Reviewerly/exHarmony huggingface dataset:
Time-optimal Trajectory Planning of Dulcimer Music Robot Based on PSO Algorithm

For a dulcimer music robot that strikes a dulcimer, fast and high-frequency striking is a key requirement, so the time optimization of the trajectory planning of the robot arm is the first problem to be solved in motion control. In this paper, the kinematics modeling and solving are firstly completed by using the D-H parameter method. Then the steps of trajectory planning by interpolation and the application of basic PSO algorithm are introduced. As for the deficiency of only velocity constraint, we propose an improve PSO algorithm considering complete kinematic constraints of all joints, which can ensure that the optimal trajectory planned is reasonable. Finally, the simulation experiments of time optimization and trajectory planning are carried out in MATLAB. Experiments show that after optimizing the interpolation time by the improved PSO algorithm under the kinematic constraints, the total time of trajectory planning of the robot is greatly reduced by 74.35%; and the planned time-optimal trajectory curves are continuous and smooth, which meets the requirements of motion stability of the manipulator. As a result, the efficiency of the improved PSO algorithm applied to time-optimal trajectory planning of robot is verified.
Density-Based Optimal UAV Path Planning for Photovoltaic Farm Inspection in Complex Topography

In order to improve power generation efficiency and reduce land cost, many large-scale photovoltaic (PV) farms are built on the mountain. The rugged terrain of the mountains leads to an irregular distribution of the PV modules' location. Compared with manual inspection, the efficiency of unmanned aerial vehicles (UAVs) inspection is much higher. The path planning of scattered PV modules has become a challenge. In this paper, we use improved Density-Based Spatial Clustering of Applications with Noise (DBSACN) to partition the entire area. In order to reduce the number of turns of UAV, the partitions tend to divide the same row of PV modules into the same area. For the PV modules in each area, the full coverage path planning (CPP) of the boustrophedon will be adopted, and the lateral spacing between the PV modules can fly directly. Finally, the path control points are smoothed using Bezier Curves interpolation.
Compositional Diagrammatic First-Order Logic

Peirce’s $$\beta $$ variant of Existential Graphs (EGs) is a diagrammatic formalism, equivalent in expressive power to classical first-order logic. We show that the syntax of EGs can be presented as the arrows of a free symmetric monoidal category. The advantages of this approach are (i) that the associated string diagrams share the visual features of EGs while (ii) enabling a rigorous distinction between “free” and “bound” variables. Indeed, this diagrammatic language leads to a compositional relationship of the syntax with the semantics of logic: we obtain models as structure-preserving monoidal functors to the category of relations. In addition to a diagrammatic syntax for formulas, Peirce developed a sound and complete system of diagrammatic reasoning that arose out of his study of the algebra of relations. Translated to string diagrams we show the implied algebraic structure of EGs sans negation is that of cartesian bicategories of relations: for example, lines of identity obey the laws of special Frobenius algebras. We also show how the algebra of negation can be presented, thus capturing Peirce’s full calculus.
Feature Selection on K-Nearest Neighbor Algorithm Using Similarity Measure

Data mining was the data processing technique to be obtained knowledge or important pattern of data. One of the popular methods was the KNN (K-Nearest Neighbor) which was computational simplicity. There was some weakness of KNN, vulnerable in the data high dimensionality. It was caused of data high dimensionality, so that space can be occupied in instance to be greater. The method approach would be proposed in the research was the method to be omitted several numbers of features were irrelevant to the KNN method by using similarity measures (Euclidean distance, Correlation distance and Cosine similarity). The testing was done by testing the different three datasets and computed the average of accurate results. The results of testing have successes to be omitted the data features without decreasing accuracy, that the accuracy average of feature selection using the KNN algorithm: KNN without selection was 88.804%, KNN-Euclidean distance was 89.120%, KNN-Correlation distance is 89.567% and KNN-Cosine similarity was 89.134%.
Path Planning for Robot Based on IACO-SFLA Hybrid Algorithm

In order to solve the problem of slow convergence, low efficiency and easily getting into local optimal solution in robot path planning which is based on basic ant colony algorithm, a new algorithm combining improved ant colony algorithm (IACO) with shuffled frog leaping algorithm (SFLA) is proposed. In the novel ant colony algorithm, dynamic state transition strategy, dynamic global pheromone updating strategy and dynamic pheromone evaporation factor updating strategy are used to ensure the accuracy of solution and the convergence speed of the algorithm. Furthermore, conditional fallback strategy is adopted to ensure the global search ability of the novel algorithm. Numerical experiments show that the optimal path can be more effectively generated by using the improved leapfrog algorithm and the simplified operator in the last step.
± 1Highly Undetectable Stegosystem Model Using Digital Still Images

