Reviewerly/exHarmony huggingface dataset:
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise Based Inter-Domain Knowledge

Face recognition in the unconstrained environment is an ongoing research challenge. Although several covariates of face recognition such as pose and low resolution have received significant attention, “disguise” is considered an onerous covariate of face recognition. One of the primary reasons for this is the scarcity of large and representative labeled databases, along with the lack of algorithms that work well for multiple covariates in such environments. In order to address the problem of face recognition in the presence of disguise, the paper proposes an active learning framework termed as A2-LINK. Starting with a face recognition machine-learning model, A2-LINK intelligently selects training samples from the target domain to be labeled and, using hybrid noises such as adversarial noise, fine-tunes a model that works well both in the presence and absence of disguise. Experimental results demonstrate the effectiveness and generalization of the proposed framework on the DFW and DFW2019 datasets with state-of-the-art deep learning featurization models such as LCSSE, ArcFace, and DenseNet.
Sequential Transfer in Reinforcement Learning with a Generative Model

We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved ones. The availability of solutions to related problems poses a fundamental trade-off: whether to seek policies that are expected to achieve high (yet sub-optimal) performance in the new task immediately or whether to seek information to quickly identify an optimal solution, potentially at the cost of poor initial behavior. In this work, we focus on the second objective when the agent has access to a generative model of state-action pairs. First, given a set of solved tasks containing an approximation of the target one, we design an algorithm that quickly identifies an accurate solution by seeking the state-action pairs that are most informative for this purpose. We derive PAC bounds on its sample complexity which clearly demonstrate the benefits of using this kind of prior knowledge. Then, we show how to learn these approximate tasks sequentially by reducing our transfer setting to a hidden Markov model and employing spectral methods to recover its parameters. Finally, we empirically verify our theoretical findings in simple simulated domains.
Decentralised Learning with Random Features and Distributed Gradient Descent

We investigate the generalisation performance of Distributed Gradient Descent with Implicit Regularisation and Random Features in the homogenous setting where a network of agents are given data sampled independently from the same unknown distribution. Along with reducing the memory footprint, Random Features are particularly convenient in this setting as they provide a common parameterisation across agents that allows to overcome previous difficulties in implementing Decentralised Kernel Regression. Under standard source and capacity assumptions, we establish high probability bounds on the predictive performance for each agent as a function of the step size, number of iterations, inverse spectral gap of the communication matrix and number of Random Features. By tuning these parameters, we obtain statistical rates that are minimax optimal with respect to the total number of samples in the network. The algorithm provides a linear improvement over single machine Gradient Descent in memory cost and, when agents hold enough data with respect to the network size and inverse spectral gap, a linear speed-up in computational runtime for any network topology. We present simulations that show how the number of Random Features, iterations and samples impact predictive performance.
Tails of Lipschitz Triangular Flows

We investigate the ability of popular flow based methods to capture tail-properties of a target density by studying the increasing triangular maps used in these flow methods acting on a tractable source density. We show that the density quantile functions of the source and target density provide a precise characterization of the slope of transformation required to capture tails in a target density. We further show that any Lipschitz-continuous transport map acting on a source density will result in a density with similar tail properties as the source, highlighting the trade-off between a complex source density and a sufficiently expressive transformation to capture desirable properties of a target density. Subsequently, we illustrate that flow models like Real-NVP, MAF, and Glow as implemented originally lack the ability to capture a distribution with non-Gaussian tails. We circumvent this problem by proposing tail-adaptive flows consisting of a source distribution that can be learned simultaneously with the triangular map to capture tail-properties of a target density. We perform several synthetic and real-world experiments to compliment our theoretical findings.
Dataset-Level Attribute Leakage in Collaborative Learning.

Secure multi-party machine learning allows several parties to build a model on their pooled data to increase utility while not explicitly sharing data with each other. We show that such multi-party computation can cause leakage of global dataset properties between the parties even when parties obtain only black-box access to the final model. In particular, a curious party can infer the distribution of sensitive attributes in other parties' data with high accuracy. This raises concerns regarding the confidentiality of properties pertaining to the whole dataset as opposed to individual data records. We show that our attack can leak population-level properties in datasets of different types, including tabular, text, and graph data. To understand and measure the source of leakage, we consider several models of correlation between a sensitive attribute and the rest of the data. Using multiple machine learning models, we show that leakage occurs even if the sensitive attribute is not included in the training data and has a low correlation with other attributes and the target variable.
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure

Complex devices are connected daily and eagerly generate vast streams of multidimensional state measurements. These devices often operate in distinct modes based on external conditions (day/night, occupied/vacant, etc.), and to prevent complete or partial system outage, we would like to recognize as early as possible when these devices begin to operate outside the normal modes. Unfortunately, it is often impractical or impossible to predict failures using rules or supervised machine learning, because failure modes are too complex, devices are too new to adequately characterize in a specific environment, or environmental change puts the device into an unpredictable condition. We propose an unsupervised anomaly detection method that creates a negative sample from the positive, observed sample, and trains a classifier to distinguish between positive and negative samples. Using the Contraction Principle, we explain why such a classifier ought to establish suitable decision boundaries between normal and anomalous regions, and show how Integrated Gradients can attribute the anomaly to specific variables within the anomalous state vector. We have demonstrated that negative sampling with random forest or neural network classifiers yield significantly higher AUC scores than Isolation Forest, One Class SVM, and Deep SVDD, against (a) a synthetic dataset with dimensionality ranging between 2 and 128, with 1, 2, and 3 modes, and with and without noise dimensions; (b) four standard benchmark datasets; and (c) a multidimensional, multimodal dataset from real climate control devices. Finally, we describe how negative sampling with neural network classifiers have been successfully deployed at large scale to predict failures in real time in over 15,000 climate-control and power meter devices in 145 Google office buildings.
An Approach for Detection and Localization of Missing Gate Faults in Reversible Circuit

Power consumption issues in VLSI circuits resulted in the emergence of promising technologies – like reversible computing. The introduction of such computing paradigm has turned out to be beneficial for the application of low power CMOS design, communications and in the emerging area of quantum computing. With these benefits, physical realization of reversible circuits has received significant attention in the semiconductor industry. Additionally, to make a robust implementation of this technology, testing has been considered an essential component and several works on this filed are being reported on a daily basis. Although, many works on the efficient detection of gate faults exist but our main focus is not only to detect the faults in the circuit but also to localize the fault with 100% accuracy. Considering this aspect, in this article we propose a fault detection and localization scheme for the existing missing gate fault models in reversible circuits. In our approach, initially test vectors (T) are computed and then an algorithm executes to generate a unique test set (U). Once the test set (U) is generated, it is applied to detect any type of faults if exist in the circuit. Our approach has been successfully tested for all types of missing gate faults – SMGF, PMGF and MMGF. In way to verify the functional correctness of our scheme, we also have executed the testing policy over a wide spectrum of benchmarks and experimental findings are cross checked. The obtained results have been compared with peer-reviewed works and improvements are reported.
The Role of Computer Music Technology in Improving the Quality of Music Teaching in Preschool Majors

Abstract The rise of computer technology has brought unprecedented opportunities for the development of music teaching, so that both music teaching and music learning have been improved efficiency and reduced difficulty. This paper explains the concept and development of computer music technology, and then analyzes the necessity and characteristics of music education in pre-school specialty. Finally, it elaborates the important role of evil in improving the quality of music teaching in pre-school specialty for the reference of people engaged in relevant teaching, learning and research.
Named Entity Recognition Method of Brazilian Legal Text based on pre-training model

Abstract Named entity recognition (NER) is a common task in Natural Language Processing (NLP). To this end, we propose a novel approach based on pre-training model to complete the sequence labeling tasks by learning the large-scale real-world data from Brazilian legal documents. Especially, combining iterated dilated convolution[1] (IDCNN) and Bi-LSTM, we develop the scalable sequence labeling model named Sequence Tagging Model (STM) and extensive experiments validate the effectiveness of STM for NER tasks. Furthermore, compared with the IDCNN-CRF model, the experimental results show that the STM is better and the F1 score is 93.23%, which provides an important basis for NER tasks.
Research on the Teaching Reform of the Basic Computer Teaching Course Assisted by Artificial Intelligence

Abstract With the rapid development of information technology, countries are striving to make efforts in the fields of information technology and artificial intelligence. The construction of the course system of artificial intelligence assisted computer basic teaching in colleges is not only related to the personal future of students, but also related to the future development of the national information industry. This paper first studies the current situation of basic computer teaching course’s teaching mode, then analyzes the existing problems in the current basic computer teaching course, and finally gives the construction strategy of the new basic computer teaching course’s teaching system.
Blood Lactate Concentration Prediction in Critical Care.

Blood lactate concentration is a reliable risk indicator of deterioration in critical care requiring frequent blood sampling. However, lactate measurement is an invasive procedure that can increase risk of infections. Yet there is no clinical consensus on the frequency of measurements. In response we investigate whether machine learning algorithms can be used to predict blood lactate concentration from ICU health records. We evaluate the performance of different prediction algorithms using a multi-centre critical care dataset containing 13,464 patients. Furthermore, we analyse impact of missing value handling methods in prediction performance for each algorithm. Our experimental analysis show promising results, establishing a baseline for further investigation into this problem.
A dependent counting INAR model with serially dependent innovation

To provide a more flexible model of count data, we extend the first-order integer-valued autoregressive model with serially dependent innovations based on the dependent thinning operator. This model is appropriate for modelling the number of dependent random events affecting each other when the number of new cases depend on the previous count through a linear functional relationship. Several statistical properties of the model are determined, parameters are estimated by some methods and their properties are studied via simulations. This study was carried out to investigate the efficiency of the new model by two real count data sets, the number of contagious diseases and robbery.
Constructive Texture Steganography Based on Compression Mapping of Secret Messages

This paper proposes a new constructive texture synthesis steganographic scheme by compressing original secret messages. First, we divide the original message into multiple bit blocks, which are transferred to decimal values... | Find, read and cite all the research you need on Tech Science Press
Active learning a convex body in low dimensions

