Reviewerly/exHarmony huggingface dataset:
Biometric applications in education

Educational institutions are acquiring novel technologies to help make their processes more efficient and services more attractive for both students and faculty. Biometric technology is one such example that has been implemented in educational institutions with excellent results. In addition to identifying students, access control, and personal data management, it has critical applications to improve the academic domain's teaching/learning processes. Identity management system, class attendance, e-evaluation, security, student motivations, and learning analytics are areas in which biometric technology is most heavily employed. A literature review is performed to present an overview of biometric technology applications for educational purposes, challenges that must overcome to implement biometric technology, and potentially foreshadowing trends effectively. The future seems promising for biometric technology; the biometric technology market is expected to reach a value of USD 94 billion by 2025 at a compound annual growth rate of 36%. New characteristics are under development for commercial applications, such as vascular pattern recognition, ear shape recognition, facial thermography, odor sensing, gait recognition, heartbeat authentication, brain waves, and human body bioacoustics. The biggest challenge this technology must overcome is security and privacy issues, which must be addressed to fully develop the technology to its full potential. It is desirable that this literature review can provide researchers with a sound vision of the potential that biometric technology will have in education.
VisuoSpatial Foresight for physical sequential fabric manipulation

Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks. We build upon the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different sequential fabric manipulation tasks with a single goal-conditioned policy. We extend our earlier work on VisuoSpatial Foresight (VSF), which learns visual dynamics on domain randomized RGB images and depth maps simultaneously and completely in simulation. In this earlier work, we evaluated VSF on multi-step fabric smoothing and folding tasks against 5 baseline methods in simulation and on the da Vinci Research Kit surgical robot without any demonstrations at train or test time. A key finding was that depth sensing significantly improves performance: RGBD data yields an $$\mathbf{80 \%}$$ improvement in fabric folding success rate in simulation over pure RGB data. In this work, we vary 4 components of VSF, including data generation, visual dynamics model, cost function, and optimization procedure. Results suggest that training visual dynamics models using longer, corner-based actions can improve the efficiency of fabric folding by 76% and enable a physical sequential fabric folding task that VSF could not previously perform with 90% reliability. Code, data, videos, and supplementary material are available at https://sites.google.com/view/fabric-vsf/ .
Finding a New Voice: Transitioning Designers from GUI to VUI Design

As Voice User Interfaces (VUIs) become widely popular, designers must handle new usability challenges. However, compared to other established domains such as Graphical User Interfaces (GUIs), VUI designers have fewer resources (training support, usability heuristics, design patterns) to guide them. On the other hand, GUI-trained designers may also be solicited upon to design VUIs given the increased demand for such interfaces. This raises the question: how can we best support such designers as they transition from GUI to VUI design? To answer this, we focus on usability heuristics as a key resource, and conduct several workshops with GUI design experts, exploring how they map their design experience onto VUI design. Based on this, we suggest that the "path of least resistance" to transitioning designers from GUI to VUI may be the adaptation of familiar resources and concepts (such as GUI heuristics) to the VUI design space, instead of the imposition of novel VUI-specific heuristics on GUI-trained designers. This finding can inform the development of design resources that can support the increase demand for VUIs.
The Challenge for the Nature-Inspired Global Optimization Algorithms: Non-Symmetric Benchmark Functions

Along with the increasing number of nature-inspired algorithms, more and more benchmark functions were also involved in the initial verification experiments. The benchmark functions were introduced to verify the capability of algorithms in optimization, but not all of them could be optimized, because they were different from each other in dimensionality, separability, scalability, and modality <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et.al.</i> . In this paper, we introduced another property called symmetry or non-symmetry, which should be another embedded characteristic of functions affecting the capability of algorithms in optimization. 67 non-symmetric benchmark functions were collected and 9 popular capability-verified algorithms were introduced in four types of simulation experiments. Experimental results show that most of the non-symmetric algorithms could not be optimized. And none of the algorithms involved could optimize them all. Efforts remain in need of new methods and improvements of nature-inspired algorithms.
Is prediction nothing more than multi-scale pattern completion of the future?

