Efektivitas Layanan Informasi dengan Format Klasikal Menggunakan Metode Small Group Discussion untuk Meningkatkan Self-Efficacy Akademik Siswa SMA Negeri 10 Padang

Abstract: Academic self-efficacy is one of the many factors that can affect student learning achievement. Someone who has good academic self-efficacy, will feel that he is always ready and alert in completing his academic tasks without any doubts about his condition. in completing their academic tasks without any doubts about their own condition. This study aims to see the effectiveness of the implementation of information services with a classical format using the small group discussion method to improve students' academic self-efficacy. This research uses a quantitative approach, with the type of research Quasi Experiment design The Non-Equivalent Control Group. The population in this study were Phase E students of SMA Negeri 10 Padang, sampling using purposive sampling method, then obtained samples of Phase E 4 and Phase E 9 classes. The technique used in data collection is by distributing questionnaires, then analyzed using the Wilcoxon Signed Ranks Test, and Mann-Whitney U 2 Independent Sample Test. The results of this study indicate that the implementation of information services with a classical format using the small group discussion method is effective in increasing students' academic self-efficacy. Specifically, the use of this method increases better academic self-efficacy.


Healthcare : A Transformer Network Based Chatbot

Abstract: Medical chatbots are becoming more and more common as the healthcare industry changes and places more and more focus on automated and remote services. These digital assistants provide cost savings, prompt answers to healthcare inquiries, and access to medical guidance around-the-clock. The effectiveness of these chatbots depends on how well-informed they are about healthcare. Our project's main goal is to employ Transformer network architecture to create a sophisticated chatbot for healthcare. It includes gathering data, choosing a model, recognizing intent and entities, managing conversations, producing responses, integrating knowledge databases, creating user-friendly designs, doing thorough testing, adhering to regulations, and continuously improving. Our objective is to develop a smart conversational agent that can respond to a broad range of healthcare inquiries, provide advice and recommendations tailored to individual diseases, and eventually improve patient satisfaction and healthcare experiences


ELAD: Explanation-Guided Large Language Models Active Distillation

Abstract: The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences. Traditional distillation methods, which transfer the capabilities of LLMs to smaller models, often fail to determine whether the knowledge has been sufficiently transferred, potentially resulting in high costs or incomplete distillation. In this paper, we propose an Explanation-Guided LLMs Active Distillation (ELAD) framework that employs an active learning strategy to optimize the balance between annotation costs and model performance. To improve efficient sample selection, we introduce an explanation-guided sample selection method that identifies samples challenging its reasoning by exploiting uncertainties in explanation steps. Additionally, we present a customized LLM-annotated explanation revision technique where the teacher model detects and corrects flaws in the student model's reasoning. Our experiments across various reasoning datasets demonstrate that our framework significantly enhances the efficiency of LLM knowledge distillation.


General multi-attack detection for continuous-variable quantum key distribution with local local oscillator

Abstract: Abstract Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD) has been extensively researched due to its simplicity and security. For the practical security of the LLO CVQKD system, there are two main attack modes referred to as reference pulse attack and polarization attack presently. However, there is currently no general defense strategy against such attacks and the security of the system needs further investigation. Here, we employ a deep learning framework called generative adversarial networks (GANs) to detect both attacks. We first analyze the data in different cases, derive a feature vector as input to a GAN model, and then show the training and testing process of the GAN model for attack classification. The proposed model has two parts, a discriminator and a generator, both of which employ a convolutional neural network (CNN) to improve accuracy. Simulation results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission distance. It only establishes a detection model by monitoring features of the pulse without adding additional devices.


A Bibliometric Analysis of Industry-Education Integration Research Based on Web of Science

Abstract: This study aims to gain a deeper understanding of the research dynamics and trends in industry-education integration through a quantitative analysis of the literature in this field. Eight hundred ninety-five relevant articles were retrieved from the Web of Science Core Collection database, and the research focused on the annual publication trends, prominent authors, institutions and countries, highly cited articles, keywords, and collaborative networks. The study found that the field of industry-education integration has undergone three stages. Secondly, researchers from China and the United States ranked first and second in the number of publications in this field. Thirdly, highly cited articles have a particular influence on industry-education integration, but the overall citation count is relatively limited. Fourthly, keywords such as “cooperative education,” “students,” and “performance” are hot topics in this field. Finally, the analysis of collaborative networks revealed the existence of multiple communities, but the overall scale of collaboration is relatively small, and the influence is limited. This study provides profound insights into the academic research in industry-education integration. It helps scholars, policymakers, and practitioners better understand this field’s development trends.


Supplemental Material: Genetic association between carbonates and gold precipitation mechanisms in the Jinshan deposit, eastern Jiangnan orogen

Abstract: Thermodynamic database for Geochemist's Workbench (GWB).


GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a  Gradient-Aware Mask and Semantic Constraints

Abstract: Self-supervised depth estimation has evolved into an image reconstruction task that minimizes a photometric loss. While recent methods have made strides in indoor depth estimation, they often produce inconsistent depth estimation in textureless areas and unsatisfactory depth discrepancies at object boundaries. To address these issues, in this work, we propose GAM-Depth, developed upon two novel components: gradient-aware mask and semantic constraints. The gradient-aware mask enables adaptive and robust supervision for both key areas and textureless regions by allocating weights based on gradient magnitudes.The incorporation of semantic constraints for indoor self-supervised depth estimation improves depth discrepancies at object boundaries, leveraging a co-optimization network and proxy semantic labels derived from a pretrained segmentation model. Experimental studies on three indoor datasets, including NYUv2, ScanNet, and InteriorNet, show that GAM-Depth outperforms existing methods and achieves state-of-the-art performance, signifying a meaningful step forward in indoor depth estimation. Our code will be available at https://github.com/AnqiCheng1234/GAM-Depth.


Global Safe Sequential Learning via Efficient Knowledge Transfer

Abstract: Sequential learning methods such as active learning and Bayesian optimization select the most informative data to learn about a task. In many medical or engineering applications, the data selection is constrained by a priori unknown safety conditions. A promissing line of safe learning methods utilize Gaussian processes (GPs) to model the safety probability and perform data selection in areas with high safety confidence. However, accurate safety modeling requires prior knowledge or consumes data. In addition, the safety confidence centers around the given observations which leads to local exploration. As transferable source knowledge is often available in safety critical experiments, we propose to consider transfer safe sequential learning to accelerate the learning of safety. We further consider a pre-computation of source components to reduce the additional computational load that is introduced by incorporating source data. In this paper, we theoretically analyze the maximum explorable safe regions of conventional safe learning methods. Furthermore, we empirically demonstrate that our approach 1) learns a task with lower data consumption, 2) globally explores multiple disjoint safe regions under guidance of the source knowledge, and 3) operates with computation comparable to conventional safe learning methods.


Unveiling Linguistic Regions in Large Language Models

Abstract: Large Language Models (LLMs) have demonstrated considerable cross-lingual alignment and generalization ability. Current research primarily focuses on improving LLMs' cross-lingual generalization capabilities. However, there is still a lack of research on the intrinsic mechanisms of how LLMs achieve cross-lingual alignment. From the perspective of region partitioning, this paper conducts several investigations on the linguistic competence of LLMs. We discover a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters. Removing this core region by setting parameters to zero results in a significant performance decrease across 30 different languages. Furthermore, this core region exhibits significant dimensional dependency, perturbations to even a single parameter on specific dimensions leading to a loss of linguistic competence. Moreover, we discover that distinct regions exist for different monolingual families, and disruption to these specific regions substantially reduces the LLMs' proficiency in those corresponding languages. Our research also indicates that freezing the core linguistic region during further pre-training can mitigate the issue of catastrophic forgetting (CF), a common occurrence observed during further pre-training of LLMs. Overall, exploring the LLMs' functional regions provides insights into the foundation of their intelligence.


AgentScope: A Flexible yet Robust Multi-Agent Platform

Abstract: With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooperation and LLMs' erratic performance pose notable challenges in developing robust and efficient multi-agent applications. To tackle these challenges, we propose AgentScope, a developer-centric multi-agent platform with message exchange as its core communication mechanism. Together with abundant syntactic tools, built-in resources, and user-friendly interactions, our communication mechanism significantly reduces the barriers to both development and understanding. Towards robust and flexible multi-agent application, AgentScope provides both built-in and customizable fault tolerance mechanisms while it is also armed with system-level supports for multi-modal data generation, storage and transmission. Additionally, we design an actor-based distribution framework, enabling easy conversion between local and distributed deployments and automatic parallel optimization without extra effort. With these features, AgentScope empowers developers to build applications that fully realize the potential of intelligent agents. We have released AgentScope at https://github.com/modelscope/agentscope, and hope AgentScope invites wider participation and innovation in this fast-moving field.


Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study

Abstract: Cyber-attacks have become increasingly prevalent with the widespread integration of technology into various aspects of our lives. The surge in social media platform usage has prompted users to share their firsthand experiences with cyber-attacks. Despite this, previous literature has not extensively investigated individuals' experiences with these attacks. This study aims to comprehensively explore and analyze the content shared by cyber-attack victims in Saudi Arabia, encompassing text, video, and audio formats. The primary objective is to investigate the factors influencing victims' perceptions of the security risks associated with these attacks. Following data collection, preparation, and cleaning, Latent Dirichlet Allocation (LDA) is employed for topic modeling, shedding light on potential factors impacting victims. Sentiment analysis is then utilized to examine the nuanced negative and positive perceptions of individuals. NVivo is deployed for data inspection, facilitating the presentation of insightful inferences. Hierarchical clustering is implemented to explore distinct clusters within the textual dataset. The study's results underscore the critical importance of spreading awareness among individuals regarding the various tactics employed by cyber attackers. Doi: 10.28991/ESJ-2024-08-01-010 Full Text: PDF


Holistic and Multidisciplinary Education Approaches in Higher Education NEP 2020

Abstract: Abstract: National Education Policy: 2020 is ground-breaking step taken by Indian Government in the field of Education. We are living in the post modern virtual world where the boundary of the education of a country is kaput and enriched with the affluent global thoughts. Indian education has the greatest heritage and glorious past. It has been altering its form according to the passage of time on the demand of the crisis influenced by social, political and economical issues. The present study will focus on the holistic and multidisciplinary dimension of New Education Policy: 2020. The researcher will discuss how holistic attitude of education plays an important role in the field of ‘all round development’ of the child; and the researcher will find out how much multidisciplinary approach is relevant in contemporary education system in the respect of global education. The researcher has gone through the report of the previous education policies, committees and studied related literature closely which are desirable for this study. The findings of this study will help its readers to understand the context to introduce NEP 2020 by Government; the reader will realize the stream of the change of education policy, he/she will aware previous history of education in brief. The researcher has taken philosophical research method to convey philosophy of education and philosophy of life as well. The novelty of this study is to recognize the importance of the approach of holistic education in modern education system and curriculum. And then the readers will be able to distinguish how much multidisciplinary educational approach is significant for survival which is the ultimate aim of all education. Keywords: holistic approach, multidisciplinary education, all round development, heritage


Stable training via elastic adaptive deep reinforcement learning for autonomous navigation of intelligent vehicles

Abstract: Abstract The uncertain stability of deep reinforcement learning training on complex tasks impedes its development and deployment, especially in intelligent vehicles, such as intelligent surface vessels and self-driving cars. Complex and varied environmental states puzzle training of decision-making networks. Here we propose an elastic adaptive deep reinforcement learning algorithm to address these challenges and achieve autonomous navigation in intelligent vehicles. Our method trains the decision-making network over the function and optimization learning stages, in which the state space and action space of autonomous navigation tasks are pruned by choosing classic states and actions to reduce data similarity, facilitating more stable training. We introduce a task-adaptive observed behaviour classification technique in the function learning stage to divide state and action spaces into subspaces and identify classic states and actions. In which the classic states and actions are accumulated as the training dataset that enhances its training efficiency. In the subsequent optimization learning stage, the decision-making network is refined through meticulous exploration and accumulation of datasets. The proposed elastic adaptive deep reinforcement learning enables the decision-making network to effectively learn from complex state and action spaces, leading to more efficient training compared to traditional deep reinforcement learning approaches. Simulation results demonstrate the remarkable effectiveness of our method in training decision-making networks for intelligent vehicles. The findings validate that our method provides reliable and efficient training for decision-making networks in intelligent vehicles. Moreover, our method exhibits stability in training other tasks characterized by continuous state and action spaces.


Integral Chatbot Solution for Efficient Incident Management and Emergency or Disaster Response: Optimizing Communication and Coordination

Abstract: Climate change, with its unstable and dynamic weather phenomena, has a significant impact on the city of Piura, Peru, making it particularly vulnerable to the El Niño phenomenon. The limited technological infrastructure for communication between local authorities and the community has resulted in decisions that are unacceptable to the population. In order to address this gap and improve coordination during emergency or disaster situations, it is proposed to develop a specialized chatbot that integrates with modern platforms such as WhatsApp, Manychat, and Google Sheets. The solution was evaluated through a questionnaire and an observation guide, addressed to 30 citizens of the districts of Piura, Castilla, and Catacaos, Peru. The degree of satisfaction on the part of the users was 26.38%. In terms of the criterion of clarity, it is 26.44%. While 22.73% said that they needed coherence and fluency in conversation, Lastly, 24.45% corresponded to the level of accuracy in forwarding information and queries. The results of the study highlight the effectiveness of this solution as a digital alternative that not only optimizes incident management but also provides efficient responses and motivates citizens to be prepared through the use of modern technologies.


Blockchain-Based Technology in Corporate Governance

Abstract: Blockchain-based technology has been recognized as a potential tool to transform corporate governance. This chapter first discusses the persisting challenges of agency costs, inefficiencies, and the need for improved transparency. It then examines the emergence of blockchain as a potential solution, offering a novel, decentralized approach to corporate governance. The analysis covers the technology's application to share registers, trading, voting, and accounting practices, emphasizing the duality of its promise for increased transparency and emergent privacy concerns. Controversial aspects, such as the threat to existing shareholder activism and the governance of blockchain itself, are critically examined. Solutions like hybrid blockchain models and regulatory nodes are proposed to address these issues. The chapter concludes with a forward-looking perspective on decentralized autonomous organizations (DAOs) as an innovative alternative to traditional corporate structures, signifying a new paradigm in corporate governance.


LLM Based Multi-Agent Generation of Semi-structured Documents from  Semantic Templates in the Public Administration Domain

Abstract: In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse. This complexity arises from the need to handle a wide range of document types, often characterized by semi-structured forms. Semi-structured documents present a fixed set of data without a fixed format. As a consequence, a template-based solution cannot be used, as understanding a document requires the extraction of the data structure. The recent introduction of Large Language Models (LLMs) has enabled the creation of customized text output satisfying user requests. In this work, we propose a novel approach that combines the LLMs with prompt engineering and multi-agent systems for generating new documents compliant with a desired structure. The main contribution of this work concerns replacing the commonly used manual prompting with a task description generated by semantic retrieval from an LLM. The potential of this approach is demonstrated through a series of experiments and case studies, showcasing its effectiveness in real-world PA scenarios.


Gotcha! Don't trick me with unanswerable questions! Self-aligning Large  Language Models for Responding to Unknown Questions

Abstract: Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ a two-stage class-aware self-augmentation approach to generate a large amount of unknown question-response data. Then we conduct disparity-driven self-curation to select qualified data for fine-tuning the LLM itself for aligning the responses to unknown questions as desired. Experimental results on two datasets across four types of unknown questions validate the superiority of the Self-Align method over existing baselines in terms of three types of task formulation.


Machine Unlearning by Suppressing Sample Contribution

Abstract: Machine Unlearning (MU) is to forget data from a well-trained model, which is practically important due to the "right to be forgotten". In this paper, we start from the fundamental distinction between training data and unseen data on their contribution to the model: the training data contributes to the final model while the unseen data does not. We theoretically discover that the input sensitivity can approximately measure the contribution and practically design an algorithm, called MU-Mis (machine unlearning via minimizing input sensitivity), to suppress the contribution of the forgetting data. Experimental results demonstrate that MU-Mis outperforms state-of-the-art MU methods significantly. Additionally, MU-Mis aligns more closely with the application of MU as it does not require the use of remaining data.


