Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Code-Mixed Roman Urdu-English Social Media Text using Deep Learning Approaches

Sentiment analysis is the computational study of attitudes, opinions, and sentiments towards certain issues, products, individuals, and organizations. Companies and customers are making decisions by seeking opinions from social media text. Sentiment analysis is getting intelligent with the advancement of artificial intelligence and natural language processing. With a stunning increase in the use of social media, a huge volume of text available on these platforms is in imperfect and informal languages like Roman Urdu mixed with the English language. Present sentiment analysis techniques do not perform precisely on these code-mixed imperfect, informal, and poorly resourced languages. A promising solution is the use of deep learning models on these code-mixed Roman Urdu and English text. Therefore, the objective of this paper is to perform a sentiment analysis of code-mixed Roman Urdu and English social media text using state-of-the-art deep learning models. Our work is independent of lexical normalization, language dictionary, and code transfer indication. We perform sentiment analysis using Multilingual BERT (mBERT) and XLM-RoBERTa (XLM-R) models. The results reveal that performance of XLM-R model with tuned hyperparameters for code-mixed Roman Urdu and English social media text is better than the mBERT model with F1 score of 71%.
Bayesian Inference Federated Learning for Heart Rate Prediction

The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users’ devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices.
Asynchronous Stochastic Gradient Descent Over Decentralized Datasets

The computational efficiency of the asynchronous stochastic gradient descent (ASGD) against its synchronous version has been well documented in recent works. Unfortunately, it usually works only for the situation that all workers retrieve data from a shared dataset. As data get larger and more distributed, new ideas are urgently needed to maintain the efficiency of ASGD for decentralized training. This article proposes a novel ASGD over decentralized datasets where each worker can only access its local privacy-preserved dataset. We first observe that due to the heterogeneity of decentralized datasets and/or workers, the ASGD will progress at wrong directions, leading to undesired solutions. To tackle this issue, we propose a decentralized asynchronous stochastic gradient descent (DASGD) method by weighting the SG via the importance sampling technique. We prove that the DASGD achieves a convergence rate of O(1/K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\frac12</sup> ) on nonconvex training problems under mild conditions. Numerical results also substantiate the performance of the proposed algorithm.
EdgeMask: An Edge-based Privacy Preserving Service for Video Data Sharing

Preserving privacy in image and video data captured from public environments is essential for any research group that leverages, publishes, or shares such data. Although there are several research efforts attempting to resolve the privacy issues, they had quality and efficiency limitations. In this work, we proposed EdgeMask as a privacy preserving service that leverages edge computing and deep learning models to propose a real-time object segmentation approach and analyze the input data using parallel computing and speed up the object removal. Our experimental results indicate that EdgeMask reduces the computational time considerably.
Emotion Expression With Fact Transfer for Video Description

Translating a video into natural language is a fundamental but challenging task in visual understanding, since there is a great gap between visual content and linguistic sentence. More attention has been paid to this research field and a number of state-of-the-art results are achieved in recent years. However, the emotions in videos are usually overlooked, leading to the generated description sentences being boring and colorless. In this work, we construct a new dataset for video description with emotion expression, which consists of two parts: a re-annotated subset of the MSVD dataset with emotion embedded and another subset annotated with long sentences and rich emotions based on a video emotion recognition dataset. A fact transfer based framework is designed, which incorporates a fact stream and an emotion stream to generate sentences with emotion expression for video description. In addition, we propose a novel approach for sentence evaluation by balancing facts and emotions. A group of experiments are conducted, and the experimental results demonstrate the effectiveness of the proposed methods, including the idea of dataset construction for video description with emotion expression, model training and testing, and the emotion evaluation metric. The project page (including the code and dataset) can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://mic.tongji.edu.cn/ce/70/c9778a183920/page.htm</uri> .
RHC: A Dataset for In-Room and Out-Room Human Counting

