Reviewerly/exHarmony huggingface dataset:
Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation

Commercial Automatic Speech Recognition (ASR) systems tend to show systemic predictive bias for marginalised speaker/user groups. We highlight the need for an interdisciplinary and context-sensitive approach to documenting this bias incorporating perspectives and methods from sociolinguistics, speech & language technology and human-computer interaction in the context of a case study. We argue evaluation of ASR systems should be disaggregated by speaker group, include qualitative error analysis, and consider user experience in a broader sociolinguistic and social context.
Locality Preserving Loss: Neighbors that Live together, Align together

We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations. Given two pretrained embedding manifolds, LPL optimizes a model to project an embedding and maintain its local neighborhood while aligning one manifold to another. This reduces the overall size of the dataset required to align the two in tasks such as crosslingual word alignment. We show that the LPL-based alignment between input vector spaces acts as a regularizer, leading to better and consistent accuracy than the baseline, especially when the size of the training set is small. We demonstrate the effectiveness of LPL-optimized alignment on semantic text similarity (STS), natural language inference (SNLI), multi-genre language inference (MNLI) and cross-lingual word alignment (CLA) showing consistent improvements, finding up to 16% improvement over our baseline in lower resource settings.
A Natural Representation of Functions that Facilitates `Exact Learning'

We present a collection of mathematical tools and emphasise a fundamental representation of analytic functions. Connecting these concepts leads to a framework for `exact learning', where an unknown numeric distribution could in principle be assigned an exact mathematical description. This is a new perspective on machine learning with potential applications in all domains of the mathematical sciences and the generalised representations presented here have not yet been widely considered in the context ofmachine learning and data analysis. The moments of a multivariate function or distribution are extracted using a Mellin transform and the generalised form of the coefficients is trained assuming a highly generalised Mellin-Barnes integral representation. The fit functions use many fewer parameters contemporary machine learning methods and any implementation that connects these concepts successfully will likely carry across to non-exact problems and provide approximate solutions. We compare the equations for theexact learning method with those for a neural network which leads to a new perspective on understanding what a neural network may be learning and how to interpret the parameters of those networks.
LibGuides: Early Childhood Education: Open Access

All information, knowledge and resources about early childhood education open access resources and journals and databases
A Study of the Search and Seizure Procedure for Remote Servers through the Practical Affairs

우리 형사소송법은 과학기술의 발달에 따른 환경변화에 적응하고자 여러 차례 개정을 통하여 디지털 시대의 흐름에 대응하여 왔다. 기본적으로 1999년 디지털 문건에 대하여 전문법칙을 적용함으로써 최초로 디지털 증거에 대한 논의가 시작되었다. 그 후 디지털 증거에 대한 진정성 입증을 위한 요건으로 해시값 확인, 포렌식 전문가에 의한 증언, 영상으로 확인 등 여러 객관적 방법을 제시하였다. 하지만 과학기술은 매일매일 발전하고 있는 상황에서 새로운 기술과 새로운 형태의 디지털 증거가 법정에 나오게 되면서 수사기관의 입장에서는 적법한 증거로서 인정받기 위한 부담이 발생하였다. 즉 정보통신기술의 발달로 디지털 증거들은 네트워크로 연결된 모든 곳으로 송수신이 될 뿐만 아니라 현재의 서버장치가 아닌 제3의 원격지에 소재하고 있는 서버에도 저장될 수 있는 디지털 환경이 구축되어있다. 수사기관은 원격지 서버에 저장되어 있는 범죄사실 관련 정보를 압수·수색하기 위해서는 영장에 장소 및 대상을 특정해야하는 부담을 안고 있으며, 더 나아가 국제관할권의 문제가 발생할 수도 있는 상황에 직면하고 있다. 이러한 상황에서 원격지 서버에 저장된 이메일 정보를 수집하기 위한 수사기관의 수사절차에 대하여 상반된 의견을 제시한 법원 판결에 주목을 하게 되었고, 법원이 유사한 사안에 대하여 서로 다른 의견을 제시한 원인으로 수사기관의 원격지 서버에 대한 압수·수색절차의 부재라고 생각하였다. 현행 형사소송법 및 각 수사기관의 디지털 증거 압수·수색 규정은 추상적일 뿐만 아니라 수가기관에 따라서도 그 절차가 상이하기 때문에 법원의 입장에서는 어떠한 기준으로 수사기관의 디지털 포렌식 절차의 적법성을 판단해야 할 지에 대한 명확한 기준이 없어 상반된 판단이 나온 것이라 생각된다. 따라서 본 연구에서는 동일 쟁점으로 상반된 판결을 한 두 사건으로부터 출발하여, 현행 법률과 판례에서 제시하고 있는 기준에 부합하면서 실무적으로도 도움이 될 수 있는 절차를 제언하고자 한다.Korean Criminal Procedure Act has responded to the trend of the digital age by revising it several times to adapt to changes in the environment in response to the development of scientific technology. However, as new technologies and new forms of digital evidence emerged in court amid the daily development of science and technology, the investigative agencies have been pressured to be recognized as legitimate evidence. In other words, with the development of information and communication technology, digital evidence is not only sent to and from any network connection, but also digital environments that can be stored on servers that are located at a third remote location instead of the current server device. Investigators are under pressure to specify a location and target for a warrant in order to confiscate and retrieve criminal information stored on remote servers, and are even facing a situation where international jurisdiction may arise. Under these circumstances, the court s decision, which suggested conflicting opinions on the investigation procedures of the investigative agency to collect e-mail information stored on the remote site server, was considered to be the absence of seizure or search procedures for the remote location servers of the investigative agency as the cause of the court s different opinions on similar cases. As the current Criminal Procedure Act and the regulations on the confiscation and search of digital evidence by each investigative agency are abstract and the procedures vary depending on the number of agencies, it is believed that the court did not have a clear standard on which criteria to judge the validity of the investigation agency s digital forensics procedure.
Accuracy in Parallel Dynamic Task Allocation for Multi-Robot Systems Under Fuzzy Environment