On the composable security of weak coin flipping

Abstract: Weak coin flipping is a cryptographic primitive in which two mutually distrustful parties generate a shared random bit to agree on a winner via remote communication. While a stand-alone secure weak coin flipping protocol can be constructed from noiseless communication channels, its composability has not been explored. In this work, we demonstrate that no weak coin flipping protocol can be abstracted into a black box resource with composable security. Despite this, we also establish the overall stand-alone security of weak coin flipping protocols under sequential composition.


A Blockchain-Enabled Framework of UAV Coordination for Post-Disaster  Networks

Abstract: Emergency communication is critical but challenging after natural disasters when ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) offer enormous potential for agile relief coordination in these scenarios. However, effectively leveraging UAV fleets poses additional challenges around security, privacy, and efficient collaboration across response agencies. This paper presents a robust blockchain-enabled framework to address these challenges by integrating a consortium blockchain model, smart contracts, and cryptographic techniques to securely coordinate UAV fleets for disaster response. Specifically, we make two key contributions: a consortium blockchain architecture for secure and private multi-agency coordination; and an optimized consensus protocol balancing efficiency and fault tolerance using a delegated proof of stake practical byzantine fault tolerance (DPoS-PBFT). Comprehensive simulations showcase the framework's ability to enhance transparency, automation, scalability, and cyber-attack resilience for UAV coordination in post-disaster networks.


A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model

Abstract: In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.


Leveraging Bidirectionl LSTM with CRFs for Pashto tagging

Abstract: Part-of-speech tagging plays a vital role in text processing and natural language understanding. Very few attempts have been made in the past for tagging Pashto Part-of-Speech. In this work, we present LSTM based approach for Pashto part-of-speech tagging with special focus on ambiguity resolution. Initially we created a corpus of Pashto sentences having words with multiple meanings and their tags. We introduce a powerful sentences representation and new architecture for Pashto text processing. The accuracy of the proposed approach is compared with state-of-the-art Hidden Markov Model. Our Model shows 87.60% accuracy for all words excluding punctuations and 95.45% for ambiguous words, on the other hand Hidden Markov Model shows 78.37% and 44.72% accuracy respectively. Results show that our approach outperform Hidden Markov Model in Part-of-Speech tagging for Pashto text.


Data Stream Processing & Integrated Research Infrastructure Across Facilities

Abstract: degradation. We then ameliorate bad behavior by throttling these processes. We demonstrate that problematic performance interference can be detected and acted on, which makes it possible to continue to share resources between applications and simultaneously maintain high utilization levels in a computing cluster. We relocate the CLAS12 data processing workflow to a remote data center for a case study, preventing file migration and temporal data persistency.


How Can LLM Guide RL? A Value-Based Approach

Abstract: Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorithms may require extensive trial-and-error interactions to collect useful feedback for improvement. On the other hand, recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities for planning tasks, lacking the ability to autonomously refine their responses based on feedback. Therefore, in this paper, we study how the policy prior provided by the LLM can enhance the sample efficiency of RL algorithms. Specifically, we develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning, particularly when the difference between the ideal policy and the LLM-informed policy is small, which suggests that the initial policy is close to optimal, reducing the need for further exploration. Additionally, we present a practical algorithm SLINVIT that simplifies the construction of the value function and employs subgoals to reduce the search complexity. Our experiments across three interactive environments ALFWorld, InterCode, and BlocksWorld demonstrate that our method achieves state-of-the-art success rates and also surpasses previous RL and LLM approaches in terms of sample efficiency. Our code is available at https://github.com/agentification/Language-Integrated-VI.


Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation

Abstract: Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial learning, etc. In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL). Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible, and we hope to leverage these external datasets to assist nucleus instance segmentation on the target dataset which only has very limited annotation. To achieve this goal, we adopt the meta-learning based FSL paradigm, which however has to be tailored in two substantial aspects before adapting to our task. First, since the novel classes may be inconsistent with those of the external dataset, we extend the basic definition of few-shot instance segmentation (FSIS) to generalized few-shot instance segmentation (GFSIS). Second, to cope with the intrinsic challenges of nucleus segmentation, including touching between adjacent cells, cellular heterogeneity, etc., we further introduce a structural guidance mechanism into the GFSIS network, finally leading to a unified Structurally-Guided Generalized Few-Shot Instance Segmentation (SGFSIS) framework. Extensive experiments on a couple of publicly accessible datasets demonstrate that, SGFSIS can outperform other annotation-efficient learning baselines, including semi-supervised learning, simple transfer learning, etc., with comparable performance to fully supervised learning with less than 5% annotations.


Pre-training Cross-lingual Open Domain Question Answering with  Large-scale Synthetic Supervision

Abstract: Cross-lingual question answering (CLQA) is a complex problem, comprising cross-lingual retrieval from a multilingual knowledge base, followed by answer generation either in English or the query language. Both steps are usually tackled by separate models, requiring substantial annotated datasets, and typically auxiliary resources, like machine translation systems to bridge between languages. In this paper, we show that CLQA can be addressed using a single encoder-decoder model. To effectively train this model, we propose a self-supervised method based on exploiting the cross-lingual link structure within Wikipedia. We demonstrate how linked Wikipedia pages can be used to synthesise supervisory signals for cross-lingual retrieval, through a form of cloze query, and generate more natural queries to supervise answer generation. Together, we show our approach, \texttt{CLASS}, outperforms comparable methods on both supervised and zero-shot language adaptation settings, including those using machine translation.


Towards Efficient Quantum Computing for Quantum Chemistry: Reducing  Circuit Complexity with Transcorrelated and Adaptive Ansatz Techniques

Abstract: The near-term utility of quantum computers is hindered by hardware constraints in the form of noise. One path to achieving noise resilience in hybrid quantum algorithms is to decrease the required circuit depth -- the number of applied gates -- to solve a given problem. This work demonstrates how to reduce circuit depth by combining the transcorrelated (TC) approach with adaptive quantum ans\"atze and their implementations in the context of variational quantum imaginary time evolution (AVQITE). The combined TC-AVQITE method is used to calculate ground state energies across the potential energy surfaces of H$_4$, LiH, and H$_2$O. In particular, H$_4$ is a notoriously difficult case where unitary coupled cluster theory, including singles and doubles excitations, fails to provide accurate results. Adding TC yields energies close to the complete basis set (CBS) limit while reducing the number of necessary operators -- and thus circuit depth -- in the adaptive ans\"atze. The reduced circuit depth furthermore makes our algorithm more noise-resilient and accelerates convergence. Our study demonstrates that combining the TC method with adaptive ans\"atze yields compact, noise-resilient, and easy-to-optimize quantum circuits that yield accurate quantum chemistry results close to the CBS limit.


IRConStyle: Image Restoration Framework Using Contrastive Learning and  Style Transfer

Abstract: Recently, the contrastive learning paradigm has achieved remarkable success in high-level tasks such as classification, detection, and segmentation. However, contrastive learning applied in low-level tasks, like image restoration, is limited, and its effectiveness is uncertain. This raises a question: Why does the contrastive learning paradigm not yield satisfactory results in image restoration? In this paper, we conduct in-depth analyses and propose three guidelines to address the above question. In addition, inspired by style transfer and based on contrastive learning, we propose a novel module for image restoration called \textbf{ConStyle}, which can be efficiently integrated into any U-Net structure network. By leveraging the flexibility of ConStyle, we develop a \textbf{general restoration network} for image restoration. ConStyle and the general restoration network together form an image restoration framework, namely \textbf{IRConStyle}. To demonstrate the capability and compatibility of ConStyle, we replace the general restoration network with transformer-based, CNN-based, and MLP-based networks, respectively. We perform extensive experiments on various image restoration tasks, including denoising, deblurring, deraining, and dehazing. The results on 19 benchmarks demonstrate that ConStyle can be integrated with any U-Net-based network and significantly enhance performance. For instance, ConStyle NAFNet significantly outperforms the original NAFNet on SOTS outdoor (dehazing) and Rain100H (deraining) datasets, with PSNR improvements of 4.16 dB and 3.58 dB with 85% fewer parameters.


