Reviewerly/exHarmony huggingface dataset:
Sample-Efficient Multimodal Dynamics Modeling for Risk-Sensitive Reinforcement Learning

When we consider dynamics of pouring viscous liquid, there are multiple modes; e.g., liquid jams when the size of container opening is narrow. Our past work showed that using multiple skills (e.g., tipping and shaking a container) is effective to handle such tasks where stochastic neural networks were used to model the multimodal dynamics in a model-based control. It was possible because we could assume the output-state probability distribution is unimodal for an input state-action distribution. However, we have found that the output-state distribution becomes multimodal where the mode of dynamics switches, and the prediction of the stochastic neural networks becomes inaccurate due to the unimodal assumption. As the consequence, the model-based control may choose a risky skill and parameters. This paper explores modeling such multimodal dynamics. Since the output distribution becomes multimodal only where the dynamics mode switches, the number of available samples might be limited. Furthermore, we do not need to explicitly handle the output multimodality since our goal is a stochastic model-based control (dynamic programming). Thus, we propose to introduce a Gaussian mixture model to expand the variance of output distribution of the stochastic neural networks. This model can be easily unified into existing stochastic dynamic programming. The simulation experiments of pouring viscous liquid demonstrated that the proposed method improves the risk sensitivity.
Intelligent Input and Analysis System of English Characters from the Perspective of Semantic Recognition

This paper absorbs some basic ideas of Lexical Functional Grammar (LFG) and knowledge of semantic recognition, and presents an expectation-driven intelligent analysis system for English Chinese character input based on grammatical and semantic analysis. The language model BERT in the field of natural language processing proposes a method for hiding Chinese and English text information based on input errors. Since the features of Chinese and English texts are quite different, and the embedding method of this article is based on words, the task of Chinese word segmentation is also involved in Chinese text. Therefore, this article proposes information embedding methods and blind extraction methods for Chinese text and English text respectively. Method, this method can replace some characters in plain text with generated input errors to embed information.
Moment-based modulation classification for Rician time-varying aeronautical channels

Modulation classification is a classical topic in the field of signal classification, and is of great significance in various applications including aeronautical communications. However, changes in the features of the signals transmitted through the Rician time-varying channels deteriorate the performance of modulation classification. In this study, a robust moment-based algorithm is proposed to counter the influence of the channels. The statistical correlation of the received samples are utilized for the estimation on the Rician factor. According to the derivation of the relationship between the moments of the transmitted samples and the received samples, the features utilized for classification are compensated mathematically. Finally, decision tree and random forest classifier are presented for classification. The estimation and classification performance of the proposed algorithm are analyzed for aeronautical en-route (EN) and arrival/takeoff (AT) channels through simulations. Meanwhile, real world signals of L-band digital aeronautical communication system (L-DACS1) are utilized for verification. The results show that our algorithm has high classification accuracy and low computational complexity.
Comparative Analysis of Different Models for Covid-19 Trend Prediction

The critical situation of Covid-19 in recent years is gradually easing. However, it is still important to keep an eye on predicting future data about the pandemic. In this study, we compared the following three models: Long short-term memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Logistic to identify and evaluate the cons and pros with the implementation of python code. The data we used is based on the COVID-19 pandemic data of China from &#x201C;Our World in Data&#x201D;. The experimental results indicated that the Logistic achieved the best prediction performance, i.e, with the optimal RMSE.
VTuckeR: Multimodal Tucker Fusion for Scene Graph Generation

Originated from object segmentation and word vector representations, Scene Graph Generation (SGG) became a complex task built on enumerous research results. Today's scene graph generation (SGG) task is still far from practical, we believe that it's due to unfair matching strategies between images and object regions. Inspired by Tucker decomposition's success in VQA area, in this paper, we propose VTuckeR, a relatively straightforward but powerful linear model based on Tucker decomposition of the tensor representation of images and object features. In VTuckeR, we control the complexity of the merging scheme while keeping itself good interpretability. We show our model is able to enforce multiple types of Scene Graph Generation models in Visual Genome dataset in the PredCls mode. What's more, a more accurate scene graph may aid prediction of wireless channel dynamics in the future, which is called V2C.
Design of Intelligent Decision System via Computer Aided Translation Software Evaluation under the Background of Internet of Things

