Reviewerly/exHarmony huggingface dataset:
FINE-TUNING DEEP LEARNING MODELS FOR PEDESTRIAN DETECTION

Object detection in high resolution images is a new challenge that the remote sensing community is facing thanks to introduction of unmanned aerial vehicles and monitoring cameras. One of the interests is to detect and trace persons in the images. Different from general objects, pedestrians can have different poses and are undergoing constant morphological changes while moving, this task needs an intelligent solution. Fine-tuning has woken up great interest among researchers due to its relevance for retraining convolutional networks for many and interesting applications. For object classification, detection, and segmentation fine-tuned models have shown state-of-the-art performance. In the present work, we evaluate the performance of fine-tuned models with a variation of training data by comparing Faster Region-based Convolutional Neural Network (Faster R-CNN) Inception v2, Single Shot MultiBox Detector (SSD) Inception v2, and SSD Mobilenet v2. To achieve the goal, the effect of varying training data on performance metrics such as accuracy, precision, F1-score, and recall are taken into account. After testing the detectors, it was identified that the precision and recall are more sensitive on the variation of the amount of training data. Under five variation of the amount of training data, we observe that the proportion of 60%-80% consistently achieve highly comparable performance, whereas in all variation of training data Faster R-CNN Inception v2 outperforms SSD Inception v2 and SSD Mobilenet v2 in evaluated metrics, but the SSD converges relatively quickly during the training phase. Overall, partitioning 80% of total data for fine-tuning trained models produces efficient detectors even with only 700 data samples.
A hybrid equilibrium optimizer algorithm for multi-level image segmentation

<abstract> Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time. </abstract>
Predicting malaria epidemics in Burkina Faso with machine learning

Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here we present the first data-driven malaria epidemic early warning system that can predict the 13-week case rate in a primary health facility in Burkina Faso. Using the extraordinarily high-fidelity data of infant consultations taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the weekly number of malaria cases over a 13 week period. We test our algorithm on historical epidemics and find that for our lowest threshold for an epidemic alert, our algorithm has 30% precision with > 99% recall at raising an alert. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively.
Security Analysis of Quantum Lightning

Quantum lightning is a new cryptographic object that gives a strong form of quantum money. Zhandry recently defined quantum lightning and proposed a construction of it based on superpositions of low-rank matrices. The scheme is unusual, so it is difficult to base the scheme’s security on any widespread computational assumptions. Instead, Zhandry proposed a new hardness assumption that, if true, could be used to prove security. In this work, we show that Zhandry’s hardness assumption is in fact false, so the proof of security does not hold. However, we note that the proposal for quantum lightning has not been proven insecure. This work is the first step in analyzing the security of [3]’s proposal and moving toward a scheme that we can prove to be secure.
Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text

Code-mixing is a frequent communication style among multilingual speakers where they mix words and phrases from two different languages in the same utterance of text or speech.Identifying and filtering code-mixed text is a challenging task due to its co-existence with monolingual and noisy text.Over the years, several code-mixing metrics have been extensively used to identify and validate codemixed text quality.This paper demonstrates several inherent limitations of code-mixing metrics with examples from the already existing datasets that are popularly used across various experiments.
Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: <b>MixGHD</b> <i>R</i> package

The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach is suitable for data that can be considered a realization of a (multivariate) continuous random variable. The GHD has the advantage of being flexible due to skewness, concentration, and index parameters; as such, clustering methods that use this distribution are capable of estimating clusters characterized by different shapes. The package provides five different models all based on the GHD, an efficient routine for discriminant analysis, and a function to measure cluster agreement. This paper is split into three parts: the first is devoted to the formulation of each method, extending them for classification and discriminant analysis applications, the second focuses on the algorithms, and the third shows the use of the package on real datasets.
Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors

Transformer networks have revolutionized NLP representation learning since they were introduced.Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient.One important reason is that there lack enough visualization tools for detailed analysis.In this paper, we propose to use dictionary learning to open up these 'black boxes' as linear superpositions of transformer factors.Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors, e.g., word-level polysemy disambiguation, sentence-level pattern formation, and long-range dependency.While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights.We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work.
Scholarly Information Share through Social Networks (SNs) and Medias among Social Science Scholars in selected State Universities in Tamilnadu.

