Reviewerly/exHarmony huggingface dataset:
Deriving the Optimal Strategy for the Two Dice Pig Game via Reinforcement Learning

Games of chance have historically played a critical role in the development and teaching of probability theory and game theory, and, in the modern age, computer programming and reinforcement learning. In this paper, we derive the optimal strategy for playing the two-dice game Pig, both the standard version and its variant with doubles, coined “Double-Trouble”, using certain fundamental concepts of reinforcement learning, especially the Markov decision process and dynamic programming. We further compare the newly derived optimal strategy to other popular play strategies in terms of the winning chances and the order of play. In particular, we compare to the popular “hold at n” strategy, which is considered to be close to the optimal strategy, especially for the best n, for each type of Pig Game. For the standard two-player, two-dice, sequential Pig Game examined here, we found that “hold at 23” is the best choice, with the average winning chance against the optimal strategy being 0.4747. For the “Double-Trouble” version, we found that the “hold at 18” is the best choice, with the average winning chance against the optimal strategy being 0.4733. Furthermore, time in terms of turns to play each type of game is also examined for practical purposes. For optimal vs. optimal or optimal vs. the best “hold at n” strategy, we found that the average number of turns is 19, 23, and 24 for one-die Pig, standard two-dice Pig, and the “Double-Trouble” two-dice Pig games, respectively. We hope our work will inspire students of all ages to invest in the field of reinforcement learning, which is crucial for the development of artificial intelligence and robotics and, subsequently, for the future of humanity.
Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer, resulting in an inaccurate diagnosis.As a result, it needs an efficient despeckling method for ultrasound images in clinical practice and telemedicine.This article proposes a novel adaptive fuzzy filter based on the directionality and translation invariant property of the Non-Sub sampled Contour-let Transform (NSCT).Since speckle-noise causes fuzziness in ultrasound images, fuzzy logic may be a straightforward technique to derive the output from the noisy images.This filtering method comprises detection and filtering stages.First, image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image.Then, the system adaptively selects the better-suited filter for the specific image region, resulting in significant speckle noise suppression and retention of detailed features.The suggested approach uses a weighted average filter to distinguish between noise and edges at the filtering stage.In addition, we apply a structural similarity measure as a tuning parameter depending on the kind of noise in the ultrasound pictures.The proposed methodology shows that the proposed fuzzy adaptive filter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.
Robust Deep Transfer Learning Based Object Detection and Tracking Approach

At present days, object detection and tracking concepts have gained more importance among researchers and business people.Presently, deep learning (DL) approaches have been used for object tracking as it increases the performance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Annotation with ResNet based Faster regional convolutional neural network (R-CNN) named (AIA-FRCNN) model.The AIA-RFRCNN method performs image annotation using a Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR) called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker, which involves region proposal network (RPN) and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals, which are utilized by Fast R-CNN for detection process.Besides, Residual Network (ResNet 101) model is used as a shared convolutional neural network (CNN) for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer, which tunes the hyperparameters namely learning rate, batch size, momentum, and weight decay.Finally, softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.
Dynamic Time and Location Information in Ciphertext-Policy Attribute-Based Encryption with Multi-Authorization

Due to the mobility of users in an organization, inclusion of dynamic attributes such as time and location becomes the major challenge in Ciphertext-Policy Attribute-Based Encryption (CP-ABE).By considering this challenge; we focus to present dynamic time and location information in CP-ABE with multi-authorization.At first, along with the set of attributes of the users, their corresponding location is also embedded.Geohash is used to encode the latitude and longitude of the user's position.Then, decrypt time period and access time period of users are defined using the new time tree (NTT) structure.The NTT sets the encrypted duration of the encrypted data and the valid access time of the private key on the data user's private key.Besides, single authorization of attribute authority (AA) is extended as multi authorization for enhancing the effectiveness of key generation.Simulation results depict that the proposed CP-ABE achieves better encryption time, decryption time, security level and memory usage.Namely, encryption time and decryption time of the proposed CP-ABE are reduced to 19% and 16% than that of existing CP-ABE scheme.
Simulating the Dilution of Sulfuric Acid on Mixed Reality Platform

