Reviewerly/exHarmony huggingface dataset:
Domain adaptation transfer learning soft sensor for product quality prediction

For multi-grade chemical processes, often, limited labeled data are available, resulting in an insufficient construction of reliable soft sensors for several modes. Additionally, the current soft sensors built in a specific mode cannot be directly extended to accurately predict the product qualities of other modes. In this paper, inspired by the idea of transfer learning, a domain adaptation extreme learning machine (DAELM) is developed to establish a simple soft sensor model suitable for multi-grade processes with limited labeled data. Additionally, an efficient model selection strategy is developed to select its model parameters. By utilizing and transferring the useful information from different operating conditions to the existing soft sensor, the prediction domain is enlarged and the prediction accuracy is enhanced. The prediction results of two multi-grade chemical processes demonstrate the advantages of DAELM as compared to the current popular soft sensors (e.g., extreme learning machine).
How to Record Quantum Queries, and Applications to Quantum Indifferentiability

The quantum random oracle model (QROM) has become the standard model in which to prove the post-quantum security of random-oracle-based constructions. Unfortunately, none of the known proof techniques allow the reduction to record information about the adversary’s queries, a crucial feature of many classical ROM proofs, including all proofs of indifferentiability for hash function domain extension. In this work, we give a new QROM proof technique that overcomes this “recording barrier”. We do so by giving a new “compressed oracle” which allows for efficient on-the-fly simulation of random oracles, roughly analogous to the usual classical simulation. We then use this new technique to give the first proof of quantum indifferentiability for the Merkle-Damgård domain extender for hash functions. We also give a proof of security for the Fujisaki-Okamoto transformation; previous proofs required modifying the scheme to include an additional hash term. Given the threat posed by quantum computers and the push toward quantum-resistant cryptosystems, our work represents an important tool for efficient post-quantum cryptosystems.
Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data

Large-scale face datasets usually exhibit a massive number of classes, a long-tailed distribution, and severe label noise, which undoubtedly aggravate the difficulty of training. In this paper, we propose a training strategy that treats the head data and the tail data in an unequal way, accompanying with noise-robust loss functions, to take full advantage of their respective characteristics. Specifically, the unequal-training framework provides two training data streams: the first stream applies the head data to learn discriminative face representation supervised by Noise Resistance loss; the second stream applies the tail data to learn auxiliary information by gradually mining the stable discriminative information from confusing tail classes. Consequently, both training streams offer complementary information to deep feature learning. Extensive experiments have demonstrated the effectiveness of the new unequal-training framework and loss functions. Better yet, our method could save a significant amount of GPU memory. With our method, we achieve the best result on MegaFace Challenge 2 (MF2) given a large-scale noisy training data set.
HaBiTs: Blockchain-based Telesurgery Framework for Healthcare 4.0

Telesurgery has a huge potential to deliver a real-time healthcare surgical services to the remote or distant locations with high quality and accuracy over the wireless communication channel. It provides benefits to the society with improved precision and accuracy in diagnosis procedures. However, the existing telesurgery system has the security, privacy, and interoperability issues, which limits its applicability in healthcare centers across the world in future. To mitigate these issues, in this paper, we propose, a framework named as HaBiTs (blockchain-based secure and flawless inter-operable telesurgery), where security can be achieved with immutability and interoperability by Smart Contracts (SCs). SC is a piece of code written in solidity or other blockchain specific languages to establish the trust between all the parties connected through blockchain and also eliminate the need of an intermediary for data sharing. Finally, we highlight some issues of the traditional telesurgery system and how they are mitigated with usage of the proposed HaBiTs framework.
Context-Aware Online Adaptation of Mixed Reality Interfaces

We present an optimization-based approach for Mixed Reality (MR) systems to automatically control when and where applications are shown, and how much information they display. Currently, content creators design applications, and users then manually adjust which applications are visible and how much information they show. This choice has to be adjusted every time users switch context, i.e., whenever they switch their task or environment. Since context switches happen many times a day, we believe that MR interfaces require automation to alleviate this problem. We propose a real-time approach to automate this process based on users' current cognitive load and knowledge about their task and environment. Our system adapts which applications are displayed, how much information they show, and where they are placed. We formulate this problem as a mix of rule-based decision making and combinatorial optimization which can be solved efficiently in real-time. We present a set of proof-of-concept applications showing that our approach is applicable in a wide range of scenarios. Finally, we show in a dual-task evaluation that our approach decreased secondary tasks interactions by 36%.
Label Enhancement for Label Distribution Learning

Label distribution is more general than both single-label annotation and multi-label annotation. It covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. To solve this problem, one way is to recover the label distributions from the logical labels in the training set via leveraging the topological information of the feature space and the correlation among the labels. Such process of recovering label distributions from logical labels is defined as label enhancement (LE), which reinforces the supervision information in the training sets. This paper proposes a novel LE algorithm called Graph Laplacian Label Enhancement (GLLE). Experimental results on one artificial dataset and fourteen real-world LDL datasets show clear advantages of GLLE over several existing LE algorithms. Furthermore, experimental results on eleven multi-label learning datasets validate the advantage of GLLE over the state-of-the-art multi-label learning approaches.
SDM

Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry. However, they are not effective to model dynamic and evolving preferences of users. In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors. Compared with existing sequence-aware recommendation methods, we tackle the following two inherent problems in real-world applications: (1) there could exist multiple interest tendencies in one session. (2) long-term preferences may not be effectively fused with current session interests. Long-term behaviors are various and complex, hence those highly related to the short-term session should be kept for fusion. We propose to encode behavior sequences with two corresponding components: multi-head self-attention module to capture multiple types of interests and long-short term gated fusion module to incorporate long-term preferences. Successive items are recommended after matching between sequential user behavior vector and item embedding vectors. Offline experiments on real-world datasets show the superior performance of the proposed SDM. Moreover, SDM has been successfully deployed on online large-scale recommender system at Taobao and achieves improvements in terms of a range of commercial metrics.
A Survey of Event Extraction From Text

Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles.How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction.Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts.As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades.This article provides a comprehensive yet up-to-date survey for event extraction from text.We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches.In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses.Last, we also present our envisions about future research directions.
Formal versus informal entrepreneurship in emerging economies: The roles of governance and the financial sector

This study contributes to the existing entrepreneurship literature by demonstrating how formal and informal entrepreneurship in emerging economies are differentially driven by the interplay between financial development and good governance. The following findings are obtained through the two-step system Generalized Method of Moments: (i) there exists an unconditional positive (negative) impact of financial development on formal (informal) entrepreneurship; (ii) the conditional effect of quality of governance increases formal entrepreneurship and decreases informal entrepreneurship; (iii) the net effects on formal entrepreneurship from the interactions of financial development with the indicators of governance quality are mostly positive, indicating that the quality of governance can be employed to enhance the positive weak effect of financial development on formal entrepreneurship; and (iv) the net effects on informal entrepreneurship from the interactions between financial development and the indicators of governance quality are negative for most estimated models, indicating that good governance can be used as a policy variable that improves the potentially weak impact of financial development on reducing informal entrepreneurship. Theoretical and empirical contributions, policy and practical implications are also discussed.
A Survey of Sentiment Analysis from Social Media Data

In the current era of automation, machines are constantly being channelized to provide accurate interpretations of what people express on social media. The human race nowadays is submerged in the idea of what and how people think and the decisions taken thereafter are mostly based on the drift of the masses on social platforms. This article provides a multifaceted insight into the evolution of sentiment analysis into the limelight through the sudden explosion of plethora of data on the internet. This article also addresses the process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks. The techniques of communalizing user data have also been surveyed in this article. Data, in its different forms, have also been analyzed and presented as a part of survey in this article. Other than this, the methods of evaluating sentiments have been studied, categorized, and compared, and the limitations exposed in the hope that this shall provide scope for better research in the future.
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

In this paper we develop a Quality Assessment approach for face recognition based on deep learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images with quality information related to their ICAO compliance level. The groundtruth quality labels are obtained using FaceNet to generate comparison scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making it capable of returning a numerical quality measure for each input image. Finally, we verify if the FaceQnet scores are suitable to predict the expected performance when employing a specific image for face recognition with a COTS face recognition system. Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development. FaceQnet is publicly available in GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Hierarchical Transformers for Long Document Classification

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm. We extend its fine-tuning procedure to address one of its major limitations - applicability to inputs longer than a few hundred words, such as transcripts of human call conversations. Our method is conceptually simple. We segment the input into smaller chunks and feed each of them into the base model. Then, we propagate each output through a single recurrent layer, or another transformer, followed by a softmax activation. We obtain the final classification decision after the last segment has been consumed. We show that both BERT extensions are quick to fine-tune and converge after as little as 1 epoch of training on a small, domain-specific data set. We successfully apply them in three different tasks involving customer call satisfaction prediction and topic classification, and obtain a significant improvement over the baseline models in two of them.
A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization

Inspired by the mathematical characteristics of sine and cosine trigonometric functions, the Sine Cosine Algorithm (SCA) has shown competitive performance among other meta-heuristic algorithms. However, despite its sufficient global search ability, its low exploitation ability and immature balance between exploitation and exploration remain weaknesses. In order to improve Sine Cosine Algorithm (SCA), this paper presents a modified version of the SCA called MSCA. Firstly, a non-linear transition rule is introduced instead of a linear transition to provide comparatively better transition from the exploration to exploitation. Secondly, the classical search equation of the SCA is modified by introducing the leading guidance based on the elite candidate solution. When the above proposed modified search mechanism fails to provide a better solution, in addition, a mutation operator is used to generate a new position to avoid the situation of getting trapped in locally optimal solutions during the search. Thus, the MSCA effectively maximizes the advantages of proposed strategies in maintaining a comparatively better balance of exploration and exploitation as compared to the classical SCA. The validity of the MSCA is tested on a set of 33 benchmark optimization problems and employed for training multilayer perceptrons. The numerical results and comparisons among several algorithms show the enhanced search efficiency of the MSCA.
Machine Learning in Computer Vision

