Reviewerly/exHarmony huggingface dataset:
Sub-Subword N-Gram Features for Subword-Level Neural Machine Translation

Neural machine translation (NMT) systems often use subword segmentation to limit vocabulary sizes. This type of segmentation is particularly useful for morphologically complex languages because their vocabularies can grow prohibitively large. This method can also replace infrequent tokens with more frequent subwords. Fine segmentation with short subword units has been shown to produce better results for smaller training datasets. Character-level NMT, which can be considered as an extreme case of subword segmentation in which each subword consists of a single character, can provide enhanced transliteration results, but also tends to produce grammatical errors. We propose a novel approach to this problem that combines subword-level segmentation with character-level information in the form of character n-gram features to construct embedding matrices and softmax output projections for a standard encoder-decoder model. We use a custom algorithm to select a small number of effective binary character n-gram features. Through four sets of experiments, we demonstrate the advantages of the proposed approach for processing resource-limited language pairs. Our proposed approach yields better performance in terms of BLEU score compared to subword- and character-based baseline methods under low-resource conditions. In particular, the proposed approach increases the vocabulary size for small training datasets without reducing translation quality.
Big Data Analytics on Matrimonial Data Set

In this paper, I propose a model that will analyze the person's sentiment and emotion using social media.Face book, using machine learning and deep learning.Nowadays, most teenagers are depressed and stressed due to the overuse of social media and emotional breakdown.This project will help them to know their status and improve emotional stability.The numerous data floating around virtually can be taken as the context of data science and big data.In this paper, I will show how useful information can be mined out of those data sets.This paper aims to retrieve and pre-process social media data for sentiment analysis, which is a part of natural language processing.I will use several techniques of NLP for data pre-processing for better sentiment analysis.This paper essential requirement is to show the realization how individuals feel certain social media status, which will be utilized for classification.
Probability model adjustment for the CNN-based lossless image coding method

Recently, convolutional neural network-based generative models of image signals have been proposed mainly for the purpose of image generation, restoration and compression. For example, PixelCNN++ approximates probability distribution of the image intensity value as a parametric function pel-by-pel, and can be used for lossless image coding tasks. However, such an approach cannot work well for specific images which have statistical properties different from the image dataset used for the network training. In this paper, we improve the coding efficiency by introducing a few parameters for adjusting the probability model generated by PixelCNN++. These parameters are numerically optimized to minimize coding rates of the given image and then encoded as side-information to enable same adjustment at the decoder side.
A Blueprint for a Trustworthy Health Data Platform Encompassing IoT and Blockchain Technologies

eHealth provides great relief for patients and physicians. This means, patients au- tonomously monitor their condition via IoT medical devices and make these data available to physicians for analyses. This requires a data platform that takes care of data acquisition, management, and provisioning. As health data are highly sensitive, there are major concerns regarding data security with respect to confidentiality, integrity, and authenticity. To this end, we present a blueprint for constructing a trustworthy health data platform called SEAL. It provides a lightweight attribute-based authentication mechanism for IoT devices to validate all involved data sources, there is a fine-grained data provisioning system to enable data provision according to actual requirements, and a verification procedure ensures that data cannot be manipulated.
On Some Fuzzy Classification Algorithms and the AEC Model

In the paper some fuzzy classification algorithms based upon a nearest neighbor decision rule areconsidered in terms of the pattern recognition algorithms which are based on the computation of estimates (theso-called AEC model). It is shown that the fuzzy K nearest neighbor algorithm can be assigned to the AECclass. In turn, it is found that some standard AEC algorithms, which depend on a number of numericalparameters, can be used as fuzzy classification algorithms. Yet among them there exist algorithms extremalwith respect to these parameters. Such algorithms provide maximum values of the associated performancemeasures.
Learning with Missing Data

Many real-world data sets contain missing values, therefore, learning with incomplete data sets is a common challenge faced by data scientists. Handling them in an intelligent way is important to develop robust data models, since there is no perfect approach to compensate for the missing values. Deleting the rows with empty cells is a commonly used approach, this naive method may lead to estimates with larger standard errors due to reduced sample size. On the other hand, imputing the missing records is a better approach, but it should be used with great caution, as it relies on often unrealistic specific assumptions which can potentially bias results. In this paper, a new greedy-like algorithm is proposed to maximize the number of records. The algorithm can be used to generate various maximized sub-sets by varying the number of columns (features) that can be used for learning. It salvages more records than the naive method, and it avoids the bias induced by imputation. The learning algorithms would be able to learn from real sub-sets without the bias induced by artificial data. Finally, the proposed algorithm is applied to a case study, the COVID-19 Open Research data set (CORD-19) that was prepared and posted by The White House and a coalition of leading research groups as a call to action to the world's artificial intelligence experts to answer high priority scientific questions. This data set contains missing records, therefore, resulting maximized sub-sets from this analysis can be further investigated by the research community.
Constructing a universe for the setoid model

