bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating Neural Fit Approaches for Sentence Embedding Model Paradigms
Authors: Balabin Helena , Liuzzi Antonietta Gabriella , Sun Jingyuan , Dupont Patrick , Vanderberghe Rik , Moens Marie-Francine
Published: September 2023

In recent years, representations from brain activity patterns and pre-trained language models have been linked to each other based on neural fits to validate hypotheses about language processing. Nonetheless, open questions remain about what intrinsic properties of language processing these neural fits reflect and whether they differ across neural fit approaches, brain networks, and models. In this study, we use parallel sentence and functional magnetic resonance imaging data to perform a comprehensive analysis of four paradigms (masked language modeling, pragmatic coherence, semantic comparison, and contrastive learning) representing linguistic hypotheses about sentence processing. We include three sentence embedding models for each paradigm, resulting in a total of 12 models, and examine differences in their neural fit to four different brain networks using regression-based neural encoding and Representational Similarity Analysis (RSA). Among the different models tested, GPT-2, SkipThoughts, and S-RoBERTa yielded the strongest correlations with language network patterns, whereas contrastive learning-based models resulted in overall low neural fits. Our findings demonstrate that neural fits vary across brain networks and models representing the same linguistic hypothesis (e.g., GPT-2 and GPT-3). More importantly, we show the need for both neural encoding and RSA as complementary methods to provide full understanding of neural fits. All code used in the analysis is publicly available: https://github.com/lcn-kul/sentencefmricomparison.
Cost-Awareness in Multi-Agent Active Search
Authors: Banerjee Arundhati , Ghods Ramina , Schneider Jeff
Published: September 2023

Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets. In a realistic setting, agents also must consider the costs that their decisions incur. Previously proposed active search algorithms simplify the problem by ignoring uncertainty in the agent’s environment, using myopic decision making, and/or overlooking costs. In this paper, we introduce an online active search algorithm to detect targets in an unknown environment by making adaptive cost-aware decisions regarding the agent’s actions. Our algorithm proposes an online lookahead planner that combines priniciples from Monte Carlo Tree Search, Thompson sampling and pareto-optimal confidence bounds for decentralized multi-agent multi-objective optimization in an unknown environment. We analyze the algorithm’s performance in simulation to show its efficacy in cost-aware active search.
Theoretical Remarks on Feudal Hierarchies and Reinforcement Learning
Authors: Carvalho Diogo S. , Melo Francisco S. , Santos Pedro A.
Published: September 2023

Hierarchical reinforcement learning is an increasingly demanded resource for learning to make sequential decisions towards long term goals. Feudal hierarchies are among the most deployed frameworks. However, there are few theoretical results for hierarchical structures. In this work, we formalize the common two-level feudal hierarchy as two Markov decision processes, with the one on the high level being dependent on the policy executed at the low level. Despite the non-stationarity raised by the dependency, we show that each of the processes presents stable behavior. We then build on the first result to show that, regardless of the convergent learning algorithm used for the low level, convergence of both prediction and control algorithms at the high-level is guaranteed. Our results contribute with theoretical support for the use of feudal hierarchies in combination with standard reinforcement learning methods at each level.
Cache-Efficient Dynamic Programming MDP Solver
Authors: Champagne Gareau Jaël , Gosset Guillaume , Beaudry Éric , Makarenkov Vladimir
Published: September 2023

Automated planning research often focuses on developing new algorithms to improve the computational performance of planners, but effective implementation can also play a significant role. Hardware features such as memory hierarchy can yield substantial running time improvements when optimized. In this paper, we propose two state-reordering techniques for the Topological Value Iteration (TVI) algorithm. Our first technique organizes states in memory so that those belonging to the same Strongly Connected Component (SCC) are contiguous, while our second technique optimizes state value propagation by reordering states within each SCC. We analyze existing planning algorithms with respect to their cache efficiency and describe domain characteristics which can provide an advantage to each of them. Empirical results show that, in many instances, our new algorithms, called eTVI and eiTVI, run several times faster than traditional VI, TVI, LRTDP and ILAO* techniques.
Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation
Authors: Dong Yihong , Li Ge , Jiang Xue , Jin Zhi
Published: September 2023

