Reviewerly/exHarmony huggingface dataset:
Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Pancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients.Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during the diagnosis and staging of PC.In addition, accurate PC staging could better help clinicians deliver the optimal therapeutic schedule for PC patients of different stages.Therefore, this study proposes a comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learning-support vector machine (EL-SVM) and computed tomography (CT) images.The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection.In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision.The EL-SVM learner was used to classify 168 CT images of normal pancreas and different stages of PC.The experimental results demonstrated that the normal pancreas (normal)-pancreatic cancer early stage (early stage) classification accuracy was 86.61%, the normal-pancreatic cancer stage III (stage III) classification accuracy was 87.04%, the normal-pancreatic cancer stage IV (stage IV) classification accuracy was 91.63%, the normal-PC classification accuracy was 87.89%, the early stage-stage III classification accuracy was 75.03%, and the early stage-stage IV classification accuracy was 81.22%, and the stage III-stage IV classification accuracy was 82.48%.Our experimental results prove that our proposed method is feasible and promising for clinical applications for the preoperative diagnosis and staging of PC via CT images.
Counterfactual explanation based on gradual construction for deep networks

To understand the black-box characteristics of deep networks, counterfactual explanation that deduces not only the important features of an input space but also how those features should be modified to classify input as a target class has gained an increasing interest. The patterns that deep networks have learned from a training dataset can be grasped by observing the feature variation among various classes. However, current approaches perform the feature modification to increase the classification probability for the target class irrespective of the internal characteristics of deep networks. This often leads to unclear explanations that deviate from real-world data distributions. To address this problem, we propose a counterfactual explanation method that exploits the statistics learned from a training dataset. Especially, we gradually construct an explanation by iterating over masking and composition steps. The masking step aims to select an important feature from the input data to be classified as a target class. Meanwhile, the composition step aims to optimize the previously selected feature by ensuring that its output score is close to the logit space of the training data that are classified as the target class. Experimental results show that our method produces human-friendly interpretations on various classification datasets and verify that such interpretations can be achieved with fewer feature modification.
Service Discovery in Mobile Ad-Hoc Environments: A Solution Space Analysis

Twitter is now one of the greatest podiums all over the world through which anyone can present his opinion regarding a particular issue.It is a social networking site.Every day, it generates almost 500 million of tweets and the total volume of tweets contains 8TB of data.This data is very much significant if examined, because we are able to draw out salient facts through opinion mining.We can keep an eye at the augmentation of a product or any important affairs or events or a novel fashion in twitter data.The prime objective of this sentiment analysis or opinion mining is to explore emotion, opinion, subjectivity and perspective from a customary text on application of a medicine for the treatment of COVID-19(Corona Virus Disease 2019).We can classify the tweets into positive and negative sentiments in twitter sentiment analysis.The expression 'Cluster' refers to an accomplished method where homogeneous substances are kept in a distinct class and thus a pack of cluster is created.We went on with an analysis and concluded that the application of clustering can hurriedly and precisely differentiate tweets on account of their sentiment scores and thus it gets weekly and firmly positive or negative tweets, if they are clustered, with outcomes of distinct dictionaries.The objective of this paper is to scrutinize discrete viewpoints of clustering sentiment analysis and thus devises a method to make connection with the tweets of the statesmen of some first world, second world and third world countries.The frame of reference in this case is the polarity and subjectivity on the application of drugs for the treatment of COVID-19.
Image Recognition Technology Based on Neural Network

Image recognition is an important part of human-computer interaction. Using deep learning algorithms to recognize and classify image has become a hot issue for scholars from all walks of life. In this paper, the traditional classification algorithm based on convolutional neural network is improved, and the feature information of the key parts of the face is used to integrate the key part features with the global features of the face image to better distinguish similar categories. Therefore, this paper designs a method to locate the key points of the face image, and optimizes the key point positioning method through multiple experiments to facilitate the extraction of the feature information of the key points. For the calculation of classification results, a multi-region test method is used. By calculating multiple regions of the image during the test, the accuracy of image recognition can be improved. The final experimental results show that the model with key point feature information has more advantages in accuracy, and the robustness of the model is improved.
Human Height Estimation by Color Deep Learning and Depth 3D Conversion

