Reviewerly/exHarmony huggingface dataset:
Semiparametric Bayesian networks

We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones. We demonstrate that semiparametric Bayesian networks generalize two well-known types of Bayesian networks: Gaussian Bayesian networks and kernel density estimation Bayesian networks. For this purpose, we consider two different conditional probability distributions required in a semiparametric Bayesian network. In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data. To realize this, we employ a score function based on cross-validation. In addition, using a validation dataset, we apply an early-stopping criterion to avoid overfitting. To evaluate the applicability of the proposed algorithm, we conduct an exhaustive experiment on synthetic data sampled by mixing linear and nonlinear functions, multivariate normal data sampled from Gaussian Bayesian networks, real data from the UCI repository, and bearings degradation data. As a result of this experiment, we conclude that the proposed algorithm accurately learns the combination of parametric and nonparametric components, while achieving a performance comparable with those provided by state-of-the-art methods.
Identification of asthma control factor in clinical notes using a hybrid deep learning model

Abstract Background There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician’s documentation is not feasible using only structured data but requires labor-intensive chart review of electronic health records (EHRs). A certain guideline element in asthma control factors, such as review inhaler techniques, requires context understanding to correctly capture from EHR free text. Methods The study data consist of two sets: (1) manual chart reviewed data—1039 clinical notes of 300 patients with asthma diagnosis, and (2) weakly labeled data (distant supervision)—27,363 clinical notes from 800 patients with asthma diagnosis. A context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) was developed to identify inhaler techniques in EHR free text. Both original BERT and clinical BioBERT (cBERT) were applied with a cost-sensitivity to deal with imbalanced data. The distant supervision using weak labels by rules was also incorporated to augment the training set and alleviate a costly manual labeling process in the development of a deep learning algorithm. A hybrid approach using post-hoc rules was also explored to fix BERT model errors. The performance of BERT with/without distant supervision, hybrid, and rule-based models were compared in precision, recall, F-score, and accuracy. Results The BERT models on the original data performed similar to a rule-based model in F1-score (0.837, 0.845, and 0.838 for rules, BERT, and cBERT, respectively). The BERT models with distant supervision produced higher performance (0.853 and 0.880 for BERT and cBERT, respectively) than without distant supervision and a rule-based model. The hybrid models performed best in F1-score of 0.877 and 0.904 over the distant supervision on BERT and cBERT. Conclusions The proposed BERT models with distant supervision demonstrated its capability to identify inhaler techniques in EHR free text, and outperformed both the rule-based model and BERT models trained on the original data. With a distant supervision approach, we may alleviate costly manual chart review to generate the large training data required in most deep learning-based models. A hybrid model was able to fix BERT model errors and further improve the performance.
<i>Deep-Confidentiality</i> : An IoT-Enabled Privacy-Preserving Framework for Unstructured Big Biomedical Data

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality . The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.
The MADE-Axis

Tangible controls-especially sliders and rotary knobs-have been explored in a wide range of interactive applications for desktop and immersive environments. Studies have shown that they support greater precision and provide proprioceptive benefits, such as support for eyes-free interaction. However, such controls tend to be expressly designed for specific applications. We draw inspiration from a bespoke controller for immersive data visualisation, but decompose this design into a simple, wireless, composable unit featuring two actuated sliders and a rotary encoder. Through these controller units, we explore the interaction opportunities around actuated sliders; supporting precise selection, infinite scrolling, adaptive data representations, and rich haptic feedback; all within a mode-less interaction space. We demonstrate the controllers' use for simple, ad hoc desktop interaction,before moving on to more complex, multi-dimensional interactions in VR and AR. We show that the flexibility and composability of these actuated controllers provides an emergent design space which covers the range of interactive dynamics for visual analysis. In a user study involving pairs performing collaborative visual analysis tasks in mixed-reality, our participants were able to easily compose rich visualisations, make insights and discuss their findings.
End-to-End Speech Recognition of Tamil Language

