Reviewerly/exHarmony huggingface dataset:
The Importance of Human-Labeled Data in the Era of LLMs

The advent of large language models (LLMs) has brought about a revolution in the development of tailored machine learning models and sparked debates on redefining data requirements. The automation facilitated by the training and implementation of LLMs has led to discussions and aspirations that human-level labeling interventions may no longer hold the same level of importance as in the era of supervised learning. This paper presents compelling arguments supporting the ongoing relevance of human-labeled data in the era of LLMs.
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework (Extended Abstract)

Creative Problem Solving (CPS) is a sub-area within artificial intelligence that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning in AI, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways, remains a challenge. To stimulate further research in CPS, we contribute a definition and a framework of CPS, which we use to categorize existing AI methods in this field. We conclude our survey with open research questions, and suggested future directions.
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention

The monocular depth estimation task has recently revealed encouraging prospects, especially for the autonomous driving task. To tackle the ill-posed problem of 3D geometric reasoning from 2D monocular images, multi-frame monocular methods are developed to leverage the perspective correlation information from sequential temporal frames. However, moving objects such as cars and trains usually violate the static scene assumption, leading to feature inconsistency deviation and misaligned cost values, which would mislead the optimization algorithm. In this work, we present CTA-Depth, a Context-aware Temporal Attention guided network for multi-frame monocular Depth estimation. Specifically, we first apply a multi-level attention enhancement module to integrate multi-level image features to obtain an initial depth and pose estimation. Then the proposed CTA-Refiner is adopted to alternatively optimize the depth and pose. During the CTA-Refiner process, context-aware temporal attention (CTA) is developed to capture the global temporal-context correlations to maintain the feature consistency and estimation integrity of moving objects. In particular, we propose a long-range geometry embedding (LGE) module to produce a long-range temporal geometry prior. Our approach achieves significant improvements (e.g., 13.5% for the Abs Rel metric on the KITTI dataset) over state-of-the-art approaches on three benchmark datasets.
QUALITY OF TEACHER TRAINING IN THE UNIVERSITY EDUCATION SYSTEM

The article presents the results of a study conducted with users of personnel from the educational practice – directors of schools and kindergartens and experts in the field of education. The purpose of the research is to establish the respondents’ point of view regarding the quality of teacher training in the university education system. The main tasks of the study are to determine the opinion of the surveyed persons regarding the current state of the professional-pedagogical training of teachers; to identify the respondents’ expectations of the competence profile of the modern teacher; to systematize the viewpoints of the respondents in the context of opportunities for improving the educational content of the university pedagogical training. The sample of the study included 15 subjects, divided into three focus groups. The research methods used in the study are theoretical analysis, qualitative analysis, and content analysis.
PENGARUH MEDIA PEMBELAJARAN GAME INTERAKTIF TERHADAP KECERDASAN EMOSIONAL PADA MATERI SUHU DAN KALOR KELAS V SEKOLAH DASAR

The formulation of the research problem is whether there is an influence of interactive game learning media on emotional intelligence in the subject of temperature and heat in grade V elementary school? This study aims to determine the effect of interactive game learning on emotional intelligence in the material temperature and heat class V Elementary School. This research was conducted in the even semester of the 2023/2024 academic year. The method used in this research is the pre-experimental method with a one group pretest-posttest design. The research sample consisted of one class of 30 students. The research instrument used was a questionnaire of 20 questions, while the data analysis technique used the t test. Based on the results of the study it was found that the average pretest value questionnaire produced was 66,30 and the posttest questionnaire average was 78,47. Thus, it can be concluded that interactive game learning media has an effect on emotional intelligence in the subject of temperature and heat in class V of elementary school.
RANCANG BANGUN SISTEM INFORMASI MONITORING ORGANISASI MAHASISWA BERBASIS WEB MENGGUNAKAN FRAMEWORK CODEIGNITER

