Reviewerly/exHarmony huggingface dataset:
A collaborative filtering recommendation algorithm based on user clustering with preference types

Abstract The traditional collaborative filtering algorithm is extensively applied in the field of personalized recommendation, but it still faces the challenges of data sparsity and scalability problems which result in low quality and efficiency of recommendation. To address the problems, a hybrid collaborative filtering recommendation algorithm is proposed based on user preference type clustering. First, by analyzing the relationship between users and item categories, we construct the user item category preference matrix. On this basis, user clustering is carried out and users with similar preference types are clustered into the same user groups. Then, to search for the nearest neighbors of target user, similarity of users is calculated in the cluster it belongs to by considering both the user ratings and their preferred item categories. Finally, an improved Slope One algorithm is proposed and applied to the nearest neighbor set to predict item ratings and then generate recommendations. The experiments indicate the proposed hybrid collaborative filtering algorithm can improve recommendation performance in the case of a great many users.
Certification of three black boxes with unsharp measurements using 3 → 1 sequential quantum random access codes

Abstract Unsharp measurements play an increasingly important role in quantum information theory. In this paper, we study a three-party prepare-transform-measure experiment with unsharp measurements based on 3 → 1 sequential random access codes (RACs). We derive optimal trade-off between the two correlation witnesses in 3 → 1 sequential quantum random access codes (QRACs), and use the result to complete the self-testing of quantum preparations, instruments and measurements for three sequential parties. We also give the upper and lower bounds of the sharpness parameter to complete the robustness analysis of the self-testing scheme. In addition, we find that classical correlation witness violation based on 3 → 1 sequential RACs cannot be obtained by both correlation witnesses simultaneously. This means that if the second party uses strong unsharp measurements to overcome the classical upper bound, the third party cannot do so even with sharp measurements. Finally, we give the analysis and comparison of the random number generation efficiency under different sharpness parameters based on the determinant value, 2 → 1 and 3 → 1 QRACs separately. This letter sheds new light on generating random numbers among multi-party in semi-device independent framework.
Human-Following of Mobile Robots Based on Object Tracking and Depth Vision

Human-following is a promising ability for robots to serve and assist humankind. Correlation Filter (CF) with excellent performance and high running speed, is widely used in single object tracking, suitable for human following. In this paper, an improved correlation filter object tracking algorithm is adopted to achieve human following. For the lack of accuracy evaluation mechanism in most object trackers, fusion of Peak Side-lobe Rate (PSR) and depth information are introduced to evaluate the credibility of the tracking result. The sample and filter model are updated with a high credibility to avoid model drift. Face matching is used to re-track human when tracking fails. Based on a depth camera Kinect and a mobile robot, the experiments of following human show that proposed method has excellent abilities to deal with different scenarios and to follow target human stably for a long time.
Secured Data Storage Framework for Land Registration using Blockchain Technology

Land registration requires a complex of sensitive data that requires a decentralized environment. Current technology only concentrates on the less secure database storage and expose to any misconduct. This is due to the characteristics of the database, which still has some problems with unstructured data and non-relational databases. As the land registration methods require complexities and challenges in terms of land tenure security at a high-risk scale, the security level of the land registration system needs to be put at the highest level. Fraud is one the major problem which is currently a severe problem in the land registration methods. Also, the land registration process takes a long duration of time to complete. The land title indicates that the land is confirmed and already registered to an owner. The reason for ownership is to perceive property rights, which incorporates data relating to land region, area, limits, just as proprietorship and title of the ardent property. However, the land is registered, but still, there are lots of causes of fraud happened in which land registration data can be quickly deleted and or edited. Since land is an asset, and any fraud can cause a loss in a lot of money, it becomes very crucial that the registration of land becomes speedy, transparent, and with less hustle. In this research, we propose a framework for secure data storage of land registration using Blockchain. Blockchain offers the solution with its underlying technology. Blockchain is decentralized, transparent, and fast compared to the traditional centralized software approach. For the validation of the proposed framework, we performed a comparison between the proposed and existing methods.
Utilization of data mining for placement of books in the library using the Apriori method

Abstract The Apriori Algorithm is an algorithm that is well known for performing frequent itemset searches using the association rule technique. The Apriori algorithm uses knowledge about the previously known frequent itemset to process data related to borrowing books in the library to be processed into useful information. This study uses a priori algorithm method for grouping books at the PGRI University Library in Semarang based on trends that appear together in a library activity visit. By gaining knowledge from this algorithm, it can be used as a reference for the PGRI University Library in Semarang in placing book collections.
Tracking quantum control for a two qutrits system under amplitude damping noise

