Reviewerly/exHarmony huggingface dataset:
An Algorithm for Reconstructing the Orphan Stream Progenitor with MilkyWay@home Volunteer Computing

We have developed a method for estimating the properties of the progenitor dwarf galaxy from the tidal stream of stars that were ripped from it as it fell into the Milky Way. In particular, we show that the mass and radial profile of a progenitor dwarf galaxy evolved along the orbit of the Orphan Stream, including the stellar and dark matter components, can be reconstructed from the distribution of stars in the tidal stream it produced. We use MilkyWay@home, a PetaFLOPS-scale distributed supercomputer, to optimize our dwarf galaxy parameters until we arrive at best-fit parameters. The algorithm fits the dark matter mass, dark matter radius, stellar mass, radial profile of stars, and orbital time. The parameters are recovered even though the dark matter component extends well past the half light radius of the dwarf galaxy progenitor, proving that we are able to extract information about the dark matter halos of dwarf galaxies from the tidal debris. Our simulations assumed that the Milky Way potential, dwarf galaxy orbit, and the form of the density model for the dwarf galaxy were known exactly; more work is required to evaluate the sources of systematic error in fitting real data. This method can be used to estimate the dark matter content in dwarf galaxies without the assumption of virial equilibrium that is required to estimate the mass using line-of-sight velocities. This demonstration is a first step towards building an infrastructure that will fit the Milky Way potential using multiple tidal streams.
Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms

The problem of finding K-nearest neighbors in the given dataset for a given query point has been worked upon since several years. In very high dimensional spaces the K-nearest neighbor search (KNNS) suffers in terms of complexity in computation of high dimensional distances. With the issue of curse of dimensionality, it gets quite tedious to reliably bank on the results of variety approximate nearest neighbor search approaches. In this paper, we survey some novel K-Nearest Neighbor Search approaches that tackles the problem of Search from the perspectives of computations, the accuracy of approximated results and leveraging parallelism to speed-up computations. We attempt to derive a relationship between the true positive and false points for a given KNNS approach. Finally, in order to evaluate the robustness of a KNNS approach against adversarial points, we propose a generic Reinforcement Learning based framework for the same.
Narrare i classici nell'era digitale. Dai tweet agli emoji: il caso di "Pinocchio"

The aim of this paper is to analyse the famous Italian novel Le avventure di Pinocchio. Storia di un Burattino (1883) in light of new rewritings in the digital era. Taking into account the main classical literary topoi that distinguish Carlo Collodi’s fairy tale, which in turn has become a classic of literature for all ages, the main purpose is to examine how this canonical text has been retold according to the rules of the so-called twitterature.
Any two-qubit state has nonzero quantum discord under global unitary operations

Quantum discord is significant in analyzing quantum nonclassicality beyond the paradigm of entanglement. Presently, we have explored the effectiveness of global unitary operations in manifesting quantum discord from a general two-qubit zero discord state. Apart from the emergence of some obvious concepts such as absolute classical-quantum and absolute quantum-classical states, more interestingly, it is observed that set of states characterized by absoluteness contains only maximally mixed state. Consequently, this marks the peak of effectiveness of global unitary operations in purview of manifesting nonclassicality from arbitrary two-qubit state when other standard methods fail to do so. A set of effective global unitaries has been provided in this context. Our observations have direct implications in remote state preparation task.
Mirror Sample Based Distribution Alignment for Unsupervised Domain Adaption

Unsupervised Domain Adaption has great value in both machine learning theory and applications. The core issue is how to minimize the domain shift. Motivated by the more and more sophisticated distribution alignment methods in sample level, we introduce a novel concept named (virtual) mirror, which represents the counterpart sample in the other domains.The newly-introduced mirror loss using the virtual mirrors establishes the connection cross domains and pushes the virtual mirror pairs together in the aligned representation space. Our proposed method does not align the samples cross domains coarsely or arbitrarily, thus does not distort the internal distribution of the underline distribution and brings better asymptotic performances. Experiments on several benchmarks validate the superior performance of our methods.
LibGuides: 3D Drop-off Services: Using Thingiverse

Interested in utilizing PPLD's 3D drop-off services? This guide covers everything you need to know for submitting and picking up your 3D drop-off.
Rain Measurement Simulation Using the Hall Effect Flow-Meter

