Reviewerly/exHarmony huggingface dataset:
Medical Image De-Speckling Using Fusion of Diffusion-Based Filters And CNN

Ultrasound images, although significantly low cost when compared to the likes of MRI scans and Biopsy, these images have the disadvantage of being laden with Speckle noise, which is a multiplicative noise, making it less accurate and competitive to the level of details provided on MRI scans. Improving the quality of ultrasound pictures so that they may be used as a viable alternative to MRI scans entails using a combination of different denoising filters to remove noise, based on their individual qualities. Following that, after the initial denoising, CNN was used to perform learning-based denoising to maximize the removal of speckle noise in the images. To simulate a real-life scenario, 750 ultrasound images were contaminated with speckle noise. After a comparative analysis of various methods, this paper's proposed model of combining filters and CNN model achieves a PSNR of approx. 52.25 dB on denoising a speckle- corrupted ultrasound. By combining the best of both worlds of Hybrid filters and CNN denoising, the proposed model achieves solid efficiencies for reducing speckle noise in Ultrasound images, making it a strong alternative.
Controlled beam splitter gate transparent to dominant ancilla errors

Abstract In hybrid circuit quantum electrodynamics (QED) architectures containing both ancilla qubits and bosonic modes, a controlled beam splitter (cBS) gate is a powerful resource. It can be used to create (up to a controlled-parity operation) an ancilla-controlled SWAP gate acting on two bosonic modes. This is the essential element required to execute the ‘swap test’ for purity, prepare quantum non-Gaussian entanglement and directly measure nonlinear functionals of quantum states. It also constitutes an important gate for hybrid discrete/continuous-variable quantum computation. We propose a new realization of a hybrid cSWAP utilizing ‘Kerr-cat’ qubits—anharmonic oscillators subject to strong two-photon driving. The Kerr-cat is used to generate a controlled-phase beam splitter operation. When combined with an ordinary beam splitter one obtains a cBS and from this a cSWAP. The strongly biased error channel for the Kerr-cat has phase flips which dominate over bit flips. This yields important benefits for the cSWAP gate which becomes non-destructive and transparent to the dominate error. Our proposal is straightforward to implement and, based on currently existing experimental parameters, should achieve cBS gates with high fidelities comparable to current ordinary beam-splitter operations available in circuit QED.
A Component Selection Framework of Cohesion and Coupling Metrics

Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development.Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity.The complexity measurement of cohesion and coupling component to analyze the relationship between the component module.In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository.It measures the interface density modules of coupling and cohesion in a modular software system.This cohesion measurement has been taken into two parameters for analyzing the result of complexity, with the help of low cohesion and high cohesion.In coupling measures between the component of inside parameters and outside parameters.The final process of coupling and cohesion, the measured values were used for the average calculation of components parameter.This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository.The better result is observed for high cohesion and low coupling in component-based software engineering.
Comparative Analysis and Development of Voice-based Chatbot System for Differently-abled

Abstract One technology that improved humans’ lifestyle is a Chatbot System, an AI-based system that conducts people’s conversations. Chatbots are used in a wide range of applications-assistive technologies, customer service, home automation, education, medical bots, entertainment, etc. Such technology could be beneficial to differently-abled for teaching, assistance, etc. But when it comes to people with a slight speech disorder or autism, available chatbot systems aren’t very effective. This paper investigates the existing speech recognition APIs based on WER and CER and tries to improve results by adding a personalized layer for the user. This chatbot system can be embedded with existing technologies-domestic robots, smart mirrors, home automation, medical applications, etc. It will open these technologies to a wide range of audiences. The proposed Chatbot will use the existing speech recognition API and improve the results based on the above performance measures.
A Missing Data Reconstruction Method Using an Accelerated Least-Squares Approximation with Randomized SVD

