Reviewerly/exHarmony huggingface dataset:
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance

Training a classification model on a dataset where the instances of one class outnumber those of the other class is a challenging problem. Such imbalanced datasets are standard in real-world situations such as fraud detection, medical diagnosis, and computational advertising. We propose an iterative data augmentation method, MixBoost, which intelligently selects (Boost) and then combines (Mix) instances from the majority and minority classes to generate synthetic hybrid instances that have characteristics of both classes. We evaluate MixBoost on 20 benchmark datasets, show that it outperforms existing approaches, and test its efficacy through significance testing. We also present ablation studies to analyze the impact of the different components of MixBoost.
Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data

Ki-67 labelling index is a biomarker which is used across the world to predict the aggressiveness of cancer. To compute the Ki-67 index, pathologists normally count the tumour nuclei from the slide images manually; hence it is timeconsuming and is subject to inter pathologist variability. With the development of image processing and machine learning, many methods have been introduced for automatic Ki-67 estimation. But most of them require manual annotations and are restricted to one type of cancer. In this work, we propose a pooled Otsu's method to generate labels and train a semantic segmentation deep neural network (DNN). The output is postprocessed to find the Ki-67 index. Evaluation of two different types of cancer (bladder and breast cancer) results in a mean absolute error of 3.52%. The performance of the DNN trained with automatic labels is better than DNN trained with ground truth by an absolute value of 1.25%.
Hydrodynamical self-interference of a scattered polariton quanta

Abstract Researchers have observed the free-propagation of a single microcavity polariton directly and its self-interference when scattering upon a defect. These experimental observations of quantum hydrodynamics in the single polariton limit test the wave-particle duality and aid in the development of polariton-based photonic circuits in quantum information processing.
A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

In the field of correlation filter object tracking, the traditional template-update method easily causes template drift, so it performs poorly in complex scenes. To enhance the robustness of the template, a novel incremental multi-template update strategy is proposed in this paper. We find that reliability varies among all historical filters and that highly reliable filters are key to achieving accurate tracking. The incremental multi-template update strategy combines the local maximum-reliability filter template with the historical filter template incrementally, which is obviously different from the traditional update method. We apply this strategy to two trackers with superior performance. The experimental results of three test benchmarks, including the VOT2016, OTB100 and UAV123 datasets, show that the performance of our trackers is superior to that of the state-of-the-art trackers.
Medical Image Digital Watermarking Algorithm Based on DWT-DFRFT

In order to solve the security problem of medical information and improve the robustness of watermarking algorithms for medical image, a noval watermarking algorithm based on discrete wavelet transform and discrete fractional fourier transform (DWT-DFRFT) was proposed. First, DWT-DFRFT transform was applied to extract the feature vectors of original medical images. And combined with cryptography technology, the extracted feature vectors are represented by a 32-bit symbol sequence, which is derived from the transformation of 4 * 4 submatrix coefficients of the middle part extracted from the energy distribution after DFRFT transformation. Then, the watermark was encrypted with tent chaotic mapping, and the encrypted binarized watermark was associated with the feature vector to realize zero watermarking and blind extraction. After performed both conventional attacks and geometric attacks on medical images, the algorithm is proved to be robust. The experimental results show that it can resist both conventional attacks and geometric attacks, and performs well under geometric attacks.
Sagitta of ophthalmic lenses

Abstract Purpose To propose alternative formulas for the sagitta calculation of ophthalmic lenses. Methods Equation factoring. Results The conventional sagitta formula is algebraically inconsistent when applied to curves with direction‐oriented radii. It is possible to correct this problem by factoring it. Conclusions The conventional sagitta formula can and should be replaced by unambiguous equations.
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data

Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data. However, collecting large-scale dialogue data is usually time-consuming and labor-intensive. To address this data dilemma, we propose a novel data augmentation method for training open-domain dialogue models by utilizing unpaired data. Specifically, a data-level distillation process is first proposed to construct augmented dialogues where both post and response are retrieved from the unpaired data. A ranking module is employed to filter out low-quality dialogues. Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data. Automatic and manual evaluation indicates that our method can produce high-quality dialogue pairs with diverse contents, and the proposed data-level and model-level dialogue distillation can improve the performance of competitive baselines.
Implementation of Recommender System Using Neural Networks and Deep Learning