Chimera: A Lossless Decoding Method for Accelerating Large Language  Models Inference by Fusing all Tokens

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their widespread application is hindered by the resource-intensive decoding process. To address this challenge, current approaches have incorporated additional decoding heads to enable parallel prediction of multiple subsequent tokens, thereby achieving inference acceleration. Nevertheless, the accuracy of these decoding heads falls short of the auto-regressive decoding approach. In light of these limitations, we propose Chimera, a novel framework specifically designed for speculative sampling. Within this framework, we introduce a lightweight draft model that effectively utilizes previously generated tokens to predict subsequent words. To ensure both accuracy and efficiency, we present two strategies within the lightweight draft model. Firstly, we focus on capturing short-range dependencies at the bottom layer. Secondly, we leverage the readily available representations from the original LLM.Through empirical evaluation on the Vicuna and LlaMA-2 series, Chimera demonstrates impressive results, achieving an average latency speedup ratio of 2.7x compared to the vanilla auto-regressive decoding approach. This highlights the potential of our proposed framework in significantly improving the efficiency of large language models during the decoding process.


SportQA: A Benchmark for Sports Understanding in Large Language Models

Abstract: A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP). This holds particular significance in the context of evaluating and advancing Large Language Models (LLMs), given the existing gap in specialized benchmarks. To bridge this gap, we introduce SportQA, a novel benchmark specifically designed for evaluating LLMs in the context of sports understanding. SportQA encompasses over 70,000 multiple-choice questions across three distinct difficulty levels, each targeting different aspects of sports knowledge from basic historical facts to intricate, scenario-based reasoning tasks. We conducted a thorough evaluation of prevalent LLMs, mainly utilizing few-shot learning paradigms supplemented by chain-of-thought (CoT) prompting. Our results reveal that while LLMs exhibit competent performance in basic sports knowledge, they struggle with more complex, scenario-based sports reasoning, lagging behind human expertise. The introduction of SportQA marks a significant step forward in NLP, offering a tool for assessing and enhancing sports understanding in LLMs.


MATHWELL: Generating Educational Math Word Problems at Scale

Abstract: Math word problems are critical K-8 educational tools, but writing them is time-consuming and requires domain expertise. We suggest that language models can support K-8 math education by automatically generating problems at scale. To be educational, generated problems must be 1) solvable, 2) accurate, and 3) appropriate. Existing datasets are unlabeled for these criteria, making them ill-suited for training problem generators. We introduce MATHWELL, a Llama-2 (70B) model iteratively finetuned to generate K-8 math word problems using data from expert annotation. Using MATHWELL, we generate the largest English word problem dataset to date, containing 20,490 problems. 3,484 are scored by domain experts who find MATHWELL has a 40% higher share of problems that have executable solutions and meet all criteria than alternatives, with 74% of its problems with executable solutions being solvable, accurate, and appropriate.


GraphWiz: An Instruction-Following Language Model for Graph Problems

Abstract: Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored. To bridge this gap, we introduce GraphInstruct, a novel and comprehensive instruction-tuning dataset designed to equip language models with the ability to tackle a broad spectrum of graph problems using explicit reasoning paths. Utilizing GraphInstruct, we build GraphWiz, an open-source language model capable of resolving various graph problem types while generating clear reasoning processes. To enhance the model's capability and reliability, we incorporate the Direct Preference Optimization (DPO) framework into the graph problem-solving context. The enhanced model, GraphWiz-DPO, achieves an average accuracy of 65% across nine tasks with different complexity levels, surpassing GPT-4 which has an average accuracy of 43.8%. Moreover, our research delves into the delicate balance between training data volume and model performance, highlighting the potential for overfitting with increased data. We also explore the transferability of the model's reasoning ability across different graph tasks, indicating the model's adaptability and practical application potential. Our investigation offers a new blueprint and valuable insights for developing LLMs specialized in graph reasoning and problem-solving.


Towards Accurate Post-training Quantization for Reparameterized Models

Abstract: Model reparameterization is a widely accepted technique for improving inference speed without compromising performance. However, current Post-training Quantization (PTQ) methods often lead to significant accuracy degradation when applied to reparameterized models. This is primarily caused by channel-specific and sample-specific outliers, which appear only at specific samples and channels and impact on the selection of quantization parameters. To address this issue, we propose RepAPQ, a novel framework that preserves the accuracy of quantized reparameterization models. Different from previous frameworks using Mean Squared Error (MSE) as a measurement, we utilize Mean Absolute Error (MAE) to mitigate the influence of outliers on quantization parameters. Our framework comprises two main components: Quantization Protecting Reparameterization and Across-block Calibration. For effective calibration, Quantization Protecting Reparameterization combines multiple branches into a single convolution with an affine layer. During training, the affine layer accelerates convergence and amplifies the output of the convolution to better accommodate samples with outliers. Additionally, Across-block Calibration leverages the measurement of stage output as supervision to address the gradient problem introduced by MAE and enhance the interlayer correlation with quantization parameters. Comprehensive experiments demonstrate the effectiveness of RepAPQ across various models and tasks. Our framework outperforms previous methods by approximately 1\% for 8-bit PTQ and 2\% for 6-bit PTQ, showcasing its superior performance. The code is available at \url{https://github.com/ilur98/DLMC-QUANT}.


Synergizing edge computing and blockchain for <scp>cyber‐physical</scp> systems

Abstract: Summary As a foundational technology for managing decentralized systems like smart grids and healthcare systems, blockchain is attracting a lot of interest. However, owing to excessive resource requirements and low scalability with frequent‐intensive transactions, its use in resource‐constrained mobile devices is restricted. To let mobile devices offload processing tasks to cloud resources, edge computing can be used. For scalability and secure transactions, the integration with edge computing guarantees consistent access, distributed computing, and non‐compromised storage. To accomplish effective integration, critical challenges such as reliability, adaptability, and resource planning must be resolved. Despite these efforts, further study is still needed to address issues with confidentiality, flexibility, and reliability to develop a workable, secure decentralized data storage system. This study utilizes edge computing to build an Internet of Things (IoT) architecture that satisfies the safety and scalability necessary criteria for integration along with the integration of peer‐to‐peer and blockchain technologies. Already‐existing blockchain and related technologies have also been investigated to suggest solutions that address issues like secrecy, reliability, and scalability in order to successfully incorporate blockchain in IoT systems. Our experimental findings indicate that achieving low rates of stale blocks and promoting decentralization requires a meticulous selection of block sizes and generation intervals. It is essential to maintain block creation intervals as short as practically possible, and utilizing blocks with a size below 1 MB is advisable.


PENGARUH KECERDASAN EMOSI TERHADAP KECEMASAN MENGHADAPI ULANGAN AKHIR SEMESTER PADA SISWA KELAS X SMK NEGERI 3 YOGYAKARTA [CORRELATION BETWEEN EMOTIONAL INTELLIGENCE WITH ANXIETY ON PREPARING FINAL SEMESTER EXAM FOR TENTH GRADE STUDENTS OF VOCATIONAL HIGH SCHOOL 3 YOGYAKARTA]

Abstract: This research aims to examine the influence of emotional intelligence on anxiety facing the Final Semester Examination in Class X students at SMK Negeri 3 Yogyakarta. This research is correlational research using a quantitative approach. The research sampling technique used random sampling technique. Data collection instruments used an emotional intelligence level scale and an anxiety scale for facing end-of-semester exams. This research concludes that there is a significant relationship between the level of emotional intelligence and the level of anxiety facing the final semester exams in class X students at SMK Negeri 3 Yogyakarta. The correlation coefficient value is -0.447 and the determination coefficient is 0.200.


Graph-CNN Hybrid Advance Model for Accurate Anomaly Detection in Multivariate Time Series IoT Streams

Abstract: Abstract The Internet of Things (IoT) has revolutionized various industries by enabling the collection of vast amounts of data from interconnected devices. However, the sheer volume and complexity of IoT data pose significant challenges for anomaly detection, especially in multivariate time series data. Traditional methods struggle to effectively capture the intricate relationships and temporal dependencies within such data. In response to these challenges, this paper introduces a pioneering approach that harnesses the combined power of Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) for anomaly detection in multivariate time series IoT data. The novelty of our approach lies in its holistic integration of GNNs and CNNs, enabling the comprehensive analysis of both global dependencies and local temporal features within the IoT data. Through rigorous experimentation on real-world datasets, showcases the efficacy and superiority of our method over existing state-of-the-art techniques. Our approach consistently achieves superior performance metrics, compared to state-of-the-art methods. Underscoring its effectiveness in accurately detecting anomalies amidst the complexities of IoT data.


Network, Cryptography, and the Next World to Come: An Everlasting American Techno Millenarianism?