The Student Organization Monitoring Information System (SIMORA) is a student monitoring system for student organizations at Banten Jaya University using electronic device technology. Student organizations at the University of Banten Jaya currently in terms of administrative management such as correspondence, funding applications, proposals, accountability reports and announcements, activity information, and so on still use a manual system that still uses paper so it is vulnerable to data storage errors. All administrative activities will be archived and used as a reference in implementing activities for each student organization in the next period. The purpose of this research is to design a website for monitoring student organization activities and to help optimize the administrative process of student organizations through SIMORA. The system design method used in this research is using the Waterfall method so that it is easier to develop and use UML for visual system modeling. SIMORA is made website-based using the CodeIgniter framework. This student organization monitoring information system acts as a means of supporting the running of a web-based organization so that it can be accessed online with various service features that are mutually integrated with one another. This information system can facilitate the head of student affairs in disseminating information, disciplining student organizations regarding programs to be implemented, and supervising student organizations in terms of administration and student activities. This system can help discipline student organizations in submitting activities because, in the SIMORA system, they cannot propose activities if they are not in accordance with the Draft Activity Budget (RAK) this system also only enters submission data so as to minimize revisions to submissions.
Developments and Evaluation of Twin Learning Algorithms: A Systematic Review

This chapter contributes a detailed study on the developments of twin learning algorithms that are happened on top of the Twin Support Vector Machine (TWSVM), Twin Extreme Learning Machine (TELM) and Twin Random Vector Functional Link (TRVFL). Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. According to the demands of the digital age, machine learning algorithms have made significant strides. Twin Algorithms' level of performance ought to be superior to that of its parents'. The improvements in the running time of TELM and TWSVM are commendable and have given confidence to the researchers. The twin models that followed Extreme Learning Machine (ELM) and the single-hidden-layer learning paradigms both attempted to overcome a number of their parents' shortcomings. Artificial intelligence will advance thanks to iterative single-hidden-layer models and their reliable performances. In this chapter, some of the works in twin learning algorithms that are either technically sound or better in the performances are taken for the study. The recent developments in twin algorithms, especially in the single hidden layered models, found more attractive because of the underlined learning procedure than the performance. The working principle and performance of the algorithms are detailed with the help of published works and findings.
Direction of University ESG Liberal Arts Education: Learner participation type with the local community Focusing on the case of ESG liberal arts education

In this paper, as a strategy for revitalizing ESG(Environmental, Social, and Governance) education, we looked at the direction of ESG liberal arts education in universities. To this end, the theoretical background of ESG and cases of companies and local governments were examined. In addition, through cases of citizen participation and learner-centered ESG education with local communities and university students, a draft educational program and learner satisfaction survey results were analyzed. Finally, as a way to expand ESG education from the perspective of civic literacy and lifelong education, the direction and teaching methods of ESG liberal arts education in universities were examined. Through this, it was confirmed that ESG liberal arts education can be a new paradigm shift in university education and an innovative education strategy.
Decoupling with Entropy-based Equalization for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation methods are the main solution to alleviate the problem of high annotation consumption in semantic segmentation. However, the class imbalance problem makes the model favor the head classes with sufficient training samples, resulting in poor performance of the tail classes. To address this issue, we propose a Decoupled Semi-Supervise Semantic Segmentation (DeS4) framework based on the teacher-student model. Specifically, we first propose a decoupling training strategy to split the training of the encoder and segmentation decoder, aiming at a balanced decoder. Then, a non-learnable prototype-based segmentation head is proposed to regularize the category representation distribution consistency and perform a better connection between the teacher model and the student model. Furthermore, a Multi-Entropy Sampling (MES) strategy is proposed to collect pixel representation for updating the shared prototype to get a class-unbiased head. We conduct extensive experiments of the proposed DeS4 on two challenging benchmarks (PASCAL VOC 2012 and Cityscapes) and achieve remarkable improvements over the previous state-of-the-art methods.
THE EDUCATIONAL ENVIRONMENT – A FACTOR FOR IMPROVING THE QUALITY OF CLASSROOM LEARNING

The educational environment is multi-component. In scientific research, there are different concepts about the nature, structure and planning of the educational environment. Studies in this direction support pedagogical practice with strategies for building an effective educational environment suitable for the educational needs of every child and student. The correlation between educational environment and learning quality is undeniable. The purpose of this report is to analyze the components of the educational environment and their influence on increasing learning outcomes in the classroom. The topicality of the set topic can be found in the National Program „Ensuring a Modern Educational Environment“ of the Ministry of Education and Culture, launched in 2021.
Explanation-Guided Reward Alignment

