Reviewerly/exHarmony huggingface dataset:
The Influence of the R2L Learning Model on the Material of the Human Digestive System on Students’ Scientific Literacy

2018 PISA results show that Indonesia is ranked 71st out of 77 countries in the world. While Indonesia has been successful in increasing the access of 15-year-olds to the school system, greater efforts are still needed to educate them so that the percentage of low-achieving students is kept as low as possible. Based on the results of observations made, students' scientific literacy at SMP Negeri in Bukittinggi is still relatively low. This research was a quasi-experimental study with a non-equivalent control group design which aims to determine the scientific literacy profile of students using the Reading to Learn (R2L) model. The R2L model has a main focus on teaching students to read texts and then gain a deeper understanding of the contents of the reading and rewriting what they have learned (note-taking). Based on the results of statistical analysis, the data is normally distributed and homogeneous so that a parametric test (T-test) is carried out. The results of the calculation of the T-test obtain a value of 0.05, thus H0 is rejected and H1 is accepted. This shows that there is an influence of the Reading to Learn (R2L) model on the digestive system material on the scientific literacy skills of junior high school students.
Analysis of the Opinion Students about The Online Learning System During the Pandemic Using The K-NN and Naïve Bayes Methods

Abstract— Coronavirus or known as Covid-19 is an outbreak that has spread to various parts of the world including Indonesia. Residents of Ambon city also felt the difficult times of surviving in the midst of a pandemic. The adverse impact of this pandemic has notharmed the people of A mbon city, especially the lower middle class. Since the entry of this pandemic into the city of Ambon, the local government has continuously taken steps to overcome the pandemic by conducting PSBB, PPKM, and implementing various rules aimed at limiting community activities outside the home so that crowds do not occur. The negative impact due to Covid-19 also spreads to all aspects such as economic, socio-cultural, tourism aspects, and also affects the learning process in the city of Ambon, where the learning process is carried out online. This online learning process is a problem for people who do not have the supporting means to learn online. Since the Ministry of Education and Culture made this online learning decision, there have been various student opinions in response to this, especially Pattimura University students. In this study, an analysis of student opinions was carried out to find out negative sentiment, positive or neutral sentiment in students related to the online learning process policy made by the government using the K-Nearest Neighbors method and the Naïve Bayes Classifier method. in this study produced an accuracy value from the classification obtained using the KNN method was 37.12%, and the Naïve Bayes method produced a greater accuracy value of about 42.89% which was positive. This shows that the student's response to the performance and rules of online learning at one of the state universities in Ambon, Pattimura University is felt to be good for the online teaching and learning process.
THE INFLUENCE OF READER COMMENTS ON STUDENTS' PERCEPTIONS ABOUT CAMPUS NEWS CONTROVERSION THROUGH THE INFLUENCE OF LINE TODAY THROUGH

Technological developments provide significant changes in the field of journalism, especially in the news by the mass media. The online news portal currently provides a comment column feature for news readers to comment on the existing news. This study aims to (1) look at the effect of reader comments on students' perceptions of controversial news about campus through the LINE Today feature, (2) analyze the difference in influence between positive comments and negative comments on students' perceptions of campus controversy news through the LINE Today feature. This study uses a quantitative approach with a questionnaire method to the research population. The sample is students of the Department of Communication, Faculty of Social and Political Sciences, Hasanuddin University who have accounts on the LINE application, totaling 199 people with the total sampling method. Regression analysis technique was used to see the effect of reader comments on student perceptions. To compare the effect of positive and negative reader comments, a comparative analysis technique is used, namely variance. The results showed that (1) There was a significant effect of reader comments on students' perceptions of campus controversy news through the LINE Today feature using both positive and negative stimuli; (2) There is a difference in the effect of positive and negative comments on students' perceptions of campus controversy news through the LINE Today feature.stimuli have a greater influence on student perceptions
Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images