The identification and classification of professional terms of machine translation are studied in this work, to improve the accuracy and professionalism of computer aided translation (CAT) software. Firstly, the current situation and related fields of machine translation are analyzed to summarize the difficulties and shortcomings in machine translation. Secondly, the concept of term is introduced to conduct targeted research on the imbalance problem of terminology classification and recognition in machine translation. Thirdly, a term recognition model based on integrated recognition method is proposed. Finally, the classification accuracy and recall rate of the model are verified using the method of confusion matrix in experiments. The results demonstrate that in comparison of the recall rate, classification accuracy, and f value in different fields, the classification accuracy of network terms by the hybrid method combining the over-sampling method and under-sampling method is the highest of 77%, that of sports terms is the lowest of 71%, and that of economic terms is 74%. Among the recall rate, accuracy rate and f value, the recall rate is the highest, reaching more than 80%, especially for economic terms of 91%. The combination of over-sampling and under-sampling performs better than the under-sampling with playback and under-sampling without playback in terms of term recognition and classification in different fields. Through the classification results before and after integration, it is obvious that the integration of each base classifier not only effectively improves the classification accuracy of terms, but also greatly improves the recall rate. This term recognition model can help CAT software in improving the recognition accuracy of term translation, which has certain practical effects and provides reference for research in related fields.
Solving nonlinear differential equations using a hybrid method between the variational iteration method and grey wolf optimization algorithm

Done in this manuscript, an analytical method, Variation Iteration Method (VIM) has been hybridized with an intelligent method the Gray Wolf Optimization (GWO) algorithm, by solving nonlinear Ordinary Differential Equations, where the results obtained from the analytical method have been improved by (GWO) algorithm. The results obtained by calculating the mean square error (MSE) and estimating the maximum absolute error (MAE) proved the efficiency of this method.
Vision-Based Recognition of Construction Workers’ Hand Signals

The development of robotic machines has shown the potential to promote automation and address issues in construction. One of the critical steps to make these machines work with onsite construction workers as teams is to provide a user-friendly interface to support their interactions. So far, little related work could be found in the construction domain. This paper tried to fill the gap and proposed a vision-based framework to recognize the construction workers’ hand signals to interact with construction robotic machines. The framework includes three main components: (1) detection and tracking, (2) region cropping, and (3) hand signal recognition. Specifically, it started with the visual detection and tracking of a construction signalman from a video sequence. Based on the detection and tracking results, the regions of the signalman were cropped to form a hand signal recognition queue. Then, if a hand signal was detected, a pre-trained classifier would be used to identify the meaning of the signal. The framework was tested with two construction site videos. The test results indicated the effectiveness of the proposed framework on common construction hand signal recognition.
Mutual Noise Estimation Algorithm for Video Denoising

An innovative algorithm is recommended to approximate spatial-time based variance of noise in videos. This procedure practices mutual data from the three-dimensional and time-based noise figures to discover standardized wedges in a video border and evaluate the occurrence of noise. In this project we describe the implementation of algorithm titled mutual noise estimation for eliminating noise in the video. Videos are besmirched by noise while transmitting and video procurement, dispensation and broadcast. Noise worsens the filmic eminence of the videos and eradicating the noise significantly influences the efficacy and enactment of video handling solicitations like compression of video signal, breakdown, format alteration, motion approximation and filtering. Many noise estimation algorithms were recommended in the proposal to guess the amount of noise available in videos. Some of the noise assessment algorithms merely take aid of time-based information of videos, while some methods virginally rely on three-dimensional information. Other approaches make the aid of spatio-temporal domain of videos to upsurge the exactitude and dependability of their algorithms. The investigational outcomes demonstrate the precision and strength of the algorithm over videos with low to high three-dimensional and time-based complexities, and varying levels of noise power and noise correlations. By using this mutual algorithm, we can find the image quality metrics.
Progressive integration of visibility constraints for implicit functions