During last few years the computer applications have gone dramatic transformation from simple data processing to machine learning, thanks to the availability and accessibility of huge volume of data collected through sensors and internet. The idea of machine learning demonstrates and propagates the facts that computer has the ability to improve itself with the passage of time. The western countries have shown great interest on the topic of machine learning, computer vision, and pattern recognition via organizing conferences, workshops, collective discussion, experimentation, and real life implementation. This study on machine learning and computer vision explores and analytically evaluates the machine learning applications in computer vision and predicts future prospects. The study has found that the machine learning strategies in computer vision are supervised, un-supervised, and semi-supervised. The commonly used algorithms are neural networks, k-means clustering, and support vector machine. The most recent applications of machine learning in computer vision are object detection, object classification, and extraction of relevant information from images, graphic documents, and videos. Additionally, Tensor flow, Faster-RCNN-Inception-V2 model, and Anaconda software development environment used to identify cars and persons in images.
Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix

Multi-view subspace clustering aims at separating data points into multiple underlying subspaces according to their multi-view features. Existing low-rank tensor representation-based multi-view subspace clustering algorithms are robust to noise and can preserve the high-order correlations of multi-view features. However, they may suffer from two common problems: (1) the local structures and different importance of each view feature are often neglected; (2) the low-rank representation tensor and affinity matrix are learned separately. To address these issues, we propose a unified framework to learn the Graph regularized Low-rank representation Tensor and Affinity matrix (GLTA) for multi-view subspace clustering. In the proposed GLTA framework, the tensor singular value decomposition-based tensor nuclear norm is adopted to explore the high-order cross-view correlations. The manifold regularization is exploited to preserve the local structures embedded in high-dimensional space. The importance of different features is automatically measured when constructing the final affinity matrix. An iterative algorithm is developed to solve GLTA using the alternating direction method of multipliers. Extensive experiments on seven challenging datasets demonstrate the superiority of GLTA over the state-of-the-art methods.
A Self-supervised Approach for Adversarial Robustness

Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e.g., for classification, segmentation and object detection. The vulnerability of DNNs against such attacks can prove a major roadblock towards their real-world deployment. Transferability of adversarial examples demand generalizable defenses that can provide cross-task protection. Adversarial training that enhances robustness by modifying target model's parameters lacks such generalizability. On the other hand, different input processing based defenses fall short in the face of continuously evolving attacks. In this paper, we take the first step to combine the benefits of both approaches and propose a self-supervised adversarial training mechanism in the input space. By design, our defense is a generalizable approach and provides significant robustness against the unseen adversarial attacks (e.g. by reducing the success rate of translation-invariant ensemble attack from 82.6% to 31.9% in comparison to previous stateof-the-art). It can be deployed as a plug-and-play solution to protect a variety of vision systems, as we demonstrate for the case of classification, segmentation and detection. Code is available at: https://github.com/ Muzammal-Naseer/NRP.
Just Go With the Flow: Self-Supervised Scene Flow Estimation

When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects. This is of particular interest in the field of autonomous driving, in which many cars, people, bicycles, and other objects need to be accurately tracked. Current state-of-the-art methods require annotated scene flow data from autonomous driving scenes to train scene flow networks with supervised learning. As an alternative, we present a method of training scene flow that uses two self-supervised losses, based on nearest neighbors and cycle consistency. These self-supervised losses allow us to train our method on large unlabeled autonomous driving datasets; the resulting method matches current state-of-the-art supervised performance using no real world annotations and exceeds state-of-the-art performance when combining our self-supervised approach with supervised learning on a smaller labeled dataset.
A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems

Whale Optimization Algorithm (WOA), as a newly developed meta-heuristic algorithm, performs well in solving optimization problems. A WOA with chaos mechanism based on quasi-opposition (OBCWOA) is proposed in this paper to overcome the slow convergence speed of the original WOA and to avoid being trapped in local optimal solutions when dealing with high-dimensional problems. We applied two strategies to the original WOA: using chaos mechanism to generate initial value to improve convergence speed of the algorithm and using the opposition-based learning method to balance exploration and development ability of the algorithm to help the algorithm jump out of local optimal solutions. The proposed algorithm is compared with other algorithms on unimodal functions, multimodal functions and fixed dimensional multimodal functions, and is applied to a famous engineering design problem. Results show that combination of the two strategies can improve convergence speed and enhance global search ability of the original WOA. OBCWOA proposed in this paper performs better than the other existing algorithms.