Reviewerly/exHarmony huggingface dataset:
Illumination-robust face recognition based on deep convolutional neural networks architectures

&lt;p&gt;&lt;span&gt;In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classiﬁcation. For learning the features followed by classiﬁcation we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.&lt;/span&gt;&lt;/p&gt;
Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning

<h2>Abstract</h2> Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided tumor bud selection. Development of computer algorithms for this purpose requires unequivocal examples of individual tumor buds. As such, we undertook a large-scale, international, and digital observer study on individual tumor bud assessment. From a pool of 46 colorectal cancer cases with tumor budding, 3000 tumor bud candidates were selected, largely based on digital image analysis algorithms. For each candidate bud, an image patch (size 256 × 256 µm) was extracted from a pan cytokeratin-stained whole-slide image. Members of an International Tumor Budding Consortium (<i>n</i> = 7) were asked to categorize each candidate as either (1) tumor bud, (2) poorly differentiated cluster, or (3) neither, based on current definitions. Agreement was assessed with Cohen's and Fleiss Kappa statistics. Fleiss Kappa showed moderate overall agreement between observers (0.42 and 0.51), while Cohen's Kappas ranged from 0.25 to 0.63. Complete agreement by all seven observers was present for only 34% of the 3000 tumor bud candidates, while 59% of the candidates were agreed on by at least five of the seven observers. Despite reports of moderate-to-substantial agreement with respect to tumor budding grade, agreement with respect to individual pan cytokeratin-stained tumor buds is moderate at most. A machine learning approach may prove especially useful for a more robust assessment of individual tumor buds.
SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification

Feature representation learning, an emerging topic in recent years, has achieved great progress. Powerful learned features can lead to excellent classification accuracy. In this article, a selective and robust feature representation framework with a supervised constraint (SRSC) is presented. SRSC seeks a selective, robust, and discriminative subspace by transforming the original feature space into the category space. Particularly, we add a selective constraint to the transformation matrix (or classifier parameter) that can select discriminative dimensions of the input samples. Moreover, a supervised regularization is tailored to further enhance the discriminability of the subspace. To relax the hard zero-one label matrix in the category space, an additional error term is also incorporated into the framework, which can lead to a more robust transformation matrix. SRSC is formulated as a constrained least square learning (feature transforming) problem. For the SRSC problem, an inexact augmented Lagrange multiplier method (ALM) is utilized to solve it. Extensive experiments on several benchmark data sets adequately demonstrate the effectiveness and superiority of the proposed method. The proposed SRSC approach has achieved better performances than the compared counterpart methods.
Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ALSC) aims at predicting the sentiment polarity of a specific aspect term occurring in a sentence. This task requires learning a representation by aggregating the relevant contextual features concerning the aspect term. Existing methods cannot sufficiently leverage the syntactic structure of the sentence, and hence are difficult to distinguish different sentiments for multiple aspects in a sentence. We perceive the limitations of the previous methods and propose a hypothesis about finding crucial contextual information with the help of syntactic structure. For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. Empirical studies show that our model outperforms recent models on most of the benchmark datasets for the ALSC task. The results suggest that our method for incorporating syntactic structure enriches the representation for the classification.
Adversarial Learning of Privacy-Preserving and Task-Oriented Representations

Data privacy has emerged as an important issue as data-driven deep learning has been an essential component of modern machine learning systems. For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks. Our work aims at learning a privacy-preserving and task-oriented representation to defend against such model inversion attacks. Specifically, we propose an adversarial reconstruction learning framework that prevents the latent representations decoded into original input data. By simulating the expected behavior of adversary, our framework is realized by minimizing the negative pixel reconstruction loss or the negative feature reconstruction (i.e., perceptual distance) loss. We validate the proposed method on face attribute prediction, showing that our method allows protecting visual privacy with a small decrease in utility performance. In addition, we show the utility-privacy trade-off with different choices of hyperparameter for negative perceptual distance loss at training, allowing service providers to determine the right level of privacy-protection with a certain utility performance. Moreover, we provide an extensive study with different selections of features, tasks, and the data to further analyze their influence on privacy protection.
Image Cropping with Composition and Saliency Aware Aesthetic Score Map

Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we propose an interpretable image cropping model to unveil the mystery. For each image, we use a fully convolutional network to produce an aesthetic score map, which is shared among all candidate crops during crop-level aesthetic evaluation. Then, we require the aesthetic score map to be both composition-aware and saliency-aware. In particular, the same region is assigned with different aesthetic scores based on its relative positions in different crops. Moreover, a visually salient region is supposed to have more sensitive aesthetic scores so that our network can learn to place salient objects at more proper positions. Such an aesthetic score map can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by our model. We show the competitive performance of our model in the image cropping task on several benchmark datasets, and also demonstrate its generality in real-world applications.
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding

Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years. Compared to the traditional pipeline system, the end-to-end ST model has potential benefits of lower latency, smaller model size, and less error propagation. However, it is notoriously difficult to implement such a model without transcriptions as intermediate. Existing works generally apply multi-task learning to improve translation quality by jointly training end-to-end ST along with automatic speech recognition (ASR). However, different tasks in this method cannot utilize information from each other, which limits the improvement. Other works propose a two-stage model where the second model can use the hidden state from the first one, but its cascade manner greatly affects the efficiency of training and inference process. In this paper, we propose a novel interactive attention mechanism which enables ASR and ST to perform synchronously and interactively in a single model. Specifically, the generation of transcriptions and translations not only relies on its previous outputs but also the outputs predicted in the other task. Experiments on TED speech translation corpora have shown that our proposed model can outperform strong baselines on the quality of speech translation and achieve better speech recognition performances as well.
DeepCPDP: Deep Learning Based Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is an active research topic in the domain of software defect prediction, since CPDP can be applied to the following scenarios: the target project for software defect prediction is a new project or the target project does not have enough labeled modules. Most of the previous work tried to utilize the labeled dataset gathered from other projects (i.e., the source projects) and then proposed transfer learning based methods to reduce the data distribution difference between different projects. In this article, we propose a deep learning based CPDP method DeepCPDP. For this method, we represent source code of each extracted program module by using simplified abstract syntax tree (SimAST). For a node of SimAST, we only keep its node type, since this is project-independent, while we ignore the name of method and variable, since these information are project-specific. Therefore, SimAST is project-independent and especially suitable for the task of CPDP. Then, we extract the token vector from each module after it is modeled via SimAST. Moreover, we design a new unsupervised based embedding method SimASTToken2Vec to learn meaningful representation for these extracted token vectors. Later, we employ Bi-directional Long Short-Term Memory (BiLSTM) neural network to automatically learn semantic features from embedded token vectors. In addition, we use attention mechanism over the BiLSTM layer to learn the weight of the vectors from the learned semantic features. Finally, we construct CPDP models via Logistic regression classifier. To show the effectiveness of DeepCPDP, ten large-scale projects from different application domains are used and AUC measure is used to measure the prediction performance of trained models. By using Scott-Knott test, we can find DeepCPDP can significantly outperform eight state-of-the-art baselines. Moreover, we also verify that the usage of SimASTToken2Vec, BiLSTM and attention mechanism is competitive in our proposed method.
Controlling the Amount of Verbatim Copying in Abstractive Summarization

An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same flexibility, i.e., learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the significance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.
Quantum Key Distribution Over Double-Layer Quantum Satellite Networks

Quantum key distribution (QKD) has attracted much attention on secure communications across global networks. QKD over satellite networks can overcome the limitations of terrestrial optical networks, such as large attenuation over long distance fiber channel and difficulty of intercontinental domain communications. Different QKD networks (around the world) can intercommunicate through quantum satellites, leading to a global quantum network in near future. This raises a new resource allocation and management problem of QKD involving multiple satellite layers and distributed ground stations. Using existing schemes, a single satellite cannot perform QKD for ground stations for the whole day. Moreover, the research problem is more challenging due to limitations of satellite coverage: limited cover time of low earth orbit (LEO) satellite, high channel losses of geostationary earth orbit (GEO) satellite, etc. To overcome these limitations, our study proposes a double-layer quantum satellite network (QSN) implemented quantum key pool (QKP) to relay keys for ground stations. We propose a new architecture of trusted-repeater-based double-layer quantum satellite networks, comprising GEO and LEO satellites. We also address the routing and key allocation (RKA) problem for key-relay services over QSNs. We propose a novel joint GEO-LEO routing and key allocation (JGL-RKA) algorithm to solve the RKA problem. Simulative results show that the proposed scheme can increase success probability of key-relay services significantly. We also present the impact of different route selections mechanisms, number of satellite links, satellite node capability, and service granularity on network performance.
Mixed Reality Office System Based on Maslow’s Hierarchy of Needs: Towards the Long-Term Immersion in Virtual Environments

