Reviewerly/exHarmony huggingface dataset:
A study on using private-permissioned blockchain for securely sharing farmers data

In agriculture, farmers are the most important entity. For supporting farmers in increasing productivity and efficiency, the government offers subsidies, loans, insurances, and so on. This paper explores the usage of Blockchain technology for securing farmer's data in the Indian scenario. The farmer needs to register through the multiple official registration systems for availing different schemes and information provided by the country. The personnel and crop-based details of each farmer are collected at the time of registration. The filing also helps in providing better services to farmers like connecting farmers and traders to ensure a fair price for quality crops, advice to farmers of agricultural practices and location. In this paper, a blockchain-based farmer's data securing system is proposed to provide data provenance and transparency of the information entered in the system. While registering, the data is collected, and it is verified. A single verified record of farmers accessed by various government agriculture departments were designed using the Hyperledger fabric framework.
A Greedy Pursuit Approach for Fitting 3D Facial Expression Models

We propose a novel fitting strategy for the expression of blendshapes. Rather than employing all of the expression blendshapes to approximate the target points, only a subset of blendshapes selected to represent an expression on the target face is utilized, which efficiently reduces redundancy among the expression models. An expression correlation map is proposed to measure the redundancies between the blendshapes under the assumption that each expression changes the facial shape regionally, which enables a few less-correlated expressions to be obtained using a greedy pursuit approach. It is demonstrated that a subset of blendshapes that represents the target more expressively and semantically can be obtained non-parametrically using the proposed selection method, which enables natural facial shapes to be reliably generated without regularization, while also coping well with target-specific or unusual expressions. The experimental results from public datasets exhibit an increase in the quality of the facial shapes and expressions over baseline methods and state-of-the-art facial fitting approaches.
Just Like the Real Thing: Fast Weak Simulation of Quantum Computation

Quantum computers promise significant speedups in solving problems intractable for conventional computers but, despite recent progress, remain limited in scaling and availability.Therefore, quantum software and hardware development heavily rely on simulation that runs on conventional computers.Most such approaches perform strong simulation in that they explicitly compute amplitudes of quantum states.However, such information is not directly observable from a physical quantum computer because quantum measurements produce random samples from probability distributions defined by those amplitudes.In this work, we focus on weak simulation that aims to produce outputs which are statistically indistinguishable from those of error-free quantum computers.We develop algorithms for weak simulation based on quantum state representation in terms of decision diagrams.We compare them to using state-vector arrays and binary search on prefix sums to perform sampling.Empirical validation shows, for the first time, that this enables mimicking of physical quantum computers of significant scale.
Theory of interactions between cavity photons induced by a mesoscopic circuit

We use a quantum path-integral approach to describe the behavior of a microwave cavity coupled to a dissipative mesoscopic circuit. We integrate out the mesoscopic electronic degrees of freedom to obtain a cavity effective action at fourth order in the light/matter coupling. By studying the structure of this action, we establish sufficient conditions in which the cavity dynamics can be described with a Lindblad equation. This equation depends on effective parameters set by electronic correlation functions. It reveals that the mesoscopic circuit induces an effective Kerr interaction and two-photon dissipative processes. We use our method to study the effective dynamics of a cavity coupled to a double quantum dot with normal metal reservoirs. If the cavity is driven at twice its frequency, the double-dot circuit generates photonic squeezing and nonclassicalities visible in the cavity Wigner function. In particular, we find a counterintuitive situation where mesoscopic dissipation enables the production of photonic Schr\"odinger cats. These effects can occur for realistic circuit parameters. Our method can be generalized straightforwardly to more complex circuit geometries with, for instance, multiple quantum dots and other types of fermionic reservoirs such as superconductors and ferromagnets.
Segmentation-based, omnifont printed Arabic character recognition without font identification