This paper takes an important step in bridging the performance gap between DETR and R-CNN for graphical object detection. Existing graphical object detection approaches have enjoyed recent enhancements in CNN-based object detection methods, achieving remarkable progress. Recently, Transformer-based detectors have considerably boosted the generic object detection performance, eliminating the need for hand-crafted features or post-processing steps such as Non-Maximum Suppression (NMS) using object queries. However, the effectiveness of such enhanced transformer-based detection algorithms has yet to be verified for the problem of graphical object detection. Essentially, inspired by the latest advancements in the DETR, we employ the existing detection transformer with few modifications for graphical object detection. We modify object queries in different ways, using points, anchor boxes and adding positive and negative noise to the anchors to boost performance. These modifications allow for better handling of objects with varying sizes and aspect ratios, more robustness to small variations in object positions and sizes, and improved image discrimination between objects and non-objects. We evaluate our approach on the four graphical datasets: PubTables, TableBank, NTable and PubLaynet. Upon integrating query modifications in the DETR, we outperform prior works and achieve new state-of-the-art results with the mAP of 96.9\%, 95.7\% and 99.3\% on TableBank, PubLaynet, PubTables, respectively. The results from extensive ablations show that transformer-based methods are more effective for document analysis analogous to other applications. We hope this study draws more attention to the research of using detection transformers in document image analysis.
Improving Performance of Clinical and Operational Workflows in Health Tech Domain using Artificial Intelligence

Abstract: Artificial Intelligence (AI) is rapidly transforming the healthcare industry, with the potential to improve patient outcomes, reduce costs, and enhance the overall quality of care. This paper explores the emerging trends in AI in health tech, including machine learning, natural language processing, robotics, and computer vision. It also examines the challenges and opportunities associated with these emerging technologies, as well as their impact on healthcare delivery, research, and patient engagement. The availability of an enormous amount of data supported by limitless cloud storage is a significant benefit that aids in the development of many AI applications. The learning algorithms are trained using this data to improve their accuracy. The algorithms interact with the practise data to provide fresh perspectives on diagnosis and therapy. As a result, it enhances patients' results. A case study on Diagnosis and Treatment of Skin Cancer was discussed and a suitable architecture is proposed.
Entangling motional atoms and an optical loop at ambient condition

Abstract The observation of quantum entanglement in macroscopic matters has implications in the fundamental studies of quantum mechanics, as well as the implementations of quantum information technologies, such as quantum communication, enhanced sensing, and distributed quantum computing. Here, we report the creation of the heralded entanglement between two different room-temperature quantum memories: a single-photon entangled state delocalized between motional atoms as a collective excitation and an all-optical loop as a flying qubit. The stored entangled state is subsequently retrieved and verified by measuring the nonclassical correlations, quantum interference, and concurrence of the mapped-out photons. Our results show that quantum entanglement can be sustained in macroscopic matters at ambient condition, which enriches the fundamental researches of the transition boundary between quantum and classical worlds. Also, it highlights the potential cooperation between atomic ensembles and all-optical loop as quantum nodes at ambient condition, bringing a significant step towards practical quantum networks.
Peer review declaration

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. Type of peer review: Double anonymous Conference submission management system: Morressier Number of submissions received: 112 Number of submissions sent for review: 109 Number of submissions accepted: 34 Acceptance Rate (Submissions Accepted / Submissions Received × 100): 30.4 % Average number of reviews per paper: 2.00 Total number of reviewers involved: 56 Contact person for queries: Name: Catherine Liu Affiliation: International Computing and Engineering Association Email: conf@i-cea.net
Circumventing superexponential runtimes for hard instances of quantum adiabatic optimization

Classical optimization problems can be solved by adiabatically preparing the ground state of a quantum Hamiltonian that encodes the problem. The performance of this approach is determined by the smallest gap encountered during the evolution. Here, we consider the maximum independent set problem, which can be efficiently encoded in the Hamiltonian describing a Rydberg atom array. We present a general construction of instances of the problem for which the minimum gap decays superexponentially with system size, implying a superexponentially large time to solution via adiabatic evolution. The small gap arises from locally independent choices, which cause the system to initially evolve and localize into a configuration far from the solution in terms of Hamming distance. We investigate remedies to this problem. Specifically, we show that quantum quenches in these models can exhibit signatures of quantum many-body scars, which in turn, can circumvent the superexponential gaps. By quenching from a suboptimal configuration, states with a larger ground state overlap can be prepared, illustrating the utility of quantum quenches as an algorithmic tool.
Entropy Cones and Entanglement Evolution for Dicke States

The $N$-qubit Dicke states $|D^N_k\rangle$, of Hamming-weight $k$, are a class of entangled states which play an important role in quantum algorithm optimization. We present a general calculation of entanglement entropy in Dicke states, which we use to describe the $|D^N_k\rangle$ entropy cone. We demonstrate that all $|D^N_k\rangle$ entropy vectors emerge symmetrized, and use this to define a min-cut protocol on star graphs which realizes $|D^N_k\rangle$ entropy vectors. We identify the stabilizer group for all $|D^N_k\rangle$, under the action of the $N$-qubit Pauli group and two-qubit Clifford group, which we use to construct $|D^N_k\rangle$ reachability graphs. We use these reachability graphs to analyze and bound the evolution of $|D^N_k\rangle$ entropy vectors in Clifford circuits.
Drishya: Object Detection And MonocuLar Depth Estimation Using Deep Learning

