Reviewerly/exHarmony huggingface dataset:
Towards Complex-query Referring Image Segmentation: A Novel Benchmark

Referring Image Understanding (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research investigating how existing algorithms should be benchmarked with complex language queries, which include more informative descriptions of surrounding objects and backgrounds (\eg \textit{"the black car."} vs. \textit{"the black car is parking on the road and beside the bus."}). Given the significant improvement in the semantic understanding capability of large pre-trained models, it is crucial to take a step further in RIS by incorporating complex language that resembles real-world applications. To close this gap, building upon the existing RefCOCO and Visual Genome datasets, we propose a new RIS benchmark with complex queries, namely \textbf{RIS-CQ}. The RIS-CQ dataset is of high quality and large scale, which challenges the existing RIS with enriched, specific and informative queries, and enables a more realistic scenario of RIS research. Besides, we present a nichetargeting method to better task the RIS-CQ, called dual-modality graph alignment model (\textbf{\textsc{DuMoGa}}), which outperforms a series of RIS methods.
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery

Learning diverse and qualified behaviors for utilization and adaptation without supervision is a key ability of intelligent creatures. Ideal unsupervised skill discovery methods are able to produce diverse and qualified skills in the absence of extrinsic reward, while the discovered skill set can efficiently adapt to downstream tasks in various ways. Maximizing the Mutual Information (MI) between skills and visited states can achieve ideal skill-conditioned behavior distillation in theory. However, it's difficult for recent advanced methods to well balance behavioral quality (exploration) and diversity (exploitation) in practice, which may be attributed to the unreasonable MI estimation by their rigid intrinsic reward design. In this paper, we propose Contrastive multi-objectives Skill Discovery (ComSD) which tries to mitigate the quality-versus-diversity conflict of discovered behaviors through a more reasonable MI estimation and a dynamically weighted intrinsic reward. ComSD proposes to employ contrastive learning for a more reasonable estimation of skill-conditioned entropy in MI decomposition. In addition, a novel weighting mechanism is proposed to dynamically balance different entropy (in MI decomposition) estimations into a novel multi-objective intrinsic reward, to improve both skill diversity and quality. For challenging robot behavior discovery, ComSD can produce a qualified skill set consisting of diverse behaviors at different activity levels, which recent advanced methods cannot. On numerical evaluations, ComSD exhibits state-of-the-art adaptation performance, significantly outperforming recent advanced skill discovery methods across all skill combination tasks and most skill finetuning tasks. Codes will be released at https://github.com/liuxin0824/ComSD.
Flux-pulse-assisted Readout of a Fluxonium Qubit

Much attention has focused on the transmon architecture for large-scale superconducting quantum devices, however, the fluxonium qubit has emerged as a possible successor. With a shunting inductor in parallel to a Josephson junction, the fluxonium offers larger anharmonicity and stronger protection against dielectric loss, leading to higher coherence times as compared to conventional transmon qubits. The interplay between the inductive and Josephson energy potentials of the fluxonium qubit leads to a rich dispersive shift landscape when tuning the external flux. Here we propose to exploit the features in the dispersive shift to improve qubit readout. Specifically, we report on theoretical simulations showing improved readout times and error rates by performing the readout at a flux bias point with large dispersive shift. We expand the scheme to include different error channels, and show that flux-pulse-assisted readout offers 5 times improvement in signal to noise ratio after 200 ns integration time. Moreover, we show that the performance improvement persists in the presence of finite measurement efficiency combined with quasi-static flux noise. We suggest energy parameters for the fluxonium architecture that will allow for the implementation of our proposed flux-pulse-assisted readout scheme.
Few-Shot Domain Adaptation for Charge Prediction on Unprofessional Descriptions

Recent works considering professional legal-linguistic style (PLLS) texts have shown promising results on the charge prediction task. However, unprofessional users also show an increasing demand on such a prediction service. There is a clear domain discrepancy between PLLS texts and non-PLLS texts expressed by those laypersons, which degrades the current SOTA models' performance on non-PLLS texts. A key challenge is the scarcity of non-PLLS data for most charge classes. This paper proposes a novel few-shot domain adaptation (FSDA) method named Disentangled Legal Content for Charge Prediction (DLCCP). Compared with existing FSDA works, which solely perform instance-level alignment without considering the negative impact of text style information existing in latent features, DLCCP (1) disentangles the content and style representations for better domain-invariant legal content learning with carefully designed optimization goals for content and style spaces and, (2) employs the constitutive elements knowledge of charges to extract and align element-level and instance-level content representations simultaneously. We contribute the first publicly available non-PLLS dataset named NCCP for developing layperson-friendly charge prediction models. Experiments on NCCP show the superiority of our methods over competitive baselines.
Source-Replacement Model for Phase-Matching Quantum Key Distribution

