Reviewerly/exHarmony huggingface dataset:
Classroom student posture recognition based on an improved high-resolution network

Abstract Due to the large number of students in a typical classroom and crowded seating, most features of student posture are often obscured, making it difficult to balance the accuracy in identifying student postures with computational efficiency. To solve this issue, a novel classroom student posture recognition method is proposed. First, to recognize the poses of multiple students in the classroom, we use the you-only-look-once (YOLOv3) algorithm for object detection and retrain it to detect human objects that are hunching on a table, creating the pose estimation network. Next, to improve the accuracy of the pose estimation network, we use the squeeze-and-excitation network structure that is embedded in the residual structure of high-resolution networks (HRNet). Finally, with the improved HRNet algorithm’s outputs of key human body points, we design a pose classification algorithm based on a support vector machine, to classify human poses in the classroom. Experiments show that the improved HRNet multi-person pose estimation algorithm yields the best mean average precision performance of 73.76% on the common objects in context (COCO) validation dataset. We further test the proposed algorithm on a customer dataset collected in a classroom and achieved a high recognition rate of 90.1% and good robustness.
Multimodal Wearable Intelligence for Dementia Care in Healthcare 4.0: a Survey

Abstract As a new revolution of Ubiquitous Computing and Internet of Things, multimodal wearable intelligence technique is rapidly becoming a new research topic in both academic and industrial fields. Owning to the rapid spread of wearable and mobile devices, this technique is evolving healthcare from traditional hub-based systems to more personalised healthcare systems. This trend is well-aligned with recent “Healthcare 4.0” which is a continuous process of transforming the entire healthcare value chain to be preventive, precise, predictive and personalised, with significant benefits to elder care. But empowering the utility of multimodal wearable intelligence technique for elderly care like people with dementia is significantly challenging considering many issues, such as shortage of cost-effective wearable sensors, heterogeneity of wearable devices connected, high demand for interoperability, etc. Focusing on these challenges, this paper gives a systematic review of advanced multimodal wearable intelligence technologies for dementia care in Healthcare 4.0. One framework is proposed for reviewing the current research of wearable intelligence, and key enabling technologies, major applications, and successful case studies in dementia care, and finally points out future research trends and challenges in Healthcare 4.0.
Ambient temperature and solar irradiance forecasting prediction horizon sensitivity analysis

Selecting the correct weather forecasting technique is a crucial task when planning an efficient solar energy generation system. Estimating accurate solar photovoltaic systems power output depends on the correct modeling of solar irradiance and ambient temperature, evidencing the need for a framework to select the correct technique to forecast these parameters. This paper presents a review of the forecasting methods to predict solar irradiance and ambient temperature, considering the sensitivity to the forecasting horizon. The methodology includes estimating an interval for ambient temperature and solar irradiance by using the Mean Absolute Error as the percentage of variation in these parameters. To provide context, the study considers best-case and worst-case scenarios for four cities, estimating the power output for a sample array and analyzing the differences between the cases. The power output estimation of the PV array varied between 36% and 50% (on average) for the short-term prediction, and 54% to 95% for the long-term. The changes in the location produced an average variation of 43% in terms of power production, and up to 187% in economic value (USD) for the short term, and 44.5% and 187% for the long term. The results suggest a marked sensitivity to the variation in the forecasting horizon and significance with regards to location selection (considering the changes in solar irradiation and the cost of electricity).
Constructions of Optimal Binary Z-Complementary Sequence Sets With Large Zero Correlation Zone

In this letter, we first propose a construction of aperiodic binary Z-complementary sequence sets (ZCSSs) with length 3·2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> employing Boolean functions. Next we present a construction of binary ZCSSs with large set sizes by using orthogonal sequences. At last, we provide a large class of optimal binary ZCSSs of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ·L with large zero correlation zone width, where L is the length of Z-complementary pairs. The lengths of the ZCSSs are more flexible than the known methods. In this letter, these systematic constructions can generate more ZCSSs of the new sequence lengths and set sizes, which have not been reported before.
Smartphone-based real-time object recognition architecture for portable and constrained systems

