Reviewerly/exHarmony huggingface dataset:
Joint Segmentation and Uncertainty Estimation of Ventricular Structures from Cardiac MRI using a Bayesian CondenseUNet

While convolutional neural networks (CNNs) have shown potential in segmenting cardiac structures from magnetic resonance (MR) images, their clinical applications still fall short of providing reliable cardiac segmentation. As a result, it is critical to quantify segmentation uncertainty in order to identify which segmentations might be troublesome. Moreover, quantifying uncertainty is critical in real-world scenarios, where input distributions are frequently moved from the training distribution due to sample bias and non-stationarity. Therefore, well-calibrated uncertainty estimates provide information on whether a model's output should (or should not) be trusted in such situations. In this work, we used a Bayesian version of our previously proposed CondenseUNet [1] framework featuring both a learned group structure and a regularized weight-pruner to reduce the computational cost in volumetric image segmentation and help quantify predictive uncertainty. Our study further showcases the potential of our deep-learning framework to evaluate the correlation between the uncertainty and the segmentation errors for a given model. The proposed model was trained and tested on the Automated Cardiac Diagnosis Challenge (ACDC) dataset featuring 150 cine cardiac MRI patient dataset for the segmentation and uncertainty estimation of the left ventricle (LV), right ventricle (RV), and myocardium (Myo) at end-diastole (ED) and end-systole (ES) phases.
Probing the link between the APOE-ε4 allele and whole-brain gray matter using deep learning

The APOE-ε4 allele is a known genetic risk for Alzheimer's disease (AD). Thus, it can be reasoned that the APOE-ε4 allele would also impact neurodegeneration-associated structural brain changes. Here we probe if the APOE-ε4 genotype directly modulates the human brain's gray matter using a neural network trained on the whole-brain gray matter images from the cognitively normally aging (CN) and AD individuals. To investigate the linkage between the APOE-ε4 allele and whole-brain (voxel-wise) gray matter, we systematically profile our investigation in multiple classification tasks, including diagnostic classification and APOE-ε4 classification conjointly as well as independently. Results suggest that although the MRI data can reliably track and reflect neurodegenerative changes in the brain cross-sectionally, the APOE-ε4 status may not be distinguishable correspondingly. The nonexistence of a direct and convincing modulative effect of APOE-ε4 on the whole-brain gray matter indicates that the gray matter changes may be independent of the APOE-ε4 status, and instead characterize a non-APOE, comorbid mechanism in AD.
Towards Adversarial Robustness with Early Exit Ensembles

Deep learning techniques are increasingly used for decision-making in health applications, however, these can easily be manipulated by adversarial examples across different clinical domains. Their security and privacy vulnerabilities raise concerns about the practical deployment of these systems. The number and variety of the adversarial attacks grow continuously, making it difficult for mitigation approaches to provide effective solutions. Current mitigation techniques often rely on expensive re-training procedures as new attacks emerge. In this paper, we propose a novel adversarial mitigation technique for biosignal classification tasks. Our approach is based on recent findings interpreting early exit neural networks as an ensemble of weight sharing sub-networks. Our experiments on state-of-the-art deep learning models show that early exit ensembles can provide robustness generalizable to various white box and universal adversarial attacks. The approach increases the accuracy of vulnerable deep learning models up to 60 percentage points, while providing adversarial mitigation comparable to adversarial training. This is achieved without previous exposure to the adversarial perturbation or the computational burden of re-training.
Geometric Mapping Evaluation for Real-Time Local Sensor Simulation

Medical augmented reality and simulated test environments struggle in accurately simulating local sensor measurements across large spatial domains while maintaining the proper resolution of information required and real time capability. Here, a simple method for real-time simulation of intraoperative sensors is presented to aid with medical sensor development and professional training. During a surgical intervention, the interaction between medical sensor systems and tissue leads to mechanical deformation of the tissue. Through the inclusion of detailed finite element simulations in a real-time augmented reality system the method presented will allow for more accurate simulation of intraoperative sensor measurements that are independent of the mechanical state of the tissue. This concept uses a coarse, macro-level deformation mesh to maintain both computational speed and the illusion of reality and a simple geometric point mapping method to include detailed fine mesh information. The resulting system allows for flexible simulation of different types of localized sensor measurement techniques. Preliminary simulation results are provided using a real-time capable simulation environment and prove the feasibility of the method.
Branching Time Active Inference with Bayesian Filtering