This article presents a model of a highly undetectable stegosystem using digital still images as covering objects. The main principle of using the model is to minimize adequately specified distortion using an effective encoding algorithm. Distortion is defined as an operation ± 1 on a specially selected image (covering object) byte which is selected from the condition of minimizing the penalty function calculated on some selected neighborhood of this byte. This allows us to preserve the original image in almost unchanged form and make it indistinguishable even after the introduction of large payloads. Thus, the embedded hidden message may not be visible at steganalysis even for the experienced steganalytic. The stegosystem model was named ±1 Highly Undetectable SteGOsystem model. In short, it can be designated as ± 1HUGO model [10]. This model can be implemented to embed hidden messages in the spatial domain of digital still images much more efficiently than using the LSB algorithm. The proposed stegosystem model provides higher performance than the LSB algorithm and allows you to send a longer message, while providing higher resistance to detection.
A Prediction of Grain Yield Based on Hybrid Intelligent Algorithm

Abstract The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm algorithm (SSA) for predicting grain yield is prone to fall into the local optimal problem. An improved SSA is proposed with combine with back propagation neural network. Using the different advantages of SSA algorithm in global search capabilities, combining the two for further optimize the weight, improve the accuracy and robustness of the grain yield prediction model. The specific implementation is selected from 1963 to 2013. These methods are used to define agricultural datasets that supports crop growth decision for grain product and its influencing factors were tested as a data set. The results show that, the improved salp swarm optimization can be classified as a good predict tool for the domestic food production trend in recent years compared with the SSA. This paper briefly introduces three artificial methods BP neural networks, SSA and improved SSA optimization algorithm. The natural behavior of salp, barrel-shaped plankton that are mostly water by weight optimization and combined with mixed-group of intelligent algorithm are simulated. The simulation results of grain production prediction illustrate that the predict precision of the improved SSA is much higher than of both conventional BPNN and SSA techniques and it’s very efficient and practicable.
Mining of Association Rules in R Using Apriori Algorithm

Association rules are the strong rules which occur frequently in the dataset. Association Mining is the technique used to explore these rules with the help of various algorithms available in data mining. This paper discusses the use of apriori() to mine the strong rules which are helpful to find out the customer purchasing pattern and help to increase the sale. In this paper, R language is used and arules() package is used to mine the rules based on the value of support and confidence.
Review on Blockchain Applications for Electric Power Systems

This article describes some of the current theoretical developments and models based on Blockchain technology, suitable for application in modern electric power systems. Because of their significant advantages over the conventional models currently in use, they have the potential to replace them completely or partially soon. Some of these are decentralized nature, transparency and authenticity of transactions, protection against malicious manipulation of data, etc.
Hybrid Deduplication System—A Block-Level Similarity-Based Approach

Deploying deduplication for primary storage is a challenging task in view of random access patterns of I/O requests and the requirement of quick response time. Existing deduplication approaches designed for primary workloads are locality based with the assumption that I/O requests follow the locality principle. However, primary workloads in cloud systems need not necessarily follow the locality principle, and hence, the existing methods for deduplication are likely to exhibit poor response time. To provide a solution to this challenging problem, we propose and implement a hybrid deduplication system (HDS), a block-based partial deduplication system with similarity-based indexing. The proposed system applies deduplication in the background to decrease the latency and also aims at decreasing the data fragmentation. It applies similarity-based indexing to reduce high number of metadata lookups arising out of random access patterns of the requests. HDS for primary workloads is simulated in the Linux environment using three different types of FIU traces, and the effectiveness of the system is compared with full deduplication based on the parameters-metadata access overhead, average segment length, and response time. The experimental results show that the system has performed consistently better in reducing the metadata overhead and increasing the average segment length for all three sets of I/O trace data.
Multi-use Deterministic Public Key Proxy Re-Encryption from Lattices in the Auxiliary-Input Setting

Proxy Re-Encryption (PRE) is a cryptographic primitive that allows a proxy to turn an Alice’s ciphertext into a Bob’s ciphertext on the same plaintext. All of the PRE schemes are public key encryption and semantic security. Deterministic Public Key Encryption (D-PKE) provides an alternative to randomized public key encryption in various scenarios where the latter exhibits inherent drawbacks. In this paper, we construct the first multi-use unidirectional D-PRE scheme from Lattices in the auxiliary-input setting. We also prove that it is PRIV1-INDr secure in the standard model based on the LWR. Finally, an identity-based D-PRE is obtained from the basic construction.
Selection of Cloud Service Provider Based on Sampled Non-functional Attribute Set

