Reviewerly/exHarmony huggingface dataset:
Online Tuning of PID Controllers Based on Membrane Neural Computing

PID controllers are still popular in a wide range of engineering practices due to their simplicity and robustness. Traditional design of a PID controller needs manual setting of its parameters in advance. This paper proposes a new method for online tuning of PID controllers based on hybridized neural membrane computing. A neural network is employed to adaptively determine the proper values of the PID parameters in terms of evolving situations/stages in the control process. Further the learning of the neural network is performed based on a membrane algorithm, which is used to locate the weights of the network to optimize the control performance. The effectiveness of the proposed method has been demonstrated by the preliminary results from simulation tests.
Incremental Bayesian Classifier for Streaming Data with Concept Drift

Classification is an important task in the field of machine learning. Most classifiers based on offline learning are invalid for open data streams. In contrast, incremental learning is feasible for continuous data. This paper presents the Incremental Bayesian Classifier “Incremental_BC”, which continuously updates the probabilistic information according to each new training sample via recursive calculation. Further, the Incremental_BC is improved to deal with the flowing data whose distribution and property evolve with time, i.e., the concept drift. The effectiveness of the proposed methods has been verified by the results of simulation tests on benchmark data sets.
Image Caption Description Generation Method Based on Reflective Attention Mechanism

Aiming at the problem that the existing image caption model cannot model the dependence between the generated words, this paper introduces a reflective attention mechanism that uses the context vector generated at each time step to model the relationship between words, so that the model can fuse the previous words information when generating image description words, and finally generate more accurate and coherent image description sentences. The experimental results on the MSCOCO dataset show that the method is effective and has a great improvement in each evaluation metric.
An Efficient Heuristic Rapidly-Exploring Random Tree for Unmanned Aerial Vehicle

Autonomous unmanned aerial vehicle (UAV) can be utilized to replace humans to do hard work or work in dangerous environment. Path planning is one of the crucial technologies for intelligent flight of UAV. The Rapidly-exploring Random Tree (RRT) has a wide range of applications in path planning with the advantage of the sampling-based path planning which avoids complex construction of the configuration space. However, these methods perform a uniform random sampling and thus do not lead to the most efficient solution. In this paper, in order to improve the efficiency of the traditional RRT, a heuristic strategy with goal-bias was exploited and incorporated in the traditional RRT. A simulation platform was built to assess the performance of the improved algorithm and analyze the influences of the parameters, and a flight test platform was established based on the DJI Matrice 100 to carry out the flight experiments of UAV path planning. The simulations proved that the heuristic RRT is much higher efficient than the standard version. The experiments validated the feasibility to apply the new RRT in the navigation for UAVs in reality.
Cross-view Geo-localization Based on Cross-domain Matching

As a recently emerging problem, cross-view geo-localization aims at finding image pairs captured from different views (e.g., drone and satellite views) or domains yet same location, which can be widely employed in various applications. However, unlike traditional scene classification problem, it faces several challenges, including large intra-class distance and small inter-class distance caused by domain gap, as well as redundant contextual information and visual distractors across views. To address the concerns, we propose a novel cross-domain matching framework to handle this task, which measures the similarity for query and candidate images from two different domains. Comparing to prior classification based framework, our matching based framework is better suited for the task by forcing the model to learn discriminative features for scenes. Moreover, to aid cross-domain matching, we propose a matching-oriented feature modulation scheme, in which we not only apply a large-view attention module to enhance spatial features but also employ channel shuffling to loose the correlation of key feature semantics and distractors in the respective domains. Last, we conduct experiments to show that our model achieves the state-of-the-art performance and surpasses the competing method by a large margin on the public benchmarks.
Depth and Pixel-Distance Based Attention for Outdoor Semantic Segmentation

Semantic segmentation has been a crucial technology for various practical applications such as autonomous driving. Recently, attempts have been made to improve the performance of semantic segmentation using depth information. However, most attempts have been focused on the indoor environment for the following reasons. First, it is relatively more difficult to obtain accurate and dense depth information outdoors. Second, a network with a new structure is required to use depth information because processing depth as an input demands an additional encoder. To overcome aforementioned difficulties, we propose a novel Depth and Pixel-distance based Attention (DPA) module, which utilizes depth information to compute the similarity between pixels. The similarity of pixels is computed using the fact that pixels belonging to the same object have similar depth values. Because only the relative difference in depth is considered, it is relatively robust despite the accuracy of the provided depth information. Furthermore, DPA is a simple plug-in module that can be applied to existing RGB-based segmentation backbones. Since no encoder is added, it is much more efficient in terms of computation. We conduct extensive experiments on the Cityscapes dataset using various baseline architectures. Regardless of the baseline models, DPA yields meaningful performance improvements in semantic segmentation tasks. It is also computationally more efficient compared to the methods that take depth information as input.
Automated detection of colon cancer using deep learning