An accelerated least-squares approach is introduced in this work by incorporating a greedy point selection method with randomized singular value decomposition (rSVD) to reduce the computational complexity of missing data reconstruction. The rSVD is used to speed up the computation of a low-dimensional basis that is required for the least-squares projection by employing randomness to generate a small matrix instead of a large matrix from high-dimensional data. A greedy point selection algorithm, based on the discrete empirical interpolation method, is then used to speed up the reconstruction process in the least-squares approximation. The accuracy and computational time reduction of the proposed method are demonstrated through three numerical experiments. The first two experiments consider standard testing images with missing pixels uniformly distributed on them, and the last numerical experiment considers a sequence of many incomplete two-dimensional miscible flow images. The proposed method is shown to accelerate the reconstruction process while maintaining roughly the same order of accuracy when compared to the standard least-squares approach.
Providing A Topic-Based LSTM Model to Re-Rank Search Results

The ranking of search results is directly affected by user click preferences and is an effective way to improve the quality of the result of search engines. To tailor the ranking, it is necessary to use the submitted query information by the user, such as click history, user profile, the previous queries history, or query click entropy. There are ranking methods that explore the issue of underlying information using traditional machine learning algorithms. Recently LSTM (Long Short-Term Memory) based models are investigated in this field. As traditional ML models require considering short-term and long-term preferences in predicting, the LSTM based models can improve prediction efficiency by considering both short and long-term. This paper proposes a topic-based LSTM model to re-rank search results on a submitted input quey using the previous queries sequence and user click history. In this model, we use the topic distribution of user documents to the LSTM model. We compare the model with topic-based ranking models with data from an AOL search engine and Session TREC 2013,2014 to show its performance. The result reveals significant improvement in the Topic-based LSTM model using topics in the Mean Reciprocal Rank by 13\% compared to the baseline topic-based models.
Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team

Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an a priori known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of a priori known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of a priori known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates.
STEB: A secure service trading ecosystem based on blockchain

A service can be an intangible commodity in which no physical goods are transferred from the seller to the buyer. However, traditional trading platforms have many limitations in trading services due to dishonest buyers and brokers. In this paper, we propose a service trading ecosystem based on blockchain, named STEB, which combines blockchain, smart contract, encryption, and digital authentication techniques for service trading. In addition, a dual-chain architecture, which contains two types of blockchains, namely TraChain and SerChain, and a hierarchical encryption scheme of the data on the chain, are proposed to ensure the integrity of transaction data and fine-grained privacy protection of users. Furthermore, we describe a new set of smart contracts to ensure safe transactions for the entire service trading. Security analysis and simulation results confirm that the proposed STEB can achieve more efficient contract execution and enhance service transaction privacy.
Classification of Hotel Reviews using Machine Learning Techniques

The discussion and critique of products and services occur across various mediums, including the realm of social media. Reviews from previous customers offer a wealth of information about products, allowing shoppers to make pre-purchase product evaluations. Positive reviews are beneficial for a business because they can improve its reputation, attract new customers, and increase sales and profitability. Negative reviews impact businesses adversely but are just as important as a five-star rating. They provide opportunities to correct the issues to show potential clients that their opinions are cared for. Online reviews for hotels can be found on various websites. These opinions shared on social media platforms can shape the public's perception of the hotel. By using sentiment analysis and machine learning algorithms on existing hotel reviews, we create a classification system that can identify what the customer post on a hotel's website and thinks about the hotel. Logistic regression and Support Vector Machines (SVMs) were chosen as the classification algorithms. The classifiers are analyzed based on three criteria: accuracy, precision, and recall. The classifier with the best performance metrics is labelled the algorithm most suited for classifying hotel reviews.
Gene Fingerprinting: Cracking Encrypted Tunnel with Zero-Shot Learning

Website Fingerprinting (WF) enables a passive attacker to identify which website a user is visiting over an encrypted tunnel. Current WF attacks have two strong assumptions: (i) specific tunnel, i.e., the attacker can train on traffic samples collected in a simulated tunnel with the same tunnel settings as the user, and (ii) pseudo-open-world, where the attacker has access to training samples of unmonitored sites and treats them as a separate class. These assumptions, while experimentally feasible, render WF attacks less usable in practice. In this paper, we present Gene Fingerprinting (GF), a new WF attack that achieves cross-tunnel transferability by generating fingerprints that reflect the intrinsic profile of a website. The attack leverages Zero-shot Learning — a machine learning technique not requiring training samples to identify a given class — to reduce the effort to collect data from different tunnels and achieve a real open-world. We demonstrate the attack performance using three popular tunneling tools: OpenSSH, Shadowsocks, and OpenVPN. The GF attack attains over 94% accuracy on each tunnel, far better than existing CUMUL, DF, and DDTW attacks. In the more realistic open-world scenario, the attack still obtains 88% TPR and 9% FPR, outperforming the state-of-the-art attacks. These results highlight the danger of our attack in various scenarios where gathering and training on a tunnel-specific dataset would be impractical.
Feature autoencoder for detecting adversarial examples