Making the right decision is an essential requirement for the task allocation process in multi-robot systems functioning in dynamic environments. Robots are often forced to make these decisions individually without any communication between them. It may be due to reasons related to uncertainty in environments or related to tasks security, such as military applications. However, robot decisions must be precise in order to increase their efficiency to perform complex tasks. This paper presents a model in which a criterion of accuracy in tasks allocation process in an uncertain environment is defined. In order to increase this precision in such environments, the robots will formulate their observations in terms of the fuzzy linguistic variables. These variables are used by a fuzzy inference system to determine a utility value of a task that most effectively increases accuracy in task allocation. Simulation results on a complex task of goods transportation by mobile robots are presented to demonstrate the effectiveness of this model.
Pengembangan Aplikasi Perpustakaan Berbasis Desktop di SD Negeri 018 Tarakan Tahun 2020

The presence of a library is an important factor to support education in school communities, especially teachers and students. SD Negeri 018 Tarakan already has a library whose management is still done semi computer, namely recording borrowing and returning book transactions using manual records on a sheet of paper, while book collection data is stored in the Microsoft Excel 2007 database. In processing library data using Microsoft Excel 2007 applications, it is quite difficult for officers , because it is still possible for errors to enter the book collection data, transaction data for borrowing / returning books, member data, data making and presenting transaction reports. To overcome this problem, it is necessary to develop a computerized library application. The software process model used is Linear or Waterfall Sequential Process models. This model proposes an approach to the development of systematic and sequential software starting from the level and progress of the system at all stages of analysis, design, code, and testing. The software produced is the Library Application (APES) of SD Negeri 018 Tarakan. APES was built using the Microsoft Visual FoxPro 9.0 programming language. The DBMS (Database Management System) used is MySQL 5.1.41. Testing of software is done by the black-box method. The software produced in addition to the process of adding, changing, searching, deleting, and printing library data reports. This application is expected to assist officers in processing library data and making reports.
Research on Text Classification Algorithm Based on BiLSTM-WSAttention

BiLSTM has been widely used in the field of text classification, but the model still cannot accurately measure the importance of each word and cannot extract text features more effectively. In order to solve this problem, this paper proposes a BiLSTM-WSAttention model. The neural network model of BiLSTM-WSAttention is used to classify text. The BiLSTM-WSAttention neural network model combines the context of words and sentences, and extracts contextual semantic information from two perspectives: front to back and back to front. At the same time, this article introduces an attention mechanism. Since text is composed of sentences, sentences are composed of words, and the importance of words and sentences depends on context information, so this article includes word-level attention mechanisms and sentence-level. The attention mechanism assigns different weight values to different words and sentences. Finally, the method proposed in this article is compared with the classification methods of Naive-Bayes, CNN, RNN, and BLSTM on the same data set. The experimental results show that: Compared with other classification methods, the neural network model BiLSTM-WSAttention proposed in this article is effective on this data set.
A Hierarchy of Algebras for Boolean Subsets.