Consider a set P ⊆ ℝ^d of n points, and a convex body C provided via a separation oracle. The task at hand is to decide for each point of P if it is in C using the fewest number of oracle queries. We show that one can solve this problem in two and three dimensions using O(⬡_P log n) queries, where ⬡_P is the largest subset of points of P in convex position. In 2D, we provide an algorithm which efficiently generates these adaptive queries.Furthermore, we show that in two dimensions one can solve this problem using O(⊚(P,C) log² n) oracle queries, where ⊚(P,C) is a lower bound on the minimum number of queries that any algorithm for this specific instance requires. Finally, we consider other variations on the problem, such as using the fewest number of queries to decide if C contains all points of P.As an application of the above, we show that the discrete geometric median of a point set P in ℝ² can be computed in O(n log² n (log n log log n + ⬡(P))) expected time.
Learning by Reusing Previous Advice in Teacher-Student Paradigm

Reinforcement Learning (RL) has been widely used to solve sequential decision-making problems. However, RL algorithms suffer from poor sample efficiency and require a long time to learn a suitable policy, especially when multiple agents are learning without prior knowledge. This problem can be alleviated through reusing knowledge from other agents during the learning process. One notable approach is advising actions based on a teacher-student relationship, where the decision of a student agent during learning is aided by an experienced teacher agent. A critical assumption in teacher-student paradigm is that the communication may be limited, so that a student may wait for a while and learn by itself before receiving the next advice. More importantly, in some noisy or stochastic environments, the student may not be able to master the advised actions when they are only performed once. We propose three methods for agents choosing between learning by exploration, asking for advice and reusing previous advice. The results show that our approaches significantly outperform existing advising methods without reusing advice.
Adaptive Trade-Offs in Off-Policy Learning

A great variety of off-policy learning algorithms exist in the literature, and new breakthroughs in this area continue to be made, improving theoretical understanding and yielding state-of-the-art reinforcement learning algorithms. In this paper, we take a unifying view of this space of algorithms, and consider their trade-offs of three fundamental quantities: update variance, fixed-point bias, and contraction rate. This leads to new perspectives of existing methods, and also naturally yields novel algorithms for off-policy evaluation and control. We develop one such algorithm, C-trace, demonstrating that it is able to more efficiently make these trade-offs than existing methods in use, and that it can be scaled to yield state-of-the-art performance in large-scale environments.
Hybrid CTC/Attention Architecture with Self-Attention and Convolution Hybrid Encoder for Speech Recognition

Abstract The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. The transformer model based on self-attention get a promising result. As you can see, the hybrid model based on Connectionist Temporal Classification (CTC)/Attention has very prominent advantages in decoding, which can combine the excellent sequence-to-sequence modeling ability of attention, and can also combine CTC to achieve temporal alignment. We propose SA-Conv-CTC/Attention model, which apply a Self-Attention and shallow Convolution based hybrid encoder to Hybrid CTC/Attention Architecture, and we also explored the method of decoding with Self-Attention language models. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder, even participated in decoding. We achieve a 0.8-4.75% error reduction compared to other hybrid CTC/Attention systems on WSJ and HKUST dataset.
What is Discussed about COVID-19: A Multi-modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis. With the development of online social media, people prefer to express their opinions and discuss the latest news online. We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time. It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online. In this paper, we take Sina Weibo, the most widely used online social media in China, for analysis and experiments. We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler, including texts and images posted by users. In order to effectively discover what is being discussed about COVID-19 without human labeling, we propose a unified multi-modal framework, including an unsupervised short-text topic model to discover and track bursty topics, and a selfsupervised model to learn image features so that we can retrieve related images about COVID-19. Experimental results have shown the effectiveness and superiority of the proposed models, and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.
Retracted: Target tracking and risk avoidance system for intelligent driving system based on 5G signal anomaly detection

The above article, published online on 29 June 2020 on Wiley Online Library ( https://onlinelibrary.wiley.com/doi/full/10.1002/dac.4518 ) has been retracted by agreement between the journal Editor‐in‐Chief, Mohammad Obaidat and John Wiley &amp; Sons Ltd. The retraction has been agreed following an investigation based on allegations raised by a third party. It found that the article contains inconsistencies including irrelevant citations and was not reviewed in line with the journal’s peer review standards. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Therefore, the article’s conclusions must be considered insufficiently supported.
Blended Learning as a form of Transition from Traditional Learning to Digital Learning

The introduction of massive open online courses (MOOCs) in the educational process is a natural response of universities to modern development trends. The article provides a brief analysis of the implementation of MOOCs in Russia and in the world. The most popular online courses that can be used in the learning process at any university are actively developing at the St. Petersburg Polytechnic University. The experience of the organization of blended learning at the Institute of Industrial Management, Economics and Trade of the St. Petersburg Polytechnic University using the developed and implemented mass open online courses is described in detail. The results of learning achievement changes, forms of communication and problems encountered in the implementation of the MOOCs are given. Directions of decision of arising up problems are offered. The next step in the development of education is the transition to digital learning with use of the developed MOOCs in SPbSPU as a form of training employees of various companies.