Reviewerly/exHarmony huggingface dataset:
Experimental test of search range in quantum annealing

We construct an Ising Hamiltonian with an engineered energy landscape such that it has a local energy minimum which is near the true global minimum solution and further away from a false minimum. Using a technique established in previous experiments, we design our experiment such that (at least on timescales relevant to our study) the false minimum is reached preferentially in forward annealing due to high levels of quantum fluctuations. This allows us to demonstrate the key principle of reverse annealing, that the solution space can be searched locally, preferentially finding nearby solutions, even in the presence of a false minimum. The techniques used here are distinct from previously used experimental techniques and allow us to probe the fundamental search range of the device in an alternative way. We perform these experiments on two flux qubit quantum annealers, one with higher noise levels than the other. We find evidence that the lower noise device is more likely to find the more distant energy minimum (the false minimum in this case), suggesting that reducing noise fundamentally increases the range over which flux qubit quantum annealers are able to search. Our work explains why reducing the noise leads to improved performance on these quantum annealers. This supports the idea that these devices may be able to search over broad regions of the solution space quickly, one of the core reasons why quantum annealers are viewed as a potential avenue for a quantum computational advantage.
Bidirectional GRU for Targeted Aspect-Based Sentiment Analysis Based on Character-Enhanced Token-Embedding and Multi-Level Attention

The user's feedback on healthcare services is usually based on ratings from post-service questionnaires.However, in order to get a clear view of the user's perspective, online text reviews need to be analyzed.We combined targeted and aspect-based sentiment analysis by multi-level attention to get a specific user sentiment on a target of an aspect.The multi-level attention consists of Target-level and Sentence-level attention.Our proposed framework is based on Bidirectional Gated Recurrent Unit.Bi-GRU is commonly known to have comparable results compared to LSTM while having lesser computational complexity.We also utilized Bidirectional LSTM based Character-Enhanced Token-Embedding to handle out of vocabulary words and misspelling to avoid error in detecting sentiment.We created a dataset of online healthcare reviews from 2018-2020, targeting the name of the hospital or department, with ten aspects: cleanliness, cost, doctor, food, nurse, parking, receptionist and billing, safety, test and examination, and waiting time.To improve the results of our proposed method, we calculated polarity weight to handle imbalanced aspects in the dataset.We classified these reviews into three polarities, which are positive, negative, and neutral.Based on our experiments, we achieved the best F1-Score of 88%.
INVESTIGATING THE E-LEARNING READINESS OF GHANAIAN PARENTS DURING COVID-19

&lt;p&gt;In recent times, the Coronavirus (COVID-19) pandemic has led to unprecedented changes in the livelihood of mankind globally bringing most socio-economic activities to a standstill. The Education sector is not exempted. With the current closure of schools to contain the spread of the virus, this study sought to investigate the readiness of Ghanaian parents to support their children with e-learning while at home. Using a survey research method, parents of children in primary one to six responded to a questionnaire. Data collected from 113 parents were analyzed. The study used a factor analysis to analyze the factors influencing parental readiness. The findings indicated that parents with a high socio-economic status were more prepared to assist children with e-learning as compared to parents with low socio-economic status during COVID-19. It is being recommended that schools invest more resources in conducting e-learning training sessions for parents. The study contributes to ongoing discussions on COVID-19 and Education.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0620/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Hybrid-Attention Network for RGB-D Salient Object Detection

