Reviewerly/exHarmony huggingface dataset:
A systematic literature mapping on secure identity management using blockchain technology

After the acceptance of blockchain technology, there have been applications which aim to use blockchain in their fields. Various approaches have been proposed in past to build a secure Identity Management (IdM) System. This is a novel systematic literature mapping of IdM in blockchain. This paper provides an extensive review on IdM with emphasis on how the emergence of blockchain has addressed the IdM challenges faced over the years. A thorough study has been done on the existing literature. The primary and secondary “search string” were identified and search was conducted on five databases; and after screening the analysis was done. Out of the total studied literature, 30 primary studies published from 2009 to 2020 were selected. Through this paper, the researchers will be able to: 1) find out the research trends in IdM using blockchain, 2) understand the challenges in IdM and report whether blockchain can solve the IdM challenges, 3) scrutinize and understand how the different frameworks of IdM would deal with security, integrity and privacy problems, 4) know about initiatives taken for IdM using blockchain, 5) which consensus algorithms are popular among blockchains, 6) know about the research projects going on in the field of IdM using blockchain.
Modeling Incongruity between Modalities for Multimodal Sarcasm Detection

Sarcasm is a sophisticated linguistic phenomenon and commonly manifests on social media platforms, which poses a great challenge for opinion mining systems. Therefore, multimodal sarcasm detection, which aims to understand the implied sentiment in the video, has gained more and more attention. However, previous works mostly focus on multimodal feature fusion without explicitly modeling the incongruity between modalities, such as expressing verbal compliments while rolling eyes, which is an obvious cue for detecting sarcasm. In this article, we propose the incongruity-aware attention network (IWAN), which detects sarcasm by focusing on the word-level incongruity between modalities via a scoring mechanism. This scoring mechanism could assign larger weights to words with incongruent modalities. Experimental results demonstrate the effectiveness of our proposed IWAN model, which not only achieves the state-of-the-art performance on the MUStARD dataset but also offers the advantages of interpretability.
Cost and makespan aware workflow scheduling in IaaS clouds using hybrid spider monkey optimization

The researcher's predilection towards the concerned infinite resources and the dynamic provisioning on rental premises encourages the scheduling of complex scientific applications in the cloud. The scheduling of workflows in the cloud is constrained to QoS parameters. Many heuristic and meta-heuristic algorithms are widely investigated for the QoS constrained workflow scheduling problem. However, it is still an open area of research, as most of the existing techniques concentrate on minimization of either cost or time and ignores the optimization of multiple QoS constraints simultaneously. To address this problem, in this paper, a Hybrid Spider Monkey Optimization (HSMO) algorithm has been proposed. The proposed algorithm optimizes the makespan and the cost while satisfying the budget and deadline constraints. The proposed algorithm is the hybridization of recently developed SMO and the other popular heuristic BDSD algorithm. BDSD is a budget and deadline constrained algorithm, which guides HSMO in generating a feasible schedule. Moreover, the proposed strategy involves the penalty function to restrict selecting those solutions that fail to satisfy the QoS constraints. Experimental results demonstrate the effectiveness of HSMO over existing ABC, Bi-Criteria PSO, and BDSD algorithms.
A Novel Approach for Credit Card Fraud Detection using Decision Tree and Random Forest Algorithms

In the world of finance, as the technology grown, new systems of business making came into picture. Credit card system is one among them. But because of lot of loop holes in this system, lot of problems are aroused in this system in the method of credit card scams. Due to this the industry and customers who are using credit cards are facing a huge loss. There is a deficiency of investigation lessons on examining practical credit card figures in arrears to privacy issues. In the manuscript an attempt has been made for finding the frauds in the credit card business by using the algorithms which adopted machine learning techniques. In this regard, two algorithms are used viz Fraud Detection in credit card using Decision Tree and Fraud Detection using Random Forest. The efficiency of the model can be decided by using some public data as sample. Then, an actual world credit card facts group from a financial institution is examined. Along with this, some clatter is supplemented to the data samples to auxiliary check the sturdiness of the systems. The significance of the methods used in the paper is the first method constructs a tree against the activities performed by the user and using this tree scams will be suspected. In the second method a user activity based forest will have constructed and using this forest an attempt will be made in identifying the suspect. The investigational outcomes absolutely show that the mainstream elective technique attains decent precision degrees in sensing scam circumstances in credit cards.
Mixed 2D and 3D convolutional network with multi-scale context for lesion segmentation in breast DCE-MRI

