batam University held a Coaching for Students and Teachers on Profession English Proficiency and Communication Law Literacy for Teachers and Students at MTs Manbaa'ul Nurul Hidayah Batam. The method used is by collecting data from observations, questionnaires and group discussions. The result of this service is that participants already understand English and communication legal literacy. There are 60% of the participants who have practiced English and communication law literacy.
Automated Quantitative Mapping of Ore Minerals by Multispectral Reflected-light Microscopy

ABSTRACT An innovative multispectral reflected-light microscopy system is able to automatically identify ore minerals by measuring their specular reflectance with non-polarized light in a number of spectral bands, and comparing the values obtained with a reference database. In this way it can provide quantitative mineralogical mapping comparable to that obtained from automated mineralogy systems based on SEM-EDS (Scanning Electron Microscopy with Energy-Dispersive X-Ray Spectroscopy), at a fraction of the cost and with less stringent environmental and operational requirements. The new system, called AMCO (for Automated Microscopic Characterization of Ores), consists of an instrument and two proprietary software applications: amcoCapture, to acquire multispectral images of a polished section prepared from an ore sample, and amcoAnalysis, to process these images and extract different types of mineralogical information from them.
Application of specialized DFA for time series forecasting

In this paper the possibility of using deterministic finite automaton to forecast time series in real time is considered. To solve this problem, a modification of the automaton with 10 reading heads designed to recognize multilinear sequences is proposed. The article presents modifications of this automaton, algorithms for their implementation, and demonstrates the results for various time series. In addition, the paper presents a method for changing the algorithm of the automaton ensuring its step-by-step execution.
Unmixing Mineral Phases, Improving Quantification: Use Machine Learning to Understand Deep-Mantle with STEM-EDS Data

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DEVELOPMENT OF AN AUTOMATED BIBLIOGRAPHIC SYSTEM

Most research and higher education institutions of Ukraine are funded by the state. Almost all research is conducted mainly in the public sector, while private universities and companies usually do not conduct any research. Public higher education institutions are financed from the state budget and lack of funds is one of the main factors hindering the introduction of new information technologies and systems into the daily routine. Analysis of scientific achievements using bibliographic information has become a significant part of scientific and technical management in many countries around the world, but unlike Slovenia and Poland, Ukraine does not have a single bibliographic database and relevant search interfaces. Therefore, the aim of the work is to develop an automated bibliographic system that will simplify the process of creating, editing, searching, sorting, statistics, deleting bibliographic information of research and teaching staff and will automate the formation of personalized reports. To implement the proposed automated system, a review of existing databases of bibliographic information and search interfaces was conducted. It is concluded that they cannot provide Ukrainian scientists with the necessary functionality to form bibliographic descriptions of their own research papers. To design the architecture of a mobile application based on the Android operating system, a multi-level architecture class and an MVVM template were selected. Designed architecture contains three levels and five layers on which separate subsystems are located. The Pixel emulator and Xiaomi Redmi 5 Plus smartphone were used to test the application on different screen sizes, as well as on different versions of the software interface. The application was developed in the AndroidStudio software environment. The design of the entered data according to two standards was implemented, namely: DSTU 8302-2015 and IEEE. Algorithms for searching and sorting records according to the necessary criteria are implemented. Saving the list of all necessary bibliographic descriptions in text format is implemented. Additionally, the possibility of sending it to e-mail is implemented. In the future it is planned to expand the list of standards for bibliographic information and improve search and sorting algorithms.
Usage of Digital Technologies in the Volyn Regional Children’s Library Activity

The purpose of the article is to identify and reveal the introduction and improvement essence of the automated library information system “Mark-SQL” in the Volyn Regional Library for Children, to study the rational use of digital technologies and library resources. The research methodology is based on the application of a systematic approach as a general scientific principle for computerization of library processes to find potential users through all possible channels of communication (directly among children). The scientific novelty of the research is in the fact that the article has further studied and developed innovative approaches to the expanded introduction of the latest library information technologies in library practice. Also, the concepts of “web-lesson” and “professional guidance project” are revealed and analyzed. Conclusions. The study focuses on the transformation of forms and methods of providing modern, high-quality library services as well as remote work with the usage of digital technologies, by using which the library, especially in times of a crisis, is looking for a new paradigm. There is a trend for the rapid growth of libraries’ electronic resources, which need not only to be accumulated, but also to be stored, used, and promoted among users for educational and training purposes. To promote the image of libraries among the younger generation, the communication campaigns aimed at students are introduced to make them understand the role of reading in the development of critical and creative thinking as well as to involve them in the usage of IT technologies. It was found that the use of automated technologies allows revealing modern innovative forms of library activities that make visitors return to literature, and social values, and help to develop, through children, the interest in books and library among parents, who, in most cases, have lost such interest.
The Development of Electronic-Worksheets (E-Worksheets) Based on Crosswords Games with Sub-Materials of The Excretory System to Improve The Motivation of High School Students

Education in the 4.0 industrial revolution era has a curriculum that utilizes the internet of things (IoT) as a source or media in the learning process. Learning media that support is e-worksheet with crosswords games that will help students be motivated in the learning process. This study aims to develop a valid and practical e-worksheet based on crossword games with sub-materials Excretory System to improve learning motivation of students. This study is development research that uses the ASSURE model with steps: Analyze learners, State objectives, Select method, media, and materials, Utilize materials, Require learners’ performance and Evaluate/Revise. The e-worksheet research trial included 20 students of XI grades from Muhammadiyah 10 Gresik. Validity was determined by material experts, media experts, and a biology teacher at SMA Muhammadiyah 10 Gresik. The practicality of the e-worksheet was determined using students’ response questionnaire, students’ motivation questionnaire before and after using the product, implementation of motivation, and student activities. Validity and practicality data were analyzed using the descriptive quantitative. The result showed that the developed e-worksheet obtained an average validity percentage of 98.14% with very valid criteria. The practical e-worksheet obtained 93.5% of students’ responses, the increase in students’ motivation improvement from 60.1% to 86.8%, and 91.67% observation motivations implementation. The data that is received shows that e-worksheet based on crossword games are very valid and practical.&#x0D; Keywords: e-Worksheet, Crosswords, Excretory System, Motivation