Reviewerly/exHarmony huggingface dataset:
Advancing Personalized Learning with Multi-modal Embedding Open Learner Model and GPT Prompt

&lt;p&gt;The Generative Pre-trained Transformer (GPT) plays a pivotal role in driving advancements and innovation in the field of Artificial Intelligence in Education (AIED) due to its remarkable capabilities in understanding, reasoning, and generating. Further refinement can enrich the model's understanding of domain-specific knowledge, and enhances the capabilities of GPT for AIED. However, there was a lack of studies closely incorporate the application of the GPT and AI with educational theories. Some research studies have been extensively explored and yielded favorable results. For instance, the integration of AI with Open Learner Models (OLMs) implements AIED that provides learners with insights into their progress and aligns with their learning objectives. Despite these, existing methods still encounter several challenges that impact their practicality. Such as, employing a single neural network to store OLM's data renders continuously modifying difficult, as well as hinders scalability to encompass larger knowledge without substantial augmentation of the neural network's capacity. Furthermore, given the semantics explicitly represented within the OLM, the neural network encounters difficulties in comprehending the nuanced interdependencies among them. These challenges can result in suboptimal performance when generating meaningful insights from the data in AI tasks, and they can also lead to security issues. To address these challenges, we introduce the first Multi-modal Embedding Open Learner Model (MeOLM) framework, which integrates course embeddings, OLM embeddings, multi-modal embedding module, and task-specific modules. It works with GPT to enhance personalized learning. It maps OLM's data with implicit neural networks as multi-modal embeddings. This approach enhances the capturing of intricate relationships between each embeddings. Within the MeOLM framework, we introduce an efficient hybrid data structure that combines embeddings using a hierarchical octree structure. This facilitates the rapid allocation and access of embeddings, which in turn benefits downstream tasks such as knowledge graph completion, relation extraction, and dynamic embeddings management. Furthermore, we integrating the MeOLM with Biggs' constructive alignment, which holds significant influence in education, to align the learning objectives of a subject with its Teaching and Learning (T&amp;L) activities and assessments. The MeOLM framework enables context-aware explanations and interactions, fostering learner engagement and understanding, identifying patterns in learning behaviors, and adjusting pathways.&lt;/p&gt;
Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs

Machine learning-based video codecs have made significant progress in the past few years. A critical area in the development of ML-based video codecs is an accurate evaluation metric that does not require an expensive and slow subjective test. We show that existing evaluation metrics that were designed and trained on DSP-based video codecs are not highly correlated to subjective opinion when used with ML video codecs due to the video artifacts being quite different between ML and video codecs. We provide a new dataset of ML video codec videos that have been accurately labeled for quality. We also propose a new full reference video quality assessment (FRVQA) model that achieves a Pearson Correlation Coefficient (PCC) of 0.99 and a Spearman's Rank Correlation Coefficient (SRCC) of 0.99 at the model level. We make the dataset and FRVQA model open source to help accelerate research in ML video codecs, and so that others can further improve the FRVQA model.
An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems

We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., particle filters or ensemble Kalman filters, is the low accuracy in handling high-dimensional and highly nonlinear problems. EnSF attacks this challenge by exploiting the score-based diffusion model, defined in a pseudo-temporal domain, to characterizing the evolution of the filtering density. EnSF stores the information of the recursively updated filtering density function in the score function, in stead of storing the information in a set of finite Monte Carlo samples (used in particle filters and ensemble Kalman filters). Unlike existing diffusion models that train neural networks to approximate the score function, we develop a training-free score estimation that uses mini-batch-based Monte Carlo estimator to directly approximate the score function at any pseudo-spatial-temporal location, which provides sufficient accuracy in solving high-dimensional nonlinear problems as well as saves tremendous amount of time spent on training neural networks. Another essential aspect of EnSF is its analytical update step, gradually incorporating data information into the score function, which is crucial in mitigating the degeneracy issue faced when dealing with very high-dimensional nonlinear filtering problems. High-dimensional Lorenz systems are used to demonstrate the performance of our method. EnSF provides surprisingly impressive performance in reliably tracking extremely high-dimensional Lorenz systems (up to 1,000,000 dimension) with highly nonlinear observation processes, which is a well-known challenging problem for existing filtering methods.
Hessian-aware Quantized Node Embeddings for Recommendation