Abstract: To understand the nexus between networked cryptography and American religiosity, it is essential to embed this inquiry within the broader context of religious imaginaries associated with communication networks in nineteenth-century North America and their evolution into the spiritual landscape of the twentieth century. Throughout United States history, network imaginaries have exerted a substantial influence, engaging in a continuous dialogue with the evolution of religious thought, attributing eschatological expectations to networks, and ascribing them with a profound sense of ultimate purpose and significance. This investigation finds its ultimate context within the early techno-libertarian communities of 1990s California, pivotal in shaping the design, development, and objectives of networked cryptography online, ultimately giving birth to cryptocurrencies and the blockchain protocol.


Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Abstract: Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the collaborative Kriging spatial interpolation method based on Kriging spatial interpolation is adopted to process the rainfall data into multi-index fused surface data. The rainfall data after spatial interpolation are used as the input sample data of the support vector machine early warning model, and the optimal parameters of the support vector machine are calculated by the sea squirt algorithm, and then the debris flow early warning results are output. After experimental analysis, the model can obtain rainfall surface data. After calculation by the model, the accuracy of the early warning probability of debris flow is improved, and the early warning result is consistent with the actual result of debris flow.


Abstract: Graph Convolutional Network (GCN) has drawn widespread attention in data mining on graphs due to its outstanding performance and rigor theoretical guarantee. However, some recent studies have revealed that GCN-based methods may mine latent information insufficiently owing to the underutilization of the feature space. Besides, the unlearnable topology also significantly imperils the performance of GCN-based methods. In this paper, we conduct experiments to investigate these issues, finding that GCN does not fully consider the potential structure in the feature space, and a fixed topology deteriorates the robustness of GCN. Thus, it is desired to distill node features and establish a learnable graph. Motivated by this goal, we propose a framework dubbed Graph Convolutional Network with elastic topology (GCNet1). With the analysis of the optimization for the proposed flexible Laplacian embedding, GCNet is naturally constructed by alternative graph convolutional layers and adaptive topology learning layers. GCNet aims to deeply explore the feature space and employ the mined information to construct a learnable topology, which leads to a more robust graph representation. In addition, a set-level orthogonal loss is utilized to meet the orthogonal constraint required by the flexible Laplacian embedding and promote better class separability. Moreover, comprehensive experiments indicate that GCNet achieves remarkable performance and generalization on several real-world datasets.


Boosting constrained teaching learning based optimization algorithm through group discussion

Abstract: Teaching learning based optimization algorithm (TLBO) is an efficient population-based nature-inspired meta-heuristic designed initially to solve unconstrained optimization problems. TLBO being a swam intelligence based algorithm is renowned for fast convergence. This characteristic, however, occasionally causes it to converge too soon to a non-globally optimal solution. Group discussion techniques are used in educational institutions all over the world to help participants to exchange ideas and information and to improve their skills. This work imitates the outlined strategy, therefore innovating TLBO. Here, a group of virtual students is selected corresponding to each student in the learner phase of TLBO to produce diversity in the population and to balance the exploration and exploitation capabilities of it, hence reducing the likelihood of early convergence. GTLBO stands for the new algorithm that incorporates group discussion. It is essential to incorporate some constraint handling techniques (CHTs) into the TLBO framework in order to handle constrained optimization problems (COPs). The most recent, effective, and enhanced version of the widely used CHT stochastic ranking (SR) is called hybrid stochastic ranking (HSR). HSR is added to the GTLBO structure as a selection operator for managing COPs; the resulting setup is referred to as HSR-GTLBO. Additionally, the suggested algorithm’s group size for discussion is sensitive, resulting in a number of constrained variants. The efficacy of these variations is assessed with the suit CEC 2017’s constrained problems. In conformity with the parameters settlement, comparison, and ranking criteria of CEC 2017, the simulation results of the proposed versions are compared and ranked using the most advanced state-of-the-art algorithms. As a result, in the competition, the newly developed HSR-GTLBO variants placed first, second, and third.


Investigating the Robustness of Vision Transformers against Label Noise  in Medical Image Classification

Abstract: Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability. The test performance of a model tends to decrease as the label noise rate increases. Over recent years, several methods have been proposed to mitigate the impact of label noise in medical image classification and enhance the robustness of the model. Predominantly, these works have employed CNN-based architectures as the backbone of their classifiers for feature extraction. However, in recent years, Vision Transformer (ViT)-based backbones have replaced CNNs, demonstrating improved performance and a greater ability to learn more generalizable features, especially when the dataset is large. Nevertheless, no prior work has rigorously investigated how transformer-based backbones handle the impact of label noise in medical image classification. In this paper, we investigate the architectural robustness of ViT against label noise and compare it to that of CNNs. We use two medical image classification datasets -- COVID-DU-Ex, and NCT-CRC-HE-100K -- both corrupted by injecting label noise at various rates. Additionally, we show that pretraining is crucial for ensuring ViT's improved robustness against label noise in supervised training.


Probing anyonic statistics via Mach-Zehnder interferometry in quantum  computers

Abstract: We introduce a synthetic Mach-Zehnder interferometer for digitized quantum computing devices to probe fractional exchange statistics of anyonic excitations that appear in quantum spin liquids. Employing an IonQ quantum computer, we apply this scheme to the toric ladder, a quasi-one-dimensional reduction of the toric code. We observe interference patterns resulting from the movement of `electric' excitations in the presence and absence of `magnetic' ones. We model the noise in IonQ via depolarizing Lindbladian dynamics, and find quantitative agreement with the measurements obtained from the quantum device. The synthetic Mach-Zehnder interferometer can thus also serve as an effective means to probe the coherence length and time scales of multi-qubit noisy quantum devices.


On the probability of a Pareto record

Abstract: Given a sequence of independent random vectors taking values in ${\mathbb R}^d$ and having common continuous distribution $F$, say that the $n^{\rm \scriptsize th}$ observation sets a (Pareto) record if it is not dominated (in every coordinate) by any preceding observation. Let $p_n(F) \equiv p_{n, d}(F)$ denote the probability that the $n^{\rm \scriptsize th}$ observation sets a record. There are many interesting questions to address concerning $p_n$ and multivariate records more generally, but this short paper focuses on how $p_n$ varies with $F$, particularly if, under $F$, the coordinates exhibit negative dependence or positive dependence (rather than independence, a more-studied case). We introduce new notions of negative and positive dependence ideally suited for such a study, called negative record-setting probability dependence (NRSPD) and positive record-setting probability dependence (PRSPD), relate these notions to existing notions of dependence, and for fixed $d \geq 2$ and $n \geq 1$ prove that the image of the mapping $p_n$ on the domain of NRSPD (respectively, PRSPD) distributions is $[p^*_n, 1]$ (resp., $[n^{-1}, p^*_n]$), where $p^*_n$ is the record-setting probability for any $F$ governing independent coordinates.


A Cardiac Audio Classification Method Based on Multidimensional Feature Expression

Abstract: Abstract Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in automatic heart sound classification; however, most methods are based on segmentation features and traditional classifiers, and do not fully use existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time, frequency and statistical domains. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional plane are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2-d coordinate representations. Finally, the one-dimensional feature component value and its divergence from the two categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is subsequently sent to a deep network such as ResNet50 for classification tasks. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that the proposed method can achieve a classification accuracy of 95.72% for the two-classification task of heart sounds when combined with the current deep network.


"Whisper and Write Game” as Pre-Writing Activity to Enhance Descriptive Writing Proficiency of Indonesian Seventh Graders

Abstract: Pre-writing is a preparation process before writing where students gather ideas, organize information, and plan their writing so that pre-writing has a significant influence on student writing outcomes. This study aims to analyze the effect of using whisper and write games as students' pre-writing activities on the descriptive writing abilities of the seventh-grade students at MTs Mamba'us Sholihin Gresik. This research uses class action research (CAR). The results showed that the improvement was seen from the cycle I and cycle II learning that had been implemented, comparing between cycle I and cycle II. The increase in learning outcomes was 76.19%, the increase obtained from the evaluation results in the first cycle was 14.28% and the second cycle was 90.47%. the learning outcomes of the last cycle show that the mastery of learning exceeds the KKM of 75%. Based on the results of the study it was explained that the use of whispering and writing as pre-writing activity games can help to understand and improve students' skills in writing descriptive text. and this can be seen from the increase in the percentage of student scores in each cycle. and the application of CAR can help students achieve the KKM target. The author admits that the writing of this research has several limitations. The limitation of this study is the inappropriate timing of the research because many agendas clashed with the school, so it is hoped that further research can adjust the time to be appropriate.


