Reviewerly/exHarmony huggingface dataset:
PSL: An Algorithm for Partial Bayesian Network Structure Learning

Learning partial Bayesian network (BN) structure is an interesting and challenging problem. In this challenge, it is computationally expensive to use global BN structure learning algorithms, while only one part of a BN structure is interesting, local BN structure learning algorithms are not a favourable solution either due to the issue of false edge orientation. To address the problem, this article first presents a detailed analysis of the false edge orientation issue with local BN structure learning algorithms and then proposes PSL, an efficient and accurate P artial BN S tructure L earning (PSL) algorithm. Specifically, PSL divides V-structures in a Markov blanket (MB) into two types: Type-C V-structures and Type-NC V-structures, then it starts from the given node of interest and recursively finds both types of V-structures in the MB of the current node until all edges in the partial BN structure are oriented. To further improve the efficiency of PSL, the PSL-FS algorithm is designed by incorporating F eature S election (FS) into PSL. Extensive experiments with six benchmark BNs validate the efficiency and accuracy of the proposed algorithms.
Security Threats to Machine Learning Systems

Machine Learning not only promises to solve issues, but it also has the potential to assist businesses in formulating forecasts and so enhancing decision-making. Machine learning, on the other hand, has security difficulties. This study examines the many security concerns that machine learning faces and how they might be mitigated. There are two types of security risks to ML systems: those that occur before model training and those that occur after model training. The document also offers information on a variety of additional threats that fall under these categories and target other sorts of data while posing varied hazards.
A Novel Social Interaction Assistive Device for Arab Deaf People

Many deaf people worldwide face problems with integrating into society and interacting with people who do not understand sign language. This can lead to isolation and difficulty in expressing feelings. In this research, our primary goal is to help deaf people communicate, express their feelings, and socialize with others. Toward that end, 40 Arabic words that are commonly used in social interactions were used to build a dataset of hand movements used by deaf people to express these words. These movements were recorded using a Leap Motion Controller ( LMC ). The resulting dataset consists of 1,579 instances and 112 features, recorded with the help of five deaf persons. Feature reduction and oversampling techniques were applied to analyze the dataset. Machine learning algorithms were then used to build a model that is able to classify any given hand posture or gesture into one of those 40 words. This work compared the performance of nine classification algorithms: Random Forest, Decision Table, Classification via Regression, K-Nearest Neighbor (KNN), Simple Logistic, Input Mapped Classifier, Random Tree, J48, and Bayes network. Results show that the Random Forest model achieved the highest accuracy with over 90%, outperforming the other eight models. Subsequently, a usability study was conducted by 10 deaf people to test the effectiveness of the proposed assistive device. The results suggest that the proposed device is useful for facilitating social communication with deaf people. It also suggests that the device was preferred, when compared with other relevant devices.
SU-UNet: A Novel Self-Updating Network for Hepatic Vessel Segmentation in CT Images

Hepatectomy is currently one of the most commonly used treatment methods for malignant liver tumors. It is of great significance to clinical surgery to perform accurate hepatic vessel segmentation in preoperative CT images. However, due to the complex structure of hepatic vessels and low contrast in the CT images, it is difficult for experienced doctors to perform accurate manual labeling. Based on this, the labels of the existing public datasets are noisy. In this paper, we propose a double UNet structure based on the soft-constraint method to more accurately segment the vessels from the noisy annotation dataset. First, two different Unet output different segmentation predictions. Then a Self-updating module (SUM) is designed to optimize the noisy vessel label based on segmentation predictions so that the optimized label can better guide the network training. This method can guide the network to get better segmentation predictions. Extensive experiments using a noisy public dataset demonstrate the superiority of our method.
Profile to frontal face recognition in the wild using coupled conditional generative adversarial network

In recent years, with the advent of deep-learning, face recognition (FR) has achieved exceptional success. However, many of these deep FR models perform much better in handling frontal faces compared to profile faces. The major reason for poor performance in handling of profile faces is that it is inherently difficult to learn pose-invariant deep representations that are useful for profile FR. In this paper, the authors hypothesise that the profile face domain possesses a latent connection with the frontal face domain in a latent feature subspace. The authors look to exploit this latent connection by projecting the profile faces and frontal faces into a common latent subspace and perform verification or retrieval in the latent domain. A coupled conditional generative adversarial network (cpGAN) structure is leveraged to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cpGAN framework consists of two conditional GAN-based sub-networks, one dedicated to the frontal domain and the other dedicated to the profile domain. Each sub-network tends to find a projection that maximises the pair-wise correlation between the two feature domains in a common embedding feature subspace. The efficacy of the authors’ approach compared with the state of the art is demonstrated using the CFP, CMU Multi-PIE, IARPA Janus Benchmark A, and IARPA Janus Benchmark C datasets. Additionally, the authors have also implemented a coupled convolutional neural network (cpCNN) and an adversarial discriminative domain adaptation network (ADDA) for profile to frontal FR. The authors have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN. Finally, the authors have also evaluated the authors’ cpGAN for reconstruction of frontal faces from input profile faces contained in the VGGFace2 dataset.
Cloud Service Recommendation Using Trust Data and Reliability Measurements

