Reviewerly/exHarmony huggingface dataset:
Semi-Blind Image de-blurring with IMU Prior

Due to fast movement of an autonomous system, motion blur is an issue for images with long-exposures, especially for those captured at dim-lighting conditions. In this paper, a simple semi-blind image de-blurring algorithm is designed by using measurements from an inertial measurement unit (IMU) embedded in the autonomous system. A prior of the blur kernel is first derived from an elegant IMU integration model. The image is then de-blurred by using the prior of the blur kernel with a refinement step to further mitigate the drift caused by the inaccurate IMU measurements. Experimental results show the improvement of the proposed algorithm for image de-blurring.
Exploration of Membership Inference Attack on Convolutional Neural Networks and Its Defenses

Recently, machine learning has been frequently applied in multiple areas, like image classification and object recognition. However, some machine learning applications train their models on sensitive data that could leak personal information, leading to potential privacy issues. Many privacy attacks against deep learning models have been proposed. A membership inference attack was demonstrated to be a type of effective attack and a real threat to privacy in machine learning. Here, we trained three different convolutional neural networks for image classification with the CIFAR10 dataset and examined the effects of a membership inference attack on these models. We also explore different defense strategies, including differential privacy against membership inference attack. We showed that membership inference attack was more effective against overfitting models and defense strategies like differential privacy and regularization can significantly lower the attack accuracy.
Design of Robot Vision Recognition System

In this paper, a robot vision recognition system is developed based on the Robot Operating System (ROS) and the Open Source Computer Vision (Open CV), which mainly implements face recognition, object detection, motion analysis and object segmentation of the robot.
Sybil Detection in Online Social Networks

The internet has become a very important part of the society especially while socializing. Number of social media users are increasing at an exponential rate. While it has opened many ways for users to become fully connected with each other, it poses a threat where identify theft can cause damage to personal influence, reputation, well being, and wealth. The fake identities also referred as Sybils can manipulate reviews, followings, ratings of genuine users or they can deceive the community by portraying as an identity they are replicating. Identification of Sybils on social network is an issue and has been approached from different dimensions. We have however, proposed an Architecture that is based upon machine learning algorithms considering features from the social ids. The application developed on this architecture helps the user to identify given account as Sybil or genuine. The information from the social handle is fed into the trained machine learning model that predicts the user genuineness and its followers. The system is validated by checking 50 profiles of social identities. There were 20% Sybil ids and remaining were genuine ids. The proposed system accurately predicted the genuineness of the identities with 88% accuracy. Furthermore, the system also visually presented the presence of Sybil identities in the social graph of the user.
Task Assignment of Unmanned Platform Cluster Considering Special State

This paper addresses the task assignment problem of unmanned platform with the special states, where the special state can be parts damage, insufficient power, insufficient ammunition, etc. To be specific, when the unmanned platform is in the special state mentioned above, it needs to give up the original mission target and then return to the designated position for supply. After reaching the designated spot and completing the supply, previous mission target can be executed again. We propose an algorithm framework that combines A * algorithm and discrete particle swarm optimization algorithm, where A * algorithm is used to deal with the path planning problem, which get the paths as well as the distances, and discrete particle swarm optimization algorithm is used to deal with the task assignment problem to realize the task assignment of unmanned platform cluster considering the special state. Illustrative examples demonstrate the effectiveness of the proposed framework.
Language Detection and Localization, for Pakistani languages, in Acoustic Channels

Language detection and localization in audio is an essential task in Natural Language Processing as it serves as the starting point in the NLP pipeline to accomplish other tasks such as detecting and localizing the language in an audio then segmenting it to perform speech to text conversion, sentiment analysis and audio-speech recognition on the text in that particular language. This task can help us achieve goals that include targeted extraction of specific references in an audio, regardless of their language. Our work leads towards generating auto-summarizations from any given audio. In our work, we propose a novel approach to detect and localize the language in an audio using a hybrid architecture of CNN-LSTM. We achieved approximately 93% accuracy for detecting a language and classifying it in the local context on native languages (Urdu, Sindhi, and Pushto) and international languages (English and Arabic), respectively.
Bolt looseness detection algorithm based on 2D color space extraction and 3D depth measurement

