Reviewerly/exHarmony huggingface dataset:
Early Feasibility Studies of Augmented Reality Navigation for Lateral Skull Base Surgery

Hypothesis: An augmented reality (AR) platform using a head mounted display (HMD) can successfully display and register virtual computerized tomography (CT) data to corresponding real world anatomy using fiducial markers. Background: AR is a growing field in surgical planning, and has been used by this group to aide in orthopedic surgery applications. Intraoperative AR registration of preoperative CT images could improve identification of critical anatomical structures during lateral skull base surgery. Methods: An AR based HMD platform was created to allow for virtual display and real time manipulation of a temporal bone phantom model. Four fiducial markers were incorporated into this model and used to allow manual alignment of surface markers in real-time. To test the accuracy of this platform virtual lines were created in the AR environment running through the visualized real-world fiducial marker points. Target registration error (TRE) was calculated by measuring the orthogonal distance between these virtual lines and the corresponding virtual fiducial marker for each of the four markers from varying angles. Ten consecutive experiments were performed. Results: The AR based platform successfully displayed CT renderings in the AR environment and allowed real time translation and rotation for manual alignment. A TRE of 10.62 ± 5.90 mm was found. Conclusions: Our results suggest that AR visualization of CT imaging can be registered to patient surface landmarks, but current limitations of AR hardware resulted in TREs too large to be used in the lateral skull base. Future advancements in AR technology will hopefully allow for reduction of registration error.
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG- Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes use of learnable latent variables to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs, we leverage multiple views in order to explicitly exploit the rotational invariance in airborne images. We further develop an adaptive class weighting loss to address the class imbalance. We demonstrate the effectiveness and flexibility of the proposed method on the Agriculture-Vision challenge dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and our model achieves very competitive results (0.547 mIoU) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with much fewer parameters and at a lower computational cost compared to related pure-CNN based work.
Malaria Parasite Detection Using Deep Learning : (Beneficial to humankind)

Malaria is one of the deadliest diseases across the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria detection techniques include manual microscopic examination and RDT. These approaches are vulnerable to human mistakes. Early detection of malaria can help in reducing the death rates across the globe. Deep Learning can emerge as a highly beneficial solution in the diagnosis of disease. This model gives a faster and cheaper method for detecting Plasmodium parasites. The custom convolutional neural network is primarily designed to distinguish between healthy and infected blood samples. The proposed model consists of three convolutional layers and fully connected layers each. The neural network presented is a cascade of several convolutional layers having multiple filters present in layers, which yields the exceptionally good accuracy as per the available resources. The model is trained and later several blood sample images are fed to test the accuracy of the designed system. The CNN classifier has performed exceptionally well under limited computational resources giving an accuracy of 95%. Blood smear sample analysis can also aid in the detection of certain other illnesses and the application of deep learning models will help in the greater good of humankind.
Integrating SIFT and CNN Feature Matching for Partial-Duplicate Image Detection

With the increasing popularity of various deep neural networks in the area of computational intelligence, the research attention for content-based image detection/retrieval has been shifted from the handcrafted local features such as scale invariant feature transform (SIFT) to the features derived from convolutional neural networks (CNN). However, the existing image-based CNN features, directly extracted from the entire images, are not suitable for detecting small duplicate regions, while region-based CNN features show limited robustness to a variety of image modifications such as rescaling, occlusion, and noise adding. These will affect the performance of partial-duplicate image detection. To address these issues, we propose an integrated feature matching scheme, which integrates the matching of SIFT features and CNN features between images for partial-duplicate image detection. In this scheme, we first implement SIFT feature matching based on the bag-of-visual-words model to detect the potential duplicate region pairs between images, and then match the CNN features of these regions extracted from the deep convolutional layer of CNN to compute image similarity. Since both the good robustness of SIFT features and the high discriminative power of CNN features are sufficiently explored, our scheme allows an accurate detection. Experimental results show that the proposed approach provides superior accuracy than the state of the arts, while achieves comparable efficiency.
A visual approach to explainable computerized clinical decision support

Clinical Decision Support Systems (CDSS) provide assistance to physicians in clinical decision-making. Based on patient-specific evidence items triggering the inferencing process, such as examination findings, and expert-modeled or machine-learned clinical knowledge, these systems provide recommendations in finding the right diagnosis or the optimal therapy. The acceptance of, and the trust in, a CDSS are highly dependent on the transparency of the recommendation's generation. Physicians must know both the key influences leading to a specific recommendation and the contradictory facts. They must also be aware of the certainty of a recommendation and its potential alternatives. We present a glyph-based, interactive multiple views approach to explainable computerized clinical decision support. Four linked views (1) provide a visual summary of all evidence items and their relevance for the computation result, (2) present linked textual information, such as clinical guidelines or therapy details, (3) show the certainty of the computation result, which includes the recommendation and a set of clinical scores, stagings etc., and (4) facilitate a guided investigation of the reasoning behind the recommendation generation as well as convey the effect of updated evidence items. We demonstrate our approach for a CDSS based on a causal Bayesian network representing the therapy of laryngeal cancer. The approach has been developed in close collaboration with physicians, and was assessed by six expert otolaryngologists as being tailored to physicians' needs in understanding a CDSS.
Exploring the human factor in cyber-enabled and cyber-dependent crime victimisation: a lifestyle routine activities approach

