Reviewerly/exHarmony huggingface dataset:
Probabilistic Programming Languages: Independent Choices and Deterministic Systems

chapter Share on Probabilistic Programming Languages: Independent Choices and Deterministic Systems Authors: David Poole University of British Columbia University of British ColumbiaSearch about this author , Frank Wood University of British Columbia University of British ColumbiaSearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 691–712https://doi.org/10.1145/3501714.3501753Online:04 March 2022Publication History 0citation10DownloadsMetricsTotal Citations0Total Downloads10Last 12 Months10Last 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 Study on a Contact-free Teaching Method for the Liberal Arts Program in Colleges－Focus on Chinese Character Class

This study aimed to examine teaching methods for a real contact-free class of Chinese characters. For this purpose, a “teaching and learning community” was organized for better instruction at colleges and focus was placed on the reinforcement of efficiency of contact-free classes. Following this, a Standard Class Plan was made for a contact-free class that would apply real instruction for ten classes of Chinese characters as a subject within the liberal arts. Curriculum consulting was presented by the Institute for Innovation in Education. All the classes of Chinese characters received a good evaluation in educational achievements and for their communication between teachers and students. In respect to the contact-free Chinese character program, in addition to video lessons and real-time Zoom classes, the followings were included: First, a Chinese character video lesson was presented and the students were tested with the use of a short quiz consisting of 40 questions. Second, to increase the participation among the students, a test for learning diagnosis was made. Third, after the consulting was completed, the results were shared between the students. In this manner, the Chinese character class was designed as a precyclic, contact-free program. Based on the results above, this study proposed strategies to increase participation.
Cloud Forensics: Threat Assessment and Proposed Mitigations

Cloud Computing is a broad but ambiguous term that can refer to a variety of dynamically allocated services. Cloud Computing may be viewed as a two-edged sword in terms of law enforcement and forensic inquiry. On the one hand, acquiring digital evidence from cloud sources may present difficult technological and inter-jurisdictional legal challenges. Using cloud storage and processing capabilities, on the other hand, can help to speed up the forensics process and focus the investigation on significant evidence earlier in the investigation. It is possible that traditional evidence gathering and recovery methods may no longer be relevant in the cloud due to the dispersed nature of data processing. Because of the dynamic genre of Cloud Computing, digital forensics has had to deal with new issues. Throughout this article, the foundations of Cloud Computing architecture will be discussed in depth. As well as that, we give thorough research of forensic threats in Cloud Computing and provide viable solutions.
Sharing Weights in Shallow Layers via Rotation Group Equivariant Convolutions

The convolution operation possesses the characteristic of translation group equivariance. To achieve more group equivariances, rotation group equivariant convolutions (RGEC) are proposed to acquire both translation and rotation group equivariances. However, previous work paid more attention to the number of parameters and usually ignored other resource costs. In this paper, we construct our networks without introducing extra resource costs. Specifically, a convolution kernel is rotated to different orientations for feature extractions of multiple channels. Meanwhile, much fewer kernels than previous works are used to ensure that the output channel does not increase. To further enhance the orthogonality of kernels in different orientations, we construct the non-maximum-suppression loss on the rotation dimension to suppress the other directions except the most activated one. Considering that the low-level-features benefit more from the rotational symmetry, we only share weights in the shallow layers (SWSL) via RGEC. Extensive experiments on multiple datasets (i.e., ImageNet, CIFAR, and MNIST) demonstrate that SWSL can effectively benefit from the higher-degree weight sharing and improve the performances of various networks, including plain and ResNet architectures. Meanwhile, the convolutional kernels and parameters are much fewer (e.g., 75%, 87.5% fewer) in the shallow layers, and no extra computation costs are introduced.
A Novel Relational Deep Network for Single Object Tracking

Virtual object tracking is an active research area in computer vision. It aims to estimate the location of the target object in video frames. For the past few years, the deep learning method has been widely used for object tracking to improve accuracy. However, there are still challenges of performance problems and accuracy. This study aims to enhance the performance of an object detection model by focusing on single object tracking using Siamese network architecture and a correlation filter to find the relationship between the target object and search object from a series of continuous images. We mitigate some challenging problems in the Siamese network by adding variance loss to improve the model to distinguish between the foreground and the background. Furthermore, we add the attention mechanism and process the cropped image to find the relationship between objects and objects. Our experiment used the VOT2019 dataset for testing object tracking and the CUHK03 dataset for the training model. The result demonstrates that the proposed model achieves promising prediction performance to solve the image occlusion problem and reduce false alarms from object detection. We achieved an accuracy of 0.608, a robustness of 0.539, and an expected average overlap (EAO) score of 0.217. Our tracker runs at approximately 26 fps on GPU.
Multi-level Image Classification Using Fuzzy Petri Net

