Reviewerly/exHarmony huggingface dataset:
LongReMix: Robust learning with high confidence samples in a noisy label environment

State-of-the-art noisy-label learning algorithms rely on an unsupervised learning to classify training samples as clean or noisy, followed by a semi-supervised learning (SSL) that minimises the empirical vicinal risk using a labelled set formed by samples classified as clean, and an unlabelled set with samples classified as noisy. The classification accuracy of such noisy-label learning methods depends on the precision of the unsupervised classification of clean and noisy samples, and the robustness of SSL to small clean sets. We address these points with a new noisy-label training algorithm, called LongReMix, which improves the precision of the unsupervised classification of clean and noisy samples and the robustness of SSL to small clean sets with a two-stage learning process. The stage one of LongReMix finds a small but precise high-confidence clean set, and stage two augments this high-confidence clean set with new clean samples and oversamples the clean data to increase the robustness of SSL to small clean sets. We test LongReMix on CIFAR-10 and CIFAR-100 with introduced synthetic noisy labels, and the real-world noisy-label benchmarks CNWL (Red Mini-ImageNet), WebVision, Clothing1M, and Food101-N. The results show that our LongReMix produces significantly better classification accuracy than competing approaches, particularly in high noise rate problems. Furthermore, our approach achieves state-of-the-art performance in most datasets. The code is available at https://github.com/filipe-research/LongReMix.
Recognition of Alzheimer’s Dementia From the Transcriptions of Spontaneous Speech Using fastText and CNN Models

Alzheimer’s dementia (AD) is a type of neurodegenerative disease that is associated with a decline in memory. However, speech and language impairments are also common in Alzheimer’s dementia patients. This work is an extension of our previous work, where we had used spontaneous speech for Alzheimer’s dementia recognition employing log-Mel spectrogram and Mel-frequency cepstral coefficients (MFCC) as inputs to deep neural networks (DNN). In this work, we explore the transcriptions of spontaneous speech for dementia recognition and compare the results with several baseline results. We explore two models for dementia recognition: 1) fastText and 2) convolutional neural network (CNN) with a single convolutional layer, to capture the n-gram-based linguistic information from the input sentence. The fastText model uses a bag of bigrams and trigrams along with the input text to capture the local word orderings. In the CNN-based model, we try to capture different n-grams (we use n = 2, 3, 4, 5) present in the text by adapting the kernel sizes to n. In both fastText and CNN architectures, the word embeddings are initialized using pretrained GloVe vectors. We use bagging of 21 models in each of these architectures to arrive at the final model using which the performance on the test data is assessed. The best accuracies achieved with CNN and fastText models on the text data are 79.16 and 83.33%, respectively. The best root mean square errors (RMSE) on the prediction of mini-mental state examination (MMSE) score are 4.38 and 4.28 for CNN and fastText, respectively. The results suggest that the n-gram-based features are worth pursuing, for the task of AD detection. fastText models have competitive results when compared to several baseline methods. Also, fastText models are shallow in nature and have the advantage of being faster in training and evaluation, by several orders of magnitude, compared to deep models.
Complex dynamical behaviors of a novel exponential hyper–chaotic system and its application in fast synchronization and color image encryption

This paper proposes a novel exponential hyper–chaotic system with complex dynamic behaviors. It also analyzes the chaotic attractor, bifurcation diagram, equilibrium points, Poincare map, Kaplan–Yorke dimension, and Lyapunov exponent behaviors. A fast terminal sliding mode control scheme is then designed to ensure the fast synchronization and stability of the new exponential hyper–chaotic system. Stability analysis was performed using the Lyapunov stability theory. One of the main features of the proposed controller is the finite time stability of the terminal sliding surface designed with high–order power function of error and derivative of error. The approach was implemented for image cryptosystem. Color image encryption was carried out to confirm the performance of the new hyper–chaotic system. For image encryption, the DNA encryption-based RGB algorithm was used. Performance assessment of the proposed approach confirmed the ability of the proposed hyper–chaotic system to increase the security of image encryption.
Assessment of urban resilience based on the transformation of resource-based cities: a case study of Panzhihua, China

Long-term development of resource utilization has caused a series of economic, social, and ecological problems in resource-based cities (RBCs).Thus, in pursuit of sustainable urban development, many RBCs have begun seriously pursuing urban transformation and have achieved good results.However, the RBCs' urban resilience also exhibits evident stage characteristics throughout the processes of urban transformation.Herein, we constructed an evaluation index system to measure the urban resilience of Panzhihua, China and analyzed the resilience time-varying characteristics and influencing factors from 2000 to 2016.The results show that: (1) after undergoing urban transformation, changes in Panzhihua's resilience can be divided into three stages consisting of a slow rising period (2000)(2001)(2002)(2003)(2004)(2005), a rapidly fluctuating rising period (2006)(2007)(2008)(2009)(2010), and a stable development period (2011)(2012)(2013)(2014)(2015)(2016).(2) Among the four divisions of urban resilience, the largest changes were found in the infrastructure and environment, and economic and social categories; only relatively small changes were observed in health and well-being, and government management capabilities.Nevertheless, all showed an upward trend.(3) Among the 12 indicators, transportation and communication, social security, education development, and comprehensive development ability were found to be closely linked to urban resilience.(4) Under the influence of urban transformation, RBCs' resilience greatly fluctuates, likely in response to actively transitioning the urban system from its previous state to its current state.Our study demonstrates that there is a high degree of correlation between the RBCs' life cycles and adaptation cycles.
Blockchain and novel coronavirus: Towards preventing COVID-19 and future pandemics