Code generation focuses on automatically converting natural language (NL) utterances into code snippets. Sequence-to-tree (Seq2Tree) approaches are proposed for code generation with the aim of ensuring grammatical correctness of the generated code. These approaches generate subsequent Abstract Syntax Tree (AST) nodes based on the preceding predictions of AST nodes. However, existing Seq2Tree approaches tend to treat both antecedent predictions and subsequent predictions equally, which poses a challenge for models to produce accurate subsequent predictions if the antecedent predictions are incorrect under the constraints of the AST. Given this challenge, it is necessary to pay more attention to antecedent predictions compared to subsequent predictions. To this end, this paper proposes a novel and effective method, named Antecedent Prioritized (AP) Loss, which prioritizes antecedent predictions by leveraging the position information of the generated AST nodes. We design an AST-to-Vector (AST2Vec) method that maps AST node positions to two-dimensional vectors, thereby modeling the position information of AST nodes. To evaluate the effectiveness of our proposed loss, we implement and train an Antecedent Prioritized Tree-based code generation model called APT. Experiments on four benchmark datasets demonstrate that with better antecedent predictions and accompanying subsequent predictions, APT achieves significant improvements, indicating the superiority and generality of our proposed method.
Distributionally Robust Cross Subject EEG Decoding
Authors: Duan Tiehang , Wang Zhenyi , Doretto Gianfranco , Li Fang , Tao Cui , Adjeroh Donald
Published: September 2023

Recently, deep learning has shown to be effective for Electroencephalography (EEG) decoding tasks. Yet, its performance can be negatively influenced by two key factors: 1) the high variance and different types of corruption that are inherent in the signal, 2) the EEG datasets are usually relatively small given the acquisition cost, annotation cost and amount of effort needed. Data augmentation approaches for alleviation of this problem have been empirically studied, with augmentation operations on spatial domain, time domain or frequency domain handcrafted based on expertise of domain knowledge. In this work, we propose a principled approach to perform dynamic evolution on the data for improvement of decoding robustness. The approach is based on distributionally robust optimization and achieves robustness by optimizing on a family of evolved data distributions instead of the single training data distribution. We derived a general data evolution framework based on Wasserstein gradient flow (WGF) and provides two different forms of evolution within the framework. Intuitively, the evolution process helps the EEG decoder to learn more robust and diverse features. It is worth mentioning that the proposed approach can be readily integrated with other data augmentation approaches for further improvements. We performed extensive experiments on the proposed approach and tested its performance on different types of corrupted EEG signals. The model significantly outperforms competitive baselines on challenging decoding scenarios.
Strategy Repair in Reachability Games
Authors: Gaillard Pierre , Patrizi Fabio , Perelli Giuseppe
Published: September 2023

We introduce Strategy Repair, the problem of finding a minimal amount of modifications to turn a strategy for a reachability game from losing into winning. The problem is relevant for a number of settings in Planning and Synthesis, where solutions essentially correspond to winning strategies in a suitably defined reachability game. We show, via reduction from Vertex Cover, that Strategy Repair is NP-complete and devise two algorithms, one optimal and exponential and one polynomial but sub-optimal, which we compare experimentally. The reported experimentation includes some heuristics for strategy modification, which proved crucial in dramatically improving performance.
Multi-Domain Learning from Insufficient Annotations
Authors: He Rui , Liu Shengcai , Wu Jiahao , He Shan , Tang Ke
Published: September 2023

Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-shared information extraction and domain-private information preservation, following the shared-private framework (SP models), which offers significant advantages over single-domain learning. However, the limited availability of annotated data in each domain considerably hinders the effectiveness of conventional supervised MDL approaches in real-world applications. In this paper, we introduce a novel method called multi-domain contrastive learning (MDCL) to alleviate the impact of insufficient annotations by capturing both semantic and structural information from both labeled and unlabeled data. Specifically, MDCL comprises two modules: inter-domain semantic alignment and intra-domain contrast. The former aims to align annotated instances of the same semantic category from distinct domains within a shared hidden space, while the latter focuses on learning a cluster structure of unlabeled instances in a private hidden space for each domain. MDCL is readily compatible with many SP models, requiring no additional model parameters and allowing for end-to-end training. Experimental results across five textual and image multi-domain datasets demonstrate that MDCL brings noticeable improvement over various SP models. Furthermore, MDCL can further be employed in multi-domain active learning (MDAL) to achieve a superior initialization, eventually leading to better overall performance.
Understanding and Improving Neural Active Learning on Heteroskedastic Distributions
Authors: Khosla Savya , Kin Whye Chew , Ash Jordan , Zhang Cyril , Kawaguchi Kenji , Lamb Alex
Published: September 2023

Models that can actively seek out the best quality training data hold the promise of more accurate, adaptable, and efficient machine learning. Active learning techniques often tend to prefer examples that are the most difficult to classify. While this works well on homogeneous datasets, we find that it can lead to catastrophic failures when performed on multiple distributions with different degrees of label noise or heteroskedasticity. These active learning algorithms strongly prefer to draw from the distribution with more noise, even if their examples have no informative structure (such as solid color images with random labels). To this end, we demonstrate the catastrophic failure of these active learning algorithms on heteroskedastic distributions and propose a fine-tuning-based approach to mitigate these failures. Further, we propose a new algorithm that incorporates a model difference scoring function for each data point to filter out the noisy examples and sample clean examples that maximize accuracy, outperforming the existing active learning techniques on the heteroskedastic datasets. We hope these observations and techniques are immediately helpful to practitioners and can help to challenge common assumptions in the design of active learning algorithms. Our code is available at https://github.com/savya08/Active-Learning-on-Heteroskedastic-Distributions.
Gated Adapters for Multi-Domain Neural Machine Translation
Authors: Klimaszewski Mateusz , Belligoli Zeno , Kumar Satendra , Stergiadis Emmanouil
Published: September 2023

