Reviewerly/exHarmony huggingface dataset:
RESEARCH OF IDENTITY DOCUMENTS AND OTHER DOCUMENTS THAT CONTAIN HIGH-TECH PROTECTION ELEMENTS

The article addresses issues relating to forensic and technical studies of newgeneration identity documents and other documents equipped with high-tech anticounterfeiting protection. In order to solve the expert problems in the forensic and technical examination of the new generation of documents, identity of the new generation, and other documents with elements of high-tech protection, it is necessary to adapt modern technical means to specific tasks. The challenge is to familiarize ourselves with the technology of the next generation of identity documents and other documents with high-tech protection elements and the introduction of appropriate additions into existing methods. Analysis of the main types of protection of printed products, namely new generation documents, identity documents, securities and other documents equipped with high-tech elements of protection against counterfeiting, shows that there are many different methods of protection. The most common among this is the attraction of appropriate printing technologies using mathematical methods, in particular, the creation of guilloche drawings with the formation of a graphic database and the development of software for computer synthesis of guilloche compositions and protective nets. The use of new elements of document protection requires the development of a new set of methods for their research and the proper modernization of existing methods. Expert research of documents includes a set of modern special equipment together with professional competence of expert and relevant information. The current level of high-tech development allows the creation of new means of protection of identity documents, such as passport and visa documents, plastic ID cards and other documents. The need to develop and implement new remedies for this category of documents is primarily due to the need to monitor interstate movement and take into account persons wanted or engaged in criminal activities. The article outlined in this article could serve as a basis for the creation and improvement of the methodology for the study of new-generation documents in order to improve the effectiveness of forensic research of documents.
Scalable Gaussian Process Regression for Time Series Modelling

Machine learning algorithms has its applications in almost all areas of our daily lives. This is mainly due to its ability to learn complex patterns and insights from massive datasets. With the inc ...
LibGuides: Marketing Research And Planning For Your Business: THE PLACE or Distribution Outlets

A guide introducing you to the basics of marketing and recommending which databases to use for effective research results.
Time-layered cultural map of Australia

This paper reports on an Australian project that is developing an onlinesystem to deliver researcher-driven national-scale infrastructure for the humanities, focused on mapping, time series, and data integration. Australian scholars and scholars of Australia worldwide are well served with digital resources and tools to deepen the understanding of Australia and its historical and cultural heritage. There are, however, significant barriers to use. The Time Layered Cultural Map of Australia (TLCMap) will provide an umbrella infrastructure related to time and space, helping to activate and draw together existing high-quality resources. TLCMap expands the use of Australian cultural and historical data for research through sharply defined and powerful discovery mechanisms. See https://tlcmap.newcastle.edu.au/.
HUBUNGAN ANTARA PENGHARGAAN DIRI DAN KEMAMPUAN MENULIS OLEH SISWA TAHUN PERTAMA DI SMAN 1 PEKANBARU

The objective of this research is to determine the correlation between self-esteem and writing ability of the first year students of SMAN 1 Pekanbaru. This is a correlational research that hastwo variables. The sample of this research is one class consisted of 36 students. The class was chosen by using Cluster Random Sampling Technique. Self-esteem questionnaire and writing test were used to collect the data. The self-esteem data were collected by using a set of questionnaire FLSES (Foreign Language Self-Esteem Scale) designed by Hassan (2001). The data of writing ability were taken by administaring test of writing. The collected data were analyzed by using Pearson Product Moment Correlation. The result shows that there is low correlation between students’ self-esteem and writing ability with the correlation value 0.312, while the coefficient correlation is as big as 9.7 %. The self-esteem of the students is categorized in “average” level and the writing ability is categorized in “good” level. It indicates that the students’ self-esteem influences their writing ability. Based on the finding, probably it would be better for the students to pay attantion on personality aspect, such as self-esteem in learning process of writing.
Evolving Meta-Level Reasoning with Reinforcement Learning and A* for Coordinated Multi-Agent Path-planning

This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm's ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.
A Characterization of Mean Squared Error for Estimator with Bagging

