Reviewerly/exHarmony huggingface dataset:
BayesDLL: Bayesian Deep Learning Library

We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our library implements mainstream approximate Bayesian inference algorithms: variational inference, MC-dropout, stochastic-gradient MCMC, and Laplace approximation. The main differences from other existing Bayesian neural network libraries are as follows: 1) Our library can deal with very large-scale deep networks including Vision Transformers (ViTs). 2) We need virtually zero code modifications for users (e.g., the backbone network definition codes do not neet to be modified at all). 3) Our library also allows the pre-trained model weights to serve as a prior mean, which is very useful for performing Bayesian inference with the large-scale foundation models like ViTs that are hard to optimise from scratch with the downstream data alone. Our code is publicly available at: \url{https://github.com/SamsungLabs/BayesDLL}\footnote{A mirror repository is also available at: \url{https://github.com/minyoungkim21/BayesDLL}.}.
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks

Keyword spotting (KWS) refers to the task of identifying a set of predefined words in audio streams. With the advances seen recently with deep neural networks, it has become a popular technology to activate and control small devices, such as voice assistants. Relying on such models for edge devices, however, can be challenging due to hardware constraints. Moreover, as adversarial attacks have increased against voice-based technologies, developing solutions robust to such attacks has become crucial. In this work, we propose VIC-KD, a robust distillation recipe for model compression and adversarial robustness. Using self-supervised speech representations, we show that imposing geometric priors to the latent representations of both Teacher and Student models leads to more robust target models. Experiments on the Google Speech Commands datasets show that the proposed methodology improves upon current state-of-the-art robust distillation methods, such as ARD and RSLAD, by 12% and 8% in robust accuracy, respectively.
Dynamic "Succincter"

Augmented B-trees (aB-trees) are a broad class of data structures. The seminal work "succincter" by Patrascu showed that any aB-tree can be stored using only two bits of redundancy, while supporting queries to the tree in time proportional to its depth. It has been a versatile building block for constructing succinct data structures, including rank/select data structures, dictionaries, locally decodable arithmetic coding, storing balanced parenthesis, etc. In this paper, we show how to "dynamize" an aB-tree. Our main result is the design of dynamic aB-trees (daB-trees) with branching factor two using only three bits of redundancy (with the help of lookup tables that are of negligible size in applications), while supporting updates and queries in time polynomial in its depth. As an application, we present a dynamic rank/select data structure for $n$-bit arrays, also known as a dynamic fully indexable dictionary (FID). It supports updates and queries in $O(\log n/\log\log n)$ time, and when the array has $m$ ones, the data structure occupies \[ \log\binom{n}{m} + O(n/2^{\log^{0.199}n}) \] bits. Note that the update and query times are optimal even without space constraints due to a lower bound by Fredman and Saks. Prior to our work, no dynamic FID with near-optimal update and query times and redundancy $o(n/\log n)$ was known. We further show that a dynamic sequence supporting insertions, deletions and rank/select queries can be maintained in (optimal) $O(\log n/\log\log n)$ time and with $O(n \cdot \text{poly}\log\log n/\log^2 n)$ bits of redundancy.
Detect Every Thing with Few Examples

Few-shot object detection aims at detecting novel categories given a few example images. Recent methods focus on finetuning strategies, with complicated procedures that prohibit a wider application. In this paper, we introduce DE-ViT, a few-shot object detector without the need for finetuning. DE-ViT's novel architecture is based on a new region-propagation mechanism for localization. The propagated region masks are transformed into bounding boxes through a learnable spatial integral layer. Instead of training prototype classifiers, we propose to use prototypes to project ViT features into a subspace that is robust to overfitting on base classes. We evaluate DE-ViT on few-shot, and one-shot object detection benchmarks with Pascal VOC, COCO, and LVIS. DE-ViT establishes new state-of-the-art results on all benchmarks. Notably, for COCO, DE-ViT surpasses the few-shot SoTA by 15 mAP on 10-shot and 7.2 mAP on 30-shot and one-shot SoTA by 2.8 AP50. For LVIS, DE-ViT outperforms few-shot SoTA by 20 box APr.
Massive End-to-end Models for Short Search Queries

In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for offline recognition of voice search queries, with up to 2B model parameters. The encoders of our models use the neural architecture of Google's universal speech model (USM), with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. We perform extensive studies on vocabulary size, time reduction strategy, and its generalization performance on long-form test sets. Despite the speculation that, as the model size increases, CTC can be as good as RNN-T which builds label dependency into the prediction, we observe that a 900M RNN-T clearly outperforms a 1.8B CTC and is more tolerant to severe time reduction, although the WER gap can be largely removed by LM shallow fusion.
On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures

Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth potential security risks. CAVs' driving decisions rely on remote untrusted data, making them susceptible to attacks carried out by malicious participants in the collaborative perception system. However, security analysis and countermeasures for such threats are absent. To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks. Our attacks demonstrate a high success rate of over 86% on high-fidelity simulated scenarios and are realizable in real-world experiments. To mitigate the vulnerability, we present a systematic anomaly detection approach that enables benign vehicles to jointly reveal malicious fabrication. It detects 91.5% of attacks with a false positive rate of 3% in simulated scenarios and significantly mitigates attack impacts in real-world scenarios.
Wordification: A New Way of Teaching English Spelling Patterns

Literacy, or the ability to read and write, is a crucial indicator of success in life and greater society. It is estimated that 85% of people in juvenile delinquent systems cannot adequately read or write, that more than half of those with substance abuse issues have complications in reading or writing and that two-thirds of those who do not complete high school lack proper literacy skills. Furthermore, young children who do not possess reading skills matching grade level by the fourth grade are approximately 80% likely to not catch up at all. Many may believe that in a developed country such as the United States, literacy fails to be an issue; however, this is a dangerous misunderstanding. Globally an estimated 1.19 trillion dollars are lost every year due to issues in literacy; in the USA, the loss is an estimated 300 billion. To put it in more shocking terms, one in five American adults still fail to comprehend basic sentences. Making matters worse, the only tools available now to correct a lack of reading and writing ability are found in expensive tutoring or other programs that oftentimes fail to be able to reach the required audience. In this paper, our team puts forward a new way of teaching English spelling and word recognitions to grade school students in the United States: Wordification. Wordification is a web application designed to teach English literacy using principles of linguistics applied to the orthographic and phonological properties of words in a manner not fully utilized previously in any computer-based teaching application.
Expressive variational quantum circuits provide inherent privacy in federated learning

Federated learning has emerged as a viable distributed solution to train machine learning models without the actual need to share data with the central aggregator. However, standard neural network-based federated learning models have been shown to be susceptible to data leakage from the gradients shared with the server. In this work, we introduce federated learning with variational quantum circuit model built using expressive encoding maps coupled with overparameterized ans\"atze. We show that expressive maps lead to inherent privacy against gradient inversion attacks, while overparameterization ensures model trainability. Our privacy framework centers on the complexity of solving the system of high-degree multivariate Chebyshev polynomials generated by the gradients of quantum circuit. We present compelling arguments highlighting the inherent difficulty in solving these equations, both in exact and approximate scenarios. Additionally, we delve into machine learning-based attack strategies and establish a direct connection between overparameterization in the original federated learning model and underparameterization in the attack model. Furthermore, we provide numerical scaling arguments showcasing that underparameterization of the expressive map in the attack model leads to the loss landscape being swamped with exponentially many spurious local minima points, thus making it extremely hard to realize a successful attack. This provides a strong claim, for the first time, that the nature of quantum machine learning models inherently helps prevent data leakage in federated learning.
ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Large Language Models (LLMs) still struggle with natural language reasoning tasks. Motivated by the society of minds (Minsky, 1988), we propose ReConcile, a multi-model multiagent framework designed as a round table conference among diverse LLM agents. ReConcile enhances collaborative reasoning between LLM agents via multiple rounds of discussion, learning to convince other agents to improve their answers, and employing a confidence-weighted voting mechanism that leads to a better consensus. In each round, ReConcile initiates discussion between agents via a 'discussion prompt' that consists of (a) grouped answers and explanations generated by each agent in the previous round, (b) their confidence scores, and (c) demonstrations of answer-rectifying human explanations, used for convincing other agents. Experiments on seven benchmarks demonstrate that ReConcile significantly improves LLMs' reasoning -- both individually and as a team -- surpassing prior single-agent and multi-agent baselines by up to 11.4% and even outperforming GPT-4 on three datasets. ReConcile also flexibly incorporates different combinations of agents, including API-based, open-source, and domain-specific models, leading to an 8% improvement on MATH. Finally, we analyze the individual components of ReConcile, demonstrating that the diversity originating from different models is critical to its superior performance. Code: https://github.com/dinobby/ReConcile
A Multi-faceted Analysis of the Performance Variability of Virtual Machines

