Reviewerly/exHarmony huggingface dataset:
Hybrid Random Forest and Particle Swarm Optimization Algorithm for Solar Radiation Prediction

Due to increased pollution, greenhouse effect and global warming resulting from power production using fossil fuels, there is increased penetration of renewable energy sources into the power system. The concept of microgrids has made solar radiation an indispensable source of power in the distribution system. But the power production using solar energy is highly variable and weather dependent which creates a power imbalance into the system when it is penetrated without forecasting. Therefore, solar power prediction plays a critical role in the proper usage of solar energy while keeping the system stable. For automating the power system, the forecast needs to be very accurate and thus, it is needed to improve the existing forecasting techniques. In this study, we have proposed a solar radiation scheme based on various meteorological factors, including temperature, humidity, wind speed, and others. We have introduced a hybrid model for prediction which optimizes the parameters of Random Forest using Particle Swarm Optimization technique.
Comparative analysis of libraries for computer vision OpenCV and AForge.NET for use in gesture recognition system

Abstract The article presents the results of the experiment, which based on a comparative analysis of two libraries for solving problems in the field of computer vision in intelligent gesture recognition system: OpenCV and AForge.NET. For the test was developed by intelligent gesture recognition system at both libraries and a block diagram of the gesture recognition system has been developed. A recognition algorithm based on neural network technologies has been developed, suitable sample descriptors and classifier structure have been selected to achieve the best results.The effectiveness of the gesture recognition test software was also evaluated. Below is a brief description of both libraries, a more detailed description of the research and test results.
Crop Recommendation via Clustering Center Optimized Algorithm for Imbalanced Soil Data

Making regional crop recommendation based on the content of soil elements and nutrient cycle periods is an efficient and environmental-friendly way in precision agriculture. However, real-world soil data distribution is always imbalanced, which seriously affects the performance of the learning-based prediction model. In order to solve this problem, this paper proposes a clustering center optimized algorithm by Synthetic Minority Over-sampling Technique (SMOTE). Firstly, the algorithm analyzes the original sample points and selects the density-based clustering centers. Then, it uses clustering center to generate the minority samples to ameliorate imbalanced data distribution. Finally, the ensemble algorithm is used to train the prediction model for accurate prediction. Experimental results show that the prediction accuracy of the proposed algorithm outperforms other state-of-the-art prediction models over imbalanced soil data.
Application of clustering methods to anomaly detection in fibrous media

The paper considers the problem of anomaly detection in 3D images of fibre materials. The spatial Stochastic Expectation Maximisation algorithm and Adaptive Weights Clustering are applied to solve this problem. The initial 3D grey scale image was divided into small cubes subject to clustering. For each cube clustering attributes values were calculated: mean local direction and directional entropy. Clustering is conducted according to the given attributes. The proposed methods are tested on the simulated images and on real fibre materials. The spatial Stochastic Expectation Maximization algorithm shows its effectiveness in comparison to Adaptive Weights Clustering.
Virtualized Logical Qubits: A 2.5D Architecture for Error-Corrected Quantum Computing

Current, near-term quantum devices have shown great progress in the last several years culminating recently with a demonstration of quantum supremacy. In the medium-term, however, quantum machines will need to transition to greater reliability through error correction, likely through promising techniques like surface codes which are well suited for near-term devices with limited qubit connectivity. We discover quantum memory, particularly resonant cavities with transmon qubits arranged in a 2.5D architecture, can efficiently implement surface codes with substantial hardware savings and performance/fidelity gains. Specifically, we virtualize logical qubits by storing them in layers of qubit memories connected to each transmon. Surprisingly, distributing each logical qubit across many memories has a minimal impact on fault tolerance and results in substantially more efficient operations. Our design permits fast transversal application of CNOT operations between logical qubits sharing the same physical address (same set of cavities) which are 6x faster than standard lattice surgery CNOTs. We develop a novel embedding which saves approximately 10x in transmons with another 2x savings from an additional optimization for compactness. Although qubit virtualization pays a 10x penalty in serialization, advantages in the transversal CNOT and in area efficiency result in fault-tolerance and performance comparable to conventional 2D transmon-only architectures. Our simulations show our system can achieve fault tolerance comparable to conventional two-dimensional grids while saving substantial hardware. Furthermore, our architecture can produce magic states at 1.22x the baseline rate given a fixed number of transmon qubits. This is a critical benchmark for future fault-tolerant quantum computers as magic states are essential and machines will spend the majority of their resources continuously producing them. This architecture substantially reduces the hardware requirements for fault-tolerant quantum computing and puts within reach a proof-of-concept experimental demonstration of around 10 logical qubits, requiring only 11 transmons and 9 attached cavities in total.
Entanglement bounds on the performance of quantum computing architectures