This project mainly uses 2D and 3D image processing technology, and develops fault detection algorithm accordingly. The research object we focused on is bolt. In reality, as for the images processing, the shooting environment is complex with different shooting angles, and the change of light in the environment will change the RGB value of the same point. Target detection based on model training is used in the processing. The bolt fault detection is based on the anti-loose line detection model based on two-dimensional image processing. In order to reduce Error detection rate, the color threshold dictionary is introduced to get the optimal threshold, and the bolt depth detection model based on three-dimensional point cloud processing is introduced to jointly complete the fault detection of target faults.
Risk Management Capability Level of Mail Information System in Surabaya Government

ICT (Information and Communication Technology) plays a strategic role in government institutions. The existence of ICT is considered capable of promoting the realization of good governance. Therefore, Dinkominfo Surabaya implemented a mail management information system called e-Surat. Dinkomifo Surabaya has established a standard operating procedure (SOP) for e-Surat. However, problems such as data loss and mail disposition errors still occurred. Since SOPs were not enough to minimize the occurrence of information security risks, Dinkominfo Surabaya needed to know its capability level of information security risk. This study aimed to help Dinkominfo Surabaya assess the extent to which risk optimization management in e-Surat using COBIT 5 and focusing on the APO12 domain. The results showed that the capability level of the risk management process on the APO12 domain was at level 1 (performed), two levels below the expected level, indicating that the risk management process has been done well, although it has not met the criteria of the practice itself. Recommendations to improve risk management-related processes to achieve the expected level 3 (established) include determining the objectives and limits of the risk management process carried out and providing work products for institution leaders so that the handling of problems related to risk management can involve policymakers.
Self-aware Multi-inductor Net: Cross Domain Person Re-identification Based on Global Self-attention and Multi-granularity Supervision

One of the biggest challenges on deep-learning-based person re-identification technology is its serious degradation of cross-domain testing performance. The poor cross-domain generalization could attribute to the deficiency on extracting typical and adaptive feature representations from pedestrian images. The pure convolutional baseline has two major defects: for one thing, its weights are relatively rigid due to its static architecture without enough dynamically selective mechanism; for another, the guidance to tune learnable parameters is insufficient because there is only one way signal for supervision. Therefore, it hinders networks to adapt inputs flexibly and fails to induce networks to utmost mine the potential information hidden in the human body structure. In this paper, a convolutional-based neural network with global self-attention and multi-granularity supervision is proposed to improve the adaptiveness of feature extraction, named as “Self-aware Multi-inductor Net (SMNet)”. It only trains on source domain in a supervised manner, but could improve the generalization performance on the target domain by a nice bit. Experiment results on classic public datasets show that our proposed method could effectively upgrade the cross-domain generalization for person re-identification tasks.
Real time road traffic sign detection and recognition systems using Convolution Neural Network on a GPU platform

Artificial Intelligence has been widely used for object detection in various applications such as text recognition and wildlife detection. Although it can detect an object at a certain level of confidence, the accuracy can be affected by the object's background, lighting condition, or when the object is moving. In this paper, a machine learning algorithm is applied to develop a traffic sign detection and recognition system (TSDR) using Convolution Neural Network (CNN) to build a localised traffic sign model using a novel multi-objects suppression approaches. The constructed model is tailored for specific country's traffic signs and can be installed on a portable microcontroller with GPU capability, to provide a real-time traffic sign recognition system. The proposed system can update the traffic sign model online or offline. Extensive experimental evaluations have shown that the traffic sign's model can be customised using multi-objects suppression with CNN and the accuracy of the model increases with the number of training samples.
A Comprehensive Review of Emerging Technologies for Anemia Detection

In recent year, anemia emerges as a major health concern among the children and pregnant woman in developing countries like India. The National Family Health Survey data also shows a spike in the anemia prevalence in children & women across most states in India. The low concentration of hemoglobin in the blood is the major indicator of anemia in an individual. Considering this fact, extensive research is carried out in the field of anemia detection. This paper gives the comprehensive review of the research work in this field. Effect of various factors such as age, gender, etc. on hemoglobin levels is also discussed in this research article. This paper gives the brief overview of various data collection and preprocessing methods. It also gives comprehensive analysis of technologies used to detect anemia with the help of hemoglobin estimation. Study of performance measures used for evaluation of results is covered while reviewing existing research work. This paper provides the input for the novel research to be carried out for anemia detection with help of state-of-the-art approached in this domain.
Characterizing Text Revisions to Better Support Collaborative

