Reviewerly/exHarmony huggingface dataset:
Whole brain segmentation with full volume neural network

Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes separately. Their representation learning is based on parts of the whole volume whereas their labeling result is produced by aggregation of partial segmentation. Learning and inference with incomplete information could lead to sub-optimal final segmentation result. To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume. The framework makes use of complete information in each volume and can be implemented easily. An effective instance in this framework is given subsequently. We adopt the 3D high-resolution network (HRNet) for learning spatially fine-grained representations and the mixed precision training scheme for memory-efficient training. Extensive experiment results on a publicly available 3D MRI brain dataset show that our proposed model advances the state-of-the-art methods in terms of segmentation performance.
A neural network for glomerulus classification based on histological images of kidney biopsy

Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists.We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153).Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.
“More Than Deep Learning”: post-processing for API sequence recommendation

In the daily development process, developers often need assistance in finding a sequence of APIs to accomplish their development tasks. Existing deep learning models, which have recently been developed for recommending one single API, can be adapted by using encoder-decoder models together with beam search to generate API sequence recommendations. However, the generated API sequence recommendations heavily rely on the probabilities of API suggestions at each decoding step, which do not take into account other domain-specific factors (e.g., whether an API suggestion satisfies the program syntax and how diverse the API sequence recommendations are). Moreover, it is difficult for developers to find similar API sequence recommendations, distinguish different API sequence recommendations, and make a selection when the API sequence recommendations are ordered by probabilities. Thus, what we need is more than deep learning. In this paper, we propose an approach, named Cook, to combine deep learning models with post-processing strategies for API sequence recommendation. Specifically, we enhance beam search with code-specific heuristics to improve the quality of API sequence recommendations. We develop a clustering algorithm to cluster API sequence recommendations so as to make it easier for developers to find similar API sequence recommendations and distinguish different API sequence recommendations. We also propose a method to generate a summary for each cluster to help developers understand the API sequence recommendations. Our evaluation results have shown that (1) three deep learning models with our heuristic-enhanced beam search achieved better performance than with the original beam search in terms of CIDEr-1, CIDEr-5 and CIDEr-10 scores, with an average improvement of 1.8, 2.3 and 2.3, respectively; and (2) our clustering algorithm achieved high performance on six metrics and outperformed two variant clustering algorithms. Moreover, our user study with 24 participants shows that Cook can help developers accomplish programming tasks faster and pass more test cases, and the participants confirm that clusters and summaries indeed help them understand and select the correct API sequence recommendations.
Improving Super-Resolution Performance Using Meta-Attention Layers

Convolutional Neural Networks (CNNs) have achieved impressive results across many super-resolution (SR) and image restoration tasks. While many such networks can upscale low-resolution (LR) images using just the raw pixel-level information, the ill-posed nature of SR can make it difficult to accurately super-resolve an image which has undergone multiple different degradations. Additional information (metadata) describing the degradation process (such as the blur kernel applied, compression level, etc.) can guide networks to super-resolve LR images with higher fidelity to the original source. Previous attempts at informing SR networks with degradation parameters have indeed been able to improve performance in a number of scenarios. However, due to the fully-convolutional nature of many SR networks, most of these metadata fusion methods either require a complete architectural change, or necessitate the addition of significant extra complexity. Thus, these approaches are difficult to introduce into arbitrary SR networks without considerable design alterations. In this paper, we introduce meta-attention, a simple mechanism which allows any SR CNN to exploit the information available in relevant degradation parameters. The mechanism functions by translating the metadata into a channel attention vector, which in turn selectively modulates the network's feature maps. Incorporating meta-attention into SR networks is straightforward, as it requires no specific type of architecture to function correctly. Extensive testing has shown that meta-attention can consistently improve the pixel-level accuracy of state-of-the-art (SOTA) networks when provided with relevant degradation metadata. For PSNR, the gain on blurred/downsampled (X4) images is of 0.2969 dB (on average) and 0.3320 dB for SOTA general and face SR models, respectively.
Improving breast cancer prediction using a pattern recognition network with optimal feature subsets

AimTo predict the presence of breast cancer by using a pattern recognition network with optimal features based on routine blood analysis parameters and anthropometric data.Methods Sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and Fowlkes-Mallows (FM) index of each model were calculated.Glucose, insulin, age, homeostatic model assessment, leptin, body mass index (BMI), resistin, adiponectin, and monocyte chemoattractant protein-1 were used as predictors. ResultsPattern recognition network distinguished patients with breast cancer disease from healthy people.The best classification performance was obtained by using BMI, age, glucose, resistin, and adiponectin, and in a model with two hidden layers with 11 and 100 neurons in the neural network.The accuracy, sensitivity, specificity, FM index, and MCC values of the best model were 94.1%, 100%, 88.9%, 94.3%, and 88.9%, respectively. ConclusionBreast cancer diagnosis was successfully predicted using only five features.A model using a pattern recognition network with optimal feature subsets proposed in this study could be used to improve the early detection of breast cancer.
Thin-film neural networks for optical inverse problem