Cloud computing and virtualization solutions allow one to rent the virtual machines (VMs) needed to run applications on a pay-per-use basis, but rented VMs do not offer any guarantee on their performance. Cloud platforms are known to be affected by performance variability, but a better understanding is still required. This paper moves in that direction and presents an in-depth, multi-faceted study on the performance variability of VMs. Unlike previous studies, our assessment covers a wide range of factors: 16 VM types from 4 well-known cloud providers, 10 benchmarks, and 28 different metrics. We present four new contributions. First, we introduce a new benchmark suite (VMBS) that let researchers and practitioners systematically collect a diverse set of performance data. Second, we present a new indicator, called Variability Indicator, that allows for measuring variability in the performance of VMs. Third, we illustrate an analysis of the collected data across four different dimensions: resources, isolation, time, and cost. Fourth, we present multiple predictive models based on Machine Learning that aim to forecast future performance and detect time patterns. Our experiments provide important insights on the resource variability of VMs, highlighting differences and similarities between various cloud providers. To the best of our knowledge, this is the widest analysis ever conducted on the topic.
Utilizing Blockchain Technology for Enhanced Governance: A Comprehensive Analysis of Potential Applications and Innovations in the Governmental Sector of the EU

&lt;p&gt;This research delineates the transformative potential of blockchain technology within the realm of European governments. By conducting a meticulous analysis of existing literature and case studies, the study seeks to identify and evaluate the opportunities and challenges associated with implementing blockchain technology in governmental operations across Europe. The methodology involves a comprehensive review of peer-reviewed articles, white papers, and governmental reports, aiming to construct a nuanced perspective on the current state of blockchain applications in European governmental structures. Key findings indicate that blockchain technology can significantly enhance transparency, efficiency, and security in governmental processes within the European context, albeit with certain challenges and barriers to implementation. This study serves as a pivotal reference point, fostering a deeper understanding of the intricate dynamics of blockchain technology in the European governmental sphere and paving the way for future research and policy developments. &lt;/p&gt;
Project based STEM activities as an effective educational technology in the context of blended learning

The study aims to investigate the implementation of project-based STEM activities within a blended learning framework in higher education. This approach encourages students' independent creativity and prepares them for contemporary work environments. STEM education is positioned as a tool to develop key competencies like multicultural understanding, linguistic skills, and adaptability. This innovative educational method emphasizes the real-world application of scientific, mathematical, technical, and engineering knowledge. The research methodology combines theoretical analysis with practical evaluation. Key findings underscore the significance of independent research, interdisciplinary integration, and skill development in a blended learning environment. A notable outcome emphasizes empowering students to craft scalable business models, such as startups, vital for success in today's world.
Analisa Faktor yang Mempengaruhi Kejadian Ikterik pada Neonatus

Jaundice is a symptom that is common in newborns. This jaundice usually disappears by the end of the first week or no later than the first ten days. This study aims to analyze the factors that influence the incidence of jaundice in neonates. This study used the literature review method by searching journals using three databases: PubMed, Google Scholar, and SpringerLink. Journal inclusion criteria published 2016 - 2021. Research results of jaundice in neonates. Factors that influence the incidence of neonatal jaundice in the Asian region, especially Southeast Asia, are maternal and perinatal. Maternal factors include gestational age, complications, pregnancy (incompatibility, ABO blood group, Rh and DM), type of delivery, and race. Perinatal factors include neonatal asphyxia, infection, birth trauma, low intake of breast milk, low birth weight (LBW), gender, and medication. The management of neonatal jaundice includes exposing the baby to the sun, phototherapy, adequate nutritional intake, and adequate breastfeeding. For this reason, health education and support from the husband and family are needed for the mother so that the baby does not experience jaundice.
Autonomous localized path planning algorithm for UAVs based on TD3 strategy

Abstract Aiming at the current local path planning methods for UAVs, the problem of weak portability due to the large influence of different controllers on its planning effect and the weak autonomous decision-making ability of UAV path planning, an autonomous local path planning algorithm for UAVs based on the TD3 policy update is proposed. On the basis of not changing the UAV's native controller, the decision-making model is designed to help the UAV realize local path planning work in unfamiliar environments based on environmental information. The model filters and extracts the UAV sensor, camera image information, and lidar information, inputs them into the reinforcement learning agent, outputs high-level command actions to the robot cooperative operating system, and transmits the movement commands from the operating system to the UAV, realizes the local path planning task of the UAV, and carries out the test experiments based on the training results. The test results show that the autonomous local path planning algorithm for UAVs based on deep reinforcement learning can effectively realize the autonomous local path planning task of UAVs, and the success rate reaches 93% under the interference of no obstacles, and 92% in the environment with obstacles. The success rate and portability of UAV implementation of path planning are improved.
Supervised machine learning for false data injection detection: accuracy sensitivity

