Reviewerly/exHarmony huggingface dataset:
MMVP: Motion-Matrix-based Video Prediction

A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their appearances across frames. This work introduces an end-to-end trainable two-stream video prediction framework, Motion-Matrix-based Video Prediction (MMVP), to tackle this challenge. Unlike previous methods that usually handle motion prediction and appearance maintenance within the same set of modules, MMVP decouples motion and appearance information by constructing appearance-agnostic motion matrices. The motion matrices represent the temporal similarity of each and every pair of feature patches in the input frames, and are the sole input of the motion prediction module in MMVP. This design improves video prediction in both accuracy and efficiency, and reduces the model size. Results of extensive experiments demonstrate that MMVP outperforms state-of-the-art systems on public data sets by non-negligible large margins (about 1 db in PSNR, UCF Sports) in significantly smaller model sizes (84% the size or smaller).
A digital teaching model for jurisprudence courses based on a linear regression model

Abstract To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to enhance the teaching effectiveness of jurisprudence courses. Regression modeling deals with the correlation between teaching variables and establishes a linear regression model in the design process. The sample likelihood function is constructed to output the teaching regression coefficients and the metrics of the model fitting effect, the raw data are dimensionless, and the dimensionless model covariance is calculated according to the nature of teaching expectations. On this basis, the digital teaching model of the jurisprudence course was explored to optimize digital teaching resources, adjust the structure, and establish a student evaluation mechanism. To verify the feasibility of this teaching model, its teaching effect was tested. The results showed that the digital teaching model of jurisprudence based on linear regression led to a teaching level of 2.6594%, and students’ mastery of knowledge reached 2.7952%. And the number of students with failing grades decreased by 480%, and the number of students receiving excellent grades increased by 3.53 times, with a teaching quality rating range of [3.95-4.39] points. It can be seen that the linear regression model promoted the jurisprudence course to break through the traditional teaching model and further improve the teaching quality of the jurisprudence course.
High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods

These lecture notes provide an overview of existing methodologies and recent developments for estimation and inference with high dimensional time series regression models. First, we present main limit theory results for high dimensional dependent data which is relevant to covariance matrix structures as well as to dependent time series sequences. Second, we present main aspects of the asymptotic theory related to time series regression models with many covariates. Third, we discuss various applications of statistical learning methodologies for time series analysis purposes.
The harmonic h-index: an impromevement of the Hirsch h-index and the Egghe g-index

In order to characterize the scientific output of scientists, in this paper we define the harmonic har-index whose values are positive integers. It is proved that $h \leq har \leq g$, where h is the Hirsch index and g is the Egghe index. Despite the fact that the har-index is defined in a completely different way (including sum of reciprocals of citations of a researcher), based on our computatioanl results, we get the surprising fact that this index is highly correlated with the hg-index ($hg=\sqrt{hg}$ ) introduced by Alonso et al. (2010). Accordingly, we believe that the har-index keep the advantages of both measures as well as to minimize their disadvantages. In addition, it is much easier to calculate the values of har-index than those of g-index and hg-index.
Bayesian questions with frequentist answers

The two statistical methods, namely the frequentist and the Bayesian methods, are both commonly used for probabilistic inference in many scientific situations. However, it is not straightforward to interpret the result of one approach in terms of the concepts of the other. In this paper we explore the possibility of finding a Bayesian significance for the frequentist's main object of interest, the $p$-value, which is the probability assigned to the proposition -- which we call the {\it extremity proposition} -- that a measurement will result in a value that is at least as extreme as the value that was actually obtained. To make contact with the frequentist language, the Bayesian can choose to update probabilities based on the {\it extremity proposition}, which is weaker than the standard Bayesian update proposition, which uses the actual observed value. We then show that the posterior probability (or probability density) of a theory is equal to the prior probability (or probability density) multiplied by the ratio of the $p$-value for the data obtained, given that theory, to the mean $p$-value -- averaged over all theories weighted by their prior probabilities. Thus, we provide frequentist answers to Bayesian questions. Our result is generic -- it does not rely on restrictive assumptions about the situation under consideration or specific properties of the likelihoods or the priors.
Neuro-augmented vision for evolutionary robotics