Despite advancement in collaborative writing tools, the track changes capability in modern editors remains limited to highlighting syntactic changes, with authors still required to manually read through each of the revisions. We envision a collaborative authoring system where an author could accept all minor edits first and then focus on the substantial changes. To support this, we define the task of significant revision identification as the task of identifying the revisions between two versions of a text according to one of four categories, i.e. formal, meaning preserving, micro- and macro-structure. Micro-structure change corresponds to minor meaning change while macro-structure change corresponds to major meaning change. Our main contribution is to define a computational approach to this task, by framing the task as bi-directional entailment between the original and revised sentences. An existing recognition of textual entailment (RTE) system is applied to evaluate whether the revised texts entails. We evaluate the approach through a novel corpus consisting of multiple versions of drafts of academic papers written by multiple authors, which were annotated with the four revision types by both authors and non-authors of the papers. The proposed bi-directional textual entailment approach performs better than baseline edit distance approaches, which is similar to the current track changes capability built into most word processors.
Knowledge Alignment Based on Negative Sampling of Potential Topic Similarities

In the entity task, negative sampling of the translation model is used to generate negative example triples by randomly selecting an entity from the entity set, resulting in poor quality of the generated negative example triples, which affects the feature learning ability of entities and relations and the entity alignment effect, to this end, we propose a method to generate high-quality negative triples based on negative sampling of potential topic similarities. All entities are first analyzed for potential topics based on the topic model LDA, then the head entity is replaced by an entity from the topic of the head entity in the triplet of positive examples, the tail entity is replaced in a similar way. The TransE-LTS was obtained by combining a negative sampling approach to topic similarity with the translation model TransE. Results show that the TransE-LTS achieves significant improvements in entity alignment tasks, link prediction and triad classification.
Row-Wise Corrective Median Filtering for Fast Impulse Noise Reduction in Color Images

The vector median filter and its variants are popularly used to reduce impulse noise in digital color images. These methods work by the principle of similarity detection within a local block of pixels, termed a window and by replacing the centre test pixel by another pixel in that window that minimises the aggregate of a suitably chosen distance measure. This processing is performed over all the pixels in the image in a sequential fashion. This renders these filtering methods computationally expensive. In this paper, we propose a fast method for reducing impulse noise in color images. The key idea here is to slice each row of the image as a univariate data vector, identify anomalies (impulse noise) using anomaly detection approach and then apply a corrective median filtering technique over these to restore the original image. This idea ensures fast filtering as the iterations are now limited to the rows. Using simulations, we show that the proposed method scales efficiently with respect to a combined measure of accuracy and time.
Cardiac Image Segmentation Based on Improved U-Net

Due to the complexity of the whole heart tissue, low segmentation accuracy, easy adhesion of tissue and incomplete segmentation in CT images, this paper proposes a method to improve the whole heart segmentation of U-Net. According to the structural morphological characteristics of the human heart, this paper introduces the ASPP module into the coding layer of the U-Net network, and adds the attention mechanism to the jump layer connection of the U-Net network. In this paper, the improved whole heart segmentation algorithm is compared with other popular segmentation algorithms using the public dataset MMWHS. Experimental results show that the segmentation accuracy of the proposed algorithm in this paper reaches 89.74%, which improves the segmentation accuracy of the whole heart structure.
Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-<i>iid</i>) Data

Natural science datasets frequently violate assumptions of independence. Samples may be clustered (e.g., by study site, subject, or experimental batch), leading to spurious associations, poor model fitting, and confounded analyses. While largely unaddressed in deep learning, this problem has been handled in the statistics community through mixed effects models, which separate cluster-invariant fixed effects from cluster-specific random effects. We propose a general-purpose framework for Adversarially-Regularized Mixed Effects Deep learning (ARMED) models through non-intrusive additions to existing neural networks: 1) an adversarial classifier constraining the original model to learn only cluster-invariant features, 2) a random effects subnetwork capturing cluster-specific features, and 3) an approach to apply random effects to clusters unseen during training. We apply ARMED to dense, convolutional, and autoencoder neural networks on 4 datasets including simulated nonlinear data, dementia prognosis and diagnosis, and live-cell image analysis. Compared to prior techniques, ARMED models better distinguish confounded from true associations in simulations and learn more biologically plausible features in clinical applications. They can also quantify inter-cluster variance and visualize cluster effects in data. Finally, ARMED matches or improves performance on data from clusters seen during training (5-28% relative improvement) and generalization to unseen clusters (2-9% relative improvement) versus conventional models.
A Novel Deep Learning Model for Medical Report Generation by Inter-Intra Information Calibration

