Reviewerly/exHarmony huggingface dataset:
Digital Image Restoration Using Modified Richardson-Lucy Deconvolution Algorithm

Since the Surveillance camera came into the market, the advanced digital image processing is getting more research importance. One of the major issues of digital photographing is motion and image blur. This can be occurring when the camera is in motion, the shape of the blurring is constant, but only the spatial extent is varying. Here, a novel algorithm is proposed for deconvoluting the image samples in the filtering process. Moreover, the proposed algorithm is successfully useful for compressed domain operations such as downsampling, translation, and filtering, etc. The recommended deconvolution algorithm produces the approximated output images that are perceptually closer to the actual image with reduced computational complexity. In this paper, the proposed multiplier less convolution algorithm is employed in the Richardson-Lucy filter for the Deblurring analysis. Also, this proposed method was compared with the existing techniques and stated that the novel multiplier less deconvolution algorithm provides better performance in terms of PSNR.
Fault Detection of Electromechanical Actuators via Automatic Generation of a Fuzzy Index

Regardless of dimensions, complexity, or purpose of a mechatronic system, fault detection (FD) represents a critical element to achieve robustness and safety, while reducing risks and maintenance costs. Important requisites for an FD component include flexibility, performance, platform independence, and interpretability. To pursue these goals, this article presents a framework for the FD on electromechanical systems based on a fuzzy inference system. Fuzzy logic (FL) is a powerful modeling tool whose applicability to complex systems, however, can be limited by the effort required to specify the fuzzy inference rules, whose number grows rapidly with the number of input variables and membership functions (MFs). To this purpose, we propose the Fuzzy INDices (F-IND) framework as the foundation for an FD system; F-IND automatically generates the fuzzy rules from the specification of qualitative best and worst cases on the MFs of each input variable, which greatly reduces the burden on model design. This article presents the mathematical model of F-IND, its implementation, and its application to the specific case of FD of electric motors, evaluating its computational performance and the accuracy in both simulated and experimental settings.
Multi-agent system applications to fight COVID-19 pandemic

A multi-agent system (MAS) uses multiple interacting intelligent agents with the support of the computerized system. During this COVID-19 pandemic, there is a requirement to improve service for patients to fulfill the ongoing challenges. This technology provides a digital service continuously for the patients. An MAS-based platform is helpful for different scenarios during this pandemic. This article discusses significant components, as used for developing an MAS to handle COVID-19 situations. Finally, it discusses various significance applications of an MAS for COVID-19 pandemic patients. The knowledge and data required to solve the COVID-19 pandemic are distributed spatially in different locations, which add constraints, toward proper planning of the treatment of patients to combat this pandemic. Governments accept that their systems cannot stop the widespread transmission of this virus. However, by flattening the curve, the mortality rate can be reduced. MAS consist of autonomous entities, also known as agents, who work collaboratively. By using MAS, lots of real-world data of the emerging situation such as an outbreak can be processed gainfully. It will allow doctors and organizations to create flexible models of potential scenarios.
Digital Signature Authentication Using Asymmetric Key Cryptography with Different Byte Number

Nowadays, each and every system needs proper security. Only proper security can save important documents. There are different types of security systems that people use for safety. Digital Signature is one kind of a security system. Digital Signature is the public key primitive of different types of message authentication. Digital signature is one kind of technique that converts the handwritten signature in digital data. It is one kind of cryptographic value that is calculated from the data and a secret private key which is only known by the signer. In this paper, we want to make the digital signature more secure by using the ED25519 algorithm, which is an asymmetric algorithm. In this algorithm, the signature will be converted into different byte number, which will make the security system more strong.
Association Loss for Visual Object Detection

Convolutional neural network (CNN) is a popular choice for visual object detection where two sub-nets are often used to achieve object classification and localization separately.However, the intrinsic relation between the localization and classification sub-nets was not exploited explicitly for object detection.In this letter, we propose a novel association loss, namely, the proxy squared error (PSE) loss, to entangle the two sub-nets, thus use the dependency between the classification and localization scores obtained from these two sub-nets to improve the detection performance.We evaluate our proposed loss on the MS-COCO dataset and compare it with the loss in a recent baseline, i.e. the fully convolutional one-stage (FCOS) detector.The results show that our method can improve the AP from 33.8 to 35.4 and AP75 from 35.4 to 37.8, as compared with the FCOS baseline.
Variational collision and obstacle avoidance of multi-agent systems on Riemannian manifolds

