Reviewerly/exHarmony huggingface dataset:
Back analysis of displacement of deep foundation pit based on genetic algorithm

At present, the deformation prediction during the construction of foundation pit is still in the exploratory stage. The more common prediction methods are to calculate the deformation based on numerical simulation software, or to find the law between the excavation conditions and the deformation according to the field measured data and use it for prediction of subsequent operating conditions. The deformation calculation accuracy of the numerical simulation software depends on the value of the model parameters, but the geotechnical parameters themselves are difficult to determine, the excavation process of the foundation pit is more complicated, and the excavation process of deep foundation pits is more complicated.The difficulty in determining the parameters can be efficiently overcome by doing an inverse analysis of the rock and soil characteristics based on field monitoring data (deformation, stress, etc.).According to the parameters obtained from the inverse analysis, the subsequent working conditions are simulated and calculated, and the deformation and stress of the foundation pit in the subsequent working conditions can be predicted.When comparing the excavation displacement inversion curve to the actual deformation curve of the soil layer parameters after inversion, the change trend of the two is essentially the same, and the relative error of the inversion is small. This paper uses the genetic algorithm to perform the inverse analysis of the engineering monitoring displacement. Experiments have proved the effectiveness of genetic algorithm prediction.
Reconstruction and teaching implementation of English teaching resources in smart classrooms

The demands of teaching English nowadays cannot be met by the traditional teaching method due to the rapid advancements in information technology. In order to meet the demands of smart classroom teaching, it is required to reconstruct traditional English teaching resources and enhance the traditional classroom’s organizational structure. This paper examines the attributes of intelligent teaching, reconstructs English teaching materials on the network platform, and employs information-based teaching organization forms to innovate teaching models to satisfy social development for talent training needs.
Kinodynamic optimal trajectories generation in cluttered environments using Bernstein polynomials

We develop a trajectory planning strategy for autonomous agents using Bernstein polynomials. The focus is on generating trajectories that are functionally controllable for under-actuated vehicles and that guarantee a safe path to avoid obstacles. The algorithm is suitable for local replanning and onboard quick re-computation of the trajectory when unplanned obstacles pop up. By leveraging on the properties of Bernstein polynomials the problem is cast into a quadratic programming problem that can be solved in polynomial time.
Inverse Cubature and Quadrature Kalman filters

Recent developments in counter-adversarial system research have led to the development of inverse stochastic filters that are employed by a defender to infer the information its adversary may have learned. Prior works addressed this inverse cognition problem by proposing inverse Kalman filter (I-KF) and inverse extended KF (I-EKF), respectively, for linear and non-linear Gaussian state-space models. However, in practice, many counter-adversarial settings involve highly non-linear system models, wherein EKF's linearization often fails. In this paper, we consider the efficient numerical integration techniques to address such nonlinearities and, to this end, develop inverse cubature KF (I-CKF) and inverse quadrature KF (I-QKF). We derive the stochastic stability conditions for the proposed filters in the exponential-mean-squared-boundedness sense. Numerical experiments demonstrate the estimation accuracy of our I-CKF and I-QKF with the recursive Cram\'{e}r-Rao lower bound as a benchmark.
Protein Sequence Design with Batch Bayesian Optimisation

Protein sequence design is a challenging problem in protein engineering, which aims to discover novel proteins with useful biological functions. Directed evolution is a widely-used approach for protein sequence design, which mimics the evolution cycle in a laboratory environment and conducts an iterative protocol. However, the burden of laboratory experiments can be reduced by using machine learning approaches to build a surrogate model of the protein landscape and conducting in-silico population selection through model-based fitness prediction. In this paper, we propose a new method based on Batch Bayesian Optimization (Batch BO), a well-established optimization method, for protein sequence design. By incorporating Batch BO into the directed evolution process, our method is able to make more informed decisions about which sequences to select for artificial evolution, leading to improved performance and faster convergence. We evaluate our method on a suite of in-silico protein sequence design tasks and demonstrate substantial improvement over baseline algorithms.
People Make Decisions Using Heuristics. I

In some cases, instead of looking for an optimal solution, people use some heuristic ideas–with the hope that these ideas will lead to reasonable quality decisions. In this chapter and in the following chapter, we will give two examples of such heuristic ideas, and we will provide a justification for these ideas.
Scale-Invariance Explains the Empirical Success of Inverse Distance Weighting and of Dual Inverse Distance Weighting in Geosciences