The study analyzed that use of social network and Medias by theResearch scholars in Selected Universities of Tamilnadu.To highest 87 (31.9%) of respondents Annamalai University,(16.8%) and (15%) of them Male and Female respectively out of 273 total respondents responded to the study.It is analysed More numbers of respondents (17.2%) of them 26-35. It followed by (27.1%) of respondents Management/ Commerce.The majority (83.5%) of respondents using “Whaatsapp” out of respondents. The respondents reported (77.7%) and (73.6%) using devices tools “Laptop” and Smartphone/Mobiles are respectively. The majority 179(65.6%) of the respondents witnessed to search “Daily” followed by 65(23.8%) of the respondents reported that Send/Receive a Messages “Rarely”.The majority 169(61.9%) of the respondents use SNs “Daily” reported SNs can be used PDF from can Read articles/Books etc. preference of research that majority 96(35.2%) of the respondents preference to “Obtain Natural/Raw data On Human Information Behavior” replied their option “Agree” (26.7%) of them less that Stated “Very Strongly Agree”.The majority 90(33%), 76(27.8) and 51(18.7%) of the respondents of them recorded that “Strongly Agree”, “Agree”, and “No Comment” respectively to prefer “Easy to access massive amount of data to analyse”.
Density Estimation in Representation Space to Predict Model Uncertainty

Deep learning models frequently make incorrect predictions with high confidence when presented with test examples that are not well represented in their training dataset. We propose a novel and straightforward approach to estimate prediction uncertainty in a pre-trained neural network model. Our method estimates the training data density in representation space for a novel input. A neural network model then uses this information to determine whether we expect the pre-trained model to make a correct prediction. This uncertainty model is trained by predicting in-distribution errors, but can detect out-of-distribution data without having seen any such example. We test our method for a state-of-the art image classification model in the settings of both in-distribution uncertainty estimation as well as out-of-distribution detection.
Broadening Narrow Money: Monetary Policy with a Central Bank Digital Currency

This paper discusses central bank digital currency (CBDC) and its potential impact on the monetary transmission mechanism. We first offer a general definition of CBDC which should make the concept accessible to a wide range of economists and policy practitioners.We then investigate how CBDC could affect the various stages of transmission, from markets for central bank money to the real economy. We conclude that monetary policy would be able to operate much as it does now, by varying the price or quantity of central bank money. Transmission may even be strengthened for a given change in policy instruments.
Training Data Augmentation for Code-Mixed Translation

Machine translation of user-generated codemixed inputs to English is of crucial importance in applications like web search and targeted advertising.We address the scarcity of parallel training data for training such models by designing a strategy of converting existing non-code-mixed parallel data sources to codemixed parallel data.We present an mBERT based procedure whose core learnable component is a ternary sequence labeling model, that can be trained with a limited code-mixed corpus alone.We show a 5.8 point increase in BLEU on heavily code-mixed sentences by training a translation model using our data augmentation strategy on an Hindi-English codemixed translation task.
Code completion for programming education based on deep learning

In solving programming problems, it is difficult for beginners to create a program from scratch. One way to navigate this difficulty is to suggest the next word following an incomplete program. In the present study, we propose a method for code completion characterised by two principal elements: the prediction of the next within-vocabulary word and the prediction of the next referenceable identifier. For the prediction of within-vocabulary words, a neural language model based on an LSTM network with an attention mechanism is proposed. Additionally, for the prediction of referenceable identifiers, a model based on a pointer network to a given incomplete program is proposed. For evaluation of the proposed method, source code accumulated in an online judge system is used. The results of the experiment demonstrate that in both statically and dynamically typed languages, the proposed method can predict the next word to a high degree of accuracy.
Aspects Based Opinion Mining for Teacher and Course Evaluation