In recent years, research in human counting from CCTV (Closed Circuit Television) images have found an increasing demand to be deployed in real-world applications. The applications have been implemented in various settings, both indoor and outdoor. In the case of indoor setting, we found a type of room setting that conveys a problem to human counting model if we need to count only humans inside a room. With this respect, we present RHC (Room Human Counting) dataset, which images are captured in the aforementioned setting. The dataset can be used to develop a robust model that can differentiate between humans inside and outside a room. The dataset is publicly available at https://data.mendeley.com/datasets/vt5c8h6kmh/1.
Tackling Feature Selection Problems with Genetic Algorithms in Software Defect Prediction for Optimization

Software defect prediction is a way to improve quality by finding and tracking defective modules in the software which helps reduce costs during the software testing process. The use of machine learning methods for predicting software defects can be applied to predict defects in each software module. However, basically the software defect prediction dataset has two problems, namely class imbalance with very few defective modules compared to non-defective modules and contains noisy attributes due to irrelevant features. With these two problems, it will result in overfitting and lead to biased classification results so that it will have an impact on significantly reducing the performance of the machine learning model. In this study, we propose the implementation of bagging techniques and genetic algorithms to improve the classification performance of machine learning models in predicting software defects based Logistic Regression, Naive Bayes, SVM, KNN, Decision Tree. Bagging techniques and Genetic algorithms are approaches that can handle two main problems in software defects prediction, each of which can handle the class imbalance and feature selection problem. We used 6 NASA Promise datasets to evaluate the classification performance results based on AUC and G-Means values. The results using 10 cross-validations show that the proposed method can improve classification performance when compared to the original algorithm. The Decision Tree shows the highest performance of the 3 datasets tested, with the highest value of 94.61 % on the KC4 dataset. We also compare GA performance with another natural algorithm, Particle Swarm Optimization (PSO). The results show that the performance of all machine learning models with GA can outperform the algorithms with PSO
Active DNN IP Protection: A Novel User Fingerprint Management and DNN Authorization Control Technique

The training process of deep learning model is costly. As such, deep learning model can be treated as an intellectual property (IP) of the model creator. However, a pirate can illegally copy, redistribute or abuse the model without permission. In recent years, a few Deep Neural Networks (DNN) IP protection works have been proposed. However, most of existing works passively verify the copyright of the model after the piracy occurs, and lack of user identity management, thus cannot provide commercial copyright management functions. In this paper, a novel user fingerprint management and DNN authorization control technique based on backdoor is proposed to provide active DNN IP protection. The proposed method can not only verify the ownership of the model, but can also authenticate and manage the user's unique identity, so as to provide a commercially applicable DNN IP management mechanism. Experimental results on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets show that the proposed method can achieve high detection rate for user authentication (up to 100% in the three datasets). Illegal users with forged fingerprints cannot pass authentication as the detection rates are all 0 % in the three datasets. Model owner can verify his ownership since he can trigger the backdoor with a high confidence. In addition, the accuracy drops are only 0.52%, 1.61 % and -0.65% on CIFAR-10, CIFAR-100 and Fashion-MNIST, respectively, which indicate that the proposed method will not affect the performance of the DNN models. The proposed method is also robust to model fine-tuning and pruning attacks. The detection rates for owner verification on CIFAR-10, CIFAR-100 and Fashion-MNIST are all 100% after model pruning attack, and are 90 %, 83 % and 93 % respectively after model fine-tuning attack, on the premise that the attacker wants to preserve the accuracy of the model.
Fully Automated DCNN-Based Thermal Images Annotation Using Neural Network Pretrained on RGB Data

One of the biggest challenges of training deep neural network is the need for massive data annotation. To train the neural network for object detection, millions of annotated training images are required. However, currently, there are no large-scale thermal image datasets that could be used to train the state of the art neural networks, while voluminous RGB image datasets are available. This paper presents a method that allows to create hundreds of thousands of annotated thermal images using the RGB pre-trained object detector. A dataset created in this way can be used to train object detectors with improved performance. The main gain of this work is the novel method for fully automatic thermal image labeling. The proposed system uses the RGB camera, thermal camera, 3D LiDAR, and the pre-trained neural network that detects objects in the RGB domain. Using this setup, it is possible to run the fully automated process that annotates the thermal images and creates the automatically annotated thermal training dataset. As the result, we created a dataset containing hundreds of thousands of annotated objects. This approach allows to train deep learning models with similar performance as the common human-annotation-based methods do. This paper also proposes several improvements to fine-tune the results with minimal human intervention. Finally, the evaluation of the proposed solution shows that the method gives significantly better results than training the neural network with standard small-scale hand-annotated thermal image datasets.
Distance on the Cairo pattern