The thin-film optical inverse problem has attracted a great deal of attention in science and industry, and is widely applied to optical coatings. However, as the number of layers increases, the time it takes to extract the parameters of thin films drastically increases. Here, we introduce the idea of exploiting the structural similarity of all-optical neural networks and applied it to the optical inverse problem. We propose thin-film neural networks (TFNNs) to efficiently adjust all the parameters of multilayer thin films. To test the performance of TFNNs, we implemented a TFNN algorithm, and a reflectometer at normal incidence was built. Operating on multilayer thin films with 232 layers, it is shown that TFNNs can reduce the time consumed by parameter extraction, which barely increased with the number of layers compared with the conventional method. TFNNs were also used to design multilayer thin films to mimic the optical response of three types of cone cells in the human retina. The light passing through these multilayer thin films was then recorded as a colored photo.
The Importance of Traditional Sports into College Physical Education Based on Big Data Dynamic Programming Algorithm

Physical education teaching is conducive to the cultivation of students’ lifelong sports consciousness, which can improve students’ health and enhance their physique. In order to explore the importance of traditional sports based on big data dynamic programming algorithm into college physical education, the video action recognition and segmentation technology based on big data dynamic programming algorithm is designed. The complex actions in traditional sports teaching video are divided into a series of atomic actions with single semantics. The human action results are modeled according to the relationship between complex actions and atomic actions, and the actions are completed, and the changes of students’ sports level were compared under different teaching modes. Compared with the no segment method, the average accuracy of the experimental design method increased by 2.80% and 3.50%, respectively, and the action recognition rate increased by 11.50%, 8.40%, 13.60%, 13.50%, and 13.60%, respectively. Before and after the experiment, there was a significant difference in the performance of the experimental group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>=</mo> <mn>0.021</mn> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The results show that the traditional sports teaching mode based on video action recognition technology of big data dynamic programming algorithm can effectively improve the teaching quality of sports teaching. This research has a certain reference value to promote the current physical education teaching reform policy.
Ants Colony Optimization Algorithm in the Hopfield Neural Network for Agricultural Soil Fertility Reverse Analysis

The Boolean Satisfiability Problem (BSAT) is one of the most important decision problems in mathematical logic and computational sciences for determining whether or not a solution to a Boolean formula.. Hopfield neural network (HNN) is one of the major type artificial neural network (NN) popularly known for it used in solving various optimization and decision problems based on its energy minimization machinism. The existing models that incorporate standalone network projected non-versatile framework as fundamental Hopfield type of neural network (HNN) employs random search in its training stages and sometimes get trapped at local optimal solution. In this study, Ants Colony Optimzation Algorithm (ACO) as a novel variant of probabilistic metaheuristic algorithm (MA) inspired by the behavior of real Ants, has been incorporated in the training phase of Hopfield types of the neural network (HNN) to accelerate the training process for Random Boolean kSatisfiability reverse analysis (RANkSATRA) based for logic mining. The proposed hybrid model has been evaluated according to robustness and accuracy of the induced logic obtained based on the agricultural soil fertility data set (ASFDS). Based on the experimental simulation results, it reveals that the ACO can effectively work with the Hopfield type of neural network (HNN) for Random 3 Satisfiability Reverse Analysis with 87.5 % classification accuracy
Colored Point Cloud Registration by Depth Filtering

In the last stage of colored point cloud registration, depth measurement errors hinder the achievement of accurate and visually plausible alignments. Recently, an algorithm has been proposed to extend the Iterative Closest Point (ICP) algorithm to refine the measured depth values instead of the pose between point clouds. However, the algorithm suffers from numerical instability, so a postprocessing step is needed to restrict erroneous output depth values. In this paper, we present a new algorithm with improved numerical stability. Unlike the previous algorithm heavily relying on point-to-plane distances, our algorithm constructs a cost function based on an adaptive combination of two different projected distances to prevent numerical instability. We address the problem of registering a source point cloud to the union of the source and reference point clouds. This extension allows all source points to be processed in a unified filtering framework, irrespective of the existence of their corresponding points in the reference point cloud. The extension also improves the numerical stability of using the point-to-plane distances. The experiments show that the proposed algorithm improves the registration accuracy and provides high-quality alignments of colored point clouds.
Interval Value Fuzzy k-Ideals of a KU-Semigroup

The notion of interval value fuzzy k-ideal of KU-semigroup was studied as a generalization of afuzzy k-ideal of KU-semigroup. Some results of this idea under homomorphism are discussed. Also, we presented some properties about the image (pre-image) for interval~ valued fuzzy~k-ideals of a KU-semigroup. Finally, the~ product of~ interval valued fuzzyk-ideals is established.&#x0D;
ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5

We present the winning entry to the Multilingual Lexical Normalization (MultiLexNorm) shared task at W-NUT 2021 (van der Goot et al., 2021a), which evaluates lexicalnormalization systems on 12 social media datasets in 11 languages.We base our solution on a pre-trained byte-level language model, ByT5 (Xue et al., 2021a), which we further pre-train on synthetic data and then fine-tune on authentic normalization data.Our system achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing.
Hyper-parameter optimization of convolutional neural network based on particle swarm optimization algorithm

Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.
ARTIFICIAL INTELLIGENCE AND COPYRIGHT

Artificial Intelligence is one of the emerging technologies with the most potential nowadays. The scale of use is increasingly broad, and many compa-nies are involved in developing this technolog y with significant investments. In this sense, intellectual property issues have been raised in several legal systems, with different approaches. Knowing who owns the copyright of works produced by an AI is crucial as it will directly impact the development of this technolog y. Furthermore, it is paramount for fully autonomous AI, as there is no human intervention in these. Another path would be creating a robotic legal personality, which raises ethical and legal issues that will take time to overcome.
Audiobook Stylistics: Comparing print and audio in the bestselling segment

The paper explores differences between bestsellers in print and the most popular audiobooks in a subscription-based streaming service for books (‘beststreamers’) by means of computational stylistics. The point of departure is the complete set of print bestsellers and digital audiobook beststreamers for the Swedish book market 2015–2019, in total 172 novels. We probed 34 linguistic measures to track differences between subsets at the stylistic level. The results indicate that there are pronounced differences between the formats. Print bestsellers are longer, syntactically more complex and varied, and seem to focus more on depiction. Beststreaming audiobooks, by contrast, are shorter, more straightforwardly written, and appear to highlight plot and dialogue. The results are replicated when the comparison is restricted to crime fiction, the most prominent genre in the commercial top segment. Given these results, it is argued that it is possible to discern a particular audiobook style as one factor affecting book consumption in digital formats, and conversely that the printed format is associated with other stylistic preferences.
Medical Data Wrangling With Sequential Variational Autoencoders

Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors that are off for some time or data collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE, which extends the capabilities of VAEs to sequential streams of data with missing observations. We compare our model against state-of-the-art solutions in an intensive care unit database (ICU) and a dataset of passive human monitoring. Furthermore, we find that standard error metrics such as RMSE are not conclusive enough to assess temporal models and include in our analysis the cross-correlation between the ground truth and the imputed signal. We show that Shi-VAE achieves the best performance in terms of using both metrics, with lower computational complexity than the GP-VAE model, which is the state-of-the-art method for medical records.
A Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding

With the early success of query-answer assistants such as Alexa and Siri, research attempts to expand system capabilities of handling service automation are now abundant.However, preliminary systems have quickly found the inadequacy in relying on simple classification techniques to effectively accomplish the automation task.The main challenge is that the dialogue often involves complexity in user's intents (or purposes) which are multiproned, subject to spontaneous change, and difficult to track.Furthermore, public datasets have not considered these complications and the general semantic annotations are lacking which may result in zero-shot problem.Motivated by the above, we propose a Label-Aware BERT Attention Network (LABAN) for zeroshot multi-intent detection.We first encode input utterances with BERT and construct a label embedded space by considering embedded semantics in intent labels.An input utterance is then classified based on its projection weights on each intent embedding in this embedded space.We show that it successfully extends to few/zero-shot setting where part of intent labels are unseen in training data, by also taking account of semantics in these unseen intent labels.Experimental results show that our approach is capable of detecting many unseen intent labels correctly.It also achieves the state-of-the-art performance on five multiintent datasets in normal cases.
Sentiment Analysis of Health Care: Review

Twitter is a microblogging service where users can send and read short messages of 140 characters called “tweets”. Many healthcare-related unstructured and free-text tweets are shared on Twitter, which is becoming a popular domain for medical research. Sentiment analysis is one of the data mining types that provides an estimate of the direction of personality sentiment analysis in natural language processing. By analyzing text, computational linguistics is used to infer and analyze mental knowledge of the web, social media, and related references. The data reviewed actually quantifies the attitudes or feelings of the global society towards specific goods, people, or thoughts and exposes the contextual duality of the knowledge. Sentiment analysis is used in various sectors such as health care. There is an incredible amount of healthcare information available online, such as social media, and websites focused on rating medical problems, that is not accessed in a methodical way. Sentiment analysis has many benefits, such as using medical information to achieve the best possible patient outcome and improve the quality of health care. This review paper focuses on the presented sentiment analysis methods that are used in the medical field.
Comparing two-qubit and multiqubit gates within the toric code

In some quantum computing (QC) architectures, entanglement of an arbitrary number of qubits can be generated in a single operation. This property has many potential applications, and may specifically be useful for quantum error correction (QEC). Stabilizer measurements can then be implemented using a single multi-qubit gate instead of several two-qubit gates, thus reducing circuit depth. In this study, the toric code is used as a benchmark to compare the performance of two-qubit and five-qubit gates within parity-check circuits. We consider trapped ion qubits that are controlled via Raman transitions, where the primary source of error is assumed to be spontaneous photon scattering. We show that a five-qubit M{\o}lmer-S{\o}rensen gate offers an approximately $40\%$ improvement over two-qubit gates in terms of the fault tolerance threshold. This result indicates an advantage of using multi-qubit gates in the context of QEC.