Reviewerly/exHarmony huggingface dataset:
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning

Federated Learning (FL) aims to learn a global model from distributed users while protecting their privacy. However, when data are distributed heterogeneously the learning process becomes noisy, unstable, and biased towards the last seen clients’ data, slowing down convergence. To address these issues and improve the robustness and generalization capabilities of the global model, we propose WiMA (Window-based Model Averaging). WiMA aggregates global models from different rounds using a window-based approach, effectively capturing knowledge from multiple users and reducing the bias from the last ones. By adopting a windowed view on the rounds, WiMA can be applied from the initial stages of training. Importantly, our method introduces no additional communication or client-side computation overhead. Our experiments demonstrate the robustness of WiMA against distribution shifts and bad client sampling, resulting in smoother and more stable learning trends. Additionally, WiMA can be easily integrated with state-of-the-art algorithms. We extensively evaluate our approach on standard FL benchmarks, demonstrating its effectiveness.
Cryptocurrencies in Blockchains Environment: The Verification Trip

A peer-to-peer network called a blockchain, a secure ledger of transactions, such as buying, selling, and transferring, is in charge of organizing and managing the Cryptocurrencies. In contrast with physical money, they are decentralized, which implies neither governments nor other financial organizations issue them. In a cryptocurrency blockchain, transaction data is permanently preserved. A block contains some or all of the most recent transactions the network still needs to confirm. The block is closed once the data has been confirmed. A series of verifications are applied to the transactions that blockchain participants submit to ensure their integrity and for most network security standards to be met. In this paper, we will discuss a set of them.
Ray-Patch: An Efficient Querying for Light Field Transformers

In this paper we propose the Ray-Patch querying, a novel model to efficiently query transformers to decode implicit representations into target views. Our Ray-Patch decoding reduces the computational footprint and increases inference speed up to one order of magnitude compared to previous models, without losing global attention, and hence maintaining specific task metrics. The key idea of our novel querying is to split the target image into a set of patches, then querying the transformer for each patch to extract a set of feature vectors, which are finally decoded into the target image using convolutional layers. Our experimental results quantify the effectiveness of our method, specifically the notable boost in rendering speed for the same task metrics.
Face Mask Detection During COVID-19 Pandemic Using NASNetMobile and CNN Deep Learning Algorithms

The coronavirus (COVID-19) pandemic has led to a health, economic, and social crisis, resulting in many casualties and disrupting daily life, trade, and global movements. The most common transmission mode of infection is through respiratory droplets loaded with viruses. They are transmitted during speaking, coughing, or exhaling when people are close to each other and without protection covering the face. Wearing a face mask is the way to reduce COVID-19 transmission from one person to another in public places praised by the World Health Organization (WHO); manually knowing people wearing a face mask is very difficult, especially in crowded places. This paper aims to build a system based on NASNetMobile and Convolution Neural Network (CNN) Deep Learning Algorithms to capture frames from the webcam in realtime, detect faces, and then classify the face whether it is wearing a mask or not. The system is built starting with training dataset collection, preprocessing dataset, classification model building, evaluation, testing, and model implementation. The accuracy of the model built by the Convolutional Neural Network reached 99.3% when evaluated on the test data, while the model built by the NASNetMobile model architecture reached 99.76%. This system helps monitor crowded places and alerts cases of violations of the laws on wearing a face mask.
Video-and-Language (VidL) models and their cognitive relevance

In this paper we give a narrative review of multi-modal video-language (VidL) models. We introduce the current landscape of VidL models and benchmarks, and draw inspiration from neuroscience and cognitive science to propose avenues for future research in VidL models in particular and artificial intelligence (AI) in general. We argue that iterative feedback loops between AI, neuroscience, and cognitive science are essential to spur progress across these disciplines. We motivate why we focus specifically on VidL models and their benchmarks as a promising type of model to bring improvements in AI and categorise current VidL efforts across multiple ‘cognitive relevance axioms’. Finally, we provide suggestions on how to effectively incorporate this interdisciplinary viewpoint into research on VidL models in particular and AI in general. In doing so, we hope to create awareness of the potential of VidL models to narrow the gap between neuroscience, cognitive science, and AI.
Enhancing Differentiable Architecture Search: A Study on Small Number of Cell Blocks in the Search Stage, and Important Branches-based Cells Selection

