Reviewerly/exHarmony huggingface dataset:
Evaluation Criteria for Trajectories of Robotic Arms

This paper presents a complex trajectory evaluation framework with a high potential for use in many industrial applications. The framework focuses on the evaluation of robotic arm trajectories containing only robot states defined in joint space without any time parametrization (velocities or accelerations). The solution presented in this article consists of multiple criteria, mainly based on well-known trajectory metrics. These were slightly modified to allow their application to this type of trajectory. Our framework provides the methodology on how to accurately compare paths generated by randomized-based path planners, with respect to the numerous industrial optimization criteria. Therefore, the selection of the optimal path planner or its configuration for specific applications is much easier. The designed criteria were thoroughly experimentally evaluated using a real industrial robot. The results of these experiments confirmed the correlation between the predicted robot behavior and the behavior of the robot during the trajectory execution.
Speech to text Community Application using Natural Language Processing

Android-based mobile applications are on-demand these days. People in a community can communicate with each other through internet-based platforms using cloud firebase. Since typing messages is a cumbersome activity, a speech to text application would cater for a hassle-free mode of communication. A flutter based android application integrated with natural language processing can capture the spoken words and digitize them into textual format. The efficacy of the application depends on the vocal parameters of the speaker that includes- pitch, loudness, intonation etc. On the basis of the audio captured, the application processes the text and displays it on the screen. The application uses a cloud firebase for user authentication and sign-in. Once a user registers on this application, they can communicate with others using the speech to text feature. The performance of the application is evaluated using hidden Markov models that take variegated forms of audio input into consideration. The precision of the application is 91.5%, recall is 95.4%, f1 score is 86.8% and accuracy is 89%.
An Analysis of the Linguistic Features of Popular Chinese Online Fantasy Novels

Previous studies found that linguistic features can be used to predict the success of novels. However, which specific linguistic features better contribute to a novel's popularity is unclear. This study addressed this issue by investigating the linguistic features of 2,008 online Chinese fantasy novels with different popularity (indicated by the Baidu Index). Specifically, word part-of-speech, personal pronouns, word complexity, and local/overall sentence semantic coherence were analyzed using a word segmentation tool (Jieba) and a latent semantic analysis software (Chinese version of Coh-Metrix). Results showed significant differences between popular and non-popular (high and low popularity) novels in the distribution of parts-of-speech, use of the second person pronoun, word complexity, and sentence semantic coherence. Moreover, the presence of the second person pronoun ("you"), local sentence semantic coherence, auxiliary words, word complexity, overall sentence semantic coherence, and adjectives better predicted the popularity of a Chinese online fantasy novel. The theoretical background and the implications of these results are detailed in the study discussion.
Human in the Loop Fuzzy Pattern Tree Evolution

Abstract Fuzzy pattern trees evolved using grammatical evolution, a system we call Fuzzy Grammatical Evolution, are shown to be a robust Explainable Artificial Intelligence technique. Experimental results show Fuzzy Grammatical Evolution achieves competitive results when compared against SVM, Random Forest and Logistic Regression on a set of real world benchmark problems. Fuzzy Grammatical Evolution allows for human input throughout the evolutionary process. Regularization methods and double tournament selection were investigated to determine what method was most successful at finding smaller, more interpretable models. A domain expert was recruited to investigate the interpretability of the models found and to give a confidence score for each model. This expert successfully identified overfit models, validating that Fuzzy Grammatical Evolution can be regarded a powerful Explainable Artificial Intelligence technique.
Research on the Improvement Method of Music Education Level under the Background of AI Technology