There are many possible architectures of qubit connectivity that designers of future quantum computers will need to choose between. However, the process of evaluating a particular connectivity graph's performance as a quantum architecture can be difficult. In this paper, we show that a quantity known as the isoperimetric number establishes a lower bound on the time required to create highly entangled states. This metric we propose counts resources based on the use of two-qubit unitary operations, while allowing for arbitrarily fast measurements and classical feedback. We use this metric to evaluate the hierarchical architecture proposed by A. Bapat et al. [Phys. Rev. A 98, 062328 (2018)], and find it to be a promising alternative to the conventional grid architecture. We also show that the lower bound that this metric places on the creation time of highly entangled states can be saturated with a constructive protocol, up to a factor logarithmic in the number of qubits.
Impact of Packet Loss to the Motion of Autonomous UAV Swarms

This paper examines the impact of packet loss to a set of path planning algorithms targeted to swarms of autonomous Unmanned Aerial Vehicles (UAV). We provide a brief overview of three deconfliction algorithms, utilizing virtual forces, velocity control and a bidding mechanism, respectively. We apply these algorithms to a use case where swarms pass through one another within a constrained urban environment. We then vary the level of packet loss within this use case and analyze the impact of packet loss to the swarm coherency produced by these algorithms.
MCA-Based Rule Mining Enables Interpretable Inference in Clinical Psychiatry

Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases and disorders in many areas of medicine. These models can serve not only as sources of predictions and estimates, but also as discovery tools for clinicians and researchers to reveal new knowledge from the data. High dimensionality of patient information (e.g., phenotype, genotype, and medical history), lack of objective measurements, and the heterogeneity in patient populations often create significant challenges in developing interpretable machine learning models for clinical psychiatry in practice. In this paper we take a step towards the development of such interpretable models. First, by developing a novel categorical rule mining method based on Multivariate Correspondence Analysis (MCA) capable of handling datasets with large numbers of features, and second, by applying this method to build transdiagnostic Bayesian Rule List models to screen for psychiatric disorders using the Consortium for Neuropsychiatric Phenomics dataset. We show that our method is not only at least 100 times faster than state-of-the-art rule mining techniques for datasets with 50 features, but also provides interpretability and comparable prediction accuracy across several benchmark datasets.
A Practice of Tourism Knowledge Graph Construction Based on Heterogeneous Information

The increasing amount of semi-structured and unstructured data on tourism websites brings a need for information extraction (IE) so as to construct a Tourism-domain Knowledge Graph (TKG), which is helpful to manage tourism information and develop downstream applications such as tourism search engine, recommendation and Q & A. However, the existing TKG is deficient, and there are few open methods to promote the construction and widespread application of TKG. In this paper, we present a systematic framework to build a TKG for Hainan, collecting data from popular tourism websites and structuring it into triples. The data is multi-source and heterogeneous, which raises a great challenge for processing it. So we develop two pipelines of processing methods for semi-structured data and unstructured data respectively. We refer to tourism InfoBox for semi-structured knowledge extraction and leverage deep learning algorithms to extract entities and relations from unstructured travel notes, which are colloquial and high-noise, and then we fuse the extracted knowledge from two sources. Finally, a TKG with 13 entity types and 46 relation types is established, which totally contains 34,079 entities and 441,371 triples. The systematic procedure proposed by this paper can construct a TKG from tourism websites, which can further applied to many scenarios and provide detailed reference for the construction of other domain-specific knowledge graphs.
Multi-Label Classification

Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.
The semi-parametric Bernstein-von Mises theorem for regression models with symmetric errors