The successful service allocation is a result of the successful decisions made by the Cloud Service Provider. The decision must be effective and on time, for survival, to get competitive advantages and to increase profitability of an organization. In this scenario, the increasing number of Cloud Service Providers posts the challenge to measure the services on the basis of non-functional attributes also. In the literature, researchers have developed the optimization techniques to distribute the workload. A lot of work is done to optimize the functional attributes but through this research work we tried to propose a mathematical model to select the service provider based on some sampled non-functional attribute set.
Numerical study of filtration process of ground and pressure waters in multilayer porous media

Abstract A article discusses the process of forecasting changes in the level of ground and pressure water. A brief analysis and computational experiments of scientific papers on mathematical and numerical modeling of the object under study are given. For a comprehensive study of the problem under consideration, a mathematical model was developed that takes into account the external source, evaporation, filtration coefficients, active porosity, filtration rate and two-way boundary conditions. An effective numerical algorithm has been developed for predicting changes in the ground water level using a combination of finite-difference schemes and run-through methods. It has been studied that changes in the level of ground and pressure water, filtration permeability, water loss coefficient and filtration rate associated with the water level can have a serious impact on the environmental process.
Application of temporal self-organizing maps to patterning short-time series of fish behavior responding to environmental stress

Response behaviors of animals to stressors are complicated. Especially, it is difficult to analyze temporal data in addressing the time course development of behavioral states to stressors. Not much study has been conducted in identifying the short-term behavior conditions of indicator organisms responding to stressors. Hence, in the present study, we selected three temporal networks, Temporal Kohonen Map (TKM), Recurrent Self-Organizing Map (RSOM), and Recursive Self-Organizing Map (RecSOM) to characterize the behavior strength of individual fish exposed to Atrazine (ATZ, 0.12 mg/L) for 15 days. The trained networks were able to detect the characteristic behavior strength segments presenting the Stepwise Behavior Response Model (SBRM). TKM was found suitable for detecting strong curves in a short training time, but clustering was limited in grouped segments. In contrast, RecSOM was found to be most suitable for detecting behavior segments with both high and low contrast response under toxic exposure. RecSOM was effective in clustering but could not detect segments with strong changes. Finally, our results presented that the temporal SOMs, especially RecSOMs, were feasible in identifying short-term time series data and could be utilized for on-line monitoring of test organisms under stressful conditions.
A Hybrid PSO-MiLOF Approach for Outlier Detection in Streaming Data

Efficient outlier detection is important for the efficient performance and reliability of different types of telecommunication networks and applications, especially in wireless sensor network (WSN) scenarios. Still it is quite challenging to detect outliers in streaming data as the bulk of data to be examined is practically unbounded and can arrive at a high data rate. Most of the Local Outlier Factor (LOF) based algorithms suffer from large memory requirements, as well as high time complexity, hence they have limited use in practice for outlier detection on streaming data. To overcome the computationally intensive iterative training data stage of the LOF algorithms Swarm Intelligence (SI) based methods can be incorporated. Such hybrid outlier detection approaches can combine the properties of both LOF and SI methods. The utilization of Particle Swarm Optimization (PSO) based on bird flocks' behavior are well known SI algorithms for solving optimization problems. This paper proposes a hybrid algorithm for outlier detection in streaming data based on PSO and Memory Efficient LOF. The major idea behind such an approach when solving the problem of finding the local outliers in streaming data is to meet the performance indicators of high detection accuracy, low computation time and limited memory usage.
Adversarial Filters for Secure Modulation Classification

Classification (MC) is the problem of classifying the modulation format of a wireless signal. In the wireless communications pipeline, MC is the first operation performed on the received signal and is critical for reliable decoding. This paper considers the problem of secure MC, where a transmitter (Alice) wants to maximize MC accuracy at a legitimate receiver (Bob) while minimizing MC accuracy at an eavesdropper (Eve). This work introduces novel adversarial learning techniques for secure MC. We present adversarial filters in which Alice uses a carefully designed adversarial filter to mask the transmitted signal, that can maximize MC accuracy at Bob while minimizing MC accuracy at Eve. We present two filtering-based algorithms, namely gradient ascent filter (GAF), and a fast gradient filter method (FGFM), with varying levels of complexity. Our proposed adversarial filtering-based approaches significantly outperform additive adversarial perturbations (used in the traditional machine-learning (ML) community and other prior works on secure MC) and have several other desirable properties. In particular, GAF and FGFM algorithms are a) computational efficient (allow fast decoding at Bob), b) power-efficient (do not require excessive transmit power at Alice); and c) SNR efficient (i.e., perform well even at low SNR values at Bob).
Fast Griffin Lim based waveform generation strategy for text-to-speech synthesis