Recommender systems is a subclass of data filtering system that seeks to predict the “rating” or “preference” a user would give to an item. It has become ubiquitous nowadays. This paper is an attempt to understand the different kinds of recommendation systems and compare their performance on the MovieLens dataset. Further, a scalable model has been developed to perform this analysis. This paper is started by preparing and comparing the two most efficient models. The performance is checked on the different types of data like in the form of text, images, etc. This paper, therefore, gives the conclusion about when to use which model and a comparison which is not yet efficiently worked upon.
Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning

MR images of knee joint are usually collected in axial, coronal, and sagittal views with large slice spacing for clinical study. Current methods either segment images in different views separately or apply super-resolution fusion before 3D segmentation. Knee images segmentation transfer between different views is still an open problem. Moreover, the majority of manual labelling works focus on the sagittal-view, and practically it is hard to collect label maps for the coronal- and axial-views, which are also invaluable for observing knee injuries. In this paper, we propose a novel algorithm to transfer sagittal-view annotations to the other views. First, we build a supervised low-resolution segmentation (LR-Seg) module based on the down-sampled sagittal-view slices to obtain the label map on the target view. And then a context transfer module is proposed to refine the segmentations using target-view context. Then by iterative learning of these two modules, the context from one result can be used to guide the training of the other. Experimental results show that our algorithm can greatly alleviate the burden of manually labeling works from clinicians and gain comparable segmentation results on axial and coronal views.
Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber Continuity

In this paper, we introduce a technique for super-resolution reconstruction of diffusion MRI, harnessing fiber-continuity (FC) as a constraint in a global whole-brain optimization framework. FC is a biologically-motivated constraint that relates orientation information between neighboring voxels. We show that it can be used to effectively constrain the inverse problem of recovering high-resolution data from low-resolution data. Since voxels are inter-related by FC, we devise a global optimization framework that allows solutions pertaining to all voxels to be solved simultaneously. We demonstrate that the proposed super-resolution framework is effective for diffusion MRI data of a glioma patient, a healthy subject, and a macaque.
Undefeatable System Using Machine Learning

In machine learning, computer learns from its own experience, i.e., it stores the tasks it performs and uses it as and when required. Machine learning is widely used for gaming as it consists of many algorithms which help the computer to self-learn and become an undefeatable system. Initially, the computer will have no domain knowledge except the rules of the game but gradually it will learn the game, and when all the combinations of the moves which can be played are learnt, it will become the master player, i.e., undefeatable. Games like chess, Go, Shogi, i.e., Japanese chess use reinforcement learning which comes under the domain of machine learning and maximizes the rewards [1–3]. The learning phase of the computer includes both self-learning and learning by playing with human. The paper presents a brief study of machine learning technologies which helps in making the system undefeatable. That includes Integration of Machine Learning for Behavior Modeling, Reinforcement Learning, and Goal-oriented Behavior.
Improving the management of poultry production: Mobile application development and validation

The broiler production chain is productive, and chicken meat has achieved high export rates to several countries in the world. This study aimed to develop a mobile application that helps the producer to audit the issues of good production practices. The application was developed for Android and programmed in Java. For its development, questions were used, with different weights according to their importance for production. These questions were removed from the items that make up the manuals of good practices used in Brazil. A test was carried out with users to determine the possibility of using the application in the field. After answering all the questions, the user gets a score ranging from 5 to 1 (excellent to bad). It also indicates which questions they did agree with good practices. The test with users showed that the application was easy to understand and enabled users to make decisions that would improve broiler production.
Extending the Integrity Constraint Support in a Graph Database

The large volume of data that has been produced every day has encouraged the development of new data storage technologies, including graph databases, which have experienced a growing adoption rate in recent years. On the other hand, the graph database management systems that are commercially available nowadays still lack means to ensure the consistency of stored data. In this scenario, this work proposes the extension of a graph database management system in order to add support for four new integrity constraints, allowing the definition of: conditions on node attributes, required edges, type of in/out nodes of an edge and edge cardinality. Support for these constraints was added to a modified version of OrientDB and experimental results show improvements in the performance of applications with integrity constraints.
SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task