To improve the demand pressure of traditional music on space, music equipment, and funds in the teaching mode, this study presents a music education model based on the model view controller (MVC) framework structure based on AI technology; the model optimizes traditional music education by intelligent adjustment of computational audio and three-frequency equalizer, through the integration of artificial intelligence and music education; seven feasible explorations are put forward; and its effects and the value and future development trend of artificial intelligence in music education were also analyzed. Subsequently, the bivariate t-test was used to check and accept the teaching effect of the implemented music education classroom, and teacher and parent satisfaction with music education in the AI background was compared. The results show that, compared with traditional teaching models, music education based on AI background has high satisfaction among students, teachers, and parents. This study lays a solid foundation for the future integration of music education and intelligent technology, and the investment of artificial intelligence can effectively expand the popularization of high-quality music education, thus expanding the level of universal music education in China, improving the national music literacy, and providing valuable development ideas for the research of artificial intelligence technology in other teaching fields.
The path-planning in radioactive environment based on HIOSD-PRM method

To ensure the safety of workers in the radioactive environment of nuclear facilities, it is necessary to develop an effective method to plan the walking path with a relatively small dose based on the information of the radiation field. In this paper, a stable and effective path planning method in the complex static radiation field is proposed based on the high-index optimization-based shrinking dimer (HIOSD) method and probabilistic roadmap (PRM) method. In a two-dimensional radiation field, local paths consist of plenty of dimers connecting saddle points to local minima can be dynamically built by the HIOSD method. With these local paths, the PRM algorithm combined with the A* algorithm is applied to optimize the whole walking path from the start point to the goal point based on the dose calculation model. To verify the effectiveness of our method, two application cases in hypothetical radiation fields are shown and we compared the results of the HIOSD-PRM method and the traditional PRM method. The results show that the average cumulative dose of the former in both cases is lower than those of the PRM method and it is worth mentioning that in case 2 with more sources, the HIOSD-PRM method shows very good stability. The built local paths which have much potential can be used multiple times and they can reduce the uncertainty of heuristic algorithms. Overall, this method is suitable for static energy fields with multiple radioactive sources and can reduce the radiation exposure to the workers in the radioactive environment of nuclear facilities.
Learning Competitive Swarm Optimization

Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.
Blockchain Technology in Smart Grids and Microgrids: A Critical Review of Challenges and Opportunities

Our Electricity or energy market was designed a long time ago. The regulation that applies to it today that disciples the innovation were put in place about a 100 years ago has been lobbied to keep in place. Since then, there have been massive inefficiencies in the entire ecosystem and infrastructure, and the control and distribution of energy today are quite antiquated. Much regulation prevents new companies from implementing a new system, so it becomes necessary to work with the incumbents to integrate this system. Therefore, this paper aims to frame today's situation of the energy markets’ state. Also, to find the opportunity for a new energy paradigm. We point out how the current public blockchain infrastructure is generally incompatible with today's energy infrastructure. Furthermore, it also enlightens the new infrastructures that will enable the transition and examples of applications in the energy infrastructure.
Online Knowledge Distillation by Temporal-Spatial Boosting

Online knowledge distillation (KD) mutually trains a group of student networks from scratch in a peer-teaching manner, eliminating the need for pre-trained teacher models. However, supervision from peers can be noisy, especially in the early stage of training. In this paper, we propose a novel method for online knowledge distillation by temporal-spatial boosting (TSB). The proposed method constructs superior "teachers" with two modules, temporal accumulator and spatial integrator. Specifically, the temporal accumulator leverages the previous outputs of networks during training and produces a representative prediction over all classes. Instead of merely imitating the outputs of other networks as in vanilla online KD, we further propose the so-called spatial integrator that consolidates the knowledge learned by all networks and yields a stronger instructor. The operations of these two modules are simple and straightforward, which can be computed efficiently on the fly during training. The proposed method can improve the efficiency of transferring effective knowledge as well as stabilize the training process. Experimental results on various benchmark datasets and network structures validate the effectiveness of the proposed method over the state-of-the-art.
A hybrid ensemble and evolutionary algorithm for imbalanced classification and its application on bioinformatics