The performance of text-to-speech (TTS) systems heavily depends on spectrogram to waveform generation, also known as the speech reconstruction phase. The time required for the same is known as synthesis delay. In this paper, an approach to reduce speech synthesis delay has been proposed. It aims to enhance the TTS systems for real-time applications such as digital assistants, mobile phones, embedded devices, etc. The proposed approach applies Fast Griffin Lim Algorithm (FGLA) instead Griffin Lim algorithm (GLA) as vocoder in the speech synthesis phase. GLA and FGLA are both iterative, but the convergence rate of FGLA is faster than GLA. The proposed approach is tested on LJSpeech, Blizzard and Tatoeba datasets and the results for FGLA are compared against GLA and neural Generative Adversarial Network (GAN) based vocoder. The performance is evaluated based on synthesis delay and speech quality. A 36.58% reduction in speech synthesis delay has been observed. The quality of the output speech has improved, which is advocated by higher Mean opinion scores (MOS) and faster convergence with FGLA as opposed to GLA.
Hierarchical Combinatorial Auction in Computing Resource Allocation for Mobile Blockchain

The mobile blockchain has been recognized as an emerging solution to address the security and privacy issues in a mobile application system. The mining process in mobile blockchain requires high computing resources which could overwhelm that which mobile devices can offer. In this case, mobile edge computing servers (MESs) can be involved to offer computing services to miners in mobile blockchain. Note that the resources of MESs are also limited; MESs could further request resources from the cloud computing server (CCS). Accordingly, the issue of hierarchical computing resource allocation arises. In this paper, we first consider a simple case with single-seller multiple buyers and a hierarchical single-seller multibuyer combinatorial auction model is proposed to solve this problem, based on which efficient and truthful frameworks are provided. We then extend the model to consider multiple CCSPs and propose a hierarchical multiple-seller multiple-buyer combinatorial auction model. For both models, the winner determination problems are formulated and computationally tractable algorithms are proposed. Also, pricing schemes are proposed to ensure the property of incentive compatibility and individual rationality. Finally, we evaluate the proposed schemes via simulations.
Conjunctive Queries: Unique Characterizations and Exact Learnability

We answer the question of which conjunctive queries are uniquely characterized by polynomially many positive and negative examples and how to construct such examples efficiently. As a consequence, we obtain a new efficient exact learning algorithm for a class of conjunctive queries. At the core of our contributions lie two new polynomial-time algorithms for constructing frontiers in the homomorphism lattice of finite structures. We also discuss implications for the unique characterizability and learnability of schema mappings and of description logic concepts.
A simple deep learning based image illumination correction method for paintings

Image illumination correction has been a long standing topic for research in the Computer Vision problem. However, all previous literature on this topic has either been statistical in nature in the sense that a specified algorithm has been developed for approaching a particular case of illumination normalization, or involves extremely complex deep learning methods for illumination correction of either one of over illuminated or under illuminated images. We present here a very simple deep learning based image illumination correction architecture which works on color images of paintings irrespective of whether they are under or over illuminated. We have tested the results using a synthetic database as well as on real world painting images of diverse nature.
Robust Learning via Ensemble Density Propagation in Deep Neural Networks

Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for robust learning that builds upon Bayesian estimation and Variational Inference. We formulate the problem of density propagation through layers of a DNN and solve it using an Ensemble Density Propagation (EnDP) scheme. The EnDP approach allows us to propagate moments of the variational probability distribution across the layers of a Bayesian DNN, enabling the estimation of the mean and covariance of the predictive distribution at the output of the model. Our experiments using MNIST and CIFAR-10 datasets show a significant improvement in the robustness of the trained models to random noise and adversarial attacks.
Deep Cross-Output Knowledge Transfer Using Stacked-Structure Least-Squares Support Vector Machines

This article presents a new deep cross-output knowledge transfer approach based on least-squares support vector machines, called DCOT-LS-SVMs. Its aim is to improve the generalizability of least-squares support vector machines (LS-SVMs) while avoiding the complicated parameter tuning process that occurs in many kernel machines. The proposed approach has two significant characteristics: 1) DCOT-LS-SVMs is inspired by a stacked hierarchical architecture that combines several layer-by-layer LS-SVMs modules. The module that forms the higher layer has additional input features that consider the predictions from all previous modules and 2) cross-output knowledge transfer is used to leverage knowledge from the predictions of the previous module to improve the learning process in the current module. With this approach, the model's parameters, such as a tradeoff parameter C and a kernel width δ , can be randomly assigned to each module in order to greatly simplify the learning process. Moreover, DCOT-LS-SVMs is able to autonomously and quickly decide the extent of the cross-output knowledge transfer between adjacent modules through a fast leave-one-out cross-validation strategy. In addition, we present an imbalanced version of DCOT-LS-SVMs, called IDCOT-LS-SVMs, given that imbalanced datasets are common in real-world scenarios. The effectiveness of the proposed approaches is demonstrated through a comparison with five comparative methods on UCI datasets and with a case study on the diagnosis of prostate cancer.