For a multi-level image classification, a knowledge representation scheme based on Fuzzy Petri Net with fuzzy inference algorithms is used. A simple graphical Petri net notation and a welldefined semantics displaying the process of reasoning through inference trees are used for visualization of the knowledge base and explanations of derived conclusion. Used knowledge representation formalism has the ability to show a probability of concepts and relations. The procedures of image multi-level classification using fuzzy recognition and inheritance algorithms on a knowledge representation scheme, as well as experimental results of image semantic interpretation, are presented.
Joint motion context and clip augmentation for spatio-temporal action detection

This paper endeavors to leverage spatio-temporal visual cues to improve video-based action detection. As a result, a NOn-Local Action detector based on anchor-free called NOLA is proposed, which is built off a recent moving center detector (MOC) and further extends it by efficiently aggregating long-range spatio-temporal information. In detail, a significantly efficient spatio-temporal motion-aware non-local block is explored to provide global motion contexts for the entire predictive branches of MOC. This byproduct can make the large batch samples run on a resource limited device. Besides, a light-weighted data augmentation method termed clip augmentation designed for video-based tasks is proposed, which serves to improve the generalization ability of the detector with economical scale-and-addition operation. NOLA works with two above schemes in real-time as well. Experiments on two benchmark datasets show that NOLA significantly exceeds MOC. Compared to other existing methods,, NOLA reaches the state-of-the-art, in terms of video-level mean of average precision (video mAP).
Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

Every year, cervical cancer (CC) is the leading cause of death in women around the world.If detected early enough, this cancer can be treated, and patients will receive adequate care.This study introduces a novel ultrasound-based method for detecting CC.The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI).Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP).The retrieved appearance is trained and tested using a feed-forward propagation neural network, and the ANFIS classifier is utilized to classify them.The purpose of this classifier is to distinguish between normal and pathological cervical pictures.Sensitivity is 97.52 percent, specificity is 99.46 percent, accuracy is 98.39 percent, precision is 97.48 percent, PPV is 97.38 percent, NPV is 92.27 percent, LRP is 141.81 percent, 0.0946 percent LRN, FPR is 96.82 percent, and NPR is 91.46 percent for the CC detection categorization.The proposed methodology outperforms standard CC identification and classification methodologies.
Fusing Attention Features and Contextual Information for Scene Recognition

Aiming to obtain more discriminative features in scene images and overcome the impacts of intra-class differences and inter-class similarities, the paper proposes a scene recognition method that combines attention and context information. First, we introduce the attention mechanism and build a multi-scale attention model. Discriminative information considers salient objects and regions by means of channel attention and spatial attention. Besides, the central loss function joint supervision strategy is introduced to further reduce the misjudgment of intra-class differences. Second, a model based on multi-level context information is proposed to describe the positional relationship between objects, which can effectively alleviate the influence of the similarity of objects between classes. Finally, the two models are merged to give full play to the compatibility of features, so that the final feature representation not only focuses on the effective discriminant information, but also manifests the relative position relationship between significant objects. Extensive experiments have proved that the method in this paper effectively solves the problem of insufficient feature representation in scene recognition tasks, and improves the accuracy of scene recognition.
An object tracking algorithm based on adaptive particle filtering and deep correlation multi-model

An object tracking algorithm based on adaptive particle filtering and deep correlation multi-model is proposed to solve the problem of large numbers of particles, as well as the defects in the generation of object model in the conventional correlation particle filter. The proposed algorithm generates multi-object model by applying different adjustment rates to each high likelihood particle, and updates and predicts the particles adaptively according to the weight of correlation response graph and particle position. The proposed algorithm can adaptively adjust the number of particles according to the complexity of the tracking scene to obtain more useful particles, solve the problem of the conventional algorithm in model generation, and improve the tracking performance. The experimental results compared with some existing tracking algorithms on OTB100 datasets show that the proposed algorithm can track the object more accurately and stably under the influence of various challenging factors.
An investigation of hypothesis testing procedures for circular and spherical mean vectors