Breast lesion segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an essential step for breast cancer analysis. 2D networks have the advantages of low inference time and greater transfer-ability while 3D networks are superior in learning the contextual information in volumetric data. However, the performance of the 3D network is severely affected when MRI has a low-resolution ratio in the third dimension. In order to integrate the advantages of 2D and 3D networks, we propose a mixed 2D and 3D convolutional network with multi-scale context (M2D3D-MC) for lesion segmentation in breast DCE-MRI with a limited number of axial slices in the scans. Through serial 2D and 3D convolution, the mixed 2D and 3D convolution module has the ability to exploit the contexts between adjacent slices. And considering the diversity of shape and size for breast lesions, we introduce a multi-scale context extractor block consisting of atrous convolutions with different sampling rates to extract multi-scale image features. We justify the proposed method through extensive experiments on 90 MRI studies. Compared with both 2D and 3D networks, M2D3D-MC achieves the best performance with DSC, SEN, and PPV of 76.4%, 75.9%, and 82.4% respectively. A new paradigm is provided for breast lesion segmentation by combining 2D and 3D convolutions to exploit the contextual information. It demonstrates stronger performance in mixed 2D and 3D model given the limited number of axial slices. Our investigation also reveals that the multi-scale context block is effective for breast lesion segmentation.
An eye tracking based virtual reality system for use inside magnetic resonance imaging systems

Patients undergoing Magnetic Resonance Imaging (MRI) often experience anxiety and sometimes distress prior to and during scanning. Here a full MRI compatible virtual reality (VR) system is described and tested with the aim of creating a radically different experience. Potential benefits could accrue from the strong sense of immersion that can be created with VR, which could create sense experiences designed to avoid the perception of being enclosed and could also provide new modes of diversion and interaction that could make even lengthy MRI examinations much less challenging. Most current VR systems rely on head mounted displays combined with head motion tracking to achieve and maintain a visceral sense of a tangible virtual world, but this technology and approach encourages physical motion, which would be unacceptable and could be physically incompatible for MRI. The proposed VR system uses gaze tracking to control and interact with a virtual world. MRI compatible cameras are used to allow real time eye tracking and robust gaze tracking is achieved through an adaptive calibration strategy in which each successive VR interaction initiated by the subject updates the gaze estimation model. A dedicated VR framework has been developed including a rich virtual world and gaze-controlled game content. To aid in achieving immersive experiences physical sensations, including noise, vibration and proprioception associated with patient table movements, have been made congruent with the presented virtual scene. A live video link allows subject-carer interaction, projecting a supportive presence into the virtual world.
Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots

Planning smooth and energy-efficient paths for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, several sampling-based motion-planning algorithms, extend functions and post-smoothing algorithms have been introduced for such motion-planning systems. Choosing the best combination of components for an application is a tedious exercise, even for expert users. We therefore present Bench-MR, the first open-source motion-planning benchmarking framework designed for sampling-based motion planning for nonholonomic, wheeled mobile robots. Unlike related software suites, Bench-MR is an easy-to-use and comprehensive benchmarking framework that provides a large variety of sampling-based motion-planning algorithms, extend functions, collision checkers, post-smoothing algorithms and optimization criteria. It aids practitioners and researchers in designing, testing, and evaluating motion-planning systems, and comparing them against the state of the art on complex navigation scenarios through many performance metrics. Through several experiments, we demonstrate how Bench-MR can be used to gain extensive insights from the benchmarking results it generates.
Research on gear fault diagnosis based on feature fusion optimization and improved two hidden layer extreme learning machine

