Reviewerly/exHarmony huggingface dataset:
Semisupervised Laplace-Regularized Multimodality Metric Learning

Distance metric learning, which aims at learning an appropriate metric from data automatically, plays a crucial role in the fields of pattern recognition and information retrieval. A tremendous amount of work has been devoted to metric learning in recent years, but much of the work is basically designed for training a linear and global metric with labeled samples. When data are represented with multimodal and high-dimensional features and only limited supervision information is available, these approaches are inevitably confronted with a series of critical problems: 1) naive concatenation of feature vectors can cause the curse of dimensionality in learning metrics and 2) ignorance of utilizing massive unlabeled data may lead to overfitting. To mitigate this deficiency, we develop a semisupervised Laplace-regularized multimodal metric-learning method in this work, which explores a joint formulation of multiple metrics as well as weights for learning appropriate distances: 1) it learns a global optimal distance metric on each feature space and 2) it searches the optimal combination weights of multiple features. Experimental results demonstrate both the effectiveness and efficiency of our method on retrieval and classification tasks.
Social Search and Task-Related Relevance Dimensions in Microblogging Sites

Social media, and in particular microblogging sites, allow users to post multiple kinds of content for different purposes. Content may be purely conversational, or news-related, or event-related. To find information relevant to users in this heterogeneous mass of content, it would be important to consider the task for which search is carried out, and the most suitable relevance dimensions. In the last years, despite the social search problem has been increasingly investigated, this aspect has not been sufficiently analyzed. For this reason, in this paper, we focus on different search tasks in the microblog search context, and we identify some related relevance dimensions. We also report some experiments we have made to verify the impact of the identified relevance dimensions on the system effectiveness, with respect to the considered search tasks.
A Hierarchical Framework for Long-term and Robust Deployment of Field Ground Robots in Large-Scale Farming

Achieving long term autonomy of robots operating in dynamic environments such as farms remains a significant challenge. Arguably, the most demanding factors to achieve this are the on-board resource constraints such as energy, planning in the presence of moving individuals such as livestock and people, and handling unknown and undulating terrain. These considerations require a robot to be adaptive in its immediate actions in order to successfully achieve long-term, resource-efficient and robust autonomy. To achieve this, we propose a hierarchical framework that integrates a local dynamic path planner with a longer term objective based planner and advanced motion control methods, whilst taking into consideration the dynamic responses of moving individuals within the environment. The framework is motivated by and synthesizes our recent work on energy aware mission planning, path planning in dynamic environments, and receding horizon motion control. In this paper we detail the proposed framework and outline its integration on a robotic platform. We evaluate the strategy in extensive simulated trials, traversing between objective waypoints to complete tasks such as soil sampling, weeding and recharging across a dynamic environment, demonstrating its capability to robustly adapt long term mission plans in the presence of moving individuals and obstacles for real world applications such as large scale farming.
Knowledge Based Versus Data Based

The advent of big data and deep learning algorithms has promoted a major shift toward data-driven methods in medical image analysis recently. However, the medical image analysis field has a long and rich history inclusive of both knowledge-driven and data-driven methodologies. In the present article, we provide a historical review of an illustrative sample of medical image analysis methods and locate them along a knowledge-driven versus data-driven continuum. In doing so, we highlight the historical importance as well as current-day relevance of more traditional, knowledge-based artificial intelligence approaches and their complementarity with fully data-driven techniques such as deep learning.
A Primal-Dual Subgradient Approach for Fair Meta Learning

The problem of learning to generalize on unseen classes during the training step, also known as few-shot classification, has attracted considerable attention. Initialization based methods, such as the gradient-based model agnostic meta-learning (MAML) [1], tackle the few-shot learning problem by “learning to fine-tune”. The goal of these approaches is to learn proper model initialization, so that the classifiers for new classes can be learned from a few labeled examples with a small number of gradient update steps. Few shot meta-learning is well-known with its fast-adapted capability and accuracy generalization onto unseen tasks [2]. Learning fairly with unbiased outcomes is another significant hallmark of human intelligence, which is rarely touched in few-shot meta-learning. In this work, we propose a Primal-Dual Fair Meta-learning framework, namely PDFM, which learns to train fair machine learning models using only a few examples based on data from related tasks. The key idea is to learn a good initialization of a fair model's primal and dual parameters so that it can adapt to a new fair learning task via a few gradient update steps. Instead of manually tuning the dual parameters as hyperparameters via a grid search, PDFM optimizes the initialization of the primal and dual parameters jointly for fair meta-learning via a subgradient primal-dual approach. We further instantiate an example of bias controlling using decision boundary covariance (DBC) [3] as the fairness constraint for each task, and demonstrate the versatility of our proposed approach by applying it to classification on a variety of three realworld datasets. Our experiments show substantial improvements over the best prior work for this setting. Our code and datasets are available at https://github.com/charliezhaoyinpeng/PDFM.git.
A Novel Visual Measurement Method for Three-Dimensional Trajectory of Underwater Moving Objects Based on Deep Learning