In a smooth semi-parametric model, the marginal posterior distribution for a finite dimensional parameter of interest is expected to be asymptotically equivalent to the sampling distribution of any efficient point-estimator.The assertion leads to asymptotic equivalence of credible and confidence sets for the parameter of interest and is known as the semi-parametric Bernstein-von Mises theorem.In recent years, it has received much attention and has been applied in many examples.We consider models in which errors with symmetric densities play a role; more specifically, it is shown that the marginal posterior distributions of regression coefficients in the linear regression and linear mixed effect models satisfy the semi-parametric Bernstein-von Mises assertion.As a consequence, Bayes estimators in these models achieve frequentist inferential optimality, as expressed e.g. through Hájek's convolution and asymptotic minimax theorems.Conditions for the prior on the space of error densities are relatively mild and well-known constructions like the Dirichlet process mixture of normal densities and random series priors constitute valid choices.Particularly, the result provides an efficient estimate of regression coefficients in the linear mixed effect model, for which no other efficient point-estimator was known previously.
Robust nonlinear adaptation algorithms for multitask prediction networks

Summary High fidelity behavior prediction of intelligent agents is critical in many applications, which is challenging due to the stochasticity, heterogeneity, and time‐varying nature of agent behaviors. Prediction models that work for one individual may not be applicable to another. Besides, the prediction model trained on the training set may not generalize to the testing set. These challenges motivate the adoption of online adaptation algorithms to update prediction models in real‐time to improve the prediction performance. This article considers online adaptable multitask prediction for both intention and trajectory. The goal of online adaptation is to improve the performance of both intention and trajectory predictions with only the feedback of the observed trajectory. We first introduce a generic ‐step adaptation algorithm of the multitask prediction model that updates the model parameters with the trajectory prediction error in recent steps. Inspired by extended Kalman filter (EKF), a base adaptation algorithm modified EKF with forgetting factor (MEKF ) is introduced. In order to improve the performance of MEKF , generalized exponential moving average filtering techniques are adopted. Then this article introduces a dynamic multiepoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with moving average and dynamic multiepoch strategy (MEKF MA − ME ). We empirically study the best set of parameters to adapt in the multitask prediction model and demonstrate the effectiveness of the proposed adaptation algorithms to reduce the prediction error.
New Word Discovery Algorithm Based on N-Gram for Multi-word Internal Solidification Degree and Frequency

For the problems of the low usability of the lexicon constructed by traditional new word discovery methods and the erroneous new word segmentation results that only consider the solidification of adjacent two characters, A new word discovery algorithm based on N-Gram's internal solidification and frequency of multiple characters is proposed. Firstly, select a fixed higher degree of coagulation n, calculate the internal coagulation degree of n under different values, and keep only the fragments higher than a certain threshold to form a set Q. Then combine named entity recognition to construct N-Gram segmentation corpus, and count the frequency. Introduce the backtracking mechanism, filter the candidate words by word frequency and K times of mutual information, use the Trie tree structure to improve the string retrieval speed, and filter the meaningless words in multiple dimensions through the rule stop vocabulary and Chinese stop vocabulary. Finally, compare the words that do not appear in the dictionary to get the new word set. Experiments show that this new word discovery algorithm effectively improves the accuracy of new word recognition, which is significantly faster than other new word discovery algorithms.
Matrix-Based Collaborative Filtering Employing Personal Values-Based Modeling and Model Relationship Learning

This paper proposes a matrix-based collaborative filtering (CF) employing personal values (MCFPV). Introduction of various factors such as diversity and long-tailedness in addition to accuracy is a recent trend in the study of recommender systems. We think recommending acceptable items while satisfying users’ preference is important when considering other factors than accuracy. Also, interpretability is one of important characteristics recommender systems should have. To recommend acceptable items on the basis of an interpretable mechanism, this paper proposes a matrix-based recommendation method based on personal values-based modeling. Whereas existing CF based on matrix factorization methods are known to be more accurate than neighborhood-based CF, latent factors obtained by existing methods are difficult to interpret. On the other hand, user/item models of the propose method (MCFPV) is expected to be interpretable, because it represents the effect of each attribute items have on user’s decision making. Regarding a model relationship matrix that connects user and item models, this paper proposes two approaches: manual setting and machine learning approaches. Experimental results using 5 datasets generated from actual review sites show that the proposed methods recommend much unpopular items than the state-of-the art matrix factorization-based methods while keeping precision and recall.
Game-Like Captchas for Intrusion Detection

