Reviewerly/exHarmony huggingface dataset:
Comparative Study of Sentiment Analysis and Text Summarization for Commercial Social Networks

The rapid shift towards digitalization today has actually transferred the market to an entirely digitalized platform. The participation of such a large number of users has given rise to a huge amount of data over the internet, proving the need for proper structuring and removal of unwanted and redundant data. The presence of a system that gives them the complete overview of a product is a dire need for the public today. Diving deep, we nd technologies that help us in the analysis and modification of data found over the internet. Sentiment analysis helps us nd the opinions people have towards a variety of entities, through a series of processes. Along with this, we have text summarisation which aids in the attainment of meaningful information from the wide range of irrelevant and redundant data found online. Clubbing these two, we can obtain concise reviews in addition to the overall sentiment towards selected entities. Here, we propose a model where we convolve into a system that provides the user with the overall recommendation found on popular e-commerce websites (Amazon, Flipkart and TripAdvisor). Starting with the collection of data from given sources, we pre-process the data, we combine machine learning with a lexicon-based approach, obtain the summaries and sentiments and eventually provide the user with the popular opinion behind the product.
A Dual-Stream architecture based on Neural Turing Machine and Attention for the Remaining Useful Life Estimation problem

Estimating in a reliable way the Remaining Useful Life (RUL) of a mechanical component is a fundamental task in the field of Prognostics and Health Management (PHM). In recent years a greater availability of high quality sensors and easiness of data gathering gave rise to data-driven models based on deep learning for this task, which has recently seen the introduction of "dual-stream" architectures. In this paper we propose a dual-stream architecture to address the RUL estimation problem through the exploitation of a Neural Turing Machine (NTM) and a Multi-Head Attention (MHA) mechanism. The NTM is a content-based memory addressing system which gives each of the streams the ability to access to and interact with the memory and acts as a fusion technique. The MHA is an attention mechanism added as a mean for our architecture to identify the existing relations between different sensor data in order to reveal hidden patterns among them. To evaluate the performance of our model, we considered the C-MAPSS dataset, a benchmark dataset published by NASA consisting of several time series related to the life of turbofan engines. We show that our approach achieves the best prediction score (which measures the safety of the predictions) in the available literature on two of the C-MAPSS subdatasets.
Using NLP to Generate MARC Summary Fields for Notre Dame ’s Catholic Pamphlets

Three NLP (Natural Language Processing) automated summarization techniques were tested on a special collection of Catholic Pamphlets acquired by Hesburgh Libraries. The automated summaries were generated after feeding the pamphlets as .pdf files into an OCR pipeline. Extensive data cleaning and text preprocessing were necessary before the computer summarization algorithms could be launched. Using the standard ROUGE F1 scoring technique, the Bert Extractive Summarizer technique had the best summarization score. It most closely matched the human reference summaries. The BERT Extractive technique yielded an average Rouge F1 score of 0.239. The Gensim python package implementation of TextRank scored at .151. A hand-implemented TextRank algorithm created summaries that scored at 0.144. This article covers the implementation of automated pipelines to read PDF text, the strengths and weakness of automated summarization techniques, and what the successes and failures of these summaries mean for their potential to be used in Hesburgh Libraries.
A Preliminary Study on the Teaching Model of College English Reading: Fragmented Reading

English reading is of vital significance in English learning. In proficiency tests such as CET-4, CET-6, IELTS and TOFEL, extensive reading accounts for a large proportion in scores. Those who do well in reading tend to get higher scores. However, the current English teaching situation doesn&amp;rsquo;t show its significance: the in-class time is limited with only two classes each week, and it&amp;rsquo;s difficult to decide appropriate textbooks for teachers. As a result, students can&amp;rsquo;t figure out the significance of extensive reading course and think little of it. Since the Internet appears in every aspect of our daily life, we can combine extensive reading course with the Internet. By using our spare time to reading fragmented information in English from the Internet selected and instructed by the teacher in any place, we bring fragmented reading into extensive reading teaching. In this paper, the author aims to assume a new teaching model concerning fragmented reading in details. Through this new teaching model, students can not only read English materials in classroom, but also after class in any place with their spare time. With much more input of English both in and out of class, their reading ability will be improved and they are more ready to deal with proficiency tests of all kinds. Meanwhile, students&amp;rsquo; interests will be aroused and their horizons will be broadened, which are also helpful for students to pass all kinds of proficiency tests.
An Image-Based Class Retrieval System for Roman Republican Coins