In recent years, differentiable neural architecture search (DARTS) method has attracted a lot of attention. This method has been proposed to reduce the search cost incurred when using reinforcement learning and evolutionary search strategies. Although several studies have been carried out to improve its performance, most of these existing methods share some common limitations: They use a stack of five to eight cells during the search process to find only two distinct cells. The usage of several cells significantly increases the computation cost of the search process. In this paper, to reduce the search time, we propose to decouple the structure of the architecture used during the search of optimal pair of cells from the final architecture by using only one normal and one reduction cells search architecture during the search stage and the same architecture structure as DARTS during the evaluation stage. We also address the stability and performance drop trade-off by inserting additional residual connection in parallel with every normal cell block. Additionally, adding A convolution skip connection to the evaluation architecture has been shown to improve the performance. Finally, we investigated the effect of searching optimal cell’s operation from highly performing branches in the internal structure of every cell. Extensive experiments showed that the proposed method significantly reduces the search cost while achieving promising results on ImageNet, CIFAR-10, and CIFAR-100 compared to existing state-of-the-art methods on DARTS search space.
Painter: Teaching Auto-regressive Language Models to Draw Sketches

Large language models (LLMs) have made tremendous progress in natural language understanding and they have also been successfully adopted in other domains such as computer vision, robotics, reinforcement learning, etc. In this work, we apply LLMs to image generation tasks by directly generating the virtual brush strokes to paint an image. We present Painter, an LLM that can convert user prompts in text description format to sketches by generating the corresponding brush strokes in an auto-regressive way. We construct Painter based on off-the-shelf LLM that is pre-trained on a large text corpus, by fine-tuning it on the new task while preserving language understanding capabilities. We create a dataset of diverse multi-object sketches paired with textual prompts that covers several object types and tasks. Painter can generate sketches from text descriptions, remove objects from canvas, and detect and classify objects in sketches. Although this is an unprecedented pioneering work in using LLMs for auto-regressive image generation, the results are very encouraging.
CoroNetGAN: Controlled Pruning of GANs via Hypernetworks

Generative Adversarial Networks (GANs) have proven to exhibit remarkable performance and are widely used across many generative computer vision applications. However, the unprecedented demand for the deployment of GANs on resource-constrained edge devices still poses a challenge due to huge number of parameters involved in the generation process. This has led to focused attention on the area of compressing GANs. Most of the existing works use knowledge distillation with the overhead of teacher dependency. Moreover, there is no ability to control the degree of compression in these methods. Hence, we propose CoroNet-GAN for compressing GAN using the combined strength of differentiable pruning method via hypernetworks. The proposed method provides the advantage of performing controllable compression while training along with reducing training time by a substantial factor. Experiments have been done on various conditional GAN architectures (Pix2Pix and CycleGAN) to signify the effectiveness of our approach on multiple benchmark datasets such as Edges → Shoes, Horse ↔ Zebra and Summer → Winter. The results obtained illustrate that our approach succeeds to outperform the baselines on Zebra → Horse and Summer → Winter achieving the best FID score of 32.3 and 72.3 respectively, yielding high-fidelity images across all the datasets. Additionally, our approach also outperforms the state-of-the-art methods in achieving better inference time on various smart-phone chipsets and data-types making it a feasible solution for deployment on edge devices.
Learning Universal Semantic Correspondences with No Supervision and Automatic Data Curation

We study the problem of learning semantic image correspondences without manual supervision. Previous works that tackled this problem rely on manually curated image pairs and learn benchmark-specific correspondences. Instead, we present a new method that learns universal correspondences once, from a large image dataset, and without using any manual curation. Despite their generality and despite using less supervision, our universal correspondences still outperform prior works, unsupervised and weakly supervised, in most benchmarks. Our approach starts from local features extracted by an unsupervised vision transformer, which obtain good semantic but poor geometric matching accuracy. It then learns a Transformer Adapter which improves the geometric accuracy of the features, as well as their compatibility between pairs of different images. The method combines semantic similarity with geometric stability obtained via cycle consistency and supervision via synthetic transformations. We use these features to also select pairs of matching images for training the unsupervised correspondences.
Boosting Semi-Supervised Learning by bridging high and low-confidence predictions

Pseudo-labeling is a crucial technique in semi-supervised learning (SSL), where artificial labels are generated for unlabeled data by a trained model, allowing for the simultaneous training of labeled and unlabeled data in a supervised setting. However, several studies have identified three main issues with pseudo-labeling-based approaches. Firstly, these methods heavily rely on predictions from the trained model, which may not always be accurate, leading to a confirmation bias problem. Secondly, the trained model may be overfitted to easy-to-learn examples, ignoring hard-to-learn ones, resulting in the "Matthew effect" where the already strong become stronger and the weak weaker. Thirdly, most of the low-confidence predictions of unlabeled data are discarded due to the use of a high threshold, leading to an underutilization of unlabeled data during training. To address these issues, we propose a new method called ReFixMatch, which aims to utilize all of the unlabeled data during training, thus improving the generalizability of the model and performance on SSL benchmarks. Notably, ReFixMatch achieves 41.05% top-1 accuracy with 100k labeled examples on ImageNet, outperforming the baseline FixMatch and current state-of-the-art methods.
Multi-Task Consistency for Active Learning

