Reviewerly/exHarmony huggingface dataset:
Atlas-based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks

We propose an atlas-based method to segment the intracochlear anatomy (ICA) in the post-implantation CT (Post-CT) images of cochlear implant (CI) recipients that preserves the point-to-point correspondence between the meshes in the atlas and the segmented volumes. To solve this problem, which is challenging because of the strong artifacts produced by the implant, we use a pair of co-trained deep networks that generate dense deformation fields (DDFs) in opposite directions. One network is tasked with registering an atlas image to the Post-CT images and the other network is tasked with registering the Post-CT images to the atlas image. The networks are trained using loss functions based on voxel-wise labels, image content, fiducial registration error, and cycle-consistency constraint. The segmentation of the ICA in the Post-CT images is subsequently obtained by transferring the predefined segmentation meshes of the ICA in the atlas image to the Post-CT images using the corresponding DDFs generated by the trained registration networks. Our model can learn the underlying geometric features of the ICA even though they are obscured by the metal artifacts. We show that our end-to-end network produces results that are comparable to the current state of the art (SOTA) that relies on a two-steps approach that first uses conditional generative adversarial networks to synthesize artifact-free images from the Post-CT images and then uses an active shape model-based method to segment the ICA in the synthetic images. Our method requires a fraction of the time needed by the SOTA, which is important for end-user acceptance.
Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences

A meaningful understanding of clinical protocols and patient pathways helps improve healthcare outcomes. Electronic health records (EHR) reflect real-world treatment behaviours that are used to enhance healthcare management but present challenges; protocols and pathways are often loosely defined and with elements frequently not recorded in EHRs, complicating the enhancement. To solve this challenge, healthcare objectives associated with healthcare management activities can be indirectly observed in EHRs as latent topics. Topic models, such as Latent Dirichlet Allocation (LDA), are used to identify latent patterns in EHR data. However, they do not examine the ordered nature of EHR sequences, nor do they appraise individual events in isolation. Our novel approach, the Categorical Sequence Encoder (CaSE) addresses these shortcomings. The sequential nature of EHRs is captured by CaSE's event-level representations, revealing latent healthcare objectives. In synthetic EHR sequences, CaSE outperforms LDA by up to 37% at identifying healthcare objectives. In the real-world MIMIC-III dataset, CaSE identifies meaningful representations that could critically enhance protocol and pathway development.
Construction of Dirichlet Mixture Allocation total probability model based on multiple class text analysis

Abstract The LDA model is a total probability generation model for analyzing a large number of documents. It extends PLSA, another text analysis model. In this model, each document is treated as a topic hybrid model, and the topic’s proportional prior distribution is a Dirichlet distribution. The LDA model does not reflect complex dependencies between underlying topics. Based on LDA, this paper introduces a new topic generation model, DMA (Dirichlet Mixture Allocation), which models document collections more accurately than LDA when documents are obtained in multiple classes. In this paper, we build a probabilistic topic model of DMA, use the method of variational inference to approximate each parameter of the model, study the model, and finally solve the estimation of each parameter.
Lossless Database Watermarking Based on Order-preserving Encryption

With the development of information technology and the popularity of Internet applications, the database outsourcing service is gaining popularity. However, since the database services provided by third parties are not completely trusted, the outsourced database model has to face a series of security issues such as privacy leakage, data theft, malicious tampering, and so forth. To both provide privacy protection and copyright tracking, this paper proposes a lossless database watermarking method based on order-preserving encryption. In the proposed method, the data owner encrypts the original data using order-preserving encryption algorithms. The outsourced database administrator then embeds the watermark bits by modifying the parities of the cover encrypted attribute values. At the database client, the database user extracts the embedded watermark bits based on the majority voting mechanism. The original database is recovered by directly decrypting the marked encrypted database. The experimental results and analysis show that the proposed method has the advantages of lossless, strong robustness, high embedding rate, no auxiliary message and low computational complexity.
An experimental study of the detection of clicks in English

Abstract This experimental study sets out to determine whether people detect click sounds in American English. Recent research has documented the use of non-phonemic clicks in a variety of languages to fulfill a range of functions such as sequence management or signaling searches and different types of attitudinal stance. While these clicks are acoustically salient and have been reported to occur with a frequency of up to 14 per minute in British English, they have not been widely investigated until relatively recently. For this experiment, we designed video stimuli consisting of A and B pairs of approximately 10 seconds of speech, one with a click and the other with the click edited out. We gave 118 participants a questionnaire and asked if they could detect a difference between the pairs of videos. The results indicate that the majority of participants, between 79% and 86%, do not detect click sounds.
FER-YOLO: Detection and Classification Based on Facial Expressions