Optical Character Recognition OCR is an essential part of many real-world applications such as digital archiving, automatic number plate recognition, handle cheques, etc. However, developing an OCR for printed Arabic text is still a challenging and open research field due to the special characteristics of Arabic cursive script. In this paper, we propose a segmentation-based, omnifont, open-vocabulary OCR for printed Arabic text. The proposed approach doesn’t require an explicit font type recognition stage. It uses an explicit, indirect character segmentation method. The presented segmentation method is baseline dependent and employs a hybrid, three-steps character segmentation algorithm to handle the problem of character overlapping. Besides, it uses a set of topological features that are designed and generalized to make the segmentation approach font independent. The segmented characters are fed as an input to a convolutional neural network for feature extraction and recognition. The APTID-MF data set has been used for testing and evaluation. The average accuracy of the proposed segmentation stage is 95%, while the average accuracy of the recognition stage is 99.97%. The whole approach achieves an average accuracy of 95% without using font-type recognition or any post-processing techniques.
Artificial intelligence based on effectiveness of inverted classroom teaching of college sports

This study takes the effectiveness analysis of inverted classroom teaching in colleges and universities as a breakthrough point, and combines artificial intelligence technology with the analysis method of inverted classroom teaching in colleges and universities to enrich the existing methods for analyzing, the behavior of inverted classroom teaching in colleges and universities to realize the effectiveness of inverted classroom teaching in colleges and universities analysis. This research first constructs an analytical framework for the teaching behaviors of college physical education inverted classrooms based on artificial intelligence technology, which consists of observation dimension and the evaluation dimension. In order to further test the scientifically and operability of the analytical framework, taking emotion recognition as an example, practical operations are combined with specific examples to obtain visual analysis results. This study expands the dimension and depth of analysis of the behavior of inverted sport in classroom teaching in sport inversion colleges and universities, and has obvious advantages in saving manpower and real-time visual display. Through the analysis of the effectiveness of physical education inverted classroom teaching in sports inversion colleges and universities through artificial intelligence technology, the use of technology to participate in the analysis of physical education inverted classroom teaching behaviors in sports inverted colleges and universities, shorten the evaluation time, expand the evaluation dimension, improve the evaluation efficiency, achieve real-time feedback, real-time attention to classroom effects. Effectively regulating the inverted classroom teaching behavior of college physical education can promote the cultivation of teachers’ professional abilities, scientifically and accurately improve and correct teaching problems, and improve the quality of education and teaching. Eventually, students will achieve comprehensive self-evaluation of students, and promote personalized and standardized growth of students.
Ant Colony Optimization-Based Streaming Feature Selection: An Application to the Medical Image Diagnosis

Irrelevant and redundant features increase the computation and storage requirements, and the extraction of required information becomes challenging. Feature selection enables us to extract the useful information from the given data. Streaming feature selection is an emerging field for the processing of high-dimensional data, where the total number of attributes may be infinite or unknown while the number of data instances is fixed. We propose a hybrid feature selection approach for streaming features using ant colony optimization with symmetric uncertainty (ACO-SU). The proposed approach tests the usefulness of the incoming features and removes the redundant features. The algorithm updates the obtained feature set when a new feature arrives. We evaluate our approach on fourteen datasets from the UCI repository. The results show that our approach achieves better accuracy with a minimal number of features compared with the existing methods.
Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering

With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to solve the incomplete multi-view clustering problem. Firstly, to eliminate the noise existing in the original space, we transform complete original data into latent representations which contribute to better graph construction for each view. Then, we incorporate feature extraction and incomplete graph fusion into a unified framework, whereas two processes can negotiate with each other, serving for graph learning tasks. A sparse regularization is imposed on the complete graph to make it more robust to the view-inconsistency. Besides, the importance of different views is automatically learned, further guiding the construction of the complete graph. An effective iterative algorithm is proposed to solve the resulting optimization problem with convergence. Compared with the existing state-of-the-art methods, the experiment results on several real-world datasets demonstrate the effectiveness and advancement of our proposed method.
Clustering Approach for Solving Traveling Salesman Problems via Ising Model Based Solver

Ising model based solver have gained increasing attention due to their efficiency in finding approximate solutions for combinatorial optimization problems. However, when solving doubly constrained problems, such as traveling salesman problem using the Ising model-based solver, both the execution speed and the quality of solutions deteriorate significantly due to the quadratically increasing spin counts and strong constraints placed on the spins. In this paper, we propose a recursive clustering approach that accelerates the calculations of the Ising model and that also helps to obtain high-quality solutions. Through evaluations using the TSP benchmarks, the qualities with the proposed method have been improved by up to 67.1% and runtime were reduced by 73.8x.
A Survey on Integrity Auditing for Data Storage in the Cloud: From Single Copy to Multiple Replicas

