Reviewerly/exHarmony huggingface dataset:
Quantum error correction on symmetric quantum sensors

The Heisenberg limit provides a quadratic improvement over the standard quantum limit, and is the maximum quantum advantage that linear quantum sensors could provide over classical methods. It remains elusive, however, because of the inevitable presence of noise decohering quantum sensors. Namely, if infinite rounds of quantum error correction corrects any part of a quantum sensor's signal, a no-go result purports that the standard quantum limit scaling can not be exceeded. We side-step this no-go result by using an optimal finite number of rounds of quantum error correction, such that even if part of the signal is corrected away, our quantum field sensing protocol can approach the Heisenberg limit in the face of a linear rate of deletion errors. Here, we focus on quantum error correction codes within the symmetric subspace. Symmetric states of collective angular momentum are good candidates for multi-qubit probe states in quantum sensors because they are easy to prepare and can be controlled without requiring individual addressability. We show how the representation theory of the symmetric group can inform practical decoding algorithms for permutation-invariant codes. These decoding algorithms involve measurements of total angular momentum, quantum Schur transforms or logical state teleportations, and geometric pulse gates. Our completion of the theory of permutation-invariant codes allows near-term implementation of quantum error correction using simple quantum control techniques.
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety

Large language models (LLMs) have exploded in popularity in the past few years and have achieved undeniably impressive results on benchmarks as varied as question answering and text summarization. We provide a simple new prompting strategy that leads to yet another supposedly "super-human" result, this time outperforming humans at common sense ethical reasoning (as measured by accuracy on a subset of the ETHICS dataset). Unfortunately, we find that relying on average performance to judge capabilities can be highly misleading. LLM errors differ systematically from human errors in ways that make it easy to craft adversarial examples, or even perturb existing examples to flip the output label. We also observe signs of inverse scaling with model size on some examples, and show that prompting models to "explain their reasoning" often leads to alarming justifications of unethical actions. Our results highlight how human-like performance does not necessarily imply human-like understanding or reasoning.
Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation

Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of deep learning models. Such PIs are useful or "high-quality" as long as they are sufficiently narrow and capture most of the probability density. In this paper, we present a method to learn prediction intervals for regression-based neural networks automatically in addition to the conventional target predictions. In particular, we train two companion neural networks: one that uses one output, the target estimate, and another that uses two outputs, the upper and lower bounds of the corresponding PI. Our main contribution is the design of a novel loss function for the PI-generation network that takes into account the output of the target-estimation network and has two optimization objectives: minimizing the mean prediction interval width and ensuring the PI integrity using constraints that maximize the prediction interval probability coverage implicitly. Furthermore, we introduce a self-adaptive coefficient that balances both objectives within the loss function, which alleviates the task of fine-tuning. Experiments using a synthetic dataset, eight benchmark datasets, and a real-world crop yield prediction dataset showed that our method was able to maintain a nominal probability coverage and produce significantly narrower PIs without detriment to its target estimation accuracy when compared to those PIs generated by three state-of-the-art neural-network-based methods. In other words, our method was shown to produce higher-quality PIs.
A picture is worth a thousand words, or Visualization of learning information at informatics lessons

The principle of visibility is one of the most effective in teaching students. The use of diagrams, graphs, tables, drawings contributes to the rapid understanding and memorization of the material being studied. Taking into account modern technical capabilities, the idea of v isualizing information in the learning process acquires new features. The article discusses modern visualization techniques that can be used at informatics lessons. Programs and free online services for creating a timeline, mind maps, scribing and infographics are listed. The proposed services will be useful for teachers in creating high-quality visual material. The article demonstrates with specific examples how, using various visualization techniques, it is possible to present educational material on informatics at different stages of the lesson.
Integrated course "Methods of approximate calculations" in classes of physics and mathematics profile and engineering profile

The Federal State Educational Standard of Secondary General Education determines the results of mastering the curricula of school disciplines at an advanced level with a focus on preparation for subsequent professional education. The article discusses the possibilities of the training course "Methods of approximate calculations" to achieve the planned results of mastering the educational program in classes with in-depth study of mathematics, physics, informatics. The content of the course is presented, one of the options for thematic planning (10–11th grades) is given. As an example, a physical problem and its solution are considered in accordance with the stages of algorithmic problem solving: from the formulation of the problem to the analysis of the received answer. Two options for implementing the created mathematical model are proposed: by writing code in the Python programming language and by tools of a spreadsheet editor.
How Does Independence Help Generalization? Sample Complexity of ERM on Product Distributions