Procedurally-defined implicit functions, such as CSG trees and recent neural shape representations, offer compelling benefits for modeling scenes and objects, including infinite resolution, differentiability and trivial deformation, at a low memory footprint. The common approach to fit such models to measurements is to solve an optimization problem involving the function evaluated at points in space. However, the computational cost of evaluating the function makes it challenging to use visibility information from range sensors and 3D reconstruction systems. We propose a method that uses visibility information, where the number of function evaluations required at each iteration is proportional to the scene area. Our method builds on recent results for bounded Euclidean distance functions by introducing a coarse-to-fine mechanism to avoid the requirement for correct bounds. This makes our method applicable to a greater variety of implicit modeling techniques, for which deriving the Euclidean distance function or appropriate bounds is difficult.
The Solution for the Branching Factor of the Alpha-Beta Pruning Algorithm and its Optimality

chapter Share on The Solution for the Branching Factor of the Alpha-Beta Pruning Algorithm and its Optimality Author: Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 91–102https://doi.org/10.1145/3501714.3501724Online:04 March 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A parallel branch-and-bound algorithm with history-based domination

In this paper, we describe a parallel Branch-and-Bound (B&B) algorithm with a history-based domination technique, and we apply it to the Sequential Ordering Problem (SOP). To the best of our knowledge, the proposed algorithm is the first parallel B&B algorithm that includes a history-based domination technique and is the first parallel B&B algorithm for solving the SOP using a pure B&B approach. The proposed algorithm takes a pool-based approach and employs a collection of novel techniques that we have developed to achieve effective parallel exploration of the solution space, including parallel history domination, history table memory management, and a thread restart technique. The proposed algorithm was experimentally evaluated using the SOPLIB and TSPLIB benchmarks. The results show that using ten threads with a time limit of one hour on the medium-difficulty instances, the proposed algorithm gives a geometric-mean speedup of 19.9 on SOPLIB and 10.23 on TSPLIB, with super-linear speedups up to 65x seen on 17 instances.
Research on Postproduction of Film and Television Based on Computer Multimedia Technology

In the postproduction of film and television, the fast movement of objects or camera shake will lead to an excessive selection of key frames, resulting in information redundancy and affecting the stability of postproduction videos and video-editing effects. Aiming at this problem, this paper proposes a method of film and television postproduction based on digital media technology. Firstly, the film and video shots are segmented to find out the changing scenes; secondly, the bottom feature vector of each image is formed according to the color space entropy, motion vector, and motion region, and the video key frame is extracted based on digital media technology; finally, the pixel label information is established in the key frame, and the feature points located in the foreground are removed, while the time feature matching points and spatial feature matching points in the background are left, so as to achieve dynamic video mosaic. The experimental results show that the film and television postproduction method based on digital media technology has strong advantages compared with other methods, which can improve the stability score of video and reduce the average stitching error.
A User-based Collaborative Filtering System for Deck Recommendation in Game Clash Royale

The decision-making process is often crucial in video games. However, this process may be hard for players to master since it often requires players to accurately analyze and balance various factors. To assist players in making choices, several machine learning algorithms were proposed, one of which is Collaborative Filtering. In this paper, a Collaborating Filtering model improved by user profiles was developed based on the context of Clash Royale, a strategic game. The collaborative Filtering model served to help decide players&#x0027; decks, collections of cards necessary to battles. Its performance for different players database was evaluated, which showed reliable predicted winning rate that owns errors less than 10&#x0025; with real testing rate. The evaluation of the model shows that the model can achieve a performance equal to the skilled player, for the winning rates of recommended decks are similar to that of decks used by skilled players.
A method for support neuron selection in NMLI

By simulating the neural mechanism of cognition, NMLI (Neural Model with Lateral Interaction) was recently proposed for learning tasks. It can realize both supervised and unsupervised learning while achieving outstanding results on few-shot learning. Obeying Hebbian rules and the one-to-one correspondence between elementary neurons and samples, the number of inter-level connections in NMLI is consistent with the number of training samples. This forming mechanism, however, would result in huge computation when the training set is large. Inspired by the fact that neurons in the human brain are sparsely connected, as well as the related cognition phenomena, we propose a method for support neuron selection in NMLI. We first evaluate the validity of the inter-level connections. Based on the evaluation, only a few parts of elementary neurons are selected as support neurons, corresponding to the typical and special samples in the training set. Then, by considering only the support neurons, NMLI can form spares inter-level connections. In this way, the computational efficiency and the biological reasonability of the model are significantly improved. In addition, the proposed method adjusts neuronal connections according to prediction error, implying that back-propagation (BP) mechanism can be realized by synaptic plasticity. Experiments show that the proposed method reduced the test time of NMLI while maintaining accuracy. Furthermore, it shows the potential to improve the efficiency of other neural models.
Flood Mitigation in Banjar Regency, South Kalimantan, Indonesia in 2021: Between Hydro-meteorological factor and Anthropogenic factor