We present a collection of axiom systems for the construction of Boolean subalgebras of larger overall algebras. The subalgebras are defined as the range of a complement-like operation on a semilattice. This technique has been used, for example, with the antidomain operation, dynamic negation and Stone algebras. We present a common ground for these constructions based on a new equational axiomatisation of Boolean algebras. All results are formally proved in Isabelle/HOL.
Remittances and Financial Inclusion: Does Financial Development Matter?

This study provides new evidence on the relationship between remittances and financial inclusion using generalized methods of moments (GMM) and panel threshold regressions (PTR). The sample consists of 64 developing countries over the period 2004-2017. The empirical results suggest that remittances improve financial inclusion, and the relationship between both variables is nonlinear, with respect to financial development. These findings are supported by PTR estimations, that confirm positive and significant relationship between remittances and financial inclusion below a given threshold of financial development. Beyond this threshold, remittances and financial inclusion nexus is not significant. These results have some policy implications.
From the Research approach in teaching to the lessons of historical Memory.

The article presents a brief methodological and methodological justification of the research approach in teaching as a technological basis of the Federal State Educational Standard of General Education. The article reveals the instrumental possibilities of the research approach in teaching to ensure project activities in the educational space of a modern school. The metasubject and polyfunctionality of the research approach are actualized, through which the developing nature of training is practically ensured and metasubject connections of educational and extracurricular activities are realized at all levels of general education. The research approach in teaching shows how the technology of forming the research culture of students in a modern school. Correlation of lessons of historical memory and project activity of students as the most important component of the research approach in training is proved. The presentation of pedagogical experience in studying the lessons of historical memory through a research approach in teaching MBOU "V. I. Peller Center of Education" of Birobidzhan district, Jewish Autonomous Region was carried out. Also in the article, the mentor reflects the experience of implementing project activities, presented in the framework of the scientific and practical conference " 85 years of the EAO. Ethnography in socio-humanitarian, natural-science education: essence, technology, prospects", organized by the education Committee of the Jewish Autonomous region, Institute of qualification increasing of pedagogical workers, Institute for complex analysis of regional problems Feb RAS, public organization "the Heritage of the Jewish Autonomous region". The article also presents a research paper that shows the result of project activities. The choice of this topic is due to the fact that the school c. The birdhouse of the Birobidzhan district of the EAO was named after Vladimir Izrailevich Peller, a full knight of the Order of Glory, and the school received the status of the MBOU "V. I. Peller Education Center". The fate of the legendary countryman became the subject of research. Official website of MBOU "V. I. Peller Center of Education" http://ptichnik.com.
Humanoid Robots for Contract Visualisation

This paper challenges two assumptions made by most lawyers: first, that contracts should consist of words alone; second, that only human beings are capable of designing the “look and feel” of contracts. These assumptions amount to taboos – even in today’s digital world. Humanoid robots for contract visualisation would or rather will break these taboos. Contract visualisation constitutes a fledgling subject concerning various fields of law (e.g. visual law, legal design, contract law, legal theory and EU law). This topic needs to be explored from different perspectives. Although humanoid robots are being increasingly implemented in the legal context, their potential for contract visualisation has not yet been investigated. This paper therefore discusses contract visualisation and how humanoid robots might use visuals of the Contract Design Pattern Library presented by the International Association for Contract & Commercial Management (IACCM). The findings prompt discussion about whether and, if so, how to communicate legally with those anthropomorphic machines. Or even more specifically, about whether and, if so, how humanoid robots might best represent contracts visually and communicate these both to humans and to other humanoid robots.
Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation

Achieving satisfying performance in machine translation on domains for which there is no training data is challenging. Traditional supervised domain adaptation is not suitable for addressing such zero-resource domains because it relies on in-domain parallel data. We show that when in-domain parallel data is not available, access to document-level context enables better capturing of domain generalities compared to only having access to a single sentence. Having access to more information provides a more reliable domain estimation. We present two document-level Transformer models which are capable of using large context sizes and we compare these models against strong Transformer baselines. We obtain improvements for the two zero-resource domains we study. We additionally provide an analysis where we vary the amount of context and look at the case where in-domain data is available.
Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems

Recent advances in neuroscience have revealed many principles about neural processing. In particular, many biological systems were found to reconfigure/recruit single neurons to generate multiple kinds of decisions. Such findings have the potential to advance our understanding of the design and optimization process of artificial neural networks. Previous work demonstrated that dense neural networks are needed to shape complex decision surfaces required for AI-level recognition tasks. We investigate the ability to model high dimensional recognition problems using single or several neurons networks that are relatively easier to train. By employing three datasets, we test the use of a population of single neuron networks in performing multi-class recognition tasks. Surprisingly, we find that sparse networks can be as efficient as dense networks in both binary and multi-class tasks. Moreover, single neuron networks demonstrate superior performance in binary classification scheme and competing results when combined for multi-class recognition.
Computationally Efficient Wasserstein Loss for Structured Labels

