bluuebunny/crossref_metadata_2025 huggingface dataset:
Model-Based Reinforcement Learning with Multi-Step Plan Value Estimation
Authors: Lin Haoxin , Sun Yihao , Zhang Jiaji , Yu Yang
Published: September 2023

A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to save real-world samples. However, when the learned model has a non-negligible model error, sequential steps in the model are hard to be accurately evaluated, limiting the model’s utilization. This paper proposes to alleviate this issue by introducing multi-step plans into policy optimization for model-based RL. We employ the multi-step plan value estimation, which evaluates the expected discounted return after executing a sequence of action plans at a given state, and updates the policy by directly computing the multi-step policy gradient via plan value estimation. The new model-based reinforcement learning algorithm MPPVE (Model-based Planning Policy Learning with Multi-step Plan Value Estimation) shows a better utilization of the learned model and achieves a better sample efficiency than state-of-the-art model-based RL approaches. The code is available at https://github.com/HxLyn3/MPPVE.
Label Aggregation with Self-Supervision Enhanced Graph Transformer
Authors: Liu Jiacheng , Tang Feilong , Hou Xiaofeng
Published: September 2023

Aggregating noisy labels produced by the crowd of workers to generate true labels is a challenging problem in crowdsourcing. The key behind label aggregation is to effectively utilize the hidden information (e.g., characteristics of workers and questions which are often missing) in the labeling process. Existing methods mainly generated aggregation models based on the complicated Bayesian model or some strong assumptions. Recently, deep learning-based methods attempt to automate label aggregation but need various labels. These all make them hard to deploy to real-world applications. In fact, abundant information in the process of crowdsourcing itself can be extremely helpful to aggregate the labels. In this paper, we propose ATHENA (lAbel aggregaTion witH sElf-supervision eNhanced grAph transformer) to aggregate labels by utilizing the self-supervision signals in crowdsourcing. Firstly, we propose a transformer-based graph neural network that can learn from the crowdsourcing topology and features. Then, we use self-supervision signals inherently included in the dataset to help to aggregate the labels. To be specific, we identify the answer-based self-supervision signal that can predict the answer of any user given to different tasks. In our evaluations, we compare the proposed ATHENA with the other 11 representative methods on 10 datasets. Our experimental results demonstrate that ATHENA is highly effective in aggregating labels and obtains much better performance than existing methods.
Efficient Information Modulation Network for Image Super-Resolution
Authors: Liu Xiao , Liao Xiangyu , Shi Xiuya , Qing Linbo , Ren Chao
Published: September 2023

Recent researches have shown that the success of Transformers comes from their macro-level framework and advanced components, not just their self-attention (SA) mechanism. Comparable results can be obtained by replacing SA with spatial pooling, shifting, MLP, fourier transform and constant matrix, all of which have spatial information encoding capability like SA. In light of these findings, this work focuses on combining efficient spatial information encoding technology with superior macro architectures in Transformers. We rethink spatial convolution to achieve more efficient encoding of spatial features and dynamic modulation value representations by convolutional modulation techniques. The large-kernel convolution and Hadamard product are utilizated in the proposed Multi-orders Long-range convolutional modulation (MOLRCM) layer to imitate the implementation of SA. Moreover, MOLRCM layer also achieve long-range correlations and self-adaptation behavior, similar to SA, with linear complexity. On the other hand, we also address the sub-optimality of vanilla feed-forward networks (FFN) by introducing spatial awareness and locality, improving feature diversity, and regulating information flow between layers in the proposed Spatial Awareness Dynamic Feature Flow Modulation (SADFFM) layer. Experiment results show that our proposed efficient information modulation network (EIMN) performs better both quantitatively and qualitatively. Codes and supplementary materials link: https://github.com/liux520/EIMN.
Comment-Aware Multi-Modal Heterogeneous Pre-Training for Humor Detection in Short-Form Videos
Authors: Liu Yang , Ping Huanqin , Zhang Dong , Sun Qingying , Li Shoushan , Zhou Guodong
Published: September 2023

Conventional humor analysis normally focuses on text, text-image pair, and even long video (e.g., monologue) scenarios. However, with the recent rise of short-form video sharing, humor detection in this scenario has not yet gained much exploration. To the best of our knowledge, there are two primary issues associated with short-form video humor detection (SVHD): 1) At present, there are no ready-made humor annotation samples in this scenario, and it takes a lot of manpower and material resources to obtain a large number of annotation samples; 2) Unlike the more typical audio and visual modalities, the titles (as opposed to simultaneous transcription in the lengthy film) and associated interactive comments in short-form videos may convey apparent humorous clues. Therefore, in this paper, we first collect and annotate a video dataset from DouYin (aka. TikTok in the world), namely DY24h, with hierarchical comments. Then, we also design a novel approach with comment-aided multi-modal heterogeneous pre-training (CMHP) to introduce comment modality in SVHD. Extensive experiments and analysis demonstrate that our CMHP beats several existing video-based approaches on DY24h, and that the comments modality further aids a better comprehension of humor. Our dataset, code and pre-trained models are available at https://github.com/yliu-cs/CMHP.
Online Algorithms for Matchings with Proportional Fairness Constraints and Diversity Constraints
Authors: Louis Anand , Nasre Meghana , Nimbhorkar Prajakta , Sankar Govind S.
Published: September 2023