The tracking control of quantum correlations between two coupled qutrits is examined, where the qutrits are coupled to a Markovian environment. The analyzed noise is the amplitude damping. We adapt the piecewise time independent quantum control method to a general N qutrits system. Effectively, the control field is an external potential applied to the system which contains two adjustable parameters, the intensity and the phase of the field. Using this method we increase the time for which the system remains coherent and entangled. Furthermore, tracking quantum control protocols with different targets are presented, considering the case where the qutrits are in resonance and the case of non-resonance. In all the dynamic evolution studied, entanglement and trace distance are analyzed.
Recognition of Natural and Computer-Generated Images Using Convolutional Neural Network

Recognizing Natural Images (NI) and Computer-Generated Images (CGI) by a human is difficult due to the use of new-age computer graphics tools for designing more photorealistic CGI images. Identifying whether an image was captured naturally or if it is a computer generated image is a fundamental research problem. For this problem, we design and implement a new Convolutional Neural Network (ConvNet) architecture along with data augmentation techniques. Experimental results show that our method outperforms existing methods by 2.09 percentage for recognizing NI and CGI images.
Privacy-Preserving Techniques for Protecting Large-Scale Data of Cyber-Physical Systems

As Cyber-Physical Systems (CPSs), such as power and gas networks, generate heterogeneous and large-scale data sources from devices and networks, they need efficient privacy-preserving techniques to protect data and systems from cyber attacks. To safeguard CPSs from potential cyber threats, it is vital to identify vulnerabilities of CPSs' components to prevent Advanced Persistent Threats (APTs) and protect their generated data using privacy-preserving techniques. This paper aims to review the current state of privacy-preserving techniques for protecting CPSs and their networks against cyber attacks. Concepts of Privacy preservation and CPSs are discussed, illustrating CPSs' components and how they could be hacked using cyber and physical hacking scenarios. Then, types of privacy preservation, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are discussed to demonstrate how they would be applied to protect the original data in CPSs and their networks. Finally, we explain existing challenges, solutions and future research directions of privacy preservation in CPSs.
Frequency-domain loss function for deep exposure correction of dark images

We address the problem of exposure correction of dark, blurry and noisy images captured in low-light conditions in the wild. Classical image-denoising filters work well in the frequency space but are constrained by several factors such as the correct choice of thresholds and frequency estimates. On the other hand, traditional deep networks are trained end to end in the RGB space by formulating this task as an image translation problem. However, that is done without any explicit constraints on the inherent noise of the dark images and thus produces noisy and blurry outputs. To this end, we propose a DCT/FFT-based multi-scale loss function, which when combined with traditional losses, trains a network to translate the important features for visually pleasing output. Our loss function is end to end differentiable, scale-agnostic and generic; i.e., it can be applied to both RAW and JPEG images in most existing frameworks without additional overhead. Using this loss function, we report significant improvements over the state of the art using quantitative metrics and subjective tests.
Client-side Cryptography Based Security for Cloud Computing System

Cryptography indicates to techniques of securing information and communication derived from mathematical perception to convert messages in ways that are tough to interpret. Cryptography is firmly associated with the department of cryptology along with cryptanalysis. It consists of techniques such as blending words with images, microdots, and alternative ways to mask data during storage or else transit. However, in the modern era, cryptography is repeatedly related to cloud computing. But, moving data into a cloud is a huge modification and has real involvement that makes users lapse before one can sign up for the desired service which can cause unwanted instruction on sensitive information and data lost. For the security of cloud data, a symmetric algorithm had been introduced by previous research work which used simple algorithms and had performance issues. In this research paper we have introduced and enforced symmetric key encryption that would encrypt a file locally at the client-side prior to uploading to the cloud and the file would decrypt after downloading on the client-side using key generated during encryption. This algorithm also uses a different algorithm to calculate the key value. As a result, our algorithm offers better security and better performance for large files. This way we can add an extra layer of security which would restrain unwanted attacks on intimated information as well as lack of standardization.
A further study of quadratic APN permutations in dimension nine