The Adapter framework introduces lightweight modules that reduce the complexity of Multi-Domain Machine Translation systems. Compared to fine-tuned models, Adapters train faster, do not overfit, have smaller memory requirements, and maintain the base model intact. However, just like fine-tuned models, they need prior information about the domain of the sentence. Otherwise, their performance decreases for out-of-domain and unknown-domain samples. In this work, we propose a solution that does not require the information and can decide on the sample’s origin on-the-fly without compromising quality or latency. We introduce a built-in gating mechanism utilising a knowledge distillation framework to activate a subset of softly-gated, domain-specific Adapters that are relevant to the sentence. The effectiveness of the proposed solution is demonstrated through our experiments on two language pairs, using both in-domain and out-of-domain datasets. Our analysis reveals that Gated Adapters provide significant benefits, particularly in the case of ambiguous, misclassified samples, resulting in an improvement of over +5 COMET points.
DeepDiscord: Dual Contrastive Coding for Transferable Time Series Anomaly Detection
Authors: Li Xin-Yi , Zhong Pei-Nan , Chen Di , Zhang Zhen-Dong , Yang Yu-Bin
Published: September 2023

Time series anomaly detection has attracted extensive research attention owing to its real-world applications. Existing deep learning based anomaly detectors usually require a separate training phase for each dataset. However, the long training time restricts their practicality in the industry use. To address this limitation, we propose a novel deep learning based discord search method named DeepDiscord, which is a multi-scale anomaly detector capable of directly examining unseen datasets after pre-training. To the best of our knowledge, our study is the first to introduce contrastive learning in the discord search, in order to provide a flexible and effective similarity measure for various kinds of data. We innovatively divide the data into two categories according to their roles in discord search, and combine dual learning with contrastive learning, which improves the efficiency and efficacy of discord search. Furthermore, a novel pretext task is proposed based on our dual contrastive learning setting. We evaluate DeepDiscord comprehensively on five anomaly detection benchmarks. Experimental results show that DeepDiscord achieves the state-of-the-art results on the four out of five benchmarks.
Multi-Agent Cooperative Games Using Belief Map Assisted Training
Authors: Luo Chen , Huang Qinwei , Wu Alex B. , Khan Simon , Li Hai , Qiu Qinru
Published: September 2023

In a multi-agent system, agents share their local observations to gain global situational awareness for decision making and collaboration using a message passing system. When to send a message, how to encode a message, and how to leverage the received messages directly affect the effectiveness of the collaboration among agents. When training a multi-agent cooperative game using reinforcement learning (RL), the message passing system needs to be optimized together with the agent policies. This consequently increases the model’s complexity and poses significant challenges to the convergence and performance of learning. To address this issue, we propose the Belief-map Assisted Multi-agent System (BAMS), which leverages a neuro-symbolic belief map to enhance training. The belief map decodes the agent’s hidden state to provide a symbolic representation of the agent’s understanding of the environment and other agents’ status. The simplicity of symbolic representation allows the gathering and comparison of the ground truth information with the belief, which provides an additional channel of feedback for the learning. Compared to the sporadic and delayed feedback coming from the reward in RL, the feedback from the belief map is more consistent and reliable. Agents using BAMS can learn a more effective message passing network to better understand each other, resulting in better performance in the game. We evaluate BAMS’s performance in a cooperative predator and prey game with varying levels of map complexity and compare it to previous multi-agent message passing models. The simulation results showed that BAMS reduced training epochs by 66%, and agents who apply the BAMS model completed the game with 34.62% fewer steps on average.
MFC: A Multishot Approach to Federated Data Clustering
Authors: Makkar Jaglike , Bhumika  , Jain Shweta , Gupta Shivam
Published: September 2023

The work explores the federated data clustering problem. The primary goal is to perform k-means clustering of data distributed over multiple clients while preserving privacy during an exchange with the central server. Existing solutions to unsupervised federated data clustering are either computationally challenging or effective only in heterogeneous regimes, i.e., when the number of clusters per client (kz) is less than the total number of clusters (k) (specifically, kz ≤ k). Moreover, existing one-shot approaches assume that the information about kz is available for each client. In this paper, we propose two multi-shot approaches which we call MFC and MFCH, that perform well on both heterogeneous and non-heterogeneous regimes, i.e., are independent of the underlying client data distribution. Both MFC and MFCH stand out as they do not rely on prior knowledge about kz. We theoretically bound the closeness of the local centers obtained by MFC to that of the optimal global centers and prove that under some well-separability assumption, the centers will be close enough. MFCH improvises MFC by only sharing a single cluster center from each client, thus ensuring more privacy. Our theoretical analysis shows that when at least O(k2log k) clients are involved, centers obtained by MFCH will closely approximate optimal global centers. Experiments on synthetic and real-world datasets validate the proposed approaches’ efficacy showcasing lower objective costs in non-heterogeneous regimes while having comparable performance in heterogeneous regimes. In addition, as a byproduct MFC exhibits higher device-level fairness in terms of the individual objective cost compared to existing state-of-the-art algorithms. The code is publicly available at https://github.com/shivi98g/MFC.
Symbolic Knowledge-Extraction Evaluation Metrics: The FiRe Score
Authors: Sabbatini Federico , Calegari Roberta
Published: September 2023