Bagging can significantly improve the generalization performance of unstable machine learning algorithms such as trees or neural networks. Though bagging is now widely used in practice and many empirical studies have explored its behavior, we still know little about the theoretical properties of bagged predictions. In this paper, we theoretically investigate how the bagging method can reduce the Mean Squared Error (MSE) when applied on a statistical estimator. First, we prove that for any estimator, increasing the number of bagged estimators $N$ in the average can only reduce the MSE. This intuitive result, observed empirically and discussed in the literature, has not yet been rigorously proved. Second, we focus on the standard estimator of variance called unbiased sample variance and we develop an exact analytical expression of the MSE for this estimator with bagging. This allows us to rigorously discuss the number of iterations $N$ and the batch size $m$ of the bagging method. From this expression, we state that only if the kurtosis of the distribution is greater than $\frac{3}{2}$, the MSE of the variance estimator can be reduced with bagging. This result is important because it demonstrates that for distribution with low kurtosis, bagging can only deteriorate the performance of a statistical prediction. Finally, we propose a novel general-purpose algorithm to estimate with high precision the variance of a sample.
Multirobot Coverage of Modular Environments

Multirobot systems for covering environments are increasingly used in applications like cleaning, industrial inspection, patrolling, and precision agriculture. The problem of covering a given environment using multiple robots can be naturally formulated and studied as a multi-Traveling Salesperson Problem (mTSP). In a mTSP, the environment is represented as a graph and the goal is to find tours (starting and ending at the same depot) for the robots in order to visit all the vertices with minimum global cost, which is typically calculated as the makespan, namely the length of the longest tour. The mTSP is an NP-hard problem for which several approximation algorithms have been proposed. These algorithms usually assume generic environments, but tighter approximation bounds can be reached focusing on specific environments. In this paper, we address the case of modular environments, namely of environments composed of sub-parts, called modules, that can be reached from each other only through some linking structures. Examples are multi-floor buildings, in which the modules are the floors and the linking structures are the staircases or the elevators, and floors of large hotels or hospitals, in which the modules are the rooms and the linking structures are the corridors. We focus on linear modular environments, with the modules organized sequentially, presenting an efficient (with polynomial worst-case time complexity) algorithm that finds a solution for the mTSP whose cost is within a bounded distance from the cost of the optimal solution. The main idea of our algorithm is to allocate disjoint blocks'' of adjacent modules to the robots, in such a way that each module is covered by only one robot. We experimentally compare our algorithm against some state-of-the-art algorithms for solving mTSPs in generic environments and show that it is able to provide solutions with lower makespan and spending a computing time several orders of magnitude shorter.
COVID-19 and the Law: A Compilation of Legal Resources

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Mixture of Discrete Normalizing Flows for Variational Inference

Advances in gradient-based inference have made distributional approximations for posterior distribution of latent-variable models easy, but only for continuous latent spaces. Models with discrete latent variables still require analytic marginalization, continuous relaxations, or specialized algorithms that are difficult to generalize already for minor variations of the model. Discrete normalizing flows could, in principle, be used as approximations while allowing efficient gradient-based learning, but as explained in this work they are not sufficiently expressive for representing realistic posterior distributions even for simple cases. We overcome this limitation by considering mixtures of discrete normalizing flows instead, and present a novel algorithm for modeling the posterior distribution of models with discrete latent variables, based on boosting variational inference.
Cultural Differences in Bias? Origin and Gender Bias in Pre-Trained German and French Word Embeddings.

Smart applications often rely on training data in form of text. If there is a bias in that training data, the decision of the applications might not be fair. Common training data has been shown to be biased towards different groups of minorities. However, there is no generic algorithm to determine the fairness of training data. One existing approach is to measure gender bias using word embeddings. Most research in this field has been dedicated to the English language. In this work, we identified that there is a bias towards gender and origin in both German and French word embeddings. In particular, we found that real-world bias and stereotypes from the 18th century are still included in today’s word embeddings. Furthermore, we show that the gender bias in German has a different form from English and there is indication that bias has cultural differences that need to be considered when analyzing texts and word embeddings in different languages.
Recovering Microsoft Azure Workloads to Another Azure Region

We have discussed Microsoft Azure’s platform capabilities to provide resiliency in previous chapters. We discussed storage, networks, VMs, and other services that are built from the ground up with capabilities to support various kinds of failures. Although applications deployed on Azure leveraging such services can benefit from their resiliency, they will not necessarily have default DR capabilities aligned to your RPO and RTO requirements.
Components of Passage

Music for solo instrument and electronics creates a special challenge in the concrete and vivid character of the live performer versus the abstract and fixed character of the speakers with their in ...
Data Curation Network: leveraging expertise to curate data at scale