Imbalanced data classification is the fundamental problem of data mining. Relevant researchers have proposed many solutions to solve the problem, such as sampling and ensemble learning methods. However, random under-sampling is easy to lose representative samples, and ensemble learning does not use the correlation information between pieces in the data set. Therefore, we proposed a Hybrid Adaptive sampling with Bagging Classifier(HABC). Specifically, we calculated the adaptive sampling rate according to the characteristics of the data set. We then performed density-based under-sampling and over-sampling on the original data set according to the sampling rate. Further, the sampled data subset was sent to the Bagging classifier, and the classifier was employed to predict the unknown data set. In addition, the multi-objective particle swarm optimization algorithm was combined to optimize the prediction result. Extensive experiments based on UCI, KEEL, and three bioinformatics datasets show that our proposed method is better than state-of-the-art algorithms.
Restoration of deteriorated text sections in ancient document images using a tri-level semi-adaptive thresholding technique

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A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

The process of recognizing manufacturing parts in real time requires fast, accurate, small, and low-power-consumption sensors. Here, we describe a method to extract descriptors from several objects observed from a wide range of angles in a three-dimensional space. These descriptors define the dataset, which allows for the training and further validation of a convolutional neural network. The classification is implemented in reconfigurable hardware in an embedded system with an RGB sensor and the processing unit. The system achieved an accuracy of 96.67% and a speed 2.25× faster than the results reported for state-of-the-art solutions. Our proposal is 655 times faster than implementation on a PC. The presented embedded system meets the criteria of real-time video processing and it is suitable as an enhancement for the hand of a robotic arm in an intelligent manufacturing cell.
Some results on the Gaussian Markov Random Field construction problem based on the use of invariant subgraphs

Abstract The study of Gaussian Markov Random Fields has attracted the attention of a large number of scientific areas due to its increasing usage in several fields of application. Here, we consider the construction of Gaussian Markov Random Fields from a graph and a positive-definite matrix, which is closely related to the problem of finding the Maximum Likelihood Estimator of the covariance matrix of the underlying distribution. In particular, it is simultaneously required that the variances and the covariances between variables associated with adjacent nodes in the graph are fixed by the positive-definite matrix and that pairs of variables associated with non-adjacent nodes in the graph are conditionally independent given all other variables. The solution to this construction problem exists and is unique up to the choice of a vector of means. In this paper, some results focusing on a certain type of subgraphs (invariant subgraphs) and a representation of the Gaussian Markov Random Field as a Multivariate Gaussian Markov Random Field are presented. These results ease the computation of the solution to the aforementioned construction problem.
Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering. The close vicinity of minimum distance measurements is done for these clusters, mapped for proper classification and decision process of behavioral pattern. To identify the data acquired, support vector machines and artificial neural networks are utilized. A method known as an adaptive-unidirectional associative memory (AUTAM) was used to map one side of a human face to the other side of the same subject. The behavioral pattern has been detected based on two-class problem classification, and the decision process has been done using a genetic algorithm with best-fit measurements. The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET, Multi-PIE, Yale Face database, RTR, CASIA, etc. The complexity measures have also been calculated under worst-case and best-case situations.
Financial structure convergence

Abstract Financial structure, the degree to which a country's financial system is market‐ or bank‐based, matters for economic outcomes. Hence, it is important to understand the drivers of financial structure. This paper sheds light on this issue and explores a specific mechanism in the evolution of financial structure, namely convergence. Financial structure is shown to converge across countries over time. This pattern remains similar even after controlling for macroeconomic and institutional factors, or for banking sector characteristics. It is not specific to a region, and appears to be similarly strong around the globe. However, financial structure convergence is somewhat weaker in the emerging market and developing economies relative to the advanced economies. Given the role of financial structure in economic performance, these findings have implications for macroeconomic and financial policies. Moreover, bridging cross‐country gaps in financial structure has potential consequences for income differences across countries.
Soft labeling constraint for generalizing from sentiments in single domain