Depth information has been widely used to improve RGB-D salient object detection by extracting attention maps to determine the position information of objects in an image. However, non-salient objects may be close to the depth sensor and present high pixel intensities in the depth maps. This situation in depth maps inevitably leads to erroneously emphasize non-salient areas and may have a negative impact on the saliency results. To mitigate this problem, we propose a hybrid attention neural network that fuses middle- and high-level RGB features with depth features to generate a hybrid attention map to remove background information. The proposed network extracts multilevel features from RGB images using the Res2Net architecture and then integrates high-level features from depth maps using the Inception-v4-ResNet2 architecture. The mixed high-level RGB features and depth features generate the hybrid attention map, which is then multiplied to the low-level RGB features. After decoding by several convolutions and upsampling, we obtain the final saliency prediction, achieving state-of-the-art performance on the NJUD and NLPR datasets. Moreover, the proposed network has good generalization ability compared with other methods. An ablation study demonstrates that the proposed network effectively performs saliency prediction even when non-salient objects interfere detection. In fact, after removing the branch with high-level RGB features, the RGB attention map that guides the network for saliency prediction is lost, and all the performance measures decline. The resulting prediction map from the ablation study shows the effect of non-salient objects close to the depth sensor. This effect is not present when using the complete hybrid attention network. Therefore, RGB information can correct and supplement depth information, and the corresponding hybrid attention map is more robust than using a conventional attention map constructed only with depth information.
Plaintext: A Missing Feature for Enhancing the Power of Deep Learning in Side-Channel Analysis?

Deep learning (DL) has proven to be very effective for image recognition tasks, with a large body of research on various model architectures for object classification. Straight-forward application of DL to side-channel analysis (SCA) has already shown promising success, with experimentation on open-source variable key datasets showing that secret keys can be revealed with 100s traces even in the presence of countermeasures. This paper aims to further improve the application of DL for SCA, by enhancing the power of DL when targeting the secret key of cryptographic algorithms when protected with SCA countermeasures. We propose a new model, CNN-based model with Plaintext feature extension (CNNP) together with multiple convolutional filter kernel sizes and structures with deeper and narrower neural networks, which has empirically proven its effectiveness by outperforming reference profiling attack methods such as template attacks (TAs), convolutional neural networks (CNNs) and multilayer perceptron (MLP) models. Our model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 40 attack traces in comparison with order of 100s traces required by straightforward machine learning (ML) application. During the profiling stage an attacker needs no additional knowledge on the implementation, such as the masking scheme or random mask values, only the ability to record the power consumption or electromagnetic field traces, plaintext/ciphertext and the key. Additionally, no heuristic pre-processing is required in order to break the high-order masking countermeasures of the target implementation.
Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification

Real data collected from different applications that have additional topological structures and connection information are amenable to be represented as a weighted graph. Considering the node labeling problem, Graph Neural Networks (GNNs) is a powerful tool, which can mimic experts’ decision on node labeling. GNNs combine node features, connection patterns, and graph structure by using a neural network to embed node information and pass it through edges in the graph. We want to identify the patterns in the input data used by the GNN model to make a decision and examine if the model works as we desire. However, due to the complex data representation and non-linear transformations, explaining decisions made by GNNs is challenging. In this work, we propose new graph features’ explanation methods to identify the informative components and important node features. Besides, we propose a pipeline to identify the critical factors used for node classification. We use four datasets (two synthetic and two real) to validate our methods. Our results demonstrate that our explanation approach can mimic data patterns used for node classification by human interpretation and disentangle different features in the graphs. Furthermore, our explanation methods can be used for understanding data, debugging GNN models, and examine model decisions.
Investigation on Blockchain Technology for Web Service Composition

The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value. Blockchain technology makes breakthroughs in business intelligence in many areas such as banking sector, finance, judiciary, commerce, and information technology. Web service compositions have a revolutionary impact on business intelligence by enabling loose coupling, data consolidation from diverse sources, consolidation of information under a single roof, easing ad-hoc querying and reporting. The objective of current work is to investigate the applicability of blockchain for the semantic web service composition process. The paper focuses on design of conceptual architecture and the algorithm for QoS-aware semantic web service composition (SWSC) using blockchain.
UENet: A Novel Generative Adversarial Network for Angiography Image Segmentation