Symbolic knowledge-extraction (SKE) techniques are becoming of key importance for AI applications since they enable the explanation of opaque black-box predictors, enhancing trust and transparency. Among all the available SKE techniques, the best option for the case at hand should be selected. However, an automatic comparison between different options can be performed only if an adequate metric – such as a scoring function resuming all the interesting features of the extractors – is provided. Regrettably, the literature currently lacks definitions of effective evaluation metrics for symbolic knowledge extractors. This paper proposes the novel FiRe score metric, which comprehensively assesses the quality of an SKE procedure by considering both its predictive performance and the readability of the extracted knowledge. FiRe is compared to another existing scoring metric and a rigorous mathematical formulation is provided along with several practical examples to highlight its effectiveness to the end of being exploited inside automatic hyper-parameter tuning procedures.
Anytime Index-Based Search Method for Large-Scale Simultaneous Coalition Structure Generation and Assignment
Authors: Taguelmimt Redha , Aknine Samir , Boukredera Djamila , Changder Narayan
Published: September 2023

Organizing agents into disjoint groups is a crucial challenge in artificial intelligence, with many applications where quick runtime is essential. The Simultaneous Coalition Structure Generation and Assignment (SCSGA) problem involves partitioning a set of agents into coalitions and assigning each coalition to a task, with the goal of maximizing social welfare. However, this is an NP-complete problem, and only a few algorithms have been proposed to address it for both small and large-scale problems. In this paper, we address this challenge by presenting a novel algorithm that can efficiently solve both small and large instances of this problem. Our method is based on a new search space representation, where each coalition is codified by an index. We have developed an algorithm that can explore this solution space effectively by generating index vectors that represent coalition structures. The resulting algorithm is anytime and can scale to large problems with hundreds or thousands of agents. We evaluated our algorithm on a range of value distributions and compared its performance against state-of-the-art algorithms. Our experimental results demonstrate that our algorithm outperforms existing methods in solving the SCSGA problem, providing high-quality solutions for a wide range of problem instances.
High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation
Authors: Tang Jiaqi , Xu Xiaogang , Hu Sixing , Chen Ying-Cong
Published: September 2023

Due to limited camera capacities, digital images usually have a narrower dynamic illumination range than real-world scene radiance. To resolve this problem, High Dynamic Range (HDR) reconstruction is proposed to recover the dynamic range to better represent real-world scenes. However, due to different physical imaging parameters, the tone-mapping functions between images and real radiance are highly diverse, which makes HDR reconstruction extremely challenging. Existing solutions can not explicitly clarify a corresponding relationship between the tone-mapping function and the generated HDR image, but this relationship is vital when guiding the reconstruction of HDR images. To address this problem, we propose a method to explicitly estimate the tone mapping function and its corresponding HDR image in one network. Firstly, based on the characteristics of the tone mapping function, we construct a model by a polynomial to describe the trend of the tone curve. To fit this curve, we use a learnable network to estimate the coefficients of the polynomial. This curve will be automatically adjusted according to the tone space of the Low Dynamic Range (LDR) image, and reconstruct the real HDR image. Besides, since all current datasets do not provide the corresponding relationship between the tone mapping function and the LDR image, we construct a new dataset with both synthetic and real images. Extensive experiments show that our method generalizes well under different tone-mapping functions and achieves SOTA performance. The code/dataset is available at https://github.com/jqtangust/EPCE-HDR.git.
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
Authors: Tu Nguyen Anh , Uyen Hoang Thi Thu , Phuong Tu Minh , Bach Ngo Xuan
Published: September 2023

Multiple intent detection and slot filling are two fundamental and crucial tasks in spoken language understanding. Motivated by the fact that the two tasks are closely related, joint models that can detect intents and extract slots simultaneously are preferred to individual models that perform each task independently. The accuracy of a joint model depends heavily on the ability of the model to transfer information between the two tasks so that the result of one task can correct the result of the other. In addition, since a joint model has multiple outputs, how to train the model effectively is also challenging. In this paper, we present a method for multiple intent detection and slot filling by addressing these challenges. First, we propose a bidirectional joint model that explicitly employs intent information to recognize slots and slot features to detect intents. Second, we introduce a novel method for training the proposed joint model using supervised contrastive learning and self-distillation. Experimental results on two benchmark datasets MixATIS and MixSNIPS show that our method outperforms state-of-the-art models in both tasks. The results also demonstrate the contributions of both bidirectional design and the training method to the accuracy improvement. Our source code is available at https://github.com/anhtunguyen98/BiSLU.
SDV: Simple Double Validation Model-Based Offline Reinforcement Learning
Authors: Wang Xun , Chen Haonan , Yang Junming , Qian Zhuzhong , Zhang Bolei
Published: September 2023