While many classical notions of learnability (e.g., PAC learnability) are distribution-free, utilizing the specific structures of an input distribution may improve learning performance. For example, a product distribution on a multi-dimensional input space has a much simpler structure than a correlated distribution. A recent paper [GHTZ21] shows that the sample complexity of a general learning problem on product distributions is polynomial in the input dimension, which is exponentially smaller than that on correlated distributions. However, the learning algorithm they use is not the standard Empirical Risk Minimization (ERM) algorithm. In this note, we characterize the sample complexity of ERM in a general learning problem on product distributions. We show that, even though product distributions are simpler than correlated distributions, ERM still needs an exponential number of samples to learn on product distributions, instead of a polynomial. This leads to the conclusion that a product distribution by itself does not make a learning problem easier -- an algorithm designed specifically for product distributions is needed.
Privacy-preserving Security Inference Towards Cloud-Edge Collaborative Using Differential Privacy

Cloud-edge collaborative inference approach splits deep neural networks (DNNs) into two parts that run collaboratively on resource-constrained edge devices and cloud servers, aiming at minimizing inference latency and protecting data privacy. However, even if the raw input data from edge devices is not directly exposed to the cloud, state-of-the-art attacks targeting collaborative inference are still able to reconstruct the raw private data from the intermediate outputs of the exposed local models, introducing serious privacy risks. In this paper, a secure privacy inference framework for cloud-edge collaboration is proposed, termed CIS, which supports adaptively partitioning the network according to the dynamically changing network bandwidth and fully releases the computational power of edge devices. To mitigate the influence introduced by private perturbation, CIS provides a way to achieve differential privacy protection by adding refined noise to the intermediate layer feature maps offloaded to the cloud. Meanwhile, with a given total privacy budget, the budget is reasonably allocated by the size of the feature graph rank generated by different convolution filters, which makes the inference in the cloud robust to the perturbed data, thus effectively trade-off the conflicting problem between privacy and availability. Finally, we construct a real cloud-edge collaborative inference computing scenario to verify the effectiveness of inference latency and model partitioning on resource-constrained edge devices. Furthermore, the state-of-the-art cloud-edge collaborative reconstruction attack is used to evaluate the practical availability of the end-to-end privacy protection mechanism provided by CIS.
Towards trustworthy phoneme boundary detection with autoregressive model and improved evaluation metric

Phoneme boundary detection has been studied due to its central role in various speech applications. In this work, we point out that this task needs to be addressed not only by algorithmic way, but also by evaluation metric. To this end, we first propose a state-of-the-art phoneme boundary detector that operates in an autoregressive manner, dubbed SuperSeg. Experiments on the TIMIT and Buckeye corpora demonstrates that SuperSeg identifies phoneme boundaries with significant margin compared to existing models. Furthermore, we note that there is a limitation on the popular evaluation metric, R-value, and propose new evaluation metrics that prevent each boundary from contributing to evaluation multiple times. The proposed metrics reveal the weaknesses of non-autoregressive baselines and establishes a reliable criterion that suits for evaluating phoneme boundary detection.
Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design

Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.
Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble

Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples. Unfortunately, the distant supervision may induce noisy labels, thus undermining the robustness of the learned models and restricting the practical application. To relieve this problem, recent works adopt self-training teacher-student frameworks to gradually refine the training labels and improve the generalization ability of NER models. However, we argue that the performance of the current self-training frameworks for DS-NER is severely underestimated by their plain designs, including both inadequate student learning and coarse-grained teacher updating. Therefore, in this paper, we make the first attempt to alleviate these issues by proposing: (1) adaptive teacher learning comprised of joint training of two teacher-student networks and considering both consistent and inconsistent predictions between two teachers, thus promoting comprehensive student learning. (2) fine-grained student ensemble that updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise. To verify the effectiveness of our proposed method, we conduct experiments on four DS-NER datasets. The experimental results demonstrate that our method significantly surpasses previous SOTA methods.
SST: Real-time End-to-end Monocular 3D Reconstruction via Sparse Spatial-Temporal Guidance

