Reviewerly/exHarmony huggingface dataset:
Super Resolution Techniques for COVID-19 Chest X-ray Images

COVID-19 is a respiratory infectious disease caused by a novel coronavirus-II. COVID-19 has affected a few millions of people across the globe with alarming death toll rate. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. X-ray images inherently possess significant amount of noise, and image resolution depends on radiation dosage. Diagnosis of COVID-19 chest X-rays would be more precise with high-resolution images. The aim of the current work is to explore selected super-resolution techniques for COVID-19 chest X-ray images. The samples are taken from COVID-19 chest X-ray public dataset. We conducted the experiments on COVID-19 chest X-ray public dataset. The super-resolution methods produced high resolution chest X-ray images the clear view of the pulmonary opacities in chest x-ray images. Evaluation metrics used in the experiments demonstrated improvement in overall quality enhancement of the chest X-ray images.
Warning-Introducing Commits vs Bug-Introducing Commits: A tool, statistical models, and a preliminary user study

This paper partially replicates prior works on building historical commits [1], commit risk modeling [2], and a comparison of statistical bug models and static bug finders [3].We examine 8 Maven-based projects with an average lifespan of 5.8 years. To historically build these projects across a total of 45k commits, we develop a series of techniques, such as flexibly selecting the version of a library that is closest to the commit date. We are able to build a per project average of 78.4% of all commits, a doubling in buildability compared to prior work. We also develop a git blame strategy to assign warnings even when a commit does not build.We run JLint and FindBugs and create a logistic regression model to predict if a commit that introduces a warning has higher odds of introducing a bug. The static bug finders model accounted for only 13% of the deviance, while the statistical bug model accounted for 19.5%. We had expected static bug finder warnings to improve the predictive power of models of bug introducing changes, but we clearly attained a negative result.To understand this negative result, we perform a preliminary user study of developers who introduced new warnings in 37 projects. We found that while warnings might not predict bugs, 53% and 21% of warnings in Findbugs and Jlint respectively are useful. We also study whether just-in-time warnings presentation on each commit impacted usefulness. We find that the later a warning is shown to a developer, the less useful it is perceived to be (a median of 11.5 days versus 23 days for non useful warnings).Based on our findings, we modify the existing COMMITGURU interface to add new warnings to the specific line in a changed file. The empirical study data [4] and WARNINGSGURU tool [5] are publicly available.
HSDP: A Hybrid Sampling Method for Imbalanced Big Data Based on Data Partition

The classical classifiers are ineffective in dealing with the problem of imbalanced big dataset classification. Resampling the datasets and balancing samples distribution before training the classifier is one of the most popular approaches to resolve this problem. An effective and simple hybrid sampling method based on data partition (HSDP) is proposed in this paper. First, all the data samples are partitioned into different data regions. Then, the data samples in the noise minority samples region are removed and the samples in the boundary minority samples region are selected as oversampling seeds to generate the synthetic samples. Finally, a weighted oversampling process is conducted considering the generation of synthetic samples in the same cluster of the oversampling seed. The weight of each selected minority class sample is computed by the ratio between the proportion of majority class in the neighbors of this selected sample and the sum of all these proportions. Generation of synthetic samples in the same cluster of the oversampling seed guarantees new synthetic samples located inside the minority class area. Experiments conducted on eight datasets show that the proposed method, HSDP, is better than or comparable with the typical sampling methods for F-measure and G-mean.
Concrete and Abstract Models of Category Learning

In this paper, we compare the rhetoric that sometimes appears in the literature on computational models of category learning with the growing evidence that different theoretical paradigms typically produce similar results. In response, we suggest that concrete computational models, which currently dominate the field, may be less useful than simulations that operate at a more abstract level. We illustrate this point with an abstract simulation that explains a challenging phenomenon in the area of category learning - the effect of consistent contrasts - and we conclude with some general observations about such abstract models.
i-Algebra: Towards Interactive Interpretability of Deep Neural Networks

Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite. Despite the plethora of work on interpreting DNNs, most existing solutions offer interpretability in an ad hoc, one-shot, and static manner, without accounting for the perception, understanding, or response of end-users, resulting in their poor usability in practice. In this paper, we argue that DNN interpretability should be implemented as the interactions between users and models. We present i-Algebra, a first-of-its-kind interactive framework for interpreting DNNs. At its core is a library of atomic, composable operators, which explain model behaviors at varying input granularity, during different inference stages, and from distinct interpretation perspectives. Leveraging a declarative query language, users are enabled to build various analysis tools (e.g., ``drill-down'', ``comparative'', ``what-if'' analysis) via flexibly composing such operators. We prototype i-Algebra and conduct user studies in a set of representative analysis tasks, including inspecting adversarial inputs, resolving model inconsistency, and cleansing contaminated data, all demonstrating its promising usability.
Advances in speech vocoding for text-to-speech with continuous parameters