The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation, emotion analysis, and semantic segmentation.We propose a tree-Wasserstein distance regularized LDL algorithm, focusing on hierarchical text classification tasks.We propose predicting the entire label hierarchy using neural networks, where the similarity between predicted and true labels is measured using the tree-Wasserstein distance.Through experiments using synthetic and real-world datasets, we demonstrate that the proposed method successfully considers the structure of labels during training, and it compares favorably with the Sinkhorn algorithm in terms of computation time and memory usage.
Environmental Informatics: Educational Opportunities at Bachelors Level — International Context and Indian Potentialities

Environmental Informatics is an interdisciplinary subject and it is the combination of Environment related subjects and Informatics related subjects. It is the utilization and applications of Informatics and IT principles and tools in Environmental Management, Environmental Monitoring, and Science. In other words, it is the practice of Sustainable Computing and Information Technology. Environmental Informatics is responsible for the applications of Information Technology in Environment, Nature and various allied subjects and areas like Geology, Agriculture, Forestry, Geography, Climatology, Oceanography and all these are valuable stakeholders of Environmental Informatics. Ecology Informatics in some contexts is related with Eco Informatics. Environmental Informatics is broader than Environmental Information Technology; as interconnects both environmental as well as information science for the complete natural processes with language common to both humans and computers. Internationally Environmental Informatics has become a field of study in many universities. Apart from PG degree in recent past Environmental Informatics has also started at Bachelors level and many universities have started an initiative on this. This is a conceptual paper and also analytical study regarding Environmental Informatics including its basic feature and importance as an academic program. The paper shows about the universities which offer Bachelors degree in brief.
Partitive Techniques in Bayesian Data Analysis

This dissertation analyses two popular methods used in Bayesian data analysis, that involve splitting the data set into disjoint sets. The analysed approximative methods include expectation propagation (EP) and leave-one-out cross-validation (LOO-CV), which are used in the context of distributed inference and model evaluation/comparison respectively. The main contribution of the dissertation is in analysing the applicability and behaviour of the methods under different situations. The EP algorithm is a popular method for approximating a factorisable density. In the Bayesian context, for tractability, it has usually been applied pointwise. However, by including multiple observations in one approximated factor component, the method can be seen as a flexible framework for distributed inference. In addition, in hierarchical settings, it provides a convenient mean for dimension reduction by concentrating parameter inferences to separate units. LOO-CV is a popular method used in model evaluation, comparison, and weighting for estimating the out-of-sample predictive performance of a model using the given observations. In some situations, obtaining the estimate is a computationally heavy operation. The dissertation addresses this issue in the context of Gaussian latent variable models (GLVM) by reviewing various more efficient methods for approximating the LOO-CV estimate. Based on the results, a suggestion of approaches with different levels of accuracy and computational complexity are proposed. As the variability of the LOO-CV estimator can be high in some problems, it is important to take into account the related uncertainty when applying the LOO-CV method in practice. The current popular ways of estimating the uncertainty often leads to considerably underestimating the variability. The dissertation studies the behaviour of the uncertainty in a model comparison setting both theoretically and experimentally and identifies problematic cases, in which the estimated uncertainty is badly calibrated. The problematic cases include small data size, models making similar predictions, and model misspecification. In addition, the dissertation proposes an improved estimator for the variance of the LOO-CV estimator in the case of a Bayesian normal model. The proposed estimator serves as an example of the possibility of obtaining improved model-specific uncertainty estimates. This approach has not been discussed in the literature before.; Tama vaitoskirja kasittelee kahta suosittua bayesilaisessa data-analytiikassa kaytettya menetelmaa, joissa data jaetaan pistevieraisiin joukkoihin. Analysoitavat menetelmat ovat odotusarvon valittaminen (expectation propagation, EP), jota sovelletaan hajautettuun paattelyyn, ja jata-yksi-pois ristiinvalidointi (leave-one-out cross-validation, LOO-CV), jota kaytetaan mallin evaluointiin ja vertailuun. Tyon paakontribuutio on menetelmien sovellettavuuden ja kayttaytymisen analysointi eri tilanteissa. EP-algoritmi on suosittu menetelma, jolla voidaan approksimoida osittuva tiheysfunktio.…