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems. Nevertheless, the process of searching and ranking from a large item corpus usually requires high latency, which limits the widespread deployment of GNNs in industry-scale applications. To address this issue, many methods compress user/item representations into the binary embedding space to reduce space requirements and accelerate inference. Also, they use the Straight-through Estimator (STE) to prevent vanishing gradients during back-propagation. However, the STE often causes the gradient mismatch problem, leading to sub-optimal results.
RevColV2: Exploring Disentangled Representations in Masked Image Modeling

Masked image modeling (MIM) has become a prevalent pre-training setup for vision foundation models and attains promising performance. Despite its success, existing MIM methods discard the decoder network during downstream applications, resulting in inconsistent representations between pre-training and fine-tuning and can hamper downstream task performance. In this paper, we propose a new architecture, RevColV2, which tackles this issue by keeping the entire autoencoder architecture during both pre-training and fine-tuning. The main body of RevColV2 contains bottom-up columns and top-down columns, between which information is reversibly propagated and gradually disentangled. Such design enables our architecture with the nice property: maintaining disentangled low-level and semantic information at the end of the network in MIM pre-training. Our experimental results suggest that a foundation model with decoupled features can achieve competitive performance across multiple downstream vision tasks such as image classification, semantic segmentation and object detection. For example, after intermediate fine-tuning on ImageNet-22K dataset, RevColV2-L attains 88.4% top-1 accuracy on ImageNet-1K classification and 58.6 mIoU on ADE20K semantic segmentation. With extra teacher and large scale dataset, RevColv2-L achieves 62.1 box AP on COCO detection and 60.4 mIoU on ADE20K semantic segmentation. Code and models are released at https://github.com/megvii-research/RevCol
Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset

Cancer is an extremely difficult and dangerous health problem because it manifests in so many different ways and affects so many different organs and tissues. The primary goal of this research was to evaluate deep learning models' ability to correctly identify breast cancer cases using the BreakHis dataset. The BreakHis dataset covers a wide range of breast cancer subtypes through its huge collection of histopathological pictures. In this study, we use and compare the performance of five well-known deep learning models for cancer classification: VGG, ResNet, Xception, Inception, and InceptionResNet. The results placed the Xception model at the top, with an F1 score of 0.9 and an accuracy of 89%. At the same time, the Inception and InceptionResNet models both hit accuracy of 87% . However, the F1 score for the Inception model was 87, while that for the InceptionResNet model was 86. These results demonstrate the importance of deep learning methods in making correct breast cancer diagnoses. This highlights the potential to provide improved diagnostic services to patients. The findings of this study not only improve current methods of cancer diagnosis, but also make significant contributions to the creation of new and improved cancer treatment strategies. In a nutshell, the results of this study represent a major advancement in the direction of achieving these vital healthcare goals.
Solving Non-Rectangular Reward-Robust MDPs via Frequency Regularization

In robust Markov decision processes (RMDPs), it is assumed that the reward and the transition dynamics lie in a given uncertainty set. By targeting maximal return under the most adversarial model from that set, RMDPs address performance sensitivity to misspecified environments. Yet, to preserve computational tractability, the uncertainty set is traditionally independently structured for each state. This so-called rectangularity condition is solely motivated by computational concerns. As a result, it lacks a practical incentive and may lead to overly conservative behavior. In this work, we study coupled reward RMDPs where the transition kernel is fixed, but the reward function lies within an $\alpha$-radius from a nominal one. We draw a direct connection between this type of non-rectangular reward-RMDPs and applying policy visitation frequency regularization. We introduce a policy-gradient method and prove its convergence. Numerical experiments illustrate the learned policy's robustness and its less conservative behavior when compared to rectangular uncertainty.
Pressmatch: Automated journalist recommendation for media coverage with Nearest Neighbor search

Slating a product for release often involves pitching journalists to run stories on your press release. Good media coverage often ensures greater product reach and drives audience engagement for those products. Hence, ensuring that those releases are pitched to the right journalists with relevant interests is crucial, since they receive several pitches daily. Keeping up with journalist beats and curating a media contacts list is often a huge and time-consuming task. This study proposes a model to automate and expedite the process by recommending suitable journalists to run media coverage on the press releases provided by the user.
Immersive Technologies in Virtual Companions: A Systematic Literature Review