Colon cancer is a type of cancer that affects the large population. In the beginning, small ploys are formed in the large intestine, which if left untreated become cancer. Detection of colon cancer in its early stages reduces the risk of life to large extent and makes treatment easier and reduces the cost of treatment. The traditional way of detection of colon cancer is very time-consuming and often can be wrong if not done by a skilled person. The development of deep learning has enabled us to detect colon cancer accurately using histopathological images. In this chapter different pretrained models and techniques are used. The accuracy of results is very good; using ResNet50 99.8% accuracy is achieved on the test data which is very good. Using these techniques, the time for detection of colon cancer can be reduced significantly.
A Customized Deep Network Based Encryption-Then-Lossy-Compression Scheme of Color Images Achieving Arbitrary Compression Ratios

As encryption masks the content of the original image and thus statistical characteristics of the original image cannot be used to compress the encrypted version, compressing an encrypted image efficiently remains a significant challenge today. In this study, a novel encryption-then-lossy-compression (ETLC) scheme was developed using nonuniform downsampling and a customized deep network. Specifically, the nonuniform downsampling method integrates both uniform and random sampling to achieve an arbitrary compression ratio for an encrypted image. Lossy reconstruction from the decrypted and decompressed image is described as a constrained optimization problem, and an ETLC-oriented customized deep neural network (ETCNN) is elaborately designed to solve this problem. ETCNN contains three parts: channel-wise non-local attention including residual group and non-local sparse attention, a residual content supplementation (RCS), and a downsampling constraint (DC), where RCS and DC are customized modules exploiting specific features of the downsampling-based ETLC system. Extensive experimental simulations show that the proposed scheme outperforms the state-of-the-art ETLC methods remarkably, indicating the feasibility and effectiveness of the proposed scheme exploiting the nonuniform downsampling and ETCNN-based reconstruction. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hujuanzp/ETCNN</uri> .
Blockchain Technologies in the Perspective of Industrial Development: The Research Challenge

The article deals with the challenge of understanding blockchain technology in the perspective of the implementation of blockchain based solutions in the industry. The authors present a new methodological approach to the study of blockchain technologies as complex systems, with emphasis on the business ecosystems including the analytical framework for the research of the blockchain technology as a complex productive phenomenon and the application of graphical model comparing visibility and production usability of new technology to the industrial blockchain. The paper discusses to what extent the introduction of distributed registry technologies can improve the efficiency and effectiveness of management methods in industry. In this regard, the most relevant avenues for blockchain technology implementation are outlined. The proposed methodological approach is used to consider the perspectives of blockchain adoption in the industry and an ability of its particular applications to pass the hype stages in the near future. The benefits of large-scale blockchain implementation are discussed for each of the three main areas: tax, financial, management accounting and audit; the support of the cyber-physical systems within business ecosystems with the facilitating of the working capital flows and decentralized finance for production companies.
New Approach in Design of a Hybrid Course for the Personnel Reserve Training for Intelligent Enterprises

The article describes a new approach to creating a hybrid online course “Building an intelligent enterprise based on SAP S/4HANA”, which formed the basis of the program “SAP S/4HANA Academy”, designed to train the personnel reserve for the SAP partners and clients−SAP ecosystem in the CIS region. The training was conducted among undergraduate and graduate students of Russian and CIS universities. The new course design is considered in comparison with similar courses previously conducted by the SPbPU SAP Academic Competence Centre (SAP ACC SPbPU), the experience of which was taken into account and revised. The course structure, its theoretical and practical components are described in details, the course landscape and the tools used in its implementation are described as well. Separately, the aspects that are traditionally difficult while conducting mass online courses are considered: technical implementation of practical assignments, individual organisational and technical support for students. The summary of the authors’ experience presented in the article can be useful in the preparation and implementation of hybrid online courses of various directions.
Analysis graduates in getting a job using the CHAID methods