Matching problems with group-fairness constraints and diversity constraints have numerous applications such as in allocation problems, committee selection, school choice, etc. Moreover, online matching problems have lots of applications in ad allocations and other e-commerce problems like product recommendation in digital marketing. We study two problems involving assigning items to platforms, where items belong to various groups depending on their attributes; the set of items are available offline and the platforms arrive online. In the first problem, we study online matchings with proportional fairness constraints. Here, each platform on arrival should either be assigned a set of items in which the fraction of items from each group is within specified bounds or be assigned no items; the goal is to assign items to platforms in order to maximize the number of items assigned to platforms. In the second problem, we study online matchings with diversity constraints, i.e. for each platform, absolute lower bounds are specified for each group. Each platform on arrival should either be assigned a set of items that satisfy these bounds or be assigned no items; the goal is to maximize the set of platforms that get matched. We study approximation algorithms and hardness results for these problems. The technical core of our proofs is a new connection between these problems and the problem of matchings in hypergraphs. Our experimental evaluation shows the performance of our algorithms on real-world and synthetic datasets exceeds our theoretical guarantees.
Degradation-Resistant Offline Optimization via Accumulative Risk Control
Authors: Lu Huakang , Qian Hong , Wu Yupeng , Liu Ziqi , Zhang Ya-Lin , Zhou Aimin , Yu Yang
Published: September 2023

Offline optimization aims to elaborately construct a solution that optimizes a black-box function with only access to the offline dataset. A typical manner of constructing the solution is to train a surrogate model of the black-box function on the offline dataset and optimize the solution guided by the surrogate model. However, this manner often encounters a fundamental challenge that the surrogate model could erroneously estimate out-of-distribution (OOD) solutions. Therefore, the optimizer would be misled to produce inferior solutions for online applications, i.e., degradation of performance. To this end, this paper formalizes the risk of degradation for OOD solutions and proposes an accumulative risk controlled offline optimization (ARCOO) method. Specifically, ARCOO learns a surrogate model in conjunction with an energy model. The energy model characterizes the risk of degradation by learning on high-risk solutions and low-risk ones contrastively. In the optimization procedure, the behavior of the optimizer in each step is controlled by a risk suppression factor calculated via the energy model, which leads to the controllable accumulative risk. Theoretically, we justify the efficacy of energy for accumulative risk control. Extensive experiments on offline optimization tasks show that ARCOO surpasses state-of-the-art methods in both degradation-resistance and optimality of the output solution.
Semantic-Aware Dual Contrastive Learning for Multi-Label Image Classification
Authors: Ma Leilei , Sun Dengdi , Wang Lei , Zhao Haifeng , Luo Bin
Published: September 2023

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://github.com/yu-gi-oh-leilei/SADCL.
Active Finetuning Protein Language Model: A Budget-Friendly Method for Directed Evolution
Authors: Qin Ming , Ding Keyan , Wu Bin , Li Zhenping , Yang Haihong , Wang Zeyuan , Ye Hongbin , Yu Haoran , Chen Huajun , Zhang Qiang
Published: September 2023

Directed evolution is a widely-used strategy of protein engineering to improve protein function via mimicking natural mutation and selection. Machine learning-assisted directed evolution (MLDE) approaches aim to learn a fitness predictor, thereby efficiently searching for optimal mutants within the vast combinatorial mutation space. Since annotating mutants is both costly and labor-intensive, how to efficiently sample and utilize informative protein mutants to train the predictor is a critical problem in MLDE. Previous MLDE works just simply utilized pre-trained protein language models (PPLMs) for sampling without tailoring to the specific target protein of interest, which has not fully exploited the potential of PPLMs. In this work, we propose a novel method, the Actively-Finetuned Protein language model for Directed Evolution(AFP-DE), which leverages PPLMs to actively sample and fine-tune themselves, continuously improving the model’s sampling and overall performance through iterations, to achieve efficient directed protein evolution. Extensive experiments have shown the effectiveness of our method in generating optimal mutants with minimal annotation effort, outperforming previous works even with fewer annotated mutants, making it budget-friendly for biological experiments.
Making Friends in the Dark: Ad Hoc Teamwork Under Partial Observability
Authors: Ribeiro João G. , Martinho Cassandro , Sardinha Alberto , Melo Francisco S.
Published: September 2023