Due to the wide application prospect and market value of emotion recognition, it has become an important research topic in today’s society. Among them, facial expression recognition (FER) plays an important role in expressing human emotional information. Generally, the FER classification process includes face pre-processing (face detection, alignment, etc.), which adds extra workload. To this end, detection and classification are carried out simultaneously in this paper. We first manually annotated the RAF-DB dataset. We then designed an end-to-end FER network with better performance and applied it to facial expressions called FER-YOLO. FER-YOLO is built on the basis of YOLOv3. We combine the squeeze-and-excitation (SE) module with the backbone network and assign a certain weight to each feature channel so that FER-YOLO can focus on learning prominent facial features. We also discussed the performance changes caused by the lightweight enhanced feature extraction networks. Experimental results show that the proposed FER-YOLO network is 3.03% mAP higher than YOLOv3 on the RAF-DB dataset.
Faster Multi-Object Segmentation using Parallel Quadratic Pseudo-Boolean Optimization

We introduce a parallel version of the Quadratic Pseudo-Boolean Optimization (QPBO) algorithm for solving binary optimization tasks, such as image segmentation. The original QPBO implementation by Kolmogorov and Rother relies on the Boykov-Kolmogorov (BK) maxflow/mincut algorithm and performs well for many image analysis tasks. However, the serial nature of their QPBO algorithm results in poor utilization of modern hardware. By redesigning the QPBO algorithm to work with parallel maxflow/mincut algorithms, we significantly reduce solve time of large optimization tasks. We compare our parallel QPBO implementation to other state-of-the-art solvers and benchmark them on two large segmentation tasks and a substantial set of small segmentation tasks. The results show that our parallel QPBO algorithm is over 20 times faster than the serial QPBO algorithm on the large tasks and over three times faster for the majority of the small tasks. Although we focus on image segmentation, our algorithm is generic and can be used for any QPBO problem. Our implementation and experimental results are available at DOI: 10.5281/zenodo.5201620
Fighting Adversarial Images With Interpretable Gradients

Adversarial images are specifically designed to fool neural networks into making a wrong decision about what they are looking at, which severely degrade neural network accuracy. Recently, empirical and theoretical evidence suggests that robust neural network models tend to have better interpretable gradients. Therefore, we speculate that improving the interpretability of the gradients of the neural network models may also help to improve the robustness of the models. Two methods are used to add gradient-dependent constraint terms to the loss function of neural network models and both improve the robustness of the models. The first method adds the fussed lasso penalty term of the saliency maps to the loss function of the neural network models, which makes the saliency maps arrange in a natural way to improve the interpretability of the saliency maps, and uses the gradient enhancement for relu instead of relu to strengthen the constraint of regularization term on saliency maps. In the second method, the cosine similarity penalty term between the input gradients and the image contour is added to the loss function of the model to constrain the approximation between the input gradients and the image contour. This method has a certain biological significance, because the contour information of the image is used in the human visual system to recognize the image. Both methods improve the interpretability of model‘s gradients and the first method exceeds most regularization methods except adversarial training on MNIST and the second method even exceeds the adversarial training under white-box attacks on CIFAR-10 and CIFAR-100.
Household PV Energy Balance Calculation by Monte Carlo Method

Two different approaches for the calculation of the household photovoltaic (PV) installations energy balance are presented and discussed in this paper. The main methodology is based on the Monte Carlo method is proposed and applied to the case study with the effort to increase the annual energy balance calculation accuracy without increasing significantly problem complexity. Results are discussed and compared to the second approach based on direct calculation using only mean values as well as with the real (measured) values of the household energy balance.
A Mask Wearing Detection System Based on Deep Learning

COVID-19 has dramatically changed people's daily life.Wearing masks is considered as a simple but effective way to defend the spread of the epidemic.Hence, a real-time and accurate mask wearing detection system is important.In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic.The system consists of three important functions, which are image detection, video detection and real-time detection.To keep a high detection rate, a deep learning-based method is adopted to detect masks.Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper.Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring.Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection.The sufficient results have shown that the developed system can perform well in the mask wearing detection task.The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.
Orthogonal Frames and Indexed Relations

We define and study the notion of an indexed frame. This is a bi-dimensional structure consisting of a Cartesian product equipped with relations which only relate pairs if they coincide in one of their components. We show that these structures are quite ubiquitous in modal logic, showing up in the literature as products of Kripke frames, subset spaces, or temporal frames for STIT logics. We show that indexed frames are completely characterised by their ‘orthogonal’ relations, and we provide their sound and complete logic. Using these ‘orthogonality’ results, we provide necessary and sufficient conditions for an arbitrary Kripke frame to be isomorphic to certain well-known bi-dimensional structures.
Weighted Fuzzy-Based PSNR for Watermark Visual Quality Evaluation