Abstract The setoid model is a model of intensional type theory that validates certain extensionality principles, like function extensionality and propositional extensionality, the latter being a limited form of univalence that equates logically equivalent propositions. The appeal of this model construction is that it can be constructed in a small, intensional, type theoretic metatheory, therefore giving a method to boostrap extensionality. The setoid model has been recently adapted into a formal system, namely Setoid Type Theory (SeTT). SeTT is an extension of intensional Martin-Löf type theory with constructs that give full access to the extensionality principles that hold in the setoid model. Although already a rich theory as currently defined, SeTT currently lacks a way to internalize the notion of type beyond propositions, hence we want to extend SeTT with a universe of setoids. To this aim, we present the construction of a (non-univalent) universe of setoids within the setoid model, first as an inductive-recursive definition, which is then translated to an inductive-inductive definition and finally to an inductive family. These translations from more powerful definition schemas to simpler ones ensure that our construction can still be defined in a relatively small metatheory which includes a proof-irrelevant identity type with a strong transport rule.
Range-Net: A High Precision Streaming SVD for Big Data Applications

In a Big Data setting computing the dominant SVD factors is restrictive due to the main memory requirements. Recently introduced streaming Randomized SVD schemes work under the restrictive assumption that the singular value spectrum of the data has exponential decay. This is seldom true for any practical data. Although these methods are claimed to be applicable to scientific computations due to associated tail-energy error bounds, the approximation errors in the singular vectors and values are high when the aforementioned assumption does not hold. Furthermore from a practical perspective, oversampling can still be memory intensive or worse can exceed the feature dimension of the data. To address these issues, we present Range-Net as an alternative to randomized SVD that satisfies the tail-energy lower bound given by Eckart-Young-Mirsky (EYM) theorem. Range-Net is a deterministic two-stage neural optimization approach with random initialization, where the main memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample dimension. The data samples are read in a streaming setting with the network minimization problem converging to the desired rank-r approximation. Range-Net is fully interpretable where all the network outputs and weights have a specific meaning. We provide theoretical guarantees that Range-Net extracted SVD factors satisfy EYM tail-energy lower bound at machine precision. Our numerical experiments on real data at various scales confirms this bound. A comparison against the state of the art streaming Randomized SVD shows that Range-Net accuracy is better by six orders of magnitude while being memory efficient.
Using METS to Express Digital Provenance for Complex Digital Objects

Today’s digital libraries consist of much more than simple 2D images of manuscript pages or paintings. Advanced imaging techniques – 3D modeling, spectral photography, and volumetric x-ray, for example – can be applied to all types of cultural objects and can be combined to create complex digital representations comprising many disparate parts. In addition, emergent technologies like virtual unwrapping and artificial intelligence (AI) make it possible to create “born digital” versions of unseen features, such as text and brush strokes, that are “hidden” by damage and therefore lack verifiable analog counterparts. Thus, the need for transparent metadata that describes and depicts the set of algorithmic steps and file combinations used to create such complicated digital representations is crucial. At EduceLab, we create various types of complex digital objects, from virtually unwrapped manuscripts that rely on machine learning tools to create born-digital versions of unseen text, to 3D models that consist of 2D photos, multi- and hyperspectral images, drawings, and 3D meshes. In exploring ways to document the digital provenance chain for these complicated digital representations and then support the dissemination of the metadata in a clear, concise, and organized way, we settled on the use of the Metadata Encoding Transmission Standard (METS). This paper outlines our design to exploit the flexibility and comprehensiveness of METS, particularly its behaviorSec, to meet emerging digital provenance metadata needs.
Extractive Summarization Using Frequency Driven Approach

In recent times, we have abundant sources for information out of which not every detail is significant. So the point of concern is identifying the most relevant and important information out of vast data. The focus should be given to the relevant information present in the entire data in such a way that we can understand the entire gist of the text. Considering the above concern as our problem statement, in this paper we discuss “Text Summarization”—it is the process of generating pertinent and significant text from the entire information. Text resources include websites, text documents, and direct text feed from users. Text Summarization can be performed using 2 methods—Extractive Summarization and Abstractive summarization. Here we are going to generate a summary from various types of text sources using extractive techniques. In extractive techniques, we have used a frequency driven approach where the relevance of a particular sentence is measured through sentence weightage and term frequency. We measured the accuracy of the model using a sequence matcher which provides us the relevance between generated summary and human perceived summary.
Super-convergence and Differential Privacy: Training faster with better privacy guarantees