This paper introduces a formal definition of the setting of ad hoc teamwork under partial observability and proposes a first-principled model-based approach which relies only on prior knowledge and partial observations of the environment in order to perform ad hoc teamwork. We make three distinct assumptions that set it apart previous works, namely: i) the state of the environment is always partially observable, ii) the actions of the teammates are always unavailable to the ad hoc agent and iii) the ad hoc agent has no access to a reward signal which could be used to learn the task from scratch. Our results in 70 POMDPs from 11 domains show that our approach is not only effective in assisting unknown teammates in solving unknown tasks but is also robust in scaling to more challenging problems. Supplementary material is available at https://github.com/jmribeiro/adhoc-teamwork-under-partial-observability.
NEHATE: Large-Scale Annotated Data Shedding Light on Hate Speech in Nepali Local Election Discourse
Authors: Thapa Surendrabikram , Rauniyar Kritesh , Shiwakoti Shuvam , Poudel Sweta , Naseem Usman , Nasim Mehwish
Published: September 2023

The use of social media during election campaigns has become increasingly popular. However, the unbridled nature of online discourse can lead to the propagation of hate speech, which has far-reaching implications for the democratic process. Natural Language Processing (NLP) techniques are being used to counteract the spread of hate speech and promote healthy online discourse. Despite the increasing need for NLP techniques to combat hate speech, research on low-resource languages such as Nepali is limited, posing a challenge to the realization of the United Nations’ Leave No One Behind principle, which calls for inclusive development that benefits all individuals and communities, regardless of their backgrounds or circumstances. To bridge this gap, we introduce NEHATE, a large-scale manually annotated dataset of hate speech and its targets in Nepali local election discourse. The dataset comprises 13,505 tweets, annotated for hate speech with further sub-categorization of hate speech into targets such as community, individual, and organization. Benchmarking of the dataset with various algorithms has shown potential for performance improvement. We have made the dataset publicly available at https://github.com/shucoll/NEHate to promote further research and development, while also contributing to the UN SDGs aimed at fostering peaceful, inclusive societies, and justice and strong institutions.
GAN-Generated Faces Detection: A Survey and New Perspectives
Authors: Wang Xin , Guo Hui , Hu Shu , Chang Ming-Ching , Lyu Siwei
Published: September 2023

Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts. Therefore, the corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. In this work, we aim to provide a comprehensive review of recent progress in GAN-face detection. We focus on methods that can detect face images that are generated or synthesized from GAN models. We classify the existing detection works into four categories: (1) deep learning-based, (2) physical-based, (3) physiological-based methods, and (4) evaluation and comparison against human visual performance. For each category, we summarize the key ideas and connect them with method implementations. We also discuss open problems and suggest future research directions.
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning
Authors: Yu Xin , Shi Rongye , Feng Pu , Tian Yongkai , Luo Jie , Wu Wenjun
Published: September 2023

Multi-agent reinforcement learning (MARL) has achieved promising results in recent years. However, most existing reinforcement learning methods require a large amount of data for model training. In addition, data-efficient reinforcement learning requires the construction of strong inductive biases, which are ignored in the current MARL approaches. Inspired by the symmetry phenomenon in multi-agent systems, this paper proposes a framework for exploiting prior knowledge by integrating data augmentation and a well-designed consistency loss into the existing MARL methods. In addition, the proposed framework is model-agnostic and can be applied to most of the current MARL algorithms. Experimental tests on multiple challenging tasks demonstrate the effectiveness of the proposed framework. Moreover, the proposed framework is applied to a physical multi-robot testbed to show its superiority.
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation
Authors: Zhou Hongyu , Zhou Xin , Zhang Lingzi , Shen Zhiqi
Published: September 2023

User-item interaction data in recommender systems is a form of dyadic relation, reflecting user preferences for specific items. To generate accurate recommendations, it is crucial to learn representations for both users and items. Recent multimodal recommendation models achieve higher accuracy by incorporating multimodal features, such as images and text descriptions. However, our experimental findings reveal that current multimodality fusion methods employed in state-of-the-art models may adversely affect recommendation performance without compromising model architectures. Moreover, these models seldom investigate internal relations between item-item and user-user interactions. In light of these findings, we propose a model that enhances the dyadic relations by learning Dual RepresentAtions of both users and items via constructing homogeneous Graphs for multimOdal recommeNdation. We name our model as DRAGON. Specifically, DRAGON constructs user-user graphs based on commonly interacted items and item-item graphs derived from item multimodal features. Graph learning on both the user-item heterogeneous and homogeneous graphs is used to obtain dual representations of users and items. To capture information from each modality, DRAGON employs an effective fusion method, attentive concatenation. Extensive experiments on three public datasets and eight baselines show that DRAGON can outperform the strongest baseline by 21.41% on average. Our code is available at https://github.com/hongyurain/DRAGON.
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents
Authors: Zhou Ziyuan , Liu Guanjun
Published: September 2023

