Reviewerly/exHarmony huggingface dataset:
Learning Cross-lingual Word Embeddings with Universal Concepts

Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages.State-of-the-art methods for learning cross-lingual word embeddings rely on the alignment of monolingual word embedding spaces.Our goal is to implement a word co-occurrence across languages with the universal concepts' method.Such concepts are notions that are fundamental to humankind and are thus persistent across languages, e.g., a man or woman, war or peace, etc.Given bilingual lexicons, we built universal concepts as undirected graphs of connected nodes and then replaced the words belonging to the same graph with a unique graph ID.This intuitive design makes use of universal concepts in monolingual corpora which will help generate meaningful word embeddings across languages via the word cooccurrence concept.Standardized benchmarks demonstrate how this underutilized approach competes SOTA on bilingual word sematic similarity and word similarity relatedness tasks.
A Study on Various Image Feature Extraction Techniques

Feature Extraction is the technique of extracting quantitative information from a image. Feature plays a very important role in the area of image processing. The purpose of feature extraction technique in image processing is to represent the image in its compact and unique form of single values or matrix vector. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. The aim of this paper is to give the overview of image feature extraction techniques for young learners and researchers.
Orthogonality Constrained Multi-Head Attention For Keyword Spotting

Multi-head attention mechanism is capable of learning various representations from sequential data while paying attention to different subsequences, e.g., word-pieces or syllables in a spoken word. From the subsequences, it retrieves richer information than a single-head attention which only summarizes the whole sequence into one context vector. However, a naive use of the multi-head attention does not guarantee such richness as the attention heads may have positional and representational redundancy. In this paper, we propose a regularization technique for multi-head attention mechanism in an end-to-end neural keyword spotting system. Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models. In addition, intra-head contextual non-orthogonality regularization encourages each attention head to have similar representations across keyword examples, which helps classification by reducing feature variability. The experimental results demonstrate that the proposed regularization technique significantly improves the keyword spotting performance for the keyword "Hey Snapdragon".
The Search for Truth through Data: NP Decision Processes, ROC Functions, $P$-Functionals, Knowledge Updating and Sequential Learning

This paper re-visits the problem of deciding between two simple hypotheses, the setting considered by Neyman and Pearson in developing their fundamental lemma. It studies the decision process induced by the most powerful test and the receiver operating characteristic function associated with this decision process. It addresses the question of how to report the decision arising from the decision function. It also examines the P-functional (the P-value statistic) and its role in the decision-making process. The impetus of this work is the continuing criticisms of statistical decision-making procedures that use the P-functional and a level of significance (LoS) of 0.05. A point made is that if one is going to use the value of the P-functional, then it should be used in an equivalent manner as the most powerful decision function, but if one wants to obtain from its value the degree of support for either hypotheses, then the value of its density under the alternative is the proper quantity to use. Replicability of results are discussed. Knowledge updating through Bayes theorem when given the decision or the value of the P-functional is also discussed, and it is argued that sequential learning is a coherent way of finding the truth. But the impact of publication bias is also demonstrated to be quite serious in the search for truth. It is argued that decision-makers are free to choose their own LoS, since the additional summary measures will automatically take their LoS choices into consideration. Three approaches for choosing an optimal LoS are discussed and a procedure for sample size determination is described. Ideas are illustrated by concrete problems and by the lady tea-tasting experiment of Fisher which ushered null hypothesis significance testing. It is hoped that by considering this fundamental setting of simple hypotheses, a better understanding of more complex settings will ensue.
Application of Management Decision Virtual Simulation in Financial Forecasting of Thermal Power Plant

The article takes the financial forecasting status of thermal power generation enterprises as the research background, applies the idea of virtual simulation of management decision to the financial forecast of thermal power enterprises, and realizes the simulation of future financial forecast of thermal power plant by designing simulation system and setting relevant rules.The virtual simulation results of management decision show that thermal power enterprises can survive and achieve sustainable development under the fierce competition environment.
The Correlation of Teachers’ Reinforcement and Students’ Achievement in English Learning Process

