Reviewerly/exHarmony huggingface dataset:
How Shift Equivariance Impacts Metric Learning for Instance Segmentation

Metric learning has received conflicting assessments concerning its suitability for solving instance segmentation tasks. It has been dismissed as theoretically flawed due to the shift equivariance of the employed CNNs and their respective inability to distinguish same-looking objects. Yet it has been shown to yield state of the art results for a variety of tasks, and practical issues have mainly been reported in the context of tile-and-stitch approaches, where discontinuities at tile boundaries have been observed. To date, neither of the reported issues have undergone thorough formal analysis. In our work, we contribute a comprehensive formal analysis of the shift equivariance properties of encoder-decoder-style CNNs, which yields a clear picture of what can and cannot be achieved with metric learning in the face of same-looking objects. In particular, we prove that a standard encoder-decoder network that takes $d$-dimensional images as input, with $l$ pooling layers and pooling factor $f$, has the capacity to distinguish at most $f^{dl}$ same-looking objects, and we show that this upper limit can be reached. Furthermore, we show that to avoid discontinuities in a tile-and-stitch approach, assuming standard batch size 1, it is necessary to employ valid convolutions in combination with a training output window size strictly greater than $f^l$, while at test-time it is necessary to crop tiles to size $n\cdot f^l$ before stitching, with $n\geq 1$. We complement these theoretical findings by discussing a number of insightful special cases for which we show empirical results on synthetic data.
Nikbot: Chatbot for Nikshay Aushadhi

Nikbot is a software application that uses natural language to interact with clients. It is designed to provide information to the users about Nikshay Aushadhi tuberculosis-related frequently asked question without handling telephonic calls which are quite hectic and not scalable for a large number of users. Building Nikbot having challenges like conversational interfaces, technical design principal issues, and data preparation. The Nikbot stores the queries and response in the database, and using the chatterbots machine learning library, the algorithm identifies the input sentence keywords asked by the user. Using N-gram, TF-IDF and cosine comparison, sentence and rank similarities are determined. Each sentence is scored by the closest matching response by finding the closest matching known statement that matches the same input sentences which is selected as a response for the query. The scheduler program at the back-end handles the questions and responses presented to the bot that are not present in the database, and the support team collects that information for further analyzing user’s sentiments and preparing those relevant responses if those questions are relevant.
Streaming approximation resistance of every ordering CSP.

An ordering constraint satisfaction problem (OCSP) is given by a positive integer k and a constraint predicate Π mapping permutations on {1,…,k} to {0,1}. Given an instance of OCSP(Π) on n variables and m constraints, the goal is to find an ordering of the n variables that maximizes the number of constraints that are satisfied, where a constraint specifies a sequence of k distinct variables and the constraint is satisfied by an ordering on the n variables if the ordering induced on the k variables in the constraint satisfies Π. Ordering constraint satisfaction problems capture natural problems including Maximum acyclic subgraph (MAS) and Betweenness. In this work we consider the task of approximating the maximum number of satisfiable constraints in the (single-pass) streaming setting, where an instance is presented as a stream of constraints. We show that for every Π, OCSP(Π) is approximation-resistant to o(n)-space streaming algorithms, i.e., algorithms using o(n) space cannot distinguish streams where almost every constraint is satisfiable from streams where no ordering beats the random ordering by a noticeable amount. This space bound is tight up to polylogarithmic factors. In the case of MAS our result shows that for every e > 0, MAS is not 1/2+e-approximable in o(n) space. The previous best inapproximability result only ruled out a 3/4-approximation in o(√ n) space.Our results build on recent works of Chou, Golovnev, Sudan, Velingker, and Velusamy who show tight, linear-space inapproximability results for a broad class of (non-ordering) constraint satisfaction problems (CSPs) over arbitrary (finite) alphabets. Our results are obtained by building a family of appropriate CSPs (one for every q) from any given OCSP, and applying their work to this family of CSPs. To convert the resulting hardness results for CSPs back to our OCSP, we show that the hard instances from this earlier work have the following small-set expansion property: If the CSP instance is viewed as a hypergraph in the natural way, then for every partition of the hypergraph into small blocks most of the hyperedges are incident on vertices from distinct blocks. By exploiting this combinatorial property, in combination with the hardness results of the resulting families of CSPs, we give optimal inapproximability results for all OCSPs.
Optimized Low Code Platform for Application Development