Learning-based solutions for vision tasks require a large amount of labeled training data to ensure their performance and reliability. In single-task vision-based settings, inconsistency-based active learning has proven to be effective in selecting informative samples for annotation. However, there is a lack of research exploiting the inconsistency between multiple tasks in multi-task networks. To address this gap, we propose a novel multi-task active learning strategy for two coupled vision tasks: object detection and semantic segmentation. Our approach leverages the inconsistency between them to identify informative samples across both tasks. We propose three constraints that specify how the tasks are coupled and introduce a method for determining the pixels belonging to the object detected by a bounding box, to later quantify the constraints as inconsistency scores. To evaluate the effectiveness of our approach, we establish multiple baselines for multi-task active learning and introduce a new metric, mean Detection Segmentation Quality (mDSQ), tailored for the multi-task active learning comparison that addresses the performance of both tasks. We conduct extensive experiments on the nuImages and A9 datasets, demonstrating that our approach outperforms existing state-of-the-art methods by up to 3.4% mDSQ on nuImages. Our approach achieves 95% of the fully-trained performance using only 67% of the available data, corresponding to 20% fewer labels compared to random selection and 5% fewer labels compared to state-of-the-art selection strategy. The code is available at https://github.com/aralhekimoglu/BoxMask.
SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation

Generalisation of deep neural networks becomes vulnerable when distribution shifts are encountered between train (source) and test (target) domain data. Few-shot domain adaptation mitigates this issue by adapting deep neural networks pre-trained on the source domain to the target domain using a randomly selected and annotated support set from the target domain. This paper argues that randomly selecting the support set can be further improved for effectively adapting the pre-trained source models to the target domain. Alternatively, we propose SelectNAdapt, an algorithm to curate the selection of the target domain samples, which are then annotated and included in the support set. In particular, for the K-shot adaptation problem, we first leverage self-supervision to learn features of the target domain data. Then, we propose a per-class clustering scheme of the learned target domain features and select K representative target samples using a distance-based scoring function. Finally, we bring our selection setup towards a practical ground by relying on pseudo-labels for clustering semantically similar target domain samples. Our experiments show promising results on three few-shot domain adaptation benchmarks for image recognition compared to related approaches and the standard random selection.
Frequency-Aware Self-Supervised Long-Tailed Learning

Data collected from the real world typically exhibit long-tailed distributions, where frequent classes contain abundant data while rare ones have only a limited number of samples. While existing supervised learning approaches have been proposed to tackle such data imbalance, the requirement of label supervision would limit their applicability to real-world scenarios in which label annotation might not be available. Without the access to class labels nor the associated class frequencies, we propose Frequency-Aware Self-Supervised Learning (FASSL) in this paper. Targeting at learning from unlabeled data with inherent long-tailed distributions, the goal of FASSL is to produce discriminative feature representations for downstream classification tasks. In FASSL, we first learn frequency-aware prototypes, reflecting the associated long-tailed distribution. Particularly focusing on rare-class samples, the relationships between image data and the derived prototypes are further exploited with the introduced self-supervised learning scheme. Experiments on long-tailed image datasets quantitatively and qualitatively verify the effectiveness of our learning scheme.
Lexical Diversity and Language Impairment

Abstract The development of artificial intelligence tools has seen an enormous growth recently. Linguistic artificial intelligence tools are being successfully applied in the field of speech analysis and discourse. In our study, we used automatic NLP tools to detect differences in picture description in the discourse of people diagnosed with Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) and healthy people. A measure of lexical diversity was used to compare discourse complexity. Transcripts of recordings of the probands within the EWA project were used in the study. From the multiple comparisons, we found that there is a statistically significant difference between healthy people and people suffering from MCI and AD. Our results indicate that healthy people have more lexical diversity than people suffering from MCI and AD – a more diverse vocabulary in spontaneous speech, in our case, when describing a picture.
Boundary Guided Feature Fusion Network for Camouflaged Object Detection