Vocoders received renewed attention as main components in statistical parametric text-to-speech (TTS) synthesis and speech transformation systems. Even though there are vocoding techniques give almost accepted synthesized speech, their high computational complexity and irregular structures are still considered challenging concerns, which yield a variety of voice quality degradation. Therefore, this paper presents new techniques in a continuous vocoder, that is all features are continuous and presents a flexible speech synthesis system. First, a new continuous noise masking based on the phase distortion is proposed to eliminate the perceptual impact of the residual noise and letting an accurate reconstruction of noise characteristics. Second, we addressed the need of neural sequence to sequence modeling approach for the task of TTS based on recurrent networks. Bidirectional long short-term memory (LSTM) and gated recurrent unit (GRU) are studied and applied to model continuous parameters for more natural-sounding like a human. The evaluation results proved that the proposed model achieves the state-of-the-art performance of the speech synthesis compared with the other traditional methods.
Image Semantic Segmentation Based on Dilated Convolution and Multi-Layer Feature Fusion

At present, most of the research methods of image semantic segmentation are based on Fully Convolutional Networks (FCN). However, FCN will cause the loss of image feature information when performing image semantic segmentation, and the details of the output image will not be processed well. Therefore, we propose to take the ResNet network as the encoder basic network. Using dilated convolution to extract context information, and designing a multi-scale feature fusion method in the decoder to make full use of features from each level to enrich representative ability of feature points, so that it can classify image pixels well. Extensive experiments demonstrate that our method shows superior performance over other methods on the PASCAL VOC2012 [10]validation dataset.
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network

While over-parameterization is widely believed to be crucial for the success of optimization for the neural networks, most existing theories on over-parameterization do not fully explain the reason---they either work in the Neural Tangent Kernel regime where neurons don't move much, or require an enormous number of neurons. In practice, when the data is generated using a teacher neural network, even mildly over-parameterized neural networks can achieve 0 loss and recover the directions of teacher neurons. In this paper we develop a local convergence theory for mildly over-parameterized two-layer neural net. We show that as long as the loss is already lower than a threshold (polynomial in relevant parameters), all student neurons in an over-parameterized two-layer neural network will converge to one of teacher neurons, and the loss will go to 0. Our result holds for any number of student neurons as long as it is at least as large as the number of teacher neurons, and our convergence rate is independent of the number of student neurons. A key component of our analysis is the new characterization of local optimization landscape---we show the gradient satisfies a special case of Lojasiewicz property which is different from local strong convexity or PL conditions used in previous work.
Training ELECTRA Augmented with Multi-word Selection

Pre-trained text encoders such as BERT and its variants have recently achieved state-of-the-art performances on many NLP tasks.While being effective, these pre-training methods typically demand massive computation resources.To accelerate pre-training, ELECTRA trains a discriminator that predicts whether each input token is replaced by a generator.However, this new task, as a binary classification, is less semantically informative.In this study, we present a new text encoder pre-training method that improves ELECTRA based on multi-task learning.Specifically, we train the discriminator to simultaneously detect replaced tokens and select original tokens from candidate sets.We further develop two techniques to effectively combine all pre-training tasks: (1) using attention-based networks for task-specific heads, and (2) sharing bottom layers of the generator and the discriminator.Extensive experiments on GLUE and SQuAD datasets demonstrate both the effectiveness and the efficiency of our proposed method.
Bodo Resources for NLP - An Overview of Existing Primary Resources for Bodo

With over 1.4 million Bodo speakers, there is a need for Automated Language Processing systems such as Machine translation, Part Of Speech tagging, Speech recognition, Named Entity Recognition, and so on. In order to develop such a system it requires a sufficient amount of dataset. In this paper we present a detailed description of the primary resources available for Bodo language that can be used as datasets to study Natural Language Processing and its applications. We have listed out different resources available for Bodo language: 8,005 Lexicon dataset collected from agriculture and health, Raw corpus dataset of 2,915,544 words, Tagged corpus consisting of 30,000 sentences, Parallel corpus of 28,359 sentences from tourism, agriculture and health and Tagged and Parallel corpus dataset of 37,768 sentences. We further discuss the challenges and opportunities present in Bodo language.
The digital ecosystem of teacher education