Real-time monocular 3D reconstruction is a challenging problem that remains unsolved. Although recent end-to-end methods have demonstrated promising results, tiny structures and geometric boundaries are hardly captured due to their insufficient supervision neglecting spatial details and oversimplified feature fusion ignoring temporal cues. To address the problems, we propose an end-to-end 3D reconstruction network SST, which utilizes Sparse estimated points from visual SLAM system as additional Spatial guidance and fuses Temporal features via a novel cross-modal attention mechanism, achieving more detailed reconstruction results. We propose a Local Spatial-Temporal Fusion module to exploit more informative spatial-temporal cues from multi-view color information and sparse priors, as well a Global Spatial-Temporal Fusion module to refine the local TSDF volumes with the world-frame model from coarse to fine. Extensive experiments on ScanNet and 7-Scenes demonstrate that SST outperforms all state-of-the-art competitors, whilst keeping a high inference speed at 59 FPS, enabling real-world applications with real-time requirements.
Modelling Stance Detection as Textual Entailment Recognition and Leveraging Measurement Knowledge from Social Sciences

Stance detection (SD) can be considered a special case of textual entailment recognition (TER), a generic natural language task. Modelling SD as TER may offer benefits like more training data and a more general learning scheme. In this paper, we present an initial empirical analysis of this approach. We apply it to a difficult but relevant test case where no existing labelled SD dataset is available, because this is where modelling SD as TER may be especially helpful. We also leverage measurement knowledge from social sciences to improve model performance. We discuss our findings and suggest future research directions.
Domain Adaptation for Dense Retrieval through Self-Supervision by Pseudo-Relevance Labeling

Although neural information retrieval has witnessed great improvements, recent works showed that the generalization ability of dense retrieval models on target domains with different distributions is limited, which contrasts with the results obtained with interaction-based models. To address this issue, researchers have resorted to adversarial learning and query generation approaches; both approaches nevertheless resulted in limited improvements. In this paper, we propose to use a self-supervision approach in which pseudo-relevance labels are automatically generated on the target domain. To do so, we first use the standard BM25 model on the target domain to obtain a first ranking of documents, and then use the interaction-based model T53B to re-rank top documents. We further combine this approach with knowledge distillation relying on an interaction-based teacher model trained on the source domain. Our experiments reveal that pseudo-relevance labeling using T53B and the MiniLM teacher performs on average better than other approaches and helps improve the state-of-the-art query generation approach GPL when it is fine-tuned on the pseudo-relevance labeled data.
SENTIMENT ANALYSIS OF LOCKDOWN IN INDIA DURING COVID-19 A CASE STUDY

With the rapid increase in the use of the Internet, sentiment analysis has become one of the most popular fields of natural language processing (NLP).Using sentiment analysis, the implied emotion in the text can be mined effectively for different occasions.People are using social media to receive and communicate different types of information on a massive scale during COVID-19 outburst.Mining such content to evaluate people's sentiments can play a critical role in making decisions to keep the situation under control.The objective of this study is to mine the sentiments of Indian citizens regarding the nationwide lockdown enforced by the Indian government to reduce the rate of spreading of Coronavirus.In this work, the sentiment analysis of tweets posted by Indian citizens has been performed using NLP and machine learning classifiers.From April 5, 2020 to April 17, 2020, a total of 12 741 tweets having the keywords "Indialockdown" are extracted.Data have been extracted from Twitter using Tweepy API, annotated using TextBlob and VADER lexicons, and preprocessed using the natural language tool kit provided by the Python.Eight different classifiers have been used to classify the data.The experiment achieved the highest accuracy of 84.4% with LinearSVC classifier and unigrams.This study concludes that the majority of Indian citizens are supporting the decision of the lockdown implemented by the Indian government during corona outburst.
Differentially Private Tree-Based Redescription Mining

Differential privacy provides a strong form of privacy and allows preserving most of the original characteristics of the dataset. Utilizing these benefits requires one to design specific differentially private data analysis algorithms. In this work, we present three tree-based algorithms for mining redescriptions while preserving differential privacy. Redescription mining is an exploratory data analysis method for finding connections between two views over the same entities, such as phenotypes and genotypes of medical patients, for example. It has applications in many fields, including some, like health care informatics, where privacy-preserving access to data is desired. Our algorithms are the first differentially private redescription mining algorithms, and we show via experiments that, despite the inherent noise in differential privacy, it can return trustworthy results even in smaller datasets where noise typically has a stronger effect.
Broadband Digital Over-the-Air Computation for Wireless Federated Edge Learning