Offline reinforcement learning (RL) aims to learn effective policies from recorded data without further interactions in the environments that are often costly or risky. Model-based algorithms, which begin by constructing an environmental model and then learn the policy under the model, have become a promising approach. However, most existing works have been over-conservative to avoid the out-of-distribution error induced by the model generated samples, leading to poor performance instead. In this work, we propose a novel model-based offline RL method, named Simple Double Validation (SDV). The main idea of SDV is to introduce an additional guidance model to assist the agent in determining the rationality of the states, combined with an advantage weighting factor to avoid effects that could potentially mislead the models due to suboptimal samples. In this way, the agent can be guided to more favourable states with reliable decisions. We evaluated SDV on the widely studied offline RL benchmarks and demonstrated its state-of-the-art performance. At the same time, our work introduces the idea of double validation and model advantage weighting into the field of model-based offline RL, providing new insights for future research.
Cardsformer: Grounding Language to Learn a Generalizable Policy in Hearthstone
Authors: Xia Wannian , Yang Yiming , Ruan Jingqing , Xing Dengpeng , Xu Bo
Published: September 2023

Hearthstone is a widely played collectible card game that challenges players to strategize using cards with various effects described in natural language. While human players can easily comprehend card descriptions and make informed decisions, artificial agents struggle to understand the game’s inherent rules and are unable to generalize their policies through natural language. To address this issue, we propose Cardsformer, a method capable of acquiring linguistic knowledge and learning a generalizable policy in Hearthstone. Cardsformer consists of a Prediction Model trained with offline trajectories to predict state transitions based on card descriptions and a Policy Model capable of generalizing its policy on unseen cards. To our knowledge, this is the first work to consider language knowledge in a card game. Experiments show that our approach significantly improves data efficiency and outperforms the state-of-the-art in Hearthstone even when there are untrained cards in the deck, inspiring a new perspective of tackling problems as such with knowledge representation from large language models. As the game constantly releases new cards along with new descriptions and new effects, the challenge in Hearthstone remains. To encourage further research, we make our code publicly available and publish PyStone, the code base of Hearthstone on which we conducted our experiments, as an open benchmark.
Domain Knowledge Injection in Bayesian Search for New Materials
Authors: Xie Zikai , Evangelopoulos Xenophon , Thacker Joseph C. R. , Cooper Andrew I.
Published: September 2023

In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that accommodates domain knowledge to tune exploration in the search space. Bayesian optimization has recently emerged as a sample-efficient optimizer for many intractable scientific problems. While various existing BO frameworks allow the input of prior beliefs to accelerate the search by narrowing down the space, incorporating such knowledge is not always straightforward and can often introduce bias and lead to poor performance. Here we propose a simple approach to incorporate structural knowledge in the acquisition function by utilizing an additional deterministic surrogate model to enrich the approximation power of the Gaussian process. This is suitably chosen according to structural information of the problem at hand and acts a corrective term towards a better-informed sampling. We empirically demonstrate the practical utility of the proposed method by successfully injecting domain knowledge in a materials design task. We further validate our method’s performance on different experimental settings and ablation analyses.
Rethinking Temporal Information in Session-Based Recommendation: A Position-Agnostic Approach
Authors: Xu Xianghong , Ouyang Kai , Zou Jiaxin , Zheng Hai-Tao , Liu Wenqiang , Huang Dongxiao , Wu Bei
Published: September 2023

Session-based Recommendation (SBR) aims to predict the next item for a session, which consists of several clicked items in a transaction. Most SBR approaches follow an underlying assumption that all sequential information should be strictly utilized. Thus, they model temporal information for items using implicit, explicit, or ensemble methods. In fact, users may recall previously clicked items but might not remember the exact order in which they were clicked. Therefore, focusing on representing item temporal information in various ways could make learning session intents challenging. In this paper, we rethink the necessity of temporal information for items in SBR. We propose Aggregating the Contextual intents of the session with Attentive networks, namely ACARec. Specifically, we avoid explicitly modeling positional embeddings and learn contextual intents through aggregation methods (convolutions or poolings). We also demonstrate that even an entirely position-agnostic aggregation approach can yield promising results. Extensive experiments on real-world datasets validate our arguments. We hope our study can provide insights into SBR and inspire future research in the community.
Towards Abstractive Timeline Summarisation Using Preference-Based Reinforcement Learning
Authors: Ye Yuxuan , Simpson Edwin
Published: September 2023

