Reviewerly/exHarmony huggingface dataset:
Dynamic Reasoning for Movie QA: A Character-Centric Approach

Movie story understanding necessitates modeling of, and reasoning about characters and their relationships with the surroundings and others as the story goes. In Movie QA, this poses the challenges of effectively capturing the visual moments relevant to questions in long videos, and efficiently navigating the web of dynamic, contextual character-centric relationships over time. This paper presents a novel character-centric method that efficiently supports reasoning about relational dynamics for Movie QA. Central to the method is a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ime- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> volving <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onditional C <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> aracter-centric graph network ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm{TECH}}$</tex-math></inline-formula> ) which models the characters, objects, and their question-conditioned relationships in space-time. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm{TECH}}$</tex-math></inline-formula> first maps the raw video data into a question-focused temporal neural graph over visual entities within and across shots and then distills the graph into a character-centric network which gives rise to the answer. At the core of this graph reasoning machine, TECH uses a two-stage feature refinement process for feature movie characters and their relationships, using their interactions with the surroundings as contextual information. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm{TECH}}$</tex-math></inline-formula> draws its efficiency over long videos from a “skim and scan” technique to rapidly localize the most query-relevant moments in the movie. Tested on the three large-scale datasets, TECH clearly shows advantages over recent state-of-the-art models.
Pengaruh Pembelajaran Jarak Jauh Masa Pandemi Covid-19 terhadap Kemampuan Baca Al-Qur’an Siswa pada Mata Pelajaran PAI di SDIT Khoiru Ummah

The Covid 19 pandemic in 2021 caused a major impact on the world of education, in the implementation of learning activities the government provided rules for implementing distance learning activities (PJJ). Therefore, PAI teachers must strive to provide appropriate learning so that students can continue to improve their reading skills of the Qur'an. The purpose of this study was to determine the effect of the implementation of Distance Learning (PJJ) on students' reading ability of the Qur'an. The method in this study uses a quantitative descriptive research approach. The findings of this study are that an average value of 81.2368 was obtained before the implementation of the PJJ, and the value after the implementation of the PJJ obtained an average value of 82.0263, which means that there is an increase in the average value during the pandemic but it is not significant. Then the results of the T test calculation obtained a significance value of 0.110 &gt; 0.05, so it can be concluded that there is no significant effect between the effect of distance learning during the covid-19 pandemic on students' reading ability of the Qur'an at SDIT Khoiru Ummah.&#x0D; Keywords: Distance Learning (PJJ); Ability to read the Qur'an
Localization Accuracy Analysis for Roadside Sensing System

Localization under GNSS-denied environments has become a significant research focus in recent years. Traditional solutions for accurate positioning often rely on expensive multi-sensor fusion technologies, such as vision, inertial measurement unit (IMU), and lidar, which are primarily used in autonomous vehicles. However, these solutions can be costly and not suitable for all applications. A new paradigm for positioning services is the use of roadside sensing systems (RSS). These systems utilize vision, radar, and/or lidar sensors to detect road users and transmit the information through cellular vehicle-to-everything (C-V2X) technology. Unlike sensor-fusion-based ego-state estimation used in autonomous vehicles, RSS offloads the computation and sensing work to the roadside, providing positioning services to all connected vehicles (CV) at a lower cost. In this work, we focus on the accuracy analysis of a roadside sensing system for localization. We first proposed a spatiotemporal decoupling association method is then used to associate the RSS positioning data with the ground truth, mitigating timing errors. Then the accuracy analysis and model fitting are performed using data collected from multiple RSS deployed in different locations in China. Finally, we establish three different noise models for the general RSS system by performing a 2-D histogram regression on localization errors, and recommend a simple linear model for general RSS. The outcome of this study provides a practical spatial-related confidence model for RSS localization service users, forming a solid foundation for future applications, particularly for the fusion of GNSS and RSS in autonomous and connected vehicle systems.
The Role of Generative AI in Games Research