Recently, Beierle and Leander found two new sporadic quadratic APN permutations in dimension 9. Up to EA-equivalence, we present a single trivariate representation of those two permutations as $C_u \colon (\mathbb{F}_{2^m})^3 \rightarrow (\mathbb{F}_{2^m})^3, (x,y,z) \mapsto (x^3+uy^2z, y^3+uxz^2,z^3+ux^2y)$, where $m=3$ and $u \in \mathbb{F}_{2^3}\setminus\{0,1\}$ such that the two permutations correspond to different choices of $u$. We then analyze the differential uniformity and the nonlinearity of $C_u$ in a more general case. In particular, for $m \geq 3$ being a multiple of 3 and $u \in \mathbb{F}_{2^m}$ not being a 7-th power, we show that the differential uniformity of $C_u$ is bounded above by 8, and that the linearity of $C_u$ is bounded above by $8^{1+\lfloor \frac{m}{2} \rfloor}$. Based on numerical experiments, we conjecture that $C_u$ is not APN if $m$ is greater than $3$. We also analyze the CCZ-equivalence classes of the quadratic APN permutations in dimension 9 known so far and derive a lower bound on the number of their EA-equivalence classes. We further show that the two sporadic APN permutations share an interesting similarity with Gold APN permutations in odd dimension divisible by 3, namely that a permutation EA-inequivalent to those sporadic APN permutations and their inverses can be obtained by just applying EA transformations and inversion to the original permutations.
Bilateral Upsampling Network for Single Image Super-Resolution With Arbitrary Scaling Factors

Single Image Super-Resolution (SISR) is essential for many computer vision tasks. In some real-world applications, such as object recognition and image classification, the captured image size can be arbitrary while the required image size is fixed, which necessitates SISR with arbitrary scaling factors. It is a challenging problem to take a single model to accomplish the SISR task under arbitrary scaling factors. To solve that problem, this paper proposes a bilateral upsampling network which consists of a bilateral upsampling filter and a depthwise feature upsampling convolutional layer. The bilateral upsampling filter is made up of two upsampling filters, including a spatial upsampling filter and a range upsampling filter. With the introduction of the range upsampling filter, the weights of the bilateral upsampling filter can be adaptively learned under different scaling factors and different pixel values. The output of the bilateral upsampling filter is then provided to the depthwise feature upsampling convolutional layer, which upsamples the low-resolution (LR) feature map to the high-resolution (HR) feature space depthwisely and well recovers the structural information of the HR feature map. The depthwise feature upsampling convolutional layer can not only efficiently reduce the computational cost of the weight prediction of the bilateral upsampling filter, but also accurately recover the textual details of the HR feature map. Experiments on benchmark datasets demonstrate that the proposed bilateral upsampling network can achieve better performance than some state-of-the-art SISR methods.
Autonomous Process Model Identification using Recurrent Neural Networks and Hyperparameter Optimization

We demonstrate the application of automated machine learning to the problem of identifying dynamic process models using recurrent neural networks (RNNs). The general concept relies on continuous monitoring of input-output data from a plant and the processing of this data by a collection of algorithms. The data is first processed by a collaborative filtering system, which suggests a classification of system dynamics per output channel. The proposed classification and the number of input channels is used to initialize a search over RNN hyperparameters. The search algorithm uses subsets of historical data for training and validation to select the RNN architecture and determine the network parameters according to preselected objectives for balancing model accuracy and model compactness. The proposed approach is demonstrated on a simulated case study for online system identification of a chemical reactor, where the underlying dynamic characteristics of the simulated system are changed during the simulation as the system undergoes a number of disturbances and handles control tasks. Process models for the system in question are obtained via the automated machine learning approach and the models are updated as the system dynamics change. The results show good prediction accuracy of the models throughout the simulation representing changes in system dynamics.
An efficient and locality-oriented Hausdorff distance algorithm: Proposal and analysis of paradigms and implementations

• I propose the first morphological implementation of the Hausdorff Distance (HD). Although Serra has proved that it can be computed using mathematics, he did not provide nor propose any tangible implementation. I am the first to propose a morphological implementation (there could be many) and I compare it using different paradigms (CPU and GPU) to the state-of-the-art. • The proposed algorithm is much faster than the state-of-the-art (work of Taha and Hanbury published in TPAMI) when we consider both the CPU and the GPU for the worst case and for tasks such as image registration. • I provide the most extensive evaluation of performance of the Hausdorff distance in the literature (considering different paradigms and programming languages). • When we consider the CPU, my proposal is up to 8 times faster than the Taha-Hanbury implementation for image registration and 22337 times faster for the worst case. In the case of the GPU, it is even faster than that. Hausdorff distance (HD) is a popular similarity metric used in the comparison of images or 3D volumes. Although popular, its main weakness is computing power consumption, being one of the slowest set distances. In this work, a novel, parallel and locality-oriented Hausdorff distance implementation is proposed. Novel as it is the first time in the literature that an actual algorithmic implementation using morphological dilations is proposed and thoroughly evaluated. Parallel, as it is more robust in terms of parallelization than the state-of-the-art algorithm and local as it has an intrinsic sensitivity to voxels that are closer in space. This proposal can be faster than the state-of-the-art in several practical cases such as in medical imaging registrations (up to 8 times faster on average in one of the CPU experiments) and is faster in the worst-case (up to 22337 times faster in one of the CPU experiments). Worst-case scenarios and high resolution volumes also favor the proposed approach. Throughout the work, several sequential and parallel CPU and GPU implementations are evaluated and compared.
Extended State based Kalman Filter for Uncertain Systems with Bias