The rapid advancement of cloud computing has promoted the development of cloud storage services. One of the biggest concerns of cloud users is whether the completeness and recoverability of data can be guaranteed when cloud servers encounter problems. Only when the integrity of data is fully guaranteed can users consume cloud storage with confidence, especially in a complicated cloud environment with multiple clouds. However, the literature still lacks a thorough survey on cloud data integrity auditing for both single copy and multiple replicas. In this article, we survey and compare existing auditing schemes for single copy and multiple replicas based on a set of criteria. Based on our review and analysis, we discuss open issues, potential applications and future directions in the field of the integrity auditing in the cloud, including the implications of such trendy topics as merging blockchain and edge computing into data integrity auditing.
A new approach for tuning control rule based on hedge algebras theory and application in structural vibration control

The hedge algebras theory has the potential to make significant applications in the field of computational intelligence. The purpose of the present study is to improve the control performance of the hedge algebras–based controller by tuning its control rules and apply the hedge algebras–based controller using the tuned rule base in vibration control of structures. The authors propose a “tuning coefficient” to express the impact of each rule of the controller in the control process. These control rules are adjusted by optimizing the above tuning coefficient. The tuned controller is then used to reduce the dynamic response of structures subjected to different excitations. The adjusted rule base is more appropriate for the model to be controlled, and it allows enhancing the control performance of the system. The proposed approach is uncomplicated and transparent, and it allows preserving the monotonous feature of the rule base.
DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision

Anomaly detection and localization in medical images is a challenging task, especially when the anomaly exhibits a change of existing structures, e.g., brain atrophy or changes in the pleural space due to pleural effusions. In this work, we present a weakly supervised and detail-preserving method that is able to detect structural changes of existing anatomical structures. In contrast to standard anomaly detection methods, our method extracts information about the disease characteristics from two groups: a group of patients affected by the same disease and a healthy control group. Together with identity-preserving mechanisms, this enables our method to extract highly disease-specific characteristics for a more detailed detection of structural changes. We designed a specific synthetic data set to evaluate and compare our method against state-of-the-art anomaly detection methods. Finally, we show the performance of our method on chest X-ray images. Our method called DeScarGAN outperforms other anomaly detection methods on the synthetic data set and by visual inspection on the chest X-ray image data set.
Colour image enhancement with brightness preservation and edge sharpening using a heat conduction matrix

In this study, an enhancement process obtained by applying the heat conduction equation of solid and stagnant fluids on colour images is proposed. After the colour channel stretching, the RGB colour image was converted to the HSI model. The heat conduction equation was applied for each pixel on the I channel of the HSI colour model. The elements of the feature matrix called heat conduction matrix (HCM) can have negative, positive or zero values. A pixel with a small negative HCM value indicates that I needs level enhancement for a good image, whereas a small positive HCM value means that the I level value will be reduced and aligned with its neighbours. High positive or negative values are defined as the edges of the objects and the I level values of such pixels are not changed to protect the edges. In addition, whether HCM is negative or positive, the balanced increment and decrement path at a level I ensures that the mean brightness value performs natural protection. Finally, an enhanced image is obtained by transitioning from the HSI to the RGB colour model. Experimental results show that this method can enhance colour image details better than other methods.
BTWalk: Branching Tree Random Walk for Multi-Order Structured Network Embedding

Multi-order proximity is useful for effective network embedding. In contrast to many previous works that only consider order-level weights, this paper proposes to explore a more expressive node-level weighting mechanism to encode the diverse local structure, with a scalable and theoretically justified sampling strategy for its learning. Specifically, we start with a formal definition of multi-order proximity matrix which leads to our new multi-order objective based on Laplacian Eigenmaps and Skip-Gram. Then we instantiate the node-specific multi-order weights in the objective with the help of neighborhood size estimation, which indicates node-specific multi-order information. For objective learning, it is implicitly fulfilled with our proposed branching tree-like random walk strategy termed by BTWalk, which differs from the dominant chain-like walk in existing sampling techniques. BTWalk is designed by a synergetic combination of BFS (breadth-first search) and DFS (depth-first search), which is modulated according to the weights of the considered proximity orders. We theoretically analyze its cost-efficiency, and further propose the so-called Vec4Cross framework that incorporates joint node embedding and network alignment for two partially overlapped networks based on the seed matchings, whereby BTWalk is also adopted for embedding. Promising experimental results are obtained on real-world datasets across popular tasks.
Gender Detection from Handwritten Documents Using Concept of Transfer-Learning