Convolutional neural networks (CNNs) have been widely used in medical image segmentation. Vessel segmentation in coronary angiography remains a challenging task. It is a great challenge to extract fine features of coronary artery for segmentation due to the poor opacification, numerous overlap of different artery segments and high similarity between artery segments and soft tissues in an angiography image, which results in a sub-optimal segmentation performance. In this paper, we propose an adapted generative adversarial networks (GANs) to complete the conversion from coronary angiography image to semantic segmentation image. We implemented an adapted U-net as the generator, and a novel 3-layer pyramid structure as the discriminator. During the training period, multi-scale inputs were fed into the discriminator to optimize the objective functions, producing high-definition segmentation results. Due to the generative adversarial mechanism, both generator and discriminator can extract fine feature of coronary artery. Our method effectively solves the problems of segmentation discontinuity and intra-class inconsistencies. Experiment shows that our method improves the segmentation accuracy effectively comparing to other vessel segmentation methods.
Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection

The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification.
Towards practical security of continuous-variable quantum key distribution

Rigorous mathematical proofs of the security of continuous-variable quantum key distribution (CV QKD) have been obtained recently. Unfortunately, these security proofs rely on assumptions that are hardly met in experimental practice. Here I investigate these issues in detail, and discuss experimentally-friendly workarounds to assess the security of CV QKD. The aim of this paper is to show that there are hidden and unsolved issues and to indicate possible partial solutions. To provide a complete and rigorous mathematical security proof is out of the scope of this contribution.
Handwritten Digit Recognition: Hyperparameters-Based Analysis

Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance. In general, given a particular dataset, a data scientist cannot do much to improve the efficiency of the model. However, by tuning certain hyperparameters, the model’s accuracy and time of execution can be improved. Hence, it is of utmost importance to select the optimal values of hyperparameters. Choosing the optimal values of hyperparameters requires experience and mastery of the machine learning paradigm. In this paper, neural network-based architectures are tested based on altering the values of hyperparameters for handwritten-based digit recognition. Various neural network-based models are used to analyze different aspects of the same, primarily accuracy based on hyperparameter values. The extensive experimentation setup in this article should, therefore, provide the most accurate and time-efficient solution models. Such an evaluation will help in selecting the optimized values of hyperparameters for similar tasks.
Chaotic Image Encryption Algorithm Based on Dynamic Spiral Scrambling Transform and Deoxyribonucleic Acid Encoding Operation

This article proposes a dynamic spiral scrambling algorithm, which is combined with the random pixel value filling operation, Deoxyribonucleic Acid (DNA) operation and used in image encryption. The random pixel value filling operation is to generate some random values and fill them around the plain-image. These values can influence all the pixels after the confusion and diffusion operations. The proposed dynamic spiral scrambling algorithm dynamically combines the chaotic sequences with the plaintext image, and scrambles the pixel values of the plaintext image, this operation makes the scrambling algorithm not fixed, and position changes are highly sensitive to the chaotic sequences, any slight change will get a completely different result. Then, this article combines Deoxyribonucleic Acid (DNA) coding and manipulation to further confuse pixel values. Finally, the average entropy is 7.9974, the average NPCR is 99.6067 and the average UACI is 33.4544, experimental results and various security analyses show that the algorithm has good encryption effects and can resist common attacks such as select plaintext attacks, cropping, and noise attacks.
Probabilistic convergence analysis of the stochastic particle swarm optimization model without the stagnation assumption

As a stochastic optimization algorithm, it is more reasonable for particle swarm optimization (PSO) to study the probabilistic convergence. In this study, we analyze its convergence with probability 1 using the theory of probabilistic metric space. Firstly, we assume that the personal best of the particle and global best of the particle swarm are updated during the run; however, we do not assume that the personal best of the particle and global best of the particle swarm must be independent of the position of the particle. Such an assumption is more pragmatic and could be implemented in all PSO variants. Then, we develop a stochastic recurrence relation of the state of a particle under this assumption. Finally, we derive a sufficient condition that ensures the stochastic PSO algorithm is τ-convergent with probability 1. In addition, we analyze the impact of the parameters in the unstable range on the individual iterations. Although these parameters could not guarantee the long-term convergence, their impact significantly influences the exploration ability of PSO; thus, it is crucial to understand them. Based on this analysis, we propose a novel strategy for balancing the exploitation and exploration abilities of the PSO algorithm.
Intelligent Food Recommendation System Using Machine Learning