Numerous testing procedures for directional data have been proposed over the years and a natural question that springs is which to use and when. The aim of this paper is to answer this question, via a large scale Monte Carlo simulation study that covers circular and spherical data for the two mean directions problem. The results evidently signify that tests assuming equal concentration parameters should be avoided as they tend to inflate the test size, while the heterogeneous test that does not make this assumption is to be preferred, but unfortunately only with large sample sizes. Permutation calibration does not improve the performance of any testing procedure, whereas bootstrap does. Specifically, bootstrap calibrated tests exhibited superior performance; they attain the type I error in the vast majority of the case scenarios examined and possess nearly indistinguishable empirical power levels. Finally, examples with real data illustrate the performance of the bootstrap calibrated tests.
The education institution in the face of the COVID-19 crisis: an accelerated transition towards the technological society of control (<i>La institución escolar ante la crisis de la covid-19: una transición acelerada hacia la sociedad de control tecnológico</i>)

The disruption of educational institutions due to the COVID-19 crisis provides an opportunity to study their internal structures. Two sociological models coexist in the school paradigm: the disciplinary model as described by Foucault and the new expressions of Deleuze’s society of control. The education institution manages to remain active in the COVID-19 context because it is able to speed up its transition towards the technological society of control. As a result, a virtual academic space takes shape which emulates the blended learning model of higher education; time becomes liquid and assessment evolves into a continuum of data collection. A new academic user who becomes completely responsible for their own educative path is born. The teacher becomes a companion guide or coach, thus leading to a greater depersonalization of the institutional power. The conclusions of this essay indicate that the rise of the new academic user may enhance the systemic inequality of the education institution. Therefore, critical analyses of the transition process are required in order to guarantee educational equity.
Reservoir Computing Based Equalization for Radio over Fiber System

Reservoir computing (RC) attracts much attention in many time-series tasks due to its nonlinear mapping capability from the lower space to the higher space and the dynamic memory ability. With conceptual simplicity and computation cheapness, RC is also suitable for hardware implementation. In this paper, the RC is applied to perform the signal equalization for wavelength division multiplexing (WDM) radio over fiber (RoF) system to compensate the distortion caused by the nonlinearity of single wavelength and the interference between multiple wavelengths. Simulation results show that RC with 500 reservoir nodes can enhance the adjacent channel power ratio (ACPR) of the WDM-RoF system from 6 dB to 9 dB after equalization, where the ACPR can be further improved by simply increasing the reservoir size.
Analysis of Aerobics Auxiliary Training Based on Deep Learning

With the in-depth integration of information technology and subject teaching, it is also an inevitable trend to apply modern information technology to aerobics teaching. In this paper, the N-best algorithm is used in the video and real-time camera in aerobics, so that the human posture parameters in a single-frame image can be estimated. By using the relative position and motion direction of each part of the human body to describe the characteristics of aerobics, the Laplace scoring method is used to reduce the dimension of the data, and the discriminant human motion feature vector with a strong local topological structure is obtained. Finally, the iterative self-organizing data analysis technology (ISODATA algorithm) is used to dynamically determine the keyframe. In the aerobics video keyframe extraction experiments, the ST-FMP model improves the recognition accuracy of nondeterministic body parts of the flexible hybrid articulated human model (FMP) by about 15 percentage points and achieves 81% keyframe extraction accuracy, which is better than the keyframe algorithms of KFE and motion block. The proposed algorithm is sensitive to human motion features and human pose and is suitable for motion video annotation review.
Review and data mining of linguistic studies of English modal verbs

Abstract Modal verbs express modality, and modality is concerned with the status of the proposition that describes an event, it also expresses the opinion and attitude of a speaker toward the proposition of an utterance. Since modalities are directly related to the objective world, subjective world, and language use, they have been a hot topic of philosophers, logicians and linguists. Philosophers concern with the relations between the objective world and the true/false values of the modality; logicians are interested in the relations among the possibility, necessity and the objective world; and linguists pay attention to the modality category, sense category, function, recognition, and application of modal verbs. In recent years, the linguistic studies of modal verbs have extended from general linguistic studies to computational linguistic studies. Since modal verbs are a complex semantic system and they are often indeterminate in senses, they have been a tough issue in linguistic studies and have attracted great attention. To clarify the status of the previous linguistic studies of modal verbs and reveal the characteristics of the studies will be of great significance for the further study. Therefore, this article will focus on the review of the previous linguistic studies of English modal verbs and the data mining of the characteristics of the previous studies, and based on the summary of the previous studies, give suggestions for the further study of the English modal verbs. This article is categorized under: Algorithmic Development &gt; Text Mining Fundamental Concepts of Data and Knowledge &gt; Big Data Mining Technologies &gt; Structure Discovery and Clustering
Identification of the Ideal Team Capabilities and Predictive Success Measure for Software Projects Using Machine Learning

