Reviewerly/exHarmony huggingface dataset:
SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning

Abstract Probabilistic programming aims to open the power of Bayesian reasoning to software developers and scientists, but identification of problems during inference and debugging are left entirely to the developers and typically require significant statistical expertise. A common class of problems when writing probabilistic programs is the lack of convergence of the probabilistic programs to their posterior distributions. We present SixthSense, a novel approach for predicting probabilistic program convergence ahead of run and its application to debugging convergence problems in probabilistic programs. SixthSense’s training algorithm learns a classifier that can predict whether a previously unseen probabilistic program will converge. It encodes the syntax of a probabilistic program as motifs – fragments of the syntactic program paths. The decisions of the classifier are interpretable and can be used to suggest the program features that contributed significantly to program convergence or non-convergence. We also present an algorithm for augmenting a set of training probabilistic programs that uses guided mutation. We evaluated SixthSense on a broad range of widely used probabilistic programs. Our results show that SixthSense features are effective in predicting convergence of programs for given inference algorithms. SixthSense obtained Accuracy of over 78% for predicting convergence, substantially above the state-of-the-art techniques for predicting program properties Code2Vec and Code2Seq. We show the ability of SixthSense to guide the debugging of convergence problems, which pinpoints the causes of non-convergence significantly better by Stan’s built-in warnings.
Multiview three-dimensional imaging using a Risley-prism-based spatially adaptive virtual camera field

We present a three-dimensional (3D) imaging system that incorporates a stationary camera and Risley prisms. By planning prism rotation to generate a spatially adaptive virtual camera field, the system allows multiple virtual cameras in the field to capture any object from different perspectives for 3D reconstruction. An automatic virtual camera calibration method based on perspective projection and geometric optics is developed to enable virtual camera field construction and characterization. Moreover, a 3D computational reconstruction framework is proposed for multiview information fusion using the virtual camera field. This framework combines nonlinear distortion correction with epipolar geometry computation to efficiently perform image rectification and stereo matching, which can further facilitate 3D object reconstruction through multiview triangulation. The experiments on synthetic and real data validate the feasibility and flexibility of our 3D imaging technique.
KRAKEN: A Knowledge-Based Recommender System for Analysts, to Kick Exploration up a Notch

During a computer security investigation, a security analyst has to explore the logs available to understand what happened in the compromised system. For such tasks, visual analysis tools have been developed to help with log exploration. They provide visualisations of aggregated logs, and help navigate data efficiently. However, even using visualisation tools, the task can still be difficult and tiresome. The amount and the numerous dimensions of the logs to analyse, the potential stealthiness and complexity of the attack may end with the analyst missing some parts of an attack. We offer to help the analyst finding the logs where her expertise is needed rapidly and efficiently. We design a recommender system called KRAKEN that links knowledge coming from advanced attack descriptions into a visual analysis tool to suggest exploration paths. KRAKEN confronts real world adversary knowledge with the investigated logs to dynamically provide relevant parts of the dataset to explore. To evaluate KRAKEN we conducted a user study with seven security analysts. Using our system, they investigated a dataset from the DARPA containing different Advanced Persistent Threat attacks. The results and comments of the security analysts show the usability and usefulness of the recommender system.
Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning

Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities (e.g., visit, disease, medication) in clinical data. With the availability of real-world clinical data such as electronic health records and insurance claims, many deep learning methods are proposed for complication risk profiling. However, these existing methods face two open challenges. First, data heterogeneity relates to those methods leveraging clinical data from a single view only while the data can be considered from multiple views (e.g., sequence of clinical visits, set of clinical features). Second, generalized prediction relates to most of those methods focusing on single-task learning, whereas each complication onset is predicted independently, leading to suboptimal models. We propose a multi-view multi-task network (MuViTaNet) for predicting the onset of multiple complications to tackle these issues. In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features. In addition, it leverages additional information from both related labeled and unlabeled datasets to generate more generalized representations by using a new multi-task learning scheme for making more accurate predictions. The experimental results show that MuViTaNet outperforms existing methods for profiling the development of cardiac complications in breast cancer survivors. Furthermore, thanks to its multi-view multi-task architecture, MuViTaNet also provides an effective mechanism for interpreting its predictions in multiple perspectives, thereby helping clinicians discover the underlying mechanism triggering the onset and for making better clinical treatments in real-world scenarios.
Classification of Autism Spectrum Disorder and Typically Developed Children for Eye Gaze Image Dataset using Convolutional Neural Network