This project focuses on object detection and depth estimation using computer vision. The aim of this project is to develop an accurate and reliable system for detecting objects and estimating their distances from a camera. The system uses deep learning-based algorithms to detect objects and estimate their distances. The results of the project show that the system can accurately detect objects and estimate their distances in some scenarios. This project presents an Android app for real-time object detection and depth estimation using computer vision with YOLOv5 and U-Net architecture.
Anticipatory Thinking Challenges in Open Worlds: Risk Management

Anticipatory thinking drives our ability to manage risk - identification and mitigation - in everyday life, from bringing an umbrella when it might rain to buying car insurance. As AI systems become part of everyday life, they too have begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and Go agents have similar capabilities to humans, implicitly managing risks presented by their opponents. To further increase performance in these tasks, out-of-distribution evaluation can characterize a model's bias, what we view as a type of risk management. However, learning to identify and mitigate low-frequency, high-impact risks is at odds with the observational bias required to train machine learning models. StarCraft and Go are closed-world domains whose risks are known and mitigations well documented, ideal for learning through repetition. Adversarial filtering datasets provide difficult examples but are laborious to curate and static, both barriers to real-world risk management. Adversarial robustness focuses on model poisoning under the assumption there is an adversary with malicious intent, without considering naturally occurring adversarial examples. These methods are all important steps towards improving risk management but do so without considering open-worlds. We unify these open-world risk management challenges with two contributions. The first is our perception challenges, designed for agents with imperfect perceptions of their environment whose consequences have a high impact. Our second contribution are cognition challenges, designed for agents that must dynamically adjust their risk exposure as they identify new risks and learn new mitigations. Our goal with these challenges is to spur research into solutions that assess and improve the anticipatory thinking required by AI agents to manage risk in open-worlds and ultimately the real-world.
AmicroN: A Framework for Generating Annotations for Human Activity Recognition with Granular Micro-Activities

Complex activities of daily living (ADLs) often consist of multiple micro-activities. When performed sequentially, these micro-activities help the user accomplish the broad macro-activity. Naturally, a deeper understanding of these micro-activities can help develop more sophisticated human activity recognition (HAR) models and add explainability to their inferred conclusions. Previous research has attempted to achieve this by utilizing fine-grained annotated data that provided the required supervision and rules for associating the micro-activities to identify the macro-activity. However, this ``bottom-up'' approach is unrealistic as getting such high-quality, fine-grained annotated sensor datasets is challenging, costly, and time-consuming. Understanding this, in this paper, we develop AmicroN, which adapts a ``top-down'' approach by exploiting coarse-grained annotated data to expand the macro-activities into their constituent micro-activities without any external supervision. In the backend, AmicroN uses \textit{unsupervised} change-point detection to search for the micro-activity boundaries across a complex ADL. Then, it applies a \textit{generalized zero-shot} approach to characterize it. We evaluate AmicroN on two real-life publicly available datasets and observe that AmicroN can identify the micro-activities with micro F\textsubscript{1}-score $>0.75$ for both datasets. Additionally, we also perform an initial proof-of-concept on leveraging the state-of-the-art (SOTA) large language models (LLMs) with attribute embeddings predicted by AmicroN to enhance further the explainability surrounding the detection of micro-activities.
Real Time Twitter Sentiment Analysis

Abstract: The purpose of tweet sentiment analysis is to determine the positive, negative, or neutral sentiment component in tweeter data. Sentiment analysis may assist any organisation in discovering people's perceptions of their firm and products. We ran sentiment analysis on the Twitter data set. Our algorithm accepts the input tweet, sentiment, and selected text that begins and ends in the input tweet. We have decided to create an end-to end project on' Sentiment analysis and visualisation of popular topics on Twitter. There will be areas such as products, compare, and so on. The user will be able to search for a topic of interest and find an analysis of that issue.
Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks

Autonomous robots are increasingly utilized in realistic scenarios with multiple complex tasks. In these scenarios, there may be a preferred way of completing all of the given tasks, but it is often in conflict with optimal execution. Recent work studies preference-based planning, however, they have yet to extend the notion of preference to the behavior of the robot with respect to each task. In this work, we introduce a novel notion of preference that provides a generalized framework to express preferences over individual tasks as well as their relations. Then, we perform an optimal trade-off (Pareto) analysis between behaviors that adhere to the user's preference and the ones that are resource optimal. We introduce an efficient planning framework that generates Pareto-optimal plans given user's preference by extending A* search. Further, we show a method of computing the entire Pareto front (the set of all optimal trade-offs) via an adaptation of a multi-objective A* algorithm. We also present a problem-agnostic search heuristic to enable scalability. We illustrate the power of the framework on both mobile robots and manipulators. Our benchmarks show the effectiveness of the heuristic with up to 2-orders of magnitude speedup.
Visual Adversarial Examples Jailbreak Aligned Large Language Models

Recently, there has been a surge of interest in integrating vision into Large Language Models (LLMs), exemplified by Visual Language Models (VLMs) such as Flamingo and GPT-4. This paper sheds light on the security and safety implications of this trend. First, we underscore that the continuous and high-dimensional nature of the visual input makes it a weak link against adversarial attacks, representing an expanded attack surface of vision-integrated LLMs. Second, we highlight that the versatility of LLMs also presents visual attackers with a wider array of achievable adversarial objectives, extending the implications of security failures beyond mere misclassification. As an illustration, we present a case study in which we exploit visual adversarial examples to circumvent the safety guardrail of aligned LLMs with integrated vision. Intriguingly, we discover that a single visual adversarial example can universally jailbreak an aligned LLM, compelling it to heed a wide range of harmful instructions that it otherwise would not) and generate harmful content that transcends the narrow scope of a `few-shot' derogatory corpus initially employed to optimize the adversarial example. Our study underscores the escalating adversarial risks associated with the pursuit of multimodality. Our findings also connect the long-studied adversarial vulnerabilities of neural networks to the nascent field of AI alignment. The presented attack suggests a fundamental adversarial challenge for AI alignment, especially in light of the emerging trend toward multimodality in frontier foundation models.
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok

This paper focuses on predicting the occurrence of grokking in neural networks, a phenomenon in which perfect generalization emerges long after signs of overfitting or memorization are observed. It has been reported that grokking can only be observed with certain hyper-parameters. This makes it critical to identify the parameters that lead to grokking. However, since grokking occurs after a large number of epochs, searching for the hyper-parameters that lead to it is time-consuming. In this paper, we propose a low-cost method to predict grokking without training for a large number of epochs. In essence, by studying the learning curve of the first few epochs, we show that one can predict whether grokking will occur later on. Specifically, if certain oscillations occur in the early epochs, one can expect grokking to occur if the model is trained for a much longer period of time. We propose using the spectral signature of a learning curve derived by applying the Fourier transform to quantify the amplitude of low-frequency components to detect the presence of such oscillations. We also present additional experiments aimed at explaining the cause of these oscillations and characterizing the loss landscape.
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning

Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task. Existing methods for such personalization either prune a global model or fine-tune a global model on a local client distribution. However, these existing methods either personalize at the expense of retaining important global knowledge, or predetermine network layers for fine-tuning, resulting in suboptimal storage of global knowledge within client models. Enlightened by the lottery ticket hypothesis, we first introduce a hypothesis for finding optimal client subnetworks to locally fine-tune while leaving the rest of the parameters frozen. We then propose a novel FL framework, FedSelect, using this procedure that directly personalizes both client subnetwork structure and parameters, via the simultaneous discovery of optimal parameters for personalization and the rest of parameters for global aggregation during training. We show that this method achieves promising results on CIFAR-10.
A First Order Meta Stackelberg Method for Robust Federated Learning (Technical Report)

Recent research efforts indicate that federated learning (FL) systems are vulnerable to a variety of security breaches. While numerous defense strategies have been suggested, they are mainly designed to counter specific attack patterns and lack adaptability, rendering them less effective when facing uncertain or adaptive threats. This work models adversarial FL as a Bayesian Stackelberg Markov game (BSMG) between the defender and the attacker to address the lack of adaptability to uncertain adaptive attacks. We further devise an effective meta-learning technique to solve for the Stackelberg equilibrium, leading to a resilient and adaptable defense. The experiment results suggest that our meta-Stackelberg learning approach excels in combating intense model poisoning and backdoor attacks of indeterminate types.