Teachers have to do several actions in the class such us encouragement, reinforcement, reward, and punishment. In this case researchers focuses on reinforcement. One of the class managements’ indicator is teacher gives reinforcement and feedback to students’ responses and students’ achievement as long as Learning Process. The study aims to identify teachers’ reinforcement in English Learning Process at Senior High School 1 Padang. A quantitative research was used where the data taken from questionnaire. It was focused on positive and negative reinforcement that used in English Learning Process and any significant correlation of teacher’ reinforcement and students’ achievement. The data was analyzed by using Pearson correlation. Based on the statement above, it can be concluded that all of the positive and negative reinforcement used by English teacher at Senior High School 1 Padang and the mostly used is positive reinforcement. The finding is there is significance correlation of teachers’ reinforcement and students’ achievement in English Learning Process. All of the teachers are expected that using reinforcement in English Learning Process due to increased the students’ achievement
Urban Media as Infrastructure for Social Change

The aim of this chapter is to explore the ways that people in the Global South engage media in ways that foster social change. More than simply a metaphor for the groundwork that makes possible sociocultural transformation, I explore media as a form of infrastructure entangled with other forms of infrastructure. I draw attention to how media infrastructure—with its complex materialities, poetics, and content—is central to the constitution and governance of cities. This approach to media as urban infrastructure, I argue, not only allows us to theorize the way that media and cities are inextricably linked, but also allows us to identify and better understand the embeddedness of media in the processes of exchange, relationality, and power. Through engagements with and demands for infrastructure, people struggle over continuity and change in the city, and by extension, the broader society.
Following a tangent of proofs

research-article Share on Following a tangent of proofs Author: Johan Håstad View Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 599–622https://doi.org/10.1145/3335741.3335764Published:04 October 2019Publication History 0citation48DownloadsMetricsTotal Citations0Total Downloads48Last 12 Months3Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Active Learning with Importance Sampling

We consider an active learning setting where the algorithm has access to a large pool of unlabeled data and a small pool of labeled data. In each iteration, the algorithm chooses few unlabeled data points and obtains their labels from an oracle. In this paper, we consider a probabilistic querying procedure to choose the points to be labeled. We propose an algorithm for Active Learning with Importance Sampling (ALIS), and derive upper bounds on the true loss incurred by the algorithm for any arbitrary probabilistic sampling procedure. Further, we propose an optimal sampling distribution that minimizes the upper bound on the true loss.
Gaussian processes for online system identification and control of a quadrotor

The aim of this master thesis was to develop adaptive control for a quadrotor using Gaussian process regression (GP). Online regression with GPs can provide many benefits as it is a flexible non-linear regression model that can provide uncertainty measures of the estimate. However, online GP regression may also gives rise to problems. Because of the high numerical complexity of GPs, sparse approximations are necessary. To this end, the algorithm Sparse Online Gaussian Processes (SOGP) was used. Adaptive control, however, requires the ability to estimate time-varying functions which SOGP does not provide any obvious solution to. To provide time-varying estimations, a Kalman filter interpretation of the update equations in SOGP was used. This addition to the classical SOGP algorithm provided good adaption to non-stationary disturbances at the same time as it was lowering the numerical complexity compared to previously proposed algorithms for nonstationary regression with SOGP. Further, the developed time-varying SOGP regression can provide valuable estimates even under the lack of persistence of excitation, which can be a great advantage in adaptive control. The SOGP algorithm was applied to estimate a model error between a nominal model of the quadrotor and the observed dynamics. The estimated model error could then be used in model-based controllers to improve performance under uncertainties in the dynamics. Two strategies were tested: Model Reference Control and Model Predictive Control. Through these two control strategies, modeled uncertainties in the form of flap- and drag-induced effects, as well as parametric uncertainties, could be estimated online for better flight control. The performance of the proposed controllers was showed through simulations. Even if it was the aim of the thesis no real-time experiments were performed due to lack of time. (Less)
The Matthew effect in education

Education and the so-called Matthew effect (the rich get richer, the poor get poorer) are inextricably linked. Over forty years ago, the psychologist Stanovisch was the first who demonstrated that children who achieve the intended results of education early (reading, writing and math skills) have a lifelong advantage in using and improving these skills.
DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images