The demand of new application development increases as the organizations grow rapidly. So, the development companies are facing various challenges to meet the new requirements of the customers, who demand for the applications within very less time.  Low code development platform (LCDP) plays vital role to overcome this gap. This platform becomes a significant step towards successful development of applications for customer facing problems. Instead of traditional hand coding, it refers to the development of application through visual modelling tools using approach of Model Driven Development (MDD). It allows developer to more focus on functionality rather than syntax of the code. Rapid app delivery, minimum coding, scalability, agile development, rich functionality makes role of low code development platform more strategic in any digital transformation. Because of these features, LCDP enables organizations to build their own quickly with less time and money. This paper is based on study different low code platforms, identification of their functionalities and services. The paper also initiates a proposed system methodology of new optimized low code platform.
Service-Oriented Data Processing for Dynamic Schema

In recent years, with the rapid development of big data technology, more and more Internet enterprises have started to transform and upgrade into big data driven enterprises. In addition, in the industrial field, information services driven by industrial big data have also attracted widespread attention. Some traditional industries, such as state-owned chemical enterprises are also actively transforming to information management and big data management. These traditional chemical enterprises have been using simple data processing tools internally in the past, such as Microsoft Excel. Although these tools are relatively simple to use and do not involve much learning cost, they cannot support situations where the volume of data increases and data types become more complex. These data processing methods suffer from insufficient capacity, performance degradation, management confusion and other problems, so that they are no longer suitable for modern data management work. For these traditional chemical companies, on the one hand they have many types of specialist data to store, such as simulation data, measurement data, formulation data, component data, process data, etc. On the other hand, they are unable to determine the full database table structure from the outset, and need to change it dynamically during use. This paper designs a new service-oriented data processing for dynamic schema to meet these needs, and applies it to the development of a data center web application platform for a state-owned chemical company.
Mathbot as a Mathmatics Blended Learning Tool

Mathematics concepts are abstract. The famous mathematician, Poincare (1908/52) has already described impressively on students’ difficulties to understand mathematical ideas in the past century (Rosken, B. & Rolka, K., 2007). It is the subject that triggers the strongest negative feelings where students may establish and even result in an attitude of refusal towards mathematics, or may block their thinking processes (Buxton, 1981). Besides, students have difficulty to get individualized support and engage effectively with lecturer in large-scale class at universities (Brinton, Rill, Ha, Chiang, Smith, & Ju, 2015). Various studies shown that lack of individualized support tend to have weak learning outcomes, high dropout and dissatisfaction (Brinton et al., 2015; Eom, Wen, & Ashill, 2006; Hone & El Said, 2016). Conversational agents have the potential as the solution where they are computer programs engage a dialogue with human users in natural language for various purposes. Conversational agents are becoming a ubiquitous trend used in many fields. They are still at the beginning of expansion stage and will spread more in the education field (Chatbot’s Magazine 2017). Its implementation in education is still found very limited (Winkler, R. & Sollner, M., 2018) especially in Malaysia. This project is to design and to develop a conversational agent called Mathbot as an intelligent tutor to help students in learning mathematics and to evaluate the effect of using it as a blended learning tool on students’ mathematics achievement.
An Accurate and Efficient Zero-Crossing Line Classifier for Multiscale Parameter Estimation of Gaussian Signals Subject to Noise