Offline gender detection from Arabic handwritten documents is a very challenging task because of the high similarity between an individual's writings and the complexity of the Arabic language as well. In this paper, we propose a new way to detect the writer gender from scanned handwritten documents that mainly based on the concept of transfer-learning. We used a pre-trained knowledge from two convolution neural networks (CNN): GoogleNet, and ResNet, then we applied it on our data-set. We use this two CNN architectures as fixed feature extractors. For the analysis and the classification stage, we used a support vector machine (SVM). The performance of the two CNN architectures concerning accuracy is 80.05% for GoogleNet, 83.32% for ResNet.
Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning

Functional magnetic resonance imaging (fMRI) is widely used in the field of brain semantic decoding. However, as fMRI data acquisition is time-consuming and expensive, the number of samples is usually small in the existing fMRI datasets. It is difficult to build an accurate brain decoding model for a subject with insufficient fMRI data. The majority of semantic decoding methods focus on designing predictive model with limited samples, while less attention is paid to fMRI data augmentation. Leveraging data from related but different subjects can be regarded as a new strategy to improve the performance of predictive model. There are two challenges when using information from different subjects: 1) feature mismatch; 2) distribution mismatch. In this paper, we propose a multi-subject fMRI data augmentation method to address the above two challenges, which can improve the decoding accuracy of the target subject. Specifically, the subject information can be translated from one to another by using multiple subject-specific encoders, decoders and discriminators. The encoder maps each subject to a shared latent space, solving the feature mismatch problem. The decoders and discriminators form multiple generative adversarial network architectures, which solves the distribution mismatch problem. Meanwhile, to ensure that the representation of the latent space preserves information of the input space, our method not only minimizes the local data reconstruction loss, but also preserves the sparse reconstruction (semantic) relation over the whole dataset of the input space. Extensive experiments on three fMRI datasets demonstrate the effectiveness of the proposed method.
Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data

In the era of deep learning, aggregation of data from several sources is a common approach to ensuring data diversity. Let us consider a scenario where several providers contribute data to a consortium for the joint development of a classification model (hereafter the target model), but, now one of the providers decides to leave. This provider requests that their data (hereafter the query dataset) be removed from the databases but also that the model 'forgets' their data. In this paper, for the first time, we want to address the challenging question of whether data have been forgotten by a model. We assume knowledge of the query dataset and the distribution of a model's output. We establish statistical methods that compare the target's outputs with outputs of models trained with different datasets. We evaluate our approach on several benchmark datasets (MNIST, CIFAR-10 and SVHN) and on a cardiac pathology diagnosis task using data from the Automated Cardiac Diagnosis Challenge (ACDC). We hope to encourage studies on what information a model retains and inspire extensions in more complex settings.
TB-CoAuth: Text based continuous authentication for detecting compromised accounts in social networks

Commencement of research towards compromised account detection in email and web services foreshadows the growth of the same in social network scenario. In this paper, continuous authentication of textual content has been performed for incessant authorship verification to detect compromised accounts in social networks. Four categories of features namely, content free, content specific, stylometric and folksonomy are extracted and evaluated. Experiments are performed with 3057 twitter users taking 4000 latest tweets for each user. It is evident from the experiments that consistency maintained on features is different for each user. Hence, various statistical and similarity-based feature selection techniques are used to rank and select optimal features for each user which are further combined using a popular rank aggregation technique called BORDA. Also, performance of various supervised machine learning classifiers is analyzed on the basis of different evaluation metrics. Experimental results state that for the undertaken problem, SVM with rbf kernel outperformed other classifiers namely, kNN, Random Forest, Gradient Boosted and Multi Layer Perceptron, attaining a maximum F-score of 94.57% under the varied parameter settings.