Reviewerly/exHarmony huggingface dataset:
Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery

When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations.
Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.
Frank-Wolfe algorithm for DC optimization problem

In the present paper, we formulate two versions of Frank--Wolfe algorithm or conditional gradient method to solve the DC optimization problem with an adaptive step size. The DC objective function consists of two components; the first is thought to be differentiable with a continuous Lipschitz gradient, while the second is only thought to be convex. The second version is based on the first and employs finite differences to approximate the gradient of the first component of the objective function. In contrast to past formulations that used the curvature/Lipschitz-type constant of the objective function, the step size computed does not require any constant associated with the components. For the first version, we established that the algorithm is well-defined of the algorithm and that every limit point of the generated sequence is a stationary point of the problem. We also introduce the class of weak-star-convex functions and show that, despite the fact that these functions are non-convex in general, the rate of convergence of the first version of the algorithm to minimize these functions is ${\cal O}(1/k)$. The finite difference used to approximate the gradient in the second version of the Frank-Wolfe algorithm is computed with the step-size adaptively updated using two previous iterations. Unlike previous applications of finite difference in the Frank-Wolfe algorithm, which provided approximate gradients with absolute error, the one used here provides us with a relative error, simplifying the algorithm analysis. In this case, we show that all limit points of the generated sequence for the second version of the Frank-Wolfe algorithm are stationary points for the problem under consideration, and we establish that the rate of convergence for the duality gap is ${\cal O}(1/\sqrt{k})$.
Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models

Large language models exhibit enhanced zero-shot performance on various tasks when fine-tuned with instruction-following data. Multimodal instruction-following models extend these capabilities by integrating both text and images. However, existing models such as MiniGPT-4 face challenges in maintaining dialogue coherence in scenarios involving multiple images. A primary reason is the lack of a specialized dataset for this critical application. To bridge these gaps, we present SparklesChat, a multimodal instruction-following model for open-ended dialogues across multiple images. To support the training, we introduce SparklesDialogue, the first machine-generated dialogue dataset tailored for word-level interleaved multi-image and text interactions. Furthermore, we construct SparklesEval, a GPT-assisted benchmark for quantitatively assessing a model's conversational competence across multiple images and dialogue turns. Our experiments validate the effectiveness of SparklesChat in understanding and reasoning across multiple images and dialogue turns. Specifically, SparklesChat outperformed MiniGPT-4 on established vision-and-language benchmarks, including the BISON binary image selection task and the NLVR2 visual reasoning task. Moreover, SparklesChat scored 8.56 out of 10 on SparklesEval, substantially exceeding MiniGPT-4's score of 3.91 and nearing GPT-4's score of 9.26. Qualitative evaluations further demonstrate SparklesChat's generality in handling real-world applications. All resources are available at https://github.com/HYPJUDY/Sparkles.
A Policy Adaptation Method for Implicit Multitask Reinforcement Learning Problems

In dynamic motion generation tasks, including contact and collisions, small changes in policy parameters can lead to extremely different returns. For example, in soccer, the ball can fly in completely different directions with a similar heading motion by slightly changing the hitting position or the force applied to the ball or when the friction of the ball varies. However, it is difficult to imagine that completely different skills are needed for heading a ball in different directions. In this study, we proposed a multitask reinforcement learning algorithm for adapting a policy to implicit changes in goals or environments in a single motion category with different reward functions or physical parameters of the environment. We evaluated the proposed method on the ball heading task using a monopod robot model. The results showed that the proposed method can adapt to implicit changes in the goal positions or the coefficients of restitution of the ball, whereas the standard domain randomization approach cannot cope with different task settings.
Curvature-based Pooling within Graph Neural Networks

Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neural networks (GNNs). While over-smoothing eliminates the differences between nodes making them indistinguishable, over-squashing refers to the inability of GNNs to propagate information over long distances, as exponentially many node states are squashed into fixed-size representations. Both phenomena share similar causes, as both are largely induced by the graph topology. To mitigate these problems in graph classification tasks, we propose CurvPool, a novel pooling method. CurvPool exploits the notion of curvature of a graph to adaptively identify structures responsible for both over-smoothing and over-squashing. By clustering nodes based on the Balanced Forman curvature, CurvPool constructs a graph with a more suitable structure, allowing deeper models and the combination of distant information. We compare it to other state-of-the-art pooling approaches and establish its competitiveness in terms of classification accuracy, computational complexity, and flexibility. CurvPool outperforms several comparable methods across all considered tasks. The most consistent results are achieved by pooling densely connected clusters using the sum aggregation, as this allows additional information about the size of each pool.
Robust GAN inversion

Recent advancements in real image editing have been attributed to the exploration of Generative Adversarial Networks (GANs) latent space. However, the main challenge of this procedure is GAN inversion, which aims to map the image to the latent space accurately. Existing methods that work on extended latent space $W+$ are unable to achieve low distortion and high editability simultaneously. To address this issue, we propose an approach which works in native latent space $W$ and tunes the generator network to restore missing image details. We introduce a novel regularization strategy with learnable coefficients obtained by training randomized StyleGAN 2 model - WRanGAN. This method outperforms traditional approaches in terms of reconstruction quality and computational efficiency, achieving the lowest distortion with 4 times fewer parameters. Furthermore, we observe a slight improvement in the quality of constructing hyperplanes corresponding to binary image attributes. We demonstrate the effectiveness of our approach on two complex datasets: Flickr-Faces-HQ and LSUN Church.
Analisis Forensik Digital Pada Whatsapp Dan Facebook Menggunakan Metode NIST

Technological developments can make it easier for Smartphone users to share various kinds of information using instant messaging applications such as Facebook Messenger and WhatsApp, the features contained in these instant messaging applications can send messages in the form of text, audio, images, videos and documents. The increase in the number of users of instant messaging applications also has a negative impact, namely the emergence of individuals who abuse instant messaging applications for digital crimes such as fraud, pornography, and drug sales. Disclosure of crimes requires valid digital evidence. In conducting investigations of digital crime cases, digital evidence is needed to solve them. Retrieval of digital evidence requires a forensic process of the physical evidence that has been obtained. This research focuses on the forensic process to obtain digital evidence from an Android Smartphone in the form of audio and video in the Facebook Messenger and WhatsApp applications. The forensic tools used in this research are Oxygen Forensic Pc Suite 2014, MOBILedit Forensic, Belkasoft evidence, and Magnet Axiom. The research uses a framework from the National Institute of Standards and Technology (NIST). The results of the research using the NIST method on an Android Smartphone were that Belkasoft succeeded in recovering deleted data with a percentage of 50% audio and 41% video on the WhatsApp application while on Facebook Messenger only 8.33% audio and 41% video, Magnet Axiom with a data percentage of 100%. audio and 100% video while Facebook Messenger has 8.33% audio and 5% video, on MOBILedit with a percentage of 0% on both applications, and Oxygen manages to return 16.67% audio and 23.5% video from the WhatsApp application and 0% for Facebook Messenger
Human Perceptions Based on Translations of Recurrent Neural Networks Principles for Low Latency Applications

The Necessity of Human resources beyond perception of human understanding towards the Evaluation of video quality or Data screening methodology is conducted based on human perception level since it is concerned with how visual content is perceived by a observer based on observations with his/ her perception on a particular video sequence. Therefore, we considered that the subject has to grade the encoded video sequences under certain test environment conditions based on ITU-Recommendations. Since Human perception is considered as the true judgment and precise measurement of visual content, data screening has became quite essential and quite comfortable to general public due to introduction of User Experience(UX) concept by User Experience community. The translations of A recurrent neural network is based on certain principles, for instance we considered natural language processing which is certainly adaptable towards understanding sequential data and use patterns to predict the consistency within observers. In our research, we adapted principles based on Recurrent Neural Networks while assuming consistency within observers for predicting video quality within data screening environment towards subjective experiments. Moreover,this research work explores the trade offs between Human perception on visual content and consistency of observations within individual observer.
Constructing a School-based Training Model based on Evidence-based Orientation