Purpose The purpose of this study was to explore human factors as the possible facilitator of cyber-dependent (hacking and malware infection) and cyber-enabled (phishing) crimes victimisation and to test the applicability of lifestyle routine activities theory (LRAT) to cybercrime victimisation. Design/methodology/approach A mixed methods research paradigm was applied to address the research questions and aims. The data set of Crime Survey of England and Wales (CSEW) 2014/2015 and 42 semi-structured interviews conducted with victims of cybercrime and non-victim control group participants were analysed via binary logistic regression and content analyses methods. Findings This research illustrated that Internet users facilitated their victimisation through their online activities. Additionally, using insecure Internet connections and public access computers emerged as risk factors for both cyber-enabled and cyber-dependent crime victimisation. Voluntary and involuntary personal information disclosure through social networking sites and online advertisement websites increased the likelihood of being a target of phishing. Deviant online activities such as free streaming or peer-to-peer sharing emerged to increase the risk of cyber-dependent crime victimisation. Research limitations/implications The binary logistic regression analysis results suggested LRAT as a more suitable theoretical framework for cyber-dependent crime victimisation. Future research may test this result with models including more macro variables. Practical implications Policymakers may consider implementing regulations regarding limiting the type of information required to login to free Wi-Fi connections. Checking trust signs and green padlocks may be effective safeguarding measures to lessen the adverse impacts of impulsive buying. Originality/value This study empirically illustrated that, besides individual-level factors, macro-level factors such as electronic devices being utilised to access the Internet and data breaches of large companies also increased the likelihood of becoming the victim of cyber-enabled and cyber-dependent crime.
KubeShare: A Framework to Manage GPUs as First-Class and Shared Resources in Container Cloud

Container has emerged as a new technology in clouds to replace virtual machines~(VM) for distributed applications deployment and operation. With the increasing number of new cloud-focused applications, such as deep learning and high performance applications, started to reply on the high computing throughput of GPUs, efficiently supporting GPU in container cloud becomes essential. While GPU virtualization has been extensively studied for VM, limited work has been done for containers. One of the key challenges is the lack of support for GPU sharing between multiple concurrent containers. This limitation leads to low resource utilization when a GPU device cannot be fully utilized by a single application due to the burstiness of GPU workload and the limited memory bandwidth. To overcome this issue, we designed and implemented KubeShare, which extends Kubernetes to enable GPU sharing with fine-grained allocation. KubeShare is the first solution for Kubernetes to make GPU device as a first class resources for scheduling and allocations. Using real deep learning workloads, we demonstrated KubeShare can significantly increase GPU utilization and overall system throughput around 2x with less than 10% performance overhead during container initialization and execution.
Contextualizing rename decisions using refactorings, commit messages, and data types

Identifier names are the atoms of program comprehension. Weak identifier names decrease developer productivity and degrade the performance of automated approaches that leverage identifier names in source code analysis; threatening many of the advantages which stand to be gained from advances in artificial intelligence and machine learning. Therefore, it is vital to support developers in naming and renaming identifiers. In this paper, we extend our prior work, which studies the primary method through which names evolve: rename refactorings. In our prior work, we contextualize rename changes by examining commit messages and other refactorings. In this extension, we further consider data type changes which co-occur with these renames, with a goal of understanding how data type changes influence the structure and semantics of renames. In the long term, the outcomes of this study will be used to support research into: (1) recommending when a rename should be applied, (2) recommending how to rename an identifier, and (3) developing a model that describes how developers mentally synergize names using domain and project knowledge. We provide insights into how our data can support rename recommendation and analysis in the future, and reflect on the significant challenges, highlighted by our study, for future research in recommending renames.
Blockchain, AI and Smart Grids: The Three Musketeers to a Decentralized EV Charging Infrastructure

The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.
Deep Learning Based Analysis of Breast Cancer Using Advanced Ensemble Classifier and Linear Discriminant Analysis

This work was
LESS is More: Code-Based Signatures Without Syndromes

Devising efficient and secure signature schemes based on coding theory is still considered a challenge by the cryptographic community. In this paper, we construct a signature scheme by exploring a new approach to the area. To do this, we design a zero-knowledge identification scheme, which we then render static via standard means (e.g. Fiat-Shamir). We show that practical instances of our protocol have the potential to outperform the state of the art on code-based signatures, achieving small data sizes with a low computational complexity.
Evolutionary optimization of deep learning activation functions