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Nonong Amalita, Alfi Azzahrah Fahira, Arnellis, Dina Fitria; Analysis graduates in getting a job using the CHAID methods. AIP Conference Proceedings 7 February 2023; 2698 (1): 020004. https://doi.org/10.1063/5.0122664 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Tight Bounds for Uncertain Time-Correlated Errors With Gauss–Markov Structure in Kalman Filtering

Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. This can be challenging for linear dynamic systems if the process noise or measurement errors have uncertain time correlation. In many systems (e.g., in satellite-based or inertial navigation systems), there are sources of time-correlated sensor errors that can be well modeled using Gauss–Markov processes (GMP). However, uncertainty in the GMP parameters, particularly in the correlation time constant, can cause misleading error bounds. In this article, we develop time-correlated models that ensure tight upper bounds on the estimation error variance, assuming that the actual error is a stationary first-order GMP with a variance and time constant that are only known to reside within an interval. We first use frequency-domain analysis to derive stationary GMP models in both the continuous and discrete-time domains, which outperform models previously described in the literature. Then, we derive an even tighter estimation error bound using a nonstationary GMP model, for which we determine the minimum initial variance that guarantees bounding conditions. Both models can easily be implemented in a linear estimator like the Kalman filter.
'Paying for Surprise?': A Systematic Review of Factors and Mechanisms Driving Mystery Box Transactions in E-Commerce

The mystery box market continues to expand in size and scope worldwide. However, little is still understood about the key factors and underlying mechanisms that drive mystery box transactions on digital platforms. In this paper, we present a synthetic literature review by first introducing related concepts and theoretical mechanisms that can help us understand this emerging phenomenon. We then synthesize key research findings and perspectives covered in recent studies. Drawing on this review, we propose three types of uncertainty: risk, ambiguity, and mystery, and discuss the key mechanisms as well as the broad effects of uncertainty in driving the popularity of such uncertainty-laden products. We conclude our review by proposing a number of high-value future research directions and discuss the relevant implications for policy makers and businesses. We contend that the insights from this study will contribute to a richer understanding of mystery boxes in both academia and practice.
Construction of linear codes over $\mathfrak{R}=\sum_{s=0}^{4} v_{5}^{s}\mathcal{A}_{4}$

The aim of this paper is to propose a new family of codes. We define this family over the ring R=∑^4_{s=0}v^s_5A_4, with v^5_5=v_5. We derive its properties, a generator matrix and Gray images. This new family of codes is illustrated by three applications.
Non Homogeneous Realistic Single Image Dehazing

A hazy image is one where atmospheric effects degrade the contrast and visibility of the image. It is often caused by the dispersion of light into the moisture particles present, smoke etc. This results in lower performance in high level vision tasks such as object detection, free space detection, scene understanding, etc. Hence the images have to be de-hazed before applying other high level algorithms. Dehazing is the process of reconstructing the original colour and contrast of the image if taken in normal conditions. Image dehazing is a non-trivial task as it is hard to collect haze free ground truth images. Further, achieving dehazed images when variable haze is present is a significantly harder challenge. In this research, we propose the Non Homogeneous RESIDE dataset (NH-RESIDE) that contains images created synthetically using the principles of randomness and representativeness. Experimental results show that the model trained on our dataset produces visually more pleasing images with a much better dehazing effect on real world images. The model implemented in this paper also outperforms the state-of-the-art models by a huge margin on the NH-Haze dataset proposed by the NTIRE Non Homogeneous Dehazing Challenge at CVPR, achieving an average PSNR of 25.69 and an average SSIM of 0.80. It also achieves much better processing times when compared to other models, thereby facilitating real-time performance.
Face image quality for actor profile image curation

Selecting an ideal profile image to represent a person is a common problem with many applications. The ideal char-acteristics of a representative or profile image differ based on the application. In this work, we focus on selecting a representative face which is easy to recognise and aesthetically pleasing. Manually curating these images is time consuming, repetitive, and subjective. This makes the quality of curated images inconsistent. We have built a solution to automate this process and make it efficient, scalable, and consistent. In this work, we describe the various factors which affect the suitability of a face image for recognition by humans. We propose efficient solutions which can solve the problem without the use of ground truth data. We train a regression model using weak supervision provided by heuristics based on features which affect face quality. Finally, we use professional photography techniques to create standardized and aesthetically pleasing profile images.
From basic approaches to novel challenges and applications in Sequential Pattern Mining