Emerging Trends and Innovations in Blockchain-Digital Twin Integration for Green Investments

Abstract: This chapter explores the dynamic interaction between blockchain technology and digital twins in the context of green investing. It examines the revolutionary influence of the synergy of these cutting-edge technologies on the efficiency, transparency, and dependability of sustainable investing methods. The chapter begins with an informative introduction to the fundamentals of blockchain and digital twins, illuminating their separate functions before revealing their joint potential for strengthening green investment initiatives. A detailed assessment of the most recent improvements in blockchain and digital twin technologies follows, focusing on smart contracts, enhanced data analytics, and real-time monitoring. The actual implementation and effects of this integration are presented via illustrative case studies of renewable energy, sustainable infrastructure, and environmental conservation. This chapter delves deeper into the problems of integrating these technologies, providing practical insights and solutions to technical, regulatory, and operational roadblocks.


Automation of complex text СAPTCHA recognition using conditional generative adversarial networks

Abstract: With the rapid development of Internet technologies, the problems of network security continue to worsen. So, one of the most common methods of maintaining security and preventing malicious attacks is CAPTCHA (fully automated public Turing test). CAPTCHA most often consists of some kind of security code, to bypass which it is necessary to perform a simple task, such as entering a word displayed in an image, solving a basic arithmetic equation, etc. However, the most widely used type of CAPTCHA is still the text type. In the recent years, the development of computer vision and, in particular, neural networks has contributed to a decrease in the resistance to hacking of text CAPTCHA. However, the security and resistance to recognition of complex CAPTCHA containing a lot of noise and distortion is still insufficiently studied. This study examines CAPTCHA, the distinctive feature of which is the use of a large number of different distortions, and each individual image uses its own different set of distortions, that is why even the human eye cannot always recognize what is depicted in the photo. The purpose of this work is to assess the security of sites using the CAPTCHA text type by testing their resistance to an automated solution. This testing will be used for the subsequent development of recommendations for improving the effectiveness of protection mechanisms. The result of the work is an implemented synthetic generator and discriminator of the CGAN architecture, as well as a decoder program, which is a trained convolutional neural network that solves this type of CAPTCHA. The recognition accuracy of the model constructed in the article was 63 % on an initially very limited data set, which shows the information security risks that sites using a similar type of CAPTCHA can carry.


Time-Restricted Double-Spending Attack on PoW-based Blockchains

Abstract: Numerous blockchain applications are designed with tasks that naturally have finite durations, and hence, a double-spending attack (DSA) on such blockchain applications leans towards being conducted within a finite timeframe, specifically before the completion of their tasks. Furthermore, existing research suggests that practical attackers typically favor executing a DSA within a finite timeframe due to their limited computational resources. These observations serve as the impetus for this paper to investigate a time-restricted DSA (TR-DSA) model on Proof-of-Work based blockchains. In this TR-DSA model, an attacker only mines its branch within a finite timeframe, and the TR-DSA is considered unsuccessful if the attacker's branch fails to surpass the honest miners' branch when the honest miners' branch has grown by a specific number of blocks. First, we developed a general closed-form expression for the success probability of a TR-DSA. This developed probability not only can assist in evaluating the risk of a DSA on blockchain applications with timely tasks, but also can enable practical attackers with limited computational resources to assess the feasibility and expected reward of launching a TR-DSA. In addition, we provide rigorous proof that the success probability of a TR-DSA is no greater than that of a time-unrestricted DSA where the attacker indefinitely mines its branch. This result implies that blockchain applications with timely tasks are less vulnerable to DSAs than blockchain applications that provide attackers with an unlimited timeframe for their attacks. Furthermore, we show that the success probability of a TR-DSA is always smaller than one even though the attacker controls more than half of the hash rate in the network. This result alerts attackers that there is still a risk of failure in launching a TR-DSA even if they amass a majority of the hash rate in the network.


Photon statistics of resonantly driven spectrally diffusive quantum  emitters

Abstract: In the solid state, a large variety of single-photon emitters present high quality photophysical properties together with a potential for integration. However, in many cases, the host matrix induces fluctuations of the emission wavelength in time, limiting the potential applications based on indistinguishable photons. A deep understanding of the underlying spectral diffusion processes is therefore of high importance for improving the stability of the light emission. Here, we theoretically investigate the photon statistics of an emitter driven by a resonant laser, and subject to either of two qualitatively different stationary spectral diffusion processes - a continuous diffusion process and a process based on discrete spectral jumps, both of which being known to model the spectral diffusion of various solid-state emitters. We show that the statistics of light emission carries several experimentally accessible signatures that allow to discriminate between the two classes of models, both at short times in the intensity correlation function, and at long times in the fluctuations of the integrated intensity. These results establish that resonant excitation combined with photon statistics offers a rich access to the spectral diffusion processes, yielding information that goes beyond the bare characterization of the inhomogeneous shape and noise correlation time. Incidentally, our findings shed a new light on recent experimental results of spectral diffusion of B centers in hexagonal boron nitride, providing more insight in their spectral diffusion mechanisms.


OccTransformer: Improving BEVFormer for 3D camera-only occupancy  prediction

Abstract: This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge.


Evaluating Quantized Large Language Models

Abstract: Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirements of both high efficiency and performance across diverse scenarios, a comprehensive evaluation of quantized LLMs is essential to guide the selection of quantization methods. This paper presents a thorough evaluation of these factors by evaluating the effect of PTQ on Weight, Activation, and KV Cache on 11 model families, including OPT, LLaMA2, Falcon, Bloomz, Mistral, ChatGLM, Vicuna, LongChat, StableLM, Gemma, and Mamba, with parameters ranging from 125M to 180B. The evaluation encompasses five types of tasks: basic NLP, emergent ability, trustworthiness, dialogue, and long-context tasks. Moreover, we also evaluate the state-of-the-art (SOTA) quantization methods to demonstrate their applicability. Based on the extensive experiments, we systematically summarize the effect of quantization, provide recommendations to apply quantization techniques, and point out future directions.


Efficiently Computable Safety Bounds for Gaussian Processes in Active  Learning

Abstract: Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their calibrated uncertainty estimations are widely used for this purpose. In many technical applications the design space is explored via continuous trajectories, along which the safety needs to be assessed. This is particularly challenging for strict safety requirements in GP methods, as it employs computationally expensive Monte-Carlo sampling of high quantiles. We address these challenges by providing provable safety bounds based on the adaptively sampled median of the supremum of the posterior GP. Our method significantly reduces the number of samples required for estimating high safety probabilities, resulting in faster evaluation without sacrificing accuracy and exploration speed. The effectiveness of our safe active learning approach is demonstrated through extensive simulations and validated using a real-world engine example.


Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal  Transport

Abstract: Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typically decomposed the UCIR problem into two distinct tasks: intra-domain representation learning and cross-domain feature alignment. However, these segregated strategies overlook the potential synergies between these tasks. This paper introduces ProtoOT, a novel Optimal Transport formulation explicitly tailored for UCIR, which integrates intra-domain feature representation learning and cross-domain alignment into a unified framework. ProtoOT leverages the strengths of the K-means clustering method to effectively manage distribution imbalances inherent in UCIR. By utilizing K-means for generating initial prototypes and approximating class marginal distributions, we modify the constraints in Optimal Transport accordingly, significantly enhancing its performance in UCIR scenarios. Furthermore, we incorporate contrastive learning into the ProtoOT framework to further improve representation learning. This encourages local semantic consistency among features with similar semantics, while also explicitly enforcing separation between features and unmatched prototypes, thereby enhancing global discriminativeness. ProtoOT surpasses existing state-of-the-art methods by a notable margin across benchmark datasets. Notably, on DomainNet, ProtoOT achieves an average P@200 enhancement of 24.44%, and on Office-Home, it demonstrates a P@15 improvement of 12.12%. Code is available at https://github.com/HCVLAB/ProtoOT.


Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust  3D Object Detection

Abstract: LiDAR-based 3D object detection models have traditionally struggled under rainy conditions due to the degraded and noisy scanning signals. Previous research has attempted to address this by simulating the noise from rain to improve the robustness of detection models. However, significant disparities exist between simulated and actual rain-impacted data points. In this work, we propose a novel rain simulation method, termed DRET, that unifies Dynamics and Rainy Environment Theory to provide a cost-effective means of expanding the available realistic rain data for 3D detection training. Furthermore, we present a Sunny-to-Rainy Knowledge Distillation (SRKD) approach to enhance 3D detection under rainy conditions. Extensive experiments on the WaymoOpenDataset large-scale dataset show that, when combined with the state-of-the-art DSVT model and other classical 3D detectors, our proposed framework demonstrates significant detection accuracy improvements, without losing efficiency. Remarkably, our framework also improves detection capabilities under sunny conditions, therefore offering a robust solution for 3D detection regardless of whether the weather is rainy or sunny


TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding

Abstract: The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However, even though the image and language representations have been aligned by cross-modal models like CLIP, we find that the image modality fails to contribute as much as the language in existing multi-modal 3D representation learning methods. This is attributed to the domain shift in the 2D images and the distinct focus of each modality. To more effectively leverage both modalities in the pre-training, we introduce TriAdapter Multi-Modal Learning (TAMM) -- a novel two-stage learning approach based on three synergetic adapters. First, our CLIP Image Adapter mitigates the domain gap between 3D-rendered images and natural images, by adapting the visual representations of CLIP for synthetic image-text pairs. Subsequently, our Dual Adapters decouple the 3D shape representation space into two complementary sub-spaces: one focusing on visual attributes and the other for semantic understanding, which ensure a more comprehensive and effective multi-modal pre-training. Extensive experiments demonstrate that TAMM consistently enhances 3D representations for a wide range of 3D encoder architectures, pre-training datasets, and downstream tasks. Notably, we boost the zero-shot classification accuracy on Objaverse-LVIS from 46.8 to 50.7, and improve the 5-way 10-shot linear probing classification accuracy on ModelNet40 from 96.1 to 99.0. Project page: \url{https://alanzhangcs.github.io/tamm-page}.


Enhancing 2D Path Search and PID Control for Autonomous Mobile Robots: A* Algorithm Optimization and Hardware

Abstract: Intelligent autonomous mobile robots find diverse applications across various fields. This article delves into the optimization of path search planning and implementation for these robots in specific scenarios. The study enhances the traditional right-hand principle's search strategy, integrating a forward-looking approach grounded in the goal-oriented law with A* algorithm path planning. Emphasizing the algorithm's lightweight and high efficiency, the study also considers its interaction with hardware systems. The real-world application scenario is abstracted into a two-dimensional planar environment. Collaborative efforts between hardware and software systems result in a comprehensive robot system, validated through tests ensuring swift navigation from the starting point to the endpoint along the planned path.


Analisis Deiksis dalam Channel YouTube Satu Persen pada Daftar Putar “Manajemen Waktu”

Abstract: The studies we took in this research are from the field of pragmatics. The pragmatics study in this research focuses on deictic analysis. Deixis is a part of pragmatics that discusses the expressions or context in a sentence. This research aims to analyze various types of deixis contained in the 'Time Management' video on the Satu Persen YouTube channel. This research was carried out using a qualitative approach, namely a method as a research procedure that produces descriptive data. The research techniques used to produce data are observation techniques, note-taking techniques, writing techniques, and listening techniques. In the analysis of deixis in a series of videos contained in the 'Time Management' playlist via the One Percent YouTube channel that we studied, five types of deixis can be found, namely persona deixis (first person singular, first person plural, second person singular, second person plural, third person singular), place deixis, time deixis (past, present, future), discourse deixis (anaphora, cataphora), and social deixis. Each type of deixis has its own function depending on its context. This research will be interesting for readers who have difficulty managing time.


DAWN: Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate

Abstract: Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.


Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and  Understanding -- A Survey

Abstract: Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This survey aims to address this gap by consolidating recent progress in these areas, offering a thorough survey and taxonomy of the datasets, metrics, and methodologies utilized. It identifies strengths, limitations, unexplored territories, and gaps in the existing literature, while providing some insights for future research directions in this vital and rapidly evolving field. It also provides relevant code and datasets references. Through this comprehensive review, we hope to provide interested readers with pertinent references and insightful perspectives, empowering them with the necessary tools and knowledge to effectively navigate and address the prevailing challenges in the field.


Determining the effect of a floating point on the Falcon digital signature algorithm security

Abstract: The object of research is digital signatures. The Falcon digital signature scheme is one of the finalists in the NIST post-quantum cryptography competition. Its distinctive feature is the use of floating-point arithmetic. However, floating-point arithmetic has so-called rounding noise, which accumulates during computations and in some cases may lead to significant changes in the processed values. The work considers the problem of using rounding noise to build attacks on implementation. The main result of the study is a novel attack on implementation, which enables the secret key recovery. This attack differs from existing attacks in using two separately secure implementations with different computation orders. As a result of the analysis, the conditions under which secret key recovery is possible were revealed. The attack requires 300,000 signatures and two implementations to recover key. The probability of successful attack ranges from 70 % to 76 %. This probability is explained by the structure of the Gaussian sampling algorithm used in the Falcon digital signature. At the same time, a necessary condition for conducting an attack is identical seed during signature generation. This condition makes the attack more theoretical than practical since the correct implementation of the Falcon makes probability of two identical seeds negligible. However, the possible usage of floating-point noise shows potential existence of additional attack vectors for the Falcon that should be covered in security models. The results could be used in the construction of digital signature security models and their implementation in existing information and communication systems


LiMAML: Personalization of Deep Recommender Models via Meta Learning

Abstract: In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members and other entities, coupled with the frequent updates based on the latest user interaction signals. Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to adapt per-task sub-networks using recent user interaction data. Given the near infeasibility of productionizing original MAML-based models in online recommendation systems, we propose an efficient strategy to operationalize meta-learned sub-networks in production, which involves transforming them into fixed-sized vectors, termed meta embeddings, thereby enabling the seamless deployment of models with hundreds of billions of parameters for online serving. Through extensive experimentation on production data drawn from various applications at LinkedIn, we demonstrate that the proposed solution consistently outperforms the baseline models of those applications, including strong baselines such as using wide-and-deep ID based personalization approach. Our approach has enabled the deployment of a range of highly personalized AI models across diverse LinkedIn applications, leading to substantial improvements in business metrics as well as refreshed experience for our members.


A study on parents' perception of the current status of kindergarten integration policy implementation: First, focusing on implementation tasks and action plans

Abstract: This study aims to analyze parents' perceptions of the priority tasks and action plans of the government's kindergarten integration policy, which is being promoted to prevent gaps in education and care from the infant and toddler period, the starting point of life. The results of an analysis of a total of 256 parents with children aged 0 to 5 are as follows. First, as a result of analyzing parents' awareness of the current status of the kindergarten integration policy implementation, overall awareness of the government's priority implementation tasks and action plans for kindergarten integration was found to be at a high level. In particular, the detailed implementation tasks include 'Providing integrated information on the status of kindergartens and daycare centers', 'Supporting improvement of indoor and outdoor safety, health, and hygienic environments and expanding safety education', and 'Using information on additional recruits to kindergartens and daycare centers, improving the convenience of applying for admission and admission'. Awareness was at the highest level. Second, as a result of analyzing the differences in parents' perception of the current status of childcare integration policy implementation according to background variables, the area of “Support for the healthy development of infants and young children” showed differences by parent age, education level, type of work hours at work, child age, and financial institution group. It was found that the area of 「Alleviating parental cost burden and support for raising children」 showed differences by parent age, type of work hours, child age, and financial institution group, and the area of 「Improvement of user convenience and strengthening of cooperation base」 showed differences by group. It was found that there were differences by parent age, child age, and funding institution group. Based on these results, it is recommended that, firstly, additional policy supplementation without discrimination in the area of 'healthy development support' is necessary for the group of parents whose children are attending kindergarten, and secondly, legal content that ensures a stable budget base needs to be included as well. Third, it suggests that systems and institutional improvements to improve the convenience of consumers using services should be continuously developed.