Deep neural networks (DNNs) have gained widespread adoption in computer vision. Unfortunately, state-of-the-art DNNs are vulnerable to adversarial example (AE) attacks, where an adversary introduces imperceptible perturbations to a test example for defrauding DNNs. The obstacles have urged intensive research on improving the DNN robustness via adversarial training, that is, the clean data set is blended with adversarial examples to carry out training. However, the adversarial example attack technologies are open-ended, and the adversarial training is insufficient to focus on improving robustness performance. To circumvent this limitation, we mitigate adversarial example attacks from another perspective, which aims at detecting adversarial examples. Feature autoencoder detector (FADetector), a novel defense framework that exploits feature knowledge is proposed. One of the hallmarks of FADetector is to not involve adversarial examples to train the detector. Our extensive evaluation on MNIST and CIFAR-10 data sets demonstrates that our defense outperforms the conventional autoencoder detectors in terms of detection accuracy.
Video-zilla: An Indexing Layer for Large-Scale Video Analytics

Pervasive deployment of surveillance cameras today poses enormous scalability challenges to video analytics systems operating over many camera feeds. Currently, there are few indexing tools to organize video feeds beyond what is provided by a standard file system. Recent video analytic systems implement application-specific frame profiling and sampling techniques to reduce the number of raw videos processed, leveraging frame-level redundancy or manually labeled spatial-temporal correlation between cameras.
One-Variable Attack on the Industrial Fault Classification System and Its Defense

Recently developed fault classification methods for industrial processes are mainly data-driven. Notably, models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns. However, these data-driven models are vulnerable to adversarial attacks; thus, small perturbations on the samples can cause the models to provide incorrect fault predictions. Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples. This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system: Only one variable can be perturbed to craft adversarial samples. Moreover, to hide the adversarial samples in the visualization space, a Jacobian matrix is used to guide the perturbed variable selection, making the adversarial samples in the dimensional reduction space invisible to the human eye. Using the one-variable attack (OVA) method, we explore the vulnerability of industrial variables and fault types, which can help understand the geometric characteristics of fault classification systems. Based on the attack method, a corresponding adversarial training defense method is also proposed, which efficiently defends against an OVA and improves the prediction accuracy of the classifiers. In experiments, the proposed method was tested on two datasets from the Tennessee–Eastman process (TEP) and steel plates (SP). We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets. For industrial fault classification systems, the attack success rate of our method is close to (on TEP) or even higher than (on SP) the current most effective first-order white-box attack method, which requires perturbation of all variables.
Multi-UUV Coordinated Path Planning with Collision Avoidance (CPP/CA) Based on Combination of Improved APF and A<sup>⁎</sup>

Aiming at the path planning problem of cooperative underwater unmanned vehicles (UUVs) in the obstacle-constrained environment, this paper proposes a new obstacle-avoidance path planning algorithm. This algorithm combines artificial potential field (APF) method and A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⁎</sup> algorithm, which can realize multi-UUV coordinated path planning with collision avoidance (CPP/CA). Different from the current APF, this algorithm increases the repulsion between UUVs on the basis of optimizing the repulsive force of obstacles, sets temporary virtual target points, and uses the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⁎</sup> algorithm to make UUVs pass through obstacles smoothly and move to the target point. This algorithm not only solves the issue of UUV falling into a local minimum, but also considers UUV companions to perform the same task, so that each UUV individually changes its path to avoid collisions with other UUVs and obstacles. Experimental simulation results show that the proposed algorithm has better obstacle avoidance performance and the ability to track targets, which can greatly improve the success rate of path planning.
Construction and Application of Talent Evaluation Model Based on Nonlinear Hierarchical Optimization Neural Network

