Reviewerly/exHarmony huggingface dataset:
Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation

Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative filtering-based multi-modal recommendation systems, research on multi-modal sequential recommendation is still in its nascent stages. Unlike traditional sequential recommendation models that solely rely on item identifier (ID) information and focus on network structure design, multi-modal recommendation models need to emphasize item representation learning and the fusion of heterogeneous data sources. This paper investigates the impact of item representation learning on downstream recommendation tasks and examines the disparities in information fusion at different stages. Empirical experiments are conducted to demonstrate the need to design a framework suitable for collaborative learning and fusion of diverse information. Based on this, we propose a new model-agnostic framework for multi-modal sequential recommendation tasks, called Online Distillation-enhanced Multi-modal Transformer (ODMT), to enhance feature interaction and mutual learning among multi-source input (ID, text, and image), while avoiding conflicts among different features during training, thereby improving recommendation accuracy. To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features. Secondly, we employ an online distillation training strategy in the prediction optimization stage to make multi-source data learn from each other and improve prediction robustness. Experimental results on a stream media recommendation dataset and three e-commerce recommendation datasets demonstrate the effectiveness of the proposed two modules, which is approximately 10% improvement in performance compared to baseline models.
Counting roots of fully triangular polynomials over finite fields

Let $\mathbb{F}_q$ be a finite field with $q$ elements, $f \in \mathbb{F}_q[x_1, \dots, x_n]$ a polynomial in $n$ variables and let us denote by $N(f)$ the number of roots of $f$ in $\mathbb{F}_q^n$. %Many authors, such as Wei Cao and Kung Jiang have used augmented degree matrices to determine $N(f)$ for different families of polynomials. In this paper we consider the family of fully triangular polynomials, i.e., polynomials of the form \begin{equation*} f(x_1, \dots, x_n) = a_1 x_1^{d_{1,1}} + a_2 x_1^{d_{1,2}} x_2^{d_{2,2}} + \dots + a_n x_1^{d_{1,n}}\cdots x_n^{d_{n,n}} - b, \end{equation*} where $d_{i,j} > 0$ for all $1 \le i \le j \le n$. For these polynomials, we obtain explicit formulas for $N(f)$ when the augmented degree matrix of $f$ is row-equivalent to the augmented degree matrix of a linear polynomial or a quadratic diagonal polynomial.
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation

This paper presents OmniDataComposer, an innovative approach for multimodal data fusion and unlimited data generation with an intent to refine and uncomplicate interplay among diverse data modalities. Coming to the core breakthrough, it introduces a cohesive data structure proficient in processing and merging multimodal data inputs, which include video, audio, and text. Our crafted algorithm leverages advancements across multiple operations such as video/image caption extraction, dense caption extraction, Automatic Speech Recognition (ASR), Optical Character Recognition (OCR), Recognize Anything Model(RAM), and object tracking. OmniDataComposer is capable of identifying over 6400 categories of objects, substantially broadening the spectrum of visual information. It amalgamates these diverse modalities, promoting reciprocal enhancement among modalities and facilitating cross-modal data correction. \textbf{The final output metamorphoses each video input into an elaborate sequential document}, virtually transmuting videos into thorough narratives, making them easier to be processed by large language models. Future prospects include optimizing datasets for each modality to encourage unlimited data generation. This robust base will offer priceless insights to models like ChatGPT, enabling them to create higher quality datasets for video captioning and easing question-answering tasks based on video content. OmniDataComposer inaugurates a new stage in multimodal learning, imparting enormous potential for augmenting AI's understanding and generation of complex, real-world data.
Probabilistic Invariant Learning with Randomized Linear Classifiers

Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memory resources. In this work, we show how to leverage randomness and design models that are both expressive and invariant but use less resources. Inspired by randomized algorithms, our key insight is that accepting probabilistic notions of universal approximation and invariance can reduce our resource requirements. More specifically, we propose a class of binary classification models called Randomized Linear Classifiers (RLCs). We give parameter and sample size conditions in which RLCs can, with high probability, approximate any (smooth) function while preserving invariance to compact group transformations. Leveraging this result, we design three RLCs that are provably probabilistic invariant for classification tasks over sets, graphs, and spherical data. We show how these models can achieve probabilistic invariance and universality using less resources than (deterministic) neural networks and their invariant counterparts. Finally, we empirically demonstrate the benefits of this new class of models on invariant tasks where deterministic invariant neural networks are known to struggle.
ISSUES WITH IMPLEMENTING TEACHING MATERIALS FROM A STANDARDISED PROFESSIONAL DEVELOPMENT PROGRAMME: PERSPECTIVES AMONG ESL TEACHERS

This study investigates the issues experienced by ESL teacher participants in implementing materials and activities from the Professional Up-skilling of English Language Teachers (ProELT) programme in their language classrooms. A review of past studies indicated limited research on large-scale, standardised professional development programmes involving English language teachers from primary and secondary schools (heterogeneous teaching levels) and urban and rural areas (heterogeneous districts) participating in the same programme. Hence, this study aims to fill this research gap by adopting a mixed methods explanatory sequential design utilising interviews, focus groups, a questionnaire survey and the ProELT coursebook. The findings reveal more than half of the modules in each section of the coursebook do not complement the Malaysia curriculum specifications for primary and secondary schools; this triangulates with the interview findings that most of the teachers found it challenging to adopt the ProELT teaching materials and activities in their lessons due to the lack of relevance of the course materials with their teaching curriculum and target students. In addition, findings from the questionnaire further substantiate the teachers’ suggestions that the ProELT content should complement the English language curriculum syllabus. The findings of this study have implications for the programme providers and the programme designers in designing future development programmes that cater to the teachers’ needs, personalising separate primary and secondary-level programme contents, and focusing on pedagogical content knowledge and subject matter vis-a-vis generic learning.
RANCANG BANGUN APLIKASI PEMBELAJARAN BATIK DI INDONESIA BERBASIS ANDROID JELLY BEAN

Traditional batik that has developed over the past centuries has produced various types and patterns of traditional batik that are typical in every region in Indonesia. Indonesian Batik is very popular in various countries. Batik as the cultural heritage of these ancestors needs to be preserved so that it does not become extinct along with the times. This study aims to create a learning application to introduce batik originating from Indonesia. Applications are built using Android Studio software and packed with an attractive interface, making it easier for users to run applications and understand information.
Pathfinding Algorithms in Games

This article describes the A*, Dijkstra's, and genetic pathfinding algorithms used in games, providing a comparison and information about them.While these are not the only algorithms used in game pathfinding, they are currently the most commonly used ones.As games increasingly demand the presentation of more data (higher-quality graphics, complex environmental communication systems, be er sound effects, advanced character movement sets, smarter AI, etc.) in shorter time frames, algorithms must be developed to become more optimal.In the near future, they will be replaced by improved versions or entirely new algorithms.
Composable Function-preserving Expansions for Transformer Architectures

Training state-of-the-art neural networks requires a high cost in terms of compute and time. Model scale is recognized to be a critical factor to achieve and improve the state-of-the-art. Increasing the scale of a neural network normally requires restarting from scratch by randomly initializing all the parameters of the model, as this implies a change of architecture's parameters that does not allow for a straightforward transfer of knowledge from smaller size models. In this work, we propose six composable transformations to incrementally increase the size of transformer-based neural networks while preserving functionality, allowing to expand the capacity of the model as needed. We provide proof of exact function preservation under minimal initialization constraints for each transformation. The proposed methods may enable efficient training pipelines for larger and more powerful models by progressively expanding the architecture throughout training.
Taxation, Corruption and Economic Growth: A Causality Analysis of Developed and Developing Countries