Aiming at gear fault diagnosis in rotating machinery, a novel state assessment method based on feature fusion optimization and improved two-hidden-layer extreme learning machine (ITELM) was proposed in order to improve the fault diagnosis accuracy. Firstly, variational mode decomposition (VMD) and wavelet packet (WP) are employed to decompose the vibration signal, and statistical parameter features are extracted. Then, ReliefF algorithm is adopted to manage the extracted features and obtain the optimal feature subset. Finally, in order to avoid the network structure limitation of the existing two-hidden layer extreme learning machine with same hidden-layer node number, ITELM with different hidden-layer node number is proposed to evaluate the gear running states. Meanwhile, the empirical formula and fuzzy logic reasoning methods are developed in order to effectively determine the number of nodes for first and second hidden layers, respectively. The experimental results from different perspectives verified the effectiveness of the proposed method.
SymNet: A Simple Symmetric Positive Definite Manifold Deep Learning Method for Image Set Classification

By representing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, visual classification with image sets has attracted much attention. Despite the success made so far, the issue of large within-class variability of representations still remains a key challenge. Recently, several SPD matrix learning methods have been proposed to assuage this problem by directly constructing an embedding mapping from the original SPD manifold to a lower dimensional one. The advantage of this type of approach is that it cannot only implement discriminative feature selection but also preserve the Riemannian geometrical structure of the original data manifold. Inspired by this fact, we propose a simple SPD manifold deep learning network (SymNet) for image set classification in this article. Specifically, we first design SPD matrix mapping layers to map the input SPD matrices into new ones with lower dimensionality. Then, rectifying layers are devised to activate the input matrices for the purpose of forming a valid SPD manifold, chiefly to inject nonlinearity for SPD matrix learning with two nonlinear functions. Afterward, we introduce pooling layers to further compress the input SPD matrices, and the log-map layer is finally exploited to embed the resulting SPD matrices into the tangent space via log-Euclidean Riemannian computing, such that the Euclidean learning applies. For SymNet, the (2-D)2principal component analysis (PCA) technique is utilized to learn the multistage connection weights without requiring complicated computations, thus making it be built and trained easier. On the tail of SymNet, the kernel discriminant analysis (KDA) algorithm is coupled with the output vectorized feature representations to perform discriminative subspace learning. Extensive experiments and comparisons with state-of-the-art methods on six typical visual classification tasks demonstrate the feasibility and validity of the proposed SymNet.
Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records

PURPOSE Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer. METHODS We used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured ( bag-of-words, doc2vec, fasttext), and combinations of both ( structured + bow, structured + doc2vec, structured + fasttext). We optimized the identification model among five machine learning methods (logistic regression, multilayer perceptrons, random forest, support vector machines, and stochastic gradient boosting). The treatment information recorded in the cancer registry is the gold standard and compares our methods to an identification baseline with billing codes. RESULTS For prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using structured + doc2vec. For oropharynx cancer, we achieved an f1-score of 0.78 (95% CI, 0.58 to 0.93) for chemoradiation and 0.83 (95% CI, 0.69 to 0.95) for surgery using doc2vec. For esophagus cancer, we achieved an f1-score of 1.0 (95% CI, 1.0 to 1.0) for both chemoradiation and surgery using all combinations of structured and unstructured data. We found that employing the free-text clinical notes outperforms using the billing codes or only structured data for all three cancer types. CONCLUSION Our results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.
Regularizing deep networks with prior knowledge: A constraint-based approach