This paper aims to improve the effectiveness of the DESTE adaptive development, considering both intramural and regional educational needs and trends of the rapidly changing digital world. Empirical and analytical research methods were used. Analysis and synthesis of domestic and foreign researches in the field of creating educational ecosystems in the conditions of digitalization in education were performed. The results form the basis of an adaptive approach to create the DESTE and investigate its effectiveness in improving the quality of learning, which was performed using regression analysis. The paper substantiates the necessity and timeliness of creating the DESTE as a condition for accelerating intramural and cooperative (together with partners) innovative educational developments for their further transfer. The key features of DESTE and its significance for the training of digital education specialists were identified. A local digital ecosystem to study the "Information and Communication Technologies" subject was developed as a part of the university DESTE.
Vibra

Variable Bitrate (VBR) video encoding can provide much high quality-to-bits ratio compared to the widely adopted Constant Bitrate (CBR) encoding, and thus receives significant attentions by content providers in recent years. However, it is challenging to design efficient adaptive bitrate algorithms for VBR-encoded videos due to the sharply fluctuating chunk size and the resulting bitrate burstiness. In this paper, we propose a neural adaptive streaming framework called Vibra for VBR-encoded videos, which can well accommodate the high fluctuation of video chunk sizes and improve the quality-of-experience (QoE) of end users significantly. Our framework takes the characteristics of VBR-encoded videos into account, and adopts the technique of deep reinforcement learning to train a model for bitrate adaptation. We also conduct extensive trace-driven experiments, and the results show that Vibra outperforms the state-of-the-art ABR algorithms with an improvement of 8.17% -- 29.21% in terms of the average QoE.
Multiple Object Detection

In the cutting-edge and swift world, finding a precise and proficient object recognition for headway in computer vision frameworks has been an integral part. With the emerge of faster (due to faster and better hardware too) and increasingly accurate deep learning techniques the prediction for finding a good and accurate model is of paramount importance. In this undertaking, we are aiming to incorporate the best possible technique to tackle object detection, with the goal of achieving higher accuracy in our Multiple Object Detection project. The major motivation in our task is to make colossal contribution to increasing the accuracy of object detection. In this venture, we resort to four models to tackle the problem, mainly Mask-RCNN (Mask Region Convolution Neural Network), Faster-RCNN (Faster Region Convolution Neural Network), SSD (Single Shot MultiBox Detection), and YOLO (You Look Only Once) trained on the most publicly and freely accessible dataset (MS-COCO).
Attention-Based DSM Fusion Network for Semantic Segmentation of High-Resolution Remote-Sensing Images

For semantic segmentation of high-resolution remote-sensing images, digital surface models (DSMs) information is useful for improving the accuracy and robustness of the segmentation models. However, since the feature distributions of spectral and DSM images vary significantly in different scenes, it is difficult to fuse them effectively in popular deep network models. To solve this issue, we propose an attention-based DSM fusion network (ADF-Net) for high-resolution remote-sensing image semantic segmentation. The proposed network makes two contributions. The first is that we design an attention-based feature fusion module, which can selectively gather features from spectral and DSM information by channel attention mechanism, and further combine them to get high-quality fusion features. The second is that we introduce a residual feature refinement module to reduce the redundant information from skip connection adaptively. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets, experimental results demonstrate that our model outperforms state-of-the-art methods.
Analysis of a Pre-Stressed Quadcopter Propeller Using Finite element Approach

Quadcopter, a mechatronic device is now widely used due to its simplestructure and vertical take-off and landing capability. It consists of fourpropellers which can lift and propelled the Quadcopter in 3-D space. Thedifference in angular speeds of propellers works as a steering system andresponsible for attitude and altitude motion. Quadcopter self weight and payload is carried by the propellers itself, and a bad design of the propellermay lead to crack. Quadcopter propellers are also subjected to thrust forceand vibration during the flight. The main objective of this study is to findout the natural failure frequency of propeller. Two types of propellers havebeen designed in Creo 2.0 and analyzed in Ansys 16.2 for their vibrationfrequencies. Both the designs are analyzed with or without the thrust forceunder the vibration frequency. First six vibration modes have been calculatedfor both the propeller designs with Carbon Fiber Reinforced Polymer (CFRP)material. The obtained simulation results have been compared and analyzedto sustain the failure frequency.
Bridging the Semantic Gap in Continuous Auditing Knowledge Representation