Multi-agent reinforcement learning (MARL) has been widely applied in many fields, such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to adversarial perturbations on agent states. Robustness testing for a trained model is an essential step for confirming the trustworthiness of the model against unexpected perturbations. This work proposes a novel Robustness Testing framework for MARL that attacks states of Critical Agents (RTCA). The RTCA has two innovations: 1) a differential evolution (DE) based method to select critical agents as victims and to advise the worst-case joint actions on them, and 2) a team cooperation policy evaluation method employed as the objective function for the optimization of DE. Then, adversarial state perturbations of the critical agents are generated based on the worst-case joint actions. This is the first robustness testing framework with varying victim agents. RTCA demonstrates outstanding performance in terms of the number of victim agents and destroying cooperation policies.
PMAA: A Progressive Multi-Scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-Temporal Satellite Imagery
Authors: Zou Xuechao , Li Kai , Xing Junliang , Tao Pin , Cui Yachao
Published: September 2023

Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application. Although existing deep cloud removal models have achieved notable outcomes, they scarcely consider contextual information. This study introduces a high-performance cloud removal architecture, termed Progressive Multi-scale Attention Autoencoder (PMAA), which concurrently harnesses global and local information to construct robust contextual dependencies using a novel Multi-scale Attention Module (MAM) and a novel Local Interaction Module (LIM). PMAA establishes long-range dependencies of multi-scale features using MAM and modulates the reconstruction of fine-grained details utilizing LIM, enabling simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale features, PMAA consistently outperforms the previous state-of-the-art model CTGAN on two benchmark datasets. Moreover, PMAA boasts considerable efficiency advantages, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These comprehensive results underscore PMAA’s potential as a lightweight cloud removal network suitable for deployment on edge devices to accomplish large-scale cloud removal tasks. Our source code and pre-trained models are available at https://github.com/XavierJiezou/PMAA.
A Neuro-Symbolic Approach for Non-Intrusive Load Monitoring
Authors: Apriceno Gianluca , Erculiani Luca , Passerini Andrea
Published: September 2023

A requirement of Smart Grids is the ability to predict the energy consumption patterns of their users. In the residential domain, this is usually not feasible due to the inability of the grid to dialog with (legacy) domestic appliances. To overcome this issue Non Intrusive Load Monitoring (NILM) was introduced, a task in which a predictor is used to disaggregate household power consumption. Many of the newer approaches make use of Neural Networks to accomplish this task, due to their superior ability to detect patterns in temporal (thus sequential) data. These models unfortunately require a huge amount of data to achieve good performance, and have the tendency to overfit the training data, making them difficult to predict future consumptions. For these reasons, adapting them to optimally predict a (future) house’s consumption requires expensive and often prohibitive data collection phases. We propose a solution in the form of a neuro-symbolic framework that refines neural network predictions via a constrained optimization problem modelling the characteristics of the appliances of a house. This combined approach achieves superior performance with respect to the neural network alone over two out of five appliances and comparable results for the remaining ones, without requiring further training data.
Improving Clinical Trials of Antioxidants in Alzheimer’s Disease
Authors: Daly Timothy
Published: September 2023

Maintaining diversity in drug development in research into Alzheimer’s disease (AD) is necessary to avoid over-reliance on targeting AD neuropathology. Treatments that reduce or prevent the generation of oxidative stress, frequently cited for its causal role in the aging process and AD, could be useful in at-risk populations or diagnosed AD patients. However, in this review, it is argued that clinical research into antioxidants in AD could provide more useful feedback as to the therapeutic value of the oxidative stress theory of AD. Improving comparability between randomized controlled trials (RCTs) is vital from a waste-reduction and priority-setting point of view for AD clinical research. For as well as attempting to improve meaningful outcomes for patients, RCTs of antioxidants in AD should strive to maximize the extraction of clinically useful information and actionable feedback from trial outcomes. Solutions to maximize information flow from RCTs of antioxidants in AD are offered here in the form of checklist questions to improve ongoing and future trials centered around the following dimensions: adhesion to reporting guidelines like CONSORT, biomarker enrichment, simple tests of treatment, and innovative trial design.
Amendment of Altered Immune Response by Curcumin in Drosophila Model of Huntington’s Disease
Authors: Dhankhar Jyoti , Shrivastava Anju , Agrawal Namita
Published: September 2023