In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task. In this shared task, we participated in four translation directions of three language pairs: English-Chinese, English-Polish on supervised machine translation track, German-Upper Sorbian on low-resource and unsupervised machine translation tracks. Based on different conditions of language pairs, we have experimented with diverse neural machine translation (NMT) techniques: document-enhanced NMT, XLM pre-trained language model enhanced NMT, bidirectional translation as a pre-training, reference language based UNMT, data-dependent gaussian prior objective, and BT-BLEU collaborative filtering self-training. We also used the TF-IDF algorithm to filter the training set to obtain a domain more similar set with the test set for finetuning. In our submissions, the primary systems won the first place on English to Chinese, Polish to English, and German to Upper Sorbian translation directions.
Is Team Always Right: Producing Risk Aware Effort Estimates in Agile Development

Agile development methods promote involvement of team members in development effort estimation using expert methods. However, valuable information might be missed out during the consensus seeking procedure and some of judgments are not properly accounted for. This paper proposes a simulation based effort estimation method. The method evaluates a risk of effort underestimation due to misrepresenting the judgments made by individual team members. It does not require additional inputs and is intended as a tool to suggest estimation process improvements if estimation errors are observed. Properties of the method are analyzed in simulation studies and preliminary empirical evidence is gathered.
Angle-Awareness Based Joint Cooperative Positioning and Warning for Intelligent Transportation Systems

In future intelligent vehicle-infrastructure cooperation frameworks, accurate self-positioning is an important prerequisite for better driving environment evaluation (e.g., traffic safety and traffic efficiency). We herein describe a joint cooperative positioning and warning (JCPW) system based on angle information. In this system, we first design the sequential task allocation of cooperative positioning (CP) warning and the related frame format of the positioning packet. With the cooperation of RSUs, multiple groups of the two-dimensional angle-of-departure (AOD) are estimated and then transformed into the vehicle's positions. Considering the system computational efficiency, a novel AOD estimation algorithm based on a truncated signal subspace is proposed, which can avoid the eigen decomposition and exhaustive spectrum searching; and a distance based weighting strategy is also utilized to fuse multiple independent estimations. Numerical simulations prove that the proposed method can be a better alternative to achieve sub-lane level positioning if considering the accuracy and computational complexity.
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks

Attention-based sequence-to-sequence modeling provides a powerful and elegant solution for applications that need to map one sequence to a different sequence. Its success heavily relies on the availability of large amounts of training data. This presents a challenge for speech applications where labelled speech data is very expensive to obtain, such as automatic speech recognition (ASR) and speech translation (ST). In this study, we propose a general multi-task learning framework to leverage text data for ASR and ST tasks. Two auxiliary tasks, a denoising autoencoder task and machine translation task, are proposed to be co-trained with ASR and ST tasks respectively. We demonstrate that representing text input as phoneme sequences can reduce the difference between speech and text inputs, and enhance the knowledge transfer from text corpora to the speech to text tasks. Our experiments show that the proposed method achieves a relative 10~15% word error rate reduction on the English Librispeech task compared with our baseline, and improves the speech translation quality on the MuST-C tasks by 3.6~9.2 BLEU.
New Threats Against Object Detector with Non-local Block

The introduction of non-local blocks to the traditional CNN architecture enhances its performance for various computer vision tasks by improving its capabilities of capturing long-range dependencies. However, the usage of non-local blocks may also introduce new threats to computer vision systems. Therefore, it is important to study the threats caused by non-local blocks before directly applying them on commercial systems. In this paper, two new threats named disappearing attack and appearing attack against object detectors with a non-local block are investigated. The former aims at misleading an object detector with a non-local block such that it is unable to detect a target object category while the latter aims at misleading the object detector such that it detects a predefined object category, which is not present in images. Different from the existing attacks against object detectors, these threats are able to be performed in long range cases. This means that the target object and the universal adversarial patches learned from the proposed algorithms can have long distance between them. To examine the threats, digital and physical experiments are conducted on Faster R-CNN with a non-local block and 6331 images from 56 videos. The experiments show that the universal patches are able to mislead the detector with greater probabilities. To explain the threats from non-local blocks, the reception fields of CNN models with and without non-local blocks are studied empirically and theoretically.
Entangled edge states of corank one with positive partial transposes