This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform established extractive methods on specialised tasks such as timeline summarisation (TLS). While extractive summaries are more faithful to their sources, they may be less readable and contain redundant or unnecessary information. This paper proposes a preference-based reinforcement learning (PBRL) method for adapting pretrained abstractive summarisers to TLS, which can overcome the drawbacks of extractive timeline summaries. We define a compound reward function that learns from keywords of interest and pairwise preference labels, which we use to fine-tune a pretrained abstractive summariser via offline reinforcement learning. We carry out both automated and human evaluation on three datasets, finding that our method outperforms a comparable extractive TLS method on two of the three benchmark datasets, and participants prefer our method’s summaries to those of both the extractive TLS method and the pretrained abstractive model. The method does not require expensive reference summaries and needs only a small number of preferences to align the generated summaries with human preferences. Code available at https://github.com/Haruhi07/PBRL-TLS.
Region-Specific Prototype Customization for Weakly Supervised Semantic Segmentation
Authors: Yu Ruiguo , Zhao Yihang , Yu Mei , Gao Jie , Wang Chenhan , Zhang Ruixuan , Li Xuewei
Published: September 2023

It is well known that weakly supervised semantic segmentation requires only image-level labels for training, which greatly reduces the annotation cost. In recent years, prototype-based approaches, which prove to substantially improve the segmentation performance, have been favored by a wide range of researchers. However, we are surprised to find that there are semantic gaps between different regions within the same object, hindering the optimization of prototypes, so the traditional prototypes can not adequately represent the entire object. Therefore, we propose region-specific prototypes to adaptively describe the regions themselves, which alleviate the effect of semantic gap by separately obtaining prototypes for different regions of an object. In addition, to obtain more representative region-specific prototypes, a plug-and-play Spatially Fused Attention Module is proposed for combining the spatial correlation and the scale correlation of hierarchical features. Extensive experiments are conducted on PASCAL VOC 2012 and MS COCO 2014, and the results show that our method achieves state-of-the-art performance using only image-level labels.
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework
Authors: Zhang Zhenshuo , Zhu Yun , Shi Haizhou , Tang Siliang
Published: September 2023

lbeit having gained significant progress lately, large-scale graph representation learning remains expensive to train and deploy for two main reasons: (i) the repetitive computation of multi-hop message passing and non-linearity in graph neural networks (GNNs); (ii) the computational cost of complex pairwise contrastive learning loss. Two main contributions are made in this paper targeting this twofold challenge: we first propose an adaptive-view graph neural encoder (AVGE) with a limited number of message passing to accelerate the forward pass computation, and then we propose a structure-aware group discrimination (SAGD) loss in our framework which avoids inefficient pairwise loss computing in most common GCL and improves the performance of the simple group discrimination. By the framework proposed, we manage to bring down the training and inference cost on various large-scale datasets by a significant margin (250x faster inference time) without loss of the downstream-task performance.
Creating Explainable Dynamic Checklists via Machine Learning to Ensure Decent Working Environment for All: A Field Study with Labour Inspections
Authors: Flogard Eirik Lund , Mengshoel Ole Jakob , Theisen Ole Magnus , Bach Kerstin
Published: September 2023

To address poor working conditions and promote United Nations’ sustainable development goal 8.8, “protect labour rights and promote safe working environments for all workers [...]”, government agencies around the world conduct labour inspections. To carry out these inspections, inspectors traditionally use paper-based checklists as a means to survey individual organisations for working environment violations. Currently, these checklists are created by domain experts, but recent research indicates that machine learning (ML) could be used to generate dynamic checklists to increase inspection efficiency. A drawback with the dynamic checklists is that they are complex and could be difficult to understand for inspectors. They have also never been field-tested. In this paper, we therefore propose user-oriented explanation methods for Context-aware Bayesian Case-Based Reasoning (CBCBR), which is the current state-of-art ML method for generating dynamic checklists. We also introduce a prototype of CBCBR and present a field study where we test it in real-world labour inspections. The results from the study indicate that using the explainable dynamic checklists increases the efficiency of the labour inspections, and inspectors also report that they find the checklists useful. The results also suggest that current ML evaluation methods, where model prediction performance is evaluated on existing data, may not fully reflect the real-world field performance of checklists.
STUDY KOMPARASI IMPLEMENTASI SISTEM PEMERINTAHAN PRESIDENSIAL ANTARA NEGARA AMERIKA SERIKAT DAN NEGARA INDONESIA
Authors: Puspito Toto Andri , Rahmadhani Desta Adinda
Published: September 2023

After the stipulation of the 1945 Constitution of the Republic of Indonesia in the 4th Amendment, the Indonesian state has declared itself to be consistent in implementing the presidential system of government as the basis for the implementation of its government. The presidential system of government or also known as the congressional system is a system of government in a country in the form of a republic in which executive power is elected through general elections and is separated from legislative power. With the complexity of this system, not many countries that implement it fail to pass the test of democratic stability. It can be stated that only the United States of America is capable of becoming the best practice of implementing a successful, effective and efficient presidential system. Therefore, many other countries use the United States as a role model in building an effective and efficient form of presidentialism system as has been done by Indonesia.
Penerapan Hukuman Mati oleh Negara Maju dan Berkembang
Authors: Ningsih Rahma , Ardliansyah Moelki Fahmi
Published: September 2023

Hukum Mati adalah Hukuman yang di berikan atau yang di jatuhkan kepada pelaku kejahatan pidana yang merupakan sangsi berat.