Digitalization paves the road for power networks to evolve towards smart grids, where new intelligent devices are numerous and communication networks are closely interrelated with the power system. Smart grids face new threats due to these networks, and cyber-attacks drive them to fail. One of the most common attacks is False Data Injection (FDI), which only needs a vulnerable device to fake data sent; therefore, it can control and make power networks react to an event that is not really happening, subsequently leading to a malfunction. In this study, a decision tree is trained with variations of the same FDI attacks&apos; dataset to study how each variation affects the accuracy results to two different labels: location and status. To determine the accuracy sensitivity of the decision tree, eight variations for the training processes were used: (1) a complete and (2) reduced features dataset, (3) a real or (4) balanced dataset, (5) division criteria and (6) weight, and (7,8) two labels with different number of distinct values. The comparison aims to determine how the different number of distinct values in labels affects to the accuracy of the detection algorithm.
Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies

Microbiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate that some well chosen sign and rank transformations also allow for valid inference with compositional data, and we show how logistic regression and probabilistic index models can be used for testing for differential abundance, while inheriting the flexibility of a statistical modelling framework. The results of a simulation study demonstrate that the new methods perform better than most other methods, and that it is comparable with ANCOM-BC. These methods are implemented in an R-package ‘signtrans’ and can be installed from Github ( https://github.com/lucp9827/signtrans ).
Reducing the Use of Smart Devices Before Bedtime through design intervention

Due to the rapid proliferation of various smart devices in recent times, the usage of smart devices has been increasing rapidly. Particularly, the time spent using smart devices before bedtime constitutes a significant portion of overall smart device usage. Excessive use of smart devices before bedtime has negative effects on physical, mental, and social aspects. Therefore, in this study, we propose methods for bringing a behavior change based on behavioral theory, going beyond technology-based physical interventions, with the aim of reducing the usage time of smart devices before sleep. To achieve this, we designed two types of design-led interventions based on the feedback and feedforward concepts of behavioral theory and conducted a three-week long experiment. These proposed interventions were provided to the participants in each experimental group at a specific time (before going to bed). Based on the differences between the two groups, the study discussed the effectiveness of design-led interventions for instigating sustainable behavior change. This study holds significance as it offers practical guidance essential for the development of models and applications that may effectively utilise this particular design strategy.
Human Blood Circular System Educational Digital Comics for Improving Learning Outcomes

The learning outcomes of class V at SDN Bendungan Semarang in science subjects on the human circulatory system are low. The average learning outcome of fifth-grade students at SDN Bendungan is 63, where the score is less than the standard of minimum value for science subjects, namely 75. This study aims to develop the learning media "Digital Comics for Education on the Human Circulatory System" as well as to test the design of media development, test the feasibility of media, as well as to find out the effectiveness of the learning media "Digital Comic Education for the Human Blood Circulation System." The method used in this study is the Research and Development method with the Borg and Gall development model. The subjects in this research and development were: (1) Grade V students at SDN Bendungan Semarang in the 2022/2023 school year with 28 students; (2) Class V teachers at SDN Bendungan; (3) experts, expert lecturers, or experts as testers of learning media and provide input to the learning media. Data collection techniques are questionnaires, interviews, documentation, observation, and tests. Data analysis used validity, reliability, normality, t-tests, and N-gain tests. Based on the study's results, suitable for use with a percentage of material experts' assessment of 86% and a percentage of media experts' assessment of 95%. The percentage of teacher responses is 91%, and the percentage of student responses is 89%. The t-test result is 0.000, so there is a significant difference between the pretest and the post-test results. The result of the N-gain test is 0.60957 in the medium criteria category. So that the results obtained are: (1) the design of learning media that is developed according to the needs of students and teachers in teaching and learning activities; (2) the assessment of media experts and material experts with very feasible criteria; (3) the use of effective learning media in increasing student effectiveness and motivation. Based on the responses of teachers and students, the results of the study show that learning media is appropriate for use in teaching and learning activities