Reviewerly/exHarmony huggingface dataset:
Frequency-based Zero-Shot Learning with Phase Augmentation

Zero-Shot Learning (ZSL) aims to recognize images from seen and unseen classes by aligning visual and semantic knowledge (e.g., attribute descriptions). However, the fine-grained attributes in the RGB domain can be easily affected by background noise (e.g., the grey bird tail blending with the ground), making it difficult to effectively distinguish them. Analyzing the features in the frequency domain assists in better distinguishing the attributes since their patterns remain consistent across different images, unlike noise which may be more variable. Nevertheless, existing ZSL methods typically learn visual features directly from the RGB domain, which can impede the recognition of certain attributes. To overcome this limitation, we propose a novel ZSL method named Frequency-based Phase Augmentation (FPA) network, which learns an effective representation of the attributes in the frequency domain. Specifically, we introduce a Hybrid Phase Augmentation (HPA) module to transform visual features into the frequency domain and augment the phase component for better retention of semantic information of the attributes. The use of phase-augmented features enables FPA to capture more semantic knowledge that can be challenging to distinguish in the RGB domain, suppress noise, and highlight significant attributes. Our extensive experiments show that FPA achieves state-of-the-art performance across four standard datasets.
CLG-INet: Coupled Local-Global Interactive Network for Image Restoration

Image restoration is an ill-posed problem due to the infinite feasible solutions for degraded images. Although CNN-based and Transformer-based approaches have been proven effective in image restoration, there are still two challenges in restoring complex degraded images: 1)local-global information extraction and fusion, and 2)computational cost overhead. To address these challenges, in this paper, we propose a lightweight image restoration network (CLG-INet) based on CNN-Transformer interaction, which can efficiently couple the local and global information. Specifically, our model is hierarchically built with a "sandwich-like" structure of coupling blocks, where each block contains three layers in sequence (CNN-Transformer-CNN). The Transformer layer is designed with two core modules: Dynamic Bi-Projected Attention (DBPA), which performs dual projection with large convolutions across windows to capture long-range dependencies, and Gated Non-linear Feed-Forward Network (GNFF), which reconstructs mixed feature information. In addition, we introduce interactive learning, which fuses local features and global representations in different resolutions to the maximum extent. Extensive experiments demonstrate that CLG-INet significantly boosts performance on various image restoration tasks, such as deraining, deblurring, and denoising.
Open-Scenario Domain Adaptive Object Detection in Autonomous Driving

Existing domain adaptive object detection algorithms (DAOD) have demonstrated their effectiveness in discriminating and localizing objects across scenarios. However, these algorithms typically assume a single source and target domain for adaptation, which is not representative of the more complex data distributions in practice. To address this issue, we propose a novel Open-Scenario Domain Adaptive Object Detection (OSDA), which leverages multiple source and target domains for more practical and effective domain adaptation. We are the first to increase the granularity of the background category by building the foundation model using contrastive vision-language pre-training in an open-scenario setting for better distinguishing foreground and background, which is under-explored in previous studies. The performance gains by introducing the pre-training have been observed and have validated the model's ability to detect objects across domains. To further fine-tune the model for domain-specific object detection, we propose a hierarchical feature alignment strategy to obtain a better common feature space among the various source and target domains. In the case of multi-source domains, the cross-reconstruction framework is introduced for learning more domain invariances. The proposed method is able to alleviate knowledge forgetting without any additional computational costs. Extensive experiments across different scenarios demonstrate the effectiveness of the proposed model.
Security and Privacy Issues in Cloud-Based Databases: A Literature Review

Nowadays, technology is getting more sophisticated. It can be seen with the emergence of Cloud Computing. One application of Cloud Computing is Cloud databases. In a cloud environment, there will be privacy and security issues. From the literature results, data leaks and attacks from outsiders are still found when using cloud databases. Cloud service providers already have the technology to maintain data privacy and security. Three procedures can be carried out to maintain data privacy and security: encryption, access control, and third-party audits. Data privacy and security will be maintained with cloud service providers and procedures technology.
Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment Localization