In this study, an estimation method for human height is proposed using color and depth information. Color images are used for deep learning by mask R-CNN to detect a human body and a human head separately. If color images are not available for extracting the human body region due to low light environment, then the human body region is extracted by comparing between current frame in depth video and a pre-stored background depth image. The topmost point of the human head region is extracted as the top of the head and the bottommost point of the human body region as the bottom of the foot. The depth value of the head top-point is corrected to a pixel value that has high similarity to a neighboring pixel. The position of the body bottom-point is corrected by calculating a depth gradient between vertically adjacent pixels. Two head-top and foot-bottom points are converted into 3D real-world coordinates using depth information. Two real-world coordinates estimate human height by measuring a Euclidean distance. Estimation errors for human height are corrected as the average of accumulated heights. In experiment results, we achieve that the estimated errors of human height with a standing state are 0.7% and 2.2% when the human body region is extracted by mask R-CNN and the background depth image, respectively.
Twin-Field Quantum Key Distribution with Discrete-Phase-Randomized Sources

Due to the single-photon interference at a third untrusted party, the twin-field quantum key distribution (TF-QKD) protocol and its variants can beat the well-known rate-loss bound without quantum repeaters and related experiments have recently been implemented. In general, quantum states in these schemes should be randomly switched between the code mode and the test mode. To adopt the standard decoy-state method, the phases of coherent-state sources in the test mode are assumed to be continuously randomized. However, such a crucial assumption cannot be satisfied well in experimental implementations. In this paper, to bridge the gap between theory and practice, we propose a TF-QKD variant with discrete-phase-randomized sources both in the code mode and the test mode and prove its security against collective attacks. Our simulation results indicate that, with only a small number of discrete phases, the performance of discrete-phase-randomized sources can overcome the rate-loss bound and approach that of continuous-phase-randomized sources.
LOss-Based SensiTivity rEgulaRization: Towards deep sparse neural networks

LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology. Let the sensitivity of a network parameter be the variation of the loss function with respect to the variation of the parameter. Parameters with low sensitivity, i.e. having little impact on the loss when perturbed, are shrunk and then pruned to sparsify the network. Our method allows to train a network from scratch, i.e. without preliminary learning or rewinding. Experiments on multiple architectures and datasets show competitive compression ratios with minimal computational overhead.
Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection

Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image annotation, etc. However, traditional multi-label feature selection methods employ cumulative summation strategy to design methods, which suffers from the problem of overestimation the redundancy of some candidate features. Additionally, the cumulative summation strategy will lead to the high value of the goal function when one candidate feature is completed related with one or few already-selected features, but it is almost independent from the majority of already-selected features. To address these issues, we propose a new multi-label feature selection method named Feature Redundancy Maximization (FRM), which combines the cumulative summation of conditional mutual information with the `maximum of the minimum' criterion. Additionally, FRM can be rewritten as another form of multi-label feature selection method that employs interaction information as a measure of feature redundancy that obtains an accurate score of feature redundancy as the number of already-selected features increases. Finally, extensive experiments are implemented on fourteen benchmark multi-label data sets in comparison to six state-of-the-art methods. The experimental results demonstrate the superiority of the proposed method.
YOLOv3: Face Detection in Complex Environments