Background: Though primarily classified as a brain disorder, surplus studies direct Huntington’s disease (HD) to be a multi-system disorder affecting various tissues and organs, thus affecting overall physiology of host. Recently, we have reported that neuronal expression of mutant huntingtin induces immune dysregulation in Drosophila and may pose chronic threat to challenged individuals. Therefore, we tested the polyphenolic compound curcumin to circumvent the impact of immune dysregulation in Drosophila model of HD. Objective: The present study examined the molecular basis underlying immune derangements and immunomodulatory potential of curcumin in HD. Methods: UAS-GAL4 system was used to imitate the HD symptoms in Drosophila, and the desired female progenies (elav > Httex1pQ25; control and elav > Httex1pQ93; diseased) were cultured on food mixed without and with 10 μM concentration of curcumin since early development. Effect of curcumin supplementation was investigated by monitoring the hemocytes’ count and their functional abilities in diseased condition. Reactive oxygen species (ROS) level in cells was assessed by DHE staining and mitochondrial dysfunction was assessed by CMXros red dye. In addition, transcript levels of pro-inflammatory cytokines and anti-microbial peptides were monitored by qRT-PCR. Results: We found that curcumin supplementation commendably reduced higher crystal cell count and phenoloxidase activity in diseased flies. Interestingly, curcumin significantly managed altered plasmatocytes count, improved their phagocytic activity by upregulating the expression of key phagocytic receptors in HD condition. Moreover, substantial alleviation of ROS levels and mitochondria dysfunction was observed in plasmatocytes of diseased flies upon curcumin supplementation. Furthermore, curcumin administration effectively attenuated transcriptional expression of pro-inflammatory cytokines and AMPs in diseased flies. Conclusions: Our results indicate that curcumin efficiently attenuates immune derangements in HD flies and may prove beneficial in alleviating complexities associated with HD.
LEOCN: Real-time and complete network simulation framework for LEO constellation networks1
Authors: Li Cong , Wang Haiyang , Liang Weipeng , Li Yunhao , Yan Hongyang , Huang Teng , Li Bo , He Jianwei
Published: September 2023

With the development of the space industry, LEO simulation platforms are increasingly important. Prior work mainly focused on the dynamics of constellation topology, routing algorithms against the topological motion, and the behavior of LEO constellation networks, but neglected real-time and completeness properties. In our work, we design a real-time and complete network simulation framework, named LEOCN, that is more meaningful and detailed for researchers than previous work on LEO satellite constellation simulations. LEOCN is supported by Linux System, which makes the framework more realistic and relieves the computational burden. Meanwhile, LEOCN deeply considers topology and routing algorithms, visualization, satellite mobility, communication protocols, and network traffic problems. On LEOCN, we can deploy routing algorithm experiments via SDN. We evaluate the performance of LEOCN in large-scale constellation scenarios, and the results demonstrate the superiority of the real-time and complete network simulation framework.
Long-term effects of transcranial direct current stimulation (tDCS) combined with speech language therapy (SLT) on post-stroke aphasia patients: A systematic review and network meta-analysis of randomized controlled trials
Authors: You Yue , Li Yue , Zhang Yin , Fan Huimin , Gao Qiang , Wang Ling
Published: September 2023

BACKGROUND: Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation tool for improving language performance in patients with aphasia after stroke. However, it remains unclear whether it has long-term effects. After consulting a large number of relevant studies, it was found that there are no definitive conclusions about the long-term effects of tDCS on post-stroke aphasia patients. OBJECTIVE: To determine whether tDCS has long-term effects on post-stroke aphasia patients (PAPs) and which type of tDCS has the most beneficial treatment effects on language performance (especially naming ability). METHODS: A network meta-analysis was conducted by searching for randomized controlled trials (RCTs) published until April 2023 in the following databases: Web of Science, Embase, Medline (from OVID and PubMed), PsycInfo and PsycARTICLES (from OVID). We only included RCTs published in English. PAPs treated by tDCS combined with speech-language therapy were selected. Sham tDCS was the control group. Naming ability or other language performance must be assessed at follow-up states. Two reviewers independently used checklists to assess the primary outcome (the long-term effects on naming ability) and the secondary outcome (other language performance, such as communication). Cochrane Collaboration guidelines were used to assess the risk of bias. RESULTS: Seven studies with 249 patients were included for data synthesis. For primary outcomes (naming nous), there was no obvious evidence to show a difference between interventions (C-tDCS vs. S-tDCS SMD = 0.06, 95% CI = –1.01, 1.12; A-tDCS vs. S-tDCS SMD = 0.00, 95% CI = –0.66, 0.65; D-tDCS vs. S-tDCS SMD = 0.77, 95% CI = –0.71, 2.24; A-tDCS vs. C-tDCS SMD = –0.06, 95% CI = –1.31,1.19; D-tDCS vs. C-tDCS SMD = 0.71, 95% CI = –1.11,2.53; D-tDCS vs. A-tDCS SMD = 0.77, 95% CI = –0.84, 2.39). In addition, no evidence showed differences in communication ability (C-tDCS vs. S-tDCS SMD = 0.08 95% CI = –1.77, 1.92; A-tDCS vs. S-tDCS SMD = 1.23 95% CI = –1.89, 4.34; D-tDCS vs. S-tDCS SMD = 0.70; 95% CI = –1.93, 3.34; A-tDCS vs. C-tDCS SMD = 1.15 95% CI = –2.48, 4.77; D-tDCS vs. C-tDCS SMD = 0.62 95% CI = –2.59, 3.84; D-tDCS vs. A-tDCS SMD = –0.52 95% CI = –4.60, 3.56). CONCLUSION: It seems that tDCS has no long-term effects on post-stroke aphasia patients in naming nouns and communication in terms of the results of our network meta-analysis. However, the results should be interpreted with caution. In the future, more RCTs with long follow-up times should be included in the research to conduct subgroup or meta-regression analyses to obtain a sufficient effect size.
Menimbang Pelaksanaan Pemilu 2024 Tanpa Protokol Kesehatan
Authors: ‎    ‎ Harmono , Khoiriyah Fatikhul , Syarifudin Ahmad , Farida
Published: September 2023