The present world has observed the SARS-CoV2 or COVID-19 spreading rapidly with a rising death toll and transmission rates with an absence of proper data management and information sharing. The current traditional database storage system has the limitations of a centralized control system and tampering of data, particularly when it is being shared with others. The Novel technology known as “Blockchain” is a distributed ledger technology that acts as a shared database, keeping all its copies synced and verified. The objective of this article is to study the concept of a Blockchain based pandemic data management system that would ensure unified patients’ data storage and reliable data management to trackdown coronavirus to combat against this and future pandemics.
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input

Named Entity Recognition (NER) remains difficult in real-world settings; current challenges include short texts (low context), emerging entities, and complex entities (e.g.movie names).Gazetteer features can help, but results have been mixed due to challenges with adding extra features, and a lack of realistic evaluation data.It has been shown that including gazetteer features can cause models to overuse or underuse them, leading to poor generalization.We propose GEMNET, a novel approach for gazetteer knowledge integration, including (1) a flexible Contextual Gazetteer Representation (CGR) encoder that can be fused with any word-level model; and (2) a Mixture-of-Experts gating network that overcomes the feature overuse issue by learning to conditionally combine the context and gazetteer features, instead of assigning them fixed weights.To comprehensively evaluate our approaches, we create 3 large NER datasets (24M tokens) reflecting current challenges.In an uncased setting, our methods show large gains (up to +49% F1) in recognizing difficult entities compared to existing baselines.On standard benchmarks, we achieve a new uncased SOTA on CoNLL03 and WNUT17.
CopyPaste: An Augmentation Method for Speech Emotion Recognition

Data augmentation is a widely used strategy for training robust machine learning models. It partially alleviates the problem of limited data for tasks like speech emotion recognition (SER), where collecting data is expensive and challenging. This study proposes CopyPaste, a perceptually motivated novel augmentation procedure for SER. Assuming that the presence of emotions other than neutral dictates a speaker’s overall perceived emotion in a recording, concatenation of an emotional (emotion E) and a neutral utterance can still be labeled with emotion E. We hypothesize that SER performance can be improved using these concatenated utterances in model training. To verify this, three CopyPaste schemes are tested on two deep learning models: one trained independently and another using transfer learning from an x-vector model, a speaker recognition model. We observed that all three CopyPaste schemes improve SER performance on all the three datasets considered: MSP-Podcast, Crema-D, and IEMOCAP. Additionally, CopyPaste performs better than noise augmentation and, using them together improves the SER performance further. Our experiments on noisy test sets suggested that CopyPaste is effective even in noisy test conditions.
Blockchain in Indonesia University: A Design Viewboard of Digital Technology Education

The challenge that has often occurred in recent years is making access to education using a different learning process path. The presence of technology now provides solutions to problems that often occur such as communication, accessing information, and business or cooperation. Blockchain is a technology that develops an evaluation model for itineraries in the learning process, both individually and in bulk. Currently the Edublocs project has been designed and implemented, which combines elements of peer-to-peer learning and the teaching team. The aim of the Edublocs project is to simplify the process of designing and implementing a system for recording activity results through blockchain technology. This ongoing project is in the process of evaluation. Conforming to some design elements as well as experimental implementation in the context of higher education enables us to further indicate the sustainability and relevance of the application of blockchain technology in education.
Change Point Enhanced Anomaly Detection for IoT Time Series Data

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.
Learning curves of generic features maps for realistic datasets with a teacher-student model*

Abstract Teacher-student models provide a framework in which the typical-case performance of high-dimensional supervised learning can be described in closed form. The assumptions of Gaussian i.i.d. input data underlying the canonical teacher-student model may, however, be perceived as too restrictive to capture the behaviour of realistic data sets. In this paper, we introduce a Gaussian covariate generalisation of the model where the teacher and student can act on different spaces, generated with fixed, but generic feature maps. While still solvable in a closed form, this generalization is able to capture the learning curves for a broad range of realistic data sets, thus redeeming the potential of the teacher-student framework. Our contribution is then two-fold: first, we prove a rigorous formula for the asymptotic training loss and generalisation error. Second, we present a number of situations where the learning curve of the model captures the one of a realistic data set learned with kernel regression and classification, with out-of-the-box feature maps such as random projections or scattering transforms, or with pre-learned ones—such as the features learned by training multi-layer neural networks. We discuss both the power and the limitations of the framework.
Deterministic Microwave-Optical Transduction Based on Quantum Teleportation