In this paper, we consider a novel method of mining biometric data for user authentication by replacing traditional captchas with game-like captchas. The game-like captchas present the user with a short game in which they attempt to get a high score. The data produced from a user's game play will be used to produce a behavior biometric based on user interactions, such as mouse movement, click patterns and game choices. The baseline expectation of interactive behavior will be used as a single factor in an intrusion detection system providing continuous authentication, considering the factors such as IP address, location, time of use, website interactions, and behavior anomalies. In addition to acting as a source of data, game-like captchas are expected to deter bots and automated systems from accessing web-based services and improving the user experience for the end-users who have become accustomed to monotonous alternatives, such as Google's re-captcha.
ROS: Resource-constrained Oracle Synthesis for Quantum Computers

We present a completely automatic synthesis framework for oracle functions, a central part in many quantum algorithms. The proposed framework for resource-constrained oracle synthesis (ROS) is a LUT-based hierarchical method in which every step is specifically tailored to address hardware resource constraints. ROS embeds a LUT mapper designed to simplify the successive synthesis steps, costing each LUT according to the resources used by its corresponding quantum circuit. In addition, the framework exploits a SAT-based quantum garbage management technique. Those two characteristics give ROS the ability to beat the state-of-the-art hierarchical method both in number of qubits and in number of operations. The efficiency of the framework is demonstrated by synthesizing quantum oracles for Grover's algorithm.
Unsupervised Discovery Of Semantically Aware Communities With Tensor Kruskal Decomposition: A Case Study In Twitter

Substantial empirical evidence, including the success of synthetic graph generation models as well as of analytical methodologies, suggests that large, real graphs have a recursive community structure. The latter results, in part at least, in other important properties of these graphs such as low diameter, high clustering coefficient values, heavy degree distribution tail, and clustered graph spectrum. Notice that this structure need not be official or moderated like Facebook groups, but it can also take an ad hoc and unofficial form depending on the functionality of the social network under study as for instance the follow relationship on Twitter or the connections between news aggregators on Reddit. Community discovery is paramount in numerous applications such as political campaigns, digital marketing, crowdfunding, and fact checking. Here a tensor representation for Twitter subgraphs is proposed which takes into consideration both the followfollower relationships but also the coherency in hashtags. Community structure discovery then reduces to the computation of Tucker tensor decomposition, a higher order counterpart of the well-known unsupervised learning method of singular value decomposition (SVD). Tucker decomposition clearly outperforms the SVD in terms of finding a more compact community size distribution in experiments done in Julia on a Twitter subgraph. This can be attributed to the facts that the proposed methodology combines both structural and functional Twitter elements and that hashtags carry an increased semantic weight in comparison to ordinary tweets.
CLS-SMT: Bringing Together Combinatory Logic Synthesis and Satisfiability Modulo Theories

We introduce an approach that aims to combine the usage of satisfiability modulo theories (SMT) solvers with the Combinatory Logic Synthesizer (CL)S framework. (CL)S is a tool for the automatic composition of software components from a user-specified repository. The framework yields a tree grammar that contains all composed terms that comply with a target type. Type specifications for (CL)S are based on combinatory logic with intersection types. Our approach translates the tree grammar into SMT functions, which allows the consideration of additional domain-specific constraints. We demonstrate the usefulness of our approach in several experiments.
Modular Integration of Crashsafe Caching into a Verified Virtual File System Switch

When developing file systems, caching is a common technique to achieve a performant implementation. Integrating write-back caches into a file system does not only affect functional correctness but also impacts crash safety properties of the file system. As parts of written data are only stored in volatile memory, special care has to be taken when integrating write-back caches to guarantee that a power cut during running operations leads to a consistent state. This paper shows how non-order-preserving caches can be added to a virtual file system switch (VFS) and gives a novel crash-safety criterion matching the characteristics of such caches. Broken down to individual files, a power cut can be explained by constructing an alternative run, where all writes since the last synchronization of that file have written a prefix. VFS caches have been integrated modularly into Flashix, a verified file system for flash memory, and both functional correctness and crash-safety of this extension have been verified with the interactive theorem prover KIV.
WITHDRAWN: Ceramic art image design based on FPGA and machine learning algorithm

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.