For deep sea equipment suspension installation used in marine engineering, the multi-camera video motion analysis method is used to calculate the three-dimensional underwater trajectory of the underwater engineering structure.Considering difficulty in underwater modeling caused by the problems of light scattering and refraction under water, the camera imaging model on land is no longer applicable in water, and a new underwater camera imaging model needs to be proposed.This paper introduces an underwater camera imaging model with light refraction, studies the calibration method of the internal and external parameters of the underwater camera, and improves the multiscale rotation dense feature pyramid convolutional neural network to detect the position of the target object in the image.The underwater motion videos of the target produced by three fixed underwater cameras are optimized to fuse and calculate the trajectory of the underwater target.This method is suitable for large-scale motion of underwater objects and can obtain more accurate trajectories.Experimental analysis and data comparison have verified the effectiveness of the method. INDEX TERMS Deep sea
INSIDE: Steering Spatial Attention with Non-imaging Information in CNNs

We consider the problem of integrating non-imaging information into segmentation networks to improve performance. Conditioning layers such as FiLM provide the means to selectively amplify or suppress the contribution of different feature maps in a linear fashion. However, spatial dependency is difficult to learn within a convolutional paradigm. In this paper, we propose a mechanism to allow for spatial localisation conditioned on non-imaging information, using a feature-wise attention mechanism comprising a differentiable parametrised function (e.g. Gaussian), prior to applying the feature-wise modulation. We name our method INstance modulation with SpatIal DEpendency (INSIDE). The conditioning information might comprise any factors that relate to spatial or spatio-temporal information such as lesion location, size, and cardiac cycle phase. Our method can be trained end-to-end and does not require additional supervision. We evaluate the method on two datasets: a new CLEVR-Seg dataset where we segment objects based on location, and the ACDC dataset conditioned on cardiac phase and slice location within the volume. Code and the CLEVR-Seg dataset are available at https://github.com/jacenkow/inside.
Aerial Worker for Skyscraper Fire Fighting using a Water-Jetpack Inspired Approach

This article is concerned with the utilization of Unmanned Aerial Systems (UASs) for fire fighting purposes in skyscrapers. The water is supplied to these systems through a hose using a water pump in a similar manner as in hydroflight sports. The UAS utilizes this hydro-propulsion to lift the water hose and the water present in it, thus extending its flight time. An adaptive backstepping controller is employed for controlling the water jet in the yaw axis, while considering as disturbance the forces acting along its roll and pitch axes. Simulation results highlighting the system's performance against forces acting on its center of mass are offered.
A Chaotic Path Planning Method for 3D Area Coverage Using Modified Logistic Map and a Modulo Tactic

This paper addresses the problem of chaotic path planning for the case of an autonomous mobile robot or an autonomous unmanned arial vehicle (UAV) moving in a 3D environment. The aim is to derive a method to generate a series of chaotic motion commands for a UAV that is tasked to explore a given 3D grid. The objective is to generate an unpredictable motion, that will also give good coverage results relative to the number of motions generated. This work adapts a combination of a modified logistic map and a modulo operator to generate the motion directions. An inverse pheromone memory approach is also considered to improve performance. Simulations are then performed to evaluate the performance of the method.
Semantic Information Supplementary Pyramid Network for Dynamic Scene Deblurring

The algorithm in this paper is called semantic information supplementary pyramid network(SIS-net). We choose Generative Adversarial Network (GAN) as its fundamental model. SIS-net's generator imitates the feature pyramid network (FPN) structure to recycle features spanning across multiple receptive scales to restore a sharp image. However, to solve the problem caused by the phenomenon of semantic dilution in the FPN network, we have innovatively designed a semantic information supplement (SIS) mechanism. SIS mechanism contains two essential components: semantic information storage box (info-box) and feature fusion expanding. In the process of feature fusion expanding, the semantic information features coming from the info-box is supplemented to make greater use of detailed clues. In addition, SIS-net uses the intermediate layer path to extract image features in a single time to obtain a multi-scale effect. The running speed of SIS-net has obvious advantages over other algorithms, and can basically complete real-time deblurring tasks. Extensive experiments show that our SIS-net achieves both qualitative and quantitative improvements against state-of-the-art methods. The code is available at https://github.com/yimingliu123/SIS-NET.
Towards Lightweight Serverless Computing via Unikernel as a Function