Software project management is always been a crucial aspect of modern software development research and practice due to the very high stake of time, financial constraints, and various external factors correlating to the outcome of the projects. In the recent past, multiple parallel research outcomes can be observed in terms of predicting the software project outcomes in the form of success or failure. Nonetheless, due to the wide variety of influencing parameters in the software project matrix, the prediction of the outcome is highly inaccurate. Thus, in this work, a novel multi-regression model is deployed to predict the outcome of the software projects. The proposed multi-regression method is a collaborative regression method, which deliberates the internal regression between the project matrix attributes and generates the most optimal regression model. The outcome of the proposed multi-regression method is nearly 98.3% and almost outperformed all the other parallel research outcomes. Also, this work delivers yet another outcome in form of a recommendation system for an optimal team design matrix based on various team capabilities. The recommendation system will analyze the existing human resources in the organization and based on the statistics of the project, client history, and human resource capabilities will produce the age-based diversity, experience-based diversity, and skill-based diversity for various project roles to make the software project management highly timely and reduce risk of project failures.
As time goes by: dynamic face aging method using CycleGAN

Achieving image-to-image style transfer using various types of generative adversarial networks is a popular question in computer vision, with numerous works being proposed. However, the current typical research on the use of neural networks to achieve image-to-image style transfer is to improve the output performance through the improvement of the model, and the intermediate process of style transfer has not been paid much attention. In this paper, based on the implementation of CycleGAN style translation, the gradients of non-leaf nodes are calculated and extracted by using the autograd function in PyTorch; and finally, the aging images of faces at different ages are dynamically generated by using the middle layer gradient. The experimental results, based on the AGFW-v2 dataset, tell us that this is capable for the initial research purpose; and the presentation of the process of style transfer intermediate enriches the stage changes of age. The processing of intermediate layer features in this study can also be used in areas such as photo enhancement, image dehazing and face swapping to achieve controllability of model processing results.
The model of Tibetan thangka sales under blockchain technology

Abstract In order to improve the reliability of Tibetan Thangka sales channels, this paper combines blockchain technology to construct an intelligent model that can be used for Tibetan Thangka sales. This paper uses cryptographic encryption technology to encrypt the uploaded data to prevent Thangka producers or logistics turnover parties from tampering with Thangka‐related information, and uses the timestamp in blockchain technology to realize the traceability of Thangka‐related information. In addition, this paper uses the timestamp technology in the blockchain system to generate a traceable, query and supervised blockchain, which runs through the whole process of thangka from planting to selling. Finally, a sales model of Tibet Thangka based on blockchain technology is designed, and system performance analysis is carried out through simulation experiments. The experimental research results verify that the designed sales model has a certain effect.
Planetary image inpainting by learning mode-specific regression models

Sophisticated imaging instruments on-board spacecraft orbiting different planets and planetary bodies in this solar system, enable humans to discover and visualize the unknown. However, these planetary orbital images suffer from some missing pixel regions, which could not be captured by the spacecraft on-board cameras because of some technical limitations. In this work, we try to inpaint these missing pixels of the planetary images using mode-specific regression models that were trained with clusters of different images with similar histogram distribution on the experimental dataset. Filling in missing data via image inpainting enables downstream scientific analysis such as the analysis of morphological features on the planetary orbital - e.g., craters and their sizes, central peaks, interior structure, etc—in comparison with other planetary bodies. Here, we use the grayscale version of Mars orbital images captured by the HiRISE (High-Resolution Imaging Science Experiment) camera on the Mars Reconnaissance Orbiter (MRO) for the experimental purpose. The results show that our method can fill in the missing pixels existing on the Mars orbital image with good visual and perceptual quality and improved PSNR values.