Abstract The year 2021 in Banjar Regency and several other regencies in South Kalimantan Province opened with floods disaster. High rainfall caused the river to overflow; one of the rivers that overflowed was the Martapura River in Banjar Regency. The flood in 2021 is not the first flood disaster to occur, but a floods disaster that has occurred frequently. This flood is a fairly large flood compared to the previous flood. The areas affected by the large flood in Banjar Regency are in the Districts of Sungai Tabuk, Martapura Kota, East Martapura, West Martapura, Astambul, Karang Intan, and Pengaron. The method used in this research is to analyze the hydro-meteorological using the rainfall-runoff model, and the anthropogenic factors in the flood events in Banjar Regency be approached by the value of the C coefficient. The hydro-meteorological factors as natural factors cannot be predicted. The extreme rainfall on January 14, 2021 as 255.3 mm is higher than 100-years return period as 244.716 mm. Its caused biggest flood disaster in January 2021. The anthropogenic factors caused by humans can be reduced. One of the anthropogenic factors that have the most impact on any flood event is land-use change. Land-use change causes rainwater that falls cannot infiltrate into the ground. One of the most common community activities is to close the yard and the drains so that rainwater does not have access to penetrate the ground. Second, the conversion of agricultural land, and swampland into residential and industrial areas. The third factor that is no less important is the role of the community in development that is not environmentally friendly, one of them by dredging for houses or other buildings. These three anthropogenic factors cause the C value to increase, and it increases the flood discharge.
A HYBRID TEXTURAL AND GEOMETRICAL FEATURE EXTRACTION TO REVEAL HIDDEN INFORMATION FROM SUSPICIOUS REGIONS ON MAMMOGRAMS

A mammographic feature extraction scheme through textural and geometrical descriptors is examined to implement in a computer-aided diagnosis system for breast cancer diagnosis in this paper. This scheme is verified on a selected subset of suspicious regions (Region of Interest – ROIs) detected on a publicly available mammogram image database constructed by the Mammographic Image Analysis Society. The ROI detection is succeeded using the Chan-Vese active contour modelling after some pre-processing operations which are median filtering, morphological operations, and a region growing method performed for digitization noise reduction, artifact suppression and background removal, and pectoral muscle removal, respectively, applied on mammogram images. Then, a new adaptive convex hull approach is introduced for extracting geometrical descriptors of the ROIs accompanied by the Haralick features extracted from the gray-level co-occurrence matrices for textural description. In addition to geometrical and textural features, a hybrid mammographic feature vector is constructed by concatenating these features. All the three feature vectors are separately utilized to diagnose the ROIs via Random Forest classifier using 5-fold cross-validation. The experimental studies show that the textural features diagnose benignity more specifically and malignancy more accurately; and they are more effective on discriminating healthy ROIs when concatenated with geometrical features. Hence, a feature combination of these three features is proposed for diagnosis. The proposed feature combination is determined to be more effective for more accurate diagnoses of benignity and malignancy.
Hierarchical secret sharing scheme for WSN based on linear homogeneous recurrence relations

Wireless sensor network is a key technology in the sensing layer of the Internet of Things. Data security in wireless sensor network is directly related to the authenticity and validity of data transmitted in the Internet of Things. Due to the large number and different types of nodes in wireless sensor networks, layered secret key sharing technology is increasingly used in wireless sensor networks. In a hierarchical secret sharing scheme, participants are divided into sections with different permissions for each team, but the same permissions for participants in the same team. In this article, we follow the approach of the hierarchical secret sharing scheme derived from the linear homogeneous recurrence relations. We design a hierarchical multi-secret sharing scheme for wireless sensor networks on the basis of the elliptic curve public key cryptosystem combined with the linear homogeneous recurrence relations. In the proposed scheme, we do not make sure that the participants are half-truthful. In addition, the participants’ shadows can be reused. Our scheme is computational security. Only one share from each member is required in our hierarchical multi-secret sharing scheme. It is more suitable for wireless sensor networks compared to the up-to-date schemes.