Agents often need to infer a reward function from observations to learn desired behaviors. However, agents may infer a reward function that does not align with the original intent because there can be multiple reward functions consistent with its observations. Operating based on such misaligned rewards can be risky. Furthermore, black-box representations make it difficult to verify the learned rewards and prevent harmful behavior. We present a framework for verifying and improving reward alignment using explanations and show how explanations can help detect misalignment and reveal failure cases in novel scenarios. The problem is formulated as inverse reinforcement learning from ranked trajectories. Verification tests created from the trajectory dataset are used to iteratively validate and improve reward alignment. The agent explains its learned reward and a tester signals whether the explanation passes the test. In cases where the explanation fails, the agent offers alternative explanations to gather feedback, which is then used to improve the learned reward. We analyze the efficiency of our approach in improving reward alignment using different types of explanations and demonstrate its effectiveness in five domains.
Probabilistic Masked Attention Networks for Explainable Sequential Recommendation

Transformer-based models are powerful for modeling temporal dynamics of user preference in sequential recommendation. Most of the variants adopt the Softmax transformation in the self-attention layers to generate dense attention probabilities. However, real-world item sequences are often noisy, containing a mixture of true-positive and false-positive interactions. Such dense attentions inevitably assign probability mass to noisy or irrelevant items, leading to sub-optimal performance and poor explainability. Here we propose a Probabilistic Masked Attention Network (PMAN) to identify the sparse pattern of attentions, which is more desirable for pruning noisy items in sequential recommendation. Specifically, we employ a probabilistic mask to achieve sparse attentions under a constrained optimization framework. As such, PMAN allows to select which information is critical to be retained or dropped in a data-driven fashion. Experimental studies on real-world benchmark datasets show that PMAN is able to improve the performance of Transformers significantly.
Graph Neural Convection-Diffusion with Heterophily

Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic graphs. The connected nodes are likely to be from different classes or have dissimilar features on heterophilic graphs. In this paper, we propose a novel GNN that incorporates the principle of heterophily by modeling the flow of information on nodes using the convection-diffusion equation (CDE). This allows the CDE to take into account both the diffusion of information due to homophily and the ``convection'' of information due to heterophily. We conduct extensive experiments, which suggest that our framework can achieve competitive performance on node classification tasks for heterophilic graphs, compared to the state-of-the-art methods. The code is available at https://github.com/zknus/Graph-Diffusion-CDE.
Feature Staleness Aware Incremental Learning for CTR Prediction

Click-through Rate (CTR) prediction in real-world recommender systems often deals with billions of user interactions every day. To improve the training efficiency, it is common to update the CTR prediction model incrementally using the new incremental data and a subset of historical data. However, the feature embeddings of a CTR prediction model often get stale when the corresponding features do not appear in current incremental data. In the next period, the model would have a performance degradation on samples containing stale features, which we call the feature staleness problem. To mitigate this problem, we propose a Feature Staleness Aware Incremental Learning method for CTR prediction (FeSAIL) which adaptively replays samples containing stale features. We first introduce a staleness-aware sampling algorithm (SAS) to sample a fixed number of stale samples with high sampling efficiency. We then introduce a staleness-aware regularization mechanism (SAR) for a fine-grained control of the feature embedding updating. We instantiate FeSAIL with a general deep learning-based CTR prediction model and the experimental results demonstrate FeSAIL outperforms various state-of-the-art methods on four benchmark datasets. The code can be found in https://github.com/cloudcatcher888/FeSAIL.
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification

Text classification is a fundamental task for natural language processing, and adapting text classification models across domains has broad applications. Self-training generates pseudo-examples from the model's predictions and iteratively trains on the pseudo-examples, i.e., minimizes the loss on the source domain and the Gibbs entropy on the target domain. However, Gibbs entropy is sensitive to prediction errors, and thus, self-training tends to fail when the domain shift is large. In this paper, we propose Meta-Tsallis Entropy minimization (MTEM). MTEM uses an instance adaptive Tsallis entropy to replace the Gibbs entropy and a meta-learning algorithm to optimize the instance adaptive Tsallis entropy on the target domain. To reduce the computation cost of MTEM, we propose an approximation technique to approximate the second-order derivation involved in the meta-learning. To efficiently generate pseudo labels, we propose an annealing sampling mechanism for exploring the model's prediction probability. Theoretically, we prove the convergence of the meta-learning algorithm in MTEM and analyze the effectiveness of MTEM in achieving domain adaptation. Experimentally, MTEM improves the adaptation performance of BERT with an average of 4 percent on the benchmark dataset.
SSML-QNet: Scale-Separative Metric Learning Quadruplet Network for Multi-modal Image Patch Matching