Research in speech recognition is progressing with numerous state-of-the-art results in recent times. However, relatively fewer research is being carried out in Automatic Speech Recognition (ASR) for languages with low resources. We present a method to develop speech recognition model with minimal resources using Mozilla DeepSpeech architecture. We have utilized freely available online computational resources for training, enabling similar approaches to be carried out for research in a low-resourced languages in a financially constrained environments. We also present novel ways to build an efficient language model from publicly available web resources to improve accuracy in ASR. The proposed ASR model gives the best result of 24.7% Word Error Rate (WER), compared to 55% WER by Google speech-to-text. We have also demonstrated a semi-supervised development of speech corpus using our trained ASR model, indicating a cost effective approach of building large vocabulary corpus for low resource language. The trained Tamil ASR model and the training sets are released in public domain and are available on GitHub.
Patient posture correction and alignment using mixed reality visualization and the HoloLens 2

The purpose of this study was to develop and preliminarily test a radiotherapy system for patient posture correction and alignment using mixed reality (MixR) visualization. The write-up of this work also provides an opportunity to introduce the concepts and technology of MixR for a medical physics audience who may be unfamiliar with the topic.A MixR application was developed for on optical-see-through head-mounted display (HoloLens 2) allowing a user to simultaneously and directly view a patient and a reference hologram derived from their simulation CT scan. The hologram provides a visual reference for the exact posture needed during treatment and is initialized in relation to the origin of a radiotherapy device using marker-based tracking. The system further provides marker-less tracking that allows the user tofreely navigate the room as they view and align the patient from various angles. The system was preliminarily tested using both a rigid (pelvis) and nonrigid (female mannequin) anthropomorphic phantom. Each phantom was aligned via hologram and accuracy quantified using CBCT and CT.A fully realized system was developed. Rigid registration accuracy was on the order of 3.0 ± 1.5 mm based on the performance of three users repeating alignment five times each. The lateral direction showed the most variability among users and was associated with the largest off-sets (approximately 2.0 mm). For nonrigid alignment, the MixR setup outperformed a setup based on three-point alignment and setup photos, the latter of which showed a difference in arm position of 2 cm and a torso roll of 6-7°.MixR visualization is a rapidly emerging domain that has the potential to significantly impact the field of medicine. The current application is an illustration of this and highlights the advantages of MixR for patient setup in radiation oncology. The key feature of the system is the way in which it transforms nonrigid registration into rigid registration by providing an efficient, portable, and cost-effective mechanism for reproducing patient posture without the use of ionizing radiation. Preliminary estimates of registration accuracy indicate clinical viability and form the foundation for further development and clinical testing.
Security of quantum communications in oceanic turbulence

The channel fading induced in the ocean quantum links is a major obstacle restricting the performance of ocean quantum key distribution. To determine the ultimate limits at which secret bits (and entanglement) can be distributed via a seawater medium, we propose an analytical model for ocean quantum links to derive the probability distribution of the transmittance, which characterizes the evolution of the quantum state over the fading channel. The proposed model includes the effects of absorption, scattering, turbulence, and ocean thermal noise due to background light. Our comprehensive numerical analysis of several practical scenarios encompasses both pure-loss bounds and thermal-loss bounds, with a submarine at various depths and in various oceans on earth. Besides identifying ultimate limits, we present a security analysis of coherent-state and squeezed-state protocols, showing the secret key rate of both the asymptotic case and finite-size regimes.
The Rise and Fall of COVID-19 Contact-Tracing Apps: when NFRs Collide with Pandemic

To complement the manual contact-tracing methods, a flood of coronavirus-related apps was launched in the first half of 2020. Despite the incredible promises made by the governments, contact-tracing apps did not live up to expectations. We provide a contextual perspective of the government commissioned contact-tracing apps from four countries to understand the non-functional requirements (NFRs) and socio-technical factors that hindered the success of these apps. We collected the user reviews from the app stores for iOS and Android versions and identified top news articles related to each app. Our analysis revealed that the dominant factors behind the negligible success of these apps are complex and entangled with the cultural and political dimensions rather than being just technical. The multilayer diversity of the target users also impacted the design and development of contact-tracing apps in an extremely challenging situation. This perspective paper brings into light important elements, such as politics and socio-cultural aspects that should be studied in the design of contact-tracing apps, and public apps in general.
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering

Detection of inconsistencies of double JPEG artifacts across different image regions is often used to detect local image manipulations, like image splicing, and to localize them. In this paper, we move one step further, proposing an end-to-end system that, in addition to detecting and localizing spliced regions, can also distinguish regions coming from different donor images. We assume that both the spliced regions and the background image have undergone a double JPEG compression, and use a local estimate of the primary quantization matrix to distinguish between spliced regions taken from different sources. To do so, we cluster the image blocks according to the estimated primary quantization matrix and refine the result by means of morphological reconstruction. The proposed method can work in a wide variety of settings including aligned and non-aligned double JPEG compression, and regardless of whether the second compression is stronger or weaker than the first one. We validated the proposed approach by means of extensive experiments showing its superior performance with respect to baseline methods working in similar conditions.
Abnormal event detection using adversarial predictive coding for motion and appearance

In this paper, we propose adversarial predictive coding (APC), a novel method for detecting abnormal events. Abnormal event detection (AED) is to identify unobserved events from a given training dataset consisting of normal events, and it is considered as one of the most important objectives in developing intelligent surveillance systems. Given videos and motion flows of normal events, the APC derives a normal event model by applying an adversarial prediction approach on the jointly learnt latent feature space from the videos and motion flows. Since latent space requires more abstracted and noise-free information than the raw data space, the APC can derive a more discriminative model for normal events compared with other deep learning-based AED methods which directly apply uni-modal losses such as mean square error and cross-entropy to low-level data such as video frames. We demonstrate the effectiveness of our method in detecting abnormal events using UCSD-Ped, Avenue, and UCF-Crime datasets. The experimental results show that the APC surpass the existing state-of-the-art AED methods by deriving a more discriminative model for normal events.
The life cycle of features in highly-configurable software systems evolving in space and time

Feature annotation based on preprocessor directives is the most common mechanism in Highly-Configurable Software Systems (HCSSs) to manage variability. However, it is challenging to understand, maintain, and evolve feature fragments guarded by #ifdef directives. Yet, despite HCSSs being implemented in Version Control Systems, the support for evolving features in space and time is still limited. To extend the knowledge on this topic, we analyze the feature life cycle in space and time. Specifically, we introduce an automated mining approach and apply it to four HCSSs, analyzing commits of their entire development life cycle (13 to 20 years and 37,500 commits). This goes beyond existing studies, which investigated only differences between specific releases or entire systems. Our results show that features undergo frequent changes, often with substantial modifications of their code. The findings of our empirical analyses stress the need for better support of system evolution in space and time at the level of features. In addition to these analyses, we contribute an automated mining approach for the analysis of system evolution at the level of features. Furthermore, we also make available our dataset to foster new studies on feature evolution in HCSSs.
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search

To discover powerful yet compact models is an important goal of neural architecture search. Previous two-stage one-shot approaches are limited by search space with a fixed depth. It seems handy to include an additional skip connection in the search space to make depths variable. However, it creates a large range of perturbation during super-net training and it has difficulty giving a confident ranking for subnetworks. In this paper, we discover that skip connections bring about significant feature inconsistency compared with other operations, which potentially degrades the supernet performance. Based on this observation, we tackle the problem by imposing an equivariant learnable stabilizer to homogenize such disparities (see Fig. 1). Experiments show that our proposed stabilizer helps to improve the supernet’s convergence as well as ranking performance. With an evolutionary search backend that incorporates the stabilized supernet as an evaluator, we derive a family of state-of-the-art architectures, the SCARLET <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> series of several depths, especially SCARLET-A obtains 76.9% top-1 accuracy on ImageNet.
An LSTM-Based Approach for Understanding Human Interactions Using Hybrid Feature Descriptors Over Depth Sensors