A B S T R A C TFace detection has been well studied for many years.However, the problem of face detection in complex environments is still being studied.In complex environments, faces is often blocked and blurred.This article proposes applying YOLOv3 to face detection problems in complex environments.First, we will re-cluster the data set in order to find the most suitable a priori box.Then we set multiple score values to make it possible to predict the results of multiple sets of images and find the optimal score value.Experimental results show that after adjustment, the model has more advantages in face detection than the original model in complex environments.The average accuracy is more than 10% higher than that of aggregate channel feature (ACF), Towstage convolutional neural network (CNN) and multi-scale Cascade CNN in face detection benchmarks WIDER FACE.
An Empirical Study of Perceived Organizational Support and Affective Commitment in the Logistics Industry

The study examines the impact of organizational rewards, procedure justice, and perceived supervisor support on perceived organizational support, and examine the impact of perceived organizational support on affective commitment to the organization in the logistic enterprises as well.Quantitative research is applied to measure relationships by regression analysis with SPSS.The research data was collected by convenient method from 180 employees who work in different departments in the logistics industry.The study results in the model 1 found that organizational rewards, procedure justice, and perceived supervisor support have a positive relationship to the perceived organizational support.The study results in the model 2 also found that perceived organizational support has strong impact on the affective commitment to the organization.The study results contribute to both management theory and management practice.For the management theory aspect, the authors suggest that perceived organizational support should be considered the key antecedent of affective commitment about which researchers should pay more attention as a concept.Based on the research results, the authors also recommend for the management practice that managers should pay attention to the implementation of rewards, procedure justice, and supervisor support to increase the perceived organizational support and affective commitment in the logistic enterprises.
Learning Document Image Features With SqueezeNet Convolutional Neural Network

The classification of various document image classes is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for training, and their very large number of weights. Previous successful attempts at learning document image features have been based on training very large CNNs. SqueezeNet is a CNN architecture that achieves accuracies comparable to other state of the art CNNs while containing up to 50 times less weights, but never before experimented on document image classification tasks. In this research we have taken a novel approach towards learning these document image features by training on a very small CNN network such as SqueezeNet. We show that an ImageNet pretrained SqueezeNet achieves an accuracy of approximately 75 percent over 10 classes on the Tobacco-3482 dataset, which is comparable to other state of the art CNN. We then visualize saliency maps of the gradient of our trained SqueezeNet's output to input, which shows that the network is able to learn meaningful features that are useful for document classification. Previous works in this field have made no emphasis on visualizing the learned document features. The importance of features such as the existence of handwritten text, document titles, text alignment and tabular structures in the extracted saliency maps, proves that the network does not overfit to redundant representations of the rather small Tobacco-3482 dataset, which contains only 3482 document images over 10 classes.
Hybrid quantum-classical algorithms for solving quantum chemistry in Hamiltonian–wave-function space

The variational quantum eigensolver (VQE) typically optimizes variational parameters in a quantum circuit to prepare eigenstates for a quantum system. Its application to many problems may involve a group of Hamiltonians, e.g., a Hamiltonian of a molecule is a function of the nuclear configurations. In this paper we incorporate derivatives of the Hamiltonian into the VQE and develop some hybrid quantum-classical algorithms, which explore both Hamiltonian and wave-function spaces for solving quantum chemistry problems more efficiently. For geometry optimization, we propose a mutual gradient-descent algorithm that updates the parameters of the Hamiltonian and wave function alternately, which can give a rapid convergence towards equilibrium structures of molecules as demonstrated in numerical simulations. Moreover, to speed up the calculation of the energy potential surface, we establish differential equations that govern how the optimized variational parameters of the wave function change with the intrinsic parameters of the Hamiltonian. Our study suggests a direction for a hybrid quantum-classical algorithm for solving quantum systems more efficiently by considering spaces of both the Hamiltonian and the wave function.
Assessment of Camouflage Effectiveness Based on Perceived Color Difference and Gradient Magnitude