This study aimed to examine the impact of corruption on the relationship between taxation and economic growth. It sought to determine whether corruption acts as a moderating factor that disrupts the positive association between taxation and economic growth rate. To achieve these objectives, the study employed panel data for the year 1985-2016 of developed and developing countries. Additionally, the study employed causal analysis techniques to establish a deeper understanding of the causal relationships between taxation, corruption, and economic growth. This was followed by estimating a co-integration regression using the Dynamic Ordinary Least Squares (DOLS) method. The empirical results of DOLS show that taxation and corruption both are lubricating the wheel of economic growth for developed while for developing countries taxation is stimulating growth, but corruption sands the wheel of growth. Similarly, the indirect effect of taxation through the channel of corruption is also negative for both samples. Likewise, the impact of the square term of taxation is also negative. The VECM Granger causality test shows that bidirectional causality exists between all variables in the long run. During the causality analysis, it was found that in developing nations, there is unidirectional causality from economic growth rate to tax revenues and from corruption to taxation. In developed countries, unidirectional causality was observed from corruption to tax revenues. However, bidirectional feedback exists between economic growth rate and corruption in developing countries, and bidirectional causality exists between taxation, economic growth rate, and corruption in developed countries. The study also suggests policy implications based on these empirical findings to improve the situation of these factors.
Evolution of Modern Literature and Film

Abstract The evolution of complex narratives, such as fictional books or films, is a fairly new area of cultural-evolutionary research. It gained momentum only recently, when researchers got access to large datasets of literature, movies, and other kinds of storytelling, allowing us to discover long-lasting historical trends and regularities. This chapter, first, discusses a theoretical question: in what sense do narratives evolve? More precisely, it focuses not on the transmission of stories themselves, but on the evolution of narrative forms, or techniques of storytelling. Then, the chapter proceeds to describing how content-based, or hedonic, selection influences the evolution of narrative forms—and briefly, the role of several other evolutionary mechanisms, such as drift and accumulation of innovations. Finally, the chapter schematically presents the methods used for studying the evolution of literature and film at a large scale: from manual coding to text mining and other computational techniques.
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning

Many multi-agent systems require inter-agent communication to properly achieve their goal. By learning the communication protocol alongside the action protocol using multi-agent reinforcement learning techniques, the agents gain the flexibility to determine which information should be shared. However, when the number of agents increases we need to create an encoding of the information contained in these messages. In this paper, we investigate the effect of increasing the amount of information that should be contained in a message and increasing the number of agents. We evaluate these effects on two different message encoding methods, the mean message encoder and the attention message encoder. We perform our experiments on a matrix environment. Surprisingly, our results show that the mean message encoder consistently outperforms the attention message encoder. Therefore, we analyse the communication protocol used by the agents that use the mean message encoder and can conclude that the agents use a combination of an exponential and a logarithmic function in their communication policy to avoid the loss of important information after applying the mean message encoder.
Engineering LaCAM$^\ast$: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding

This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the eventual finding of optimal solutions for cumulative transition costs. While it has demonstrated remarkable planning success rates, surpassing various state-of-the-art MAPF methods, its initial solution quality is far from optimal, and its convergence speed to the optimum is slow. To overcome these limitations, this paper introduces several improvement techniques, partly drawing inspiration from other MAPF methods. We provide empirical evidence that the fusion of these techniques significantly improves the solution quality of LaCAM*, thus further pushing the boundaries of MAPF algorithms.
Towards an AI to Win Ghana's National Science and Maths Quiz

Can an AI win Ghana's National Science and Maths Quiz (NSMQ)? That is the question we seek to answer in the NSMQ AI project, an open-source project that is building AI to compete live in the NSMQ and win. The NSMQ is an annual live science and mathematics competition for senior secondary school students in Ghana in which 3 teams of 2 students compete by answering questions across biology, chemistry, physics, and math in 5 rounds over 5 progressive stages until a winning team is crowned for that year. The NSMQ is an exciting live quiz competition with interesting technical challenges across speech-to-text, text-to-speech, question-answering, and human-computer interaction. In this ongoing work that began in January 2023, we give an overview of the project, describe each of the teams, progress made thus far, and the next steps toward our planned launch and debut of the AI in October for NSMQ 2023. An AI that conquers this grand challenge can have real-world impact on education such as enabling millions of students across Africa to have one-on-one learning support from this AI.
VulMatch: Binary-level Vulnerability Detection Through Signature