We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, but also the retrieval of its information from standard reference book. Such classification and information retrieval is performed by our proposed system via a user friendly graphical user interface (GUI). The query coin image gets matched with exemplar images of each coin class stored in the database. The retrieved coin classes are then displayed in the GUI along with their descriptions from a reference book. However, it is highly impractical to match a query image with each of the class exemplar images as there are 10 exemplar images for each of the 60 coin classes. Similarly, displaying all the retrieved coin classes and their respective information in the GUI will cause user inconvenience. Consequently, to avoid such brute-force matching, we incrementally vary the number of matches per class to find the least matches attaining the maximum classification accuracy. In a similar manner, we also extend the search space for coin class to find the minimal number of retrieved classes that achieve maximum classification accuracy. On the current dataset, our system successfully attains a classification accuracy of 99% for five matches per class such that the top ten retrieved classes are considered. As a result, the computational complexity is reduced by matching the query image with only half of the exemplar images per class. In addition, displaying the top 10 retrieved classes is far more convenient than displaying all 60 classes.
Integer Programming on the Junction Tree Polytope for Influence Diagrams

Influence diagrams (ID) and limited memory influence diagrams (LIMID) are flexible tools to represent discrete stochastic optimization problems, with the Markov decision process (MDP) and partially observable MDP as standard examples. More precisely, given random variables considered as vertices of an acyclic digraph, a probabilistic graphical model defines a joint distribution via the conditional distributions of vertices given their parents. In an ID, the random variables are represented by a probabilistic graphical model whose vertices are partitioned into three types: chance, decision, and utility vertices. The user chooses the distribution of the decision vertices conditionally to their parents in order to maximize the expected utility. Leveraging the notion of rooted junction tree, we present a mixed integer linear formulation for solving an ID, as well as valid inequalities, which lead to a computationally efficient algorithm. We also show that the linear relaxation yields an optimal integer solution for instances that can be solved by the “single policy update,” the default algorithm for addressing IDs.
The Internet and Minority Languages of Russia: Symbolic Presence or a Revitalization Tool? (a Case Study of the Buryat Language)

The purpose of this article is to assess the nature of the presence of Russian minority languages (primarily of the Buryat language) on the Internet: is this presence symbolic or is it a real instrument for revitalization? The author considers the main publications on this issue, briefly characterizes the current situation with the Buryat language and analyzes the nature of the presence and the functioning of the Buryat language on the Internet in comparison with other minority languages of the Russian Federation. The material is constituted by the statistical data of the Higher School of Economics’ project on Russian minority languages and online resources on the Buryat language (websites, social network VKontakte, video hosting site YouTube and Instagram application). As a result of the comparative analysis, the conclusion is made about the moderate language activism of the Buryats on the Internet and the mixed language code and translingual practices that have now become the norm for minority language speakers both on the Internet and beyond. The author also notes the emergence of new actors and the effect of globalization which is visible in the fact that most Internet projects are initiated by young people driven by the need for identity in a globalized world. Finally, the author concludes that the presence of the Buryat language (and of many other minority languages in Russia) on the Internet is mainly symbolic and that there is a great variability in the degree of Internet activism of their speakers. As a result, it can be argued that the Internet does not create a new reality, it only reflects the current linguistic situation with minority languages in Russia.
Quasi-orthonormal Encoding for Machine Learning Applications

Most machine learning models, especially artificial neural networks, require numerical, not categorical data. We briefly describe the advantages and disadvantages of common encoding schemes. For example, one-hot encoding is commonly used for attributes with a few unrelated categories and word embeddings for attributes with many related categories (e.g., words). Neither is suitable for encoding attributes with many unrelated categories, such as diagnosis codes in healthcare applications. Application of one-hot encoding for diagnosis codes, for example, can result in extremely high dimensionality with low sample size problems or artificially induce machine learning artifacts, not to mention the explosion of computing resources needed. Quasi-orthonormal encoding (QOE) fills the gap. We briefly show how QOE compares to one-hot encoding. We provide example code of how to implement QOE using popular ML libraries such as Tensorflow and PyTorch and a demonstration of QOE to MNIST handwriting samples.
Analysis of Twitter data with the Bayesian fused graphical lasso

We propose a method to simplify textual Twitter data into understandable networks of terms that can signify important events and their possible changes over time. The method allows for common characteristics of the networks across time periods and each period can comprise multiple unknown sub-networks. The networks are described by Gaussian graphical models and their parameter values are estimated through a Bayesian approach with a fused lasso-type prior on the precision matrices of the underlying mixtures of the sub-models. A flexible data allocation scheme is at the heart of an MCMC algorithm to recover mean and covariance parameters of the mixture components. Several implementations of the outlined estimation procedure are studied and compared based on simulated data. The procedure with the highest predictive power is used for mining tweets regarding the 2009 Iranian presidential election.
Nowcasting of Irradiance Using a Network of All-Sky-Imagers