Serverless computing, also known as “Function as a Service (FaaS)”, is emerging as an event-driven paradigm of cloud computing. In the FaaS model, applications are programmed in the form of functions that are executed and managed separately. Functions are triggered by cloud users and are provisioned dynamically through containers or virtual machines (VMs). The startup delays of containers or VMs usually lead to rather high latency of response to cloud users. Moreover, the communication between different functions generally relies on virtual net devices or shared memory, and may cause extremely high performance overhead. In this paper, we propose Unikernel-as-a-Function (UaaF), a much more lightweight approach to serverless computing. Applications are abstracted as a combination of different functions, and each function are built as an unikernel in which the function is linked with a specified minimum-sized library operating system (LibOS). UaaF offers extremely low startup latency to execute functions, and an efficient communication model to speed up inter-functions interactions. We exploit an new hardware technique (namely VMFUNC) to invoke functions in other unikernels seamlessly (mostly like inter-process communications), without suffering performance penalty of VM Exits. We implement our proof-of-concept prototype based on KVM and deploy UaaF in three unikernels (MirageOS, IncludeOS, and Solo5). Experimental results show that U aaF can significantly reduce the startup latency and memory usage of serverless cloud applications. Moreover, the VMFUNC-based communication model can also significantly improve the performance of function invocations between different unikernels.
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature

Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day. Numerous scientific articles are being published on the disease raising the need for a service which can organize, and query them in a reliable fashion. To support this cause we present AWS CORD-19 Search (ACS), a public, COVID-19 specific, neural search engine that is powered by several machine learning systems to support natural language based searches. ACS with capabilities such as document ranking, passage ranking, question answering, knowledge graph based ranking and biomedical topic classification provides a scalable solution to COVID-19 researchers and policy makers in their search and discovery for answers to high priority scientific questions. We present a quantitative evaluation and qualitative analysis of the system against other leading COVID-19 search platforms. ACS is top performing across these systems yielding quality results which we detail with relevant examples in this work.
Hyper Parameter Tuning of Random Forest Algorithm for Affective Learning System

This paper has proposed an affective learning system to detect the affective state of learner by using random forest algorithm. The proposed system analyzes various hyper parameters of the algorithm for achieving best performance in terms of accuracy and Cohen's Kappa for affective learning dataset.
Gold Partitioning in a Model Multiphase Mineral-Hydrothermal Fluid System: Distribution Coefficients, Speciation and Segregation

The characteristics of Au partitioning in a multiphase, multicomponent hydrothermal system at 450 °C and 1 kbar pressure were obtained using experimental and computational physicochemical modelling and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) analysis. Sphalerite and magnetite contained 0.1–0.16 ± 0.02 µg/g Au and coexisted with galena and bornite which contained up to 73 ± 5 and 42 ± 10 µg/g Au, respectively. Bornite and chalcopyrite were the most effective Au scavengers with cocrystallization coefficients Au/Fe and Au/Cu in mineral-fluid system n–n × 10−2. Sphalerite and magnetite were the weakest Au absorbers, although Fe impurity in sphalerite facilitated Au uptake. Using the phase composition correlation principle, Au solubility in minerals was estimated (µg/g Au): low-Fe sphalerite = 0.7, high-Fe sphalerite = 5, magnetite = 1, pyrite = 3, pyrite-Mn = 7, pyrite-Cu = 10, pyrrhotite = 21, chalcopyrite = 110, bornite = 140 and galena = 240. The sequence reflected increasing metallicity of chemical bonds. Gold segregation occurred at crystal defects, and on surfaces, and influenced Au distribution due to its segregation at crystal interblock boundaries enriched in Cu-containing submicron phases. The LA-ICP-MS analysis of bulk and surficial gold admixtures revealed elevated Au content in surficial crystal layers, especially for bornite and galena, indicating the presence of a superficial nonautonomous phase (NAP) and dualism in the distribution of gold. Thermodynamic calculations showed that changes in experimental conditions, primarily in sulfur regime, increased the content of the main gold species (AuCl2− and AuHS0) and decreased the content of FeCl20, the prevailing form of iron in the fluid phase. The elevation of S2 and H2S fugacity affected Au partitioning and cocrystallization coefficients. Using Au content in pyrite, chalcopyrite, magnetite and bornite from volcanic-sedimentary, skarn-hosted and magmatic-hydrothermal sulfide deposits, the ranges of metal ratios in fluids were estimated: Au/Fe = n × 10−4−n × 10−7 and Au/Cu = n × 10−4−n × 10−6. Pyrite and magnetite were crystallized from solutions enriched in Au compared to chalcopyrite and bornite. The presence of NAP, and associated dualism in distribution coefficients, strongly influenced Au partitioning, but this effect does not fully explain the high gold fractionation into mineral precipitates in low-temperature geothermal systems.
Deep Shapely Portraits