Video moment localization aims to retrieve the target segment of an untrimmed video according to the natural language query. Weakly supervised methods gains attention recently, as the precise temporal location of the target segment is not always available. However, one of the greatest challenges encountered by the weakly supervised method is implied in the mismatch between the video and language induced by the coarse temporal annotations. To refine the vision-language alignment, recent works contrast the cross-modality similarities driven by reconstructing masked queries between positive and negative video proposals. However, the reconstruction may be influenced by the latent spurious correlation between the unmasked and the masked parts, which distorts the restoring process and further degrades the efficacy of contrastive learning since the masked words are not completely reconstructed from the cross-modality knowledge. In this paper, we discover and mitigate this spurious correlation through a novel proposed counterfactual cross-modality reasoning method. Specifically, we first formulate query reconstruction as an aggregated causal effect of cross-modality and query knowledge. Then by introducing counterfactual cross-modality knowledge into this aggregation, the spurious impact of the unmasked part contributing to the reconstruction is explicitly modeled. Finally, by suppressing the unimodal effect of masked query, we can rectify the reconstructions of video proposals to perform reasonable contrastive learning. Extensive experimental evaluations demonstrate the effectiveness of our proposed method. The code is available at https://github.com/sLdZ0306/CCR https://github.com/sLdZ0306/CCR.
Enhancing Family Nutrition in Aceh Through Association Rules Analysis Utilizing Mixed Data Types

The increasing amount of data in the era of Industry 4.0 brings complexity to data mining implementation. SUSENAS data, which is complex with a large number of mixed data type attributes, has not been fully utilized by the government. The Keluarga Harapan Program (PKH), as a social assistance program, also faces issues regarding improper beneficiary selection. Therefore, this study aims to maximize the processing of SUSENAS consumption module data to support the PKH program and provide insights into the relationship between consumption patterns and family nutrition. The focus of this research is attribute selection and processing of mixed data types to align with input association rules and provide information about attribute relationships. The CRISP-DM methodology is used as the research approach, with the FP-Growth algorithm chosen for association rules analysis. After performing data transformation using one-hot encoding, 37 association rules were discovered with an average confidence value of 0.89 and an average support value of 0.30. One interesting rule identified is the relationship between family size, poverty status, and nutritional adequacy, where medium-sized families tend to experience protein and calorie deficiencies. By employing this approach, this research contributes to understanding consumption patterns and family nutrition through association rule analysis on SUSENAS data. The findings of this study are expected to support the PKH program and assist the government in making more informed decisions regarding social assistance for impoverished families.
Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges

&lt;p class="MsoNormal" style="margin-top: 6pt; text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: arial, helvetica, sans-serif;"&gt;The dawn of the Artificial Intelligence (AI) era presents a plethora of new possibilities for analyzing regional economic development. The present article provides an in-depth exploration of the methods employed in this field, highlighting the immense opportunities that AI offers while also addressing potential challenges. The role of AI is crucial in complex data handling, enabling efficient analyses of intricate regional economic patterns. This capacity is paramount in shaping economic policies and strategies that are reflective of each region's unique needs and potential. The article firstly explores various AI methods used in economic analysis, including but not limited to machine learning, deep learning, and natural language processing. It delves into the application of these methods in discerning development trends, predicting economic shifts, and identifying strategic economic drivers unique to various regions. Subsequently, the potential of AI to transform regional economic analysis is discussed, encompassing its capability to process large and complex datasets, its power to predict future trends based on past and present data, and its ability to aid in strategic decision-making. However, this new era of AI-driven economic analysis is not without challenges. The latter part of this article thus confronts the issues related to data privacy, ethical use of AI, and the necessity of interdisciplinary skills in AI and economics. This exploration contributes to a broader understanding of how AI is transforming the landscape of regional economic development analysis, illuminating both its present use and future implications. By understanding these dynamics, we can better harness the potential of AI to advance economic prosperity in various regions around the globe.&lt;/span&gt;&lt;/p&gt;
NIF: A Fast Implicit Image Compression with Bottleneck Layers and Modulated Sinusoidal Activations

In Implicit Neural Representations (INRs) a discrete signal is parameterized by a neural network that maps coordinates to the signal samples. INRs were successfully employed for encoding and compression, but such approaches are in their early stage and are still overcome by traditional codecs and autoencoders. Despite this, they have recently gained the attention of the research community due to their promising results as novel representation strategies for encoding visual content. In this paper, we propose Neural Imaging Format (NIF), an open-source INR-based image compression codec which takes advantage of a novel neural architecture which consists of two modules: a Genesis network, for mapping coordinates to pixels through bottleneck layers with sinusoidal activation units, and a Modulation network, for varying the period of the sinusoidal activations. Additionally, a final weights quantization step leads to an improvement in the compression ratio. Our proposal (NIF) consistently outperforms state-of-art INR-based compressors in terms of PSNR, by achieving comparable or better results with an outstanding up to X26 encoding speed. We also show that NIF reduces the gap between INR-based methods with respect to traditional approaches. Interestingly, our approach outperforms established codecs such as JPEG and WebP when one encodes high-resolution images at low-bitrate regimes. Extensive experiments on different datasets, a visual comparison, and an ablation study, prove the validity of the proposed approach.
User Experience on Augmented Reality Application for Promoting Asnaf Care