The multiscale parameter estimation framework is a method for estimating the true parameters of signals subject to noise. The method is based on detecting lines of zero-crossings within the Continuous Wavelet Transform and substituting their locations in time into analytical equations directly expressing the unknown signal parameters. Evidently, this approach depends on selecting the correct lines, corresponding to the signal of interest and not to other phenomena related to noise. This task can be posed as the binary classification problem of determining for each zero-crossing line found whether or not it should be used for parameter estimation. It has been shown that even for very high noise levels, a correct classification leads to very accurate estimates, while a wrong classification results in highly inaccurate estimates. Therefore, with this particular approach the classification of zero-crossing lines poses the limiting factor to the accuracy of the estimated parameters. In this work, we propose a novel, efficient and more robust classifier called “stencil operator” which accurately detects the best combination of zero-crossing lines of Gaussian input signals. We evaluate the performance of this new classifier using synthetic Gaussian signals subject to white (Gaussian) noise with signal-to-noise ratios ranging from 50 dB to - 20 dB. By studying the error between estimated and ground truth parameters, we show that the new classifier outperforms the current method for all noise levels considered and for a noise level of e.g. -12 dB improves the median error from 132% to 28%. The proposed classifier pushes the boundary for analyzing heavily disturbed signals using multiscale parameter estimation to a new level.
InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images

In this paper, we present InSeGAN, an unsupervised 3D generative adversarial network (GAN) for segmenting (nearly) identical instances of rigid objects in depth images. Using an analysis-by-synthesis approach, we design a novel GAN architecture to synthesize a multiple-instance depth image with independent control over each instance. InSeGAN takes in a set of code vectors (e.g., random noise vectors), each encoding the 3D pose of an object that is represented by a learned implicit object template. The generator has two distinct modules. The first module, the instance feature generator, uses each encoded pose to transform the implicit template into a feature map representation of each object instance. The second module, the depth image renderer, aggregates all of the single-instance feature maps output by the first module and generates a multiple-instance depth image. A discriminator distinguishes the generated multiple-instance depth images from the distribution of true depth images. To use our model for instance segmentation, we propose an instance pose encoder that learns to take in a generated depth image and reproduce the pose code vectors for all of the object instances. To evaluate our approach, we introduce a new synthetic dataset, consisting of 100,000 depth images, each with 5 instances of an object from one of 10 classes. Our experiments on Insta-10, as well as on real-world noisy depth images, show that InSeGAN achieves state-of-the-art performance, often outperforming prior methods by large margins.
LibGuides: Engaging in Science, Engineering, and Math (SEM) PD 126: Getting Started

Research information for students enrolled in PD 126: Engaging in Science, Engineering, and Math
Adaptive Speed Collision Avoidance For Dynamic Environments

This paper presents a dynamic collision avoidance framework using predictive approaches. This predictive model exploits temporal and spatial characteristics of dynamic obstacles to predict future events thus avoiding collisions. To achieve this we implement a novel combination of two Poisson processes with independent outcomes and test this on a series of dynamic events with a crazyflie drone. Our approach initiates a speed adjustment when needed, which is a natural human response to avoid collisions with obstacles in crowded and congested environments. Our results in simulation and realworld tests indicate that our method removes the need for replanning and reduces the cost of time which is a huge bottleneck in trajectory planning. Our method outperforms baseline algorithms in terms of well-accepted metrics like the number of collisions, percentage accuracy, and execution time.
베이지안 최적화를 통한 저서성 대형무척추동물 종분포모델 개발

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.
FEATURES OF DESIGNING FUZZY REGULATORS IN THE COMPOSITION OF AUTOMATIC CONTROL SYSTEMS OF SEMICONDUCTOR COMPENSATORS

The article discusses the design features of fuzzy controllers as part of automatic control systems for semiconductor compensators. Tendencies to the use of fuzzy logic and neural networks to solve problems with a large number of variables that are random are analyzed. The functional scheme of the automatic control system on the basis of fuzzy logic is considered. Some features of the fuzzy regulator are given. Questions of optimization of parameters of digital fuzzy controllers are investigated. The importance of the choice of the method of parametric adjustment of the fuzzy controller is shown. The options for choosing the quality criterion for fuzzy controllers are considered. The market of commercial software products for working with fuzzy logic is analyzed.
A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images

Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis.
BLOCKCHAIN CHANGING THE CURRENT FINANCIAL ENVIRONMENT

The main focus of this research remains on analysing the merits and limitations of Blockchain Technology in the financial sectors. Furthermore, it has focused on creating a Blockchain-based Framework for successful financial Transaction.The research is a positivism philosophy based Inductive research that has focused on creating research aims and objectives, and is focused on analysing those objectives through discussing existing pieces of literature. Furthermore, the research will focus on being descriptive throughout the whole research. The secondary data collection of the research will collect the current scenario of the topic-based discussion. Furthermore, the qualitative analysis of these secondary data will be done for determining the success of the research.The research will try to evaluate its success by reaching all its objectives. The researcher has already undergone almost ten previous articles, and journals for creating a pattern from the existing Data related to the topic. Now, it will focus on finding out the most suitable Blockchain Technology in creating competency of the financial transactions. Furthermore, the research has focused on providing enhancement of the knowledge related to the advancement of Blockchain Implementation in financial sectors. The research helps in enhancing the periphery of drawbacks, and barriers of implementing Blockchain Technology in the finance sector.
A Multilingual Systematic Review on the Use of Easy Language in Educational Settings

Text simplification techniques have been used for decades with multiple purposes and tested in various linguistic and situational contexts. Over time, different simplified language varieties, such as Easy-to-Read and plain language, have emerged. Although they all aim to enhance textual accessibility, the lack of a solid distinction between them has led to a patchwork of scattered literature, hindering proper advancement of knowledge. Drawing on the results of a previous study investigating the potential of easy language in the classroom, we conducted a systematic review of studies, published in different languages, including English, French and Spanish, examining all forms of text simplification for educational purposes. The review had three main aims, namely to identify empirical work carried out in an educational context (including both process- and product-oriented research), to analyse and compare the methodology and target population of such work, and to flag research gaps within this topic. We have searched a wide range of databases and journals, and selected the studies that match a set of predetermined inclusion criteria. Our preliminary findings suggest that the number of studies with a focus on education is relatively small, compared to similar studies in other fields (e.g. healthcare or administration). Moreover, very few studies address students as a group, most focusing on sub-populations such as students with intellectual disabilities. The results of this review will contribute to shaping our project, which seeks to investigate text simplification as a tool to enhance Universal Design for Learning.
DeriNet 2.1

DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent word-formational relations between a derived word and its base word / words. The present version, DeriNet 2.1, contains 1,039,012 lexemes (sampled from the MorfFlex CZ 2.0 ​dictionary) connected by 782,814 derivational, 50,533 orthographic variant, 1,952 compounding, 295 univerbation and 144 conversion relations.Compared to the previous version, version 2.1 contains annotations of orthographic variants, full automatically generated annotation of affix morpheme boundaries (in addition to the roots annotated in 2.0), 202 affixoid lexemes serving as bases for compounding, annotation of corpus frequency of lexemes, annotation of verbal conjugation classes and a pilot annotation of univerbation. The set of part-of-speech tags was converted to Universal POS from the Universal Dependencies project.
Disentangled Lifespan Face Synthesis

A lifespan face synthesis (LFS) model aims to generate a set of photo-realistic face images of a person's whole life, given only one snapshot as reference. The generated face image given a target age code is expected to be age-sensitive reflected by bio-plausible transformations of shape and texture, while being identity preserving. This is extremely challenging because the shape and texture characteristics of a face undergo separate and highly nonlinear transformations w.r.t. age. Most recent LFS models are based on generative adversarial networks (GANs) whereby age code conditional transformations are applied to a latent face representation. They benefit greatly from the recent advancements of GANs. However, without explicitly disentangling their latent representations into the texture, shape and identity factors, they are fundamentally limited in modeling the nonlinear age-related transformation on texture and shape whilst preserving identity. In this work, a novel LFS model is proposed to disentangle the key face characteristics including shape, texture and identity so that the unique shape and texture age transformations can be modeled effectively. This is achieved by extracting shape, texture and identity features separately from an encoder. Critically, two transformation modules, one conditional convolution based and the other channel attention based, are designed for modeling the nonlinear shape and texture feature transformations respectively. This is to accommodate their rather distinct aging processes and ensure that our synthesized images are both age-sensitive and identity preserving. Extensive experiments show that our LFS model is clearly superior to the state-of-the-art alternatives. Codes and demo are available on our project website: \url{this https URL}.