The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same product. Each person has his or her own eating patterns, based on the preferences and dislikes of the user, indicating that personalized diet is important to sustain the success and health of the user. The proposed recommendation method uses a deep learning algorithm and a genetic algorithm to provide the best possible advice.
Density Peaks Clustering Algorithm Based on Weighted <i>k</i>-Nearest Neighbors and Geodesic Distance

Density peaks clustering (DPC) is a density-based clustering algorithm with excellent clustering performance including accuracy, automatically detecting the number of clusters, and identifying center points.However, DPC has some shortcomings to be addressed before it can be widely applied.For example, sensitive predefined parameter is not suitable for manifold datasets, and decision graph easily causes the wrong center points.To address these issues, a new DPC algorithm based on weighted k-nearest neighbors and geodesic distance (DPC-WKNN-GD) is proposed in this article to improve the clustering performance for manifold and non-manifold datasets.The DPC-WKNN-GD introduces the weighted k-nearest neighbors based on Euclidean distance to optimize the local density ρ.Inspired by DPC-GD, the DPC-WKNN-GD redefines the distance δ based on geodesic distance.The experimental results on artificial and real-world datasets, including image datasets, show that the DPC-WKNN-GD outperforms the state-of-the-art comparison algorithms on both manifold and non-manifold datasets.In addition, the DPC-WKNN-GD completely overcomes the DPC-GD defect, in which the decision graph displays misleading center points.
Effective Complex Airport Object Detection in Remote Sensing Images Based on Improved End-to-End Convolutional Neural Network

Airport objects are hotspots in the field of image object detection because of their specific features and value for applications.In this study, we developed a complex object detection method based on improved Faster R-CNN to achieve higher detection precision to detect seven types of remote sensing image objects in airport areas under complex conditions such as different scales, different visual angles, and different backgrounds.When building the network, we used deeper basic networks and feature fusion components to extract more robust features.At the same time, we had also modified the selection of positive and negative samples to improve sample imbalance.The main improvements in the algorithm concern the anchor size generation rule, and the addition of an a priori judgment network for the network.The effectiveness of the improved algorithm was verified in experiments.Compared with the original Faster R-CNN, the improved network brings a 12.7% increase in mAP, at the detection time of 0.307s.Finally, the model with trained weights was used to test the detection of the seven types of objects in airport areas on different data sets, and comparisons were conducted with other algorithms.The experimental results showed that the method improved the average detection accuracy and had a good performance in remote sensing airport object detection tasks.
Exploring Level Blending Across Platformers via Paths and Affordances

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.
Human Aware Task Planning Using Verbal Communication Feasibility and Costs

This paper addresses the problem of taking into account the feasibility and cost of verbal communication actions at the task planning level in the context of human-robot interaction. By determining the human and robot context in which the referring expressions to entities can be effectively achieved and at which cost, our planner can 1) prevent potential deadlocked situations where it is not possible to produce a feasible communication act 2) find plans reducing the overall communication complexity 3) evaluate different communications strategies and select the most suitable one. Our approach is based on the extension of a multi agent hierarchical task planner capable of maintaining, throughout the planning process, an estimate of the human partner knowledge about the environment using a semantic representation. A Referring Expression Generation (REG) can then be executed for each communication action allowing the task planner to be informed about its feasibility and cost during the plan elaboration process. Three scenarios are presented to validate the method, where a robot drives the human actions to perform a cube arrangement task.