Branching time active inference is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in active inference, a neuroscientific framework widely used for brain modeling, as well as in Monte Carlo tree search, a method broadly applied in the reinforcement learning literature. Up to now, the inference of the latent variables was carried out by taking advantage of the flexibility offered by variational message passing, an iterative process that can be understood as sending messages along the edges of a factor graph. In this letter, we harness the efficiency of an alternative method for inference, Bayesian filtering, which does not require the iteration of the update equations until convergence of the variational free energy. Instead, this scheme alternates between two phases: integration of evidence and prediction of future states. Both phases can be performed efficiently, and this provides a forty times speedup over the state of the art.
Cardiac Anomaly Detection from Cine MRI Images using Physiological Features and Random Forest Classifier

Computer-aided diagnosis (CAD) with cine MRI is a foremost research topic to enable improved, faster, and more accurate diagnosis of cardiovascular diseases (CVD). However, current approaches that use manual visualization or conventional clinical indices can lack accuracy for borderline cases. Also, manual visualization of 3D/4D MR data is time-consuming and expert-dependent. We try to simplify this process by creating an end-to-end automated CAD system that segments the critical substructures of the heart. The new domain-related physiological features are then calculated from the segmented regions. These features are fed to a random forest classifier that identifies the anomaly. We have obtained a very high accuracy when testing this end-to-end approach on the Automated Cardiac Diagnosis challenge (ACDC) dataset (4 pathologies, 1 normal). To prove the generalizability of the method we have blind-tested this approach on M&Ms-2 dataset which is a multi-center, multi-vendor, and multi-disease dataset with better than 90% accuracy.
Single feature constrained manual registration method for Augmented Reality applications in gynecological laparoscopic interventions

Augmented Reality (AR) can avoid some of the drawbacks of Minimally Invasive Surgery and may provide opportunities for developing innovative tools to assist surgeons. In laparoscopic surgery, the achievement of easy and sufficiently accurate registration is an open challenge. This is particularly true in procedures, such as laparoscopic abdominal Sacro-Colpopexy, where there is a lack of a sufficient number of visible anatomical landmarks to be used as a reference for registration. In an attempt to address the above limitations, we developed and preliminarily testes a constrained manual procedure based on the identification of a single anatomical landmark in the laparoscopic images, and the intraoperative measurement of the laparoscope orientation. Tests in a rigid in-vitro environment show good accuracy (median error 2.4 mm obtained in about 4 min) and good preliminary feedback from the technical staff who tested the system. Further experimentation in a more realistic environment is needed to validate these positive results. Clinical Relevance - This paper provides a new registration method for the development of AR educational videos and AR-based navigation systems for laparoscopic interventions.
Development of a Normalized Hadith Narrator Encyclopedia with TEI

The investigation in the narrator list of prophetic tradition (hadith) is considered an important task for different hadith sciences such as the biography of narrators. In fact, the authenticity of hadith is intensely related to the chain of narrators how transmitted the story. In addition, this science is interested essentially in analyzing the hadith narrator profile. Indeed, having a standardized encyclopedia of hadith narrators can help researchers to explore and manipulate the various hadith documents and can simplify different analyzes. For this reason, we aim to develop a standardized hadith narrator encyclopedia with Text Encoding Initiative (TEI) language. To achieve this, we propose a TEI model for the hadith narrator properties. To experiment our TEI model, firstly, we construct a corpus of articles about hadith narrators from Wikipedia. Secondly, we use a system allowing the named entity recognition in relation with narrator data. Thirdly, we perform a post-processing to complete the corpus TEI annotation. Fourthly, we generate the hadith narrator encyclopedia based on our TEI model. The obtained result is encouraging despite some problems related to exceptional cases.
Tunisian Dialect Agglutination Processing with Finite Transducers

The agglutination is a widespread phenomenon in the formation of words in the Tunisian dialect. So, it requires a treatment at the morphological level. In this context, we present our way of dealing with this phenomenon for the different grammatical categories in Tunisian dialect. The proposed method is based on the construction of morphological grammars using a set of finite state transducers. These transducers offer great flexibility in the construction of morphological grammars and allow their maintenance and reuse. Our goal is to create a set of linguistic resources allowing the treatment of the phenomenon of agglutination in the Tunisian dialect. The NooJ linguistic platform with new technologies makes it possible to elaborate and experiment our constructed resources. The obtained results are ambitious and highlight our proposed method.
Distributional Word Vectors as Semantic Maps Framework

Distributional Semantics Models are one of the most ubiquitous tools in Natural Language Processing. However, it is still unclear how to optimise such models for specific tasks and how to evaluate them in a general setting (having ability to be successfully applied to any language task in mind). We argue that benefits of intrinsic distributional semantic models evaluation could be questioned since the notion of their “general quality” possibly does not exist; distributional semantic models, however, can be considered as a part of Semantic Maps framework which formalises the notion of linguistic representativeness on the lexical level.
Low-rank constraint bipartite graph learning