Abstract This paper presents neuro-augmented vision for evolutionary robotics (NAVER), which aims to address the two biggest challenges in camera-equipped robot evolutionary controllers. The first challenge is that camera images typically require many inputs from the controller, which greatly increases the complexity of optimising the search space. The second challenge is that evolutionary controllers often cannot bridge the reality gap between simulation and the real world. This method utilises a variational autoencoder to compress the camera image into smaller input vectors that are easier to manage, while still retaining the relevant information of the original image. Automatic encoders are also used to remove unnecessary details from real-world images, in order to better align with images generated by simple visual simulators. NAVER is used to evolve the controller of a robot, which only uses camera inputs to navigate the maze based on visual cues and avoid collisions. The experimental results indicate that the controller evolved in simulation and transferred to the physical robot, where it successfully performed the same navigation task. The controller can navigate the maze using only visual information. The controller responds to visual cues and changes its behaviour accordingly. NAVER has shown great potential as it has successfully completed (so far) the most complex vision-based task controller in evolutionary robotics literature.
Analysis on the Configuration Path of Improving the Quality and Efficiency of County-Level Libraries in China

County-level libraries have an inescapable responsibility for cultivating local cultural identity and realizing rural revitalization. Therefore we analyzed factors that affect the service efficiency of grass-roots libraries in Henan Province through a field investigation and qualitative comparative analysis QCA and proposed a high-efficiency configuration. In addition we put forward the strategy of improving the quality and efficiency of our country􀆶 s county-level libraries in the electronic information age.
Publishing Wikipedia usage data with strong privacy guarantees

For almost 20 years, the Wikimedia Foundation has been publishing statistics about how many people visited each Wikipedia page on each day. This data helps Wikipedia editors determine where to focus their efforts to improve the online encyclopedia, and enables academic research. In June 2023, the Wikimedia Foundation, helped by Tumult Labs, addressed a long-standing request from Wikipedia editors and academic researchers: it started publishing these statistics with finer granularity, including the country of origin in the daily counts of page views. This new data publication uses differential privacy to provide robust guarantees to people browsing or editing Wikipedia. This paper describes this data publication: its goals, the process followed from its inception to its deployment, the algorithms used to produce the data, and the outcomes of the data release.
Implementation Of MNIST Dataset Learning Using Analog Circuit

There have been many attempts to implement neural networks in the analog circuit. Most of them had a lot of input terms, and most studies implemented neural networks in the analog circuit through a circuit simulation program called Spice to avoid the need to design chips at a high cost and implement circuits directly to input them. In this study, we will implement neural networks using a capacitor and diode and use microcontrollers (Arduino Mega 2560 R3 boards) to drive real-world models and analyze the results.
Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art

Since their inception in 2014, Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas. Consisting of a discriminative network and a generative network engaged in a Minimax game, GANs have revolutionized the field of generative modeling. In February 2018, GAN secured the leading spot on the ``Top Ten Global Breakthrough Technologies List'' issued by the Massachusetts Science and Technology Review. Over the years, numerous advancements have been proposed, leading to a rich array of GAN variants, such as conditional GAN, Wasserstein GAN, CycleGAN, and StyleGAN, among many others. This survey aims to provide a general overview of GANs, summarizing the latent architecture, validation metrics, and application areas of the most widely recognized variants. We also delve into recent theoretical developments, exploring the profound connection between the adversarial principle underlying GAN and Jensen-Shannon divergence, while discussing the optimality characteristics of the GAN framework. The efficiency of GAN variants and their model architectures will be evaluated along with training obstacles as well as training solutions. In addition, a detailed discussion will be provided, examining the integration of GANs with newly developed deep learning frameworks such as Transformers, Physics-Informed Neural Networks, Large Language models, and Diffusion models. Finally, we reveal several issues as well as future research outlines in this field.
Noise Adaptive Fuzzy Switching Median Filters for Removing Gaussian Noise