Autism is a neurobehavioral problem that hinders to interact with others.Autistic Spectrum Disorder (ASD) is a psychological disorder that hampers procurement of etymological, communication, cognitive, social skills and Stereotypical motor behaviors and capabilities.Recent research revealing that Autism Spectrum Disorder can be diagnosed using gaze structures which has opened up a new field where visual focus modelling could be highly used.Diagnosis of ASD becomes a difficult task due to wide range of symptoms and severity of ASD.Deep neural networks have been widely employed and have shown to perform well in a variety of visual data processing applications.In this paper, typical developed (TD) or ASD is classified using Convolution neural Networks (CNN) for the fixation maps of the corresponding observer's gaze at a given image.The objective of this paper is to observe whether eyetracking data of fixation map could classify children with ASD and typical development (TD).We further investigated whether features on visual fixation would attain better classification performance.The proposed CNN model achieves 75.23% accuracy for validation.
Learning Deep Blind Quality Assessment for Cartoon Images

Although the cartoon industry has developed rapidly in recent years, few studies pay special attention to cartoon image quality assessment (IQA). Unfortunately, applying blind natural IQA algorithms directly to cartoons often leads to inconsistent results with subjective visual perception. Hence, this brief proposes a blind cartoon IQA method based on convolutional neural networks (CNNs). Note that training a robust CNN depends on manually labeled training sets. However, for a large number of cartoon images, it is very time-consuming and costly to manually generate enough mean opinion scores (MOSs). Therefore, this brief first proposes a full reference (FR) cartoon IQA metric based on cartoon-texture decomposition and then uses the estimated FR index to guide the no-reference IQA network. Moreover, in order to improve the robustness of the proposed network, a large-scale dataset is established in the training stage, and a stochastic degradation strategy is presented, which randomly implements different degradations with random parameters. Experimental results on both synthetic and real-world cartoon image datasets demonstrate the effectiveness and robustness of the proposed method.
Automated Object Detection in Experimental Data Using Combination of Unsupervised and Supervised Methods

Deep neural networks (DNN) have shown their success through computer vision tasks such as object detection, classification, and segmentation of image data including clinical and biological data. However, supervised DNNs require a large volume of labeled data to train and great effort to tune hyperparameters. The goal of this study is to segment cardiac images in movie data into objects of interest and a noisy background. This task is one of the essential tasks before statistical analysis of the images. Otherwise, the statistical values such as means, medians, and standard deviations can be erroneous. In this study, we show that the combination of unsupervised and supervised machine learning can automatize this process and find objects of interest accurately. We used the fact that typical clinical/biological data contain only limited kinds of objects. We solve this problem at the pixel level. For example, if there is only one object in an image, there are two types of pixels: object pixels and background pixels. We can expect object pixels and background pixels are quite different and they can be grouped using unsupervised clustering methods. In this study, we used the k-means clustering method. After finding object pixels and background pixels using unsupervised clustering methods, we used these pixels as training data for supervised learning. In this study, we used logistic regression and support vector machine. The combination of the unsupervised method and the supervised method can find objects of interest and segment images accurately without predefined thresholds or manually labeled data.
Enhancing Competitive Capabilities of Healthcare SCM through the Blockchain: Big Data Business Model’s Viewpoint

The reasons why supply chain management (SCM) needs blockchain technology include simplification of transaction procedures, time and cost reductions, and reliability improvement. This study emphasizes the necessity of introducing a blockchain-based joint logistics system to strengthen the competency of medical SCM and proposes a healthcare supply chain management (HSCM) competency measurement item through an analytic hierarchy process. The variables needed for using blockchain-based joint logistics are the performance expectations, effort expectations, promotion conditions, and social impact of the unified theory of acceptance and use of technology (UTAUT) model, as well as the HSCM competency results in increased reliability and transparency, enhanced SCM, and enhanced scalability. By analyzing the importance of securing reliability based on blockchain technology in the establishment of a supply chain network for HSCM competency, we reveal that joint logistics can be achieved, and synergistic effects can be created by implementing the integrated database to secure HSCM competency. Strengthening partnerships, such as joint logistics, will eventually lead to HSCM competency. In particular, HSCM should seek ways to upgrade its competitive capabilities through big data analysis based on the establishment of a joint logistics system.
Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems

When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to motivational indices, e.g., the probability of winning a game. This idea of concentration offers insights into reinforcement learning of sophisticated Large-scale Multi-Agent Systems (LMAS) participated by hundreds of agents. In such an LMAS, each agent receives a long series of entity observations at each step, which can overwhelm existing aggregation networks such as graph attention networks and cause inefficiency. In this paper, we propose a concentration network called ConcNet. First, ConcNet scores the observed entities considering several motivational indices, e.g., expected survival time and state value of the agents, and then ranks, prunes, and aggregates the encodings of observed entities to extract features. Second, distinct from the well-known attention mechanism, ConcNet has a unique motivational subnetwork to explicitly consider the motivational indices when scoring the observed entities. Furthermore, we present a concentration policy gradient architecture that can learn effective policies in LMAS from scratch. Extensive experiments demonstrate that the presented architecture has excellent scalability and flexibility, and significantly outperforms existing methods on LMAS benchmarks.
Explored Normalized Cut With Random Walk Refining Term for Image Segmentation

The Normalized Cut (NCut) model is a popular graph-based model for image segmentation. But it suffers from the excessive normalization problem and weakens the small object and twig segmentation. In this paper, we propose an Explored Normalized Cut (ENCut) model that establishes a balance graph model by adopting a meaningful-loop and a k-step random walk, which reduces the energy of small salient region, so as to enhance the small object segmentation. To improve the twig segmentation, our ENCut model is further enhanced by a new Random Walk Refining Term (RWRT) that adds local attention to our model with the help of an un-supervising random walk. Finally, a move-making based strategy is developed to efficiently solve the ENCut model with RWRT. Experiments on three standard datasets indicate that our model can achieve state-of-the-art results among the NCut-based segmentation models.
Quantum-Memory-Enhanced Preparation of Nonlocal Graph States

Graph states are an important class of multipartite entangled states. Previous experimental generation of graph states and in particular the Greenberger-Horne-Zeilinger (GHZ) states in linear optics quantum information schemes is subjected to an exponential decay in efficiency versus the system size, which limits its large-scale applications in quantum networks. Here, we demonstrate an efficient scheme to prepare graph states with only a polynomial overhead using long-lived atomic quantum memories. We generate atom-photon entangled states in two atomic ensembles asynchronously, retrieve the stored atomic excitations only when both sides succeed, and further project them into a four-photon GHZ state. We measure the fidelity of this GHZ state and further demonstrate its applications in the violation of Bell-type inequalities and in quantum cryptography. Our work demonstrates the prospect of efficient generation of multipartite entangled states in large-scale distributed systems with applications in quantum information processing and metrology.
Time Series Anomaly Detection in Power Electronics Signals with Recurrent and Convlstm Autoencoders

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Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation

A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, user behavior modeling is essential in recommendation and advertising (e.g., CTR prediction and ads allocation). Most previous works only model point-level positive feedback (i.e., click), which neglect the page-level information of feedback and other types of feedback. To this end, we propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. Specifically, we introduce four different types of page-level feedback, and capture user preference for item arrangement under different receptive fields through the multi-channel interaction module. Through extensive offline and online experiments on Meituan food delivery platform, we demonstrate that DPIN can effectively model the page-level user preference and increase the revenue.
GCP-Net: A Gating Context-Aware Pooling Network for Cervical Cell Nuclei Segmentation

Accurate segmentation of cervical nuclei is an essential step in the early diagnosis of cervical cancer. Still, there are few studies on the segmentation of clustered nuclei in clusters of cells. Because of the complexities of high cell overlap, blurred nuclei boundaries, and clustered cells, the accurate segmentation of clustered nuclei remains a pressing challenge. In this paper, we purposefully propose a GCP-Net deep learning network to handle the challenging cervical cluster cell images. The proposed U-Net-based GCP-Net consists of a pretrained ResNet-34 model as encoder, a Gating Context-aware Pooling (GCP) module, and a modified decoder. The GCP module is the primary building block of the network to improve the quality of feature learning. It allows the GCP-Net to refine details of feature maps leveraging multiscale context gating and Global Context Attention for the spatial and texture dependencies. The decoder block including Global Context Attention- (GCA-) Residual Block helps build long-range dependencies and global context interaction in the decoder to refine the predicted masks. We conducted extensive comparative experiments with seven existing models on our ClusteredCell dataset and three typical medical image datasets, respectively. The experimental results showed that the GCP-Net obtained promising results on three evaluation metrics AJI, Dice, and PQ, demonstrating the superiorities and generalizability of our GCP-Net for automatic medical image segmentation in comparison with some SOAT baselines.
Biometric Template Protection for Dynamic Touch Gestures Based on Fuzzy Commitment Scheme and Deep Learning