Due to its promised advantages, Continuous Auditing has become a research direction in the computer technology-assisted audit field. Unfortunately, auditors usually cannot thoroughly carry out the Continuous Auditing method because of their lack of Information Technology proficiency and inefficient communication as well as interaction with IT personnel and information system. Overcoming the “semantic gap” between heterogeneous information sources to facilitate an understanding of Continuous Auditing concept domain is a key challenge to support the Continuous Auditing implementation. The primary aim of this study is to bridge the “Semantic Gap” between auditors and information system in Continuous Auditing knowledge. It is necessary to narrow the semantic gap between high-level concepts employed by the auditor and low-level feature presentations of the system to create Continuous Auditing capacity.
A Textbook Evaluation On English Textbook Entitled “Grow With English” Used By Students Of MI Nurul Huda Bengkulu City

Based on the perception of the teacher and students in Madrasah Ibtidaiyyah (MI) Nurul Huda Bengkulu City when the researchers conducted an observation, the textbook is suitable for use in the teaching and learning process. Based on those perceptions, this study was intended to evaluate the quality on English textbooks Grow with English published by Erlangga Publisher used by students grade V and IV of MI Nurul Huda Bengkulu city with how much it meets the requirement of good EFL textbook considered four criteria, namely: (1) Subjects and Contents; (2) Skills and Sub-skills; (3) Layout and Physical Make-up; and (4) Practical Consideration viewed by teacher and see students perception about these textbooks. This study used a quantitative research approach. Total population of this study were 114 students and 1 english teacher. Samples of this research were 25% of the population or 29 students which divided into 2 grades. 15 students from 5th grade and 14 students taken from 4th grade. In collecting data, researchers used checklist as the instrument which adapted from Demir and Ertas. Both textbooks was qualified based on the score ranges which divided into very bad (0-20), bad (21-40), fairly good (41-60), good (61-80), very good (81-100). The result of this study was first, Grow with English for grade five was categorized as good toward the four criteria of good EFL textbook requirements. It reached 79,78% of total score while 80,48% by students’ perception and 79,08% viewed by teachers’s perspective. Second, Grow with English for grade four was categorized as good because the total score toward the four criteria is 77,77%. The total score was gained from 80,9% based on students’ perception and 74,65% viewed by teacher’s perspective. So based on the result of this study, it can concluded that both of textbooks met the criteria in fulfilling the requirements of good EFL textbook criteria covering the availability of materials based on Subjects and Contents, Skills and Sub-skills, Layout and Physical Make-up, and the Practical.
The Effects of Adding Reachability Predicates in Quantifier-Free Separation Logic

The list segment predicate ls used in separation logic for verifying programs with pointers is well suited to express properties on singly-linked lists. We study the effects of adding ls to the full quantifier-free separation logic with the separating conjunction and implication, which is motivated by the recent design of new fragments in which all these ingredients are used indifferently and verification tools start to handle the magic wand connective. This is a very natural extension that has not been studied so far. We show that the restriction without the separating implication can be solved in polynomial space by using an appropriate abstraction for memory states, whereas the full extension is shown undecidable by reduction from first-order separation logic. Many variants of the logic and fragments are also investigated from the computational point of view when ls is added, providing numerous results about adding reachability predicates to quantifier-free separation logic.
Accelerating Large-Scale Deep Convolutional Neural Networks on Multi-core Vector Accelerators

This paper proposes an efficient algorithm mapping method for accelerating deep convolutional neural networks, which includes: (1) Proposing an efficient transformation method, which converts CNN’s convolutional layer and fully connected layer computations into efficient large-scale matrix multiplication computations, and converts pooling layer computations into efficient matrix row computations; (2) Designing a set of general and efficient vectorization method for convolutional layer, fully connected layer and pooling layer on the vector accelerator. The experimental results on the accelerator show that the average computing efficiency of convolution layer and full connected layer of AlexNet, VGG-19, GoogleNet and ResNet-50 are 93.3% and 93.4% respectively, and the average data access efficiency of pooling layer is 70%.
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example.

Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response to inputs drawn from the test set. However, the test set may have few examples that trigger some model behaviors, such as high-confidence failures or ambiguous classifications. To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx. Given a data distribution, Bayes-TrEx finds in-distribution examples which trigger a specified prediction confidence. We demonstrate several use cases of Bayes-TrEx, including revealing highly confident (mis)classifications, visualizing class boundaries via ambiguous examples, understanding novel-class extrapolation behavior, and exposing neural network overconfidence. We use Bayes-TrEx to study classifiers trained on CLEVR, MNIST, and Fashion-MNIST, and we show that this framework enables more flexible holistic model analysis than just inspecting the test set. Code and supplemental material are available at https://github.com/serenabooth/Bayes-TrEx.