Multi-modal image matching is very challenging due to the significant diversities in visual appearance of different modal images. Typically, the existing well-performed methods mainly focus on learning invariant and discriminative features for measuring the relation between multi-modal image pairs. However, these methods often take the features as a whole and largely overlook the fact that different scale features for a same image pair may have different similarity, which may lead to sub-optimal results only. In this work, we propose a Scale-Separative Metric Learning Quadruplet network (SSML-QNet) for multi-modal image patch matching. Specifically, SSML-QNet can extract both relevant and irrelevant features of imaging modality with the proposed quadruplet network architecture. Then, the proposed Scale-Separative Metric Learning module separately encodes the similarity of different scale features with the pyramid structure. And for each scale, cross-modal consistent features are extracted and measured by coordinate and channel-wise attention sequentially. This makes our network robust to appearance divergence caused by different imaging mechanism. Experiments on the benchmark dataset (VIS-NIR, VIS-LWIR, Optical-SAR, and Brown) have verified that the proposed SSML-QNet is able to outperform other state-of-the-art methods. Furthermore, the cross-dataset transferring experiments on these four datasets also have shown that the proposed method has powerful ability of cross-dataset transferring.
Faster Exact MPE and Constrained Optimization with Deterministic Finite State Automata

We propose a concise function representation based on deterministic finite state automata for exact most probable explanation and constrained optimization tasks in graphical models. We then exploit our concise representation within Bucket Elimination (BE). We denote our version of BE as FABE. FABE significantly improves the performance of BE in terms of runtime and memory requirements by minimizing redundancy. Indeed, results on most probable explanation and weighted constraint satisfaction benchmarks show that FABE often outperforms the state of the art, leading to significant runtime improvements (up to 2 orders of magnitude in our tests).
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation

Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheless, in facilitating semi-supervised controllable language generation, ST faces two key challenges. First, augmented by self-generated pseudo text, generation models tend to over-exploit the previously learned text distribution, suffering from mode collapse and poor generation diversity. Second, generating pseudo text in each iteration is time-consuming, severely decelerating the training process. In this work, we propose KEST, a novel and efficient self-training framework to handle these problems. KEST utilizes a kernel-based loss, rather than standard cross entropy, to learn from the soft pseudo text produced by a shared non-autoregressive generator. We demonstrate both theoretically and empirically that KEST can benefit from more diverse pseudo text in an efficient manner, which allows not only refining and exploiting the previously fitted distribution but also enhanced exploration towards a larger potential text space, providing a guarantee of improved performance. Experiments on three controllable generation tasks demonstrate that KEST significantly improves control accuracy while maintaining comparable text fluency and generation diversity against several strong baselines.
VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs

Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using the voxel grids to represent the radiance field can significantly accelerate the optimization process, we observe that for sparse inputs, the voxel grids are more prone to overfitting to the training views and will have holes and floaters, which leads to artifacts. In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues. To improve the performance of voxel-based radiance field in sparse input scenarios, we propose two methods: (a) We introduce an incremental voxel training strategy, which prevents overfitting by suppressing the optimization of peripheral voxels in the early stage of reconstruction. (b) We use several regularization techniques to smooth the voxels, which avoids degenerate solutions. Experiments demonstrate that VGOS achieves state-of-the-art performance for sparse inputs with super-fast convergence. Code will be available at https://github.com/SJoJoK/VGOS.
On Discovering Interesting Combinatorial Integer Sequences

We study the problem of generating interesting integer sequences with a combinatorial interpretation. For this we introduce a two-step approach. In the first step, we generate first-order logic sentences which define some combinatorial objects, e.g., undirected graphs, permutations, matchings etc. In the second step, we use algorithms for lifted first-order model counting to generate integer sequences that count the objects encoded by the first-order logic formulas generated in the first step. For instance, if the first-order sentence defines permutations then the generated integer sequence is the sequence of factorial numbers n!. We demonstrate that our approach is able to generate interesting new sequences by showing that a non-negligible fraction of the automatically generated sequences can actually be found in the Online Encyclopaedia of Integer Sequences (OEIS) while generating many other similar sequences which are not present in OEIS and which are potentially interesting. A key technical contribution of our work is the method for generation of first-order logic sentences which is able to drastically prune the space of sentences by discarding large fraction of sentences which would lead to redundant integer sequences.