Over the past few years, automatic recognition of human interactions has drawn significant attention from researchers working in the field of Artificial Intelligence (AI).And feature extraction is one of the most critical tasks in developing efficient Human Interaction Recognition (HIR) systems.Moreover, recent researches in computer vision suggest that robust features lead to higher recognition accuracies.Hence, an improved HIR system has been proposed in this paper that combines 2D and 3D features extracted using machine learning and deep learning techniques.These discriminative features result in accurate classification and help avoid misclassification of similar interactions.Ten keyframes have been extracted from each video to reduce computational complexity.Next, these frames have been preprocessed using image normalization and noise removal techniques.The Region Of Interest (ROI), which contains the two humans involved in the interaction, has been extracted using motion detection.Then, the human silhouettes have been segmented using the GrabCut algorithm.Next, the extracted silhouettes have been converted into 3D meshes and their heat kernel signatures (HKS) have been obtained to extract key body points.A Convolutional Neural Network (CNN) has been used to extract full-body features from 2D full-body silhouettes.Then, topological and geometric features have been extracted from the key body points.Finally, the combined feature vector has been fed into Long Short-Term Memory (LSTM) and each interaction has been recognized using a Softmax classifier.The proposed system has been validated via extensive experimentation on three challenging RGB+D datasets.The recognition accuracies of 91.63%, 90.54%, and 90.13% have been achieved with the SBU Kinect Interaction, NTU RGB+D, and ISR-UoL 3D social activity datasets respectively.The results of extensive experiments performed on the proposed system suggest that it can be used effectively for various applications, such as security, surveillance, health monitoring, and assisted living.
Uncertainty-aware deep co-training for semi-supervised medical image segmentation

Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we proposed a novel uncertainty-aware scheme to make models learn regions purposefully. Specifically, we employ Monte Carlo Sampling as an estimation method to attain an uncertainty map, which can serve as a weight for losses to force the models to focus on the valuable region according to the characteristics of supervised learning and unsupervised learning. Simultaneously, in the backward process, we joint unsupervised and supervised losses to accelerate the convergence of the network via enhancing the gradient flow between different tasks. Quantitatively, we conduct extensive experiments on three challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts.
Bert-Enhanced Text Graph Neural Network for Classification

Text classification is a fundamental research direction, aims to assign tags to text units. Recently, graph neural networks (GNN) have exhibited some excellent properties in textual information processing. Furthermore, the pre-trained language model also realized promising effects in many tasks. However, many text processing methods cannot model a single text unit's structure or ignore the semantic features. To solve these problems and comprehensively utilize the text's structure information and semantic information, we propose a Bert-Enhanced text Graph Neural Network model (BEGNN). For each text, we construct a text graph separately according to the co-occurrence relationship of words and use GNN to extract text features. Moreover, we employ Bert to extract semantic features. The former part can take into account the structural information, and the latter can focus on modeling the semantic information. Finally, we interact and aggregate these two features of different granularity to get a more effective representation. Experiments on standard datasets demonstrate the effectiveness of BEGNN.
A neo-institutional perspective on the establishment of information security knowledge sharing practices

Information security knowledge sharing (ISKS) among an organization's employees is vital to the organization's ability to protect itself from any number of prevalent threats, yet for many organizations, their ability to establish ISKS practices is hampered by a lack of understanding of where and how the key drivers of these practices will emerge. Based on neoinstitutional theory and a multi-study field survey of 834 professional managers in the USA, we develop and test a model that explains the establishment of ISKS practices in an organization as a product of the institutional forces abut to the organization providing normative, mimetic, and coercive influences on top management beliefs and participations in ISKS. Our findings also emphasize the importance of establishing ISKS practices for ensuring employee compliance with information security policies and an effective culture of security. Prior research has shown the importance of institutional forces on organizational processes as well as the importance of ISKS to organizational security efforts. However, this study is one of the early studies to provide insight into the manner, in which institutional forces hold sway over the people responsible for establishing the ISKS practices of a firm; insight that it is essential for firms that have yet to establish such practices or have struggled in their attempts to do so.