Privacy plays an important role in biometric authentication systems. Touch authentication systems have been widely used since touch devices reached their current level of development. In this work, a fuzzy commitment scheme (FCS) is proposed based on deep learning (DL) to protect the touch-gesture template in a touch authentication system. The binary Bose–Ray-Chaudhuri code (BCH) is used with FCS to deal with touch variations. The BCH code is described by the triplet (n, k, t) where n denotes the code word’s length, k denotes the length of the key and t denotes error-correction capability. In our proposed system, the system performance is investigated using different lengths k. The learning-based approach is applied to extract touch features from raw touch data, as the recurrent neural network (RNN) is used based on a convolutional neural network (CNN). The proposed system has been evaluated on two different touch datasets: the Touchalytics dataset and BioIdent dataset. The best results obtained were with a key length k = 99 and n = 255; the false accept rate (FAR) was 0.00 and false reject rate (FRR) was 0.5854 for the Touchalytics dataset, while the FAR was 0.00 and FRR was 0.5399 with the BioIdent dataset. The FCS shows its effectiveness in dynamic authentication systems, as good results are obtained and compared with other works.
Quantum computational study of chloride attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes

Abstract Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and entanglement, to perform computations. Quantum mechanical (QM) methods are candidates for various applications and can provide accurate absolute energy calculations in structure-based methods. QM methods are powerful tools for describing reaction pathways and their potential energy surfaces (PES). In this study, we applied quantum computing to describe the PES of the bimolecular nucleophilic substitution (S N 2) reaction between chloromethane and chloride ions. We performed noiseless and noise simulations using quantum algorithms and compared the accuracy and noise effects of the ansatzes. In noiseless simulations, the results from UCCSD and k-UpCCGSD are similar to those of full configurational interaction (FCI) with the same active space, which indicates that quantum algorithms can describe the PES of the S N 2 reaction. In noise simulations, UCCSD is more susceptible to quantum noise than k-UpCCGSD. Therefore, k-UpCCGSD can serve as an alternative to UCCSD to reduce quantum noisy effects in the noisy intermediate-scale quantum era, and k-UpCCGSD is sufficient to describe the PES of the S N 2 reaction in this work. The results showed the applicability of quantum computing to the S N 2 reaction pathway and provided valuable information for structure-based molecular simulations with quantum computing.
Multi-Color Space Network for Salient Object Detection

The salient object detection (SOD) technology predicts which object will attract the attention of an observer surveying a particular scene. Most state-of-the-art SOD methods are top-down mechanisms that apply fully convolutional networks (FCNs) of various structures to RGB images, extract features from them, and train a network. However, owing to the variety of factors that affect visual saliency, securing sufficient features from a single color space is difficult. Therefore, in this paper, we propose a multi-color space network (MCSNet) to detect salient objects using various saliency cues. First, the images were converted to HSV and grayscale color spaces to obtain saliency cues other than those provided by RGB color information. Each saliency cue was fed into two parallel VGG backbone networks to extract features. Contextual information was obtained from the extracted features using atrous spatial pyramid pooling (ASPP). The features obtained from both paths were passed through the attention module, and channel and spatial features were highlighted. Finally, the final saliency map was generated using a step-by-step residual refinement module (RRM). Furthermore, the network was trained with a bidirectional loss to supervise saliency detection results. Experiments on five public benchmark datasets showed that our proposed network achieved superior performance in terms of both subjective results and objective metrics.
A Multi-tenant Usage Access Model for Cloud Computing

Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures. It offers tremendous advantages for enterprises and service providers. It is anticipated that ... | Find, read and cite all the research you need on Tech Science Press
Cryptocurrency Frauds

There are scams of millions of dollars happening on a daily basis in the world of cryptocurrency. Awareness and the latest 21st century technology- “Artificial Intelligence,” may prove to be the key to fight this battle against Cryptocurrency scams. This research paper provides a comprehensive analysis discussing the various “Cryptocurrency scams” across the world, while giving case analysis of the biggest scams regarding the same, later exploring various solutions to this problem. This research paper, goes beyond the usual theft from hacking and ransomware attacks, and explores the in-depths of many of the frauds that have not been most commonly heard of. When we say „Cryptocurrency frauds,‟ we directly associate with hacking and theft due to unauthorized access, but it goes more than that, and that is what this paper seeks to explore.