Electric power grids are increasingly penetrated by generation from photovoltaic (PV) systems. The volatility of solar irradiance can therefore cause problematic fluctuations of grid voltage and frequency. For local low voltage power grid sections highly accurate nowcasts of the short-term local PV-power generation may enable higher PV penetration by employing short-term flexibility options while saving investments in grid-infrastructure. Systems of up to four All-Sky-Imagers (ASI) have been used successfully to nowcast direct and global irradiance as well as the production of solar power plants. In Oldenburg and the surrounding area these systems are enhanced by interconnecting 34 All-Sky-Imagers in a measurement network called Eye2Sky. Intermediate results can be combined and exchanged between systems with overlapping observation areas but also between systems up to 100 km apart. To demonstrate the potential of the approach three state-of-the-art 2-ASI-systems from the network are selected. Nowcasts of the individual systems are averaged weighting each prediction by its expected accuracy in the single points of the forecast domain. In a validation period of 86 days the network outperforms a single 2-ASI-system. For all lead times greater than zero, significantly lower root mean square errors are found compared to the 2-ASI system. For highly variable time intervals the root mean square error is reduced by up to 50 %. The network´s combined nowcast is found to reduce forecast errors related to the often uncertain cloud edge positions and further systematic errors found in single 2-ASI-systems.
Type your matrices for great good: a Haskell library of typed matrices and applications (functional pearl)

We study a simple inductive data type for representing correct-by-construction matrices. Despite its simplicity, it can be used to implement matrix-manipulation algorithms efficiently and safely, performing in some cases faster than existing alternatives even though the algorithms are written in a direct and purely functional style. A rich collection of laws makes it possible to derive and optimise these algorithms using equational reasoning, avoiding the notorious off-by-one indexing errors when fiddling with matrix dimensions. We demonstrate the usefulness of the data type on several examples, and highlight connections to related topics in category theory.
A Propositional Metric Logic with Fixed Finite Ranges

The aim of this article is developing a formal system suitable for reasoning about the distance between propositional formulas. We introduce and study a formal language which is the extension of the classical propositional language obtained by adding new binary operators D≤s and D≥s, s ∈ Range, whe re Range is a fixed finite set. In our language it is allowed to make formulas of the form D≤s(α; β) with the intended meaning ’distance between formulas α and β is less than or equal to s’. The semantics of the proposed language consists of possible worlds with a distance function defined between sets of worlds.
Outbound Student Mobility in Russia: Creating a Path for Brain Circulation through Higher Education

Given the ambitious national strategies for science, innovations and university development inRussia, the availability for young highly qualiﬁed specialists who can be competitive at the global job market, is vital. At the same time, as of now,Russia, unlike many other countries, does not have a comprehensive set of initiatives addressing the brain drain among Russian students that obtain their degrees abroad. This article provides an analysis of motivations of Russian perspective graduate (master and doctoral) students who plan to study abroad and emigrate after graduation, as well as the factors that may positively aﬀect their decision to return toRussia. In addition, the research provides an overview of international policies and practices to prevent the brain drain, and the opportunities for developing such policies in our country.
Camera parameters estimation from pose detections

Some computer vision tasks become easier with known camera calibration. We propose a method for camera focal length, location and orientation estimation by observing human poses in the scene. Weak requirements to the observed scene make the method applicable to a wide range of scenarios. Our evaluation shows that even being trained only on synthetic dataset, the proposed method outperforms known solution. Our experiments show that using only human poses as the input also allows the proposed method to calibrate dynamic visual sensors.
A Probabilistic Moving Horizon Estimation Framework Applied to the Visual-Inertial Sensor Fusion Problem

We propose a novel method to compute the arrival cost for the moving horizon estimator. The choice of the arrival cost is an important challenge and is known to have significant influence on the performance of the estimator. Most common approaches are based on implementing a complementary extended Kalman filter to propagate an approximate measure of the uncertainty. Our approach is based on the probabilistic interpretation of the moving horizon estimator and its analogy to the maximum a posteriori estimator. We derive a method to directly obtain the required uncertainties from the Hessian of the moving horizon estimation objective function. We showcase our novel approach with the challenging visual-inertial sensor fusion problem that commonly arises in visual navigation systems. The estimation performance is significantly better compared to our previous results based on the extended Kalman filter. Additionally, the proposed algorithm calibrates the inertial sensor online and is immediately ready for operation.
Log-seed pseudorandom generators via iterated restrictions