Community Service Data Visualization at PT Surya Data Infocreasi in Retail Companies X, Y and Z

Abstract: This Community Service (PKM) is carried out at PT Surya Data Infokreasi with a focus on data visualization using the Power BI platform. This service aims to increase the efficiency of understanding customer data and the company's business performance. Design and implement interactive reports using various Power BI visualizations such as graphs, maps, and dynamic tables. Interactive slicers and filters are applied to give users flexibility in exploring data in greater depth or detail. The main focus of visualization is sales analysis, customer trends, and inventory monitoring. Selection of performance metrics including total sales, customer returns, and profit analysis. The implication of this service is to help facilitate understanding of data in terms of patterns, trends and information contained in the data.


CLLMs: Consistency Large Language Models

Abstract: Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation. However, in practice, it achieves little speedup compared to traditional autoregressive (AR) decoding, primarily because Jacobi decoding seldom accurately predicts more than one token in a single fixed-point iteration step. To address this, we develop a new approach aimed at realizing fast convergence from any state to the fixed point on a Jacobi trajectory. This is accomplished by refining the target LLM to consistently predict the fixed point given any state as input. Extensive experiments demonstrate the effectiveness of our method, showing 2.4$\times$ to 3.4$\times$ improvements in generation speed while preserving generation quality across both domain-specific and open-domain benchmarks.


Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by  Exploring Refusal Loss Landscapes

Abstract: Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer. To reduce harm and misuse, efforts have been made to align these LLMs to human values using advanced training techniques such as Reinforcement Learning from Human Feedback (RLHF). However, recent studies have highlighted the vulnerability of LLMs to adversarial jailbreak attempts aiming at subverting the embedded safety guardrails. To address this challenge, this paper defines and investigates the Refusal Loss of LLMs and then proposes a method called Gradient Cuff to detect jailbreak attempts. Gradient Cuff exploits the unique properties observed in the refusal loss landscape, including functional values and its smoothness, to design an effective two-step detection strategy. Experimental results on two aligned LLMs (LLaMA-2-7B-Chat and Vicuna-7B-V1.5) and six types of jailbreak attacks (GCG, AutoDAN, PAIR, TAP, Base64, and LRL) show that Gradient Cuff can significantly improve the LLM's rejection capability for malicious jailbreak queries, while maintaining the model's performance for benign user queries by adjusting the detection threshold.


Manajemen Pembelajaran Terintegrasi dalam Model Pembelajaran Blended Learning, Learning Management System

Abstract: The primary objective of this research is to examine the impact of the blended learning model based on the Learning Management System (LMS) on both student learning outcomes and motivation. Within the realm of education, students often encounter various challenges, such as a lack of enthusiasm, boredom, and difficulties in grasping learning materials due to the limited variety in teaching and learning approaches. To address these issues, this study adopts a survey method employing a quantitative approach. The study encompasses all students at STKIP PGRI Sukabumi, with a sample size of 100 students chosen through the Simple Random Sampling technique. Data collection is conducted via a questionnaire distributed through Google Forms. The analysis involves multiple linear regression utilizing the SPSS 23 program, and the findings are as follows: 1. The LMS-based blended learning model demonstrates a significant influence on learning outcomes, as indicated by the t-count value of 6.835, surpassing the t-table value of 1.984. 2. Learning motivation is identified as a significant factor affecting learning outcomes, with a t-count value of 10.216, exceeding the t-table value of 1.984. 3. Both the LMS-based blended learning model and learning motivation collectively exhibit a substantial impact on learning outcomes, evidenced by an F-count value of 59.851, surpassing the F-table value of 3.089. In summary, the study concludes that the integration of the LMS-based blended learning model and enhanced learning motivation plays a pivotal role in advancing student learning outcomes.


In ternet of Things (IoT) relies on Wireless Technology for its major Functionalities

Abstract: Abstract: A decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This article presents the changes that the IoT has undertaken since its conception and research on how wireless technological advances have shaped it and fostered the arising of derived names suitable to specific domains. In this article, we provide a study of some wireless communication standards, evaluating their main features and behaviours in terms of various metrics, including the transmission time, data coding efficiency, complexity, and power consumption. It is believed that the comparison presented in this paper would benefit application engineers in selecting an appropriate protocol.


Research on Chaotic Image Encryption Based on Fibonacci-ILogistic-IHenon

Abstract: To further improve the security of image encryption, we propose an image encryption algorithm that combines Fibonacci with improved logistic and improved Henon chaotic mapping, i.e., the Fibonacci-ILogistic-IHenon algorithm. The algorithm addresses that the existing logistic chaotic algorithm is controlled by the parameter resulting in an uneven distribution, and the Henon chaotic algorithm may have the problem of periodic orbits. Using the mode-taking and sinusoidal functions for optimization, the output sequence region generated by the logistic chaotic mapping is adjusted to the input of the Henon chaotic mapping, which enables the construction of a new chaotic system (ILogistic-IHenon). In the image encryption implementation, Fibonacci is used to cure the chaos, and ILogistic-IHenon is utilized to generate chaotic sequences to complete the encryption. In the simulation experiments, we choose the improved logistic encryption algorithm, the improved Henon encryption algorithm, and the improved logistic-ent encryption algorithm as the comparison algorithms, and the results illustrate that the Fibonacci-ILogistic-IHenon algorithm has better performance in image encryption statistical analysis, encryption speed, pixel correlation analysis, information entropy analysis, differential attack analysis and other metrics.


Rendezvous problem for chains of kinematic points

Abstract: Abstract This paper studies the dynamic of a chain of infinitely many kinematic points guaranteeing the uniform convergence in the sense. A sufficient condition and some necessary conditions for infinite many kinematic points rendezvousing are given in terms of the spectrum and the invariant subspaces of the dynamic operator. The necessary and sufficient condition for finitely many kinematic points rendezvousing is characterized by the eigenvalues and the invariant subspaces of the dynamic operator. The estimate on the rate of the rendezvous is derived based on the spectrum of the dynamic operator.


THE STRUCTURE OF INFORMATION AND EDUCATION ENVIRONMENT OF A MEDICAL UNIVERSITY

Abstract: The authors examine the process of transformation of the educational infrastructure of medical universities during the transition to a mixed form of studying while COVID – 19 quarantine. Analysis of scientific publications on the digital transformation of the higher education system has shown the effectiveness of a hybrid model for organizing a virtual learning environment, which combines the ground-based infrastructure of the university and can include private and public cloud services. Hybrid model allows providing appropriate technological conditions for quality organization of teachers' work in online mode of distance learning system. Cloud services allow organizing a virtual group educational environment - a virtual classroom and a personal environment for students. The authors analyzed the changes in the infrastructure of Zaporizhzhia State Medical University in the process of digital transformation of the educational process during the period of COVID-19 quarantine, which became the ground for the development of a structural model of the information and educational environment of the university. The work focuses on the fact that one of the features of the educational process in medical universities is the usage of special learning tools: software and hardware systems for examination of a patient's condition, technical means of telemedicine, classroom self-training sessions for Licensing Integrated Exams “KROK” 1,2,3 on the basis of licensed software, etc. The authors conducted a systematic analysis and identified the technological components of the unified information educational environment of medical HEIs, namely: corporate computer network; workplaces of participants in the development of online courses (designer, lecturer of videoconferencing, moderator of video lectures or webinars, designer of video processing; specialized modeling systems; simulation complexes; software and hardware complexes for processing biological signals; specialized classrooms: computer room of the library, lecture halls for remote video lectures with interactive communication, lecture halls for interactive lectures, media centers for webinars and telemedicine consultations; cloud services . The proposed structure of information and educational environment of a medical university was implemented in the educational process of Zaporizhzhia State Medical University which made possible the creation of a cloud-oriented educational environment. The usage of hybrid cloud model allowed to solve the issue of integration of virtual environments of departments, in particular, territorially remote ones, and the ground infrastructure of the university.


PEMANFAATAN MEDIA SOSIAL SEBAGAI LITERASI MEMBACA DALAM MENINGKATKAN SEMANGAT BELAJAR PADA PANTI YATIM PELITA KAMIL

Abstract: This community service activity aims to increase interest in reading at the Pelita Kamil orphanage through the use of social media. The method of implementing this community service uses assistance by implementing reading activities for 20minutes every day and writing down the reading results obtained from social media. The results of this community service activity can increase children's interest in reading with a satisfaction level of 80% from the indicator of usefulness for activity participants. The indicators of the success of this service also include the equipment used during the activity, the atmosphere and situation during the implementation of the activity, the time of implementation, media, reading materials, guidance from the resource person, reading literacy practices, the resource person's ability to adapt, and mastery of the material from theresource person.