Indonesia merupakan negara hukum yang mana tertuang dalam Pasal 1 Ayat (3) UUD 1945 mengatakan bahwa, Negara Indonesia adalah negara hukum,”di mana indonesia sendiri menerapkan aturan hukum dalam penyelenggaraan pemerintahan dan dalam kehidupan bermasyarakat, berbangsa, dan bernegara.Hukuman mati sudah ada sejak zaman dahulu di mulai sejak kerjasaan Babilonia di Mesopotamia. Sampai saat ini masih banyak negara yang menggunakan hukum mati guna menghukum pelaku kejahatan pidana,ada juga Negara yang sudah mulai menghapuskan hukuman mati dalam peraturan negara mereka, ada jugayang berpendapat hukuman mati tidak efektif melihat dari segi proses sebelum dan saat eksekusinya yang dianggap  sama sekali tidak manusiawi.
The formation of atmospheric air pollution emissions from separate explosions as a result of military operations
Authors: Goncharenko Artem V. , Sipakov Rostyslav V.
Published: September 2023

As is well known, military operations in urbanized areas lead to deterioration of atmospheric air quality. The article examines the model of the formation of atmospheric polluted air from individual explosions as a result of military operations. A technique based on the theory of the convective jet is proposed for estimating the concentrations of hazardous substances formed above the burning area as a result of the explosion. The proposed approach makes it possible to obtain the concentration of pollutants in the conditions of emission into the atmosphere open to solar radiation at a height of three or more diameters of the explosion funnel (or the conventional diameter of the warm surface), the temperature and speed of hot air jets over the warm surface, the consumption of the desired substance. The obtained data make it possible to calculate the emission of hazardous substances into the atmosphere according to the formulas for a high unshaded source of pollution depending on the meteorological conditions of the area, to determine the concentration of the main acid-forming substances when assessing the probability of acid precipitation, and to serve as a basis for modern innovative atmospheric air monitoring systems.An assessment of the existing approach to determining atmospheric air pollution due to the impact of X-101 missile fragments in Kyiv on December 16, 2022 was made, which is a confirmation of the validity of this approach when calculating the impact of a separate explosion on atmospheric air quality. Calculated emissions of pollutants and their concentrations in the air in the upper part of the convective polluted jet in the case of separate explosions of the Kalibr cruise missile, the Kh-101 missile and the BUK-M1 projectile. The error from the comparison of these calculation results with the atmospheric air monitoring system depends on the nearest active observation points. This approach to assessing and forecasting concentrations of pollutants in atmospheric air can be used under neutral atmospheric conditions that determine the emission of pollutants and transformations of chemical substances in the air.
ANALYSIS OF THE STUDY OF THE ARCHITECTURE OF ELIZABETHGRAD IN THE SECOND HALF OF THE 19TH CENTURY - THE BEGINNING OF THE 20TH CENTURY IN THE HISTORICAL AND SCIENTIFIC WORKS
Authors: Penyaz Tetiana
Published: September 2023

Specific features of the forming of architecture in the second half of the 19th - beginning of the 20th centuries of cities of the central region of Ukraine need a detailed study to understand the ways of forming modern architectural space. The article is dedicated to the analysis of the state of study of historical and scientific sources about to the architecture of Yeolysavetgrad (Kropyvnytskyi) in the second half of the 19th and early 20th centuries. In the process of studying literature, based on the received on primary information and its systematization, little-studied and unexplored problems were identified that require their further study. The materials of the article are an attempt to classify literary sources by its types and complexity. Most studies are statistical or descriptive in nature and consider only a few questions about the development of Yelisavetgrad architecture in the late 19th - early 20th centuries. The main attention in historical and local history sources was paid to the city-forming architectural monuments.

The sources of the research of architecture of the city are divided according to the time of publicatons into four periods: the first - the middle of the 19th century; the second - the end of the 19th century - until 1917; the third - the Soviet period: from 1917 to 1991; the fourth - the period of independence from 1991 to 2022.

By type, they are divided into: scientific researches, historical-architectural studies, local history studies, encyclopedic literature, periodicals.

The performed analysis showed that there are no systematic research of features of development of the architecture of Yelisavetgrad in the second half of the 19th - early 20th centuries with its legible classification. There is insufficiently research into prerequisites and factors for the formation of the architecture of this period and its importance in the further architectural and planning development of the city.