Abstract Machine learning algorithms based on convolutional neural networks (CNNs) have recently been explored in a myriad of object detection applications. Nonetheless, many devices with limited computation resources and strict power consumption constraints are not suitable to run such algorithms designed for high-performance computers. Hence, a novel smartphone-based architecture intended for portable and constrained systems is designed and implemented to run CNN-based object recognition in real time and with high efficiency. The system is designed and optimised by leveraging the integration of the best of its kind from the state-of-the-art machine learning platforms including OpenCV, TensorFlow Lite, and Qualcomm Snapdragon informed by empirical testing and evaluation of each candidate framework in a comparable scenario with a high demanding neural network. The final system has been prototyped combining the strengths from these frameworks and led to a new machine learning-based object recognition execution environment embedded in a smartphone with advantageous performance compared with the previous frameworks.
A Leaf Disease Detection Mechanism Based on L1-Norm Minimization Extreme Learning Machine

The disease-free growth of a plant is highly influential for both environment and human life, as numerous microorganisms/viruses/fungus may affect the growth and agricultural production of a plant. Early detection and treatment thus becomes necessary and must be treated on time. The existing vision techniques either involve image segmentation or feature classification/regression applied over aerial images. This results in an increase in time and cost consumption due to various challenges, such as generalization ability and learning cost. Therefore, a feature-based disease detection approach with minimal learning time and generalization ability could be fairly befitting such as an extreme learning machine (ELM). In this letter, we demonstrate an algorithm, L1-ELM, after employing Kuan filtering for preprocessing and different feature computations. At the evaluation stage, the experimentation performed over benchmark plant datasets confirms that L1-ELM outperforms all existing one-class classification algorithms, preserving optimal learning and better generalization.
3D hemisphere-based convolutional neural network for whole-brain MRI segmentation

Whole-brain segmentation is a crucial pre-processing step for many neuroimaging analyses pipelines. Accurate and efficient whole-brain segmentations are important for many neuroimage analysis tasks to provide clinically relevant information. Several recently proposed convolutional neural networks (CNN) perform whole brain segmentation using individual 2D slices or 3D patches as inputs due to graphical processing unit (GPU) memory limitations, and use sliding windows to perform whole brain segmentation during inference. However, these approaches lack global and spatial information about the entire brain and lead to compromised efficiency during both training and testing. We introduce a 3D hemisphere-based CNN for automatic whole-brain segmentation of T1-weighted magnetic resonance images of adult brains. First, we trained a localization network to predict bounding boxes for both hemispheres. Then, we trained a segmentation network to segment one hemisphere, and segment the opposing hemisphere by reflecting it across the mid-sagittal plane. Our network shows high performance both in terms of segmentation efficiency and accuracy (0.84 overall Dice similarity and 6.1 mm overall Hausdorff distance) in segmenting 102 brain structures. On multiple independent test datasets, our method demonstrated a competitive performance in the subcortical segmentation task and a high consistency in volumetric measurements of intra-session scans.
Cogent: uniqueness types and certifying compilation

Abstract This paper presents a framework aimed at significantly reducing the cost of proving functional correctness for low-level operating systems components. The framework is designed around a new functional programming language, Cogent. A central aspect of the language is its uniqueness type system, which eliminates the need for a trusted runtime or garbage collector while still guaranteeing memory safety, a crucial property for safety and security. Moreover, it allows us to assign two semantics to the language: The first semantics is imperative, suitable for efficient C code generation, and the second is purely functional, providing a user-friendly interface for equational reasoning and verification of higher-level correctness properties. The refinement theorem connecting the two semantics allows the compiler to produce a proof via translation validation certifying the correctness of the generated C code with respect to the semantics of the Cogent source program. We have demonstrated the effectiveness of our framework for implementation and for verification through two file system implementations.
Improved Harris Hawks Optimization Adapted for Artificial Neural Network Training