The Cairo pattern is a dual of a semiregular tiling of the plane; its name reflects the fact that various streets in Cairo are paved in this way. The cells of this grid are identical pentagons which are put in four different orientations. In this paper we present a coordinate system addressing the cells of the grid. Also a neighborhood relation and the concept of lanes are defined by the help of the coordinates. We prove a formula for the digital (path based) distance between any two pentagons where the paths are based on the steps to neighbor pentagons.
Vi-Net: A Deep Violent Flow Network for Violence Detection in Video Sequences

Video surveillance cameras are widely used due to security concerns. Analyzing these large amounts of videos by a human operator is a difficult and time-consuming job. To overcome this problem, automatic violence detection in video sequences has become an active research area of computer vision in recent years. Early methods focused on hand-engineering approaches to construct hand-crafted features, but they are not discriminative enough for complex actions like violence. To extract complex behavioral features automatically, it is required to apply deep networks. In this paper, we proposed a novel Vi-Net architecture based on the deep Convolutional Neural Network (CNN) to detect actions with abnormal velocity. Motion patterns of targets in the video are estimated by optical flow vectors to train the Vi-Net network. As violent behavior comprises fast movements, these vectors are useful for the extraction of distinctive features. We performed several experiments on Hockey, Crowd, and Movies datasets and results showed that the proposed architecture achieved higher accuracy in comparison with the state-of-the-art methods.
Feature-Sensitive Deep Convolutional Neural Network for Multi-Instance Breast Cancer Detection

To obtain a well-performed computer-aided detection model for detecting breast cancer, it is usually needed to design an effective and efficient algorithm and a well-labeled dataset to train it. In this paper, first, a multi-instance mammography clinic dataset was constructed. Each case in the dataset includes a different number of instances captured from different views, it is labeled according to the pathological report, and all the instances of one case share one label. Nevertheless, the instances captured from different views may have various levels of contributions to conclude the category of the target case. Motivated by this observation, a feature-sensitive deep convolutional neural network with an end-to-end training manner is proposed to detect breast cancer. The proposed method first uses a pre-train model with some custom layers to extract image features. Then, it adopts a feature fusion module to learn to compute the weight of each feature vector. It makes the different instances of each case have different sensibility on the classifier. Lastly, a classifier module is used to classify the fused features. The experimental results on both our constructed clinic dataset and two public datasets have demonstrated the effectiveness of the proposed method.
Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (&lt;1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.
Experience Reviewing Digital Pap Tests using a Gallery of Images

Hologic is developing a digital cytology platform. An educational website was launched for users to review these digitized Pap test cases. The aim of this study was to analyze data captured from this website.ThinPrep® Pap test slides were scanned at ×40 using a volumetric (14 focal plane) technique. Website cases consisted of an image gallery and whole slide image (WSI). Over a 13 month period data were recorded including diagnoses, time participants spent online, and number of clicks on the gallery and WSI.51,289 cases were reviewed by 918 reviewers. Cytotechnologists spent less time (M [Median] = 65.0 s) than pathologists (M = 82.2 s) reviewing cases (P < 0.001). Longer times were associated with incorrect diagnoses and cases with organisms. Cytotechnologists matched the reference diagnoses in 85% of cases compared to pathologists who matched in 79.8%. While in 62% of cases reviewers only examined the gallery, they attained the correct diagnosis 92.7% of the time. Pathologists made more clicks on the gallery and WSI than cytotechnologists (P < 0.001). Diagnostic accuracy decreased with increasing clicks.Website participation provided feedback about how cytologists interact with a digital platform when reviewing cases. These data suggest that digital Pap test review when comprised of an image gallery displaying diagnostically relevant objects is quick and easy to interpret. The high diagnostic concordance of digital Pap tests with reference diagnoses can be attributed to high image quality with volumetric scanning, image gallery format, and ability for users to freely navigate the entire digital slide.
Safety Risk Assessment of Prefabricated Building Construction Based on Bayesian Network