Generative artificial intelligence (generative AI) has seen a meteoric rise in capability, popularity, and use. As the popularity of this technology grows, protocols are being developed around how to manage the use of generative AI in academic spaces. It is imperative that the games and play research community join this important conversation, and discuss the appropriateness of using generative AI in both digital games, and our own academic works.
Joint Instance and Feature Adaptation for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) predicts defects for the current project (target) using heterogeneous data from other projects (source), which can improve software quality effectively. The ability to reduce the distribution divergence between the source and target project is crucial to the performance of HDP. Existing HDP methods address this issue only by using feature adaptation techniques, i.e., feature matching and feature transformation. However, without considering instance adaptability differences, they cannot fully exploit the potential of instance adaptation and thus cannot make the source and target distribution completely matched. To address this problem, we propose a novel HDP approach called joint instance and feature adaptation (JIFA) combining both instance adaptation and feature adaptation to narrow the gap between projects. Making joint use of both adaptation techniques, JIFA not only further reduces the distribution discrepancy but also enhances its robustness to abnormal outliers. Besides, discriminant information is also preserved in JIFA so that a better classification boundary can be obtained. Particularly, JIFA applies to both heterogeneous and homogeneous cross-project defect prediction (CPDP) tasks. The experimental results from 22 projects indicate that JIFA outperforms a range of advanced heterogeneous and homogeneous CPDP approaches.
Filtering for Nonlinear and Linear Markov Jump Systems

In this paper we consider the finite horizon filtering problem of discrete-time Markov jump systems (MJS). In the first part we consider a MJS satisfying some general nonlinear conditions. It is obtained, for a fixed set of auxiliary constants, filter gains, based on a set of coupled Riccati like difference equations, that yield a minimum upper bound for the estimation error covariance matrix. When the nonlinear parameters are set to zero the MJS becomes a Markov jump linear system (MJLS) and, in the second part of the paper, it is shown that the obtained filter gains derived from a set of coupled Riccati like difference equations provide the optimal “prediction-correction” Markovian filter for the nominal MJLS (which reduces to the standard Kalman filter for the no jump case). The paper is concluded with some numerical examples.
Human Activity Recognition for Packing Processes using CNN-biLSTM

Human activity recognition has several applications in healthcare, sports, and industrial settings. In the latter, it can monitor industry workers and evaluate if the required activities are appropriately performed. In this paper, we employ state-of-the-art Deep Learning techniques to recognize ten distinct packing activities performed by sixteen participants from the Openpack dataset. Our proposed architecture combines data from various sensors and leverages Convolutional Neural Networks and Long Short-Term Memory networks to process spatial and temporal data. Additionally, we incorporate Transformers into our network, resulting in an improved F1-Score performance of 98.21.
BM-SMIL: A Breast Cancer Molecular Subtype Prediction Framework from H&amp;E Slides with Self-supervised Pretraining and Multi-instance Learning

Breast cancer is the most commonly diagnosed cancer, and accurate molecular subtype prediction plays a crucial role in determining treatment strategies. While immunohistochemistry (IHC) is commonly used for molecular subtype diagnosis, it suffers from cost and labor limitations. The prediction of molecular subtypes using hematoxylin and eosin (H&E) stained slides has gained importance. However, the task is challenged by limited samples, weak annotations, and strong tumor heterogeneity. This paper proposes a scalable framework, BM-SMIL, for molecular subtype prediction of breast cancer based on self-supervised pretraining and multi-instance learning. Firstly, a self-supervised pretraining framework utilizing multi-scale knowledge distillation is introduced to obtain a representative patch encoder. Then, an attention-based instance selection strategy is employed to filter out noise instances. Finally, a Transformer integrated with subtype contrastive loss is proposed for effective aggregation and WSI-level prediction. Experimental results on the dataset from cooperative hospital demonstrate the effectiveness of our proposed framework. The BM-SMIL framework has the potential to enhance molecular subtype prediction performance and can be extended to other pathology image classification tasks.
Improving Few-Shot and Zero-Shot Entity Linking with Coarse-to-Fine Lexicon-Based Retriever

Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios. The mainstream method is the “retrieve and rerank” two-stage framework. In this paper, we propose a coarse-to-fine lexicon-based retriever to retrieve entity candidates in an effective manner, which operates in two layers. The first layer retrieves coarse-grained candidates by leveraging entity names, while the second layer narrows down the search to fine-grained candidates within the coarse-grained ones. In addition, this second layer utilizes entity descriptions to effectively disambiguate tail or new entities that share names with existing popular entities. Experimental results indicate that our approach can obtain superior performance without requiring extensive finetuning in the retrieval stage. Notably, our approach ranks the 1st in NLPCC 2023 Shared Task 6 on Chinese Few-shot and Zero-shot Entity Linking.
Research on Named Entity Recognition Based on Bidirectional Pointer Network and Label Knowledge Enhancement