Bipartite Graph-based multi-view clustering has become an active topic recently due to its efficiency in tackling large scale multi-view data. However, most existing bipartite graph-based multi-view clustering methods have the following disadvantages: 1) the clustering performance heavily depends on the predefined bipartite graph; 2) they fail to explore the complementary information embedded in multiple bipartite graphs and spatial low-rank structure hidden in each bipartite graph. To address these issues, we propose an efficient multi-view clustering method, Learning Constraint Bipartite Graphs for Multi-view Clustering (LCBG). LCBG adaptively learns each graph S(v)∈RN×M such that it well characterizes the relationship between M (M≪N) anchors and N samples in the v-th view, and takes both the intra-view and inter-view spatial low-rank structures of the learned bipartite graphs into account by minimizing tensor Schatten p-norm and nuclear norm, respectively. Finally, an efficient algorithm, which scales linearly with the data size, is proposed to solve LCBG. Extensive experimental results on five benchmark datasets indicate our proposed LCBG is superior to the state-of-the-art methods.
Generalized representation of local relationships for few-shot incremental learning

小样本学习作为计算机视觉的新兴研究热点，旨在通过少量的已知样本实现对全新概念的快速识别和理解. 现有的小样本学习技术只针对给定的新类别物体进行分类和识别，忽略了旧有基础类别的表示和区分. 针对小样本增量学习问题中的类间可区分性差、增量类别难泛化两大研究问题，本文引入局部关系学习的思想，提出局部关系泛化表征的模型学习方法. 为确保增量过程中的类别可区分性，本文首先使用局部空间关联约束增量表征能力，在此基础上，使用特征重映射方法将查询向量与支撑集映射在同一度量空间，有效确保了微小差异的可区分性. 同时，为缓解增量过程中样本不足导致的归纳偏置问题，文章提出空间泛化式原型生成算法，利用分布特性快速生成虚拟原型，促进样本的有效表征. 在此基础上，本文利用元学习训练方法，提出了局部泛化联合的增量学习框架，通过联合基础类别的局部表征和增量类别的快速泛化约束，有效缓解了灾难性遗忘和表征不足的困难，实验证明本方法在经典小样本增量任务取得了当前最好的性能表现。
On the relation between completely bounded and (1,cb)-summing maps with applications to quantum XOR games

In this work we show that, given a linear map from a general operator space into the dual of a C⁎-algebra, its completely bounded norm is upper bounded by a universal constant times its (1,cb)-summing norm. This problem is motivated by the study of quantum XOR games in the field of quantum information theory. In particular, our results imply that for such games entangled strategies cannot be arbitrarily better than those strategies using one-way classical communication.
Estimation of the Local and Global Coherence of Ukrainian Texts Using Transformer-Based, LSTM, and Graph Neural Networks

In this paper, the different models for the estimation of both local and global coherence of Ukrainian-language texts have been considered. In order to evaluate the local coherence of a document, Transformer-based and LSTM neural networks have been proposed with further training on a Ukrainian-language news corpus. It has been shown that the LSTM-based approach outperforms the corresponding network based on the Transformer architecture according to the accuracy metrics while solving typical tasks on both test datasets. In order to investigate the connection between sentences revealed by the neural network, the Uniform Manifold Approximation and Projection dimension reduction technique has been utilized for the projection of sentences’ embedding into 2D space. The clusters obtained may indicate the consideration of both the structure of a sentence and different types of connections between them by the designed model. In order to estimate the global coherence of a document, a model based on a graph convolutional neural network has been suggested. The appropriateness of taking into account the connection between all sentences despite their positions has been shown. The results obtained for the designed and trained global coherence estimation model may indicate the different aspects of the analysis of a text by the designed models that can lead to the usage of both local and global coherence estimation models according to an assigned task.
Efficient Identity-Based Integrity Auditing for Cloud Storage and Data Sharing

Users can save and access cloud storage for their data remotely because of cloud storage services, the exchange of knowledge with others. The auditing of data integrity from a distance is called data accuracy auditing from afar is maintained at regular intervals in the cloud. The server file in certain traditional cloud-based computing services, as an example electronic health reports, may contain classified information. Once it is possible to share a cloud file, the private information cannot be revealed to anyone. The knowledge that is kept private can be detected by archiving the shared data file, so it will make this shared file more available unusable for everyone. In auditing the accuracy of data stored remotely, the thanks to note sharing of knowledge for confidential data concealment has yet to be investigated. The theme of data security auditing recognizes data sharing of classified details. A sanitizer is used to sanitize the knowledge the info the information blocks of the file, such as the confidential info, which converts the data blocks’ signatures into correct those for the sanitized file. Integrity auditing includes checking the integrity of the sanitized entry. As a result, our theme allows data that will be kept in the server, as well as exchanged and used by others as long as confidential knowledge is concealed, and technique for ensuring the integrity of data stored remotely allowed. It is always possible to be efficient when you are gone. In the meanwhile, the planned theme depends on identity-based cryptography.
The Benefits of Using Immersive Technologies at General School