There are only a few known general approaches for constructing explicit pseudorandom generators (PRGs). The approach, pioneered by Ajtai and Wigderson [2], has provided PRGs with seed length polylog n or even O(log n) for several restricted models of computation. Can this approach ever achieve the optimal seed length of O(log n)?In this work, we answer this question in the affirmative. Using the iterated restrictions approach, we construct an explicit PRG for read-once depth-2 AC0[⊕] formulas with seed length[MATH HERE].In particular, we achieve optimal seed length O(log n) with near-optimal error e = exp(-ΩT(log n)). Even for constant error, the best prior PRG for this model (which includes read-once CNFs and read-once F2-polynomials) has seed length Θ(log n · (log log n)2) [22].A key step in the analysis of our PRG is a tail bound for subset-wise symmetric polynomials, a generalization of elementary symmetric polynomials. Like elementary symmetric polynomials, subset-wise symmetric polynomials provide a way to organize the expansion of [MATH HERE]. Elementary symmetric polynomials simply organize the terms by degree, i.e., they keep track of the number of variables participating in each monomial. Subset-wise symmetric polynomials keep track of more data: for a fixed partition of [m], they keep track of the number of variables from each subset participating in each monomial. Our tail bound extends prior work by Gopalan and Yehudayoff [17] on elementary symmetric polynomials.
Comparison of the effect of temperature parameter on tracking and fixed photovoltaic systems: a case study in Tehran, Iran

The production of energy by renewable energy, including photovoltaic systems, is always dependent on the environmental and geographical parameters at which the system is installed. Temperature is one of the most important environmental parameters affecting the performance of photovoltaic systems. The effect of this parameter on the fixed and tracking photovoltaic system is not the same. The tracking photovoltaic system, because it is exposed to the sun from sunrise to sunset, has a higher temperature at the surface of the panels than the fixed photovoltaic systems. The result obtained from the experiments in this study shows that the temperature-induced efficiency drop in the fixed and tracking photovoltaic systems is more than 7.98% and 10.02%, respectively. According to calculations, the temperature-induced efficiency drop in tracking photovoltaic systems is about 25.55% higher than that of fixed photovoltaic systems. Observations show that this temperature difference is most extent at sunrise and sunset, and as we approach noon this difference is reduced and minimized
Mineralogical study and enhanced gravity separation of gold-bearing mineral, South Eastern Desert, Egypt

El-Hudi gold deposit, located in the South Eastern Desert of Egypt, represent large vein-type gold occurrence.The representative sample revealed the abundance of quartz as main constituent with minor amounts of mineral impurities.Gold was detectable (12 g/t) as determined using atomic absorption.The petrographic study revealed that the gold grains ranged from 10-40 µm.The grain boundaries of quartz are highly stained with iron minerals as hematite and limonite.Sericite mineral is common in discrete gold-bearing veins.Eroded pyrite was detected with high alteration leaving only cubic-shaped cavities behind.Different techniques for gravity separation were used to separate gold from the quartz mineral.After crushing and grinding of the sample, shaking table was used to upgrade the coarser fractions while Falcon concentrator was employed to upgrade the fine fraction.The best concentrate was obtained through grinding the whole sample to less than 0.2 mm, followed by cleaning steps.The gold content is increased from 12 to 145 g/t with total recovery of 78%.
A HYBRID GLOBAL IMAGE ORIENTATION METHOD FOR SIMULTANEOUSLY ESTIMATING GLOBAL ROTATIONS AND GLOBAL TRANSLATIONS

Abstract. In recent years, the determination of global image orientation, i.e. global SfM, has gained a lot of attentions from researchers, mainly due to its time efficiency. Most of the global methods take relative rotations and translations as input for a two-step strategy comprised of global rotation averaging and global translation averaging. This paper by contrast presents a hybrid approach that aims to solve global rotations and translations simultaneously, but hierarchically. We first extract an optimal minimum cover connected image triplet set (OMCTS) which includes all available images with a minimum number of triplets, all of them with the three related relative orientations being compatible to each other. For non-collinear triplets in the OMCTS, we introduce some basic characterizations of the corresponding essential matrices and solve for the image pose parameters by averaging the constrained essential matrices. For the collinear triplets, on the other hand, the image pose parameters are estimated by relative orientation using the depth of object points from individual local spatial intersection. Finally, all image orientations are estimated in a common coordinate frame by traversing every solved triplet using a similarity transformation. We show results of our method on different benchmarks and demonstrate the performance and capability of the proposed approach by comparing with other global SfM methods.