This paper presents the first orthogonal frequency-division multiplexing(OFDM)-based digital over-the-air computation (AirComp) system for wireless federated edge learning, where multiple edge devices transmit model data simultaneously using non-orthogonal OFDM subcarriers, and the edge server aggregates data directly from the superimposed signal. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog transmission for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation for phase-asynchronous multi-user OFDM systems. To realize a digital AirComp system, we develop a medium access control (MAC) protocol that allows simultaneous transmissions from different users using non-orthogonal OFDM subcarriers, and put forth joint channel decoding and aggregation decoders tailored for convolutional and LDPC codes. To verify the proposed system design, we build a digital AirComp prototype on the USRP software-defined radio platform, and demonstrate a real-time LDPC-coded AirComp system with up to four users. Trace-driven simulation results on test accuracy versus SNR show that: 1) analog AirComp is sensitive to phase asynchrony in practical multi-user OFDM systems, and the test accuracy performance fails to improve even at high SNRs; 2) our digital AirComp system outperforms two analog AirComp systems at all SNRs, and approaches the optimal performance when SNR $\geq$ 6 dB for two-user LDPC-coded AirComp, demonstrating the advantage of digital AirComp in phase-asynchronous multi-user OFDM systems.
PERKEMBANGAN TEKNOLOGI INFORMASI DAN KOMUNIKASI DALAM MENDUKUNG PENGGUNAAN MEDIA PEMBELAJARAN

Learning media is a means of channeling learning messages and information. Learning media that are well designed really help students digest and understand the subject matter. The existence of learning media as a tool in the learning process is a fact that cannot be denied. The teacher as a messenger has a great interest in facilitating his task in conveying messages or learning materials to students. The teacher also realizes that without media, learning material will be difficult for students to digest and understand, especially if the learning material that must be delivered is complicated and complex. For this reason, the use of media is absolutely necessary so that material can reach students effectively and efficiently. In general, the benefits of media in the learning process are facilitating interactions between teachers and students so that learning activities will be more effective and efficient. The development of information technology in the current era of globalization and information has spurred the development of more advanced learning media as well. The use of Information and Communication Technology (ICT) as a learning medium is already a requirement. Although the design of ICT-based media requires special expertise, it does not mean that the media is avoided and abandoned. ICT-based learning media can be in the form of internet, intranet, mobile phone, and CD Room/Flash Disk. Advances in Information Technology have led to many changes.
KETERBACAN BAHAN AJAR SMA/SMK KELAS X PADA BAB VIII MENDALAMI PUISI DAN RELEVANSINYA DENGAN PEMAHAMAN SISWA

Teaching and learning activities in schools are not uncommon to find an obstacle in understanding a material in teaching materials. Based on the data obtained by the researchers related to the revised 2017 edition of the Indonesian language textbook for class X, especially regarding the material for studying poetry in chapter VIII, it was found that students had difficulty understanding the contents of the diction as outlined in poetry verses and students had difficulty in concluding the overall meaning of the diction contained in the text of the poem. The purpose of this study is to show and describe the level of readability of poetry texts in the revised 2017 edition of Indonesian textbooks, and to describe the relevance of poetry texts in class X to students' understanding. Qualitative descriptive is the approach used in this research process. Readability analysis using Fry's graphic formula, discourse text of poetry J1 "Sajak Anak Muda" (W. S. Rendra) is in reader class 8, code J2 "Doa" (Chairil Anwar) is in reader class 4, code J3 "Have You Torn Blue Cloth in That Flag” (Aming Aminoedin) is in the 8th reading class, the j4 code “Sajak Matahari” (W. S. Rendra) is in the 7th reading class, the J5 code “Mother” (D. Zamawi Imron) is in the 8th reading class. less able to understand and still having difficulty in concluding the poetry text presented. This is considered irrelevant to the results of the readability analysis obtained, because based on the results of the analysis the readability index of the five poetry texts presented is low, and indicates that the reading area is at the 7th, 8th, and 9th class level of SMP/MTs equivalent, which means it is under class 10.