In this work, we deal with domain generalization in sentiment analysis. In traditional domain generalization systems, multiple source domains are used to generalize to a single target domain. However, we tackle the scenario where examples of sentiments from only one domain are available. Recent works have proposed to generate target domain examples from a single source domain by means of an adversarial training, ensuring that generated examples performs well on classifier trained on source domain. However, the inherent assumption is that domain shift is only due to covariate shift. In our work, we argue that, in realistic scenarios such as sentiment analysis, there is significant change in label distribution across domains as well. Subsequently, we propose a soft labeling formulation that provides better generalization and more robust classifiers across unseen sentiment domains. Experimental results on the Amazon-reviews benchmark dataset show the effectiveness of the proposed formulation.
Software mitigation of coherent two-qubit gate errors

Abstract Two-qubit gates are important components of quantum computing. However, unwanted interactions between qubits (so-called parasitic gates) can be particularly problematic and degrade the performance of quantum applications. In this work, we present two software methods to mitigate parasitic two-qubit gate errors. The first approach is built upon the Cartan’s KAK decomposition and keeps the original unitary decomposition for the error-free native two-qubit gate. It counteracts a parasitic two-qubit gate by only applying single-qubit rotations and therefore has no two-qubit gate overhead. We show the optimal choice of single-qubit mitigation gates. The second approach applies a numerical optimisation algorithm to re-compile a target unitary into the error-parasitic two-qubit gate plus single-qubit gates. We demonstrate these approaches on the CPhase-parasitic iSWAP-like gates. The KAK-based approach helps decrease unitary infidelity by a factor of 3 compared to the noisy implementation without error mitigation. When arbitrary single-qubit rotations are allowed, recompilation could completely mitigate the effect of parasitic errors but may require more native gates than the KAK-based approach. We also compare their average gate fidelity under realistic noise models, including relaxation and depolarising errors. Numerical results suggest that different approaches are advantageous in different error regimes, providing error mitigation guidance for near-term quantum computers.
Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks

Encoding-decoding (ED) CNNs have demonstrated state-of-the-art performance for noise reduction over the past years. This has triggered the pursuit of better understanding the inner workings of such architectures, which has led to the theory of deep convolutional framelets (TDCF), revealing important links between signal processing and CNNs. Specifically, the TDCF demonstrates that ReLU CNNs induce low-rankness, since these models often do not satisfy the necessary redundancy to achieve perfect reconstruction (PR). In contrast, this paper explores CNNs that do meet the PR conditions. We demonstrate that in these type of CNNs soft shrinkage and PR can be assumed. Furthermore, based on our explorations we propose the learned wavelet-frame shrinkage network, or LWFSN and its residual counterpart, the rLWFSN. The ED path of the (r)LWFSN complies with the PR conditions, while the shrinkage stage is based on the linear expansion of thresholds proposed Blu and Luisier. In addition, the LWFSN has only a fraction of the training parameters (<1%) of conventional CNNs, very small inference times, low memory footprint, while still achieving performance close to state-of-the-art alternatives, such as the tight frame (TF) U-Net and FBPConvNet, in low-dose CT denoising.
Few shot object detection for headdresses and seats in Thangka Yidam based on ResNet and deformable convolution

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Strong entanglement distribution of quantum networks

Large-scale quantum networks have been employed to overcome practical constraints of transmission and storage for single entangled systems. Our goal in this paper is to explore the strong entanglement distribution of quantum networks. We firstly show that any connected network consisting of generalized Einstein-Podolsky-Rosen states and Greenberger-Horne-Zeilinger states satisfies the strong Coffman-Kundu-Wootters monogamy inequality in terms of the bipartite entanglement measure; in addition, the monogamy inequalities are also considered for generic entangled quantum networks. This reveals the interesting feature of high-dimensional entanglement with local tensor decomposition going beyond qubit entanglement. We then apply the entanglement distribution relation in entangled networks to get a quantum maximum-flow minimum-cut theorem in terms of von Neumann entropy and R\'enyi-$\ensuremath{\alpha}$ entropy. We finally classify entangled quantum networks by distinguishing network configurations under local unitary operations. These results provide insights into characterizing quantum networks in quantum information processing.