We construct a parameterized family of n ⊗ n PPT (positive partial transpose) states of corank one for each n ≥ 3. With a suitable choice of parameters, we show that they are n ⊗ n PPT entangled edge states of corank one for 3 ≤ n ≤ 1000. They violate the range criterion for separability in the most extreme way. Note that corank one is the smallest possible corank for such states. The corank of the partial transpose is given by 2n − 3, which is also the smallest possible corank for the partial transposes of PPT entangled edge states of corank one. They provide the first explicit examples of such states for n ≥ 4.
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

Causal discovery aims to recover causal structures or models underlying the observed data. Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e.g., image pixels), but are generated by latent causal variables or confounders that are causally related. To this end, in this paper, we consider Linear, Non-Gaussian Latent variable Models (LiNGLaMs), in which latent confounders are also causally related, and propose a Generalized Independent Noise (GIN) condition to estimate such latent variable graphs. Specifically, for two observed random vectors $\mathbf{Y}$ and $\mathbf{Z}$, GIN holds if and only if $\omega^{\intercal}\mathbf{Y}$ and $\mathbf{Z}$ are statistically independent, where $\omega$ is a parameter vector characterized from the cross-covariance between $\mathbf{Y}$ and $\mathbf{Z}$. From the graphical view, roughly speaking, GIN implies that causally earlier latent common causes of variables in $\mathbf{Y}$ d-separate $\mathbf{Y}$ from $\mathbf{Z}$. Interestingly, we find that the independent noise condition, i.e., if there is no confounder, causes are independent from the error of regressing the effect on the causes, can be seen as a special case of GIN. Moreover, we show that GIN helps locate latent variables and identify their causal structure, including causal directions. We further develop a recursive learning algorithm to achieve these goals. Experimental results on synthetic and real-world data demonstrate the effectiveness of our method.
An Approach for Improving DBpedia as a Research Data Hub

Extracted from Wikipedia content, DBpedia is considered one of the most important knowledge bases of the Semantic Web, which has editions in several languages, among which those in English (DBpedia EN) and Portuguese (DBpedia PT). All DBpedia editions are subject to quality issues, more especially DBpedia PT suffers from inconsistencies and lack of data in several domains. This paper describes a semi-automatic and incremental process for publishing data on DBpedia, coming from reliable external sources, while seeking to improve aspects of its quality. In an open science context, the proposal aims at consolidating DBpedia as a reference hub for research data, so that research from any area supported by the Semantic Web data can use its data reliably. Although the approach is independent from a specific DBpedia edition, the supporting prototype tool, named ETL4DBpedia, was built for DBpedia PT, based on ETL workflows (Extract, Transform, Load). This paper also describes the assessment of the approach, applying the tool in a real-usage scenario involving data from the field of botany. This application resulted in an increase by 127% in the completeness of species of medicinal plants in DBpedia PT, besides showing satisfactory performance for ETL4Bpedia components.
Humble Teacher and Eager Student: Dual Network Learning for Semi-supervised 2D Human Pose Estimation.

Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations. However, when applied to pose estimation, the methods degenerate and predict every pixel in unlabeled images as background. This is because contradictory predictions are gradually pushed to the background class due to highly imbalanced class distribution. But this is not an issue in supervised learning because it has accurate labels. This inspires us to stabilize the training by obtaining reliable pseudo labels. Specifically, we learn two networks to mutually teach each other. In particular, for each image, we compose an easy-hard pair by applying different augmentations and feed them to both networks. The more reliable predictions on easy images in each network are used to teach the other network to learn about the corresponding hard images. The approach successfully avoids degeneration and achieves promising results on public datasets. The source code will be released.