The sensor analysis or flow-meter measuring instrument has been successfully carried out on the signal output to see the stability or accuracy of a measurement. The flowmeter measurement value was analyzed using a rainfall simulator. The rainfall intensity value will then be predicted using the Kalman filter. Kalman filters can predict various data or output signals so that the measurement results can be more stable and accurate. This research methodology consists of several stages, namely the stages of literature study, designing research tools and components, designing systems, making or assembling tools, testing all components, programs and testing the flow-meter output signal record. The flowmeter is controlled by the Arduino Nano microcontroller. Tests were carried out in this study ten times, with a time span of 60 seconds for each experiment. The increase in water flow was detected by the flow-meter which was then captured by the hercules application and the data was then copied to Ms. Excel. After the rainfall intensity value is obtained, the value will be estimated using the Kalman filter. The estimation results will show the stability and accuracy value of the flow-meter.
Quantitative evaluation of a new gamma correction method for endoscopic image improvement

Endoscopic illumination system designs are inherently space restricted resulting in non-uniform illumination patterns. Tubular tissues and organs have significant lengths relative to these fixed illumination patterns, resulting in dark areas further away from the imaging sensor and over-saturated areas closer to the sensor. Occlusions and obstructions in the illumination path can also result in significant shadowing. A gamma correction is normally applied to map luminance levels and adjust for the significant variations that can occur. During the process, the color information isn’t taken into consideration. In this paper, a new gamma correction method was proposed to increase image contrast and sharpness by utilizing pixel information from all color channels. Raw data was collected for various scenes and processed using the regular and proposed gamma correction method. Images were quantitatively compared using entropy and sharpness metrics. Both subjective interpretation and these statistics show that the proposed gamma correction method offers enhancement over the regular gamma correction method by increasing the entropy and maintaining the sharpness closer to the original image.
DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments. In prior works, Domain Randomization (DR) has been used to address the reality gap for both robotic locomotion and manipulation tasks. In this paper, we propose Domain Randomization Optimization IDentification (DROID), a novel framework to exploit single-shot human demonstration for identifying the simulator's distribution of dynamics parameters, and apply it to training a policy on a door opening task. Our results show that the proposed framework can identify the difference in dynamics between the simulated and the real worlds, and thus improve policy transfer by optimizing the simulator's randomization ranges. We further illustrate that based on these same identified parameters, our method can generalize the learned policy to different but related tasks.
NODE-SELECT: A graph neural network based on a selective propagation technique

While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during the message-passing procedure. Additionally, a very important challenge that significantly affects graph neural networks is the issue of scalability which limits their application to larger graphs. In this paper we propose our method named NODE-SELECT: an efficient graph neural network that uses subsetting layers which only allow the best sharing-fitting nodes to propagate their information. By having a selection mechanism within each layer which we stack in parallel, our proposed method NODE-SELECT is able to both reduce the amount noise propagated and adapt the restrictive sharing concept observed in real world graphs. Our NODE-SELECT significantly outperformed existing GNN frameworks in noise experiments and matched state-of-the art results in experiments without noise over different benchmark datasets.
Deep Positive Unlabeled Learning with a Sequential Bias

For many domains, from video stream analytics to human activity recognition, only weakly-labeled datasets are available. Worse yet, the given labels are often assigned sequentially, resulting in sequential bias. Current Positive Unlabeled (PU) classifiers, a state-of-the-art family of robust semi-supervised methods, are ineffective under sequential bias. In this work, we propose DeepSPU, the first method to address this sequential bias problem. DeepSPU tackles the two interdependent subproblems of learning both the latent labeling process and the true class likelihoods within one architecture. We achieve this by developing a novel iterative learning strategy aided by theoretically-justified cost terms to avoid collapsing into a naive classifier. Our experimental studies demonstrate that DeepSPU outperforms state-of-the-art methods by over 10% on diverse real-world datasets.
An Intrinsic and Extrinsic Evaluation of Learned COVID-19 Concepts using Open-Source Word Embedding Sources