Quantum key distribution has emerged as a promising solution for constructing secure communication networks, offering information-theoretic security guaranteed by the principles of quantum mechanics. One of the most advanced quantum key distribution protocols to date is the phase-matching protocol. Its security was initially established using an abstract method known as symmetry-protected privacy. In this study, we reevaluate the security of the phase-matching protocol using an intuitive source-replacement model, and we arrive at conclusions that align with the original proof. This model provides a fresh perspective on the protocol's security. As an application of this approach, we introduce a beam-splitting attack scheme. Leveraging the source-replacement model, we derive a lower bound on the phase error rate under this attack, further underscoring the robustness of our security analysis method.
Optimal Bounds on Private Graph Approximation

We propose an efficient $\epsilon$-differentially private algorithm, that given a simple {\em weighted} $n$-vertex, $m$-edge graph $G$ with a \emph{maximum unweighted} degree $\Delta(G) \leq n-1$, outputs a synthetic graph which approximates the spectrum with $\widetilde{O}(\min\{\Delta(G), \sqrt{n}\})$ bound on the purely additive error. To the best of our knowledge, this is the first $\epsilon$-differentially private algorithm with a non-trivial additive error for approximating the spectrum of the graph. One of the subroutines of our algorithm also precisely simulates the exponential mechanism over a non-convex set, which could be of independent interest given the recent interest in sampling from a {\em log-concave distribution} defined over a convex set. Spectral approximation also allows us to approximate all possible $(S,T)$-cuts, but it incurs an error that depends on the maximum degree, $\Delta(G)$. We further show that using our sampler, we can also output a synthetic graph that approximates the sizes of all $(S,T)$-cuts on $n$ vertices weighted graph $G$ with $m$ edges while preserving $(\epsilon,\delta)$-differential privacy and an additive error of $\widetilde{O}(\sqrt{mn}/\epsilon)$. We also give a matching lower bound (with respect to all the parameters) on the private cut approximation for weighted graphs. This removes the gap of $\sqrt{W_{\mathsf{avg}}}$ in the upper and lower bound in Eli{\'a}{\v{s}}, Kapralov, Kulkarni, and Lee (SODA 2020), where $W_{\mathsf{avg}}$ is the average edge weight.
Tree Cross Attention

Cross Attention is a popular method for retrieving information from a set of context tokens for making predictions. At inference time, for each prediction, Cross Attention scans the full set of $\mathcal{O}(N)$ tokens. In practice, however, often only a small subset of tokens are required for good performance. Methods such as Perceiver IO are cheap at inference as they distill the information to a smaller-sized set of latent tokens $L < N$ on which cross attention is then applied, resulting in only $\mathcal{O}(L)$ complexity. However, in practice, as the number of input tokens and the amount of information to distill increases, the number of latent tokens needed also increases significantly. In this work, we propose Tree Cross Attention (TCA) - a module based on Cross Attention that only retrieves information from a logarithmic $\mathcal{O}(\log(N))$ number of tokens for performing inference. TCA organizes the data in a tree structure and performs a tree search at inference time to retrieve the relevant tokens for prediction. Leveraging TCA, we introduce ReTreever, a flexible architecture for token-efficient inference. We show empirically that Tree Cross Attention (TCA) performs comparable to Cross Attention across various classification and uncertainty regression tasks while being significantly more token-efficient. Furthermore, we compare ReTreever against Perceiver IO, showing significant gains while using the same number of tokens for inference.
Directly Fine-Tuning Diffusion Models on Differentiable Rewards

We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-tuning diffusion models to maximize differentiable reward functions, such as scores from human preference models. We first show that it is possible to backpropagate the reward function gradient through the full sampling procedure, and that doing so achieves strong performance on a variety of rewards, outperforming reinforcement learning-based approaches. We then propose more efficient variants of DRaFT: DRaFT-K, which truncates backpropagation to only the last K steps of sampling, and DRaFT-LV, which obtains lower-variance gradient estimates for the case when K=1. We show that our methods work well for a variety of reward functions and can be used to substantially improve the aesthetic quality of images generated by Stable Diffusion 1.4. Finally, we draw connections between our approach and prior work, providing a unifying perspective on the design space of gradient-based fine-tuning algorithms.
Multi-task View Synthesis with Neural Radiance Fields