School-based training carries the mission of training teachers and serves the important task of educational reform. in the new era. Under the advantages of China’s teaching and research system, schoolbased training should also break through the narrow vision of empiricism and individualism, and needs to be supported by the evidence-based orientation system based on data and evidence, to clarify the direction of the evidence-based orientation of school-based training, to understand the value of evidence-based schoolbased training, to construct the 5A model of evidence-based school-based training, to build the operational mechanism of evidence-based school-based training, and to promote the scientific and professional decision making and practice of school-based training through evidence-based orientation.
Fully Automated Density-Based Clustering Method

Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, and inconsistent performance concerning data size and structure. To address these challenges, a novel clustering algorithm called the fully automated density-based clustering method (FADBC) is proposed. The FADBC method consists of two stages: parameter selection and cluster extraction. In the first stage, a proposed method extracts optimal parameters for the dataset, including the epsilon size and a minimum number of points thresholds. These parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find clusters. The proposed method was evaluated on different benchmark datasets and metrics, and the experimental results demonstrate its competitive performance without requiring manual inputs. The results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method, k-means, spectral clustering, DBSCAN, FCDCSD, Gaussian mixtures, and density-based spatial clustering methods. It can handle any kind of data set well and perform excellently.
Metadata as Relations Between Identifiers

An item of metadata is a relationship that someone claims to exist between two entities.&lt;sup&gt;1&lt;/sup&gt; With linked data, then, all metadata can be expressed in terms of relationships between identifiers.&lt;sup&gt;2&lt;/sup&gt; Consider that in RDF, even literals are objects.
Reasoning Lets You See What Isn't There

What is the “killer use case” for Reasoning, i.e., inferencing over a semantic graph database? Data in a relational database “knows nothing.” Data in a graph database “knows how it is connected” – you can ask an entity to tell you who its neighbors are 3 hops out, or if two pieces are connected up to 20 hops. Data with Reasoning “knows how to think.” But what does that mean exactly? What do you get?
A Journal of FAIR Overlays

A &lt;em&gt;translucent&lt;/em&gt; overlay journal could publish FAIR assets based on existing open-access publications. Typically, an overlay journal is transparent – it is a window to selected already-available content. The value added to the existing body of content is that of curation. However, scholarly digital assets – data, algorithms, tools, workflows, etc.
Data, Information, Knowledge, Wisdom (DIKW)

The DIKW&lt;sup&gt;1&lt;/sup&gt; decomposition expresses a layering of context on top of raw observations. Data points are attributes – components of facts. They are actuals (e.g., measured numbers) that can be connected to contexts. Information is data tied to context, i.e. processed data (“history”) that reduces uncertainty wrt what, when, where, and who.
Pengenalan Bahasa Inggris dalam Pembelajaran PAUD di Taman Kanak-Kanak Kota Bandung

Introducing English from an early age is a positive and absolute thing to improve the quality of Indonesian human resources, especially in facing the current era of globalization. This study aims to provide a factual and accurate picture of learning at Darul Hikam Kindergarten in introducing English to early childhood, which includes planning, implementation and evaluation processes. The method used to support this research is descriptive analytic. The subjects of this study were group B students of Daul Hikam Kindergarten. The research data were obtained through interview guides, observation guidelines, documentation studies and literature studies. It can be concluded that English for early childhood at Darul Hikam Kindergarten has been introduced through methods that can support the learning process so that it can run well and be fun, and can be an advantage of Darul Hikam Kindergarten schools.
Media Flashcard untuk Optimalisasi Hasil Belajar Siswa di Sekolah Dasar