The choice of activation function can have a large effect on the performance of a neural network. While there have been some attempts to hand-engineer novel activation functions, the Rectified Linear Unit (ReLU) remains the most commonly-used in practice. This paper shows that evolutionary algorithms can discover novel activation functions that outperform ReLU. A tree-based search space of candidate activation functions is defined and explored with mutation, crossover, and exhaustive search. Experiments on training wide residual networks on the CIFAR-10 and CIFAR-100 image datasets show that this approach is effective. Replacing ReLU with evolved activation functions results in statistically significant increases in network accuracy. Optimal performance is achieved when evolution is allowed to customize activation functions to a particular task; however, these novel activation functions are shown to generalize, achieving high performance across tasks. Evolutionary optimization of activation functions is therefore a promising new dimension of metalearning in neural networks.
Medical idioms for clinical Bayesian network development

Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.
Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems

Undersampling is a popular method to solve imbalanced classification problems. However, sometimes it may remove too many majority samples which may lead to loss of informative samples. In this article, the hashing-based undersampling ensemble (HUE) is proposed to deal with this problem by constructing diversified training subspaces for undersampling. Samples in the majority class are divided into many subspaces by a hashing method. Each subspace corresponds to a training subset which consists of most of the samples from this subspace and a few samples from surrounding subspaces. These training subsets are used to train an ensemble of classification and regression tree classifiers with all minority class samples. The proposed method is tested on 25 UCI datasets against state-of-the-art methods. Experimental results show that the HUE outperforms other methods and yields good results on highly imbalanced datasets.
Bitcoin and liquidity risk diversification

This paper investigates the potential diversification benefits of adding Bitcoin from a liquidity perspective. Using different multivariate GARCH-Type specifications to model the joint dynamics of the selected financial assets, we find that VARMA (1, 1)-cDCC-GARCH is the best-fit model. By examining the dependence structure between Bitcoin and several financial assets, we find evidence of a low time-varying correlation of liquidity innovations over the period 2014–2019. This finding suggests the existence of a potential gain in diversifying the liquidity risk using Bitcoin instead of traditional assets. The results of the portfolio optimization problem when adding the liquidity constraint show that holding Bitcoin can reduce the liquidity risk under the Mean-Variance-Liquidity framework.
Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.
Industrial Pervasive Edge Computing-Based Intelligence IoT for Surveillance Saliency Detection

Numerous surveillance data processing is crucial in the Internet-of-Things systems with pervasive edge computing. In this process, salient object detection from surveillance videos plays an important role because it provides the human-concerned semantic cue for various industrial tasks. However, it is still challenging for the existing studies with two aspects. The first one is the redundant saliency information from moving background to disturb the detection of salient objects. The second one is the difficulty to model the spatiotemporal saliency uncertainty. To overcome these challenges. In this article, an intelligent approach is proposed for surveillance saliency detection. It enables a region-proposal-based optical flow strategy to suppress the saliency enhancement of non-salient regions due to the moving background. Besides, it develops the bidirectional Bayesian state transition strategy to model the motion uncertainty for refining the spatiotemporal saliency feature. Extensive experiments have been performed on two datasets (the increase of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</sub> is larger than 0.01 for DAVIS, and larger than 0.015 for UVSD), and the comparison with seven methods to evaluate the effectiveness of the proposed approach.
EMUCF: Enhanced multistage user-based collaborative filtering through non-linear similarity for recommendation systems

The data sparsity is an acute challenge in most of the collaborative filterings (CFs) as their performance is affected by the known ratings of target users. Recently, active learning has become a prevalent and straight forward approach to cope with the data sparsity. In this approach, the newly entered users are requested to rate certain items while they signup to the underlying recommendation system. This work proposes an Enhanced Multistage User-based CF (EMUCF) algorithm, which uses the concept of active learning and predicts the unknown ratings for target users in two stages. Here, the anonymous ratings of each intermediary stage are predicted with traditional User_CF algorithm. However, the similarity models commonly used in User_CF are not adequate to compute the similarity among users. Therefore, the most recently introduced Bhattacharyya Coefficient based nonlinear similarity model Bhat_sim is used for similarity computations; it utilizes all rating pairs of items in the final estimates of users similarities. Later, an extension of simple EMUCF, the (n > 2)-stage EMUCF is proposed to increase the prediction accuracy by progressively increasing the density of the original rating matrix. The performance of simple EMUCF and its extension is evaluated on two benchmark Movielens-100K and Movielens-1M datasets. They obtain far superior results for prediction accuracy and recommendation precision compared to several prominent competing algorithms. Finally, the potential improvement in the n-stage EMUCF algorithm is assessed by establishing the connection between rating prediction accuracy and matrix density.