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Research on Architecture of Local Domain Application Based on Radio Frequency Identification

Abstract Along with the speedy improvement of sensing systems, wireless transmission technology, and faster and faster chip, the scope of application of the Internet of things (IoT) has substantially and rapidly increased, and research and innovation that integrate artificial intelligence. At same time, campus application construction as the foundation of intelligence education is being on the march, which demands newly emerging computer technologies support, such as IoT, AI, Big data and so on. In this study, we provide an architecture of smart campus based on Radio Frequency Identification (RFID) which can offer new solutions to solve some problems such as position, identification, store information and so on because of its strong anti-interference capability and non-intrusiveness, as core technology of IoT. Then based on this architecture, a kind of intelligent campus application system scheme is proposed, which can realize the function of automatic identity recognition, book reading, internal consumption, student positions. Moreover, for some special exact location demands, this paper proposes a method to precisely measure RFID tag location based on triangulation principle, which is embedded in the architecture. The application of Internet of things technology can better promote some origination or company efficiency, such as more efficient service for teachers’ and students’ work, study and life.
Learning to segment cell nuclei in phase-contrast microscopy from fluorescence images for drug discovery

We describe a method for analyzing geometrical properties of cell nuclei from phase contrast microscopy images. This is useful in drug discovery for quantifying the effect of candidate chemical compounds, bypassing the need for fluorescence imaging. Fluorescence images are then only used for training our nuclei segmentation, avoiding the need for the time consuming expert annotations. Geometry based descriptors are calculated and aggregated and fed into a classifier to distinguish the different types of chemical treatments. The drug treatment can be distinguished from no treatment with accuracy better than 95% from fluorescence images and better than 77% from phase contrast images.
Improving Chinese-Vietnamese Neural Machine Translation with Linguistic Differences

We present a simple, efficient data augmentation approach for boosting Chinese-Vietnamese neural machine translation performance by leveraging the linguistic difference between the two languages. We first define the formalized representation of modifier symmetry, which is one of the most representative linguistic differences between Chinese and Vietnamese. We then propose and test two data augmentation strategies for leveraging the linguistic difference, which can be integrated naturally with different translation models. Results indicate that both strategies can introduce linguistic rules to boost translation accuracy. Tests on Chinese-Vietnamese benchmarks show significant accuracy improvements. To facilitate studies in this domain, we also release an open-source toolkit 1 with flexible implementation for Chinese-Vietnamese linguistic difference tagging.
Multi-objective dual simplex-mesh based deformable image registration for 3D medical images - proof of concept

Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical image analysis. Most existing methods have two key shortcomings: first, they require extensive up-front parameter tuning to each specific registration problem, and second, they have difficulty capturing large deformations and content mismatches between images. There have however been developments that have laid the foundation for potential solutions to both shortcomings. Towards the first shortcoming, a multi-objective optimization approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be capable of producing a diverse set of registrations for 2D images in one run of the algorithm, representing different trade-offs between conflicting objectives in the registration problem. This allows the user to select a registration afterwards and removes the need for up-front tuning. Towards the second shortcoming, a dual-dynamic grid transformation model has proven effective at capturing large differences in 2D images. These two developments have recently been accelerated through GPU parallelization, delivering large speed-ups. Based on this accelerated version, it is now possible to extend the approach to 3D images. Concordantly, this work introduces the first method for multi-objective 3D deformable image registration, using a 3D dual-dynamic grid transformation model based on simplex meshes while still supporting the incorporation of annotated guidance information and multi-resolution schemes. Our proof-of-concept prototype shows promising results on synthetic and clinical 3D registration problems, forming the foundation for a new, insightful method that can include bio-mechanical properties in the registration.
Scale-aware network with scale equivariance

Convolutional neural network (CNN) has achieved good performance in object classification due to its inherent translation equivariance, but its ability of scale equivariance is poor. A Scale-Aware Network (SA Net) with scale equivariance is proposed, which can estimate scale, that is, the size of image, while classifying. In the training stage, only one scale pattern is learned. In the testing stage, firstly, the testing sample with unseen scale is zoomed-in and zoomed-out into a set of images with different scales, which form an image pyramid. The image zooming-in channels are up-sampled by bilinear interpolation. The image zooming-out channels are down-sampled, and the combination of dyadic discrete wavelet transform (DWT) and bilinear interpolation are used to avoid spectrum aliasing. Then, the image pyramid with different scales is sent to siamese CNNs with weight-sharing for inferencing. A two-dimensional classification score matrix is obtained. Through the position of the maximum of the classification score matrix, the classification and scale estimation can be carried out at the same time. Experiments are carried out on MNIST Large Scale testing set. In scale estimation experiments, the relative value of root mean square error (RMSE) can be obtained by scaling the testing sample images in a geometric series with common ration of <sup>4</sup>&radic;2 in the range of [1/2，2]. The classification experiments show that when the scale is greater than 1.0, the classification accuracy can surpass 90%. SA Net can estimate the scale while improving the classification accuracy, and mis-estimated samples are always near the ground-truths (GTs), so the correct scale of the unseen scale can always be obtained roughly.
Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps

Semantic information is important in eye movement control. An important semantic influence on gaze guidance relates to object-scene relationships: objects that are semantically inconsistent with the scene attract more fixations than consistent objects. One interpretation of this effect is that fixations are driven toward inconsistent objects because they are semantically more informative. We tested this explanation using contextualized meaning maps, a method that is based on crowd-sourced ratings to quantify the spatial distribution of context-sensitive "meaning" in images. In Experiment 1, we compared gaze data and contextualized meaning maps for images, in which objects-scene consistency was manipulated. Observers fixated more on inconsistent versus consistent objects. However, contextualized meaning maps did not assign higher meaning to image regions that contained semantic inconsistencies. In Experiment 2, a large number of raters evaluated image-regions, which were deliberately selected for their content and expected meaningfulness. The results suggest that the same scene locations were experienced as slightly less meaningful when they contained inconsistent compared to consistent objects. In summary, we demonstrated that - in the context of our rating task - semantically inconsistent objects are experienced as less meaningful than their consistent counterparts and that contextualized meaning maps do not capture prototypical influences of image meaning on gaze guidance.
Visual Servoing of a Differential Robot with Image Feature Size and Experimental Verification

Feedback control methods using the image features have been proposed thus far. By using the size of features such as length and area, more robust control may be achieved to measurement noise and disturbance. In this paper, we propose a method to control the velocity of a differential robot by feedback of the size of image features. In addition, by applying the orientation estimation method using image features in the existing work, we propose the orientation control method to avoid the stagnation during motion to the desired image. Experimental results of guiding the differential robot toward the desired image demonstrate the effectiveness of the proposed approaches.
Hybrid fuzzy clustering to improve services availability in P2P-based SaaS-cloud

Software as a Service is evolving as a leader model for cloud service delivery, enabling service providers to remotely deliver hosted, developed and managed software over the Internet. In parallel, some IT services are moving from traditional Internet services to cloud services based on peer-to-peer technologies. However, the P2P-based cloud is a large-scale, heterogeneous and highly dynamic environment whose performance is highly dependent on its ability to maintain persistent availability of SaaS services. In this paper, we propose an approach for improving SaaS service availability in order to meet service quality requirements and maintain performance in a P2P-Based cloud environment. It is mainly based on a new hybrid clustering mechanism that aims to provide a virtual and optimal infrastructure in order to organize the system peers into distinct clusters represented by virtual nodes forming together a virtual layer. This layer allows not only the distribution of peer providers but also the formation of condensed areas of each service of interest for a set of neighboring peers, which improve the availability probability of services in specific regions. In addition, a service availability measurement model was proposed based on the use of the system’s virtual layer taking into account different entities at different levels. The experimental results show that the proposed approach improves the probability of SaaS service availability and the reliability of the P2P-Cloud system. It responds mainly to the large-scale nature of distributed systems as well as making the best trade-off of maintaining QOS in terms of availability, performance and cost.
Codes with burst distance and periodical burst errors

Burst-b distance introduced by Wainberg and Wolf (1972) has been found to be useful for correction of multiple burst errors and multiple erasures. Villalba et al. (2016) have derived extended Reiger and Singleton bound for linear code with minimum burst-b distance db and then present a class of Maximum Distance Separable (MDS) codes (named as Cb code). In this paper, we derive an upper bound on db for any linear code and a lower bound on db for constant burst-b weight linear codes. We also present the existence of linear code with burst-b distance db−1 from code with burst distance db. The cardinality of a linear code and the connection of linearly independent columns of the parity check matrix of any MDS code with the distance db are also given. Further, we consider periodical burst error which is found in many communication channels and investigate periodical burst-detection and -correction capability of linear codes having distance db. Then, we do the same investigation for Cb and its dual code Cb⊥. Finally, we give decoding procedure for the code Cb in case of periodical burst errors.
Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)

Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias.With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline.We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses.A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively.Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.
Automatic Search Dense Connection Module for Super-Resolution

The development of display technology has continuously increased the requirements for image resolution. However, the imaging systems of many cameras are limited by their physical conditions, and the image resolution is often restrictive. Recently, several models based on deep convolutional neural network (CNN) have gained significant performance for image super-resolution (SR), while extensive memory consumption and computation overhead hinder practical applications. For this purpose, we present a lightweight network that automatically searches dense connection (ASDCN) for image super-resolution (SR), which effectively reduces redundancy in dense connection and focuses on more valuable features. We employ neural architecture search (NAS) to model the searching of dense connections. Qualitative and quantitative experiments on five public datasets show that our derived model achieves superior performance over the state-of-the-art models.