Reviewerly/exHarmony huggingface dataset:
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs

Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maintained an encoder-decoder based segmentation network, but focused on a modification of network training process that minimizes redundancy under perturbations. Given a set trained networks, we further introduce a confidence based ensembling techniques to further improve the performance. We evaluated the method on BraTS 2021, and in terms of dice for enhanced tumor core, tumor core and whole tumor, we achieved 0.8600, 0.8868 and 0.9265 average dice for the validation set, and 0.8769, 0.8721, 0.9266 average dice for the testing set. Our team (NVAUTO) submission was the top performing in terms of ET and TC scores, and using the Brats ranking system (based on the dice and Hausdorff distance ranking per case) achieved the 2nd place on the validation set, and the 4th place on the testing set.
An Evaluation of Patch Match-based Copy-Move Forgery Detection (CMFD) on Social Media Images

As society is ever more dependent on technology and digital media, the law depends more on digital forensics to find, keep, evaluate, and analyze digital evidence such as digital images and digital documents. The effective image editing application that constantly improves allows the user to change the image material or alter the image effortlessly. Copy-move forgery (CMF) is a very difficult form of forgery to detect. CMF involves copying part of an image and pasting it into one or more regions of the same image. However, the existing Copy-Move Forgery Detection (CMFD) method was only utilized on the existing image dataset, while social media images are on the common media today. In this paper, the PatchMatch-based CMFD method is evaluated with different platforms of social media images: Facebook, WhatsApp, and Twitter. The average performance generated by the PatchMatch-based CMFD method is 91% for the existing CMFD dataset. By replacing the dataset with the social media images dataset, the average performance slightly decreases to 84%.
The Nominal Group in Khorchin Mongolian: A Systemic Functional Perspective

The main focus on nominals in traditional descriptions of Mongolian is in terms of word class. Their combinations are described either with respect to their role as clause constituents or in terms of a simple modifier-modified relationship. This paper, on the other hand, recognizes the Khorchin Mongolian nominal group as a unit, whose structural configuration is realized mainly through nominals, as well as embedded nominal groups and clauses. The paper describes the relationship between nominal group systems and structures, focusing on the major systems of thing type, classification, title, experiential marking, determination, orientation, qualification, theme marking, numeration, and epithesis. These systems are interpreted in terms of the division of labor between textual, interpersonal, and experiential meaning in SFL. The systemic functional description presented in this paper provides a functionally motivated interpretation of the ordering of elements in Khorchin Mongolian nominal group and the interactions between systems.
SimplePose V2: Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation

We propose a simple yet reliable bottom-up approach with a good trade-off between accuracy and efficiency for the problem of multi-person pose estimation. To encode the multi-person pose information in the image, we employ Gaussian response heatmaps to encode the keypoint position information of all persons. And we propose a set of guiding offsets to encode the pairing information between keypoints belonging to the same individuals. We use an Hourglass Network to infer the said heatmaps and guiding offsets simultaneously. During testing, we greedily assign the detected keypoints to different individuals according to the guiding offsets. Besides, we introduce a peak regularization into the pixel-wise \(L_{2}\) loss for keypoint heatmap regression, improving the precision of keypoint localization. Experiments validate the effectiveness of the introduced components. Our approach is comparable to the state of the art on the challenging COCO dataset under fair conditions.
Combining Deep Convolutional Neural Networks With Stochastic Ensemble Weight Optimization for Facial Expression Recognition in the Wild

Although recent emotion recognition methods (based on facial expression cues) achieve excellent performance in controlled scenarios, the recognition of emotion in the wild remains a challenging problem because of occlusion, large head poses, illumination variations, etc. Recent advances in deep learning show that combining an ensemble of deep learning models can considerably outperform the approach of using only a single deep learning model for challenging recognition problems. This paper presents a novel ensemble deep learning method, “deep convolutional neural network (DCNN) ensemble classifier”, for improved facial expression recognition (FER) in the wild. Our proposed DCNN ensemble classifier is novel in terms of the following aspects: (1) the process of finding ensemble weights for combining DCNN decision outputs is formulated as a stochastic optimization problem (via simulated annealing) in which the energy to be minimized represents the generalized (test) classification error of the DCNN ensemble and (2) for the creation of DCNN ensemble members, we propose the combined use of different types of face representations and bagging (T. G. Dietterich, 2000), which is quite useful in increasing the diversity of the DCNN ensemble. Extensive and comparative experiments on three wild FER datasets, namely FER2013, SFEW2.0, and RAF-DB, show that the proposed DCNN ensemble classifier achieves competitive FER performances when compared with other recently developed methods—76.69%, 58.68%, and 87.13% of FER accuracy under the FER2013, SFEW2.0, and RAF-DB evaluation protocols, respectively.
Improving Medical Data Annotation Including Humans in the Machine Learning Loop