The combination of deep neural networks and Differential Privacy has been of increasing interest in recent years, as it offers important data protection guarantees to the individuals of the training datasets used. However, using Differential Privacy in the training of neural networks comes with a set of shortcomings, like a decrease in validation accuracy and a significant increase in the use of resources and time in training. In this paper, we examine super-convergence as a way of greatly increasing training speed of differentially private neural networks, addressing the shortcoming of high training time and resource use. Super-convergence allows for acceleration in network training using very high learning rates, and has been shown to achieve models with high utility in orders of magnitude less training iterations than conventional ways. Experiments in this paper show that this order-of-magnitude speedup can also be seen when combining it with Differential Privacy, allowing for higher validation accuracies in much fewer training iterations compared to non-private, non-super convergent baseline models. Furthermore, super-convergence is shown to improve the privacy guarantees of private models.
Online Baum-Welch algorithm for Hierarchical Imitation Learning

The options framework for hierarchical reinforcement learning has increased its popularity in recent years and has made improvements in tackling the scalability problem in reinforcement learning. Yet, most of these recent successes are linked with a proper options initialization or discovery. When an expert is available, the options discovery problem can be addressed by learning an options-type hierarchical policy directly from expert demonstrations. This problem is referred to as hierarchical imitation learning and can be handled as an inference problem in a Hidden Markov Model, which is done via an Expectation-Maximization type algorithm. In this work, we propose a novel online algorithm to perform hierarchical imitation learning in the options framework. Further, we discuss the benefits of such an algorithm and compare it with its batch version in classical reinforcement learning benchmarks. We show that this approach works well in both discrete and continuous environments and, under certain conditions, it outperforms the batch version.
Towards User-in-the-Loop Online Fashion Size Recommendation with Low Cognitive Load

One of the major challenges facing e-commerce fashion platform is that of recommending to customers the right size and fit for fashion apparel. In this work we study this topic in depth and demonstrate its various complexities focusing in particular on the challenging cold-start problem that arises when no order history is available for a specific customer. We demonstrate the multifaceted value of data obtained by involving the customer in the loop and show how it allows for an effective cold-start recommender system. We highlight our findings via detailed experiments performed on hundreds of thousands of customers and items in real world e-commerce scenarios. In addition, results and discussions are provided investigating the trade-off between the recommender’s effectiveness and the customer’s experience with the goal of introducing accurate solutions with low user cognitive load.
Measuring world-locking accuracy in AR/MR head-mounted displays

Reliable world locking in augmented and mixed reality (AR/MR) devices is important for achieving immersion and critical for technical applications that rely on stable anchoring of virtual objects in the real world. To achieve this, a head-mounted display (HMD) must maintain accurate knowledge of its real-world position and orientation. We describe a method for measuring the six-degrees-of-freedom (DOF) positioning accuracy of an HMD, and how the same method can be extended to quantify the accuracy of anchoring virtual objects in the real world, i.e., world locking. An HMD is placed on a 6-DOF test jig comprising a motion system with high precision encoders and a time-synchronized imaging system. The HMD is made to display a 3D grid of unique identification markers that are detected by a machine vision camera in real time, while the robot is moving. This allows us to track the position and pose of the virtual camera and compare that with the known HMD position and pose. Using similar methodology, we can display virtual objects with a suitable number of unique identification markers. Their virtual object position and pose can then be compared to the real HMD position, thereby quantifying the accuracy of world locking. For improved accuracy, markers can also be printed out and pasted onto real-world objects. Other temporal parameters can also be computed, including motion-to-photon latency, spatial jitter, pose drift and prediction over/undershoot. The obtained results can be used to improve or recalibrate the positioning software and hardware of the head-mounted device.
Vectorized implementation of K-means

K-means is a widely used clustering technique that seeks to minimize the squared distance among points in the same cluster. K-means is an appealing clustering method in terms of computational speed. Also, K-means is the simplest clustering; thus, researchers select K-means as a first choice. Therefore, accelerating K-means is essential. In this study, we transform data structure from the array of structures to the structure of arrays for accelerating K-means by SIMD vectorization. Experimental results show that our implementation is faster than OpenCV’s implementation.
Visions of a Future Research Workplace Arising from Recent Foresight Exercises