Pemilu 2024 akan dilaksanakan pada 14 Februari 2024. Meskipun tahapan verifikasi partai politik telah dimulai dan menetapkan 24 partai politik lolos administrasi, namun pelaksanaannya dengan atau tanpa protokol kesehatan belum terdapat keputusan. Bila pelaksanaan Pemilu 2024 masih menggunakan protokol kesehatan, maka dapat dipastikan pelaksanaannya akan relatif sama dengan gelaran Pilkada Serentak 2020 yang menimbulkan banyak persoalan karena didesain di tengah kondisi pandemi covid-19. Artikel ini mengkaji tentang pertimbangan pelaksanaan Pemilu 2024 tanpa protokol kesehatan dan menyimpulkan bahwa saat ini masyarakat telah hidup normal dengan menerapkan protokol kesehatannya secara individu. Disamping kasus covid-19 yang relatif kecil dan pelonggaran terhadap kebijakan baik pembatasan sosial maupun penggunaan masker. Efisiensi anggaran akan sangat berpengaruh bila didistribusikan pada bidang lain dalam rangka pemulihan ekonomi dibandingkan dengan membeli pembersih tangan, membuat TPS baru, dan lain sebagainya.
Ancaman Perang Modern dalam Perspektif Hukum Humaniter
Authors: Ningsih Rahmah , Nurbaiti Nurbaiti
Published: September 2023

Fenomena perang modern yang muncul sebagai kekuatan baru seiring berkembangnya teknologi informasi tidak hanya berbentuk serangan bersenjata melainkan perang pemikiran dan pembangunan opini. Perang yang berbentuk infiltrasi ke dalam suatu negara melalui berbagai aspek kehidupan mulai dari ekonomi, ideologi, politik, sosial budaya, agama, hukum dan pertahanan keamanan. Ancaman perang modern lebih dahsyat jika dibandingkan perang konvensional karena memiliki spektrum yang luas, dominasi koalisi global, melibatkan aktor negara dan aktor bukan negara untuk melemahkan kemampuan negara, mengancam potensi dan melemahkan kedaulatan. Negara harus siap dengan strategi untuk memperkuat pondasi keutuhan negara dengan penguasaan teknologi dan regulasi, menjaga persatuan dan kesatuan bangsa serta meningkatkan keadilan sosial bagi seluruh masyarakat.
Tranformasi Digital dan Ancaman Cybercrime
Authors: Rolando Dede Mercy , Aulia Hanna Hilyati , Andini Mutia Tanseba
Published: September 2023

Jurnal ini bertujuan untuk mengetahui ke-efektifitas-an Undang-Undang ITE atas terjadinya  transformasi digital yang menjadi ancaman cybercrime di Indonesia. Banyak hal buruk dan tidak di inginkan terjadi melalui teknologi informasi. Sebab itu pemerintah merasa bahwa teknologi informasi tidak hanya perlu diperhatikan, tetapi juga perlu diatur dalam hukum melalui penerapan tindak pidana Undang-Undang ITE. Namun, hanya sedikit pasal yang mengatur demi terciptanya kelancaran perdagangan secara elektronik, seperti aturan-aturan yang harus dipatuhi bagi pelaku perdagangan elektronik. Isi dari UU ITE yang paling krusial adalah justru menekankan pada perbuatan-perbuatan yang tidak bersinggungan langsung dengan perdagangan elektronik, seperti pasal 27 ayat 1 dan 3, pasal 28 ayat 2, dan pasal 29 yang cenderung menekankan masalah sosial seperti asusila, perjudian, penghinaan, pemerasan, berita bohong dan menyesatkan, berita kebencian dan permusuhan, ancaman kekerasan dan menakut-nakuti.masalah tersebut jauh dari tujuan pasal 4 ayat 2 yang telah disebutkan diatas. Ada beberapa hal yang terlewatkan yang sebenarnya harus lebih difokuskan dalam UU ITE, antara lain masalah Spamming, baik untuk email spamming maupun masalah penjualan data pribadi oleh perbankan, asuransi, Virus, worm komputer (masih implisit di Pasal 33) terutama untuk pengembangan dan penyebarannya, dan jenis-jenis phising seperti typo pirates yang membuat tidak nyaman proses perdagangan elektronik. Masalah-masalah tersebut adalah juga krusial yang seharusnya secara eksplisit diatur dalam UU ITE, karena menyangkut juga demi terciptanya kelancaran transaksi elektronik khususnya perdagangan elektronik.
Design and Cogging Torque Reduction of Radial Flux Brushless DC Motors with Varied Permanent Magnet Pole Shapes for Electric Vehicle Application
Authors: Jhankal Tanuj , N. Patel Amit
Published: September 2023