Multi-task visual learning is a critical aspect of computer vision. Current research, however, predominantly concentrates on the multi-task dense prediction setting, which overlooks the intrinsic 3D world and its multi-view consistent structures, and lacks the capability for versatile imagination. In response to these limitations, we present a novel problem setting -- multi-task view synthesis (MTVS), which reinterprets multi-task prediction as a set of novel-view synthesis tasks for multiple scene properties, including RGB. To tackle the MTVS problem, we propose MuvieNeRF, a framework that incorporates both multi-task and cross-view knowledge to simultaneously synthesize multiple scene properties. MuvieNeRF integrates two key modules, the Cross-Task Attention (CTA) and Cross-View Attention (CVA) modules, enabling the efficient use of information across multiple views and tasks. Extensive evaluation on both synthetic and realistic benchmarks demonstrates that MuvieNeRF is capable of simultaneously synthesizing different scene properties with promising visual quality, even outperforming conventional discriminative models in various settings. Notably, we show that MuvieNeRF exhibits universal applicability across a range of NeRF backbones. Our code is available at https://github.com/zsh2000/MuvieNeRF.
LLM-grounded Video Diffusion Models

Text-conditioned diffusion models have emerged as a promising tool for neural video generation. However, current models still struggle with intricate spatiotemporal prompts and often generate restricted or incorrect motion (e.g., even lacking the ability to be prompted for objects moving from left to right). To address these limitations, we introduce LLM-grounded Video Diffusion (LVD). Instead of directly generating videos from the text inputs, LVD first leverages a large language model (LLM) to generate dynamic scene layouts based on the text inputs and subsequently uses the generated layouts to guide a diffusion model for video generation. We show that LLMs are able to understand complex spatiotemporal dynamics from text alone and generate layouts that align closely with both the prompts and the object motion patterns typically observed in the real world. We then propose to guide video diffusion models with these layouts by adjusting the attention maps. Our approach is training-free and can be integrated into any video diffusion model that admits classifier guidance. Our results demonstrate that LVD significantly outperforms its base video diffusion model and several strong baseline methods in faithfully generating videos with the desired attributes and motion patterns.
The Effectiveness of the Sustainable Natural Disaster Management Program in North Kalimantan Province

Natural disaster management programs aim to reduce risks, protect the community, and minimize the impact of natural disasters in the area. The purpose of this research is to describe and analyze the effectiveness of sustainable natural disaster management programs in the North Kalimantan Province. The effectiveness of this program is very important for achieving the goals that have been previously set.. A qualitative approach was used in this study. Data collection techniques were conducted through in-depth interviews with key informants who played a role in natural disaster management programs, observation of stakeholder activities, and documentation studies. The research data sources consist of primary and secondary data. Data analysis techniques included data collection, data reduction, data presentation, and drawing conclusions. Results show that North Kalimantan Province’s natural disaster management program has been running effectively on the dimensions of achieving goals, where the budget allocated follows the existing realization. As for integration in program implementation, it is not well established because of the lack of rehearsals for disaster management as well as adaptation to implementation, which is difficult due to topographical conditions that are still difficult to reach.&#x0D; Keywords: program effectiveness, sustainable disaster management
Evaluation of flood disaster management planning: a comparative method for Bostwana and Indonesia

Disaster management planning is an activity that is translated into documents to be used as a reference in carrying out disaster risk reduction.. Furthermore, based on data from UN-ESCAP for 2022, disaster events in Indonesia dan Bostwana are dominated by extreme weather, especially storms, floods and droughts. so that disaster management planning, especially for flood disasters, is considered important as one of the efforts to reduce disaster risk. This study aims to compare the evaluation results of the disaster management planning documents owned by Botswana and Indonesia. This study uses a systematic literature review method and a comparative study with a qualitative descriptive approach to evaluation of disaster management planning between Gaborone, Botswana and Yogyakarta, Indonesia. The results of the research show that the evaluation of disaster management planning in Gaborone City, Botswana still requires updating and involving the public more in the preparation of this document. In addition, the results of the evaluation of disaster management planning documents in the City of Yogyakarta, Indonesia still require efforts to integrate disaster documents with other planning documents so that they can become a reference for the implementation of disaster risk reduction activities by various stakeholders involved in the disaster sector. The problem with disaster management planning documents in the two countries is the lack of public involvement and the lack of integration of this disaster management planning document with other development planning documents, including issues regarding validity periods. The offered solution is to update the disaster management planning document by involving more public argument.
Exploring Clustered Federated Learning’s Vulnerability against Property Inference Attack