LetScale-invariance Inverse distance weighting Distance weightinginverse Dual inverse distance weighting Distance weightingdual inverse us now consider the cases when we have a large number of observations and measurement results. Since we have many observations that cover many possible situations, a natural idea is to use these observations to predict what will happen in other cases. We can do it on a purely mathematical level–by simply interpolating the known observations, or we can also take into account the corresponding physics and related causality relation. In this chapter, we analyze the interpolation scenarios; in the following two chapters, we analyze how to take causality into account.
Construction and Application of Bid Fraud Prediction Model Based on AdaBoost Algorithm

In recent years, with the development of Internet big data technology and e-commerce platform, many active offline transaction methods have gradually shifted to online. Online auctions have come a long way due to COVID-19, but bidding fraud has seriously disrupted the health of the industry. In this paper, the AdaBoost model is used to build a bidding fraud prediction model, and the prediction performance of the model is verified by data experiments, and it is found that it has a high accuracy for identifying bidding fraud. At present, there are few prediction models for bidding fraud, and it has broad development prospects.
The Method of Processing Abnormal Values for Flight Test Data Based on Wavelet Transform

In the flight test data, there usually are abnormal values due to uncertainties in acquisition, storage and transmission. Processing the abnormal values is essential to ensure the accuracy of subsequent test data analysis. In this paper, we propose an abnormal value processing method based on wavelet transform for flight test data. We use the summation of high- frequency wavelet details as the characteristic quantity for abnormal value detection. Moreover, the detection threshold is constructed based on the idea of constant false alarm detection to locate and process the abnormal values. Simulation results show that the method can detect and process the abnormal values in the time-series flight test data fast and accurately.
Segmentation free text recognition for overlapping characters using spectral features and bidirectional recurrent wavelet neural network

This paper addresses the problem of text recognition with overlapping and touching characters which is very challenging task due to its inability to be segmented effectively. A novel framework based on word level recognition has been presented in this work which does not require the character level segmentation. Dynamic window selection technique for different aspect ratios of input images is presented to overcome limitations of existing techniques whose performance is subject to the normalised window size and uniform aspect ratio prior to the feature extraction. The combination of discrete wavelet transform (DWT) and histogram of oriented gradients (HOG) is used to represent the characteristics of overlapping and touching characters. The robustness and accuracy in the recognition phase is enhanced through bidirectional adaptive recurrent Wavelet Neural Network (BRWNN). The word accuracy in the proposed work is achieved to be 83.14% which is better than the conventional techniques over the dataset IAM-DB.
Improving the performance of web-streaming by super-resolution upscaling techniques

In recent years, we have seen significant progress in advanced image upscaling techniques, sometimes called super-resolution, ML-based, or AI-based upscaling. Such algorithms are now available not only in form of specialized software but also in drivers and SDKs supplied with modern graphics cards. Upscaling functions in NVIDIA Maxine SDK is one of the recent examples. However, to take advantage of this functionality in video streaming applications, one needs to (a) quantify the impacts of super-resolution techniques on the perceived visual quality, (b) implement video rendering incorporating super-resolution upscaling techniques, and (c) implement new bitrate+resolution adaptation algorithms in streaming players, enabling such players to deliver better quality of experience or better efficiency (e.g. reduce bandwidth usage) or both. Towards this end, in this paper, we propose several techniques that may be helpful to the implementation community. First, we offer a model quantifying the impacts of super resolution upscaling on the perceived quality. Our model is based on the Westerink-Roufs model connecting the true resolution of images/videos to perceived quality, with several additional parameters added, allowing its tuning to specific implementations of super-resolution techniques. We verify this model by using several recent datasets including MOS scores measured for several conventional up-scaling and super-resolution algorithms. Then, we propose an improved adaptation logic for video streaming players, considering video bitrates, encoded video resolutions, player size, and the upscaling method. This improved logic relies on our modified Westerink-Roufs model to predict perceived quality and suggests choices of renditions that would deliver the best quality for given display and upscaling method characteristics. Finally, we study the impacts of the proposed techniques and show that they can deliver practically appreciable results in terms of the expected QoE improvements and bandwidth savings.
Efficient Deep Reinforcement Learning via Policy-Extended Successor Feature Approximator