This paper addresses the state estimation for a class of stochastic systems with both uncertain dynamics and measurement bias. By using the idea of uncertainty/disturbance estimation, an extended state based Kalman filter algorithm is developed to estimate the original state, the uncertain dynamics and the measurement bias. Furthermore, a necessary and sufficient condition for the observability of augmented system is presented. Also, the stability of the proposed algorithm is analyzed. It is shown that the proposed filter can achieve unbiased estimation of measurement bias, such that the influence of measurement bias is eliminated. Finally, a simulation study is provided to illustrate the effectiveness of proposed method.
Informative discriminator for domain adaptation

In this paper, we consider the problem of domain adaptation for multi-class classification, where we are provided a labeled set of examples in a source dataset and target dataset with no supervision. We tackle the mode collapse problem in adapting the classifier across domains. In this setting, we propose an adversarial learning-based approach using an informative discriminator. Our observation relies on the analysis that shows if the discriminator has access to all the information available, including the class structure present in the source dataset, then it can guide the transformation of features of the target set of classes to a more structured adapted space. Further, by training the informative discriminator using the more robust source samples, we are able to obtain better domain invariant features. Using this formulation, we achieve state-of-the-art results for the standard evaluation on benchmark datasets. We also provide detailed analysis, which shows that using all the labeled information results in an improved domain adaptation.
Energy and Spectrum Efficient Blind Equalization With Unknown Constellation for Air-to-Ground Multipath UAV Communications

In unmanned aerial vehicle (UAV) communications, frequency-selective fading can severely deteriorate the quality of transmitted signal by generating undesired and disordered constellation diagrams due to scatters in the air-to-ground (ATG) mutipath channels. In this paper, we propose a low-overhead blind equalization method to combat frequency-selective fading in air-ground multipath UAV channels. Specifically, a pre-equalization method is proposed based on a constant modulus algorithm to restore the contour of the constellation diagram. Moreover, the similarity measure function and the difference measure function are derived using template matching to identify the constellation of M-ary quadrature amplitude modulation. Furthermore, we propose a weighted constant cross algorithm (WXA) to reduce the residual mean square error and construct a cross-shaped modulus value, by utilizing the statistical information of the identified normalized standard constellation diagrams and the equalizer output decision symbols' weighting value. The proposed method requires less information and no training sequences and pilots, therefore, if achieves energy and spectrum efficient ATG multipath UAV communications. Simulation results show that the proposed WXA algorithm can reduce the residual mean square error convergence value between -22dB and -25dB, making it very useful for the equalization of the frequency-selective fading channel in typical UAV communication scenarios.
A Decoder-Free Variational Deep Embedding for Unsupervised Clustering

In deep clustering frameworks, autoencoder (AE)- or variational AE-based clustering approaches are the most popular and competitive ones that encourage the model to obtain suitable representations and avoid the tendency for degenerate solutions simultaneously. However, for the clustering task, the decoder for reconstructing the original input is usually useless when the model is finished training. The encoder-decoder architecture limits the depth of the encoder so that the learning capacity is reduced severely. In this article, we propose a decoder-free variational deep embedding for unsupervised clustering (DFVC). It is well known that minimizing reconstruction error amounts to maximizing a lower bound on the mutual information (MI) between the input and its representation. That provides a theoretical guarantee for us to discard the bloated decoder. Inspired by contrastive self-supervised learning, we can directly calculate or estimate the MI of the continuous variables. Specifically, we investigate unsupervised representation learning by simultaneously considering the MI estimation of continuous representations and the MI computation of categorical representations. By introducing the data augmentation technique, we incorporate the original input, the augmented input, and their high-level representations into the MI estimation framework to learn more discriminative representations. Instead of matching to a simple standard normal distribution adversarially, we use end-to-end learning to constrain the latent space to be cluster-friendly by applying the Gaussian mixture distribution as the prior. Extensive experiments on challenging data sets show that our model achieves higher performance over a wide range of state-of-the-art clustering approaches.