In this paper we study a path planning problem from a variational approach to collision and obstacle avoidance for multi-agent systems evolving on a Riemannian manifold. The problem consists of finding non-intersecting trajectories between the agent and prescribed obstacles on the workspace, among a set of admissible curves, to reach a specified configuration, based on minimizing an energy functional that depends on the velocity, covariant acceleration and an artificial potential function used to prevent collision with the obstacles and among the agents. We apply the results to examples of a planar rigid body, and collision and obstacle avoidance for agents evolving on a sphere.
Efficient designs of reversible BCD to EX-3 Converter with low quantum cost in nanoscale

As an interesting and significant research domain, reversible logic is massively utilized in technologies, including optical computing, cryptography, quantum computing, nanotechnology, and so on. The realization of quantum computing is not possible without the implementation of reversible logic, and reversible designs are presented mainly to minimize the thermal loss because of the data input bits lost in the irreversible circuit. Digital converters, as the most important logic circuits, are used to connect computing systems with different binary codes. This paper first proposes a new reversible gate called Reversible Noorallahzadeh[Formula: see text]Mosleh Gate (RNMG). Then, using the proposed RNMG gate as well as existing NMG1, NMG6, and PG gates, three different designs of reversible Binary-Coded Decimal (BCD) to EX-3 code converter are proposed. Our results indicate that the proposed BCD to EX-3 code converters are superior to previous designs in terms of quantum cost. Moreover, the proposed converters are comparable or better than previous designs in terms of gate count, constant inputs, and garbage outputs.
Fine-tuning the BERTSUMEXT model for Clinical Report Summarization

Background: Medical personnel are expected to parse through scores of reports each day, covering the medical history of their patients. This reading task is crucial to the effectiveness of the healthcare provided. However, it has been noticed that doctors often have to spend a lot of time going through these documents, in order to get a concise gist of the most medically relevant details. This could even affect the amount of time left for doctor-patient interaction. It is in this scenario, that the potential usefulness of an automatic clinical report summarization tool becomes apparent. Such a system would save a lot of effort for the doctor, and make a lot of time available for quality patient-doctor interaction. The focus of this paper is on extractive summarization.Method: Due to its vast pre-training, BERT (Bidirectional Encoder Representations from Transformers) is one of the most knowledgeable NLP (Natural Language Processing) models currently available- making it one of the best choices for a task like summarization. BERTSUM is the BERT version fine-tuned for summarization, BERTSUMEXT being the extractive summarization variant. The BERTSUMEXT architecture has previously been used to create a model that has been extensively pre-trained on the CNN/DailyMail dataset of news articles and corresponding summaries. It was noticed through testing that this pre-trained version of BERTSUMEXT does not perform very well on clinical reports and therefore needs to be improved to be employed in a clinical report summarization system. The method adopted here is to further train the BERTSUMEXT model using different training strategies on a clinical report summarization dataset and assess the performance improvement. The idea is to expand BERTSUMEXT's knowledge to give it a `medical edge' that it lacks.Results: The training strategy that modifies the parameter values of the extractive summarization layers of the BERTSUMEXT architecture shows a clear improvement on all nine parameters of the ROUGE (Recall Oriented Understudy for Gisting Evalution) automatic evaluation metric and the human evaluation paradigm. The ROUGE metric evaluates summary quality by measuring the overlap between the reference gold summary and the candidate summary generated by the model. The Human Evaluation Paradigm is a method where we obtain a professional doctor's opinion on the summary quality produced by the model.
Callisto: Entropy-based Test Generation and Data Quality Assessment for Machine Learning Systems

Machine Learning (ML) has seen massive progress in the last decade and as a result, there is a pressing need for validating ML-based systems. To this end, we propose, design and evaluate CALLISTO- a novel test generation and data quality assessment framework. To the best of our knowledge, CALLISTO is the first black box framework to leverage the uncertainty in the prediction and systematically generate new test cases for ML classifiers. Our evaluation of CALLISTO on four real world data sets reveals thousands of errors. We also show that leveraging the uncertainty in prediction can increase the number of erroneous test cases up to a factor of 20, as compared to when no such knowledge is used for testing.CALLISTO has the capability to detect low quality data in the datasets that may contain mislabelled data. We conduct and present an extensive user study to validate the results of CALLISTO on identifying low quality data from four state-of-the-art real world datasets.
Data Augmentation Using Gaussian Mixture Model on CSV Files