BACKGROUND: Scientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes These efforts could be improved by leveraging documented, COVID-19-related symptoms, findings, and disorders from clinical text sources in the electronic health record Word embeddings can identify terms related to these clinical concepts from both the biomedical and non-biomedical domains and are being shared with the open-source community at large However, it's unclear how useful openly-available word embeddings are for developing lexicons for COVID-19-related concepts OBJECTIVE: Given an initial lexicon of COVID-19-related terms, characterize the returned terms by similarity across various, open-source word embeddings and determine common semantic and syntactic patterns between the COVID-19 queried terms and returned terms specific to word embedding source METHODS: We compared 7 openly-available word embedding sources Using a series of COVID-19-related terms for associated symptoms, findings, and disorders, we conducted an inter-annotator agreement study to determine how accurately the most similar returned terms could be classified according to semantic types by three annotators We conducted a qualitative study of COVID-19 queried terms and their returned terms to detect informative patterns for constructing lexicons We demonstrated the utility of applying such learned synonyms to discharge summaries by reporting the proportion of patients identified by concept among three cohorts: pneumonia (n=6410 patients), acute respiratory distress syndrome (n=8647 patients), and COVID-19 (n=2397 patients) cohorts RESULTS: We observed high, pairwise inter-annotator agreement (Cohen's Kappa) for symptoms (0 86 to 0 99), findings (0 93 to 0 99), and disorders (0 93 to 0 99) Word embedding sources generated based on characters tend to return more synonyms (mean count of 7 2 synonyms) compared to token-based embedding sources (mean counts range from 2 0 to 3 4) Word embedding sources queried using a qualifier term (e g , cough or muscle pain) more often returned qualifiers of the similar semantic type (e g , dry returns consistency qualifiers like wet, runny) compared to a single term (e g , cough or pain) queries A higher proportion of patients had documented fever (0 61-0 84), cough (0 41-0 55), shortness of breath (0 40-0 59), and hypoxia (0 51-0 56) retrieved than other clinical features Terms for cough returned a higher proportion of COVID-19 patients (0 07) than pneumonia (0 05) and ARDS (0 03) populations CONCLUSIONS: Word embeddings are a valuable technology for learning related terms, including synonyms When leveraging openly-available word embedding sources, choices made for the construction of the word embeddings can significantly influence the words learned CLINICALTRIAL: Not applicable
KONTRIBUSI KETERAMPILAN MENYIMAK BERITA TERHADAP KETERAMPILAN MENULIS TEKS BERITA SISWA KELAS VIII SMP PEMBANGUNAN LABORATORIUM UNP

ABSTRACTThere are three aims of this study. First, describe the skills of listening to the news of VIII grade students of the Middle School Development at the UNP Laboratory Development. Second, describe the writing text skills of VIII grade students of the Middle School Development in the Construction of the UNP Laboratory. Third, describe the contribution of listening to the news with the writing text skills of VIII grade students at the UNP Laboratory Development.The type of this research is quantitative in terms of descriptive methods. The design used in this study is correlational design. The population of this study was the eighth grade students of the Middle School Development of the UNP Laboratory which was registered in the 2017/2018 school year, it was 168 students.The sample in this study was determined by proportional random sampling of 25% of the population, namely 42 students. The research data is in the form of scores on the skills test results of listening to the news and scores on the results of the test writing news text skills. The instrument of this study is an objective test to measure news listening skills and performance tests to measure news text writing skills.The results of this study are three. First, the skills to write news texts of VIII grade students of the Middle School Development.The development of the UNP Laboratory is in accordance with the Good qualifications (B). Second, the skill of listening to the news text of VIII grade students of the Middle School Development of the UNP Laboratory Development in accordance with Good qualifications (B). Third, listening to news text skills contributed 32.72% to the news text writing skills of VIII grade students of the Middle School Development at the Construction of the UNP Laboratory. Kata Kunci: Kontribusi, Keterampilan Menyimak Berita, Keterampilan Menulis Teks Berita
Formation of the skill of searching for information necessary for self-education and gaining new knowledge during practical, seminars on disciplines "Physics", "Concepts of modern natural science"

The article discusses the methodology for practical classes and seminars in the disciplines of Physics and Concepts of Modern Natural Science, which allows developing self-education skills using the Internet.
Learning and Performing with Drum Kit Technologies (Advanced)

The activities in this chapter are designed for undergraduates in their third year of drum kit studies who have developed advanced skills on the acoustic drum kit. This activity introduces students to the features and musical affordances of drum kit technologies. Students learn how to use percussion controllers and electronic drum triggers, in conjunction with music software they have experience of, to perform pieces of music in solo performance settings on hybrid, and fully electronic, drum kits. The three-stage approach includes: 1) exploring the built-in features of the percussion controller and associated music software, 2) using the controller in a familiar context, for example, to extend the possibilities of an acoustic drum kit in a hybrid setup, and, 3) creating a performance using only the technology (i.e., removing the familiar acoustic drum kit).
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery

The monitoring of rotating machinery is an essential task in today's production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis in rotating machinery is here proposed. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. In the first part, the vibration features in the time and frequency domains are extracted. Secondly, in the fault detection, the presence of fault is verified in an unsupervised manner based on anomaly detection algorithms. The modularity of the methodology allows different algorithms to be implemented. Finally, in fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret black-box models, is used. Through the feature importance ranking obtained by the model explainability, the fault diagnosis is performed. Two tools for diagnosis are proposed, namely: unsupervised classification and root cause analysis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults in rotating machinery. The study also presents a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local- DIFFI). Lastly, an analysis of several state-of-art anomaly detection algorithms in rotating machinery is included.
PLM: Partial Label Masking for Imbalanced Multi-label Classification

Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in the ratio of positive and negative samples for each class skews network output probabilities further from ground-truth distributions. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. By stochastically masking labels during loss computation, the method balances this ratio for each class, leading to improved recall on minority classes and improved precision on frequent classes. The ratio is estimated adaptively based on the network's performance by minimizing the KL divergence between predicted and ground-truth distributions. Whereas most existing approaches addressing data imbalance are mainly focused on single-label classification and do not generalize well to the multi-label case, this work proposes a general approach to solve the long-tail data imbalance issue for multi-label classification. PLM is versatile: it can be applied to most objective functions and it can be used alongside other strategies for class imbalance. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.
PEMODELAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DAN JAKARTA ISLAMIC INDEX (JII) MENGGUNAKAN REGRESI BIRESPON SPLINE TRUNCATED BERBASIS GUI R

The capital market is one of the economic drivers and representations for assessing the condition of companies in a country. Indonesia Stock Exchange (IDX) as one of the institutions in the capital market has 24 types of indexes that can be used as main indicators that reflect the performance of capital market, two of them are the Composite Stock Price Index (CSPI) and the Jakarta Islamic Index (JII). CSPI and JII movements are influenced by several factors, both from domestic and from foreign, such as inflation and the Dow Jones Industrial Average (DJIA). Modeling of CSPI and JII in this study was carried out using biresponses spline truncated nonparametric regression methods using Graphical User Interface (GUI) R with the intention of facilitating the analysis process. This method is used because there is a correlation between CSPI and JII and there is no specific relationship pattern between the response variable (CSPI and JII) and the predictor variable (inflation and DJIA). The best biresponses spline truncated model is determined by the order, number and location of the knots seen based on minimum GCV criteria. By using monthly data from January 2016 to December 2019, the best biresponses spline truncated model is obtained when the model for CSPI is in order 2 and the model for JII is in order 3 with 2 knots for each predictor variable. This model has a coefficient of determination of 85,54437% and MAPE of 2,65595% so that it has a very good ability in forecasting.
LP2PB: Translating Answer Set Programs into Pseudo-Boolean Theories

Answer set programming (ASP) is a well-established knowledge representation formalism. Most ASP solvers are based on (extensions of) technology from Boolean satisfiability solving. While these solvers have shown to be very successful in many practical applications, their strength is limited by their underlying proof system, resolution. In this paper, we present a new tool LP2PB that translates ASP programs into pseudo-Boolean theories, for which solvers based on the (stronger) cutting plane proof system exist. We evaluate our tool, and the potential of cutting-plane-based solving for ASP on traditional ASP benchmarks as well as benchmarks from pseudo-Boolean solving. Our results are mixed: overall, traditional ASP solvers still outperform our translational approach, but several benchmark families are identified where the balance shifts the other way, thereby suggesting that further investigation into a stronger proof system for ASP is valuable.
Motivational and Value-based Component as One of the Structural Elements of Engineering Educators’ Readiness for Professional Mobility

In theoretical study of readiness of engineers-teachers of computer sciences for professional mobility, a valuative and motivated component is stated as a systemforming element and plays an important role in the process of forming a system of personal and professional values and goals. Valuative and motivated goals of an engineer-teacher reproduce social, professional and personal importance of vocational education, and influence formation of a purposeful creative mastering of professional competences necessary for development of professional mobility. The valuative and motivated sphere of would-be specialists is proved to be a significant instrument of socialization and adaptation to social and professional activities.