Abstract The results of recent foresight projects reveal the impact of future ICT tools on the practice of scientific research. This paper presents several aspects of the process of building scenarios and trends of selected advanced ICT technologies. We point out the implications of emerging global expert systems (GESs) and AI-based learning platforms (AILPs). GESs will be capable of using and processing global knowledge from all available sources, such as databases, repositories, video streams, interactions with other researchers and knowledge processing units. In many scientific disciplines, the high volume, density and increasing level of interconnection of data have already exhausted the capacities of any individual researcher. Three trends may dominate the development of scientific methodology. Collective research is one possible coping strategy: Group intellectual capacity makes it possible to tackle complex problems. Recent data flow forecasts indicate that even in the few areas, which still resist ICT domination, research based on data gathered in non-ICT supported collections will soon reach its performance limits due to the ever-growing amount of knowledge to be acquired, verified, exchanged and communicated between researchers. Growing automation of research is the second option: Automated expert systems will be capable of selecting and processing knowledge to the level of a professionally edited scientific paper, with only minor human involvement. The third trend is intensive development and deployment of brain–computer interfaces (BCIs) to quickly access and process data. Specifically, GESs and AILPs can be used together with BCIs. The above approaches may eventually merge, forming a few AI-related technological scenarios, as discussed to conclude the paper.
Design and Implementation of Memory Assistant Based on Ebbinghaus Forgetting Curve

Abstract In view of the single stereotype of memorizing knowledge points with the help of traditional books and easy to distract attention in the process of memorizing with the help of mobile phones and computers, a mobile handheld device, memory assistant, is designed and developed based on Ebbinghaus forgetting law curve, embedded software and hardware development technology, and memory list cycle pushing intelligent algorithm designed by forgetting curve law as the core. The experiment shows that the device is convenient and flexible like electronic devices such as mobile phones and computers, and also has the characteristics of high efficiency and concentration of book memorizing, at the same time, scientific and reasonable learning plan can effectively improve the efficiency of users’ Recitation and memory.
Surviving Software Dependencies

Software reuse is finally here, and its benefits should not be understated, but we’ve accepted this transformation without completely thinking through the potential consequences. The Copay and Equifax attacks are clear warnings of real problems in the way software dependencies are consumed today. There’s a lot of good software out there. Let’s work together to find out how to reuse it safely.
A VR-based piano self-training portable system on standalone HMD

A Piano is a popular musical instrument. Development of playing piano requires frequent and suitable practice. However, the learners often have difficulty on space and time for practice due to less portable size and weight of the piano. In this article, a portable and virtual-reality (VR) based self-training system of the piano is proposed, which is installed on a standalone see-through Head-Mounted-Display (HMD). A learner wearing the HMD can see both of superimposed virtual piano keyboard and virtual his/her own fingers. According to his/her keying by the fingers, the system represents the situation acoustically. Evaluation of training outcome (number of incorrect keying) and subjective assessment shows advantages of the proposed system in comparison of a piano playing application on a tablet device.
Enactment of tf-idf and word2vec on Text Categorization

Text categorization has a variety of applications, such as sentiment analysis of user’s tweet, categorizing blog posts into different categories, etc. The real-time data available for categorization is usually unstructured. An efficient algorithm for preprocessing the data can help to achieve better accuracy. Term frequency–inverse document frequency (tf-idf) and word2vec word embedding techniques are used widely before applying the text classification model. In order to show the enactment of these techniques on text categorization, we are comparing the accuracies of different multi-class text categorization algorithms such as Support Vector Machine (SVM), Logistic Regression and K-Nearest Neighbor (KNN) on these techniques. TagMyNews dataset is used to train the model. The results indicate that word2vec is efficient word embedding technique as it possesses higher accuracies for all the classification methods (KNN: 79.38%, SVM: 93.59%, Logistic Regression: 87.46%) as compared to tf-idf (KNN: 73.37%, SVM: 84%, Logistic Regression: 73.98%).
COVID-19 Pandemic and Balance of Constitutional Rights.

ABSTRACT: In 2020 the World Health Organization declared the Covid-19 to be a pandemic. Governments around the world are facing a tough challenge that could jeopardise the national healthcare systems. Safeguarding the rights of Covid patients and all citizens - especially those who are now even more vulnerable than they were before - is part of this challenge. This work aims to examine the issues arising from the current emergency in terms of how individuals' constitutional rights have been balanced and how much people at the highest risk amid this pandemic - the homeless, the inmates in prison, geriatric and psychiatric patients, and doctors on the Covid frontline - have been protected. This analysis focuses mostly on one of the worst-hit countries by the SARS-CoV-2: Italy.