One of the biggest challenges in training supervised models is the lack of amount of labeled data for training the model and facing overfitting and underfitting problems. One of the solutions for solving this problem is data augmentation. There have been many developments in data augmentation of the image files, especially in medical image type datasets, by doing some changes on the original file such as Random cropping, Filliping, Rotating, and so on, in order to make a new sample file. Or use Deep Learning models to generate similar samples like Generative Adversarial Networks, Convolutional Neural Networks and so on. However, in numerical dataset, there have not been enough advances. In this paper, we are proposing to use the Gaussian Mixture Models (GMMs) to augment more data very similar to the original Numerical dataset. The results demonstrated that the Mean Absolute Error decreases meaning that the regression model became more accurate.
Unsupervised Feature Learning by Cross-Level Discrimination between Instances and Groups

Unsupervised feature learning has made great strides with invariant mapping and instance-level discrimination, as benchmarked by classification on common datasets. However, these datasets are curated to be distinctive and class-balanced, whereas naturally collected data could be highly correlated within the class (with repeats at the extreme) and long-tail distributed across classes. The natural grouping of instances conflicts with the fundamental assumption of instance-level discrimination. Contrastive feature learning is thus unstable without grouping, whereas grouping without contrastive feature learning is easily trapped into degeneracy. We propose to integrate grouping into instance-level discrimination, not by imposing group-level discrimination, but by imposing cross-level discrimination between instances and groups. Our key insight is that grouping results from not just attraction, but also repulsion. While invariant mapping is achieved by local attraction between augmented instances, instance similarity emerges from long-range repulsion against common instance groups. To further avoid the clash between grouping and discrimination objectives, we also impose them on separate features derived from the common feature. Our extensive experimentation demonstrates not only significant gain on datasets with high correlation and long-tail distributions, but also leading performance on multiple self-supervision and semi-supervision benchmarks, bringing unsupervised feature learning closer to real data applications.
Dynamic Risk-Aware Patch Scheduling

Every month, many new software vulnerabilities are discovered and published which will pose security risks to power grid systems if they are exploited by attackers. Thus the vulnerabilities must be patched in a timely manner to reduce the chance of being exploited. However, not all vulnerabilities can be patched quickly due to limited security resources at many electric utility companies. This paper studies dynamic risk-aware patch scheduling to determine the order of patching vulnerabilities and minimize the security risk brought by vulnerabilities. We first predict the dynamic probability of exploit over time for each vulnerability and define a metric to compute the vulnerability's dynamic risk based on the predicted probability. We then formulate two patch scheduling approaches. Evaluations on real datasets show high accuracy in predicting the dynamic probability of exploit and high effectiveness of our solutions in risk reduction compared with other scheduling methods.
Adaptive cognitive intelligence in analyzing employee feedback using LSTM

Cognitive computing is the mirroring of human brain and this is made possible by using natural language processing, pattern recognition and data mining. By mirroring the human brain (Cognitive computing system), helps to solve some of the complicated problems without much of human supervision. In the fast-changing world, the major challenge every organization facing is difficulty in retaining its employees. Employees may leave an organization due to low salary, overwork, lack of opportunities and recognition, work culture, work-life imbalance etc. Better ways to retain employees is to understand their requirements and fulfill them. The proposed employee feedback sentiment analysis system collects the employee feedback reviews from open forums and perform sentiment analysis using Recurrent Neural Network – Long Short-term Memory (RNN-LSTM) algorithm. On performing Sentiment analysis, employee review comments are classified as Positive or Negative. A report is generated and sent to the HR of the organization as webapp or mobile app. The report has total number of positive and negative comments and positive and negative counts with respect to salary, work pressure etc. With the report, the organization can arrive at identifying social sentiments of their brand and may take corrective actions to retain employees which benefits both organization and employees. This paper also captures the performance of various models in training and predicting the employee feedback dataset and the models evaluated are Logistic Regression, Support Vector Machine, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, Decision Tree Classifier and Gaussian Naïve Bayes. The classification report and accuracy of each model is captured. The dataset size was gradually increased from 200 to 1000 and accuracy was predicted for each model. It was identified that the accuracy of machine learning algorithms was ranging between 66% to 85%. On training RNN-LSTM algorithm with dataset of size 30 k, the accuracy was 88%. It was identified that Deep learning algorithm RNN-LSTM performs better with huge dataset. Increasing dataset size still increase the performance of RNN-LSTM algorithm in training and prediction. Thus, the objective function of the proposed model to perform sentiment analysis on employee feedback review comments is achieved successfully.
Rounding in the Rings