The conducted research allows to set the priority tasks and directions for further researches.
Private sector perception of reducing deforestation in brazil: analysis of challenges from 2010 to 2019
Authors: Gallo Patricia , Gebara Maria Fernanda , Micheletti Tatiane , Brites Alice Dantas
Published: September 2023

Purpose: This paper aims to shed light on the private sector's perspective on REDD+ in Brazil, and how this perspective has evolved over time. Methodology/Approach: This research is part of the Global Comparative Study on REDD+ (GCS REDD+) on policies and political processes from the Center for International Forestry Research (CIFOR).  Findings: Our results indicate that national business organizations believe that REDD+ is an affordable way to mitigate climate change. However, it suggests that while this sector is seeking financial benefits from REDD+ activities, it is taking a very cautious and risk-averse approach. The private sector is not engaged and does not self-identify within the operational challenges that REDD+ policymakers are grappling with as they seek to embrace the possibilities of this mechanism. Research Limitation/Implication: To explore how these private sector actors perceive REDD+, whether such a perspective has changed from 2010 to 2019, and its implications for further REDD+ design in the national context. Originality/Value of the paper: private actors' positions on key statements about financing, benefit sharing and equity, governance, and challenges over three different time periods. A better understanding of how the private sector perceives REDD+ will contribute to national framing and more effective multi-level governance.
Rancang Bangun Alat Press Tahu Otomatis Berbasis Raspberry Pi
Authors: Aji Totok Surya , Kalsum Toibah Umi , Riska Riska
Published: September 2023

Tahu merupakan salah satu makanan yang paling banyak diminati oleh orang Indonesia, di mana tahu adalah salah satu makanan yang hampir setiap harinya diolah untuk dijadikan lauk maupun cemilan. Disadari ataupun tidak sebagai hasil olahan kacang kedelai, tahu merupakan makanan andalan untuk perbaikan gizi karena tahu mempunyai mutu protein nabati terbaik. Hal tersebut disebabkan oleh adanya kandungan komposisi asam amino paling lengkap dan diyakini memiliki daya cerna yang tinggi (sebesar 85%- 98%). Pada proses pembuatan tahu diperlukan alat press yang digunakan untuk memadatkan tahu. Umumnya sumber tenaga untuk alat press tahu kebanyakan berasal dari tangan manusia. Hal ini terlihat pada sebagian besar perusahaan pembuat tahu di Bengkulu rata-rata masih menggunakan alat dan tenaga secara manual, sehingga akan berdampak pada waktu proses pembuatan tahu yang cukup lama. Penelitian ini bertujuan untuk merancang dan  membuat alat press tahu  secara otomatis menggunakan raspberry pi. Hal ini dilakukan agar dapat  membantu proooses pengepresan  secara otomatis. Selain itu ala ini diharapkan dapat  meringan kan tenaga kerja dan mempercepat proses kerja dalam pembuatan tahu. Penelitian ini dilaksanan tepatnya pada januari sampai dengan juni tahun 2023, bertempat di laboratorium hadware Universitas Dehasen Bengkulu. Metode Penelitian yang digunakan adalah metode eksperimen, yakni untuk percobaan alat press tahu otomatis yang menggunakan sensor ultrasonik untuk menangkap ukuran tahu yang akan dipress, alat pres dilengkapi dengan sebuah cetakan yang terbuat dari kayu dengan ukuran panjang 20cm lebar 15cm dan tebal 5cm. Hasil penelitian menujukan bahwa tahu yang di press memiliki 3 ukuran ketebalan yaitu 3cm, 5cm, dan 7cm yang dapat dipress dengan waktu rata-rata 7 menit.
About Methods of Remote Control of the Heat Transfer Surface of Aerial Coolers
Authors:   LLC «Gazprom transgaz Ufa», Gilmutdinov D. V. ,   Ufa State Petroleum Technical University, Islamov I. M. ,   LLC «Gazprom transgaz Ufa», Ivanov E. S. ,   LLC «Gazprom transgaz Ufa», Kolchin A. V. ,   Ufa State Petroleum Technical University
Published: September 2023

To ensure uninterrupted and energy-efficient gas supply, consumers need to maintain a given temperature for pumping. The main unit for lowering the temperature at compressor stations are aerial coolers. Most cooling units, despite their simple design, today have a low-level digitalization and automation. Long service life (more than 20 years), as well as predominantly imported production in modern Russia, requires the introduction of new methods and methodologies of control for specified purposes. As a result of scientific research, the existing gas cooling system was studied, the processes of aging and ensuring the reliability of cooling units were analyzed, and a method was developed for remote monitoring of the technical condition of aerial coolers in terms of their heat transfer capacity. Based on the studied data on the values of thermal resistance after many years in operation are proposed measures for assessing their technical condition.