Brushless direct current motors have more attractive features, making them a promising solution for electric vehicle applications. A 1 kW, 510 rpm, 24-slots and 8-pole inner runner type surface permanent magnet mounted radial flux brushless DC motor with seven different permanent magnet pole shape rotor is investigated. Motors with different permanent magnet shape rotors were designed, and finite element modelling and simulation were carried out. For performance comparison, the initial design with a radial-type pole shape was regarded as a reference design. Cogging torque is detrimental to the overall performance of the motor, typically in low-speed applications like electric vehicles. The primary aim of this paper is to reduce the cogging torque & study its effect on the overall performance of the motor and minimize torque ripples with reduced permanent magnet requirements. The proposed designs are analyzed in terms of cogging torque, flux density, torque, efficiency, flux linkage and back-EMF. The comparative analysis shows that the motor with bump-shaped permanent magnet rotor poles has betterperformance than the others.
On the Question of the Attitude to Sports Practices in Orthodoxy
Authors: Gonashvili A. S.
Published: September 2023

Introduction.  The  article   is  aimed   at  a  comprehensive  and  holistic  analysis  of  the judgments  existing in Orthodox circles regarding physical culture and sports, reflecting the Orthodox   worldview and  its  values.  The  novelty  of  the   article   lies  in  the   empirical substantiation of the  role of the  physical and spiritual development  of believers  against the  background  of sports  practices,  as  well as  the  substantiation of contradictions  and problems  of perception,  sports practices  in the conditions of the Orthodox worldview. Methodology and  sources. A  comprehensive  approach  to the  problem  of studying and analyzing sports practices  is used as a methodological  basis. The main research method  in the work is an Internet survey.

Results and discussion. According  to the  study, it should be  noted  that  there  is no unity among Orthodox people in understanding  the role of physical culture in Orthodox spiritual development.  Most people believe that the call for the development  of their body is not the work of the church, they do not see  any sin in the underdevelopment of their body. This is the  result  of the  influence  of various values  that  are  in one  or another  society  in which people  live. Therefore,  it is especially important  to emphasize  the role of the church in the educational process  of physical culture among young people and the younger generation.

Conclusion. In general,  the author  of the article comes  to the conclusion  that there  is no unity among  Orthodox  people  in understanding  the  role of physical culture  in Orthodox spiritual development.
The EU Taxonomy: Financing the Transition through Sustainable Investing
Authors: Viñes Fiestas Helena
Published: September 2023

The EU Taxonomy is the cornerstone of the European Sustainable Finance Strategy. It is a robust, science-based transparency tool for determining environmentally sustainable economic activities. It plays a crucial role in enabling informed investment decisions and driving additional investments towards a climate-neutral and sustainable economy. But the Taxonomy is, first and foremost, a tool for companies to access the capital they need to finance their transition to net zero and green their activities.
Citric Acid and Hydro-Priming and Exogenous Application Alleviate Salt-Inhibited Seed Germination and Seedling Growth of Chilli (Capsicum annuum L.)
Authors: Jannatul Ferdous Md. Monir Hossain , Akter Kaya
Published: September 2023

Salinity is a major barrier for seed germination, plant growth and production. Seed pre-treatment and exogenous chemical usage can effectively confer salt tolerance. We evaluated the potential of citric acid (CA) as priming and exogenous agents to ameliorate salt-inhibited germination and growth of Chilli (Capsicum annuum L.) in this study. The seeds were primed with 0.5 mM and 1 mM CA, and soaked in distilled water (hydro-priming) for 30 min. In addition, untreated seeds were used for the control experiment. Finally, primed seeds were subjected to 100 mM NaCl stress. Our results demonstrated that salinity significantly reduced the germination percentage (GP), germination index (GI), germination energy (GE), seed vigor index (SVI), root length (RL), shoot length (SL), shoot–root fresh and dry weight, and plant height and increased mean germination time (MGT). The results also indicated that salinity stress considerably decreased the relative water content (RWC) and photosynthetic pigments such as chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids, and increased relative water loss (RWL). The CA- and hydro-priming improved the GP, GI, GE SL, RL, SVI, and growth of seedlings, and reduced MGT under salt stress. Data also demonstrated that the supplementation of CA enhanced RWC and photosynthetic pigments and lowered RWL in the state of saline condition. When compared to other treatments, pretreatment, and exogenous use of 1 mM CA was determined to be comparatively more effective at imparting the salt tolerance of Chilli.
Features of the course of pulmonary tuberculosis against the background of coronavirus infection according to computed tomography of the chest organs
Authors: Lynnyk М.І. , Іgnatieva V.І. , Gumeniuk G.L. , Svyatnenko V.А. , Ivashchenko V.Ye. , Chobotar О.P. , Palivoda М.G.
Published: September 2023

BACKGROUND. Quarantine measures regarding the coronavirus disease (COVID-19) pandemic, initiated in early 2020, and subsequently large-scale hostilities in Ukraine, led to a decrease in the detection of tuberculosis (TB) patients and an increase in mortality from this disease. Particular attention needs to be paid to the peculiarities of the course of pulmonary TB against the background of a coronavirus infection, which may be due to both the features of the impact of SARS-CoV-2 and the features of the immunological status of patients with pulmonary TB with preserved sensitivity to antimycobacterial drugs (DSTB) and with drug resistance.