Paper-intensive industries like insurance, law, and government have long leveraged optical character recognition (OCR) to automatically transcribe hordes of scanned documents into text strings for downstream processing. Even in 2019, there are still many scanned documents and mail that come into businesses in non-digital format. Text to be extracted from real world documents is often nestled inside rich formatting, such as tabular structures or forms with fill-in-the-blank boxes or underlines whose ink often touches or even strikes through the ink of the text itself. Further, the text region could have random ink smudges or spurious strokes. Such ink artifacts can severely interfere with the performance of recognition algorithms or other downstream processing tasks. In this work, we propose DeepErase, a neural-based preprocessor to erase ink artifacts from text images. We devise a method to programmatically assemble real text images and real artifacts into realistic-looking dirty text images, and use them to train an artifact segmentation network in a weakly supervised manner, since pixel-level annotations are automatically obtained during the assembly process. In addition to high segmentation accuracy, we show that our cleansed images achieve a significant boost in recognition accuracy by popular OCR software such as Tesseract 4.0. Finally, we test DeepErase on out-of-distribution datasets (NIST SDB) of scanned IRS tax return forms and achieve double-digit improvements in accuracy. All experiments are performed on both printed and handwritten text. Code for all experiments is available at this https URL
미래의 초등학교 듣기·말하기 교육을 위한 의사소통 모형

Given the communicative situation of current society with the upcoming Artificial Intelligence (AI) era or the Fourth Revolution, future speech education must be reconstituted based on the essential question: why must we listen and speak in a certain way? This study critically discusses various theories on “listening·speaking” and “caring” to adapt “caring thinking,” which is recently explored in Korean language education and other fields, into the listening·speaking education. Further, a communication model is developed to reflect the intentions of listening·speaking education. The model is based on the characteristics of elementary school education which sets forming a desirable personality of learners as its aim. It is also derived from the awareness that Korean language education is a basic subject of thinking and the activity of listening/speaking is the core part of the acts of caring. The study consists of two main subjects. First, it suggests a model of communication for elementary listening·speaking education which is manipulated to activate caring thinking as the filter before creative thinking or critical thinking that have been conventionally emphasized in Korean language education. Second, it discusses the direction for the contents and means of the future listening·speaking education. Along with self-reflection on Korean language education which has mainly stressed rationality and efficiency, this study is part of the efforts to materialize a new direction for listening·speaking education for future generations.
Towards Storage of Sub-Megahertz Single Photons in Gradient Echo Memory

Quantum repeaters are foundational to establishing the long distance quantum communication channels necessary to create a global communication network [1]. A quantum memory capable of storing optical quantum states is an integral component of the quantum repeater.
Building context-related dialogue systems based on Chinese-Script-Dialogue Corpus

High-quality corpora are of great importance for training dialogue generation models. We have developed a Chinese open-domain dialogue corpus which is collected from movie and television scripts, named the Chinese-Script-Dialogue. There are a total amount of 888,967 dialogues in the corpus and each dialogue consists of 4.58 turns on average. We asked human annotators to evaluate the quality of the corpus. Annotation results show that most of the dialogues are qualified. Based on this corpus, we designed a series of multi-turn dialogue systems, named Context-Related Dialogue Systems based on Transformer (CDST). Experiment results show that the CDSTs tend to generate more semantically related replies than the simple Transformer, i.e., to achieve higher BLEU score.
EEG Source Estimation using Multiple Unscented Particle Filtering

In this paper, we propose a multiple unscented particle filters approach to reconstruct the dynamic brain source localization. Localization of the brain sources that generate Electroencephalographs (EEG) has been a challenging problem in research, clinical and technological applications associated with the brain. The active areas in the brain are designed as equivalent current dipoles, and the moments and positions of these dipoles are the target to be estimated. We are formulating this EEG dynamic brain source localization problem as a nonlinear state-space model. Nowadays, particle filters (PF) represent the optimality in estimating the nonlinear and non-Gaussian systems. However, PF are inefficient in high-dimensional state-spaces because the number of particles desired increases super-exponentially with the dimension of the state. Unscented particle filter (UPF) has been emerged recently to improve the performance of PF. Therefore, we introduce a newly Multiple Unscented Particle Filter (MUPF) to deal with the problems that have high-dimensional spaces. In addition, we propose the MUPF algorithm to estimate the positions and the moments of the dipoles over time. Simulation results on synthetic and real EEG data using the MUPF provide effective tracking for dipole localization compared to the standard PF, multiple PF and UPF.
Usage Analytics: A Process to Extract and Analyse Usage Data to Understand User Behaviour in Cloud

Usage in the software field deals with knowledge about how end-users use the application and how the application responds to the users’ action. Understanding usage data can help developers optimise the application development process by prioritising the resources such as time, cost and man power on features of the application which are critical for the user. However, in a complex cloud computing environment, the process of extracting and analysing usage data is difficult since the usage data is spread across various front-end interfaces and back-end underlying infrastructural components of the cloud that host the application and are of different types and formats. In this paper, we propose usage analytics, a process to extract and analyse usage to understand the behavioural usage patterns of the user with the aim to identify features critical to user. We demonstrate how to identify the features in a cloud based application, how to extract and analyse the usage data to understand the user behaviour.
Measuring Causality: The Science of Cause and Effect