Abstract A safety risk assessment index system including four types of risk conditions: foundation pit construction risk status, construction and shipment risk status, hoisting operation risk status and prefabricated component installation risk status is established. Based on Bayesian network, fabricated by Netica software to evaluate the construction safety risk probability, through the function of Sensitivity to Findings conduct sensitivity analysis on the probability of risk occurrence. Based on the risk calculation, get the more prominent risk factors in each type of risk situation. According to the result of risk assessment, the countermeasures of construction safety risk of prefabricated building are put forward.
Investigating User Perceptions and Stereotypic Responses to Gender and Age of Voice Assistants

Technologies such as voice assistants can aid older adults aging in place by assisting with basic home and health tasks in daily routines. However, currently available voice assistants have a common design-they are vastly represented as young and female. Humans may apply stereotypes to human-computer interactions similarly to human-human interactions. When stereotypes are activated, users may lose trust or confidence in the abilities of the device, or even stop using the device all together. The two purposes of this study are to 1) investigate if users can detect the age and gender of voice assistants, and 2) understand the extent to which a voice assistant’s perceived gender, age, and reliability elicit stereotypic responses. A series of health-related vignettes will be utilized to assess perceptions of and stereotypic responses toward voice assistants in younger and older adults. In line with previous research examining healthcare agents (Pak et al., 2014), we hypothesize that voice assistants with younger male voices will be rated as more trustworthy and that high reliability will have a positive impact on ratings of trust.
BWCFace: Open-set Face Recognition using Body-worn Camera

With computer vision reaching an inflection point in the past decade, face recognition technology has become pervasive in policing, intelligence gathering, and consumer applications. Recently, face recognition technology has been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic using traditional techniques on datasets with small sample size. This paper aims to bridge the gap in the state-of-the-art face recognition using body-worn cameras (BWC). To this aim, the contribution of this work is two-fold: (1) collection of a dataset called BWCFace consisting of a total of 178K facial images of 132 subjects captured using the body-worn camera in in-door and daylight conditions, and (2) open-set evaluation of the latest deep-learning-based Convolutional Neural Network (CNN) architectures combined with five different loss functions for face identification, on the collected dataset. Experimental results on our BWCFace dataset suggest a maximum of 33.89% Rank-1 accuracy obtained when facial features are extracted using SENet-50 trained on a large scale VGGFace2 facial image dataset. However, performance improved up to a maximum of 99.00% Rank-1 accuracy when pretrained CNN models are fine-tuned on a subset of identities in our BWCFace dataset. Equivalent performances were obtained across body-worn camera sensor models used in existing face datasets. The collected BWCFace dataset and the pretrained/ fine-tuned algorithms are publicly available to promote further research and development in this area. A downloadable link of this dataset and the algorithms is available by contacting the authors.
Accurate, Low-Latency Visual Perception for Autonomous Racing: Challenges, Mechanisms, and Practical Solutions

Autonomous racing provides the opportunity to test safety-critical perception pipelines at their limit. This paper describes the practical challenges and solutions to applying state-of-the-art computer vision algorithms to build a low-latency, high-accuracy perception system for DUT18 Driverless (DUT18D), a 4WD electric race car with podium finishes at all Formula Driverless competitions for which it raced. The key components of DUT18D include YOLOv3-based object detection, pose estimation, and time synchronization on its dual stereovision/monovision camera setup. We highlight modifications required to adapt perception CNNs to racing domains, improvements to loss functions used for pose estimation, and methodologies for sub-microsecond camera synchronization among other improvements. We perform a thorough experimental evaluation of the system, demonstrating its accuracy and low-latency in real-world racing scenarios.