One of the problems of conventional visual quality evaluation criteria such as PSNR and MSE is the lack of appropriate criteria based on the human visual system (HVS). They are calculated based on the difference of the corresponding pixels in the original and manipulated image. Hence, they practically do not provide a correct understanding of the image quality. Watermarking is an image processing application in which the image's visual quality is an essential criterion for its evaluation. Watermarking requires a criterion based on the HVS that provides more accurate values compared to conventional criteria such as PSNR. This paper proposes a weighted fuzzy-based criterion that tries to find essential parts of an image based on the HVS. Then these parts will have larger weights in computing the final value of PSNR. We compare our results against standard PSNR, and our experiments show considerable consequences.
Machine Learning Based Data Security Model Using Blockchain for Secure Data Transmission in IoT

The Internet of Things (IoT) Internet typically provides information on all Internet properties. Without human interference, it monitors and handles the functions remotely. It can immediately or through its experiences adapt to the conditions. Because of the participation of IoT devices in many applications, security and privacy of the users have become a matter of major concern. The current security and privacy policies are being undermined by cyber-attacks at an explosive pace. Blockchain technology recently became one of the most advanced technique in IOT. Blockchain has been developed to facilitate digital transactions and to provide safe and reliable access to the distributed ledger. Blockchain is able to provide safe transactions among users without the need for reliable third parties or intermediaries with intelligent contracts. Each node data transmission packets rate will be recorded using the blockchain. Consequently, Machine Learning (ML) algorithms are employed to produce precise outputs from large and complex databases that enable produced outputs to predict and detect IoT-based systems' vulnerabilities. Data from heterogeneous sensors can be collected by translating the differing types of values for various sensor types. In addition to fast IoT system expansion via global protection, it is not expected to reach its highest level. As a result of the omnipresent existence of IoT, most users have no experience or ability to protect devices alone. In IoT world, machine learning can be extremely successful in addressing safety challenges. In this research work, a Machine Learning based Data Security Model with Blockchain (MLDSMB) for IoT secure data transmission is proposed. The proposed model is contrasted with the traditional models and the proposed technique shows that the secure data transmission levels are high than existing models.
Detection of Abnormal Red Blood Cells Using Features Dependent on Morphology and Rotation

Automatic detection of abnormal Red Blood Cells (RBCs) is an important problem in medical imaging. A new algorithm is proposed in this paper to detect abnormal RBCs, the first step in this algorithm identifies the regions of interest depending on morphology operations and the second step uses the rotate features to verify that the detected object is an abnormal RBCs. Two criteria were used to measure detection accuracy depending on the detection and false alarm rate, from analyzing the results they reach about 86 and 14%.
Path Planning for Mobile Robots with Courteous Behaviors in Domestic Environments

Along with the development of artificial intelligence, it has become a trend for mobile robots to serve and coexist with humans in domestic environments. It is significant to research path planning and make robots realize courteous behavioral patterns. These patterns can make robots avoid moving obstacles more regularly, and promote robots to get along with humans better. In this paper, we analyze common path planning algorithms for mobile robots. In order to realize courteous behaviors of mobile robots, we consider global path planning and local path planning comprehensively. We propose a method combining the smoothing ARA* algorithm for global path planning and the improved DWA algorithm for local path planning. The simulated experiments verify the effectiveness of the proposed algorithm.
Severe Convection Weather Identification Model Based on Rough Set Theory and Artificial Fish Swarm Algorithm

In order to solve the problem of identifying severe convection weather, this paper proposes a model of identifying severe convection weather based on rough set theory and artificial fish swarm algorithm. First, based on the radar reflectivity images, the echo attributes of the severe convection area are analyzed and extracted. Second, this paper proposes a discretization method based on rough set theory. This method discretizes the meteorological data and establishes a decision table. Third, the improved artificial fish swarm algorithm is used to reduce attributes and obtain the minimum attribute set. Finally, the classification and regression tree (CART) algorithm and the cost-complexity pruning (CCP) principle are used to establish an objective model for identifying severe convection weather. Experimental results show that the model in this paper can recognize severe convection weather under the condition of a few attributes and the identification model has high accuracy.
Indian Currency Detection Using Image Recognition Technique