At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance.Particularly, Machine Learning (ML) systems could greatly benefit from their expertise or knowledge.Thus, there is an increasing interest around how humans interact with those systems to obtain the best performance for both the AI system and the humans involved.Several approaches have been studied and proposed in the literature that can be gathered under the umbrella term of Human-in-the-Loop Machine Learning.The application of those techniques to the health informatics environment could provide a great value on prognosis and diagnosis tasks contributing to develop a better health service for Cancer related diseases.
IMAS++

This paper demonstrates an intelligent medical analysis system. We aim to address two main challenges: 1) medical data often contain heterogeneous information which are usually valuable but difficult to be modeled; 2) medical data are often lacking of large scale labeled data which usually require huge efforts to build. To resolve the first challenge, we propose a novel multi-modal heterogeneous graph model to represent the medical data. Based on this model, graph neural networks can be directly applied to effective medical case clustering. This helps to resolve the second challenge for label assignment in the same cluster. To further evaluate the practical use of the proposed model, the system also proposes an effective similar medical case retrieval framework based on a novel graph similarity learning model. We have implemented the system and the source codes are published at https://github.com/emmali808/ADDS. With our system, users can easily pinpoint valuable historical medical information they are interested in and obtain closely relevant medical cases for further diagnosis.
Hybrid Model for Sentiment Analysis Based on Both Text and Audio Data

A model for positive/negative sentiment analysis of phone conversations audio data is considered. Over six thousand features are retrieved employing opensmile Python library, afterward the most significant features are selected. Sentiment analysis of the related text data of conversations is also carried out. An attempt to combine features retrieved from audio and text is performed to increase the quality of binary sentiment classification. Practically acceptable results are obtained. An original algorithm is developed and implemented, which includes data preprocessing, model training, and verification. A research software package has been developed to solve the problem of bank scoring.Tolstoukhov, D. E. Egorov, D. P. Verina, Y. V. Kravchenko, V.
Multi-Task Learning with Task-Specific Feature Filtering in Low-Data Condition

Multi-task learning is a computationally efficient method to solve multiple tasks in one multi-task model, instead of multiple single-task models. MTL is expected to learn both diverse and shareable visual features from multiple datasets. However, MTL performances usually do not outperform single-task learning. Recent MTL methods tend to use heavy task-specific heads with large overheads to generate task-specific features. In this work, we (1) validate the efficacy of MTL in low-data conditions with early-exit architectures, and (2) propose a simple feature filtering module with minimal overheads to generate task-specific features. We assume that, in low-data conditions, the model cannot learn useful low-level features due to the limited amount of data. We empirically show that MTL can significantly improve performances in all tasks under low-data conditions. We further optimize the early-exit architecture by a sweep search on the optimal feature for each task. Furthermore, we propose a feature filtering module that selects features for each task. Using the optimized early-exit architecture with the feature filtering module, we improve the 15.937% in ImageNet and 4.847% in Places365 under the low-data condition where only 5% of the original datasets are available. Our method is empirically validated in various backbones and various MTL settings.
Opinion Texts Clustering Using Manifold Learning Based on Sentiment and Semantics Analysis

Nowadays, opinion texts are quickly published on websites and social networks by various users in the form of short texts and also in high volumes and various fields. Because these texts reflect the opinions of many users, their processing and analysis, such as clustering, can be very useful in a variety of applications including politics, industry, commerce, and economics. High dimensions of the text representation decrease efficiency of clustering, and an effective solution for this challenge is reducing dimensions of texts. Manifold learning is a powerful tool for nonlinear dimension reduction of high-dimensional data. Therefore, in this paper, for increasing efficiency of opinion texts clustering, by manifold learning, dimensions of the represented opinion texts are reduced based on sentiment and semantics, and their intrinsic dimensions are extracted. Then, the clustering algorithm is applied to dimension-reduced opinion texts. The proposed approach helps us to cluster opinion texts with simultaneous consideration of sentiment and semantics, which has received very little attention in the previous works. This type of clustering helps users of opinion texts to obtain more useful information from texts and also provides more accurate summaries in applications, such as the summarization of opinion texts. Experimental results on three datasets show better performance of the proposed approach on opinion texts in terms of important measures for evaluating clustering efficiency. An improvement of about 9% is observed in terms of accuracy on the third dataset and clustering based on sentiment and semantics.
An Auxiliary Training Method for Single-Player Badminton