We present deep shapely portraits, a novel method based on deep learning, to automatically reshape an input portrait to be better proportioned and more shapely while keeping personal facial characteristics. Different from existing methods that may suffer from irrational face artifacts when dealing with portraits with large pose variations or reshaping adjustments, we utilize dense 3D face information and constraints instead of sparse facial landmarks based on 3D morphable models, resulting in better reshaped faces lying in rational face space. To this end, we first estimate the best shapely degree for the input portrait using a convolutional neural network (CNN) trained on our newly developed ShapeFaceNet dataset. Then the best shapely degree is used as the control parameter to reshape the 3D face reconstructed from the input portrait image. After that, we render the reshaped 3D face back to 2D and generate a seamless portrait image using a fast image warping optimization. Our work can deal with pose and expression free (PE-Free) portrait images and generate plausible shapely faces without noticeable artifacts, which cannot be achieved by prior work. We validate the effectiveness, efficiency, and robustness of the proposed method by extensive experiments and user studies.
ShapeRE: Towards a Multi-Dimensional Representation for Requirements of Sustainable Software

In this paper we introduce the preliminary design of a new framework named ShapeRE, which will address two problems potentially impeding the development of sustainable software. The first problem is the common differentiation between “functional” and “non-functional” requirements, which hides relevant sustainability requirements and hampers their identification. The second problem is the lack of developer-oriented representation approaches to ensure that all requirements relevant to sustainable software will be finally implemented. To address these two issues ShapeRE provides an alternative multi-dimensional approach for identifying requirements and a developer-oriented representation guideline. We suggest nine dimensions which are social, economic, environmental, technical, individual, purpose, design-aesthetics, integrative and legal. The design of our frame-work relies on insights from the fields of linguistics and empirical aesthetics, requirements engineering, and building architecture. We introduce the framework and discuss our evaluation strategy consisting of an online-experiment, a case study, and a survey.
ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

People with asthma have heterogeneous triggers and symptoms, which they need to be aware of in order to implement the strategies to manage their condition. Context-aware reasoning has the potential to provide the personalisation that is required to address the heterogeneity of asthma by helping people to define the information that is relevant considering the characteristics of their condition and delivering services based on this information. This research work proposes the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), whose aim is to facilitate the creation of solutions allowing the required customisation to address the heterogeneity of asthma. ADAPT is the result of the constant interaction with people affected by asthma throughout the research project, which was possible to achieve thanks to the collaboration formed with the Centre for Applied Research of Asthma UK. ADAPT context dimensions facilitate the development of preventive and reactive features that can be configured depending on the characteristics of the person with asthma. The approach also provides support to people not knowing their triggers through case-based reasoning and includes virtual assistant as a complementing technology supporting asthma management. ADAPT is validated by people with asthma, carers and experts in respiratory conditions, who evaluated a mobile application that was built based on the approach. • An approach to create context-aware solutions for personalised asthma management. • Development and validation of a system built based on the approach. • Evaluation of the case-based reasoning cycle for personalised asthma management. • A virtual assistant to monitor the health status of a person with asthma.
Performance Evaluation of Containerization Platforms for Control and Monitoring Devices

Containerization platforms such as Docker are now common practice in the IT industry and are frequently used in combination with virtual machines in cloud-scenarios. Using containerization platforms for embedded control and monitoring devices has so far been much less common. However, the advantages such as increased modularization and portability are of relevance for embedded control devices as well. On the other hand, embedded control devices are often characterized by a limited amount of computational resources and may face other constraints such as requirements on low power consumption. This paper presents first steps on estimating the resource needs for different containerization platforms. We present empirical work on resource consumption of containerization platforms for a Raspberry Pi-based control and monitoring device and look at CPU load, memory usage and power consumption.