Reviewerly/exHarmony huggingface dataset:
Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.
Improving the Variational Quantum Eigensolver Using Variational Adiabatic Quantum Computing

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm for finding the minimum eigenvalue of a Hamiltonian that involves the optimization of a parameterized quantum circuit. Since the resulting optimization problem is in general nonconvex, the method can converge to suboptimal parameter values that do not yield the minimum eigenvalue. In this work, we address this shortcoming by adopting the concept of variational adiabatic quantum computing (VAQC) as a procedure to improve VQE. In VAQC, the ground state of a continuously parameterized Hamiltonian is approximated via a parameterized quantum circuit. We discuss some basic theory of VAQC to motivate the development of a hybrid quantum-classical homotopy continuation method. The proposed method has parallels with a predictor-corrector method for numerical integration of differential equations. While there are theoretical limitations to the procedure, we see in practice that VAQC can successfully find good initial circuit parameters to initialize VQE. We demonstrate this with two examples from quantum chemistry. Through these examples, we provide empirical evidence that VAQC, combined with other techniques (an adaptive termination criteria for the classical optimizer and a variance-based resampling method for the expectation evaluation), can provide more accurate solutions than “plain” VQE, for the same amount of effort.
A Crowd Counting Framework Combining with Crowd Location

In the past ten years, crowd detection and counting have been applied in many fields such as station crowd statistics, urban safety prevention, and people flow statistics. However, obtaining accurate positions and improving the performance of crowd counting in dense scenes still face challenges, and it is worthwhile devoting much effort to this. In this paper, a new framework is proposed to resolve the problem. The proposed framework includes two parts. The first part is a fully convolutional neural network (CNN) consisting of backend and upsampling. In the first part, backend uses the residual network (ResNet) to encode the features of the input picture, and upsampling uses the deconvolution layer to decode the feature information. The first part processes the input image, and the processed image is input to the second part. The second part is a peak confidence map (PCM), which is proposed based on an improvement over the density map (DM). Compared with DM, PCM can not only solve the problem of crowd counting but also accurately predict the location of the person. The experimental results on several datasets (Beijing-BRT, Mall, Shanghai Tech, and UCF_CC_50 datasets) show that the proposed framework can achieve higher crowd counting performance in dense scenarios and can accurately predict the location of crowds.
WITHDRAWN: NA-DLSTM – A neural attention based model for context aware Aspect-based sentiment analysis

Sentiment Analysis makes use of natural language processing approaches to methodically recognize, mine, and learn user sentiment. As such it can be useful for finding the mood of the public about a specific topic. Aspect based sentiment analysis is an important technique that analyses and breaks down text into aspects which are nothing but attributes or constituents of a product or service and then allocates a sentiment level (positive, negative or neutral) for each aspect. The difference between Sentiment analysis and Aspect based sentiment analysis is that the former identifies only the sentiment of an overall text, while the latter examines and categorizes the entire text into various aspects and finds the corresponding sentiment for each one. But sentiment context for a given aspect also plays an important rule for correct sentiment prediction. So, we developed a new model which is a combination of neural attention mechanism and LSTM called NA-DLSTM (Neural Attention based Deep Long Short-Term Memory) for Context-aware Aspect based Sentiment Analysis. We have compared our model with baseline models in terms of accuracy and F1 score on datasets (Rest14, Rest15, Rest16).
A Self-Adaptive Deep Learning-Based Algorithm for Predictive Analysis of Bitcoin Price

Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin's price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.
UML Diagrams in Software Engineering Research: A Systematic Literature Review

Software engineering is a discipline utilizing Unified Modelling Language (UML) diagrams, which are accepted as a standard to depict object-oriented design models. UML diagrams make it easier to identify the requirements and scopes of systems and applications by providing visual models. In this manner, this study aims to systematically review the literature on UML diagram utilization in software engineering research. A comprehensive review was conducted over the last two decades, spanning from 2000 to 2019. Among several papers, 128 were selected and examined. The main findings showed that UML diagrams were mostly used for the purpose of design and modeling, and class diagrams were the most commonly used ones.
Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology

The influence of artificial intelligence technology on teaching design is explored to improve teaching efficiency. First, artificial intelligence is introduced and its impacts on teaching design are analyzed. Second, the connotation of the paradigm of teaching design and the paradigm shift for teaching design are explored using the paradigm shift analysis framework. Finally, the changes in teaching design under artificial intelligence are analyzed, and the impacts of artificial intelligence on teaching activities are investigated. The results show that the application of artificial intelligence technology has led to different levels of change in the six elements of teaching design, including teaching objectives, service objects (teachers and students), teaching content, teaching media, teaching environment, and teaching evaluation. The connotation and paradigm shift of the teaching design are introduced from the four elements based on the artificial intelligence technology. It is found that artificial intelligence technology can enhance the learning ability and cognitive ability of students to a certain extent while improving the teaching efficiency and learning efficiency. The investigation proves that the teaching design based on artificial intelligence technology can be applied to teaching activities, thereby improving the learning efficiency of students and the teaching efficiency of teachers.
Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree

Support vector machines (SVMs) are designed to solve the binary classification problems at the beginning, but in the real world, there are a lot of multiclassification cases. The multiclassification methods based on SVM are mainly divided into the direct methods and the indirect methods, in which the indirect methods, which consist of multiple binary classifiers integrated in accordance with certain rules to form the multiclassification model, are the most commonly used multiclassification methods at present. In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm. In this algorithm, it considers not only the influence of “between-classes distance” and “class variance” in traditional measures of between-classes separability but also takes “between-classes variance” into consideration and proposes a new improved “between-classes separability measure.” Based on the new “between-classes separability measure,” it finds out the two classes with the largest between-classes separability measure and uses them as the positive and negative samples to train and learn the classifier. After that, according to the principle of the class-grouping-by-majority, the remaining classes are close to these two classes and merged into the positive samples and the negative samples to train SVM classifier again. For the samples with uneven distribution or sparse distribution, this method can avoid the error caused by the shortest canter distance classification method and overcome the “error accumulation” problem existing in traditional binary decision tree to the greatest extent so as to obtain a better classifier. According to the above algorithm, each layer node of the decision tree is traversed until the output classification result is a single-class label. The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems.
A new model of Hopfield network with fractional-order neurons for parameter estimation

Abstract In this work, we study an application of fractional-order Hopfield neural networks for optimization problem solving. The proposed network was simulated using a semi-analytical method based on Adomian decomposition,, and it was applied to the on-line estimation of time-varying parameters of nonlinear dynamical systems. Through simulations, it was demonstrated how fractional-order neurons influence the convergence of the Hopfield network, improving the performance of the parameter identification process if compared with integer-order implementations. Two different approaches for computing fractional derivatives were considered and compared as a function of the fractional-order of the derivatives: the Caputo and the Caputo–Fabrizio definitions. Simulation results related to different benchmarks commonly adopted in the literature are reported to demonstrate the suitability of the proposed architecture in the field of on-line parameter estimation.
An Adversarial Attack System for Face Recognition

Deep neural networks (DNNs) are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers. Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain. As a most common application of DNNs, face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples. In this paper, we implement an adversarial attack system for face recognition in both digital domain that generates adversarial face images to fool the recognition system, and physical domain that generates customized glasses to fool the system when a person wears the glasses. Experiments show that our system attacks face recognition systems effectively. Furthermore, our system could misguide the recognition system to identify a person wearing the customized glasses as a certain target. We hope this research could help raise the attention of artificial intelligence security and promote building robust recognition systems.
Improving Factual Consistency Between a Response and Persona Facts

Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona.These models are trained with fully supervised learning where the objective function barely captures factual consistency.We propose to finetune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility 1 .Our automatic and human evaluations on the PersonaChat corpus confirm that our approach increases the rate of responses that are factually consistent with persona facts over its supervised counterpart while retaining the language quality of responses.
Robust Open-Vocabulary Translation from Visual Text Representations

Machine translation models have discrete vo cabularies and commonly use subword seg mentation techniques to achieve an 'open vo cabulary.'This approach relies on consis tent and correct underlying unicode sequences, and makes models susceptible to degrada tion from common types of noise and vari ation.Motivated by the robustness of hu man language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by process ing visually rendered text with sliding win dows.We show that models using visual text representations approach or match per formance of traditional text models on small and larger datasets.More importantly, mod els with visual embeddings demonstrate sig nificant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character per muted German-English task where subword models degrade to 1.9.
Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.
Hybrid MEMS-CMOS ion traps for NISQ computing

Surging interest in engineering quantum computers has stimulated significant and focused research on technologies needed to make them manufacturable and scalable. In the ion trap realm this has led to a transition from bulk three-dimensional macro-scale traps to chip-based ion traps and included important demonstrations of passive and active electronics, waveguides, detectors, and other integrated components. At the same time as these technologies are being developed the system sizes are demanding more ions to run noisy intermediate scale quantum (NISQ) algorithms, growing from around ten ions today to potentially a hundred or more in the near future. To realize the size and features needed for this growth, the geometric and material design space of microfabricated ion traps must expand. In this paper we describe present limitations and the approaches needed to overcome them, including how geometric complexity drives the number of metal levels, why routing congestion affects the size and location of shunting capacitors, and how RF power dissipation can limit the size of the trap array. We also give recommendations for future research needed to accommodate the demands of NISQ scale ion traps that are integrated with additional technologies.
Toward the Minimal Floating Operation Count Cholesky Decomposition of Electron Repulsion Integrals

As quantum chemistry calculations deal with molecular systems of increasing size, the memory requirement to store electron-repulsion integrals (ERIs) greatly outpaces the physical memory available in computing hardware. The Cholesky decomposition of ERIs provides a convenient yet accurate technique to reduce the storage requirement of integrals. Recent developments of a two-step algorithm have drastically reduced the memory operation (MOP) count, leaving the floating operation (FLOP) count as the last frontier of cost reduction in the Cholesky ERI algorithm. In this report, we introduce a dynamic integral tracking, reusing, and compression/elimination protocol embedded in the two-step Cholesky ERI method. Benchmark studies suggest that this technique becomes particularly advantageous when the basis set consists of many computationally expensive high-angular-momentum basis functions. With this dynamic-ERI improvement, the Cholesky ERI approach proves to be a highly efficient algorithm with minimal FLOP and MOP count.
Nonlocal advantage of quantum coherence and entanglement of two spins under intrinsic decoherence*

We investigate the nonlocal advantage of quantum coherence (NAQC) and entanglement for two spins coupled via the Heisenberg interaction and under the intrinsic decoherence. Solutions of this decoherence model for the initial spin-1/2 and spin-1 maximally entangled states are obtained, based on which we calculate the NAQC and entanglement. In the weak region of magnetic field, the NAQC behaves as a damped oscillation with the time evolves, while the entanglement decays exponentially (behaves as a damped oscillation) for the spin-1/2 (spin-1) case. Moreover, the decay of both the NAQC and entanglement can be suppressed significantly by tuning the magnetic field and anisotropy of the spin interaction to some decoherence-rate-determined optimal values.
Analyzing and repairing concept drift adaptation in data stream classification

Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.