Clustered federated learning (CFL) is an advanced technique in the field of federated learning (FL) that addresses the issue of catastrophic forgetting caused by non-independent and identically distributed (non-IID) datasets. CFL achieves this by clustering clients based on the similarity of their datasets and training a global model for each cluster. Despite the effectiveness of CFL in mitigating performance degradation resulting from non-IID datasets, the potential risk of privacy leakages in CFL has not been thoroughly studied. Previous work evaluated the risk of privacy leakages in FL using the property inference attack (PIA), which extracts information about unintended properties (i.e., attributes that differ from the target attribute of the global model’s main task). In this paper, we explore the potential risk of unintended property leakage in CFL by subjecting it to both passive and active PIAs. Our empirical analysis shows that the passive PIA performance on CFL is substantially better than that on FL in terms of the attack AUC score. Moreover, we propose an enhanced active PIA method tailored for CFL to improve the attack performance. Our method introduces a scale-up parameter that amplifies the impact of malicious local updates, resulting in better performance than the previous technique. Furthermore, we demonstrate that the vulnerability of CFL can be alleviated by applying differential privacy (DP) mechanisms at the client-level. Unlike previous works, which have shown that applying DP to FL can induce a high utility loss, our empirical results indicate that DP can be used as a defense mechanism in CFL, leading to a better trade-off between privacy and utility.
The Gamifikasi Pembelajaran Berbasis Quizizz Sebagai Upaya Peningkatan Kompetensi Pemanfaatan Teknologi Pengajar

Post-pandemic, the implementation of IT makes learning more creative, innovative, and interactive. The presence of interaction and collaboration between students through ICT-based learning means is important for them to become enthusiastically engaged in learning. With the development of technology, educators must have knowledge and skills in using IT to support learning in the smart society era. However, based on the basic research conducted by one of the students of the SMA Negeri 8 Semarang department, the department found that there is still no optimal way to use ICT as a means of learning due to the time it takes to prepare materials. learning is limited. On the other hand, thanks to Quizizz, preparing to use it does not take much time because Quizizz offers many different features to help teachers learn more easily. Quizizz is a game-based educational app that features fun and free games that entertain you during the learning process. Quizizz training aims to ensure that teachers have equal opportunities to maximize learning using ICT-based media, so that Quizizz can be a solution embedded in the learning media creation framework. IT-based learning takes a lot of time to create an accessible, student-centered environment. The classroom atmosphere in the development of critical thinking (critical thinking), cooperation (collaboration), communication (communication) and creativity (creativity) are called the 4Cs.
The Anti-Money Laundering Challenges of FinTech and Cryptocurrencies

In the aftermath of the 2008 global financial crisis, the foundation was developed for the protocol we know as blockchain. Blockchain is a system through which money can be sent from one person to another without using any financial service providers. It has been argued that the security of blockchain technology could be the answer to people’s mistrust and lost confidence in the financial market. However, blockchain technology and cryptocurrencies go much further than a mere transfer of money. New forms of value exchange have been created, in addition to providing access to financial services in locations where only limited services are offered by traditional banks. As the use of such innovation gains popularity, the legislator is slowly catching up, but how much is covered? This paper studies FinTech and cryptocurrencies in respect of money laundering. It is found that the current regulatory landscape has several weaknesses that can be attractive for those wishing to exploit them.
Prosodic markers of indian seamen’ speech behavior

International Scientific Conference Philological sciences and translation studies: European potential : Conference Proceedings (September 6–7, 2023. Wloclawek, the Republic of Poland). Riga, Latvia : Baltija Publishing, 2023. 240 pages.
RANCANG BANGUN E-INVENTORY SPARE PART KAPAL BERBASIS CODEIGNITER PADA PT PELAYARAN NASIONAL SANDICO OCEAN LINE BATAM

With the rapid advancement of information technology, computers have become highly useful devices for humans in various fields. In the context of companies, an inventory system is needed to ensure smooth operational activities. Inventory encompasses a complete list of items owned by an office, institution, factory, or company, and is used to carry out specific tasks. PT Pelayaran Nasional Sandico Ocean Line Batam is one company that still uses a manual or conventional inventory system to record ship spare parts inventory. Therefore, a web-based information system is proposed to replace the existing system. In the development of this website, the PHP programming language is used to create dynamic and interactive web pages. For the framework, Codeigniter is chosen based on the previously explained concepts. Codeigniter is selected as one of the best frameworks for building web applications because it provides various tools that aid in faster development, a stable structure, and ease of maintenance. The development of this Codeigniter-based website aims to assist developers in efficiently building web applications. By using Codeigniter, developers can easily handle complex PHP code, seamlessly integrate components, and achieve useful functionality with simple configuration.&#x0D;
AI-based extraction of requirements from regulations for automotive engineering

Automotive engineering requires compliance with regulations for certification. In specifications, regulations are referenced, which need to be analyzed manually to elicit requirements. This process is time-consuming and leads to high costs. The aim of this research is to evaluate artificial intelligence (AI) models in terms of extracting requirements automatically from regulations. Relevant AI models are identified in a systematic literature analysis and evaluated using success criteria. The most promising AI models are implemented in a pipeline for requirements extraction. The performance of these AI models is assessed in a comparative study using automotive regulations. The results show which AI models are best suited for this task.