Deep Learning architectures can develop feature representations and classification models in an integrated way during training. This joint learning process requires large networks with many parameters, and it is successful when a large amount of training data is available. Instead of making the learner develop its entire understanding of the world from scratch from the input examples, the injection of prior knowledge into the learner seems to be a principled way to reduce the amount of require training data, as the learner does not need to induce the rules from the data. This paper presents a general framework to integrate arbitrary prior knowledge into learning. The domain knowledge is provided as a collection of first-order logic (FOL) clauses, where each task to be learned corresponds to a predicate in the knowledge base. The logic statements are translated into a set of differentiable constraints, which can be integrated into the learning process to distill the knowledge into the network, or used during inference to enforce the consistency of the predictions with the prior knowledge. The experimental results have been carried out on multiple image datasets and show that the integration of the prior knowledge boosts the accuracy of several state-of-the-art deep architectures on image classification tasks.
Theory of relaxation time of stochastic nanomagnets

Reducing the relaxation time between binary spin states is crucial to enhance the speed and accuracy of a spintronics-based approach for a probabilistic computer, as envisioned by R.P. Feynman. Contrary to previous perception that the reduction of the energy barrier between the states is the most efficient way, the authors discover here that the relaxation time in in-plane easy-axis nanomagnets is widely controllable by changing the effective perpendicular anisotropy field, even though it has nothing to do with the energy barrier.
An adaptive fuzzy penalty method for constrained evolutionary optimization

Penalty function is well-known for constrained evolutionary optimization. An open question in the penalty function is how to tune the penalty coefficient. This paper proposes an adaptive fuzzy penalty method to address this issue, where the coefficient is adjusted at both the individual level and the population level. At the individual level, each individual chooses a penalty coefficient from a predefined domain according to some fuzzy rules. At the population level, the domain of the crisp output is adjusted adaptively by using population information. To enhance the population diversity, an effective mutation scheme is developed. Due to its numerous merits, differential evolution is used to design a search algorithm. By the above processes, a constrained optimization evolutionary algorithm called AFPDE is proposed. Since the objective function value and the degree of constraint violation are normalized, AFPDE is less problem-dependent than the seminal work of the fuzzy penalty method. AFPDE introduces a lower penalty value in the early stage of AFPDE while a higher one in the later stage. Thus, it can escape local optima in the infeasible region. Experiments on three well-known benchmark test sets and two mechanical design problems validate that AFPDE is competitive.
Passive RFID Module With LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient-Assisted Living

Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient-assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive, and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using four volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyperparameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers a pervasive sensing environment for the elderly, persons with disability and chronic illness.
Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

Facing the massive data of higher education institutions, data mining technology is an intelligent information processing technology that can effectively discover knowledge from the massive data and can discover important information that people have previously ignored from the huge data information. This article is dedicated to the development of applied mathematics education resource mining technology based on edge computing and data stream classification. First of all, this article establishes a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining. Secondly, the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data. This method provides potential and valuable information for decision-makers and education practitioners. Finally, the simulation and performance test of the system verify that it has the functions of mathematical information mining and data processing. This system will provide strong support for applied mathematics education reform.
From natural language processing to neural databases

In recent years, neural networks have shown impressive performance gains on long-standing AI problems, such as answering queries from text and machine translation. These advances raise the question of whether neural nets can be used at the core of query processing to derive answers from facts, even when the facts are expressed in natural language. If so, it is conceivable that we could relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. Furthermore, such technology would enable combining information from text, images, and structured data seamlessly. This paper introduces neural databases , a class of systems that use NLP transformers as localized answer derivation engines. We ground the vision in NeuralDB, a system for querying facts represented as short natural language sentences. We demonstrate that recent natural language processing models, specifically transformers, can answer select-project-join queries if they are given a set of relevant facts. However, they cannot scale to non-trivial databases nor answer set-based and aggregation queries. Based on these insights, we identify specific research challenges that are needed to build neural databases. Some of the challenges require drawing upon the rich literature in data management, and others pose new research opportunities to the NLP community. Finally, we show that with preliminary solutions, NeuralDB can already answer queries over thousands of sentences with very high accuracy.