The emergence of virtual companions is transforming the evolution of intelligent systems that effortlessly cater to the unique requirements of users. These advanced systems not only take into account the user present capabilities, preferences, and needs but also possess the capability to adapt dynamically to changes in the environment, as well as fluctuations in the users emotional state or behavior. A virtual companion is an intelligent software or application that offers support, assistance, and companionship across various aspects of users lives. Various enabling technologies are involved in building virtual companion, among these, Augmented Reality (AR), and Virtual Reality (VR) are emerging as transformative tools. While their potential for use in virtual companions or digital assistants is promising, their applications in these domains remain relatively unexplored. To address this gap, a systematic review was conducted to investigate the applications of VR, AR, and MR immersive technologies in the development of virtual companions. A comprehensive search across PubMed, Scopus, and Google Scholar yielded 28 relevant articles out of a pool of 644. The review revealed that immersive technologies, particularly VR and AR, play a significant role in creating digital assistants, offering a wide range of applications that brings various facilities in the individuals life in areas such as addressing social isolation, enhancing cognitive abilities and dementia care, facilitating education, and more. Additionally, AR and MR hold potential for enhancing Quality of life (QoL) within the context of virtual companion technology. The findings of this review provide a valuable foundation for further research in this evolving field.
Two Games on Arithmetic Functions: SALIQUANT and NONTOTIENT

We investigate the Sprague-Grundy sequences for two normal-play impartial games based on arithmetic functions, first described by Iannucci and Larsson in \cite{sum}. In each game, the set of positions is N (natural numbers). In saliquant, the options are to subtract a non-divisor. Here we obtain several nice number theoretic lemmas, a fundamental theorem, and two conjectures about the eventual density of Sprague-Grundy values. In nontotient, the only option is to subtract the number of relatively prime residues. Here are able to calculate certain Sprague-Grundy values, and start to understand an appropriate class function.
On the Long Tails of Specimen Data

A recent article by K.R. Johnson and I.F.P. Owens in Science (Johnson and Owens 2023) suggested that the 73 main natural history museums around the world collectively hold over 1 billion records of accessioned "specimens" (taken as collection units), a result remarkably close to, but obtained through a completely different method from, research published a decade earlier by A.H. Ariño in Biodiversity Informatics (Ariño 2010). Both sets of approaches have benefitted from information available at the Global Biodiversity Information Facility (GBIF), which in the intervening years has grown by an order of magnitude, although mostly through observation-based occurrences rather than through accretion of specimen records in collections. When comparing the estimated size of collections and the amount of digital data from those collections, there is still a huge gap, as there was then. Digitization efforts have been progressing, but they are still far from reaching the goal of bringing information about all specimens into the digital domain. While the larger institutions may doubtlessly have greater overall resources to try and make their data available than smaller institutions, how do they compare in terms of data mobilization and sharing? Not surprisingly, the distribution of the collection sizes shows a long tail of small institutions that, nonetheless, are also embarking on digitization efforts. Will this long tail of science actually manage to have all their biodiversity data available sooner than the larger institutions? It is becoming more widely recognized that data usability is predicated on data becoming findable, accessible, interoperable and reusable (FAIR, Wilkinson et al. 2016). What could be the consequences of having a data availability bias towards having many tiny collections available for ready use, rather than a much smaller (although surely very significant) fraction of larger collections of a comparable type? This presentation explores and compares the distribution of potential versus readily available data in 2010 and in 2023, examines what trends might exist in the race to universal specimen data availability, and whether the digitization efforts might be better targeted to achieve greater overall scientific benefit.
The Research Progress of Path Planning Algorithms

At present, path-planning for mobile robots is a hot problem. In the previous scientific research, A*, Dijkstra, rapidly-exploring random tree (RRT) and many other algorithms have appeared. To provide a clearer understanding of these algorithms, this article organized the idea and development of Q-learning, A* algorithm, ant colony algorithm, RRT algorithm and Dijkstra algorithm. According to the time order, this article researched several applications of the five algorithms respectively. Besides, optimization of the five algorithms in the past few years were listed. There are also examples of combined algorithms, which have better efficiency compared to using one algorithm alone. By analyzing the five algorithm and their optimization, the common problem and solution was found. The common problem of these algorithms is that a shorter and smoother path needs to be solved and the convergence speed needs accelerating. At present, the main solution can be divided into two aspects. One is to improve the algorithm itself, and the other is to combine different algorithms. In the future, more kinds of combined algorithms will emerge, and a better solution of path planning can be obtained to improve efficiency and reduce energy cost.
Research on the Pattern of Pet Fostering Based on Internet Technology