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. Virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students.
Are<i>Daojing</i>and<i>Dejing</i>stylistically independent of each other: A stylometric analysis with activity and descriptivity

Abstract It has been qualitatively evidenced that Daojing and Dejing, the two sections of DaoDejing, are independent of each other in content, style, and authorship. However, this claim remains controversial because there has been scant quantitative study of them that probes into their syntactic structure to quantitatively evidence the independence due to methodological challenges to quantitative approaches. To address this problem, the present study employed two indexes of quantitative linguistics, activity and descriptivity, to capture the syntactic features of the inner structure of Daojing and Dejing to validate the independence as of the above. Results of this study reveal that (1) the chi-square test shows that both sections are significantly active texts; (2) the u-test and nonparametric two-related-sample test show that the two texts do not differ from each other significantly either holistically or by the 300-character segments in terms of activity and descriptivity; and (3) the activity sequences (Q-sequence) in the two sections can be both better fitted by two different functions, viz. the beta-function and the Morse function. Moreover, with the development of the text, a synergic relationship is found in the distribution of verbs and adjectives, indicative of a dynamic and complex self-regulating process of the text development. To conclude, contrary to the claim of prior studies that they were written by different authors, our stylometric study has quantitatively demonstrated that Daojing and Dejing are not independent of each other stylistically, especially in terms of activity and descriptivity.
Saliency detection network with two-stream encoder and interactive decoder

In recent years, there has been an increasing amount of works on improving the salient object detection performance based on the edge information. However, previous studies applied a parameter-sharing encoder to extract edge features and region features, leading to incomplete regions and blurry edges. In this paper, we focus on the independence and complementarity between salient edges and regions, and propose a novel salient object detection network with two-stream encoder and interactive decoder(TSEID). Specifically, instead of a parameter-sharing encoder, we construct a two-stream encoder consisting of the region extraction branch and the edge extraction branch to balance the feature domain differences between the regions and edges. Meanwhile, we develop a super-pixel segmentation-based method to extract the salient edges, so that the generated distinctive edges will ensure the integrality of edge features. The region features are obtained via a feature pyramid network and hierarchically enhanced with Channel Adaptation (CA) and Spatial Adaptation (SA). Further, the extracted region features and edge features are fed into an interactive decoder to explore their complementarity. Accordingly, a location guidance module (LGM) and an edge compensation module (ECM) are designed to refine object positions, enrich object boundaries, and generate saliency features. Extensive experiments on five challenging benchmarks well demonstrate that our approach outperforms existing deep learning-based methods, including the latest edge-guided ones.
Cross-domain knowledge distillation for text classification

Most text classification methods achieve great success based on the large-scale annotated data and the pre-trained language models. However, the labeled data is insufficient in practice, and the pre-trained language models are difficult to deploy due to their high computing resources and slow inference speed. In this paper, we propose cross-domain knowledge distillation, where the teacher and student tasks belong to different domains. It not only acquires knowledge from multiple teachers but also accelerates inference and reduces model size. Specifically, we train the pre-trained language models on factual knowledge obtained by aligned Wikipedia text to Wikidata triplets and fine-tune it as the teacher model. Then we use the heterogeneous multi-teacher knowledge distillation to transfer knowledge from the multiple teacher models to the student model. Multi-teacher knowledge vote can distill knowledge related to the target domain. Moreover, we also introduce the teacher assistant to help distill large pre-trained language models. Finally, we reduce the difference between the source domain and target domain by multi-source domain adaptation to solve the domain shift problem. Experiments on the multiple public datasets demonstrate that our method can achieve competitive performance while having fewer parameters and less inference time.
Fuzzy system design for automatic irrigation of agricultural fields