Similar vulnerability repeats in real-world software products because of code reuse, especially in wildly reused third-party code and libraries. Detecting repeating vulnerabilities like 1-day and N-day vulnerabilities is an important cyber security task. Unfortunately, the state-of-the-art methods suffer from poor performance because they detect patch existence instead of vulnerability existence and infer the vulnerability signature directly from binary code. In this paper, we propose VulMatch to extract precise vulnerability-related binary instructions to generate the vulnerability-related signature. VulMatch detects vulnerability existence based on binary signatures. Unlike previous approaches, VulMatch accurately locates vulnerability-related instructions by utilizing source and binary codes. Our experiments were conducted using over 1000 vulnerable instances across seven open-source projects. VulMatch significantly outperformed the baseline tools Asm2vec and Palmtree. Besides the performance advantages over the baseline tools, VulMatch offers a better feature by providing explainable reasons during vulnerability detection. Our empirical studies demonstrate that VulMatch detects fine-grained vulnerability that the state-of-the-art tools struggle with. Our experiment on commercial firmware demonstrates VulMatch is able to find vulnerabilities in real-world scenario.
The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models

Static analysis is a widely used technique in software engineering for identifying and mitigating bugs. However, a significant hurdle lies in achieving a delicate balance between precision and scalability. Large Language Models (LLMs) offer a promising alternative, as recent advances demonstrate remarkable capabilities in comprehending, generating, and even debugging code. Yet, the logic of bugs can be complex and require sophisticated reasoning and a large analysis scope spanning multiple functions. Therefore, at this point, LLMs are better used in an assistive role to complement static analysis. In this paper, we take a deep dive into the open space of LLM-assisted static analysis, using use-before-initialization (UBI) bugs as a case study. To this end, we develop LLift, a fully automated framework that interfaces with both a static analysis tool and an LLM. By carefully designing the framework and the prompts, we are able to overcome a number of challenges, including bug-specific modeling, the large problem scope, the non-deterministic nature of LLMs, etc. Tested in a real-world scenario analyzing nearly a thousand potential UBI bugs produced by static analysis, LLift demonstrates a potent capability, showcasing a reasonable precision (50%) and appearing to have no missing bugs. It even identified 13 previously unknown UBI bugs in the Linux kernel. This research paves the way for new opportunities and methodologies in using LLMs for bug discovery in extensive, real-world datasets.
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain

Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series. Monitoring and analyzing these time series are crucial for researchers, service operators, and on-call engineers to detect outliers or anomalies indicating service failures or significant events. Numerous advanced anomaly detection methods have emerged to address availability and performance issues. This review offers a comprehensive overview of time series anomaly detection in Artificial Intelligence for IT operations (AIOps), which uses AI capabilities to automate and optimize operational workflows. Additionally, it explores future directions for real-world and next-generation time-series anomaly detection based on recent advancements.
Moving Force Identification based on Dictionary Learning

This paper presents a moving force identification (MFI) approach based on dictionary learning (DL) with double sparsity.Firstly, the MFI equation is established, and a sparse dictionary model is designed.Then the sparse K-singular-value-decomposition (K-SVD) algorithm is employed for dictionary learning.Finally, the moving forces is identified via force dictionary and double sparse codes.Numerical example of a simply supported beam subjected to a moving force is used to verify the effectiveness of the proposed approach.
A Digital Visualization Approach for Bridge Structural Health Monitoring based on Building Information Modelling

Dealing with the unreadability of monitoring data poses a significant challenge in bridge structural health monitoring (SHM).This paper proposes a digital visualization approach of monitoring information based on building information modelling (BIM), which consists of establishing of the bridge information model (BRIM) and the dynamic visualization techniques.Multi-scale finite element models (FEMs) serve as the critical point for investigating the hidden information of the monitored data with the aid of big-data methods.Then the life-circle information is saved in the BRIM to realize information integration, which includes the structural designing data, operation data, monitoring system, load and environmental condition, damage information, FEM analysis results, etc.A customized four-dimensional (4D) visualization platform is subsequently developed via a secondary development interface to present heterogeneous information, reflecting the long-term state changes of infrastructures.Finally, the proposed digital visualization approach is applied to a three-span continuous model bridge case for verification.
A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.