Teacher and course evaluation by students at the end of each term&nbsp; is an important task in almost every academic institution world wide. It helps&nbsp; in assessing faculty performance and suitability of the course in any academic program. The data collected from evaluation comprises of two parts|Likert&nbsp; Scale and open-ended feedback. Computationally, the Likert Scale form can be&nbsp; handled easily as it is numerical in nature but to handle open-ended feedback&nbsp; is a challenging task. Presently, in most of the organizations, it is processed&nbsp; manually, which is error-prone, tedious and full of human biases. To solve this&nbsp; problem, this paper proposes a two-step strategy based on Machine Learning and Natural Language Processing (NLP) techniques. The first step is to&nbsp; extract overall topic of the feedback text using supervised machine learning&nbsp; followed by exploitation of NLP rules to find out specific aspect about which&nbsp; the feedback is given along with orientation of the opinion either positive, negative or neutral. Using, this two-step strategy combining with NLP, machine&nbsp; learning techniques and data from past seven years of real feedback at a public&nbsp; sector university in Pakistan, we are able to achieve a recall and precision of&nbsp; 83.89% and 84% on topic identification i.e. to classify a feedback in teacher and&nbsp; course category. The system is able to extract different aspects of teacher and&nbsp; course with a precision of 83% and recall of 80%, whereas overall sentiment&nbsp; classification accuracy is 90%.
Strategic Information Moderated By Effectiveness Management Accounting Information Systems: Business Strategy Approach

The industrial revolution 4.0 increases business competition and the increasing need for accounting information for strategic decision making. Accounting information is financial information needed by managers to predict and determine the company's strategic policies in the future, however, the current condition of many companies has not been able to produce quality accounting information which has an impact on the production of inappropriate financial policies. This study aims to determine how much the business strategy affects the effectiveness of the use of accounting software and how much the effectiveness of accounting software affects the quality of accounting information. The population in this study were 472 managers of state-owned companies and a total sample of 312 managers of BUMN which were determined by simple random sampling technique. Testing data using covariance-based structural equation modeling (SEM) with Lisrell software. The results of this study prove that State-Owned Enterprises in Indonesia effective application software affects quality accounting information, as well as the company's business strategy affects the effectiveness of accounting software.
DoT: An efficient Double Transformer for NLP tasks with tables

Transformer-based approaches have been successfully used to obtain state-of-the-art accuracy on natural language processing (NLP) tasks with semi-structured tables.These model architectures are typically deep, resulting in slow training and inference, especially for long inputs.To improve efficiency while maintaining a high accuracy, we propose a new architecture, DoT , a double transformer model, that decomposes the problem into two sub-tasks: A shallow pruning transformer that selects the top-K tokens, followed by a deep task-specific transformer that takes as input those K tokens.Additionally, we modify the task-specific attention to incorporate the pruning scores.The two transformers are jointly trained by optimizing the task-specific loss.We run experiments on three benchmarks, including entailment and question-answering.We show that for a small drop of accuracy, DoT improves training and inference time by at least 50%.We also show that the pruning transformer effectively selects relevant tokens enabling the end-to-end model to maintain similar accuracy as slower baseline models.Finally, we analyse the pruning and give some insight into its impact on the task model.
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation

In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.TextBox aims to support a broad set of text generation tasks and models.In our library, we implement 21 text generation models on 9 benchmark datasets, covering the categories of VAE, GAN, and pretrained language models.Meanwhile, our library maintains sufficient modularity and extensibility by properly decomposing the model architecture, inference, and learning process into highly reusable modules, which allows users to easily incorporate new models into our framework.The above features make TextBox especially suitable for researchers and practitioners to quickly reproduce baseline models and develop new models.TextBox is implemented based on PyTorch, and released under Apache License 2.
Impacts of openness on financial development in developing countries: Using a Bayesian model averaging approach