• Intelligent systems in the irrigation lead to better scheduling according to climatic and regional conditions. • The use of remote sensing in agricultural lands is less expensive than sensor-based hardware equipment. • Fuzzy systems based on remote sensing have more accuracy in calculating the water consumption. One of the main challenges in arid and semi-arid regions is the lack of water for various purposes such as industrial and agricultural applications. The problem is even more significant in traditional agriculture. The geographical location, climatic conditions, average annual rainfall, vegetation, air pollution and turbidity, and canopy of plants are important and lead to different water consumption in plants. Therefore, proper water consumption in agriculture in most countries leads to food security and a better supply of crops. In this research, we propose a fuzzy system that has eight inputs and seven outputs. Inputs include soil moisture, relative humidity, air temperature, wind speed, soil permeability, daily rainfall, vegetation, and air pollution. The Fuzzy outputs are the reference and water stress evapotranspiration, water pump condition, duration of water pump operation, the volume of water requirements for crops, irrigation efficiency, and water losses. The main feature of the fuzzy system is the prediction of water consumption in different climates with high accuracy. To prove high efficiency and accuracy, we have performed many analyzes in various geographical locations. The geographical locations are tropical, semi-mountainous, and mountainous areas. In addition, we have performed three different analyses in four periods using our fuzzy system. In the first and second modes are used the average daily and hourly data, respectively. Finally, in the third mode, some data of the second method, such as the impacts of air pollution, soil moisture, vegetation, and water infiltration rate in the soil, are not considered because most of the proposed approaches do not consider the factors. The results show that the second mode is more efficient, and in some farms saves about 70% in water consumption. In addition, the results show that the water consumption in autumn and winter is lower than in spring and summer. The fuzzy system has high efficiency on all crops in different climates and can prevent water stress or waterlogging. It irrigates as much water as the plants need and prevents water loss. One of the main factors in increasing the accuracy of the fuzzy system is the use of vegetation level, soil moisture, air pollution, soil type, and plant canopy. The research results show that the proposed system can be used in different climates and significantly reduce water consumption in agricultural lands. Regression, correlation coefficient, and RMSE analysis have been used to validate the fuzzy system.
Embedded Estimation Sequential Bayes Parameter Inference for the Ricker Dynamical System

The dynamical systems are comprised of two components that change over time: the state space and the observation models. This study examines parameter inference in dynamical systems from the perspective of Bayesian inference. Inference on unknown parameters in nonlinear and non-Gaussian dynamical systems is challenging because the posterior densities corresponding to the unknown parameters do not have traceable formulations. Such a system is represented by the Ricker model, which is a traditional discrete population model in ecology and epidemiology that is used in many fields. This study, which deals with parameter inference, also known as parameter learning, is the central objective of this study. A sequential embedded estimation technique is proposed to estimate the posterior density and obtain parameter inference. The resulting algorithm is called the Augmented Sequential Markov Chain Monte Carlo (ASMCMC) procedure. Experiments are performed via simulation to illustrate the performance of the ASMCMC algorithm for observations from the Ricker dynamical system.
Application of Big Data Technology in News Media Scene Visualization Based on Internet of Things (IoTs)

The rapid development of information technology has made people quickly enter the data era, and the big data era has made visualization technology that can process and analyze data with more and more attention. At present, most of the data news visualization explorations of domestic network media remain at the level of static infographics. The Data News and Visualization Laboratory of New Media has developed a series of visual news works displayed in the form of interactive dynamic graphs. This article will discuss the application scenarios, technical architecture, and key technologies of the Internet of Things in news media through the analysis of the correlation between the Internet of Things and news media.
Automatic English Pronunciation Evaluation Algorithm Based on Sequence Matching and Feature Fusion