This paper reports on the ongoing project related to the development of an Augmented Reality (AR) mobile application for promoting a series of services provided by a Kedah Zakat Board (LZNK) called Asnaf Care. Currently, the services provided by Asnaf Care are dependent on donations by the public people which are collected through the online system. However, the donation received is insufficient to satisfy the asnaf's demands due to the poor promotion technique. As a result, this study proposes an AR mobile application to promote the Asnaf Care fundraising system to increase the amount of money raised by the public. The prototype of the proposed AR mobile application has been developed by utilizing the User Centred Design (UCD) approach and Design Science Research Methodology (DSRM) as the whole research methodology. Next, it has been validated through the expert review method which has been reported in the previous study. Having developed and validated the prototype, this study then conducted the user experience testing as reported in this paper.
Search and Permission Request Over Encrypted Database

Data sharing has become increasingly important in order for organizations to maintain competitiveness in the AI-empowered digital economy. Whereas, in the face of stringent data protection and privacy regulations, such data sharing needs to be designed with extreme care. This motivates the study of privacy-preserving data sharing schemes in distributed systems. In this work, we consider a basic scenario where shared data are stored in an encrypted database while still allowing for search requests and permission verification. In contrast to the scenario of existing SSE schemes, in our case, we consider the scenario where each file comes with an access function while each user has a dynamic attribute. So a file is returned to the requester if (1) The file contains the keyword that is being searched; (2) The requester's attribute values tuple in the time of query satisfies the access function of the files. We propose a scheme that is designed to satisfy these requirements and analyze its security and efficiency.
A Novel Deep Video Watermarking Framework with Enhanced Robustness to H.264/AVC Compression

The recent success of deep image watermarking has demonstrated the potential of deep learning for watermarking, which has drawn increasing attention to deep video watermarking with the objective to improve its robustness and perceptual quality. Compared to images, video watermarking is much more challenging due to the rich structures of video data and the diversity of attacks in video transmission pipeline. The existing deep video watermarking schemes are far from satisfactory in dealing with temporal attacks, e.g., frame averaging, frame dropping and transcoding. To this end, a novel deep framework for Robustness Enhanced Video watermarking (REVMark) is proposed in this paper, aiming at improving the overall robustness, especially in dealing with H.264/AVC compression, while maintaining good visual quality. REVMark has an encoder/decoder structure with a pre-processing block (TAsBlock) to effectively extract the temporal-associated features on aligned frames. To ensure the end-to-end robust training, a distortion layer is integrated into the REVMark to resemble various attacks in real-world scenarios, among which, a new differentiable simulator of video compression, namely DiffH264, is developed to approximately simulate the process of H.264/AVC compression. In addition, the mask loss is incorporated to guide the encoder to embed the watermark in the human-imperceptible regions, thus improving the perceptual quality of the watermarked video. Experimental results demonstrate that the proposed scheme can outperform other SOTA methods while achieving 10X faster inference.
Ilhan New's Independence Movement in the United States and the Korea Economy Society

Compared to other representative indepedence movements, Ilhan New’s activity as a chairman of the Korea Economic Society has received less attention than it deserves.&#x0D; In order to create positive public opinion about Korea in the U.S Ilhan New insisted on the necessity to utilize Korean military power during the Pacific War and on Korean’s capability of building and running their nation post-war. It played a positive role in encouraging the U.S. policy toward Korea to be more beneficial to Korea. However, Ilhan New judged that working through Planning and Research Board and the United Korean Committee in America Mission, to which he belonged, to be limited.&#x0D; Therefore, he created the Korea Economic Society as a non-profit organization.&#x0D; The Korea Economic Society’s main activity was to publish it’s own journal called Korea Economic Digest. It supplemented the limitations of Ilhan New’s previous press strategy that used only Korean activists and KED’s writers consisted of many American experts on Korea. These writers supported Ilhan New’s independence movement and attempted to persuade the U.S.&#x0D; government-related ministries and public-private agencies by citing Korea’s strengths and potential values as seen by non-Koreans.&#x0D; In other words, the Korea Economic Society could be considered a valuable association in that sense that defended and promoted Korea through journalism in response to the U.S. government’s tendency to draw negative conclusions through public researches on Korea during the Pacific War period. In addition, it was unique because it was a private association that reflected New Ilhan’s special independence movement strategy which was to promote Korea through the journalism.
Conversational Composed Retrieval with Iterative Sequence Refinement