This study investigates the influences of trade and financial openness on financial development over the period 2003–2017 from a sample of 64 developing countries, employing a Bayesian model averaging approach to take into account model uncertainty. The results demonstrate that the contribution of trade openness to financial development is important in developing economies with better institutions. However, financial openness has an insignificant positive effect on financial development. There is no evidence to support the Rajan and Zingales hypothesis that the simultaneous openness to both trade and capital flows promotes financial development. Our findings also indicate that a better institutions environment allows a developing economy to exploit the benefits of openness to financial development.
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains

Large pretrained models have achieved great success in many natural language processing tasks.However, when they are applied in specific domains, these models suffer from domain shift and bring challenges in fine-tuning and online serving for latency and capacity constraints.In this paper, we present a general approach to developing small, fast and effective pretrained models for specific domains.This is achieved by adapting the offthe-shelf general pretrained models and performing task-agnostic knowledge distillation in target domains.Specifically, we propose domain-specific vocabulary expansion in the adaptation stage and employ corpus level occurrence probability to choose the size of incremental vocabulary automatically.Then we systematically explore different strategies to compress the large pretrained models for specific domains.We conduct our experiments in the biomedical and computer science domain.The experimental results demonstrate that our approach achieves better performance over the BERT BASE model in domain-specific tasks while 3.3× smaller and 5.1× faster than BERT BASE .The code and pretrained models are available at https://aka.ms/adalm.
The Correlation Between Students’ Reading Anxiety and Their Reading Comprehension in ESP Context

This study aimed to analyze the relationship between reading anxiety and reading comprehension of ESP students in Universitas Muhammadiyah Malang. The participants in this study were 50 first-year Governmental Science students who took intensive reading I. This study used correlational design to analyze the relationship between two variables (Reading anxiety and Reading comprehension). In collecting the data, the English Foreign Language Reading Anxiety Inventory (EFLRAI) questionnaire was used to investigate the level and potential causes of reading anxiety. Moreover, in assembling students' reading comprehension data, the researcher constructed a reading comprehension test specifically for governmental science students. Based on the study results, the non-English department students belong to moderate a level of reading anxiety. Specifically, reading anxiety is divided into three main aspects; top-down reading anxiety, bottom-up reading anxiety, and classroom reading anxiety. It was found out that the most potent sources of reading anxiety were in bottom-up reading anxiety, namely unclear-meaning vocabulary in English written text. Regarding students reading comprehension level, the ESP students have a low reading comprehension score. Besides, the research findings illustrated that there is a negative moderate-strong relationship between reading anxiety and Comprehension of ESP students. In means, the higher anxiety the students have, the lower their comprehension score they obtained. Thus, by knowing this, the teacher should select a carefully teaching method in their ESP classroom to decrease their reading anxiety and encourage them to improve their reading comprehension and deal with their anxiety.
Towards multilingual end‐to‐end speech recognition for air traffic control

In this work, an end-to-end framework is proposed to achieve multilingual automatic speech recognition (ASR) in air traffic control (ATC) systems. Considering the standard ATC procedure, a recurrent neural network (RNN) based framework is selected to mine the temporal dependencies among speech frames. Facing the distributed feature space caused by the radio transmission, a hybrid feature embedding block is designed to extract high-level representations, in which multiple convolutional neural networks are designed to accommodate different frequency and temporal resolutions. The residual mechanism is performed on the RNN layers to improve the trainability and the convergence. To integrate the multilingual ASR into a single model and relieve the class imbalance, a special vocabulary is designed to unify the pronunciation of the vocabulary in Chinese and English, i.e., pronunciation-oriented vocabulary. The proposed model is optimized by the connectionist temporal classification loss and is validated on a real-world speech corpus (ATCSpeech). A character error rate of 4.4% and 5.9% is achieved for Chinese and English speech, respectively, which outperforms other popular approaches. Most importantly, the proposed approach achieves the multilingual ASR task in an end-to-end manner with considerable high performance.