Determining and measuring cause-effect relationships is fundamental to most scientific studies of natural phenomena. The notion of causation is distinctly different from correlation which only looks at association of trends or patterns in measurements. In this article, we review different notions of causality and focus especially on measuring causality from time series data. Causality testing finds numerous applications in diverse disciplines such as neuroscience, econometrics, climatology, physics and artificial intelligence.
Pengaruh Aktivitas Part Time Terhadap Aktivitas Belajar dan Prestasi Mahasiswa Pendidikan Biologi Universitas Muhammadiyah Surakarta Tahun Angkatan 2016

Education is an important effort to support the quality of human resources. Thequality of human resources is determined by success in the teaching and learningprocess, including in universities. Student learning activities are a very importantprinciple in the teaching and learning process. Student achievement is a measureof student success that has been achieved during the learning process. This studyaims to determine the effect of part time activities on learning activities andachievements of students of Biology Education at the University ofMuhammadiyah Surakarta in the Year 2016. The study population was the 2016class with 203 people. The sampling technique uses random sampling totaling 135respondents. The technique of collecting data uses questionnaires anddocumentation. Data analysis uses MANOVA (Multivariate Analysis of Variance).The results of the research conducted can be concluded that (1) lectures takingwork have an effect on learning activities and learning achievement can be seenin multivariate test scores of 7.650 with a significance value of 0.001. (2) Parttime activities have an effect on learning activities indicated by the value of F10.488 with a significance value of 0.002, which is obtained by the averagelearning activity of students who do not work higher than students who work ie3.40> 3.23. (3) There is the influence of part time activities on student learningachievement with a value of F 9.913 with a significance value of 0.002 studentswho do not work tend to get cumlaude GPA compared to students who work invery satisfying categories.
Tomographic entanglement indicators in multipartite systems

We assess the performance of an entanglement indicator which can be obtained directly from tomograms, avoiding state reconstruction procedures. In earlier work, we have examined this tomographic entanglement indicator, and a variant obtained from it, in the context of continuous variable systems. It has been shown that, in multipartite systems of radiation fields, these indicators fare as well as standard measures of entanglement. In this paper, we assess these indicators in the case of two generic hybrid quantum systems, the double Jaynes-Cummings model and the double Tavis-Cummings model using, for purposes of comparison, the quantum mutual information as a standard reference for both quantum correlations and entanglement. The dynamics of entanglement is investigated in both models over a sufficiently long time interval. We establish that the tomographic indicator provides a good estimate of the extent of entanglement both in the atomic subsystems and in the field subsystems. An indicator obtained from the tomographic indicator as an approximation, however, does not capture the entanglement properties of atomic subsystems, although it is useful for field subsystems. Our results are inferred from numerical calculations based on the two models, simulations of relevant equivalent circuits in both cases, and experiments performed on the IBM computing platform.
ABILITY OF GROUND WATER IN PRECIPITATION DISSOLUTION MINERAL SALTS IN RELATION TO GEOLOGICAL FORMATION IN NINEVEH PROVENCE

This study was conducted by using the chemical analysis of soluble ions data from nine ground water wells of Nineveh Provence to study their tendency for precipitation and dissolution of associated minerals by using Geo-Chemo equilibrium program Visual MinteqA2 to calculate the saturation index values for predicted associated minerals. The results of the output of geo – chemo program showed a different tendency of these water in dissolution / precipitation of salts minerals with location, also the sulfate water at Hamam Al-Alil has a high quality in these different minerals like (calcite, order and disorder dolomite, aragonite, vaterite, and gypsum). While the well waters at shykan and fadlia were less ability to precipitate only (order dolomite). The results were correlated with the geological formation at these locations , the results show that was high content of salt minerals as calcite and gypsum within fatha formation at Hamam Al-Alil, while there are increasing in clastic and carbonate minerals inconstract the salts minerals at shykan and kadia locations.
ACPNS Legal Case Notes Series: 2019-74 In The Matter Of The D Macdonald Steele Memorial Prize

Supreme Court of South Australia, Justice Hinton, 13 September 2019. A letter by the executors of an estate was found to create a charitable trust for an educational prize.