The learning process is one of the most difficult problems in artificial neural networks. This process goal is to find the appropriate values for connection weights and biases and has a direct influence on the neural network classification and prediction accuracy. Since the search space is huge, traditional optimization techniques are not suitable as they are prone to slow convergence and getting trapped in the local optima. In this paper, an enhanced harris hawks optimization algorithm is proposed to address the task of neural networks training. Conducted experiments include 2 well-known classification benchmark datasets to evaluate the performance of the proposed method. The obtained results indicate that the devised algorithm has promising performance, as that it is able to achieve better overall results than other state-of-the-art metaheuristics that were taken into account in comparative analysis, in terms of classification accuracy and converging speed.
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation

Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis. While methods that use style-based GANs can generate strikingly photorealistic face images, it is often difficult to control the characteristics of the generated faces in a meaningful and disentangled way. Prior approaches aim to achieve such semantic control and disentanglement within the latent space of a previously trained GAN. In contrast, we propose a framework that a priori models physical attributes of the face such as 3D shape, albedo, pose, and lighting explicitly, thus providing disentanglement by design. Our method, MOST-GAN, integrates the expressive power and photorealism of style-based GANs with the physical disentanglement and flexibility of nonlinear 3D morphable models, which we couple with a state-of-the-art 2D hair manipulation network. MOST-GAN achieves photorealistic manipulation of portrait images with fully disentangled 3D control over their physical attributes, enabling extreme manipulation of lighting, facial expression, and pose variations up to full profile view.
Personalized Vehicular Edge Computing in 6G

Sixth-generation (6G) space-air-ground integrated vehicular networks (SAGIVNs) are expected to provide customized edge computing services for moving vehicles (MVs). In this article, by integrating 6G SAGIVNs and edge computing, we propose a secure and personalized vehicular edge computing framework in 6G to satisfy the diversified requirements of MVs. In the framework, we first develop a collaborative edge computing mechanism, where each 6G infrastructure (e.g., satellite, drone, and base station) cooperates with parked vehicles to provide services for MVs in order to improve the efficiency of the edge computing services. Then, considering the security issues, a smart-contract-based secure collaboration mechanism is designed to establish a reliable transaction environment for edge computing in 6G. Next, based on the personalized service requirements of MVs and the available resources in 6G SAGIVNs, the resource allocation strategy of each infrastructure and the competitiveness of different infrastructures are discussed to provide MVs with the optimal personalized service. After that, compared with the conventional schemes, a case is studied to evaluate the effectiveness of the proposed framework. Finally, we outline open research topics to identify future research opportunities and directions.
Predicting CEFR levels in learners of English: The use of microsystem criterial features in a machine learning approach

Abstract This paper focuses on automatically assessing language proficiency levels according to linguistic complexity in learner English. We implement a supervised learning approach as part of an automatic essay scoring system. The objective is to uncover Common European Framework of Reference for Languages (CEFR) criterial features in writings by learners of English as a foreign language. Our method relies on the concept of microsystems with features related to learner-specific linguistic systems in which several forms operate paradigmatically. Results on internal data show that different microsystems help classify writings from A1 to C2 levels (82% balanced accuracy). Overall results on external data show that a combination of lexical, syntactic, cohesive and accuracy features yields the most efficient classification across several corpora (59.2% balanced accuracy).
Ohm's Law + Kirchhoff's Current Law = Better AI: Neural-Network Processing Done in Memory with Analog Circuits will Save Energy

Machine learning and artificial intelligence (AI) have already penetrated so deeply into our life and work that you might have forgotten what interactions with machines used to be like. We used to ask only for precise quantitative answers to questions conveyed with numeric keypads, spreadsheets, or programming languages: “What is the square root of 10?” “At this rate of interest, what will be my gain over the next five years?”
FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.
Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks

Quantum annealing is a practical approach to approximately implement the adiabatic quantum computational model in a real-world setting. The goal of an adiabatic algorithm is to prepare the ground state of a problem-encoded Hamiltonian at the end of an annealing path. This is typically achieved by driving the dynamical evolution of a quantum system slowly to enforce adiabaticity. Properly optimized annealing schedules often considerably accelerate the computational process. Inspired by the recent success of deep reinforcement learning such as DeepMind’s AlphaZero, we propose a Monte Carlo tree search (MCTS) algorithm and its enhanced version boosted by neural networks—which we name QuantumZero (QZero)—to automate the design of annealing schedules in a hybrid quantum–classical framework. Both the MCTS and QZero algorithms perform remarkably well in discovering effective annealing schedules even when the annealing time is short for the 3-SAT examples considered in this study. Furthermore, the flexibility of neural networks allows us to apply transfer-learning techniques to boost QZero’s performance. We demonstrate in benchmark studies that MCTS and QZero perform more efficiently than other reinforcement learning algorithms in designing annealing schedules. Quantum annealers are computational models implemented on quantum hardware that can efficiently solve combinatorial optimization problems. Annealing schedules with enhanced performance can be discovered with a Monte Carlo tree search algorithm and an enhanced version incorporating value and policy neural networks—as inspired by DeepMind’s AlphaZero.
Dynamic defense against byzantine poisoning attacks in federated learning

Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzantine poisoning adversarial attacks. We argue that the federated learning model has to avoid those kind of adversarial attacks through filtering out the adversarial clients by means of the federated aggregation operator. We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model. We assess it as a defense against adversarial attacks deploying a deep learning classification model in a federated learning setting on the Fed-EMNIST Digits, Fashion MNIST and CIFAR-10 image datasets. The results show that the dynamic selection of the clients to aggregate enhances the performance of the global learning model and discards the adversarial and poor (with low quality models) clients.
Performance Evaluation Of Supervised Machine Learning Techniques For Efficient Detection Of Emotions From Online Content

Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.
Dangerous Speech

Philosophy & Public AffairsVolume 47, Issue 2 p. 208-254 Original Article Dangerous Speech Jeffrey W. Howard, Corresponding Author Jeffrey W. Howard jeffrey.howard@ucl.ac.uk orcid.org/0000-0002-6521-9228 Search for more papers by this author Jeffrey W. Howard, Corresponding Author Jeffrey W. Howard jeffrey.howard@ucl.ac.uk orcid.org/0000-0002-6521-9228 Search for more papers by this author First published: 16 September 2019 https://doi.org/10.1111/papa.12145Citations: 7 I thank the British Academy and the Leverhulme Trust for research funding. The arguments here have benefitted enormously from discussions with, and feedback from, David Anderson, Chinmayi Arun, Joe Atkinson, Eric Barendt, Christian Barry, Susan Benesch, David Birks, Corey Brettschneider, Matilda Carter, Helen Coverdale, Daniel D'Amico, Tom Dannenbaum, Josie Delap, Adam Etinson, Timothy Garton Ash, Sarah Goff, Bob Goodin, Jonathan Gould, Amanda Greene, Malte Ibsen, Joseph Jaconelli, Kasim Khorasanee, Cécile Laborde, Jonathan Leader Maynard, George Letsas, Abigail Levin, Saladin Meckled-Garcia, Emily McTernan, David Miller, Chris Mills, Shmulik Nili, Tom Parr, Avia Pasternak, Carmen Pavel, Jacob Rowbottom, Scott Shapiro, David Skarbek, Emily Skarbek, Robert Simpson, Jiewuh Song, Nic Southwood, Adam Swift, John Tomasi, Markus Trengove, Laura Valentini, Jeremy Waldron, Albert Weale, Han van Wietmarschen, and John Wilesmith. Thanks also to audiences at Brown University, the Australian National University, the London School of Economics, University College London, the American Philosophical Association, and the Universities of Canberra, Kiel, Oxford, St. Andrews, Warwick, and York. I am very grateful to the two anonymous reviewers for their feedback, which led to many improvements. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume47, Issue2Spring 2019Pages 208-254 RelatedInformation