Automatic generation of medical reports can provide diagnostic assistance to doctors and reduce their workload. To improve the quality of the generated medical reports, injecting auxiliary information through knowledge graphs or templates into the model is widely adopted in previous methods. However, they suffer from two problems: 1) The injected external information is limited in amount and difficult to adequately meet the information needs of medical report generation in content. 2) The injected external information increases the complexity of model and is hard to be reasonably integrated into the generation process of medical reports. Therefore, we propose an Information Calibrated Transformer (ICT) to address the above issues. First, we design a Precursor-information Enhancement Module (PEM), which can effectively extract numerous inter-intra report features from the datasets as the auxiliary information without external injection. And the auxiliary information can be dynamically updated with the training process. Secondly, a combination mode, which consists of PEM and our proposed Information Calibration Attention Module (ICA), is designed and embedded into ICT. In this method, the auxiliary information extracted from PEM is flexibly injected into ICT and the increment of model parameters is small. The comprehensive evaluations validate that the ICT is not only superior to previous methods in the X-Ray datasets, IU-X-Ray and MIMIC-CXR, but also successfully be extended to a CT COVID-19 dataset COV-CTR.
Application of the forward selection strategy to the C4.5 Algorithm to improve the classification's accuracy of a breast cancer data set

The purpose of this study is to improve the classification accuracy of the C4.5 Algorithm utilizing the forward selection technique. Breast Cancer from the UCI Machine Learning Repository is the dataset utilized. There are 286 records in the dataset with nine attributes and one class (label). The suggested model was evaluated with two existing classification models (C4.5 and Naïve Bayes) using the RapidMiner program. The procedure consists of multiple stages, the first of which consists of selecting the dominant trait using the feature selection technique (weight by information gain). The second step is forward selection based on the outcome of feature selection. Before processing, the dataset is separated into training and testing halves, where the ratios of comparison are 70:30, 80:20, and 90:10. The final step is examining the output. The experimental results demonstrate that the forward selection methodology employing the C4.5 (C4.5 + FS) method outperforms the C4.5 and Naïve Bayes classification techniques. C4.5 + FS (Split Data 70:30) has an accuracy value of 76.74%, C4.5 + FS (Split Data 80:20) has an accuracy value of 78.95%, C4.5 + FS (Split Data 90:10) has an accuracy value of 78.57%, C4.5 (Split Data 70:30) has an accuracy value of 65.12%, and Naïve Bayes (Split Data is 70:30) has an accuracy value 85.55%. In comparison to typical classification algorithms (C4.5 and Naïve Bayes), the average accuracy values increased by 12.97% and 8.32%, respectively. In terms of precision, recall, and F-measure, the forward selection strategy utilizing the C4.5 method beat all other classification techniques, achieving 79.84%, 92.50%, and 85.55%, respectively. In addition, the results demonstrated an increase in the average Area Under Curve (AUC) from 0.628 to 0.732%. Therefore, it can be inferred that the forward selection strategy can be applied to the Breast Cancer Data Set in order to increase the accuracy value of classification method C4.5.
An emerging era of computational cytology

The significant advancement in digital imaging, data management, advanced computational power, and artificial neural network have an immense impact on the field of cytology. The amalgamation of these areas has generated a newer discipline known as computational cytology.In To discuss the various important aspects of computational cytology.We reviewed the different studies published in English during the last few years on computational cytology.Computational cytology is a newer and emerging discipline in pathology that deals with the patient's meta-data and digital image data to make a mathematical model to produce diagnostic interpretations and predictions. The role of the cytologist is now changing from a simple observational scientist and slide interpreter to a dynamic and integrated multi-parametric prediction-based scientist.In the current stage, the cytologist must understand the situation and should have a vision of the complete scenario on computational cytology.
Optimal sampling for positive only electronic health record data

Identifying a patient's disease/health status from electronic medical records is a frequently encountered task in electronic health records (EHR) related research, and estimation of a classification model often requires a benchmark training data with patients' known phenotype statuses. However, assessing a patient's phenotype is costly and labor intensive, hence a proper selection of EHR records as a training set is desired. We propose a procedure to tailor the best training subsample with limited sample size for a classification model, minimizing its mean-squared phenotyping/classification error (MSE). Our approach incorporates "positive only" information, an approximation of the true disease status without false alarm, when it is available. In addition, our sampling procedure is applicable for training a chosen classification model which can be misspecified. We provide theoretical justification on its optimality in terms of MSE. The performance gain from our method is illustrated through simulation and a real-data example, and is found often satisfactory under criteria beyond MSE.