In a mixed reality (MR) environment that combines the physical objects with the virtual environments, users' feelings are immersed in the virtual world, while their bodies remain in the physical world. Compared to the purely physical environments, such characteristic has led to some special needs for users' long-term immersion. However, the deficiency needs that we have to face for long-term immersion still need further research. In this paper, we apply the theory of Maslow's Hierarchy of Needs (MHN) to guide the design of MR systems for long-term immersion. Taking the normal biological rhythm of human beings as the basic unit (24 hours), we propose the fundamental needs for long-term immersion in VEs through combining the theory of MHN with the special needs of virtual reality (VR). In order to verify whether those needs can satisfy users' long-term immersion, we design an MR office system for basic operations based on the theory of MHN. A long-term exposure experiment (duration of 8 hours) is designed to evaluate those needs by comparing the results with a physical work environment after a short-term preliminary study. The physiological and psychological effects are tested in both two environments and the deficiency needs for short-term immersion and long-term immersion are also compared. The results showed that the design based on the theory of MHN can support users' long-term immersion, which means that it can be a guideline for long-term use of MR systems.
Neural Network Generalization: The Impact of Camera Parameters

We quantify the generalization of a convolutional neural network (CNN) trained to identify cars.First, we perform a series of experiments to train the network using one image dataset -either synthetic or from a camera -and then test on a different image dataset.We show that generalization between images obtained with different cameras is roughly the same as generalization between images from a camera and ray-traced multispectral synthetic images.Second, we use ISETAuto, a soft prototyping tool that creates ray-traced multispectral simulations of camera images, to simulate sensor images with a range of pixel sizes, color filters, acquisition and post-acquisition processing.These experiments reveal how variations in specific camera parameters and image processing operations impact CNN generalization.We find that (a) pixel size impacts generalization, (b) demosaicking substantially impacts performance and generalization for shallow (8-bit) bit-depths but not deeper ones (10-bit), (c) the network performs well using raw (not demosaicked) sensor data for 10-bit pixels.
Multi-Tenant Provisioning for Quantum Key Distribution Networks With Heuristics and Reinforcement Learning: A Comparative Study

Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization.
Fine-grained facial expression analysis using dimensional emotion model

Automated facial expression analysis has a variety of applications in human-computer interaction. Traditional methods mainly analyze prototypical facial expressions of no more than eight discrete emotions as a classification task. However, in practice, spontaneous facial expressions in a naturalistic environment can represent not only a wide range of emotions, but also different intensities within an emotion family. In such situations, these methods are not reliable or adequate. In this paper, we propose to train deep convolutional neural networks (CNNs) to analyze facial expressions explainable in a dimensional emotion model. The proposed method accommodates not only a set of basic emotion expressions, but also a full range of other emotions and subtle emotion intensities that we both feel in ourselves and perceive in others in our daily life. Specifically, we first mapped facial expressions into dimensional measures so that we transformed facial expression analysis from a classification problem to a regression one. We then tested our CNN-based methods for facial expression regression and these methods demonstrated promising performance. Moreover, we improved our method by a bilinear pooling which encodes second-order statistics of features. We showed that such bilinear-CNN models significantly outperformed their respective baseline models.
Bright-Sun: A globally applicable 1-min irradiance clear-sky detection model

Clear-sky detection (CSD) is a crucial process in numerous solar energy applications. Many CSD models have been proposed over the years, though model performance is generally found unsatisfactory for worldwide use. We demonstrate this qualitatively on 22 CSD models at five climatologically-diverse radiometric stations; all exhibit one or more limitations: (1) unreliability at high zenith; (2) unrealistic “clear” periods immediately before or after cloudy conditions; (3) relaxed (short-term false positives); (4) over-relaxed during clear conditions (longer-term false positives); (5) conservative (short-term false negatives); and (6) over-conservative during clear conditions (longer-term false negatives). A new globally applicable CSD methodology is proposed for a posteriori detection of apparent “cloudless sky” conditions on 1-min irradiance time series, named the Bright-Sun model. This new tool requires measured global horizontal irradiance (GHI) and diffuse horizontal irradiance (DIF), and consists of three stages: (1) clear-sky irradiance optimisation, (2) tri-component CSD analysis with the newly derived Modified-Reno method, and (3) a cascading durational filters to determine periods of apparent cloudless sky. Through qualitative evaluation and exploring sensitivity to clear-sky model selection, the Bright-Sun model does not suffer any of the aforementioned limitations at any of the five stations, despite their distinctive climates. Due to the significant influence of bright or dark clouds on DIF, which have much lower impact on GHI, the new model also exhibits extra discretionary power by including analysis on DIF and can thus identify apparently clear periods with zero or near-zero cloudiness. The Bright-Sun CSD model is coded in Matlab® and freely available (future releases in R and Python are anticipated). A script is attached as supplementary material in the original form. For a supported and version controlled release of the Bright-Sun model, as well as other CSD models mentioned within this document, the reader can refer to the CSD Library at https://jamiembright.github.io/csd-library/.
Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.