Successor Features (SFs) improve the generalization of Reinforcement Learning across unseen tasks by decoupling the dynamics of the environment from the rewards. However, the decomposition highly depends on the policy learned on the task, which may not be optimal in other tasks. To improve the generalization of SFs, in this paper, we propose a novel SFs learning paradigm, Policy-extended Successor Feature Approximator (PeSFA) which decouples the SFs from the policy by learning a policy representation module and inputting the policy representation to SFs. In this way, when we fit SFs well in the policy representation space, we can directly obtain a better SFs corresponding to any task by searching the policy representation space. Experimental results show that PeSFA significantly improves the generalizability of SFs and accelerates the learning process in two representative environments.
Applications of virtual reality in marketing (2000-2020): a 4C marketing-mix based review and future research agenda

The last decade has seen a rapid technological transformation in the way marketing is thought and performed. Virtual reality (VR) is one such technology that is being applied across marketing channels to creatively engage, communicate, sell, and service the consumers. This paper is a systematic literature review of the last 20 years of research on applications of virtual reality in marketing. This paper provides a descriptive review of literature, tracks the evolution of the virtual reality concept from technological to experiential view, and outlines the application of VR across Lauterborn's 4C marketing mix - consumer, cost, communication, and convenience. These practical applications will help marketers to better understand this technology and use it to transform the way they engage their consumers. The future research areas will help the academicians to advance the research in the field.
Learning Assistance using Image Media in Increasing Reading Interest of Students of Grade 1 Madrasah Ibtidaiyah Darul ' Ula Tarokan Lor Village

This activity was motivated by the use of image media in increasing students' reading interest. learning that is used by someone by using a tool made to facilitate the delivery of material when teaching at school. There are still many schools that are only concerned with cognitive aspects without looking at student learning problems, thus making students bored and lazy to study. The importance of using picture media is that teaching attracts students' attention and teaching materials will be more clearly understood by students. The implementation method in this service is the preparation, implementation, and unstructured interviews. The results of learning assistance using image media have a positive impact on both students and teachers, this can be seen from the results of interviews revealed by the homeroom teacher of class 1 MI and the responses of students and teachers who feel helped by the existence of learning mentoring activities because this activity makes students become better to understand the material being taught.
Quantum Challenges for EDA

Though early in its development, quantum computing is now available on real hardware and via the cloud through IBM Quantum. This radically new kind of computing holds open the possibility of solving some problems that are now and perhaps always will be intractable for "classical" computers.
Development of Speech Recognition System for Remote Vocal Music Teaching based on Markov model

Abstract With the popularization of smart homes, car audio systems and various speech recognition software, speech recognition systems have gradually entered people's sights, and are favored by most users because of their practicability and accuracy. Cognition is an important interface for human-computer interaction. It will become a research focus in the field of artificial intelligence. It plays an important role in cultivating the basic characteristics of music and cultivating students' interest in music, and vocal music teaching. Teaching traditional vocal music education to students in the form of classrooms, such as vocal music, arrangement, and bel canto. The disadvantage is the lack of communication between the classroom and teachers and students. On the other hand, the development of Internet technology provides a new teaching method for traditional vocal music teaching, and provides a network infrastructure for building a vocal teaching system platform. Therefore, this article provides a preliminary construction of a remote vocal music education platform by combining vocal music education with Internet technology. The remote audio and video training system is a complex and relatively large project with multiple functions. Introduce important functions in this system. At the same time, register and log in to the remote voice and video implementation requirements and system functions respectively to realize functions such as video training and video-on-demand training.
JIT Compiler Security through Low-Cost RISC-V Extension

Language Virtual Machines (VM) need to be extremely efficient and hence use complex engines such as a JIT compiler to speed up the usual bytecode interpretation loop. Their usage of low-level and security-critical tasks make them targets of choice. Enforcing low-cost fine-grained memory isolation has been an important research focus as a countermeasure to the most advanced JIT attacks. Memory isolation splits the components of an application with controlled communication and verified access to other resources. We present how custom instructions linked to hardware-enforced domain-checking could protect JIT code and data. We present incremental solutions and their corresponding custom instructions. The generated machine code and extended RISC-V Rocket come at a low-cost both in performance and intrusiveness.
Simulating Malaria Detection in Laboratories using Deep Learning

Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. While the WHO has set audacious goals of reducing malaria incidence and mortality rates by 90% in 2030 and eliminating malaria in 35 countries by that time, it still remains a difficult challenge. Computer-assisted diagnostics are on the rise these days as they can be used effectively as a primary test in the absence of or providing assistance to a physician or pathologist. The purpose of this paper is to describe an approach to detecting, localizing and counting parasitic cells in blood sample images towards easing the burden on healthcare workers.
Outlook: Agile as the New Normal?