We propose a new model to assess the effectiveness of camouflage in terms of perceived color difference and gradient magnitude. The “image color similarity index” (ICSI) and gradient magnitude similarity deviation (GMSD) were employed to analyze color and texture differences, respectively, between background and camouflage images. Information entropy theory was used to calculate weights for each metric, yielding an overall camouflage effectiveness metric. During the analysis process, both spatial and color perceptions of the human visual system (HVS) were considered, to mimic real-world observations. Subjective tests were used to compare our proposed method with previous methods, and our results confirmed the validity of assessing camouflage effectiveness based on perceived color difference and gradient magnitude.
EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON

Abstract. Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods.
A Mechanism to Resolve the Unauthorized Access Vulnerability Caused by Permission Delegation in Blockchain-Based Access Control

Permission delegation in access control provides the subject with a second method to obtain object permissions in addition to permission granting. It is especially applicable when the owner and manager of the object are inconsistent. With the development of the Internet of Things, there are more and more scenes where object owners and managers are inconsistent, but the research on permission delegation in access control based on blockchain is not perfect. Therefore, once implemented in these blockchain-based access control algorithms, the permission delegation tends to have an unauthorized access. Based on the analysis of the causes for the unauthorized access vulnerability, this paper proposes a token-constrained permission delegation algorithm (TCPDA), which converts the access control policy corresponding to permissions into constraints for permission use, embeds the constraints in the permission token, and forms constraints on the transfer of tokens. Only subjects that meet the constraint conditions can receive tokens, thereby solving unauthorized access vulnerability caused by permission delegation. Since not all access control models can transform strategies into constraints and integrate them into blockchain tokens, this paper also proposes a permission delegation algorithm for decision-making entities to make desirable decisions. Finally, the security analysis shows that the two proposed schemes can overcome the unauthorized access vulnerability caused by permission delegation, and the algorithm performance is analyzed through experiments.
LEARNING THROUGH SERIOUS GAMES: A DIGITAL DESIGN MUSEUM FOR EDUCATION

Abstract. This project aims to demonstrate the communicative and educational possibilities offered by ICT in the promotion and enhancement of museum heritage. To this end, the researchers have outlined a workflow that includes both a technical-engineering component and a psychological component for the development of a serious game, designed to educate users in a pleasant way and applied to a small museum institution located in an ancient building in the city of Milan: the Studio Museo Achille Castiglioni, which hosts a collection of design objects created by one of the most important designers of the 20th century. Virtual Reality was considered the most effective means to make it possible to remotely enjoy the 3D space, thanks to its ability to fully immerse the user in the game space and to physically and emotionally involve him or her through interactive elements, allowing to learn by acting, to live a first-person experience as the protagonist of the visit and not just as a spectator.
Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields

Arabic diacritics play a significant role in distinguishing words with the same orthography but different meanings, pronunciations, and syntactic functions. The presence of Arabic diacritics can be useful in many natural language processing applications, such as text-to-speech tasks, machine translation, and part-of-speech tagging. This article discusses the use of bidirectional long short-term memory neural networks with conditional random fields for Arabic diacritization. This approach requires no morphological analyzers, dictionary, or feature engineering, but rather uses a sequence-to-sequence schema. The input is a sequence of characters that constitute the sentence, and the output consists of the corresponding diacritic(s) for each character in that sentence. The performance of the proposed approach was examined using four datasets with different sizes and genres, namely, the King Abdulaziz City for Science and Technology text-to-speech (KACST TTS) dataset, the Holy Quran, Sahih Al-Bukhary, and the Penn Arabic Treebank (ATB). For training, 60% of the sentences were randomly selected from each dataset, 20% were selected for validation, and 20% were selected for testing. The trained models achieved diacritic error rates of 3.41%, 1.34%, 1.57%, and 2.13% and word error rates of 14.46%, 4.92%, 5.65%, and 8.43% on the KACST TTS, Holy Quran, Sahih Al-Bukhary, and ATB datasets, respectively. Comparison of the proposed method with those used in other studies and existing systems revealed that its results are comparable to or better than those of the state-of-the-art methods.