While the notion of the brain as a prediction machine has been extremely influential and productive in cognitive science, there are competing accounts of how best to model and understand the predictive capabilities of brains. One prominent framework is of a "Bayesian brain" that explicitly generates predictions and uses resultant errors to guide adaptation. We suggest that the prediction-generation component of this framework may involve little more than a pattern completion process. We first describe pattern completion in the domain of visual perception, highlighting its temporal extension, and show how this can entail a form of prediction in time. Next, we describe the forward momentum of entrained dynamical systems as a model for the emergence of predictive processing in non-predictive systems. Then, we apply this reasoning to the domain of language, where explicitly predictive models are perhaps most popular. Here, we demonstrate how a connectionist model, TRACE, exhibits hallmarks of predictive processing without any representations of predictions or errors. Finally, we present a novel neural network model, inspired by reservoir computing models, that is entirely unsupervised and memoryless, but nonetheless exhibits prediction-like behavior in its pursuit of homeostasis. These explorations demonstrate that brain-like systems can get prediction "for free," without the need to posit formal logical representations with Bayesian probabilities or an inference machine that holds them in working memory.
An improved graph cut algorithm in stereo matching

In stereo matching tasks, the matching effect is often very poor when the texture of the region is weak or repeated. To solve this problem, an improved Graph Cut stereo matching algorithm based on Census transform is proposed. The Hamming distance of the corresponding pixels in the left and right images after Census transform is introduced as the similarity measure in the data term of the energy function. In this way, the dependence on the pixel value is reduced. The stereo matching experiments are implemented on the standard images of Middlebury stereo benchmark and the real scene images, and it demonstrates that our algorithm is robust and can obtain better performance in weak texture or repeated texture region.
All the attention you need: Global-local, spatial-channel attention for image retrieval

We address representation learning for large-scale instance-level image retrieval. Apart from backbone, training pipelines and loss functions, popular approaches have focused on different spatial pooling and attention mechanisms, which are at the core of learning a powerful global image representation. There are different forms of attention according to the interaction of elements of the feature tensor (local and global) and the dimensions where it is applied (spatial and channel). Unfortunately, each study addresses only one or two forms of attention and applies it to different problems like classification, detection or retrieval.We present global-local attention module (GLAM), which is attached at the end of a backbone network and incorporates all four forms of attention: local and global, spatial and channel. We obtain a new feature tensor and, by spatial pooling, we learn a powerful embedding for image retrieval. Focusing on global descriptors, we provide empirical evidence of the interaction of all forms of attention and improve the state of the art on standard benchmarks.
Deep learning-based single image face depth data enhancement

Face recognition can benefit from the utilization of depth data captured using low-cost cameras, in particular for presentation attack detection purposes. Depth video output from these capture devices can however contain defects such as holes or general depth inaccuracies. This work proposes a deep learning face depth enhancement method in this context of facial biometrics, which adds a security aspect to the topic. U-Net-like architectures are utilized, and the networks are compared against hand-crafted enhancer types, as well as a similar depth enhancer network from related work trained for an adjacent application scenario. All tested enhancer types exclusively use depth data as input, which differs from methods that enhance depth based on additional input data such as visible light color images. Synthetic face depth ground truth images and degraded forms thereof are created with help of PRNet, to train multiple deep learning enhancer models with different network sizes and training configurations. Evaluations are carried out on the synthetic data, on Kinect v1 images from the KinectFaceDB, and on in-house RealSense D435 images. These evaluations include an assessment of the falsification for occluded face depth input, which is relevant to biometric security. The proposed deep learning enhancers yield noticeably better results than the tested preexisting enhancers, without overly falsifying depth data when non-face input is provided, and are shown to reduce the error of a simple landmark-based PAD method.
Autonomous Off-Road Navigation over Extreme Terrains with Perceptually-Challenging Conditions

We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages. Environments are GPS-denied and perceptually-degraded with variable lighting from dark to lit and obscurants (dust, fog, smoke). Lack of prior maps and degraded communication eliminates the possibility of prior or off-board computation or operator intervention. This necessitates real-time on-board computation using noisy sensor data. To address these challenges, we propose a resilient architecture that exploits redundancy and heterogeneity in sensing modalities. Further resilience is achieved by triggering recovery behaviors upon failure. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots, as a part of Team CoSTAR’s effort to the DARPA Subterranean Challenge, where the team won 2nd and 1st place in the Tunnel and Urban Circuits, respectively.
Sentiment Analysis using Deep Learning in Cloud