Camouflaged object detection (COD) refers to the process of detecting and segmenting camouflaged objects in an environment using algorithmic techniques. The intrinsic similarity between foreground objects and the background environment limits the performance of existing COD methods, making it difficult to accurately distinguish between the two. To address this issue, we propose a novel Boundary Guided Feature fusion Network (BGF-Net) for camouflaged object detection in this paper. Specifically, we introduce a Contour Guided Module (CGM) aimed at modeling more explicit contour features to improve COD performance. Additionally, we incorporate a Feature Enhancement Module (FEM) with the goal of integrating more discriminative feature representations to enhance detection accuracy and reliability. Finally, we present a Boundary Guided Feature Fusion Module (BGFM) to boost object detection capabilities and perform camouflaged object predictions. BGFM utilizes multi-level feature fusion for contextual semantic mining, we incorporate the edges extracted by the CGM into the fused features to further investigate semantic information related to object boundaries. By adopting this approach, we are able to better integrate contextual information, thereby improving the performance and accuracy of our model. We conducted extensive experiments, evaluating our BGF-Net method using three challenging datasets.
ANALISIS PERAN PERPUSTAKAAN DALAM MENINGKATKAN KETERAMPILAN LITERASI PESERTA DIDIK DI SEKOLAH

One of Malang's top libraries, Muhammadiyah Elementary School No. 4 Library offers excellent facilities, infrastructure, and a range of literacy-boosting programs. The goal of this study is to determine how Muhammadiyah Elementary School No. 4's library contributes to children' increased literacy in the current digital era. The study's technique was qualitative. To gather information about the study subject and study objects in the field, the researcher employed observation, interviewing, and documenting techniques. The chief librarian and the research documents were the study's topics and objects. The chief librarian's interview served as the major data source, while the results of the observation and documentation research served as the secondary data source. The outcomes of the results of the survey analysis showed that both the volume of books checked out and the number of library visitors are not constant. However, it was discovered through the numerous activities carried out by the library that the function of the library in raising the literacy rate of Malang Muhammadiyah Elementary School fourth grade pupils is entirely doable.
MENINGKATKAN HASIL BLAJAR PESERTA DIDIK MENGGUNAKAN MODEL PROBLEM BASED LEARNING PADA MATA PELAJARAN BAHASA INDONESIA KELAS IV SEKOLAH DASAR

This classroom action research aims to describe the use of the Problem Based Learning learning model in an effort to improve student learning outcomes which was carried out in class IV of SD Negeri 238 Palembang on the content of the Indonesian language subject, material on transitive sentences and class IV intrasynthetic sentences. This type of research uses classroom action research (PTK) with data collection techniques consisting of interviews, tests and observations. Data collection procedures are carried out through tests at the end of each cycle and observations during learning. The average learning outcomes of students in cycle I, cycle II, cycle III have increased. In cycle I the average student score was 67.16 with learning completeness 63%. In cycle II the average student score was 76.04 with learning completeness 77%. And in cycle III the average student score was 80.03 with learning completeness of 87%. The results of this research show that the application of the Problem Based Learning learning model can improve learning outcomes in Indonesian Language Subjects.
APPLYING LISTEN, READ, DISCUSS (LRD) STRATEGY TO PORTRAY STUDENT’S READING COMPREHENSION

The purpose of this study is to know the portrayal of student's in mastering reading comprehension after taught by LRD strategy. To improve students ' reading skills, researchers used the Listen Reading Discuss (LRD) method as a teaching method to assist students in improving their reading skills. The type of Applied Research is class action research consisting of two cycles, each cycle consists of planning, action, observation and reflection. Based on the results of this study, there is an increase in reading ability. This can be seen from the average pre-test score of 56.28, post-test cycle I of 69.17, and post-test cycle II of 89.05. by increasing the percentage of mastery of classical learning by 20% in the pre-test, 60% in the first cycle and 97.14% in the second cycle. Learning English with the Listen Reading Discuss (LRD) method has succeeded in improving student skill achievement in Class X of SMA Brigjen Katamso II for the 2022/2023 academic year. Finally, the researcher suggested to the teachers to use the LRD method as a method of teaching and learning process in terms of reading skills.
Optimization of K Value in Clustering Using Silhouette Score (Case Study: Mall Customers Data)

Clustering is an important phase in data mining. The grouping method commonly used in data mining concepts is using K-Means. Choosing the best value of k in the k-means algorithm can be difficult. In this study the technique used to determine the value of k is the silhouette score. Then, to evaluate the k-means model uses the Davies Bouldin Index (DBI) technique. The best DBI value is close to 0. The parameters used are total consumer income and spending. Based on the results of this study it can be concluded that the silhouette score method can provide a k value with optimal results. For mall customer data of 200 data, the most optimal silhouette score is obtained at K = 5 with a DBI = 0.57.
УЧЕЊЕ У ДИГИТАЛНОМ ОКРУЖЕЊУ ИЗМЕЂУ ПРИВАТНЕ И ЈАВНЕ СФЕРЕ

The paper discusses the possible consequences of the changes that learning has experienced in the digital environment from the perspective of its position between the private and public spheres. Two basic possible flows are indicated. The first leads to achieving complete control over teaching and learning in order to achieve the set standards and outcomes. The second leads to the strengthening of the role of the individual in education through the application of the principle of participation.