May 2019 presentation at the International Association for Social Science Information Service & Technology Annual Conference in Sydney, Australia.
Approximating Text-To-Pattern Distance via Dimensionality Reduction

Text-to-pattern distance is a fundamental problem in string matching, where given a pattern of length m and a text of length n, over an integer alphabet, we are asked to compute the distance between pattern and the text at every location. The distance function can be e.g. Hamming distance or 𝓁_p distance for some parameter p > 0. Almost all state-of-the-art exact and approximate algorithms developed in the past ∼ 40 years were using FFT as a black-box. In this work we present 𝒪~(n/e²) time algorithms for (1±e)-approximation of 𝓁₂ distances, and 𝒪~(n/e³) algorithm for approximation of Hamming and 𝓁₁ distances, all without use of FFT. This is independent to the very recent development by Chan et al. [STOC 2020], where 𝒪(n/e²) algorithm for Hamming distances not using FFT was presented - although their algorithm is much more combinatorial, our techniques apply to other norms than Hamming.
Enumerating All Subgraphs Under Given Constraints Using Zero-Suppressed Sentential Decision Diagrams

Subgraph enumeration is a fundamental task in computer science. Since the number of subgraphs can be large, some enumeration algorithms exploit compressed representations for efficiency. One such representation is the Zero-suppressed Binary Decision Diagram (ZDD). ZDDs can represent the set of subgraphs compactly and support several poly-time queries, such as counting and random sampling. Researchers have proposed efficient algorithms to construct ZDDs representing the set of subgraphs under several constraints, which yield fruitful results in many applications. Recently, Zero-suppressed Sentential Decision Diagrams (ZSDDs) have been proposed as variants of ZDDs. ZSDDs can be smaller than ZDDs when representing the same set of subgraphs. However, efficient algorithms to construct ZSDDs are known only for specific types of subgraphs: matchings and paths.We propose a novel framework to construct ZSDDs representing sets of subgraphs under given constraints. Using our framework, we can construct ZSDDs representing several sets of subgraphs such as matchings, paths, cycles, and spanning trees. We show the bound of sizes of constructed ZSDDs by the branch-width of the input graph, which is smaller than that of ZDDs by the path-width. Experiments show that our methods can construct ZSDDs faster than ZDDs and that the constructed ZSDDs are smaller than ZDDs when representing the same set of subgraphs.
The Role of Local Language in Intercultural Communication among Societies of Buru Island

The cross-cultural communication often triggers the inter-community conflict. This research aimed at exploring the process and the function of the intercultural communication amongst societies in Buru Island. The subject of the study was the societies of Bassalale Village which has a diversity of the cultural background. This research employed a descriptive qualitative design. The researchers were applicable as the principal investigators (key instrument) which conduct the research process directly and actively in observing, interviewing, and gathering various materials relating to the intercultural communication among societies in Buru Island. To analyze the data, the researchers applied a model analysis by Miles and Huberman which consist of three elements; they are data reduction, display, and conclusion drawing. The results found that the role of Buru language in the intercultural communication process occurred through adjusting to other culture and trying to understand the otherâ€™s culture. The role of Buru language in intercultural communication are; a) social identity; b) social integration; c) cognitive, and d) escape function. Thus, the local language played an important role as a unifier of a multicultural society.
LibGuides: City View Library Programming Blog: Volunteer and Assessment

A guide to everything the City View Children's Area is planning for 2021! Created by Erin Redihan for INFO 260A Spring 2020
Hotet från cyberrymden : regeringens formulering av informations- och cybersäkerhet - vad är problemet?

This thesis examines the Swedish Government Cyber Security Strategy laid out for the years 2016-2022 using Carol Bacchi’s discourse method and theory concept “What’s the problem represented to be” ...
Artificial intelligence, robots and the law