Sentiments are the emotions or opinions of an individual encapsulated within texts or images. These emotions play a vital role in the decision-making process for a business. A cloud service provider and consumer are bound together in a Service Level Agreement (SLA) in a cloud environment. SLA defines all the rules and regulations for both parties to maintain a good relationship. For a long-lasting and sustainable relationship, it is vital to mine consumers' sentiment to get insight into the business. Sentiment Analysis or Opinion Mining refers to the process of extracting or predicting different point of views from a text or image to conclude. Various techniques, including Machine Learning and Deep Learning, strives to achieve results with high accuracy. However, most of the existing studies could not unveil hidden parameters in text analysis for optimal decision-making. This work discusses the application of sentiment analysis in the cloud-computing paradigm. The paper provides a comparative study of various textual sentiment analysis using different deep learning approaches and their importance in cloud computing. The paper further compares existing approaches to identify and highlight gaps in them.
Non-negative Matrix Factorization: A Survey

Abstract Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.
Performance vs. hardware requirements in state-of-the-art automatic speech recognition

Abstract The last decade brought significant advances in automatic speech recognition (ASR) thanks to the evolution of deep learning methods. ASR systems evolved from pipeline-based systems, that modeled hand-crafted speech features with probabilistic frameworks and generated phone posteriors, to end-to-end (E2E) systems, that translate the raw waveform directly into words using one deep neural network (DNN). The transcription accuracy greatly increased, leading to ASR technology being integrated into many commercial applications. However, few of the existing ASR technologies are suitable for integration in embedded applications, due to their hard constrains related to computing power and memory usage. This overview paper serves as a guided tour through the recent literature on speech recognition and compares the most popular ASR implementations. The comparison emphasizes the trade-off between ASR performance and hardware requirements, to further serve decision makers in choosing the system which fits best their embedded application. To the best of our knowledge, this is the first study to provide this kind of trade-off analysis for state-of-the-art ASR systems.
Compact scanning confocal chromatic sensor enabling precision 3-D measurements

This paper proposes a compact and lightweight scanning confocal chromatic sensor (SCCS) for robot-based precision three-dimensional (3-D) surface measurement applications. The integrated system design includes a 2-D fast steering mirror (FSM) to manipulate the optical path of a high precision 1-D confocal chromatic sensor (CCS). A data-driven calibration procedure is used to accurately combine the FSM deflection angles and the correspondingly measured distances to the sample surface in order to obtain a correctly reconstructed 3-D image. Lissajous scan trajectories are applied to enable efficient scans of the sample surface. The SCCS provides 3-D images at frame rates of up to 1 fps and a measurement volume of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>0.35</mml:mn> <mml:mo>×<!-- × --></mml:mo> <mml:mn>0.25</mml:mn> <mml:mo>×<!-- × --></mml:mo> <mml:mn>1.8</mml:mn> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mn>3</mml:mn> </mml:msup> </mml:mrow> </mml:math> , as well as the measurement of arbitrary regions of interest. Using a calibration standard including structures with defined sizes, the lateral and axial resolutions are determined to 2.5 µm and 76 nm, respectively.
The Utility of Unsupervised Machine Learning in Anatomic Pathology

Developing accurate supervised machine learning algorithms is hampered by the lack of representative annotated datasets. Most data in anatomic pathology are unlabeled and creating large, annotated datasets is a time consuming and laborious process. Unsupervised learning, which does not require annotated data, possesses the potential to assist with this challenge. This review aims to introduce the concept of unsupervised learning and illustrate how clustering, generative adversarial networks (GANs) and autoencoders have the potential to address the lack of annotated data in anatomic pathology.A review of unsupervised learning with examples from the literature was carried out.Clustering can be used as part of semisupervised learning where labels are propagated from a subset of annotated data points to remaining unlabeled data points in a dataset. GANs may assist by generating large amounts of synthetic data and performing color normalization. Autoencoders allow training of a network on a large, unlabeled dataset and transferring learned representations to a classifier using a smaller, labeled subset (unsupervised pretraining).Unsupervised machine learning techniques such as clustering, GANs, and autoencoders, used individually or in combination, may help address the lack of annotated data in pathology and improve the process of developing supervised learning models.
Low c-differential and c-boomerang uniformity of the swapped inverse function