The article is devoted to the problem and benefits of the use immersive technologies at general school. Immersive technologies are an important tools for future improving the educational process, and important to study their impact on the mental and physical condition of young people, which will depend on the effectiveness of the use of virtual reality in education at various levels. The purpose of the article is to analyze the use of immersive technologies for school learning and to identify the basic benefits of using immersive technologies at general school. We made a comparative description of traditional earning and learning using immersive technologies. WE can highlight the main advantages of immersive technologies: providing the ability to change the relative size of the pre-investigated objects, which leads to the visualization of objects of the micro and macro world; creation of models of the phenomena or processes which cannot be directly and clearly registered by bodies of senses of the person; visualization of abstract models and production of objects that have no form in the real world; focusing students on the study of specific objects without distraction to external stimuli, which gives them the opportunity to focus fully on the material.
Music and Affect: Self, Sound Embodiment, and the Music of Feldman and Xenakis

This paper is a phenomenological investigation of the relationship between music and affect on the basis of three different approaches. In Part I, I explore the enactive approach of the Self as a fundamental of subjective inquiry. Here I discuss the theory of autopoiesis, the principles of self-organization and self-realization, as well as Varela’s enactive account of the human experience. The Self is also examined from the perspective of Buddhism and Seebok’s theory of Semiotic Self. In Part II, I illustrate the enactive approach with research on electroacoustic music that considers the relationship between sound, space, and affect and discuss sound embodiment and immersion. In Part III, I examine the music of Morton Feldman and Iannis Xenakis, two twentieth century avant-garde composers who were pioneers in musical composition vis-à-vis their experiences of time. Using the ideas of Derrida and Lyotard on postmodern phenomenology, I conclude with a final reflection on the inquiry on the relationship between music and affect.
Hybrid Cryptosystem’s Design with AES and SHA-1 Algorithms

Web turned into a remarkable part in our consistently life. We use Web for making exchanges and sending significant and delicate records. In the event that the data to be sent are vital, and we require a solid method for correspondence in order to send the information. Cryptography is intended for that reason which will give security to our information. Here, security is given to the information by executing both AES and SHA calculations. The plan is synthesized utilizing Verilog. The plan of AES calculation is tweaked to execute just on Xilinx programming. AES is a symmetric key code calculation that can be utilized for move of information from sender to recipient in a powerful way. Encryption is performed at sender side which is a method for changing over unique message into specific example of codes called figure message for security reasons. Unscrambling is performed at beneficiary side to receive unique message from the coded message. In AES relying on adjusts, distinctive key sizes (194, 128, 256) p can be utilized. AES comprises of various advances like preround change, key development, shift lines, and blend segments. Some vital benefits of AES encryption and unscrambling calculation are quick in execution, and it utilizes higher length size 128 bits for encryption which gives greater security to the information as it requires 2^128 endeavors to send or hack the information. Secure hashing is utilized for creating hash codes from the information, and this technique is viewed as secure on the grounds that it is difficult to convey the first message from hash code. In this task, SHA-1 is executed. SHA-1 deals with information having length of 512 pieces and produces a 160 cycle information called as hash codes or digest. SHA utilizes diverse rationale tasks like and shift and XOR. SHA is utilized for creating the way to encryption calculation which prompts greater security.
Anomaly Detection Algorithm Based on Broad Learning System and Support Vector Domain Description

Deep neural network-based autoencoders can effectively extract high-level abstract features with outstanding generalization performance but suffer from sparsity of extracted features, insufficient robustness, greedy training of each layer, and a lack of global optimization. In this study, the broad learning system (BLS) is improved to obtain a new model for data reconstruction. Support Vector Domain Description (SVDD) is one of the best-known one-class-classification methods used to solve problems where the proportion of sample categories of data is extremely unbalanced. The SVDD is sensitive to penalty parameters C, which represents the trade-off between sphere volume and the number of target data outside the sphere. The training process only considers normal samples, which leads to a low recall rate and weak generalization performance. To address these issues, we propose a BLS-based weighted SVDD algorithm (BLSW_SVDD), which introduces reconstruction error weights and a small number of anomalous samples when training the SVDD model, thus improving the robustness of the model. To evaluate the performance of BLSW_SVDD model, comparison experiments were conducted on the UCI dataset, and the experimental results showed that in terms of accuracy and F1 values, the algorithm has better performance advantages than the traditional and improved SVDD algorithms.