Recently, in all image processing systems, image restoration plays a major role and it forms the major part of image processing systems. Medical images such as brain Magnetic Resonance Imaging (MRI), ultrasound images of liver and kidney, retinal images and images of uterus images are often affected by various types of noises such as Gaussian noise and salt and pepper noise. All image restoration techniques attempts to remove various types of noises. This paper deals with various filters namely Mean Filter, Averaging Filter, Median Filter, Adaptive Median Filter, Adaptive Weighted Median Filter, Gabor Filter and Noise Adaptive Fuzzy Switching Median Filter (NAFSM) for removing salt and pepper noise. Among all the filters, NAFSM removes the Gaussian noise better than the other filters and the performance of all the filters are compared using metrics such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), NAE (Normalized Absolute Error), Normalized Cross Correlation (NK), Average Difference (AD), Maximum Difference (MD), SC (Structural Content) and time elapsed to produce the denoised image.
Correlation the Components of Social Intelligence with Other Types of Intelligence in Person’s Maturity

In the research of the world level we can see that the main issue of the problem of personality development is aimed at determining the types, indicators and criteria of development. In these research works, the authors propose various methodological tools for diagnosing personality maturity. Although foreign and Uzbek scientists have conducted research on the factors and problems that affect the psychological aspects of personal development, the relevant correlation between the level of person’s education attainment and acquiring a qualification and profession at different ages, the level of development in different periods of career activity and determinant of social intelligence has not been studied as an object of special scientific research. Hence, it is important to study and research the formation of new types of personality development in modern life, the manifestation and development of social intellectual activity of a person. The article construes an experimental study of the correlation of social intelligence with other types of intelligence in personality maturity. It presents the analysis of students' general and social intelligence indicators, as well as the psychological analysis of relationship indicators of female and male students’ social intelligence with other types of intelligence. According to the results of the research, the processes of social intelligence of students have more obvious correlations in relation to the progression of general intelligence, and they have been found to have differences according to the gender differences of the entity.
A new provably stable weighted state redistribution algorithm

We propose a practical finite volume method on cut cells using state redistribution. Our algorithm is provably monotone, total variation diminishing, and GKS stable in many situations, and shuts off continuously as the cut cell size approaches a target value. Our analysis reveals why original state redistribution works so well: it results in a monotone scheme for most configurations, though at times subject to a slightly smaller CFL condition. Our analysis also explains why a pre-merging step is beneficial. We show computational experiments in two and three dimensions.
Violent Physical Behavior Detection using 3D Spatio-Temporal Convolutional Neural Networks

The use of surveillance cameras has made it possible to analyze a huge amount of data for automated surveillance. The use of security systems in schools, hotels, hospitals, and other security areas is required to identify the violent activities that can cause social, economic, and environmental damage. Detecting the mobile objects on each frame is a fundamental phase in the analysis of the video trail and the violence recognition. Therefore, a three-step approach is presented in this article. In our method, the separation of the frames containing the motion information and the detection of the violent behavior are applied at two levels of the network. First, the people in the video frames are identified by using a convolutional neural network. In the second step, a sequence of 16 frames containing the identified people is injected into the 3D CNN. Furthermore, we optimize the 3D CNN by using the visual inference and then a neural network optimization tool that transforms the pre-trained model into an average representation. Finally, this method uses the toolbox of OPENVINO to perform the optimization operations to increase the performance. To evaluate the accuracy of our algorithm, two datasets have been analyzed, which are: Violence in Movies and Hockey Fight. The results show that the final accuracy of this analysis is equal to 99.9% and 96% from each dataset.
A Lean-Congruence Format for EP-Bisimilarity

Enabling preserving bisimilarity is a refinement of strong bisimilarity, which preserves safety as well as liveness properties. To define it properly, labelled transition systems needed to be upgraded with a successor relation, capturing concurrency between transitions enabled in the same state. We enrich the well-known De Simone format to handle inductive definitions of this successor relation. We then establish that ep-bisimilarity is a congruence for the operators, as well as lean congruence for recursion, for all (enriched) De Simone languages.
Emoji Promotes Developer Participation and Issue Resolution on GitHub

Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-verbal cues such as facial expressions and body language, which hinders the effective communication and negatively impacts the work outcomes. Prevalent on social media platforms, emojis, as alternative non-verbal cues, are gaining popularity in the virtual workspaces well. In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces. To this end, we collect GitHub issues for a one-year period and apply causal inference techniques to measure the causal effect of emojis on the outcome of issues, controlling for confounders such as issue content, repository, and author information. We find that emojis can significantly reduce the resolution time of issues and attract more user participation. We also compare the heterogeneous effect on different types of issues. These findings deepen our understanding of the developer communities, and they provide design implications on how to facilitate interactions and broaden developer participation.
Deformation Robust Text Spotting with Geometric Prior

The goal of text spotting is to perform text detection and recognition in an end-to-end manner. Although the diversity of luminosity and orientation in scene texts has been widely studied, the font diversity and shape variance of the same character are ignored in recent works, since most characters in natural images are rendered in standard fonts. To solve this problem, we present a Chinese Artistic Dataset, termed as ARText, which contains 33,000 artistic images with rich shape deformation and font diversity. Based on this database, we develop a deformation robust text spotting method (DR TextSpotter) to solve the recognition problem of complex deformation of characters in different fonts. Specifically, we propose a geometric prior module to highlight the important features based on the unsupervised landmark detection sub-network. A graph convolution network is further constructed to fuse the character features and landmark features, and then performs semantic reasoning to enhance the discrimination for different characters. The experiments are conducted on ARText and IC19-ReCTS datasets. Our results demonstrate the effectiveness of our proposed method.
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks

To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal several limitations regarding four critical issues: 1) inconsistent datasets, 2) inconsistent evaluation pipelines, 3) lacking workload diversity, and 4) lacking efficient comparison. Overall, there lacks an empirical study that puts TGNN models onto the same ground and compares them comprehensively. To this end, we propose BenchTemp, a general benchmark for evaluating TGNN models on various workloads. BenchTemp provides a set of benchmark datasets so that different TGNN models can be fairly compared. Further, BenchTemp engineers a standard pipeline that unifies the TGNN evaluation. With BenchTemp, we extensively compare the representative TGNN models on different tasks (e.g., link prediction and node classification) and settings (transductive and inductive), w.r.t. both effectiveness and efficiency metrics. We have made BenchTemp publicly available at https://github.com/qianghuangwhu/benchtemp.
Unexpected Averages of Mixing Matrices

The (standard) average mixing matrix of a continuous-time quantum walk is computed by taking the expected value of the mixing matrices of the walk under the uniform sampling distribution on the real line. In this paper we consider alternative probability distributions, either discrete or continuous, and first we show that several algebraic properties that hold for the average mixing matrix still stand for this more general setting. Then, we provide examples of graphs and choices of distributions where the average mixing matrix behaves in an unexpected way: for instance, we show that there are probability distributions for which the average mixing matrices of the paths on three or four vertices have constant entries, opening a significant line of investigation about how to use classical probability distributions to sample quantum walks and obtain desired quantum effects. We present results connecting the trace of the average mixing matrix and quantum walk properties, and we show that the Gram matrix of average states is the average mixing matrix of a certain related distribution. Throughout the text, we employ concepts of classical probability theory not usually seen in texts about quantum walks.
Knowledge Distillation from Non-streaming to Streaming ASR Encoder using Auxiliary Non-streaming Layer

Streaming automatic speech recognition (ASR) models are restricted from accessing future context, which results in worse performance compared to the non-streaming models. To improve the performance of streaming ASR, knowledge distillation (KD) from the non-streaming to streaming model has been studied, mainly focusing on aligning the output token probabilities. In this paper, we propose a layer-to-layer KD from the teacher encoder to the student encoder. To ensure that features are extracted using the same context, we insert auxiliary non-streaming branches to the student and perform KD from the non-streaming teacher layer to the non-streaming auxiliary layer. We design a special KD loss that leverages the autoregressive predictive coding (APC) mechanism to encourage the streaming model to predict unseen future contexts. Experimental results show that the proposed method can significantly reduce the word error rate compared to previous token probability distillation methods.
On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint

Implicit neural networks have demonstrated remarkable success in various tasks. However, there is a lack of theoretical analysis of the connections and differences between implicit and explicit networks. In this paper, we study high-dimensional implicit neural networks and provide the high dimensional equivalents for the corresponding conjugate kernels and neural tangent kernels. Built upon this, we establish the equivalence between implicit and explicit networks in high dimensions.