In the post pandemic era, people's social and travel needs have gradually increased, and the concept of pet care has gradually become popular. The demand for pet foster care is also increasing day by day. In this environment, we carry out innovative reforms based on customer needs, supported by big data cloud computing technology and artificial intelligence technology. The core innovation of the "Mingchongwu" project lies in utilizing internet technology to achieve precise integration of resources and needs. By serving both supply and demand simultaneously, utilizing the precise positioning capabilities, powerful information processing capabilities, and data analysis capabilities of internet technology, we can find the most suitable foster care point for the foster care provider and seek additional business cooperation for the service provider, achieving a win-win situation for both parties.
An Overview of Redirected Walking Approaches and Techniques in Virtual Reality

One major obstacle to the ideal of virtual reality is the physical constraints of the user's location, primarily its limited size.A commonly proposed solution is using redirected walking, defined as manipulation of the user's experience to alter their walking path, to keep the user within a confined physical space without causing any perceivable sensory distortion for the user.This paper discusses various redirected walking approaches which have been proposed, including predictions of user movement via navigation meshes and simulated users, and subtle redirection techniques using blink-induced change blindness and avatar manipulation.
Implementation Of A* Algorithm In A Great Elephant Game With Unity 2D

Video game is a game play which related to interesting User Interface through a picture which processed and transmitted into a video, as it alive and moving. At early 2014, smartphone users were increased, affected to decreasing of computer users.Pathfinding is a route search method. Google maps is one example of an application which using pathfinding method. A* Algorithm through pathfinding method is the development of dijkstra algorithm using heuristic to produce best and optimum solution. So it is possible to discover fastest path without checking in every path. Therefore, the objective of this study is implementing A* algorithm in Great Elephant game. This algorithm can be implemented to the character for discovering nearest route and increase the score easily. Because of the route is in maze form, it will be time wasting to looking for any food. This algorithm help the character to not wasting time in looking for the food. The result study is a game using A* algorithm in the player game which can access in android smartphone. The test result is using ISO 25010 in functionality suitablity aspect and produced 100% success.&#x0D;
Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across all input layers and samples. This research shows how to use the Statistical Fractal Search (SFS)-Guided whale optimization algorithm (GWOA). To get the best solution for the GWOA, we adopt the SFS diffusion algorithm, which uses the random walk with a Gaussian distribution method common to growing systems. Continuous values are transformed into binary to apply to the feature-selection problem in conjunction with the BlazePose skeletal topology and stochastic fractal search to construct a novel implementation of the BlazePose topology for action recognition. In our experiments, we employed the Kinetics and the NTU-RGB+D datasets. The achieved actiona accuracy in the X-View is 93.14% and in the X-Sub is 96.74%. In addition, the proposed model performs better in numerous statistical tests such as the Analysis of Variance (ANOVA), Wilcoxon signed-rank test, histogram, and times analysis.
Privacy-Preserving Probabilistic Data Encoding for IoT Data Analysis

&lt;p&gt;In this research, we have proposed a novel differentially private Bloom Filter data encoding technique, that enables the use of the Deep Learning Algorithm for downstream analysis. &lt;/p&gt; &lt;p&gt;We have used public datasets to evaluate the proposed method.&lt;/p&gt;
Anomaly Detection Model of Network Dataflow Based on an Improved Grey Wolf Algorithm and CNN

With the popularization of the network and the expansion of its application scope, the problem of abnormal network traffic caused by network attacks, malicious software, traffic peaks, or network device failures is becoming increasingly prominent. This problem not only leads to a decline in network performance and service quality but also may pose a serious threat to network security. This paper proposes a hybrid data processing model based on deep learning for network anomaly detection to improve anomaly detection performance. First, the Grey Wolf optimization algorithm is improved to select high-quality data features, which are then converted to RGB images and input into an anomaly detection model. An anomaly detection model of network dataflow based on a convolutional neural network is designed to recognize network anomalies, including DoS (Denial of Service), R2L (Remote to Local), U2R (User to Root), and Probe (Probing). To verify the effectiveness of the improved Grey Wolf algorithm and the anomaly detection model, we conducted experiments on the KDD99 and UNSW-NB15 datasets. The proposed method achieves an average detection rate of 0.986, which is much higher than all the counterparts. Experimental results show that the accuracy and the detection rates of our method were improved, while the false alarm rate has been reduced, proving the effectiveness of our approach in network anomaly classification tasks.