Aiming at the simulation training of badminton movement, this paper proposes a method of adjusting the deflection angle of the limbs based on monocular video movement reconstruction. The confidence and recognition rate were used to judge the effect of gesture recognition by detecting the key joints and their connections. We extracted depth information based on a residual learning full convolutional network and realized monocular video depth estimation. We also designed a neural network regressor to reconstruct 3D motions. The method was verified by comparing the motion capture data with 3D motion data generated by the monocular video.
Low illumination image enhancement based on multi-scale CycleGAN with deep residual shrinkage

Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discriminator, different convolution kernels are used to extract image features from two paths. Compared with the training and testing results of Deep-Retinex network, GLAD network, KinD and other network methods on LOL-dataset and Brightening dataset, CycleGAN based on multi-scale depth residuals contraction proposed in this experiment on LOL-dataset results image quality evaluation indicators PSNR = 24.62, NIQE = 4.9856, SSIM = 0.8628, PSNR = 27.85, NIQE = 4.7652, SSIM = 0.8753. From the visual effect and objective index, it is proved that CycleGAN based on multi-scale depth residual shrinkage has excellent performance in low illumination enhancement, detail recovery and denoising.
Individual Emotion Recognition Approach Combined Gated Recurrent Unit with Emoticon Distribution Model

With the rapid development of social media, a large increase in text data is occurring in social networks. Mining individual emotions from the data is extremely challenging work in many fields, such as opinion monitoring, business decisions, and information prediction. In previous studies, many studies recognized texts containing positive, negative and neutral sentiments, but few studies focus on emotions according to multiple categories, such as happiness, sadness, anger, fear, disgust and surprise. Sentiment and emotions are two different aspects of individual feelings about matters. In this paper, we propose an individual emotion-identifying model called semantic emoticon emotion recognition (SEER). First, we divide the input short text into four categories by using the emotion dictionary and emoticons. Second, we combine a bidirectional gated recurrent unit (Bi-GRU) network with an attention mechanism to capture the emotion vectors of input text from the word aspect. Third, we construct an emoticon distribution model to obtain the emotion vectors in a large quantity of social network data. Fourth, according to different types of input short texts, we select different fusion weights to fuse the emoticon emotion features in text and the semantic emotion features of the texts. Finally, we classify the short text emotions into six categories depending on the final emotion vector. The experimental results show that the SEER is an effective method for improving the accuracy of emotion recognition. In addition, our proposed approach achieves the highest accuracy of 86.35% in emotion recognition, which indicates that the accuracy of our proposed approach achieves improvements of 12.66%, 14.56%, 5.38%, 4.48%, 4.9%, 2.68%, and 3.17% compared to SVM, WL, LSTM, BiLSTM, GRU, Bi-GRU, and CNN+BiLSTM, respectively.
Pose-Guided Person Image Synthesis for Data Augmentation in Pedestrian Detection

In this paper, we present a data augmentation framework for pedestrian detection using a pose-guided person image synthesis model. The proposed framework can boost the performance of state-of-the-art pedestrian detectors by generating new and unseen pedestrian training samples with controllable appearances and poses. This is achieved by a new latent-consistent adversarial variational auto-encoder (LAVAE) model, leveraging the advantages of conditional variational auto-encoders and conditional generative adversarial networks to disengage and reconstruct person images conditioned on target poses. An additional latent regression path is introduced to preserve appearance information and to guarantee a spatial alignment during transfer. LAVAE goes beyond existing works in restoring structural information and perceptual details with limited annotations and can further benefit the pedestrian detection task in automated driving scenarios. Extensive pedestrian detection and person image synthesis experiments are performed on the EuroCity Person dataset. We show that data augmentation using LAVAE improves the accuracy of state-of-the-art pedestrian detectors significantly. Furthermore, a competitive performance can be observed when we compare LAVAE with other generative models for person image synthesis.
Serverless Computing and the Emergence of Function-as-a-Service