&lt;abstract&gt;&lt;p&gt;Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding statistically relevant regularities of patterns in sequentially ordered data. It has been an active area of research since mid 1990s. Even if many prime algorithms for SPM have a long history, the field is nevertheless very active. The literature is focused on novel challenges and applications, and on the development of more efficient and effective algorithms. In this paper, we present a brief overview on the landscape of algorithms for SPM, including an evaluation on performances for some of them. Further, we explore additional problems that have spanned from SPM. Finally, we evaluate available resources for SPM, and hypothesize on future directions for the field.&lt;/p&gt;&lt;/abstract&gt;
Comparison Slice Inverse Regression Method with Machine Learning Techniques in Multivariate Data

In this study, the research aims to use some methods that deal with several independent variables with a dependent variable, where two methods were used to deal with, which is the method of slice inverse regression (SIR), which is considered a non-classical method, and two methods of machine learning, which is (TLBO, PSO), which is most popular of the teaching methods machine learning, the work of (SIR), (TLBO, PSO) is based on making reduced linear combinations of a partial set of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of multicollinearity between most of the explanatory variables. These new combinations of linear compounds resulting from the two methods will reduce the largest number of explanatory variables to reach one or more new dimensions called the effective dimension. The root mean square error criterion will be used to compare the two methods to indicate the preference of the methods.
Action-Oriented Learning for Real-Life Language Use: Theory and Practice of Tasks Meeting the Criteria of the CEFR Companion Volume

The Spanish version of the Common European Framework of Reference for Languages (CEFR) Companion Volume, published in 2021, presented an interpretation of the developmental vision of tasks for language learning and use, introducing the concept of action-oriented learning. This study argues that a task enabling action-oriented learning for real-life language use needs to satisfy three criteria―collaboration, interaction, and real-life use―and allow for both situational and interactional authenticity. Based on the theoretical discussion around this concept, this study aimed to analyze the design and execution of an action-oriented learning task in an actual Spanish-language education. First, to explore task design, action-oriented tasks presented in four Spanish textbooks were analyzed to determine whether they satisfied the three criteria. Next, for consideration of the execution of such a task, a case involving the use of an action-oriented learning task that meets the requirements of collaboration, interaction, and real-life use in a conversational Spanish language class at a Korean university is presented and discussed. Through this analysis, the theory and practice of designing and executing collaborative, interactive, real-life tasks based on the communication needs of Korean university-level learners of Spanish is explored .
Safe Reinforcement Learning using Data-Driven Predictive Control

Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-making and continuous control tasks. However, applying RL algorithms on safety-critical systems still needs to be well justified due to the exploration nature of many RL algorithms, especially when the model of the robot and the environment are unknown. To address this challenge, we propose a data-driven safety layer that acts as a filter for unsafe actions. The safety layer uses a data-driven predictive controller to enforce safety guarantees for RL policies during training and after deployment. The RL agent proposes an action that is verified by computing the data-driven reachability analysis. If there is an intersection between the reachable set of the robot using the proposed action, we call the data-driven predictive controller to find the closest safe action to the proposed unsafe action. The safety layer penalizes the RL agent if the proposed action is unsafe and replaces it with the closest safe one. In the simulation, we show that our method outperforms state-of-the-art safe RL methods on the robotics navigation problem for a Turtlebot 3 in Gazebo and a quadrotor in Unreal Engine 4 (UE4).
SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer

Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems. Conventionally, the MIMO-OFDM receiver is performed by multiple cascaded blocks with different functions and the algorithm in each block is designed based on ideal assumptions of wireless channel distributions. However, these assumptions may fail in practical complex wireless environments. The deep learning (DL) method has the ability to capture key features from complex and huge data. In this paper, a novel end-to-end MIMO-OFDM receiver framework based on transformer, named SigT, is proposed. By regarding the signal received from each antenna as a token of the transformer, the spatial correlation of different antennas can be learned and the critical zero-shot problem can be mitigated. Furthermore, the proposed SigT framework can work well without the inserted pilots, which improves the useful data transmission efficiency. Experiment results show that SigT achieves much higher performance in terms of signal recovery accuracy than benchmark methods, even in a low SNR environment or with a small number of training samples. Code is available at https://github.com/SigTransformer/SigT.