ABSTRAK In their intellectual development, elementary school students are at the concrete thinking stage. Everything that the teacher conveys must be realistic and can develop all the abilities that exist in children. There needs to be a solution, namely by using flashcard image media. In particular, graphics also function to attract attention, clarify presentation of ideas, illustrate or decorate facts that would be quickly forgotten or ignored if not graphically. Flashcards are very suitable for practicing spontaneous speaking skills by using certain sentence patterns so that learning objectives will be achieved optimally, especially in student learning outcomes.
Blockchain-Based Secure Smart Health IoT solution Using RBAC Architecture

The revolution of Internet technology, which led to connecting everything through the World Wide Web, the Internet of Things (IoT) emerged to serve this need. IoT technology connects different devices, hardware, and software together, which are created to provide services and facilitate human life. The most critical application is in the health sector, where data privacy, security, and accessibility are demanding and agonizing challenges. In this paper, we use a block chain (BC) model that enforces the Role-Based Access Control (RBAC) model to address privacy and security concerns as well as the limited nature of IoT devices. The paper's concept is to divide the network according to the assigned role, and authentication and data transfer are thoroughly discussed. The Architecture of the proposed system is shown and displayed in flowcharts, where each node has different permissions and privileges. In the experimental part, two scenarios are applied; one where the network (IoT devices) is connected through an MQTT broker as an intermediary, and the second with BC that controls the IoT, which uses proof of assignment as a consensus model. The data flow shows that with BC, the network is much more secure and holds the privacy and integrity of the data.
Basabali Wiki, Penggunaan Website Kamus Digital Sebagai Langkah untuk Melestarikan Bahasa Bali

This study aims to discuss the use of the website by the BASAbali Wiki community to preserve Balinese language, script and cultural literature. This research uses a case study approach by analyzing digital dictionaries and supporting features provided by the BASAbali Wiki website, and in-depth interviews with one of the BASAbali Wiki community administrators. The results of the study show that the BASAbali Wiki community is able to utilize information technology through the process of digitizing dictionaries and libraries through the website by involving practitioners and community leaders who are experts in their fields. The public can also actively participate in website development by adding data or editing data that is already available. With the presence of a digital Balinese dictionary and other supporting features on the BASAbali Wiki website, it is hoped that it can increase the frequency of use of the Balinese language by Balinese people, as well as introduce Balinese language and culture on a massive and more in-depth basis to the general public. From the results of the study it can be concluded that the BASAbali Wiki organization actively participates in efforts to preserve Balinese language, script, culture and literature by utilizing digital technology&#x0D; &#x0D; Keywords: BASAbali WIKI, Balinese Language, Digitalization, Website
Pengembangan LKPD Berbasis Web pada Materi Kingdom Animalia untuk Meningkatkan Hasil Belajar Siswa

This study aims to develop Web-based LKPD on animalia material, to find out the improvement of students' biology learning outcomes through the use of valid, practical, and effective Web-based LKPD. This study uses a development research approach (Research and Development). The object of this research is a web-based LKPD on the material kingdom animalia. The validation and trial stages were carried out on the feasibility of Web-based LKPD. Validation by material experts was carried out to obtain advice regarding the feasibility of web-based worksheets in terms of material. The limited trial was carried out using the response questionnaire method from students in class X MIPA 3 SMA Negeri 1 Wajo. The data analysis technique uses a quantitative descriptive analysis technique with a score of five (Likert scale) against a predetermined rating scale category. The results of the study were obtained, the results of the effectiveness test of limited and wide-scale learning outcomes from the results of the T test with a value of α &lt;0.01 so that they were declared to have a significant effect. The N-Gain test result is 0.75 in the "Low" category. In conclusion, the results of the study show that the development of web-based worksheets for biology subjects in Kingdom Animalia can improve the learning outcomes of SMA Negeri 1 Wajo students, especially in XMIPA 3 class. &#x0D; &#x0D; Keywords: Kingdom Animalia, LKPD Development, Web.