In this work, we conduct a comprehensive study on establishing hardness reductions for (Module) Learning with Rounding over rings (RLWR). Towards this, we present an algebraic framework of LWR, inspired by a recent work of Peikert and Pepin (TCC ’19). Then we show a search-to-decision reduction for Ring-LWR, generalizing a result in the plain LWR setting by Bogdanov et al. (TCC ’15). Finally, we show a reduction from Ring-LWE to Module Ring-LWR (even for leaky secrets), generalizing the plain LWE to LWR reduction by Alwen et al. (Crypto ’13). One of our central techniques is a new ring leftover hash lemma, which might be of independent interests.
A quantum circuit simulator and its applications on Sunway TaihuLight supercomputer

Classical simulation of quantum computation is vital for verifying quantum devices and assessing quantum algorithms. We present a new quantum circuit simulator developed on the Sunway TaihuLight supercomputer. Compared with other simulators, the present one is distinguished in two aspects. First, our simulator is more versatile. The simulator consists of three mutually independent parts to compute the full, partial and single amplitudes of a quantum state with different methods. It has the function of emulating the effect of noise and support more kinds of quantum operations. Second, our simulator is of high efficiency. The simulator is designed in a two-level parallel structure to be implemented efficiently on the distributed many-core Sunway TaihuLight supercomputer. Random quantum circuits can be simulated with 40, 75 and 200 qubits on the full, partial and single amplitude, respectively. As illustrative applications of the simulator, we present a quantum fast Poisson solver and an algorithm for quantum arithmetic of evaluating transcendental functions. Our simulator is expected to have broader applications in developing quantum algorithms in various fields.
Asymmetrical interference between item and order information in short-term memory.

To recall a list of items just after the end of the presentation, participants must encode both the items and the order in which they were presented. Despite a long history of studying item and order information, little is known regarding the relation between them. Here, we examined this issue with a novel task in which participants saw two 4- or 6-item lists on each trial, along with specific instructions for each list to be encoded for subsequent item information retrieval or order reconstruction. In Experiments 1, 2, and 5, words were used for both item and order lists, whereas in Experiments 3 and 4, words were used for one list and characters for the other. An item recognition task was used in Experiments 1-4, and item reconstruction from a fragment was used in Experiment 5. The general finding was that order retention was hindered when both lists required order reconstruction compared to when one list required item information only. In certain circumstances, retention of items in the first list was impaired when the second list also required item retention. We address the pattern of results with a new theoretical account in which overwriting occurs for similar materials and in which there is a capacity limit specific to order information. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
A Middleware for Polyglot Persistence and Data Portability of Big Data PaaS Cloud Applications

Vendor lock-in can occur at any layer of the cloud stack-Infrastructure, Platform, and Software-as-a-service. This paper covers the vendor lock-in issue at Platform as a Service (PaaS) level where applications can be crea... | Find, read and cite all the research you need on Tech Science Press
Finite element simulation of physical systems in augmented reality

We present a system for immersive and interactive physics simulation in augmented reality, implemented as an application on the Microsoft HoloLens. The system allows a user to simulate physical phenomena in real-world geometries by scanning the surroundings and automatically creating a finite element discretization (mesh). The system also provides a user interface based on voice commands for specification of boundary conditions and source terms, and for immersive visualization of simulation results. All computations are carried out on the HoloLens. The current implementation, named HoloFEM, demonstrates the application of augmented reality simulation for the time-dependent advection–diffusion equation. In the current work, we consider a simplified model problem with simplified parameters and data. The model is however still conceptually applicable to the simulation of indoor climate (temperature and air quality), which has implications for interactive HVAC (heating, ventilation, and air conditioning) simulation.
Attackability Characterization of Adversarial Evasion Attack on Discrete Data

Evasion attack on discrete data is a challenging, while practically interesting research topic. It is intrinsically an NP-hard combinatorial optimization problem. Characterizing the conditions guaranteeing the solvability of an evasion attack task thus becomes the key to understand the adversarial threat. Our study is inspired by the weak submodularity theory. We characterize the attackability of a targeted classifier on discrete data in evasion attack by bridging the attackability measurement and the regularity of the targeted classifier. Based on our attackability analysis, we propose a computationally efficient orthogonal matching pursuit-guided attack method for evasion attack on discrete data. It provides provably computational efficiency and attack performances. Substantial experimental results on real-world datasets validate the proposed attackability conditions and the effectiveness of the proposed attack method.