Serverless computing is one of the most recent additions to the long list of services provided by cloud computing. Improving upon the attributes of scalability, affordability, and granularity present in earlier services offered by cloud computing, serverless computing is the next major development. Function as a Service has further modularized applications and enabled the individual execution of functions using triggers which has led to reduced costs, improved scaling of applications and almost no configuration expenses. Several cloud computing providers have developed their serverless computing services, each having its advantages and disadvantages. This paper aims to describe the need for serverless computing, its working, its economics, applications, its pros and cons in the current state, and the developments over existing technologies. Various serverless computing providers and their services have also been compared and studied.
Pedestrian detection based on I-HOG feature

Pedestrian detection is a hot and difficult topic in the computer vision field. The Histograms of Oriented Gradients (HOG) feature, because of its high performance in accuracy, is widely used in pedestrian detection. Nonetheless, its information description capacity needs further improvement. so, I-HOG (Improved HOG) was proposed. I-HOG has two major improvements. First, I-HOG enhances the description of edge features. Through the different scales for the block histograms of a set of correlation graphs, makes the correlation between characteristic information. Second, I-HOG using multi-scale feature extraction methods, include wider edge feature description information, make up for the deficiencies of the HOG feature, because HOG features are only extracted in fixed block size, The experimental results show that in the INRIA database, using I-HOG, detection rate increased by 5.4% and 4.3% respectively, combined with the feature of CSS after detection rate increased by 2.8% and 4.0% respectively compared to the HOG.
RAAH.ai: An interactive chatbot for stress relief using Deep Learning and Natural Language Processing

the ongoing lockdown where the accessibility to people you want to interact with or who could provide to you the much needed relief has drastically reduced, be it doctors or therapists. Keeping this in mind, along with the huge amounts being spent by people on consultation, we thought of coming up with a system which would be relevant to people going through a tough time. There is always a need for people to talk with their loved ones even when they are not around and our voice cloning system would serve the purpose. There is a need for real-time artificial human conversation that is customized and personalized according to a person's needs. In addition, there is a requirement of security for preventing voice spoofing which might lead to malpractices. This led us to materialise a solution which would allow a person to interact with people. The system uses NLP methods of chatbot to identify the emotion/problem that the user is trying to express. It uses CNN and RNN to respond to the user in the voice of a loved one. Using the LibriSpeech and VCTK datasets, a Tacotron-based architecture was generated by us which resulted in a similarity percentage of 91% along with a response time of 4 seconds which came out to be better than the various methods studied during the literature survey.
Strengthening Software Make vs. Buy Decision: A Mixed-Method Approach

Over the past few decades, multiple software development process models, tools, and techniques have been used by practitioners. Despite using these techniques, most software development organizations still fail to meet customer’s needs within time and budget. Time overrun is one of the major reasons for project failure. There is a need to come up with a comprehensive solution that would increase the chances of project success. However, the “make vs. buy” decision can be helpful for “in time” software development. Social media have become a popular platform for discussion of all sorts of topics, so software development is no exception. Software developers discuss all the pros and cons of making vs. buy decisions on Twitter and other social media platforms. Twitter trending is a typical feature that evaluates the level of popularity of a specific event on online networking. A mixed-method approach comprising of interviews of software industry experts and Twitter data extraction is applied to scrutinize the effective decision of software build vs. buy decision. The findings of the analysis show that software makes vs. buy decisions depend on several factors including cost, development technology, software development team skills, and time. Based on the finding of the study a framework is proposed for the decision to build versus buy in Small and medium-sized enterprises (SMEs). Furthermore, the framework has been designed to statistically indicate make versus buy decisions of the organization and to suggest appropriate choices based on different parameters.
Improving Generalization of Deepfake Detection with Domain Adaptive Batch Normalization

Deepfake, a well-known face forgery technique, has raised serious concerns about personal privacy and social media security. Therefore, a plenty of deepfake detection methods come out and achieve outstanding performance in the single dataset case. However, current deepfake detection methods fail to perform strong generalization ability in cross-dataset case due to the domain gap. To tackle this issue, we propose Domain Adaptive Batch Normalization (DABN) strategy to mitigate the domain distribution gap on different datasets. Specifically, DABN utilizes the distribution statistics of the testing dataset in replace of the original counterparts so as to avoid distribution mismatch and restore the effectiveness of BN layers. Equipped with our DABN, detection method can be more robust when generalized into a broader usage. Note that our method is flexible and can be further employed on most existing deepfake detection methods during testing, which shows a great practical value. Extensive experiments on multiple datasets and models demonstrate the effectiveness of DABN. The proposed method achieves an average accuracy improvement by nearly 20% of existing strategies on Celeb-DF dataset under black-box settings, indicating strong enhancement of generalization ability of the deepfake detection models.