Today, Counterfeit notes are one of the biggest problems occurring in currency note transactions. This has become an acute problem to almost all the countries today. In developing countries like India, it is becoming a serious issue. Due to advancement in color printing, duplication and scanning, this problem has become more serious. Our approach follows a simple pattern matching algorithm to detect counterfeit notes. Initially the required features will be extracted and stored in a MATLAB file. Later the input is processed using the algorithms to find fake notes and also the denomination of the provided input notes.
Enhanced Hierarchical Structure Features for Automated Essay Scoring

Automated Essay Scoring (AES) aims to evaluate the quality of an essay automatically. In practice, an essay is usually organized in a hierarchical structure, which means that the writer needs to organize the main ideas into different paragraphs, and organize coherent sentences and appropriate words for the essay. Therefore, it is crucial to model the hierarchical structure of essays for AES. For addressing this issue, most of the existing works used neural network-based architectures (e.g., CNNs and LSTMs) to model the hierarchical structure of essays. Different from previous studies, we propose a novel hierarchical graph structure based on graph convolutional networks (GCN) to encode the hierarchical structure of essays and hope to obtain those structured coherence and discourse information from the graph aggregation. We conduct several experiments on ASAP dataset and the experimental results demonstrate the effectiveness of our method.
Investigating and Quantifying the Reproducibility of Graph Neural Networks in Predictive Medicine

Graph neural networks (GNNs) have gained an unprecedented attention in many domains including dysconnectivity disorder diagnosis thanks to their high performance in tackling graph classification tasks. Despite the large stream of GNNs developed recently, prior efforts invariably focus on boosting the classification accuracy while ignoring the model reproducibility and interpretability, which are vital in pinning down disorder-specific biomarkers. Although less investigated, the discriminativeness of the original input features -biomarkers, which is reflected by their learnt weights using a GNN gives informative insights about their reliability. Intuitively, the reliability of a given biomarker is emphasized if it belongs to the sets of top discriminative regions of interest (ROIs) using different models. Therefore, we define the first axis in our work as reproducibility across models, which evaluates the commonalities between sets of top discriminative biomarkers for a pool of GNNs. This task mainly answers this question: How likely can two models be congruent in terms of their respective sets of top discriminative biomarkers? The second axis of research in our work is to investigate reproducibility in generated connectomic datasets. This is addressed by answering this question: how likely would the set of top discriminative biomarkers by a trained model for a ground-truth dataset be consistent with a predicted dataset by generative learning? In this paper, we propose a reproducibility assessment framework, a method for quantifying the commonalities in the GNN-specific learnt feature maps across models, which can complement explanatory approaches of GNNs and provide new ways to assess predictive medicine via biomarkers reliability. We evaluated our framework using four multiview connectomic datasets of healthy neurologically disordered subjects with five GNN architectures and two different learning mindsets: (a) conventional training on all samples (resourceful) and (b) a few-shot training on random samples (frugal). Our code is available at https://github.com/basiralab/Reproducible-Generative-Learning.
Fuzzy C means application for area mapping of poor populations in Maluku Province: Efforts to reduce the number of poor populations

Based on data from Badan Pusat Statistik (BPS) in March 2016 stated that the number of poor people in Indonesia reached 28.01 million people. This figure is around 10.86 percent of the national population. The province of Maluku as the third poor contributor from all provinces in Indonesia reached 27.74 percent. Note that, there are 8 districts/cities in the Province of Maluku which are determined as disadvantaged areas, Maluku Barat Daya (MBD) is one of them. Based on data from BPS, in 2014 the percentage of poor people in the district of MBD was the second highest after the district of Maluku Tenggara Barat (MTB) of 11 districts/cities in Maluku where it reached 28.33 percent. The poverty level in a district is relatively difficult to reduce this is due to a large number of villages that have economic access isolation constraints due to unsupported geographical locations. Various programs and policies, both in the social, health, and so on, have been attempted to solve the problem, but they cannot overcome the existing poverty problem yet. The use of the Fuzzy C-Means (FCM) method to classify 11 districts/cities in Maluku Province based on data that is characteristic of poverty and consists of 10 variables. Grouping Process Using Number of Clusters: 5, Maximum iteration: 100, Expected velocity error: 10−5. The results using this method: grouping district/cities in Maluku based on poverty factors are: cluster 1 consists of Maluku Tenggara Barat, Buru, Kep. Aru, and Tual; cluster 2 consists of Maluku Barat Daya and Buru Selatan; cluster 3 consists of Maluku Tengah; cluster 4 consists of Maluku Utara, Seram Bagian Barat, and Seram Bagian Timur; and cluster 5 consists of Ambon. Each cluster describes the similarity level poverty based on Partition matrix U which loads the fuzzy centroid.