This article focuses on the question answering type of automatic scoring system for large-scale spoken English examinations and scores using a method called multifeature fusion. Three types of features are extracted to score using speech recognition text as the research object. The three types of features are similarity, syntactic, and phonetic. There are nine distinct characteristics that describe the relationship between examinee responses and expert ratings. Manhattan distance is improved as a measure of similarity in the similarity feature. Simultaneously, a feature of keyword coverage based on editing distance is proposed, and the phenomenon of word variation in text recognition is fully considered, in order to provide examinees with an objective and fair score. To obtain the machine score, all extracted features were fused using a multiple linear regression model. The experimental results demonstrate that the extracted features are extremely effective for machine scoring, with the system scoring performance of the examinee as a unit equaling 98.4 percent of expert scoring performance.
English Writing Feedback Based on Online Automatic Evaluation in the Era of Big Data

Under the influence of the continuous development of modern information technology, the online automatic evaluation system for English writing teaching was born in response to demand. The rise of the online automatic assessment and correction system has broken through the limitations of the campus, making English writing teaching no longer restricted by time and space. Adapting to the needs of English writing development in the era of big data, making full use of modern information technology to improve writing effect, further promoting the development of educational informatization and the transformation of writing teaching, and realizing the mutual blending and innovative development of education and information technology have become the inevitable trend of future development. This paper applies the combination of teacher feedback and automatic feedback to the teaching of English writing in senior high school and discusses whether the combination of the two can change the disadvantages of the automatic writing evaluation system and solve the problem of traditional single teacher evaluation. In this paper, the text feedback system is introduced into the context information, and the context information is used to filter the difference between the active context and the active context, so as to further reduce the number of participants in collaborative filtering and improve the real-time computing efficiency of the algorithm. By distinguishing dynamic text quality indicators from stable text quality indicators, this paper proposes a similar context-aware neighborhood model. Based on experimental analysis, the accuracy of feedback results is 45.2% higher than that of the original online automatic evaluation system.
Big Data Analysis of Benign Interaction of Great Power Relations and New International Relations Based on Deep Learning

The development of a new type of international relations is the advancement and improvement of diplomatic thinking among contemporary nations. It also serves as a crucial yardstick for assessing the future global pattern and the direction of order changes. Proper interaction between major powers can foster the growth of new international relations and has a significant impact on advancing global cooperation and the promotion of human peace. The goal of this essay is to examine how friendly interactions between major powers have affected the development of new international relations. A deep learning network model is presented for this purpose. The deep learning model was used to identify the emotions of the survey results, analyze each person's emotional tendencies, and summarize and compare the data. Relevant questionnaire surveys were conducted using the online questionnaire survey method on individuals in various countries. The survey results in this paper demonstrate that 96.5 percent of Chinese, 89.3 percent of Russians, and 81.6 percent of Americans support friendly relations between major nations. Only a very small percentage of the investigators supported hostile relations, with their support being 1.06 percent, 3.11 percent, and 2.94 percent, respectively. Therefore, creating a win-win partnership between major powers is exactly what the people of all nations are calling for. In contrast to the past, it is no longer hostile and violent. People anticipate that more great powers will coexist peacefully.
PruneFaceDet: Pruning lightweight face detection network by sparsity training

Face detection is the basic step of many face analysis tasks. In practice, face detectors usually run on mobile devices with limited memory and computing resources. Therefore, it is important to keep face detectors lightweight. To this end, current methods usually focus on directly designing lightweight detectors. Nevertheless, it is not fully explored whether the resource consumption of these lightweight detectors can be further suppressed without too much sacrifice on accuracy. In this study, we propose to apply the network pruning method to the lightweight face detection network, to further reduce its parameters and floating point operations. To identify the channels of less importance, we perform network training with sparsity regularisation on channel scaling factors of each layer. Then, we remove the connections and corresponding weights with near-zero scaling factors after sparsity training. We apply the proposed pruning pipeline to a state-of-the-art face detection method, EagleEye, and get a shrunken EagleEye model, which has a reduced number of computing operations and parameters. The shrunken model achieves comparable accuracy as the unpruned model. By using the proposed method, the shrunken EagleEye achieves a 56.3% reduction of parameter size with almost no accuracy loss on the WiderFace dataset.