Due to the progress of large-scale multimodal model pretraining, existing cross-modal retrieval techniques is accurate to align text description to the target image when they show close and clear semantic correspondence. However, in real situations, users only provide ambiguous text queries, making it difficult to retrieve the desired images. To address this issue, we introduce the conversational composed retrieval paradigm, inspired by conversational search which models complex user intent through iterative interaction. This paradigm enhances the model capacity in learning fine-grained correspondences. To train the cross-modal conversational retrieval, we propose the Iterative Refining Retrieval (IRR) framework. It formalizes the reference images and modification texts in each session as a multimodal sequence, which is fed into the generative model to predict the information in the sequence autoregressively, and ultimately predicting the target image feature. In the conversational retrieval paradigm, the model refines the learned correspondences based on the interaction in the later stage of the retrieval session, thus captures fine-grained semantic correspondence to enforce the cross-modal representation. We propose a domain-specific multimodal pretraining method and the full sequence sampling augmentation method to fully utilize the session information. Extensive experiments demonstrate that the iterative refining retrieval method achieves state-of-the-art performance on sessions of varying lengths.
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification

Data-Free Knowledge Distillation (DFKD) has shown great potential in creating a compact student model while alleviating the dependency on real training data by synthesizing surrogate data. However, prior arts are seldom discussed under distribution shifts, which may be vulnerable in real-world applications. Recent Vision-Language Foundation Models, e.g., CLIP, have demonstrated remarkable performance in zero-shot out-of-distribution generalization, yet consuming heavy computation resources. In this paper, we discuss the extension of DFKD to Vision-Language Foundation Models without access to the billion-level image-text datasets. The objective is to customize a student model for distribution-agnostic downstream tasks with given category concepts, inheriting the out-of-distribution generalization capability from the pre-trained foundation models. In order to avoid generalization degradation, the primary challenge of this task lies in synthesizing diverse surrogate images driven by text prompts. Since not only category concepts but also style information are encoded in text prompts, we propose three novel Prompt Diversification methods to encourage image synthesis with diverse styles, namely Mix-Prompt, Random-Prompt, and Contrastive-Prompt. Experiments on out-of-distribution generalization datasets demonstrate the effectiveness of the proposed methods, with Contrastive-Prompt performing the best.
Deep Multimodal Learning for Information Retrieval

Information retrieval (IR) is a fundamental technique that aims to acquire information from a collection of documents, web pages, or other sources. While traditional text-based IR has achieved great success, the under-utilization of varied data sources in different modalities (i.e., text, images, audio, and video) would hinder IR techniques from giving its full advancement and thus limits the applications in the real world. Within recent years, the rapid development of deep multimodal learning paves the way for advancing IR with multi-modality. Benefiting from a variety of data types and modalities, some latest prevailing techniques are invented to show great facilitation in multi-modal and IR learning, such as CLIP, ChatGPT, GPT4, etc. In the context of IR, deep multi-modal learning has shown the prominent potential to improve the performance of retrieval systems, by enabling them to better understand and process the diverse types of data that they encounter. Given the great potential shown by multimodal-empowered IR, there can be still unsolved challenges and open questions in the related directions. With this workshop, we aim to provide a platform for discussion about multi-modal IR among scholars, practitioners, and other interested parties.
Preliminary Investigation of Balanced Stratified Reduction (BSR) for Imbalanced Datasets

The prevalence of imbalanced datasets in machine learning poses significant challenges, often leading to models with suboptimal performance, especially in recognizing underrepresented classes. This paper introduces a novel technique, Balanced Stratified Reduction (BSR), aiming to address these challenges by optimizing the sampling process. BSR utilizes horizontal stratification uniquely, drawing inspiration from the quintessence of boxplots. Preliminary results, based on two medical datasets - PIMA Indian Diabetes and Haberman's Survival, showcase BSR's potential in not only managing the class imbalance but also in retaining crucial information from the majority class. The paper outlines the foundational principles of BSR, the motivation behind its inception, a detailed experimental setup for its broader application, and preliminary findings. While the initial outcomes are promising, comprehensive evaluation and application across diverse datasets remain a focal point for future work. BSR's approach, grounded in its methodological rigor, holds promise for more effective handling of imbalanced datasets in real-world scenarios.