[Product description] Artificial Intelligence, Robots and the Law is a call to students, lawyers, technologists, academics, regulators and others to engage with the issues raised by new developments in these technologies. The authors take a systematic approach to defining the field and the many terms of art that have come to distinguish it. By doing so, they add to the creation of a consistent common language through which all parties can converse. Theoretically rich, the book engages with socio-technical changes caused by developments in artificial intelligence and robotics, while identifying critical ethical and legal issues. It explains the application of law to new circumstances in areas such as tort, criminal and contract law, in addition to exploring the need for general law reform and technology-specific regulation. While written from an Australian perspective, the authors add relevant examples from other jurisdictions, recognising the global impact and boundary-free nature of artificial intelligence. The authors explore several theoretical frameworks to clarify regulatory approaches to new technologies. They explain the theoretical underpinnings of law and regulation through which they explore the most critical areas affected by this area, including automated decision-making, privacy, liability and insurance, competition and consumer law, and the future of work. The book is highly relevant to legal practitioners grappling with the law as it applies to these new technologies, regulators whose fields intersect with automated decision-making and robotics, and academics. It is invaluable for students seeking to understand the many legal and regulatory issues involved in this dynamic and challenging area. Features Comprehensive, wide-ranging and accessible discussion Examines the Australian legal position Covers both theory and practical application Relevant for both practitioners and students
Heterogeneous Double-Head Ensemble for Deep Metric Learning

The structure of a multi-head ensemble has been employed by many algorithms in various applications including deep metric learning. However, their structures have been empirically designed in a simple way such as using the same head structure, which leads to a limited ensemble effect due to lack of head diversity. In this paper, for an elaborate design of the multi-head ensemble structure, we establish design concepts based on three structural factors: designing the feature layer for extracting the ensemble-favorable feature vector, designing the shared part for memory savings, and designing the diverse multi-heads for performance improvement. Through rigorous evaluation of variants on the basis of the design concepts, we propose a heterogeneous double-head ensemble structure that drastically increases ensemble gain along with memory savings. In verifying experiments on image retrieval datasets, the proposed ensemble structure outperforms the state-of-the-art algorithms by margins of over 5.3%, 6.1%, 5.9%, and 1.8% in CUB-200, Car-196, SOP, and Inshop, respectively.
The Politeness of Language in The Interaction of Buying And Selling Airplane Tickets in Brint One Travel, Kolaka, Southeast Sulawesi

Politeness politeness is very important for the continuity of daily communication. Therefore, attention needs to be paid to in the form of research on the politeness of the language of various community groups. This study aims to describe the compliance and violation of the language politeness principle in the interaction of buying and selling airplane tickets at Brint One Travel Kolaka, Southeast Sulawesi. This study also aims to describe whether the seller applies the principle of politeness in the language of serving the buyer. This research uses descriptive qualitative research methods. Research data in the form of speech conversations between employees of Kolint Brint One Travel and buyers of airplane tickets. The research data were analyzed based on the principle of politeness in language according to Leech. The results showed that the obedience of the language politeness principle in the interaction of buying and selling airplane tickets at Brint One Travel Kolaka included the maxim of wisdom maxim, generosity maxim, appreciation maxim, humility maxim, compatibility maxim, and sympathy maxim. Violation of the principle of politeness in language in the interaction of buying and selling airplane tickets at Brint One Travel Kolaka includes violations of the maxim of wisdom and maxim of appreciation. Brint One Travel Kolaka employees apply the principle of politeness in serving the buyer and do not violate the politeness principle.
Сетевая идентичность и цифровой дуализм

The author analyzes the results of the current development of digital culture, significantly affecting the course of social change. The article deals with the phenomenon of network identity in the context of increasing mixing (convergence) of different levels of reality – online and offline. The author substantiates the difference between modern strategies for building network identity and those used in the early development of the Global Network. It is argued that the key reason for applying new approaches to network identity building is the growing transparency of the digital space.
The Effect of Implementation of 5S on Kaizen and Quality

This study aims to analyze the effect of implementation of 5S on Quality, the effect of implementing implementation of 5S on Kaizen and the effect of Kaizen on Quality in this case is Out-of-Pit Overburden Crusher dan Conveyor system (OPCC) Coal Mining Industry.Analysis of the effect of Implementation of 5S and Kaizen on site operation area in 2018.By using data collection methods in the form of library studies and questionnaires to 220 respondents.The method of data analysis in this study uses Structural Equation Modeling (SEM).The results showed that implementation of 5S had a significant and positive effect on Quality with the strongest relationship of variables explained by the indicators of "Productivity Level" and Kaizen with the strongest relationship of variables explained by the indicator "The scope of internal improvement of the department and or section (group)".And Kaizen has a significant and positive effect on Quality with the strongest relationship of variables explained by the indicator "Identification and classification of material (required and not required)".