Named Entity Recognition (NER) is one of the fundamental tasks in natural language processing (NLP) and serves as a foundation for many downstream tasks. Currently, most span-based methods utilize sequence labeling to detect entity boundaries. This method may cause too many invalid entities and the number of entity fragments explosion when combining entity boundaries to generate entity fragments. To solve these problems, we propose a model based on bidirectional pointer network and label knowledge enhancement. We used pointer network mechanism to detect entity boundary, pointing entity beginning boundary to entity ending boundary, and non-entity to sentinel word. This processing method can effectively alleviate the problem of excessive invalid entity and explosion of entity fragment quantity. In addition, we also fully integrate the label knowledge into the model to improve the effect of the model. We conducted extensive experiments on the ACE2004 dataset and achieved better results than most existing named entity recognition models.
A Quantum Moving Target Segmentation Algorithm for Grayscale Video

The moving target segmentation (MTS) aims to segment out moving targets in the video, however, the classical algorithm faces the huge challenge of real-time processing in the current video era. Some scholars have successfully demonstrated the quantum advantages in some video processing tasks, but not concerning moving target segmentation. In this paper, a quantum moving target segmentation algorithm for grayscale video is proposed, which can use quantum mechanism to simultaneously calculate the difference of all pixels in all adjacent frames and then quickly segment out the moving target. In addition, a feasible quantum comparator is designed to distinguish the grayscale values with the threshold. Then several quantum circuit units, including three-frame difference, binarization and AND operation, are designed in detail, and then are combined together to construct the complete quantum circuits for segmenting the moving target. For a quantum video with $2^m$ frames (every frame is a $2^n\times 2^n$ image with $q$ grayscale levels), the complexity of our algorithm can be reduced to O$(n^2 + q)$. Compared with the classic counterpart, it is an exponential speedup, while its complexity is also superior to the existing quantum algorithms. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Towards Better Translations from Classical to Modern Chinese: A New Dataset and a New Method

Classical Chinese (Ancient Chinese) is the written language that was used in ancient China and has been an important carrier of Chinese culture for thousands of years. Numerous ideas of modern disciplines have been influenced or derived from it, including mathematics, medicine, engineering, etc., which demonstrated the necessity for us to understand, inherit and disseminate it. Consequently, there is an urgent need to develop neural machine translation to facilitate the comprehension of classical Chinese sentences. In this paper, we introduce a high-quality and comprehensive dataset called C2MChn, consisting of about 615K sentence pairs for the translation between classical and modern Chinese. To the best of our knowledge, this is the first dataset covering a wide range of domains including history books, Buddhist classics, Confucian classics, etc. Furthermore, through the analysis of classical and modern Chinese, we have proposed a simple yet effective method, named Syntax-Semantics Awareness Transformer (SSAT). It’s capable of leveraging both syntactic and semantic information which are indispensable for better translating classical Chinese. Experiments show that our model can achieve better BLEU scores than several state-of-the-art methods as well as two general translation engines including Microsoft and Baidu APIs. The dataset and related resources will be released at: https://github.com/Zongyuan-Jiang/C2MChn .
GenFair: A Genetic Fairness-Enhancing Data Generation Framework

Bias in the training data can be inherited by Machine Learning models and then reproduced in socially-sensitive decision-making tasks leading to potentially discriminatory decisions. The state-of-the-art of pre-processing methods to mitigate unfairness in datasets mainly considers a single binary sensitive attribute. We devise GenFair, a fairness-enhancing data pre-processing method that is able to deal with two or more sensitive attributes, possibly multi-valued, at once. The core of the approach is a genetic algorithm for instance generation, which accounts for the plausibility of the synthetic instances w.r.t. the distribution of the original dataset. Results show that GenFair is on par or even better than state-of-the-art approaches.
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings

Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they typically struggle to reason rare or emerging unseen entities. In this paper, we propose kNN-KGE, a new knowledge graph embedding approach with pre-trained language models, by linearly interpolating its entity distribution with k-nearest neighbors. We compute the nearest neighbors based on the distance in the entity embedding space from the knowledge store. Our approach can allow rare or emerging entities to be memorized explicitly rather than implicitly in model parameters. Experimental results demonstrate that our approach can improve inductive and transductive link prediction results and yield better performance for low-resource settings with only a few triples, which might be easier to reason via explicit memory (Code is available at: https://github.com/zjunlp/KNN-KG ).
Uptake and Utilization of the COVID-19 Alert App within a University Community in New York

The rapid onset of the COVID-19 pandemic elicited a swift response to control the virus ubiquitous within the United States. Expanded telehealth and health informatics became critical components of the pandemic response. The aim of this study was to assess the utilization of the COVID-19 New York (NY) Alert App and identify the perceived benefits and limitations of the App. A cross-sectional design was employed to collect data by using questionnaires with closed-ended questions. The survey was developed and administered during March through April 2021. The study found that the highest rated benefit from using the COVID-19 NY Alert App was receiving alerts about being in close proximity to individuals diagnosed with COVID-19. Results showed that ineffective (insufficient and inappropriate) usage was the highest rated potential challenge for using the App. Study subjects were likely to download this Alert App when they perceived more benefits and less barriers to using the App. Findings from this study can help improve utilization of the App and inform development of similar tele-education tools. The study illuminated considerations for health information applications in scaling-up traditional COVID-19 tracing efforts and may facilitate the design of similar emergency preparedness health technology.
Image Steganalysis Based on Deep Content Features Clustering

The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image. Then, the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification. Finally, the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis. We experimented on publicly available combined datasets of Bossbase1.01, Bows2, and ALASKA#2 with a quality factor of 75. The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84% for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion (J-UNIWARD) at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient (bpnzAC). Furthermore, at the payload of 0.2 bpnzAC, the improvement effect is minimal but also reaches 1.39%. Compared with the previous steganalysis based on deep learning, this method considers the differences between the training contents. It selects the proper detector for the image to be detected. Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images. The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis.
Blockchain Security Threats and Collaborative Defense: A Literature Review

As a distributed database, the system security of the blockchain is of great significance to prevent tampering, protect privacy, prevent double spending, and improve credibility. Due to the decentralized and trustless nature of blockchain, the security defense of the blockchain system has become one of the most important measures. This paper comprehensively reviews the research progress of blockchain security threats and collaborative defense, and we first introduce the overview, classification, and threat assessment process of blockchain security threats. Then, we investigate the research status of single-node defense technology and multi-node collaborative defense technology and summarize the blockchain security evaluation indicators and evaluation methods. Finally, we discuss the challenges of blockchain security and future research directions, such as parallel detection and federated learning. This paper aims to stimulate further research and discussion on blockchain security, providing more reliable security guarantees for the use and development of blockchain technology to face changing threats and challenges through continuous updating and improvement of defense technologies.
On connection among quantum-inspired algorithms of the Ising model

Various combinatorial optimization NP-hard problems can be reduced to finding the minimizer of an Ising model, which is a discrete mathematical model. It is an intellectual challenge to develop some mathematical tools or algorithms for solving the Ising model. Over the past decades, some continuous approaches or algorithms have been proposed from physical, mathematical or computational views for optimizing the Ising model such as quantum annealing, the coherent Ising machine, simulated annealing, adiabatic Hamiltonian systems, etc.. However, the mathematical principle of these algorithms is far from being understood. In this paper, we reveal the mathematical mechanism of dynamical system algorithms for the Ising model by Morse theory and variational methods. We prove that the dynamical system algorithms can be designed to minimize a continuous function whose local minimum points give all the candidates of the Ising model and the global minimum gives the minimizer of Ising problem. Using this mathematical mechanism, we can easily understand several dynamical system algorithms of the Ising model such as the coherent Ising machine, the Kerr-nonlinear parametric oscillators and the simulated bifurcation algorithm. Furthermore, motivated by the works of C. Conley, we study transit and capture properties of the simulated bifurcation algorithm to explain its convergence by the low energy transit and capture in celestial mechanics. A detailed discussion on $2$-spin and $3$-spin Ising models is presented as application.