OBJECTIVE. To investigate the dynamics of the course of pathological changes in the lungs in patients with pulmonary TB against the background of coronavirus infection by analyzing the data of computed tomography (CT) of the chest organs.

MATERIALS AND METHODS. Establishing the diagnosis and treatment of pulmonary TB were carried out in accordance with the Order of the Ministry of Health of Ukraine No. 530. The diagnosis of COVID-19 was established in patients who were treated for pulmonary TB in accordance with the current protocols for the treatment of the coronavirus infection. All patients underwent dynamic CT of the chest organs was performed on the Aquilion TSX-101A scanner (Toshiba, Japan).

RESULTS AND DISCUSSION. Clinical, laboratory and X-ray data of patients with pulmonary TB and COVID-19 were analyzed in dynamics. It has been established that with timely diagnosis and adequate treatment of DSTB of the lungs, viral pneumonia (COVID-19) does not complicate the course of the TB process. The period of the course of COVID-19 is characterized by the continuation of the positive dynamics of the TB process. In patients with TB with multiple drug resistance, viral pneumonia (COVID-19) does not cause serious complications, but the period of the course of COVID-19 is characterized by the absence of positive dynamics of the TB process. With untreated pulmonary TB on the background of viral pneumonia (COVID-19), the disease can become severe and end in death.

CONCLUSIONS. CT of the chest organs in patients with pulmonary TB against the background of COVID-19 is highly informative in monitoring the pathological process, detecting complications and evaluating the effectiveness of treatment.
New and little-known possibilities of edaravone in the treatment of cerebral stroke and extracranial pathology
Authors: Halushko O.A.
Published: September 2023

BACKGROUND. Edaravone is a free radical scavenger and inhibits lipid peroxidation and thus reduces oxidative damage to brain cells and other organs. Edaravone is mainly known as an effective agent in the treatment of ischemic stroke and amyotrophic lateral sclerosis. OBJECTIVE. To investigate the little-known possibilities of edaravone when it is used in clinical practice. MATERIALS AND METHODS. To solve the task, a search and analysis of full-text articles was conducted in the PubMed, Web of Science, Google Scholar, and Scopus databases. The search was conducted using the key word “edaravone” and included English-language and Ukrainian-language publications over the past 5 years (from April 2018 to April 2023). RESULTS. A total of 518 publications were identified and analyzed. Edaravone has been found to improve the clinical course and may be useful in the treatment of central nervous system diseases, depression, post-traumatic stress disorder, cognitive dysfunction, oncological and infectious diseases (in particular, coronavirus disease), many poisonings, etc. All this dictates the need for further clinical studies to explore new and unexpected possibilities of edaravone.
FEATURES OF THE STUDY OF GEO-INFORMATION SYSTEMS IN THE TRAINING OF STUDENTS OF THE SPECIALTY «GEODESY AND LAND SURVEYING»
Authors: Gorb O. , Mamonov K. , Nalivayko T. , Pomortseva O.
Published: September 2023

The article highlights the problems and tasks that have intensified in higher education under the conditions of a long period of distance learning and martial law. Questions about the need to create integrated education are particularly argued and lapidary. This has a positive effect on the development of independence, cognitive activity and interests of modern higher education seekers. In this way, the acquirers show the development of creative thinking according to their own architecture. At the same time, significant complications of its content and a significant increase in the amount of knowledge are becoming characteristic features of modern education. Therefore, it is necessary to develop and introduce the latest educational paradigms into pedagogical practice. This should be based on the results of a significant rethinking of the very goals of education, its nature and technology. In the general structure of the set of these attributes of education, an extremely important place should be occupied by pedagogical influence on student youth. At the same time, this influence should consist in the formation of students' motivation to properly master the educational material. It is also necessary to properly organize pedagogical influence and transdisciplinary connections when teaching educational material. This should be considered as the main way of effectively solving the main tasks of education. From this follows the need to train a new generation of specialists. Who will be able to creatively adapt to the fast-moving environment with knowledge of the matter. They will be able to move from one type of work to another, all this ultimately determines the very heuristic approach to education. It is this approach that will help to change the usual reproductive activity of the student in classes. The student will be involved in active assimilation of new material and independent analysis in solving problem situations. Then the student will be able to enter the context of future professional activity. All these measures will contribute to the intensification of the educational process. This will ultimately have a positive effect on the quality of education and the value of the future graduate in the eyes of a potential employer.
Keywords: integrated learning, cognitive activity, self-development, levels of integration of educational material, transdisciplinary learning, interdisciplinary connections, competence.