The coherent transduction between microwave and optical frequencies is critical to interconnect superconducting quantum processors over long distances. However, it is challenging to establish such a quantum interface with high efficiency and small added noise based on the standard direct conversion scheme. Here, we propose an electro-optic transduction system based on continuous-variable quantum teleportation. Reliable quantum-information transmission can be realized with an arbitrarily small cooperativity, in contrast to the direct conversion scheme, which requires a large minimum cooperativity. We show that the teleportation-based scheme maintains a significant rate advantage robustly for practical thermal noise and all values of cooperativity. We further investigate the performance in the transduction of complex quantum states such as cat states and Gottesman-Kitaev-Preskill (GKP) states and show that a higher fidelity can be achieved with the teleportation-based scheme. Our scheme significantly reduces the device requirement, and makes quantum transduction between microwave and optical frequencies feasible in the near future.
SSMix: Saliency-Based Span Mixup for Text Classification

Data augmentation with mixup has shown to be effective on various computer vision tasks.Despite its great success, there has been a hurdle to apply mixup to NLP tasks since text consists of discrete tokens with variable length.In this work, we propose SSMix, a novel mixup method where the operation is performed on input text rather than on hidden vectors like previous approaches.SSMix synthesizes a sentence while preserving the locality of two original texts by span-based mixing and keeping more tokens related to the prediction relying on saliency information.With extensive experiments, we empirically validate that our method outperforms hidden-level mixup methods on a wide range of text classification benchmarks, including textual entailment, sentiment classification, and questiontype classification.Our code is available at https://github.com/clovaai/ssmix.
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report

As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos. In this Mobile AI challenge, the target was to develop an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs. For this, the participants were provided with a novel learned ISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102-megapixel medium format camera. The runtime of all models was evaluated on the MediaTek Dimensity 1000+ platform with a dedicated AI processing unit capable of accelerating both floating-point and quantized neural networks. The proposed solutions are fully compatible with the above NPU and are capable of processing Full HD photos under 60-100 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.
Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks

A majority of research interests in irregular (e.g., nested or discontinuous) named entity recognition (NER) have been paid on nested entities, while discontinuous entities received limited attention. Existing work for discontinuous NER, however, either suffers from decoding ambiguity or predicting using token-level local features. In this work, we present an innovative model for discontinuous NER based on pointer networks, where the pointer simultaneously decides whether a token at each decoding frame constitutes an entity mention and where the next constituent token is. Our model has three major merits compared with previous work: (1) The pointer mechanism is memory-augmented, which enhances the mention boundary detection and interactions between the current decision and prior recognized mentions. (2) The encoder-decoder architecture can linearize the complexity of structure prediction, and thus reduce search costs. (3) The model makes every decision using global information, i.e., by consulting all the input, encoder and previous decoder output in a global view. Experimental results on the CADEC and ShARe13 datasets show that our model outperforms flat and hypergraph models as well as a state-of-the-art transition-based model for discontinuous NER. Further in-depth analysis demonstrates that our model performs well in recognizing various entities including flat, overlapping and discontinuous ones. More crucially, our model is effective on boundary detection, which is the kernel source to NER.
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions.To address this gap, we design annotation protocols motivated by an issues-viewpoints-assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads.We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data.To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain.Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.
Initiative Defense against Facial Manipulation

Benefiting from the development of generative adversarial networks (GAN), facial manipulation has achieved significant progress in both academia and industry recently. It inspires an increasing number of entertainment applications but also incurs severe threats to individual privacy and even political security meanwhile. To mitigate such risks, many countermeasures have been proposed. However, the great majority methods are designed in a passive manner, which is to detect whether the facial images or videos are tampered after their wide propagation. These detection-based methods have a fatal limitation, that is, they only work for ex-post forensics but can not prevent the engendering of malicious behavior. To address the limitation, in this paper, we propose a novel framework of initiative defense to degrade the performance of facial manipulation models controlled by malicious users. The basic idea is to actively inject imperceptible venom into target facial data before manipulation. To this end, we first imitate the target manipulation model with a surrogate model, and then devise a poison perturbation generator to obtain the desired venom. An alternating training strategy are further leveraged to train both the surrogate model and the perturbation generator. Two typical facial manipulation tasks: face attribute editing and face reenactment, are considered in our initiative defense framework. Extensive experiments demonstrate the effectiveness and robustness of our framework in different settings. Finally, we hope this work can shed some light on initiative countermeasures against more adversarial scenarios.
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing prac-tices focus on the image-based vehicle matching, including vehicle re-identification and vehicle tracking. In this paper, we apply one new modality, i.e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario. The natural language-based vehicle search poses one new challenge of fine-grained understanding of both vision and language modalities. To connect language and vision, we propose to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner. Except for the network structure design and the training strategy, several optimization objectives are also revisited in this work. The qualitative and quantitative experiments verify the effectiveness of the proposed method. Our proposed method has achieved the 1st place on the 5th AI City Challenge, yielding competitive performance 18.69% MRR accuracy on the private test set. We hope this work can pave the way for the future study on using language description effectively and efficiently for real-world vehicle retrieval systems. The code will be available at https://github.com/ShuaiBai623/AIC2021-T5-CLV.