This chapter deals with the rise of agile practices in the disruptive environment of the “new normal”. It explains why a broad introduction of home office forces organisations into agility. In this situation, the use of agile tools and a reliable output measurement provides much needed support. The chapter closes with an outlook of the introduction of the concept of agility into the EFQM model as well as into ISO management standards.
Utilisation of Online Public Access Catalogue (OPAC) among Students of Federal University of Agriculture, Abeokuta, Nigeria

Libraries house information in various forms such as prints and non-print materials. Considering the avail ability of various resources in the library, there is need for information retrieval systems that will enable users to have access to required information as at when needed. Modern libraries are increasingly being rede fined to provide unrestricted access to information in various sources and formats. Academic libraries differ from one another in many respects, but they all have the same basic function, which is to aid the parent institu tion to carry out its objectives in the areas of teaching, learning, and community development. Libraries con tribute to the realisation of these objectives and support
OSS Supply-Chain Security: What Will It Take?

A discussion with Maya Kaczorowski, Falcon Momot, George Neville-Neil, and Chris McCubbin
Interaction-based Fault Detection and Classification Using Randomly Masked 1D Convolutional Neural Network

Fault detection and classification (FDC) refers to a data analysis technique that classifies normal and abnormal situations. Data for FDC is being collected in various processes such as manufacturing processes, and various algorithms such as machine learning and deep learning are used in FDC. In particular, convolutional neural network (CNN) models have the advantage of automatically extracting data features and are widely used in analysis, which has a limitation that it is difficult to directly reflect the relationship between variables in the model in multivariate time series data. This work proposes a randomly masked convolutional neural network (RM-CNN) model that enhances FDC performance by adding intervariable relationships to a 1D CNN algorithm. The proposed methodology can directly reflect the relationship between variables in the model to improve FDC performance and identify variables that have a lot of influence. The experiments were conducted by comparison with a typical 1D CNN model, and evaluation indicators used Accuracy, Precision, Recall, and F1 score. As a result of the experiment, the proposed methodology performed better than the comparative models.
The Problem of Developing Scientific and Methodological Support of Subjects of Interdepartmental Cooperation in the Implementation of the Strategy for the Development of Education of the Russian Federation

The paper discloses the structure of scientific and methodological support for interagency cooperation in the implementation of the Strategy for the Development of Upbringing in the Russian Federation that includes the coordination of work of legislative authorities and public administration bodies of the education system, scientific institutions of the Russian Academy of Education system, institutions of secondary professional and higher education system and the system of advanced training of education workers, educational institutions and additional images. The content and structure of scientific and methodological support of interdepartmental cooperation in the implementation of the Strategy for the Development of Upbringing in the Russian Federation are proposed.
An Analysis of Quality Assurance and Quality Analysis Techniques

An Analysis of Quality Assurance and Quality Analysis Techniques
Data-Parallel Momentum Diagonal Empirical Fisher (DP-MDEF):Adaptive Gradient Method is Affected by Hessian Approximation and Multi-Class Data

Although data-parallel learning can efficiently reduce training time, the accuracy becomes poor when the batch size is extremely large. Adam, a prominent adaptive gradient optimizer which uses second-order information, is known to have better convergence than SGD, but its performance in large batch training highly depends on actual problems and the reasons for this remain unclear. While attempting answer to this, we found there are two factors playing import roles: the estimation accuracy of Hessian and the number of classes in data. Aiming to improve the former one, we introduce a new method called data-parallel momentum diagonal empirical Fisher (DP-MDEF), which collects the variance of the gradients between parallel workers for gradient adaptation and obtains superior precision compared to Adam and Momentum-SGD in large batch training. To discuss the latter one, we found that when the number of classes in a data-set is large, the variances of gradients form a long-tailed distribution, in which case gradient adaptive methods give bad solutions.
Extremism on Social Media: Lynching of Priyantha Kumara Diyawadana

Extremist content on social media platforms has led to tragic acts of violence. A context-aware extremist content framework is the need of the hour to ensure the detection and mitigation of this type of content. This work provides an outline of our recently launched initiative to develop a context-aware framework. We also present the rudimentary results of the lynching of “Priyantha Kumara Diyawadana” to illustrate the impact of online extremist propaganda on social media platforms. Our results indicate that nearly 25 % of the total population included in the gathered data have a negative sentiment toward the lynching of Priyantha Kumara Diyawadana, demonstrating how extreme hate-mongering ex-tremist narratives are affecting social media users.