Modifying the binary inverse function in a variety of ways, like swapping two output points has been known to produce a 4-differential uniform permutation function. Recently, in [21] it was shown that this swapped version of the inverse function has boomerang uniformity exactly 10, if n≡0(mod6), 8, if n≡3(mod6), and 6, if n≢0(mod3). Based upon the c-differential and c-boomerang uniformity notions we defined in [16], respectively, [30], in this paper we characterize the c-differential and c-boomerang uniformity for the (0,1)-swapped inverse function in even characteristic, x2n−2+x2n−1+(x+1)2n−1 on F2n: we show that for all c≠1, the c-differential uniformity is upper bounded by 4 and the c-boomerang uniformity by 5 with both bounds being attained for n≥4.
Blockchain-Empowered Edge Intelligence for Internet of Medical Things Against COVID-19

We have witnessed an unprecedented public health crisis caused by the new coronavirus disease (COVID-19), which has severely affected medical institutions, our common lives, and social-economic activities. This crisis also reveals the brittleness of existing medical services, such as over-centralization of medical resources, the hysteresis of medical services digitalization, and weak security and privacy protection of medical data. The integration of the Internet of Medical Things (IoMT) and blockchain is expected to be a panacea to COVID-19 attributed to the ubiquitous presence and the perception of IoMT as well as the enhanced security and immutability of the blockchain. However, the synergy of IoMT and blockchain is also faced with challenges in privacy, latency, and context absence. The emerging edge intelligence technologies bring opportunities to tackle these issues. In this article, we present blockchain-empowered edge intelligence for IoMT in addressing the COVID-19 crisis. We first review IoMT, edge intelligence, and blockchain in addressing the COVID-19 pandemic. We then present an architecture of blockchain-empowered edge intelligence for IoMT after discussing the opportunities of integrating blockchain and edge intelligence. We next offer solutions to COVID-19 brought by blockchain-empowered edge intelligence from 1) monitoring and tracing COVID-19 pandemic origin, 2) traceable supply chain of injectable medicines and COVID-19 vaccines, and 3) telemedicine and remote healthcare services. Moreover, we also discuss the challenges and open issues in blockchain-empowered edge intelligence.
Few-shot action recognition with implicit temporal alignment and pair similarity optimization

Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application. Although there has been a lot of work in this area recently, most of the existing work is based on image classification tasks. Video-based few-shot action recognition has not been explored well and remains challenging: (1) the differences of implementation details among different papers make a fair comparison difficult; (2) the wide variations and misalignment of temporal sequences make the video-level similarity comparison difficult; (3) the scarcity of labeled data makes the optimization difficult. To solve these problems, this paper presents (1) a specific setting to evaluate the performance of few-shot action recognition algorithms; (2) an implicit sequence-alignment algorithm for better video-level similarity comparison; (3) an advanced loss for few-shot learning to optimize pair similarity with limited data. Specifically, we propose a novel few-shot action recognition framework that uses long short-term memory following 3D convolutional layers for sequence modeling and alignment. Circle loss is introduced to maximize the within-class similarity and minimize the between-class similarity flexibly towards a more definite convergence target. Instead of using random or ambiguous experimental settings, we set a concrete criterion analogous to the standard image-based few-shot learning setting for few-shot action recognition evaluation. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.
Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. Day-ahead solar irradiance (SI) forecasting has various applications for system operators and market agents such as unit commitment, reserve management, and biding in the day-ahead market. To this end, a hybrid recurrent neural network is presented herein that uses the long short-term memory recurrent neural network (LSTM-RNN) approach to forecast day-ahead SI. In this approach, k-means clustering is first used to classify each day as either sunny or cloudy. Then, LSTM-RNN is used to learn the uncertainty and variability for each type of cluster separately to predict the SI with better accuracy. The exogenous features such as the dry-bulb temperature, dew point temperature, and relative humidity are used to train the models. Results show that the proposed hybrid model has performed better than a feed-forward neural network (FFNN), a support vector machine (SVM), a conventional LSTM-RNN, and a persistence model.