Talent assessment attracts the attention of researchers because of its important influence on business management issues. The traditional talent evaluation model has high sample selection cost and large calculation volume ratio, so it has become an urgent problem to be solved. Based on the nonlinear hierarchical optimization neural network, this article improves the corresponding talent evaluation index system and builds a talent evaluation model on the basis of demonstrating the feasibility of using a nonlinear hierarchical optimization neural network for talent evaluation. This article conducts an empirical analysis on the talent evaluation model of the talent evaluation team members and designs a prototype system of the talent evaluation index system based on the nonlinear hierarchical optimization neural network. In the simulation process, MATLAB software is used to complete the program realization of the model, and the accuracy and feasibility of the model are verified by combining the case. Through the evaluation and research of the implementation personnel of the case enterprise, the enterprise talent evaluation based on the nonlinear hierarchical optimization neural network is demonstrated. The empirical results show that it is feasible and accurate to use the nonlinear hierarchical optimization neural network to evaluate the talent evaluation team. The comprehensive prediction accuracy rate of T-1 year is 88.5%, and the comprehensive prediction accuracy rate of T-2 year was 83.45%, which effectively promoted the evaluation of enterprise talents.
Evaluation Model of Mathematics Teaching Quality Based on Recurrent Neural Network

This study proposed an evaluation model of mathematics teaching quality under recurrent neural network for the sake of making the evaluation model of mathematics teaching quality have good fault tolerance. This model decomposes the initial data sequence of mathematics teaching quality evaluation into high- and low-frequency sequence by wavelet analysis and reconstructs it by using phase space. After introducing the recurrent neural network model, the data is reconstructed after model training, and the data mining is carried out for the evaluation of mathematics teaching quality. In the process of constructing the evaluation model, the evaluation index system should be constructed based on three dimensions firstly, and the evaluation index of association rules should be defined, so as to realize deep dig of data and obtain the phase space distribution of data and then carry out the constraint test of parameters to evaluate the mathematics teaching quality scientifically and accurately. After verification, it is known that the average values of training error and test error of the model proposed in this paper are 3.02% and 2.61%, and the average values of absolute error and relative error are 0.58 and 3.82%. This model can retain the valid data information in the initial sequence, and the evaluation results of mathematics teaching quality are relatively ideal, which greatly improves the efficiency and level of mathematics teaching.
G-NMP: Accelerating Graph Neural Networks with DIMM-based Near-Memory Processing

Graph Neural Networks (GNNs) are of great value in numerous applications and promote the development of cognitive intelligence, due to the capability of modeling non-euclidean data structures. However, the inherent irregularity makes GNNs memory-bound, and the hybrid computing paradigm of GNNs poses significant challenges for efficient deployment on existing hardware architectures. Near-Memory Processing (NMP) is a promising solution for alleviating the memory wall problem. In this paper, we present G-NMP, a practical and efficient DIMM-based NMP solution for accelerating GNNs, which accelerates both sparse Aggregation and dense Combination computations on DIMM for the first time. We propose a novel G-NMP hardware architecture to exploit rank-level memory parallelism efficiently, and the G-ISA instructions to reduce host memory requests significantly. We conduct several data flow optimizations on the G-NMP to improve memory-compute overlap and to realize efficient matrix computation. Then we develop an adaptive data allocation strategy for diverse vector sizes to further exploit feature-level parallelism. We also propose a novel memory request scheduling method to achieve flexible and low-overhead DRAM ownership transition between host and G-NMP. Overall, G-NMP achieves consistent performance advantages across diverse GNN models and datasets, and offers 1.46 × overall performance and 1.29 × energy efficiency on average compared with the state-of-the-art work. • G-NMP exploits rank-level parallelism and leverages off-the-shelf CPU and DRAM chips. • G-ISA instruction sets reduces memory requests and alleviates C/A bandwidth pressure. • Data flow optimization improves memory-compute overlap and reduces memory accesses. • Adaptive data allocation ensures memory parallelism for diverse vector sizes. • Propose a flexible and low-overhead memory request scheduling between CPU and G-NMP.